Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis.
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SUBMITTER: Kang H
PROVIDER: S-EPMC8612454 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPrimary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such institutions. This study is designed to gauge the efficiency of PHC institutions by using 2012-2020 panel data covering 31 provinces in China.MethodsThis study applied an improved three-stage data envelopment analysis (DEA) model to evaluate the efficiency of PHC institutions in China. Unlike the traditional three-stage DEA model, the input-oriented global super-efficiency slack-based measurement (SBM) DEA model is used to calculate the efficiency in the first and third stages of the improved three-stage DEA model, which not only allows the effects of environmental factors and random noise to be taken into account but also deal with the problem of slack, super-efficiency and the comparability of interperiod efficiency values throughout the efficiency measurement.ResultsThe results show that the efficiency of PHC institutions has been overestimated due to the impact of external environmental factors and random noise. From 2012 to 2020, the efficiency of PHC institutions displayed a downward trend. Moreover, there are significant differences in the efficiency of PHC institutions between regions, with the lowest efficiency being found in the northeast region. The efficiency of PHC institutions is significantly affected by residents' annual average income, per capita GDP, population density, the percentage of the population aged 0-14, the percentage of the population aged 65 and older, the number of people with a college education and above per 100,000 residents, and the proportion of the urban population.ConclusionsSubstantial investment in PHC institutions has not led to the expected efficiency gains. Therefore, more effective measures should be taken to improve the efficiency of PHC institutions in China based on local conditions. This study provides a new analytical approach to calculating the efficiency of PHC institutions, and this approach can be applied to efficiency evaluation either in other fields or in other countries.
| S-EPMC10503195 | biostudies-literature
Project description:BACKGROUND:The technical efficiency (TE) of care among the elderly in long-term care facilities (LTCF) have become increasingly crucial policy concerns faced by developing countries and Asia, especially China. The purpose of this study was to evaluate the TE and the quality of care and identify its influencing factors among LTCF. METHODS:A total of 32 registered LTCF in Xiamen of China were surveyed in 2016. The Banker-Charnes-Cooper (BCC) model and Slacks-Based Measure (SBM) model of Data Envelopment Analysis (DEA) were used to evaluate the TE of LTCF. The TE has been decomposed into pure technical efficiency and scale efficiency. Utilization of DEA with human, financial, and material resources as inputs and quantity, quality of nursing care as outputs allowed estimation of the relative TE of care in LTCF. In addition, this study applied SBM to measuring the efficiencies and slacks. Furthermore, Tobit model was performed to explore factors associated with TE. RESULTS:There were 7 public and 25 private LTCF respectively, with a total of 6729 beds and 3154 elderly people. 17 LTCF were technically efficient (53.1%). In the BCC model, the average TE was 0.963. The average pure technical efficiency and scale efficiency of LTCF were 0.979, 0.984, respectively. There were 5 LTCF with increasing returns to scale, 8 LTCF with decreasing returns to scale. In the SBM model, the average TE was 0.813, and it had the same effective decision-making unit with SBM model. Depending on TE score from high to low, the top eight are private LTCF, and the last four were public LTCF. The slack analysis showed that they can be reduced in 8 LTCF with decreasing returns to scale such as 53.31% administrative staffs, 67.73% medical staffs, 33.1% caregivers, 51.66% paramedical staffs and 4.1% beds on average. The TE of private LTCF was higher than that of public LTCF. The LTCF in urban were more effective than rural. The TE of LTCF raised by increasing of working hours, training frequency and institutional occupancy. CONCLUSIONS:The overall TE of LTCF in Xiamen of China was relatively high, especially in private institutions. However, LTCF still needs to further improve the utilization of physical resources and the management and training of human resources. The TE of LTCF was associated to their location, institutional nature, allocation of human resources and occupancy rate. It was needed to focus on promoting the efficiency and quality of LTCF in order to achieve sustainability.
| S-EPMC6729073 | biostudies-literature
Project description:BackgroundPeople in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it.MethodsWe obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis (DDEA) model was adopted to estimate all medical centers' efficiencies during 2015-2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency.ResultsThe findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015-2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals.ConclusionsThe study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.
