The influence of health insurance on hospital admission and length of stay—The case of Vietnam

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Social Science & Medicine 63 (2006) 1757–1770 www.elsevier.com/locate/socscimed

The influence of health insurance on hospital admission and length of stay—The case of Vietnam Ardeshir Sepehria,, Wayne Simpsona, Sisira Sarmab a

Department of Economics, University of Manitoba, Winnipeg, Man., Canada, R3T 5V5 Applied Research and Analysis Directorate, Microsimulation and Modelling Division, 5th Floor, Room C574, Jeanne Mance Building Tunney’s Pasture, Ottawa, ON, Canada K1A 0K9

b

Available online 12 June 2006

Abstract Few studies analyze the effects of health insurance on inpatient care in low income countries. This paper provides an empirical assessment of the influence of Vietnam’s health insurance schemes on both hospital admission and the length of stay (LOS) using the Vietnam National Health Survey 2001–2002 and an appropriate count data regression model. Our findings suggest that the influence of health insurance on hospital admission and the LOS varies across insurance schemes. The compulsory insurance scheme and the insurance scheme for the poor increase the expected LOS by factors of 1.18 and 1.39, respectively, while the voluntary insurance scheme has minimal effect on the expected LOS. Insurance also increases the likelihood of hospital admission far more for compulsory members than for members of the other two insurance schemes. The positive influence of insurance on hospital admission and the LOS also varies across income quintiles, regions and types of health facilities. While the compulsory and voluntary schemes increase the likelihood of hospital admission more for lower and middle income individuals, the influence of the compulsory scheme on the expected LOS is more pronounced for patients in the middle income groups. The influence of insurance on the LOS is also found to be stronger in the North than in the South and stronger for patients admitted to provincial hospitals rather than district hospitals. r 2006 Elsevier Ltd. All rights reserved. Keywords: Health insurance; Length of hospital stay; Hospitalization; Vietnam

Introduction In recent years a small but growing number of studies examine the impact of insurance schemes on health seeking behavior in developing countries (Jowett, 2000; Jowett, Deolalikar, & Martinsson, 2004; Trivedi, 2002; Trujillo, 2003; Waters, 1999; Yip & Berman, 2001). The focus of these studies has Corresponding author.

E-mail addresses: [email protected] (A. Sepehri), [email protected] (S. Sarma). 0277-9536/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2006.04.029

mainly been on access to health services as measured by the likelihood of using outpatient and inpatient facilities. Comparatively little attention is given to the intensity of services per hospital admission, including the length of stay (LOS), although hospitals consume a significant proportion of total health care resources in most health care systems. Thus, it is necessary to address the issue of the appropriate LOS and ascertain efficiency implications for health policy. This paper empirically assesses the influence of Vietnam’s non-profit health insurance on hospital

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admission and the LOS. This is achieved by using the latest Vietnam National Health Survey 2001–2002 (VNHS) (MoH & GSO, 2002) and an appropriate count data regression model, the zero-inflated negative binomial (ZINB) regression model. The VNHS, a large nationally representative sample of 36,000 households, provides extensive information on household health seeking behavior, including inpatient contacts, the type of health facilities used and the type of health insurance. The ZINB regression model allows us to simultaneously estimate the influence of insurance on the likelihood of hospital admission and the LOS while adjusting for socio-economic factors, self-reported health status, the type of provider, and the distance and traveling time to a health facility. Particular attention is given to Vietnam’s three schemes: compulsory health insurance (CHI), voluntary health insurance (VHI) and health insurance for the poor (HIP). Theoretically, health insurance may affect utilization in a number of ways. First, it may reduce the price of health care at the time of purchase to increase utilization of health services, often referred to as ex post moral hazard (Zweifel & Manning, 2000). Health insurance may also affect the pattern of utilization by shifting service contacts among health facilities where insurance benefits can be accessed. Second, health insurance might cause ex ante moral hazard by reducing preventive efforts (Zweifel & Manning, 2000). Third, health insurance might also increase utilization due to supplyinduced demand, especially under a fee-for-service system in which providers have the financial incentive to do more medical and surgical procedures (Evans, 1984). These risks are potentially large in transitional low-income countries where regulation and control mechanisms are weak or not enforced, where there is no professional self-regulation and where the public health sector is underfunded (Bloom & Gu, 1997; Sepehri, Chernomas, & Akram-Lodhi, 2005; Yip & Eggleston, 2004). However, increased use of health services by the insured may enable individuals with substantial unmet needs to access otherwise unaffordable care (Nyman, 1999). Existing empirical studies on the influence of Vietnam’s public health insurance on access to inpatient care focus either on the voluntary component of Vietnam’s health insurance (Jowett et al., 2004), or the insured population as a single group (Trivedi, 2002). Trivedi was unable to differentiate Vietnams’ three insurance schemes because of data

