Health Economics, Policy and Law http://journals.cambridge.org/HEP Additional services for Health

Economics, Policy and Law:

Email alerts: Click here Subscriptions: Click here Commercial reprints: Click here Terms of use : Click here

The impact of health insurance on health services utilization and health outcomes in Vietnam G. Emmanuel Guindon Health Economics, Policy and Law / Volume 9 / Issue 04 / October 2014, pp 359 - 382 DOI: 10.1017/S174413311400005X, Published online: 25 March 2014

Link to this article: http://journals.cambridge.org/abstract_S174413311400005X How to cite this article: G. Emmanuel Guindon (2014). The impact of health insurance on health services utilization and health outcomes in Vietnam. Health Economics, Policy and Law, 9, pp 359-382 doi:10.1017/ S174413311400005X Request Permissions : Click here

Downloaded from http://journals.cambridge.org/HEP, IP address: 138.251.14.35 on 13 Mar 2015

Health Economics Policy and Law (2014), 9, 359–382 © Cambridge University Press 2014 doi:10.1017/S174413311400005X First published online 24 March 2014

The impact of health insurance on health services utilization and health outcomes in Vietnam G. EMMANUEL GUINDON Centre for Health Economics and Policy Analysis, McMaster University, Hamilton, ON, Canada Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

Abstract: In recent years, a number of low- and middle-income country governments have introduced health insurance schemes. Yet not a great deal is known about the impact of such policy shifts. Vietnam’s recent health insurance experience including a health insurance scheme for the poor in 2003 and a compulsory scheme that provides health insurance to all children under six years of age combined with Vietnam’s commitment to universal coverage calls for research that examines the impact of health insurance. Taking advantage of Vietnam’s unique policy environment, data from the 2002, 2004 and 2006 waves of the Vietnam Household Living Standard Survey and single-difference and difference-in-differences approaches are used to assess whether access to health insurance – for the poor, for children and for students – impacts on health services utilization and health outcomes in Vietnam. For the poor and for students, results suggest health insurance increased the use of inpatient services but not of outpatient services or health outcomes. For young children, results suggest health insurance increased the use of outpatient services (including the use of preventive health services such as vaccination and check-up) but not of inpatient services. Submitted 15 July 2011; revised 3 May 2013; accepted 10 February 2014

Introduction In September 2009, the member states of the World Health Organization (WHO) from the Asia-Pacific Region adopted a health financing strategy. They agreed to take steps to reduce out-of-pocket payments, increase total health expenditures, cover over 90% of the population with prepayment schemes, and cover 100% of vulnerable populations with social assistance or safety-net programmes (World Health Organization, 2009). Although a number of low- and middle-income country governments have introduced health insurance schemes in recent years,

*Correspondence to: G. Emmanuel Guindon, McMaster University, Centre for Health Economics and Policy Analysis, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1. Email: emmanuel. [email protected]

359

360

G. EMMANUEL GUINDON

not a great deal is known about their impact. Vietnam, one of the signatories of the WHO Asia-Pacific health financing strategy, has launched on a number of reforms of its health insurance system. Since the onset of the doi moi economic reforms initiated in 1986, the government of Vietnam has made a number of attempts at reforming its health insurance system. The current system is the result of reforms introduced throughout the 1990s and 2000s. Public health insurance in Vietnam currently includes two main programmes. One is compulsory. The other voluntary.1 The compulsory program incorporates four sub-schemes. Social Health Insurance (SHI) in Vietnam, a compulsory employment-based programme introduced in 1993, provides insurance to civil servants and private sector workers in the formal sector. Family members are not covered. Second, a noncontributory scheme provides insurance to ‘people of merit’ including retired government officials, war veterans, members of Parliament, Communist Party officials and war heroes. A third scheme is a compulsory programme that provides health insurance to all children under six years of age. The 1991 Law on Protection, Care, and Education for Children stipulates that children under six years are entitled to primary health care and curative care free of charge at government health facilities. However, it was not until 2005 that free care began to be provided. A fourth scheme, the Health Care Fund for the Poor (HCFP) introduced in 2003, provides insurance to those that are officially classified as poor, ethnic minority households living in remote mountainous areas and households living in communes officially classified as poor. The HCFP is financed from general government revenues at both national (75%) and provincial (25%) levels. The Voluntary Health Insurance (VHI) scheme, introduced in 1994, targets various groups. Full-time students and school children are an important target group and are often enrolled as a whole at the school or college level (Lieberman and Wagstaff, 2009). Family members of those with compulsory health insurance can purchase VHI with the restriction that all household members have to enroll. The latter restriction was removed in December 2007. As of January 2010, all fulltime students are required to adhere to the government compulsory SHI scheme. Other groups such as communes are also allowed to enroll in the VHI scheme.2 The benefit package is more or less the same across all types of health insurance, whether compulsory or voluntary. It covers most outpatient and inpatient care received at government facilities and drugs on the Ministry of Health list. However, costs of treating injuries from traffic accidents,3 of nonprescription 1 This section draws from two recent excellent reviews by Ekman et al. (2008) and Lieberman and Wagstaff (2009) and the Ministry of Health’s annual review of health financing (Ministry of Health and Health Partnership Group, 2008). 2 Prior to December 2007, at least 20% of the group was required to enroll. For example, at least 20% of the residents of a particular commune had to enroll and enrollment remained voluntary for residents who did not choose to initially enroll. 3 Costs of treating injuries from traffic accidents are typically covered by specific employment or vehicle insurance schemes.

The impact of health insurance in Vietnam 361 Table 1. Overview of health insurance schemes Target group(s) SHI Civil servants and private sector workers in the formal sector ‘People of merit’ including retired government officials, war veterans, members of Parliament, Communist Party officials and war heroes HCFP: households officially classified as poor; ethnic minority households living in remote mountainous areas; households living in communes officially classified as poor Law on Protection and Care of Children: all children under six years of age Students and school children

VHI Students and school children, selfemployed, informal sector workers, dependents of SHI-members

Financing

Year of introduction

3% payroll tax (1% paid by workers, 2% paid by employers)* General government revenues

1993 1993

General government revenues

2003

General government revenues

2005

Private contributions based on ability to pay and general government revenues

2010

Private contributions based on ability to pay; subsidy from general government revenues for ‘near-poor’

1994

SHI = Social Health Insurance; HCFP = Health Care Fund for the Poor; VHI = Voulntary Health Insurance. Note: * = Increased to a maximum of 6% from 1 July 2009 (1/3 paid by workers, 2/3 paid by employers). Source: Ekman et al. (2008); Ministry of Health and Health Partnership Group (2008); National Assembly (2008); Lieberman and Wagstaff (2009).

drugs bought from drug vendors and pharmacies, and of informal payments to providers are not covered.4 There is no deductible but there was a 20% coinsurance rate (with a VND 1,500,000 annual limit) between 2003 and 2005. The coinsurance was waved if the costs of a visit were less than VND 20,000. The health insurance scheme for children under six provides the only exception to this mode of coverage. Benefits include consultations, diagnosis and treatment at government primary care facilities and, if referred by a primary care provider, treatment at provincial and central level facilities. In the event of an emergency, a referral is not needed and diagnosis and treatment can be obtained at any government facilities. Out-of-pocket payments may be required for the use of specialized medical devices and equipment.5 Table 1 provides an overview of health insurance schemes in Vietnam. With one exception private health insurance is negligible in Vietnam. Most for-profit 4 See Lieberman and Wagstaff (2009: 64–65) for more details. 5 Since 2009, the benefit package for children under six years is the same as other types of health insurance.

