research about health insurance

• Subscribe to journal Subscribe
• Get new issue alerts Get alerts

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Why Health Insurance Matters—and Why Research Evidence Should Too

Sommers, Benjamin D. MD, PhD

B.D. Sommers is associate professor of health policy and economics, Harvard T.H. Chan School of Public Health, and assistant professor of medicine, Brigham & Women’s Hospital/Harvard Medical School, Boston, Massachusetts.

Funding/Support: The research discussed in this essay was supported by the Agency for Healthcare Research & Quality and the Commonwealth Fund, but the views expressed here are solely the author’s.

Other disclosures: None reported.

Ethical approval: Reported as not applicable.

Previous presentations: This essay was adapted from speeches delivered at the AcademyHealth National Health Policy Conference, Washington, DC, January 2017, and the City Club of Cleveland, Cleveland, Ohio, February 2017.

Correspondence should be addressed to Benjamin D. Sommers, Harvard School of Public Health, 677 Huntington Ave., Kresge 406, Boston, MA 02115; e-mail: [email protected] .

In the current debate over the future of the Affordable Care Act (ACA), research evidence on the impact of the law and the effects of health insurance coverage in general is critical. Studies of health insurance expansion over the past decade have demonstrated that coverage expansions can produce significant reductions in mortality—particularly among minorities, those living in poorer areas, and those with chronic conditions potentially treatable with timely medical care. More recent studies of the ACA in particular demonstrate that the law has produced historically large reductions in the uninsured rate, with resulting improvements in access to care, perceived quality of care, and self-reported health. Yet much of the general public and many policy makers remain unaware of this evidence. Researchers and clinicians in academic medicine have a role to play in ensuring that critically important health policy decisions are made using rigorous evidence to best protect the interests of our patients.

Since the Affordable Care Act (ACA) was signed into law in 2010, it has been a lightning rod for controversy. Conservative policy makers have decried it as a costly and ineffective government intrusion into health care, while some liberals argue that it did too little to expand coverage and too much to enrich private health insurers. Now, with the fate of the ACA in serious doubt, taking stock of what we know about the effects of the law thus far—and more generally, the impact of expanding health insurance coverage to previously uninsured patients—is critical to informed policy making.

Over the past six years, my colleagues and I have been conducting a range of studies designed to provide evidence that would improve our understanding of the impacts of health insurance on patients. I care about this both as a health policy researcher and as a primary care physician. As any clinician knows, there are so many factors that affect our patients’ health outside what occurs in the office. How long did my patients wait before coming in to be seen? Can they pay for the medications I prescribe? Can they see the specialists I refer them to? And do they get better? Our work has aimed to answer some of these questions at the population level, and we have identified several key lessons from this work.

How Does Health Insurance Affect Patients?

Lesson 1: coverage can be a matter of life or death.

The first lesson is that health insurance coverage matters to patients’ lives. Some of the most useful evidence in support of this observation comes from expansions in health insurance that occurred prior to the ACA. Studying expansions of Medicaid in several states in the early 2000s by comparing them to neighboring states without expansions, we found large reductions in the uninsured rate, improved self-reported health, and a drop in mortality of 6% over the following five years. 1 These changes were largest in lower-income areas and among racial and ethnic minorities. We also studied Massachusetts’s 2006 health reform, the model for the ACA, and found that the coverage expansion led to a significant reduction in mortality for the state compared with what was happening in demographically similar counties outside the state. 2 Most of the deaths prevented were due to causes potentially more amenable to health care, such as cancer, heart disease, and infections. Overall, we concluded that one life was saved for every 830 adults who gained coverage. Most recently, I examined the costs of Medicaid expansion in relation to these mortality changes, and found that the increase in spending was a good investment compared with how much we as a society spend on other public policies that affect survival. 3

Now, with Congress debating a potential repeal of the ACA, there has been renewed interest in this work as a gauge for how many deaths might occur if the law is repealed. Policy makers and analysts have proposed various extrapolations from these studies, including the White House Council of Economic Advisors, which estimated that the law could be saving as many as 24,000 lives a year. 4 This is a challenging calculation to make with precision, and no one can know the exact numbers for sure, but our research indicates that these are indeed life and death decisions. Taking coverage from people will likely lead some to forego medical care that could have saved their lives.

Lesson 2: The ACA has succeeded in expanding coverage and access to care

These studies of pre-ACA expansions in coverage are illuminating, but what lessons can we draw about the ACA’s effects in particular? Using a variety of data sources, our team and other researchers have documented that not only has the ACA lowered the uninsured rate to its lowest level in U.S. history but that coverage has also produced meaningful benefits for patients. The earliest ACA studies examined the “dependent coverage” provision that allowed young adults to remain on their parents’ plans until age 26 starting in 2010. Studies show that this policy was more successful than even the law’s drafters had hoped, with two million to three million more adults covered. The coverage helped young adults better afford their care, reduced their use of nonurgent emergency department care, 5 and improved their perceived physical and mental health. 6

Next came the ACA’s 2014 expansions. Medicaid expansion in the roughly 30 participating states and new subsidized Marketplace coverage led to about 20 million more Americans with insurance. From 2010 to 2014, as policy makers scrambled to implement the law, researchers scrambled to figure out how to study it—and, in particular, how to study it rigorously and quickly. Standard data sources from the federal government sometimes take a year or more to become available. For a policy as large and consequential as the ACA, we needed results faster. Working with colleagues at the U.S. Department of Health & Human Services, we obtained and evaluated a new data source—the Gallup Healthways Well-Being Index. With it, we published some of the first journal articles showing—within months of real time—how the law was increasing coverage and also improving trends in rates of having a primary care doctor, ease of access to prescription medications, affordability of care, and self-reported health. 7

Following up on this, my colleagues at Harvard and I then conducted our own rapid-turnaround scientific survey to evaluate the Medicaid expansion in several southern states. This work showed that in Kentucky’s traditional Medicaid expansion and Arkansas’s private insurance expansion, low-income adults saw major improvements in health care, compared with those in Texas, which did not expand. Adults in the two expansion states reported more primary care visits, better chronic disease care, more preventive care, less ER use, and again—better self-reported health. 8

Given this pattern of findings, some have questioned whether policy makers and society in general should care about changes in self-reported health. I would argue that we should for two reasons. First, self-reported health turns out to be a strong predictor of survival; people who say they are in poor health die younger. 9 Second, subjective well-being is a key part of health. If you’re a doctor and you don’t care whether your patients feel better, you should quit. We should hold our policy makers to the same standard.

Lesson 3: Challenges in coverage and access remain

The final lesson from our research is that all is not perfect. Yes, health insurance matters, and the ACA has helped expand coverage and improve access to care. But as many as 30 million Americans are still uninsured, and millions more find themselves switching between various types of coverage each year. Some of this is related to the ACA, but much of it is due to the United States’ underlying patchwork health insurance system. This switching in coverage—sometimes called churning—has real impacts on patients. In one recent study, we found that roughly one in four low-income adults experienced a change in coverage each year. Although this is lower than many had predicted would occur under the ACA, these changes in coverage were harmful—patients reported reduced continuity with providers, disruptions in medication regimens, and negative effects on overall quality of care and health. 10 In part, this research has been useful to states and federal policy makers as they try to streamline some of the transitions in coverage. But now, with ACA repeal on the table, our findings have another implication. While taking coverage away from people would clearly be quite harmful, even transient disruptions in coverage from dismantling parts of the law could also cause significant distress.

An Uncertain Future—and the Need for Evidence-Based Policy

Despite this body of evidence, the political future of the ACA’s coverage expansion remains uncertain. In fact, the rhetoric in the debate over the ACA raises fundamental questions about what role research evidence plays at all. Despite hundreds of high-quality studies by researchers across the country probing the law’s successes and shortcomings, many people still don’t know the basic facts about the law. For instance, one recent survey by National Public Radio found that only 49% of Americans knew that the ACA had reduced the number of Americans without health insurance; 27% didn’t know or said it was unchanged, and a stunning 24% thought the uninsured rate had gone up. 11 Meanwhile, some physicians, politicians, and pundits continue to argue that the ACA expanded coverage but did not meaningfully improve access to care. But these claims are flatly contradicted by the research evidence. That so many might still not know the basic facts about this law is, at least in part, the fault of researchers like me in health policy and academic medicine. We certainly are not alone in this—there are other factors too, including some media outlets’ desire for an evenhanded cross-fire debate rather than a focus on facts, and rhetoric from some politicians that ranges from misleading to simply wrong.

But already there is some indication that the facts are starting to sink in and affect the policy debate. The steady drumbeat for repeal among congressional Republicans has met the reality of millions of Americans who have gained coverage under the ACA and are benefiting from it. Republican governors from expansion states have stepped forward to say that a repeal and large cuts in federal funding for Medicaid would not be good policy. 12 As of this writing, the debate continues and is unsettled. For those of us in academic medicine who believe evidence should guide what we do for our patients, now is the time to bring a similar mentality to the policy discussions that will affect our patients just as surely as the next prescription we write or the next test we order. The future of health care is too important not to have the major policy decisions in it driven by evidence.

• Cited Here |
• Google Scholar
• + Favorites
• View in Gallery

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Health Sciences, University of York, York, England, United Kingdom

Roles Investigation, Methodology, Supervision, Writing – review & editing

Affiliations Centre of Health Economics, University of York, York, England, United Kingdom, Luxembourg Institute of Socio-economic Research (LISER), Luxembourg

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliations Department of Health Sciences, University of York, York, England, United Kingdom, Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada

Roles Conceptualization, Investigation, Supervision, Writing – review & editing

• Darius Erlangga, 
• Marc Suhrcke, 
• Shehzad Ali, 
• Karen Bloor

• Published: August 28, 2019
• https://doi.org/10.1371/journal.pone.0219731
• Reader Comments

7 Nov 2019: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) Correction: The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLOS ONE 14(11): e0225237. https://doi.org/10.1371/journal.pone.0225237 View correction

Expanding public health insurance seeks to attain several desirable objectives, including increasing access to healthcare services, reducing the risk of catastrophic healthcare expenditures, and improving health outcomes. The extent to which these objectives are met in a real-world policy context remains an empirical question of increasing research and policy interest in recent years.

We reviewed systematically empirical studies published from July 2010 to September 2016 using Medline, Embase, Econlit, CINAHL Plus via EBSCO, and Web of Science and grey literature databases. No language restrictions were applied. Our focus was on both randomised and observational studies, particularly those including explicitly attempts to tackle selection bias in estimating the treatment effect of health insurance. The main outcomes are: (1) utilisation of health services, (2) financial protection for the target population, and (3) changes in health status.

8755 abstracts and 118 full-text articles were assessed. Sixty-eight studies met the inclusion criteria including six randomised studies, reflecting a substantial increase in the quantity and quality of research output compared to the time period before 2010. Overall, health insurance schemes in low- and middle-income countries (LMICs) have been found to improve access to health care as measured by increased utilisation of health care facilities (32 out of 40 studies). There also appeared to be a favourable effect on financial protection (26 out of 46 studies), although several studies indicated otherwise. There is moderate evidence that health insurance schemes improve the health of the insured (9 out of 12 studies).

Interpretation

Increased health insurance coverage generally appears to increase access to health care facilities, improve financial protection and improve health status, although findings are not totally consistent. Understanding the drivers of differences in the outcomes of insurance reforms is critical to inform future implementations of publicly funded health insurance to achieve the broader goal of universal health coverage.

Citation: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLoS ONE 14(8): e0219731. https://doi.org/10.1371/journal.pone.0219731

Editor: Sandra C. Buttigieg, University of Malta Faculty of Health Sciences, MALTA

Received: March 19, 2018; Accepted: July 2, 2019; Published: August 28, 2019

Copyright: © 2019 Erlangga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The search strategy for this review is available in Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent decades, achieving universal health coverage (UHC) has been a major health policy focus globally.[ 1 – 3 ] UHC entitles all people to access healthcare services through publicly organised risk pooling,[ 4 ] safeguarding against the risk of catastrophic healthcare expenditures.[ 5 ] Low- and middle-income countries (LMICs) face particular challenges in achieving UHC due to particularly limited public resources for health care, inefficient allocation, over-reliance on out-of-pocket payments, and often large population size.[ 5 ] As a result, access to health care and the burden of financial cost in LMICs tends to be worse for the poor, often resulting in forgone care.[ 6 – 8 ]

Introducing and increasing the coverage of publicly organised and financed health insurance is widely seen as the most promising way of achieving UHC,[ 9 , 10 ] since private insurance is mostly unaffordable for the poor.[ 11 ] Historically, social health insurance, tax-based insurance, or a mix of the two have been the dominant health insurance models amongst high income countries and some LMICs, including Brazil, Colombia, Costa Rica, Mexico, and Thailand.[ 12 ] This is partly influenced by the size of the formal sector economy from which taxes and payroll contributions can be collected. In recent decades, community-based health insurance (CBHI) or “mutual health organizations” have become increasingly popular among LMICs, particularly in Sub-Saharan Africa (e.g. Burkina Faso,[ 13 ] Senegal[ 14 ] and Rwanda[ 15 ]) as well as Asia (e.g. China[ 16 ] and India[ 17 ]). CBHI has emerged as an alternative health financing strategy, particularly in cases where the public sector has failed to provide adequate access to health care.[ 18 ]

We searched for existing systematic reviews on health insurance in the Cochrane Database for Systematic Reviews, Medline, Embase, and Econlit. Search terms “health insurance”, “low-middle income countries”, and “utilisation” were used alongside methodological search strategy to locate reviews. Seven systematic reviews were identified of varying levels of quality, [ 19 – 26 ] with Acharya et al.[ 27 ] being the most comprehensive. The majority of existing reviews has suggested that publicly-funded health insurance has typically shown a positive impact on access to care, while the picture for financial protection was mixed, and evidence of the impact on health status was very sparse.

This study reviews systematically the recent fast-growing evidence on the impact of health insurance on health care utilisation, financial protection and health status in LMICs. Since the publication of Acharya et al. (which conducted literature searches in July 2010), the empirical evidence on the impact of health insurance has expanded significantly in terms of quantity and quality, with growing use of sophisticated techniques to account for statistical challenges[ 28 ] (particularly insurance selection bias). This study makes an important contribution towards our understanding of the impact of health insurance in LMICs, taking particular care in appraising the quality of studies. We recognise the heterogeneity of insurance schemes implemented in LMICs and therefore do not attempt to generalise findings, but we aim to explore the pattern emerging from various studies and to extract common factors that may affect the effectiveness of health insurance, that should be the focus of future policy and research. Furthermore, we explore evidence of moral hazard in insurance membership, an aspect that was not addressed in the Acharya et al review.[ 27 ]

This review was planned, conducted, and reported in adherence with PRISMA standards of quality for reporting systematic reviews.[ 29 ]

Participants

Studies focusing on LMICs are included, as measured by per capita gross national income (GNI) estimated using the World Bank Atlas method per July 2016.[ 30 ]

Intervention

Classification of health insurance can be complicated due to the many characteristics defining its structure, including the mode of participation (compulsory or voluntary), benefit entitlement, level of membership (individual or household), methods for raising funds (taxes, flat premium, or income-based premium) and the mechanism and extent of risk pooling [ 31 ]. For the purpose of this review, we included all health insurance schemes organised by government, comprising social health insurance and tax-based health insurance. Private health insurance was excluded from our review, but we recognise the presence of community-based health insurance (CBHI) in many LMICs, especially in Africa and Asia [ 18 ]. We also therefore included CBHI if it was scaled up nationally or was actively promoted by national government. Primary studies that included both public and private health insurance were also considered for inclusion if a clear distinction between the two was made in the primary paper. Studies examining other types of financial incentives to increase the demand for healthcare services, such as voucher schemes or cash transfers, were excluded.

Control group

In order to provide robust evidence on the effect on insurance, it is necessary to compare an insured group with an appropriate control group. In this review, we selected studies that used an uninsured population as the control group. Multiple comparison groups were allowed, but an uninsured group had to be one of them.

Outcome measures

We focus on three main outcomes:

• Utilisation of health care facilities or services (e.g. immunisation coverage, number of visits, rates of hospitalisation).
• Financial protection, as measured by changes in out-of-pocket (OOP) health expenditure at household or individual level, and also catastrophic health expenditure or impoverishment from medical expenses.
• Health status, as measured by morbidity and mortality rates, indicators of risk factors (e.g. nutritional status), and self-reported health status.

The scope of this review is not restricted to any level of healthcare delivery (i.e. primary or secondary care). All types of health services were considered in this review.

Types of studies

The review includes randomized controlled trials, quasi-experimental studies (or “natural experiments”[ 32 ]), and observational studies that account for selection bias due to insurance endogeneity (i.e. bias caused by insurance decisions that are correlated with the expected level of utilisation and/or OOP expenditure). Observational studies that did not take account of selection bias were excluded.

Databases and search terms

A search for relevant articles was conducted on 6 September 2016 using peer-reviewed databases (Medline, Embase, Econlit, CINAHL Plus via EBSCO and Web of Science) and grey literatures (WHO, World Bank, and PAHO). Our search was restricted to studies published since July 2010, immediately after the period covered by the earlier Acharya et al. (2012) review. No language restrictions were applied. Full details of our search strategy are available in the supporting information ( S1 Table ).

Screening and data extraction

Two independent reviewers (DE and MS) screened all titles and abstracts of the initially identified studies to determine whether they satisfied the inclusion criteria. Any disagreement was resolved through mutual consensus. Full texts were retrieved for the studies that met the inclusion criteria. A data collection form was used to extract the relevant information from the included studies.

Assessment of study quality

We used the Grades of Assessment, Development and Evaluation (GRADE) system checklist[ 33 , 34 ] which is commonly used for quality assessment in systematic reviews. However, GRADE does not rate observational studies based on whether they controlled for selection bias. Therefore, we supplemented the GRADE score with the ‘Quality of Effectiveness Estimates from Non-randomised Studies’ (QuEENS) checklist.[ 35 ]

cRandomised studies were considered to have low risk of bias. Non-randomised studies that account for selection on observable variables, such as propensity score matching (PSM), were categorised as high risk of bias unless they provided adequate assumption checks or compared the results to those from other methods, in which case they may be classed as medium risk. Non-randomised studies that account for selection on both observables and unobservables, such as regression with difference-in-differences (DiD) or Heckman sample selection models, were considered to have medium risk of bias–some of these studies were graded as high or low risk depending on sufficiency of assumption checks and comparison with results from other methods.

Heterogeneity of health insurance programmes across countries and variability in empirical methods used across studies precluded a formal meta-analysis. We therefore conducted a narrative synthesis of the literature and did not report the effect size. Throughout this review, we only considered three possible effects: positive outcome, negative outcome, or no statistically significant effect (here defined as p-value > 0.1).

Results of the search

Our database search identified 8,755 studies. Five additional studies were retrieved from grey literature. After screening of titles and abstracts, 118 studies were identified as potentially relevant. After reviewing the full-texts, 68 studies were included in the systematic review (see Fig 1 for the PRISMA diagram). A full description of the included studies is presented in the supporting information ( S2 Table ). Of the 68 included studies, 40 studies examined the effect on utilisation, 46 studies on financial protection, and only 12 studies on health status (see Table 1 ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0219731.g001

https://doi.org/10.1371/journal.pone.0219731.t001

Utilisation of health care

Table 2 collates evidence on the effects of health insurance on utilisation of healthcare services. Three main findings were observed:

• Evidence on utilisation of curative care generally suggested a positive effect, with 30 out of 38 studies reporting a statistically significant positive effect.
• Evidence on preventive care is less clear with 4 out of 7 studies reporting a positive effect, two studies finding a negative effect and one study reporting no effect.
• Among the higher quality studies, i.e. those that suitably controlled for selection bias reflected by moderate or low GRADE score and low risk of bias (score = 3) on QuEENS, seven studies reported a positive relationship between insurance and utilisation. One study[ 36 ] reported no statistically significant effect, and another study found a statistically significant negative effect.[ 37 ]

https://doi.org/10.1371/journal.pone.0219731.t002

Financial protection

Overall, evidence on the impact of health insurance on financial protection is less clear than that for utilisation (see Table 3 ). 34 of the 46 studies reported the impact of health insurance on the level of out-of-pocket health expenditure. Among those 34 studies, 17 found a positive effect (i.e. a reduction in out-of-pocket expenditure), 15 studies found no statistically significant effect, and two studies–from Indonesia[ 59 ] and Peru[ 62 ]–reported a negative effect (i.e. an increase in out-of-pocket expenditure).

https://doi.org/10.1371/journal.pone.0219731.t003

Another financial protection measure is the probability of incurring catastrophic health expenditure defined as OOP exceeding a certain threshold percentage of total expenditure or income. Of the 14 studies reporting this measure, nine reported reduction in the risk of catastrophic expenditure, three found no statistically significant difference, and two found a negative effect of health insurance. Only four studies reported sensitivity analysis varying changes in the threshold level,[ 59 , 62 , 75 , 76 ] though this did not materially affect the findings.

• Two studies used a different measure of financial protection, the probability of impoverishment due to catastrophic health expenditure, reporting conflicting findings.[ 77 , 78 ] Finally, four studies evaluated the effect on financial protection by assessing the impact of insurance on non-healthcare consumption or saving behaviour, such as non-medical related consumption[ 79 ], probability of financing medical bills via asset sales or borrowing[ 40 ], and household saving[ 80 ]. No clear pattern can be observed from those four studies.

Health status

Improving health is one of the main objectives of health insurance, yet very few studies thus far have attempted to evaluate health outcomes. We identified 12 studies, with considerable variation in the precise health measure considered (see Table 4 ). There was some evidence of positive impact on health status: nine studies found a positive effect, one study reported a negative effect, and two studies reported no effect.

https://doi.org/10.1371/journal.pone.0219731.t004

Type of insurance and countries

Considering the heterogeneity of insurance schemes among different countries, we attempted to explore the aggregate results by the type of insurance scheme and by country. Table 5 provides a summary of results classified by three type of insurance scheme: community-based health insurance, voluntary health insurance (non-CBHI), and compulsory health insurance. This division is based on the mode of participation (compulsory vs voluntary), which may affect the presence of adverse selection and moral hazard. Premiums are typically community-rated in CBHI, risk-rated in voluntary schemes and income-rated in compulsory schemes.

https://doi.org/10.1371/journal.pone.0219731.t005

In principle, CBHI is also considered a voluntary scheme, but we separated it to explore whether the larger size of pooling from non-CBHI schemes may affect the outcomes. Social health insurance is theoretically a mandatory scheme that requires contribution from the enrolees. However, in the context of LMICs, the mandatory element is hard to enforce, and in practice the scheme adopts a voluntary enrolment. Additionally, the government may also want to subsidise the premium for poor people. Therefore, in this review SHI schemes can fall into either the voluntary health insurance (non-CBHI) or compulsory health insurance (non-CBHI), depending on the target population defined in the evaluation study. Lastly, we chose studies with high quality/low risk only to provide more robust results.

Based on the summary in Table 5 , the effect on utilisation overall does not differ based on type of insurance, with most evidence suggesting an overall increase in utilisation by the insured. The two studies showing no effect or reduced consumption of care were conducted in two different areas of India, which may–somewhat tentatively–suggest a common factor unique to India’s health system that may compromise the effectiveness of health insurance in increasing utilisation.

Regarding financial protection, the evidence for both CBHI and non-CBHI voluntary health insurance is inconclusive. Furthermore, there is an indication of heterogeneity by supply side factors captured by proximity to health facilities. Evidence from studies exploring subsidised schemes suggests no effect on financial protection, even a negative effect among the insured in Peru.

Lastly, evidence for health status may be influenced by how health outcomes are measured. Studies exploring specific health status, (examples included health indexes, wasting, C-reactive protein, and low birth weight), show a positive effect, whereas studies using mortality rates tends to show no effect or even negative effects. Studies exploring CBHI scheme did not find any evidence of positive effect on health status, as measured either by mortality rate or specific health status.

