Inspiration

Extensive population based health data is being collected nationwide, however, there is a need to efficiently process and present this data before it can be used to improve lives.

What it does

"PlacePredicts!" provides information on the health/disease trends together with information on the demographics and the environment of the patient’s/client’s specific ZIP code. This resource can be accessed through medical health records (MHRs) by the practitioner, allowing them to integrate these factors before reaching medical decisions. Understanding physiology in the context of one's socio-environmental landscape, can help reduce health disparities by providing better diagnosis and treatment plans.

How we built it

Using publicly available databases, provided by CDC and EPA, we integrated county level data regarding variables that code for environmental conditions and disease prevalence. The detailed information regarding the functioning of the prototype is available in the attached file.

Challenges we ran into

ZIP code level data is not publicly available due to data privacy. Given the short time span, we were not able to request for these RDC files

Accomplishments that we're proud of

We were able to model our idea using county level data by including environmental variables from national databases administered by Center of Disease Control and Prevention (CDC) and Environmental Protection Agency (EPA). This model is also able to provide relevant warnings to the practitioner based on the comparison with national estimates.

What we learned

Geographical tags are packed with valuable information regrading social, political, demographic and environmental factors. They can serve as a potential determinant of individual health, more accurate and precise than national/state level health indicators.

What's next for PlacePredicts!

The next step would be to go through the required approval process and gain access to ZIP-code level data. Using, this data, we want to include a greater number of variables that can precisely capture the health landscape in each ZIP code, while focusing on the socio-environmental determinants of health. Integrating it in existing MHRs is the ultimate goal that we want to achieve.

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