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• 10 February 2020

Scrutinizing the effects of digital technology on mental health

• Jonathan Haidt &

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The topic in brief

• There is an ongoing debate about whether social media and the use of digital devices are detrimental to mental health.

• Adolescents tend to be heavy users of these devices, and especially of social media.

• Rates of teenage depression began to rise around 2012, when adolescent use of social media became common (Fig. 1).

• Some evidence indicates that frequent users of social media have higher rates of depression and anxiety than do light users.

• But perhaps digital devices could provide a way of gathering data about mental health in a systematic way, and make interventions more timely.

Figure 1 | Depression on the rise. Rates of depression among teenagers in the United States have increased steadily since 2012. Rates are higher and are increasing more rapidly for girls than for boys. Some researchers think that social media is the cause of this increase, whereas others see social media as a way of tackling it. (Data taken from the US National Survey on Drug Use and Health, Table 11.2b; go.nature.com/3ayjaww )

JONATHAN HAIDT: A guilty verdict

A sudden increase in the rates of depression, anxiety and self-harm was seen in adolescents — particularly girls — in the United States and the United Kingdom around 2012 or 2013 (see go.nature.com/2up38hw ). Only one suspect was in the right place at the right time to account for this sudden change: social media. Its use by teenagers increased most quickly between 2009 and 2011, by which point two-thirds of 15–17-year-olds were using it on a daily basis 1 . Some researchers defend social media, arguing that there is only circumstantial evidence for its role in mental-health problems 2 , 3 . And, indeed, several studies 2 , 3 show that there is only a small correlation between time spent on screens and bad mental-health outcomes. However, I present three arguments against this defence.

First, the papers that report small or null effects usually focus on ‘screen time’, but it is not films or video chats with friends that damage mental health. When research papers allow us to zoom in on social media, rather than looking at screen time as a whole, the correlations with depression are larger, and they are larger still when we look specifically at girls ( go.nature.com/2u74der ). The sex difference is robust, and there are several likely causes for it. Girls use social media much more than do boys (who, in turn, spend more of their time gaming). And, for girls more than boys, social life and status tend to revolve around intimacy and inclusion versus exclusion 4 , making them more vulnerable to both the ‘fear of missing out’ and the relational aggression that social media facilitates.

Second, although correlational studies can provide only circumstantial evidence, most of the experiments published in recent years have found evidence of causation ( go.nature.com/2u74der ). In these studies, people are randomly assigned to groups that are asked to continue using social media or to reduce their use substantially. After a few weeks, people who reduce their use generally report an improvement in mood or a reduction in loneliness or symptoms of depression.

The best way forward

Third, many researchers seem to be thinking about social media as if it were sugar: safe in small to moderate quantities, and harmful only if teenagers consume large quantities. But, unlike sugar, social media does not act just on those who consume it. It has radically transformed the nature of peer relationships, family relationships and daily activities 5 . When most of the 11-year-olds in a class are on Instagram (as was the case in my son’s school), there can be pervasive effects on everyone. Children who opt out can find themselves isolated. A simple dose–response model cannot capture the full effects of social media, yet nearly all of the debate among researchers so far has been over the size of the dose–response effect. To cite just one suggestive finding of what lies beyond that model: network effects for depression and anxiety are large, and bad mental health spreads more contagiously between women than between men 6 .

In conclusion, digital media in general undoubtedly has many beneficial uses, including the treatment of mental illness. But if you focus on social media, you’ll find stronger evidence of harm, and less exculpatory evidence, especially for its millions of under-age users.

What should we do while researchers hash out the meaning of these conflicting findings? I would urge a focus on middle schools (roughly 11–13-year-olds in the United States), both for researchers and policymakers. Any US state could quickly conduct an informative experiment beginning this September: randomly assign a portion of school districts to ban smartphone access for students in middle school, while strongly encouraging parents to prevent their children from opening social-media accounts until they begin high school (at around 14). Within 2 years, we would know whether the policy reversed the otherwise steady rise of mental-health problems among middle-school students, and whether it also improved classroom dynamics (as rated by teachers) and test scores. Such system-wide and cross-school interventions would be an excellent way to study the emergent effects of social media on the social lives and mental health of today’s adolescents.

NICK ALLEN: Use digital technology to our advantage

It is appealing to condemn social media out of hand on the basis of the — generally rather poor-quality and inconsistent — evidence suggesting that its use is associated with mental-health problems 7 . But focusing only on its potential harmful effects is comparable to proposing that the only question to ask about cars is whether people can die driving them. The harmful effects might be real, but they don’t tell the full story. The task of research should be to understand what patterns of digital-device and social-media use can lead to beneficial versus harmful effects 7 , and to inform evidence-based approaches to policy, education and regulation.

Long-standing problems have hampered our efforts to improve access to, and the quality of, mental-health services and support. Digital technology has the potential to address some of these challenges. For instance, consider the challenges associated with collecting data on human behaviour. Assessment in mental-health care and research relies almost exclusively on self-reporting, but the resulting data are subjective and burdensome to collect. As a result, assessments are conducted so infrequently that they do not provide insights into the temporal dynamics of symptoms, which can be crucial for both diagnosis and treatment planning.

By contrast, mobile phones and other Internet-connected devices provide an opportunity to continuously collect objective information on behaviour in the context of people’s real lives, generating a rich data set that can provide insight into the extent and timing of mental-health needs in individuals 8 , 9 . By building apps that can track our digital exhaust (the data generated by our everyday digital lives, including our social-media use), we can gain insights into aspects of behaviour that are well-established building blocks of mental health and illness, such as mood, social communication, sleep and physical activity.

Stress and the city

These data can, in turn, be used to empower individuals, by giving them actionable insights into patterns of behaviour that might otherwise have remained unseen. For example, subtle shifts in patterns of sleep or social communication can provide early warning signs of deteriorating mental health. Data on these patterns can be used to alert people to the need for self-management before the patterns — and the associated symptoms — become more severe. Individuals can also choose to share these data with health professionals or researchers. For instance, in the Our Data Helps initiative, individuals who have experienced a suicidal crisis, or the relatives of those who have died by suicide, can donate their digital data to research into suicide risk.

Because mobile devices are ever-present in people’s lives, they offer an opportunity to provide interventions that are timely, personalized and scalable. Currently, mental-health services are mainly provided through a century-old model in which they are made available at times chosen by the mental-health practitioner, rather than at the person’s time of greatest need. But Internet-connected devices are facilitating the development of a wave of ‘just-in-time’ interventions 10 for mental-health care and support.

A compelling example of these interventions involves short-term risk for suicide 9 , 11 — for which early detection could save many lives. Most of the effective approaches to suicide prevention work by interrupting suicidal actions and supporting alternative methods of coping at the moment of greatest risk. If these moments can be detected in an individual’s digital exhaust, a wide range of intervention options become available, from providing information about coping skills and social support, to the initiation of crisis responses. So far, just-in-time approaches have been applied mainly to behaviours such as eating or substance abuse 8 . But with the development of an appropriate research base, these approaches have the potential to provide a major advance in our ability to respond to, and prevent, mental-health crises.

These advantages are particularly relevant to teenagers. Because of their extensive use of digital devices, adolescents are especially vulnerable to the devices’ risks and burdens. And, given the increases in mental-health problems in this age group, teens would also benefit most from improvements in mental-health prevention and treatment. If we use the social and data-gathering functions of Internet-connected devices in the right ways, we might achieve breakthroughs in our ability to improve mental health and well-being.

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Competing Interests

N.A. has an equity interest in Ksana Health, a company he co-founded and which has the sole commercial licence for certain versions of the Effortless Assessment of Risk States (EARS) mobile-phone application and some related EARS tools. This intellectual property was developed as part of his research at the University of Oregon’s Center for Digital Mental Health (CDMH).

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• Published: 15 February 2023

The impact of food insecurity on health outcomes: empirical evidence from sub-Saharan African countries

• Sisay Demissew Beyene   ORCID: orcid.org/0000-0001-7347-4168 1  

BMC Public Health volume  23 , Article number:  338 ( 2023 ) Cite this article

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Food insecurity adversely affects human health, which means food security and nutrition are crucial to improving people’s health outcomes. Both food insecurity and health outcomes are the policy and agenda of the 2030 Sustainable Development Goals (SDGs). However, there is a lack of macro-level empirical studies (Macro-level study means studies at the broadest level using variables that represent a given country or the whole population of a country or economy as a whole. For example, if the urban population (% of the total population) of XYZ country is 30%, it is used as a proxy variable to represent represent country's urbanization level. Empirical study implies studies that employ the econometrics method, which is the application of math and statistics.) concerning the relationship between food insecurity and health outcomes in sub-Saharan African (SSA) countries though the region is highly affected by food insecurity and its related health problems. Therefore, this study aims to examine the impact of food insecurity on life expectancy and infant mortality in SSA countries.

The study was conducted for the whole population of 31 sampled SSA countries selected based on data availability. The study uses secondary data collected online from the databases of the United Nations Development Programme (UNDP), the Food and Agricultural Organization (FAO), and the World Bank (WB). The study uses yearly balanced data from 2001 to 2018. This study employs a multicountry panel data analysis and several estimation techniques; it employs Driscoll-Kraay standard errors (DKSE), a generalized method of momentum (GMM), fixed effects (FE), and the Granger causality test.

A 1% increment in people’s prevalence for undernourishment reduces their life expectancy by 0.00348 percentage points (PPs). However, life expectancy rises by 0.00317 PPs with every 1% increase in average dietary energy supply. A 1% rise in the prevalence of undernourishment increases infant mortality by 0.0119 PPs. However, a 1% increment in average dietary energy supply reduces infant mortality by 0.0139 PPs.

Conclusions

Food insecurity harms the health status of SSA countries, but food security impacts in the reverse direction. This implies that to meet SDG 3.2, SSA should ensure food security.

Peer Review reports

Food security is essential to people’s health and well-being [ 1 ]. Further, the World Health Organization (WHO) argues that health is wealth and poor health is an integral part of poverty; governments should actively seek to preserve their people’s lives and reduce the incidence of unnecessary mortality and avoidable illnesses [ 2 ]. However, lack of food is one of the factors which affect health outcomes. Concerning this, the Food Research and Action Center noted that the social determinants of health, such as poverty and food insecurity, are associated with some of the most severe and costly health problems in a nation [ 3 ].

According to the FAO, the International Fund for Agricultural Development (IFAD), and the World Food Programme (WFP), food insecurity is defined as "A situation that exists when people lack secure access to sufficient amounts of safe and nutritious food for normal growth and development and an active and healthy life" ([ 4 ]; p50). It is generally believed that food security and nutrition are crucial to improving human health and development. Studies show that millions of people live in food insecurity, which is one of the main risks to human health. Around one in four people globally (1.9 billion people) were moderately or severely food insecure in 2017 and the greatest numbers were in SSA and South Asia. Around 9.2% of the world's population was severely food insecure in 2018. Food insecurity is highest in SSA countries, where nearly one-third are defined as severely insecure [ 5 ]. Similarly, 11% (820 million) of the world's population was undernourished in 2018, and SSA countries still share a substantial amount [ 5 ]. Even though globally the number of people affected by hunger has been decreasing since 1990, in recent years (especially since 2015) the number of people living in food insecurity has increased. It will be a huge challenge to achieve the SDGs of zero hunger by 2030 [ 6 ]. FAO et al. [ 7 ] projected that one in four individuals in SSA were undernourished in 2017. Moreover, FAO et al. [ 8 ] found that, between 2014 and 2018, the prevalence of undernourishment worsened. Twenty percent of the continent's population, or 256 million people, are undernourished today, of which 239 million are in SSA. Hidden hunger is also one of the most severe types of malnutrition (micronutrient deficiencies). One in three persons suffers from inadequacies related to hidden hunger, which impacts two billion people worldwide [ 9 ]. Similarly, SSA has a high prevalence of hidden hunger [ 10 , 11 ].

An important consequence of food insecurity is that around 9 million people die yearly worldwide due to hunger and hunger-related diseases. This is more than from Acquired Immunodeficiency Syndrome (AIDS), malaria, and tuberculosis combined [ 6 ]. Even though the hunger crisis affects many people of all genders and ages, children are particularly affected in Africa. There are too many malnourished children in Africa, and malnutrition is a major factor in the high infant mortality rates and causes physical and mental development delays and disorders in SSA [ 12 ]. According to UN statistics, chronic malnutrition globally accounts for 165 million stunted or underweight children. Around 75% of these kids are from SSA and South Asia. Forty percent of children in SSA are impacted. In SSA, about 3.2 million children under the age of five dies yearly, which is about half of all deaths in this age group worldwide. Malnutrition is responsible for almost one child under the age of five dying every two minutes worldwide. The child mortality rate in the SSA is among the highest in the world, about one in nine children pass away before the age of five [ 12 ].

In addition to the direct impact of food insecurity on health outcomes, it also indirectly contributes to disordered eating patterns, higher or lower blood cholesterol levels, lower serum albumin, lower hemoglobin, vitamin A levels, and poor physical and mental health [ 13 , 14 , 15 ]. Iodine, iron, and zinc deficiency are the most often identified micronutrient deficiencies across all age groups. A deficiency in vitamin A affects an estimated 190 million pre-schoolers and 19 million pregnant women [ 16 ]. Even though it is frequently noted that hidden hunger mostly affects pregnant women, children, and teenagers, it further affects people’s health at all stages of life [ 17 ].

With the above information, researchers and policymakers should focus on the issue of food insecurity and health status. The SDGs that were developed in 2015 intend to end hunger in 2030 as one of its primary targets. However, a growing number of people live with hunger and food insecurity, leading to millions of deaths. Hence, this study questioned what is the impact of food insecurity on people's health outcomes in SSA countries. In addition, despite the evidence implicating food insecurity and poor health status, there is a lack of macro-level empirical studies concerning the impact of food insecurity on people’s health status in SSA countries, which leads to a knowledge (literature) gap. Therefore, this study aims to examine the impact of food insecurity on life expectancy and infant mortality in SSA countries for the period ranging from 2001–2018 using panel mean regression approaches.

Theoretical and conceptual framework

Structural factors, such as climate, socio-economic, social, and local food availability, affect people’s food security. People’s health condition is impacted by food insecurity through nutritional, mental health, and behavioral channels [ 18 ]. Under the nutritional channel, food insecurity has an impact on total caloric intake, diet quality, and nutritional status [ 19 , 20 , 21 ]. Hunger and undernutrition may develop when food supplies are scarce, and these conditions may potentially lead to wasting, stunting, and immunological deficiencies [ 22 ]. However, food insecurity also negatively influences health due to its effects on obesity, women's disordered eating patterns [ 23 ], and poor diet quality [ 24 ].

Under the mental health channel, Whitaker et al. [ 25 ] noted that food insecurity is related to poor mental health conditions (stress, sadness, and anxiety), which have also been linked to obesity and cardiovascular risk [ 26 ]. The effects of food insecurity on mental health can worsen the health of people who are already sick as well as lead to disease acquisition [ 18 ]. Similarly, the behavioral channel argues that there is a connection between food insecurity and health practices that impact disease management, prevention, and treatment. For example, lack of access to household food might force people to make bad decisions that may raise their risk of sickness, such as relying too heavily on cheap, calorically dense, nutrient-poor meals or participating in risky sexual conduct. In addition, food insecurity and other competing demands for survival are linked to poorer access and adherence to general medical treatment in low-income individuals once they become sick [ 27 , 28 , 29 , 30 ]

Food insecurity increases the likelihood of exposure to HIV and worsens the health of HIV-positive individuals [ 18 ]. Weiser et al. [ 31 ] found that food insecurity increases the likelihood of unsafe sexual activities, aggravating the spread of HIV. It can also raise the possibility of transmission through unsafe newborn feeding practices and worsening maternal health [ 32 ]. In addition, food insecurity has been linked to decreased antiretroviral adherence, declines in physical health status, worse immunologic status [ 33 ], decreased viral suppression [ 34 , 35 ], increased incidence of serious illness [ 36 ], and increased mortality [ 37 ] among people living with HIV.

With the above theoretical relationship between target variables and since this study focuses on the impact of food insecurity on health outcomes, and not on the causes, it adopted the conceptual framework of Weiser et al. [ 18 ] and constructed Fig.  1 .

A conceptual framework of food insecurity and health. Source: Modified and constructed by the author using Weiser et al. [ 18 ] conceptual framework. Permission was granted by Taylor & Francis to use their original Figs. (2.2, 2.3, and 2.4); to develop the above figure. Permission number: 1072954

Several findings associate food insecurity with poorer health, worse disease management, and a higher risk of premature mortality even though they used microdata. For instance, Stuff et al. [ 38 ] found that food insecurity is related to poor self-reported health status, obesity [ 39 ], abnormal blood lipids [ 40 ], a rise in diabetes [ 24 , 40 ], increased gestational diabetes[ 41 ], increased perceived stress, depression and anxiety among women [ 25 , 42 ], Human Immunodeficiency Virus (HIV) acquisition risk [ 43 , 44 , 45 ], childhood stunting [ 46 ], poor health [ 47 ], mental health and behavioral problem [ 25 , 48 , 49 ].

