Realigning demand and supply side incentives to improve primary health care seeking in rural China.

HEALTH ECONOMICS Health Econ. 24: 755–772 (2015) Published online 8 May 2014 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.3060

REALIGNING DEMAND AND SUPPLY SIDE INCENTIVES TO IMPROVE PRIMARY HEALTH CARE SEEKING IN RURAL CHINA TIMOTHY POWELL-JACKSONa,*, WINNIE CHI-MAN YIPb and WEI HANb a

Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK b Blavatnik School of Government, University of Oxford, Oxford, UK

ABSTRACT China’s recent and ambitious health care reform involves a shift from the reliance on markets to the reaffirmation of the central role of the state in the financing and provision of services. In collaboration with the Government of the Ningxia province, we examined the impact of two key features of the reform on health care utilisation using panel household data. The first policy change was a redesign of the rural insurance benefit package, with an emphasis on reorientating incentives away from inpatient towards outpatient care. The second policy change involved a shift from a fee-for-service payment method to a capitation budget with pay-for-performance amongst primary care providers. We find that the insurance intervention, in isolation, led to a 47% increase in the use of outpatient care at village clinics and greater intensity of treatment (e.g. injections). By contrast, the two interventions in combination showed no effect on health care use over and above that generated by the redesign of the insurance benefit package. Copyright © 2014 John Wiley & Sons, Ltd. Received 18 June 2013; Revised 20 February 2014; Accepted 02 April 2014 KEY WORDS:

impact evaluation; China; health insurance; capitation payment; pay-for-performance

1. INTRODUCTION As countries seek to make progress towards universal coverage, policymakers are turning towards wholesale health sector reforms to improve the performance of their health system. Such reforms may transform the way health providers are paid or change how financial resources are pooled, for example, through the creation of new insurance mechanisms. Often, health system reforms involve the introduction of several changes together, acting on both the demand side and supply side. A prime example is China’s ambitious 3-year $125 billion health care reform, launched in 2009. It involves a shift from the reliance on markets to the reaffirmation of the central role of the state in financing and providing health care to the population. The reform has five core components: expansion of insurance coverage, establishment of an essential medicine system, improvement in the delivery of primary care and the referral system, expansion of public health services, and the piloting of public hospital reforms. There are enormous challenges in assessing the impact of such reforms. The first concerns the fact that health policy changes are typically applied across an entire country. This means that there is limited scope for establishing an appropriate counterfactual that is key to making any claim to causality. The identification challenge is not insurmountable. Various studies on competition (Cooper et al., 2011), pay regulation (Propper and van Reenen, 2010), targets (Propper et al., 2010), health insurance (Wagstaff et al., 2009a; King et al., 2010; Finkelstein et al., 2012), conditional cash transfers (Lagarde et al., 2007) and pay-for-performance (p4p) (Basinga

*Correspondence to: Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK. E-mail: [email protected]

Copyright © 2014 John Wiley & Sons, Ltd.

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et al., 2011) are notable for providing a convincing account of the impact of large scale health policies. However, these studies focus on single policy interventions. When studies evaluate broad-based health sector reforms, a second challenge emerges. Precisely because of the complexity of such reforms, it becomes difficult to tease out which of the multiple components has driven the impacts, even when the identification strategy is credible. Interpretation of the findings thus becomes unclear, and the lessons for future policymaking less evident. This apparent trade-off between attribution and narrowness of scope, much discussed in the evaluation literature (Deaton, 2010), is particularly relevant for health systems research. It explains in part why there is a paucity of rigorous quantitative evidence on the benefits of health care reforms. In this paper, we evaluate two key features of China’s health care reform through the means of a social experiment conducted in the Ningxia province, one of the least developed areas of the country. The first policy change is a redesign of the benefit package of China’s flagship health insurance scheme, with a particular emphasis on reorientating incentives away from inpatient care towards outpatient care. The second policy change concerns the supply side—a shift from a fee-for-service payment method to a capitation budget with p4p amongst primary care providers. We worked in collaboration with the leadership of the Ningxia government to introduce these policy changes in such a way as to provide scope for evaluation of their impacts. Our research design—by allowing us to identify separately the effect of the insurance intervention in isolation and in combination with the change to the provider payment system—goes some way to address the limitations of previous research on the impact of health care reforms. To aid interpretation, it is important to note that we use data collected no more than 1 year after the start of implementation. The assessment is therefore of early impacts. Moreover, we focus solely on outcomes concerning health care use. Other outputs from the research project will address a broader range of outcomes relevant to the evaluation of the two policy changes. The paper is structured as follows. Section 2 provides a brief review of the relevant literature and an overview of the Ningxia project. Section 3 describes the research design, including the data used and the main study outcomes. Section 4 outlines the empirical strategy, and section 5 presents the results. Finally, Section 6 provides a discussion of the study limitations and implications for policy and future research.

2. BACKGROUND 2.1. Literature It is notable in the literature how individual health policies or programmes tend to be studied in isolation. Yet, health system reforms in most countries involve multiple components that aim to increase demand for health services, while at the same time improving the availability, quality and efficiency of health care provision. Given the objective of this paper, we are primarily interested in reviewing studies that examine the interaction between demand-side and supply-side incentive changes. Before doing so, we give a brief overview of the literature on the impact of health insurance and provider payment methods. There is burgeoning literature on the impact of health insurance on health care use and financial strain. A systematic review provides a critical summary of this literature in the context of low and middle income countries (Acharya et al., 2012). China-specific literature on insurance, particularly the flagship New Cooperative Medical Scheme (NCMS; Wagstaff et al., 2009b; Yip et al., 2012), is also extensive, with the most rigorous exploiting panel data to address the problem of endogenous take-up of insurance (Lei and Lin, 2009; Wagstaff et al., 2009a,2009b; Babiarz et al., 2010, 2012).1 1

The NCMS is a public health insurance scheme for the rural population. The government subsidises 90% of the premium, and although enrolment is voluntary, the enrolment rate has steadily reached over 95%. Risk is pooled at the county level. The government started piloting this program in 2003, and by 2006, it has been rolled out nationwide. Government subsidies have increased substantially from 2003 to 2012, and there has been an expansion of the benefit package from hospitalisation only to outpatient services.


