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Table 3 Estimates of HIV Prevalence among Men in Ghana (2003)

From: Using interviewer random effects to remove selection bias from HIV prevalence estimates

Model

HIV prevalence

Analytic 95% CI

 

Bootstrap 95% CI

 

All Men - Fixed Effects Selection Model

1.4%

1.1%

1.7%

  

All Men - Random Effects Selection Model

1.4%

1.1%

1.7%

1.2%

1.6%

All Men - Random Effects Bias Correction Selection Model

1.4%

1.1%

1.7%

1.2%

1.6%

Men with Non-Missing Data (Valid HIV Test)

1.6%

1.2%

2.0%

  

Men with No Contact - Imputation Model

1.6%

1.4%

1.8%

  

All Men - Imputation Model

1.6%

1.3%

2.0%

  
  1. Consent to test and HIV status are jointly estimated using a bivariate probit with the following covariates: education, wealth quintile, marital status, had a sexually transmitted disease, age at first intercourse, had high risk sex, number of partners, condom use, would care for an HIV-infected relative, knows someone who died of AIDS, previously tested for HIV, smokes, language, age group, region, ethnicity and religion. The selection variable which predicts consent but not HIV status is interviewer identity. Full parameter estimates are presented in tables A8-A10 in the appendix (see Additional file 1). Analytic standard errors are shown for the fixed effects and random effects models, with bootstrap errors for random effects and random effects bias correction models based on 1,000 replications. Our cluster bootstrap takes account of survey design by drawing a fixed number of clusters (the same as in the original data) from each stratum in each sample. Results from an imputation model are also shown in rows 5–6, along with estimates only using those without missing data (respondents with a valid HIV test). HIV prevalence estimates are weighted. Source: DHS Ghana 2003 (men).
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BMC Medical Research Methodology

ISSN: 1471-2288

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