Division of Health Policy & Management, College of Health Science, Korea University, Seoul, South Korea.
Harvard Center for Population & Development Studies, Cambridge, Massachusetts, USA.
J Glob Health. 2020 Dec;10(2):020405. doi: 10.7189/jogh.10.020405.
The conventional indicators of infant and under-five mortality are aggregate deaths occurring in the first year and the first five years, respectively. Monitoring deaths by <1 month (neonatal), 1-11 months (post-neonatal), and 12-59 months (child) can be more informative given various etiological causes that may require different interventions across these three mutually exclusive periods. For optimal resource allocation, it is also necessary to track progress in robust estimates of child survival at a smaller geographic and administrative level.
Data on 259 627 children came from the 2015-2016 Indian National Family Health Survey. We used a random effects model to account for the complex survey design and sampling variability, and predicted district-specific probabilities of neonatal, post-neonatal, and child mortality. The resulting precision-weighted estimates are more reliable as they pool information and borrow strength from other districts that share the same state membership. The Pearson correlation and Spearman's rank correlation were assessed for the three mortality estimates, and the Moran's I measure was used to detect spatial clustering of high burden districts for each outcome.
The majority of under-five deaths was disproportionately concentrated in the neonatal period. Across all districts, the predicted probability of neonatal, post-neonatal, and child mortality varied from 6.0 to 63.9 deaths, 3.8 to 47.6 deaths, and 1.7 to 11.8 deaths per 1000 live births, respectively. The overall correlation between district-wide probabilities of mortality for the three mutually exclusive periods was moderate (Pearson correlation = 0.47-0.58, Spearman's rank correlation = 0.58-0.64). For each outcome, a relatively strong spatial clustering was detected across districts that transcended state boundaries (Moran's I = 0.61-0.76).
Sufficiently breaking down the under-five mortality to distinct age groups and using the precision-weighted estimations to monitor performances at smaller geographic and administrative units can inform more targeted interventions and foster accountability to improve child survival.
婴儿和五岁以下儿童死亡率的传统指标分别是第一年和前五年的总死亡人数。由于各种病因可能需要在这三个相互排斥的时期进行不同的干预，因此按<1 个月（新生儿期）、1-11 个月（新生儿后期）和 12-59 个月（儿童期）监测死亡情况可能更具信息量。为了在更小的地理和行政级别上跟踪儿童生存的稳健估计值的进展，还需要优化资源分配。
2015-2016 年印度全国家庭健康调查的数据来自于 259627 名儿童。我们使用随机效应模型来解释复杂的调查设计和抽样变异性，并预测了各地区新生儿、新生儿后期和儿童死亡率的具体概率。由于这些精确加权的估计值汇总了信息并从具有相同州成员身份的其他地区借用了优势，因此更可靠。评估了这三种死亡率估计值的皮尔逊相关系数和斯皮尔曼等级相关系数，并使用莫兰指数来检测每种结果的高负担地区的空间聚类。
大多数五岁以下儿童死亡不成比例地集中在新生儿期。在所有地区，新生儿、新生儿后期和儿童死亡率的预测概率分别为每 1000 例活产 6.0-63.9 例、3.8-47.6 例和 1.7-11.8 例。三个相互排斥时期的全区死亡率之间的总体相关性中等（皮尔逊相关系数=0.47-0.58，斯皮尔曼等级相关系数=0.58-0.64）。对于每个结果，都在跨越州界的地区检测到了相对较强的空间聚类（莫兰指数=0.61-0.76）。
将五岁以下儿童死亡率充分细分为不同的年龄组，并使用精确加权估计值来监测更小的地理和行政单位的表现，可以提供更有针对性的干预措施，并促进问责制以改善儿童生存。