limitations. These insurance schemes vary not only in terms of enrollees’ socio-economic profiles but also in terms of the types of benefits offered, benefit caps, coinsurance and the types of designated health facilities where the benefits can be accessed. The schemes also vary in terms of how providers are reimbursed for services rendered under the insurance plans. In this paper we use the most recent Vietnam National Health Survey 2001–2002, which differentiates the three insurance schemes, and a ZINB regression model to empirically assess the influence of Vietnam’s three health insurance schemes on both hospital admission and LOS. The paper is organized as follows. The next section provides a brief review of Vietnam’s health care sector, followed by a description of the data and methods. The paper then presents and discusses the results. Overview of Vietnam’s health insurance A formal social insurance program was introduced in Vietnam following a government decree in August of 1992. The scheme was initially compulsory but was later extended to include a voluntary scheme and a free health-card scheme for the poor. The CHI scheme covers current and retired civil servants and the employees of state enterprises as well as those in large private enterprises with more than ten employees, employees of foreign owned enterprises and organizations, the disabled, people of merit (such as mothers, widows and orphans of veterans), army invalids, and the elderly aged 90 and over. The scheme does not cover family members except for the armed forces, nor does it cover government employees at or below the district level in certain provinces. The scheme covers the cost of inpatient and outpatient treatment at hospitals, subject to some ceilings. Currently employed members are required to pay 20% coinsurance and 50% coinsurance for certain high tech diagnostic services and treatments. The VHI scheme is in principle open to all those not eligible for coverage under the CHI scheme, including the self-employed, employees of small enterprises, family members of the insured and government employees at or below the district level. However, some provinces have been too cautious in offering voluntary insurance owing to potential adverse selection and the difficulties involved in collecting premiums, while experiments with VHI

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programs in other provinces have generally been less successful (Knowles et al., 2004). Currently, the scheme is open to all community groups provided that minimum community thresholds are met, including 20% of households in a commune or ward and 30% of students in a given school (Nguyen & Akal, 2003). VHI premiums vary according to the benefit coverage chosen and the place of residence, ranging from 25,000 to 140,000 Vietnamese Dong per annum (about US$1.60 to $9.30). Initially, the VHI scheme had two packages, one lower-priced package that covered only inpatient care and another higherpriced package that covered both inpatient and outpatient care and, in some situations, pharmaceuticals as well. In 2003, VHI benefits were standardized and made similar to those of the CHI scheme, although some high cost surgical and medical treatments are subject to benefit caps. There is also an insurance scheme for the poor covering residents of communes with very difficult socio-economic circumstances and ethnic minorities in disadvantaged provinces. The scheme is funded by a variety of charity and donor organizations and the national government through the poverty alleviation program. More recently, the scheme has been reorganized and funded more adequately under a national program according to which provinces and centrally run cities are instructed to establish Health Care Funds for the Poor. The funds either provide beneficiaries with a free health insurance card with an annual premium of 50,000 Vietnamese Dong per person or pay expenses incurred at public health facilities (subject to some ceilings). In principle, beneficiaries are exempt from coinsurance for covered services. Insurance benefits can mainly be accessed at public hospitals where providers are reimbursed on a fee-forservices basis. Fees are cost-based and are generally much higher than user charges paid by the uninsured (World Bank, SIDA, AusAID, Royal Netherlands Embassy, & MoH, 2001). Fees are also higher at higher level health facilities. Both benefits and the amount of the ‘‘health care fund’’ (total insurance premium contributions adjusted for administrative expenses) made available to the health facilities are more generous under the compulsory scheme. For each insurance scheme, however, reimbursements are limited to a budget cap that is a fixed percentage (40% for outpatient care and 50% for inpatient care) of the ‘‘health care fund.’’ This provider reimbursement system implies that the health facilities that attract relative highly paid

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enrollees, especially high income CHI enrollees, receive higher average outpatient care reimbursements than those that attract less well paid members (Knowles et al., 2004). It also means that the budget cap on inpatient care reimbursements is less binding under the CHI scheme than other two schemes, encouraging providers to provide more services to the CHI members. In 2000, the average premium contribution per CHI enrollee was almost five times larger than the average premium contribution per VHI enrollee (Knowles et al., 2004). Initially, there was no risk pooling across the three insurance schemes but, since the merging of the health insurance portfolio under Vietnam Social Security in 2003, the financial regulations have been modified to pool CHI and VHI. The insurance coverage rate for the population remains low (about 16.5% of the population), with the coverage rate varying from 9% for the poorest population quintile to 36% for the richest quintile (MoH & GSO, 2002). Ethnic minorities and rural residents are also underrepresented among insurance enrollees—the coverage rate for ethnic minorities is less than half the coverage rate for the ethnic majority kinh (9.6% versus 21.2%) and the coverage rate for rural residents is 13.2% compared to 31.6% for urban residents (MoH & GSO, 2002). Data and methods Data The data in this study are from VNHS (MoH & GSO, 2002). The VNHS covers about 158,000 individuals from 36,000 households collected as a three-stage random stratified cluster sample with a non-response rate of less than 2% for the survey at the national level. The survey contains information on disease and disability, reproductive health, utilization of outpatient and inpatient services, the choice of provider, travel time and distance, types of insurance coverage and out-of-pocket expenditures as well as various socio economic variables such as education, gender, marital status, age, and household food and non-food expenditure. A 4-week and a 12-month recall period are used to collect information on inpatient contacts. In this study we use the 12-month data on hospital admission and the LOS rather than the 4-week data to assess the impact of Vietnam’s public health insurance on inpatient use. While the use of the 4-week data might be preferable to the 12-month data because of

