Open AccessComparison of cohort characteristics in Central Africa International Epidemiology Databases to Evaluate AIDS and Demographic Health Surveys: Rwanda and Burundi
Author(s) -
Anna Mageras,
Ellen Brazier,
T Niyongabo,
Gad Murenzi,
Jean d’Amour Sinayobye,
Adebola Adedimeji,
Christella Twizere,
Elizabeth A. Kelvin,
Kathryn Anastos,
Denis Nash,
Heidi E. Jones
Publication year - 2021
Publication title -
international journal of std and aids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.673
H-Index - 74
eISSN - 1758-1052
pISSN - 0956-4624
DOI - 10.1177/0956462420983783
Subject(s) - medicine , epidemiology , cohort , demography , population , underweight , marital status , cohort study , gerontology , environmental health , body mass index , overweight , pathology , sociology
Clinical health record data are used for HIV surveillance, but the extent to which these data are population representative is not clear. We compared age, marital status, body mass index, and pregnancy distributions in the Central Africa International Databases to Evaluate AIDS (CA-IeDEA) cohorts in Burundi and Rwanda to all people living with HIV and the subpopulation reporting receiving a previous HIV test result in the Demographic and Health Survey (DHS) data, restricted to urban areas, where CA-IeDEA sites are located. DHS uses a probabilistic sample for population-level HIV prevalence estimates. In Rwanda, the CA-IeDEA cohort and DHS populations were similar with respect to age and marital status for men and women, which was also true in Burundi among women. In Burundi, the CA-IeDEA cohort had a greater proportion of younger and single men than the DHS data, which may be a result of outreach to sexual minority populations at CA-IeDEA sites and economic migration patterns. In both countries, the CA-IeDEA cohorts had a higher proportion of underweight individuals, suggesting that symptomatic individuals are more likely to access care in these settings. Multiple sources of data are needed for HIV surveillance to interpret potential biases in epidemiological data.