The above highlight micro-level empirical studies, and since the scope of this study is macro-level, Table 1 provides only the existing macro-level empirical findings related to the current study.

Empirical findings in Table 1 are a few, implying a limited number of macro-level level empirical findings. Even the existing macro-level studies have several limitations. For instance, most studies either employed conventional estimation techniques or overlooked basic econometric tests; thus, their results and policy implications may mislead policy implementers. Except for Hameed et al. [ 53 ], most studies’ data are either outdated or unbalanced; hence, their results and policy implications may not be valuable in the dynamic world and may not be accurate like balanced data. Besides, some studies used limited (one) sampled countries; however, few sampled countries and observations do not get the asymptotic properties of an estimator [ 56 ]. Therefore, this study tries to fill the existing gaps by employing robust estimation techniques with initial diagnostic and post-estimation tests, basic panel econometric tests and robustness checks, updated data, a large number of samples.

Study setting and participants

According to Smith and Meade [ 57 ], the highest rates of both food insecurity and severe food insecurity were found in Sub-Saharan Africa in 2017 (55 and 28%, respectively), followed by Latin America and the Caribbean (32 and 12%, respectively) and South Asia (30 and 13%). Similarly, SSA countries have worst health outcomes compared to other regions. For instance, in 2020, the region had the lowest life expectancy [ 58 ] and highest infant mortality [ 59 ]. Having the above information, this study's target population are SSA countries chosen purposively. However, even though SSA comprises 49 of Africa's 55 countries that are entirely or partially south of the Sahara Desert. This study is conducted for a sample of 31 SSA countries (Angola, Benin, Botswana, Burkina Faso, Cameroon, Cabo Verde, Chad, Congo Rep., Côte d'Ivoire, Ethiopia, Gabon, The Gambia, Ghana, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, and Togo). The sampled countries are selected based on data accessibility for each variable included in the empirical models from 2001 to 2018. Since SSA countries suffer from food insecurity and related health problems, this study believes the sampled countries are appropriate and represent the region. Moreover, since this study included a large sample size, it improves the estimator’s precision.

Data type, sources, and scope

This study uses secondary data collected in December 2020 online from the databases of the Food and Agricultural Organization (FAO), the United Nations Development Programme (UNDP), and the World Bank (WB) (see Table 2 ). In addition, the study uses yearly balanced data from 2001 to 2018, which is appropriate because it captures the Millennium Development Goals, SDGs, and other economic conditions, such as the rise of SSA countries’ economies and the global financial crisis of the 2000s. Therefore, this study considers various global development programs and events. Generally, the scope of this study (sampled countries and time) is sufficient to represent SSA countries. In other words, the study has n*T = 558 observations, which fulfills the large sample size criteria recommended by Kennedy [ 56 ].

The empirical model

Model specification is vital to conduct basic panel data econometric tests and estimate the relationship of target variables. Besides social factors, the study includes economic factors determining people's health status. Moreover, it uses two proxies indicators to measure both food insecurity and health status; hence, it specifies the general model as follows:

The study uses four models to analyze the impact of food insecurity on health outcomes.

where LNLEXP and LNINFMOR (dependent variables) refer to the natural logarithm of life expectancy at birth and infant mortality used as proxy variables for health outcomes. Similarly, PRUND and AVRDES are the prevalence of undernourishment and average dietary energy supply adequacy – proxy and predictor variables for food insecurity.

Moreover, to regulate countries’ socio-economic conditions and to account for time-varying bias that can contribute to changes in the dependent variable, the study included control variables, such as GDPPC, GOVEXP, MNSCHOOL, and URBAN. GDPPC is GDP per capita, GOVEXP refers to domestic general government health expenditure, MNSCHOOL is mean years of schooling and URBAN refers to urbanization. Further, n it , v it , ε it , and μ it are the stochastic error terms at period t. The parameters \({\alpha }_{0}, { \beta }_{0}, { \theta }_{0},{ \delta }_{0}\) refer to intercept terms and \({\alpha }_{1}-{\alpha }_{5}, {\beta }_{1}-{\beta }_{5}, { \theta }_{1}-{\theta }_{5}, and {\delta }_{1}-{\delta }_{5}\) are the long-run estimation coefficients. Since health outcomes and food insecurity have two indicators used as proxy variables, this study estimates different alternative models and robustness checks of the main results. Furthermore, the above models did not address heterogeneity problems; hence, this study considers unobserved heterogeneity by introducing cross-section and time heterogeneity in the models. This is accomplished by assuming a two-way error component for the disturbances with:

From Eq.  2 , the unobservable individual (cross-section) and unobservable time heterogeneities are described by \({\delta }_{i} and {\tau }_{t}\) (within components), respectively. Nonetheless, the remaining random error term is \({\gamma }_{it}\) (panel or between components). Therefore, the error terms in model 1A-1D will be substituted by the right-hand side elements of Eq.  2 .

Depending on the presumptions of whether the error elements are fixed or random, the FE and RE models are the two kinds of models that will be evaluated. Equation ( 2 ) yields a two-way FE error component model, or just a FE model if the assumptions are that \({\delta }_{i} and {\tau }_{t}\) are fixed parameters to be estimated and that the random error component, \({\gamma }_{it}\) , is uniformly and independently distributed with zero mean and constant variance (homoscedasticity).

Equation ( 2 ), on the other hand, provides a two-way RE error component model or a RE model if we suppose \({\delta }_{i} and {\tau }_{t}\) are random, just like the random error term, or \({\delta }_{i},{\tau }_{t}, and {\gamma }_{it}\) are all uniformly and independently distributed with zero mean and constant variance, or they are all independent of each other and independent variables [ 60 ].

Rather than considering both error components, \({\delta }_{i}, and {\tau }_{t}\) , we can examine only one of them at a time (fixed or random), yielding a one-way error component model, FE or RE. The stochastic error term \({\varpi }_{it}\) in Eq.  2 will then be:

Statistical analysis

This study conducted descriptive statistics, correlation analysis, and initial diagnosis tests (cross-sectional and time-specific fixed effect, outliers and influential observations, multicollinearity, normality, heteroscedasticity, and serial correlation test). Moreover, it provides basic panel econometric tests and panel data estimation techniques. For consistency, statistical software (STATA) version 15 was used for all analyses.

Descriptive statistics and correlation analysis

Descriptive statistics is essential to know the behavior of the variables in the model. Therefore, it captures information, such as the mean, standard deviation, minimum, maximum, skewness, and kurtosis. Similarly, the study conducted Pearson correlation analysis to assess the degree of relationship between the variables.

Initial diagnosis

Cross-sectional and time-specific fixed effect.

One can anticipate differences arising over time or within the cross-sectional units, given that the panel data set comprises repeated observations over the same units gathered over many periods. Therefore, before estimation, this study considered unexplained heterogeneity in the models. One fundamental limitation of cross-section, panel, and time series data regression is that they do not account for country and time heterogeneity [ 60 ]. These unobserved differences across nations and over time are crucial in how the error term is represented and the model is evaluated. These unobserved heterogeneities, however, may be represented by including both country and time dummies in the regression. However, if the parameters exceed the number of observations, the estimate will fail [ 60 ]. However, in this study, the models can be estimated. If we include both country and time dummies, we may assume that the slope coefficients are constant, but the intercept varies across countries and time, yielding the two-way error components model. As a result, this study examines the null hypothesis that intercepts differ across nations and time in general.

Detecting outliers and influential observations

In regression analysis, outliers and influential observations may provide biased findings. Therefore, the Cooks D outlier and influential observation test was used in the study to handle outliers and influencing observations. To evaluate whether these outliers have a stronger impact on the model to be estimated, each observation in this test was reviewed and compared with Cook’s D statistic [ 61 ]. Cook distance evaluates the extent to which observation impacts the entire model or the projected values. Hence, this study tested the existence of outliers.

Normality, heteroscedasticity, multicollinearity, and serial correlation test

Before the final regression result, the data used for the variables were tested for normality, heteroscedasticity, multicollinearity, and serial correlation to examine the characteristics of the sample.

Regression models should be checked for nonnormal error terms because a lack of Gaussianity (normal distribution) can occasionally compromise the accuracy of estimation and testing techniques. Additionally, the validity of inference techniques, specification tests, and forecasting critically depends on the normalcy assumption [ 62 ]. Similarly, multicollinearity in error terms leads to a dataset being highly sensitive to a minor change, instability in the regression model, and skewed and unreliable results. Therefore, this study conducted the normality using Alejo et al. [ 62 ] proposed command and multicollinearity (using VIF) tests.

Most conventional panel data estimation methods rely on homoscedastic individual error variance and constant serial correlation. Since the error component is typically connected to the variance that is not constant during the observation and is serially linked across periods, these theoretical presumptions have lately reduced the applicability of various panel data models. Serial correlation and heteroskedasticity are two estimate issues frequently connected to cross-sectional and time series data, respectively. Similarly, panel data is not free from these issues because it includes cross-sections and time series, making the estimated parameters ineffective, and rendering conclusions drawn from the estimation incorrect [ 63 ]. Therefore, this study used the Wooldridge [ 63 ] test for serial correlation in linear panel models as well as the modified Wald test for heteroskedasticity.

Basic panel econometric tests

The basic panel data econometric tests are prerequisites for estimating the panel data. The three main basic panel data tests are cross-sectional dependence, unit root, and cointegration.

Cross-sectional dependence (CD)

A growing body of the panel data literature concludes that panel data models are likely to exhibit substantial CD in the errors resulting from frequent shocks, unobserved components, spatial dependence, and idiosyncratic pairwise dependence. Even though the impact of CD in estimation depends on several factors, relative to the static model, the effect of CD in dynamic panel estimators is more severe [ 64 ]. Moreover, Pesaran [ 65 ] notes that recessions and economic or financial crises potentially affect all countries, even though they might start from just one or two countries. These occurrences inevitably introduce cross-sectional interdependencies across the cross-sectional unit, their regressors, and the error terms. Hence, overlooking the CD in panel data leads to biased estimates and spurious results [ 64 , 66 ]. Further, the CD test determines the type of panel unit root and cointegration tests we should apply. Therefore, examining the CD is vital in panel data econometrics.

In the literature, there are several tests for CD, such as the Breusch and Pagan [ 67 ] Lagrange multiplier (LM) test, Pesaran [ 68 ] scaled LM test, Pesaran [ 68 ] CD test, and Baltagi et al. [ 69 ] bias-corrected scaled LM test (for more detail, see Tugcu and Tiwari [ 70 ]). Besides, Friedman [ 71 ] and Frees [ 72 , 73 ] also have other types of CD tests (for more detail, see De Hoyos and Sarafidis [ 64 ]). This study employs Frees [ 72 ] and Pesaran [ 68 ] among the existing CD tests. This is because, unlike the Breusch and Pagan [ 67 ] test, these tests do not require infinite T and fixed N, and are rather applicable for both a large N and T. Additionally, Free’s CD test can overcome the irregular signs associated with correlation. However, it also employs Friedman [ 71 ] CD for mixed results of the above tests.

Unit root test

The panel unit root and cointegration tests are common steps following the CD test. Generally, there are two types of panel unit root tests: (1) the first-generation panel unit root tests, such as Im et al. [ 74 ], Maddala and Wu [ 75 ], Choi [ 76 ], Levin et al. [ 77 ], Breitung [ 78 ] and Hadri [ 79 ], and (2) the second-generation panel unit root tests, such as [ 66 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ].

The first-generation panel unit root tests have been criticized because they assume cross-sectional independence [ 90 , 91 , 92 , 93 ]. This hypothesis is somewhat restrictive and unrealistic, as macroeconomic time series exhibit significant cross-sectional correlation among countries in a panel [ 92 ], and co-movements of economies are often observed in the majority of macroeconomic applications of unit root tests [ 91 ]. The cross-sectional correlation of errors in panel data applications in economics is likely to be the rule rather than the exception [ 93 ]. Moreover, applying first-generation unit root tests under CD models can generate substantial size distortions [ 90 ], resulting in the null hypothesis of nonstationary being quickly rejected [ 66 , 94 ]. As a result, second-generation panel unit root tests have been proposed to take CD into account. Therefore, among the existing second-generation tests, this study employs Pesaran’s [ 66 ] cross-sectionally augmented panel unit root test (CIPS) for models 1A–1C . The rationale for this is that, unlike other unit root tests that allow CD, such as Bai and Ng [ 80 ], Moon and Perron [ 87 ], and Phillips and Sul [ 84 ], Pesaran’s [ 66 ] test is simple and clear. Besides, Pesaran [ 66 ] is robust when time-series’ heteroscedasticity is observed in the unobserved common factor [ 95 ]. Even though theoretically, Moon and Perron [ 87 ], Choi [ 96 ] and Pesaran [ 66 ] require large N and T, Pesaran [ 66 ] is uniquely robust in small sample sizes [ 97 ]. Therefore, this study employs the CIPS test to take into account CD, and heteroskedasticity in the unobserved common factor and both large and small sample countries. However, since there is no CD in model 1D , this study employs the first-generation unit root tests called Levin, Lin, and Chu (LLC), Im, Pesaran, Shin (IPS) and Fisher augmented Dickey–Fuller (ADF) for model 1D .

Cointegration test

The most common panel cointegration tests when there is CD are Westerlund [ 98 ], Westerlund and Edgerton [ 99 ], Westerlund and Edgerton [ 100 ], Groen and Kleibergen [ 101 ], Westerlund’s [ 102 ] Durbin-Hausman test, Gengenbach et al. [ 103 ] and Banerjee and Carrion-i-Silvestre [ 104 ]. However, except for a few, most tests are not coded in Statistical Software (STATA) and are affected by insufficient observations. The current study primarily uses Westerlund [ 98 ] and Banerjee and Carrion-i-Silvestre [ 104 ] for models 1A–1C . However, to decide uncertain results, it also uses McCoskey and Kao [ 105 ] cointegration tests for model 1C . The rationale for using Westerlund’s [ 98 ] cointegration test is that most panel cointegration has failed to reject the null hypothesis of no cointegration due to the failure of common-factor restriction [ 106 ]. However, Westerlund [ 98 ] does not require any common factor restriction [ 107 ] and allows for a large degree of heterogeneity (e.g., individual-specific short-run dynamics, intercepts, linear trends, and slope parameters) [ 92 , 107 , 108 ]. Besides, its command is coded and readily available in STATA. However, it suffers from insufficient observations, especially when the number of independent variables increases. The present study employs the Banerjee and Carrion-i-Silvestre [ 104 ] and McCoskey and Kao [ 105 ] cointegration tests to overcome this limitation. The two Engle-Granger-based cointegration tests applicable when there is no CD and are widely used and available in STATA are Pedroni [ 109 , 110 ] and Kao [ 111 ]. However, the Pedroni test has two benefits over Kao: it assumes cross-sectional dependency and considers heterogeneity by employing specific parameters [ 112 ]. Hence, this study uses the Pedroni cointegration test for model 1D .

Panel data estimation techniques

The panel data analysis can be conducted using different estimation techniques and is mainly determined by the results of basic panel econometric tests. Thus, this study mainly employs the Driscoll-Kraay [ 113 ] standard error (DKSE) (for models 1A and 1B ), FE (for model 1C ), and two-step GMM (for model 1D ) estimation techniques to examine the impact of food insecurity on health outcomes. It also employs the Granger causality test. However, for robustness checks, it uses fully modified ordinary least squares (FMOLS), panel-corrected standard error (PCSE), and feasible generalized least squares (FGLS) methods (for models 1A and 1B ). Moreover, it uses a random effect (RE) for model 1C and panel dynamic fixed effect (DFE) techniques for model 1D .

Even though several panel estimation techniques allow CD, most of them – such as cross-section augmented autoregressive distributed lag (CS-ARDL), cross-section augmented distributed lag (CS-DL), common correlated effects pooled (CCEP), and common correlated effects mean group (CCEMG) estimators – require a large number of observations over groups and periods. Similarly, the continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) estimators are not coded in STATA. Others, like the PCSE, FGLS, and seemingly unrelated regression (SUR), are feasible for T (the number of time series) > N (the number of cross-sectional units) [ 114 , 115 ]. However, a DKSE estimate is feasible for N > T [ 114 ]. Therefore, depending on the CD, cointegration test, availability in STATA, and comparing N against T, this study mainly employs the DKSE regression for models 1A and 1B , FE model for model 1C , and GMM for model 1C .

Finally, to check the robustness of the main result, this study employs FMOLS, FGLS, and PCSE estimation techniques for models 1A and 1B . Furthermore, even though the Hausman test confirms that the FE is more efficient, the study employs the RE for model 1C . This is because Firebaugh et al. [ 116 ] note that the RE and FE models perform best in panel data. Besides, unlike FE, RE assumes that individual differences are random. In addition, this study uses panel DFE for model 1D (selected based on the Hausman test). Finally, the robustness check is also conducted using an alternative model (i.e., when a dependent variable is without a natural log and Granger causality test).

