Big Data and Public Health: The Effectiveness of Health Interventions in Latin America
DOI:
https://doi.org/10.70577/h04n1a89Keywords:
Big Data, indicators, interventions, mortality, public health.Abstract
This study evaluates the effectiveness of public health interventions in Latin America through the use of Big Data and advanced statistical analysis. A time-series model and a Random Forest predictive algorithm were applied to a simulated database spanning multiple countries and intervention types, focusing on two key indicators: the overall mortality rate and the infant mortality rate. The results show stable patterns with moderate variability in both indicators and reveal that socioeconomic factor such as per capita income, educational level, and availability of health personnel are the main predictors. The analysis also demonstrates an interrelationship between the two mortality rates, suggesting that they should be addressed in an integrated manner. The study highlights the usefulness of Big Data for monitoring trends in real time, personalizing interventions, and improving the precision of public policies, in line with precision public health approaches. However, limitations associated with data quality, ethical challenges, and lack of institutional sustainability are recognized. The methodology implemented offers a solid foundation for future empirical studies aimed at optimizing resource allocation and improving equity in the region's health systems. The conclusion is that the strategic use of big data can transform healthcare management, provided it is accompanied by ethical frameworks, intersectoral policies, and local capacity building.
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