Equity in health care access to: assessing the urban health insurance reform in China $

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1 Social Science & Medicine 55 (2002) Equity in health care access to: assessing the urban health insurance reform in China $ Gordon G. Liu a, *, Zhongyun Zhao b, Renhua Cai c, Tetsuji Yamada d, Tadashi Yamada e a University of North Carolina at Chapel Hill, CB# 7360, Beard Hall, Chapel Hill, NC , USA b Health Outcomes Evaluation Group, Eli Lilly, USA c China National Institute of Health Economics, China d Rutgers University, USA e University of Tsukuba, Japan Abstract This study evaluates changes in access to health care in response to the pilot experiment of urban health insurance reform in China. The pilot reform began in Zhenjiang and Jiujiang cities in 1994, followed by an expansion to 57 other cities in 1996, and finally to a nationwide campaign in the end of Specifically, this study examines the pre- and post-reform changes in the likelihood of obtaining various health care services across sub-population groups with different socioeconomic status and health conditions, in an attempt to shed light on the impact of reform on both vertical and horizontal equity measures in health care utilization. Empirical estimates were obtained in an econometric model using data from the annual surveys conducted in Zhenjiang City from 1994 through The main findings are as follows. Before the insurance reform, the likelihood of obtaining basic care at outpatient setting was much higher for those with higher income, education, and job status at work, indicating a significant measure of horizontal inequity against the lower socioeconomic groups. On the other hand, there was no evidence suggesting vertical inequity against people of chronic disease conditions in access to care at various settings. After the reform, the new insurance plan led to a significant increase in outpatient care utilization by the lower socioeconomic groups, making a great contribution to achieving horizontal equity in access to basic care. The new plan also has maintained the measure of vertical equity in the use of all types of care. Despite reform, people with poor socioeconomic status continue to be disadvantaged in accessing expensive and advanced diagnostic technologies. In conclusion, the reform model has demonstrated promising advantages over pre-reform insurance programs in many aspects, especially in the improvement of equity in access to basic care provided at outpatient settings. It also appears to be more efficient overall in allocating health care resources by substituting outpatient care for more expensive care at emergency or inpatient settings. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Equity; Reform; Urban health care; Insurance; China; Access Introduction $ The earlier version of this study was presented at the 2000 Allied Social Science Associations Annual Meetings, Boston, MA. *Corresponding author. Tel.: ; fax: address: ggliu@unc.edu (G.G. Liu). The urban health insurance reform in China was initiated in 1994 with a pilot model implemented in two cities, Zhenjiang and Jiujiang. Following the pilot model, similar reforms were extended to 57 other cities in By the end of 1998, a nationwide reform campaign was carried out. While there is a certain /02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S (01)

2 1780 G.G. Liu et al. / Social Science & Medicine 55 (2002) degree of variation in model design across communities undergoing the health insurance reform, they all share some common key features of the Zhenjinag model following the government guidelines: ensuring access to basic care; wide coverage; joint premium contributions by employers and employees; and the integration of individual medical savings account (MSA) and social pooling account (SPA) (Cai, 1999). Therefore, to a large extent, the pilot model in Zhenjiang provides some unique and valuable observations for assessing the possible impact and consequences of China s urban health insurance reform. Prior to the insurance reform, urban health care in China had been financed primarily through two major public programs: the Government Insurance Program (GIP) and the Labor Insurance Program (LIP). The former was primarily for government employees, veterans, educators, and college students, whereas the latter was for workers of all state-owned, and some non-state owned, enterprises (Henderson et al., 1995; Yuen, 1996). Beginning in the early 1980s, when China launched its economic reform, the health care market has undergone many radical changes, resulting in some fundamental challenges to the existing health insurance system in general, and LIP in particular (Hsiao & Liu, 1996; Hu, Ong, Lin, & Li, 1999). These challenges were driven by the roles of all major market stakeholders. On the employee side, for instance, the most notable change was an increasing pressure for better access to quality care, mostly driven by factors such as aging, increased income, and the disease transition from infectious to chronic conditions (World Bank, 1993). On the medical provider side, the market economy reform led to substantial changes in hospital management and financing policies towards a widespread adoption of advanced medical technologies (Hu, 1991; Hu & Meng, 1991). This factor, coupled with inflation, has been widely believed to be a major driving force for the health care cost escalation during the past two decades. The growth rate of health care costs was as high as 24.4% annually between 1985 and1989 (Liu & Hsiao, 1995). On the employer side, health care financing reached critical proportions. Many enterprises were downsizing as a result of increased competitive pressure caused by China s economic reform. Firms with a relatively large number of retirees were under even greater pressure to pay for employees health care. To a large extent, this crisis was inevitable because all state-owned enterprises were, in essence, self-insured through the labor insurance program, and thus were unable to pool any large health risks. Prior to China s economic reform, such problems did not appear to be a vital issue to many firms as the centrally planned economy would serve as the payer of last resort. The economic reform beginning in the early 1980s, however, led to a large degree of decentralization of both financial decision-making and self-recovery responsibility to the individual firm level. As a result, a significant portion of state-owned enterprises had little capacity to reimburse large medical bills for their employees and retirees, who thus had to pay out-of-pocket for their health care costs (Henderson et al., 1995; Hu et al., 1999). This created even greater financial hardships for the elderly and those with chronic diseases. On the other hand, government sectors and many enterprises with sound financial conditions could continue to provide generous health care benefits, which often contained little patient cost-sharing responsibilities. Consequently, the urban health care system reached at a major health crisis due to rapid cost escalation and increasing inequality in health care financing, leading to the pilot reform efforts in Zhengjiang and Jiujiang. The pilot reform contained three innovative constructs. First, it developed a community-based insurance plan, mandating all community wide employees to participate, aiming to achieve an equitable universal coverage and to minimize adverse selection. Second, the new plan is financed through the joint contributions of employers, employees and the local government subsidies. The premiums are then pooled together and redistributed to two central accounts: the individual MSA and the public SPA. Third, the insurance benefits are comprehensive, covering major services including inpatient care, outpatient care, ER, and medications, coupled with various demand and supply side cost sharing mechanisms in an attempt to control costs. To evaluate the economic aspects of the pilot model tested in Zhenjiang, Liu et al. (1999, 2001) conducted several cost and utilization analyses indicating some significant cost savings and reduced utilization of inpatient and emergency care resulting from the reform. In explaining the cost savings, these studies identified an effect of strong substitution of outpatient care utilization for inpatient/emergency care. Similar observations on the cost savings were provided in other studies as well (Yip & Hsiao, 1997). Previous studies also demonstrated that the reform was associated with an overall improvement in the general population s access to care (Liu et al., 1999). Access to care is one s ability to obtain health services when needed, which is contingent upon two conditions: financial constraints or non-financial constraints. To the extent that the pilot model has increased its risk pooling substantially through the community-wide social account, it is clear that there has been an overall improvement in financial access for the total population. However, research is lacking with respect to whether or not the pilot model led to any changes in non-financial access barriers such as the availability of transportation, health care personnel and facilities. Also lacking is an analysis of the relative changes in access to