362

G. EMMANUEL GUINDON

insurance schemes focus on students. Although little information is available, several million students were believed to be enrolled with domestic for-profit insurance companies in 2006. For example, the Bao Viet Insurance Company, a governmentowned for-profit general insurance company, reported enrollment of more than 8,000,000 students (Ministry of Health and Health Partnership Group, 2008). Despite Vietnam’s recent expansion of health insurance, out-of-pocket payments still accounted for about two-thirds of all health spending in 2006. It is important to note that even insured individuals can experience substantial out-of-pocket expenses. In addition to cost-sharing, other out-of-pocket expenses include drugs not covered by the insurance plan, most visits to private providers and informal payments (Lieberman and Wagstaff, 2009). Literature review Although the RAND Health Insurance Experiment (HIE) was completed more than two decades ago, it remains the only long-term experimental study of the effect of health insurance on the use of health services and on health outcomes. The HIE demonstrated that participants who received free care used more health services than those who paid for a share of their health care. It also showed that those who paid for a share of their health care did not report worse general health than those receiving free care. There were, however, exceptions, especially among the sickest and poorest participants. For example, free care led to improvements in hypertension and dental and vision health (Newhouse and the RAND Insurance Experiment Group, 1996). The RAND HIE, however, did not randomize indviduals to receive no health insurance. Rather, they were assigned to plans with varying degrees of cost sharing. This design feature combined with the fact that the RAND HIE was conducted in the United States in the 1970s and 1980s limits the generalizability of its findings in the context of low- and middle-income countries. Research from systematic reviews suggests that health insurance increases utlization in the United States (Hadley, 2003; Buchmueller et al., 2005; Levy and Meltzer, 2008). For example, Buchmueller et al. (2005) found that insured children average one more physician visit per year than uninsured children. This effect was smaller for poor children. They also found that, for children, insurance increases the use of outpatient preventive care (e.g. immunizations, well-child visit) and acute care services but not inpatient services. For adults, health insurance was found to increase outpatient utilization by between one to two visits per year and inpatient utilization by between 0.16 and 0.24 days per year. These estimates represented 30 to 50% increases for children and 60 to 100% for adults. Lagarde and Palmer (2006) systematically reviewed studies that examine the impact of community-based insurance and SHI on extending access to health services to poor people in low- and middle-income countries and found there was ́ (2010) too little evidence to make any conclusive statements. Giedion and Diaz assessed the evidence on the impact of health insurance in low- and middle-income

The impact of health insurance in Vietnam 363

countries and found that health insurance improved access and use but had no conclusive impact on health status. Spaan et al. (2012) evaluated the impact of health insurance in low- and lower-middle-income countries in Africa and Asia and found an evidence base that is “incomplete, …, patchy and of variable quality” (pp. 687–688). A number of studies have examined the impact of health insurance on health service utilization in Vietnam. Axelson et al. (2009), Lieberman and Wagstaff (2009), Nguyen (2009) and Wagstaff (2007, 2010) examined the early impact of Vietnam’s HCFP on health services utilization. Wagstaff (2010) used singledifference (2006), double-difference (2004–2006) and triple-difference (2002– 2004–2006) and regression analysis, and found that HCFP had no effect on utilization (number of outpatient visits and inpatient admissions in 12 months preceding the interview).6 Using single-difference (2004) and double-difference (2002–2004) with propensity score matching, Axelson et al. found that HCFP had a modest positive, but not statistically significant, effect on the number of inpatient and outpatient visits. They also found that individuals with HCFP insurance substituted from private to public providers and from primary to secondary and tertiary level care. Wagstaff (2007) used single-difference with propensity score matching and found that HCFP substantially increased health services utilization. The impact on utilization, however, was found to be larger among the better off.7 As Axelson et al., Nguyen (2009) used single-difference (2004) with propensity score matching. Additionally, Nguyen used an intention to treat approach with instrumental variable and fixed effects estimations. Nguyen also found that HCFP led to increased utilization of government health services, while substituting away from uncovered services in the private sector. Lieberman and Wagstaff (2009) used single-difference (2006) with poisson regressions and found that health insurance and HCFP substantially increased health services utilization.8 As noted above, systematic reviews found little evidence that health insurance effects health status in low- and middle-income countries. Two sets of studies are notable, those in Vietnam and China. Wagstaff and Pradhan (2005) studied the effect of VHI on body mass index (BMI) and on the height and weight of young children and found that VHI had a significant positive impact on the BMI of adults and on the height and weight of young children (children aged between 0 and 4 in 1992/93) but not of older children. Wang et al. (2009) evaluated the impact of a community-based health insurance scheme in rural China in 2003. Using data from a social experiment (2002 and 2005) and double-difference with propensity score matching, Wang et al. found that insurance had a positive impact on overall 6 The results of Wagstaff (2010) should be interpreted with caution as there are obvious discrepancies between the text and the results presented in Table III (page 205). 7 The conflicting results between Axelson et al. (2009) and Wagstaff (2007) who use the same data and similar methods are probably due to the use of different ‘control groups’. 8 This analysis is part of a broad overview of health financing and delivery in Vietnam. Relatively limited methodological information is provided.

364

G. EMMANUEL GUINDON

self-reported health and on specific dimensions of quality of life, including pain/ discomfort and anxiety/depression but not mobility, self-care or usual activity. Wagstaff and Yu (2007) evaluated the impact of a World Bank project that combined supply-side interventions with demand-side measures including measures that aimed at expanding health insurance in China. Using two survey waves conducted after the start of the project (2000 and 2004) and difference-in-differences with propensity score matching, Wagstaff and Yu found some evidence that the project led to a reduction in the number of days of sickness and in chronic sickness, but no evidence that the project had any impact on self-assessed health or infant mortality. The mixed evidence of the impact of health insurance on health outcomes for high-income populations and the limited evidence for populations of low- and middle-income countries calls for additional research on the impact of health insurance. While a number of studies have already explored the impact of HCFP on health services utilization in Vietnam, none explore its impact over a period longer than two years: Axelson et al. (2009), Wagstaff (2007) and Nguyen (2009) examine the effect between 2002 and 2004, Wagstaff (2010) examine the effect between 2004 and 2006 while Lieberman and Wagstaff (2009) compares individuals with and without insurance in 2006. Additionally, the impact of the policy to provide free health care to children under six and the impact of gaining student health insurance have not been previously examined. As pointed out by Buchmueller et al., the effects of expanding insurance coverage may vary across the population and may depend on what type of coverage is offered to currently uninsured individuals (Buchmueller et al., 2005). As Vietnam moves toward universal coverage, understanding the probable magnitude of the impact of health insurance on health services utilization for various subpopulations is critical for assessing the costs and benefits of its expansion strategy. I first examine the impact of the HCFP on health services utilization and health outcomes. Second, I examine the impact of the Law on Protection and Care of Children that mandated free care for children under six. Third, I examine the impact of student and school children health insurance. Data The Vietnam Household Living Standards Survey (VHLSS) is a large-scale national survey with a sample in 2006 of more than 9000 households and 39,000 individuals, representative for the whole country, eight regions, urban/rural strata and provinces.9 VHLSS has a rich panel component (2002, 2004 and 2006) of more than 6500 individuals. Using a matching algorithm developed by McCaig (2009), it is possible to link 6575 individuals from 1790 households over the three waves of the survey. 9 VHLSS 2002 and 2004 have similar sample sizes.

