The Behavioral Risk Factor Surveillance System (BRFSS) is an annual statewide random telephone and cellular surveillance survey designed by the Centers for Disease Control and Prevention (CDC). The survey is conducted in all 50 states and US territories. BRFSS monitors modifiable risk behaviors and other factors contributing to the leading causes of morbidity and mortality in the population. Data from the BRFSS are useful for planning, initiating, and supporting health promotion and disease prevention programs at the state and federal level, and monitoring progress toward achieving health objectives for the state and nation.

Our modeling approach uses spatial microsimulation techniques (iterative proportional fitting) to identify COVID-19 associated health factors for specified counties. Our modeling framework utilizes BRFSS (Behavior Risk Factor Surveillance System) survey questions (heart disease, obesity, diabetes, smoking, depression, pulmonary disease, kidney disease) in combination with American Community Survey (ACS) Census data. This approach uses county-level demographics to “downscale” estimations of health variables from a regional view to a county view.

Several advantages can be gained from our approach. This methodology allows for validation of results within the model, as well as externally – in comparison to other datasets. Additionally, our approach is modular, in that it can be applied to all counties in the state of Idaho – or other states. Finally, our methodology uses statistical testing approaches to ensure that we choose the best constraining variables (race, income, age, sex, education) to model these variables in differing Idaho counties.

The American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation.

For this dashboard, we are presenting the following ACS data (2021: ACS 5-Year Estimates) for comparison purposes against various health risk indicators:

• Age - Percent of population under age 18, and ages 15 to 17
• Disability Status - Percent having a disability
• Educational Attainment - Percent with No High School Diploma, ages 18-24 and 25+
• Employment - Unemployment rate, ages 16 and above
• Ethnicity - Percent of population Hispanic or Latino
• Health Insurance Coverage - Percent of population uninsured, ages 19 to 64 years
• Limited English Speaking Households - Percent limited English-speaking households
• Poverty Status - Percent of population living below poverty level, ages 18-64 and 65+
• Veteran Status - Percent veterans, civilian population 18 years and over

Relative error is a measure of the uncertainty of measurement compared to the size of the measurement. Higher percentages of relative error indicate greater uncertainty in the results.

Margin of error is the degree of error in results received from random sampling surveys. The higher the margin of error, the lower the confidence in the results.

Using our downscaled county-level variables from the Idaho Behavioral Risk Factors Surveillance System (BRFSS) from 2020 and 2021, we have constructed a number of predictive models to evaluate health factor relationships to COVID-19 deaths. Figure 1 shows the change of COVID-19 deaths (adjusted for population) from 2020 to 2021.

Figure 1. Change in COVID-19 deaths from 2020 to 2021, adjusted for population.

The results of our best performing model (fixed effects regression with time effects) suggest that the following variables are the most influential in predicting COVID-19 deaths:

Stroke
Diabetes
Obesity
Getting a flu shot
Heart disease
Heavy drinking
Pulmonary Disease

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Proportion of variation of COVID-19 deaths that is predictable from the 30+ health outcomes (adjusted R2):
76%