This review synthesises the recent, burgeoning empirical literature on the impact of health insurance in LMICs. We identified a total of 68 eligible studies over a period of six years–double the amount identified by the previous review by Acharya et al. over an approximately 60-year time horizon (1950—July 2010). We used two quality assessment checklists to scrutinise the study methodology, taking more explicit account of the methodological robustness of non-experimental designs.

Programme evaluation has been of interest to many researchers for reporting on the effectiveness of a public policy to policymakers. In theory, the gold standard for a programme evaluation is the randomised control trial, in which the treatment is randomly assigned to the participants. The treatment assignment process has to be exogenous to ensure that any observed effect between the treated and control groups can only be caused by the difference in the treatment assignment. Unfortunately, this ideal scenario is often not feasible in a public policy setting. Our findings showed that only three papers between 2010 and 2016 were able to conduct a randomised study to evaluate the impact of health insurance programmes in developing countries, particularly CBHI [ 38 , 75 , 103 ]. Policymakers may believe in the value of an intervention regardless of its actual evidence base, or they may believe that the intervention is beneficial and that no one in need should be denied it. In addition, policymakers are inclined to demonstrate the effectiveness of an intervention that they want implemented in the most promising contexts, as opposed to random allocation [ 104 ].

Consequently, programme evaluators often have to deal with a non-randomised treatment assignment which may result in selection bias problems. Selection bias is defined as a spurious relationship between the treatment and the outcome of interest due to the systematic differences between the treated and the control groups [ 105 ]. In the case of health insurance, an individual who chooses to enrol in the scheme may have different characteristics to an individual who chooses not to enrol. When those important characteristics are unobservable, the analyst needs to apply more advanced techniques and, sometimes, stronger assumptions. Based on our findings, we noted several popular methods, including propensity score matching (N = 8), difference-in-difference (N = 10), fixed or random effects of panel data (N = 6), instrumental variables (N = 12) and regression discontinuity (N = 6). Those methods have varying degree of success in controlling the unobserved selection bias and analysts should explore the robustness of their findings by comparing initial findings with other methods by testing important assumptions. We noted some papers combining two common methods, such as difference-in-difference with propensity score matching (N = 10) and fixed effects with instrumental variables (N = 8), in order to obtain more robust results.

Overall effect

Compared with the earlier review, our study has found stronger and more consistent evidence of positive effects of health insurance on health care utilisation, but less clear evidence on financial protection. Restricting the evidence base to the small subset of randomised studies, the effects on financial protection appear more consistently positive, i.e. three cluster randomised studies[ 39 , 75 , 76 ] showed a decline in OOP expenditure and one randomised study[ 36 ] found no significant effect.

Besides the impact on utilisation and financial protection, this review identified a number of good quality studies measuring the impact of health insurance on health outcomes. Twelve studies were identified (i.e. twice as many as those published before 2010), nine of which showed a beneficial health effect. This holds for the subset of papers with stronger methodology for tackling selection bias.[ 39 , 49 , 89 , 103 ] In cases where a health insurance programme does not have a positive effect on either utilisation, financial protection, and health status, it is particularly important to understand the underlying reasons.

Possible explanation of heterogeneity

Payment system..

Heterogeneity of the impact of health insurance may be explained by differences in health systems and/or health insurance programmes. Robyn et al. (2012) and Fink et al (2013) argued that the lack of significant effect of insurance in Burkina Faso may have been partially influenced by the capitation payment system. As the health workers relied heavily on user fees for their income, the change of payment system from fee-for-services to capitation may have discouraged provision of high quality services. If enrolees perceive the quality of contracted providers as bad, they might delay seeking treatment, which in turn could impact negatively on health.

Several studies from China found the utilisation of expensive treatment and higher-level health care facilities to have increased following the introduction of the insurance scheme.[ 41 , 44 , 45 , 88 ] A fee-for-service payment system may have incentivised providers to include more expensive treatments.[ 43 , 83 , 88 ] Recent systematic reviews suggested that payment systems might play a key role in determining the success of insurance schemes,[ 23 , 106 ] but this evidence is still weak, as most of the included studies were observational studies that did not control sufficiently for selection bias.

Uncovered essential items.

Sood et al. (2014) found no statistically significant effect of community-based health insurance on utilisation in India. They argued that this could be caused by their inability to specify the medical conditions covered by the insurance, causing dilution of a potential true effect. In other countries, transportation costs[ 69 ] and treatments that were not covered by the insurance[ 59 , 60 ] may explain the absence of a reduction in out-of-pocket health expenditures.

Methodological differences.

Two studies in Georgia evaluated the same programme but with different conclusions.[ 50 , 51 ] This discrepancy may be explained by the difference in the estimated treatment effect: one used average treatment effect (ATE), finding no effect, and another used average treatment effect on the treated (ATT), reporting a positive effect. ATE is of prime interest when policymakers are interested in scaling up the programme, whereas ATT is useful to measure the effect on people who were actually exposed to insurance.[ 107 ]

Duration of health insurance.

We also found that the longer an insurance programme has been in place prior to the timing of the evaluation, the higher the odds of improved health outcomes. It is plausible that health insurance would not change the health status of population instantly upon implementation.[ 21 ] While there may be an appetite among policymakers to obtain favourable short term assessments, it is important to compare the impact over time, where feasible.

Moral hazard.

Acharya et al (2012) raised an important question about the possibility of a moral hazard effect as an unintended consequence of introducing (or expanding) health insurance in LMICs. We found seven studies exploring ex-ante moral hazard by estimating the effect on preventive care. If uninsured individuals expect to be covered in the future, they may reduce the consumption of preventive care or invest less in healthy behaviours.[ 108 , 109 ] Current overall evidence cannot suggest a definite conclusion considering the heterogeneity in chosen outcomes. One study found that the use of a self-treated bed nets to prevent malaria declined among the insured group in Ghana[ 54 ] while two studies reported an increase in vaccination rates[ 62 ] and the number of prenatal care visits[ 55 , 62 ]among the insured group. Another study reported no evidence that health insurance encouraged unhealthy behaviour or reduction of preventive efforts in Thailand.[ 66 ]

Two studies from Colombia found that the insured group is more likely to increase their demand for preventive treatment.[ 47 , 49 ] As preventive treatment is free for all, both authors attributed this increased demand to the scheme’s capitation system, incentivising providers to promote preventive care to avoid future costly treatments.[ 110 ] Another study of a different health insurance programme in Colombia found an opposite effect.[ 48 ]

Study limitations.

This review includes a large variety of study designs and indicators for assessing the multiple potential impacts of health insurance, making it hard to directly compare and aggregate findings. For those studies that used a control group, the use of self-selected controls in many cases creates potential bias. Studies of the effect of CBHI are often better at establishing the counterfactual by allowing the use of randomisation in a small area, whereas government schemes or social health insurance covering larger populations have limited opportunity to use randomisation. Non-randomised studies are more susceptible to confounding factors unobserved by the analysts. For a better understanding of the links between health insurance and relevant outcomes, there is also a need to go beyond quantitative evidence alone and combine the quantitative findings with qualitative insights. This is particularly important when trying to interpret some of the counterintuitive results encountered in some studies.

The impact of different health insurance schemes in many countries on utilisation generally shows a positive effect. This is aligned with the supply-demand theory in whichhealth insurance decreases the price of health care services resulting in increased demand. It is difficult to draw an overall conclusion about the impact of health insurance on financial protection, most likely because of differences in health insurance programmes. The impact of health insurance on health status suggests a promising positive effect, but more studies from different countries is required.

The interest in achieving UHC via publicly funded health insurance is likely to increase even further in the coming years, and it is one of the United Nation’s Sustainable Development Goals (SDGs) for 2030[ 111 ]. As public health insurance is still being widely implemented in many LMICs, the findings from this review should be of interest to health experts and policy-makers at the national and the international level.

Supporting information

S1 table. search strategies..

https://doi.org/10.1371/journal.pone.0219731.s001

S2 Table. Study characteristic and reported effect from the included studies (N = 68).

https://doi.org/10.1371/journal.pone.0219731.s002

S3 Table. PRISMA 2009 checklist.

https://doi.org/10.1371/journal.pone.0219731.s003

• View Article
• Google Scholar
• PubMed/NCBI
• 4. World Health Organization. Research for universal health coverage: World health report 2013. WHO. Geneva: World Health Organization; 2014.
• 5. World Health Organization. The world health report 2010. Health systems financing: the path to universal coverage. World Health Organization; 2010.
• 9. Maeda A, Araujo E, Cashin C, Harris J, Ikegami N, Reich MR. Universal Health Coverage for Inclusive and Sustainable Development: A Synthesis of 11 Country Case Studies. The World Bank; 2014. https://doi.org/10.1596/978-1-4648-0297-3
• 10. Jowett M, Kutzin J. Raising revenues for health in support of UHC: strategic issues for policy makers. world Health Organization. Geneva; 2015. Report No.: 1. https://doi.org/10.1080/13545701.2015.1088658
• 12. Wang H, Switlick K, Ortiz C, Zurita B, Connor C. Health Insurance Handbook. The World Bank; 2011. https://doi.org/10.1596/978-0-8213-8982-9
• 21. Giedion U, Alfonso EA, Díaz Y, Andrés Alfonso E, Díaz Y. The Impact of Universal Coverage Schemes in the Developing World: A Review of the Existing Evidence. Univers Heal Cover Stud Ser (UNICO), No 25. Washington DC: World; 2013;
• 31. A System of Health Accounts 2011. OECD; 2017. https://doi.org/10.1787/9789264270985-en
• 34. Cochrane . Cochrane handbook for systematic reviews of interventions. Higgins JPT, Green SE, editors. Wiley-Blackwell; 2008.
• 37. Sheth K. Evaluating Health-Seeking Behavior, Utilization of Care, and Health Risk: Evidence from a Community Based Insurance Model in India. 2014. Report No.: 36.
• 105. Wooldridge JM. Introductory Econometrics: A Modern Approach. Fifth Inte. Mason, Ohio: South-Western Cengage Learning; 2013.
• 111. World Health Organization. World Health Statistics 2016: Monitoring health for the SDGs. WHO. Geneva: World Health Organization; 2017.

• Health Insurance
• Best And Most Affordable Health Insurance Plans

Best Affordable Health Insurance Plans Of 2024

Expert Reviewed

Updated: May 1, 2024, 12:25pm

Kaiser Permanente is the best affordable health insurance company on the Affordable Care Act (ACA) marketplace.

The ACA marketplace at HealthCare.gov lets you compare health plan options, including information about costs, deductibles and coinsurance. It’s a great way to find health insurance if you can’t get coverage through work.

We compared health plans that offer ACA coverage to find the best and most affordable health insurance plan.

• Best Dental Insurance
• Best Disability Insurance
• Best Health Insurance
• Best Short-Term Health Insurance
• Best Vision Insurance

Summary: The Best Affordable Health Insurance Companies

Best affordable health insurance companies, how can i get affordable health insurance.

• How Much Does Health Insurance Cost?

What to Consider When Searching for Affordable Health Insurance

Methodology, other health insurance companies we rated, affordable health insurance frequently asked questions (faqs).

Kaiser Permanente and Blue Cross Blue Shield top our list, with UnitedHealthcare also receiving high marks.

Source: Healthcare.gov. Based on unsubsidized ACA plans. Average costs can vary significantly depending on your state and age.

How We Chose the Best Affordable Health Insurance Companies

We collected data such as complaints to state insurance departments, quality ratings, deductibles, breadth of health plans and metal-tier offerings. Our editors are committed to bringing you unbiased ratings and information. Our editorial content is not influenced by advertisers. You can read more about our editorial guidelines and the methodology for the ratings below.

• 259 health insurance plan costs crunched
• 84 coverage and quality data points analyzed
• 102 years of insurance experience on the editorial team

Cheapest health insurance company

Kaiser permanente.

NCQA Quality Rating

4.2 out of 5

Average cost for a bronze plan for a 40-year-old

$351 a month

Average bronze plan deductible

$6,700 a year

Kaiser Permanente has the cheapest prices among the health insurance companies we evaluated. It has superior ratings from the National Committee for Quality Assurance and offers four different types of metal tiers on the ACA marketplace. We like that Kaiser Permanente provides many choices, which can help you to find the right premium/out-of-pocket cost balance for you.

• Best average ACA plan ratings among the insurers we reviewed.
• Operates an integrated health system, which may reduce potential claims problems and make healthcare more seamless.
• Highest NCQA quality rating of the health insurance companies we analyzed.
• Cheapest health insurance deductible for bronze plans, on average, compared to large insurers.

More:   Kaiser Permanente Health Insurance Review

• Only available in eight states and Washington, D.C.
• Consumer complaints to state insurance commissioners are higher than the industry average.
• Washington, D.C.

Best Provider Network

Blue cross blue shield.

3.5 out of 5

$458 a month

$7,173 a year

We like that you can find Blue Cross Blue Shield providers in any state. Blue Cross Blue Shield has over 1.7 million in-network providers that are composed of 33 independent insurers. That means it’s easier to find an in-network provider when you travel. It offers three types of health plans and four metal tiers on the ACA marketplace.

• Lower than average silver plan deductible costs.
• Available nationwide.
• Better-than-average quality ratings from the National Committee for Quality Assurance.

More:  Blue Cross Blue Shield Health Insurance Review

• Level of complaints to state insurance commissioners is slightly above average compared to the rest of the industry.
• Higher ACA marketplace premiums than competitors we analyzed.

All 50 states and Washington, D.C.

Best for customer satisfaction

Unitedhealthcare.

$427 a month

$8,177 a year

UnitedHealthcare was the only insurer in our analysis with a lower-than-average level of complaints to state insurance departments. That may mean higher customer satisfaction than competitors. We also like that it has better-than-average plan ratings from the National Committee for Quality Assurance.

• Large provider network across the country with 1.5 million providers and 7,000 hospitals and facilities.
• Three metal tiers offered on ACA marketplace.
• Provides other types of insurance, including dental, vision, accident and critical illness plans.

More:   UnitedHealthcare Health Insurance Review

• Premiums are higher than some competitors.
• Doesn’t offer as many metal tiers or types of health plans in the ACA marketplace compared to competitors.
• Massachusetts
• Mississippi
• North Carolina

There are multiple avenues to finding cheap health insurance .

A bronze health insurance plan on the Affordable Care Act marketplace costs an average of $373 monthly for a 30-year-old.

• A 40-year-old pays an average of $420 monthly for the same coverage.
• A 50-year-old pays $587 monthly.
• A 60-year-old pays $890 monthly.

Bronze plans have the lowest health insurance premiums on the ACA marketplace. Silver plans have the next lowest premiums. A silver health insurance plan costs an average of $488 monthly for a 30-year-old, $549 for a 40-year-old, $767 for a 50-year-old and $1,164 for a 60-year-old.

None of these averages factor in subsidies or tax credits, which will lower your overall cost. ACA plans are the only ones that have those tax credits, which can reduce your costs. People with household incomes below 400% of the federal poverty level qualify for subsidies on ACA plans.

Average Cost of Health Insurance by Age

Age plays an important role in how much you pay for ACA marketplace coverage.

Health insurance costs in the ACA marketplace vary based on multiple factors, including:

• The health insurance company.
• The metal tier.
• The type of health plan.
• The out-of-pocket costs, including deductibles and coinsurance.
• Your smoking status.

Average Monthly Health Insurance Costs by Tier

Ask an expert

How to Find the Best Cheap Health Insurance

Patrick Padgett

Advisory Board Member

Les Masterson

Insurance Editor

Insurance Lead Editor

Insurance Managing Editor

Michelle Megna

Get Subsidies if You Can

If you are purchasing a plan through the ACA marketplace, subsidies make all of the difference in how much you will pay in premiums and are dependent on your annual income. If you are not yet 65 and retired, look for ways to keep your taxable income below the maximum level to obtain subsidies.

Compare Quotes

If you’re buying a health plan through the ACA marketplace, I think it’s wise to compare health insurance quotes on the marketplace website. Make sure to compare the costs for the same metal tier, so you can gauge the plans accurately.

Consider an HMO or EPO

If you want the most affordable health plan benefit design, I think you may want to go with a health maintenance organization (HMO) or exclusive provider organization (EPO) plan. Those plans require you to stay in the plan’s provider network, but they have lower premiums than a preferred provider organization (PPO) plan.

Make Sure Your Doctors Take the Plan

Make sure that your providers accept a health insurance plan before you buy it. I would check with your providers to confirm that they take that specific insurance plan. Don’t rely on the health insurance company’s online provider directory, which can be incorrect or out of date.

Get on a Spouse or Parent's Plan

Getting added to a spouse or parent’s health plan may cost less than buying your own health insurance. I suggest reviewing all of your health insurance options, including a spouse or parent’s health plan, before choosing a plan.

When looking for a cheap health insurance plan, consider your family’s current and near-future healthcare needs. Are you planning to start a new family? Are you on many prescription medications? Do you expect you’ll need to get that trick knee fixed in the coming year?

All of those factors should be taken into account so you can decide whether or not you should choose a plan with a high deductible. A high-deductible health plan (HDHP) typically has lower premiums, but you pay more out-of-pocket when you need healthcare.

We analyzed 84 data points about coverage and quality for seven large health insurance companies to determine the best and most affordable health insurance companies. Our ratings are based on:

• Complaints made to state insurance departments (30% of score): We used complaint data from the National Association of Insurance Commissioners .
• Plan ratings from the National Committee for Quality Assurance (30% of score): The National Committee for Quality Assurance is an independent, nonprofit organization that accredits health plans and produces ratings based on specific metrics, including patient experience, prevention, treatment, overall rating of the health plan and rating of care.
• Average silver plan deductible (20% of score): The deductible is how much you have to pay for healthcare in a year before the health plan begins picking up a portion of the costs. Companies with health plans that had low deductibles got more points. Source: HealthCare.gov .
• Breadth of health plans (10% of score): Health insurance companies may offer up to four types of plan benefit designs (PPO, HMO, EPO and POS). Companies that offered more types of plans got more points. Source: HealthCare.gov .
• Metal tier offerings (10% of score): The ACA marketplace has four metal tier levels. We gave points to companies that offered more tier plan options. Source: HealthCare.gov .

Read more: How Forbes Advisor rates health insurance companies

Here are other health insurance companies we analyzed as part of our research.

Get Forbes Advisor’s ratings of the best insurance companies and helpful information on how to find the best travel, auto, home, health, life, pet, and small business coverage for your needs.

What is the most affordable health insurance company?

Kaiser Permanente is the most affordable health insurance company, according to our analysis of seven health insurers.

Our research found that Kaiser Permanente has the cheapest home insurance rates, on average, for multiple ages and metal tiers on the Affordable Care Act marketplace. For instance, a 40-year-old pays $351 monthly on average for a Kaiser Permanente unsubsidized Bronze plan, compared to $378 for an Aetna plan and $392 for an Oscar plan.

Can I buy affordable health insurance any time?

You usually can only buy a new health insurance plan or change your coverage during the annual open enrollment period unless you have a qualifying life event. For instance, open enrollment for ACA marketplace plans runs from November 1 to January 15 in most states, though some states have longer open enrollment periods.

A qualifying life event, such as losing your health coverage, getting married or having a baby, typically kicks off a special enrollment period . During a special enrollment period, you can sign up for overage or change your existing health coverage.

Can I negotiate the cost of health insurance?

No, you can’t typically negotiate health insurance costs. The health insurance company may offer another plan that would be a better fit for you.

For instance, an insurer may suggest a lower-cost bronze plan, going with a higher deductible or choosing a more restrictive plan like a health maintenance organization (HMO) plan. Those options often have lower premiums than more expensive coverage options.

Next Up In Health Insurance

• How To Get Health Insurance?
• Compare Health Insurance Quotes

Michelle is a lead editor at Forbes Advisor. She has been a journalist for over 35 years, writing about insurance for consumers for the last decade. Prior to covering insurance, Michelle was a lifestyle reporter at the New York Daily News, a magazine editor covering consumer technology, a foreign correspondent for Time and various newswires and local newspaper reporter.

Patrick T. Padgett is executive vice president/CEO of the Kentucky Medical Association. He served two tours with the United States Navy Judge Advocate General’s Corps, one as a military prosecutor and a second working with Naval Intelligence. Patrick joined the KMA staff in 1996 and served for 11 years as KMA’s counsel before being elevated to CEO in 2007.   He has authored a number of papers and booklets, including the KMA Legal Handbook for Kentucky Physicians and is a frequent speaker on such topics as the basics of health insurance, laws and regulations impacting health insurance, healthcare policy, leadership and personal finance.   He is the former chair of the American Medical Association Litigation Center Executive Committee; a former member of the Ronald McDonald House of Kentuckiana Board of Directors; and for the 2023-2024 term, the chair of the Kentucky Humane Society Board of Directors. Patrick is married to his wife Elizabeth, and has two dogs named Henry and Sophie.

• Search Search Please fill out this field.

How We Research Health Insurance Plans

Health insurance evaluation categories.

• Health Plan Quality & Customer Satisfaction

Plan Features

State availability, articles that use our methodology.

• Meet the Insurance Research & Reviews Team
• Health Insurance

How We Review Health Insurance Companies

FG Trade Latin / Getty Images

Investopedia is dedicated to helping you find the right health insurance provider for your needs. We’ve completed in-depth research into nine companies that provide health insurance on the federal Marketplace, which was set up after the passage of the  Affordable Care Act (ACA) . We compared plan details across four categories—cost, features, availability, and plan quality and customer satisfaction. This guide explains these categories, the criteria we used to evaluate each health insurance provider, and how we generate scores for each insurer.

Our editors and researchers independently evaluate all recommended products and services. If you click on links we provide, we may receive compensation. Our advertising partnerships are not a factor in how we evaluate products, though they may affect the order of products you see listed in our articles.

To identify which health insurance companies to review, we analyzed business and market insight databases, including Statistia, Plunkett, and Gale; considered health insurance company market share; and researched user-generated data from Google to determine public interest and trends in health insurance companies and plans. 

We collected data from the National Committee for Quality Assurance (NCQA) , an independent organization that rates health care plans on quality and patient satisfaction. We also gathered data from state and federal government health care marketplace websites and databases, and directly from companies via websites, media contacts, and existing partnerships. The data collection process took place between Sept. 29 and Oct. 23, 2023.

We then developed a quantitative model that scores each health care plan based on four major categories and 27 criteria that are crucial in evaluating the plan’s offerings and benefits.

Data Collection and Scoring 

• Data points were scored on a 0.00 - 1.00 scale
• Binary criteria received a 0 or 1 score
• Scaled criteria (e.g., 5-point) were scored as such: [0.00, 0.25, 0.50, 0.75, 1.00]
• Continuous criteria were scored such that the minimum value in the database was re-scaled to 0.00 and the maximum value was re-scaled to 1.00

After determining four key categories with which to evaluate health insurance plans and providers, we weighted them differently depending on the article.

These categories were then broken down into 27 criteria, resulting in 243 data points that make up our scoring rubric.

Health Plan Quality & Customer Satisfaction

We scored customer satisfaction on a continuous scale using NCQA ratings.

NCQA Ratings

National Committee for Quality Assurance (NCQA) star ratings measure the performance of managed care plans in terms of effectiveness, availability and accessibility, communication about the plan, and more, on a scale of one to five stars. We then scored this rating on a continuous scale.

This criterion helped us understand how well a company’s health plans deliver for patients and how satisfied customers are with the different companies considered for our rankings.

We weighted these scores as such across our different articles:

• Best Health Insurance Companies: 20%
• Best Affordable Health Insurance: 10%
• Best Health Insurance for the Self-Employed: 15%
• Best Health Insurance in Florida: 25% 

Plan features measured types of plans (HMO, PPO, EPO), metal levels (six levels in all), health management programs (nine specific programs), and adult dental coverage.