The impact of health insurance in Vietnam 365

Dependent variables Following Axelson et al. (2009) and Wagstaff (2007, 2010), I use the number of outpatient visits and inpatient admissions in the last 12 months as measures of health services utilization. As measures of health outcomes, I use the number of ‘sickness days’ (number of days in the past 12 months when illness or injuries precluded the respondent from carrying out her/his regular activities, such as work or school); and ‘bed days’ in the past 12 months (number of days in the past 12 months when illness or injuries confined the respondent to stay in bed).10 Measures of utilization in VHLSS 2002 differ slightly from the 2004 and 2006 measures. The wording and subsequent coding of the 2002 question do not allow for a record of multiple visits to the same facility for the same episode of care. VHLSS 2004 and 2006 allow such possibility. For example, a handful of respondents in 2004 and 2006 report having visited a specific facility such as a commune health center or a private clinic more than 20 times. Independent variables To mitigate selection bias, the following individual and household characteristics are used: age, age squared, sex, marital status, HCFP qualifying criteria (1) whether the individual lived in a household officially classified as poor; (2) whether the household is an ethnic minority household living in especially disadvantaged provinces in the Northern Mountainous region;11 and (3) whether the household is living in a ‘decision 135’ commune12), geographical regions, household per capita expenditures, student status, ever smoking and household head’s sex, age, age squared and education. Methods Table 2 presents a timeline of health insurance policy implementations and Vietnam Household Living Standard Surveys.

HCFP HCFP, introduced in 2003, provides insurance to those that are officially classified as poor, ethnic minority households living in remote mountainous areas and households living in communes officially classified as poor. I take advantage of the gradual uptake of HCFP health insurance from 2003 to study individuals who had obtained coverage by 2004 and 2006 (14% and 21% of individuals reported 10 Wagstaff and Yu (2007) use a similar measure of ‘sickness days’ in their impact assessment of the World Bank’s Health VIII project in China. 11 Ethnic minorities (not Kinh or Chinese) living in the province of Thai Nguyen and the six mountainous provinces designated by Decision 186 as facing special difficulties: Cao Bang, Bac Kan, Lao Cai, Ha Giang, Son La and Lai Chau. 12 Decision 135 established a poverty-alleviating programme in 1998 to implement government policies targeting the most vulnerable communes.

366

G. EMMANUEL GUINDON

Table 2. Timeline of policy implementations and VHLSS Year Quarter

2002

2003

2004

2005

2006

I II III IV I II III IV I II III IV I II III IV I II III IV

VHLSS 2002 x x x HCFP created (Decision 139) HCFP reimbursement of health services VHLSS 2004 Free health care for children under six* Under six free health care VHLSS 2006

x x

x

x x x x x

x x

x x x

x

x x x

x

x

x x x x x

x x

x x x

VHLSS = Vietnam Household Living Standard Surveys; HCFP = Health Care Fund for the Poor. Note: *Law on Protection and Care of Children.

being covered under HCFP in 2004 and 2006, respectively).13 First, I compare individuals who had HCFP coverage in 2006 (and had no health insurance in 2004 and were not covered by the predecessor of HCFP in 2002) to individuals who had no health insurance in 2004 and 2006 and were not covered by the predecessor of HCFP in 2002. Second, I compare individuals who had HCFP coverage in 2004 and 2006 (and were not covered by the predecessor of HCFP in 2002) to individuals who had no health insurance in 2004 and 2006 and were not covered by the predecessor of HCFP in 2002. As a sensitivity check, I also follow Wagstaff (2010) and compare individuals who acquired HCFP coverage between 2004 and 2006 (and who were not covered by the predecessor of HCFP in 2002) to those who had no HCFP coverage in 2004 and 2006 (and who were not covered by the predecessor of HCFP in 2002).14 Because HCFP coverage is not randomly assigned it may not possible to control fully for factors such as health status that may influence the uptake of HCFP insurance.15 13 The reasons given by the HCFP management boards to explain the gradual uptake were difficulties in identifying those from low-income groups and ensuring that cards were issued and received by beneficiaries (Ministry of Health & Health Partnership Group, 2008). 14 The subtle but key difference in Wagstaff’s approach is the inclusion of individuals who had other forms of health insurance in both treatment and control groups. The chosen approach gets at the change in utilization that would result from providing HCFP coverage to the uninsured. Wagstaff’s approach gets at the change in utilization that would result from providing HCFP coverage to the uninsured as well as the insured. 15 Differences in utilization and health outcomes for individuals with and without insurance will reflect the combination of a causal effect of insurance and the effect of unmeasured characteristics that are correlated with insurance coverage (Buchmueller et al., 2005). Two of the three eligibility HCFP criteria are unlikely to be manipulated by individuals or officials (ethnicity and residence in a commune covered by Decision 135) but may be correlated with characteristics that are not observed. The direction and size of this bias is, however, unclear (Wagstaff, 2007). Poverty status, on the other hand, can be more easily manipulated by officials. For example, individuals just above the poverty cut-off line with known unmet medical needs may be provided HCFP coverage. If such is the case, the estimates of the impact on health services utilization will be overestimated.

The impact of health insurance in Vietnam 367

Free health care for children under six (Law on Protection and Care of Children) Because the panel component of VHLSS contains few children under six, I use single difference with the full 2006 VHLSS sample to examine the impact of the Law on Protection and Care of Children (i.e. I compare children under six with and without coverage in 2006). I also examine the impact of the Law on Protection and Care of Children using difference-in-differences by pooling the 2004 and 2006 waves of VHLSS and using an intention-to-treat approach: I examine the impact of under six mandated free care regardless of whether or not children under six had acquired coverage by 2006. In 2006, 70% of children under six had already acquired coverage. As a control group, I use children aged 7–10. Consequently, the treatment group includes children under six who were eligible but did not actually report having coverage while the comparison group contains children who were too old to qualify for coverage.16 Estimates obtained using the intention to treat approach are unlikely to be biased due to selection as date of birth is difficult to manipulate. Moreover, the inclusion of children who were eligible but did not report having coverage in the treatment group, would if anything, result in underestimations of the impact of mandated free care for under six. It is possible, however, that children with greater unmet need for health services were provided coverage earlier than children with lesser unmet need. Hence, single-difference estimates of the impact of mandated free care for under six children may be overestimated. As a sensitivity check, as control group, I use children aged 7–9 and 7–11. Students and school children health insurance Using the VHLSS panel sub-sample of individuals who were 30 years of age or less in 2006, I examine the impact of student health insurance by comparing individuals who had student health insurance coverage in 2006 (and had no health insurance in 2004) to individuals who had no health insurance in 2004 and 2006. As a sensitivity check, I use the VHLSS panel sub-sample of individuals who were 25 or less in 2006. Because full-time students and school children are typically enrolled as a whole at the school or college level, it is unlikely that estimates of the impact of student health insurance are biased due to selection at the individuallevel. Selection, however, could occur at the level of the school. Econometric analysis To assess the impact of health insurance on health services utilization and health outcomes, I use single difference (SD) and difference-in-differences, or doubledifference (DD) approaches to estimate average treatment effects on the treated (ATT). Following Ravallion’s notation (Ravallion, 2007), let there be two groups 16 Pre-intervention trends (2002 to 2004) between the two groups are remarkably similar.

