Financial Protection Effect of Health Insurance Evidence from Ghana National Health Insurance Scheme Ha Nguyen, Abt Associates Inc. Yogesh Rajkotia, USAID Hong Wang, Abt Associates Inc. November 10, 2010 APHA Conference, Denver

2 Rational Background: Increasing interest in health insurance in developing countries Conflicting evidence on insurance s protective effect against financial burden of health care Objectives: Evaluate financial protection effects of insurance in Ghana (2 districts): Amount of out-of-pocket payment (OOP) Likelihood of catastrophic OOP expenditure

3 Health financing in Ghana Important milestones Free services in public facilities after Independence (1957) Nominal user fees early 1970s Significant user fees starting 1985 Exemption policy for indigents and other disadvantaged groups ~ unfunded mandate Implications of the cash and carry system Delay in or forego seeking care Low quality, inadequate services High OOP payment (50% vs. sub-Saharan Africa average of 39% )

4 National Health Insurance Scheme (NHIS) Timing: Enacted in 2003 and started in 2005 Coverage: Open to all population, covered ~ 45% as of 2008 Revenue collection: 2.5% sales tax, 2.5% from formal sector contribution, premium contribution from other members Premium exemption for indigenous and other disadvantaged populations Benefit package: 95% of conditions (inpatient and outpatient care) Public sector and accredited private facilities Management: centralized financing but decentralized implementation

5 NHIS early experience and impact Implementation issues Delay in card issuance and provider reimbursement Low incentives to improve quality of insured care Provider discrimination against insured patients Informal payment to providers Early impact evaluation (Chankova, Atim, and Hatt 2009; Frempong et al., 2009) Increase service utilization of curative care Conflicting evidence on impact on MCH services

6 Data and variables Survey of 2500 households in 2 rural districts, Offinso and Nkoranza, in late 2007 (11,617 individuals) Dependent variables: One year OOP expenditure on curative care Likelihood of having catastrophic expenditure (thresholds: 5% income, 10% income, 10% non-food consumption, 20% non-food consumption) Independent variables: Main interest: Membership in NHIS Covariates: Household SES, ethnicity, urbanicity, self-reported health status and chronic diseases

7 Methods Model specification: Yi = F (HIi, Xi, ei) Y: OOP amount, likelihood of catastrophic expenditure HI: membership in NHIS X: covariates E: error terms F: Two-part model for OOP amount and probit for catastrophic expenditure Direction of bias if adverse selection exists: Y=service utilization: positive bias Y=OOP exp: negative bias

8 Sample description: breakdowns of OOP expenditure on curative care Expenditure breakdowns (Amount in Ghana Cedi) Non-members (N=6,718) Members (N=4,899) Acute illnesses and injuries Informal care 2,839 4,913 Fees 3, Lab expenses 1,354 1,036 Other expenses Unofficial payment to providers Drugs purchased at facility 6,500 2,709 Drugs purchased outside facility 2,348 3,743 Antenatal care and delivery 6,442 4,475 Surgery and hospitalization 6,121 2,819 Total 29,843 21,503

9 Sample description: incidence of catastrophic expenditure by quintile and HI status

10 Results: NHIS effects on OOP expenditure (1)(2) Has HI from NHIS-33,821-30,094 (20,379)*-20,157 Chronic health condition40, (38,229) Bad health (self-assessed)125, (90,323) District dummyYes Individual and household characteristicsYes Assets and living conditionsYes N11,617 Note: unit is Ghana Cedi. Robust standard errors in parenthesis. *significant at p<0.10. Effects are estimated with a 2-part model

11 Results: NHIS effects on the incidence of catastrophic expenditure Note: figures represent marginal effects of insurance obtained from probit estimation. Horizontal bars denote 95% CI

12 Results: NHIS effects among poor versus non-poor population Indicators Quintile 1 (poorest) N = 1,762 Rest of population N = 9,855 Exceeds 5% of income (0.005)*** (0.004) Exceeds 10% of non-food expenditure (0.005)*** (0.004) Exceeds 10% of income (0.005)** (0.003) Exceeds 20% of non-food expenditure (0.005)*** (0.002)* Note: figures represent marginal effects of insurance obtained from probit estimation. Robust standard errors are in parentheses. * significant at p<0.10; ** p<0.05; *** p<0.01

13 Limitations Potential adverse selection in insurance is not fully addressed However, bias is likely negative, rendering assurance that effect is truly significant Survey was conducted in 2 out of 138 districts, so results cannot be generalized

14 Discussion Small effects on absolute amount of OOP payment raise concerns about implementation issues (informal payment, use of informal care, quality of insured services, etc.) NHIS confirms function of HI as a safety net, i.e., protect against risk of catastrophic expenditure Stronger effects among the poor justifies premium subsidies Ghana experience is highly applicable to many developing countries, especially in sub-Saharan Africa, with similar health system features

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