RISK ASSESSMENT AND BEHAVIORAL HEALTH STATISTICS: MODELING LIFESTYLE FACTORS AND EXPOSURE IMPACTS ON PUBLIC HEALTH OUTCOMES DOI Open Access
Sylvester Chibueze Izah,

ANDREW SAMPSON UDOFIA,

Ian Johnson

et al.

Greener Journal of Epidemiology and Public Health, Journal Year: 2024, Volume and Issue: 12(1), P. 21 - 34

Published: Nov. 19, 2024

Risk assessment in public health is a vital and evolving process that seeks to understand the various factors influencing outcomes, particularly those related lifestyle environmental exposures. This paper focuses on role of statistical modeling evaluating predicting risks associated with behaviors, exposures, their cumulative impacts outcomes. The found essential for understanding complex relationships between factors, Advances artificial intelligence (AI) machine learning have significantly improved accuracy risk predictions, allowing more personalized effective interventions. such as diet, physical activity, smoking was shown significant impact chronic disease prevention management. Environmental occupational exposure assessments are critical identifying disproportionately affecting vulnerable populations. effect multiple including social determinants health, highlighted driver disparities. Finally, integrating these techniques into practice can improve overall effectiveness recommends enhancing advanced methods AI prediction models identify at-risk populations target interventions better. It also advocates incorporating promote equity reduce disparities across communities.

Language: Английский

Environmental Risk Assessment of Trace Metal Pollution: A Statistical Perspective DOI Creative Commons
Matthew Chidozie Ogwu, Sylvester Chibueze Izah,

Wisdom Ebiye Sawyer

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(4)

Published: Feb. 28, 2025

Abstract Trace metal pollution is primarily driven by industrial, agricultural, and mining activities presents complex environmental challenges with significant implications for ecological human health. Traditional methods of risk assessment (ERA) often fall short in addressing the intricate dynamics trace metals, necessitating adoption advanced statistical techniques. This review focuses on integrating contemporary methods, such as Bayesian modeling, machine learning, geostatistics, into ERA frameworks to improve precision, reliability, interpretability. Using these innovative approaches, either alone or preferably combination, provides a better understanding mechanisms transport, bioavailability, their impacts can be achieved while also predicting future contamination patterns. The use spatial temporal analysis, coupled uncertainty quantification, enhances hotspots associated risks. Integrating models ecotoxicology further strengthens ability evaluate health risks, providing broad framework managing pollution. As new contaminants emerge existing pollutants evolve behavior, need adaptable, data-driven methodologies becomes ever more pressing. advancement tools interdisciplinary collaboration will essential developing effective management strategies informing policy decisions. Ultimately, lies diverse data sources, analytical techniques, stakeholder engagement, ensuring resilient approach mitigating protecting public

Language: Английский

Citations

2
Statistical Approaches in Medical Social Work: Enhancing Health Surveillance and Evaluating Intervention Outcomes DOI Open Access

Tatiana Jack,

Sylvester Chibueze Izah

Greener Journal of Epidemiology and Public Health, Journal Year: 2025, Volume and Issue: 13(1), P. 6 - 18

Published: Jan. 30, 2025

Statistical approaches are critical in advancing medical social work, particularly health surveillance, outbreak detection, and evaluating intervention outcomes. This paper focuses on how integrating advanced statistical methods enhances the effectiveness of work by informing evidence-based practices improving public interventions. Using syndromic surveillance space-time scan statistics has revolutionized monitoring disease outbreaks, enabling timely responses targeted interventions to mitigate threats. These methodologies can also foster data-driven decision-making, allowing workers tailor based rigorous evidence a deeper understanding patient needs determinants health. However, challenges remain effectively these tools into practice, including data accessibility, interdisciplinary collaboration, potential for misinterpretation complex findings. Despite barriers, opportunities presented vast. They enhance contribute identifying trends disparities, more equitable healthcare delivery. As background increasingly shifts toward models, must embrace inform their address diverse populations' multifaceted challenges. The successful incorporation is essential outcomes, advocating vulnerable communities, promoting equity.

Language: Английский

Citations

0
Water Quality Management: Processes Influencing Waterborne Diseases and Sustainable Solutions DOI

Wisdom Ebiye Sawyer,

Kurotimipa Frank Ovuru,

Nsikak Godwin Etim

et al.

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 53 - 85

Published: Jan. 1, 2025

Language: Английский

Citations

0
Waste Management and Health: Addressing the Processes Behind Hazardous Waste and Pollution DOI

Sylvester Chibueze Izah,

Matthew Chidozie Ogwu, Milan Hait

et al.

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 143 - 171

Published: Jan. 1, 2025

Language: Английский

Citations

0
RISK ASSESSMENT AND BEHAVIORAL HEALTH STATISTICS: MODELING LIFESTYLE FACTORS AND EXPOSURE IMPACTS ON PUBLIC HEALTH OUTCOMES DOI Open Access
Sylvester Chibueze Izah,

ANDREW SAMPSON UDOFIA,

Ian Johnson

et al.

Greener Journal of Epidemiology and Public Health, Journal Year: 2024, Volume and Issue: 12(1), P. 21 - 34

Published: Nov. 19, 2024

Risk assessment in public health is a vital and evolving process that seeks to understand the various factors influencing outcomes, particularly those related lifestyle environmental exposures. This paper focuses on role of statistical modeling evaluating predicting risks associated with behaviors, exposures, their cumulative impacts outcomes. The found essential for understanding complex relationships between factors, Advances artificial intelligence (AI) machine learning have significantly improved accuracy risk predictions, allowing more personalized effective interventions. such as diet, physical activity, smoking was shown significant impact chronic disease prevention management. Environmental occupational exposure assessments are critical identifying disproportionately affecting vulnerable populations. effect multiple including social determinants health, highlighted driver disparities. Finally, integrating these techniques into practice can improve overall effectiveness recommends enhancing advanced methods AI prediction models identify at-risk populations target interventions better. It also advocates incorporating promote equity reduce disparities across communities.

Language: Английский

Citations

1