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the shorter recall period, the 4-week data yields a much smaller sample which contains less than 14% of those households that experienced hospital admissions during the preceding 12-month period. The VNHS provides three measures of household living standards. One measure is constructed from household consumption of 13 food items and 13 specific household durable goods. A second measure is derived from household total expenditures using the estimated expenditure parameters obtained from the 1998 Vietnam Living Standard and the common variables in both surveys. The third measure is based on the commune leaders’ ranking of living standards for the 30 selected households in each commune. In this study we focus on estimated household total consumption expenditure as a measure of household living standards, since we believe that it is more comprehensive and comparable across communities, but we assess the robustness of our results by using the other two measures of living standards as well. Methods Hospital admission and LOS are likely to be influenced by a wide range of factors, including types of health insurance, income, age, gender, education, marital status, health status, household size, ethnicity, and type and severity of illness. The inclusion of different types of insurance is important to the extent that the real price of services is affected by co-insurance and the generosity of the benefits offered by Vietnam’s three schemes. Since LOS is a non-negative integer dependent variable, count data models such as Poisson or negative binomial regression are more appropriate than standard linear regression (Greene, 2003). The negative binomial model is generally considered to be superior to the Poisson model, which restricts the conditional variance of the count variable to be equal to the conditional mean. This equidispersion property turns out to be a very restrictive in empirical applications and leads to inefficient estimates in the presence of overdispersion in the data (Cameron & Trivedi, 1986). Our raw data display overdispersion—the unconditional variance of the LOS for those admitted to hospital is almost 22 times larger than the unconditional mean. Although this does not rule out the use of a Poisson model, there is clearly a need to account for overdispersion. The negative binomial model generalizes the Poisson model to account for over-

dispersion by allowing the mean of the count variable to be a random variable that follows a gamma distribution. Although the negative binomial model accounts for overdispersion, it is not suitable to capture ‘‘excess zeros’’ (more zeros than would be expected under a Poisson or negative binomial process). The distribution of our raw data is markedly skewed, with more than 93% of the sample having zeros (no hospital admissions) over a 12-month period. The ZINB model accounts both for the preponderance of excess zeros and overdispersion by assuming that the population is characterized by two latent (unobserved) groups, one in which members always have zero counts and one in which members may have zero or positive counts. An individual in the former group has an outcome of zero with a probability of one, while an individual in the latter group has a nonzero probability that the count is positive (Long & Freese, 2003). The likelihood of being in the former group is estimated using a logit specification, while counts in the latter group are estimated using a negative binomial specification. The ZINB model may also be conceptually more appropriate for modeling the use of inpatient care than two-part or hurdle models, which have been commonly applied in outpatient visit count data with excess zeros. These two-part models assume that separate processes generate decisions whether to use services and to what extent. This assumption might apply to visits to a general practitioner, but hospital admission and the LOS are not totally disjoint to the extent that physicians act as gatekeepers in both processes. To assess the robustness of the results from the ZINB model, we also apply the two-part model. Finally, since the VNHS data uses a three-stage stratified cluster sampling methodology, the clustering of responses by the primary sampling unit (commune) raises the possibility of intra-commune correlation. Standard errors of the estimated coefficients are thus corrected for intra-commune correlation (heteroscedasticity). We also apply appropriate sampling weights to produce unbiased population estimates. Results Descriptive results Fig. 1 displays hospital admission rates by health insurance status and income quintile. Individuals

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with CHI and HIP coverage are more likely to be admitted to hospital than the uninsured. The differences in hospital admission rates between the insured and uninsured are more pronounced for those in the middle income quintiles. The average hospital admission rate for the near-poor with CHI coverage is three times the admission rate for the near-poor with no health insurance. The differences in hospital admission rates between the insured and uninsured are also more pronounced for the CHI enrollees than the HIP enrollees. In contrast, the relationship between health insurance and hospital admission rates is less clear for the VHI enrollees. The VHI enrollees’ low hospital admission rate reflects mainly the younger age composition of VHI members who are, as noted above, almost entirely school children. The average hospital admission rate for those 18 yr and younger with VHI coverage exceeds the rate for the uninsured by 106% for those in the lowest two income quintiles and only by 36% for those in the richest quintile. The upper panel of Table 1 reports the distribution of inpatient contacts across health facilities by health insurance status and income quintile. The insured and uninsured poor and near-poor rely more on lower level health facilities. Inpatient contacts at the commune health centers (CHCs) and district hospitals account for 62% of their total inpatient contacts. In contrast, inpatient contacts at the provincial and central hospitals account for over two-thirds of all inpatient contacts by the better-off and rich with health insurance.

