research methodology in sip report

Cookies on GOV.UK

We use some essential cookies to make this website work.

We’d like to set additional cookies to understand how you use GOV.UK, remember your settings and improve government services.

We also use cookies set by other sites to help us deliver content from their services.

You have accepted additional cookies. You can change your cookie settings at any time.

You have rejected additional cookies. You can change your cookie settings at any time.

Research report: heritage sector statistics methodology

A feasibility study and preliminary framework for an alternative heritage sector statistics methodology.

A Feasibility Study and Preliminary Framework for an alternative Heritage Sector statistics methodology

PDF , 867 KB , 59 pages

The Department for Culture, Media & Sport commissioned Alma Economics to carry out a feasibility study of different approaches that could be used to produce a single reliable estimate of the economic contribution of heritage organisations to the UK economy.

The report considers 4 approaches:

• dynamic mapping
• ‘SIC-SOC’ mapping
• satellite accounts

The research assesses these approaches against 6 criteria (coverage, disaggregation, robustness,  feasibility, replicability, comparability) and sets out recommendations for improving economic estimates of the heritage sector.

Updates to this page

PDF version of report added

First published.

Sign up for emails or print this page

Is this page useful.

• Yes this page is useful
• No this page is not useful

Help us improve GOV.UK

Don’t include personal or financial information like your National Insurance number or credit card details.

To help us improve GOV.UK, we’d like to know more about your visit today. Please fill in this survey (opens in a new tab) .

• Open access
• Published: 25 August 2024

Modelling efficiency in primary healthcare using the DEA methodology: an empirical analysis in a healthcare district

• Silvia González-de-Julián   ORCID: orcid.org/0000-0003-0274-6060 1 ,
• David Vivas-Consuelo   ORCID: orcid.org/0000-0003-2945-7525 1 &
• Isabel Barrachina-Martínez   ORCID: orcid.org/0000-0003-3710-1190 1  

BMC Health Services Research volume  24 , Article number:  982 ( 2024 ) Cite this article

133 Accesses

Metrics details

Primary healthcare management efficiency conditions the functioning of specialized care and has a direct impact on the outcomes of the health system and its sustainability. The objective of this research is to develop models to evaluate the efficiency, including health outcomes, of the primary healthcare centres (PHC) of the Clínico – La Malvarrosa Health District in Valencia.

To evaluate efficiency, Data Envelopment Analysis (DEA) was used with output orientation and variable returns to scale, with panel data from the years 2015 to 2019. In rates per 10,000 inhabitants, the inputs are: medical and nursing staff and pharmacy cost. The outputs are: number of consultations, hospital emergencies, referrals, avoidable hospitalisations, avoidable mortality and pharmaceutical prescription efficiency. As exogenous variables: the percentage of population over 65 years old, over 80 and case-mix. Three models were developed, all of them with the same inputs and different combinations of outputs related to: healthcare activity, outcomes, and both, in order to study the influence of the different approaches on efficiency. Each model is analysed both without exogenous variables and with each of them.

The efficiency results vary depending on the model used, although certain PHCs are always on, or very close to, the efficient frontier, while others are always inefficient. When healthcare activity outputs are considered, efficiency scores improve and the number of efficient PHCs increases. However, in general, the PHC score decreases throughout the evaluated period. This decrease is more pronounced when only activity outputs are included.

Conclusions

DEA allows the inefficiencies of PHCs to be analysed and the efficient ones are clearly distinguished from the inefficient, although different efficiency scores are obtained depending on the model used. Evaluation can be according to healthcare activity, health outcomes or both, making it necessary to identify the expected objectives of the PHCs, as the perspective of the analysis influences the results.

Peer Review reports

In 2000, an upward trend in health spending began, driven by the progressive ageing of the population, a growing demand for services by patients and the incorporation of highly advanced and increasingly expensive technologies. While the economy in general grew by 2.8% between 2000 and 2015, health spending increased by 4% [ 1 ], with no foreseeable changes in this trend for the coming years.

In 2020, health spending in developed countries approached 9.7% of GDP [ 2 ]. In Spain, at 122,800 million euros, it represented 10.7% of the GDP [ 3 ], below the European Union, at 10.9%. Health spending per capita in Spain, at 2,588 euros, remains very far from the European Union average, which stands at 3,159 euros [ 4 ].

The problem becomes greater when this growth in health spending is not accompanied by an improvement in health outcomes for the population [ 5 ]. In fact, one of the main threats facing the healthcare sector, especially in developing countries, is inefficient management in terms of optimizing resources [ 6 ].

Among the main objectives of the Spanish National Health System is the promotion of health in this environment of high healthcare expenditure and progressive increase [ 7 ], which makes it necessary to adopt containment measures to avoid a reduction in the quality of healthcare services [ 8 ].

Since the mid-1980s, a relatively powerful primary healthcare structure has been developed in Spain, which has always ranked well in international comparisons. However, in a process that has been going on for years, and caused by various factors, primary healthcare has presented problems, some of an acute nature, that have given rise to social concern [ 9 ]. In this sense, the recent pandemic caused by SARS-CoV-2 has revealed many of the weaknesses of both European and global healthcare systems, as well as the need to introduce changes in organizations and give primary healthcare the importance it deserves and that it has been losing [ 10 , 11 , 12 , 13 ]. In some European countries such as Spain, the United Kingdom and Portugal, the primary healthcare network constitutes a fundamental pillar that has supported the main virus containment measures [ 14 ].

Therefore, the evaluation of the efficiency of primary healthcare services is essential to detect the set of varied problems that affect the ability to offer high-quality services to the population, within the limitations of health spending. Furthermore, evaluation and analysis allows for better distribution and use of healthcare resources.

In this sense, frontier estimation methods that measure the inefficiency of an organization as the distance between a frontier generated by best practices and the actual performance of the units evaluated have been widely used in economic studies on productivity and technical efficiency in many areas: hospital costs, electrical energy, fishing and agriculture, manufacturing industry, public provision of transport or education services [ 15 ]. Their development has significantly advanced the practice of efficiency measurement in healthcare [ 16 ], although most studies focus on measuring the efficiency of healthcare and do not consider the results for care quality and the impact on the health of the population.

Within the frontier estimation methods for measuring the efficiency of service organizations, there are two groups: parametric models and non-parametric models. Both methodologies aim to evaluate production units using productivity indicators, which provide measurements that characterize the operations of the analysed units [ 17 ]. Every estimate using parametric functions has a defined mathematical form that is not always easy to identify [ 18 ]. The stochastic frontier is the most used parametric approach, as it assumes that it is not possible to completely specify the function, allowing the existence of error or random noise which is caused by exogenous factors outside the control of the managers [ 19 ].

Within the non-parametric models, one of the most used is data envelopment analysis (DEA), since it allows evaluation of the relative efficiency of decision making units (DMUs) by creating a production frontier using the best practice within the observed data.

One of the limitations of DEA methodology is that the number of efficiency-determining variables that can be introduced into the models depends on the number of DMUs considered in the analysis [ 20 ]. In order to avoid this dimensionality problem, when the ratio between the number of observations and the number of variables (inputs + outputs) is very small, as in our case, a panel data that includes data from several years is used. This approach groups together all cross sections, forming a single intertemporal data set that uses and treats separately all observations or DMUs from all the periods included in the analysis. In this way, each DMU is treated as if it were a different unit in each of the reference periods, which allows a unit in a specific period to compare its own performance for multiple years or periods, as well as with the performance of the other units, as well as to discriminate between efficient units, to provide greater robustness to the data, and to reduce the problem of studies with small samples [ 21 , 22 ].

When using this approach, it is implicitly assumed that there are no substantial technological changes throughout the entire time period analysed, given all units within the panel are compared with each other. This may be questionable when long periods are to be analysed, but for our study, which is only 5 years, it is a perfectly acceptable hypothesis [ 21 , 23 ].

In the review of the methodology of efficiency analyses with DEA, no standard approach is observed in the selection of input and output variables. In a systematic review from 2020, the main inputs were identified as: personnel costs, gross expenditure, referrals and days of hospitalization, as well as prescriptions and research; while the outputs included consultations or visits, registered patients, procedures, treatments and services, prescriptions and research [ 24 ]. Other authors distinguish between desirable and undesirable output variables, such as avoidable hospitalizations, which are variables to minimize [ 25 , 26 ]. Furthermore, it is important to consider the existence of exogenous variables in the analysis [ 27 , 28 ].

The development of information systems has allowed the use of real-world data from the PHCs of a health district of the Valencian Community, a region in the east of Spain, from which are drawn the output, input and exogenous variables that allow the development and comparison of useful models to measure the efficiency of the PHC.

The objective of this study is to develop models to measure the efficiency of the PHCs and to evaluate how the variables introduced in the models influence the efficiency scores, in order to design models that incorporate healthcare quality variables and healthcare outcomes as outputs, as well as developing a methodology that allows their evolution to be monitored.

The study period was from 2015 to 2019. The efficiency analysis includes the 18 PHCs of the Clínico – La Malvarrosa Health District in the Valencian Community, with an approximate covered population of 320,000 inhabitants.

The data are obtained from each of the patients with an assigned medical code as of January 1 for each year included in the study period, previously anonymized. This data is linked to a unique key per centre, used to generate a database in which records are grouped by PHC. The data used to draw up the variables was collected at an individual level and, subsequently, grouped by PHC, given the efficiency analysis is carried out at the PHC level. For grouping by PHC, the administrative grouping is followed. Each patient is assigned a Health Center by the health authority. This is included in the official health card.

The information sources used were: the Population Information System (SIP), the Hospital Minimum Data Set (MDS), the Patient Classification System (SCP-CV), mortality data (Mortality Registry of the General Directorate of Public Health), the centralized data of emergencies and referrals from Alumbra, the electronic outpatient clinical records (ABUCASIS) that encompasses the Ambulatory Information System (SIA), and the Pharmacy Prescriptions Manager (GAIA).

For efficiency analysis, traditional Data Envelopment Analysis (DEA) is used with panel data, output orientation and variable returns to scale (VRS), using the following expression:

Given the characteristics of the Spanish health system, most of the inputs used by health organizations (personnel, equipment, etc.) are not easily controllable from primary health care centres. The use of output-oriented models is justified because the health sector must focus on obtaining the best health results, which is associated with greater technical efficiency [ 29 ].

