Microeconomic impact Microeconomic impact of of

HIV disease among HIV disease among female bar/hotel female bar/hotel

workers in northern workers in northern Tanzania:Tanzania:

methodological methodological considerationsconsiderationsTony AoTony Ao

Advisor: Dr. Saidi KapigaAdvisor: Dr. Saidi KapigaHarvard School of Public HealthHarvard School of Public Health

Population Impacts on Economic Development Research Population Impacts on Economic Development Research ConferenceConference

03 NOV 200603 NOV 2006

BackgroundBackground

HIV disproportionately affects womenHIV disproportionately affects women 59% of infections are women in SSA 59% of infections are women in SSA (UNAIDS 2005)(UNAIDS 2005)

Male: 6.4% Female:

7.7%

At-risk populations in At-risk populations in TanzaniaTanzania

Women working in bars/hotels have Women working in bars/hotels have highest risk:highest risk:

Arusha: 75% (Nkya 1991)Arusha: 75% (Nkya 1991) Moshi: 26% (Kapiga 2002)Moshi: 26% (Kapiga 2002) Mbeya: 68% (Reidner 2006)Mbeya: 68% (Reidner 2006)

Macroeconomics & HIVMacroeconomics & HIVNo clear link between HIV and economic No clear link between HIV and economic

growthgrowth

Negative effect:Negative effect: Kambou et al (1992)Kambou et al (1992) Cuddington (1993)Cuddington (1993) Cuddington and Hancock (1994)Cuddington and Hancock (1994) Bonnel (2000)Bonnel (2000) Papageorgiou and Stoytcheva (2004)Papageorgiou and Stoytcheva (2004) Corrigan, Gloom, Mendez (2005)Corrigan, Gloom, Mendez (2005)

No effect:No effect: Bloom and Mahal (1997)Bloom and Mahal (1997) Werker, Ahuja, Wendell (2006)Werker, Ahuja, Wendell (2006)

Microeconomics & HIVMicroeconomics & HIV

Examples:Examples: Household verbal autopsies (Ngalula et al 2002)Household verbal autopsies (Ngalula et al 2002) Kenyan tea plantation workers (Fox et al 2004)Kenyan tea plantation workers (Fox et al 2004) Household surveys in Kenya and Rwanda (UNAIDS 2004)Household surveys in Kenya and Rwanda (UNAIDS 2004) Elderly health and AIDS death (Dayton & Ainsworth 2004)Elderly health and AIDS death (Dayton & Ainsworth 2004)

Microeconomic impact of HIVMicroeconomic impact of HIV Mostly assessed within formal sector or householdsMostly assessed within formal sector or households No study with female bar/hotel workersNo study with female bar/hotel workers Important for intervention and policy implicationsImportant for intervention and policy implications

Proposed FrameworkProposed Framework

Clinical Factors

Behavioral factors

Environmental factors

HIV Infection

Microeconomic impact

Clinical signs & symptoms

Health seeking behavior

Objective and hypothesesObjective and hypotheses

Objective:Objective: To investigate the microeconomic impact of To investigate the microeconomic impact of

HIV disease among female bar/hotel workersHIV disease among female bar/hotel workers

Hypotheses:Hypotheses: Compared to HIV negative women, HIV Compared to HIV negative women, HIV

positive women are expected to:positive women are expected to: Report lower monthly incomeReport lower monthly income Report higher health care expenditureReport higher health care expenditure Report higher health seeking behaviorReport higher health seeking behavior Report lower level of savingsReport lower level of savings

Possible ApproachesPossible Approaches

Randomized controlled trialRandomized controlled trial Longitudinal study Longitudinal study Cross sectionalCross sectional

Instrumental variable (IV)Instrumental variable (IV) Propensity score matching Propensity score matching

(PSM)(PSM)

MethodMethod Study designStudy design: cross sectional with : cross sectional with

retrospective retrospective questionnaire (adapted questionnaire (adapted LSMS)LSMS)

Study populationStudy population: bar/hotel workers : bar/hotel workers presenting presenting for screening for existing for screening for existing CHAVI study at CHAVI study at clinicclinic

OutcomesOutcomes: : Monthly incomeMonthly income Health care utilization in past 3 monthsHealth care utilization in past 3 months Health care spending in past 3 monthsHealth care spending in past 3 months Household savingsHousehold savings

Propensity Score Propensity Score MatchingMatching

Propensity score matchingPropensity score matching Uses predicted probability of HIV status based Uses predicted probability of HIV status based

on observed predictors from logistic on observed predictors from logistic regression to create counterfactual group for regression to create counterfactual group for comparisoncomparison

Advantages:Advantages: Improves causal inferenceImproves causal inference Ethically appropriateEthically appropriate Logistically feasibleLogistically feasible

AnalysisAnalysisPropensity score matchingPropensity score matching

Step 1Step 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 2Step 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

Data collectionData collection

Issues to consider:Issues to consider: Reliability of self-report of income Reliability of self-report of income

and sexual behaviorand sexual behavior Recall biasRecall bias Income not a sufficient variableIncome not a sufficient variable

Data collectionData collection

ACASI ACASI

(audio computer-assisted self-interviewing)(audio computer-assisted self-interviewing)

Source: Waruru et al. 2005

Data collectionData collection

Advantages of ACASIAdvantages of ACASI Using tablets vs. conventional laptopsUsing tablets vs. conventional laptops Local written and spoken languageLocal written and spoken language Accurate reporting of sensitive dataAccurate reporting of sensitive data Accurate data entryAccurate data entry Validated in ZimbabweValidated in Zimbabwe11 and Kenya and Kenya22

Builds local research capacityBuilds local research capacity

1van de Wijgert, J., N. Padian, et al. 2000 2Waruru et al. 2005

Ethical considerationsEthical considerations

Screening study has been approved, Screening study has been approved, no additional specimen collection no additional specimen collection neededneeded

Sensitive information will be obtainedSensitive information will be obtained

Confidentiality and data management Confidentiality and data management is paramountis paramount

LimitationsLimitations PSM does not match on unobserved PSM does not match on unobserved

contextual characteristics contextual characteristics matching matching might not be 100% perfectmight not be 100% perfect

Retrospective data may not capture Retrospective data may not capture outcome accuratelyoutcome accurately

Generalizability Generalizability

Acceptability of ACASIAcceptability of ACASI

Thank youThank you

William & Flora Hewlett FoundationWilliam & Flora Hewlett Foundation

Population Reference BureauPopulation Reference Bureau

David CanningDavid Canning

Ajay MahalAjay Mahal

Grace WyshakGrace Wyshak

Saidi KapigaSaidi Kapiga

ReferencesReferencesBloom, 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, JuneCuddington, 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.

HIV in TanzaniaHIV in Tanzania

Men: 6.3% Men: 6.3% Women: 7.7% Women: 7.7% (DHS 2005)(DHS 2005)

Age and sex-specific HIV prevalence, 2003

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.