180 Poor Near-poor Average Better-off Rich Total

160

(Per 1,000 persons)

140 120 100 80 60 40 20 0

Uninsured

Compulsory insurance

Voluntary insurance

Insurance for the poor

Fig. 1. Hospital admission rate by insurance status, type of insurance and per capita expenditure quintile.

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The middle panel of Table 1 displays the average LOS across health facilities by health insurance status and type of health insurance. Two features of the LOS are worth noting. First, the average LOS is longer for the insured than it is for the uninsured across all government health facilities. Second, the average LOS varies with insurance type. The average LOS for CHI and HIP enrollees exceeds the LOS for the uninsured by 4.5 and 4.9 days, respectively.1 The difference in the average LOS for the CHI enrollees and the uninsured varies from 3 days for contacts at provincial hospitals to as low as 1.4 days for contacts at the CHCs. In contrast, the average LOS for the voluntary enrollees is slightly lower than the LOS for the uninsured, reflecting the younger age composition of these enrollees. The average LOS for those 18 yr and younger with voluntary and no insurance were 6.8 and 7.4 days, respectively. Econometric results It is found that inpatient contacts and LOS are both influenced by a wide range of variables that we observe, including health insurance status, income (approximated by consumption expenditure level), self-reported health status, sex, age, marital status, education, ethnicity and geographical location (measured by urban/rural and northern/southern regions). Type of health facility, categories of illness, readmission, and distance and travel times to hospitals are only observed once there is an admission and hence can only be included in the first (LOS) part of the model. We also interact insurance dummies with income quintiles, region and types of health facilities to assess how health insurance affects use of inpatient care differentially across these dimensions. Descriptive statistics for the variables used in this study are provided in Table 2 for both the sample population with inpatient contacts and the entire sample population. LOS is measured by the number of days spent in a health facility. Health insurance status is represented by three dummies indicating Vietnam’s three schemes. Household economic status is measured by the per capita consumption expenditure quintile, referred to as the income quintile throughout this 1

The average LOS for HIP enrollees should be treated with caution due to small number of observations. Of 268 patients with HIP coverage only 21 and 8 were admitted to CHCs and central hospitals, respectively.

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Table 1 Distribution of inpatient contacts and average length of hospital stay by type of health facility and insurance statusa All hospitals

Government health facilities CHCb

Distribution of inpatient contacts (%) Insured Poor and near poor Better-off and rich Uninsured Poor and near poor Better-off and rich Average length of hospital stay (days) Insured Uninsured By type of insurance Compulsory (CHI) Voluntary (VHI) Health insurance for the poor (HIP)

Districtc

Provinciald

Central

Private clinic/hospital

100.0 100.0

2.9 0.5

59.2 31.3

35.7 56.0

2.3 12.2

na na

100.0 100.0

14.9 5.5

44.4 24.1

31.3 52.9

5.1 11.8

4.3 5.7

11.0 8.3

5.7e 3.5

8.7 6.9

12.1 10.0

15.6 13.8

na 6.4

11.8 7.3 12.2

4.9 4.3 7.9

9.1 6.0 10.7

12.9 8.4 14.0

16.4 9.7 18.0

na na na

Inpatient admission rate (per 1000 persons) Insured 90.8 Uninsured 53.3 By type of insurance Compulsory (CHI) 125.5 Voluntary (VHI) 42.8 Health insurance for the poor (HIP) 113.1 a

Only 6 yr and older patients with 2 or more days of hospital stays are included. Includes state maternity wards and others. c Includes regional polyclinics. d Includes other state health facilities. e Only 73 insured patients were admitted to the CHCs. b

paper; marital status is represented by a dummy variable equals to one if the individual is married and the respondent level of education is measured by four dummies—no education (reference category), primary/literate, secondary and post-secondary education. Respondent’s perceived health status is represented by three dummies—good (reference category), average and weak.2 Ethnicity is represented by a dummy variable equals to one if an individual is a member of the ethnic majority kinh. Econometric results for the expected LOS are reported in the first two columns of Table 3 and those of the likelihood of hospital admission in the last two columns of Table 3. For ease of interpreta2 A five-point response scale is used in NVHS to measure respondent’s perceived health status (very good, good, average, very weak and weak). Given the limited number of observations for very good and very weak categories, very good or good categories are recoded as good and very weak and weak as weak.

tion both parameters estimates and factor changes are reported in Table 3 under the negative binomial part and the zero-inflated part. To facilitate comparson of the results from the two parts of the model, the signs of the coefficients in the inflated (logit)-part of the model have been changed so that they reflect the probability of being admitted to hospital rather than the probability of not experiencing hospital admission. The estimated measure of dispersion (alpha) is positive and the likelihood ratio (LR) test clearly rejects the zero-inflated Poisson model in favor of the ZINB model. The significant and positive value of the Vuong statistic also supports the ZINB over the negative binomial regression model. The results suggest that the influence of insurance on the LOS varies across the three insurance schemes. Both the CHI and HIP schemes have a positive and statistically significant influence on the expected LOS with more pronounced influence for the latter scheme. The estimated coefficient for the

