合作医疗保险改革与中国农村地区乡镇卫生院效率外文翻译.doc

济南大学毕业论文外文资料翻译毕业论文外文资料翻译题 目 合作医疗保险改革与中国农村地区 乡镇卫生院效率：从调查数据分析学 院 经济学院 专 业 经济学 班 级 0901 学 生 王强 学 号 20092221328 指导教师 王雷 二一三年四月十九日- 20 -China Economic Review， 2013（4）：13-26.Health insurance reform and efficiency of township hospitals in rural China:An analysis from survey dataMartine Audibert, Jacky Mathonnat, Aurore Pelissier, Xiao Xian Huang, Anning MaABSTRACTIn the rural health-care organization of China, township hospitals ensure the delivery of medical services above village health stations and below county hospitals. Particularly damaged by the economic reforms implemented from 1975 to the end of the 1990s, the efficiency of township hospitals has been questioned, mainly because of the implementation since 2003 of the reform of health insurance in rural areas (New Rural Cooperative Medical Scheme). From a database of 24 randomly selected township hospitals observed over the period 20002008 in Weifang Prefecture (Shandong), this study examines the efficiency of township hospitals through a two-stage approach.As curative and preventive medical services delivered at township hospital level use different production processes, two data envelopment analysis models are estimated with different orientations to compute scores. The results show that technical efficiency has declined over time.The factors explaining technical efficiency are mainly environmental characteristics rather than internal ones. Among these environmental factors, NRCMS have in average a negative effect on the evolution of THs efficiency, although efficiency have improved for some of them.Our results suggest also that, in the context of China, the efficiency of township hospitals is influenced by unobservable factors. From our findings, we suggest five main orientations to improve THs efficiency.1.IntroductionIn the rural Chinese healthcare system, township hospitals (THs) play an essential role: they represent the main providers of primary healthcare in rural areas (Hillier & Shen, 1996). THs constitute the intermediate level of healthcare facilities and ensure the link between village health stations at the grassroots level and county or above-level hospitals. They supervise healthcare delivery at the level of village health stations, and act as gate keepers, orienting patients toward higher health facility levels. They offer a wide-ranging set of general medical services by delivering curative and preventive activities, from vaccinations and laboratory tests to outpatient visits and inpatient care.The Chinese rural healthcare system has greatly changed since the 1950s. From 1950 to 1975, China achieved significant improvements in health outcomes, thanks to the definition of an efficient three-tier system of healthcare delivery and a successful community-based rural health insurance scheme (Hsiao, 1995; World Bank, 1997). However, the economic transition (19751990) caused the deterioration of these two pillars of the rural healthcare system (Liu, Xu, & Wang, 1996; Wagstaff, Lindelow, Wang, &Zhang, 2009).1 First, the CooperativeMedical System(CMS) collapsed.While in 1975 quasi-universal coverage was achieved in rural areas, less than 10% of the rural population was still insured in the 1990s (World Bank, 1997). Secondly, the three-tier system, consisting of village health stations, township and county hospitals (from the lower level to the upper one), was disrupted. The efficiency of THs has declined due to the economic reforms (Hsiao, 1995; Liu, Rao, & Hu, 2003). The budget decentralization (1979)and the management reform of THs (1983) led them to look for profitable activities (Hillier & Shen, 1996; Liu et al., 1996). As a consequence, negative externalities came to light: the quality of healthcare declined, healthcare prices increased, expensive technologies were overused, drugs were overprescribed, the average length of stay increased and preventive activities were neglected to privilege expensive curative activities (Eggleston, Ling, Qingyue, Lindelow, &Wagstaff, 2008; Hillier & Shen, 1996; Hsiao,1995; World Bank, 1997). The collapse of the CMS disturbed the referral system. Moreover, as THs suffered from a bad reputation, patients bypassed them to go directly to county hospitals (World Bank, 1997). This phenomenon was enhanced by the increase of rural incomes, leading patients to look for higher quality services and enhancing their capacity to pay for health (Liu et al., 1996). Thus,the activity of THs fell off.Since 2003, the implementation of the New Rural Cooperative Medical Scheme (NRCMS) has served mainly two objectives.The first one is to offer an insurance system to the rural population, in order to lower the financial barrier to accessing the healthcare system and to improve the rural populations health (Wagstaff, Lindelow, Wang, & Zhang, 2009). The second one is to make the THs, which suffered from the economic liberalization, more attractive by re-orientating patients toward this level.The question of the THs efficiency is crucial, with regard to their strategic position in the healthcare delivery chain and the changes they experienced over the preceding years, but also in a context of scarce resources, of vertical and horizontal competition and health insurance reform. By targeting THs more than other health facilities in Weifang Prefecture, on which this study is focused, the NRCMS can influence the activity and the efficiency of these facilities. The main channels are the likely increase in demand induced by insurance and the implementation of contracts. Therefore, identifying the determinants of THs efficiency can help the design of relevant policy measures by highlighting the factors on which policy makers can act.This study investigates technical efficiency by examining the production process of healthcare services in a sample of 24 randomly selected THs, observed over the period 20002008 in