Health Econ. 24: 755–772 (2015) DOI: 10.1002/hec

IMPROVING PRIMARY HEALTH CARE SEEKING IN RURAL CHINA

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These studies broadly show that the introduction of the NCMS has increased the use of inpatient and outpatient health services. That the NCMS provides financial protection against ill health is less evident. The majority of studies find no protective effect of insurance on health spending and other measures of financial strain. In fact, several studies find that the NCMS is associated with higher out-of-pocket spending (Wagstaff et al., 2009a; Zhou et al., 2009). Because policy design responsibilities are decentralised, two studies have examined whether there is heterogeneity in the impact of the NCMS with respect to variation in the design of benefit package. Babiarz et al. (2010) find that impacts vary across different policy bundles in the case of health spending but not utilisation. Wagstaff et al. (2009a), however, find little in a way of heterogeneity according to type of scheme, at least in the counties that they study. Both studies used data before 2007 when the NCMS premium was still at a mere 40 renminbi (RMB) per person. One of the few studies to examine (self-reported) health outcomes finds that community-based health insurance in China reduced pain/discomfort and anxiety/depression in the study population (Wang et al., 2009). The evidence on provider payment methods is summarised in various literature reviews (Bitran and Yip, 1998; Gosden et al., 2001, 2003; Lagarde et al., 2010; Flodgren et al., 2011; Scott et al., 2011; Witter et al., 2012). Studies on provider payment reforms in China are not common. The government has experimented with different payment methods, but few have been assessed, and hence, there is little convincing evidence of their impact on quality of care and health outcomes. Of the existing studies, the evidence tends to support the theory and is consistent with that from industrialised countries. Prospective payment methods versus fee-for-service reduce unnecessary care and health care costs (Yip and Eggleston, 2001, 2004; Liu and Mills, 2003, 2005; Wang et al., 2011). One study in the Guizhou province, for example, examined the effect of moving from a fee-for-service payment system to one in which village doctors were paid a salary plus performance bonus (Wang et al., 2011). The change to incentives reduced prescribing of unnecessary drugs although did little to reduce total health spending because sicker patients were more likely to be referred up the health system where costs were higher. A second set of studies found that switching from low powered incentives to a system linking remuneration to services delivered increased hospital revenue, admissions and unnecessary care (Liu and Mills, 2003, 2005). While the previously mentioned literature is relevant to this study, they concern discrete policy adjustments that act on either patients or health providers. Studies in which several policy changes are introduced together, perhaps operating on demand and supply simultaneously, are few and far between. Wagstaff and Yu (2007) evaluated a World Bank project in China that resurrected a rural health insurance scheme known as the cooperative medical scheme, introduced a health expense safety net for the poor, invested in upgrading township health centres (e.g. infrastructure and equipment) and implemented treatment protocols. The difference-in-difference estimates suggest that the project reduced out-of-pocket health spending but had little impact on health care use. The results with respect to health outcomes are mixed. A second study in China examined the effect of rural health insurance and a set of supply-side interventions referred to as the drug policy (Zhou et al., 2009).2 Using panel data, the study finds that the coinsurance rate and the drug policy were negatively associated with utilisation. Furthermore, the drug policy appears to have reduced outpatient health spending per visit. These two studies make a valuable contribution to the literature, but it remains unclear which components of the reforms worked because they could not differentiate between the demand-side and supply-side interventions. 2.2. Ningxia project While the central government specifies policies on premium contributions to the NCMS and the respective share between the central and local government, the exact design of the benefit package and how providers are paid are at the discretion of the local government. With the goal of improving access to health services and quality of care, the leadership of the Ningxia government worked with the project team to introduce several policy changes in the health sector. The collaboration provided an opportunity to evaluate the reforms in Ningxia province through the means of a social experiment. 2

The drug policy included contracting village doctors such that they were no longer allowed to charge fee-for-service or drug mark-ups and instead paid on a salary basis, a ban on village doctors purchasing drugs themselves, and intense monitoring of village clinic drug prescriptions.



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Ningxia Autonomous Region is a low income province in the north-west part of China. It has a population of 6.25 million. The ethnic composition is 63% Han and 36% Hui. With a per capita annual income of 15 551 RMB in urban areas and 6627 RMB in rural areas, Ningxia was the third poorest province in China in 2009 (Ningxia Statistical Yearbook, 2010). Life expectancy at the time of the study was 74 years. The most prevalent infectious diseases are viral hepatitis, tuberculosis, syphilis, dysentery and scarlet fever, and the most common non-communicable diseases are hypertension and cardiovascular diseases (Ningxia Centres for Diseases Prevention Control, 2011). Analysis of the situation suggested two major problems in the incentives faced by patients and providers prior to the start of the project. First, on the demand side, the NCMS covered primarily inpatient services. Incentives for patients were therefore skewed heavily in favour of inpatient care even if primary health services were more appropriate to address the health condition. This had implications for the efficiency of health services and the role of preventative services within the broader health system. Baseline data in 2009 showed that 54% of individuals did not seek any formal outpatient care when ill. Moreover, individuals were more likely to resort to township health centres and county hospitals than village doctors for outpatient care.3 Second, on the supply side, health providers in China charged a mark up of 15 to 20% on drug prescriptions. This has led to major problems of overprescription, encouragement of profitable drugs and, more generally, health expenditure growth. Recognising the problem, in 2006, the Government of Ningxia introduced a zero drug profit policy for village clinics and township health centres. This had a substantial impact on these providers’ income, especially village clinics, that used to earn over 95% of their revenue from drug sales. Baseline data on village doctors found that their income fell from about 12 000 RMB to only 4500 RMB after drug mark ups were removed. In the absence of prescription charges, the main source of village doctor income became a 100 RMB per month direct government subsidy, charges for intravenous (IV) drips and injections, and fees for vaccinations. Consequently, about one third of the village doctors stopped providing basic health care and instead worked as migrant workers in the urban areas. Township health centres fared slightly better because the government compensated them for the loss of drug profit by providing direct government support to pay for the basic salary of permanent staff. However, township health centres were still left to fend for themselves—in terms of compensating staff beyond the basic salary level (e.g. bonuses) and paying contract staff that typically accounted for one third of all personnel. In summary, prior to the reforms, primary care providers in Ningxia were faced with low powered incentives. Two major policy changes were initiated in select counties in Ningxia, acting on the demand side and supply side respectively. These policy interventions were introduced under the provincewide increase of government subsidies to NCMS premium from 80 to 120 RMB per person. The demand-side intervention consists of redesigning the benefit design of NCMS to reduce financial barriers when seeking primary health care at village clinics and township health centres. This is done by providing insurance coverage of outpatient services, with the highest reimbursement rates for village clinics (65%) followed by township health centres (50%) and county hospitals (30%) to motivate patients to use lower level rather than higher level facilities.4 In designing the provider payment intervention, the objectives were to provide incentives for village doctors and township health centres to improve efficiency and quality but also to pay them at a level that would motivate them to provide care, that is, a contract that satisfies the participation constraints. The intervention consists of a capitated budget with p4p. The capitation rate is estimated to include outpatient services at both the township health centres and village doctors. Seventy percent of the budget is disbursed at the beginning of a year, while the rest is withheld for performance assessment at mid-year and end of the year. Performance indicators during the study period included antibiotic (or multiple antibiotic) prescription rates and measures of patient satisfaction. These were later supplemented after the study period in year two of 3

Use of village clinics, township health centres, country hospitals and provincial hospitals conditional of being ill at baseline was 8%, 12%, 13%, and 6%, respectively. 4 While we refer to reimbursement rates, in practice, patients do not get reimbursed for payments made upfront. Rather, they pay only their co-payment share at the point of care without any need for ‘reimbursement’. Copyright © 2014 John Wiley & Sons, Ltd.