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Table 2 Description of the variables Variables

Hospital admission Hospital days Insurance status Compulsory insurance Voluntary insurance Insurance for the poor Income quintile1 (reference category) Income quintile2 Income quintile3 Income quintile4 Income quintile5 Sex Age Marital status Household size No education (reference category) Primary education Secondary education Post-secondary education Ethnicity Urban North Good health status (reference category) Average health status Weak health status Readmission Distance (km) Travel time (min) Commune health center District hospital Provincial hospital (reference category) Central hospital Private clinic/hospital Acute Injury Chronic (reference category) Obstetrics Others

Sub-sample with inpatient contacts (n ¼ 7909)

Entire sample (n ¼ 138; 295)

Mean

Standard deviation

Mean

9.1931 0.3358 0.2385 0.0635 0.0339 0.1693 0.1953 0.2055 0.2232 0.2067 0.4165 40.7144 0.7854 4.9015 0.0731 0.4329 0.3853 0.1087 0.8669 0.3543 0.4940 0.1376 0.5157 0.3467 0.1353 26.7755 43.4285 0.0779 0.3720 0.4310 0.0821 0.0370 0.3252 0.1234 0.3765 0.1687 0.0062

10.2908 0.4423 0.4262 0.2438 0.1809 0.3750 0.3965 0.4041 0.4164 0.4050 0.4930 20.8545 0.4105 2.0087 0.2603 0.4955 0.4867 0.3113 0.4782 0.3397 0.5000 0.3445 0.4998 0.4759 0.3420 77.2316 151.9875 0.2680 0.4834 0.4952 0.2745 0.1888 0.4685 0.3289 0.4845 0.3745 0.0785

voluntary scheme is positive but statistically insignificant. The estimated factor change for CHI suggests that individuals with CHI coverage have an expected LOS that is 1.18 ( ¼ e0.1695) times longer than individuals with no insurance, or simply that CHI increases the expected LOS by a factor of 1.18. The estimated coefficients of the three insurance dummies in the zero-inflated part are positive and statistically significant, implying that all three insurance schemes significantly increase the likelihood of hospital admission. The influence is

Standard deviation

0.0611

0.2395

0.2197 0.1118 0.0894 0.0185 0.1979 0.1859 0.1956 0.2104 0.2103 0.4764 31.7368 0.5572 5.1005 0.0847 0.4376 0.3885 0.0891 0.8317 0.3289 0.4858 0.2422 0.6144 0.1434

0.4141 0.3151 0.2854 0.1347 0.3984 0.3890 0.3966 0.4076 0.4075 0.4994 19.5187 0.4967 1.9692 0.2785 0.4961 0.4874 0.2849 0.3741 0.4698 0.4998 0.4284 0.4867 0.3504

more pronounced for the CHI enrollees. According to the estimated factor changes, having CHI increases the odds of hospital admission by a factor of 2.17 compared with 1.53 for VHI enrollees and 1.85 for HIP enrollees. The estimated coefficients for income quintiles are all negative in the first part of the model indicating that the expected LOS is lower for the non-poor than the poor (reference category), but the income effect is only significant for the third and the highest income quintiles. In contrast, the coefficients for the income quintiles in

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Table 3 Regression results for the expected LOS and the likelihood of hospital admission Negative binomial part—LOS Coefficient estimate Insurance status Compulsory (CHI) Voluntary (VHI) Poor (HIP)

Zero-inflated part—admission Factor changea

Coefficient estimate

Factor changeb

0.1695*** (0.0307) 0.0212 (0.0618) 0.3309*** (0.0917)

1.1848 1.0215 1.3922

0.7758*** (0.0382) 0.4285*** (0.0623) 0.6172*** (0.0762)

2.1724 1.5350 1.8538

Income quintiles Near poor Average Better-off Rich Sex Age Age-squared Marital status Household size


0.0096 (0.0397)
0.0351 (0.0398) 0.0056 (0.0467)
0.0841** (0.0426) 0.0842*** (0.0292) 0.0195*** (0.0052)
0.0001*** (0.0000)
0.2072** (0.0823) 0.0094 (0.0062)

0.9904 0.9655 1.0057 0.9193 1.0878 1.0197 0.9999 0.8128 1.0095

0.1314*** (0.0477) 0.0848* (0.0490) 0.0220 (0.0509)
0.0344 (0.0576)
0.2368*** (0.0270)
0.0376*** (0.0044) 0.0003*** (0.0000) 1.3335*** (0.0683) 0.0138* (0.0073)

1.1404 1.0885 1.0223 0.9662 0.7892 0.9631 1.0003 3.7944 1.0139

Education Primary Secondary Post-secondary Ethnicity Urban North


0.0320 (0.0471)
0.0401 (0.0507) 0.0231 (0.0621)
0.0665* (0.0378) 0.0055 (0.0354) 0.0754** (0.0296)

