Abstract
BackgroundSurvey data from low income countries on birth weight usually pose a persistent problem. The studies conducted on birth weight have acknowledged missing data on birth weight, but they are not included in the analysis. Furthermore, other missing data presented on determinants of birth weight are not addressed. Thus, this study tries to identify determinants that are associated with low birth weight (LBW) using multiple imputation to handle missing data on birth weight and its determinants.MethodsThe child dataset from Nepal Demographic and Health Survey (NDHS), 2011 was utilized in this study. A total of 5,240 children were born between 2006 and 2011, out of which 87% had at least one measured variable missing and 21% had no recorded birth weight. All the analyses were carried out in R version 3.1.3. Transform-then impute method was applied to check for interaction between explanatory variables and imputed missing data. Survey package was applied to each imputed dataset to account for survey design and sampling method. Survey logistic regression was applied to identify the determinants associated with LBW.ResultsThe prevalence of LBW was 15.4% after imputation. Women with the highest autonomy on their own health compared to those with health decisions involving husband or others (adjusted odds ratio (OR) 1.87, 95% confidence interval (95% CI) = 1.31, 2.67), and husband and women together (adjusted OR 1.57, 95% CI = 1.05, 2.35) were less likely to give birth to LBW infants. Mothers using highly polluting cooking fuels (adjusted OR 1.49, 95% CI = 1.03, 2.22) were more likely to give birth to LBW infants than mothers using non-polluting cooking fuels.ConclusionThe findings of this study suggested that obtaining the prevalence of LBW from only the sample of measured birth weight and ignoring missing data results in underestimation.
Highlights
Survey data from low income countries on birth weight usually pose a persistent problem
NDHS data The child dataset from Nepal Demographic and Health Survey (NDHS), 2011 was analyzed in this study
The overall prevalence of low birth weight (LBW) was 15.4% after imputation
Summary
Survey data from low income countries on birth weight usually pose a persistent problem. This study tries to identify determinants that are associated with low birth weight (LBW) using multiple imputation to handle missing data on birth weight and its determinants. Missing data occur almost in all types of studies and cause inefficient and biased estimates of parameters if they are handled improperly. The weighting adjustment technique is applied, in which weight of respondents are increased to represent non-respondents [3], whereas for item nonresponse, imputation methods are employed [1, 4]. When missing data are MCAR, the probability of missingness does not depend on the missing and other observed data. When missing data are MNAR, the probability of missingness depends on both observed and missing data. Multiple imputation yields unbiased estimates of parameters, when missing data hold MAR assumption [4], and as aforementioned, missing data in this study held MAR assumption. The overall regression coefficient is the average of all Qi and shown in Eq (1)
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