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implementation with a set of process of care measures associated with common acute and chronic health conditions. To prevent providers from reducing the volume of service, quantity thresholds are specified in the contract. Pre-paid funds would have to be returned to NCMS if a provider does not meet the volume threshold. To further overincentivise a village doctor to provide basic health care, they are also paid a per visit fee of 2 RMB or 4 RMB if it is a home visit. The capitation budget is estimated such that if providers satisfy the volume threshold and meet performance standards, village doctors will on average earn 12 000 RMB a year, while township health centres will earn enough to break even, covering operating expenses.5 Interventions were introduced in 2010. From 2009 to 2010, there were a few provincial policies that affected both the intervention and comparison sites equally; in particular, government subsidies for NCMS premium increased from 80 to 120 RMB per person per year, and monthly subsidies for village doctors increased from 100 to 200 RMB per month.

3. RESEARCH DESIGN 3.1. Study design The impact evaluation is based on a quasi-experimental design in five counties of the Ningxia province, with a cluster randomised experiment embedded within several of the counties. The Ningxia province has 22 counties, about half of which are mountainous while the others lie in the plains or valleys. With the provincial government, we selected two mountainous counties with no recent pilot projects of health sector reforms. We then matched three other counties to act as comparison sites. These counties are also located in mountainous areas and have similarly low levels of income and poor access to health care. They serve as comparison sites in the sense that they did not experience the specific interventions of our project but did experience the same provincewide policy changes as the two intervention counties. Our choice of counties means that the results are most generalisable to rural areas in China with similarly low levels of development. In the two intervention counties, some areas were exposed to the redesign of the NCMS benefit package only (the demand-side intervention), while other areas were introduced to changes in both the NCMS benefit package and the provider payment system (the supply-side and demand-side intervention combined). More precisely, half of the 28 township health centres (along with the village clinics within the town catchment) in the two intervention counties were randomly selected to receive the capitation plus p4p intervention, while no changes were made to the payment system in the remaining areas. Matched-pair randomisation was used with the township health centre and its catchment area as the unit of randomisation (King et al., 2007). Township health centres were paired before randomisation in such a way as to ensure matches were as similar as possible on the basis of a Mahalanobis distance measure derived from data on a set of baseline characteristics. In each pair, one township health centre was randomly assigned to receive the provider payment intervention. Our analysis is based on a comparison between three study arms. In one arm, the NCMS benefit package was expanded. In the second arm, there were changes to both the NCMS benefit package and the provider payment system. Finally, the third arm comprises counties with neither of the two interventions and thus serves as a comparison site. We identify the impact of the two intervention packages by comparing over time outcomes in the first and second arm with those in the third arm. For reasons given in the succeeding texts, we do not exploit randomisation of the provider payment intervention by comparing outcomes in the first and second arm. Table I summarizes the demand and supply-side policies in the three arms at baseline and post-intervention. Both the first and second arms experienced the same NCMS benefit package redesign to provide outpatient reimbursement, whereas the NCMS benefit package in the third arm remains similar between baseline and post-interventions, with the benefit package primarily covering only inpatient services. In terms of supply-side 5

A more detailed description of the capitation plus pay-for-performance intervention is given elsewhere (Yip et al., 2014).




Provider payment

Benefit design

County hospital

Township health centre

Township health centre County hospital Ceiling (per capita per year) Village clinic

Deductible Reimbursement rate for the following: Village clinic

Fee-for-service (with zero drug profit); government subsidy for basic salary of permanent staff Fee-for-service (with drug profit)

2010

Same as baseline

Same as baseline

Same as baseline

30% 150

0% 120 Fee-for-service (with zero drug profit); government subsidy of 100 RMB per month

50%

65%

0% 30; 35%

0

0

Baseline

Benefit package only

Fee-for-service (with zero drug profit); government subsidy for basic salary of permanent staff Fee-for-service (with drug profit)

Fee-for-service (with zero drug profit); government subsidy for basic salary of permanent staff

0% 120

30; 35%;

0%

0

Baseline

Same as baseline

Capitation + pay-forperformance (p4p); government subsidy for basic salary of permanent staff Capitation + p4p

30% 150

50%

65%

0

2010

Benefit package + provider payment

Table I. Description of interventions by study arm

Fee-for-service (with zero drug profit); government subsidy for basic salary of permanent staff Government subsidy for permanent staffs; fee for IV/injection/tests with zero drug profit Fee-for-service (with drug profit)

1 RMB co-payment for two counties; 0% for one county 0% for two counties; 45% for one county 0% 120

0

Baseline

2010

Same as baseline

Same as baseline

Same as baseline

0% 150

Same as baseline

Same as baseline

0

Comparison

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policies, the second arm experienced a change from fee-for-service (with zero profit for drug prescription) to capitation plus p4p, whereas the first and third arm continued to pay primary care providers by fee-for-service (with zero profit for drugs). We hypothesize that both the demand only and demand plus supply-side interventions would increase the probability of seeking care, especially at the village level where the increase in reimbursement rate is largest. We further posit that the increase is greatest for the demand plus supply arm through attracting more village doctors to provide basic health care in the village. Finally, we anticipate that, with the redesign of the insurance benefit package, providers paid by fee-for-service will increase use of IV drip and injection as they can charge a fee for these services separately. This increase is predicted to be smaller in towns that experienced reform in provider payment methods to capitation and p4p. 3.2. Data collection We use data from two rounds of a panel household survey that we conducted in the five study counties. The sampling frame consists of the universe of all towns, villages and households in the five study counties. In each of the five study counties, we sampled every town and then stratified villages according to their economic situation (i.e. rich, middle and poor). We selected 40% of villages in each stratum using a random number generator. Within the two intervention counties, in each sampled village, we randomly selected 33 households, and in the three comparison counties, in each sampled village, we randomly select 20 households. This yields a similar sample size of households in the intervention and comparison counties.6 The first round of data collection was conducted in February 2009, before the interventions described previously were introduced. The second round of the survey was carried out in early 2011, after the policy reforms were initiated in the two intervention counties. This round sought to reinterview the same households as at baseline using a similar questionnaire. Households that could not be followed, for whatever reason (e.g. family moved away), were replaced by a randomly selected household in the same village. Moreover, if a person in the baseline sample established a separate household in the same village, all persons in the new household were followed up. The two intervention counties consist of 28 towns, 266 villages and 133 400 households, and the control counties consist of 47 towns, 651 villages and 224 000 households. A total of 6702 households (30 393 individuals) were interviewed in 260 villages in the first survey round. Of these, 5407 households (23 750 individuals) were contacted in the second round survey and reinterviewed—a household attrition rate of 19%. The households lost to follow-up were replaced by 1161 new households in the second round survey. The replacement households are similar to the households lost to follow-up on characteristics (e.g. gender and ethnicity) that are not expected to change over time. The household questionnaire consists of 10 modules, the majority of which collect information from each household member. It includes a module on the following: (i) basic household and individual characteristics; (ii) illness, injury and outpatient visits; (iii) inpatient care sought; (iv) chronic disease patients; (v) self-reported health of adults; (vi) maternal health care; (vii) child health care; (viii) elderly individuals; (ix) health behaviour and knowledge; and (x) household consumption. This paper exploits data mainly from the first three modules. 3.3. Study outcomes and descriptive statistics We examine the effect of the policy reforms on various measures of health care utilisation. As for outpatient care, our main outcome is whether the individual sought care in the past 2 weeks. Outpatient care can be sought at different levels of the public health system: village clinic, township health centre, county hospital and provincial hospital. The descriptive statistics in panel A of Table II show that almost 50% of individuals visited a doctor when sick at baseline. The most common type of health provider for outpatient care is the county hospital, followed by the township health centre, village clinic and provincial hospital. 6

As of 2009, the population was about 600 000 in the intervention counties and 1 000 000 in the comparison counties.