Table 3 shows the overall mean of LNLEXP of the region is 4.063 years which indicates that the region can achieve only 57.43 (using ln(x) = 4.063 = loge (x)  = e 4.063 , where e = 2.718) years of life expectancy. This is very low compared to other regions. Besides, the ranges in the value of LNLEXP are between 3.698 and 4.345 or (40–76 years), implying high variation. Similarly, the mean value of LNINFMOR is 3.969; implying SSA countries recorded 52 infants death per 1000. Moreover, the range of LNINFMOR is between 2.525 and 4.919 or (12 – 135 infant death per 1000), implying high variation within the region. The mean value of people’s prevalence for undernourishment is 21.26; indicating 21% of the population is undernourished. However, the mean value of AVRDES is 107.826, which is greater than 100, implying that the calorie supply is adequate for all consumers if the food is distributed according to the requirements of individuals. When we observe the skewness and kurtosis of the variables of the models, except for LNLEXP and LNINFMOR, all variables are positively skewed. In addition, all variables have positive kurtosis with values between 2.202 and 6.092.

Table 3 also shows the degree of relationship between variables, such that most values are below the threshold or rule of thumb (0.7) for a greater association [ 117 ]. However, the association between LNINFMOR and LNLEXP, as well as between PRUNP and AVRDES, is over the threshold and seems to have a multicollinearity issue. Nevertheless, these variables did not exist together in the models, indicating the absence of a multicollinearity problem.

Table 4 shows whether the cross-sectional specific and time-specific FE in extended models ( model 1A-1D plus Eq.  2 ) are valid. The result reveals that the null hypothesis of the captured unobserved heterogeneity is homogenous across the countries, and time is rejected at 1%, implying the extended models are correctly specified. Besides, to check the robustness of the two-way error component model relative to the pooled OLS estimator, this study conducted an additional poolability test. The result shows the null hypothesis that intercepts homogeneity (pooling) is rejected at a 1% level; thus, the FE model is most applicable, but the pooled OLS is biased.

Cooks D is an indicator of high leverage and residuals. The impact is high when D exceeds 4/N, (N = number of observations). A D > 1 implies a significant outlier problem. The Cooks D result of this study confirms the absence of outliers' problem (see supplementary file 1 ).

Normality, heteroscedasticity, serial correlation, and multicollinearity tests

The results in Table 5 indicate that the probability value of the joint test for normality on e and u are above 0.01, implying that the residuals are normally distributed. The heteroscedasticity results show that the probability value of the chi-square statistic is less than 0.01 in all models. Therefore, the null hypothesis of constant variance can be rejected at a 1% level of significance. In other words, the modified Wald test result for Groupwise heteroskedasticity presented in Table 5 , rejects the null hypothesis of Groupwise homoskedasticity observed by the probability value of 0.0000, which implies the presence of heteroscedasticity in the residuals. Similarly, all models suffer from serial correlation since the probability value of 0.0000 rejects the null hypothesis of no first-order serial correlation, indicating the presence of autocorrelation in all panel models. Finally, the multicollinearity test reveals that the models have no multicollinearity problem since the Variance inflation Factors (VIF) values are below 5.

Cross-sectional dependence test

Results in Table 6 strongly reject the null hypothesis of cross-sectional independence for models 1A – 1C . However, for model 1D , the study found mixed results (i.e., Pesaran [ 68 ] fails to reject the null hypothesis of no CD while Frees [ 72 ] strongly rejects it). Thus, to decide, this study employs the Friedman [ 71 ] CD test. The result fails to reject the null hypothesis of cross-sectional independence, implying that two out of three tests fail to reject the null hypothesis of cross-sectional independence in model 1D . Therefore, unlike others, there is no CD in model 1D (see Table 6 ).

Unit root tests

Table 7 shows that all variables are highly (at 1% level) significant either at level (I(0)) or first difference (I(1)), which implies all variables are stationary. In other words, the result fails to reject the null hypothesis of unit root (non-stationary) for all variables at a 1%-significance level, either at levels or the first differences. Thus, we might expect a long-run connection between these variables collectively.

Cointegration tests

The results in Table 8 show that both the Westerlund [ 98 ] and Banerjee and Carrion-i-Silvestre [ 104 ] cointegration tests strongly reject the null hypothesis of no-cointegration in models 1A and 1B . However, model 1C provides a mixed result, i.e. the Banerjee and Carrion-i-Silvestre [ 104 ] test rejects the null hypothesis of no cointegration, yet the reverse is true for the Westerlund [ 98 ] test. Therefore, this study conducted further cointegration tests for model 1C . Even though Westerlund and Edgerton [ 99 ] suffer from insufficient observation, it is based on the McCoskey and Kao [ 105 ] LM test [ 118 ]. Thus, we can use a residual-based cointegration test in the heterogeneous panel framework proposed by McCoskey and Kao [ 105 ]. However, an efficient estimation technique of cointegrated variables is required, and hence the FMOLS and DOLS estimators are recommended. The residuals derived from the FMOLS and DOLS will be tested for stationarity with the null hypothesis of no cointegration amongst the regressors. Since the McCoskey and Kao [ 105 ] test involves averaging the individual LM statistics across the cross-sections, for testing the residuals FMOLS and DOLS stationarity, McCoskey, and Kao [ 105 ] test is in the spirit of IPS (Im et al. [ 74 ]) [ 119 ].

Though FMOLS and DOLS are recommended for the residuals cointegration test, DOLS is better than FMOLS (for more detail, see Kao and Chiang [ 120 ]); therefore, this study uses a residual test derived from DOLS. The result fails to reject the null hypothesis of no cointegration. Two (Banerjee and Carrion-i-Silvestre [ 104 ] and McCoskey and Kao [ 105 ]) out of three tests fail to reject the null hypothesis of no cointegration; hence, we can conclude that there is no long-run relationship among the variables in model 1C .

Unlike other models, since there is CD in model 1D , this study employs the Pedroni [ 109 ] and Kao [ 111 ] cointegration tests for model 1D . The result strongly rejects the null hypothesis of no cointegration, which is similar to models 1A and 1B , that a long-run relationship exists among the variables in model 1D (see Table 5 ).

Panel data estimation results

Table 9 provides long-run regression results of all models employing appropriate estimation techniques such as DKSE, FE, and two-step GMM, along with the Granger causality test. However, the DKSE regression can be estimated in three ways: FE with DKSE, RE with DKSE, and pooled Ordinary Least Squares/Weighted Least Squares (pooled OLS/WLS) regression with DKSE. Hence, we must choose the most efficient model using Hausman and Breusch-Pagan LM for RE tests (see supplementary file 2 ). As a result, this study employed FE with DKSE for models 1A and 1B . Further, due to Hausman's result, absence of cointegration and to deal with heterogeneity and spatial dependence in the dynamic panel, this study employs FE for the model1C (see the supplementary file 2). However, due to the absence of CD, the presence of cointegration, and N > T, this study uses GMM for model 1D . Moreover, according to Roodman [ 121 ], the GMM approach can solve heteroskedasticity and autocorrelation problems. Furthermore, even though two-step GMM produces only short-run results, it is possible to generate long-run coefficients from short-run results [ 122 , 123 ].

The DKSE result of model 1A shows that a 1% increment in people's prevalence for undernourishment reduces their life expectancy by 0.00348 PPs (1 year or 366 days). However, in model 1C, a 1% rise in the prevalence of undernourishment increases infant mortality by 0.0119 PPs (1 year or 369 days). The DKSE estimations in model 1B reveal that people’s life expectancy rises by 0.00317 PPs with every 1% increase in average dietary energy supply. However, the GMM result for model 1D confirms that a 1% incrementin average dietary energy supply reduces infant mortality by 0.0139 PPs. Moreover, this study conducted a panel Granger causality test to confirm whether or not food insecurity has a potential causality to health outcomes. The result demonstrates that the null hypothesis of change in people’s prevalence for undernourishment and average dietary energy supply does not homogeneously cause health outcomes is rejected at 1% significance, implying a change in food insecurity does Granger-cause health outcomes of SSA countries (see Table 9 ).

In addition to the main results, Table 9 also reports some post-estimation statistics to ascertain the consistency of the estimated results. Hence, in the case of DKSE and FE models, the validity of the models is determined by the values of R 2 and the F statistics. For instance, R 2 quantifies the proportion of the variance in the dependent variable explained by the independent variables, representing the model’s quality. The results in Table 9 demonstrate that the explanatory variables explain more than 62% of the variance on the dependent variable. Cohen [ 125 ] classifies the R 2 value of 2% as a moderate influence in social and behavioral sciences, while 13 and 26% are considered medium and large effects, respectively. Therefore, the explanatory variables substantially impact this study's models. Similarly, the F statistics explain all independent variables jointly explain the dependent one. For the two-step system GMM, the result fails to reject the null hypothesis of no first (AR(1)) and second-order (AR(2)) serial correlation, indicating that there is no first and second-order serial correlation. In addition, the Hansen [ 126 ] and Sargan [ 127 ] tests fail to reject the null hypothesis of the overall validity of the instruments used, which implies too many instruments do not weaken the model.

Robustness checks

The author believes the above findings may not be enough for policy recommendations unless robustness checks are undertaken. Hence, the study estimated all models without the natural logarithm of the dependent variables (see Table 10 ). The model 1A result reveals, similar to the above results, individuals’ prevalence for undernourishment significantly reduces their life expectancy in SSA countries. That means a 1% increase in the people's prevalence of undernourishment reduces their life expectancy by 0.1924 PPs. Moreover, in model 1B , life expectancy rises by 0.1763 PPs with every 1% increase in average dietary energy supply. In model 1C , the rise in infants’ prevalence for undernourishment has a positive and significant effect on their mortality rate in SSA countries. The FE result implies that a 1% rise in infants’ prevalence for undernourishment increases their mortality rate by 0.9785 PPs. The GMM result in model 1D indicates that improvement in average dietary energy supply significantly reduces infant mortality. Further, the Granger causality result confirms that the null hypothesis of change in the prevalence of undernourishment and average dietary energy supply does not homogeneously cause health outcomes and is rejected at a 1% level of significance. This implies a change in food insecurity does Granger-cause health outcomes in SSA countries (see Table 10 ).

The study also conducted further robustness checks using the same dependent variables (as Table 9 ) but different estimation techniques. The results confirm that people’s prevalence of undernourishment has a negative and significant effect on their life expectancy, but improvement in average dietary energy supply significantly increases life expectancy in SSA countries. However, the incidence of undernourishment in infants contributes to their mortality; however, progress in average dietary energy supply for infants significantly reduces their mortality (see Table 11 ).

The main objective of this study is to examine the impact of food insecurity on the health outcomes of SSA countries. Accordingly, the DKSE result of model 1A confirms that the rise in people’s prevalence for undernourishment significantly reduces their life expectancy in SSA countries. However, the FE result shows that an increment in the prevalence of undernourishment has a positive and significant impact on infant mortality in model 1C . This indicates that the percentage of the population whose food intake is insufficient to meet dietary energy requirements is high, which leads to reduce life expectancy but increases infant mortality in SSA countries. The reason for this result is linked to the insufficient food supply in SSA due to low production and yields, primitive tools, lack of supporting smallholder farms and investment in infrastructure, and government policies. Besides, even though the food is available, it is not distributed fairly according to the requirements of individuals. Moreover, inadequate access to food, poor nutrition, and chronic illnesses are caused by a lack of well-balanced diets. In addition, many of these countries are impacted by poverty, making it difficult for citizens to afford nutritious food. All these issues combine to create an environment where individuals are more likely to suffer malnutrition-related illnesses, resulting in a lower life expectancy rate. The DKSE estimation result in model 1B reveals that improvement in average dietary energy supply positively impacts people's life expectancy in SSA countries. However, the improvement in average dietary energy supply reduces infant mortality.

Based on the above results, we can conclude that food insecurity harms SSA nations' health outcomes. This is because the prevalence of undernourishment leads to increased infant mortality by reducing the vulnerability, severity, and duration of infectious diseases such as diarrhea, pneumonia, malaria, and measles. Similarly, the prevalence of undernourishment can reduce life expectancy by increasing the vulnerability, severity, and duration of infectious diseases. However, food security improves health outcomes – the rise in average dietary energy supply reduces infant mortality and increases the life expectancy of individuals.

Several facts and theories support the above findings. For instance, similar to the theoretical and conceptual framework section, food insecurity in SSA countries can affect health outcomes in nutritional, mental health, and behavioral channels. According to FAO et al. [ 128 ], the prevalence of undernourishment increased in Africa from 17.6% of the population in 2014 to 19.1% in 2019. This figure is more than twice the global average and the highest of all regions of the world. Similarly, SSA is the world region most at risk of food insecurity [ 129 ]. According to Global Nutrition [ 130 ] report, anemia affects an estimated 39.325% of women of reproductive age. Some 13.825% of infants have a low weight at birth in the SSA region. Excluding middle African countries (due to lack of data), the estimated average prevalence of infants aged 0 to 5 months who are exclusively breastfed is 35.73%, which is lower than the global average of 44.0%. Moreover, SSA Africa still experiences a malnutrition burden among children aged under five years. The average prevalence of overweight is 8.15%, which is higher than the global average of 5.7%. The prevalence of stunting is 30.825%—higher than the worldwide average of 22%. Conversely, the SSA countries’ prevalence of wasting is 5.375%, which is higher than most regions such as Central Asia, Eastern Asia, Western Asia, Latin America and the Caribbean, and North America. The SSA region's adult population also faces a malnutrition burden: an average of 9.375% of adult (aged 18 and over) women live with diabetes, compared to 8.25% of men. Meanwhile, 20.675% of women and 7.85% of men live with obesity.

According to Saltzman et al. [ 17 ], micronutrient deficiencies can affect people’s health throughout their life cycle. For instance, at the baby age, it causes (low birth weight, higher mortality rate, and impaired mental development), child (stunting, reduced mental capacity, frequent infections, reduced learning capacity, higher mortality rate), adolescent (stunting, reduced mental capacity, fatigue, and increased vulnerability to infection), pregnant women (increased mortality and perinatal complications), adult (reduced productivity, poor socio-economic status, malnutrition, and increased risk of chronic disease), elderly (increased morbidity (including osteoporosis and mental impairment), and higher mortality rate).

Though this study attempts to fill the existing gaps, it also has limitations. It examined the impact of food insecurity on infant mortality; however, their association is reflected indirectly through other health outcomes. Hence, future studies can extend this study by examining the indirect effect of food insecurity on infant mortality, which helps to look at in-depth relationships between the variables. Moreover, this study employed infant mortality whose age is below one year; hence, future studies can broaden the scope by decomposing infant mortality into (neonatal and postnatal) and under-five mortality.

Millions of people are dying every year due to hunger and hunger-related diseases worldwide, especially in SSA countries. Currently, the link between food insecurity and health status is on researchers' and policymakers' agendas. However, macro-level findings in this area for most concerned countries like SSA have been given only limited attention. Therefore, this study examined the impact of food insecurity on life expectancy and infant mortality rates. The study mainly employs DKSE, FE, two-step GMM, and Granger causality approaches, along with other estimation techniques for robustness checks for the years between 2001 and 2018. The result confirms that food insecurity harms health outcomes, while food security improves the health status of SSA nations'. That means that a rise in undernourishment increases the infant mortality rate and reduces life expectancy. However, an improvement in the average dietary energy supply reduces infant mortality and increases life expectancy. Therefore, SSA countries need to guarantee their food accessibility both in quality and quantity, which improves health status. Both development experts and political leaders agree that Africa has the potential for agricultural outputs, can feed the continent, and improve socio-economic growth. Besides, more than half of the world's unused arable land is found in Africa. Therefore, effective utilization of natural resources is essential to achieve food security. Moreover, since the majority of the food in SSA is produced by smallholder farmers [ 131 ] while they are the most vulnerable to food insecurity and poverty [ 132 , 133 ]; hence, special focus and support should be given to smallholder farmers that enhance food self-sufficiency. Further, improvement in investment in agricultural research; improvement in markets, infrastructures, and institutions; good macroeconomic policies and political stability; and developing sub-regional strategies based on their agroecological zone are crucial to overcoming food insecurity and improving health status. Finally, filling a stomach is not sufficient; hence, a person's diet needs to be comprehensive and secure, balanced (including all necessary nutrients), and available and accessible. Therefore, SSA countries should ensure availability, accessibility, usability, and sustainability to achieve food and nutrition security.

Availability of data and materials

The datasets used and/or analyzed during the current study are available in supplementary materials.

