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Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: A
Daniela Testoni Costa-Nobre1, Mandira Daripa Kawakami1, Kelsy Catherina Nema Areco1
1Escola Paulista de Medicina-Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil.
Insights
High rates of neonatal mortality due to asphyxia are clustered in specific areas of São Paulo State, Brazil. These clusters often overlap with regions of lower per capita GDP, indicating a link between economic status and infant health outcomes.
Area of Science:
- Public Health
- Spatial Epidemiology
- Neonatal Mortality Research
Background:
- Infant mortality rate is a key indicator of population health.
- Neonatal mortality constitutes a significant proportion of infant deaths.
- Underdevelopment and associated factors can increase neonatal mortality risk.
Purpose of the Study:
- Investigate spatial clusters of asphyxia-associated neonatal mortality.
- Explore the association between these mortality clusters and per capita GDP in São Paulo State, Brazil.
Main Methods:
- Ecological study of live births in São Paulo State (2004-2013).
- Defined neonatal deaths (0-27 days) related to perinatal asphyxia.
- Employed geoprocessing, spatial autocorrelation (Moran, LISA), and Bayesian smoothing.
- Correlated asphyxia-associated neonatal mortality with per capita GDP using Spearman correlation.
Main Results:
- Identified 6,713 asphyxia-associated neonatal deaths (1.13/1000 live births).
- Spatial analysis revealed significant clusters of neonatal mortality (p=0.001 after Bayesian smoothing).
- Clusters were concentrated in the south, southeast, and northwest regions.
- Areas with high neonatal mortality showed partial overlap with areas of low per capita GDP.
Conclusions:
- Spatial analysis successfully identified high-risk areas for asphyxia-associated neonatal mortality.
- A significant negative correlation exists between high neonatal mortality clusters and low per capita GDP.
- This approach can guide public health interventions to reduce neonatal mortality.
Background:
Infant mortality rate is a measure of population health and neonatal mortality account for great proportion of these deaths. Underdevelopment might be associated to higher neonatal mortality risk due to assistant related factors. Spatial and temporal distribution of mortality help identifying and developing strategies for interventions.
Objective:
To investigate the cluster areas of asphyxia-associated neonatal mortality and to explore its association with per capita gross domestic product (GDP) in São Paulo State (SP), Brazil.
Methods:
Ecological study including live births residents in SP from 2004-2013. Neonatal deaths (0-27 days) with perinatal asphyxia were defined as intrauterine hypoxia, birth asphyxia or meconium aspiration syndrome written in any line of the Death Certificate. Geoprocessing analytical approach included detection of first order effects through quintiles and spatial moving average maps, followed by second order effects by global and local spatial autocorrelation (Moran and LISA, respectively) before and after smoothing with local Bayesian estimates. Finally, Spearman correlation was applied between asphyxia-associated neonatal mortality and mean per capita GDP rates for the municipalities with significant LISA.
Results:
There were 6,713 asphyxia-associated neonatal deaths among 5,949,267 live births (rate: 1.13/1000) in SP. Spatial moving average maps showed a non-random distribution among municipalities, with presence of clusters (I = 0.048; p = 0.023). LISA map identified clusters of asphyxia-associated neonatal mortality in the south, southeast and northwest. After applying local Bayes estimates, clusters were more pronounced (I = 0.589; p = 0.001). There was a partial overlap of the areas of higher asphyxia-associated neonatal mortality and lower mean per capita GDP.
Conclusions:
Spatial analysis identified cluster areas of high asphyxia-associated neonatal mortality and low per capita GDP rates, with a significant negative correlation. This optimized, structured, and hierarchical approach to identify high-risk areas of cause-specific neonatal mortality may be helpful for guiding public health efforts to decrease neonatal mortality.
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