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Table II. Descriptive statistics at baseline in the three study arms Benefit package

Benefit package + provider incentives

Comparison counties

Mean (1)

SD (2)

Mean (3)

SD (4)

Mean (5)

SD (6)

Panel A: outcomes Doctor visit past 2 weeks if ill At village clinic At township health centre At county hospital At provincial hospital No treatment sought or self-treated Injection (if sought care) Intravenous drip (if sought care) Oral medicine (if sought care) Inpatient visit past year At township health centre At county hospital At provincial hospital Number of admission past year

0.460 0.087 0.136 0.116 0.072 0.540 0.171 0.378 0.853 0.075 0.023 0.033 0.019 0.097

0.499 0.282 0.342 0.320 0.259 0.499 0.376 0.485 0.355 0.263 0.149 0.179 0.136 0.399

0.442 0.082 0.084 0.149 0.044 0.558 0.176 0.402 0.820 0.064 0.012 0.040 0.012 0.082

0.497 0.274 0.278 0.356 0.205 0.497 0.381 0.491 0.384 0.245 0.107 0.197 0.109 0.373

0.473 0.079 0.137 0.138 0.059 0.527 0.234 0.368 0.823 0.069 0.025 0.031 0.012 0.095

0.499 0.270 0.344 0.345 0.235 0.499 0.424 0.482 0.382 0.253 0.156 0.173 0.110 0.432

Panel B: covariates Self-reported ill in past 2 weeks Chronic illness diagnosed Male Han Hui Other ethnicity Age (years) No education Elementary school Middle school High school or above Migrant worker Wealth asset score Distance from village clinic (km) Distance town health centre (km) Distance from county hospital (km) Head of the household Female head of household Household size (members)

0.181 0.141 0.518 0.470 0.527 0.003 30.3 0.295 0.399 0.233 0.072 0.177 0.251 3.9 19.9 107.4 0.223 0.037 5.0

0.385 0.348 0.500 0.499 0.499 0.054 19.4 0.456 0.490 0.423 0.259 0.382 0.927 13.6 16.4 66.5 0.416 0.188 1.4

0.154 0.131 0.522 0.608 0.387 0.005 31.4 0.293 0.360 0.252 0.095 0.182 0.289 5.5 20.4 82.4 0.231 0.038 4.8

0.361 0.337 0.500 0.488 0.487 0.067 19.4 0.455 0.480 0.434 0.293 0.386 1.014 27.9 16.9 56.9 0.422 0.192 1.4

0.151 0.073 0.520 0.393 0.602 0.005 29.2 0.306 0.398 0.217 0.079 0.166 -0.164 3.2 14.1 76.5 0.212 0.039 5.2

0.358 0.261 0.500 0.489 0.489 0.068 19.4 0.461 0.489 0.413 0.270 0.372 0.987 20.7 13.1 53.4 0.409 0.194 1.4

Data are from the first wave of the household survey. The unit of observation is an individual. Descriptive statistics on utilisation are shown for doctor visits conditional on being ill in the past 2 weeks.

During outpatient care, patients may receive an injection, IV drip, oral medicine or various combinations of the three. As shown in panel A and Table II, oral medicine is the most common type of treatment, given in more than 80% of outpatient cases. Patients are given an IV drip in more than one third of outpatient cases. Injections are the least common type of treatment, but still, around one fifth of patients report receiving one when they visited a doctor in the past 2 weeks. We measure inpatient care both at the extensive margin and in terms of the number of admissions in the past year. The descriptive statistics in panel A of Table II show that 7% of individuals used inpatient care in the previous year, and the number of admissions was approximately 0.09 per capita each year. Inpatient care was most frequently sought at the county hospital, followed by the township health centre and lastly the provincial hospital. It is worth noting that outpatient care use at baseline was fairly similar in the intervention and comparison counties. If anything, utilisation was slightly higher at baseline in the comparison counties. As for inpatient care, intervention and comparison counties are also similar at baseline. Descriptive statistics on the characteristics of individuals in our sample are presented in panel B of Table II. Except for chronic illness, asset wealth and distance to the county hospital, the intervention and comparison samples appear fairly similar. Copyright © 2014 John Wiley & Sons, Ltd.



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4. EMPIRICAL STRATEGY We estimate the effect of the interventions using a difference-in-difference approach. The empirical strategy compares changes in utilisation of health care over time in the two counties where the reforms were introduced compared with the three counties that continued with the status quo. We further exploit within county variation in the package of interventions to separate out the effect of expanding the NCMS benefit package only (the demand-side intervention) from that of changing both the insurance benefit package and the provider payment system (the supply-side and demand-side intervention combined). More precisely, we run a regression of health care use on a dummy for whether the town introduced both reforms and a dummy for whether the town expanded the insurance package only, while controlling for year and village fixed effects. Formally, let yivt denote our measure of health care utilisation for individual i in village v in year t. Let NCMSwt be a dummy for whether the insurance benefit package alone was expanded and PPSwt a dummy for whether changes to both the insurance package and provider payment system were introduced in town w at time t. The model is of the form yivt ¼ β0 þ β1 NCMSwt þ β2 PPSwt þ X ivt β3 þ γv þ δt þ εivt ;

(1)

where γv and δt are the village and year fixed effects respectively, and Xivt is a vector of individual and household characteristics that include whether the individual has a chronic disease, age, age squared, gender, gender of the household head, household size, asset wealth, education, distance from the nearest health facility of each type, ethnicity, whether the individual is the household head, and migrant status. Treatment effects are estimated by ordinary least squares, and we accounted for the clustered nature of the data by clustering standard errors at the village level. We analyse the effect of the reforms on various measures of health care use. Utilisation is measured at each level of the health provider: village clinic, town clinic, county hospital and provincial hospital. We also distinguish between inpatient and outpatient care. We use measures of outpatient care utilisation that are conditional on being sick. No screening question on illness was used when asking respondents about inpatient care seeking in the household survey. The identifying assumption underpinning the analysis is that health care utilisation in counties that enacted the policy reforms would not in the absence of the programme have changed differently from the comparison counties. In other words, we assume that NCMSwt and PPSwt are orthogonal to the disturbance term in Eqn 1. When this assumption holds, β1 identifies the causal effect of expanding the benefit package only on the use of health care. Similarly, β2 identifies the effect on utilisation of changing the insurance package and the provider payment method in combination. Given the design, there is no guarantee of balance between the two intervention and three comparison counties. However, in several respects, the data are reassuring for the identifying assumption that Cov(NCMSwt, εivt) = 0 and Cov(PPSwt, εivt) = 0. First, as previously mentioned, health care utilisation at baseline was similar in the two intervention arms and the comparison counties. Second, when we estimate the propensity score of being in an intervention county for each household based on our set of baseline covariates, we find the distribution of the score by treatment status to be fairly balanced in observables (Figure A1 and Figure A2).7 Our econometric strategy then further controls for unobserved time-invariant effects between villages. A common way of assessing the parallel trend assumption underpinning the difference-in-difference approach is to analyse pre-trends. If trends in the outcome diverge between treatment and control prior to the intervention, we can have little confidence that the difference-in-difference estimator will provide an unbiased measure of impact. With only two survey waves, unfortunately, we lack the data to perform this robustness check. However, it is important to note that we sought to minimise the risk of confounding trends by selecting comparison counties that were similar to the intervention counties in that none of them has been exposed to any health policy or 7

As the basis of a comparison, refer to the propensity score histograms in the paper by Wagstaff et al. (2009) that uses a difference-in-difference approach to estimate the impact of the NCMS in China.