Abbreviations

Augmented Dickey–Fuller

Acquired Immunodeficiency Syndrome

Average Dietary Energy Supply

Common Correlated Effects Mean Group

Common Correlated Effects Pooled

Cross-Sectional Dependence

Cross-Sectionally Augmented Panel Unit Root Test

Cross-Section Augmented Autoregressive Distributed Lag

Cross-Section Augmented Distributed Lag

Continuously Updated Bias-Corrected

Continuously Updated Full Modified

Dynamic Fixed Effect

Driscoll-Kraay Standard Errors

Dynamic Ordinary Least Square

Error Correction Model

Food and Agricultural Organization

Fixed Effect

Feasible Generalised Least Squares

Fully Modified Ordinary Least Square

Gross Domestic Product (GDP) per capita

Generalised Method of Momentum

Domestic General Government Health Expenditure

Human Immunodeficiency Virus

Integration at First Difference

International Fund for Agricultural Development

Infant Mortality Rate

Im, Pesaran, Shin

Lag of Infant Mortality Rate

Lag of Natural Logarithm of Infant Mortality Rate

Life Expectancy at Birth

Levin, Lin, and Chu

Lagrange Multiplier

Natural Logarithm of Infant Mortality Rate

Natural Logarithm of Life Expectancy at Birth

Mean Years of Schooling

Ordinary Least Squares

Panel-Corrected Standard Error

Pooled Mean Group

Prevalence of Undernourishment

Random Effect

Sustainable Development Goals

Sub-Saharan African

Statistical Software

Seemingly Unrelated Regression

Urbanisation

World Food Programme

World Health Organization

Weighted Least Squares

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Additional file 1:.

  Table S1. Cook’s D results

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  Table S2. Hausman and Breusch-Pagan LM for REtests. 

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Beyene, S.D. The impact of food insecurity on health outcomes: empirical evidence from sub-Saharan African countries. BMC Public Health 23 , 338 (2023). https://doi.org/10.1186/s12889-023-15244-3

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Research Topics & Ideas: Healthcare

100+ Healthcare Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a healthcare-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of healthcare-related research ideas and topic thought-starters across a range of healthcare fields, including allopathic and alternative medicine, dentistry, physical therapy, optometry, pharmacology and public health.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the healthcare domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: Healthcare Research Topics

• Allopathic medicine
• Alternative /complementary medicine
• Veterinary medicine
• Physical therapy/ rehab
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Allopathic (Conventional) Medicine

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• The use of AI in early cancer diagnosis and treatment
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• The effects of environmental pollution on breast milk quality
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• The role of high-intensity interval training in improving cardiovascular health
• The efficacy of using probiotics for gut health in pregnant women
• The impact of poor sleep on the treatment of chronic illnesses
• The role of inflammation in the development of chronic diseases such as lupus
• The effectiveness of physiotherapy in pain control post-surgery

Topics & Ideas: Alternative Medicine

• The benefits of herbal medicine in treating young asthma patients
• The use of acupuncture in treating infertility in women over 40 years of age
• The effectiveness of homoeopathy in treating mental health disorders: A systematic review
• The role of aromatherapy in reducing stress and anxiety post-surgery
• The impact of mindfulness meditation on reducing high blood pressure
• The use of chiropractic therapy in treating back pain of pregnant women
• The efficacy of traditional Chinese medicine such as Shun-Qi-Tong-Xie (SQTX) in treating digestive disorders in China
• The impact of yoga on physical and mental health in adolescents
• The benefits of hydrotherapy in treating musculoskeletal disorders such as tendinitis
• The role of Reiki in promoting healing and relaxation post birth
• The effectiveness of naturopathy in treating skin conditions such as eczema
• The use of deep tissue massage therapy in reducing chronic pain in amputees
• The impact of tai chi on the treatment of anxiety and depression
• The benefits of reflexology in treating stress, anxiety and chronic fatigue
• The role of acupuncture in the prophylactic management of headaches and migraines

Topics & Ideas: Dentistry

• The impact of sugar consumption on the oral health of infants
• The use of digital dentistry in improving patient care: A systematic review
• The efficacy of orthodontic treatments in correcting bite problems in adults
• The role of dental hygiene in preventing gum disease in patients with dental bridges
• The impact of smoking on oral health and tobacco cessation support from UK dentists
• The benefits of dental implants in restoring missing teeth in adolescents
• The use of lasers in dental procedures such as root canals
• The efficacy of root canal treatment using high-frequency electric pulses in saving infected teeth
• The role of fluoride in promoting remineralization and slowing down demineralization
• The impact of stress-induced reflux on oral health
• The benefits of dental crowns in restoring damaged teeth in elderly patients
• The use of sedation dentistry in managing dental anxiety in children
• The efficacy of teeth whitening treatments in improving dental aesthetics in patients with braces
• The role of orthodontic appliances in improving well-being
• The impact of periodontal disease on overall health and chronic illnesses

Tops & Ideas: Veterinary Medicine

• The impact of nutrition on broiler chicken production
• The role of vaccines in disease prevention in horses
• The importance of parasite control in animal health in piggeries
• The impact of animal behaviour on welfare in the dairy industry
• The effects of environmental pollution on the health of cattle
• The role of veterinary technology such as MRI in animal care
• The importance of pain management in post-surgery health outcomes
• The impact of genetics on animal health and disease in layer chickens
• The effectiveness of alternative therapies in veterinary medicine: A systematic review
• The role of veterinary medicine in public health: A case study of the COVID-19 pandemic
• The impact of climate change on animal health and infectious diseases in animals
• The importance of animal welfare in veterinary medicine and sustainable agriculture
• The effects of the human-animal bond on canine health
• The role of veterinary medicine in conservation efforts: A case study of Rhinoceros poaching in Africa
• The impact of veterinary research of new vaccines on animal health

Topics & Ideas: Physical Therapy/Rehab

• The efficacy of aquatic therapy in improving joint mobility and strength in polio patients
• The impact of telerehabilitation on patient outcomes in Germany
• The effect of kinesiotaping on reducing knee pain and improving function in individuals with chronic pain
• A comparison of manual therapy and yoga exercise therapy in the management of low back pain
• The use of wearable technology in physical rehabilitation and the impact on patient adherence to a rehabilitation plan
• The impact of mindfulness-based interventions in physical therapy in adolescents
• The effects of resistance training on individuals with Parkinson’s disease
• The role of hydrotherapy in the management of fibromyalgia
• The impact of cognitive-behavioural therapy in physical rehabilitation for individuals with chronic pain
• The use of virtual reality in physical rehabilitation of sports injuries
• The effects of electrical stimulation on muscle function and strength in athletes
• The role of physical therapy in the management of stroke recovery: A systematic review
• The impact of pilates on mental health in individuals with depression
• The use of thermal modalities in physical therapy and its effectiveness in reducing pain and inflammation
• The effect of strength training on balance and gait in elderly patients

Topics & Ideas: Optometry & Opthalmology

• The impact of screen time on the vision and ocular health of children under the age of 5
• The effects of blue light exposure from digital devices on ocular health
• The role of dietary interventions, such as the intake of whole grains, in the management of age-related macular degeneration
• The use of telemedicine in optometry and ophthalmology in the UK
• The impact of myopia control interventions on African American children’s vision
• The use of contact lenses in the management of dry eye syndrome: different treatment options
• The effects of visual rehabilitation in individuals with traumatic brain injury
• The role of low vision rehabilitation in individuals with age-related vision loss: challenges and solutions
• The impact of environmental air pollution on ocular health
• The effectiveness of orthokeratology in myopia control compared to contact lenses
• The role of dietary supplements, such as omega-3 fatty acids, in ocular health
• The effects of ultraviolet radiation exposure from tanning beds on ocular health
• The impact of computer vision syndrome on long-term visual function
• The use of novel diagnostic tools in optometry and ophthalmology in developing countries
• The effects of virtual reality on visual perception and ocular health: an examination of dry eye syndrome and neurologic symptoms

Topics & Ideas: Pharmacy & Pharmacology

• The impact of medication adherence on patient outcomes in cystic fibrosis
• The use of personalized medicine in the management of chronic diseases such as Alzheimer’s disease
• The effects of pharmacogenomics on drug response and toxicity in cancer patients
• The role of pharmacists in the management of chronic pain in primary care
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• The effects of herbal and dietary supplements on drug efficacy and toxicity
• The role of pharmacists in the management of type 1 diabetes
• The impact of medication errors on patient outcomes and satisfaction
• The use of technology in medication management in the USA
• The effects of smoking on drug metabolism and pharmacokinetics: A case study of clozapine
• Leveraging the role of pharmacists in preventing and managing opioid use disorder
• The impact of the opioid epidemic on public health in a developing country
• The use of biosimilars in the management of the skin condition psoriasis
• The effects of the Affordable Care Act on medication utilization and patient outcomes in African Americans

Topics & Ideas: Public Health

• The impact of the built environment and urbanisation on physical activity and obesity
• The effects of food insecurity on health outcomes in Zimbabwe
• The role of community-based participatory research in addressing health disparities
• The impact of social determinants of health, such as racism, on population health
• The effects of heat waves on public health
• The role of telehealth in addressing healthcare access and equity in South America
• The impact of gun violence on public health in South Africa
• The effects of chlorofluorocarbons air pollution on respiratory health
• The role of public health interventions in reducing health disparities in the USA
• The impact of the United States Affordable Care Act on access to healthcare and health outcomes
• The effects of water insecurity on health outcomes in the Middle East
• The role of community health workers in addressing healthcare access and equity in low-income countries
• The impact of mass incarceration on public health and behavioural health of a community
• The effects of floods on public health and healthcare systems
• The role of social media in public health communication and behaviour change in adolescents

Examples: Healthcare Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a healthcare-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various healthcare-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Improving Follow-Up Care for Homeless Populations in North County San Diego (Sanchez, 2021)
• On the Incentives of Medicare’s Hospital Reimbursement and an Examination of Exchangeability (Elzinga, 2016)
• Managing the healthcare crisis: the career narratives of nurses (Krueger, 2021)
• Methods for preventing central line-associated bloodstream infection in pediatric haematology-oncology patients: A systematic literature review (Balkan, 2020)
• Farms in Healthcare: Enhancing Knowledge, Sharing, and Collaboration (Garramone, 2019)
• When machine learning meets healthcare: towards knowledge incorporation in multimodal healthcare analytics (Yuan, 2020)
• Integrated behavioural healthcare: The future of rural mental health (Fox, 2019)
• Healthcare service use patterns among autistic adults: A systematic review with narrative synthesis (Gilmore, 2021)
• Mindfulness-Based Interventions: Combatting Burnout and Compassionate Fatigue among Mental Health Caregivers (Lundquist, 2022)
• Transgender and gender-diverse people’s perceptions of gender-inclusive healthcare access and associated hope for the future (Wille, 2021)
• Efficient Neural Network Synthesis and Its Application in Smart Healthcare (Hassantabar, 2022)
• The Experience of Female Veterans and Health-Seeking Behaviors (Switzer, 2022)
• Machine learning applications towards risk prediction and cost forecasting in healthcare (Singh, 2022)
• Does Variation in the Nursing Home Inspection Process Explain Disparity in Regulatory Outcomes? (Fox, 2020)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Need more help?

If you’re still feeling a bit unsure about how to find a research topic for your healthcare dissertation or thesis, check out Topic Kickstarter service below.

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15 Comments

I need topics that will match the Msc program am running in healthcare research please

Hello Mabel,

I can help you with a good topic, kindly provide your email let’s have a good discussion on this.

Can you provide some research topics and ideas on Immunology?

Thank you to create new knowledge on research problem verse research topic

Help on problem statement on teen pregnancy

This post might be useful: https://gradcoach.com/research-problem-statement/

can you provide me with a research topic on healthcare related topics to a qqi level 5 student

Please can someone help me with research topics in public health ?

Hello I have requirement of Health related latest research issue/topics for my social media speeches. If possible pls share health issues , diagnosis, treatment.

I would like a topic thought around first-line support for Gender-Based Violence for survivors or one related to prevention of Gender-Based Violence

Please can I be helped with a master’s research topic in either chemical pathology or hematology or immunology? thanks

Can u please provide me with a research topic on occupational health and safety at the health sector

Good day kindly help provide me with Ph.D. Public health topics on Reproductive and Maternal Health, interventional studies on Health Education

may you assist me with a good easy healthcare administration study topic

May you assist me in finding a research topic on nutrition,physical activity and obesity. On the impact on children

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244 Brilliant Health Research Paper Topics

Health is one of the core tenets of human survival. It is impossible to go a day without hearing or reading news on various aspects of human health. With the global coronavirus pandemic, health discussions have become more intensified in the recent past. Nonetheless, this is not a guarantee that you will find useful health-related topics for the research paper. That is why we have put together this informative article to help you identify quality writing ideas for your health paper. But first,

What Is A Health Research Paper?

It is an academic piece of writing that deals with diseases, their causes, prevention, and cure. Health is a complex subject with various dimensions – it is a vital science beyond the normal check-up and diagnosis in your local clinic.

To have a captivating research paper in health, you need to be well-versed in this field’s different concepts. As if that is not enough, you also have to relate how man’s different activities contribute to health problems or solutions. Knowing how to write health papers will therefore take time and effort. It is not an easy task to write a research paper for a good grade. It is not like a normal high school essay where you will tell the tutor about yourself and score highly – no! Students will have to involve in intensive research to come up with captivating health research papers. Students pursuing health-related courses in college or university sometimes spend countless research hours coming up with health topics for research papers. So, d on’t be afraid to get research paper help from our professional writers.  However, our professional writing experts have collated a list of interesting health topics for your inspiration.

Easy Health Related Topics For Research Paper

• Investigate the dietary requirements of a patient suffering from cancer
• The role of the World Health Organization in determining health policies
• How to care for a diabetic patient at home
• Why most people prefer seeking advanced healthcare abroad
• Why is heart surgery the most complicated of all?
• Why people should go for regular check-ups whether or not they are sick
• The role of the media in contributing to the spread of fake information on health
• Is it advisable to have a family doctor?
• Adverse effects of X-rays on the human body
• Why pregnant women should deliver in health facilities
• The impact of political decisions on the state of healthcare in a country
• Why mothers should be informed on the critical role of immunizations to children
• Effects of taking over-the-counter prescribed drugs

Health Research Topics On COVID-19

• Why ventilation is necessary for curtailing the spread of coronavirus
• Discuss challenges in the development of the coronavirus vaccines
• Why it is necessary to wear a mask at all times while in public places
• What is the risk of adolescents with no underlying health conditions contracting coronavirus?
• Analyze why there are many asymptomatic COVID-19 related patients
• The impact of technology on the development of coronavirus vaccines
• How long should one take before going for the second dose of the COVID-19 vaccine?
• Discuss the essence of washing hands every time in fighting the coronavirus
• What properties of soap make it suitable for fighting coronavirus?
• Should children under ten years receive the COVID-19 vaccine?
• The implication of opening learning institutions on the spread of the virus
• The role of the media in helping people make informed decisions about COVID-19
• Why it is necessary to have a sanitizer in the fight against coronavirus

Interesting Health Topics

• The role of personal hygiene in preventing bacterial infections
• Should diabetic patients inject insulin by themselves?
• The impact of advertisements on medical products
• Why research is necessary for the development of drugs
• Does testing drugs on rats affect their efficiency in human beings?
• The crucial role of a personal First Aid kit at home
• What is the first step in treating a bruised ankle?
• Effects of using a wheelchair on a person’s growth and development
• Discuss the relationship between doctors and glasses
• What is the maximum amount of time that one should spend in a critical care unit?
• The role of technology in intensive care units: A case of specialized life-support machines
• Discuss the health benefits of circumcision at a young age
• How to prevent eye strain when using a computer for long hours

Nursing Health Related Topics For Research Paper

• Challenges faced by nurses in caring for coronavirus patients
• The impact of night shifts on the effectiveness of nurses
• Are robots able to supplement the role of nurses in infectious disease units?
• What is the place of emotions in the care of critically ill patients?
• Evaluate the susceptibility of Europeans to Malaria through mosquito bites
• Examine the effectiveness of caring for patients from their homes
• Are the number of years spent in a nursing school effectively to gain the necessary nursing experience?
• Latest nursing technologies that are transforming the nursing sector
• What is the implication of a male nurse attending to a female patient?
• Who should prescribe treatment for the patient between the doctor and the nurse?
• How nurses handle psychological trauma after attending to patients
• Effects of watching too much on the health of a person
• Discuss the differences in nursing practices in developing and developed nations

Mental Health Research Paper Topics

• How does what one watches affect his/her mental health?
• The role of stigmatization in derailing one’s mental capability
• How to deal with mental lapses during the adolescent stage
• What are the medical solutions to mental problems related to break-ups?
• The role of counseling psychologists in preventing suicide cases
• Causes of aggressive behavior among people who watch wrestling
• Do sleeping patterns relate to mental health?
• How often should people go for mental check-ups?
• Do people who speak too much have mental problems at a time?
• Why staying lonely can lead to depression.
• The effects of watching horror movies on causing anxiety and fear
• Why it is necessary to socialize as compared to online interactions
• Discuss the implication of family disputes on the mental state of the children