One of the requirements of DEA methodology is that the DMUs analysed (in this study, the PHCs) must have a similar size to obtain more robust results [ 30 ]. Previous analyses have revealed the heterogeneity of the different PHCs in terms of covered population and, therefore, in terms of the level of healthcare activity and resources used. To remedy this drawback, the original data are transformed into rates per 10,000 inhabitants assigned to each PHC. These rates constitute the variables that were used in the models.

Among the variables available, we selected those that were identified in a previous study as producing the greatest power of discrimination or explanation of the variability. Other studies carried out in this field by other researchers were also considered [ 31 , 32 , 33 ].

The following are considered as variables indicating resources or inputs for each PHC: rate of doctors and nurses, and pharmacy costs. The medical and nursing staff is considered a non-discretionary variable, as the number of professionals in the centres are determined by the Regional Ministry of Health, and the directors of the PHCs have little room for manoeuvre.

As health outcome indicator variables for the outputs, which quantify the activity and quality of health care, the following healthcare activity indicators are included: rate of medical and nursing consultations, rate of referrals and rate of hospital emergencies; and for health outcomes: rate of avoidable hospitalisations, avoidable mortality and an efficiency indicator in pharmaceutical prescription.

Avoidable hospitalisations are obtained from the number of hospital admissions caused by pathologies that should be controlled from the PHCs and that represent a high percentage of the interactions of chronic patients with the healthcare system [ 34 , 35 ]. The indicator of pharmaceutical prescription efficiency is measured through the prior development of other indicators, which consider whether, for a group of pathologies which represent a high percentage of total pharmaceutical expenditure, the most economical and effective drug has been correctly prescribed. This last indicator needs to be prepared in each centre.

Emergencies, avoidable hospitalisations and avoidable mortality are considered undesirable outputs. To introduce them into the program, it is necessary to identify them and carry out a prior transformation so that they are properly imputed in the analysis, especially when using output orientation, which tries to maximize the results for a given level of inputs. Therefore, the original values are replaced with modified values by subtracting a sufficiently high fixed amount (multiplying the result by -1 so that it has a positive value) [ 36 , 37 , 38 , 39 ]. By doing this, the traditional DEA model can be used, although it must be applied with VRS.

Finally, as exogenous variables, the ageing of the population attended by the PHCs is considered at two levels: the percentage of people aged 65 years or older and the percentage of people aged 80 years or older; and the morbidity of the population using case-mix. These are variables that are fixed exogenously and which PHCs cannot control, such as the characteristics of the population assigned to each centre in terms of age and burden of disease, and which determine the greater or lesser activity of the PHCs and the results they obtain, given that attending a younger and healthier population is not the same as serving one that is older and/or with greater morbidity.

The case-mix is obtained from the Clinical Risk Group (CRG) classification of the covered population of each PHC. Based on the CRG, a weight is assigned to each health status according to the clinical complexity of its treatment in economic terms [ 40 ]. In this way, the case-mix is a figure that indicates the burden of disease for patients in the different PHCs.

To treat these exogenous or non-controllable variables, we chose the approach proposed by Banker and Morey (1986) [ 41 , 42 ], an alternative offered by practically all DEA specific software.

Previously, a correlation analysis was carried out between the variables to identify possible multicollinearity.

The DEA models were made with the interactive web application deaR, programmed in R [ 43 ].

Three models were developed which include the 18 PHCs in a panel of 90 observations, each model with four different specifications regarding the exogenous variables introduced (Table  1 ). All models took as inputs: pharmacy cost and the number of doctors and nurses. The variables related to personnel are considered non-discretionary inputs, given the rigidity of the Spanish public health system and the limited capacity that PHCs have to manage the number of doctors or nurses available to them.

Regarding the outputs, the first model was designed to evaluate the healthcare activity of the centres, with the rate of consultations, emergencies and referrals as variables. The rate of emergencies is treated as an undesirable output.

The second model evaluates the health outcomes of the population and uses the following as output variables: avoidable hospitalisations, avoidable mortality, and prescription efficiency. Avoidable hospitalisations and avoidable mortality are treated as undesirable outputs.

In the third and final model, all the output variables are included, so that both the healthcare activity of the centres and the health outcomes are evaluated.

Furthermore, each of these models is carried out both with and without each of the exogenous variables of percentage of people older than 65 years, percentage of people older than 80 years, or case-mix as non-controllable inputs. Thus, a total of 12 different models are evaluated.

Several models were evaluated with the objective of comparing them and selecting the one that most clearly differentiates the efficiency of the PHCs and that considers the outcomes for the health of the population, rather than the healthcare activity.

Our choice of the variables to include was based on previous analysis and on the review of the variables used in other studies, but also considered the limitations of the existing data collection process in the District, with the ultimate goal of obtaining a model that is useful for the best management of healthcare resources.

The correlation analysis between the variables shows high coefficients between the percentage of the population over 65 years of age and over 80 years of age, which are not simultaneously introduced into the models. There is also a high correlation between the case-mix and the pharmacy cost and the consultations rate. A greater morbidity or disease burden (case-mix) implies a greater consumption of medications and usually leads to a greater number of consultations. We included these variables in the analyses, as excluding them may lead to an incomplete representation of the activity carried out by the DMUs.

The descriptive statistical results of the entire sample, that is, 18 PHCs over a period of 5 years (2015–2019), making a total of 90 observations, reveal the existence of significant heterogeneity between PHCs, with very diverse sizes and large variations both in their resource allotment and in their outcomes (Table  2 ).

Figure  1 illustrates the evolution of all these variables throughout the 5-year period analysed. The graph shows a growing trend in personnel, as hiring took place in 2018 (more evident in the case of doctors), and in pharmacy costs, due to the increase in the prescription of medications and, in particular, the incorporation of increasingly expensive drugs.

Regarding the exogenous variables (or non-controllable inputs), no major variations are observed throughout these five years. These are the characteristics of the covered population in terms of population ageing and burden of disease, and it is usual that significant changes do not occur in such short periods of time.

Evolution of the average values over the period studied

Regarding the outputs, an upwards trend is observed in the rate of emergencies and referrals (represented by the scale on the right axis of the graph), while the rate of consultations (left axis) shows a clear drop, especially since 2018. This trend reflects a change in the way of recording some of the tasks that nursing staff usually perform, such as extractions, injectables, dressings, etc., and which are not strictly considered consultations and, in some centres, were not recorded. Since the end of 2018, none of these tasks have been recorded within this indicator, in order to reflect only and homogeneously nursing consultations. The drop in activity caused by this must be taken into account when analysing the results of the DEA models.

The evolution of the variables that measure health outcomes shows that large variations are not produced and the rates of avoidable hospitalisations and mortality are maintained at similar values over five years. Regarding pharmaceutical prescription efficiency, this presents a clear upward trend, indicating that an effort is being made by the centres to prescribe the most appropriate drugs at all times, for example, antibiotics or anti-inflammatories only when they are strictly necessary, or those active ingredients that are recommended in clinical guides for certain pathologies.

Table  3 summarizes the main descriptive statistics (average, standard deviation, maximum and minimum) of all the units evaluated in a dynamic context (90 observations), and for the different models analysed: activity, health outcomes and activity and health outcomes together, with output orientation, and both with and without including any non-controllable variable.

The average values of the estimated efficiency scores with the 3 models (activity, health outcomes and activity + health outcomes) differ considerably, being significantly lower in model 2, where the average value (0.8302) and the lowest minimum value (0.4147) are obtained when no exogenous variable is included. Furthermore, it is in model 2 where the greatest differences between the evaluated units are also observed, as can be verified when analysing the standard deviation.

When the variables that measure the activity of the centres (number of consultations, referrals and emergencies) are taken into account, the scores obtained are higher, and there is less dispersion between the units. This occurs in models 1 and 3, being much more evident in the latter. It is in model 3 where the average score is highest with case-mix (0.9934), and the highest minimum value is obtained (0.9168) when the percentage of population older than 65 is used.

This indicates that some of the PHCs analysed obtain better results when evaluating activity (model 1), while others obtain better results when health outcomes are included (model 2). When all the variables (model 3) are used together, some indicators are compensated by others and improve the global scores of the PHC.

One of the main objectives of this study is to analyse the efficiency of the PHCs in a dynamic context. Therefore, once the global results are analysed, Fig.  2 gives the temporal evolution of the average estimated efficiency scores for each of the 3 models and their different specifications throughout the 2015–2019 period.

Evolution of estimated efficiency scores by year

It can be seen that scores follow a clearly downward trend over time, especially when activity indicators are used (models 1 and 3). It is in 2015 when the evaluated units obtain the highest scores and then descend, especially in 2016 and 2019. This trend lessens slightly when the non-controllable variables are incorporated into the models. In model 2 the trend is not so clear, but a slight descent is still observed from 2016. It must be taken into account that the variables used in this model imply few cases per year in each PHC (around 20 avoidable hospitalisations and 15 cases of avoidable mortality per 10,000 inhabitants) and therefore small variations in these indicators in each of the years can significantly affect the results. On the other hand, the downward evolution of the scores is influenced by an increase in the inputs or resources used (more personnel and higher pharmacy costs) and a decrease in outputs or results, especially in the number of consultations. Thus, the increase in the level of inputs and the reduction in the level of outputs jointly explain the decrease in the efficiency scores for these years.

Below, the scores of the different models are presented individually for each PHC. Table  4 shows the average score for the 5 years analysed for each PHC. A score of 1 (maximum value) is because the PHC obtained the maximum score in the 5 years and it is therefore considered to be totally efficient.

Significant differences can be seen between the results obtained from each of the models, and when some of the exogenous variables are incorporated, the scores improve in general.

In the first model, which evaluates activity, no PHC is efficient in all cases when exogenous variables are not taken into account. PHC14 and PHC18 are efficient when considering the age of the covered population, while PHC13 and PHC16 are totally efficient in the 5 years when case-mix is used. In model 2, where health outcomes are evaluated, it is observed that the scores obtained are lower in general, although PHC17, which was already among the most efficient units in model 1, is fully efficient in all years, both with and without exogenous variables, having a very low rate of avoidable hospitalisations and mortality, the best in the District, and an above average prescription efficiency indicator. All this means it achieves the best scores.

