Reliability of demographic and socioeconomic variables in predicting early initiation of breastfeeding: a replication analysis using the Kenya Demographic and Health Survey data
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Objectives Examine the reliability of sociodemographic variables in predicting initiation of breastfeeding within an hour of birth (EarlyBF), using data from 1998, 2003 and 2008–2009. Study design A replication analysis using the Kenya Demographic and Health Survey (KDHS) data collected in 1998, 2003 and 2008–2009. The candidate predictor variables were child's gender, home or health facility place of birth, vaginal or caesarean mode of birth, urban or rural setting, province of residence, Wealth Index and maternal education, occupation, literacy and media exposure. Setting Kenya. Participants 6375 dyads of mothers aged 15–49 and their children aged 0–23 months (2125 dyads in each of the survey years). Results Mode of birth and province were statistically significant predictors of EarlyBF in 1998, 2003 and 2008–2009. Children delivered through caesarean section were non-EarlyBF in 1998 (OR 2.63, 95% CI 1.72 to 4.04), 2003 (OR 3.36, 95% CI 1.83 to 6.16) and 2008 (OR 3.51, 95% CI 2.17 to 5.69). The same was true of those living in the Western province in 1998 (OR 2.67, 95% CI 1.61 to 4.43), 2003 (OR 4.92, 95% CI 3.01 to 8.04) and 2008 (OR 6.07, 95% CI 3.54 to 10.39). Conclusions The 1998 KDHS data do not provide the basis for reliable prediction of EarlyBF, with reliability conceptualised as replicability of findings using highly similar data sets from 2003 and 2008–2009. Most of the demographic and socioeconomic variables were unreliable predictors of EarlyBF. We speculate that activities in parts or all of Kenya changed the analysis context in the period between 1998 and 2008–2009, and these changes were of a sufficient magnitude to affect the relationships under investigation. The degree to which this is a general problem in child health research is not known, calling for further research to investigate this methodological issue with other health end points and other data.