For the best health insurance companies and best health insurance for the self-employed, this group of criteria was worth 40% of a company’s overall score. 

For the best affordable health insurance companies, it was worth 30%. For the best health insurance companies in Florida, it was worth 50%.

Plan features are an indicator of the breadth of coverage, features, and options available for consumers, which can be an important factor when choosing a health insurance provider . 

Types of Plans Available (HMO, PPO, EPO)

We scored the availability of each plan type first with a binary scale (0 for no, 1 for yes) and then assigned subweights for each plan type: 33% for health management organization (HMO) , 33% for preferred provider organization (PPO) , and 33% for exclusive provider organization (EPO) . We multiplied each binary score by the percentage weight, took the sum, and got a weighted average on a scale of 0 to 1 to get a total plan availability score for each company.

Metal Levels Available 

We counted the number of metal levels a company offers (bronze, expanded bronze, silver, gold, platinum, and catastrophic) and scored on a fixed interval scale by dividing that number by the maximum number of levels offered by any company on our list (six). 

Health Management Programs Available

We measured whether companies offer the following nine health management programs: 

• Heart disease
• High blood pressure/cholesterol
• Low back pain
• Pain management
• Weight loss

We evaluated each health management program and scored on a binary scale. If a company offers the program, it scored 1; if the company doesn’t offer the program, it scored 0. Then we took the sum of binary scores across all nine and divided that number by the maximum programs offered by any company on our list (nine). That number was then scored on a fixed interval scale. We did that calculation for two ZIP codes: 33012 (Miami) and 79936 (El Paso, Texas), then averaged the scores for the two ZIP codes to come up with one score. 

Adult Dental Coverage

We used a binary scale to measure whether companies offer adult dental coverage in at least one of the two ZIP codes we researched, granting scores of 1 for companies that offered it and 0 for those that did not.

We gathered quotes for coverage in two different ways:

• By looking at the copays associated with each provider’s bronze and silver plans with the lowest copays for a visit to a physician
• By examining the costs associated with each provider’s bronze, silver, and gold plans with the lowest premiums

We did this across two ZIP codes: 33012 (Miami) and 79936 (El Paso, Texas). For companies without a presence in either of these states, we used 90011 (Los Angeles) or 30369 (Atlanta).

Our final cost score encompasses data from both collection methods described below, incorporating copays, premiums, and deductibles across age groups, metal tiers, and ZIP codes.

The final cost score was weighted across our different articles as follows:

• Best Health Insurance Companies: 25%
• Best Affordable Health Insurance: 45%
• Best Health Insurance for the Self-Employed: 30%

Copays of Plans With the Lowest Physician Copays

For each of these plans, we collected physician copays and specialist copays in each ZIP code. We calculated a weighted average for silver (66% for physician copay and 33% for specialist copay) and bronze (same weights) and averaged those together to get a copay score. We then averaged copay scores for each ZIP code together to come up with an average copay score. We called this score A. 

Premiums & Deductibles for Plans With the Lowest Premiums

For each of these plans, we collected premiums and their associated deductibles across four age groups: 25-year-olds, 35-year-olds, 45-year-olds, and 60-year-olds in each of three metal tiers (bronze, silver, and gold) and the two ZIP codes mentioned above. Each premium received its own score on a continuous scale of 0 to 1 based on how that premium compared to the highest and lowest premiums we gathered. Then that same method was used to score each deductible. We averaged the premium and deductible scores together to get one premium-deductible score for each age group in each of the three separate metal tiers and each ZIP code. 

We then took those individual premium-deductible scores and averaged them together across the metal tiers and ZIP codes so that we had a score that could tell us, for instance, which company had the best premium-deductible score for 25-year-olds or for bronze plans. 

Those scores were averaged for each company for a premium-deductible metal/age/ZIP code score we call B. This allowed us to say that a particular company had the best premiums and deductibles overall.

Our final cost score covers even more. We averaged scores A and B together for an overall cost score that incorporates copays, premiums, and deductibles across age groups, metal tiers, and ZIP codes. 

Percentage of Plans With 100% of Premiums Eligible for Tax Credits

Using data from Healthcare.gov, we looked at what percentage of a company’s plans are 100% eligible for tax credits. Premium tax credits subsidize the purchase of health insurance offered on the federal and state health insurance exchanges. 

To qualify for premium tax credits, your income must fall within certain limits, and you can’t be eligible for affordable insurance through an eligible employer-sponsored plan or a government program like Medicare or Medicaid. We counted the number of plans each company has with 100% of premiums eligible for premium tax credits. That number was then divided into the total number of health insurance plans that the company offered nationwide.

We counted up the number of states in which each company made its ACA plans available, using data from both the federal and state marketplaces. That tally was then divided by the highest number of states that any company offered (36) and scored on a continuous scale.

State availability was weighted as follows for our lists:

• Best Health Insurance Companies: 15% 
• Best Affordable Health Insurance Companies: 15%
• Best Health Insurance Companies for the Self-Employed: 15%
• Best Health Insurance Companies in Florida: 0%

We have many articles about the best health insurance companies to meet your needs. The data collected for this methodology have been used to compile the following lists: 

• Best Health Insurance Companies 
• Best Affordable Health Insurance Companies
• Best Health Insurance Companies for the Self-Employed
• Best Health Insurance Companies in Florida

The order of providers on these lists may be based on additional product-specific criteria plus subjective insights from our editors and industry experts. 

Meet the Insurance Research & Reviews Team

Shanker narayan, yasmin ghahremani.

Yasmin Ghahremani is an Associate Editorial Director at Investopedia, where she oversees educational content about consumer financial products, ranging from checking accounts to life insurance. She joined the team in January 2023, after working for nearly four years in a similar role at The Balance. She has more than a decade of experience educating consumers about personal finance, which also includes stints as a managing editor at CreditCards.com and Wise Bread, and a contract editor at LendingTree.

Yasmin has also had an extensive international career covering business, technology, and the environment for broadcast and print outlets, including CNN, CNBC, and Asiaweek magazine. She has a Master of International Affairs degree from Columbia University.

IRS. “ Eligibility for the Premium Tax Credit .”

• Terms of Service
• Editorial Policy
• Privacy Policy

The independent source for health policy research, polling, and news.

Inflation Reduction Act Health Insurance Subsidies: What is Their Impact and What Would Happen if They Expire?

Jared Ortaliza , Anna Cord, Matt McGough , Justin Lo , and Cynthia Cox Published: Jul 26, 2024

As a candidate in 2020, President Biden campaigned on building upon the Affordable Care Act (ACA) by increasing the amount of financial assistance available to people buying their own health insurance coverage through the ACA Marketplaces. Temporary subsidies were originally passed as part of the American Rescue Plan Act (ARPA) in 2021, which included two years of enhanced subsidies (2021 and 2022). The Inflation Reduction Act (IRA), which passed in 2022, extended these enhanced subsidies for an additional three years, ending after 2025.

The IRA and ARPA’s enhanced health insurance subsidies both increase the amount of financial help available to those already eligible for assistance under the ACA and also newly expand subsidies to middle-income people (with incomes over four times the poverty level, $103,280 for a family of three in 2024), many of whom were previously priced out of coverage. These subsidies, combined with increased funding for outreach and marketing , have led to record-high enrollment in the ACA Marketplaces.

By the time these enhanced subsidies are currently set to expire at the end of next year, they will have been an integral part of the ACA Marketplaces for 5 years, or nearly half as long as the ACA Marketplaces have existed. Millions of enrollees have come to rely on the enhanced subsidies, with more people gaining Marketplace coverage since President Biden took office than had signed up for ACA Marketplace when the markets first launched in 2014. If the enhanced subsidies expire, almost all ACA Marketplace enrollees will experience steep increases in premium payments in 2026. However, the subsidies come at a steep cost to taxpayers, with the CBO projecting that a permanent extension of the subsidies would cost $335 billion over the next ten years.

The charts below show the impact these subsidies have had on enrollment and premium payments, and the potential implications if the enhanced subsidies expire. This analysis finds that:

• The recent growth in ACA Marketplace plan enrollment has been driven primarily by low-income people, with signups by people with incomes up to 2.5 times poverty growing 115% since 2020.
• Enhanced subsidies have cut premium payments by an estimated 44% ($705 annually) for enrollees receiving premium tax credits. If the subsidies expire, most Marketplace enrollees will see premium payment increase substantially.
• Without these enhanced subsidies, premiums would double or more, on average, for subsidized enrollees in 12 states using Healthcare.gov.

While enhanced subsidies expire at the end of 2025, insurers and regulators will want to know well in advance whether the subsidies will be renewed or discontinued so they can set accurate premiums for 2026.

Since 2020, the year before the enhanced subsidies went into effect, the number of people with ACA Marketplace coverage has grown by 88% from 11.4 million to 21.4 million.

All the growth in Marketplace enrollment in the last four years is among people receiving an advanced payment of the premium tax credit. Subsidized enrollment is up 106%, from 9.6 million (84% of Marketplace enrollees) in 2020 to 19.7 million people (92% of the total number of Marketplace enrollees). If the Inflation Reduction Act’s enhanced subsidies expire, the Congressional Budget Office (CBO) expects ACA Marketplace enrollment to drop sharply from an estimated 22.8 million in 2025 to 18.9 million the following year. CBO projects that enrollment would continue to fall in the subsequent years reaching as low as 15.4 million in 2030.

Spurred by the availability of plans with no or very low premium payment – often with very low deductibles – made possible by enhanced subsidies, low-income enrollees (those with incomes up to 2.5 times the federal poverty level) have driven most (83%) of the enrollment growth in the ACA Marketplaces from 2020 to 2024. While these plans with little or no premium payment are available nationwide, they are available to a larger share of ACA Marketplace enrollees in the ten states that have not expanded Medicaid.

While most of the recent growth in enrollment is from low-income enrollees, all income groups have seen substantial growth. From 2020 to 2024, the number of Marketplace enrollees with incomes up to 2.5 times poverty grew by 115%, whereas enrollment for those with incomes between 2.5 and 4 times poverty grew by 36%, and enrollment for those with incomes above 4 times poverty grew by 57%.

ACA Marketplace enrollees with incomes just above the federal poverty level (up to 2.5 times poverty) are eligible for cost sharing reductions (CSR) that reduce deductibles and other cost sharing. From 2020 to 2024, the number of enrollees receiving cost sharing reductions increased by 91% from 5.6 million to 10.6 million enrollees.

The Inflation Reduction Act’s enhanced subsidies make the reduced cost sharing plans more affordable. For example, $0 premium silver plans with very low deductibles are available to the lowest income enrollees (those with incomes up to 1.5 times poverty), whereas before the enhanced subsidies became available, these enrollees would have had to pay about 2%-4% of their household income for a plan with a reduced deductible.

The enhanced subsidies in the Inflation Reduction Act reduce net premium costs by 44%, on average, for enrollees receiving premium tax credits, though the amount of savings varies by person. In 2024, the average annual premium payment would have been $1,593, but instead was $888 because of the Inflation Reduction Act subsidies, which average $705 per enrollee.

On average, the total annual premium is similar in 2024 ($7,320) to what it was in 2020 ($7,132), but the federal government is paying a larger share of the total premium (a subsidy of $6,432 or 88% of the average annual premium in 2024, compared to a subsidy of $5,942 dollars or 83% of the average annual premium in 2020).

Enhanced subsidies work by reducing the amount an enrollee has to pay for a benchmark silver plan. Under the Inflation Reduction Act, the amount of money enrollees are required to contribute toward their monthly silver premium varies by income, on a sliding scale with lower income enrollees paying as little as $0 and higher income enrollees paying as much as 8.5% of their household income.

Without enhanced subsidies, an enrollee making just above poverty would be required to pay around 2% of their income for a benchmark silver plan. With enhanced subsidies, however, most enrollees with incomes around the poverty level are eligible for zero-dollar benchmark silver plans. Similarly, without enhanced subsidies, an enrollee with an income just above 400% of the poverty level would have to pay full price for their monthly premium (because they would be ineligible for financial assistance), but with the enhanced subsidies, they pay no more than 8.5% of their household income.

The chart above depicts the percent increase in premium payments for a 45-year-old buying a silver plan, if enhanced subsidies were to expire. (Because 2025 premiums and federal poverty guidelines are not yet available, the chart is based on 2024 premiums and poverty guidelines.)

Low-income enrollees would experience the steepest percent increase in their annual premium payments if enhanced subsidies were unavailable. A 45-year old enrollee making $25,000 (166% of poverty) would see their annual premium payments grow by an average of 573%, or $917, for a benchmark silver plan (an increase from $160 for the annual premium payment with enhanced subsidies to $1,077 without enhanced subsidies). Prior to the enhanced subsidies, enrollees making above 400% of poverty were ineligible for premium assistance. Without enhanced subsidies, a 45-year old individual making $65,000 (432% of poverty) would experience a premium increase of $941 annually from $5,525 to $6,466 (the full cost of the benchmark silver premium).

Prior to the ARPA and Inflation Reduction Act, individuals making above 400% of poverty were ineligible for ACA Marketplace premium subsidies and had to pay the full cost of monthly health insurance premiums. In 2024, CMS estimates that individuals making above four times poverty in HealthCare.gov states save an average of $4,248 annually due to the Inflation Reduction Act’s enhanced subsidies. Without the Inflation Reduction Act subsidies, middle income ACA Marketplace enrollees with incomes just above four times poverty would, in many cases, be priced out of health insurance coverage. The number of ACA Marketplace enrollees making above four times of poverty quadrupled from approximately 400 thousand in 2021 to 1.5 million in 2024.

Inflation Reduction Act subsidies are available nationwide, but current data on the amount of the enhanced subsidies are only available in the 32 states that use Healthcare.gov. In these states, 15.5 million people are receiving an average of $624 per year in enhanced subsidies because of the Inflation Reduction Act. On an annual basis, this translates to nearly $10 billion in enhanced Inflation Reduction Act subsidies going to enrollees in these 32 states in 2024. Among states using Healthcare.gov, the majority (52%) of this federal funding is going to enrollees in Florida ($2.2 billion, or 22%), Texas ($1.5 billion, or 16%), Georgia and North Carolina ($660 million, or 7%, each). These are all high-population states, but also stand out because most have not expanded Medicaid, and therefore have more low-income residents who qualify for substantial ACA subsidies.

The Congressional Budget Office estimates that making enhanced subsidies permanent would result in an increase of $275 billion in direct outlays and a reduction in revenues of $60 billion, for a net impact of $335 billion on the federal budget over the 10-year period from 2025 to 2034. This amount reflects higher enrollment induced by the enhanced subsidies and projections of premium growth over time.

If the Inflation Reduction Act’s enhanced subsidies expire, the vast majority of ACA Marketplace enrollees will see their premium payments increase significantly in 2026.

The results of the 2024 elections will likely play a major role in whether enhanced subsidies are extended beyond 2025. The map above shows 2024 ACA Marketplace enrollment by congressional district in the 118 th Congress. (Though some states have redrawn their congressional district lines ahead of the 2024 election for the 119 th Congress, they remain the same for the majority of states as in the 2022 elections for the 118 th Congress).

Generally, enrollment in Marketplace coverage by congressional district is largest in the South. At least 10% of the population is enrolled in ACA Marketplace plans throughout all congressional districts in Florida and South Carolina, along with most in Texas, Georgia, and Utah. In Florida, there are nine congressional districts where at least 20% of the population is enrolled in in a Marketplace plan.

If the Inflation Reduction Act’s enhanced subsidies were to expire at the end of next year, the vast majority of ACA Marketplace enrollees would see significant increases in their premium payments. However, these increases would vary by state because of differences in the incomes and ages of people living in each state, as well as differences in the premiums charged by insurers in each state.

For subsidized enrollees in states using Healthcare.gov, premium payments average about $672 per year in 2024 ($56 per month). Without enhanced subsidies, the average annual premium payment would rise by 93% ($624) to $1,296.

Based on 2024 premiums, if these enhanced premium subsidies were to expire, subsidized Marketplace enrollees in at least 12 states would see their annual premium payments double or more, on average. As these data are only available in states using Healthcare.gov, there could be additional states that would see average premium payments double. Among states using Healthcare.gov, average annual premium payments for subsidized enrollees would grow the most in Wyoming (195%, or $1,872), Alaska (125%, or $1,836), and West Virginia (133%, or $1,404). In Texas, annual premium payments would increase by an average of 115%, or $456, for the 3.4 million people receiving premium tax credits, if these subsidies were to expire.

If enhanced subsidies expire, gains in Marketplace enrollment are projected to reverse and the health status of remaining enrollees may be sicker, on average, than it is with enhanced subsidies. If insurers expect to lose their healthier enrollees, they may raise premiums heading into 2026.

Every year, in early spring, insurers compile and then submit detailed rate filings proposing premium changes for the following year, for review by state regulators. Regulators evaluate insurer justifications for premium increases, provide feedback to the insurers, and request revisions or additional justifications as deemed necessary. This process stretches into the summer each year.

For the 2026 plan year, when the Inflation Reduction Act subsidies are set to expire, insurers will have to submit their proposed premiums and justifications in early 2025 and finalize their premiums by August 2025, in advance of the 2026 open enrollment period beginning November 1, 2025.

Because of this lengthy process, insurers and state and federal regulators will want to know whether enhanced subsidies will expire or be renewed well in advance of their expiration or renewal. In the leadup to the passage of the Inflation Reduction Act, uncertainty over whether the enhanced subsidies would be extended led some insurers to increase premiums . An April 2022 letter to Congress from the National Association of Insurance Commissioners and signed by regulators from Idaho, Missouri, Connecticut, and North Dakota urged Congress to “to act by July of this year to extend the enhanced premium tax credits beyond their current end date,” which, at the time, was the end of 2022.

• Affordable Care Act
• Health Costs
• Private Insurance
• Affordability

news release

• ACA Marketplace Enrollees Will See Steep Increases in Premium Payments in 2026 if Enhanced Subsidies Expire

Also of Interest

• Where ACA Marketplace Enrollment is Growing the Fastest, and Why
• Another Year of Record ACA Marketplace Signups, Driven in Part by Medicaid Unwinding and Enhanced Subsidies
• Five Things to Know about the Renewal of Extra Affordable Care Act Subsidies in the Inflation Reduction Act

• Download PDF
• Share X Facebook Email LinkedIn
• Permissions

Does a Patient’s Ability to Pay For Health Care Make Their Life Worth Saving?

• 1 Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
• Original Investigation Insurance Type and Withdrawal of Life-Sustaining Therapy in Critically Injured Trauma Patients Graeme Hoit, MD; Duminda N. Wijeysundera, MD, PhD; Doulia M. Hamad, MD; Aaron Nauth, MD, MSc; Amit Atrey, MD, MSc; Mansur Halai, MD; Eric Walser, MD, MSc; Anton Nikouline, MD; Avery B. Nathens, MD, PhD, MPH; Amir Khoshbin, MD, MSc JAMA Network Open

In this well-executed study using data from the American College of Surgeons Trauma Quality Improvement Program (TQIP), Hoit et al 1 demonstrated that the timing of withdrawal of life-sustaining treatment (WLST) in critically injured adults between ages 18 and 64 years was statistically associated with the type of insurance they carried. Specifically, the authors showed that even after accounting for patient and hospital characteristics, individuals without insurance were approximately 50% more likely to undergo WLST earlier than others who were insured (eg, private insurance or Medicaid). 1 As the authors noted, the law in the United States requires that critically ill individuals taken to hospitals receive the best possible care regardless of financial means. 1 However, the law does not include provisions to pay for such care. Thus, more than two-thirds of uninsured patients in the United States are at risk for health expenditures that exceed either their ability to pay or 10% of their total income. 2 Catastrophic health expenditures effect over 11 million Americans and remain a leading cause of personal bankruptcy. 3 It is cynical but reasonable for the authors to hypothesize that inability to pay for future health care needs could factor into decisions to withdraw life-sustaining treatment.

Like most good studies, especially those using secondary data, this evaluation of associations between insurance coverage and treatment decisions raises more questions than it answers. Compared with other national retrospective data used to study trauma care, the TQIP data uniquely describes clinical factors associated with WLST, including important descriptions of the limitations, the timing of WLST, and injury severity. However, a lack of contextual data leaves substantial gaps in our understanding of how these decisions occur at the bedside.

First, medical decisions for adults who are critically ill typically depend on surrogates. In the context of sudden and unexpected illness, surrogates may be especially unprepared for their role. Even when present, advance directives are rarely sufficient to guide clinical decisions in emergent scenarios because the directives are frequently too general, too specific, or unable to be located. It is also noteworthy that among almost 370 000 patients, only 18 had preexisting do not resuscitate documents; the infrequency of advance directives in trauma patients further limits their use as clinicians are unlikely to include them in routine workflows. Consequently, surrogates must base decisions on few data aside from prognostic information from clinicians and their own assumptions about the treatment and quality of life the patient would find acceptable. Detailed data about the attributes among the surrogates who made decisions about WLST are necessary to characterize how surrogate preparedness and understanding of future health needs are associated with the timing of WLST.

Second, diseases of despair, including suicide, substance use, and alcoholism, are ongoing threats to American life expectancy. Sociologists Case and Deaton note that there were 150 000 deaths of despair in the United States in 2017 (coincident with the first year of data in this study). White non-Hispanic males in midlife were among the hardest hit. At its core, injury is fundamentally a social disease, and the data in Hoit et al 1 bears this out. The study cohort demonstrated clinically meaningful differences by insurance type with respect to alcohol use, mental health or personality disorder, and substance use disorder, which may have factored into clinician decisions. 4 , 5 Conspicuously, self-inflicted harm was associated with 54% higher likelihood of earlier WLST, suggesting that even though depression is a treatable disorder, comorbid depression may have biased clinicians and surrogates toward WLST. Others have shown that when presented with hypothetical scenarios, depressed individuals were significantly more likely to reject life sustaining treatment if treatment would confer a negative fiscal effect. 6 Clinicians, researchers, and policymakers must closely examine how comorbid depression, alcoholism, and substance use disorder influence treatment decisions after injury and amend biases to ensure equitable treatment.

Third, stark geographic differences in health measures, insurance coverage, and attitudes about medical care across the United States may influence study findings and are yet unmeasured. One of the more important findings in this study is that having any insurance vs no insurance is more important than having public vs private insurance in terms of risk of treatment withdrawal. Specifically, the Patient Protection and Affordable Care Act in 2010 was a catalyst for Medicare expansion, which has since occurred in all but 10 states. 7 Compared with injured adults in non-Medicare expansion states, individuals in Medicaid expansion are more likely to survive hospitalization, have shorter hospitalizations, and are more likely to receive rehabilitation postdischarge. Furthermore, Medicaid expansion is associated with less medical debt, improved access to behavioral health care, and greater funding for rural hospitals. It is plausible that limited access to health care to address the underlying contributors to injury and the sequelae of the injury itself can influence families and clinicians as they determine the coping and resilience necessary to meet the health states that would be acceptable to an individual patient. Nearly all US citizens who are in the coverage gap (ie, they are ineligible for Medicaid or subsidies for private insurance) live in the South. 7 Thus, an exploration of how insurance coverage weighs into these life-and-death treatment decisions is incomplete without contextualizing these events within the policy structures in which they occur.

Trauma disparities researchers will continue to identify treatment differences until every injured patient has access to timely, high-quality medical care and other social determinants of health throughout their lifespan. Until then, it is incumbent upon individual clinicians and health systems to closely and uncomfortably examine how bias either creeps or marches into the life-and-death decisions we make for everyone under our care.

Published: July 24, 2024. doi:10.1001/jamanetworkopen.2024.29146

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Cooper Z. JAMA Network Open .

Corresponding Author: Zara Cooper, MD, MSc, Brigham and Women’s Hospital, Harvard Medical School, Department of Surgery, Brigham and Women’s Hospital, 1620 Tremont St, Ste 2-016, Boston, MA 02120 ( [email protected] ).