368

G. EMMANUEL GUINDON

indexed by treatment status T = 0, 1 where 0 indicates individuals who do not receive treatment (i.e. the control group) and 1 indicates individuals who do receive treatment (i.e. the treatment group). Let Yi be the observable outcome indicator for each individual i and let YiT be the value of Yi under treatment and YiC the value of Yi under the counterfactual of not receiving the treatment (i.e. the control group). For simplicity, let us assume individuals are observed in two time periods, t = 0, 1 where 0 indicates a time period before the treatment group receives treatment and 1 indicates a time period after the treatment group receives treatment. Let X be a vector of observed covariates such as individual and household characteristics. The single difference (D) in mean outcomes between individuals with and without health insurance (i.e. treatment) is

DðXÞ ¼ E Y T jX; T ¼ 1 E Y C jX; T ¼ 0 The double-difference estimator for period 1 is

DDðXÞ ¼ E Y1T Y0T jX; T1 ¼ 1 E Y1C Y0C jX; T1 ¼ 0 Conveniently, a regression approach can be used to estimate this double difference design: Yit ¼ α + γTi1 + δt + βXit + DDðTi1 tÞ + εi ðt ¼ 0; 1; i ¼ 1; ::: ; nÞ where α is a constant term; the dummy Ti1 captures possible differences between the treatment and control groups prior to the policy change; the dummy t captures aggregate factors that would cause changes in Y even in the absence of a policy change; and Ti1 t is the interaction term taking on a value of 1 if individual i has health insurance at time t. The coefficient of interest is DD (the coefficient on the interaction term). The key assumption to ensure validity requires that trends would have been the same in the absence of treatment (i.e. differences between treatment and control group would have remained constant in the absence of treatment). In other words, this DD analysis assumes that the paths of health utilization and outcome for the individuals with and without health insurance would not have been systematically different in the absence of intervention. The estimated impact (DD – the coefficient on the interaction term) is often estimated using traditional linear-regression methods (e.g. Wagstaff (2010)). Traditional linear-regression methods, however, impose the restriction that the controls enter in a linear-in-parameters form (Ravallion, 2007). During a 12-month period, a substantial number of individuals do not use any health services. Similarly, a substantial number of individuals do not experience any illness or injuries that preclude them from carrying out their regular activities, such as work or school, or that confine them to stay in bed. Such characteristics of the data render estimates obtained using linear-regressions inconsistent (Cameron and Trivedi, 2005). Several alternative models have been proposed17 including 17 See, for example, Buntin and Zaslavsky (2004) that compare the performance of eight alternative estimators.

The impact of health insurance in Vietnam 369

two-part models that allow for the possibility that the zero and positive values are generated by different processes (Cameron and Trivedi, 2005). For completeness and in order to allow comparisons with recent studies, I first present single-difference estimates obtained using ordinary least-squares (OLS) regressions. Second, I present difference-in-differences estimates obtained using: (1) OLS regressions; (2) fixed effects regressions (to control for unobserved heterogeneity); and (3) two-part models (to account for the substantial number of individuals who do not experience the outcome and to allow for the possibility that the zero and positive values are generated by different processes). The first part of the two-part model is estimated using linear probability models (LPM) while the second part is estimated using linear regressions. Sensitivity analyses are conducted using a probit specification for the first part and linear and loglinear regressions for the second part. I also use difference-in-differences with propensity score matching (PSM) using various matching methods to adjust for observables as sensitivity checks. All models are estimated using Stata/MP 11.0 for Macintosh. Results

Descriptive statistics Table 3 presents descriptive statistics of the outcome variables for all treatment and control groups. Overall use of outpatient services increased for both HCFP treatment and control samples between 2004 and 2006 while use of inpatient services decreased during the same period. The number of sickness and bed days increased among all HCFP samples. There was one exception: the number of bed days of the HCFP control group sample. Health services utilization increased among young children treated and control samples with the exception of the number of inpatient admissions among the control group sample that remained more or less the same. Trends among the student health insurance treated and control samples are less evident. Among the treated, use of outpatient services decreased slightly while use of inpatient services and number of sickness and bed days increased between 2004 and 2006. Among the control sample, the converse occurred: use of outpatient services increased slightly while use of inpatient services and number of sickness and bed days decreased. Table 4 provides descriptive statistics of the characteristics of participants and non-participants. Compared with individuals with no health insurance in 2004 and 2006, individuals who obtained HCFP coverage after 2004, are on average younger, are less likely to be married, to have ever smoked and to reside in richer and more urban regions (e.g. Red River Delta and Mekong River Delta), and are more likely to be students, to qualify for HCFP, and to live in a household with lower monthly expenditures and whose head is less educated. The only unexpected systematic difference is the relatively younger age of ‘treated’ individuals, which in turn explains differences in marital, student and smoking status.

370

Table 3. Descriptive statistics of outcome variables Student health insurance

2004 Treated (n = 441)

2006 Control (n = 2541)

Treated (n = 441)

2004 Control (n = 2541)

Treated (n = 313)

2006 Control (n = 806)

Treated (n = 313)

Control (n = 806)

Variables

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

No. of outpatient visits No. of inpatient admissions No. of sickness days No. of bed days

0.68 0.13 4.42 2.33

1.92 0.62 12.21 18.31

0.99 0.09 5.58 1.63

2.65 0.53 21.28 12.58

1.05 0.10 5.54 2.77

2.54 0.38 22.68 22.87

1.21 0.07 6.04 1.25

3.38 0.37 26.22 9.30

0.56 0.02 1.67 0.48

1.50 0.17 4.60 2.00

0.60 0.06 3.31 0.77

1.89 0.26 16.43 4.34

0.50 0.08 1.96 0.56

1.12 0.29 3.88 3.15

0.62 0.05 2.65 0.60

1.58 0.32 13.78 3.48

HCFP

Under six Health Insurance

2002 Treated (n = 425)

2004–06 Control (n = 2541)

Treated (n = 425)

2004

Control (n = 2541)

Treated (n = 3685)

2006 Control (n = 3238)

Treated (n = 3685)

Control (n = 3238)

Variables

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

No. of outpatient visits No. of inpatient admissions

0.12 0.09

0.39 0.30

0.16 0.06

0.45 0.26

1.00 0.15

2.65 0.48

1.10 0.08

3.04 0.46

1.27 0.08

2.68 0.46

0.73 0.05

2.21 0.26

1.61 0.10

2.99 0.42

0.86 0.05

2.07 3.74

HCFP = Health Care Fund for the Poor. Note: No. of outpatient visits: No. of outpatient visits in last 12 months; No. of inpatient admissions: No. of inpatient admissions in last 12 months; No. of sickness days: No. of days in past 12 months when illness/injuries precluded respondent from carrying out regular activities, such as work/school; No. of bed days: No. of days in past 12 months when illness or injuries confined respondents to stay in bed.