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reform intervention beyond those that were introduced across the entire province. The research team had a permanent presence in the study area, and to the best of our knowledge, there was no differential introduction of a health policy or any macroshock that would have affected the outcome variables of interest. We recognise that our research design provides an opportunity to isolate the effect of changes to the provider payment system by exploiting town level random assignment of the supply-side intervention. However, such an analysis is beyond the scope of this paper, and we have reported the results elsewhere (Yip et al., 2014). Given that the purpose of the provider payment intervention is to improve quality of care and efficiency, it would be inappropriate to rely solely on data from the household survey—the focus of this paper—as the primary basis for evaluation.

5. RESULTS 5.1. Use of outpatient care Table III presents difference-in-difference estimates of the effect of the policy reforms enacted in Ningxia province on use of outpatient care at the extensive margin. As previously described, our design allows us to estimate separately the effect of expanding the NCMS benefit package (the demand-side intervention) only and the effect of both expanding the insurance package and changing the provider payment system (the supply-side and demand-side intervention combined). We show the statistic and p-value of the test for a difference between the two point estimates under each study outcome. The effect of expanding the benefit package on the probability of visiting a doctor in the past 2 weeks was positive although imprecisely estimated (column 1). The combined effect of the two interventions is also positive and of a greater magnitude but not statistically significant. The estimate of effect in column 1 suggests the supply-side and demand-side interventions in combination were associated with a 3.6 percentage point increase (from a base of 49.3%) in the probability of visiting a doctor if ill in the past 2 weeks. The F-statistics suggest that there is no difference in the effect of the two intervention packages. Column 2 reports the effect on the probability of no formal care being sought, that is, self-treatment or no treatment. The results, as expected, mirror those in column 1. Table III. Use of outpatient medical care at the extensive margin By type of health facility Doctor visits in past 2 weeks (1) Benefit package Benefit package + provider incentives 2011 F-statistics on test (single = combined) p-value Comparison group mean Villages Observations R squared

Self-treated or no treatment sought (2)

Village clinic (3)

Township health centre (4)

Country hospital (5)

Provincial hospital (6)

0.015 (0.037) 0.036

0.015 (0.037) 0.036

0.053 (0.026)** 0.060

0.001 (0.024) 0.021

0.010 (0.020) 0.008

0.031 (0.015)** 0.007

(0.038) 0.052 (0.022)** 0.243 0.622 0.493 260 8583 0.014

(0.038) 0.052 (0.022)** 0.245 0.621 0.507 260 8583 0.014

(0.030)** 0.052 (0.017)*** 0.043 0.835 0.112 260 8583 0.031

(0.022) 0.002 (0.017) 0.954 0.330 0.140 260 8583 0.005

(0.024) 0.012 (0.012) 0.506 0.478 0.122 260 8583 0.010

(0.013) 0.011 (0.010) 2.504 0.115 0.057 260 8583 0.007

Data are from the two waves of the household survey. Regressions are estimated by ordinary least squares (OLS). Robust standard errors clustered at the village level are reported in parentheses. The models include fixed effects for survey wave and village. Demographics include controls for age, age squared, family size, household wealth, distance from the nearest of each type of health provider and dummies for chronic disease, gender, migrant status, household head, ethnicity, and educational attainment. Measures of utilisation are conditional on being ill in the past 2 weeks. The unit of observation is an individual. *denotes significance at 10%; **at 5%; ***at 1% level. Copyright © 2014 John Wiley & Sons, Ltd.


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Table IV. Specific treatments administered during outpatient visit

Benefit package Benefit package + provider incentives 2011 F stat on test (single = combined) p value Comparison group mean Villages Observations R squared

Injection in the past 2 weeks (any) (1)

IV drip in the past 2 weeks (any) (2)

Oral medicine in the past 2 weeks (any) (3)

0.116 (0.039)*** 0.090 (0.044)** 0.087 (0.028)*** 0.360 0.549 0.206 260 4169 0.030

0.116 (0.051)** 0.053 (0.045) 0.058 (0.029)** 1.353 0.246 0.339 260 4169 0.026

0.061 (0.036)* 0.028 (0.045) 0.051 (0.026)** 0.553 0.458 0.839 260 4169 0.008

Data are from the two waves of the household survey. Regressions are estimated by OLS. Robust standard errors clustered at the village level are reported in parentheses. The models include fixed effects for survey wave and village. Demographics include controls for age, age squared, family size, household wealth, distance from the nearest of each type of health provider and dummies for gender, migrant status, household head, ethnicity, and educational attainment. The outcomes are conditional on an outpatient visit in the past 2 weeks. The unit of observation is an individual. *denotes significance at 10%; **at 5%; ***at 1% level.

In columns 3 to 6, we analyse the impact of the interventions on use of care at each level of public health provider. At the village clinic level, there was a positive and significant effect of expanding the insurance package of 5.3 percentage points or 47% on use of outpatient care (column 3). When combined with changes to the provider payment system, the effect on the probability of a village clinic visit if sick was 6.0 percentage points (column 3). This is equivalent to a 54% increase in utilisation and is significant with 95% confidence. We find no effect of either of the set of interventions on utilisation at township health centres (column 4) or county hospitals (column 5). At provincial hospitals, there is a negative effect of the insurance intervention (column 6), an unexpected finding that is difficult to explain. In no case is the impact of the two intervention packages statistically different from each other. The findings on outpatient care suggest that the redesign of the insurance benefit package increased use of outpatient care in village clinics. There is little evidence to suggest that the supply-side and demand-side reforms in combination had any greater impact. Taken together, the findings on outpatient care are mixed as to whether the two interventions had the anticipated effect. The reimbursement rate was made considerably more generous for care received at village clinics and, to a lesser extent, at township health centres and county hospitals. These changes to the benefit package are in line with our finding of a large effect on health care use at village clinics. We had anticipated that the capitation with p4p would provide better incentives for village doctors to stay in the village to provide care (rather than spending some time as migrant workers) and therefore had a bigger impact on increasing use of village clinics. However, we did not find such effect. Perhaps it will take more time for village doctors to readjust their labour input than that covered by this present paper. 5.2. Outpatient treatment For individuals who report using outpatient services, we asked whether they had received any of the following treatment: an injection, an IV drip, and oral medicine.8 We examine the effect of the interventions on whether an individual received each treatment in the past 2 weeks, conditional on using outpatient care. Defined in this way, these outcomes can be considered a proxy measure for the intensity of treatment during outpatient care. 8

The responses are not mutually exclusive since an individual can report receiving more than one treatment.



