Explaining discrepancies in reproductive health indicators from population-based surveys and exit surveys: a case from Rwanda.
OBJECTIVE
Reproductive health programmes often need exit surveys and population-based surveys for monitoring and evaluation. This study investigates why such studies produce discrepant estimates of condom use, sexual behaviour and condom brand knowledge, and discusses the implications for future use of exit surveys for programme monitoring.


METHODS
Logistic regression is used to explain differences between a household survey of 1295 persons and an exit survey among a random sample of 2550 consumers at retail outlets in RWANDA:


RESULTS
Discrepancies in ever use of condoms and risky sexual behaviours are due to differences in socioeconomic status of the two samples. After controls, exit surveys at most outlet types have the same results as the household survey. Only exit surveys at bars, nightclubs and hotels yield significantly different estimates. However, the above-average knowledge of Prudence Plus condoms in the exit interviews is not attributable to socioeconomic or demographic variables, most likely because respondents have seen the product at the outlets.


CONCLUSIONS
Information about condom use and sexual behaviour obtained from exit surveys appears as accurate as that obtained through household surveys. Nevertheless, exit surveys must be used cautiously. Because exit surveys may include wealthier and better-educated respondents, they are not representative of the general population. The composition of exit survey samples should be validated through existing household surveys. Comparisons across survey types are generally unadvisable, unless they control for sample differences. When generalizing to the population at large is not needed (e.g. for studies aimed at identifying the characteristics and behaviour of users of particular products or services), exit surveys can provide an appropriate alternative to household surveys.
Meekers
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Population Services International, Washington, DC 20036, USA. dmeekers@psiwash.org
Ogada
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EA
D000293:Adolescent; D000328:Adult; D015982:Bias; D003625:Data Collection; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D011159:Population Surveillance / Q000379:methods*; D018456:Reproductive Medicine / Q000706:statistics & numerical data*; D012432:Rwanda / Q000453:epidemiology; D012923:Social Class