Jana Arup, Saha Unnati Rani, Reshmi R S, Muhammad T
International Institute for Population Sciences, Deonar, Mumbai, 400088, India.
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Arch Public Health. 2023 Feb 21;81(1):28. doi: 10.1186/s13690-023-01037-y.
Low birth weight (LBW) predisposes physical and mental growth failure and premature death among infants. Studies report that LBW predominately explains infant mortality. However, existing studies rarely demonstrate the phenomenon of both observed and unobserved factors, which may influence the likelihood of birth and mortality outcomes simultaneously. In this study, we identified the spatial clustering of the prevalence of LBW along with its determinants. Further, the relationship between of LBW and infant mortality, considering the unobserved factors, has been explored in the study.
Data for this study have been extracted from the National Family Health Survey (NFHS) round 5, 2019-21. We used the directed acyclic graph model to identify the potential predictors of LBW and infant mortality. Moran's I statistics have been used to identify the high-risk areas of LBW. We applied conditional mixed process modelling in Stata software to account for the simultaneous nature of occurrences of the outcomes. The final model has been performed after imputing the missing data of LBW.
Overall, in India, 53% of the mothers reported their babies' birth weight by seeing health card, 36% reported by recall, and about 10% of the LBW information was observed as missing. The state/union territory of Punjab and Delhi were observed to have the highest levels of LBW (about 22%) which is much higher than the national level (18%). The effect of LBW was more than four times larger compared to the effect in the analysis which does not account for the simultaneous occurrence of LBW and infant mortality (marginal effect; from 12 to 53%). Also, in a separate analysis, the imputation technique has been used to address the missing data. Covariates' effects showed that female children, higher order births, births that occur in Muslim and non-poor families and literate mothers were negatively associated with infant mortality. However, a significant difference was observed in the impact of LBW before and after imputing the missing values.
The current findings showed the significant association of LBW with infant deaths, highlighting the importance of prioritising policies that help improve the birth weight of new-born children that may significantly reduce the infant mortality in India.
低出生体重（LBW）易导致婴儿身体和智力发育迟缓以及过早死亡。研究报告称，低出生体重是婴儿死亡的主要原因。然而，现有研究很少能同时证明观察到的和未观察到的因素，这些因素可能会同时影响出生和死亡结果的可能性。在本研究中，我们确定了低出生体重患病率及其决定因素的空间聚集情况。此外，本研究还探讨了低出生体重与婴儿死亡率之间的关系，并考虑了未观察到的因素。
本研究的数据取自2019 - 2021年第五轮全国家庭健康调查（NFHS）。我们使用有向无环图模型来确定低出生体重和婴儿死亡率的潜在预测因素。莫兰指数（Moran's I）统计量用于确定低出生体重的高风险地区。我们在Stata软件中应用条件混合过程模型来解释结果出现的同时性。在对低出生体重的缺失数据进行插补后，进行了最终模型分析。
总体而言，在印度，53%的母亲通过查看健康卡报告婴儿出生体重，36%通过回忆报告，约10%的低出生体重信息被视为缺失。旁遮普邦和德里联邦属地的低出生体重水平最高（约22%），远高于全国水平（18%）。与未考虑低出生体重和婴儿死亡率同时发生情况的分析相比，低出生体重的影响要大四倍多（边际效应；从12%增至53%）。此外，在单独的分析中，采用插补技术来处理缺失数据。协变量效应表明，女童、多胎生育、穆斯林家庭和非贫困家庭出生的婴儿以及识字母亲与婴儿死亡率呈负相关。然而，在插补缺失值前后，低出生体重的影响存在显著差异。
当前研究结果表明低出生体重与婴儿死亡之间存在显著关联，突出了优先制定有助于提高新生儿出生体重政策的重要性，这可能会显著降低印度的婴儿死亡率。