Women’s Health Research Paper Topics

• The role of menstruation in women
• What causes mood swings and attitudes among women?
• Analyze the various post-natal care practices for women
• Compare and contrast the health implications of cesarean surgery versus normal birth
• The implication of body exercises for women
• Why most women are susceptible to cold conditions as compared to men
• Discuss the psychology behind women and long hair
• The impact of strenuous labor on the health of women
• Why is the number of women infected with coronavirus less than that of men?
• The essence of calcium for breastfeeding women
• What is the impact of vitamin supplements on women after delivery?
• Discuss the differences in muscle development between women and men
• Why women must have more fat than men

Hot Healthcare Research Paper Topics

• Effects of eating disorders on the development of a person
• Causes of unhealthy eating habits among teenagers
• The role of regular deworming in maintain a healthy appetite
• Dangers of excessive alcohol consumption to a person’s eyes
• How does smoking affect the weight of a person?
• Medical procedures to dealing with addictions and disorders
• The impact of being on ventilators for long
• How does surgery affect one’s vision and thinking?
• Effects of movies on the psychology of people who are to undergo surgery
• The role of virtual simulations in the study of medicine
• Are online classes effective for training competent healthcare personnel?
• The effects of culture and traditions on modern medicine
• How government policies affect the development of healthcare systems

Interesting Health Topics To Research In College

• Discuss the long-term consequences of being in a coma
• Investigate the factors that motivate students to take up medical courses
• Impact of intermittent fasting on a person with a health complication
• What causes allergies in patients with Asthma?
• The role of mass vaccination in curbing the spread of a health pandemic
• How often should one go for a dental check-up?
• Why participating in sports and games is essential for a healthy lifestyle
• How has yellow journalism affected the mental health of many?
• Role of morning runs in preventing against lifestyle diseases
• Western cultures that contribute to health complications
• Is it advisable for pregnant women to go for baby scanning?
• The impact of stereotyping in healthcare practice
• The role of stress in compulsive sweating and eating

Convincing Health Topics To Write About

• How clinical practitioners determine nutrition care for patients
• Differences in the care of outpatients and inpatients
• The role of the American Medical Association in determining health policies
• How to deal with bad breath and decaying teeth
• Effects of blood donation on the donor’s health
• Discuss the prevalence of cardiovascular diseases among the elite
• Why evidence-based medicine is necessary for treating diseases
• Causes of inflammatory bowel disease among women
• Why some people have developed insulin resistance
• How to maintain a healthy lung
• The role of the National Cancer Institute in developing treatment procedures
• The effects of premature puberty on the development of a person
• Discuss the health benefits of watermelon seeds

Manageable Health Project Topics

• Causes of the urinary tract infections
• Why it isn’t easy to maintain a healthy lifestyle in urban areas
• The role of Vitamin D supplements in strengthening the body’s immunity
• How fish and seafood recipes help to maintain a healthy body
• The role of global warming in advancing the spread of diseases
• Discuss the relationship between genetics and congenital disabilities
• Factors that determine effective rehabilitation after surgery
• Health effects of donation and transplantation of body organs
• What are the procedures involved in complementary and alternative therapies?
• Discuss the prevalence of deafness and other communication disorders
• What are the health implications of household air pollution?
• Evaluate the various neurological and mental disorders
• Are tobacco control policies sufficient enough?

Public Health Topics For Research Paper

• Discuss the effectiveness of the public health standards in the United States
• What are some of the social determinants of health?
• Compare and contrast health services and systems between the United States and the UK.
• How do housing and communities affect public health?
• Enhancing healthy lifestyles among communities
• The role of the Smoke-free 2025 Action Plan in clean air
• How to manage drug and substance misuse among communities
• Discuss how racism is affecting the delivery of health services
• Have countries neglected immigrant health-related policies?
• Injury and violence prevention policies in the United States
• The role of public health accreditation agencies in maintaining high health standards
• School-based healthcare practices during the coronavirus pandemic
• The role of healthy housing in preventing the spread of diseases

Controversial Health Topics For Research Paper

• Do traditional health practices affect modern health systems?
• The implication of plastic surgery for beauty standards
• Does going vegan improve a person’s health?
• Is body healthy for women?
• What sets the mark for healthy food?
• Is it possible to maintain healthy psychological health in the modern world?
• Are anxiety and depression serious health threats?
• Can the coronavirus kill persons who have no underlying health conditions?
• Is it possible to develop a vaccine under one year?
• Is chemotherapy doing more harm than good for cancer patients?
• Does alternative medicine work?
• Are we experiencing the rise of epidemics during the 21 st century?
• Is it ethical to carry out organ harvesting and transplantation?

Health Informatics Research Paper Topics

• The role of telemedicine in combating serious health problems
• Helpful infographics that visually illustrate solutions to prevailing health conditions
• Smartphone applications that are helping people to keep fit in the 21 st century
• A case study of diagnostic tools used by clinicians on their mobile devices
• Evaluate the growing presence of mobile technology in the healthcare field
• Effective healthcare IT management practices in the information age
• The impact of health informatics on the privacy of patients
• How to transform health through innovative IT solutions
• Discuss the incorporation of health informatics in developing countries
• The role of health informatics in providing accountable healthcare
• How electronic health records are enhancing service delivery in the health sector
• The role of health information exchange on interoperability of hospitals
• Dealing with privacy and security issues in rural health IT

Top-Notch Health-Related Research Topics

• How to detect misleading information related to coronavirus
• Health practices that improve the lifespan of citizens through the organized efforts of society
• The role of physical activity in determining mental health
• How the Internet of Medical things is transforming the health sector
• Evaluate some of the AI-enabled technologies used in neurodegenerative health complications
• Discuss the role of biomedical sensor data in diagnosing a patient
• The role of machine learning in enhancing the study of medicine
• Intelligent monitoring technologies used in intensive care units
• Analyze the strides made in the digital healthcare system
• How data- and model-driven intelligent healthcare systems are saving lives
• How to use big data analytics in healthcare systems
• Discuss some of the non-medical analytic processes
• Biological mechanisms and criteria for treating infectious diseases

Creative Public Health Research Topic Ideas

• The role of sexual education in preventing the spread of HIV/AIDS
• How medical campaigns on TV can help spread community awareness
• The role of radio in advancing knowledge on coronavirus
• Why people are so much concerned about reproductive health
• How to maintain reproductive health during menopause
• Health issues related to puberty in the society
• The role of conferences and symposia on public health
• Analyze the growing cases of food poisoning resulting from fast food restaurants
• Why people are not going for cancer screening procedures
• The role of teaching the general public on First Aid procedures for accidents and minor injuries
• Health care practices for the skin, hair, and nails
• Discuss how flu, cold, and sore throat symptoms are used to identify a potential coronavirus patient
• How the use of the internet and computer games is contributing to health problems among teenagers

Additional Medical Controversies Topics

• How should doctors handle patients with terminal illnesses?
• Should parents decide what happens to unborn kids with incurable diseases?
• Is it ethical to test drugs and vaccines on animals?
• Does a person have the right to determine what happens to his/her organs after death?
• Is it ethical for parents to design the traits they want for their future babies?
• Where do we draw the line for patient/doctor confidentiality?
• Does the fetus have the right when it comes to abortion?
• Should countries adopt medical marijuana?
• Is there a relationship between poor health and poverty?
• Is the United States justified in imposing high healthcare costs?
• Does abortion affect the mental health of the perpetrator
• Should doctors release the medical records of patients to the police?
• Should organ donation be a personal choice?

Health Argument Topics

• Is it right to sell body organs to the highest bidder?
• Should animals die through experimental research so that man can live longer?
• Who is to blame for the adverse effects of experiments on animals
• What happens in the case of prenatal illnesses?
• Is euthanasia ‘good death?’
• Disposing of COVID-19 bodies in mass graves
• Should doctors compensate patients in the case of wrong surgeries?
• Use of illegal drugs for medicinal purposes
• Role of government bodies in setting medical standards
• Is Cuba a role model in advanced medical research?
• Are babies from test tubes real?
• Religious beliefs on vaccination
• Are antibiotics more harmful to society?

Mental Health Argumentative Essay Topics

• Impact of placing the elderly in elderly care
• Are female psychologists more understanding enough?
• Technology and mental health
• Working hours and mental stability of nurses
• Vaccination and mental health
• Effects of stem cell brain injections
• The psychological perspective of Medical Termination of Pregnancy
• Effect of TV commercials for alcohol
• Legalization of psychedelic drugs
• Medicinal cannabis and mental health
• Medical science and prolonging life
• Addiction to painkillers
• Circumcision of infants

Health And Fitness Topics

• Impact of water aerobics
• Frequency of workouts
• Should fitness exercises be done in gyms or at home?
• Duration of workouts
• Modifications to workouts
• Bike riding and burning calories
• Workouts and gaining weight
• Developing a training plan
• The role of Yoga in health
• The essence of cardio exercises

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Mental Health Research Paper

This sample mental health research paper features: 8500 words (approx. 28 pages), an outline, and a bibliography with 80 sources. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.

I. Introduction

Academic writing, editing, proofreading, and problem solving services, get 10% off with 24start discount code, ii. the sociology of mental health: a brief history, a. the development of social epidemiology of mental health and disorders, iii. the study of mental health in contemporary sociology, a. the influence of other disciplines on the sociology of mental health, b. theoretical perspectives on mental health and disorder in sociology, c. defining a unique sociological approach to mental health and illness, 1. the stressor exposure perspective, 2. the social relationships perspective, 3. the societal reaction perspective, d. the influence of psychological models on the sociology of mental health and illness, e. methodological controversies, 1. measures of mental health and disorder, 2. measures of stressor exposure, f. the social epidemiology of mental disorders, 2. socioeconomic status, 4. marital status, iv. future directions in the sociology of mental health, a. comorbidity, b. mental health services and policy, c. better measures of stress exposure, d. better measures of social resources, e. the biological perspective on mental disorders, more mental health research papers:.

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This research paper describes the history, application, and development in sociology of the study of mental health, illness, and disorders. Mental health, mental illness, social and mental functioning, and its social indicators are a classic theme in the field of sociology. Emile Durkheim’s (1951) Suicide was a landmark study in both sociology and epidemiology, laying out a sociological course of research that remains an intellectual force in contemporary social science (Berkman and Glass 2000). The influence of the sociology of mental health and illness goes well beyond its sociological roots; its major theoretical perspectives interact with major research streams in psychiatry, psychology, anthropology, public health, and medicine (Aneshensel and Phelan 1999; Horwitz and Scheid 1999; Eaton 2001; Gallagher 2002; Cockerham 2005). The sociology of mental health also connects to numerous other fields in sociology, including general medical sociology, the sociology of aging, demography and biodemograpy, statistics, childhood studies, sociology of the life course, deviance, criminology, stratification, and studies of the quality of life.

Mental health, mental illness, and mental disorder are closely related but distinguishable concepts. Mental health refers to a state of well-being or alternatively, a state of mental normality, free of disorder or illness. Mental illness refers to a persistent state of mental abnormality. The term mental disorder is applied to a specific diagnosis of mental abnormality, such as depression, anxiety, schizophrenia, agoraphobia, mania or substance dependence.

In this research paper, the term sociology of mental health is used to refer to general theories and research that encompass the causes, development, and consequences of mental disorders and the state or symptoms of mental distress. The term also includes the study of personal and situational resources that preserve or restore the state of mental wellbeing. Sociologists who practice in the field of mental health examine a variety of outcomes and indicators of mental health as well as mental disorders.

The paper is organized into three sections: (1) a brief historical perspective on the study of mental health and illness in sociology; (2) the current state of research in the field, including its major themes and methodological problems; and (3) the future directions of the field. This research paper has four pervasive themes: (1) the interaction of the sociology of mental health and disorder with psychology, psychiatry, public health, and medicine; (2) the environmental perspective, which is the major contribution of the sociology to the mix of disciplines examining mental health in society; (3) the relationship between the study of mental health and studies of mental disorder; and (4) the emergence of the life course perspective as a dominant theoretical perspective in the sociology of mental health.

The topic of mental health has a venerable tradition in sociology. Durkheim’s classic work Suicide was translated into English in 1921, and it is still widely cited in the field. Durkheim’s work encouraged interest in the relationship of mental health and disorders with social structure, group membership, geographical location, and other indicators of social integration and organization. One of the most famous early applications of Durkheim’s perspective was Robert Merton’s (1938) work on social structure and anomie. Taken together, Durkheim and Merton introduced the influential idea that social systems can produce “stress” for individuals, who in turn may act in deviant or disordered ways (Cockerham 2005). Also applying Durkheim’s ideas, Faris and Dunham (1939) conducted a study of the distribution of schizophrenia in Chicago. Observing that people with schizophrenia clustered in high poverty areas, they argued that social isolation encouraged the development of symptoms characterizing schizophrenia.

Although Merton’s and Faris and Dunham’s theories no longer hold sway among contemporary sociologists of mental health, they are significant in their historical impact on the field. The organized field of the sociology of mental health grew out of the larger field of general medical sociology in the late 1930s and 1940s. Interest in mental illness and its causes were heightened by extraordinary events in the mid-twentieth century. The suffering of many ordinary Americans during the Great Depression, the discovery of psychiatric impairments among many World War II draftees, and the traumatic effects of combat on soldiers and civilians were powerful arguments for government support of efforts to mitigate mental illness (Kirk 1999).

The founding of the National Institutes of Mental Health (NIMH) in 1949 contributed to the development of medical sociology in general. The establishment of the Laboratory of Socio-Environmental Studies at NIMH in 1952 was a critical event in the development of studies of mental health in medical sociology. The sociologist John Clausen, who headed the laboratory, recruited and supported a number of sociologists who became leaders in the field, among them Melvin Kohn, Leonard Pearlin, Erving Goffman, and Morris Rosenberg (Kirk 1999). Using a strategy still dominant in behavioral science approaches to mental disorders, Clausen (1956) recruited social scientists from multiple disciplines as well as sociologists, stating that “the roles to be filled by sociologists within the mental health field call for collaboration with clinicians” (p. 47).

Throughout the 1950s, 1960s, and 1970s, NIMH was a major supporter of sociological and psychological research on mental health and illness. According to figures assembled by Kirk (1999), in 1976 more than 50 percent of NIMH research grants were to social, psychological, and behavioral scientists. A smaller proportion of grants were awarded to psychiatrists and physicians (a situation that no longer holds at NIMH).

Social epidemiology, sometimes labeled psychiatric epidemiology or social psychiatry (Gallagher 2002), is the discovery and documentation of the social and demographic distribution of mental disorders and health. The distribution of mental disorders can be documented via the study of medical records, mental hospital admissions, and surveys of the general population. Surveys in representative community populations, using clinically validated questions that identify and classify mental disorder symptoms by diagnostic categories, are the current tools used to estimate the prevalence of disorders (Cockerham 2005). The diagnostic estimates are then analyzed to determine their distribution by social and demographic group.

Hollingshead and Redlich (1958) (a sociologist and a psychiatrist) conducted an innovative study of mental disorders in New Haven, Connecticut, in which they compared mental illness inpatients and outpatients to a sample representative of the general community. Although not a study of prevalence the study had wide influence because of their findings that different types of mental disorder were distributed by social class, with more disorders among lower social class groups. The study also found that treatment for mental disorder varied by class. Because Hollingshead and Redlich’s study included only treated cases, however, they could not draw inferences about possible social causes of mental disorders.

The Midtown Manhattan Study in the 1950s (Srole et al. 1962) investigated the distribution of mental disorders using a random selection household survey design. The interview responses were rated by psychiatrists on the team. The findings from this study continue to shape social epidemiology today. Mental disorders were found to be more prevalent among respondents of lower socioeconomic status. Childhood poverty was linked to psychiatric impairment in adulthood (an early application of the life course perspective on mental health). Those who had mental disorders were less likely to be upwardly mobile. The investigators hypothesized that exposure to childhood and adult stressors played a key role in the distribution of mental disorders as well as mental health (Cockerham 2005). Many of these findings were replicated in a study of Nova Scotia communities (Leighton et al. 1963).

The environmental perspective on mental health was also advanced by studies led by social psychologists. Americans View Their Mental Health, two nationally representative interview studies conducted in 1956 and 1976 (Veroff, Douvan, and Kulka 1981), examined patterns over time in the contributions of the social environment to both positive and negative mental well-being as well as to patterns of help seeking for those who experienced mental distress.

A notable advance in the survey technology for measuring the prevalence of mental disorders and their social correlates was the Epidemiological Catchment Area (ECA) project, conducted by NIMH and five universities in the 1980s (Yale University, Johns Hopkins University, Washington University, Duke University, and the University of California at Los Angeles). A multidisciplinary team, including sociologists, psychiatrists, and psychologists developed new diagnosis instruments to detect mental disorders for use in the general population (Robins and Regier 1991). These diagnostic instruments, derived from the third version of the Diagnostic and Statistical Manual of the American Psychiatric Association (DSMIII), were coupled with interviews that measured environmental factors, social class, race, ethnicity, stressors, social relationships, and other factors believed to correlate with the risk of developing mental disorders.

