Singh Usha, Ueranantasun Attachai, Kuning Metta
Nepal Institute of Health Sciences, Gokarneswor Municipality-12, Jorpati, Kathmandu, Nepal.
Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, 94000, Thailand.
BMC Pregnancy Childbirth. 2017 Feb 20;17(1):67. doi: 10.1186/s12884-017-1252-5.
Survey 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.
The 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.
The 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.
The 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.
低收入国家有关出生体重的调查数据通常存在一个长期问题。关于出生体重的研究已承认存在出生体重数据缺失的情况，但这些数据未纳入分析。此外，出生体重决定因素方面呈现的其他缺失数据也未得到处理。因此，本研究尝试使用多重填补法来处理出生体重及其决定因素的缺失数据，以确定与低出生体重（LBW）相关的决定因素。
本研究使用了2011年尼泊尔人口与健康调查（NDHS）中的儿童数据集。2006年至2011年间共出生了5240名儿童，其中87%至少有一个测量变量缺失，21%没有记录出生体重。所有分析均在R 3.1.3版本中进行。采用先变换后填补法来检验解释变量与填补的缺失数据之间的相互作用。将调查软件包应用于每个填补后的数据集，以考虑调查设计和抽样方法。应用调查逻辑回归来确定与低出生体重相关的决定因素。
填补后低出生体重的患病率为15.4%。与那些健康决策涉及丈夫或其他人的女性相比，对自身健康拥有最高自主权的女性（调整后的优势比（OR）为1.87，95%置信区间（95%CI）=1.31，2.67），以及丈夫和女性共同决策的情况（调整后的OR为1.57，95%CI =1.05，2.35）生下低出生体重婴儿的可能性较小。使用高污染烹饪燃料的母亲（调整后的OR为1.49，95%CI =1.03，2.22）比使用无污染烹饪燃料的母亲生下低出生体重婴儿的可能性更大。
本研究结果表明，仅从有测量出生体重的样本中获取低出生体重的患病率而忽略缺失数据会导致低估。
