Spatiotemporal analysis of hospital admissions for primary care-sensitive conditions in women and children in the first 1000 days of life

PLoS One. 2022 Jun 9;17(6):e0269548. doi: 10.1371/journal.pone.0269548. eCollection 2022.

Abstract

Objective: To analyze the spatiotemporal distribution of hospital admission rates for primary care-sensitive conditions (PCSC) in women and children in the first 1000 days of life in Brazil.

Methods: Ecological study, with spatiotemporal analyses, using secondary data from Brazilian municipalities. PCSC in women, related to prenatal care and childbirth, and in children under two years old, from 2008 to 2019 were used to characterize trends and formations of spatiotemporal clusters/outliers. Crude PCSC rates were calculated and adjusted by the local empirical Bayesian method, presented in choropleth maps. We also used Anselin Local Moran I type analyses to identify spatial clusters, and space-time cube with clustering by emerging hotspot, followed by time series clustering, for analysis of spatiotemporal trends (alpha = 5%).

Results: A total of 1,850,776 PCSC were registered in pregnant women, puerperae, and children under two years of age in Brazil, representing 1.7% of the total number of hospital admissions in the period. PCSC rates showed different behaviors when the groups of women and children were evaluated, with a predominant growing trend of 109% in admissions in the first group and a reduction of 34.4% in the second. The North, Northeast, and Midwest regions had larger high-risk clusters and more significant increasing trends in PCSC in the two subpopulations studied.

Conclusions: Health actions and services in primary care may be reducing hospital admissions for children, but they are not being effective in reducing hospital admissions for women for causes related to prenatal care and childbirth, especially in the North, Northeast, and Midwest of Brazil. Investments in the qualification of care over the thousand days are urgent in the country.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Brazil / epidemiology
  • Child
  • Female
  • Hospitalization*
  • Hospitals
  • Humans
  • Infant
  • Pregnancy
  • Primary Health Care*
  • Spatio-Temporal Analysis

Grants and funding

This work was supported, in whole or in part, by: i) The Bill & Melinda Gates Foundation [OPP1202186]: ii) The National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq acronym in Portuguese) [Grants 443834/2018-0 and 306592/2018-5]; iii) The Maranhão Foundation for Research and Scientific and Technological Development (Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão – FAPEMA acronym in Portuguese) [RCUK-01538/19]; and iv) The Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES acronym in Portuguese) [finance code 001 and process nº 88887.468042/2019-00]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.