| S-EPMC8976980 | biostudies-literature
Project description:BackgroundIn order to measure and analyse the technical efficiency of district hospitals in Ghana, the specific objectives of this study were to (a) estimate the relative technical and scale efficiency of government, mission, private and quasi-government district hospitals in Ghana in 2005; (b) estimate the magnitudes of output increases and/or input reductions that would have been required to make relatively inefficient hospitals more efficient; and (c) use Tobit regression analysis to estimate the impact of ownership on hospital efficiency.MethodsIn the first stage, we used data envelopment analysis (DEA) to estimate the efficiency of 128 hospitals comprising of 73 government hospitals, 42 mission hospitals, 7 quasi-government hospitals and 6 private hospitals. In the second stage, the estimated DEA efficiency scores are regressed against hospital ownership variable using a Tobit model. This was a retrospective study.ResultsIn our DEA analysis, using the variable returns to scale model, out of 128 district hospitals, 31 (24.0%) were 100% efficient, 25 (19.5%) were very close to being efficient with efficiency scores ranging from 70% to 99.9% and 71 (56.2%) had efficiency scores below 50%. The lowest-performing hospitals had efficiency scores ranging from 21% to 30%.Quasi-government hospitals had the highest mean efficiency score (83.9%) followed by public hospitals (70.4%), mission hospitals (68.6%) and private hospitals (55.8%). However, public hospitals also got the lowest mean technical efficiency scores (27.4%), implying they have some of the most inefficient hospitals.Regarding regional performance, Northern region hospitals had the highest mean efficiency score (83.0%) and Volta Region hospitals had the lowest mean score (43.0%).From our Tobit regression, we found out that while quasi-government ownership is positively associated with hospital technical efficiency, private ownership negatively affects hospital efficiency.ConclusionsIt would be prudent for policy-makers to examine the least efficient hospitals to correct widespread inefficiency. This would include reconsidering the number of hospitals and their distribution, improving efficiency and reducing duplication by closing or scaling down hospitals with efficiency scores below a certain threshold. For private hospitals with inefficiency related to large size, there is a need to break down such hospitals into manageable sizes.
| S-EPMC4108084 | biostudies-literature
Project description:BackgroundStudies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis.MethodsWe performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit.ResultsWe performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79-1.21] and SRU was 1.15 [IQR: 0.95-1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18-1.88] vs. 1.7 [IQR: 1.36-2.00]) and nursing workload (168 hours [IQR: 168-291] vs 396 hours [IQR: 336-672]) but higher nurses per bed ratio (2.02 [1.16-2.48] vs. 1.71 [1.43-2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the "most efficient" quadrant.ConclusionData envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.
| S-EPMC8601512 | biostudies-literature
Project description:IntroductionMost cases of neonatal mortality are preventable but a substantial number of cases get complicated and become irreversible not only due to scarcity but also due to inefficient utilization of available resources in the health service. However, limited evidence shows the efficiency level of health facilities in Ethiopia. Therefore, this study aimed to assess the technical efficiency of neonatal health service and its associated factors among primary hospitals in three zones of Northwest Ethiopia.MethodsA facility-based cross-sectional study was conducted among fifteen randomly selected primary hospitals from March 17 to April 17, 2021. Input data on non-salary recurrent costs, salary costs, and the number of beds, and output data on neonatal admissions, outpatient visits, and referrals for the 2019/20 fiscal year were collected using a document review. In the first stage of analysis, input-oriented data envelopment analysis with a variable return-to-scale assumption was employed to estimate the efficiency scores using DEAP 2.1. A Tobit regression model was fitted in the second stage to identify the associated factors with technical efficiency. Variables with a p-value <0.05 were declared as statistically associated factors.ResultsIn this study, 80% of the primary hospitals were pure technical efficient whereas 46.67% of the facilities were scale efficient with all of the scale inefficient hospitals operating below their scale. The mean pure technical and scale efficiency score of primary hospitals was 0.948±0.113 and 0.887±0.143, respectively. Total catchment population, incentive packages for the clinical staff, and the educational status of the manager were positively associated with the technical efficiency of hospitals. On the other hand, technical efficiency was negatively associated with the presence of a health facility that provides neonatal health services near the hospital and the distance of the manager's residence.ConclusionThough most of the primary hospitals in Northwest Ethiopia were technical efficient, more than half of them were working below their scale of operation. Our results also indicated that introducing the performance-based provision of incentive packages for clinical staff and employing master's and above-educated health professionals as a manager might improve the efficient utilization of resources in primary hospitals.
| S-EPMC9671419 | biostudies-literature
Project description:The traditional data envelopment analysis (DEA) literatures generally concentrated on the efficiency evaluation of single decision making unit (DMU). However, in many practical problems, the decision makers are required to choose a number of DMUs instead of a single one from the DMUs set. Therefore, it is necessary to study the combinatorial efficiency evaluation problem which can be illustrated as a knapsack problem naturally. It is indicated that the basic model proposed by Cook and Green may have some drawbacks and a modified model, which is combined with the super efficiency model, is proposed in this paper. What is more, our proposed model is more persuasive to the decision makers because it is able to provide a unique best combination of DMUs. An adapted local search algorithm is developed as a solver of this problem. Finally, numerical examples are provided to examine the validity of our proposed model and the adapted local search algorithm.