A notable case is PHC18, which has the highest rate of avoidable hospitalisations and avoidable mortality of the District, being considerably above the rest of PHCs. It can be seen it has one of the worst scores in the model without the exogenous variable, but once the burden of disease or age is considered, it becomes efficient.

Finally, in model 3, which evaluates activity and health outcomes together, the scores of all the PHCs improve. In this model, virtually all PHCs obtain very high scores close to 1, in all cases being greater than 0.9, and few differences between units can be observed. This indicates that the PHCs have a quite homogeneous performance when a more global analysis of their activity and outcomes is made. PHCs that obtained higher scores in model 1 compensate to some extent their poorer results in health outcome indicators, while the PHCs that are more efficient in health outcome indicators compensate for not scoring so highly in activity.

To sum up, there are PHCs that are more efficient when evaluating their activity, while others are more efficient when evaluating their health outcomes, and analysing the centres more globally, some aspects compensate for others and the PHCs present a more homogeneous behaviour and obtain good scores in all cases.

Finally, we examined the evolution of the PHCs individually. Table  5 shows the estimated efficiency scores for each PHC for each of the years 2015 to 2019. The average value of the 5 years is also included, which has already been commented on. Only the results of model 2 (outcomes) with output orientation and case-mix are presented, since this is considered the most relevant model, capable of detecting most differences between the PHCs, and which aims to maximize the outcomes in population health - the ultimate goal of the health system - and also takes into account the morbidity of the population served. In the event of the morbidity indicator not being available, it is be possible to replace it with the percentage of population older than 80, given the high correlation that exists between both variables. The results of the rest of the models are presented in the appendix.

In this model (see Table  5 ) significantly lower scores and more differences between the units are observed, although when using case-mix as a non-controllable variable, these differences are mitigated. When examining the evolution of some of the PHCs throughout the period studied, certain interesting aspects can be observed. Many of the PHCs obtain the highest score in 2015 and start to decline later.

In this case, 3 PHCs are efficient in all 5 years: PHC13, PHC17 and PHC18. Other PHCs such as PHC7, PHC9, PHC12, PHC14 and PHC16 are efficient in all years apart from one, which coincided with the years 2018 or 2019, when personnel were hired and, therefore, the level of inputs increased. In this model a downward trend of the efficiency score is not clearly appreciated, although units such as PHC5, PHC11 and PHC15 show a progressive worsening. The case of PHC6 is noteworthy, as it obtained the worst score in 2015, improved considerably in 2016 and 2017 and gave the lowest scores in 2018 and 2019. In addition, it is the PHC with the worst average score of the 5 years.

DEA methodology identifies the efficient PHCs as a whole. After carrying out the analysis, it can be observed that there is no clear combination of inputs and outputs that allows the units to obtain higher results. It is, however, evident that the results of each PHC are largely affected by the characteristics of their covered population. Nevertheless, it can be seen that certain PHCs are always efficient or remain close to the efficient frontier, while others are always inefficient.

The use of outputs that measure activity produces changes in the scores and increases the number of efficient PHCs. In addition, it can be seen that the PHCs underwent, in general, a clear decrease in their efficiency levels throughout the period evaluated. This decrease is more pronounced when only activity variables are included.

This study analyses the efficiency of the 18 PHCs of the Valencia Clínico – La Malvarrosa Health District for a period of 5 years (2015–2019). This is the first efficiency evaluation of primary healthcare conducted in the Valencian Community. We used the technique of data envelopment analysis, a methodology widely used in previous studies in the healthcare sector [ 24 , 31 , 32 , 44 , 45 , 46 ], to estimate the efficiency scores of the PHCs with panel data [ 21 , 23 ] and to compare the efficiency results obtained from three models with different specifications.

Data envelopment analysis, despite its limitations, is shown to be a useful methodology for the evaluation of the efficiency of PHCs and provides very valuable information for managers. It is of interest to compare the PHCs with best practices and determine possible improvements for those that are below that frontier, that is, the resources that should be reduced or the outcomes that must be improved.

Three models have been developed with different specifications to allow evaluation of PHC performing from different perspectives. Although the variables included in the models have a strong influence on the results, we observed that some PHCs are always efficient, or are very close to the efficient frontier, regardless of the model or the year analysed, while other PHCs are always inefficient or systematically obtain the lowest scores.

Those models that include variables for activity (models 1 and 3) and therefore carry out the analysis from a healthcare point of view, show a greater number of efficient units and the estimated efficiency scores achieved by the PHCs are higher, which implies that healthcare activity is taking place homogeneously in most units.

By incorporating variables for quality or healthcare outcomes (avoidable hospitalisations, avoidable mortality and prescription efficiency), more differences between centres are detected (especially in model 2). The introduction of only health outcomes as outputs assigns a lot of weight to these indicators and they discriminate more strongly in the evaluation of efficiency. Therefore, it is important to observe the evolution of the analysis over time to give greater consistency to the observed measurements. This demonstrates the importance of a suitable selection of the variables to be used, as evidenced in other studies carried out [ 24 ].

The inclusion of variables for characteristics of the covered population in terms of ageing and morbidity affects the efficiency results, making their incorporation in the analysis essential, as also demonstrated by other authors [ 25 , 27 ]. It is also observed that the use of one or another of the exogenous or non-controllable variables (age or case-mix) does not substantially modify the results, which makes it easier to replace one variable with the other if one of them is not available.

The treatment of undesirable outputs is a complex issue and different alternative approaches can be found in the literature [ 25 ]. In this study, the simplest approach has been chosen, in which the original values are modified by subtracting a sufficiently high fixed amount (multiplying the result by -1 so that it has a positive value) [ 36 , 37 , 38 , 39 ]. This allows the use of the traditional DEA model, necessarily applying variable returns to scale.

The treatment of exogenous or uncontrollable variables is also complex as there are multiple methodological options, each with advantages and disadvantages. The simplest is that proposed by Banker and Morey (1986) [ 41 ], which is the one used in this work. It does, however, present important limitations, such as the influence on the results of the choice of constant or variable returns to scale, or requiring some restrictive assumptions such as the free availability and convexity of the achievable set, or the estimated efficiency scores may be systematically biased, increasing the potential production targets of inefficient DMUs [ 47 ]. The methodological option that is considered most appropriate by other authors is the non-parametric conditional model proposed by Daraio and Simar [ 48 , 49 ]. This conditional efficiency model was used by Cordero et al. (2016), although its use is still scarce in the healthcare context [ 33 ]. Its main advantage is that it is not necessary to assume the assumption of separability and it allows the effect of exogenous variables to be incorporated directly into the calculation of efficiency scores, conditioning the production process to certain values of these variables. This option, although not used in this work due to its complexity, will be included in future research.

The number of variables that can be used in the models is limited, since to obtain reliable results it is recommended that the total inputs and outputs do not exceed one third of the PHCs analysed (in our case 18 PHCs) [ 50 ]. Once more, this implies a suitable selection of the variables and, in some cases, the prior use of other methodologies. To avoid this problem of dimensionality, as the complete information for the variables was available for the 5 years, panel data methodology was used, which also allowed us to analyse the evolution of efficiency in these units throughout the period. In this way, the results show that there has been a decrease in the efficiency levels of the PHCs over the period studied, especially when including variables for activity. When using variables that measure health outcomes, the worsening is not so evident, and it is possible to identify more differences between the PHCs.

Despite the limitations in the number of variables that can be included in the models, the introduction of correlated variables in the analysis is justified by the need to capture complete information on the performance of the DMUs [ 42 ]. By considering all the relevant dimensions, it is ensured that the DEA model reflects the complexity of the production process more precisely, and by reflecting the operational reality of the DMUs, they show that, in many practical situations, the input and output variables are naturally correlated due to the structure of the production process of the health care sector [ 41 , 51 ]. Excluding correlated variables could lead to an incomplete or distorted representation of the performance of DMUs. Furthermore, the introduction of correlated variables can improve the fit and accuracy of the DEA model by allowing better discrimination between DMUs. Although it increases the dimensionality of the model, it can also provide a more detailed and accurate assessment of performance [ 52 ]. Lastly, in many applied studies, the inclusion of correlated variables is not only common but necessary to capture all relevant dimensions of performance. Case studies in health, education and other sectors show how these variables contribute to a more complete evaluation [ 53 ].

There are still relatively few studies that evaluate efficiency in primary healthcare, especially in Spain. In most of them, the analysis is carried out from the point of view of the activity, given the impossibility on many occasions of accessing indicators that allow the evaluation of the quality of the care provided by the centres. However, not taking these quality indicators into account can end up rewarding, in some way, those centres that have greater activity than others, simply because they are operating with lower quality standards [ 54 ].

Likewise, incorporating other quality variables that are not usually available, such as user satisfaction surveys, or including as exogenous variables the deprivation index of the population assigned to each PHC [ 55 ], or other types of variables such as per capita income or education level [ 56 ], would allow different results to be obtained that more adequately reflect the real activity of the centres and the characteristics of their population. This would contribute to proposing more useful recommendations for the management of the PHC, which in turn would help to achieve a more efficient and higher quality health care. For this, it is also necessary to involve healthcare managers in the analysis, so that their preferences (and the goals they pursue) can be taken into account through the selection of the most appropriate input and output variables [ 57 ].

The efficiency scores found using this methodology only allows comparison within the set of PHCs considered. In this case, no major differences were observed in the scores obtained between the components of the group, especially with models 1 and 3, something that implies that healthcare activity is occurring homogeneously in the majority of units.

In order to establish a unified production frontier and thus achieve holistic comparability between regions within the field of efficiency evaluation, standardization and normalization of the variables used, including the exogenous, would be required, and a single DEA model would be applied for the evaluation. In this way, a meta-frontier of the set of DMUs could be obtained. These strategies would ensure that efficiency assessments are fair, accurate and sufficiently reflect the operating conditions for each region, enabling a valid and useful comparison between different regional contexts.