3 G.G. Liu et al. / Social Science & Medicine 55 (2002) care across different population groups after the insurance reform. It is worth noting a few studies about China s economic reform and its impact on equality in health care and population health outcomes. Using the 1989 China Health and Nutrition Survey (CHNS), Henderson et al. (1994) analyzed a sub-sample of 6513 working adults located in eight provinces, with over half of the sample living in rural villages and the rest residing in urban cities. Pooling both urban and rural individuals together in their model, Henderson and colleagues found that very few enabling factors were related to the use of health services. Their enabling factors included income, insurance type (GIP, LIP, Cooperative Insurance, and the uninsured), and location. They concluded that the Chinese health care system (including both urban and rural areas as a whole) is relatively equitable in terms of access to care. In a follow-up study, Henderson et al. (1998) further documented utilization trends in health services for interviewed people in eight provinces using the multiyear series of CHNS ( ). This study showed that health care accessibility, in general, remained unchanged from 1989 through Similar to their previous findings, these findings also produced an insignificant income effect where income was measured in the amount of consumer durables. Due to data limitations, however, this study found an inconclusive health insurance impact on the likelihood of health care use, after controlling a number of co-morbidity variables. A study by Shen, Habicht, and Chang (1996) raised a critical question concerning the distributional effects of the economic reform on the health status of younger Chinese generations. Using data from a series of large surveys between 1975 and 1992, the researchers revealed varying growth rates for height among children aged two to five years old living in urban versus rural areas. While there was an overall increase in height for the entire study population, the study found that the average gains were significantly lower in the rural children. More recently, Liu, Hsiao, and Eggleston (1999) examined changes in health and health care access between rural and urban populations during the economic reform era. Based on data from three national health surveys in 1985, 1986, and 1993, they demonstrated that the nation s rapid economic growth has produced some undesirable consequences relative to the goal of achieving equality of health for all. In particular, their data on infant mortality rate suggested an increasing gap in health status between rural and urban populations along with the economic reform. The authors also demonstrated some rising disparities in health care utilization rates and access to care between the rural and urban populations for the same time period. A central message from this study is that the economic reform, while yielding enormous improvements in income, has led to sizable inequities in health and health care access across different sub-populations. However, no studies have directly assessed the impact of the urban health insurance reform on equity measures in health care utilization. Equity has been widely considered a major objective of health care policies in international communities (e.g., WHO, 1985; Aday et al., 1993; Wagstaff and van Doorslaer, 1992; Newbold, Eyles, & Birch, 1995; Smaje & Le Grand, 1997; Liu & Hsiao, 1999). Equity in social welfare also has long played a major role in shaping China s national policies. In fact, equitable care has been officially declared by the State Council to be a principal aim of the urban health insurance reform (Cai, 1999). This aim was the main reason for making the new insurance plan a mandatory policy applied universally to every employee across all citywide government sectors and enterprises. Benefit coverage, premiums, and reimbursement policies are the same for all enrollees regardless of individual characteristics such as gender, occupation, education, pre-exiting conditions, income, and age. Equity also has become a growing issue of concern among the public after the reform. For instance, some may feel relatively worse off in the new plan that reduces their relative advantages in obtaining care (such as government officials or those working in companies with solid financial status). Others (such as ordinary workers with low income and chronic diseases) may benefit substantially from the reform under which their access to basic care services has been largely improved. However, data is lacking on a well-defined scale as to who benefited and who lost from the reform. This study seeks to fill the gap in the literature by analyzing the relative changes in access to care across different population groups due to the reform. The analysis focuses on four comparison groups defined by individual health and socioeconomic status. The empirical results are obtained from estimating a set of difference-in-difference model specifications for the use of various services at different settings (Gruber, 1994). Study design The pilot reform model The pilot model of China s urban health insurance reform started in December 1994 in Zhenjiang and Jiujiang, two medium-sized cities located in Jiangsu and Jiangxi provinces, respectively. Compared to the previous GIP and LIP, the reform developed several key components in the new plan. First, it is a local government run program that organizes universal health insurance for all community wide employees. For the very first time, the total insurance funds across all local

