Intersecting global health metrics with personal change

What is it about?

Changing poor health behaviors is a persistent challenge to both practitioner and patient. We rely on aggregated data on health behaviors and metabolic risk indicators to measure health status and point to likely mortality. However, the scale of empirical evidence focusing on high-profile global disease indicators may lead to less than optimal care when local populations are experiencing indicators with less widespread concern. The multilevel scale of data in case studies here demonstrates variations in important trends, giving rise to methods that merge the institutional and social responsibility of data collection and analysis with healthcare and individual accountability. Repeatable analysis incorporating global trends with individual characteristics is one way to translate aggregated health data to predict mortality susceptibility in individual patients. We suggest that statistical modeling of health behaviors intersecting global health metrics with personal change objectives requires new methods of collecting, analyzing, and applying local knowledge from patient history.

Featured Image

Photo by National Cancer Institute on Unsplash

Photo by National Cancer Institute on Unsplash

Why is it important?

Today, we have access to a tremendous amount of data, much of which is compiled across a wide variety of people groups. Instead, we suggest that looking at data at the local level for local solutions can best suit immediate care needs. While aggregated data is valuable across national indicators, localized data can be more useful in direct patient care.

Perspectives