» Articles » PMID: 35597940

US County-level Estimation for Maternal and Infant Health-related Behavior Indicators Using Pregnancy Risk Assessment Monitoring System Data, 2016-2018

Overview
Publisher Biomed Central
Specialty Public Health
Date 2022 May 21
PMID 35597940
Authors
Affiliations
Soon will be listed here.
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.

References
1.
Taylor J, Moon G, Twigg L . Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates. Soc Sci Res. 2016; 56:108-16. DOI: 10.1016/j.ssresearch.2015.12.006. View

2.
Zhang X, Holt J, Lu H, Wheaton A, Ford E, Greenlund K . Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the behavioral risk factor surveillance system. Am J Epidemiol. 2014; 179(8):1025-33. DOI: 10.1093/aje/kwu018. View

3.
Congdon P . A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates. Int J Health Geogr. 2009; 8:6. PMC: 2647533. DOI: 10.1186/1476-072X-8-6. View

4.
Ferreira L, Blumenberg C, Utazi C, Nilsen K, Hartwig F, Tatem A . Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys. Int J Health Geogr. 2020; 19(1):41. PMC: 7552506. DOI: 10.1186/s12942-020-00239-9. View

5.
Zhang X, Holt J, Yun S, Lu H, Greenlund K, Croft J . Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system. Am J Epidemiol. 2015; 182(2):127-37. PMC: 4554328. DOI: 10.1093/aje/kwv002. View