4 1782 G.G. Liu et al. / Social Science & Medicine 55 (2002) government and business units are pooled into a single plan. This integrated approach serves two primary roles: minimizing the risk of selection bias (both adverse selection by patients and favorable selection by providers); and maximizing the pooling of health risks. Secondly, the new plan is financed through two accounts: the MSA for each subscriber, and a shared SPA for all subscribers. For people under the age of 45, MSAs are funded jointly by employers who provide 4% of employees salaries, and by the employees themselves who contribute 1% of their salaries. For those aged 45 or above, the employer s contribution is 6% of their salary. The SPA is fully funded through the employers contributions of 4 6% of each employee s salary, depending upon the individual s age. From a patient s perspective, when seeking care, a three-tier payment system is instituted to pay for rendered services. Payment for any services first draws funds from the MSAs as the first line, followed by an out-of-pocket deductible up to 5% of the individual s salary. After meeting the deductible, the SPA kicks in as the payment of last resort, coupled with a decreasing co-payment schedule ranging from 10% under 5000 Yuan ($1D8.3 Yuan); 8% for ,000 Yuan; and 2% for over 10,000 Yuan. Recently, there have been some policy changes on the use of funds in SPA, including a cap limit equivalent to four times one s annual salary (Yuen, 1996; Cai, 1999). Thirdly, health care providers are managed to contain costs through various fee schedules, contracting, and drug formularies (Yuen, 1996). All participating providers obtain reimbursement for services directly from the City Social Security Bureau. The reimbursement rates are determined according to fee schedules varying with the level and type of provider. For example, at tertiary hospitals, the reimbursement rate is 47 Yuan for an outpatient visit and 110 Yuan for a hospital day (capped at 19 days per patient on average). At secondary hospitals, the rate is 75 Yuan for an outpatient visit and 90 Yuan for a hospital day (capped at an average of 16 days per patient). Government conducts evaluations of providers performance (mostly on annual basis) to determine contract renewals. Yet, data is lacking as to what criteria and measures were used in the evaluations. In addition, formularies are applied to medication use. Data Data for this study were derived from a multiyear survey series conducted in Zhenjiang. The survey instruments were designed by the Chinese Ministry of Health, in collaboration with the Jiangsu Provincial Health Department. Between 1994 and 1997, the survey was conducted annually in a stratified representative sample of the working population of Zhenjiang. The survey samples excluded children, the spouses of employees and rural populations because the urban health insurance program was primarily for current employees and retirees. Questionnaires were administered by trained staff through face-to-face interviews. Unfortunately, the data documentation did not include response rates. Nevertheless, through our personal communications with the Ministry of Health and local government agencies, we believe that the response rates were quite high. In China, few people would refuse to participate in a survey conducted through government official channels. In 1996, Zhenjiang had a population of about 2.6 million people, of which 525,000 were urban residents. Prior to the insurance reform, the GIP covered 78,887 people, and LIP covered 360,004 employed individuals. The survey database contains information on individual employee demographic and socioeconomic characteristics, including age, sex, marital status, education, chronic disease status, occupation and assigned providers for regular visits. The database also contains cost and utilization information on outpatient care, inpatient care, emergency visits, and usage of expensive diagnostic technologies. In order to minimize recall bias, all of the utilization variables were assessed for three time periods: the previous two weeks; month; and year. Visits to all providers, both insured and uninsured, were all counted in the utilization data. Equity measurement At the conceptual level, there is a broad consensus that equity should be a principal aim of a health care system. In practice, however, a large body of previous research has shown that there is a considerable disagreement over the definitions, measurements, and interpretations of equity and its determinants (Mooney, Hall, Donaldson, & Gerard, 1991; Le Grand, 1991; Culyer & Wagstaff, 1993; Wagstaff, van Doorslaer, & Paci, 1991; Carr-Hill, 1994). This study is not intended to address the general issues of equity. Instead, it examines the variations in access to care among different socioeconomic groups, shedding light on the equity of urban health insurance plans before and after reform. This study takes a simple, but widely used, approach to defining equity in two dimensions: horizontal equity and vertical equity. Horizontal equity requires that persons in equal need have an equal opportunity to obtain care, whereas vertical equity demands that the people with the greatest needs be given the most care (Smaje & Le Grand, 1997; Gerdtham, 1997; Alberts, Sanderman, Eimers, & Heuvel, 1997). Previous research on health care utilization has suggested the importance of distinguishing between factors that affect the propensity to use health care and factors that determine the volume of utilization