Conflict of Interest Disclosures: None reported.

See More About

Cooper Z. Does a Patient’s Ability to Pay For Health Care Make Their Life Worth Saving? JAMA Netw Open. 2024;7(7):e2429146. doi:10.1001/jamanetworkopen.2024.29146

Manage citations:

© 2024

Select Your Interests

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

Warning: The NCBI web site requires JavaScript to function. more...

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Institute of Medicine (US) Committee on the Consequences of Uninsurance. Care Without Coverage: Too Little, Too Late. Washington (DC): National Academies Press (US); 2002.

Care Without Coverage: Too Little, Too Late.

• Hardcopy Version at National Academies Press

3 Effects of Health Insurance on Health

This chapter presents the Committee's review of studies that address the impact of health insurance on various health-related outcomes. It examines research on the relationship between health insurance (or lack of insurance), use of medical care and health outcomes for specific conditions and types of services, and with overall health status and mortality. There is a consistent, positive relationship between health insurance coverage and health-related outcomes across a body of studies that use a variety of data sources and different analytic approaches. The best evidence suggests that health insurance is associated with more appropriate use of health care services and better health outcomes for adults.

The discussion of the research in this chapter is organized within sections that encompass virtually all of the research literature on health outcomes and insurance status that the Committee identified. The chapter sections include the following:

• Primary prevention and screening services
• Cancer care and outcomes
• Chronic disease management, with specific discussions of diabetes, hypertension, end-stage renal disease (ESRD), HIV disease, and mental illness
• Hospital-based care (emergency services, traumatic injury, cardiovascular disease)
• Overall mortality and general measures of health status

The Committee consolidated study results within categories that reflect both diseases and services because these frameworks helped in summarizing the individual studies and subsumed similar research structures and outcome measures. Older studies and those of lesser relevance or quality are not discussed within this chapter devoted to presenting study results and reaching Committee findings. However, all of the studies reviewed are described briefly in Appendix B .

The studies presented in some detail in this chapter are those that the Committee judged to be both methodologically sound and the most informative regarding health insurance effects on health-related outcomes. 1 Most studies report a positive relationship between health insurance coverage and measured outcomes. However, all studies with negative results that are contrary to the Committee's findings are presented and discussed in this chapter. Appendix B includes summaries of the complete set of studies that the Committee reviewed.

In the pages that follow, the Committee's findings introduce each of the five major sections listed above and also some of the subsections under chronic disease and hospital-based care. All of the Committee's specific findings are also presented together in Box 3.12 in the concluding section of this chapter. These findings are the basis for the Committee's overall conclusions in Chapter 4 .

Specific Committee Findings. Uninsured adults are less likely than adults with any kind of health coverage to receive preventive and screening services and less likely to receive these services on a timely basis. Health insurance that provides more extensive (more...)

• PRIMARY PREVENTION AND SCREENING SERVICES

Finding: Uninsured adults are less likely than adults with any kind of health coverage to receive preventive and screening services and less likely to receive these services on a timely basis. Health insurance that provides more extensive coverage of preventive and screening services is likely to result in greater and more appropriate use of these services.

Finding: Health insurance may reduce racial and ethnic disparities in the receipt of preventive and screening services.

These findings have important implications for health outcomes, as can be seen in the later sections on cancer and chronic diseases. For prevention and screening services, health insurance facilitates both the receipt of services and a continuing care relationship or regular source of care, which also increases the likelihood of receiving appropriate care.

Insurance benefits are less likely to include preventive and screening services ( Box 3.2 ) than they are physician visits for acute care or diagnostic tests for symptomatic conditions. However, over time, coverage of preventive and screening services has been increasing. In 1998, about three-quarters of adults with employment-based health insurance had a benefit package that included adult physical examinations; two years later in 2000, the proportion had risen to 90 percent (KPMG, 1998; Kaiser Family Foundation/HRET, 2000). Yet even if health insurance benefit packages do not cover preventive or screening services, those with health insurance are more likely to receive these recommended services because they are more likely to have a regular source of care, and having a regular source of care is independently associated with receiving recommended services (Bush and Langer, 1998; Gordon et al., 1998; Mandelblatt et al., 1999; Zambrana et al., 1999; Cummings et al., 2000; Hsia et al., 2000; Breen et al., 2001). The effect of having health insurance is more evident for relatively costly services, such as mammograms, than for less costly services, such as a clinical breast exam (CBE) or Pap test (Zambrana et al., 1999; Cummings et al., 2000; O'Malley et al., 2001).

Screening Services. The U.S. Preventive Services Task Force (USPSTF) recommends screening for the following conditions in the general adult population under age 65: cervical cancer (above age 18), breast and colorectal cancer (above age 50), hypertension (more...)

According to several large population surveys conducted within the past decade, adults without health insurance are less likely to receive recommended preventive and screening services and are less likely to receive them at the frequencies recommended by the United States Preventive Services Task Force than are insured adults. 2 The 1992 National Health Interview Survey (NHIS) documented receipt of mammography, CBE, Pap test, fecal occult blood test (FOBT), sigmoidoscopy, and digital rectal exam by adults under 65 (Potosky et al., 1998). Those with no health insurance had significantly lower screening rates compared to those with private coverage and compared to those with Medicaid for every service except sigmoidoscopy. The odds ratios (ORs) for receiving a screening service if uninsured compared with having private health insurance ranged from 0.27 for mammography to 0.43 for Pap test. 3

The 1998 NHIS found that, although rates of screening at appropriate intervals had increased generally over the preceding decade, they remained substantially lower for uninsured adults than for those with any kind of health insurance (Breen et al., 2001). 4 In a multivariable analysis that adjusted for age, race, education, and a regular source of care, uninsured adults were significantly less likely than those with any kind of coverage to receive a Pap test, mammography, and colorectal screening (FOBT or sigmoidoscopy) (ORs ranged from 0.37 to 0.5) (Breen et al., 2001). The study reported a strong relationship between having a regular source of care and timely receipt of these screening services in addition to the relationship between health insurance and screening.

Studies using other national samples report results consistent with those of the NHIS. A study of more than 31,000 women between ages 50 and 64 who responded to telephone surveys conducted between 1994 and 1997 about their receipt of mammograms, Pap smears, and colorectal cancer screening (either FOBT or sigmoidoscopy) found that uninsured women were significantly less likely to have received these tests than were women with private prepaid plan insurance (ORs ranging from 0.30 to 0.50) (Hsia et al., 2000). This study also found a strong relationship between having a regular source of care and receipt of screening services. Health insurance was an independently significant predictor. Another study based on several years of the Behavioral Risk Factor Surveillance System (BRFSS) for older adults (55 through 64) found that uninsured men and women were much less likely than their insured counterparts to receive cancer or heart disease screening tests and also much less likely to have a regular source of care (Powell-Griner et al., 1999; see Table 4.1 ).

Disparities Among Population Groups

A review of the literature on the interaction of race, ethnicity, and socioeconomic status (SES) with health insurance, concluded that health insurance makes a positive contribution to the likelihood of receiving appropriate screening services, although racial and ethnic disparities persist independent of health insurance (Haas and Adler, 2001). Studies of the use of preventive services by particular ethnic groups, such as Hispanics and African Americans, find that health insurance is associated with increased receipt of preventive services and increased likelihood of having a regular source of care, which improves one's chances of receiving appropriate preventive services (Solis et al., 1990; Mandelblatt et al., 1999; Zambrana et al., 1999; Wagner and Guendelman, 2000; Breen et al., 2001; O'Malley et al., 2001).

Breen and colleagues (2001) modeled the expected increase in screening rates for different ethnic groups if they were to gain health insurance coverage and a regular source of care. This “what-if” model suggests that those groups for whom screening rates are particularly low (e.g., receipt of mammography by Hispanic women, colorectal screening of African-American men) would make the largest gains (an 11 percentage-point increase in mammography rates for Hispanic women [to 77 percent] and a 5 percentage-point increase in colorectal screening for African-American men [to 31 percent] (Breen et al., 2001).

Extensiveness of Insurance Benefits

The type of health insurance and the continuity of coverage have also been found to affect receipt of appropriate preventive and screening services. Faulkner and Schauffler (1997) examined receipt of physical examinations, blood pressure screening, lipid screening for detection of cardiovascular disease, Pap test, CBE, and mammography and identified a positive and statistically significant “dose– response” relationship between the extent of coverage for preventive services (e.g., whether all such services, most, some, or none were covered by health insurance). Insurance coverage for preventive care increased men's receipt of preventive services more than it did that of women. Men with no coverage for preventive services were much less likely than men with complete coverage for such services to receive them (ORs for receipt of specific services ranged from 0.36 to 0.56). Women with no preventive services coverage also received fewer of these services than did women with full coverage for them (ORs for specific services ranged from 0.5 to 0.83) (Faulkner and Schauffler, 1997).

Ayanian and colleagues (2000) used the 1998 BRFSS data set to analyze the effect of length of time without coverage on receipt of preventive and screening services for adults between ages 18 and 65. Those without coverage for a year or longer were more likely than those uninsured for less than one year to go without appropriate preventive and screening services. For every generally recommended service (mammography, CBE, Pap smear, FOBT, sigmoidoscopy, hypertension screening, and cholesterol screening), the longer-term uninsured were significantly less likely than persons with any form of health insurance to receive these services (Ayanian et al., 2000).

Negative Findings

In the Committee's review, the one study that did not find a positive effect of insurance coverage compared mammography use among clients of various sites of care in Detroit, Michigan: two health department clinics, a health maintenance organization (HMO), and a private hospital (Burack et al., 1993). This study found no significant differences among women according to their health insurance status but did find that patients with more visits annually for any service (seven or more) were more likely to receive mammography. All women in this study had access to a primary care provider and, in the case of uninsured women, to clinics with the mission of serving the uninsured. These factors may explain why uninsured women had mammography rates as high as those of women with insurance.

• CANCER CARE AND OUTCOMES

Finding: Uninsured cancer patients generally have poorer outcomes and are more likely to die prematurely than persons with insurance, largely because of delayed diagnosis. This finding is supported by population-based studies of breast, cervical, colorectal, and prostate cancer and melanoma.

The studies analyzing health-related outcomes for cancer patients provide some of the most compelling evidence for the effect of health insurance status on health outcomes ( Box 3.3 ). This evidence comes from research based on area or statewide cancer registries, which provide large numbers of observations and reflect almost all cases occurring in a geographic region. Multivariable data analysis is used to determine the independent effects of health insurance, by controlling for demographic, SES, and clinical differences among study subjects.

Cancer. Cancers of all kinds have an overall incidence nationally of 400 cases per 100,000 people each year. More than 8.9 million Americans alive today have a history of cancer. Cancers account for approximately 550,000 deaths each year in the United (more...)

In addition to receiving fewer cancer screening services, uninsured adults are at greater risk of late-stage, often fatal cancer. Early diagnosis frequently improves the chances of surviving cancer. Generally, in studies examining the stage at which cancer is diagnosed, those with private health insurance have the best outcomes and those with no insurance have the worst (i.e., the highest proportion of late-stage diagnoses), with intermediate outcomes for Medicaid enrollees. In some studies however, the outcomes for Medicaid enrollees are comparable to those for uninsured cancer patients (Roetzheim et al., 1999). Both because of an assumption of similarity in SES between uninsured and Medicaid patients and because of small numbers of observations in the separate categories, some studies report combined results for Medicaid and uninsured patients and compare these findings with those for privately insured patients (e.g., Lee-Feldstein et al., 2000).

In studies assessing the outcomes for adults with cancer—stage of disease at diagnosis and mortality—Medicaid enrollees often do no better, and sometimes do worse, than uninsured patients. This similarity in experience between patients enrolled in Medicaid and those without any coverage may reflect the fact that uninsured persons in poor health, once they seek care, may become enrolled in Medicaid as a result of their frequent interactions with the health care system (Davidoff et al., 2001; see Box 2.1 ). Also, Medicaid enrollees tend to have discontinuous coverage and thus may have had less regular access to screening services. Consequently, persons with Medicaid at the time of a cancer diagnosis may have been without coverage for some prior period (Carrasquillo et al., 1998; IOM, 2001a; Perkins et al., 2001). For example, one study of women under 65 with Medi-Cal coverage (California's Medicaid and indigent care program) who were diagnosed with breast cancer found that, among those who had been uninsured during the year prior to their diagnosis (18 percent of all Medi-Cal enrollees), late-stage diagnosis was much more likely than among those who had been continuously enrolled for the previous 12 months (ORs of 3.9 for those who had been uninsured and 1.4 for those continuously covered by Medi-Cal, compared with all other women ages 30–64 diagnosed with breast cancer) (Perkins et al., 2001).

With this general background on the nature of the research examining health insurance status effects, the remainder of this section discusses study results for five specific cancers.

Breast Cancer

Uninsured women and women with Medicaid are more likely to receive a breast cancer diagnosis at a late stage of disease (regional or distant) and have a 30– 50 percent greater risk of dying than women with private coverage, as shown in studies based on three different state or regional cancer registries (Ayanian et al., 1993; Roetzheim et al., 1999, 2000; Lee-Feldstein et al., 2000).

In a study using the New Jersey Cancer Registry, Ayanian and colleagues (1993) identified 4,675 women 35 to 65 years of age diagnosed with breast cancer and assessed their stage of disease at diagnosis and their survival rates 4.5 to 7 years after diagnosis. The authors found that uninsured women were significantly more likely than privately insured women to be diagnosed with regional or late-stage cancer, as were patients with Medicaid. After controlling for stage of disease at diagnosis and other factors, uninsured women had an adjusted risk of death 49 percent higher than that of privately insured women, and women with Medicaid had a 40 percent higher risk of death than those who were privately insured.

Using a regional cancer registry and Census data for 1987 through 1993, Lee-Feldstein and colleagues (2000) examined the stage of disease at diagnosis, treatment, and survival experience of about 1,800 northern California women under the age of 65 diagnosed with breast cancer. They found that women who were uninsured and publicly insured (primarily Medicaid), taken together, were twice as likely as privately insured women with indemnity coverage to be diagnosed at a late stage of disease. Over a four- to ten-year follow-up, uninsured and publicly insured women had higher risks of death from both breast cancer (42 percent higher) and all causes (46 percent higher) than did privately insured women with indemnity coverage. The likelihood of receiving breast-conserving surgery did not differ between these two groups.

In a review of approximately 9,800 Florida residents diagnosed with breast cancer in 1994, Roetzheim and colleagues calculated that, after controlling for age, education, income, marital status, race, and comorbidity, women without insurance were more likely to be diagnosed with late-stage disease than women with private indemnity coverage (OR = 1.43) (Roetzheim et al., 1999). Women with Medicaid had an even greater likelihood of late-stage diagnosis compared with privately insured women (OR = 1.87). In a subsequent analysis of mortality using the same registry data, the authors estimated that the relative risk (RR) of dying was 31 percent higher for uninsured women and 58 percent higher for women with Medicaid over a three to four-year follow-up period (Roetzheim et al, 2000a). Further analysis suggested that stage of disease at diagnosis and, to a lesser extent, treatment modality appeared to account for the differences in survival by insurance status. Finally, uninsured women were less likely than women with private coverage to receive breast-conserving surgery when stage at diagnosis, comorbidities, and other personal characteristics were taken into account (OR = 0.70) (Roetzheim et al., 2000a).

Cervical Cancer

Uninsured women are more likely to receive a late-stage diagnosis for invasive cervical cancer than are privately insured women. Ferrante and colleagues (2000) analyzed 852 cases of invasive cervical cancer reported in the Florida tumor registry for 1994 to determine factors associated with late-stage diagnosis. In bivariate analysis, being uninsured was associated with an increased likelihood of late-stage diagnosis (OR = 1.6). In a multivariable analysis that adjusted for age, education, income, marital status, race, comorbidities, and smoking, uninsured women were more likely to present with a late-stage cancer compared to women with private indemnity coverage, although this finding was not statistically significant (OR = 1.49, confidence interval [CI]: 0.88–2.50). The outcome for Medicaid enrollees was similar to that of privately insured women in both bivariate and multivariable analysis (Ferrante et al., 2000).

Colorectal Cancer

Uninsured patients with colorectal cancer have a greater risk of dying than do patients with private indemnity insurance, even after adjusting for differences in the stage at which the cancer is diagnosed and the treatment modality. Using the Florida cancer registry for 1994, Roetzheim and colleagues (1999) analyzed the relative likelihood of late-stage diagnosis by insurance status for more than 8,000 cases of colorectal cancer. In a multivariable analysis adjusting for sociodemographic characteristics, smoking status, and comorbidities, uninsured patients were more likely to be diagnosed with late-stage colorectal cancer than were patients with private indemnity coverage (OR = 1.67). Medicaid enrollees had a statistically insignificant greater likelihood of late-stage disease compared to patients with indemnity coverage (OR = 1.44, CI: 0.92–2.25).

A subsequent analysis of largely the same data set (9,500 cases) that adjusted for sociodemographic factors and comorbidities but not for smoking estimated the adjusted mortality risk for uninsured patients with colorectal cancer to be 64 percent greater over a three- to four-year follow-up period than that for patients covered by private indemnity plans (Roetzheim et al., 2000b). 5 Even after adjusting for stage of disease at diagnosis, the risk of death for uninsured patients was 50 percent higher than that for the privately insured, and after further adjustment for treatment modality, the risk for uninsured patients was 40 percent higher (Roetzheim et al., 2000b).

Prostate Cancer

In addition to delayed diagnosis and greater risk of death, uninsured prostate cancer patients have been found to experience a decrease in health-related quality of life after their diagnosis and during treatment, unlike publicly and privately insured patients. A study of about 8,700 cases of newly diagnosed prostate cancer reported to the Florida cancer registry in 1994 found that uninsured men were more likely to be diagnosed at a late stage of the disease than were men with private indemnity insurance (OR = 1.47) (Roetzheim et al., 1999). A study of 860 men in 26 medical practices with newly diagnosed prostate cancer evaluated their health-related quality of life (HRQOL) at three- to six-month intervals over a two-year period (Penson et al., 2001). Although uninsured men diagnosed with prostate cancer did not have a lower HRQOL at diagnosis, their HRQOL decreased over the course of their disease and treatment, in contrast to that of HMO and Medicare patients. The authors suggest that “patients undergoing aggressive treatment, which can itself have deleterious effects on quality of life, are exposed to further hardships when they do not have comprehensive health insurance upon which to support their care” (Penson et al., 2001, p. 357).

Uninsured patients, as well as Medicaid patients have been found to be more likely to be diagnosed with late-stage melanoma than are privately insured patients. Among 1,500 patients diagnosed with melanoma, uninsured patients were more likely to have late-stage (regional or distant) disease than those with private indemnity coverage (OR = 2.6) (Roetzheim et al., 1999). The small number of Medicaid patients with melanoma (13) included in this study also had a much greater chance of being diagnosed with late-stage cancer.

• CHRONIC DISEASE CARE AND OUTCOMES

Finding: Uninsured people with chronic diseases are less likely to receive appropriate care to manage their health conditions than are those who have health insurance. For the five disease conditions that the Committee examined (diabetes, cardiovascular disease, end-stage renal disease, HIV infection, and mental illness), uninsured patients have worse clinical outcomes than insured patients.

Effective management of chronic conditions such as diabetes, hypertension, HIV, and depression ( Box 3.4 ) includes not only periodic services and care from health care professionals but also the active involvement of patients in modifying their behavior, monitoring their condition, and participating in treatment regimens (Wagner et al., 1996; Davis et al., 2000). Identifying chronic conditions early and providing appropriate health care on an ongoing and coordinated basis are health care system goals that have been developed over several decades and have been continuously refined as evidence for cost-effective interventions and practices has accumulated. Maintaining an ongoing relationship with a specific provider who keeps records, manages care, and is available for consultation between visits is a key to high-quality health care, particularly for those with chronic illnesses (O'Connor et al., 1998; IOM, 2001b).

Chronic Conditions. Chronic conditions are the leading causes of death, disability, and illness in the United States, accounting for one-third of the potential life years lost before age 65 (CDC, 2000a). Almost 100 million Americans have chronic conditions. (more...)

For persons with a chronic illness, health insurance may be most important in that it enhances the opportunities to acquire a regular source of care. If someone has coverage through a private or public managed care plan, a relationship with a primary care provider may be built into the insurance. Indemnity or fee-for-service (FFS) insurance coverage also improves the chances of having a regular source of care because having the resources to pay for services is often a prerequi-site to being seen in a medical practice. Uninsured adults are much less likely to have a regular source of care and are more likely to identify an emergency department as their regular source of care than are adults with any form of coverage (Weinick et al., 1997; Cunningham and Whitmore, 1998; Zuvekas and Weinick, 1999; Haley and Zuckerman, 2000). Loss of coverage also interrupts patterns of use of health care and results in delays in seeking needed care (Burstin et al., 1998; Kasper et al., 2000; Hoffman et al., 2001). For uninsured adults under age 65, 19 percent with heart disease and 14 percent with hypertension lack a usual source of care, compared to 8 and 4 percent, respectively, of their insured counterparts (Fish-Parcham, 2001). For uninsured patients without a regular source of care or those who identify an emergency department as their usual source, obtaining care that is consistent with recognized standards for effective disease management is a daunting challenge.

Providers with a commitment to serving uninsured clients, such as local public health and hospital clinics and federally funded community health centers, have sometimes instituted special interventions and programs for the chronically ill to promote continuity of care and disease management. These innovations are critically important to the identified, chronically ill patients who routinely receive care at such clinics and centers. The efforts of these providers, however, are limited in scale by funding and service capacity relative to the high need for care within their service areas (Baker et al., 1998; Chin et al., 2000; Piette, 2000; Philis-Tsimikas and Walker, 2001). As demonstrated in the following review of studies examining the care and outcomes for patients with specific chronic conditions, those who do not have health insurance coverage of any kind fare measurably worse than their insured counterparts.

Cardiovascular Disease

Finding: Uninsured adults with hypertension or high cholesterol have diminished access to care, are less likely to be screened, are less likely to take prescription medication if diagnosed, and experience worse health outcomes.

Across the spectrum of services and the course of development of cardiovascular disease ( Box 3.5 ), uninsured adults receive fewer services and experience worse health. They are less likely to receive screening for hypertension and high cholesterol and to have frequent monitoring of blood pressure once they develop hypertension. Uninsured adults are less likely to stay on drug therapy for hypertension both because they lack a regular provider and because they do not have insurance coverage. Loss of insurance coverage has been demonstrated to disrupt therapeutic relationships and worsen control of blood pressure.

Cardiovascular Disease. “Cardiovascular disease” encompasses a variety of diseases and conditions that affect the heart and blood vessels, including hypertension (high blood pressure), heart disease, and stroke. One-quarter of all Americans (more...)

Uninsured adults are less likely to receive routine screening services for cardiovascular disease. A nationwide household survey in 1997 found that adults who had been without health insurance for one year or longer were less likely than insured adults to have received recommended hypertension screening within the previous two years (80 percent compared with 94 percent) or cholesterol screening (60 percent compared with 82 percent) (Ayanian et al., 2000). Adults who were uninsured for less than one year received these screening services at rates intermediate between those for long-term uninsured and insured adults.

