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

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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)

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• Male 6.4
• Female 7.7

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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)

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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)

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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

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

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Possible Approaches

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

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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

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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

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Analysis

• Propensity score matching
• Step 1 Run Multivariate Logistic Regression
• Dependent variable Y1 if HIV Y 0, otherwise
• Include all observed characteristics except
  outcomes
• Obtain PS predicted probability (p) or
  logp/(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

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Data collection

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

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Data collection

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

Source Waruru et al. 2005
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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
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Ethical considerations

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

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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

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Thank you

• William Flora Hewlett Foundation
• Population Reference Bureau
• David Canning
• Ajay Mahal
• Grace Wyshak
• Saidi Kapiga

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References
Bloom, David and Ajay Mahal. Does the AIDS
Epidemic threaten Economic Growth? Journal of
Econometrics. 1997. 77105-124. 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 36368. 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) 10930. 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 199124325. 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 2 524.
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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.