5 G.G. Liu et al. / Social Science & Medicine 55 (2002) once the person has entered the system (Andersen & Newman, 1973; Duan, Manning, Morris, & Newhouse, 1983; Newbold et al., 1995). In general, patient characteristics and demand-side factors play an important role in initial contact, shedding light on the degree of accessibility. In contrast, the volume of overall utilization tends to depend on both the patient role and health care system or provider behaviors such as treatment and referral patterns (Alberts et al., 1997; Diehr, Yanez, Ash, Hornbrook, & Lin, 1999). In our study, equity in access to health care is addressed in the context of the propensity to obtain a visit at different settings. Hence, the main outcome variable is the likelihood of health care utilization in a given year by an individual. Four types of services are examined in this analysis: outpatient care, hospital care, emergency care and expensive diagnostic procedures. A central hypothesis of this study is that individual health and socioeconomic status are among the major determinants of care-seeking behavior in various settings. Specifically, health status is thought to be the most important factor. Thus, it is hypothesized that poor health status increases the propensity to use health care, holding constant all other factors. Based on the study database, health status is measured with the selfreported chronic disease status. Chronic disease status has been widely used in previous research as a proxy variable for patient health status (van Doorslaer & Wagstaff, 1992; Benzeval & Judge, 1994; Alberts et al., 1997; Smaje & Le Grand, 1997). To measure variations in horizontal equity, three comparison groups are defined based on their socioeconomic status including individual annual income, educational level, and jobstatus. While income and education have been studied extensively, findings have been inconclusive regarding the direction of the impact. This is partly because both income and education can function as enabling factors, leading to an increase in health care use (e.g., Newbold et al., 1995; Alberts et al., 1997). On the other hand, it is also likely that both variables could act as a surrogate marker for better health status, resulting in lower needs for health care (Rosenberg & Hanlon, 1996; Roos & Mustard, 1997). In addition to income and education, this study also investigates whether or not the propensity to initiate utilization of health care services is associated with the individual s position at work. Although no existing studies have explicitly considered such an effect in other countries, the position variable seems to be particularly appropriate in the case of China. In fact, most state welfare benefits in China have long been granted to state government officials. Given these considerations, this study hypothesizes that Chinese managerial officers are more likely than common workers to have better access to health care facilities. Modeling As stated above, this study is intended to demonstrate the multi-group distribution patterns of utilization propensity, and how the patterns changed over time in response to the insurance reform. To accomplish this goal, this study employs a simple difference-in-difference model, which provides a straightforward framework for pursuing our empirical analysis. The model is of the following form: I i ¼ a 1 D T þ X b k D ki þ X l k ðd T D ki ÞþG 1 dx i þ e i ; k k i ¼ 1; yn In this equation, i indexes each individual, and k (1,2,3,4) indicates four comparison groups by chronic disease conditions, income, education, and jobstatus, respectively. I i is a service utilization dummy variable being 1 for users, and 0 for non-users. D T is a policy dummy variable being 1 for the post-reform period and 0 for the pre-reform period, and D ki is a dummy variable representing the kth comparison group. X i is a vector of the individual s characteristics, including age, marital status, occupation, type of designated regular providers, and opinions about the health insurance reform. The stochastic variable e i ; controlling for unobserved residuals, is assumed to follow a logistic distribution. Among the coefficients, a 1 measures the reform impact for the baseline groups, b k captures the pre-reform difference across the kth group, and the post-reform change in such a difference is identified by l k : G 1 estimates the impact of all other covariates included in vector X i : In this model specification, dummy variable D T measures the policy intervention, combining years for the pre-reform period, and years for the post-reform period. Thus, the pre-reform period contains 25,100 individuals, and the post-reform period contains 24,700 individuals. This integration approach is considered in an attempt to minimize the potential stock-up effect on people s behavior in using health care. The stock up effect is referred to as the individual s tendency to overuse health services before leaving a less-managed plan for a more-managed plan (Long et al., 1998; Newhouse, 1993). There was likely a well-perceived stock-up effect on the use of care right before the reform. Compared to GIP and LIP, while the reform model ensures basic access to care for a lot more employees, it was also widely known among the public that it would be well managed with patient cost-sharing responsibilities and some regulations. Relative to the insurance reform dummy, all other variables are also defined accordingly in a pre- and postreform fashion. Specifically, four utilization dummy variables are defined to measure the pre- and postreform use of outpatient care, inpatient care, emergency

6 1784 G.G. Liu et al. / Social Science & Medicine 55 (2002) care, and expensive diagnostic procedures. The diagnostic procedures include the use of six major medical technologies: CT, MRI, TCT, ECT, color-doppler, and beta-ultrasound. Four comparison groups are defined according to the individual s health and socioeconomic status (D ki ): chronic morbidity, income, education, and jobstatus. In the study survey, a chronic disease condition was defined by a physician s diagnosis of any chronic disease during the previous six months. Accordingly, the value of the dummy variable was 1 if a person had been diagnosed with any chronic disease in the past six months and 0 otherwise. The ten most frequently diagnosed chronic diseases in all years were as follows: chronic gastroenteritis; hypertension (except hypertensive cardiopathy); cholelithiasis and cholecystitis; chronic bronchitis; other diseases of the motor system; other diseases of the digestive system; other cardiac diseases; pharyngitis, laryngitis, tonsillitis, and trachitis; rheumatic arthritis; and digestive ulcer. The chronic disease variable plays two important roles in the model. It gives a direct estimate of the difference in utilization across groups due to chronic diseases, shedding light on vertical equity. It also controls the individual s need for health care when examining the effects of socioeconomic class variables. To define the income group, it is important to take into account changes over the past few decades in income distribution due to China s rapid economic growth and inflation (Liu & Hsiao, 1995; World Bank, 1998). Considering both the growth and inflation effects, a year-specific scale is used to define relative income levels: a low-income group (bottom 20%), a middleincome group (middle 60%), and a high-income group (top 20%). Following these criteria, the year-specific cut-offs are estimated in Table 1: With the year-specific relative income data, this study focuses on the changes in utilization between the highest and the lowest income groups. Education has been shown in previous research to have considerable impact on health care use. In Table 1 Year-specific income cut-offs from 1993 to 1996 (in Chinese Yuan) Year Low income Middle income High income 1993 Below Above Below Above Below Above Below Above 7000 particular, research in Western countries suggests that educational level is a strong predictor of socioeconomic status and inequity in health care (Ranchor, Sanderman, & Van den Heuvel, 1990; Alberts et al., 1997). This study defines three education categories: (a) below a middle school education; (b) a middle/high school education; and (c) a college education. Similar to income, education is modeled to focus on any differences between the groups with the lowest and highest education. As for jobstatus, government officials or those in managerial positions in state-owned enterprises generally were believed to have greater advantages in using health care than common workers under both the GIP and LIP. With the insurance reform, it was anticipated that these two groups would experience the greatest relative changes in health care utilization. Thus, a dummy variable is defined to be 1 for those in managerial positions and 0 otherwise. Empirical results Following the illustrated analytical framework, we obtain the empirical results by estimating four sets of service-specific logistic functions for outpatient care, inpatient care, emergency care, and expensive diagnostic procedures, respectively. Each service-specific function includes all the comparison group dummy variables: chronic disease conditions, income, education, and job status. In addition, other controlled variables include gender, age, marital status, occupation, and the type of designated regular care providers. The definitions of the variables and the full set of regression results for each service-specific function are provided in Appendix A (see Tables 6 10). Before discussing the estimates, we note a few points on the dependent utilization variables and some of the controlled variables employed in the regressions. First, as shown in appendix (Table 6), outpatient care utilization, on the one hand, increased after the reform both in total number of users and number of visits per person. Conversely, the use of inpatient care, emergency visits, and diagnoses all decreased. Such changes in utilization pattern may be, in part, indicative of a substitution effect of the demandside cost sharing developed through the institution of individual MSA and SPA in the new plan. Given that outpatient care is generally less costly than other three services, the substitution effect could be an important factor contributing to the observed cost savings. Liu et al. (1999, 2001) provided more detailed analyses on this issue. Second, all four sets of the regression results demonstrate a consistent pattern of effects by the controlled variables as expected. For example, the likelihood of using all types of care is higher for married

