Spatial patterns of mortality in low birth weight infants at term and its determinants in the State of São Paulo, Brazil

Abstract

Objective: Low birth weight (LBW) is a public health problem strongly associated with infant mortality. This study aimed to identify the spatial distribution of infant mortality in newborns with LBW (750–2,500 g) at term (≥37 weeks of gestation), due to their being small for gestational age, analyzing its association with mother-related determinants, as well as to identify priority areas of mortality in the State of São Paulo, 2010–2019. Methods: Infant mortality rate was analyzed in the division of neonatal mortality and postneonatal mortality of newborns with LBW at term. The empirical Bayesian method smoothed the rates, the univariate Moran index was used to measure the degree of spatial association between the municipalities, and the bivariate Moran index was employed to identify the existence of a spatial association between the rates and the selected determinants. Thematic maps of excess risk and local Moran were prepared to identify spatial clusters, adopting 5% as a significance level. Results: The excess risk map showed that more than 30% of the municipalities had rates above the state rate. High-risk clusters were identified in the southwest, southeast, and east regions, mainly among more developed municipalities. The determinants of adolescent mothers, mothers over 34 years of age, low education, human development index, social vulnerability index, gross domestic product, physicians, and pediatric beds showed a significant association with the rates evaluated. Conclusions: Priority areas and significant determinants associated with reduced mortality in newborns with LBW were identified, suggesting the need for intervention measures to achieve the Sustainable Development Goal.

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

“…For the analysis, variables related to different determinants were also calculated, classified as maternal, social, and health service indicators. Note that, according to the reviewed literature, these variables have already been analyzed in regard to spatial patterns of mortality in full-term and low birth weight newborns 6 , but not based on the spatial approach proposed here for prematurity. These determinants are easy to obtain and are available on official Brazilian websites such as DATASUS 26 .…”

“…The univariate global Moran index (I m ) can have values between [-1, +1], indicating a positive spatial autocorrelation (0<I m ≤1), where municipalities with high values in the proportions of premature births tend to cluster together, and similarly for municipalities with low values 23 . A negative spatial autocorrelation (-1≤I m <0) is observed when municipalities with high proportions of premature births tend to be very close to municipalities with low values and vice versa, as well as the absence of spatial autocorrelation can also be observed (spatial independence, I m =0) 6 , 29 .…”

“…The bivariate global Moran index (I m ) was used to identify the presence of spatial autocorrelation between the proportion of premature births and each of the different indicators studied in the municipalities of MRSP. Likewise, its values vary between [-1, +1], indicating the existence of an inverse or direct spatial autocorrelation between the values of two analyzed variables 6 , 29 .…”

“…Therefore, reducing prematurity and its determinants is one of the keys to reducing the number of deaths, especially in the neonatal period, given the relationship between mortality and prematurity and its consequences, such as low birth weight 6 and other complications 7 .…”

“…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 .…”