0.9685 0.9606 1.0233 0.9356 1.0055 1.0783

0.1709*** (0.0539) 0.1336** (0.0579) 0.17053** (0.0738) 0.1438*** (0.0449) 0.0280 (0.0326)
0.0782** (0.0314)

1.1864 1.1430 1.1859 1.1547 1.0284 0.9247

0.0339 (0.0340) 0.2146*** (0.0560) 0.0662 (0.0421) 0.0008*** (0.0003) 0.0003* (0.0002)

1.0345 1.2394 1.0684 1.0008 1.0003

0.2259*** (0.0400) 1.2001*** (0.0475)

1.2534 3.3204

(0.0430) (0.0346) (0.0657) (0.0633)

0.4863 0.7876 1.2600 0.7139


0.0967** (0.0428) 0.0201 (0.0820)
0.2778*** (0.0647) 0.3969** (0.1829) 1.7721*** (0.1184)

0.9078 1.0203 0.7574 1.4872

Health status Average Weak Readmission Distance Travel time Type of hospital CHC District hospital Central hospital Private clinic/hospital Illness category Acute Injury Obstetrics Others Cons Alpha LR test statisticc Vuong statisticd


0.7210***
0.2387*** 0.2311***
0.3370***

3.6846*** (0.0998) 0.4329 (0.0560) 9.9e+06*** 35.86***

Robust standard errors in parenthesis. *10% Significant level; **5% significant level; ***1% significant level. a Exp (parameter estimate) ¼ factor change in expected LOS for unit increase in a covariate. b Exp (parameter estimate) ¼ factor change in odds for unit increase in a covariate. c Comparing ZINB against zero-inflated Poisson (ZIP) regression model. d Comparing ZINB against negative binomial regression model. The Vuong statistic is computed using the standard ZINB regression model with common regressors and no sampling weights.

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the second part of the model are positive and statistically significant only for the second and third highest income quintiles. The estimated factor changes suggest that being from the near-poor and average households increases the odds of a hospital admission by factors of 1.14 and 1.09, respectively. The income effect was also found to be negative when alternative measures of living standards, such as household expenditures on selected food and durable items, and community ranking of households were used. Males have a longer expected LOS than females and are less likely to have hospital admission. The estimated coefficients for age and age-squared suggest that the expected LOS increases with age but at a smaller rate as age rises. Married individuals have a shorter expected LOS than singles and are more likely to have a hospital admission. While the likelihood of hospital admission increases with household size, the effect of household size on the LOS is insignificant. Similarly, education has no significant influence on the LOS but significantly increases the likelihood of hospital admission. Ethnic majority patients have a lower LOS and a higher likelihood of hospital admission by factors of 0.93 and 1.15, respectively. The expected LOS is higher for residents of northern Vietnam, although they are less likely to be admitted to hospital. As expected, individuals who report average or weak health status have a longer LOS than those reporting good health status, but only the latter is significant. The likelihood of hospital admission is also, as expected, higher for individuals reporting average or weak health status. Readmitted patients have a longer expected LOS. Both distance and travel time to a health facility have a positive and significant effect on the LOS. The estimated coefficients for the four types of health facilities suggests that patients admitted at a CHC, district or private clinic/hospital have a shorter LOS than those admitted at a provincial hospital (the reference hospital), while patients admitted at a central hospital have a longer LOS. To assess the differential influence of health insurance on inpatient use across income quintiles, regions and types of health facilities, the ZINB regression model is extended by including three sets of interaction terms, one between insurance and each income quintile, one between insurance and North, and one between insurance and each type of health facilities. The results for the interaction terms are reported in Table 4.

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Model 1 in Table 4 was estimated using an interaction term for each income quintile in both the negative binomial and zero-inflated parts of the ZINB regression. Testing for equality among the interaction terms suggested that the second through fourth income quintiles could be aggregated. The results are summarized under model 1 in columns 1 through 4 of Table 4. Results from the negative binomial part of the ZINB regression model 1 suggest that the positive effect of compulsory insurance on the LOS is higher for middle income households. Compared to individuals without health insurance, CHI increases the expected LOS by a factor of 1.22 for middle income individuals, 1.13 for the highest income individuals and has no significant effect on the lowest income individuals. In contrast, the effect of VHI on the LOS is minimal for all but the highest income group. The estimated coefficient for the interaction term between the VHI and the highest income quintile is negative and marginally significant, indicating that VHI decreases the LOS by a factor of 0.87 for the highest income individuals. Results for the zero-inflated part of model 1 suggest that CHI increases the likelihood of hospital admission for all income groups but is more pronounced for middle income individuals. VHI also increases the odds of hospital admission more for low and middle income individuals, but the influence of insurance is smaller than for the CHI enrollees. Model 2 in Table 4, incorporating three interaction terms combining each insurance scheme and the North, suggests that the positive influence of the CHI scheme on both hospital admission and the LOS is larger in the North. The VHI enrollees from northern Vietnam are also more likely to have a longer LOS than their southern counterparts, although there is no significant regional difference in the use of inpatient care by these two insured groups. In contrast to the other two schemes, the influence of the HIP plan on the LOS does not vary across the regions, but northern residents with HIP coverage are more likely to have inpatient contacts.3 Finally, model 3 in Table 4 incorporates interaction terms between insurance and the three government health facilities—district, provincial and central hospitals—in the negative binomial part of the ZINB regression. These health facilities account 3 Given the small numbers of HIP enrollees admitted to hospital in the two regions, these results should be interpreted with caution.