Health insurance coverage is associated with better blood pressure control for lower-income persons with hypertension, according to two studies, one prospective and experimental and the other a longitudinal analysis of a cohort of patients that either lost or maintained Medicaid coverage. The prospective study, the RAND Health Insurance Experiment, found that for patients with diagnosed hypertension, patients in the plan without any cost sharing had significantly lower blood pressure than those in health plans with any form of cost sharing (an overall difference of 1.9 mm Hg) (Keeler et al., 1985). A much greater effect of cost sharing on average blood pressure was found for low-income patients than for high-income patients (3.5 mm Hg. versus 1.1 mm Hg.). Patients in the plan without cost sharing also had greater compliance with drug and behavioral therapies. These differences were attributed to more frequent contact with health providers in the free care plan (Keeler et al., 1985). 6

In the longitudinal analysis, Lurie and colleagues (1984, 1986) followed a cohort of patients at a university ambulatory care clinic for one year after some lost their Medi-Cal coverage consequent to a state policy change. At six months after loss of coverage and again at one year, hypertensive patients who lost coverage had significantly worse blood pressure than did those who remained covered by MediCal, with an average increase in diastolic blood pressure of 6 mm Hg compared with a decrease in the insured control group of 3 mm Hg after a full year (Lurie et al., 1984, 1986). The percentage of patients with diastolic blood pressure greater than 100 mm Hg increased in the group that lost coverage from 3 percent at baseline to 31 percent at six months, and then declined to 19 percent at one year, while the proportion with diastolic blood pressure > 100 mm Hg in the continuously covered control group did not change significantly over the year (Lurie et al., 1986).

Deficits in the care of uninsured persons with hypertension place them at risk of complications and deterioration in their condition. The 1987 National Medical Expenditures Survey afforded an in-depth examination of the use of antihypertensive medications by health insurance status. Uninsured persons younger than 65 who had hypertension were less likely than either those with private insurance or Medicaid to have any antihypertensive medication therapy (ORs = 0.62 and 0.44, respectively) (Huttin et al., 2000). 7 An analysis of the third round of the National Health and Nutrition Examination Survey (NHANES), with data on 40,000 respondents for the period 1988–1994, found that 22 percent of uninsured adults under age 65 with diagnosed hypertension had gone for more than one year without a blood pressure check, compared to 10 percent of insured adults with hypertension (Fish-Parcham, 2001). While 75 percent of insured adults under 65 who had ever been diagnosed with high blood pressure and been told to take medication for it were in fact taking blood pressure medication, only 58 percent of their uninsured counterparts who had been advised to take medication were doing so. Among those adults under 65 who had been advised to take cholesterol-lowering medication, 43 percent of those without insurance failed to take such medication, compared to 29 percent among those with health insurance who did not comply with this advice (Fish-Parcham, 2001).

A study by Shea and colleagues (1992a, 1992b) of patients presenting to two New York hospital emergency departments between 1989 and 1991 found that uninsured patients were more likely to have severe, uncontrolled hypertension than were sociodemographically similar patients with any health insurance (OR = 2.2), while patients without a regular source of care had an even greater risk of severe and uncontrolled disease (OR = 4.4). When insurance status, having a regular source of care, and complying with a therapeutic regimen were all included in the analysis, the odds ratio for being uninsured was no longer statistically significant (OR = 1.9, CI: 0.8–4.6). This result is not surprising, given the strong association between having health insurance and having a regular source of care.

Finding: Uninsured persons with diabetes are less likely to receive recommended services. Lacking health insurance for longer periods increases the risk of inadequate care for this condition and can lead to uncontrolled blood sugar levels, which, over time, put diabetics at risk for additional chronic disease and disability.

Despite the demanding and costly care regimen that persons with diabetes face, adults with diabetes are almost as likely to lack health insurance as those without this disease. Of diabetic adults under age 65, 12 percent are uninsured compared with 15 percent of the comparable general population (Harris, 1999). Persons with diabetes who are uninsured are less likely to receive the professionally recommended standard of care than are those who have health insurance ( Box 3.6 ). One result of not receiving appropriate care may be uncontrolled blood sugar levels, which puts diabetics at increased risk of hospitalization for either hyper- or hypoglycemia, in addition to increasing the likelihood of comorbidities and disabilities (Palta et al., 1997).

Diabetes. Diabetes mellitus is a prevalent chronic disease that has been increasing in the U.S. population by 5–6 percent each year during the past decade. Approximately 800,000 new cases are diagnosed each year. More than 16 million Americans (more...)

Based on a 1994 survey, among adults diagnosed with diabetes who did not use insulin, those without health insurance were less likely than those with any kind of coverage to self-monitor blood glucose (OR = 0.5) or, within the past year, to have had their feet examined (OR = 0.4), or a dilated eye exam (OR = 0.5) (Beckles et al., 1998). 8 Persons with diabetes who used insulin and were uninsured were also less likely than those with health insurance to have had a foot examination (OR = 0.25) or a dilated eye examination (OR = 0.34) (Beckles et al., 1998).

A later analysis, using 1998 data from the same annual survey, found that 25 percent of adults younger than 65 who had diabetes and were uninsured for a year or more had not had a routine checkup within the past two years, compared with 7 percent of diabetics who were uninsured for less than a year and 5 percent of diabetics with health insurance (Ayanian et al., 2000). Adjusting results for the demographic characteristics of the national population, persons with diabetes who were uninsured for a year or longer were significantly less likely to have had a foot examination, a dilated eye examination, a cholesterol measurement, or a flu shot than were insured diabetics ( Figure 3.1 ) (Ayanian et al., 2000).

Diabetes management among insured and uninsured adults, ages 18–64. NOTE: Proportions adjusted to demographic characteristics of study cohort.

End-Stage Renal Disease

Finding: Uninsured patients with end-stage renal disease begin dialysis at a later stage of disease than do insured patients and have poorer clinical measures of their condition at the time they begin dialysis.

Insurance status affects the timing and quality of care ( Box 3.7 ) and may contribute to the longevity of dialysis patients, which is substantially lower than that of others of the same age (Obrador et al., 1999). The clinical goals for patients with kidney disease are to slow the progression of renal failure, manage complications, and prevent or manage comorbidities effectively. Although professional consensus about when dialysis should begin is not complete, there is agreement that the point in the progression of the disease at which dialysis begins affects patient outcomes (Kausz et al., 2000).

End-Stage Renal Disease. In 2000, 90,000 people in the United States developed end-stage renal disease (kidney failure). Dialysis and transplantation are the two standard treatments. Approximately 300,000 patients are on dialysis and 80,000 have received (more...)

The Medicare ESRD program maintains extensive clinical and sociodemographic information on all dialysis patients, including information on patient health insurance status before beginning dialysis. This database provides opportunities to analyze the health care experience of all Americans who eventually develop ESRD, rather than just a sample of the population. One study that used this database analyzed the characteristics of 155,000 chronic dialysis patients who entered dialysis over a 27-month period between 1995 and 1997 (Obrador et al., 1999). This study found that uninsured patients were sicker at initiation of dialysis and less likely to have received erythropoietin (EPO) therapy than patients with any kind of insurance pre-ESRD. Uninsured patients also had an increased likelihood of hypoalbuminemia than those who had previously been privately insured (OR = 1.37) and a greater likelihood of low hematocrit (<28 percent) 9 than the privately insured (OR = 1.34), after controlling for patients' sociodemographic and clinical characteristics, including comorbidities. Uninsured patients were also less likely than privately insured patients to have received EPO prior to dialysis (OR = 0.49) (Obrador et al., 1999). A second study based on the same data set found that patients without insurance were more likely to begin dialysis late 10 than were patients with any form of insurance (OR = 1.55) (Kausz et al., 2000).

Human Immunodeficiency Virus (HIV) Infection

Finding: Uninsured adults with HIV infection are less likely to receive highly effective medications that have been shown to improve survival.

A strong body of research about HIV infection confirms the findings of the general literature on insurance status and access to and use of services: uninsured adults diagnosed with HIV face greater delays in care than those with health insurance. They are less likely to receive regular care and drug therapy and are more likely to go without needed care than patients with any kind of coverage (Cunningham et al., 1995, 1999; Katz et al., 1995; Shapiro et al., 1999).

BOX 3.8 HIV Infection

• As of the beginning of 2000, the Centers for Disease Control and Prevention estimated that about 800,000 to 900,000 people were living with HIV infection or AIDS in the United States (CDC, 2001a).
• In each of the years 1997, 1998, and 1999, between 40,000 and 50,000 new cases of AIDS were reported.
• By 1996, combination antiretroviral therapy including protease inhibitors and nonnucleoside reverse transcriptase inhibitors, referred to as highly active antiretroviral therapies were becoming established as the treatment of choice for HIV infection (Carpenter et al., 1996). Largely as a result of these therapies, deaths among persons with AIDS dropped for the first time between 1996 and 1997 (by 42 percent) and declined 8 percent between 1998 and 1999 (CDC, 2001a).
• About half of all adults with HIV infection see a provider at least once every six months (Bozzette et al., 1998).
• Studies of HIV infection and health insurance examine a variety of health-related outcomes: general measures of access and utilization such as routine care visits and emergency department visits without hospitalization, delays between diagnosis and initiation of therapy, use of recommended drug therapies, and clinical outcomes such as CD4 lymphocyte counts.

A number of analyses have been based on national, longitudinal surveys evaluating access to care for persons with HIV infection (Niemcryk et al., 1998; Joyce et al., 1999; Shapiro et al., 1999; Andersen et al., 2000; Cunningham et al., 1999, 2000; Turner et al., 2000, Goldman et al., 2001). 11 These surveys allow assessment of the relationship between health insurance and access to care, use of services, receipt and timeliness of recommended therapies, and mortality as related to health insurance status. The research based on one of these surveys, the HIV Cost and Services Utilization Study (HCSUS), represents some of the most carefully designed studies of access to care and receipt of recommended therapies for specific conditions. In addition, there are several smaller, local studies based on hospital records or patient surveys (Katz et al., 1992, 1995; Bennett et al., 1995; Cunningham et al., 1995, 1996; Palacio et al, 1999; Sorvillo et al., 1999).

Access to a Regular Source of Care

Several studies suggest that the positive effects of health insurance for HIV-infected adults are achieved through the mechanism of having a regular source of care. Sorvillo and colleagues (1999) surveyed 339 HIV-positive adults in Los Angeles county in 1996–1997, and found that two-thirds of insured patients used protease inhibitors (PIs), while just half of uninsured patients were using them. When the site of care (private clinic, HMO, or public clinic) was included in a multivariable analysis, insurance status was no longer significantly related to receipt of PIs because of the concentration of uninsured patients in public clinics, which were less likely to prescribe PIs, especially at the beginning of the study period (Sorvillo et al., 1999).

Uninsured patients appear to face greater delays in beginning care following a diagnosis of HIV infection. In bivariate analysis of HCSUS data, uninsured patients were significantly more likely to have their first office visit more than three months after diagnosis with HIV than were privately insured patients (37 percent of uninsured patients had delays compared to 25 percent of privately insured patients in 1993; by 1995, those patients with delays decreased to 22 percent of uninsured patients and 14 percent of privately insured) (Turner et al., 2000). However, in a multivariable analysis, being uninsured was no longer a significant predictor of late initiation, while not having a regular source of care remained an important predictor (Turner et al., 2000).

Findings regarding emergency department (ED) use and hospitalization have changed over time. The most recent analysis, based on HCSUS, finds greater use of EDs, without hospitalization, and hospitalization more frequently than every six months for uninsured HIV patients (Shapiro et al., 1999), suggesting poorer access to other kinds of outpatient care. Studies based on earlier data report that uninsured patients had lower use of emergency rooms and hospitalization than either publicly or privately insured patients (Mor et al., 1992; Fleishman and Mor, 1993; Niemcryk et al., 1998; Joyce et al., 1999), suggesting poorer access even at high levels of acuity.

Receipt of Drug Therapies

Adults with HIV infection are more likely to receive effective drug therapies and to receive them earlier in the course of disease if they have health insurance. In an HCSUS analysis with extensive adjustments for sociodemographic and clinical factors, those without health insurance were much less likely to have ever received antiretroviral therapy (OR = 0.35) (Shapiro et al., 1999). Waiting times from diagnosis to the start of therapy with either PIs or nonnucleoside reverse transcriptase inhibitors, were 9.4 months for the privately insured, 12.4 months for Medicaid enrollees, and 13.9 months for uninsured patients (Shapiro et al., 2000).

Overall, many HIV-infected patients abandon recommended drug therapy over time. However, uninsured patients are more likely to stop drug therapy than are those with coverage. At the second follow-up interview of HCSUS respondents in 1997–1998, only half (53 percent) of all HIV-positive patients in care were receiving the recommended combination drug therapy, highly active antiretroviral therapy (HAART), although 71 percent had received HAART at some time in their treatment history (Cunningham et al., 2000). Uninsured patients were significantly less likely than privately insured patients with indemnity coverage (OR = 0.71) to be receiving HAART at the time of follow-up, indicating less appropriate care for uninsured patients with this disease (Cunningham et al., 2000).

Clinical Outcomes and Mortality

Studies of clinical outcomes for HIV patients present an evolving picture of both the efficacy of treatments and the impact of health insurance. A relatively early study of patients hospitalized with Pneumocystis carinii pneumonia (1987– 1990) found that uninsured patients had a higher in-hospital mortality rate than did those with private insurance (OR = 1.49), and Medicaid patients had an even higher in-hospital mortality, relative to private patients (OR = 2.1) (Bennett et al., 1995). Another early and small study (96 patients in one university clinic) found that patients with private insurance had significantly lower CD4 lymphocyte counts (a worse outcome) than either uninsured or Medicaid patients (who had the highest counts), when first treated at the clinic (Katz et al., 1992). The authors hypothesize that some relatively healthy patients with private health insurance coverage may have been reluctant to use it and thus reported their status as uninsured.

More recently, an analysis based on HCSUS examined the mortality experience of insured and uninsured HIV-infected adults and found that having health insurance of any kind reduced the risk of dying within six months of being surveyed between 71 and 85 percent, when severity of illness (measured by CD4 lymphocyte count) and sociodemographic characteristics were controlled (Goldman et al., 2001). The greater reduction in mortality risk (85 percent) was estimated for a surviving subset (2,466 participants) of the original 1996 sample of 2,864 participants a year later, when HAART was in wider use and was reducing mortality among HIV patients who used it. This impact of health insurance on mortality for HIV-infected adults within a short follow-up period, six months, demonstrates how sensitive health outcomes can be to coverage when it facilitates receipt of effective therapy.

Mental Illness

Finding: Health insurance that covers any mental health treatment is associated with the receipt of mental health care and with care consistent with clinical practice guidelines from both general medical and specialty mental health providers.

Mental disorders or illnesses are health conditions that are characterized by changes in thinking, mood, or behavior. They are often chronic conditions but may also occur as single or infrequent episodes over a lifetime. Mental illnesses represent a major source of disability in the United States that is often underestimated by the public and health care professionals alike (USDHHS, 2000). In industrialized economies, mental illness is equivalent to heart disease and cancer in terms of its impact on disability (Murray and Lopez, 1996).

Despite the differential treatment of mental health services in both public and private insurance plans, the studies reviewed by the Committee document a positive association between health insurance coverage and more appropriate care for mental illnesses ( Box 3.9 ). Health insurance plans and programs historically have excluded services related to treatment for mental illness, strictly limited coverage of mental health services, and administered mental health benefits separately from other kinds of medical care. Thus, studies that attempt to measure the effects of health insurance status on health care and outcomes for mental illnesses may be affected by the diversity of health insurance benefits and of cost sharing and administrative requirements for these services and conditions. Variability in benefits among health insurance plans and types of insurance complicates the interpretation of all observational studies of health insurance effects but poses a particular problem vis–a–vis mental health. (See the discussion of measurement bias in Chapter 2 .)

Mental Illness. About 38 million people ages 18 and older are estimated to have a single mental disorder of any severity or both a mental and an addictive disorder in a given year (Narrow et al., 2002). The most common conditions fall into the broad categories (more...)

The use of mental health services in both the general and specialty mental health sectors by adults is positively associated with health insurance coverage (Cooper-Patrick et al., 1999; Wang et al., 2000; Young et al., 2001). Between 1987 and 1997, the overall rate of treatment for depression among American adults under age 65 tripled from 1 person per 100 to 3.2 persons per 100, yet the treatment rate among those without health insurance was half that of the overall population rate in 1997, 1.5 persons treated per 100 population (Olfson et al., 2002). A longitudinal, community-based study in Baltimore, Maryland, between 1981 and 1996 documented increased use of mental health services over this period (Cooper-Patrick et al., 1999). Analyzing the experience of African Americans and whites separately, the authors found that for African Americans specifically, this increase was achieved predominantly with services provided in the general medical sector. For both African Americans and whites, being uninsured reduced the likelihood of receiving any mental health services.

At the same time, insurance coverage for adults with mental illness is less stable than average for those without this condition (Sturm and Wells, 2000; Rabinowitz et al., 2001). In a recent (1998) follow-up survey of participants in the Community Tracking Study, those who reported having symptoms of mental disorders were found to be more likely to lose coverage within a year following their diagnosis than those without a mental disorder (Sturm and Wells, 2000). As discussed below, those with severe mental illness also experience transitions in insurance coverage, frequently ending up with public program coverage (Rabinowitz et al., 2001).

The findings reported below are grouped into those for depression and anxiety disorders and those for severe mental illnesses. Depression and anxiety disorders are often treatable in the general medical sector and primarily require outpatient services. Severe mental illnesses (schizophrenia, other psychoses, and bipolar depression) require the attention of specialty mental health professionals and may require inpatient and other forms of more extensive services (e.g., partial or day hospitalization). Public health insurance, both Medicare and Medicaid, is an important source of coverage for specialty mental health services for those disabled by severe mental illness (SMI).

Depression and Anxiety Disorders

Receipt of appropriate (guideline-concordant) care for depression is associated with improved functional outcomes at two years (Sturm and Wells, 1995). Health insurance coverage specifically for mental health services is associated with an increased likelihood of receiving such care. Two studies support this claim.

The first, a nationally representative study of three prevalent disorders— depression, panic disorder, and generalized anxiety disorder—investigated the contribution of insurance coverage and health care utilization to guideline-con-cordant treatment (Wang et al., 2000). Mental health diagnoses were determined in a structured interview using a well-defined operational definition of mental health care over the previous 12 months. Treatment criteria included the combination of a prescription medication for depression or anxiety from a general medical doctor or a psychiatrist in addition to at least four visits to the same type of provider or, where medication was not prescribed, a minimum of eight visits to either a psychiatrist or a mental health specialist (Wang et al., 2000). A multivariable analysis estimated the effects of sociodemographic characteristics, various measures of clinical status including a measure of mental illness severity, insurance coverage for mental health visits, number and reasons for use of general medical services, other medications, and alternative therapies. Patients diagnosed with depression, panic disorder, or a generalized anxiety disorder who had no health insurance coverage for mental health visits were less likely to receive any mental health services (OR = 0.43). They were also less likely to receive guideline-concordant care in the general medical sector (OR = 0.24) or in the mental health treatment sector (OR = 0.36) (Wang et al., 2000).

A second study of adults with a probable 12-month diagnosis of depression or anxiety examined factors associated with receipt of appropriate care (psychiatric medication and counseling) (Young et al, 2001): 1,636 respondents were identified as having one or more depressive or anxiety disorders based on a structured diagnostic interview. Respondents with a depressive or anxiety disorder who had more education and a greater number of medical disorders were more likely to have had contact with providers than those with less education and fewer medical conditions. Those with no health insurance were less likely to have had any provider contact than were those with any form of health insurance (OR = 0.46). However, for those receiving any care, insurance status was not related to receipt of appropriate care (Young et al., 2001). These findings suggest that health insurance alone may not ensure appropriate mental health care.

Severe Mental Illness

Uninsured adults with severe mental illnesses are less likely to receive appropriate care than are those with coverage and may experience delays in receiving services until they gain public insurance.

In a study using the same sample and survey as that used by Young and colleagues, McAlpine and Mechanic (2001) investigated the association of current insurance coverage and specialty mental health utilization within the past 12 months (i.e., visits to a psychiatrist or psychologist, hospital admission, or emergency room visit for an emotional or substance use problem) for SMI. Two diagnostic indices, including a global measure of mental health, measured the need for care. Potential confounding factors such as physical symptoms and degree of dangerousness and disruptiveness were also measured. One in five respondents identified with an SMI was uninsured. Among persons with SMI, those without health insurance were far less likely to use specialty mental health services than those with Medicare or Medicaid (OR = 0.17) (McAlpine and Mechanic, 2000).

Individuals with SMIs typically lack insurance at the time of hospitalization (Rabinowitz et al., 2001). An important question regarding insurance coverage in this patient population is whether a first hospitalization for SMI results in a change in insurance status and whether such a change influences subsequent mental health care. Rabinowitz and colleagues followed the progress of 443 individuals enrolled in a county mental health project to determine whether changes in coverage followed first admission for psychosis and the association between type of insurance coverage and future care. Overall, the proportion of patients with no insurance 24 months after hospitalization decreased from 42 percent at baseline to 21 percent as a result of enrollment in public insurance programs. Men were more likely to remain uninsured than were women. The total number of days of care received (inpatient, outpatient, day hospital) was significantly higher for the publicly insured group compared to both those with private insurance and those with no insurance during the first 6 months after initial hospitalization and over the entire 24-month period. Uninsured patients with SMI were much less likely to receive outpatient care after hospitalization than patients with Medicaid or Medicare (OR = 0.24) and also less likely than those with private health insurance to receive outpatient care subsequent to hospitalization (OR = 0.56) (Rabinowitz et al., 2001).

An earlier study using the same data also reported an association between health insurance and receipt of mental health services prior to a first admission for psychotic disorder (Rabinowitz et al., 1998). Forty-four percent of patients (n = 525) were uninsured at first admission. Uninsured patients were less likely than those with private insurance to have had

• any mental health treatment prior to admission (OR = 0.53),
• specific psychotherapeutic contact (OR = 0.43),
• voluntary admission (OR = 0.56),
• less than three months between onset of psychosis and admission (OR = 0.56)

and were less likely to have been admitted to a community (versus public) hospital (OR = 0.14) (Rabinowitz et al., 1998). Uninsured patients were also less likely than those with either Medicaid or Medicare to have received antipsychotic medication (OR = 0.4), had voluntary admission (OR = 0.53), and be admitted to a community hospital (OR = 0.33).

• HOSPITAL-BASED CARE

Finding: Uninsured patients who are hospitalized for a range of conditions experience higher rates of death in the hospital, receive fewer services, and are more likely to experience an adverse medical event due to negligence than are insured patients.

Americans assume and expect that hospital-based care for serious and emergency conditions is available to everyone, regardless of health insurance coverage, while recognizing that uninsured patients may be limited to treatment at public or otherwise designated “safety-net” hospitals (IOM, 2001a). Professional and institutional standards of practice grounded in ethics, law, and licensure dictate that the care received by all patients, regardless of financial or insurance status, be of equal and high quality. Yet studies of hospital-based care conducted over the past two decades have documented differences in the services received by insured and uninsured patients, differences in the quality of their care (sometimes but not always related to the site of care), and differences in patient outcomes such as in-hospital mortality rates. 12

One of the most comprehensive of these studies of hospitalization analyzed more than 592,000 hospital discharge abstracts in 1987 (Hadley et al., 1991). The authors report that for adults ages 18–65, uninsured hospital inpatients had a significantly higher risk of dying in the hospital than their privately insured counterparts in 8 of 12 age–sex–race-specific population cohorts (relative risks ranged from 1.1 for black women ages 50–64 to 3.2 for black men ages 35–49). This analysis adjusted for patient condition on admission to the hospital. Uninsured patients were also less likely to receive endoscopic procedures in the hospital than privately insured patients, and when they did receive these diagnostic services, the resultant pathology reports were more likely to be abnormal (OR = 1.56) (Hadley et al., 1991).

This study by Hadley and colleagues also examined the relative resource use (length of stay) of uninsured hospital patients compared to privately insured patients and found that for conditions that afford high discretion in treatment decisions (e.g., tonsillitis, bronchitis, hernia), uninsured patients had significantly shorter lengths of stay (Hadley et al., 1991). However, for diagnoses that afford little discretion in treatment (e.g., gastrointestinal hemorrhage, congestive heart failure), lengths of stay were not significantly different for uninsured and privately insured patients, although uninsured patients tended to have shorter stays. This underscores the possibility that when uninsured patients are found to receive fewer services than insured patients, it may be the result of overtreatment of patients with insurance, rather than undertreatment of those without coverage.