G. EMMANUEL GUINDON

HCFP

Table 4a. Descriptive statistics of control variables (pre-intervention year) HCFP, 2004 Treated

Control

Treated

Control

Mean

SD

Mean

SD

Mean

SD

Mean

SD

31.5 0.49 0.47

20.0 0.50 0.50

39.3 0.49 0.65

17.6 0.50 0.48

12.8 0.49 0.00

4.6 0.50 0.00

19.4 0.55 0.17

6.5 0.50 0.37

0.60 0.07 0.26 2734 0.24 0.19

0.49 0.25 0.44 1569 0.43 0.39

0.02 0.01 0.07 4250 0.12 0.24

0.15 0.10 0.26 2892 0.32 0.43

0.06 0.01 0.08 3601 0.84 0.02

0.23 0.10 0.27 2054 0.37 0.13

0.03 0.01 0.11 3874 0.36 0.20

0.17 0.11 0.31 2701 0.48 0.40

45.0 0.82

12.3 0.39

49.8 0.83

13.3 0.38

43.9 0.88

10.4 0.32

48.0 0.83

12.9 0.37

0.34 0.27 0.36 0.04 0.00

0.47 0.44 0.48 0.20 0.00

0.29 0.30 0.32 0.08 0.01

0.45 0.46 0.47 0.27 0.09

0.19 0.27 0.41 0.11 0.01

0.40 0.27 0.49 0.32 0.11

0.31 0.31 0.30 0.06 0.01

0.46 0.46 0.46 0.24 0.09

0.10 0.26 0.09 0.20 0.11 0.10 0.04 0.11

0.29 0.44 0.28 0.40 0.31 0.30 0.20 0.31

0.21 0.11 0.01 0.11 0.09 0.05 0.14 0.28

0.41 0.31 0.11 0.31 0.29 0.21 0.35 0.45

0.21 0.14 0.01 0.20 0.09 0.08 0.10 0.17

0.41 0.35 0.11 0.40 0.29 0.27 0.30 0.37

0.19 0.13 0.02 0.12 0.06 0.06 0.14 0.29

0.39 0.33 0.13 0.33 0.23 0.24 0.35 0.45

SD = standard deviation; VND = Vietnam Dong. Note: * = whether the household is an ethnic minority household living in especially disadvantaged provinces in the Northern Mountainous region.

The impact of health insurance in Vietnam 371

Age Sex (male) Married HCFP qualifying criteria Household officially classified as poor Ethnic minority household* Household is living in a ‘decision 135’ commune Household monthly per capita expenditures (VND) Student Ever smoked Household Head: Age Sex (male) Education Less than primary Primary Lower secondary Upper secondary College/University Regions Red River Delta North East North West North Central South Central Coast Central Highlands South East Mekong River Delta

Student Health Insurance, 2004

372

Table 4b. Descriptive statistics of control variables (pre-intervention year)

Treated

Age Sex (male) Household officially classified as poor Household monthly per capita expenditures (VND) Household head Age Sex (male) Education Less than primary Primary Lower secondary Upper secondary College/University Regions Red River Delta North East Northwest North Central South Central Coast Central Highlands South East Mekong River Delta SD = standard deviation; VND = Vietnam Dong.

Under six Health Insurance, 2006

Control

Treated

Control

Mean

SD

Mean

SD

Mean

SD

Mean

SD

3.17 0.54 0.13 3768

2.02 0.50 0.34 3441

8.62 0.51 0.13 3588

1.11 0.50 0.33 2987

2.72 0.53 0.16 4431

1.75 0.50 0.37 2960

3.00 0.47 0.12 4775

2.36 0.50 0.32 3731

44.3 0.82

15.2 0.39

42.4 0.84

12.6 0.37

45.6 0.80

15.1 0.40

45.5 0.78

14.6 0.41

0.35 0.28 0.24 0.11 0.03

0.48 0.45 0.42 0.31 0.18

0.31 0.28 0.26 0.12 0.03

0.46 0.45 0.44 0.33 0.17

0.32 0.28 0.26 0.11 0.04

0.47 0.45 0.44 0.31 0.18

0.35 0.28 0.26 0.09 0.02

0.48 0.45 0.44 0.29 0.13

0.17 0.14 0.08 0.11 0.09 0.10 0.14 0.18

0.37 0.35 0.27 0.31 0.28 0.30 0.34 0.38

0.15 0.15 0.07 0.14 0.10 0.10 0.12 0.18

0.35 0.36 0.26 0.34 0.29 0.31 0.32 0.38

0.18 0.20 0.09 0.12 0.07 0.06 0.12 0.16

0.38 0.40 0.28 0.32 0.26 0.25 0.33 0.37

0.15 0.12 0.06 0.09 0.07 0.09 0.18 0.24

0.36 0.33 0.23 0.29 0.26 0.28 0.39 0.43

G. EMMANUEL GUINDON

Under six Health Insurance, 2004

The impact of health insurance in Vietnam 373

On average, individuals who acquired student health insurance after 2004 compared with individuals with no health insurance in 2004 and 2006, are younger, less likely to be male, married, and to live in a household whose head is less educated. There are no obvious unexpected differences between these two groups. Table 4 also provides descriptive statistics of the characteristics of the treatment and control groups for the under six health insurance scheme for both approaches utilized (i.e. single difference with the full 2006 sample and difference-in-differences that pools the 2004 and 2006 samples and use an intention-to-treat approach). For the intention-to-treat approach, with the exception of age (which is expected given how the control group is constructed), there are no systematic differences between both groups. Children under the age of 6 with health insurance are slightly younger, more likely to be male and live in a poor households, and reside in the North East region and less likely to reside in the South East and Mekong River Delta regions.