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Our empirical approach remains the same. While informative, we recognise that this analysis may suffer from selection problems given that the outcomes are conditional on seeking care and the intervention may have affected patient mix. Table IV shows that the redesign of the insurance package was associated with an increase in injections (column 1) and the use of IV drips (column 2) conditional on outpatient care. The effect is large, on both the probability of an injection (11.6 percentage points or 56%) and the probability of an IV drip (11.6 percentage points or 34%). The negative effect on oral medicines suggests possible substitution between different treatments as a result of expanding the benefit package (column 3). These results may reflect both greater demand from patients and supplier-induced demand. On the one hand, patients may perceive injections to be more efficacious than oral medicines. Redesign of the insurance package provides greater purchasing power that, in turn, increases demand for services that may be perceived to be of higher quality. On the other hand, because providers receive zero profit from prescription of oral medication but a fee from IV drip and injection, they may change how drugs are administered to maximise revenue. The combined effect of the demand and supply intervention package is in the same direction, although in each case, the magnitude of effect is not as large (columns 1 to 3). The differences in the point estimates of the single and combined intervention are not statistically different, but there is a suggestion that reforms to the provider payment system may have moderated the shift towards greater treatment intensity resulting from the redesign of the insurance package. With these outcome measures, it is not possible to comment on what these results mean for the appropriateness of care. 5.3. Use of inpatient care We now turn to various measures of inpatient care utilisation. Table V first reports results on the extensive margin. The point estimates in column 1 show that redesign of the benefit package is associated with a reduction in the probability of being admitted in the past year (0.4 percentage points), and the corresponding treatment effect for the combined package of interventions is positive, albeit small (0.3 percentage points). In neither case are the point estimates significant at conventional levels. Table V. Admissions

Benefit package Benefit package + provider incentives 2011 F-statistics on test (single = combined) p-value Comparison group mean Villages Observations R squared

Inpatient care in past year (any) (1)

Inpatient care at township health centre (any) (2)

0.004 (0.006) 0.003

0.000 (0.003) 0.002

(0.005) 0.005 (0.004) 1.308

(0.003) 0.003 (0.002) 0.262

0.254 0.073 260 57 921 0.067

0.609 0.024 260 57 921 0.021

Inpatient care at county hospital (any) (3) 0.001 (0.004) 0.002

Inpatient care at provincial hospital (any) (4)

Number of admissions in past year (5)

0.003 (0.003) 0.001

0.003 (0.009) 0.008

(0.004) 0.006 (0.002)** 0.375

(0.002) 0.003 (0.002)* 0.616

(0.009) 0.006 (0.006) 1.288

0.541 0.034 260 57 921 0.034

0.433 0.014 260 57 921 0.013

0.257 0.100 260 57 910 0.063

Data are from the two waves of the household survey. Regressions are estimated by OLS. Robust standard errors clustered at the village level are reported in parentheses. The models include fixed effects for survey wave and village. Demographics include controls for age, age squared, family size, household wealth, distance from the nearest of each type of health provider and dummies for gender, migrant status, household head, ethnicity, and educational attainment. The unit of observation is an individual. *denotes significance at 10%; **at 5%; ***at 1% level. Copyright © 2014 John Wiley & Sons, Ltd.


Copyright © 2014 John Wiley & Sons, Ltd. 0.050 (0.028)* 0.061 (0.035)* 0.053 (0.019)*** 0.091 0.763 0.114 260 6365 0.031

0.063 (0.052) 0.072

(0.053) 0.041 (0.035) 0.025

0.875 0.108

246 2218 0.041

Some education (2)

252 3212 0.039

0.433 0.127

(0.041) 0.043 (0.019)** 0.617

0.082 (0.038)** 0.045

Poorest (3)

254 2885 0.031

0.687 0.092

(0.040)*** 0.012 (0.025) 0.163

0.107 (0.037)*** 0.124

Middle (4)

0.479 0.105 250 4038 0.044

0.479 0.115

(0.049)** 0.057 (0.023)** 0.502

0.086 (0.042)** 0.126

0.044 (0.054) 0.007 (0.057) 0.118 (0.041)*** 0.503

1 km or less (6)

251 4654 0.020

0.165 0.108

(0.031) 0.048 (0.021)** 1.936

0.030 (0.033) 0.016

More than 1 km (7)

Distance to village clinic Richest (5)

227 2486 0.033

Household wealth

260 3772 0.034

0.820 0.116

(0.038) 0.068 (0.023)*** 0.052

0.040 (0.033) 0.031

Male (8)

0.523 0.108

(0.033)** 0.039 (0.019)** 0.409

0.062 (0.029)** 0.085

Female (9)

260 4811 0.031

Gender

238 1049 0.050

0.394 0.124

(0.075)** 0.045 (0.042) 0.729

0.089 (0.058) 0.150

Yes (10)

260 7534 0.032

0.803 0.110

(0.031)* 0.051 (0.017)*** 0.062

0.048 (0.028)* 0.056

No (11)

Migrant worker

Data are from the two waves of the household survey. Each column represents a separate regression, estimated by OLS. Robust standard errors clustered at the village level are reported in parentheses. The models include fixed effects for survey wave and village. Demographics include controls for age, age squared, family size, household wealth, distance from the nearest of each type of health provider and dummies for gender, migrant status, household head, ethnicity, and educational attainment. The outcomes are conditional on an outpatient visit in the past 2 weeks. The unit of observation is an individual. *denotes significance at 10%; **at 5%; ***at 1% level.

F stat on test (single = combined) p value Comparison group mean Villages Observations R squared

2011

Benefit package + provider incentives

Benefit package

No education (1)

Education of household head

Table VI. Heterogeneity in impact of expanded NCMS package and provider incentives on village clinic utilisation


767


768


We next disaggregate the results by type of health provider. The treatment effect for both intervention packages is close to zero and statistically insignificant for admissions at township health centres (column 2), county hospitals (column 3) and provincial hospitals (column 4). Consistent with the main results on the extensive margin, the point estimates are small and statistically insignificant when we specify the number of admissions over the past year as the dependent variable (column 6). These findings suggest that any increase in the use of outpatient care at village clinics reflects not a shift away from inpatient care but rather a change in care seeking behaviour amongst those who, in the absence of the policy change, would have not used any formal care. On balance, this is probably a positive development even if the primary intention of the combined reform was to shift patients away from the inappropriate use of more costly inpatient services. 5.4. Heterogeneity We examine heterogeneity in the impact of expanding the insurance benefit package with respect to characteristics of the household. Table VI reports estimates from a subgroup analysis in which we explore heterogeneity with respect to baseline values of education of the household head, household wealth, distance to the nearest village clinic, gender and migrant worker status. The dependent variable is use of any village clinic visit if ill in the past 2 weeks. We first split the sample according to whether the household head has any education. Columns 1 and 2 show that individuals living in households in which the head has no education and those in which the head has some education were similarly responsive to the intervention packages. We then use household wealth to split the sample into terciles. The results show that individuals in the middle tercile were most responsive to the interventions (column 4). Meanwhile, the point estimates are positive albeit smaller for the poorest individuals (column 3) and close to zero or negative for the richest individuals (column 5). The pattern of results is very similar when we use non-medical consumption instead of wealth to split the sample (result not shown). The interventions had a large impact on individuals living within 1 km of a village clinic (column 6) and a negligible effect on those living further away (column 7). The results indicate that women (column 8) were slightly more responsive to the policy changes than men (column 9). Finally, we can see that the interventions had a larger effect on migrant workers (column 10) than on non-migrant workers (column 11). Across the subgroup analyses, we find little evidence of differences in the impact of the two intervention packages.

