Microeconomic impact of HIV disease among female bar/hotel workers in northern Tanzania: methodological considerations

1. Microeconomic impact of HIV disease among female bar/hotel workers in northern Tanzania:methodological considerations Tony Ao Advisor: Dr. Saidi Kapiga Harvard School of Public Health Population Impacts on Economic Development Research Conference 03 NOV 2006

2. Background • HIV disproportionately affects women • 59% of infections are women in SSA (UNAIDS 2005)

3. Male: 6.4% • Female: 7.7%

4. At-risk populations in Tanzania • Women working in bars/hotels have highest risk: • Arusha: 75% (Nkya 1991) • Moshi: 26% (Kapiga 2002) • Mbeya: 68% (Reidner 2006)

5. Macroeconomics & HIV No clear link between HIV and economic growth Negative effect: • Kambou et al (1992) • Cuddington (1993) • Cuddington and Hancock (1994) • Bonnel (2000) • Papageorgiou and Stoytcheva (2004) • Corrigan, Gloom, Mendez (2005) No effect: • Bloom and Mahal (1997) • Werker, Ahuja, Wendell (2006)

6. Microeconomics & HIV Examples: • Household verbal autopsies (Ngalula et al 2002) • Kenyan tea plantation workers (Fox et al 2004) • Household surveys in Kenya and Rwanda (UNAIDS 2004) • Elderly health and AIDS death (Dayton & Ainsworth 2004) Microeconomic impact of HIV • Mostly assessed within formal sector or households • No study with female bar/hotel workers • Important for intervention and policy implications

7. Proposed Framework Clinical Factors Clinical signs & symptoms Health seeking behavior Behavioral factors HIV Infection Environmental factors Microeconomic impact

8. Objective and hypotheses Objective: • To investigate the microeconomic impact of HIV disease among female bar/hotel workers Hypotheses: Compared to HIV negative women, HIV positive women are expected to: • Report lower monthly income • Report higher health care expenditure • Report higher health seeking behavior • Report lower level of savings

9. Possible Approaches • Randomized controlled trial • Longitudinal study • Cross sectional • Instrumental variable (IV) • Propensity score matching (PSM)

10. Method • Study design: cross sectional with retrospective questionnaire (adapted LSMS) • Study population: bar/hotel workers presenting for screening for existing CHAVI study at clinic • Outcomes: • Monthly income • Health care utilization in past 3 months • Health care spending in past 3 months • Household savings

11. Propensity Score Matching • Propensity score matching • Uses predicted probability of HIV status based on observed predictors from logistic regression to create counterfactual group for comparison • Advantages: • Improves causal inference • Ethically appropriate • Logistically feasible

12. Analysis Propensity score matching • Step 1: Run Multivariate Logistic Regression • Dependent variable: Y=1 if HIV+; Y = 0, otherwise • Include all observed characteristics except outcomes • Obtain PS: predicted probability (p) or log[p/(1-p)] for each woman • Step 2: Match each HIV+ to one HIV- woman based on PS  New sample of “randomized” individuals • Nearest neighbor matching • Caliper matching • Mahalanobis metric matching in conjunction with PSM • Stratification matching • Difference-in-differences matching (kernel & local linear weights) • Step 3: Run multivariate analyses using newly matched sample

13. Data collection Issues to consider: • Reliability of self-report of income and sexual behavior • Recall bias • Income not a sufficient variable

14. Data collection ACASI (audio computer-assisted self-interviewing) Source: Waruru et al. 2005

15. Data collection Advantages of ACASI • Using tablets vs. conventional laptops • Local written and spoken language • Accurate reporting of sensitive data • Accurate data entry • Validated in Zimbabwe1 and Kenya2 • Builds local research capacity 1van de Wijgert, J., N. Padian, et al. 2000 2Waruru et al. 2005

16. Ethical considerations • Screening study has been approved, no additional specimen collection needed • Sensitive information will be obtained • Confidentiality and data management is paramount

17. Limitations • PSM does not match on unobserved contextual characteristics  matching might not be 100% perfect • Retrospective data may not capture outcome accurately • Generalizability • Acceptability of ACASI

18. Thank you William & Flora Hewlett Foundation Population Reference Bureau David Canning Ajay Mahal Grace Wyshak Saidi Kapiga

19. References Bloom, David and Ajay Mahal. Does the AIDS Epidemic threaten Economic Growth? Journal of Econometrics. 1997. 77:105-124. Bonnel, Rene. HIV/AIDS: Does it Increase or Decrease Growth in Africa? World Bank, mimeo (2000). Corrigan, Paul & Glomm, Gerhard & Mendez, Fabio, 2005. "AIDS crisis and growth," Journal of Development Economics. 77(1), pages 107-124, June Cuddington, John T. and John D. Hancock (1994) ‘Assessing the Impact of AIDS on the Growth Path of the Malawian Economy’, Journal of Development Economics 43: 363–68. Dayton J and Martha Ainsworth. The elderly and AIDS: coping with the impact of adult death in Tanzania. Soc Sci Med. 2004 Nov; 59(10):2161-72. Fox, M. P., S. Rosen, et al. (2004). "The impact of HIV/AIDS on labour productivity in Kenya." Trop Med Int Health 9(3): 318-24. KAMBOU, G., S. Devarajan and Mead Over (1992) ‘The Economic Impact of AIDS in an African Country: Simulations with a General Equilibrium Model of Cameroon’, Journal of African Economies 1(1): 109–30. Ngalula, J., M. Urassa, et al. (2002). "Health service use and household expenditure during terminal illness due to AIDS in rural Tanzania." Trop Med Int Health 7(10): 873-7. Nkya WM, Gillespie SH, Howlett W, et al. Sexually transmitted diseases in prostitutes in Moshi and Arusha, Northern Tanzania. Int J STD AIDS 1991;2:432–5. Riedner, G., M. Rusizoka, et al. (2003). "Baseline survey of sexually transmitted infections in a cohort of female bar workers in Mbeya Region, Tanzania." Sex Transm Infect 79(5): 382-7 Tanzania Commission for AIDS (TACAIDS), National Bureau of Statistics (NBS), and ORC Macro. 2005. Tanzania HIV/AIDS Indicator Survey 2003-04. Calverton, Maryland, USA: TACAIDS, NBS, and ORC Macro. Over, Mead. The Macroeconomic Impact of AIDS in Sub-Saharan Africa. World Bank Working Paper 1992. Papageorgiou, Chris and Petia Stoytcheva. What Do We Know About the Impact of AIDS on Cross-Country Income So Far? LSU, mimeo (2004). UNAIDS (2004). 2004 Report on the Global HIV/AIDS Epidemic: 4th Global Report. Geneva, Switzerland, WHO/UNAIDS. van de Wijgert, J., N. Padian, et al. (2000). "Is audio computer-assisted self-interviewing a feasible method of surveying in Zimbabwe?" Int J Epidemiol 29(5): 885-90. Waruru AK, NduatiR, Tylleskar T. Audio computer assisted self interviewing (ACASI) may avert socially desirable responses about infant feeding in the context of HIV. BMC Med Inform Decis Mak. 2005 Aug 2; 5:24.

20. Age and sex-specific HIV prevalence, 2003 HIV in Tanzania Men: 6.3% Women: 7.7% (DHS 2005) Source: Tanzania Commission for AIDS (TACAIDS), National Bureau of Statistics (NBS), and ORC Macro. 2005. Tanzania HIV/AIDS Indicator Survey 2003-04. Calverton, Maryland, USA: TACAIDS, NBS, and ORC Macro.