Impact of HCFP on health services utilization and health Table 5 presents the estimates of the impact of HCFP on changes in health services utilization and health outcomes between 2004 and 2006 and between 2002 and 2004/2006. The first column under each alternative specification shows the estimated ATTs, the second column shows the standard errors. For example, the last estimate of the first column (0.068) indicates that HCFP was associated with an increase of 0.068 in the annual number of inpatient admissions. From pre-intervention level (i.e. 0.09), an ATT of 0.068 represents an increase of nearly 75%. On the whole, the results do not suggest HCFP had any statistically significant impact on health services utilization and health outcomes between 2004 and 2006 (top part of Table 5). The lone exception is the finding that individuals who were covered by HCFP in 2006 had a higher probability (0.115) of having had at least one outpatient visit in the past 12 months. When examining individuals who had HCFP coverage in 2004 and 2006, compared with individuals who had no health insurance (bottom part of Table 5), the results suggest HCFP had a statistically significant impact on utilization of inpatient services. The average treatment effects are about 0.05, which represent an increase of more than 50% from pre-HCFP levels (0.09). Of note is the relatively higher estimates of the impact of HCFP on inpatient admissions obtained using DD relative to SD (last row of Table 5): DD estimates are about 50% higher than the SD estimates (0.033 v. 0.049 and 0.053). Whether individuals gained HCFP coverage by 2004 or 2006 does not appear to alter the estimated effect of HCFP on the utilization of outpatient services. HCFP coverage is not found to impact the utilization of outpatient services with the exception that HCFP coverage appears to increase the probability of having had at least one outpatient visit.

374 G. EMMANUEL GUINDON

Table 5. Impact of Health Care Fund for the Poor on health services utilization and health SD OLS

DD OLS

Pooled OLS

No adjustments for observables 2004 vs 2006

Pooled OLS

Fixed-Effects

No adjustments for observables

Two-part model

LPM

Estimate

SE

Estimate

SE

Estimate

SE

Estimate

SE

No. of outpatient visits No. of inpatient admissions No. of sickness days No. of bed days

− 0.154 0.031 − 0.499 1.862

0.165 0.021 1.222 1.657

0.399 0.030 0.096 1.923

0.278 0.035 2.335 1.647

0.150 − 0.011 0.660 0.814

0.168 0.038 1.322 0.814

0.198 − 0.015 0.900 0.736

2002 vs 2004–06 No. of outpatient visits No. of inpatient admissions

− 0.100 0.068

0.150 0.020

0.183 0.033

0.219 0.032

− 0.056 0.037

0.148 0.021

− 0.018 0.149 0.049 0.021

Estimate

SE

Estimate

0.165 0.224 0.167 0.115 0.039 − 0.014 0.039 0.015 1.308 1.185 1.307 0.029 0.849 0.812 0.858 − 0.011 0.022 0.150 0.053 0.021

Pooled OLS SE

Estimate

0.036 − 0.21 0.020 − 0.42 0.034 2.91 0.023 7.88

SE 0.37 0.33 3.83 7.97

0.077 0.033 − 0.420 0.368 0.027 0.017 0.046 0.112

SD = single difference; DD = differences-in-differences; OLS = ordinary least squares; SE = standard errors; LPM = Linear Probability Model. Note: Unless otherwise noted, estimates of impact are adjusted for observables. Bold indicates significance at 5%. SE are adjusted for clustering at the household level.

The impact of health insurance in Vietnam 375 Table 6. Impact of under six years old health insurance on health services utilization SD No. of outpatient visits Estimate OLS; no adjustments for observables OLS Nearest neighbor matching Radius matching (radius = 0.0001) Kernel matching Stratification matching Two-part model PartI: LPM Part II: OLS Preventive health services Vaccination –LPM Vaccination and check-up–LPM

No. of inpatient admissions

SE

Estimate

SE

0.405 0.625 0.481 0.665 0.414 0.469

0.148 0.159 0.21 0.521 0.171 0.188

0.035 0.036 0.050 0.002 0.042 0.041

0.020 0.021 0.029 0.071 0.021 0.020

0.202 − 0.093

0.027 0.306

0.038 − 0.197

0.013 0.184

0.056 0.081

0.014 0.017

na na

na na

DD Pooled OLS; no adjustments for observables Pooled OLS

0.203 0.223

0.088 0.087

0.013 0.013

0.013 0.013

Two-part model Part I: LPM Part II: OLS

0.047 0.201

0.017 0.196

0.014 − 0.087

0.008 0.127

SD = single difference; DD = differences-in-differences; OLS = Ordinary Least Squares; SE = standard errors; LPM = Linear Probability Model; na = not applicable. Note: Unless otherwise noted, estimates of impact are adjusted for observables. Bold indicates significance at 5%. SE (OLS and LPM) are adjusted for clustering at the household level.

Impact of the Law on Protection and Care of Children on health services utilization Table 6 presents the estimates of the impact of mandated free care on changes in health services utilization by those under the age of 6. The top portion of Table 6 presents average treatment effects obtained using single difference while the bottom portion presents the intention-to-threat effects obtained using a difference-indifferences approach. The results suggest that providing free care to under six children had a statistically significant and fairly substantial impact on the utilization of outpatient services. Single-difference estimates range from ∼ 0.4 to 0.6 (which represents an increase of about 30 to 50% from pre-intervention level [1.27]). Single-difference estimates also suggest that the Law on Protection and Care of Children may have impacted utilization of inpatient services, but this finding is not

376

G. EMMANUEL GUINDON

robust across alternative specifications. Looking only at the utilization of preventive health services (i.e. vaccination and check-up and consulting), single-difference estimates suggest that providing free care to under six children had a statistically significant and substantial impact on the utilization of outpatient preventive services. For example, the results indicate under six coverage increased outpatient vaccination visits nearly two-fold. As pointed out by Buchmueller et al., relative to other types of care, estimates of the effect of insurance on preventive care visits are less likely to be confounded by unobserved morbidity. Other sources of bias, such as preferences, however, cannot be ruled out (Buchmueller et al., 2005). Difference-in-differences estimates also suggest that under six coverage had a statistically significant and fairly substantial impact on the number of outpatient visits but not on the number of inpatient admissions (0.22, which represents an increase of about 17%). As expected, the DD estimates are somewhat smaller than the SD estimates. As discussed earlier, SD estimates may be overestimated due to selection while DD estimates may be underestimated due to the inclusion of children who were eligible but did not report being covered in the treatment group.

Impact of student and school children health insurance on health services utilization and health Table 7 presents the estimates of the impact of student health insurance on changes in health services utilization and health outcomes between 2004 and 2006. More specifically, the estimates represent the impact of gaining student health insurance coverage (from no health insurance coverage) between 2004 and 2006 relative to individuals without health insurance coverage in both 2004 and 2006. The results suggest that student health insurance had a statistically significant impact on utilization of inpatient services but not on the utilization of outpatient services or health outcomes. The average treatment effects for inpatient admissions are about 0.06 and represent a more than two-fold increase from 2004 (pre-student health insurance) levels. Robustness: sensitivity analyses Following Wagstaff’s approach and including individuals who had other forms of health insurance in both HCFP treatment and control groups yields qualitatively similar results. Including FE or using a probit specification for the first part of the two-part models and linear and log-linear regressions for the second part also yields results that are qualitatively similar. Using regression and propensity score matching approaches to examine the impact of under six years old health insurance on health services utilization yields results that are similar in direction but not in effect size (Table 6). The estimates of the impact of health insurance on the number of out-patient visits obtained using propensity score matching methods yields estimates that are somewhat smaller. Conversely, the estimates of the impact of health insurance on the number of inpatient admissions obtained using

Table 7. Impact of student health insurance on health services utilization and health SD OLS

DD OLS

Pooled OLS

2004 vs 2006 No. of outpatient visits No. of inpatient admissions No. of sickness days No. of bed days