In this study, the traditional or radial DEA model has been used, which is the most common, although there are other methodological options with different perspectives, such as non-radial efficiency measures that include the Russell index, the additive models or the slack-based efficiency indicators [ 58 ], and which will be explored in future studies.

Data envelopment analysis is shown to be a valuable methodology to evaluate the efficiency of PHCs and is useful as a management tool in terms of resource allocation. It allows the inefficiencies of the PHC to be analysed, although it is necessary to identify the objectives of the centres, since the variables included in the models and the perspective of the analyses influence the results.

It is important that management focus its objectives on improving the health of the population (fewer emergencies, fewer avoidable hospitalisations, lower avoidable mortality) and incorporate variables for healthcare quality and health outcomes, and focus less on the activity (the number of consultations is not so important, but rather that they are necessary), as well as keeping in mind the characteristics of the covered population when performing the analysis.

It is essential to carry out this type of evaluation, since the identification of anomalies in efficiency behaviour can help in the management of primary healthcare centres and provide a better allocation of healthcare resources.

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Abbreviations

Clinical Risk Groups

Data Envelopment Analysis

Decision making units

Primary Healthcare Center

Variable returns to scale

World Health Organization. New Perspectives on Global Health Spending for Universal Health Coverage [Internet]. 2018. http://apps.who.int/bookorders

OECD. Health resources - Health Spending (indicator) [Internet]. OECD Data. 2023 [cited 2023 Jan 21]. https://data.oecd.org/healthres/health-spending.htm

Ministerio de Sanidad. Ministerio de Sanidad - Portal Estadístico del SNS - Sanidad en datos [Internet]. 2023 [cited 2023 Jan 21]. p. 1. https://www.sanidad.gob.es/estadEstudios/sanidadDatos/home.htm

OECD/European Union. Health at a Glance: Europe 2022: State of Health in the EU Cycle [Internet]. OECD Publishing. Paris. 2022. https://doi.org/10.1787/507433b0-en

Ogloblin C. Health care efficiency across countries: a stochastic frontier analysis. Appl Econom Int Dev. 2011;11(1).

Mohammadpour S, Javan-Noughabi J, Vafaee Najar A, Zangeneh M, Yousefi S, Nouhi M et al. Factors affecting the technical efficiency of rural primary health care centers in Hamadan, Iran: data envelopment analysis and Tobit regression. Cost Eff Resour Alloc [Internet]. 2020;18(1):1–8. https://doi.org/10.1186/s12962-020-00249-1

Ministerio de Sanidad SS e I. Monografía en internet [Internet]. 2012. pp. 16–29. http://www.msssi.gob.es/organizacion/sns/docs/sns2012/SNS012__Espanol.pdf

Ministerio de Sanidad. Informe anual del Sistema Nacional de Salud 2020–2021. Inf Estud e Investig 2022 [Internet]. 2019;267. https://www.sanidad.gob.es/estadEstudios/estadisticas/sisInfSanSNS/tablasEstadisticas/InfAnSNS.htm

Rotaeche del Campo R, Gorroñogoitia Iturbe A. Reflexiones sobre la atención primaria del siglo XXI. Atención Primaria Práctica. 2022;4.

Sarti TD, Lazarini WS, Fontenelle LF, Almeida APSC. Qual o papel Da Atenção Primária à Saúde Diante Da pandemia provocada pela COVID-19? Epidemiol E Serv Saude Rev do Sist Unico Saude do Bras. 2020;29(2):e2020166.

Google Scholar  

Saint-Lary O, Gautier S, Le Breton J, Gilberg S, Frappé P, Schuers M, et al. How GPs adapted their practices and organisations at the beginning of COVID-19 outbreak: a French national observational survey. BMJ Open. 2020;10(12):e042119.

Article   PubMed   Google Scholar  

Wynn A, Moore KM. Integration of primary health care and public health during a public health emergency. American Journal of Public Health. Volume 102. American Public Health Association; 2012. p. e9.

Joy M, Mcgagh D, Jones N, Liyanage H, Sherlock J, Parimalanathan V et al. Reorganisation of primary care for older adults during COVID-19: a cross-sectional database study in the UK.

Lim WH, Wong WM. COVID-19: notes from the front line, Singapore’s primary health care perspective. Ann Fam Med. 2020;18(3):259–61.

Article   PubMed   PubMed Central   Google Scholar  

Arsad R, Isa Z, Shaari SNM. Estimating efficiency performance of decision-making unit by using SFA and DEA Method: a cross-sectional data approach. Int J Eng Technol. 2018;7(4):25–31.

Article   Google Scholar  

Mutter RL, Rosko MD, Greene WH, Wilson PW. Translating frontiers into practice: taking the next steps toward improving hospital efficiency. Med Care Res Rev. 2011.

Nepomuceno TCC, Piubello Orsini L, de Carvalho VDH, Poleto T, Leardini C. The core of Healthcare Efficiency: a Comprehensive Bibliometric Review on Frontier Analysis of hospitals. Healthc. 2022;10(7):1–27.

Scippacercola S, Sepe E. Critical comparison of the main methods for the technical efficiency. Electron J Appl Stat Anal. 2016;9(4):760–80.

Martin Martin JJ. López del Amo González M del P. La medida de la eficiencia en las organizaciones sanitarias. Presup y gasto público [Internet]. 2007;49(2007):139–61. http://www.ief.es/documentos/recursos/publicaciones/revistas/presu_gasto_publico/49_medidaeficiencia.pdf

Paradi JC, Sherman HD, Tam FK. Data Envelopment Analysis in the Financial Services Industry [Internet]. Vol. 266. 2018. 379 p. http://link.springer.com/ https://doi.org/10.1007/978-3-319-69725-3

Cordero JM, García-García A, Lau-Cortés E, Polo C. Assessing Panamanian hospitals’ performance with alternative frontier methods. Int Trans Oper Res. 2023;30(1):394–420.

O’Neill L, Rauner M, Heidenberger K, Kraus M. A cross-national comparison and taxonomy of DEA-based hospital efficiency studies. Socioecon Plann Sci. 2008;42(3):158–89.

Siciliani L. Estimating technical efficiency in the hospital sector with panel data: a comparison of parametric and non-parametric techniques. Appl Health Econ Health Policy. 2006;5(2):99–116.

Zakowska I, Godycki-Cwirko M. Data envelopment analysis applications in primary health care: a systematic review. Fam Pract. 2020;37(2):147–53.

PubMed   Google Scholar  

Cordero JM, Alonso-Morán E, Nuño-Solinis R, Orueta JF, Arce RS. Efficiency assessment of primary care providers: a conditional nonparametric approach. Eur J Oper Res. 2015;240(1):235–44.

Cordero Ferrera JM, Cebada EC, Murillo Zamorano LR. The effect of quality and socio-demographic variables on efficiency measures in primary health care. Eur J Heal Econ. 2014;15(3):289–302.

Cordero-Ferrera JM, Crespo-Cebada E, Murillo-Zamorano LR. Measuring technical efficiency in primary health care: the effect of exogenous variables on results. J Med Syst. 2011;35(4):545–54.

González-De-julián S, Barrachina-Martínez I, Vivas-Consuelo D, Bonet-Pla Á, Usó-Talamantes R. Data envelopment analysis applications on primary health care using exogenous variables and health outcomes. Sustain. 2021;13(3):1–18.

Charnes A, Cooper WW, Rhodes E. Measuring efficiency of decision-making units. Eur J Oper Res. 1978;2(6):429–44.

Dyson RG, Allen R, Camanho AS, Podinovski VV, Sarrico CS, Shale EA. Pitfalls and protocols in DEA. Eur J Oper Res. 2001;132(2):245–59.

Caballer-Tarazona M, Moya-Clemente I, Vivas-Consuelo D, Barrachina-Martínez I. A model to measure the efficiency of hospital performance. Math Comput Model. 2010;52(7–8):1095–102.

Pérez-Romero C, Ortega-Díaz MI, Ocaña-Riola R, Martín-Martín JJ. Análisis De La Eficiencia técnica en Los hospitales del Sistema Nacional De Salud español. Gac Sanit. 2017;31(2):108–15.

Cordero JM, Nuño-Solinís R, Orueta JF, Polo C, del Río-Cámara M, Alonso-Morán E. Evaluación De La Eficiencia técnica De La atención primaria pública en el País Vasco, 2010–2013. Gac Sanit. 2016;30(2):104–9.

Angulo-Pueyo E, Ridao-López M, Martínez-Lizaga N, García-Armesto S, Peiró S, Bernal-Delgado E. Factors associated with hospitalisations in chronic conditions deemed avoidable: ecological study in the Spanish healthcare system. BMJ Open. 2017;7(2):1–7.

Caminal J, Starfield B, Sánchez E, Casanova C, Morales M. The role of primary care in preventing ambulatory care sensitive conditions. Eur J Public Health [Internet]. 2004 Sep [cited 2021 Jan 8];14(3):246–51. https://pubmed.ncbi.nlm.nih.gov/15369028/

Pastor JT. Translation invariance in data envelopment analysis: a generalization. Ann Oper Res. 1996;66:93–102.

Seiford LM, Zhu J. Modeling undesirable factors in efficiency evaluation. Eur J Oper Res. 2002;142(1):16–20.

Färe R, Grosskopf S. Modeling undesirable factors in efficiency evaluation: comment. Eur J Oper Res. 2004;157(1):242–5.

Seiford LM, Zhu J. A response to comments on modeling undesirable factors in efficiency evaluation. Eur J Oper Res. 2005;161(2):579–81.

Usó Talamantes R. Análisis y desarrollo de un modelo predictivo del gasto farmacéutico ambulatorio ajustado a morbilidad y riesgo clínico. [Internet]. 2015. https://riunet.upv.es/handle/10251/56149

Banker RD, Morey RC. Efficiency Analysis for Exogenously Fixed Inputs and Outputs. https://doi.org/10.1287/opre.34.4.513 [Internet]. 1986 Aug 1 [cited 2023 Feb 15];34(4):513–21. https://pubsonline.informs.org/doi/abs/

Cooper WW, Seiford LM, Tone K. Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software: Second edition. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software: Second Edition. 2007. 1–490 p.