7 G.G. Liu et al. / Social Science & Medicine 55 (2002) individuals, women, and those over the age of 45 compared to their counterparts. Another interesting finding is that blue collar workers were much more likely to seek emergency visits than any other comparison groups including teachers, government officials, scientists, health and financing professionals, and retirees. On the other hand, these workers were less likely to use all other services at outpatient and inpatient settings, and expensive diagnostic technologies. We also controlled for the type of designated regular provider for each individual. Compared to those with small clinics designated as their regular providers, individuals with county and city government hospitals as regular providers had a significantly higher likelihood of using all services included in the model. As many other individual characteristics such as age, chronic health status, and occupation are controlled in the multivariate regressions, these results may be an indication of some unequal access to care associated with the type of health care providers designated to the study individuals. As the study is primarily aimed at investigating how the reform experimental model altered the likelihood of health care utilization across sub-groups with different socioeconomic status and health conditions, the following section will focus the discussion on the estimated odds ratios by the four group dummy variables: chronic disease status; income; education; and job-status at work. For convenience, the estimated odds ratios and p- values for each sub-group are summarized in Tables 2 5. Table 2 Changes in chronic disease effect on utilization Services D (93/94) DD (95/96) Odds ratio p-value Odds ratio p-value Outpatient Inpatient Emergency Diagnosis Table 3 Changes in income effect on utilization Services D (93/94) DD (95/96) Odds ratio p-value Odds ratio p-value Outpatient Inpatient Emergency Diagnosis Table 4 Changes in educational effect on utilization Services D (93/94) DD (95/96) Chronic disease As expected, chronic disease status appears to be the most significant determinant of health care utilization (Table 2). This is the case for the use of all four types of services. Compared to individuals without chronic diseases, it is found that prior to the insurance reform, those with chronic illness were two to four times more likely to use outpatient care, inpatient and emergency care, and expensive diagnostic services (p ¼ 0:0001). After the reform, the across-group difference further increased, but was barely significant, in the use of outpatient care (p ¼ 0:07), meaning that chronic patients become even more likely than before to seek outpatient visits. In the meantime, the insurance reform reduced the likelihood of emergency visits by chronic patients (p ¼ 0:0001). For the use of other services, the differences in utilization across the groups remained unchanged. Income Odds ratio p-value Odds ratio p-value Outpatient Inpatient Emergency Diagnosis Table 5 Changes in job-status effect on utilization Services D (93/94) DD (95/96) Odds ratio p-value Odds ratio p-value Outpatient Inpatient Emergency Diagnosis Table 3 compares the differences in health care utilization between the highest and lowest income groups. Prior to the insurance reform, the high-income group was more likely to use outpatient care (p ¼ 0:003) and diagnostic procedures (p ¼ 0:03); and less likely to use inpatient care (p ¼ 0:01) and emergency care (p ¼ 0002). After the reform, the high-income group increased the likelihood of using both inpatient care

8 1786 G.G. Liu et al. / Social Science & Medicine 55 (2002) (p ¼ 0:024) and diagnostic services (p ¼ 0:02). In the meantime, the across-group difference in using outpatient care narrowed in favor of the low-income group (0.02). Education Educational effects are summarized in Table 4. In comparison with the poorly educated group (below middle school), the well-educated group (at college level or above) had a higher propensity to use outpatient and emergency services (p ¼ 0:001). Similar to the income effect, after the insurance reform, the education-related difference in outpatient care use narrowed in favor of the low-education group (p ¼ 0:002). However, the welleducated group became more likely than before to use diagnostic technologies, while the less-educated group lagged behind in the use of these services. Job status Table 5 shows the across-group variations in service utilization due to jobstatus. Estimates indicate that managerial officers were more likely than workers to use both outpatient care (p ¼ 0:0001) and diagnostic services (p ¼ 0:0005). On the other hand, workers were more likely to rely upon the use of emergency care (p ¼ 0:0005). Insurance reform significantly narrowed the difference in outpatient utilization between managerial officers and workers. Yet, differences in the use of diagnostic and emergency services persisted after the reform. In the case of inpatient care, jobstatus was found to be unrelated to utilization both before and after the reform. Discussion There are nearly 400 million people residing in urban areas in China, accounting for over 30% of the total population. On average, about half of the urban population are employed in government sectors or state-owned enterprises. Historically, health care benefits for the vast majority of urban employees have long been provided through the employers-based insurance programs, GIP and LIP. As discussed elsewhere, however, both programs have a number of significant problems, which eventually led to the reform of the urban health insurance system. These problems include the lack of risk pooling capacity; the absence of patient-cost sharing; and inequitable access to care across employers with different financial conditions. The urban insurance reform carried out a wide range of fundamental changes in health care financing and organization. The key changes may be characterized by the development of the individual MSA and SPA; and the application of various managed care measures with a focus on demand-side cost sharing. As a result, it was highly expected, at least from the policy makers perspective, that the reform should lead to an improvement in cost containment and access to basic care coverage. A few previous studies provided some empirical evidence in support of this expectation (Yip & Hsiao, 1997; Liu et al., 1999, 2001). However, there has been an increasing issue of concern to both the government and the public, regarding the reform impact on equity scale of health care utilization. Using a multiyear survey data from the Zhenjiang experiment, this analysis made the first attempt to address the equity issue by assessing the relative changes in access to care across four comparison groups before and after the reform. Major findings and policy implications are discussed as follows. The likelihood of using all specified health care services was significantly higher among those with chronic disease conditions, indicating a measure of vertical equity in access to care among the Chinese urban working population. Compared to the previous GIP and LIP, the pilot reform led to changes towards more use of outpatient services and less use of emergency visits by the people with chronic diseases. This study finds a strong evidence that people with chronic illnesses received more care than those without chronic illness, regardless of individual characteristics. Yet, this study cannot discern whether the degree of increased utilization by sick people adequately met their relative needs. Further support for improvements in equity following the reform is our finding that the chronic patients as a group became even more likely to use outpatient care, and less likely to seek emergency care after the reform. From an economic standpoint, such a change will help improve the efficiency of health care use and allocation. Generally, outpatient settings are more likely than emergency facilities to provide preventive and less-costly services, contributing to total cost savings (Liu et al., 1999, 2001). From a patient standpoint, this change could also be partly attributed to changes in the health behavior of chronically ill patients who may have sought more preventive care, thus reducing their risk of acute care utilization. It is likely that the built-in patient cost-sharing mechanisms in the new plan are a major driving force for the behavioral change. Under the previous GIP and LIP, the horizontal distribution of outpatient care and diagnostic technology appeared to be inequitable, favoring people with higher socioeconomic status indexed by income, education, and job status. The reform significantly narrowed the gap in access to outpatient care between people with different socioeconomic status. Yet, the gap in the use of diagnostic technologies seemed to be widened. Controlling for health