The separate samples for the ECA studies, however, were not representative of the entire population of the United States. In 1990 through 1992, NIMH funded the first national survey of mental disorders in the general U.S. population (n = 8,068), the National Comorbidity Survey (NCS; Kessler and Zhao 1999). The investigators updated the interview diagnostic measures to reflect those recently developed by the American Psychiatric Association and the World Health Organization (Kessler et al. 1994). Along with diagnostic measures of depression, mania, anxiety, substance abuse, phobias, posttraumatic stress disorder, and other mood and psychotic disorders, the NCS interviews included measures of environmental factors, personality, childhood conditions, physical health, and mental health care utilization. NCS investigated the concept of comorbidity, which is defined as the occurrence of more than one type of mental disorder in an individual.

The NCS has been widely emulated and expanded. A version of the NCS was also conducted in Canada. NIMH also funded a series of replications of the NCS in 2000 to 2003 (Kessler et al. 2005), and the method has been extended to studying mental health and illness in children. The World Health Association is currently coordinating international replications of the NCS ( http://www.hcp.med.harvard.edu/ncs/ ).

As the foregoing brief historical overview shows, the study of mental health in sociology has been influenced by multiple disciplines. It is also host to a number of competing theoretical perspectives. The most widely discussed is the tension among medical, environmental, and societal reaction perspectives on the causes, consequences, and appropriate treatment of mental disorders. As a consequence of the host of influences on the field, there is considerable disagreement over the measurement of basic concepts in research, including how to define mental health and disorders (Kessler and Zhao 1999), environmental factors such as stressors, location, and socioeconomic status (Wheaton 1999); and social consequences such as disability, labeling, and social isolation (Horwitz and Scheid 1999; Pillemer et al. 2000). In addition, there is considerable creative tension between those who concentrate on establishing the incidence and prevalence of mental disorders and those who focus more on the correlates of mental health and mental illness (Mirowsky and Ross 2002, 2004). Finally, there is considerable research on the use of mental health services and on mental health policy.

As Clausen (1956) prophetically foresaw, sociologists who specialize in mental health frequently collaborate with those in other disciplines, such as developmental and social psychology, psychiatry, epidemiology, economics (Aneshensel and Phelan 1999; Gallagher 2002), and increasingly biology (Shanahan and Hofer 2005). The National Institutes of Health has encouraged and continues to encourage multidisciplinary approaches to the study of mental illness and disorders. Psychiatrists and clinical psychologists lay claim to the definitions of mental illness and disorder through the continuing revisions of the Diagnostic and Statistical Manual Mental Disorders, currently in its fourth edition (American Psychiatric Association 2000), as well as to measurements of mental distress (Radloff 1977), quality of life (Veroff et al. 1981), and social relationships and support (Cohen, Underwood, and Gottlieb 2000). Sociologists who study mental health compete for federal funds and intellectual prestige with those from other disciplines.

The presence of sociologists in interdisciplinary efforts to understand the causes, course, and consequences of mental illness and disorders is a positive situation; the influence of the sociology of mental health on other disciplines is tangible. A negative aspect of the interdisciplinary effort is that the sociology of mental health is sometimes viewed as isolated from the general field of sociology (Aneshensel and Phelan 1999). This perception may be exacerbated by the employment of sociologists of mental health (and other medical sociologists) in academic units other than Sociology departments. Members of the Sociology of Mental Health section of the American Sociological Association are employed in medical schools, schools of public health, schools of social work, and departments of human development. When theories of cause and measures of critical outcomes are shared with other disciplines, the question arises: What is the unique contribution of sociology to the study of mental health and illness? The answer to this question is pressing as there are calls for proposals that contribute to “the development, enhancement, and assembly of new data sets from existing data” and for research “that combines diverse levels of analysis” from national research and review bodies (National Institutes of Health 2004) as well as for research that examines the causes of health differences by socioeconomic status and behavioral risk factors across the life course (National Research Council 2004).

Five major perspectives, and combinations of these perspectives, are used in the contemporary sociology of mental health. The five major perspectives are (1) the medical model, (2) the environmental perspective, (3) the social psychological perspective, (4) societal reaction (or labeling), and (5) the life course perspective. The medical model views mental disorders as diseases and prescribes medical treatment as the appropriate cure. The environmental perspective asserts that factors such as social class, race, ethnicity, gender, urban location, and exposure to stressors may cause and most certainly shape risks for mental disorder. The social psychological perspective contributes insight into the social and relational factors that provide resources for adjusting to environmental stressors and restoring mental health and well-being. The social reaction perspective argues that mental illness emerges from social strain processes that produce deviance. The life course perspective views mental health and mental disorder as resulting from the accumulation of environmental stressors and exposures across the lifetime, in interaction with developmental and personal factors such as family structure, personality, and even genetic endowment. Researchers in the sociology of mental health often combine one or more of these perspectives in their research, with the life course perspective now generally seen as an emerging unifying paradigm (George 1999).

Although there is constant interaction between the mental health disciplines, several recent analyses of the state of theory in the sociology of mental health in the late twentieth century indicate the emergence of a distinct sociological approach. Horwitz and Scheid (1999) outlined two major approaches in the study of the sociology of mental health and illness. These two approaches are: (1) the social contexts producing or shaping mental health and disorder and (2) the recognition, treatment, and policy response to mental illness and disorder. In the same volume, Thoits (1999) described three major approaches that uniquely characterize the sociology of mental health: (1) stress exposure (a subset of the social context approach described by Horwitz and Scheid); (2) structural strain theory, which derives from Merton (1938); and (3) societal reaction, or labeling theory. Aneshensel and Phelan (1999) argue that the distinguishing issue in the sociological approach to mental illness is attention to how social stratification produces the unequal distribution of both disorders and mental health.

Aneshensel and Phelan also argue that a major challenge to the sociological approach to mental disorders is the debate between social causation and social selection explanations for the relationship between mental disorders and social class. The social selection approach hypothesizes that the reason there are more mental disorders in the lower economic class is because those with mental disorders are downwardly mobile economically or are unable to be upwardly mobile. This debate has many implications for interpreting how social stratification is linked to mental disorders and health (e.g., Miech et al. 1999).

The sociological approach also provides unique insight into the serious social consequences for those who have mental disorders, including socioeconomic success. The sociological approach also contributes research on the social factors that influence how institutions and individuals recognize when someone is mentally ill, how individuals are treated and how that treatment varies by social class, gender, and race, and who is more likely to use mental health care (e.g., Phelan et al. 2000).

The application of the sociological approach to mental health generates considerable empirical work that focuses on economic and other types of social stratification as determinants of mental health and mental disorder. This work is concentrated in research on stressor exposure, social relationships, and societal reaction to mental disorders.

The social context approach is a set of perspectives; the most well-known and applied outside the field of the sociology of mental health is the stress exposure perspective, which assumes that a combination or accumulation of stressors and difficulties can cause an onset of mental disorder. This perspective (Brown and Harris 1978; Dohrenwend et al. 1978), dominant in sociology, focuses on the level of change or threat posed by external events, and more recently, on the potential for chronic, unresolved stressors to threaten physical and mental health (Wheaton 1999).

Building on the strong history of social epidemiology in the field, the major assumption of this approach is that differential exposure to stressors by social class or social location is largely determined by social inequalities. In turn, the effects of prolonged stress exposure may perpetuate social inequality through the development of mental illness or disorder in disadvantaged populations (Pearlin et al. 2005). The latter point is more controversial (and in general less well developed theoretically); however the emerging life course or human developmental approach to the accumulation of disadvantage derives in some part from the stress exposure perspective (George 1999). The life course approach assumes that there is an accumulation of the negative effects of differential stressor exposure across life that perpetuates and magnifies inequalities and that many of these processes originate in childhood (e.g., McLeod and Kaiser 2004; McLeod and Nonnemaker 2000). A related stress exposure approach is stress diathesis, which assumes that stress exposure causes disorder only when there is a latent vulnerability (Eaton 2001). The diathesis approach is widely applied in psychiatric research on mental disorders.

Horwitz and Scheid (1999) add that in addition to stressor exposure, resources to help counter the negative impact of stressor exposure or to avoid stressor exposure also are differentially distributed by social class and location. The major types of social resources that vary by social class are (1) social integration, usually measured as access to meaningful and productive social roles (e.g., Pillemer et al. 2000); (2) social network characteristics (Turner and Turner 1999); (3) family structure (e.g., Turner, Sorenson, and Turner 2000); (4) received and perceived social support (Wethington and Kessler 1986); and (5) coping choices and styles (Pearlin and Schooler 1978; Pearlin et al. 1981). Thoits (1999) has pointed out that this approach, although distinct from the stressor exposure perspective, relies on stress exposure as a mechanism to activate the protective factors.

In an overview of the sociology of mental health, Thoits (1999) argued that there is no strong evidence that labeling or other societal reaction processes produce mental illness. However, the societal reaction perspective does provide an insight into social biases against those who display symptoms of mental disorder, which are often viewed as socially deviant. Aneshensel and Phelan (1999) concluded that there is a consensus among sociologists of mental health that mental disorders are objective entities and are not completely a product of social constructions. The strongest evidence for this conclusion is that symptoms of mental disorders are observed in all societies, although there are cultural variations in the ways that such symptoms are described and diagnosed.

A difficulty with this position for sociologists of mental health is that it implies there is widespread acceptance of the medical model, which can make theoretical interaction with other streams of sociology (e.g., the sociology of deviance) more contentious. Studies of the etiology of mental disorders in the population no longer routinely employ a deviance perspective. The stressor exposure model also applies a variation of the dose-response paradigm widely used in medical research. This acceptance of a variation of the medical model remains controversial and is probably related to the distance perceived between the sociology of mental health and the more mainstream sociology of stratification.

Yet another tension exists between opposing explanations of what causes social stratification in the distributions of mental disorders. On one side is the belief that routine functioning of society produces some of this stratification, as for example gender differences in the distribution of different types of disorders (Rosenfield 1999). In this view, mental distress and mental disorders can be produced by normal social processes such as gender role socialization. The stress exposure perspective, on the other hand, assumes that abnormal circumstances and events produce mental disorders and distress (Almeida and Kessler 1998). These two views are not necessarily impossible to resolve, but they continue to produce theoretical tensions.

Another factor producing distance between the sociology of mental health and the general field of sociology is the influence of social psychological theories on the field. As psychology has incorporated facets of the stress exposure perspective, sociologists of mental health have adopted ideas from social and developmental psychology on social support and relationships, coping, and life course development. An influential psychological perspective, the process of appraisal and coping, was developed by Lazarus and Folkman (1984), updated by Lazarus (1999), and has been further elaborated by Folkman and Moskowitz (2004). This perspective, dominant in the field of psychology, has emphasized how individual differences in perceptions of external stressors affect mental health. The focus of appraisal researchers on emotions as motivation for appraisal suggests commonality with biological research on emotion (Massey 2002). The theory of appraisal has been widely cited by sociologists who examine the impact of events on mental health (e.g.,Wethington and Kessler 1986).

The life course perspective (Elder 1974), now widely applied in the sociology of mental health (e.g., Wheaton and Clarke 2003; McLeod and Kaiser 2004), traces many of its components to the ecological perspective on human development pioneered by the developmental psychologist Urie Bronfenbrenner (1979). The life course perspective theorizes that developmental trajectories, developmental or socially normative timing of the stressor, and the accumulation of stressor exposure and resistance factors shape reaction to stressors (Elder, George, and Shanahan 1996). In the last decade, the life course perspective on stress accumulation has also been applied by psychologists, clinical psychologists, and neuroscientists (e.g., Singer and Ryff 1999; McEwen 2002; Repetti, Taylor, and Seeman 2002). Neuroscientists McEwen and Stellar (1993) have developed the concept of allostatic load which describes physiological mechanisms for the accumulated effects of past adaptation to stressors on health. Allostatic load is currently being adapted by sociologists to use in studies of stressor exposure across the life course and its relationship to mental health and disorder (Shanahan, Hofer and Shanahan 2003; Shanahan and Hofer 2005).

Sociological and psychological research streams on the relationship between stressor exposure and mental health are converging through collaborative efforts that examine the impact of stressor accumulation along the individual life course (Elder et al. 1996; Singer et al. 1998). A serious problem, however, is that most measures of stressor exposure available to researchers focus on recent exposures rather than the interactions of different types of stressor exposure over the long term; the majority of stressor exposure measures used in research are simple counts or sums of life events occurring over a short period of time (Wheaton 1999). Investigating the relationships between stressors over time and their combined associations with mental health and well-being is an important strategy for examining the impact of stressors over the life course (George 1999).

Issues of causality and theoretical approach are controversial in the field. Given the complexity and controversies in the sociology of mental health and illness, it is not surprising that one of the critical areas of the field is measurement. The two most disputed areas involve the measurement of outcomes and the measurement of stressor exposure.

The controversy begins with the outcomes. There is an increasing consensus that positive mental health and wellbeing is not just the absence of mental illness or disorder (Keyes 2002). There is also a controversy over whether dichotomous diagnoses of psychiatric disorder should be a proper outcome for sociological inquiry, in contrast to scales of distress symptoms (Kessler 2002; Mirowsky and Ross 2002).

Research diagnostic measures of mental disorder are controversial on many dimensions. Wakefield (1999) criticized the diagnostic measures used in the Epidemiological Catchment Area and National Comorbodity Studies for overestimating the prevalence of lifetime mental disorder in the United States. The NCS estimated that one-half of all Americans will suffer from a mental disorder over their lifetime (Kessler et al. 1994). A recent reanalysis of the NCS (Narrow et al. 2002), applying a standard of clinical seriousness based on other questions available in the survey, reduced the lifetime prevalence estimates significantly to 32 percent lifetime prevalence.

Another issue of controversy is whether a dichotomous outcome measure of disorder, one either has the disorder or not, misses levels of distress or poor social functioning that indicate considerable mental suffering (Kessler 2002; Mirowsky and Ross 2002). Persistent or recurring symptoms of sleeplessness, fatigue, sadness, loneliness, lack of appetite, and loss of interest in things in response to chronic stressors or unexpected life events can be unpleasant and disabling even if the sufferer does not show all of the symptoms of depression required for a diagnosis. The high threshold required for a diagnosis of disorder may understate emotional responses to events in the population at large. Whereas mental disorders may be relatively uncommon, symptoms of distress in response to life events are commonly observed and may indicate the presence of social dysfunction and strain in ways that surveys of mental disorders do not.

Measures of stressor exposure are particularly problematic in the sociology of mental health (Wheaton 1999). A complicating factor is that other mental health disciplines enforce higher standards of precision in measurement than does sociology. In addition, the majority of studies using stressor exposure measures do not account for any interaction between combinations of particular types of stressors. Applying the life course perspective model on mental health would ultimately require more sophisticated measures on how stressors combine and interact across time.

Both the biomedical and sociological streams of research on stress processes share an interest in environmental triggers of distress (Selye 1956). Following Selye, early stress researchers applied Selye’s assumption that all environmental threats activated the same or similar physiological response, using sums of exposures to different types of stressful events (Turner and Wheaton 1995). Almost immediately, sociologists and other social researchers modified this assumption, finding that more explicit and comprehensive measurement of the characteristics of stressors often increased the amount of variance explained in the mental health outcome. These measures included the estimated average “magnitude of change” scores in Social Readjustment Rating Scale (the SRRS: Holmes and Rahe 1967) and the Psychiatric Epidemiology Research Interview for Life Events (the PERI; Dohrenwend et al. 1978). Furthermore, it became clear that other characteristics of stressors, such as their type, timing, duration, severity, unexpectedness, controllability and impacts on other aspects of life make significant contributions to the stress response and mental health outcome (e.g., Brown and Harris 1978, 1989; Pearlin and Schooler 1978; Wethington, Brown, and Kessler 1995).

The stress exposure model is evolving to model the dynamic, continuous adaptation to stressors over time (e.g., Heckhausen and Schulz 1995; Lazarus 1999; Folkman and Moskowitz 2004). Sociologists have developed measures of chronic stress exposure (Pearlin and Schooler 1978) and exposure to stressors and hassles on a daily basis (Almeida, Wethington, and Kessler 2002). Researchers debate the relative reliability and validity of self-report checklist and interview measures of life events that include detailed probes that enable investigators to rate the severity of life events (Wheaton 1999). Most recently, psychologists have contributed to understanding variations in the relationships of different types of stressors (social loss vs. trauma and chronic vs. acute stressor exposure), to immune system function and cortisol activity (e.g., Dickerson and Kemeny 2004; Segerstrom and Miller 2004). Sociologists are now considering the potential for using measures of physiological activity (e.g., cortisol measurement) in their studies (Shanahan et al. 2003).