6. DISCUSSION In this paper, we assessed the impact of two key elements of China’s health care reform in the province of Ningxia. Our findings suggest that redesigning the NCMS benefit package to cover outpatient services with a tiered structure that reimburse lower level facilities more generously increased the likelihood of people using outpatient care at village clinics. The increase in use of village clinics did not arise from a substitution away from inpatient use, perhaps because the reimbursement rates for hospitalisations did not change. The two policy changes in combination showed no effect on utilisation of village clinics over and above that generated by the redesigned NCMS benefit package, implying that the supply-side intervention failed to change care seeking behaviour. Before discussing the implications of the findings, it is important to recognise a number of limitations of the paper. First, we considered utilisation outcomes only. Although central to the evaluation of the health reforms, these outcomes only tell part of the story, and a full account of implications for welfare will require an analysis of out-of-pocket spending and health status. We have examined the effects of the capitation plus p4p intervention on a range of quality and efficiency outcomes, and these are reported elsewhere (Yip et al., 2014). Second, as with any quasi-experimental approach, there is the possibility of bias in our estimates of impact. While we Copyright © 2014 John Wiley & Sons, Ltd.



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controlled for unobserved heterogeneity through the village fixed effects, time-varying confounding remains a concern. Third, the paper gave limited space to the question of how the policy changes were implemented. While research of the process of implementation will make for a richer understanding, the analysis of impacts is a key element of an evaluation. Finally, our findings were applicable to Ningxia province, and we are cautious in generalising to other provinces. Broader conclusions must come from a complete examination of the available evidence in China, including this paper. We view the impact on village clinic care seeking behaviour in a positive light but note that without an analysis of health outcomes and measures of financial strain, the welfare implications of the insurance intervention remain unclear. Any gains from improved health and financial risk protection must be weighed against the costs of accessing more care. The analysis of heterogeneity with respect to wealth suggested that the interventions had the largest effect on the care seeking behaviour of households in the middle tercile, implying that the reforms have been relatively neutral in terms of the equity impact. Low and middle income countries striving to achieve universal health coverage in recent years have typically introduced insurance benefit packages that cover inpatient services only. While such insurance schemes were intended to protect people from major financial risk associated with medical care, such a design can lead to a growth in hospitalisations. By comparing different benefit designs, this paper goes beyond the existing literature that has tended to evaluate health insurance against a counterfactual of no insurance. As insurance coverage in countries increases, the more important policy question is how to design the benefit package for maximum gain. This paper shows that an expanded insurance package can change care seeking patterns, but there does not appear to have been a shift away from more costly inpatient care. The findings are therefore inconclusive with respect to the question of whether a more rational design of insurance benefits can lead to behavioural change in patients’ demand for less costly outpatient care services. In relation to the combined effect of the two policy changes, there are several possible explanations for why demand for health care was not affected. First, village doctors in both the intervention and comparison areas experienced an increase in income. It is possible that the income effect of attracting (or retaining) village doctors swamped any effect resulting from the change in incentives. Second, implementation of the new payment mechanism, particularly the p4p element, was initially slow, and it took the best part of a year until the system was firmly in place. It seems unlikely any resulting improvements in the quality of care could have influenced demand in the short period of time prior to the household survey. However, even from a theoretical standpoint, it is not clear that the change to a capitation payment mechanism increases demand for health care, particularly when patients are unable to perfectly observe provider behaviour. It is questionable how sensitive health seeking behaviour will be to improvements in quality, at least in the short term. This is an area that requires further evaluation with more time elapsed. Redesign of the insurance benefit package to cover outpatient services was associated with large increases in injections and the use of IV drips, which, in part, substituted oral medicines at village clinics, consistent with supply-side moral hazard behaviour. With these data, we are not able to understand whether these impacts reflected an increase in the provision of unnecessary care. What is clear is that the fee-for-service system in operation at the time of study contained strong incentives to administer drugs via injections and IV drips instead of orally. In other words, there were few mechanisms in place to counter demand-side and supply-side moral hazard behaviours generated by the incentives in place. Recent evidence from China also provides reasons for concern. Various studies have found that the provision of unnecessary care is rife. Liu and Mills (1999) find that 20% of expenditures for the treatment of appendicitis and pneumonia were justified on clinical grounds. Zhang et al. (2003) show that only a small proportion of prescriptions at village clinics and township health centres are clinically appropriate. Finally, (Zhan et al. (2004)) argue that the financial incentives behind the delivery of TB services led to inappropriate practices, including the provision of unnecessary procedures and drugs. Although far from conclusive, there was a suggestion from our findings that the introduction of the capitation payment system may have dampened the increase in injections and IV drips resulting from the redesign of the insurance package. In sharp contrast to fee-for-service, the capitation budget provides doctors with a strong Copyright © 2014 John Wiley & Sons, Ltd.


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nudge to keep costs to a minimum. In the Chinese context, this is likely to be a positive development. Given the statistical uncertainty in these results, it will be important to conduct a follow-up analysis with the third wave of household data, collected once the new payment system was firmly institutionalised. We also plan to measure quality of care processes using data extracted directly from the project’s health management information system that is operational in village clinics and township health centres. Together, these data will provide a stronger foundation with which to examine the question of whether the payment system influences the extent of unnecessary care.

APPENDIX

0

.2

.4 .6 Propensity Score Untreated

.8

1

Treated

Figure A1. Propensity scores for insurance package only and comparison counties at baseline

0

.2

.4 .6 Propensity Score Untreated

.8

1

Treated

Figure A2. Propensity scores for combined intervention and comparison counties at baseline ACKNOWLEDGEMENTS

This work was funded by the Bill and Melinda Gates Foundation and through an EU-FP7 research grant (HEALTH-F2-2009-223166-HEFPA) on ‘Health Equity and Financial Protection in Asia’. Copyright © 2014 John Wiley & Sons, Ltd.



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ETHICS The study received ethical approval from University of Oxford Ethical Review Committee and Ningxia Medical University Ethical Review Board.

