Home Value and ZIP Code Predict Obesity Rates

SEATTLE, Aug. 30 -- ZIP codes and property values will provide a better handle on the scope of the nation's obesity problem than income, race, or education, investigators here found.

Property values proved to be the best predictor of obesity prevalence, , Adam Drewnowski, Ph.D., of the University of Washington here, and colleagues reported online and in the September issue of Social Science & Medicine.

And, within a given ZIP code, each ,000 increase in median home value was associated with a 2% decline in obesity prevalence (P

The current study examined obesity rates in King County, Wash., by ZIP codes. The primary objective was to identify any associations between obesity and area-based measures of race/ethnicity, income, poverty, and property values.

Data for the study came from the CDC-sponsored behavioral risk factor surveillance system, a random telephone survey of adults in all 50 states. Survey responses included self-reported height and weight, which were used to calculate BMI. A cutoff of 30 was used to define obesity.

The final analysis was based on 74 ZIP code areas comprising 8,803 residents of King County. The number of respondents per ZIP code ranged from 13 to 275. The authors noted that 95% of King County residents live within the ZIP codes included in the study.

Crude obesity rates by ZIP code ranged from 5% to 30%, and adjusted rates ranged from 10% to 25%. The median value of owner-occupied housing units and the Hispanic proportion of the population were the two strongest predictors of obesity prevalence (P

The authors noted several limitations of the study:

• Heights and weights, used to calculate BMI values, were based on telephone self-report. Both men and women under-report weight, and men may over-report height in telephone surveys.

• The ZIP code area is a problematic scale for spatial analysis. Because population counts per ZIP code area can vary widely, many ZIP code areas were too small to provide area-based prevalence estimates.

• Potential sources of bias in state-specific data include low response rates and non-response biases within certain demographic groups. Such biases

may also occur at the neighborhood level, which might make the present population samples nonrepresentative.