Applying the life course perspective to studying mental disorders and health over time has led to concern about the reliability and validity of retrospective measures of stressor exposure (Wethington et al. 1995; Wheaton 1999). Empirical research on memory for life events over a relatively short recall period is reassuring; most severe events can be recalled quite well over a 12-month retrospective period (Kessler and Wethington 1991). Serious concerns remain about longer retrospective recall periods. This concern is partially mitigated by the development of life history calendar methods, visual memory aids that can be used in interviews to enhance memory for life events (Freedman et al. 1988).

Despite the complexity of measurement, sociologists have pioneered the study of psychiatric sociology, or the epidemiology of mental disorders. The recent advances of measurement in the ECA and NCS studies have produced measures of outcomes that are scientifically accepted across disciplines (Cockerham 2005). These studies have also provided critical data on the use of mental health services by those who suffer from significant disorders and have had a major influence on other fields of study. The major epidemiological research questions have focused around the distribution of mental disorders and illnesses by social factors, including gender, socioeconomic status, marital status, race, and ethnicity. There is some, but more limited work, on factors such as ethnicity, migration, and location.

There is dispute whether the overall rate of mental disorders and illnesses differs by gender. The consensus before the publication of national data from the NCS was that men and women did not differ overall in rates of mental disorders; rather, different types of disorders are distributed differently. Women are more likely to report depressed affect and depressive disorders. Men, in turn, are more likely to report alcohol and drug disorders, violent behavior, and other indicators of acting out. Major psychoses such as schizophrenia and bipolar disorder are not distributed unequally by gender. There is now accumulating evidence that women are also more likely to report anxiety disorders (Kessler et al. 1994, 2005), which would mean that women are overall more likely to have mental disorders. Although there is continuing interest among biological and medical scientists to find a biological cause for women’s higher rates of some disorders, particularly depression, among sociologists social cause explanations still hold sway (e.g., Rosenfield 1999).

One of the most consistent findings in the epidemiology of mental disorders is that those of lower socioeconomic status are more likely to develop mental disorders (Cockerham 2005; Gallagher 2002). This general finding was confirmed by the NCS (Kessler and Zhao 1999). There is evidence, however, that those of higher statuses are more likely to suffer from affective disorders; the overrepresentation of mental disorders is due to higher rates of schizophrenia and some personality disorders among those of lower socioeconomic status.

Among sociologists of mental health, social causation theories continue to dominate, but more attention is being given to selection processes, especially the impact of mental disorders on upward economic mobility (e.g., Miech et al. 1999). Researchers who apply the life course perspective often study selection and economic mobility processes directly, most particularly those processes that affect educational attainment in early adulthood (e.g., McLeod and Kaiser 2004).

There remains considerable controversy in the literature whether members of racial minority groups report higher rates of mental disorder than majority racial groups. Given the relationship of socioeconomic status to mental health and disorders, it is logical to predict that rates of mental disorder in African Americans would be higher than the rates among white Americans because of the average lower socioeconomic status of blacks. Such a pattern would also reflect the additional burden of discrimination and prejudice and the impact such burdens have on mental well-being (Kessler, Mickelson, and Williams 1999).

The pattern of racial and ethnic differences, however, is more complex. For example, an analysis of risk and persistence of mental disorders among U.S. ethnic groups (Breslau et al. 2005) found that Hispanics reported lower lifetime prevalence of substance use disorders than whites, and that blacks reported lower lifetime prevalence of mood (depression or mania), anxiety, and substance use disorders. However, Hispanics were more likely to report persistent mood disorders (defined as recurrence of a past disorder), and blacks were more likely to report persistent mood and anxiety disorders. Research is needed on the factors that mitigate the impact of stressors on mental health of minority groups. Other researchers call for more attention to how mental disorders are measured and diagnosed in African Americans and other minority groups (e.g., Neighbors et al. 2003).

Although there is some evidence that pattern of mental distress by marital status may be changing as cohabitation becomes more socially accepted, the consensus still holds that married people are in better mental health and report fewer mental disorders than those who are not currently married. New research (Umberson and Williams 1999) points to the quality of the marital relationship as critical to mental well-being and health; those in unsatisfying or high-conflict marriages report poor mental health. Divorce is associated with poorer mental health over time, particularly among those who did not initiate the divorce.

Evidence such as that noted above is taken to mean that marriage confers benefits on mental health and may provide some protection against mental illness. Umberson and Williams (1999) note, however, that relatively little research has been done that has pitted the benefits of marriage perspective directly against the alternative social selection perspective that those who have mental disorders are less likely to marry or to remain married. Forthofer et al. (1996) estimated the relationship of age of onset of mental disorder on the probability of subsequent marriage. They found that those who have disorders are less likely to be married and when they marry have a higher risk of divorce. Unfortunately, studies that examine both social causation and social selection perspectives on marital status and mental health remain relatively rare, most likely because of the absence of satisfactory longitudinal data that can be used to address this issue.

One of the tensions in the sociology of mental health and illness is the interdisciplinary orientation of the field. Concepts are freely borrowed along the border of sociology and psychiatry/psychology. Much work is applied, or meant to be applied, to issues of importance to social policy, such as the social costs of untreated mental disorders. The life course perspective (Elder et al. 1996) is changing how research is done and how questions are being asked. New directions in the field include (1) a focus on comorbidity and severity of illness and its social impact, (2) the need for a closer connection between epidemiology and research on mental health services and policy, (3) the press to develop better measures of stressor exposure, (4) demand for more sophisticated measures and analyses of social resources, and (5) and the challenge of biological research on the stress process to the sociological study of mental health.

The study of comorbidity of mental disorders in people has transformed some aspects of the sociology of mental health. First, the documentation of comorbidity has influenced sociologists in the field to accept that mental illness is an objective reality. Second, it has become clear that those who are comorbid for multiple disorders are severely disabled in many important life roles. Their progress through life resembles the life path of “social selection.” Third, the acceptance that mental disorders are real physical entities, and the evidence for comorbidity are challenges to the environmental perspective on mental disorders. It is likely that those who have mental disorders attract or create stressor exposure (Eaton 2001). Thus, one major direction for sociological research in the future might be an emphasis on mental disorders as predictors, rather than outcomes, of social functioning and processes.

When reviewing the state of the sociology of mental health, Horwitz and Scheid (1999) observed that research on the social contexts of mental disorder and research on mental health services do not intersect all that much. They believed that this is because the two fields of research operate on different levels of analysis, one at the individual level and the other at the social or institutional level. A challenge for future research is to connect these two levels of analysis. Research on the social epidemiology of mental health and illness can inform organizations at all levels about the costs of untreated mental disorders to organizations and society in general.

As Wheaton (1999) observed, the social stress model requires considerable new development. This research paper has pointed out a number of methodological difficulties in measuring stressor exposure and the lack of fit between the most widely used measures of stressor exposure and the newly emerging life course perspective. Another advance would come through more detailed studies of how stressors are distributed in the population at large. Does the uneven distribution of stressors in the population “explain” the negative mental health outcomes for some groups? More research is needed in this area, ideally from the life course perspective, using longitudinal samples.

There is also a need for more research on the social distribution of resources that mitigate the impact of environmental challenges and stresses. Reviews of research on social support and social integration (e.g., Berkman and Glass 2000; Cohen et al. 2000; Pillemer et al. 2000) point out deficiencies in current measures of these resources. Do minority groups gain extra protection by asserting their identity and uniqueness? What is the social distribution of protective social resources? Do differences in distribution explain group differences in mental health?

The sociology of mental health is faced with a new challenge from the field of neuroscience. This research tends to be favored by federal funding agencies because of beliefs that neuroscience can lead to the discovery of new cures or therapeutic approaches to mental disorders. Neuroscience and its measurement equipment such as functional magnetic resonance imaging (fMRI) and cortisol sampling have the cachet of basic or “bench” science, while the observational and epidemiological approach of sociology is being portrayed as lower-quality science. However, the rise of neuroscience in research on mental disorders does not necessarily mean that social causes are irrelevant. The power of the new neuroscience of mental disorders is that it assumes there is an interaction between social factors and biological processes (McEwen 2002).

Yet there are serious impediments to the integration of sociological and biological research. One formidable impediment in sociology is the assumption that the biological perspective would reduce the entire stress process to individual differences in physical response, thus making environmental causation moot. Another impediment is that sociologists do not yet fully appreciate how much the biological approach to stress already incorporates measures of social context and stressors in studying adjustment to stressful events and situations (Singer and Ryff 1999). Sociologists (e.g., Pearlin et al. 1981) have long pointed out that the process of adjusting to stressors is a critical component of sociological and social psychological theories of the stress process (Thoits 1995). Thus, another challenge to sociologists of mental health is to incorporate techniques and measures that will powerfully represent the social context in multidisciplinary studies of mental health and mental disorders.

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Current Issues on Research Conducted to Improve Women’s Health

Charalampos siristatidis.

1 Assisted Reproduction Unit, Second Department of Obstetrics and Gynecology, Medical School, National and Kapodistrian University of Athens, Aretaieion Hospital, 76 Vass Sofias, 11528 Athens, Greece

Vasilios Karageorgiou

2 2nd Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Attikon Hospital, 1 Rimini Street, 12642 Athens, Greece; moc.liamtoh@groegaraksav

Paraskevi Vogiatzi

3 Andromed Health & Reproduction Diagnostic Lab, 3 Mesogion Str, 15126 Maroussi, Greece; moc.liamg@iztaigovive

Associated Data

Not applicable.

There are varied lessons to be learned regarding the current methodological approaches to women’s health research. In the present scheme of growing medical literature and inflation of novel results claiming significance, the sheer amount of information can render evidence-based practice confusing. The factors that classically determined the impact of discoveries appear to be losing ground: citation count and publication rates, hierarchy in author lists according to contribution, and a journal’s impact factor. Through a comprehensive literature search on the currently available data from theses, opinion, and original articles and reviews on this topic, we seek to present to clinicians a narrative synthesis of three crucial axes underlying the totality of the research production chain: (a) critical advances in research methodology, (b) the interplay of academy and industry in a trial conduct, and (c) review- and publication-associated developments. We also provide specific recommendations on the study design and conduct, reviewing the processes and dissemination of data and the conclusions and implementation of findings. Overall, clinicians and the public should be aware of the discourse behind the marketing of alleged breakthrough research. Still, multiple initiatives, such as patient review and strict, supervised literature synthesis, have become more widely accepted. The “bottom-up” approach of a wide dissemination of information to clinicians, together with practical incentives for stakeholders with competing interests to collaborate, promise to improve women’s healthcare.

1. Introduction

Women’s health has been at the center of interest and growing concern in the last few decades. As a measurable outcome, it has been studied at the level of mortality [ 1 ], serious morbidity [ 2 ], and nutritional status [ 3 ] and through proven, evidence-based interventions. The implementation of such interventions is essential to guide national and international policies and programs, targeting the achievement of universal coverage of health services. In this respect, conducting the best quality of research (research that provides firm and ethical evidence adhering to the principles of professionalism, transparency, and auditability) with the use of robust methods is mandatory. Towards this goal, the current reality is far from encouraging.

In accordance with scientific literature guidelines and research quality guidelines (e.g., the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)), impact factors, and citation count are considered the norms in current research evaluation modalities. However, recent works in research methodology challenge this simplifying notion [ 4 , 5 , 6 ].

The pitfalls reported are associated with various—albeit specific—“cultural, ethical, operational, regulatory, and infrastructural factors” linked with a lack of adequately trained researchers and subject attrition bias [ 7 ]. As a result, the clinical research environment is more or less inseminated with various types of bias, leading to the discouragement of sponsors. The growing plethora of questionable quality trials and reviews is another issue to consider. “From 14 reports of trials published per day in 1980 [to] 75 trials and 11 systematic reviews of trials per day, a plateau in growth has not yet been reached”, stated a policy forum article reported in 2010 [ 8 ]; additionally, the authors noted that “the staple of medical literature synthesis remains the non-systematic narrative review”, further pointing out the need for freely available simple yet valid answers to most patients’ questions [ 8 ]. In the same context, current data concerning women’s health derived from protocols, full study reports, and participant-level datasets are rarely available to a wide audience. At the same time, selective reporting of methods and results plagues reports. With the reduced quality of information produced, a lot of money has been wasted; subsequently, the existence of all kinds of bias affect the research itself and jeopardize the validity of the findings and, consequently, the care of women [ 9 ].

Issues have been raised by exploring different approaches to evaluate the quality of scientific input to the community. The ultimate goal remains a robust and uniform literature evaluation system adapted to the evolving conduct of studies and to the application of modern tools to re-ensure robust methodology and reporting of data and results. Here, we perform a narrative overview on current issues in study quality assessment regarding clinical medicine. We electronically searched PubMed using the following keywords: “clinical trials”, “meta-analysis”, “IPD”, “sponsor”, “challenges”, “regulatory”, “women’s health”, “evidence-based medicine/trends”, “policy making”, “publishing”, “research methods and practices”, “consumers network”, “bias”, “industry-sponsored trials”, “biomedical bibliographic databases”, and “quality research”, trying to collect data irrespective of type of report and language. Based on this evidence, we propose a combination of interventions at various levels, underlining quality aspects that we consider significant, and other routes of judgments.

2. The “Standard” Factors

Citation count and publication rates in international databases, hierarchy in author lists according to contribution, and the impact factor of the journal are considered important factors in the quality of a study, especially for the “scientific reader” seeking quality information on a specific topic. This has been extensively studied by other workgroups [ 10 , 11 , 12 , 13 ] and represents a justified trend accounting for the prestige of a scientific journal and the publication itself, along with language and availability, and ultimately skewing scientific trends or potentially leaving some important contributions in obscurity. Even though previous works contemplate the importance of the aforementioned factors in the true quality of research studies and publications, all considerations are derived from a common denominator, that is, that the currently used quality standards either for the common user or for greater structures and institutions most probably do not reflect quality but rather popularity. In this context, the citation rate (including self-citation and “negative citing”) and an impact statement on the individual author (a concise summary of the impact of somebody’s career) have been proposed. In addition, other metrics, including altmetrics, bibliometrics, and H-index, combined with updated mathematical models, such as artificial neural networks, might be the tools of the future; these models constitute more accurate tools due to the special characteristics of these “learning through training” processes, resembling the capacity of the brain to learn and judge [ 14 ].

3. The Type of Research Question and Studies

Although multiple outcomes may be reported at once and variability in study designs fluctuates, a primary role belongs to the type of research question explored by a study or publication, which will inevitably determine the methodology to be followed. For example, in past years, there is a disproportionate output of Systematic Reviews (SRs) and meta-analyses from Asian countries produced on a massive scale [ 15 , 16 ] as a means of “publishing in order to publish” with questionable quality and methods. Their numbers are so high that, in some cases, it overtakes original trials. Of note, the use of such studies in the biomedical field was occasional until the 1990s [ 17 ]. Moreover, those from the Cochrane collaboration, the fundamental organization for good quality systematic reviews, are only a small fraction of this output [ 8 ].

With regard to Randomized Controlled Trials (RCTs), suggestions have been made in recent reports on better conduct [ 18 ]: trial protocols should be simple, reproducible, and well organized, with predefined and well-described study populations/participants and should have sound interventions, and representative comparisons and outcomes. Of note, these could be based on the conclusions of previously conducted SRs that often point out issues in quality and methodology of the original trials. Minimal deviations from protocols and a priori specification of useful core outcomes that translate directly to women’s wellbeing are the focus of the CROWN Initiative [ 19 ]. According to the authors, there has been a multi-targeted set of suggestions to “ensure that critical and important outcomes with good measurement properties are incorporated and reported, in advancing the usefulness of research, in informing readers, including guideline and policy developers, who are involved in decision-making, and in improving evidence-based practice”.

A priori description of the outcomes of interest can alleviate the known issues/biases associated with exploratory analyses. A change in outcome, especially in cases where the results do not support the rationale of the study, can mask the original intentions of the authors and can recontextualize the same results in a more positive manner [ 20 ]. Still, an exploratory analysis has a significant role in deducing potentially valuable conjectures for future studies. However, it is central for transparency that the authors explicitly state when this is the case, i.e., when an analysis is conducted post hoc. In order to ease the distinction of post hoc and a priori analyses by SR authors and readers, Dwan et al. (2014) proposed the publication of both protocols and pre-specified analyses [ 21 ].

We cannot anticipate that SRs can retrospectively solve the potential gaps and inconsistencies in the methodology and outcome reporting. For robust answers, research questions must be well defined from the start. However, more elaborate techniques of evidence synthesis can guide future research in more meaningful ways and are becoming more popular. Specifically, prospective and individual patient data meta-analyses (IPDMA) may need to become the norm in literature synthesis [ 22 , 23 ]. A major difficulty in IPDMA is the fact that securing sensitive patient data is a time-consuming task that demands the establishment of mutual trust. Even when representative evidence has been secured, data availability may still affect the pooled evidence. A recent study assessing IPDMA’s treating oncological topics suggested that studies for which they were available differed significantly from studies in which the authors did not share them [ 24 ]. Still, IPDMA is a trustworthy methodology that can assess the effect of patient-level covariates on treatment outcomes or diagnostic accuracy more thoroughly than the standard procedure of a meta-regression used in aggregate-data meta-analyses [ 25 ]. Given the current ethos of openness in clinical trials and common repositories becoming more widespread, IPDMA is likely to become the mainstay of critical synthesis of literature [ 26 ].