In addition to differences in the resources devoted to the care of insured and uninsured patients, the quality of the care provided may differ. One study of more than 30,000 hospital medical records in 51 hospitals in New York State for 1984 found that the proportion of adverse medical events due to negligence was substantially greater among patients without health insurance than among privately insured patients (OR = 2.35), while the experience of Medicaid patients did not differ significantly from that of the privately insured population (Burstin et al., 1992). This increased risk for uninsured patients was attributable only in part to receiving care more frequently in emergency departments, which generally were found to have higher rates of adverse events.

Because most studies of hospital-based care and outcomes are observational, including only those who literally “show up” for care, and because appropriateness criteria are not available for many conditions, some of the strongest research on health insurance effects involves studies of specific conditions. Studies of certain conditions are less likely to be compromised by nonrandom or unrepresentative samples (selection bias) simply because a larger proportion of the population of interest—namely, acutely ill adults—is likely to be captured in the hospital-based study population. Furthermore, condition-specific studies are more likely to include evidence-based criteria for judging the appropriateness of care.

The following two sections consider research that has examined the effect of health insurance on care and outcomes for patients with (1) emergency conditions and traumatic injuries and (2) cardiovascular disease. For both categories, selection bias among those reaching treatment is minimized, and appropriateness guidelines and outcomes criteria (e.g., mortality) are definitive. Traumatic injuries (specifically automobile accidents), for example, reduce some of the unmeasured differences in propensity to seek care between insured and uninsured patients (Doyle, 2001). Another area of hospital-based services for which there is sufficient professional consensus about appropriate treatment is the use of angiography and revascularization procedures following acute myocardial infarction (AMI) or heart attack, at least for a subset of patients with severe coronary artery disease. 13

Emergency and Trauma Care

Finding: Uninsured persons with traumatic injuries are less likely to be admitted to the hospital, receive fewer services when admitted, and are more likely to die than insured trauma victims.

Two studies based on large, statewide data sets have found substantial and significant differences in the risk of dying for insured and uninsured trauma patients ( Box 3.10 ) who were admitted to hospitals as emergencies. Doyle (2001) analyzed more than 10,000 police reports of auto accidents linked to hospital records maintained by Wisconsin over 1992–1997 to ascertain the care received and the mortality of insured and uninsured crash victims. After controlling for personal, crash, and hospital characteristics, it was found that uninsured accident victims received 20 percent less care, as measured by hospital charges and length of stay, and had a 37 percent higher mortality rate than did privately insured accident victims (5.2 percent versus 3.8 percent, respectively) (Doyle, 2001). The authors conclude that these differences are attributable to provider response to insurance status because extensive patient characteristics were accounted for in the analysis and because unmeasured patient characteristics that might influence these outcomes were unlikely to be related to patients' health insurance status.

Trauma. Throughout the United States in 1997, approximately 34.4 million episodes of injury and poisoning received medical attention and 40.9 million injuries and poisonings were reported as a result (Warner et al., 2000). For injury-related deaths, 43 (more...)

Haas and Goldman (1994) evaluated the treatment experience and mortality of more than 15,000 insured and uninsured trauma patients admitted to hospitals on an emergency basis in Massachusetts in 1990. Adjusting the data for injury severity and comorbidities as well as for age, sex, and race, the authors found that uninsured trauma patients received less care and had higher in-hospital mortality than did patients with private insurance or Medicaid. Uninsured patients were just as likely to receive care in an intensive care unit (ICU) as privately insured trauma patients but were less likely to undergo an operative procedure (OR = 0.68) or to receive physical therapy (OR = 0.61). Uninsured patients were much more likely than privately insured patients to die in the hospital (OR = 2.15) (Haas and Goldman, 1994). The differences in services and mortality experience between Medicaid and privately insured patients were small and were not statistically significant.

Other studies of emergency department use and admissions and care for traumatic injuries shed some light on patient behavior and institutional responses related to health insurance status. Both lacking health insurance and not having a regular source of care have been found in surveys of patients who eventually do arrive at an ED to be related to delays in seeking care (Ell et al., 1994; Rucker et al., 2001). Braveman and colleagues (1994) examined hospital discharge records of more than 91,000 adults diagnosed with acute appendicitis in California hospitals between 1984 and 1989. They found that the risk of a ruptured appendix was 50 percent higher for both uninsured and Medicaid patients, than for privately insured patients in prepaid plans, in an analysis that controlled for age, sex, race, psychiatric diagnoses, diabetes, and hospital characteristics. Admission to a public hospital also was associated with rupture, as were diagnoses of psychiatric illness or diabetes (Braveman et al., 1994). The authors hypothesized that both Medicaid and uninsured patients incurred avoidable delays before seeking care for appendicitis.

Three separate studies that analyzed Medicaid and uninsured trauma patients together report mixed findings regarding patient outcomes and hospital care. Rhee and colleagues (1997) examined patient information for more than 2,800 persons hospitalized at a Level 1 trauma center after a motor vehicle crash in Seattle, Washington, between 1990 and 1993. 14 This study found no significant differences in mortality, hospital charges, or length of stay (LOS) between privately insured patients and those who either had Medicaid coverage or were uninsured, except for patients who ultimately were transferred to a long-term care or rehabilitation facility. In the case of patients awaiting transfer, those with Medicaid or no insurance had an adjusted LOS that was 11 percent longer than privately insured patients (Rhee et al., 1997). The authors speculate that the similarity in treatment and outcomes for patients of different insurance status could be due to the mission of the public, Level 1 trauma center to which they were admitted, which was to serve the entire state population needing that level of care and act as a provider of last resort for uninsured patients. Because this study did not differentiate results for Medicaid and uninsured patients, it provides less information about outcomes for uninsured patients than studies that analyze these groups separately.

Uninsured trauma patients may also be treated differently from insured patients in interhospital transfer decisions. Using Washington State trauma registry information, Nathens and colleagues (2001) identified 2,008 trauma patients between 16 and 64 years of age injured in King County (Seattle) and originally transported to one of seven Level 3 or 4 trauma centers in the county between 1995 and 1999. Adjusting for age, sex, type of injury, and injury severity, they looked at independent predictors of transfer to the Level 1 trauma center in the county—a public, safety-net hospital, and estimated that patients who either had Medicaid or were uninsured were more than twice as likely to be transferred to the higher level facility than were privately insured patients (OR = 2.4) and that many of these transferred patients had low injury severity scores (ISS). 15 The authors conclude that this “payer-based triage” may undermine the effectiveness of Level 1 trauma centers in serving the more critically injured patients by diverting resources to patients who could have been treated appropriately in their original hospital (Nathens et al., 2001).

Finally, the differences found between uninsured and insured patients in highly discretionary cases may reflect overtreatment of those with health insurance rather than undertreatment of uninsured patients. Svenson and Spurlock (2001) evaluated the experience of more than 8,500 patients with head injuries treated in four Kentucky hospitals between 1995 and 1997. For those with less severe head injuries (lacerations, contusion, or concussion), uninsured patients were substantially less likely than privately insured patients to be admitted to the hospital (OR = 0.14 for laceration, 0.38 for contusion or concussion). The likelihood of admission for Medicaid was also substantially lower than for privately insured patients, but not as low as for uninsured patients (ORs = 0.33 and 0.45, respectively). Little difference was found in hospital admissions for more severe head injuries among patients with different insurance status. The authors were unable to determine whether the differences in admissions for less severe head trauma are due to undertreatment of uninsured and Medicaid patients or overtreatment of privately insured patients (Svenson and Spurlock, 2001).

Finding: Uninsured patients with acute cardiovascular disease are less likely to be admitted to a hospital that performs angiography or revascularization procedures, are less likely to receive these diagnostic and treatment procedures, and are more likely to die in the short term.

Finding: Health insurance reduces the disparity in receipt of these services by members of racial and ethnic minority groups.

Health insurance is positively associated with receipt of hospital-based treatments for cardiovascular disease (specifically, coronary artery disease) and with lower patient mortality ( Box 3.11 ). One meta-analysis has credited medical advances in the treatment of cardiovascular disease, including hospital-based care following AMI, with roughly half of the reduction in post-AMI mortality between 1975 and 1995 (with a range of 20 to 85 percent) (Cutler et al., 1998). Some of the most recent studies have used appropriateness criteria to identify when a given procedure is considered necessary according to professional consensus, reducing the chances that differences in rates between uninsured and insured patients are a result of overtreatment of the insured population (i.e., Sada et al.,1998; Leape et al., 1999).

In 2001, an estimated 1.1 million Americans suffered a diagnosed heart attack. An estimated 7.3 million Americans have a history of AMI (American Heart Association, 2001). During 1998, coronary heart disease accounted for about 460,000 deaths; AMI was (more...)

Five studies that examined the mortality experience of patients hospitalized for cardiovascular disease (including AMI, angina, and chest pain) reported higher in-hospital or 30-day posthospitalization mortality for uninsured patients (Young and Cohen, 1991; Blustein et al., 1995; Kreindel et al., 1997; Sada et al., 1998; Canto et al., 2000).

The first study, of about 5,000 patients admitted on an emergency basis for AMI in 1987, found that uninsured patients were more likely to die within 30 days of admission than privately insured patients (OR = 1.5) (Young and Cohen, 1991). In a second study, Blustein and colleagues (1995) examined records for 5,800 patients under 65 who were admitted to California hospitals for AMI in 1991 and found that uninsured patients were more likely to die in the hospital than privately insured patients (OR = 1.9) and still had an increased risk of dying after adjusting for receipt of a revascularization procedure (OR = 1.7). Finally, a study in a single Massachusetts community of 3,700 patients hospitalized for AMI between 1986 and 1993 reported that uninsured patients had a slight, but statistically insignificant greater in-hospital mortality than privately insured patients (OR = 1.2, CI: 0.6–2.4) (Kreindel et al., 1997).

Two larger studies that used more recent data (1994–1996) from the National Registry of Myocardial Infarction reported higher in-hospital mortality for uninsured than for privately insured patients. In the first, Sada and colleagues (1998) reviewed records for 17,600 patients under age 65 who were admitted to hospital for AMI and found that uninsured patients had an in-hospital mortality rate of 5.4 percent, compared with 3.8 percent for private FFS patients and 3.9 percent for private HMO patients. Medicaid patients had the highest in-hospital mortality rate, 8.9 percent. In a model that adjusted for demographic and clinical factors, the likelihood of uninsured patients dying in the hospital was still higher but was not statistically significantly different from that of privately insured patients (OR = 1.2, CI: 0.8–1.6) (Sada et al, 1998). The second national study examined records for more than 332,000 patients admitted with AMI and found that after adjusting for demographics, prior disease history, and clinical characteristics, uninsured patients were more likely to die in the hospital than privately insured FFS patients (OR = 1.29) (Canto et al., 2000). The mortality experience of Medicaid patients was the same as that of uninsured patients.

Only one study, a review of hospital records of 1,556 patients undergoing coronary artery bypass graft surgery in a single Louisiana teaching hospital, found that uninsured patients had better long-term survival than did insured patients (Mancini et al., 2001). However, this study did not control for age or characteristics of the patients. The average age of uninsured patients at the time of surgery was 55, and of insured patients, 65 years. Furthermore, only 7 percent of the insured study population had private insurance, so the population was not representative of the insured population at large.

Coronary Procedures

The body of research on the use of specific procedures to diagnose and treat cardiovascular disease as a function of the insurance status of the patient consistently reports differences in utilization, with uninsured patients generally less likely to receive coronary angiography, CABG, or percutaneous transluminal coronary angioplasty (PTCA) than privately insured patients (Young and Cohen, 1991; Blustein et al., 1995; Kuykendall et al., 1995; Sada et al., 1998; Leape et al., 1999; Canto et al., 2000; Daumit et al., 2000). However, only some of these studies applied appropriateness criteria to identify cases in which the use of these procedures was considered nondiscretionary or necessary. In the studies that examined overall utilization rates, the differences found by insurance status could be attributed to overutilization as well as underutilization.

Angiography (cardiac catheterization) is an invasive diagnostic procedure that provides information to guide decisions about subsequent treatment options, including revascularization procedures. Sada and colleagues (1998) applied the criteria of the American College of Cardiology and American Heart Association Joint Task Force to a national data set of 17,600 myocardial infarction patients under 65 to identify nondiscretionary angiography for revascularization candidates considered to be at high risk. They estimated that in hospitals providing these cardiac procedures, patients with private FFS coverage who were deemed high-risk and for whom angiography was nondiscretionary were more likely than similarly high-risk uninsured patients or Medicaid patients to receive angiography. Among high-risk FFS patients, 84 percent received this service compared to 73 percent of high-risk uninsured patients and 60 percent of similar Medicaid patients (Sada et al., 1998).

Revascularization procedures (either CABG or PTCA) following a heart attack are also more likely to be performed on insured than uninsured patients. In two studies, uninsured patients were less likely to receive revascularization (either CABG or PTCA) than privately insured FFS patients (OR = 0.6 in the 1991 study and 0.8 in the 2000 study) (Young and Cohen, 1991; Canto et al., 2000). Blustein and colleagues (1995) and Kuykendall and colleagues (1995) reported similar comparative findings regarding the revascularization of uninsured and privately insured patients (ORs in these studies ranged from 0.4 to 0.6).

InterHospital Transfers to Receive Services. For patients with AMI, health insurance facilitates access to hospitals that perform angiography and revascularization, whether admission is initial or by means of an interhospital transfer (Blustein et al., 1995; Canto et al., 1999; Leape et al., 1999).

In a study of California hospital admissions for AMI, Blustein and colleagues (1995) found that uninsured patients were less likely than privately insured patients to be admitted initially to a hospital that offered revascularization and much less likely to be transferred if admitted initially to one that did not (ORs = 0.71 and 0.42, respectively).

Leape and colleagues (1999) reviewed 631 records for patients who had received angiography and subsequently met expert panel criteria for necessary revascularization. Overall, 74 percent of patients meeting these criteria received revascularization. Leape et al. found that in hospitals that also performed CABG and PTCA, there were no differences in rates of revascularization for patients with different insurance status. However, for patients initially hospitalized in facilities that did not perform CABG and PTCA, who required a transfer to another hospital to receive revascularization, the rates differed significantly by insurance status: 91 percent of Medicare patients, 82 percent of privately insured patients, 75 percent of Medicaid patients, and just 52 percent of uninsured patients received this indicated surgery (Leape et al., 1999).

Insurance Status and Racial and Gender Disparities. Health insurance has been shown to lessen disparities in the care for cardiovascular disease received by men compared to women and among members of racial and ethnic groups (Carlisle et al., 1997; Daumit et al., 1999, 2000).

An analysis of more than 100,000 hospital discharges with a principal diagnosis of cardiovascular disease in Los Angeles County between 1986 and 1988 revealed significant differences in rates of angiography, CABG, and PTCA between uninsured African-American and white patients but not between members of these ethnic groups who were privately insured (Carlisle et al., 1997). In a multivariate analysis that controlled for demographic and clinical characteristics and hospital procedure volume, the odds ratios for uninsured African Americans to receive one of these services compared with uninsured whites ranged from 0.33 to 0.5 (Carlisle et al., 1997).

A longitudinal study with a seven-year follow-up of a national random sample of patients who initially became eligible for the Medicare ESRD program in 1986 or 1987 found that once uninsured patients qualified for ESRD benefits, pronounced disparities by gender or race in their likelihood of receiving either angiography, CABG, or PTCA were eliminated (Daumit et al., 1999, 2000). In the period prior to qualifying for Medicare, uninsured African Americans were far less likely than uninsured whites to undergo a cardiac procedure (OR = 0.07) (Daumit et al., 1999). Uninsured women were also less likely than uninsured men to receive a cardiac procedure before qualifying for Medicare (OR = 0.4), and uninsured men were much less likely than men with private insurance to receive one (OR = 0.47) (Daumit et al., 2000). In the case of both race and gender, differences in the receipt of these cardiac procedures were eliminated after gaining Medicare ESRD coverage.

• GENERAL HEALTH OUTCOMES

Finding: Longitudinal population-based studies of the mortality of uninsured and privately insured adults reveal a higher risk of dying for those who were uninsured at baseline than for those who initially had private coverage.

Finding: Relatively short (one- to four-year) longitudinal studies document relatively greater decreases in general health status measures for uninsured adults and for those who lost insurance coverage during the period studied than for those with continuous coverage.

This chapter concludes with a review of the studies evaluating the overall health status and mortality experience of insured and uninsured populations. Assessments of general health outcomes such as self-reported health status and mortality or survival rates for uninsured adults under 65 compared to those with some form of health insurance (i.e., employment-sponsored, Medicaid, Medicare, individually purchased policies), present researchers with even greater challenges of analytic adjustment than those encountered in studies of specific health conditions. Not only might health insurance affect health status, but health status can affect health insurance status. Thus, it is difficult to interpret cross-sectional studies of health insurance and health status. However, several well-designed longitudinal studies with extensive analytic adjustments for covariates have found higher mortality and worse overall functional and health status among uninsured adults than among otherwise similar insured adults.

Two studies provide evidence that uninsured adults are more likely to die prematurely than are their privately insured counterparts.

Franks and colleagues (1993a) followed a national cohort of 4,700 adults age 25 or older for 13 to 17 years who, at the baseline interview, were either privately insured or uninsured. At the end of the follow-up period (1987), about twice as many participants who were uninsured at the time of the first interview had died as had those with private health insurance (18.4 percent compared with 9.6 percent). Controlling for sociodemographic characteristics, health examination findings, self-reported health status, and health behaviors, the risk of death for adults who initially were uninsured was 25 percent greater than for those who had private health insurance at the time of the initial interview (mortality hazard ratio = 1.25, CI: 1.00–1.55). The magnitude of this independent health insurance effect on mortality risk was comparable to that of being unemployed, to lacking a high school diploma, or to being in the lowest income category (Franks et al., 1993a). 16 Because insurance status was measured only at the initial interview and thus did not reflect the subjects' cumulative insurance experience over the 13–17 year follow-up period, the difference found in mortality between uninsured and privately insured persons most likely is an underestimate of differences in the mortality experience of those who are continuously uninsured and those who are continuously insured.

A study by Sorlie and colleagues (1994) tracked the mortality experience of 148,000 adults between 25 and 65 years of age until 1987, a two- to five-year follow-up period. After adjusting for age and income, this study found that uninsured white men had a 20 percent higher risk of dying than white men with employment-based health insurance. Uninsured black men and white women each had a 50 percent higher mortality risk than their counterparts with employment-based coverage (Sorlie et al., 1994). Among black women, insurance was not statistically associated with mortality. The authors also examined the mortality experience of insured and uninsured employed white men and women, adjusted for age and income. (Because of small sample size, they did not perform this analysis for black men and women.) Uninsured employed white men had a 30 percent greater risk of dying than their working counterparts with health insurance, and uninsured employed white women had a 20 percent greater risk over two to five years than their counterparts with health insurance (Sorlie et al., 1994).

Loss of Coverage and Changes in Health Status Over Time

Persons who lose health insurance have been found to experience declines in their health status. Longitudinal studies that follow a cohort of individuals over time can provide a “before-and-after” picture of health status, comparing a group that maintained coverage with one that lost it. Such a design helps to minimize the possibility that unmeasured factors that vary along with health insurance status account for differences in health, a competing hypothesis that cannot be eliminated in cross-sectional studies.

Lurie and colleagues (1984, 1986) took advantage of a natural experiment in the mid-1980s when California eliminated Medi-Cal coverage for a group of medically indigent adults. Following matched cohorts of adults seen at an internal medicine practice at a university clinic who either maintained or lost Medi-Cal coverage, the authors found that the patients who lost coverage reported significant decreases in perceived overall health at both six months and a year later, unlike those who maintained coverage. As discussed earlier in this chapter, participants in this study with hypertension who lost coverage also experienced worsening blood pressure control, while those who maintained coverage did not.

Like those with chronic health conditions, adults in late middle age are particularly susceptible to deteriorations of function and health status if they lack or lose health insurance coverage. Baker and colleagues (2001) followed a group of more than 7,500 participants in the longitudinal Health and Retirement Survey (adults ages 51 to 61 at the outset) between 1992 and 1996. The authors compared three groups:

those who were continuously insured over the first two years (measured in 1992 and 1994);

those who were continuously without insurance over that period; and

those who were intermittently uninsured , defined as those who lacked health insurance either in 1992 or in 1994, but not at both times (Baker et al., 2001).

Of those who were continuously uninsured, 22 percent had a major decline 17 in self-reported health, 16 percent of the intermittently uninsured experienced a major decline, and 8 percent of the continuously insured reported a major decline in health. In an analysis that controlled for sociodemographic characteristics, preexisting medical conditions, and health behaviors, the authors estimated a 60 percent greater risk of a major decline in health for continuously uninsured persons and a 40 percent greater risk for intermittently insured persons, as compared with continuously insured persons. Continuously or intermittently uninsured persons also had a 20 to 25 percent greater risk of developing a new difficulty in walking or climbing stairs than did those who were continuously insured (Baker et al., 2001).

Cross-Sectional Studies of Health Status

Cross-sectional studies based on large national population surveys (Medical Expenditure Panel Survey [MEPS], National Medical Expenditure Survey [NMES], and Behavioral Risk Factor Surveillance System, provide snapshots of the subjective or self-reported health status of populations according to insurance status. These surveys report worse health status among those without insurance than among those with coverage. Two large studies with careful and extensive analytic adjustments for covarying personal characteristics are presented here.

Franks and colleagues (1993b) examined the relationship between health insurance status and subjective health across several dimensions, including a general health perceptions scale, physical and role functions, and mental health, for 12,000 adults ages 25 through 64. The authors compared participants who had private health insurance for an entire year with those who had been without health insurance the entire year. In an analysis that controlled for age, sex, race, education, presence of a medical condition, and attitude toward medical care and insurance, uninsured adults had significantly lower subjective health scores across all dimensions. The effect on these measures of health of being uninsured was greater for lower-income persons than for those in families with incomes above 200 percent of the federal poverty level, although the effect persisted in both income groups. For both lower- and higher-income adults, the negative effect on perceived health of being uninsured was greater than that of having minority racial or ethnic status. Overall, the extent to which being uninsured negatively affected subjective health (a decrement of 4 points on a 100-point scale) was greater than that of having either of two diseases, cancer or gall bladder disease, and slightly lower than that for arteriosclerosis (Franks et al., 1993b).

Ayanian and colleagues' (2000) analysis of the 1998 BRFSS compared self-reported health status among adults 18-64 who were uninsured for a year or longer, those uninsured for less than a year, and those with any kind of insurance, public or private. Table 3.1 presents the unadjusted results for the approximately 163,000 adults surveyed. One in five adults uninsured for a year or longer reported being in fair or poor health, compared with one in seven among those uninsured for less than a year, and one in nine for those with health insurance.

Unadjusted Self-Reported Health Status for 18–64 Year-Old Adults, BRFSS, 1998 (percent).

The RAND Health Insurance Experiment

In an experimental study conducted between 1975 and 1982, about 4,000 participants between 14 and 61 years were randomly assigned (in family units) to health insurance plans that differed in the amount of patient cost sharing required, ranging from free care to major deductible plans (95 percent cost sharing, with a maximum of $1,000 per family per year) (Brook et al., 1983; Newhouse et al., 1993). Participants received a lump-sum payment at the beginning of the study to compensate them for their expected out-of-pocket costs if they were in cost-sharing plans. Participants were studied for a three- to five-year period. While persons in plans with any cost sharing had significantly fewer physician visits and hospitalizations than persons in a free-care plan, no difference was found overall between plans with any amount of cost sharing and those with no cost sharing. Free care did result in better outcomes for adults with hypertension, as discussed earlier in this chapter, and in improved visual acuity. This experiment demonstrates both the sensitivity of health care utilization in the general population to cost sharing and the relative insensitivity of short-term (three- to five-year) health outcomes for the general population to cost sharing.