Benítez R, Coll-Serrano V, Bolós VJ. Dear-shiny: an interactive web app for data envelopment analysis. Sustain. 2021;13(12):1–19.

Amado CA, Santos SP. Challenges for performance assessment and improvement in primary health care: the case of the Portuguese health centres. Health Policy [Internet]. 2009 Jun [cited 2022 Nov 21];91(1):43–56. https://pubmed.ncbi.nlm.nih.gov/19118917/

Kohl S, Schoenfelder J, Fügener A, Brunner JO. The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Heal Care Manag Sci 2018 222 [Internet]. 2018 Feb 24 [cited 2022 Nov 21];22(2):245–86. https://link.springer.com/article/ https://doi.org/10.1007/s10729-018-9436-8

Hollingsworth B, Dawson PJ, Maniadakis N. Efficiency measurement of health care: a review of non-parametric methods and applications. Health Care Manag Sci. 1999;2(3):161–72.

Article   PubMed   CAS   Google Scholar  

Cordero JM, Polo C, Santín D, Sicilia G. Monte-Carlo comparison of conditional nonparametric methods and traditional approaches to include exogenous variables. Pac Econ Rev. 2016;21(4):483–97.

Daraio C, Simar L. Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach. J Prod Anal. 2007;28(1–2):13–32.

Daraio C, Simar L. Introducing environmental variables in nonparametric frontier models: a probabilistic approach. J Prod Anal. 2005;24(1):93–121.

Banker RD, Charnes A, Cooper WW. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Manage Sci [Internet]. 1984 [cited 2022 Nov 21];30(9):1078–92. http://www.jstor.org/stable/2631725

Ozcan YA. Health Care Benchmarking and performance evaluation: an Assessment using data Envelopment Analysis (DEA). Int Ser Oper Res Manage Sci. 2014. 329 p.

Lovell CAK, Pastor JT. Units invariant and translation invariant DEA models. Oper Res Lett. 1995;18(3):147–51.

Zhu J. Quantitative Models for Performance Evaluation and Benchmarking [Internet]. Third edit. International Series in Operations Research & Management Science Introduction. Springer International Publishing; 2014. (International Series in Operations Research & Management Science; vol. 213). http://link.springer.com/ https://doi.org/10.1007/978-3-319-06647-9

Pelone F, Kringos DS, Romaniello A, Archibugi M, Salsiri C, Ricciardi W. Primary Care Efficiency Measurement Using Data Envelopment Analysis: a systematic review. J Med Syst. 2015;39(1).

Colls C, Mias M, García-Altés A. A deprivation index to reform the financing model of primary care in Catalonia (Spain). Gac Sanit [Internet]. 2020 Jan 1 [cited 2021 Apr 12];34(1):44–50. http://www.gacetasanitaria.org/es-un-indice-privacion-reformar-el-articulo-S0213911118302176

Su W, Hou Y, Huang M, Xu J, Du Q, Wang P. Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach. BMC Health Serv Res [Internet]. 2023;23(1):1–19. https://doi.org/10.1186/s12913-023-09979-3

Gouveia MC, Dias LC, Antunes CH. Additive DEA based on MCDA with imprecise information. J Oper Res Soc. 2008;59(1):54–63.

Aparicio J, Cordero JM, Gonzalez M, Lopez-Espin JJ. Using non-radial DEA to assess school efficiency in a cross-country perspective: An empirical analysis of OECD countries. Omega (United Kingdom) [Internet]. 2018;79:9–20. https://doi.org/10.1016/j.omega.2017.07.004

Download references

Acknowledgements

The authors would like to thank Inma Saurí and José Luis Trillo for his helpful assistance in the collection and elaboration of the variables and to Javier Díaz Carnicero for his help in the mathematical models. We would like to thank John Wright for help with English editing.

This research was funded by “Conselleria de Hacienda y Modelo Económico de la Comunitat Valenciana (Spain)”, file number HIECPU/2019/1, in the context of the Project “Desarrollo de un Modelo para el análisis de la eficiencia en las Unidades Básicas de Salud de atención primaria en un departamento de Salud perteneciente al mapa sanitario de la Comunidad Valenciana”.

Author information

Authors and affiliations.

Research Unit for Health Economics and Management, Universitat Politècnica de València, Valencia, 46022, Spain

Silvia González-de-Julián, David Vivas-Consuelo & Isabel Barrachina-Martínez

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, D.V.-C. and I.B.-M.; methodology, D.V.-C. and I.B.-M.; software, S.G.-d.-J.; validation, D.V.-C. and I.B.-M.; formal analysis, S.G.-d.-J.; investigation, S.G.-d.-J.; resources, S.G.-d.-J.; data curation, I.B.-M.; writing—original draft preparation, S.G.-d.-J. and I.B.-M.; writing—review and editing, S.G.-d.-J. and I.B.-M.; supervision, D.V.-C.; project administration, I.B.-M. and D.V.-C.; funding acquisition, I.B.-M. and D.V.-C. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Silvia González-de-Julián .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of HOSPITAL CLINICO UNIVERSITARIO DE VALENCIA (protocol code 2020/170 and date of approval 25 June 2020). Informed consent statement was deemed unnecessary according to national regulations, specifically according to the assessment of the Ethics Committee (CEIm) of HOSPITAL CLINICO UNIVERSITARIO DE VALENCIA, and in accordance with the Spanish Biomedical Research Law 14/2007 for observational studies. This study is retrospective and does not contain individual personal information, since the data were obtained from a secondary database with anonymized and dissociated information as stablished by the current legislation at the time of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

González-de-Julián, S., Vivas-Consuelo, D. & Barrachina-Martínez, I. Modelling efficiency in primary healthcare using the DEA methodology: an empirical analysis in a healthcare district. BMC Health Serv Res 24 , 982 (2024). https://doi.org/10.1186/s12913-024-11420-2

Download citation

Received : 04 June 2024

Accepted : 09 August 2024

Published : 25 August 2024

DOI : https://doi.org/10.1186/s12913-024-11420-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Primary healthcare centres
• Health outcomes
• Data envelopment analysis

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

• Data library

New York City Rental Report: Rents Continue To Increase in July 2024

• In July 2024, the median asking rent in New York City registered at $3,421, increasing by $73, or 2.2%, compared with a year ago.
• The median asking rent for 0-2 bedrooms in the city was $3,322, reflecting an increase of $72, or 2.2%, from the previous year, while rent for 3-plus bedroom units declined by $262, or 5.0%, compared with July 2023, reaching $4,996 .
• While the median asking rent in Manhattan continued to decrease at an annual rate of 2.0%, rents in relatively affordable Brooklyn, Queens, and the Bronx continued to rise, indicating stronger demand in more affordable areas.

In July 2024, the median asking rent for all rental properties listed on Realtor.com® in New York City was $3,421. In contrast to the overall declining trend seen across the top 50 markets , the median asking rent in New York City continues to rise annually, increasing by $73, or 2.2%, compared with a year ago. Although New York City was one of the rental markets that saw the steepest rent declines during the COVID-19 pandemic, its median asking rent rebounded to pre-pandemic levels by spring 2022 and has continued to rise annually since then. As of July 2024, the median asking rent in New York City was $413, or 13.7%, higher than the same time in 2019 (pre-pandemic). 

Figure 1: Rents Continue To Increase in New York City–July 2024

Higher demand seen in affordable smaller units

There was greater demand for smaller rental units with 0-2 bedrooms compared with those with 3 or more bedrooms in New York City. In July 2024, the median asking rent for 0-2 bedrooms in the city was $3,322, marking an increase of $72, or 2.2%, from the previous year. Meanwhile, the median asking rent among larger units with 3-plus bedrooms fell to $4,996, experiencing a year-over-year rent decline of $262, or 5.0%, compared with July 2023.

Figure 2: Rents by Unit Size in New York City–July 2024

Table 1: New York City Rents by Unit Size–July 2024

Higher demand seen in relatively affordable boroughs

In July 2024, the median asking rent for all rental units in Manhattan was $4,489, down $91 or 2.0% from a year ago. It was the 13th consecutive month of annual declines, and rent was $362 (-7.5%) below the peak seen in August 2019.

Additionally, in July 2024, Manhattan’s median asking rent was still $171 (-3.7%) lower than its pre-pandemic level, suggesting a relatively lower demand in this most expensive borough, perhaps indicating an ongoing willingness of workers to commute and leverage flexible working arrangements to find housing affordability, as Realtor.com previously found in the for-sale market .

In fact, to afford renting a typical home in Manhattan without spending more than 30% of income on housing (including utilities)—the standard measure of affordability—a gross household income of $14,963 per month, or $179,560 per year, is required.  

Unlike the cooling rental market in Manhattan, the three relatively lower-rent boroughs of the Bronx, Brooklyn, and Queens saw rents continue to increase yearly. Among these three, Queens saw the fastest annual rental growth in July, where the median asking rent reached $3,380, up $256 or 8.2% from the same time last year. It was the highest rent level seen in our data history and was $967 (40.1%) higher than five years ago. 

Meanwhile, the median asking rent in the Bronx increased by 7.7%, or $226, to $3,175 from a year ago. It was the second-highest rent level seen since March 2019 and was $1,202 (60.9%) higher than five years ago.

In Brooklyn, the median asking rent increased by 3.5%, or $124, on an annual basis, to $3,718 from a year ago. It was also the highest rent level seen in our data history and was $916 (32.7%) higher than five years ago. 

To afford renting a typical home in these three boroughs while adhering to the 30% rule of thumb, the gross monthly household income required for tenants in Queens, Brooklyn, and the Bronx was $11,267, $12,393, and $10,583, respectively, or annual income of $135,200, $148,720, and $127,000 .

Figure 3: Rents by Borough in New York City–July 2024

Table 2: Rents by Borough in New York City 

Note: Data for Staten Island is currently under review.

Methodology.

New York City rental data as of July 2024 for all units advertised as for rent on Realtor.com®. Rental units include apartments as well as private rentals (condos, townhomes, single-family homes). We use rental sources that reliably report data each month within New York City and each of its boroughs. Data for Staten Island is currently under review.

Realtor.com began publishing regular monthly rental trends reports for New York City in August 2024 with data history stretching back to March 2019.