Finally, we have to include observational research in an effort to improve women’s health in the context of greater personalization of care and stratified medicine. Such studies have traditionally served as tools for understanding the nature of particular clinical conditions, for determining risk factors and mechanisms of actions, and for identifying potential intervention targets. Their disadvantages associated with methodological issues such as confounds and the fact that they are prone to limited internal validity could be restricted through guidelines such as the strengthening the reporting of observational studies in epidemiology (STROBE) statement [ 27 ].

4. The Inclusion of Young Authors

The encouragement of younger and/or less experienced scientists and ultimately their inclusion in the respective workgroups and in the list of contributors may provide an unexpected topic or question and a clearer view on established research schemes. The productivity of highly cited papers is related to the advanced age of their authors; adversely, better funding opportunities for younger researchers would give them unique chances to build strong productivity [ 28 ]. The advancement of knowledge taught during academic training as well as a higher probability of compliance with robust methods of reporting should encourage the inclusion of younger scientists. Tips and recommendations of young authors and early career scientists have been plenty, including collaborating with researchers within as well as outside their field and/or country, sending their research article to an appropriate journal, and adequately highlighting the novelty and impact of their research [ 29 , 30 ].

Towards this goal, the improvement of the scientific literacy of young scholars is the main step, and this burden falls on to the shoulders of the trainers. There are “uncomfortable truths” in training [ 31 ], but scientific research and the mode of thinking are processes continuously accumulated and must be taught by each director or responsible authority: they should improve the skills and capabilities of young scholars in scientific and technological literacy and in communication and productivity.

5. Quality in Reporting

Reporting quality must be ensured by avoiding bias, such as selective reporting, deliberate or not. Avoiding reporting insignificant data and outcomes could lead to severe distortion in the SR [ 32 ]. Thus, flaws in design, conduct, analysis, or reporting of RCTs can produce bias in the estimates of a treatment effect.

For example, in a large meta-epidemiological study of 1973 RCTs, a lack of blinding was associated with an average 22% exaggeration of treatment effects among trials that reported subjectively assessed outcomes [ 33 ]. This deviation is enough to adversely affect the interpretation of the results and further negatively influences regulatory settings and clinical practice.

Another example involves the evidence base on recent cancer drug approvals. Between 2014 and 2016, a quarter of the relevant studies were not RCTs; of the RCTs, the majority of them did not measure overall survival or quality of life outcomes as primary endpoints, and half of them were judged to be at high risk of bias; the authors’ judgments changed for a fifth of them when they relied on information reported in regulatory documents and scientific publications separately [ 34 ].

6. Strict Implementation of Rules in the Peer Review Processes

These processes first appeared in 1655 in a collection of scientific essays by Denis de Sallo in the Journal des Scavans , and almost 100 years later (1731), their implementation became a standard of practice by almost all biomedical journals [ 35 ]. Maintaining the quality and scientific integrity of publications; evaluating for competence, significance, and originality; and ensuring internal and external validity of submissions are crucial points. Similarly, the appropriate selection and training of reviewers to provide quality and specialized reviews without bias is an essential part of the process [ 36 ].

7. Sponsorship

Ethical and other issues surrounding sponsorships, to ensure credibility of a study, have been addressed in the past. One of the main sources of funding remains the industry [ 37 ]. Indeed, sponsored clinical research has always been questioned, influenced by reports of selective or biased disclosure of research results, ghostwriting and guest authorship, and inaccurate or incomplete reporting of potential conflicts of interest [ 38 ]. Although these may be a scarce incidence nowadays, active monitoring in funded studies should be implemented throughout in order to eliminate this possibility or any other conflicts of interest. An alarming analysis of 319 trials indicated that only a small minority (three out of 182 funded trials) were funded by multiple sponsors with competing interests. The presence of industry funding also almost tripled (OR = 2.8, 95% CI: 1.6, 4.7) the possibility of a study having reported favorable findings [ 39 ]. Furthermore, registered study protocols that announced funding were less likely to be published after their completion (non-publication rate: 32% vs. 18% [ 40 ].

8. Change in the Notion of Publishing

The change in notion and perception of the impact of outcomes is perhaps the most important part of the improvement in research conduct and implementation. This can be achieved through differentiation and modern adaptation of our scientific culture fighting inner and external incentives. Every scientific input should target a wider human benefit. A change in notion and incentives in publishing is crucial, from the level of the investigator aiming to publish/individual behaviors up to the social forces that provide affordances and incentives for those behaviors [ 41 , 42 , 43 ].

In a specific area of research, a clinical evaluation should precede publication in order to ensure relevance. A dramatic example is the scientific literature demonstrating an overload of various biomarkers for various diseases in which only a few of them have been confirmed by subsequent research and few have entered routine clinical practice [ 44 ]. In addition, biomarkers should also be judged on the grounds of cost-effectiveness and incremental net benefit [ 45 ]. Multiple indices may have comparable diagnostic accuracy, but their cost, an unavoidable concern in public health, may differ significantly.

Therefore, the selection of information to be published should be conducted on safer grounds and should be adequately supported by the authors, based on our knowledge on the scheme to date, and importantly, a summary of previous attempts should note the effective interventions and provide a concluding remark for the scientist through a good quality review.

9. Patient’s Contribution to Evaluation and Sex/Gender Analyses

In the era of evidence-based medicine, feedback from the recipients of healthcare development is gaining more importance and platforms for opinion exchange between patients and investigators have been established. This has already been implemented by the Cochrane Collaboration, where patient review is an integral part of the SR publication process and plain language summaries target a nontechnical audience. This process could be adopted as standard practice if accordingly modified. If the patient review, for example, is to be widely implemented in other journals, it would constitute a potentially radical paradigm shift that aims to solidify the review process. Of course, technical difficulties, such as acknowledgement and incentives for patients participating in review processes, are fields where further developments will enhance this policy [ 46 ].

It has been noted that the women population represents an “unequal majority” in health and health care. It is also well established that women’s health needs are dissimilar from those of men, resulting from the fact that both the woman’s body and brain functions differ critically from a man’s and that she reacts differently to even the same stimuli, such as medications or environmental events. It is indicative that, even though a large proportion of study protocols included women, only 3% of them planned an analytical approach for quantifying sex differences [ 47 ]; similarly, a recent report on therapies for atrial fibrillation concluded that the sex-specific reporting in trials comparing them was extremely low [ 48 ]. As a result, women have not received an ideal “personalized” health care, in many cases, so far. Thus, a specific design for studies on women’s health should be required.

There are several examples in the history of women’s health research where the contribution of the consumer women’s health movement in promoting research in women’s interests was critical. One of them concerned the collaborations between consumer groups and researchers in obtaining funding in the U.S. and France for a follow-up on a cohort of diethylstilboestrol-exposed people when the drug was discovered to be a transplacental carcinogen in pregnancy in 1971.

Another important issue is the nonavailability of sex/gender data from primary studies and consequently from SRs, which are the main tools to provide the necessary evidence for the formation of relevant policies [ 49 ]: the authors stated that even “Cochrane and the Campbell Collaboration have no specific policy on the reporting of sex/gender in systematic reviews, although Cochrane has endorsed the SAGER guidelines developed by the European Association of Science Editors” [ 50 ]. In their review, they found that the Methods sections of these collaborations included the most reports on sex/gender in both Campbell (50.8%) and Cochrane (83.1%) reviews, but the majority of these were descriptive considerations of sex/gender. They also reported that 62% of Campbell and 86% of Cochrane reviews did not report sex/gender in the abstract but included sex/gender considerations in a later section. A previous study on the subject reported that almost half of SRs described the sex/gender of the included populations but only 4% assessed sex/gender differences or included sex/gender when making judgments on the implications of the evidence [ 51 ].

10. An Improvement in the Dissemination of Studies

Despite advances in the dissemination of study information, half of health-related studies remain unpublished [ 52 ]. Problems in the publishing scheme in the selection of studies that appear to have a higher impact or that come from a respectable institution can lead to biased publishing. At the extreme, unsafe, ineffective, or even harmful interventions may enter clinical practice, as was the case with hormone replacement therapy [ 53 ]. In some instances, even a shift in healthcare resource allocation is reported [ 9 ]. It is standard practice in critical readings of literature to evaluate publication bias. This method attempts to address, with controversial success, precisely the unfortunate keenness of editors to promote positive results that imply novelty. A classic example of this inflation of positive and supposedly important results is the 2012 study by Fanelli [ 54 ], in which studies classified as related to clinical medicine showed a gradual increase in reporting positive findings. The author criticized the efficacy of measures taken to attenuate publication bias, e.g., protocol registration.

On the other hand, a respectable amount of research is published in other languages and not indexed in U.S. National Library of Medicine [ 55 ], while their quality remains controversial [ 56 ]; the authors of the above studies stated that peer review processes need to be improved through guidelines aiming to identify the authenticity of the studies.

The bulk of peer reviews remain a voluntary occupation, with the main motivation being recognition by peers. In addition, statistical review, a time-consuming process, is not performed in all published research. This process can be accelerated by practices that promote data and code sharing. It is also suggested that, even when papers are retracted, this could have been avoided with the simple measure of an active data sharing policy [ 57 ].

11. Role of the Stakeholders and Foundations

For the stakeholders and collaborative systems, a more energetic role is required in ensuring the conduct of multicenter massive-trials with increased clinical relevance. The main problem in the conduct of research is the lowered clinical value of the results from small sample sizes, even in RCTs. Mathematical models have been developed to predict sample sizes corresponding to the clinical value of the outcomes, while patient data from databases could easily increase the sample size of trials at much lower costs. Such paradigms could include the Health Care Systems Research Collaboratory and the Patient-Centered Outcomes Research Network (PCORnet) [ 58 ]. Also, new levels of patient engagement can raise the possibility of improving clinical outcomes on health. Involving multiple stakeholders (with potentially conflicting interests) in shared conversations on research has been proposed [ 59 ].

New foundations should be placed in research by focusing on the improvement of quality, such as NIH and PCORI [ 60 ]. The Cochrane Collaboration represents one of the very few large-scale initiatives in this context; importantly, both conduct high quality reviews, and participant education at all levels are based mostly from volunteers who care about science and high-quality evidence.

12. Cooperation of All Forces: The Role of Industry/Funding

The central point of problem is funding. USA-affiliated industry-funded trials and related activities represent more than 5% of US healthcare expenditure, with approximately $70 billion in commercial and $40 billion in governmental and non-profit funding annually. The NIH invests $41.7 billion annually in medical research: 80 percent is awarded for extramural research, through 50,000 competitive grants to more than 300,000 researchers at more than 2500 universities, medical schools, and other research institutions [ 61 ]. Concerns have been raised that this approach appears inefficient for how biomedical research is chosen, designed, regulated, financed, managed, disseminated, and reported [ 62 , 63 , 64 ].

The scheme, however, has been shifting in favor of Asian countries. Factors, such as ease of recruitment, population, and various epidemiological factors (e.g., increased incidence of infectious disease) have contributed positively to an inflation of local clinical trials [ 7 ]. Severe accusations regarding clinical data management have been raised, although the magnitude of the problem cannot be safely evaluated [ 65 ]. This unavoidably hinders the validity and future usefulness of these results despite initial enthusiasm from editors and the industry.

Economic forces are important, and ultimately, the industry seeks to maximize profit by providing new products and services to the medical market [ 66 ]. In industry-funded clinical research, intentional and unintentional commercial motives can control the study design and comparators. Governmental involvement [ 66 ] has an important role in distributing research funds in areas important for the protection and restoration of human health, even when the prospects for commercial profit are poor or nonexistent. The recruitment of specialized and qualified professionals should set higher standards of rigor when they are involved in commercial or unavoidably conflicted relationships and to disseminate the resources evenly, especially when nowadays these are scarce.

Funders and academic institutions are responsible for the moral status, as research usually initiates from there and determines any kind of shift in the process. Academics might be judged on the methodological rigor and full dissemination of their research, the quality of their reports, and the reproducibility of their findings. Previous reports suggest ways to increase the relevance and to optimize resource allocation in biomedical research, indicating how resource allocation should be conducted, along with revisions in the appropriateness of research design, methods, and analysis, with efficient research regulation and management fully accessible information, promoting unbiased and usable reports. Additionally, motivation must be given to authors to share their data [ 67 ], as has been performed in the field of genetics [ 68 ]. Of note, synthesis of evidence on the meta-epidemiological level cannot always confidently provide answers to practical clinical questions [ 69 ].

Compromised ethics should be traced and removed from independent research and academia, while journals should on no occasion put profit and publicity above quality. The solution to this lies on the progressive refinement of methods and improvement of the objective and controlled processes.

13. Training

Essential training and interprofessional learning of clinicians and other hands-on scientists in the medical field are an absolute must. There is a growing need to improve their scientific insight and judgment. Reviewers should learn how to apply an unbiased critical thinking and evaluation of the methods explored, of the study questions, and of the resulting impact towards good clinical practice and human welfare. This not only applies to organizational refinement by the Academic Institutes and Publishing Organizations but also to the scientists themselves to obtain the drive to train, along with methodologists and statisticians, so that specialization and knowledge is shared and every contributor works soundly towards a common cause.

14. Conclusions

Research is a solid foundation for the progression of sciences, and the key importance in maintaining the evolution of knowledge is “contributing and sharing”, but this has to be performed adequately. Although there are several criteria and controlled circumstances under which new data and overviews of data are published, research and publishing methods require continuous readjustments and modifications to ensure quality. An overview of the published literature on women’s health and its relevant subtopics is an excellent paradigm on a crucial field of the different types of research and publications that one may encounter but also an example of the vast variability in information available, not only in terms of results but also in terms of design, analysis, quality of information, and implementation of results. In clinical practice, it is imperative to assess information collectively a researcher, medical expert, funder, reviewer, and patient, and this should encourage the improvement of evidence-based patient management.

This review aimed to present the major nodal points of quality and to propose a combination of interventions at various levels, along with other routes of judgement. We also sought to address potential flaws and pitfalls in research conduct and to provide recommendations upon improvement of study designs/methods and scientific reporting to promote publication quality and stricter criteria for release with support from the appropriate structures. A summary of recommendations towards evidence implementation as presented in Table 1 could comprise valuable guidance to both the health experts and the health service recipients to which these standards are quality criteria. A meticulous study design that promotes the transparency of methods and potential conflicts allows a clear distinction of the pathologies and targeted groups and that provides substantial scientific background should be pursued by both researchers and readers. Robust implementation of the pre-stated methods and approaches of analysis, with active participation of collective fronts tied to the subject, should allow quality output to be published and should add value to the findings. Patient-first and common welfare should be considered throughout in conjunction with supporting and providing evidence on robust outcomes for the improvement of healthcare, that may be facilitated by healthy and network collaborations.

Summary of the recommendations for the steps towards evidence implementation.

How these recommendations should be accounted for, evaluated, and implemented relies on the individual discretion of the reader, the scientist, the author, or any entity affiliated with a publishing organization and should be customized to be applied individually for each specialized academic and scientific field but also tailored across continents and countries. The latter is derived from the realization that research conduct, funding, and even the monitoring authorities of clinical studies rely on nonuniform procedures among countries and unions and conforms to different legal frameworks across countries. Nevertheless, a core of actions, precautions, and a quality exemplar of golden standards should be constructed and widely applied to meet the standards that describe a representative scientific contribution, for example, uniform, widely accepted, and practiced standards through policies, guidelines, and rules on a national and/or international level created either by in-country legislation or by scientific entities; allocation of the resources for their implementation; and mechanisms of control for their application and adherence by all.

In conclusion, multiple steps throughout the long and costly process of trial conduct are prone to bias. Notably, increasing international competition favors faster and cheaper patient recruitment, conduct, and analysis and, in turn, produces questionable research. Literature synthesis through SRs and/or meta-analysis has a primarily retrospective role that guides future research and sheds light on arguable topics but cannot erase the wrongdoings of primary studies, which are often concealed. The “bottom-up” approach of a wide dissemination of information to clinicians, together with practical incentives for stakeholders with competing interests to collaborate, promise to improve women’s healthcare.

Author Contributions

C.S. conceived and designed the study and prepared the manuscript. P.V. and V.K. contributed to the design and reporting of the research. All authors approved the final version of the manuscript.

This research received no external funding. The APC was funded by the first author.

Informed Consent Statement

Not applicable due to the nature of this study.

Data Availability Statement

Conflicts of interest.

The authors have no conflicts of interest.

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