Using Applied Regression and Biostatistics to Assess Outcomes of Community-Based Health Programs

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@article{1710079,
      author = {Olaitan Kemi Atobatele, Opeoluwa Oluwanifemi Ajayi, Akonasu Qudus Hungbo, Christiana Adeyemi},
      title = {Using Applied Regression and Biostatistics to Assess Outcomes of Community-Based Health Programs},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {12},
      pages = {307-325},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710079.pdf},
      abstract = {Assessing the outcomes of community-based health programs requires rigorous analytical methods that can account for complex, real-world variables influencing health interventions at the population level. Applied regression analysis and biostatistics offer robust tools for evaluating the effectiveness, efficiency, and equity of these programs by enabling precise estimation of intervention impacts while adjusting for confounding factors. Regression models, including linear, logistic, and multilevel regressions, facilitate the quantification of relationships between program interventions and health outcomes, such as service utilization, morbidity reduction, and behavioral change. These models allow researchers to control for socio-demographic, environmental, and programmatic variables, providing clearer causal inferences in non-randomized, observational study designs typical of community interventions. Biostatistical methods further enhance the evaluation process by guiding sampling strategies, determining appropriate statistical power, and addressing issues related to missing data, measurement errors, and intra-cluster correlations. Advanced techniques, such as propensity score matching and difference-in-differences (DiD) analysis, strengthen causal inference by mitigating selection bias and isolating program effects from external influences. The integration of time-series and geospatial regression models also allows for the dynamic assessment of intervention outcomes across temporal and spatial dimensions. Incorporating applied regression and biostatistics into community health program evaluations not only improves the accuracy and validity of findings but also supports data-driven decision-making by program managers and policymakers. These methodologies provide actionable insights that inform program refinement, resource allocation, and scaling strategies. As global health systems increasingly emphasize evidence-based interventions, enhancing the capacity of public health practitioners to apply these analytical approaches is essential for achieving more effective and equitable health outcomes in diverse community settings.},
      keywords = {Applied regression, Biostatistics, Assess outcomes, Community-based, Health programs},
      month = {June},
  }