Negative Results

Some studies have reported worse health status for those with health insurance compared to uninsured adults. This result may be attributable to the fact that worse health status may lead to coverage by Medicare or Medicaid, as discussed in Chapter 2 (see Box 2.1 ) and Chapter 4 . However, the competing hypothesis, that health insurance is not associated with overall health status, must also be considered.

Hahn and Flood (1995) used NMES to examine health status by both income level and type and duration of insurance coverage. When SES and demographic characteristics, health behaviors, health care utilization, and Social Security disability status were controlled for in the analysis, self-reported health status was seen to be arrayed from highest to lowest as follows:

• privately insured for the full year,
• privately insured for part of the year and uninsured for part of the year,
• uninsured for the full year,
• publicly insured for part of the year, and
• publicly insured for the full year.

The authors concluded that the likeliest explanation for their results was that the poorer health status of those who qualify for public coverage was not fully accounted for in their analytic model, even though qualification on the basis of disability was considered explicitly (Hahn and Flood, 1995). An alternative (and possibly supplementary) hypothesis was that public insurance—Medicaid specifically—provided enrollees with access and services that were less effective than those provided by private insurance. Neither of these possible explanations can be eliminated based on the research that the Committee has reviewed.

A second study by Ross and Mirowsky (2000) based on the Survey of Aging, Status and the Sense of Control (ASOC) examined the claim that being uninsured contributes to the worse health of persons of lower SES. The ASOC survey included 2,600 adults between ages 18 and 95 at baseline in 1995, 38 percent of whom were 60 years or older. Participants were reinterviewed in 1998 (44 percent were lost to follow-up) (Ross and Mirowsky, 2000). Health status, functional status, and chronic conditions reported by participants at baseline were used to predict health status, functional status, and chronic conditions three years later. Changes in these measures between baseline and follow-up were also included as predictors of health status, functional status, and number of chronic conditions at follow-up in 1998. The authors concluded that privately insured and uninsured persons had similar health status at a three-year follow-up, adjusted for baseline health status, chronic conditions, and sociodemo-graphic characteristics, and that publicly insured persons had worse health status than privately insured and uninsured adults (Ross and Mirowsky, 2000).

The Committee does not find this study convincing in its conclusions because of both the study sample and its analytic design. The sample included a large proportion of persons over 65, all of whom have Medicare, and the substantial fraction of participants lost to follow-up differed systematically from those who were reinterviewed. By including changes in health condition over the study period as independent variables along with health measures at baseline, the authors may have built their findings into the predictive model itself. In addition, Medicare beneficiaries with supplemental health insurance were classified as privately insured; thus, those who counted as publicly insured included only those Medicare beneficiaries without supplemental policies (a lower-income subset of all Medicare beneficiaries) and Medicaid beneficiaries. This atypical classification scheme distorts the comparison between those with public and private health insurance.

This chapter has presented studies examining the impact of health insurance status on general measures of population health, on health care and clinical outcomes for specific conditions, and on the appropriate use of preventive services for the nonelderly adult population in the United States. This body of research yields largely consistent and significant findings about the relationship between health insurance and health-related outcomes. In summary, uninsured adults receive health care services that are less adequate and appropriate than those received by patients who have either public or private health insurance, and they have poorer clinical outcomes and poorer overall health than do adults with private health insurance. The specific findings discussed throughout this chapter are presented in Box 3.12 .

The Committee has assessed the research regarding the effects of health insurance status across a range of health conditions and services affecting adults. In each domain examined—

• preventive care and screening services,
• cancer care and outcomes,
• chronic disease management and patient outcomes,
• acute care services and outcomes for hospitalized adults, and
• overall health status and mortality,

health insurance improved the likelihood of appropriate care and was associated with better health outcomes. Health insurance appears to achieve these positive effects in part through facilitating ongoing care with a regular health care provider and reducing financial barriers to obtaining those services that constitute or contribute to appropriate care, including screening services, prescription drugs, and specialty mental health services.

Chapter 4 specifically addresses the question of the difference that providing health insurance to uninsured individuals and populations would make to their health and health care. The Committee assesses the potential impact of health insurance coverage on those uninsured adults who are most at risk for poor or adverse health-related outcomes, including the chronically ill, adults in late middle age, members of ethnic minorities, and adults in lower-income households. The chapter also reviews the features and characteristics of health insurance that account for its effectiveness in achieving better health outcomes, including both continuity of coverage and scope of benefits.

BOX 4.1 Conclusions

The Committee's conclusions are supported by the evidence and findings presented in Chapter 3 , which are largely based on observational studies.

• Health insurance is associated with better health outcomes for adults and with their receipt of appropriate care across a range of preventive, chronic, and acute care services. Adults without health insurance coverage die sooner and experience greater declines in health status over time than do adults with continuous coverage.
• Adults with chronic conditions, and those in late middle age, are the most likely to realize improved health outcomes as a result of gaining health insurance coverage because of their high probability of needing health care services.
• Population groups that are most at risk of lacking stable health insurance coverage and that have worse health status, including racial and ethnic minorities and lower-income adults, particularly would benefit from increased health insurance coverage. Increased coverage would likely reduce some of the racial and ethnic disparities in the utilization of appropriate health care services and might also reduce disparities in morbidity and mortality among ethnic groups.
• When health insurance affords access to providers and includes preventive and screening services, outpatient prescription drugs, and specialty mental health care, it is more likely to facilitate the receipt of appropriate care than when insurance does not have these features.
• Broad-based health insurance strategies across the entire uninsured population would be more likely to produce the benefits of enhanced health and life expectancy than would “rescue” programs aimed only at the seriously ill.

Chapter 2 discusses the features of observational (nonexperimental) studies that are necessary for methodological soundness. All quantified study results that are presented in this chapter and in Chapter 4 are significant at least at the 95 percent confidence interval. If results do not meet this level of statistical significance, the confidence interval is reported. See “confidence interval” in Appendix C for further discussion.

Earlier studies based on the 1986 Access to Care Survey and the 1982 NHIS had findings consistent with those of the more recent nationally representative sample surveys regarding receipt of preventive and screening services by those without health insurance (Hayward et al., 1988; Woolhandler and Himmelstein, 1988).

Enrollees in private managed care plans is the reference group; however, fee-for-service enrollees did not have significantly different screening rates from those of managed care enrollees. The odds ratio is the relative odds of having an outcome in the uninsured and insured groups. For example, if the odds of receiving a Pap test are 2:1 in a group of uninsured women (i.e., two of every three women or 67 percent receive the test) and the odds are 4:1 in a group of women with insurance (i.e., four of every five women, or 80 percent, receive the test), the odds ratio of uninsured compared to insured women is 0.5 (2:1/4:1). The OR is not a good estimate of the relative risk (the probability of been screened in the uninsured group divided by the probability of being screened in the insured group) because screening is not a rare event. Throughout this report the results of particular studies, if reported as odds ratios or as relative risks, will be presented as the ratio of the uninsured to the insured rates (in this example, as an OR of 0.5).

Comparing results presented in Potosky et al., 1998, and Breen et al., 2001, the gap in screening rates between insured and uninsured adults decreased between 1992 and 1998.

Smoking has been associated with an increased risk of colorectal cancer (Chao et al., 2000).

This hypertension result was an exception to the overall results for the RAND study, which did not find significant differences in outcomes for most conditions and dimensions of health. These results are discussed further in the General Health Outcomes section later in this chapter.

Notably, this same study found that persons with hypertension who had Medicare coverage only (which does not pay for outpatient prescription drugs) did not have a statistically significant difference in their likelihood of receiving antihypertensive medication than uninsured persons, while those who had Medicare plus Medicaid coverage or Medicare with private supplemental insurance were significantly more likely to have received drug therapy than uninsured persons with hypertension.

BRFSS has documented the use of recommended services among insured and uninsured persons with diabetes for two recent years. BRFSS collected information on diabetes management in 1994 in 22 jurisdictions (21 states and the District of Columbia) and in 1998 in 37 jurisdictions, representing 70 percent of the U.S. population (Beckles et al., 1998; Ayanian et al., 2000).

This standard of low hematocrit is below the hematocrit target range of 33–36 percent recommended by the National Kidney Foundation's Dialysis Outcomes Quality Initiative (NKF, 2001).

In this study, “late initiation” is defined as glomerular filtration rate of serum creatinine of <5 ml/min per 1.73 m 2 —a level substantially below both that recommended by the National Kidney Foundation (<10.5 ml/min) and the U.S. mean value at initiation (<7.1 ml/min) (Kausz et al., 2000).

The HIV Cost and Services Utilization Study (HSCUS), conducted by RAND and the Agency for Healthcare Research and Quality, was a probability sample of persons 18 years and older in the contiguous United States known to have HIV infection who had one visit for regular care (except in a military, prison, or emergency treatment facility) within a two-month period in 1996. Three rounds of interviews were conducted over a two-year period, 1996–1998, with between 2,267 and 2,864 subjects (Shapiro et al., 1999). The AIDS Costs and Utilization Survey, a predecessor study to HCSUS, with six waves over 18 months in 1991 and 1992, was not a probability sample (see Box 2.4 for further detail on these surveys).

Older studies that examine hospital-based care and outcomes according to insurance status across a range of diagnoses are summarized in Appendix B . The results of these studies are consistent with the findings discussed in text; however, many are based on hospital records that may be less relevant to the current hospital practice environment.

See Leape et al. (1999) for a description of the RAND methodology for determining appropriateness and its application to developing criteria for revascularization procedures.

The American College of Surgeons designates hospital EDs as trauma centers based on qualifying criteria related to staffing, resources, and services. There are four designations: Level 1, the most stringent requirements, for providing tertiary care on a regional basis; Level 2, similar services to a Level 1 center but without clinical research and prevention activities; Level 3, presence of emergency services, often in a rural area, with fewer specialized services and resources than Level 1 or 2 centers; and Level 4, usually in a rural area, describing hospitals and clinics that serve a triage function (Bonnie et al., 1999).

The authors designated an ISS of <16 as “minimal to moderate injury” and >16 as more severe. Overall, 59 percent of transferred patients had an ISS of <9.

The lowest income category included those with a family income of less than $7,000 at the initial interview (1971–1975).

A “major decline” in health was defined as a change from excellent, very good, or good health in 1992 to fair or poor health in 1996, or from fair health in 1992 to poor health in 1996 (Baker et al., 2001).

• Cite this Page Institute of Medicine (US) Committee on the Consequences of Uninsurance. Care Without Coverage: Too Little, Too Late. Washington (DC): National Academies Press (US); 2002. 3, Effects of Health Insurance on Health.
• PDF version of this title (4.6M)

In this Page

Recent activity.

• Effects of Health Insurance on Health - Care Without Coverage Effects of Health Insurance on Health - Care Without Coverage

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

• Health Equity
• Managed Care
• Maternal & Early Childhood Health
• Rural Health
• Unwinding the PHE
• State Data Hub
• Research & Reports
• Comments on Federal Regulations
• Faculty and Staff
• LinkedIn LinkedIn

Another Sign That Trump 2 Will Target Medicaid for Deep, Damaging Cuts

• share this post on Facebook
• share this post on Twitter
• share this post on LinkedIn
• share this post through Email

What would a second Trump Administration have in store for Medicaid?  The Project 2025 blueprint includes truly draconian cuts just like the House Republican Study Committee budget plan does.  Now, a new report from the Paragon Institute provides yet another sign that Medicaid will be targeted – it recommends deep and damaging cuts in federal Medicaid funding resulting in millions of people becoming uninsured, and an overall depletion of resources for a vital piece of America’s health safety net. Medicaid covers half of the children in the United States, is the largest payer for behavioral health and substance abuse treatment, the primary payor for long term care, and covers more than forty percent of all births , so cuts of this magnitude would be devastating for crucial aspects of our health care system. Medicaid dollars are also the largest inflow for state budgets so the ripple effects would be substantial.

Both of the report’s authors have worked at the Heritage Foundation, and the lead author, Brian Blase , served in the White House at the National Economic Council during the first Trump Administration. So this is another look of what we could expect. The two major proposals in the report are similar to those proposed in the past – sharply shifting costs to states to roll back the Medicaid expansion with additional cuts to higher income states which would overwhelmingly affect blue states. Overall the report authors expect the Congressional Budget Office to estimate that together the two proposals would cut federal spending by $592.4 billion from 2026 to 2034, though it is likely that the actual spending cut would be significantly higher . [1]

We are not surprised by this – after all Medicaid has long been a target for Republicans. Its importance has only grown in the last decade – today providing health insurance to approximately 80 million people – more than Medicare. And while it is doubtful that former President Trump will hold to statements that he will not cut Medicare and Social Security, as Project 2025 includes radical cuts to both, Medicaid is the obvious remaining target for massive spending cuts.

There are two main proposals – (1) phasing out the current, permanent 90 percent federal matching rate for the Affordable Care Act’s Medicaid expansion so that the regular FMAP would apply after eight years; and (2) phasing down the minimum regular matching rate for states from 50 percent to 40 percent after eight years.  Both would have the effect of shifting substantial costs to states.  States would either have to increase their own contributions to the cost of Medicaid by raising taxes or cutting other parts of their budget like education or, as is far more likely, deeply cutting their Medicaid programs in the area of eligibility, benefits and provider payments.

In the case of the expansion, the report expects some states will entirely drop their Medicaid expansion, resulting in one-quarter of expansion enrollees below the federal poverty line losing coverage.  However, it is very likely that most or all states would eventually drop their expansions, as the expansion FMAP was a critical factor influencing the decision by all but 10 states that have adopted the expansion so far.  (Some states also have explicit “trigger” laws that automatically drop the expansion if the expansion FMAP is lowered.) Otherwise, they would have to institute large cuts to the rest of their Medicaid program – especially those states targeted for additional cuts through the lowering of the FMAP floor.

The report’s authors make the preposterous claim that their proposals will protect children, people with disabilities and pregnant women. Nothing could be further from the truth. In fact, the report is quite explicit in proposal two about lowering the minimum regular match for those very populations if these children happen to live in California, Colorado, Connecticut, the District of Columbia, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Washington and Wyoming (with Wyoming the only all “red” state and only non-expansion state) which currently have 50% matching rates.  If states want to maintain the Medicaid expansion (and the report unrealistically assumes that most states will retain the expansion), states will have to cut other parts of their Medicaid program and thus directly harm children, people with disabilities, pregnant women and seniors who rely on the program today.  Or states would have to cut other parts of their budget, and the largest general fund spending item is K-12 education.

The paper’s claim to protect children and other “traditional” enrollees through its plan to lower the Medicaid match rates for the adult expansion group to the regular match rate that children are covered under is specious for many reasons including:

• 11 states including the District of Columbia would sustain cuts from proposal two to lower the minimum Medicaid matching rate as described above; some states would see larger cuts than others – creating more budget pressures on those states impacting programs important to children such as education. This cost-shift would force states to institute cuts to their Medicaid programs. So are these children less worthy because they live in mostly blue states?
• The ACA Medicaid expansion has had many benefits for children as we have argued before – mainly because their parents/caregivers have insurance and the whole family is protected from medical debt. For example, a mom who has insurance has better access to treatments for conditions like depression and is better able to care for her children. In addition, eligible children are more likely to be enrolled when their parents are also covered – this is called the “welcome mat” effect.
• The Children’s Health Insurance Program (CHIP) has a higher match rate for slightly higher income children; if the authors are concerned about the moral hazard of the expansion they would be better off suggesting that the Medicaid match rate for all children be raised to the CHIP match rather than suggesting at least a trillion dollars in cuts for a program that is so vital to children.

The authors are clear (p. 34) that this paper is just the first in a series and they will write in the future about additional radical, draconian cuts to Medicaid so as to enable further thought on how to block grant or impose a per capita cap on Medicaid (block grant/per capita caps also included in Project 2025 and in the Republican Study Committee budget plan) . In other words, the kind of deep, damaging cuts proposed in this report are part of a broader agenda to gut the entire Medicaid program that is critical to children, pregnant women, people with disabilities, and seniors needing long term care , and is not limited to rolling back the Medicaid expansion.

[1] It is unclear from the report what were all of the assumptions and data used by Paragon to construct its baseline

[Editor’s Note: This blog has been updated to correct the estimated size of the cuts to Medicaid.]

Subscribe to Updates from our Team

Transparency in medicaid managed care: cms posts the mcpars, shades of blue project making shift happen for black maternal mental health.

CCF.GEORGETOWN.EDU

Navigation on swissre.com

Our applied research covers emerging risks, industry trends and topical deep dives.

Topics & Risk Dialogues

• Climate and natural catastrophe risk
• Health and longevity
• Digital business models and cyber risk
• Economy and insurance outlook
• Casualty risk
• Risk Dialogue: Underwriting
• Risk Dialogue: Digital Ecosystems

sigma research

• Expertise Publications
• Economic Insights
• Economic Outlook
• Insurance Monitoring
• sigma data explorer
• sigma 3/2024
• sigma 2/2024

Publication Multi-Cancer Early Detection: cancer screening beyond today's boundaries

Your global partner for risk and insurance education. We help you navigate tomorrow.

• Open programmes
• Learning success factors

Swiss Re Institute Campus

Book a training

Our events examine topics relevant to understanding risk and re/insurance.

Client Training Programme 2024

Your digital place for risk & insurance education

Partnerships

With selected research partners, we explore the future of risk coverage, assess changes in the risk landscape and act as a catalyst for industry change.

Collaboration Open research challenge: Modelling biodiversity and ecosystem services loss to advance resilience

Meetings of the Swiss Re Strategic Council explore emerging issues and provide strategic insights, advice and recommendations on the global economic, political, regulatory ...

Superior research driving better decisions.

Locations and regional sites

Asia pacific, europe, middle east, & africa.

• Brazil Country Website
• Colombia Country Website
• Mexico Country Website
• United States of America
• Australia Country Website
• China Country Website
• Hong Kong SAR
• Japan Country Website
• Republic of Korea Country Website
• New Zealand Country Website
• Ivory Coast
• Slovak Republic
• Switzerland
• United Kingdom
• South Africa

sigma 3/2024 – World insurance: strengthening global resilience with a new lease of life

Prevailing economic conditions have given insurance business a new lease of life. Economic resilience, reflected in slowing but still robust economic growth, and high interest rates are driving much-improved industry profitability. An insurance sector in healthy earnings mode will attract more capital. This, in turn, will drive industry growth and expand risk transfer capacity, enabling the industry to contribute more to narrowing existing protection gaps in many parts of the world.

Article information and share options

The key takeaways of this sigma are:.

• Today's higher interest rates have transformed the operating environment for insurers, most notably for asset-intensive business, from low yields and lows returns to one of higher yields and higher returns.
• We estimate an aggregate 15% improvement in profitability for the life insurance sector across major and advanced markets, with an expected uptick in life savings products as a result of stronger investment returns.
• We also see stronger results in the non-life sector, with newly-underwritten business benefitting as the effects of high interest rates come through, and also due to improved investment returns.

The insurance industry has reached a new equilibrium. The global economy has surprised on the upside, which should drive more demand for insurance.

Fernando Casanova, Senior Economist ; Caroline Da Souza Rodrigues Cabral, Senior Economist ; James Finucane, Senior Economist ; Roman Lechner, P&C Economic Research Lead ; Mahesh Puttaiah, Head Insurance Market Analysis ;  Weijia Yao,Economist ; John Zhu, Chief Economist Asia Pacific

• macroeconomic growth
• life insurance
• interest rates

Growth resilience The world economy has shown to be more resilient than widely anticipated. Recession fears have faded, and in real terms the pace of growth is not far off the 2.8% average of the past two decades. This year, and for the third year running, economies in emerging Asia (excluding China) will be the main engine of global growth.

The economic environment is supporting the insurance industry The prevailing economic backdrop has generated a new and favourable operating environment for insurers. Steady growth, strong labour markets, rising real incomes as inflation moderates from recent highs, and higher interest rates are driving and will continue to drive demand for insurance. At the same time, the higher interest rates are supporting industry profitability.

Insurance markets set for growth The insurance markets demonstrated recovery in 2023, in large part due to a return to positive premium growth in the life sector. This year, still-strong labour markets and improving real wages will underpin demand for both life and non-life insurance, while in life, higher interest rates will fuel strong sales in fixed-rate savings business. We estimate that global life premiums will grow by 2.9% in real terms in 2024, well above the annual average of the previous decade (0.8%), and by 2.7% in 2025. In non-life, rate hardening, especially in personal lines, will drive growth. Global non-life premiums will grow by an estimated 3.3% in 2024 and slightly slower (forecast 3%) in 2025, as insurance prices moderate alongside an easing in (claims) inflation.

Life insurance: a new lease of life In addition to a recovery in premium growth in life insurance, we see a strong 15% gain in sector profitability this year, driven by a 14% increase in investment income as a result of the higher interest rate regime that now prevails. For many years, life insurance business has been in the doldrums due to the very low interest rates in place since the global financial crisis (2008-09) and until after 2021. A notable development of the now higher interest rate environment will be a marked turnaround in life insurance business, in the advanced markets in particular. We estimate that in absolute terms, the advanced markets will contribute about half of all additional life premiums over the next 10 years, a significant improvement from the 9% in the low-interest rate decade before the pandemic. The contribution of incremental premiums from advanced Asia Pacific and western Europe to global volumes will turn strongly positive, having been negative.

Non-life insurance: healthy earnings trend to continue The profitability of non-life business remains on an upward trend. After rising to 6% in 2023, we estimate that sector return on equity will improve to about 10.0% in 2024 and 10.7% in 2025, with progress on both the underwriting and investment fronts. We see significant improvements in underwriting results. Specifically, the profitability of newly-underwritten business is much higher than legacy across most lines of business, due to the full benefits of higher interest rates coming through. Average investment returns will also improve, albeit more gradually. We forecast an improvement in investment yields to 3.6% in 2024 and 3.9% in 2025 as bond portfolios move away from pre-pandemic compositions.

Life insurance sector growth 2023, by market Life insurance premium volumes in advanced markets were down 1.1% in real terms last year, an improvement from the 4.7% slump in 2022. This was due to declines in both advanced Asia Pacific and western Europe, as high inflation eroded real growth and reduced disposable incomes. Emerging market life premiums were up 8.4%, mostly driven by China, where growth recovered to 12.5% from 2.0% in 2022 on the back of strong sales in saving products.

Non-life insurance sector growth 2023, by market Non-life insurance premium volumes in advanced markets grew by 3.6% in 2023, boosted by price increases in personal and, to a lesser degree, commercial insurance lines. The markets in emerging economies grew by 5.3% in 2023, slightly below the 5.9% average level seen in the previous decade. Below-trend economic growth in China, which accounts for half of emerging markets' total premiums, was the main drag.

sigma 3/2024 World insurance

Strengthening global resilience with a new lease of life

Contact Get in contact with our experts

Jerome Jean Haegeli

Group Chief Economist

Swiss Re Institute

Mahesh H Puttaiah

Head Insurance Market Analysis

Find more sigma related content

Publication sigma resilience index 2024: encouraging resilience gains, but more is needed, publication sigma 2/2024: life insurance in the higher interest rate era, article sigma 1/2024 in short, publication sigma 1/2024: natural catastrophes in 2023, publication sigma 6/2023 – risks on the rise as headwinds blow stronger, world insurance series.

The world insurance sigma covers premiums written in the global primary insurance industry. Published annually, it has become one of the fixtures of the sigma programme since 1968, the publication's inaugural year. This page gives quick access to all the resources.

Overlay with sharing options

Tom Pepper, MBBS, BDS, MSc, FRCS

Oral and maxillofacial surgeon, my locations, training and education, clinical focus and research, external relationships.