0.0371 (0.0760) 0.1969*** (0.0363) 0.1260** (0.0543) 0.0667 (0.0698)
0.1325 (0.0853) 0.8759

1.0690

1.1343

1.2177

1.0379

0.7626*** (0.1430) 0.8968*** (0.0447) 0.5175*** (0.0677) 0.5537*** (0.0689) 0.0501 (0.1138) 1.0514

1.7397

1.6778

2.4517

2.1439

0.1139** (0.0526) 0.2338*** (0.0889)
0.1021 (0.1785) 0.9030

1.2634

1.1207

Factor change

Robust standard errors in parenthesis. *Significant at 1%, **significant at 5%, ***significant at 10%. a Other regressors for each model correspond to those listed in Table 3. b CHI (compulsory health insurance); VHI (voluntary health insurance); HIP (health insurance for the poor).

CHI central

CHI provincial

Insurance type of health facility CHI district

HIP North

VHI North

Insurance region CHI North

VHI income5

VHI income1–4

CHI income5

CHI income2–4

Insuranceb income CHI income1

Coefficient estimate

Coefficient estimate

Coefficient estimate

Factor change

Negative binomial part

Zero-inflated part

Negative binomial part Factor change

Model 2

Model 1

Table 4 Results for interaction terms between insurance and income, region and type of health facilitya

0.1487** (0.0682) 0.0182 (0.1081) 0.2558* (0.1510)

Coefficient estimate

1.2915

1.0184

1.1604

Factor change

Zero-inflated part

0.1410*** (0.0407) 0.2955*** (0.0515) 0.0756 (0.1119)

Coefficient estimate

1.0785

1.3438

1.1514

Factor change

Negative binomial part

Model 3

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for 96% of inpatient contacts by the insured. We focus on the CHI enrollees because of the small number of inpatient contacts by VHI and HIP enrollees across the three health facilities. The results are summarized under model 3 in columns 5 and 6 of Table 4 where only the results for the negative binomial part of the ZINB regression are reported. The estimated coefficients for the three interaction terms are positive indicating that insurance increases the expected LOS at all three types of health facilities. The insurance effect is stronger at provincial than district health facilities, with insurance increasing the LOS by a factor of 1.34 at provincial hospitals and by 1.14 at district hospitals. The estimated coefficient for the interaction term between central hospitals and insurance should be interpreted with care considering the small number of observations and the large dispersion in LOS— the ratio of the standard deviation of LOS at the central hospitals to its mean was 1.2, compared with 0.73 and 0.82 for the district and provincial hospitals. Our results, including the positive and significant association between insurance and the LOS, are robust to small changes in the conditioning information set.4 Discussion and conclusion This paper assesses the influence of health insurance in Vietnam on hospital admission and the LOS while adjusting for the type of provider, self-reported health status and socio-economic factors. The econometric results suggest that the influence of insurance on the likelihood of hospital admission and the expected LOS varies across three insurance schemes. While all three schemes increase the odds of hospital admission the influence is more pronounced for CHI and HIP than for VHI. Similarly, both CHI and HIP increase the expected LOS, while VHI has minimal effect on the LOS. The influence of insurance on hospital admission and the LOS also varies across income quintiles, regions and health facilities. The positive influence of the CHI scheme on the LOS is larger for middle income individuals than the highest income individuals. Similarly, middle income individuals with CHI coverage are more likely to have hospital admission than the highest income enrollees. VHI also 4 The results are also robust to such alternative estimation techniques as the two-part and truncated negative binomial models. Results are available upon request.

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increases hospital admission for lower and middle income individuals but has no significant influence on the LOS. The positive influence of CHI and VHI on the LOS is larger in the North than in the South. Similarly, the positive influence of insurance on the LOS is greater at the higher level health facilities. The results in Table 3 assume that insurance is exogenous, but our estimates of the insurance effect may be subject to endogeneity bias if there is adverse selection in Vietnam’s health insurance schemes. It is unlikely that CHI (and possibly not HIP) is affected by selection because of categorical eligibility. While self-selection into VHI is more likely, the results do not suggest that endogeneity bias is a serious problem.5 In a study of the effect of Vietnam’s VHI scheme on patterns of health seeking behavior in three provinces, the association between voluntary insurance status and the incidence of illness and its severity was also found to be weak (Jowett et al., 2004). Our results on the influence of insurance on hospital admission and the LOS are consistent with other studies. Many of these studies find that financial incentives under insurance schemes based on a fee-for-service system encourage providers to admit and provide more services to insured patients (Bertranou, 1998; Cameron, Trivedi, Milne, & Piggott, 1988; Dafny & Gruber, 2005; Harmon & Nolan, 2001; Hunt-McCool, Kiker, & Ng, 1994; Manning et al., 1987; Sapelli, & Vial, 2003; Trujillo, 2003). Increased consumption of inpatient care amongst the insured, especially low income individuals, may not necessarily be inappropriate if insurance facilitates access to otherwise unaffordable care (Nyman, 1999) or a cost effective medical service (Zweifel & Manning, 2000). However, our results suggest that the positive influence of insurance on the likelihood of inpatient contacts is weaker under HIP than under CHI. Moreover, the effect is smaller for individuals in the poorest quintile under the compulsory scheme. To the extent that higher utilization of inpatient care and longer LOS amongst the insured is induced by providers, our results suggest that the influence has been more pronounced under the compulsory scheme than the voluntary scheme and more pronounced at the higher level provincial hospitals than the lower level district hospitals. The attraction of high income CHI enrollees to the higher level health facilities combined with a more generous 5

