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Risk Pooling to Achieve Universal Coverage: Ghana ’ s National Health Insurance Scheme

Risk Pooling to Achieve Universal Coverage: Ghana ’ s National Health Insurance Scheme. Slavea Chankova. I. BACKGROUND. The National Health Insurance Scheme (NHIS). Established by legislation in 2003 Goal: equitable and universal access to health care Coverage reached 66% in 2010

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Risk Pooling to Achieve Universal Coverage: Ghana ’ s National Health Insurance Scheme

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  1. Risk Pooling to Achieve Universal Coverage: Ghana’s National Health Insurance Scheme Slavea Chankova

  2. I. BACKGROUND

  3. The National Health Insurance Scheme (NHIS) • Established by legislation in 2003 • Goal: equitable and universal access to health care • Coverage reached 66% in 2010 • Evaluation of NHIS • Designed in anticipation of NHIS implementation • Collaboration between Health Systems 20/20 project and Health Research Unit - Ghana Health Service

  4. Key Features of the NHIS • Managed by district-level mutual health insurance schemes • Providers: all public health facilities and accredited private providers • Benefits: 95% of disease conditions, essential drugs • Enrollment • Open to all with sliding-scale premium contributions starting at about $5 per adult • Premium exemptions for children (under 18), elderly (70+), indigent, and pregnant women (as of 2008)

  5. II. EVALUATION DESIGN

  6. Evaluation Questions • Who has enrolled in the NHIS? • Do enrollment rates differ among different socio-economic groups? • Is there evidence of adverse selection in NHIS enrollment? • How well-targeted have premium exemptions been? • What is the impact of the NHIS on the utilization of health services? • What is the impact of the NHIS on out-of-pocket expenditures for health care?

  7. Evaluation Design • Pre-post study design • Baseline in September 2004 • Endline in September 2007 • Implementation of NHIS in study sites started in 2005 • Cross-sectional household surveys in 2 districts • Nkoranza (had CBHI at baseline) • Offinso

  8. Study Sample

  9. Analytic Methods • Pre-post comparison of means for key indicators • Regression models • Control for differences in socio-economic characteristics between baseline and endline samples • Probit and logistic regression models • Results were robust to analytic methods

  10. III. RESULTS

  11. Sample Characteristics • Poor rural population • General improvements in socio-economic characteristics between 2004 and 2007 • Health insurance coverage:

  12. Who Enrolls in NHIS? • Enrollment increases with wealth quintile • Poorest are 3 times less likely to enroll compared to the richest

  13. Who Enrolls in NHIS? Factors associated with higher likelihood of NHIS enrollment* • Richer wealth quintile • Education of household head • Female headed household • Female gender • Age: children and the elderly more likely to enroll, compared to 18-49 yr old • Self-reported chronic illness • At least one household member is part of a community solidarity scheme * Results from multivariate regression (variables with statistically significant coefficients)

  14. Targeting of NHIS: Premium Exemptions for Children & Elderly • Age-based exemptions have worked as intended • But nearly all enrolled (97%) had paid a registration fee

  15. Targeting of NHIS: Premium Exemptions for the Poor • Exemptions have not benefited primarily those in the lowest wealth quintile

  16. Adverse Selection in Enrollment • Strong evidence of adverse selection based on health status • NHIS-insured almost 3 times as likely to report illness in past 2 weeks, compared to uninsured • 55% of those with chronic illness insured, compared to 34% of those without • No evidence of self-selection in enrollment related to pregnancy • 36% of women with delivery in the past 12 months were insured at time of delivery, compared to 33% of women who did not have a delivery

  17. Utilization of Care for Recent Illness or Injury

  18. Utilization of Maternal Health Care • No significant changes between 2004 and 2007 in proportion of pregnant women receiving key maternal health services

  19. Likelihood of OOP Expenditures for Care • Significant decrease in probability of incurring OOP expenditures for recent curative care, hospitalization, antenatal care (ANC), and delivery

  20. Changes in OOP Expenditures for Care • Average expenditures for treatment declined significantly for most services: • 41% decrease for curative care (from $2 at baseline) • 44% decrease for hospitalization (from $25 at baseline) • No significant decrease for ANC (remained at about $3) • 30% decrease for delivery (from $8 at baseline) • No significant changes in average amount paid by those who had positive OOP expenditures

  21. Limitations • Results from 2 districts (out of 138) so cannot be generalized to whole country • Changes between 2004 and 2007 likely reflect impact of NHIS, but may also be influenced by other factors (e.g. other socioeconomic or policy changes occurring simultaneously) • Small samples for some indicators (e.g. hospitalization) limit the ability of the study to detect significant changes

  22. IV. CONCLUSIONS & POLICY IMPLICATIONS

  23. NHIS Enrollment • Age-based exemptions from NHIS premiums for children and the elderly have worked as intended • But this may have potential implications for NHIS sustainability • Strong wealth effects observed for NHIS enrollment • Exemptions for the poorest groups need to be strengthened to ensure equitable enrollment in NHIS • Evidence of adverse selection: those with poorer health status are more likely to enroll and more likely to utilize care • Implications for DMHIS sustainability

  24. Utilization and OOP Expenditures • Substantial increase in use of formal medical services for illness; decrease in self-treatment and informal care-seeking • However, no improvement in maternal care-seeking • Need to explore non-financial barriers for seeking care • Insurance has been very effective at reducing out-of-pocket expenditures for curative care and hospitalization, as well as for maternal care

  25. Acknowledgements • Abt Associates -- Health Systems 20/20: • Laurel Hatt, Sara Sulzbach, Hong Wang, Ha Nguyen • Ghana Health Service/Health Research Unit: • Dr. John Gyapong, Bertha Garshong • USAID: • Yogesh Rajkotia, Karen Cavenaugh, Mary Ellen Stanton

  26. Reports related to this presentation are available at: www.HS2020.org Presentation will be posted at: http://www.abtassociates.com/HSRsymposium

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