A contextual-compositional approach to discover associations between health determinants and health indicators for neonatal mortality rate monitoring in situations of anomalies.
<h4>Introduction</h4>Epidemiology is considered both a field of research and a methodological approach within the broader health sciences. It aims to understand health-related events' causes and effects and provide the evidence necessary to prevent disease and implement effective co...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0310413 |
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| Summary: | <h4>Introduction</h4>Epidemiology is considered both a field of research and a methodological approach within the broader health sciences. It aims to understand health-related events' causes and effects and provide the evidence necessary to prevent disease and implement effective control and prevention strategies. One of the main focuses of epidemiology is identifying the determinant factors in the health situation of populations since health-related anomalies are not randomly distributed among people. This understanding brings up the necessity of considering each place's particularities and observing the regularity of diseases in a population context.<h4>Methods</h4>We present the Contextual-Compositional Approach (CCA) for the discovery of associations between Health Indicators (HI) and Health Determinants (HD) for neonatal mortality rate monitoring in situations of anomalies. CCA uses time series concepts, anomaly detection, and data distribution between classes for studying HD under expected conditions and comparing them to the anomaly conditions indicated by the anomaly detection in the HI. CCA is evaluated using a neonatal mortality database in health facilities in Rio de Janeiro, Brazil.<h4>Results</h4>The results show that CCA can reveal essential associations between the health condition and the population's social, economic, and cultural characteristics on different scales.<h4>Conclusion</h4>CCA stands out because it is easy to apply and understand, requiring little computational resources and parameters. |
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| ISSN: | 1932-6203 |