9 G.G. Liu et al. / Social Science & Medicine 55 (2002) status, horizontal equity measures the degree of equal care for equal needs, regardless of the ability to pay. Income and education have been suggested in previous studies as important predictors of one s socioeconomic status. Yet, little consensus was reached on the impact of the two variables on the use of various health services. For instance, several studies found that individual income and education were positively associated with one s decision regarding office visits as well as inpatient care (Alberts et al., 1997; Newbold et al., 1995; Gerdtham, 1997). Others suggested an inverse relationship between socioeconomic status and the use of outpatient and inpatient care (Keskimaki, Salinto, & Aro, 1995; Roos & Mustard, 1997; Kephart, Thomas, & MacLean, 1998). Our study finds a higher likelihood of using outpatient care and diagnostic technology among those with higher income, education, and position at work, indicating a poorer access to these services for people of lower socioeconomic status. However, the reform led to a great reduction in the across-group difference for the use of outpatient care, directly contributing to the goal of achieving more equitable access to basic care under the reform initiatives. The likelihood of using diagnostic technologies, on the other hand, became even greater for those with higher income and education after the reform. To a large extent, this finding is not surprising as the reform was primarily aimed at targeting basic care coverage, so that great deal of measures were taken to improve equal access to inexpensive services which are mostly provided at outpatient settings. In contrast, the use of expensive diagnostic technology and medications was widely contained through tight drug formulary, co-payment and cap policies. As a result, a large share of the costs for expensive diagnostic services would be self-paid, and thus access to these services would be affected by individual socioeconomic status, especially at income level. Given the fact that such constraints were not popular before the reform, it seems logical that higher socioeconomic status played a larger role in the demand for expensive technologies after the reform. Lower socioeconomic groups were more likely to rely upon the use of emergency or inpatient facilities, implying that they were at higher risk for severe health shocks when they were entering the health care system. This pattern did not change much before and after the reform. This also represents an inefficient allocation of health care resources. Individuals with poor socioeconomic status were at a higher risk for being admitted to hospital or emergency rooms. This pattern could be attributed to two factors. First, people in the low socioeconomic groups may be in poorer health conditions compared to people in higher classes. As such, it is possible that this utilization pattern was partly reflective of their higher needs for intensive or urgent care (O Donnell & Propper, 1991; Keskimaki et al., 1995; Gerdtham, 1997). This is likely so when health conditions are not well controlled in analyzing horizontal utilization patterns across different subgroups. This might be the case in our study where health conditions are controlled with chronic sickness, which may be inadequate to predict the needs for acute care visits. The other factor that may have contributed to the pattern is that, as the lower socioeconomic groups have had poorer access to outpatient care, this would lead to a higher probability that their needs for early on care were often delayed. As a consequence, they were at a higher risk for being in inpatient or acute care settings due to more severe cases when first seeking care. This possibility in fact concurs with the findings by Roos and Mustard (1997), suggesting that in Canada, poorer and less healthy groups in fact received more acute hospital care than their counterparts. Conclusions Based on the multi-year survey data from the pilot health insurance reform conducted in Zhenjiang, this study concludes that the new insurance model is more equitable in ensuring access to primary care at outpatient settings than the previous GIP and LIP. And to a large extent, it also appears to be more efficient in allocating health care resources by substituting outpatient care for more expensive services at emergency or inpatient settings. The reform lessened the extent of horizontal inequity, while maintaining the degree of vertical equity in access to primary care services. Furthermore, this study has also revealed an increased difference, after the reform, in access to expensive diagnostic technologies between people with different socioeconomic status. As equity and efficiency are two primary goals of the insurance reform, future studies should look into the cost and health implications of the increased differences, in order to shed light on the overall cost-effectiveness of health care under the new model. This study is subject to some limitations. First, it was designed in a pre- and post-intervention fashion to examine the changes in healthcare propensity across different population groups. Due to the lack of data on a control group, however, conceptually the changes identified could also be attributed partly to some confounding factors such as policy shocks or market forces other than the insurance reform. In general, such a problem would exist with most natural experimental studies in the absence of a control group. Nevertheless, the problem seems to be attenuated in the case of Zhenjiang. As the central government s