Fixed-effects

No adjustments for observables

Two-part model

LPM

Estimate

SE

Estimate

SE

Estimate

SE

Estimate

− 0.114 0.022 − 0.688 − 0.034

0.090 0.021 0.553 0.231

− 0.065 0.022 − 0.338 0.024

0.118 0.026 0.561 0.358

− 0.074 0.056 0.953 0.258

0.125 0.025 0.804 0.267

− 0.067 0.055 1.090 0.429

SE

Estimate

SE

0.135 − 0.109 0.138 0.026 0.058 0.026 0.704 0.788 0.790 0.302 0.307 0.259

Pooled OLS

Estimate

SE

Estimate

SE

0.041 0.059 0.073 −0.001

0.039 0.022 0.042 0.026

− 0.63 0.14 1.81 5.38

0.45 0.24 2.81 2.77

OLS = Ordinary Least Squares; SE = standard errors; LPM = Linear Probability Model. Note: Unless otherwise noted, impact estimates are adjusted for observables. Bold indicates significance at 5%. SE are adjusted for clustering at the household level.

The impact of health insurance in Vietnam 377

No adjustments for observables

Pooled OLS

378

G. EMMANUEL GUINDON

propensity score matching methods yields estimates that are somewhat larger. Using alternative control groups when examining the impact of under six years old health insurance using an intention treat approach yields results that are essentially identical. Similarly, restricting the VHLSS panel sub-sample to individuals who were 25 years of age (vs 30) or less in 2006 when examining the impact of student health insurance yields results that are qualitatively similar. Discussion These results suggest that HCFP did not have an impact on health outcomes if measured by the number of sickness or by the number of bed days. The results, however, provide some support that HCFP led to increased use of inpatient services. The conflicting results between individuals who acquired HCFP coverage after 2004 and those who acquired HCFP coverage in 2003 or 2004 suggest that too little time may have elapsed between the onset of HCFP coverage for those who acquired coverage after 2004 and 2006 for HCFP to impact health services utilization and health outcomes. Moreover, official Vietnam Social Security (VSS) figures indicate that a substantial number of individuals who are covered under HCFP acquired coverage in 2005 and 2006 (Wagstaff, 2010).18 Changes in health outcomes may be particularly sensitive to the short time frame examined (i.e. changes between 2004 and 2006). The data utilized do not allow one to examine changes in health outcomes from 2002 as VHLSS 2002 did not ask respondents about illness/injuries that precluded them from carrying out regular activities or that confined them to stay in bed. Using data from China, Wagstaff and Yu (2007) find a statistically significant reduction in the number of days of sickness between 2000 and 2004, which suggests that significant changes in the number of sickness days may be uncovered using relatively short time frames, but perhaps not as short as just two years. Results also suggest that providing health insurance to students who did not previously have any health insurance had a large impact on utilization of inpatient services but not on the utilization of outpatient service or health outcomes. Estimates of the impact of the Law on Protection and Care of Children mandating free care for children under six suggest that providing medical coverage to young children had a fairly substantial impact on the number of outpatient visits but not on the number of inpatient admissions. As pointed out by Buchmueller et al. (2005), both theory and empirical evidence such as the RAND HIE suggest a positive effect of insurance on the utilization of outpatient services 18 The retrospective nature of the health services utilization and health outcome measures (number of visits/admissions/days in the past 12 months) combined with the non-negligible number of individuals who acquired HCFP coverage less than 12 months prior to the data collection interview may bias estimates toward zero (i.e. some individuals may have obtained HCFP coverage after their outpatient visit or inpatient admission). However, if ill-health and unmet health care needs caused eligible individuals to obtain insurance more promptly, the bias may be in the opposite direction, away from zero.

The impact of health insurance in Vietnam 379

by children. Because outpatient visits include in addition to treatment of acute illness, preventive care services such as immunizations, increases in outpatient services may lead to decreases in inpatient admissions. This is precisely what was observed in the RAND HIE: coinsurance rates did not effect the probability of admission for children (Newhouse and the RAND Insurance Experiment Group, 1996). One possible interpretation of the finding that mandated free care for children under six led to increases in outpatient services but not inpatient admissions, is that by improving access to preventive and primary care, mandated free care led to a more efficient use of health care resources (Buchmueller et al., 2005). The finding that providing free care to under six children had a statistically significant and substantial impact on the utilization of outpatient preventive services supports this interpretation. The effect sizes (i.e. increases of between 30 and 50%) are remarkably similar to the effect sizes found by Buchmueller et al. in their systematic review (Buchmueller et al., 2005). For all three insurance schemes examined, the results of the two-part models suggest that the main pathway by which health insurance impacts health services utilization may be through increases in the probability of a first visit or admission and not through increases in the number of visits/admissions given at least one visit/admission. The positive association between underreporting of self-reported utilization with increased frequency of utilization is well documented (Bhandari and Wagner, 2006). In other words, high users tend to underreport more than low users, as individuals likely forget some visits as the number of visits increase (Bhandari and Wagner, 2006). Such measurement error may explain, at least partially, the finding that health insurance tends to increase the probability of a first visit or admission but not the number of visits/admissions. This study’s contributions to the health insurance literature are three-fold. First, I examine the impact of three different health insurance schemes that target three distinct groups (the poor, young children and students). Consequently, this study is more relevant than previous studies for forecasting the effects of Vietnam’s health insurance strategy. Second, in addition to examining the impact of health insurance on health services utilization, I also examine the impact of health insurance on health outcomes and thus contribute to the small but growing literature that explores whether expanding health insurance in low- and middleincome countries actually improves health outcomes. Third, I use a number of alternative specifications to ensure that findings are robust. There are, however, some limitations that merit discussion. First, as discussed earlier, the possibility of selection bias exists (i.e. the selection into health insurance schemes on unobservables). Buchmueller et al. in their systematic review find a high degree of concordance among the results of studies that use an extensive array of variables to control for the nonrandom assignment of insurance status and studies that use instrumental variables or quasi-experimental regression techniques. They concluded that endogeneity bias due to adverse selection may not be a major problem (Buchmueller et al., 2005). Such findings provide some