Sign up for updates

Join our mailing list to receive the latest data and research.

Along with Stanford news and stories, show me:

• Student information
• Faculty/Staff information

We want to provide announcements, events, leadership messages and resources that are relevant to you. Your selection is stored in a browser cookie which you can remove at any time using “Clear all personalization” below.

A new technology can extract lithium from brines at an estimated cost of under 40% that of today’s dominant extraction method, and at just a fourth of lithium’s current market price. The new technology would also be much more reliable and sustainable in its use of water, chemicals, and land than today’s technology, according to a study published today in Matter by Stanford University researchers.

Global demand for lithium has surged in recent years, driven by the rise of electric vehicles and renewable energy storage. The dominant source of lithium extraction today relies on evaporating brines in huge ponds under the sun for a year or more, leaving behind a lithium-rich solution, after which heavy use of potentially toxic chemicals finishes the job. Water with a high concentration of salts, including lithium, occurs naturally in some lakes, hot springs, and aquifers, and as a byproduct of oil and natural gas operations and of seawater desalination.

The benefits to efficiency and cost innate to our approach make it a promising alternative to current extraction techniques and a potential game changer for the lithium supply chain.” Yi Cui Senior author and professor of materials science and engineering

Many scientists are searching for less expensive and more efficient, reliable, and environmentally friendly lithium extraction methods. These are generally direct lithium extraction that bypasses big evaporation ponds. The new study reports on the results of a new method using an approach known as “redox-couple electrodialysis,” or RCE, along with cost estimates.

“The benefits to efficiency and cost innate to our approach make it a promising alternative to current extraction techniques and a potential game changer for the lithium supply chain,” said Yi Cui , the study’s senior author and a professor of materials science and engineering in the School of Engineering .

The research team estimates its approach costs $3,500 to $4,400 per ton of high-purity lithium hydroxide, which can be converted to battery-grade lithium carbonate inexpensively, compared with costs of about $9,100 per ton for the dominant technology for extracting lithium from brine. The current market price for battery-grade lithium carbonate is almost $15,000 per ton, but a shortage in late 2022 drove the volatile lithium market price to $80,000.

Meeting growing demand

Lithium, so far, has had a critical role in the global transition to sustainable energy. The demand for lithium is expected to rise from approximately half a million metric tons in 2021 to an estimated 3 million to 4 million metric tons by 2030, according to a report by McKinsey & Co. This sharp increase is driven mostly by the rapid adoption of electric vehicles and renewable energy storage systems, both of which rely heavily on batteries.

Related story

Supervolcanoes: A key to America’s electric future?

Traditionally, lithium has been extracted from mined rocks, a method that is even more expensive, energy intensive, and driven by toxic chemicals than brine extraction. As a result, the dominant method for lithium extraction today has switched to evaporating salt-lake brines, though still at high financial and environmental costs. This method is also heavily dependent on specific climatic conditions that limit the number of commercially viable salt lakes, throwing into doubt the lithium industry’s ability to meet rising demand.

The new method from Cui and his team uses electricity to move lithium through a solid-state electrolyte membrane from water with a low lithium concentration to a more concentrated, high-purity solution. Each of a series of cells increases the lithium concentration to a solution from which final chemical isolation is relatively easy. This approach uses less than 10% of the electricity required by current brine extraction technology and has a lithium selectivity of almost 100%, making it very efficient.

“The advantages displayed by our approach over conventional lithium extraction techniques enhance its feasibility in eco-friendly and cost-effective lithium production,” said co-lead author of the study, Rong Xu , a former postdoctoral researcher in Cui’s lab, now a faculty member at Xi'an Jiaotong University in China. “Eventually, we hope our method will significantly advance electrified transportation and the ability to store renewable energy.”

Cost and environmental benefits

The study includes a brief techno-economic analysis comparing the costs of current lithium extraction with those of the RCE approach. The new method is expected to be relatively inexpensive due mostly to lower capital costs. It eliminates the need for large-scale solar evaporation ponds, which are expensive to build and maintain. The new method’s use of significantly less electricity, water, and chemical agents – aside from the sustainability benefits – further lowers costs.

By avoiding the extensive land use and water consumption of traditional methods, the RCE approach also reduces the ecological footprint of lithium production.

The RCE method works with a variety of saline waters, including those with varying concentrations of lithium, sodium, and potassium. Study experiments showed that the new technology could extract lithium, for example, from wastewater resulting from oil production. It could potentially be used to extract lithium from seawater, which has lower lithium concentrations than brines. Lithium extraction from seawater using conventional methods is not commercially viable today.

“Direct lithium extraction techniques like ours have been in development for a while. The main contending technologies to date have significant drawbacks, like the inability to operate continuously, high energetic costs, or relatively low efficiency,” said Ge Zhang , a Stanford postdoctoral scholar and co-author of the study. “Our method seems to have none of these drawbacks. Its continuous operation could contribute to a more reliable lithium supply and calm the volatile lithium market.”

Looking ahead

The scalability of the RCE method is also promising. In experiments where the scale of the device was increased fourfold, the RCE method continued to perform well, with both energy efficiency and lithium selectivity remaining very high.

“This suggests that the method could be applied on an industrial scale, making it a viable alternative to current extraction technologies,” said Cui.

Nevertheless, the study highlights some areas for further research and development. The researchers experimented with two versions of their method. One extracted the lithium more quickly and used more electricity. The other was slower and used less electricity. The slower extraction resulted in lower costs and a more stable membrane for extracting the lithium continuously and for a long time, compared with the faster extraction. Under high current densities and faster water flow, the membranes degraded, leading to reduced efficiency over time. Even though this was not evident in the slower extracting experiment, the researchers want to optimize the design of their device for potentially faster extraction. They are already testing other promising materials for the membrane.

Also, the researchers did not demonstrate lithium extraction from seawater in this study.

“In principle, our method is applicable for seawater as well, but there could be stability problems for the membrane in seawater,” said Zhang.

Still, the team remains quite optimistic.

“As our research continues, we think our method could soon move from the laboratory to large-scale industrial applications,” said Xu.

For more information

The other co-lead author of the study, Xin Xiao, was a postdoc at Stanford when this work was done, and is now a faculty member at Zhejiang University. Other co-authors are Yusheng Ye, Pu Zhang, Yufei Yang, and Sanzeeda Baig Shuchi, all at Stanford. Yi Cui is also the Fortinet Founders Professor in the School of Engineering, faculty director of the Sustainability Accelerator in the Stanford Doerr School of Sustainability , a professor of energy science and engineering and of photon science, senior fellow and former director of the Precourt Institute for Energy , and senior fellow of the Woods Institute for the Environment . This research was funded by the StorageX Initiative , an industrial affiliates program within Stanford’s Precourt Institute for Energy.

Media contact Mark Golden, Precourt Institute for Energy: (650) 724-1629, [email protected]

• All topics »
• Fact sheets
• Feature stories
• Publications
• Questions & answers
• Tools and toolkits
• Coronavirus disease (COVID-19) pandemic
• Ukraine emergency
• Environment and health

Mpox (monkeypox)

• Calls for experts
• Initiatives
• European Programme of Work
• Sustainable Development Goals
• The Pan-European Mental Health Coalition
• Empowerment through Digital Health
• The European Immunization Agenda 2030
• Healthier behaviours: incorporating behavioural and cultural insights
• Moving towards UHC
• Protecting against health emergencies
• Promoting health and well-being
• News stories
• Media releases
• Photo stories
• Questions and answers

Media Contacts

Newsletters

• European Health Information Gateway
• European health report
• Core health indicators
• WHO Immunization Data portal
• Noncommunicable diseases (NCD) dashboard 
• Events 
• Teams »
• Data and digital health
• Policy & Governance f. Health through the Life Course
• Groups and networks »
• Health Evidence Network (HEN)

The European Health Report 2021 »

• Conflict in Israel and the occupied Palestinian territory
• Armenian refugee health response
• Climate crisis: extreme weather
• Türkiye and Syria earthquakes
• About health emergencies
• Health emergencies newsletter 
• Health emergencies list

• Regional Director
• Executive Council
• Technical centres
• Faces of WHO
• Regional Committee for Europe
• Standing Committee
• Partners 
• Groups and networks
• WHO collaborating centres

74th session of the WHO Regional Committee for Europe

Alarming decline in adolescent condom use, increased risk of sexually transmitted infections and unintended pregnancies, reveals new WHO report

Copenhagen, 29 August 2024

New report reveals high rates of unprotected sex among adolescents across Europe, with significant implications for health and safety

An urgent report from the WHO Regional Office for Europe reveals that condom use among sexually active adolescents has declined significantly since 2014, with rates of unprotected sex worryingly high. This is putting young people at significant risk of sexually transmitted infections (STIs) and unplanned pregnancies. The new data were published as part of the multi-part Health Behaviour in School-aged Children (HBSC) study, which surveyed over 242 000 15-year-olds across 42 countries and regions in 2014–2022.

Far-reaching consequences of unprotected sex

Overall, the report highlights that a substantial proportion of sexually active 15-year-olds are engaging in unprotected sexual intercourse, which WHO warns can have far-reaching consequences for young people, including unintended pregnancies, unsafe abortions and an increased risk of contracting STIs. The high prevalence of unprotected sex indicates significant gaps in age-appropriate comprehensive sexuality education, including sexual health education, and access to contraceptive methods.

Worrying decline in condom use

Compared to 2014 levels, the new data show a significant decline in the number of adolescents reporting condom use during last sexual intercourse. From the data, it is clear that the decrease in condom use is pervasive, spanning multiple countries and regions, with some experiencing more dramatic reductions than others.

The report underscores the urgent need for targeted interventions to address these concerning trends and promote safer sexual practices among young people within the wider context of equipping them with the foundation they need for optimal health and well-being.