10 1788 G.G. Liu et al. / Social Science & Medicine 55 (2002) first initiative in reforming urban insurance, it was unlikely, if not impossible, that the Zhenjiang model would be intervened by many other policy shocks during the experimental period. In fact, there were no significant changes in national health policies from 1994 to Moreover, market forces did not seem to play a key role in shaping the observed patterns, as the pilot model was a mandatory plan and was run by the local government. Such a universal policy would leave little room for other non-reform market factors to confound the reform intervention. However, factors such as changes in the available workforce and health care infrastructure may have had some impact on the observed difference. Second, this study assumed that one s health status could be well controlled using chronic morbidity status. To some extent, this assumption may be problematic. In general, chronic sickness is a vital indicator of health status. However, a majority of individuals in the study population were free of chronic diseases. Although these people did not have chronic diseases, their healthy status could vary substantially. Thus, a failure to control for such intra-group variations could bias the study results. Third, this study used multiple cross-sectional samples rather than panel data. While stratification criteria were used in sampling, individuals included in each year may have varied by unobserved characteristics. These unobserved differences may have biased the study estimates. In addition, this study is subject to measurement error due to the survey data based on annual recall by surveyed respondents. Unfortunately, no information was available to assess the validity of the data. Also, the study sample was drawn from Zhenjiang. While this is a typical Chinese city in terms of socioeconomic and demographic characteristics, to a large extent the sample is still lacking national representation. Therefore, it is suggested that the study findings be considered with caution when drawing policy implications for other Chinese cities and rural areas. Fourth, this study addresses equity issues in the context of access to care by analyzing the distribution patterns in health care utilization propensity across various groups. As noted earlier, the probability of service use conveys information mainly on initial visits, and reveals little about the overall volume and quality of services used. However, both the volume and quality measures determine the equity measure of health care distribution. Thus, future research should address this issue when data becomes available. With information on both the propensity and the volume of overall utilization, we expect to gain a better understanding of equity measures in health care utilization and allocation in urban China after the reform. Acknowledgements The authors thank Ten-Wei Hu at UC Berkeley, Erick Wu at the University of Southern California, Jill Lavigne and Joshua Thorpe at the University of North Carolina Chapel Hill for their assistance and suggestions; editor Steve Birch and two anonymous reviewers for their comments. The views expressed here are those of the authors and do not necessarily represent the views of their affiliated institutions. Appendix A Table 6 Definitions and distributions of the key variables Variable Definition Pre-reform ( ) Mean/STD Post-reform ( ) Mean/STD Age 45 A dummy variable being 1 for aged 45/over and / /0.463 for the younger group Male A dummy variable being 1 for male and 0 for female 0.517/ /0.500 Married A dummy variable being 1 for married and 0 otherwise 0.848/ /0.344 EduH A dummy variable being 1 for college graduates or higher 0.172/ /0.379 and 0 otherwise EduM A dummy variable being 1 for high school graduates and 0 otherwise 0.359/ /0.486 EduL A dummy variable being 1 for those with less than high school 0.469/ /0.497 education and 0 otherwise Teacher A dummy variable being 1 for educators and 0 otherwise 0.066/ /0.255 Officer A dummy variable being 1 for officers in government sectors or managerial positions in industry and 0 otherwise 0.149/ /0.362

11 G.G. Liu et al. / Social Science & Medicine 55 (2002) Table 6 (continued) Variable Definition Pre-reform ( ) Mean/STD Post-reform ( ) Mean/STD Scientist A dummy variable being 1 for being scientists and 0 otherwise 0.050/ /0.204 Health A dummy variable being 1 for health care professionals and 0 otherwise 0.072/ /0.223 Finance A dummy variable being 1 for those in economic or 0.034/ /0.207 financial positions and 0 otherwise Other occup A dummy variable being 1 for other occupation and 0 otherwise 0.011/ /0.112 Worker A dummy variable being 1 for blue collar workers and 0 otherwise 0.534/ /0.499 Retiree A dummy variable being 1 for the retired and 0 otherwise 0.079/ /0.285 Chronic A dummy variable being 1 for those with chronic disease and 0 otherwise 0.264/ /0.435 Incomel A dummy variable being 1 for those with income 0.220/ /0.408 in the bottom 20% quartile and 0 otherwise; IncomeM A dummy variable being 1 for those with income in the middle 20%-80% 0.601/ /0.494 quartiles and 0 otherwise; IncomeH A dummy variable being 1 for those with income 0.179/ /0.406 in the top 20% quartile and 0 otherwise; Clinic A dummy variable indicating a clinic as 0.388/ /0.489 the designated regular service provider County A dummy variable indicating a county hospital as the designated 0.233/ /0.441 regular service provider City A dummy variable indicating a city hospital as the designated 0.336/ /0.465 regular service provider Other_hosp A dummy variable indicating any other hospitals 0.012/ /0.086 as the designated regular service provider DOP A dummy variable being 1 for outpatient service users, 0.648/ /0.410 and 0 otherwise QOP A discrete variable indicating total number of outpatient visits 3.863/ /6.548 DIP A dummy variable being 1 for inpatient service users, and 0 otherwise 0.048/ /0.198 QIP A discrete variable indicating total number of inpatient admissions 0.055/ /0.234 DER A dummy variable being 1 for ER service users, and 0 otherwise 0.100/ /0.285 QER A discrete variable indicating total number of ER visits 0.183/ /0.767 DHITECH A dummy variable being 1 for diagnostic technology users, and 0 otherwise 0.114/ /0.280 QHITECH A discrete variable indicating total number of diagnostic technology utilization 0.281/ /0.731 Table 7 Changes in chronic disease effect on utilization Variables Outpatient Inpatient Emergency Diagnosis odds ratio odds ratio odds ratio odds ratio (p-value) (p-value) (p-value) (p-value) Reform (95/96) (0.0001) (0.0022) (0.4960) (0.0001) Reform*Chronic (0.0728) (0.6682) (0.0001) (0.9355) Chronic IncomeH (0.0495) (0.1451) (0.0001) (0.0001) IncomeM (0.0001) (0.2035) (0.0053) (0.9219) EduH (0.0536) (0.1579) (0.0001) (0.0004) EduM (0.0001) (0.0270) (0.0001) (0.4540) Officer (0.0707) (0.460) (0.0001) (0.0001)