| S-EPMC4119746 | biostudies-other
Project description:OBJECTIVE:This study aims to estimate the technical efficiency of health systems in Asia. SETTINGS:The study was conducted in Asian countries. METHODS:We applied an output-oriented data envelopment analysis (DEA) approach to estimate the technical efficiency of the health systems in Asian countries. The DEA model used per-capita health expenditure (all healthcare resources as a proxy) as input variable and cross-country comparable health outcome indicators (eg, healthy life expectancy at birth and infant mortality per 1000 live births) as output variables. Censored Tobit regression and smoothed bootstrap models were used to observe the associated factors with the efficiency scores. A sensitivity analysis was performed to assess the consistency of these efficiency scores. RESULTS:The main findings of this paper demonstrate that about 91.3% (42 of 46 countries) of the studied Asian countries were inefficient with respect to using healthcare system resources. Most of the efficient countries belonged to the high-income group (Cyprus, Japan, and Singapore) and only one country belonged to the lower middle-income group (Bangladesh). Through improving health system efficiency, the studied high-income, upper middle-income, low-income and lower middle-income countries can improve health system outcomes by 6.6%, 8.6% and 8.7%, respectively, using the existing level of resources. Population density, bed density, and primary education completion rate significantly influenced the efficiency score. CONCLUSION:The results of this analysis showed inefficiency of the health systems in most of the Asian countries and imply that many countries may improve their health system efficiency using the current level of resources. The identified inefficient countries could pay attention to benchmarking their health systems within their income group or other within similar types of health systems.
| S-EPMC6475137 | biostudies-literature
Project description:BACKGROUND:Disparity in resource allocation is an issue among various health delivery units in Ethiopia. To sufficiently address this problem decision-makers require evidence on efficient allocation of resources. Therefore, the purpose of this study was to assess the technical efficiency of primary health care units providing neonatal health services in Southwest Ethiopia. METHODS:Two-stage data envelopment analysis was conducted based on one-year (2016/17) data from 68 health posts and 23 health centers in Southwest Ethiopia. Primary data were collected from each of the facility, respective district health offices and finance and economic cooperation offices. Technical efficiency scores were calculated using data envelopment analysis software version 2.1. Tobit regression was then applied to identify determinants of technical efficiency. STATA version 14 was used in the regression model and for descriptive statistics. RESULTS:By utilizing the best combination of inputs, eight health posts (11.76%) and eight health centers (34.78%) were found to be technically efficient in delivering neonatal health services. Compared with others included in the analysis, inefficient health delivery units were using more human and non-salary recurrent resources. The regression model indicated that there was a positive association between efficiency and the health center head's years of experience and the facility's catchment population. Waiting time at the health posts was found to negatively affect efficiency. CONCLUSIONS:Most of health posts and the majority of health centers were found to be technically inefficient in delivering neonatal health services. This indicates issues with the performance of these facilities with regards to the utilization of inputs to produce the current outputs. The existing resources could be used to serve additional neonates in the facilities.
| S-EPMC6815357 | biostudies-literature
Project description:BackgroundPrimary care is widely seen as a core component of resilient and sustainable health systems, yet its efficiency is not well understood and there is a lack of evidence about how primary care efficiency is associated with health system characteristics. We examine this issue through the lens of diabetes care, which has a well-established evidence base for effective treatment and has previously been used as a tracer condition to measure health system performance.MethodsWe developed a conceptual framework to guide the analysis of primary care efficiency. Using data on 18 European countries during 2010-2016 from several international databases, we applied a two-stage data envelopment analysis to estimate (i) technical efficiency of primary care and (ii) the association between efficiency and health system characteristics.ResultsCountries varied widely in terms of primary care efficiency, with efficiency scores depending on the range of population characteristics adjusted for. Higher efficiency was associated with bonus payments for the prevention and management of chronic conditions, nurse-led follow-up, and a financial incentive or requirement for patients to obtain a referral to specialist care. Conversely, lower efficiency was associated with higher rates of curative care beds and financial incentives for patients to register with a primary care provider.ConclusionsOur results underline the importance of considering differences in population characteristics when comparing country performance on primary care efficiency. We highlight several policies that could enhance the efficiency of primary care. Improvements in data collection would enable more comprehensive assessments of primary care efficiency across countries, which in turn could more effectively inform policymaking.
| S-EPMC10694950 | biostudies-literature