Geographical variations in the risk of adverse birth outcomes in Spain

Abstract

The objective of this study was to describe the spatial risk-patterns of prematurity and low birth weight in Spain. A descriptive spatial analysis of births registered in the Spanish Vital Statistics during 2004-2008 using municipalities as the observation unit was carried out. Besag-York-Mollié autoregressive spatial models were adjusted using the Integrated Nested Laplace approximation to calculate relative risks and posterior probabilities of having very and moderate preterm or low weight newborns. Results were represented in maps to assess geographic risk-patterns. Spatial analysis shows geographical variations in the risk of adverse reproductive outcomes in Spain highlighting three main high-risk zones, namely, municipalities in Asturias, Madrid City and Murcia. The specific risk patterns identified on each zone suggests some differences regarding the potential underlying risk factors and specific areas for future research. A differential exposure during pregnancy to some risks potentially related to industry or agriculture and other contextual factors could underlie an unequal vulnerability to adverse reproductive outcomes in some Spanish regions.

Spatial patterns of prematurity and its determinants in the metropolitan region of São Paulo, Brazil, 2010-2019

“…As spatial analysis has been a good way to carry out epidemiological studies by mapping various problems such as prematurity, Bloch 20 used a spatial approach through the census in Philadelphia to study the geographic pattern of premature births to black mothers born abroad and in the United States, and systematic variation across communities was observed in race, poverty, and crime. Another study carried out by Castelló et al 21 , in Spain, also showed spatial variations in the risk of prematurity and low birth weight, highlighting some areas with high risk that could be related to industry or agriculture, in addition to other factors, such as vulnerability unequal social status.…”

“…Among the studies where geoprocessing was used, the analyses on the profile of births 14 , morbidity 7 , 15 , and neonatal, post-neonatal (28 to 364 days of life) or infant mortality (under 1 year of life) 6 stand out 16 , 17 . Specifically, studies were found on the spatial distribution of premature births in Brazil 7 , 18 , 19 and in other countries 20 , 21 , 22 , 23 , whose risk areas were identified and spatial associations with some factors were studied, such as those related to health 7 , industry 21 , agriculture 22 , the environment 23 or some socioeconomic conditions 19 .…”