Factors associated with mortality from 5 to 14 years of age according to geographically weighted regression

Datos Bibliográficos

ID23310773
AutoresMônia Maia de Lima (0000-0002-5481-4726, Prefeitura Municipal de Primavera do Leste, Brasil), Mônia Maia Lima (Prefeitura Municipal de Vitória), Silvana Granado Nogueira Da Gama (0000-0002-9200-0387, Fundação Oswaldo Cruz), Alexsandra Rodrigues De Mendonça Favacho (0000-0002-4950-2357, Fiocruz Mato Grosso do Sul, Brasil), Cosme Marcelo Furtado Passos Da Silva (0000-0001-7789-1671, Fundação Oswaldo Cruz), Reinaldo Souza-Santos (0000-0003-2387-6999, Fundação Oswaldo Cruz)
Año2026
Volumen31
Número6
Páginase15622024-e15622024
Fecha de publicación2026-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaCiência & Saúde Coletiva (JOURNAL)
Identificadores de la revistaISSN: 1413-8123 • E-ISSN: 1678-4561
EditorialFapUNIFESP (SciELO) (PUBLISHER)
DOI10.1590/1413-81232026316.15622024
PMID42525041
OpenAlexW7171429738
IdiomaEN
Referencias citadas10

Mortality data for the 5 to 14 age group is scarce and often of low quality, despite most of these deaths being preventable. Social determinants of health offer an alternative perspective to understand the context of these deaths. An analytical ecological study using geographically weighted regression was conducted to identify the association between social determinants and deaths among 5 to 14-year-olds in Mato Grosso from 2009 to 2020. The model included variables related to demographic, geopolitical, environmental factors, living conditions, and access to health services. The model effectively explained mortality patterns in both age groups. For 5 to 9-year-olds, deaths were influenced by demographic, environmental, geopolitical, and health services factors, while for 10 to 14-year-olds, demographic, environmental factors, and living conditions played a larger role. The models were similar but showed varying variable compositions and behaviors depending on location and age group. The strongest associations for ages 5 to 9 were concentrated in the northeast and southeast regions, characterized by major grain production and state/international borders. For ages 10 to 14, associations were more heterogeneous.

Context (archaeology) · Geographically Weighted Regression · Linear regression · Population · Public health · Regression · Regression analysis · Variables · Health disparities and outcomes · Indigenous Health and Education · Maternal and Neonatal Healthcare