380

G. EMMANUEL GUINDON

assurance that estimates may not be overly biased because of selection. Second, as noted earlier, measures of utilization in VHLSS 2002 differ slightly from the measures used in 2004 and 2006. This difference likely explains at least partly the substantial increase in reported utilization of outpatient services between 2002 and 2004. The slight difference in measures of utilization between the first wave of VHLSS and the second and third wave, however, would only affect the estimates of impact if it leads to the failure of the parallel trend assumption (i.e. changes in outcomes follow a different trend for the treatment and control group). Third, VHLSS does not differentiate between student insurance provided by the government’s VHI scheme and student health insurance provided by for-profit general insurance companies. This feature limits the generalizability of the findings. Fourth, as discussed earlier, changes in health services utilization and particularly health outcomes may be particularly sensitive to the short time frame examined. Fifth, errors in the measurement of utilization and insurance status may bias the results. Although there is no consistent evidence that the accuracy of self-report utilization varies by socio-demographic characteristics, as discussed earlier, there is a well documented positive association between underreporting of self-reported utilization and increased frequency of utilization (Bhandari and Wagner, 2006). Moreover, studies in the United States and Canada have highlighted the potential for error in self-reported data on source of insurance (Braveman et al. 1998; Nelson et al. 2000; Buchmueller et al., 2003; Grootendorst et al., 2003). Sixth, the relatively small sample sizes may not allow one to identify effects that are small but nevertheless socially meaningful. In 2006, more than half of Vietnam’s population had some health insurance coverage. However, health insurance in Vietnam provides limited financial protection. Even under universal coverage, out-of-pocket payments are expected to remain the dominant source of health care financing. They account for more than half of all health spending in Vietnam (Lieberman and Wagstaff, 2009). The shallowness of the health insurance coverage may explain, at least in part, the results that health insurance did not increase the use of outpatient services among the poor and among students. Anticipated out-of-pocket payments may deter even those with health insurance to seek health care. As Vietnam moves toward universal health insurance coverage, its decision makers ought to examine the potential contribution to population health of expanding the benefit package for vulnerable groups. Acknowldgements The author thanks Jeremiah Hurley, Michel Grignon and Michael Boyle for their comments and guidance, Henrik Axelson, Sarah Bales, Nguyen-Tuan Lam and Jinhu Li for helpful discussions, Brian McCaig for his helpful advice regarding the construction of the 2002–2006 VHLSS panels, Henrik Axelson for sharing his Stata codes and Daniel Canty for his editing assistance.

The impact of health insurance in Vietnam 381

Funding Generous financial support from the Social Sciences and Humanities Research Council of Canada and the McMaster University Centre for Health Economics and Policy Analysis is acknowledged. Competing Interests None.

References Axelson, H., S. Bales, P. D. Minh, B. Ekman and U.-G. Gerdtham (2009), ‘Health financing for the poor produces promising short-term effects on utilization and out-of-pocket expenditure: evidence from Vietnam’, International Journal for Equity in Health, 8: 20. Bhandari, A. and T. Wagner (2006), ‘Self-reported utilization of health care services: improving measurement and accuracy’, Medical Care Research Review, 63(2): 217–235. Braveman, P., M. Pearl, S. Egerter, K. Marchi and R. Williams (1998), ‘Validity of insurance information on California birth certificates’, American Journal of Public Health, 88(5): 813–816. Buchmueller, T. C., M. E. Allen and W. Wright (2003), ‘Assessing the validity of insurance coverage data in hospital discharge records: California OSHPD data’, Health Services Research, 38(5): 1359–1372. Buchmueller, T. C., K. Grumbach, R. Kronick and J. G. Kahn (2005), ‘The effect of health insurance on medical care utilization and implications for insurance expansion: a review of the literature’, Medical Care Research Review, 62(1): 3–30. Buntin, M. B. and A. M. Zaslavsky (2004), ‘Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures’, Journal of Health Economics, 23(3): 525–542. Cameron, A. C. and P. K. Trivedi (2005), Microeconometrics: methods and applications, Cambridge: Cambridge University Press. Ekman, B., N. T. Liem, H. A. Duc and H. Axelson (2008), ‘Health insurance reform in Vietnam: a review of recent developments and future challenges’, Health Policy and Planning, 23(4): 252–263. ́ (2010), ‘A Review of the Evidence’, in M.-L. Escobar, C. C. Griffin Giedion, U. and B. Y. Diaz and R. P. Shaw (eds), Impact of Health Insurance in Low- and Middle-income Countries, Washington, DC: Brookings Institution. Grootendorst, P., E. C. Newman and M. A. Levine (2003), ‘Validity of self-reported prescription drug insurance coverage’, Health Reports, 14(2): 35–46. Hadley, J. (2003), ‘Sicker and poorer–the consequences of being uninsured: a review of the research on the relationship between health insurance, medical care use, health, work, and income’, Medical Care Research and Review, 60, Supplement 2: 3S–75S. Lagarde, M. and N. Palmer (2006), Evidence from systematic reviews to inform decision making regarding financing mechanisms that improve access to health services for poor people. A policy brief prepared for the International Dialogue on Evidence-Informed Action to Achieve Health Goals in Developing Countries (IDEAHealth), Geneva: The Alliance for Health Policy and Systems Research.

382

G. EMMANUEL GUINDON

Levy, H. and D. Meltzer (2008), ‘The Impact of Health Insurance on Health’, Annual Review of Public Health, 29(1): 399–409. Lieberman, S. S. and A. Wagstaff (2009), Health financing and delivery in Vietnam: looking forward. Health, Nutrition and population series, No. 47388. Washington: The World Bank. McCaig, B. (2009), The Reliability of Matches in the 2002–2004 Vietnam Household Living Standards Survey Panel. Discussion Paper No. 622. Camberra: Centre for Economic Policy Research, Research School of Social Sciences, Australian National University. Ministry of Health, and Health Partnership Group (2008), Joint Annual Health Review 2008: Health Financing in Vietnam, Hanoi: Ministry of Health, Vietnam. National Assembly (2008), Law on Health Insurance. No. 25/2008/QH12, Hanoi: National Assembly, Vietnam. Nelson, D. E., B. L. Thompson, N. J. Davenport and L. J. Penaloza (2000), ‘What people really know about their health insurance: a comparison of information obtained from individuals and their insurers’, American Journal of Public Health, 90(6): 924–928. Newhouse, J. P. and the RAND Insurance Experiment Group (1996), Free for all? Lessons from the Rand Health Insurance Experiment. Cambridge: Harvard University Press. Nguyen, H. (2009), Effects of Demand Side Targeted Health Programs in Developing Countries: Evidence from Vietnam, Berkeley: University of California. Ravallion, M. (2007), ‘Evaluating Anti-Poverty Programs’, in T. P. Schultz and J. A. Strauss (eds) Handbook of Development Economics, Amsterdam: Elsevier. Spaan, E., J. Mathijssen, N. Tromp, F. McBain, A. ten Have and R. Baltussen (2012), ‘The impact of health insurance in Africa and Asia: a systematic review’, Bull World Health Organ, 90(9): 685–692. Wagstaff, A. (2007), Health insurance for the poor: initial impacts of Vietnam’s health care fund for the poor. In Impact Evaluation Series no. 11. Policy, Research Working Paper no. WPS 4134. Washington: The World Bank. Wagstaff, A. (2010), ‘Estimating health insurance impacts under unobserved heterogeneity: the case of Vietnam’s health care fund for the poor’, Health Economics, 19(2): 189–208. Wagstaff, A. and M. Pradhan (2005), ‘Health insurance impacts on health and nonmedical consumption in a developing country’, World Bank Policy Research Working Paper 3563, Washington: The World Bank. Wagstaff, A. and S. Yu (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(3): 505–535. Wang, H., W. Yip, L. Zhang and W. Hsiao (2009), ‘The impact of rural mutual health care on health status: evaluation of a social experiment in rural China’, Health Economics, 18, Supplement 2: S65–S82. World Health Organization (2009), WHO Health Financing Strategy for the Asia Pacific Region 2010–2015, Manila: World Health Organization.