Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies. Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded. We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure. The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

中文翻译：

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生殖、孕产妇、新生儿和儿童健康指标的地理空间估计：基于家庭调查的研究方法方面的系统回顾

地理空间方法越来越多地用于对低收入和中等收入国家 (LMIC) 的生殖、孕产妇、新生儿和儿童健康 (RMNCH) 指标进行精细的空间尺度估计。本研究旨在描述应用于 RMNCH 覆盖范围和影响结果的地理空间方法的重要方法论方面和特殊性，并使非专业读者能够批判性地评估和解释这些研究。使用 Medline、Web of Science、Scopus、SCIELO 和 LILACS 电子数据库进行了两次独立检索。基于使用地理空间方法对中低收入国家 RMNCH 进行调查数据的研究被认为是合格的。其结果不是发生率测量的研究被排除在外。我们确定了 82 项研究，重点关注 30 多种不同的 RMNCH 结果。贝叶斯分层模型是 62 项研究中发现的主要建模方法。5 × 5 公里估计是最常见的分辨率，主要信息来源是人口统计和健康调查。模型验证的报告不足，只有 56% 的研究报告了样本外方法，13% 的研究没有提供单一的验证指标。不确定性评估和报告缺乏标准化，超过四分之一的研究未能报告任何不确定性测量。以 RMNCH 结果为重点的地理空间估计领域正在明显扩大。然而，尽管采用标准化概念建模框架来生成更精细的空间尺度估计，但模型验证和不确定性等方法方面仍需要进一步关注，因为它们对于帮助读者评估所提出的估计至关重要。