利用妊娠风险评估监测系统数据对美国县一级母婴健康相关行为指标进行估计，2016-2018 年。

US county-level estimation for maternal and infant health-related behavior indicators using pregnancy risk assessment monitoring system data, 2016-2018.

机构信息

Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, 30341, USA.

Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, 30341, USA.

出版信息

Popul Health Metr. 2022 May 21;20(1):14. doi: 10.1186/s12963-022-00291-6.

DOI:10.1186/s12963-022-00291-6

PMID:35597940

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9124401/

Abstract

BACKGROUND

There is a critical need for maternal and child health data at the local level (for example, county), yet most counties lack sustainable resources or capabilities to collect local-level data. In such case, model-based small area estimation (SAE) could be a feasible approach. SAE for maternal or infant health-related behaviors at small areas has never been conducted or evaluated.

METHODS

We applied multilevel regression with post-stratification approach to produce county-level estimates using Pregnancy Risk Assessment Monitoring System (PRAMS) data, 2016-2018 (n = 65,803 from 23 states) for 2 key outcomes, breastfeeding at 8 weeks and infant non-supine sleeping position.

RESULTS

Among the 1,471 counties, the median model estimate of breastfeeding at 8 weeks was 59.8% (ranged from 34.9 to 87.4%), and the median of infant non-supine sleeping position was 16.6% (ranged from 10.3 to 39.0%). Strong correlations were found between model estimates and direct estimates for both indicators at the state level. Model estimates for both indicators were close to direct estimates in magnitude for Philadelphia County, Pennsylvania.

CONCLUSION

Our findings support this approach being potentially applied to other maternal and infant health and behavioral indicators in PRAMS to facilitate public health decision-making at the local level.

摘要

背景

地方层面（例如县）急需妇幼健康数据，但大多数县缺乏收集地方数据的可持续资源或能力。在这种情况下，基于模型的小区域估计（SAE）可能是一种可行的方法。针对小区域母婴健康相关行为的 SAE 从未进行过或评估过。

方法

我们应用了多层回归后分层方法，使用妊娠风险评估监测系统（PRAMS）数据（来自 23 个州的 65803 例，2016-2018 年）来生成县级估计值，用于 2 个关键结果，即 8 周时的母乳喂养率和婴儿非仰卧睡眠姿势。

结果

在 1471 个县中，8 周时母乳喂养率的中位数模型估计值为 59.8%（范围为 34.9%至 87.4%），婴儿非仰卧睡眠姿势的中位数为 16.6%（范围为 10.3%至 39.0%）。这两个指标在州级水平上，模型估计值与直接估计值之间存在很强的相关性。宾夕法尼亚州费城的这两个指标的模型估计值在数量级上与直接估计值接近。