I completed my residency training in London, England, which involved broad exposure to trauma, head and neck cancer, skin cancer, reconstructive surgery, orthognathic surgery, salivary surgery, aesthetic surgery, TMJ surgery, and dentoalveolar surgery. I have thoroughly enjoyed the breadth of the specialty to date, which has allowed me to assist a wide range of patients with conditions in the head and neck. My focus during consultations is putting patients at ease so that all concerns are covered, and on clear, jargon-free communication to reach a mutually understood surgical plan together. When time allows, I enjoy relaxing by running, sailing, skiing, or scuba diving.

• Plastic, Maxillofacial, and Oral Surgery

Call for an Appointment

I am passionate about research and have two aims in this regard: firstly, to advance surgical science for the benefit of patients, and secondly, to enhance surgical education for the benefit of patients.

Insurance Accepted

Duke Health contracts with most major health insurance carriers and transplant networks, including the ones listed below.

• Aetna Choice POS, Aetna Choice POS II
• Aetna Elect Choice HMO, Aetna Open Access Elect Choice
• Aetna Health Network Only, Aetna Health Network Option
• Aetna Limited Benefit Insurance PPO
• Aetna Managed Choice POS
• Aetna Medicare Assure Plan (HMO D-SNP)
• Aetna Medicare Eagle Plan (PPO)
• Aetna Medicare Essential Plan (PPO)
• Aetna Medicare Value Plan (HMO)
• Aetna Open Access HMO, Open Access Aetna Select, Aetna Open Access Managed Choice
• Aetna Open Choice PPO
• Aetna PCP Coordinated POS Plan
• Aetna Quality Point of Service (QPOS)
• Aetna Select HMO
• Aetna Traditional Choice
• Aetna Voluntary Indemnity Group Plan
• Aetna Whole Health – Duke WakeMed WKCC
• Aetna/CVS Health
• Ambetter of North Carolina
• Blue Advantage
• Blue Medicare (HMO, PPO)*
• Blue Options (123, PPO, HSA)
• Blue Select
• NC State Employees Health Plan

*Duke HomeCare and Hospice does not participate in the plan.

• Cigna Behavioral Health (*Limited eligibility)
• Cigna Choice Plus
• Cigna Connect Individual Family Plan
• Cigna Open Access
• Cigna Open Access Plus

*Please call Cigna Behavioral Heath to see if the provider is participating in your plan.

• Duke Select
• Experience Health Medicare Advantage (HMO) Plan
• Gateway Health Alliance
• Humana Choice (PPO)
• Humana Choice - Medicare Advantage (PPO)
• Humana ChoiceCare - Medicare Advantage (PPO)
• Humana Gold Choice - Medicare Advantage (PFFS)
• Humana Gold Plus - Medicare Advantage (HMO)
• Humana Medicare Advantage Group Plan - NC State Retirees
• First Medicare Direct
• Medicare Part A
• Medicare Part B
• AmeriHealth Caritas North Carolina
• Carolina Complete Health
• Healthy Blue
• NC Medicaid Direct
• WellCare of North Carolina
• TRICARE Prime
• TRICARE Prime Remote
• TRICARE Select
• AARP Medicare Advantage Plan 2 (HMO-POS)
• AARP Medicare Complete (HMO, PPO)*
• AARP Medicare Complete Essential (HMO)*
• All Savers Alternate Funding
• All Savers Fully Insured
• United Healthcare (HMO, PPO, POS)
• United Healthcare Charter/Charter Balance/Charter Plus
• United Healthcare Choice/Choice Plus
• United Healthcare Core/Core Essential
• United Healthcare Navigate/Navigate Plus/Navigate Balanced
• United Healthcare Option PPO
• United Healthcare Passport Connect Choice/Choice Plus
• United Healthcare Passport Connect Options PPO
• United Healthcare Select/Select Plus
• United Healthcare Shared Services - Harvard Pilgrim/UHC Options PPO Network
• United Medical Resources (UMR)

*Duke HomeCare and Hospice and mental health providers do not participate with the plan.

Before scheduling your appointment, we  strongly recommend you contact your insurance company  to verify that the Duke Health location or provider you plan to visit is included in your network. Your insurance company will also be able to inform you of any co-payments, co–insurances, or deductibles that will be your responsibility. If you proceed in scheduling an appointment and your health insurance benefits do not participate with Duke, your out of pocket liability may be higher. We will contact you regarding your coverage and patient liability. If you are uninsured, learn more about our  financial assistance policy .

Explore your options for easing the burden of student loan repayments with Savi.

AARP daily Crossword Puzzle

Hotels with AARP discounts

Life Insurance

AARP Dental Insurance Plans

AARP MEMBERSHIP 

AARP Membership — $12 for your first year when you sign up for Automatic Renewal

Get instant access to members-only products, hundreds of discounts, a free second membership, and a subscription to AARP the Magazine. Find how much you can save in a year with a membership.  Learn more.

• right_container

Work & Jobs

Social Security

• AARP en Español

• Membership & Benefits
• Members Edition
• AARP Rewards
• AARP Rewards %{points}%

Conditions & Treatments

Drugs & Supplements

Health Care & Coverage

Health Benefits

AARP Hearing Center

Advice on Tinnitus and Hearing Loss

Get Happier

Creating Social Connections

Brain Health Resources

Tools and Explainers on Brain Health

Your Health

8 Major Health Risks for People 50+

Scams & Fraud

Personal Finance

Money Benefits

View and Report Scams in Your Area

AARP Foundation Tax-Aide

Free Tax Preparation Assistance

AARP Money Map

Get Your Finances Back on Track

How to Protect What You Collect

Small Business

Age Discrimination

Flexible Work

Freelance Jobs You Can Do From Home

AARP Skills Builder

Online Courses to Boost Your Career

31 Great Ways to Boost Your Career

ON-DEMAND WEBINARS

Tips to Enhance Your Job Search

Get More out of Your Benefits

When to Start Taking Social Security

10 Top Social Security FAQs

Social Security Benefits Calculator

Medicare Made Easy

Original vs. Medicare Advantage

Enrollment Guide

Step-by-Step Tool for First-Timers

Prescription Drugs

9 Biggest Changes Under New Rx Law

Medicare FAQs

Quick Answers to Your Top Questions

Care at Home

Financial & Legal

Life Balance

LONG-TERM CARE

​Understanding Basics of LTC Insurance​

State Guides

Assistance and Services in Your Area

Prepare to Care Guides

How to Develop a Caregiving Plan

End of Life

How to Cope With Grief, Loss

Recently Played

Word & Trivia

Atari® & Retro

Members Only

Staying Sharp

Mobile Apps

More About Games

Right Again! Trivia

Right Again! Trivia – Sports

Atari® Video Games

Throwback Thursday Crossword

Travel Tips

Vacation Ideas

Destinations

Travel Benefits

Beach Vacation Ideas

Fun Beach Vacations

Road Trips For Every Personality

Passport Access

Passports Can Be Renewed Online

AARP National Park Guide

Black Canyon of the Gunnison

Entertainment & Style

Family & Relationships

Personal Tech

Home & Living

Celebrities

Beauty & Style

Movies for Grownups

Summer Movie Preview

Jon Bon Jovi’s Long Journey Back

Looking Back

50 World Changers Turning 50

Sex & Dating

7 Dating Dos and 7 Don'ts

Friends & Family

Veterinarians May Use AI to Treat Pets

Home Technology

Caregiver’s Guide to Smart Home Tech

Virtual Community Center

Join Free Tech Help Events

Creative Ways to Store Your Pets Gear

Meals to Make in the Microwave

Wearing Shoes Inside: Pros vs. Cons

Driver Safety

Maintenance & Safety

Trends & Technology

AARP Smart Guide

How to Clean Your Car

We Need To Talk

Assess Your Loved One's Driving Skills

AARP Smart Driver Course

Building Resilience in Difficult Times

Tips for Finding Your Calm

Weight Loss After 50 Challenge

Cautionary Tales of Today's Biggest Scams

7 Top Podcasts for Armchair Travelers

Jean Chatzky: ‘Closing the Savings Gap’

Quick Digest of Today's Top News

AARP Top Tips for Navigating Life

Get Moving With Our Workout Series

You are now leaving AARP.org and going to a website that is not operated by AARP. A different privacy policy and terms of service will apply.

Why Do We Forget Names?

Struggling to recall a name or calling someone the wrong name is common —and usually not worrisome.

Many people can remember being called by the wrong name as a kid, often by an exhausted mother who ran through the names of every creature in the household — including the family dog — until hitting the name she meant to say in the first place.

As adults, people often repeat the same mistakes, calling one child or grandchild by the name of another.

Get instant access to members-only products and hundreds of discounts, a free second membership, and a subscription to AARP the Magazine. Find out how much you could save in a year with a membership.  Learn more.

Does this mean we’re cracking up?

Not at all.

“It’s completely normal to mix up names, especially within categories of related names,” such as children’s names, says Neil Mulligan, professor of psychology and neuroscience at the University of North Carolina at Chapel Hill.

In a study led by Samantha Deffler, an associate professor of psychology at York College of Pennsylvania, and her colleagues, researchers found that about half of college students interviewed reported being called the wrong name by someone familiar to them. In 95 percent of those cases, the naming mistake was made by a family member.

Parents and grandparents aren’t the only ones who slip up. In the study, 38 percent of students also reported having called a familiar person by the wrong name, most often a family member.

You don’t have to be a scientist to notice a pattern here.

When we call someone by the wrong name, we typically use the name of a similar or related person, such as a family member or close friend. That’s because “the brain stores information in networks” of related terms, says Judith Heidebrink, M.D., a research professor of Alzheimer’s disease, a professor of neurology and co-division chief of the cognitive disorders program at the University of Michigan Medical School.

“You’re much more likely to substitute names that sound similar or that are tied into a category in the strongest way,” Heidebrink says.

Deffler’s study uncovered other patterns about our verbal blunders. For example, the people making the mistakes are almost always older than the people they misname, who tend to be people that the speakers see frequently. Women were slightly more likely than men to mix up names, as well as report having their own names mixed up.

AARP NEWSLETTERS

%{ newsLetterPromoText  }%

%{ description }%

Privacy Policy

ARTICLE CONTINUES AFTER ADVERTISEMENT

Mood can affect our memory too, says Deffler.

More than 40 percent of the time, study participants reported that the person mixing up the name was tired, frustrated or angry. Trying to juggle multiple tasks at once likely increases the odds of making a naming mistake, Deffler said.

AARP® Vision Plans from VSP™

Vision insurance plans designed for members and their families

Deffler’s study also suggests that dogs have a special place in their families.

Although participants in Deffler’s study were as likely to own cats as dogs, people reported that their parents rarely confused children’s names with those of the family feline. But parents did sometimes mix up the names of their kids and canines.

Inside the brain’s filing cabinet

Many people have trouble remembering names. Research shows that people are more likely to remember a person’s occupation than name, Mulligan says.

And while naming errors are some of the most obvious memory mistakes we make, our brain actually substitutes one word for another all the time, Mulligan said.

Often, that’s because our brain is scrambling to retrieve words quickly enough to keep pace with a conversation.

While we’re chatting away, our brain is working frantically behind the scenes, scanning through a list of potential answers, Mulligan says. The process involves calling up the words we want and rejecting those we don’t.

As part of the normal aging process, people lose some of the dexterity with which they once suppressed unwanted words. That can cause people to make more verbal gaffes, Mulligan says.

When we ask our brain to retrieve information, we send it cues or specifications, such as “an adjective to describe my neighbor’s Great Dane.” But those instructions may not be precise enough for our brain to locate the word we really want. Instead, our brain may select a related word, Mulligan said.

Join AARP today for $16 per year. Get instant access to members-only products and hundreds of discounts, a free second membership, and a subscription to AARP The Magazine. Find out how much you could save in a year with a membership.  Learn more.

For example, a person searching for the word “gigantic” to describe that Great Dane may instead come out with the word “huge.”

“The listener may not even realize I had a small glitch in memory retrieval, because my response is still perfectly understandable,” Mulligan says. 

Cause for concern

It can be frustrating to find that we can’t recall a specific name that’s on the tip of the tongue.

In many cases, however, our brain keeps plugging away on the problem, long after we’ve forgotten about our memory lapse. That’s why people may recall a forgotten word a short time later, when the word suddenly pops into our mind, Heidebrink says. “Even though you’re not consciously thinking about it, your brain is still working,” she adds.

Both younger and older adults are better at recognizing names than recalling them, Mulligan says. Although many people forget the name of a former neighbor or Hollywood celebrity, they instantly recognize the name when reminded by a friend.

Heidebrink says such memory lapses are concerning if the speaker, even when prompted, can’t remember the person at all — as if the name for which they’re searching is not just delayed, but completely gone.

And while mixing up names may be more common in older people, Heidebrink says there’s good reason for that.

“As we age, we have more names to keep track of,” Heidebrink says. “It’s not a sign of impending dementia.”

Liz Szabo is an award-winning health writer based in Washington and previously worked at USA TODAY and KFF Health News .

Unlock Access to AARP Members Edition

Already a Member? Login

More From AARP

Brain Health Resource Center

AARP Smart Guide to Keeping Your Memory Sharp

22 science-backed ways to growing a healthier, happier brain, now and in the future

Faces & Names

Member Benefit: Learn specific techniques and strategies that may help you get better with names

Is This Normal Memory Loss or Memory Problems?

Tips from experts on how to tell the difference

Recommended for You

Benefits recommended for you.

SAVE MONEY WITH THESE LIMITED-TIME OFFERS

• Skip to main content
• Keyboard shortcuts for audio player

Harris’ struggles with immigration policy expose political vulnerabilities

Sergio Martínez-Beltrán

Jasmine Garsd

A look at Vice President Kamala Harris’ record on immigration

Vice President Kamala Harris, center, along with Department of Homeland Security Secretary, Alejandro Mayorkas, Rep. Veronica Escobar, a Democrat from Texas, and Sen. Dick Durbin, a Democrat from Illinois, tour a U.S. Customs and Border Protection facility in El Paso, Texas, U.S., in June 2021. Bloomberg/via Getty Images hide caption

As Vice President Kamala Harris works to secure the presidential nomination of the Democratic Party next month, her role on immigration policy is now in the spotlight.

Minutes after the President Joe Biden announced he was dropping out of the race and was endorsing Harris, Republicans started attacking her record on immigration and border policy.

“Joe Biden has now endorsed and fully supports his ‘Borders Czar’ Kamala Harris to be the Democrat candidate for president,” Gov. Greg Abbott, R-Texas, posted on X . “I think I will need to triple the border wall, razor wire barriers and National Guard on the border.”

Conservatives have often referred to Harris as the Biden administration’s "Border Czar," incorrectly claiming she was tasked with repairing the border.

“Kamala had one job,” said Nikki Haley earlier this month at the Republican National Convention. “One job. And that was to fix the border. Now imagine her in charge of the entire country.”

In reality, that was not Harris’ job.

She was tasked by Biden in 2021 to examine the root causes of migration from Central America, including poverty, violence, and corruption. At that time, unauthorized migration came primarily from Mexico and Central America.

She was never tapped to head immigration policy, which is the responsibility of Homeland Security Secretary Alejandro Mayorkas, who oversees all agencies in charge of the enforcement of immigration laws.

Three years later, this role could be Harris’ Achilles' heel. Her role in pushing for Biden’s immigration proposals have disenchanted Democrats and immigrant rights groups.

“I do think there is an opportunity here for Vice President Harris to have a more hopeful message around immigration than even the Biden administration has had in the past,” said Adriel Orozco, a senior policy counsel with the American Immigration Council.

Biden’s policy proposals have included severely restricting most asylum claims at the border and expediting the removal of unauthorized migrants, something immigrant rights groups have opposed.

Suyapa Portillo, a professor of Chicano/a-Latino/a Transnational Studies at Pitzer College, says Harris should try to separate herself from the Biden administration’s “slow move towards immigration reform,” and from the message of deterrence that “represents that conservatism from the Biden administration and the Democratic Party — the old guard.”

Vice President Kamala Harris speaks from the South Lawn of the White House in Washington on Monday during an event with NCAA college athletes. This was her first public appearance since President Joe Biden endorsed her to be the next presidential nominee of the Democratic Party. Susan Walsh/AP hide caption

A changed immigration landscape

If Harris secures the presidential nomination, she will be facing a very different immigration landscape than back in 2021, when she was tasked with addressing its root causes.

Last year, unauthorized crossings at the U.S.-Mexico border hit an all-time high. In December 2023, the number of encounters reached nearly 250,000, according to U.S. Customs and Border Protection.

For the last four months, the number of migrants trying to cross illegally has dramatically dropped. That’s due in part due to Mexico’s enforcement, and Biden’s policies, which include severely restricting most asylum claims at the border .

But migration has diversified in the last few years. There is an unprecedented crisis of global displacement. When Harris was elected in 2020, 90% of immigration hailed from Mexico and Central America, according to an analysis by the Migration Policy Institute .

In 2023, only 49 percent of the encounters were with migrants from those four countries.

Today, immigrants arriving at the US Mexico border are fleeing from the crisis in Venezuela, the war in Ukraine and cartel violence in Ecuador, just to name a few.

A mixed track record

Harris’ record on immigration has been marred by policy blunders.

Her first international trip as vice president made clear her approach on immigration: addressing root causes to stop illegal migration.

In the summer of 2021, she traveled to Guatemala to meet with then-President Alejandro Giammattei. In a speech, she said that the Biden administration was committed to helping Guatemalans find “hope at home.”

But she also warned prospective migrants.

“I want to be clear to folks in this region who are thinking about making that dangerous trek to the United States-Mexico border,” Harris said. “Do not come. Do not come.”

Those three words: Do not come, were seen by many as a blunder . Latino advocates criticized the statement as paternalistic and tone-deaf, given the violent crises rattling the region.

For many immigrant advocates, that statement continues to haunt Harris’ candidacy.

“She needs to separate from Biden,” Portillo says. “She needs to speak to TPS holders and DACA holders for a plan for legalization, and a border plan that does not include throwing children in jail.”

But Harris has maintained that deterrence is essential: last year she announced $950 million in pledges from private companies to support Central American communities.

Judith Browne Dianis, the executive director of the D.C.-based civil rights organization Advancement Project, says Harris will now have to explain how she would tackle immigration if she were elected president.

“Is it a humanitarian response, or is there a criminalization response?” Dianis says. “We don’t need more criminalization. We don’t need a border wall. We need to get to the root causes. We need to make sure that people are taken care of.”

Criticism from GOP for not visiting the border enough

In early June 2021, Harris came under fire for not visiting the border. In an interview with NBC News , she was asked about Republican critiques.

“And I haven’t been to Europe,” Harris fired back. “I mean, I don’t understand the point that you are making.”

Her response was criticized by conservatives as disconnected and flippant towards border communities and agencies which have felt overwhelmed by the influx of migrants in recent years.

Harris’ first trip to the border came later that month, to El Paso, Texas. At a press conference there, she stated that migration “cannot be reduced to a political issue. We’re talking about children, we’re talking about families, we are talking about suffering.”

Earlier this year, Harris backed a Biden-endorsed bipartisan bill on border enforcement.

The measure would have added immigration detention beds, increased the number of U.S. Customs and Border Protection personnel and asylum officers, and funded technology to detect fentanyl smuggling at the Southern border. It passed in the Senate but failed to move forward after former President Donald Trump urged House Republicans to kill it.

But for many immigration advocates, Harris is their candidate.

Kerri Talbot, the executive director of the national advocacy organization Immigration Hub, called Harris a “strong defender and champion of American families, including their immigrant family members” in a statement Sunday.

“We have no doubt that she can step up to the challenge, counter Trump and JD Vance’s rhetoric and dark vision for democracy, and protect the progress we’ve made while delivering transformative change for our immigration system,” Talbot said.

Before VP, Harris was already pushing for reform

But Harris involvement with immigration goes way beyond her vice presidency, and her actions show a shift in policies.

When she was the district attorney in San Francisco, she backed a city policy that turned over to federal immigration authorities migrant juveniles suspected of committing a felony. In 2019, Harris’ campaign told CNN “this policy could have been applied more fairly.”

But as California’s attorney general, she had a different stance. In a 2015 interview with CBS Los Angeles, Harris said, “Unfortunately, I know what crime looks like. I know what a criminal looks like who's committing a crime. An undocumented immigrant is not a criminal.”

Harris became U.S. senator from California in 2017.

She was part of a Senate hearing on the Trump administration’s highly controversial separation policy, in which undocumented migrant children were separated from their parents at the U.S.-Mexico border, as a form of immigration deterrence. She questioned Trump officials, and said separating families can cause “irreparable harm.”

In 2019, she and several other Democratic senators reintroduced the Reunite Every Unaccompanied Newborn Infant, Toddler and Other Children Expeditiously (REUNITE) Act , “to expedite the reunification of separated immigrant families and promote humane alternatives for asylum-seeking immigrant families.”

When she ran for president in 2019, Harris unveiled an immigration plan that called for a path to citizenship for recipients of Deferred Action on Childhood Arrivals program, best known as DACA.

That’s similar to what the Biden-Harris campaign promised when they run in 2020. However, none of that has happened during the administration.

The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Future frontiers: Navigating the next wave of tech innovations

Join Lareina Yee and Roger Roberts on Tuesday, July 30, at 12:30 p.m. EDT/6:30 p.m. CET as they discuss the future of these technological trends, the factors that will fuel their growth, and strategies for investing in them through 2024 and beyond.

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

Explore a career with us

Related articles.

Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale

A generative AI reset: Rewiring to turn potential into value in 2024

Implementing generative AI with speed and safety

Advertisement

Chaos and Confusion: Tech Outage Causes Disruptions Worldwide

Airlines, hospitals and people’s computers were affected after CrowdStrike, a cybersecurity company, sent out a flawed software update.

• Share full article

By Adam Satariano Paul Mozur Kate Conger and Sheera Frenkel

• July 19, 2024

Airlines grounded flights. Operators of 911 lines could not respond to emergencies. Hospitals canceled surgeries. Retailers closed for the day. And the actions all traced back to a batch of bad computer code.

A flawed software update sent out by a little-known cybersecurity company caused chaos and disruption around the world on Friday. The company, CrowdStrike , based in Austin, Texas, makes software used by multinational corporations, government agencies and scores of other organizations to protect against hackers and online intruders.

But when CrowdStrike sent its update on Thursday to its customers that run Microsoft Windows software, computers began to crash.

The fallout, which was immediate and inescapable, highlighted the brittleness of global technology infrastructure. The world has become reliant on Microsoft and a handful of cybersecurity firms like CrowdStrike. So when a single flawed piece of software is released over the internet, it can almost instantly damage countless companies and organizations that depend on the technology as part of everyday business.

“This is a very, very uncomfortable illustration of the fragility of the world’s core internet infrastructure,” said Ciaran Martin, the former chief executive of Britain’s National Cyber Security Center and a professor at the Blavatnik School of Government at Oxford University.

A cyberattack did not cause the widespread outage, but the effects on Friday showed how devastating the damage can be when a main artery of the global technology system is disrupted. It raised broader questions about CrowdStrike’s testing processes and what repercussions such software firms should face when flaws in their code cause major disruptions.

How a Software Update Crashed Computers Around the World

Here’s a visual explanation for how a faulty software update crippled machines.

How the airline cancellations rippled around the world (and across time zones)

Share of canceled flights at 25 airports on Friday

50% of flights

Ai r po r t

Bengalu r u K empeg o wda

Dhaka Shahjalal

Minneapolis-Saint P aul

Stuttga r t

Melbou r ne

Be r lin B r anden b urg

London City

Amsterdam Schiphol

Chicago O'Hare

Raleigh−Durham

B r adl e y

Cha r lotte

Reagan National

Philadelphia

1:20 a.m. ET

CrowdStrike’s stock price so far this year

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .