N-PHAM uses the latest demographic and built environment data to estimate chronic disease prevalence and physical activity. N-PHAM is intended for use by public agencies to integrate community health considerations into their transportation, land use, and community design decisions. This tool is can evaluate existing and future conditions to support all levels of community planning, including project siting, comprehensive planning, project prioritization, aspirations planning, and equity analysis.
N-PHAM’s integration of demographic and built environment factors allows estimates to be stratified by socioeconomic class and location. The tool also offers a suite of cross-sectional statistical models that estimate current and future community health and transport-related physical activity levels for states, counties, regions, cities, census tracts, or census block groups. The models rely on an extensive set of independent variables that describe demographic and built/natural environmental conditions. UD4H developed N-PHAM with partial support from the US Environmental Protection Agency’s National Health and Environmental Effects Research Lab.
Click here to explore NPHAM for Washington DC (MWCOG) region.
US Census Block Group: Population density, employment density, intersection density, road network density, land use mix, employment accessibility, transit accessibility, transit service frequency, park accessibility, tree canopy, land cover, bike network accessibility, bike services, bike/ped safety, violent crime
US Census Block Group: Population, age, income, race, family type, education, poverty level, sex, home ownership, vehicle ownership
US Census Block Group: Average BMI, % BMI>30, % BMI>25, coronary heart disease prevalence, hypertension prevalence, type 2 diabetes prevalence, depression prevalence, distress, % walk for transport, walk for transport duration, % bike for transport, bike for transport duration, % that use transit, % that use a personal vehicle, % that walk for leisure, leisure walk participation, annual cost of illness
Health outcomes are modeled using a simulation-assisted heterogeneity-based modeling framework, known as random-parameter (RP) models. This method is applied to large scale health survey data linked to built environment characteristics.
Rochester (in progress)
Chicago (in progress)
Las Vegas (in progress)