We thank an anonymous referee for this point.

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reimbursement system provide these facilities with more resources than the lower level health facilities. The positive influence of insurance on utilization is also more pronounced in the South than in the North. One plausible explanation for this regional difference is that health facilities in the South generally charge higher fees for services provided to uninsured patients which may encourage health facilities to rely more on increasing the intensity of services provided per hospital day than on prolonging the LOS. According to the VNHS data uninsured patients in the South spend far more on hospital fees per hospital day, including consultation, diagnostic and imaging fees, and drugs and beds than those in the North—117,000 versus 66,000 Vietnamese Dong. There is some anecdotal evidence indicating that the mean number of diagnostic tests administrated to patients in public hospitals differs significantly depending on the way patient care is financed (Phong et al., 2002). The financial incentives for hospital overprovision of high technology services are documented for several developing countries (Barnum & Kutzin, 1993). However, further research is required into the regional differences in the LOS and the intensity of inpatient services per hospital day. Our results on the influence of socio-economic variables are also generally in line with those reported elsewhere. The negative influence of marital status on the LOS is consistent with other findings suggesting that married people are more likely to have supportive and stable home environments than the unmarried, and hence have less need for extended hospital stays (Meer & Rosen, 2004; Shi, 1996). Men are also less likely to use inpatient care and more likely to have an extended stay once they are hospitalized (Bertranou, 1998; Shi, 1996; Trivedi, 2002). This may reflect women’s excess morbidity, particularly reproduction events—obstetrics accounted for about 58% of all hospital admissions by women 17–38 yr old. Finally, the effect of education on hospital use is found to be mixed (Bertranou, 1998; Gertler & Litvack, 1998; Trivedi, 2002). The positive influence of education on the likelihood of hospital admission suggests that educated people may be more knowledgeable about health issues than the uneducated and may therefore make use of more health services. Education may, however, reduce inpatient care if educated people are more efficient in the production of health (Grossman, 1972).

These findings have important implications for health policy. First, the finding that all three insurance schemes, including the insurance scheme for the poor, increase the likelihood of inpatient use represents a positive result for health planners and policy makers in Vietnam and other lower income countries who have already introduced or are about to introduce a health care fund for the poor. Greater access is likely to enhance both efficiency and equity to the extent that insurance allows lower income individuals to seek timely and efficient treatment. Indeed, evidence from the North of Vietnam suggests that the poor generally delay treatment and, when they seek treatment, the illness is more serious on average (Ensor & San, 1996). However, it is not clear whether a longer LOS by the insured means a better health outcome. Evidence from high income countries suggests no clear association between the LOS and health outcomes (for a review see Clarke & Rosen, 2001). To improve efficiency and equity in the use of scarce health care resources, policy makers and health planners should consider strategies to deal with the problem of provider’s moral hazard, including the introduction of alternative reimbursement mechanisms (such as paying physicians through capitation or funding hospitals through episode-based prospective reimbursement) and the establishment of state and professional regulatory bodies to formulate and implement standards and guidelines for clinical practice. Second, the finding that the difference in the LOS between the insured and uninsured is lower at the lower level health facilities than at the higher level health facilities suggests that it may be more cost effective to channel higher inpatient service contacts under insurance schemes to the lower level facilities. Allowing the insured to access benefits at the CHCs and private clinics/ hospitals may also make it more convenient and less expensive for patients to access health services. However, this may require an improvement in the quality of services provided by the CHCs, which is generally perceived by patients to be of a lower quality (Tipping, Troung, Nguyen, & Segall, 1994). Some caveats are in order. The regression models do not control for differences in the quality of care received by the insured and the uninsured nor for differences in the quality of care received by insurance enrollees at high quality provincial hospitals and lower quality district hospitals. The regression models also do not adequately control for the effect of patient case-mix, although inclusion

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of four broad illness categories and the level of health facility among the covariates may act as a proxy for patient case-mix to the extent that higher level health facilities tend to treat more severe and complex conditions. Finally the estimated coefficients of health status and readmission and income may be subject to endogeneity bias if these variables are endogenous to the processes of utilization of hospital services.

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