Research On Efficiency Evaluation And Influencing Factors Analysis Of County-Level Centers For Disease Control And Prevention In China: Based On Data Envelopment Analysis

SignificancesAs the process of administrative reform, Chinese government has changed its role and function gradually and has become public-service-oriented government. The primary tasks of public-service-oriented government are to implement public service and to increase service efficiency. As an important part of public service, disease control and prevention has been highly appreciated since SARS breaking in 2003. With the increasing financial investment into disease control and prevention system year by year, the issue that how is the efficiency of disease control and prevention service has been paid more and more attention from our governments and the public.In consideration of the multiple inputs and outputs of the center for disease control and prevention （CDC）, Data Envelopment Analysis （DEA） could be an appropriate method for CDC’s efficiency evaluation. Although DEA’s theory system has been developed maturly and DEA has been used extensively in the fields of public service and medical institutions, it has been merely utilized to evaluate efficiency of CDCs.This study, thus, applied the DEA model to evaluate the efficiency of county-level CDCs in China. Then this study used Multilevel Model to analyze the internal and external influencing factors of efficiency for the purpose of identifying key factors and developing strategies for improving the efficiency.Materials and MethodsFirstly, by using the principle of "structure-process-outcome" and Macro-Model of Health System, index set for inputs and outputs and index set for influencing factors were collected.Secondly, typical input and output indicators were selected by using cluster analysis and expert consultations. Then, DMUs （county-level CDCs samples） were selected by systematic sampling, and output-oriented CCR model and BCC model were used to evaluate overall, pure technical and the scale efficiency of DMUs. What’s more, weakness in current work was identified by projection analysis.Finally, by using Two-level Model, the influence of region-level and institution-level factors on overall efficiency were analyzed. Based on above, key factors for improving efficiency were known and the corresponding countermeasures and suggestions were put forward.458 county-level CDCs were selected by systematic sampling across the country and the effect recovery rate was 90.8%. Basic data including staff, financial and material resource, and capacity building of sample CDCs were collected via information platform of performance evaluation of DCPS.Result1. Select input and output indicators for DEA modelAccording to the principle of "structure-process-outcome" and Macro-Model of Health System, this study constructed indicator set for inputs and outputs via analysis of literatures and expert consultations. Then, ten typical input and output indicators were selected by cluster analysis and defined. Input indicators included the number of staff, regular budget plus special disease control funding, and the total value of fixed assets （housing facilities and equipment assets）. Output indicators included the number of children having the five basic vaccines, the number of categories of emergency supplies, the number of categories of analyzed information, the number of projects that monitoring drinking water samples, the number of Class A laboratory testing projects, the number of health promotion campaigns and the average time for on-site professional staffs guiding works.2. Establish a DEA model and evaluate the efficiency of county-level CDCsThis study calculated overall, pure technical and the scale efficiency values of sample county-level CDCs by output-oriented CCR model and BCC model. What’s more, weakness in current work were identified by projection analysis.（1） Public service efficiency of Chinese county-level CDCs was relatively lowThe average overall efficiency score of county-level CDCs in 2012 was 0.505, decreasing by 6.09% compared with that in 2008. Only 7.21% of CDCs were overall-efficient. The pure technical efficiency score was 0.687, which means CDCs only completed 68.7% of their optimal output by their input resources. In comparison with 2008, the average pure technical efficiency score decrease by 4,92%. As for returns to scales,87.98% of CDCs had decreasing returns to scale. This value was higher than that in 2008.（2） Public health service of county-level CDCs was under-produced, CDCs in defferent regions needed to increase output targetlyThe projection analysis showed seven outputs were all under-produced; indicators standing for management of public health emergencies and laboratory testing had the greatest gap between their actual values and projection values. For CDCs in eastern, middle and western regions, different output indicators should be increase primarily.3. Analyze influencing factors of county-level CDCs’efficiencyThis study selected index set for influence factors by using Macro-Model of Health System and constructed Two-level Model in which overall efficiency score was dependent variable and influencing factors were independent variables. The results illustrated county-level CDCs in different provinces had different overall efficiency scores. The Two-level model constructed was Y= 0.413+0.101×population density₁+0.122×population density₂+ 0.002 X the proportion of health technical staff-0.00005 X paid service income+0.003×area of laboratory per capita. This model illustrated that county-level CDCs in the provinces with higher population density tended to have higher overall efficiency scores than those in the provinces with lower population density; the proportion of health technical staff and area of laboratory per capita of CDC had positive effect to overall efficiency score while paid service income of CDC had negative effect.4. Put forward optimizing strategies for increasing county-level CDCs’efficiency（1） To increase pure technical efficiency and scale efficiency at the same time, county-level CDCs should pay more attention on augmenting output, increasing allocative and utilizing efficiency of input resources and enhancing standard management and quality control during working process. Since seven output indicators were all under-produced, CDCs could take different measures to increasing different outputs. Take the number of categories of emergency supplies for example, CDCs should establish comprehensive coordination mechanism, and governments should add the budget for emergency supplies to their regular spending.（2） Since the proportion of health technical staff had positive effect on overall efficiency score, county-level CDCs should focus more on establishing their health technical staff team with remaining their current staff scale by technical training and continuing education. In addition, personnel access mechanism should be built to guarantee high quality of new staff. What’s more, incentive mechanism could prevent the outflow of staff.（3） Since area of laboratory per capita had positive effect on overall efficiency score, county-level CDCs should build enough laboratories and increasing the proportion of laboratory area.（4） Since paid service income had negative effect to overall efficiency score, paid service income should be separated from the whole income of county-level CDCs gradually. What’s more, governments should establish stable investment mechanism to promote CDCs to deliver public service better.ConclusionsThis study selected input and output indicators and constructed DEA model to evaluate public service efficiency of county-level CDCs in China. The efficiency score was relatively low, which illustrated county-level CDCs should increase their efficiency of utilizing resousrce. Projection analysis showed seven functions were all under-produced. Management of public health emergencies was the function which was the most under-produced and was required to increase output.This study also applied Multilevel Model to analyze how region-level factors and institution-level factors affected CDC’s overall efficiency. The results showed that, in region-level indicators, population density had positive effect to overall efficiency; in institution-level, the proportion of health technical staff and area of laboratory per capita of CDC had positive effect to overall efficiency score while paid service income of CDC had negative effect. According to these results, this study put forward some optimized suggestions which would help county-level CDCs to increase service efficiency and deliver public health better.