rural areas of Weifang Prefecture in China. According to the reviews ofHollingsworth (2003) and ONeill, Rauner, Heidenberger, and Kraus (2008), the literature on the efficiency of health facilities mainly concentrates on North American and European case studies. But there is a growing literature on developing countries, such as Ersoy,Kavuncubasi, Ozcan and Harris (1997) for Turkey, La Forgia and Couttolenc (2008) for Brasil, Hajialiafzali,Moss and Mahmood (2007) for Iran, Kirigia, Emrouznejad and Sambo (2002) for Kenya, Puenpatom and Rosenman (2008) for Thailand, among others. In addition, there are two articles related to technical efficiency in Taiwan (Chang, 1998; Chang, Cheng, & Das, 2004), and two recent studies which examined hospital efficiency in China using a Data Envelopment Analysis (DEA) approach (Hu, Qi, & Yang, 2012; Ng,2011). The first one (Ng, 2011) focuses on Guangdong Province while the second (Hu et al., 2012) is considering regional hospitals nationwide. Our study adds complementary findings to the recent literature in examining THs, well-described as facing efficiency issues (Hsiao, 1995; Liu et al., 2003), but never studied through an efficiency analysis. National studies revealed serious discrepancies throughout China, both in terms of the effects of reformand the functioning of the healthcare system (Brown, de Brauw, & Du, 2008;Feng & Song, 2009; Hu et al., 2012), stressing the importance of investigating more at local level through case studies to deepen knowledge and guide specific regional policies, as mentioned by Eggleston et al. (2008). A two-stage approach is applied: technical efficiency of THs is computed fromDEA and then technical efficiency scores are regressed on a set of explanatory variables through a Tobit approach.2. DataThe original dataset covers 24 randomly selected THs of Weifang Prefecture, in Shandong Province (about 14% of total THs in Weifang Prefecture) observed over a nine-year period, from 2000 to 2008. Information was collected from the Weifang Health Bureau database and the registers/books of the THs during the third quarter of 2009 in collaboration with the Weifang Medical University and Chinese authorities. Data were checked and when necessary new investigations were implemented in THs and completed with interviews.The size of the THs is relatively small with on average 39 beds, 45 curative medical staff and six preventive staff over the period.However, the number of beds varied from16 to 150, reflecting large disparities in the size of THs. The dataset consists of nine central THs and 15 general THs. Overall, the size of THs, as measured by the human and physical resources available, increased over the period.3. Technical efficiency3.1. Estimation of technical efficiencyDeveloped in 1957 by Farrell (1957), the concept of technical efficiency refers to the capacity of a decision making unit (DMU) to transform a quantity of inputs into an amount of outputs. The technical efficiency of DMUs is studied through the framework of the efficiency frontiers (Frsund, Lovell Knox, & Schmidt, 1980).The parametric stochastic approach, Stochastic Frontier Analysis (SFA) and the non-parametric determinist approach, Data Envelopment Analysis (DEA) are the most employed methods in production frontier analysis literature. The first is used when the production technology is well-known. It is based on an econometric specification of the production technology, for which the shape is determined by micro-econometric theory. The second is based on a mathematical linear program comparing decision making units with each other in order to draw an efficiency frontier and compute efficiency scores.Considering hospitals, their optimization behavior diverges from traditional neoclassical theory (Hollingsworth, Dawson, &Maniadakis, 1999). Thus, the difficulty in assessing the nature of the optimization behavior of hospitals leads to a poorly known shape of the production function and pushes many practitioners to prefer a nonparametric approach such as DEA rather than the parametric SFA. In fact, DEA digs out the shape of the production frontier from the data and so does not require assumptions on the functional form of the production function or on the distribution of the error term (Charnes, Cooper, Lewin, & Seiford, 1994;Jacobs, Smith, & Street, 2006). Additionally, DEA is more convenient than a parametric approach in the context of this study, as a “case-study” in a managerial perspective, as the method defines efficiency in terms of the best practice observed in the sample, and not in terms of the best practice as defined by a theoretical technology of production. The scores estimated are relative measures of technical efficiency. Moreover, it allows reaching conclusions directly linked to the practices observed in the studied THs. But, in contrast, the results dont provide us with generalizable results outside the sample. However, due to these advantages,the method has shown its value as a relevant analytical tool of hospital efficiency as stressed in Hollingsworth (2003), ONeill et al.