REFERENCES

Acharya A, Vellakkal S, Taylor F, Masset E, Satija A, Burke M, Ebrahim S. 2012. The Impact of Health Insurance Schemes for the Informal Sector in Low- and Middle-Income Countries: A Systematic Review. The World Bank Research Observer. Babiarz KS, Miller G, Yi H, Zhang L, Rozelle S. 2010. New evidence on the impact of China’s New Rural Cooperative Medical Scheme and its implications for rural primary healthcare: multivariate difference-in-difference analysis. BMJ 341: 1–9. Babiarz KS, Miller G, Yi H, Zhang L, Rozelle S. 2012. China’s new cooperative medical scheme improved finances of township health centers but not the number of patients served. Health Affairs 31: 1065–1074. Basinga P, Gertler PJ, Binagwaho A, Soucat ALB, Sturdy J, Vermeersch CMJ. 2011. Effect on maternal and child health services in Rwanda of payment to primary health-care providers for performance: an impact evaluation. The Lancet 377: 1421–1428. Bitran R, Yip W. 1998. A Review of Health Care Provider Payment Reform in Selected Countries in Asia and Latin America. Partnerships for Health Reform, Major Applied Research 2, Working Paper 1. Cooper Z, Gibbons S, Jones S, McGuire A. 2011. Does hospital competition save lives? evidence from the english NHS patient choice reforms. The Economic Journal 121: f228–f260. Deaton A. 2010. Instruments, randomization, and learning about development. Journal of Economic Literature 48: 424–455. Finkelstein A, Taubman S, Wright B, Bernstein M, Gruber J, Newhouse JP, Allen H, Baicker K, Group OHS. 2012. The oregon health insurance experiment: evidence from the first year*. The Quarterly Journal of Economics 127: 1057–1106. Flodgren G, Eccles MP, Shepperd S, Scott A, Parmelli E, Beyer FR. 2011. An overview of reviews evaluating the effectiveness of financial incentives in changing healthcare professional behaviours and patient outcomes. Cochrane Database of Systematic Reviews 7: 1–94. Gosden T, Forland F, Kristiansen IS, Sutton M, Leese B, Giuffrida A, Sergison M, Pedersen L. 2001. Impact of payment method on behaviour of primary care physicians: a systematic review. Journal of Health Services Research & Policy 6: 44–55. Gosden T, Sibbald B, Williams J, Petchey R, Leese B. 2003. Paying doctors by salary: a controlled study of general practitioner behaviour in England. Health Policy 64: 415–423. King G, Gakidou E, Imai K, Lakin J, Moore RT, Nall C, Ravishankar N, Vargas M, Téllez-Rojo MM, Ávila JEH, Ávila MH, Llamas HH. 2010. Public policy for the poor? A randomised assessment of the Mexican universal health insurance programme. The Lancet 373: 1447–1454. King G, Gakidou E, Ravishankar N, Moore R, Lakin J, Vargas M, Téllez-Rojo M, Hernández Ávila J, Ávila M, Llamas H. 2007. “Politically robust” experimental design for public policy evaluation, with application to the mexican universal health insurance program. Journal of Policy Analysis and Management 26: 479–506. Lagarde M, Haines A, Palmer N. 2007. Conditional cash transfers for improving uptake of health interventions in low- and middle-income countries: A systematic review. JAMA 298: 1900–1910. Lagarde M, Powell-Jackson T, Blaauw D. 2010. Managing incentives for health providers and patients in the move towards universal coverage. (Ed)^(Eds), Global Symposium on Health Systems Research: Montreux, Switzerland. Lei X, Lin W. 2009. The new cooperative medical scheme in rural china: does more coverage mean more service and better health? health economics 18: s25–s46. Liu X, Mills A. 1999. Evaluating payment mechanisms: how can we measure unnecessary care? Health Policy and Planning 14: 409–413. Liu X, Mills A. 2003. The influence of bonus payments to doctors on hospital revenue: results of a quasi-experimental study. Applied Health Economics and Health Policy 2: 91–98. Liu X, Mills A. 2005. The effect of performance-related pay of hospital doctors on hospital behaviour: a case study from Shandong, China. Human Resources for Health 3: 11. Propper C, Sutton M, Whitnall C, Windmeijer F. 2010. Incentives and targets in hospital care: Evidence from a natural experiment. Journal of Public Economics 94: 318–335. Propper C, van Reenen J. 2010. Can pay regulation kill? panel data evidence on the effect of labor markets on hospital performance. Journal of Political Economy 118: 222–273. Scott A, Sivey P, Ait OD, Willenberg L, Naccarella L, Furler J, Young D. 2011. The effect of financial incentives on the quality of health care provided by primary care physicians. Cochrane Database of Systematic Reviews 9: 1–59. Copyright © 2014 John Wiley & Sons, Ltd.


772


Wagstaff A, Lindelow M, Jun G, Ling X, Juncheng Q. 2009a. Extending health insurance to the rural population: an impact evaluation of China’s new cooperative medical scheme. Journal of Health Economics 28: 1–19. Wagstaff A, Yip W, Lindelow M, Hsiao WC. 2009b. China’s health system and its reform: a review of recent studies. Health Economics 18: S7–S23. Wagstaff A, Yu S. 2007. Do health sector reforms have their intended impacts?: The World Bank’s Health VIII project in Gansu province, China. Journal of Health Economics 26: 505–535. Wang H, Yip W, Zhang L, Hsiao WC. 2009. The impact of rural mutual health care on health status: evaluation of a social experiment in rural China. Health Economics 18: S65–S82. Wang H, Zhang L, Yip W, Hsiao W. 2011. An experiment in payment reform for doctors in rural china reduced some unnecessary care but did not lower total costs. Health Affairs 30: 2427–2436. Witter S, Fretheim A, Kessy FL, Lindahl AK. 2012. Paying for performance to improve the delivery of health interventions in low- and middle-income countries. Cochrane Database of Systematic Reviews 2: 1–82. Yip W, Eggleston K. 2001. Provider payment reform in China: the case of hospital reimbursement in Hainan province. Health Economics 10: 325–339. Yip W, Eggleston K. 2004. Addressing government and market failures with payment incentives: hospital reimbursement reform in Hainan, China. Social Science & Medicine 58: 267–277. Yip WC, Hsiao WC, Chen W, Hu S, Ma J, Maynard A. 2012. Early appraisal of China’s huge and complex health-care reforms. Lancet 379: 833–842. Yip WC, Powell-Jackson T, Chen W, Hu M, Fe E, Hu M, Jian W, Lu M, Han W, Hsiao WC. 2014. Capitation with payfor-performance improves primary care providers’ antibiotic prescribing practices in rural China. Health Affairs 33(3): 502–510. Zhan S, Wang L, Yin A, Blas E. 2004. Revenue-driven in TB control—three cases in China. The International Journal of Health Planning and Management 19: S63–S78. Zhang X, Feng Z, Zhang L. 2003. Analysis on quality of prescription of township hospitals in poor areas. Journal of Rural Health Service Management 23: 33–35. Zhou Z, Gao J, Xue Q, Yang X, Yan J. 2009. Effects of Rural Mutual Health Care on outpatient service utilization in Chinese village medical institutions: evidence from panel data. Health Economics 18: S129–S136.



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[Strategies to improve influenza vaccination coverage in Primary Health Care].

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Preliminary Hawai'i Public Health Workforce Supply and Demand Assessment.

Dementia care in rural China.

Myocardial oxygen supply and demand.

Supply and demand for long-term care services from the perspective of leaders of health care institutions.

Non-dental primary care providers' views on challenges in providing oral health services and strategies to improve oral health in Australian rural and remote communities: a qualitative study.