12 1790 G.G. Liu et al. / Social Science & Medicine 55 (2002) Table 7 (continued) Variables Outpatient Inpatient Emergency Diagnosis odds ratio odds ratio odds ratio odds ratio (p-value) (p-value) (p-value) (p-value) Age (0.0001) (0.2031) (0.4037) (0.0001) Male Married (0.0001) (0.0001) (0.0038) (0.0001) Teacher (0.0001) (0.3683) (0.0001) (0.0001) Scientist (0.0001) (0.6246) (0.0001) (0.0040) Health (0.0125) (0.0008) (0.0001) (0.0001) Finance (0.0001) (0.0242) (0.0001) (0.0001) Other_Occup (0.0311) (0.9996) (0.1032) (0.9307) Retiree (0.0001) (0.0001) (0.0001) (0.0002) County (0.0032) (0.0001) (0.0001) (0.0001) City Other_hosp (0.0001) (0.9927) (0.0011) (0.0005) N log Lðw 2 Þ 4873 (p ¼ 0:0001) 758 (p ¼ 0:0001) 1115 (p ¼ 0:0001) 3339 (p ¼ 0:0001) Table 8 Changes in income effect on utilization Variables Outpatient Inpatient Emergency Diagnosis odds ratio odds ratio odds ratio odds ratio (p-value) (p-value) (p-value) (p-value) Reform (95/96) Reform*IncomeH (0.0190) (0.0241) (0.6249) (0.0189) IncomeH (0.0032) (0.0132) (0.0002) (0.0316) IncomeM (0.0001) (0.1885) (0.0067) (0.8999) Chronic EduH (0.0571) (0.1580) (0.0001) (0.0004) EduM (0.0001) (0.0256) (0.0001) (0.4650) Officer (0.0709) (0.0425) (0.0001) (0.0001) Age (0.0001) (0.1975) (0.3864) (0.0001) Male

13 G.G. Liu et al. / Social Science & Medicine 55 (2002) Table 8 (continued) Variables Outpatient Inpatient Emergency Diagnosis odds ratio odds ratio odds ratio odds ratio (p-value) (p-value) (p-value) (p-value) Married (0.0001) (0.0001) (0.0038) (0.0001) Teacher (0.0001) (0.3562) (0.0001) (0.0001) Scientist (0.0001) (0.6512) (0.0001) (0.0034) Health (0.0168) (0.0005) (0.0001) (0.0001) Finance (0.0001) (0.0231) (0.0001) (0.0001) Other_occup (0.0324) (0.9809) (0.1063) (0.9546) Retiree (0.0001) (0.0001) (0.0001) (0.0002) County (0.0044) (0.0001) (0.0001) (0.0001) City Other_hosp (0.0001) (0.9890) (0.0011) (0.0005) N log Lðw 2 Þ 4876 (p ¼ 0:0001) 763 (p ¼ 0:0001) 1099 (p ¼ 0:0001) 3344 (p ¼ 0:0001) Table 9 Changes in educational effect on utilization Variables Outpatient Inpatient Emergency Diagnosis odds ratio odds ratio odds ratio odds ratio (p-value) (p-value) (p-value) (p-value) Reform (95/96) Reform*EduH (0.0019) (0.3495) (0.1874) (0.0121) EduH (0.0011) (0.0912) (0.0001) (0.1025) EduM (0.0001) (0.0270) (0.0001) (0.4480) Chronic IncomeH (0.0498) (0.1432) (0.0001) (0.0001) IncomeM (0.0001) (0.1962) (0.0066) (0.9050) Officer (0.0632) (0.0465) (0.0001) (0.0001) Age (0.0001) (0.1986) (0.3768) (0.0001) Male Married (0.0001) (0.0001) (0.0037) (0.0001) Teacher (0.0001) (0.3729) (0.0001) (0.0001)

14 1792 G.G. Liu et al. / Social Science & Medicine 55 (2002) Table 9 (continued) Variables Outpatient Inpatient Emergency Diagnosis odds ratio odds ratio odds ratio odds ratio (p-value) (p-value) (p-value) (p-value) Scientist (0.0001) (0.6441) (0.0001) (0.0031) Health (0.0139) (0.0007) (0.0001) (0.0001) Finance (0.0001) (0.0244) (0.0001) (0.0001) Retiree (0.0001) (0.0001) (0.0001) (0.0002) Other_occup (0.0320) (0.9940) (0.1068) (0.9497) County (0.0045) (0.0001) (0.0001) (0.0001) City Other_hosp (0.0001) (0.9921) (0.0011) (0.0005) N log Lðw 2 Þ 4880 (p ¼ 0:0001) 759 (p ¼ 0:0001) 1101 (p ¼ 0:0001) 3345 (p ¼ 0:0001) Table 10 Changes in job-status effect on utilization Variables Outpatient Inpatient Emergency Diagnosis odds ratio odds ratio odds ratio odds ratio (p-value) (p-value) (p-value) (p-value) Reform (95/96) (0.0001) (0.0001) (0.0004) (0.0001) Reform*Officer (0.0001) (0.5958) (0.5649) (0.5553) Officer (0.0001) (0.2248) (0.0005) (0.0039) Chronic IncomeH (0.0473) (0.1454) (0.0001) (0.0001) IncomeM (0.0001) (0.2004) (0.0068) (0.9244) EduH (0.0523) (0.1568) (0.0001) (0.0004) EduM (0.0001) (0.0272) (0.0001) (0.4498) Age (0.0001) (0.2068) (0.3854) (0.0001) Male Married (0.0001) (0.0001) (0.0039) (0.0001) Teacher (0.0001) (0.3643) (0.0001) (0.0001) Scientist (0.0001) (0.6210) (0.0001) (0.0040) Health (0.0099) (0.0008) (0.0001) (0.0001)

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