(2008), and Hollingsworth et al. (1999) in spite of its limitations. One is that the technical efficiency obtained from this method does not give information on the quality of services, nor does it reflect the needs of patients. This being said, one essential assumption on our sample THs being the existence of under-utilization of resources, this implies that, in general terms, increasing the volume of THs activities should not harm the quality of services.3.2. The DEA modelsAs the purpose is to model the production function of the DMUs, DEA requires the specification of the inputs used to produce the outputs, the orientation of the model, the nature of the returns to scale and the temporal dimension to run the DEA model.THs deliver two main kinds of medical activities: curative and preventive healthcare services, which are characterized by two different production processes with two different objectives. Thus, two distinct DEA models are defined.The model characterizing the production process of curative activities consists of one output and three inputs. THs delivered a large set of curative activities. Therefore, the number of outputs used in the DEA framework needs to be minimized,3 but the diversity of activities has to be taken into account. A composite index is calculated using a workload equivalent weighting system4 suggested by a Chinese experts committee5 and already used in a previous study (Audibert et al., 2008). Two main categories of input enter in the curative production process of THs: staff and equipment. The former are measured by the number of curative staff members, while equipment includes the number of operational beds and a composite index which gives the endowment of the TH in equipment. It is computed from principal component analysis and incorporates the number of operational X-rays, echographs, endoscope and electrocardiograms.6 The objective assigned to THs is to maximize the volume of healthcare delivered,i.e. an output orientation is relevant.The model characterizing the production process of preventive activities consists of one output (the total volume of vaccinations delivered) and one input (the number of staff for preventive activities), as only human resources are used to produce preventive activities. The level of production is previously defined by the government. The input orientation appears more suitable as THs can minimize their use of preventive resources in order to produce the target volume of preventive activitiesIn both models, the case-mix was not taken into consideration because of the lack of data. Yet, the potential bias occurrence is not an important limitation to this specific study as THs are homogeneous in terms of disease treated (source: personal communication from Weifang Health Bureau). They are located in the same prefecture and face similar disease patterns. They belong to the same hierarchical level in the Chinese health delivery system and have therefore common missions defined by the government.Furthermore, according to our data, severe cases are treated in county hospitals and THs mainly deal with respiratory and cardiovascular pathologies, diabetes and injuries. Inpatient activity represents a weak share of the curative activities delivered by THs (less than 5% of patients on average).4. Determinants of technical inefficiencyIn line with the existing literature and the discussion with our Chinese partners, this paper focuses on two kinds of factors which can contribute to explain the efficiency level of THs: the internal characteristics of THs and the characteristics of the environment in which THs are situated. Two Tobit models are estimated, one for efficiency scores calculated from the curative DEA model and one from the preventive DEA model, as explaining factors may differ for both estimations.4.1. Internal factorsThe composition of the staff (balance between qualified and unqualified staff), the staff work load and staff incentives are considered as important channels for technical efficiency (Puenpatom & Rosenman, 2008; Yip et al., 2010). Variables are different according to the production process. We consider the proportion of qualified staff in the total staff for the regression on the curative technical efficiency as a high ratio is expected to have an attractive effect on patients. The number of households per staff is used in the regression on preventive technical efficiency as the delivery of preventive activities is often much more managed by coverage rate considerations than by considerations of staff qualifications.The efficiency of THs may be subjected to financial constraints (Preker & Harding, 2003), creating a hard or a “soft budget constraint” (SCB). As pointed out by Kornai (2009) (p. 119120), SBC is “not a single event, () but a mental condition, present in the headthe thinking, the perception of a decision maker (). There are grades of hardness and softness”. That means that indicators should be continuous, not discrete. Theoretical and empirical literatures provide us with some evidence showing that SBC can decrease efficiency. Regarding THs, one of the most important factors of budget constraint comes from the current share of subsidies in total expenditures (excluding staff related expenditures), rather than from the deficit, as the deficit implies a kind of informal “agreement” (the so called “guanxi”) between each TH, the health authorities and local municipalities. Therefore, a high proportion of subsidies may have a negative effect on efficiency as they lower the financial constraint of THs, creating a “soft budget constraint”. Selecting the current amount of subsidies (ratio), instead of the lagged one, is relevant because the volume of subsidies cannot be anticipated by the TH as it is decided at the beginning of the year.We then also take into consideration the potential effect that the efficiency of a TH in a specific production process (for example, in curative healthcare delivery) can also have an effect on the efficiency of this same TH in other production processes (for example, in preventive healthcare delivery). To assess cross services potential additional effects on efficiency behavior in different production technologies, efficiency scores of the curative DEA model are introduced into the regression of the determinants of preventive efficiency, and vice-versa.4.2. Environmental factorsThe structural characteristic of the township is measured by the density of the population in the township. It can influence the demand addressed to the THs and thus the volume of medical services they delivered.The relationship of the TH with its environment is measured by two sets of variables. The density of villa