“While the report’s findings are dismaying, they are not surprising,” noted Dr Hans Henri P. Kluge, WHO Regional Director for Europe. “Age-appropriate comprehensive sexuality education remains neglected in many countries, and where it is available, it has increasingly come under attack in recent years on the false premise that it encourages sexual behaviour, when the truth is that equipping young persons with the right knowledge at the right time leads to optimal health outcomes linked to responsible behaviour and choices. We are reaping the bitter fruit of these reactionary efforts, with worse to come, unless governments, health authorities, the education sector and other essential stakeholders truly recognize the root causes of the current situation and take steps to rectify it. We need immediate and sustained action, underpinned by data and evidence, to halt this cascade of negative outcomes, including the likelihood of higher STI rates, increased health-care costs, and – not least – disrupted education and career paths for young persons who do not receive the timely information and support they need.”

Key findings from the report

• Decline in condom use: the proportion of sexually active adolescents who used a condom at last intercourse fell from 70% to 61% among boys and 63% to 57% among girls between 2014 and 2022.
• High rates of unprotected sex: almost a third of adolescents (30%) reported using neither a condom nor the contraceptive pill at last intercourse, a figure that has barely changed since 2018.
• Socioeconomic differences: adolescents from low-affluence families were more likely to report not using a condom or the contraceptive pill at last sexual intercourse than their peers from more affluent families (33% compared with 25%).
• Contraceptive pill use: the report indicates that contraceptive pill use during last sexual intercourse remained relatively stable between 2014 and 2022, with 26% of 15-year-olds reporting that they or their partners used the contraceptive pill at their last sexual intercourse.

Need for comprehensive sexuality education

The findings underscore the importance of providing comprehensive sexual health education and resources for young people. “As teenagers, having access to accurate information about sexual health is vital,” said Éabha, a 16-year-old from Ireland. “We need education that covers everything from consent to contraception, so we can make informed decisions and protect ourselves.”

“Comprehensive sexuality education is key to closing these gaps and empowering all young people to make informed decisions about sex at a particularly vulnerable moment in their lives, as they transition from adolescence to adulthood,” said Dr András Költő of the University of Galway, the lead author of the report. “But education must go beyond just providing information. Young people need safe spaces to discuss issues like consent, intimate relationships, gender identity and sexual orientation, and we – governments, health and education authorities, and civil society organizations – should help them develop crucial life skills including transparent, non-judgmental communication and decision-making.”

Roadmap for action, despite worrying trends

While the findings are sobering, they also offer a roadmap for the way ahead.

The report calls for sustainable investments in age-appropriate comprehensive sexuality education, youth-friendly sexual and reproductive health services, and enabling policies and environments that support adolescent health and rights.

“The findings of this report should serve as a catalyst for action. Adolescents deserve the knowledge and resources to make informed decisions about their sexual health. We have the evidence, the tools and the strategies to improve adolescent sexual health outcomes. What we need, though, is the political will and the resources to make it happen,” said Dr Margreet de Looze of Utrecht University, one of the report’s co-authors.

Call to action for policy-makers and educators

The WHO Regional Office for Europe calls upon policy-makers, educators and health-care providers to prioritize adolescent sexual health by:

• Investing in comprehensive sexuality education: implementing and funding evidence-based sexuality education programmes in schools that cover a wide range of topics, including contraception, STIs, consent, healthy relationships, gender equality and LGBTQIA+ (lesbian, gay, bisexual, transgender, queer, questioning, intersex, asexual, plus) issues. In this, the International Technical Guidance on Sexuality Education, produced by a consortium of United Nations agencies and partners, is key.
• Enhancing access to youth-friendly sexual health services: ensuring that adolescents everywhere have access to confidential, non-judgmental and affordable sexual health services that meet their specific needs and preferences.
• Promoting open dialogue: encouraging open and honest conversations about sexual health within families, schools and communities to reduce stigma and increase awareness.
• Training educators: providing specialized training for teachers and health-care providers to deliver effective and inclusive sex education. Such resources should be made available in both school and out-of-school settings.
• Conducting further research: investigating the underlying reasons for the decline in condom use and the variations in sexual health behaviours across different populations to inform targeted interventions. This includes analysing messages and other content adolescents are exposed to across social media and online platforms, given their reach and impact.

“Ultimately, what we are seeking to achieve for young persons is a solid foundation for life and love,” said Dr Kluge. “Sexual and reproductive health and rights, informed by the right knowledge at the right time along with the right health and well-being services, is critical. By empowering adolescents to make informed decisions about their sexual health, we ultimately safeguard and improve their overall well-being. This is what all parents and families should want for their children, everywhere.”

Communications officer

Bhanu Bhatnagar

Press & Media Relations Officer WHO Regional Office for Europe

Joseph Hancock

Communications Officer for the HBSC study

WHO/Europe Press Office

A focus on adolescent sexual health in Europe, central Asia and Canada: Health Behaviour in School-aged Children international report from the 2021/2022 survey

Health Behaviour in School-aged Children (HBSC) study

• Skip to main content
• Keyboard shortcuts for audio player

• Your Health
• Treatments & Tests
• Health Inc.
• Public Health

Shots - Health News

The new covid shot is now available. here's what you need to know.

New COVID Vaccines

A new round of COVID-19 vaccines will be rolled out soon. Scott Olson/Getty Images hide caption

It’s that time of year again.

New COVID-19 shots are now available all over the country.

That comes after the Food and Drug Administration last week greenlighted the two updated vaccines, which are aimed at helping protect people from the latest strains of the virus.

The arrival of the new shots may come as a relief to those who’ve tried to dodge a summer surge in cases, fueled by the FLiRT variants.

Whether or not you decide to rush out and get the vaccine could depend on a few factors, including when you last had COVID-19 and your underlying risk of getting seriously ill.

Here’s what you need to know:

Is COVID endemic yet? Yep, says the CDC. Here's what that means

What exactly are these new shots.

The Pfizer-BioNTech and Moderna vaccines rely on the same mRNA technology as the earlier versions of the vaccine, but they now target the KP.2 variant – a member of the omicron family that rose to prominence over the summer.

As many of us know by now, the virus continues evolving to better evade our immune defense, which means regularly updating the vaccines to keep up with the latest strain.

It turns out the KP.2 variant has already been overtaken by newer variants. Because those are also descendants of omicron, the hope is that the new vaccines are close enough matches that they can still boost immunity and protect people in the coming months – ideally reducing the chances of a big winter wave.

“The vaccine is not intended to be perfect. It’s not going to absolutely prevent COVID-19," Dr. Peter Marks from the FDA told NPR in an interview.

"But if we can prevent people from getting serious cases that end up in emergency rooms, hospitals or worse — dead — that’s what we’re trying to do with these vaccines.”

On average across all age groups, the new vaccines should cut the risk of having COVID-19 by 60% to 70% and reduce the risk of getting seriously ill by 80% to 90% during the three to four months after receiving the shot, Marks says.

A third vaccine is also expected to get the FDA’s stamp of approval soon.

That one, made by Novavax, is based on older technology (not mRNA), and targets an earlier strain of the virus, called JN.1.

Who should get them?

The FDA gave the OK for anyone ages 6 months and older to get one of the new shots. The Centers for Disease Control and Prevention is recommending the vaccines for those age groups.

“In my opinion, everyone should get one of the new vaccines,” says Dr. George Diaz , chief of medicine at Providence Regional Medical Center Everett and a spokesperson for the Infectious Disease Society of America.

That said, it’s most important for those at high risk of becoming seriously ill from COVID-19, namely those over the age of 65 or who have other underlying health problems like a weakened immune system.

Studies suggest getting vaccinated can also reduce the risk of long COVID, Diaz adds.

While anyone can get a shot, Dr. Paul Offit says not everyone necessarily needs another one.

“Anyone who wants to get this vaccine should get it,” says Offit, a vaccine expert at the University of Pennsylvania and Children's Hospital of Philadelphia who advises the FDA.

The vaccine does lessen your chance of getting a mild or moderate infection for about four to six months and to “some extent lessens your chances of spreading the virus,” he says.

But the calculation could be different for younger people who may have enough immunity from previous COVID shots and infections that they’re already protected from getting very sick.

“Were I a 35-year-old healthy adult who’d already had several doses of vaccine and one or two natural infections, I wouldn’t feel compelled to get it,” he says.

And regardless of the public health advice, it’s far from clear how many people will want one of the new shots. Only about 22% of eligible adults got one of the last ones.

Should I get the shot now? Or wait?

That’s a personal judgment call.

Marks suggests most people get vaccinated sooner rather than later because there’s an ongoing surge in COVID cases and the current vaccine is a “reasonably close match” to the current strain that’s circulating.

“Right now we’re in a wave, so you’d like to get protection against what’s going on right now,” Marks says. “You’re probably going to get the most benefit.”

However, it would be wise to hold off if you had COVID-19 over the summer.

People should wait at least two or three months since their last bout, or their last shot, in order to maximize the chances of getting the best protection from this new vaccine, says Marks.

Some people may want to get vaccinated later in September or October if they are primarily concerned about fending off COVID during a potential winter surge and staying healthy over the holiday season.

“This [protection] is not like something that suddenly cuts off at three or four months,” says Marks, “It’s just that the immunity will decrease with time.”

Where can I find the shots? Do I have to pay?

All the major pharmacy chains, including CVS, Rite Aid and Walmart, say the shots should be available at all their stores this week.

Insured people can get vaccinated for free if they get their shot from an in-network provider. But it won’t necessarily be free for those without health coverage.

A federal program that paid for the vaccines for uninsured adults expired. The uninsured may be able to still get the shots for free at some places, such as federally-funded health clinics.

“In the public health community we’re very concerned about how they will access protection,” says Dr. Kelly Moore , who runs Immunize.org , an advocacy group.

“We know that the people who are uninsured are the least likely to be able to afford becoming ill – missing work, staying home from school.”

Can I double up and get the COVID and flu shots at the same time?

Yes, health officials say it’s perfectly safe to get both shots at the same time. In fact, officials are recommending that, especially if that makes it more likely that people will get vaccinated because it’s more convenient.

What about kids? Can they get the same shots?

Yes, children can get the same vaccines that adults receive. But kids get different doses and may need more than one dose, depending on their age and whether they’ve been vaccinated before. They may also need to get their shots from a pediatrician.

• covid-19 vaccines
• covid vaccines
• omicron variants
• COVID surge
• COVID boosters

• quicklinks Academic admin council Academic calendar Academic stds cte Admission Advising African studies Alumni engagement American studies Anthropology/sociology Arabic Arboretum Archives Arcus center Art Assessment committee Athletics Athletic training Biology Biology&chem center Black faculty&staff assoc Bookstore BrandK Business office Campus event calendar Campus safety Catalog Career & prof dev Health science Ctr for civic engagement Ctr for international pgrms Chemistry Chinese Classics College communication Community & global health Community council Complex systems studies Computer science Copyright Counseling Council of student reps Crisis response Critical ethnic studies Critical theory Development Dining services Directories Disability services Donor relations East Asian studies Economics and business Educational policies cte Educational quality assmt Engineering Environmental stewardship Environmental studies English Experiential education cte Facilities management Facilities reservations Faculty development cte Faculty executive cte Faculty grants Faculty personnel cte Fellowships & grants Festival playhouse Film & media studies Financial aid First year experience Fitness & wellness ctr French Gardens & growing spaces German Global crossroads Health center Jewish studies History Hornet hive Hornet HQ Hornet sports Human resources Inclusive excellence Index (student newspaper) Information services Institutional research Institutional review board Intercultural student life International & area studies International programs Intramural sports Japanese LandSea Learning commons Learning support Lgbtqai+ student resources Library Mail and copy center Math Math/physics center Microsoft Stream Microsoft Teams Moodle Movies (ch 22 online) Music OneDrive Outdoor programs Parents' resources Payroll Phi Beta Kappa Philharmonia Philosophy Physics Physical education Political science Pre-law advising Provost Psychology Public pol & urban affairs Recycling Registrar Religion Religious & spiritual life Research Guides (libguides) Residential life Safety (security) Sexual safety Shared passages program SharePoint online Sophomore experience Spanish Strategic plan Student accounts Student development Student activities Student organizations Study abroad Support staff Sustainability Teaching and learning cte Teaching commons Theatre arts Title IX Webmail Women, gender & sexuality Writing center

Library Guide to SIP Research

• Interlibrary Loan & MeLCat
• Annotated Bibliography
• Literature Review

What's the purpose of a Literature Review?

Points to keep in mind, helpful sites.

• Using Statistics & Data
• Advanced Tools
• Writing & Citing
• Posters & Presentations
• Previous SIPs
• Submit a SIP & Printing Tips

Suggested Resource

• Annual Reviews Annual Reviews articles are highly-cited scientific and social science literature reviews written by top experts in each discipline. They synthesize the vast amount of primary research literature and identify the principal contributions in a given field.

Explain the background of research on a topic, illuminates what has been researched

Demonstrate why a topic is significant to a subject area

Identify major themes, concepts, and researchers on a topic

Highlight critical gaps, points of disagreement, or potentially flawed methodology or theoretical approaches

Discuss further research questions that logically come out of the previous studies

A literature review is a systematic review of the published literature on a specific topic or research question. The literature review is designed to analyze-- not just summarize-- scholarly writings that are related directly to your research question.

Literature Reviews are sometimes one part of a scholarly article, sometimes they are the whole article.

• A literature review is organized around ideas that are illuminated in sources, not the sources themselves. 
• A literature review is not a list of sources with detail about each one of them.
• As you read widely and also selectively on your topic area, consider what themes or issues connect your sources.

Do the sources present one or different solutions?

Is there an aspect of the field that is missing in the sources you read?

How well do the sources present the material and do they portray it according to an appropriate theory?

Do they reveal a trend in the field?  Or a raging debate?

Be selective

Use quotes sparingly

Summarize and synthesize

Keep your own voice

Use caution when paraphrasing

• Chemistry Literature Review
• Business Literature Review
• Psychology Literature Review

Thank you to these universities for providing inspiration for the above content.

• Literature Reviews from the Writing Center at the Univ. of North Carolina Chapel Hill
• What is a Literature Review? from the University of Florida
• Demystifying the Literature Review From the University of Illinois Library.
• Writing a Literature Review from Boston College
• Learn How to Write a Review of Literature From the University of Wisconsin.
• << Previous: Annotated Bibliography
• Next: Using Statistics & Data >>
• Last Updated: Aug 7, 2024 1:34 PM
• URL: https://libguides.kzoo.edu/sip

As the World Revolves We Evolve at JIMS

How to write a Summer Internship Project Report?

DR. NUPUR RAO

Summer Internship Project (SIP) is an indispensable part of the two-year PGDM & PGDM-IB Programme at Jagannath International Management School, Kalkaji.

SIP aims at widening the pupil’s outlook by providing an exposure to a realistic organizational and environmental situation. This will empower the students to explore an industry/organization, build a relationship with a prospective employer, or simply sharpen their skills in a familiar field. SIP also provides extremely useful knowledge and networking ordeal to the students. During the internship, the student gets a chance to put whatever he/she has learned in the first year of PGDM into use while working on a business plan, The organisation, in turn, benefits from the objective and unbiased perspective the student provides based on concepts and skills imbibed in the first year of the PGDM.

During the training the student stays under the supervision of a person in the organization who acts as his/her corporate guide. He/she will give direction on how the student should work during his/her association with the organization. In addition, each student is guided by a faculty of JIMS, Kalkaji , one of the best MBA college in Delhi , who is in constant touch with the student guiding and hand holding him throughout.

At the end of the training all the students have to prepare and submit a written project report. This need not necessarily be a statistical or analytical report; it could be a learning and experience sharing report. The project report has to be certified by the organization. 

Summer Internship Project Report Format

1. Cover Page

This is the first page of the report which consists of the Title of the project, Institute’s name, Internal and External guide’s name and the details of the student.

2. Certificates

• Declaration of the student
• Certificate from the organization where training has been conducted
• Certificate from faculty guide

3. Content Page

Headings and subheading with page numbers

4. Acknowledgement

5. List of tables

6. List of Figures

7. List of symbols, Abbreviations or Nomenclature (optional)

8. Executive Summary

9. Chapter 1: Introduction and Conceptual Overview :

• Origin & development of the industry.
• Growth, present & future of industry.
• Defining the concept.
• objective of the project

Introduction about the topic, rationale behind the study Introduce the project. Explain the project. Explain the relevance of the topic & the study. Explain the  concepts/ Theories which are being referred to in your study or which are important in doing this study. Also explain how these concepts are interrelated.

For Example:

Topic: Study of advertisement effectiveness of Astral adhesive.

Initially introduce and explain this study. You are required to spot light the importance of this study by referring to role of advertisements in creating brand position etc. You should also be referring to advertising expenditures of companies in general and how it is used to compete in market etc. Some data related to growth of advertising spends can also be shared.

 This should be followed by explaining various relevant concepts like meaning and use of advertising, why effectiveness is important, what does effectiveness means in terms of brand recall, repeat purchase, creating brand perception and building brand loyalty. How it is measured. Tools of measurements etc.

10. Chapter 2 – Company Profile

• Growth & present strategy
• Products & Services
• Market profile

Sectoral overview –

In this chapter you need to explain/ cover the sector/ industry (Adhesive here in our case) in terms of basic nature of sector, size of sector, growth of the sector, customer segments it serves, competitors’ details, opportunities in the industry, issues and policy matters etc. A sectoral SWOT can also be done here. Data has to be provided regarding the growth etc.

Most of the information is readily available in reports like Industry surveys, IBEF, MOSPI, Maps of India, KPMG, PWC, AC Nielsen, Ernyst & Young, Deolittes, Money control, CRISIL etc.

11. Chapter 3 – Research Methodology –

Title of Project/ Objectives

Methodology – Type of research – Explorative in your case. Type of Data – Secondary/ and some case primary. Identification of Population, Sample Size. Sampling method – Convenient Sampling in your case. Tools of analysis etc.

12. Chapter 4- Data Analysis and Interpretation

(USING SPSS OR ANY OTHER STATISTICS TOOL)

Presenting Data

Analysing the data using the statistical tools

Interpretation

13. Chapter 5- Findings & Inferences

14. Chapter 6- Recommendations and Conclusions

15. Appendices

An appendix is used for additional or supplementary materials, which has not found place in the main text. The materials that can be included here are original interview schedules/questionnaire, copies of covering letters used, documents and long explanatory notes to the text, statistical tests used and tables referred and any other material of considerable reference value.

JIMS Kalkaji provides a great opportunity of placement through a good summer internship project, Live projects and early internships for which trained and experienced faculties hand hold their students.

JIMS,  Kalkaji

#jims #jimsdelhi #managementcollegeindelhi #pgdmcollegesindelhi #mbacollegesindelhi #toppgdmCollegesindelhi #topbschoolsindelhi  # pgdmadmissions2022 #pgdm(ib)admissions2022 #JIMS Kalkaji #SIP #PGDM#summer Internship Project #SIP Report

For more information visit:  www.jagannath.org/

Written by admin

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Recent Posts

• Enhancing Field Operations in Business: The Importance of Safety Software
• Dominating Your Funds: A Pathway to Independence from the rat race
• What is Operations Management: A Comprehensive Guide
• Exploring the Benefits of Cross-Disciplinary Education in Business and IT
• The Significance of Continuous Learning in Professional Development

Recent Comments

• ishika on Navigating Career Success: Choosing the Right MBA College in Delhi NCR
• Shivani on Navigating Career Success: Choosing the Right MBA College in Delhi NCR
• nitesh on Navigating Career Success: Choosing the Right MBA College in Delhi NCR
• Aashna Sharma on Navigating Career Success: Choosing the Right MBA College in Delhi NCR
• smriti malhotra on Navigating Career Success: Choosing the Right MBA College in Delhi NCR
• August 2024
• February 2024
• January 2024
• December 2023
• November 2023
• October 2023
• September 2023
• August 2023
• February 2023
• January 2023
• December 2022
• November 2022
• October 2022
• September 2022
• August 2022
• February 2022
• January 2022
• December 2021
• February 2021
• January 2021
• December 2020
• November 2020
• October 2020
• Digital Marketing
• Economics & Finance
• Entrepreneurship
• Human Resource Management
• Information technology
• International Business
• Marketing Management
• Operations Management
• Personal well being
• Personal Well being
• Entries feed
• Comments feed
• WordPress.org

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

FINAL SIP REPORT

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024