Using Small-Area Estimation to Calculate the Prevalence of Smoking by Subcounty Geographic Areas in King County, Washington, Behavioral Risk Factor Surveillance System, 2009-2013.

INTRODUCTION: King County, Washington, fares well overall in many health indicators. However, county-level data mask disparities among subcounty areas. For disparity-focused assessment, a demand exists for examining health data at subcounty levels such as census tracts and King County health reporting areas (HRAs). METHODS: We added a "nearest intersection" question to the Behavioral Risk Factor Surveillance System (BRFSS) and geocoded the data for subcounty geographic areas, including census tracts. To overcome small sample size at the census tract level, we used hierarchical Bayesian models to obtain smoothed estimates in cigarette smoking rates at the census tract and HRA levels. We also used multiple imputation to adjust for missing values in census tracts. RESULTS: Direct estimation of adult smoking rates at the census tract level ranged from 0% to 56% with a median of 10%. The 90% confidence interval (CI) half-width for census tract with nonzero rates ranged from 1 percentage point to 37 percentage points with a median of 13 percentage points. The smoothed-multiple-imputation rates ranged from 5% to 28% with a median of 12%. The 90% CI half-width ranged from 4 percentage points to 13 percentage points with a median of 8 percentage points. CONCLUSION: The nearest intersection question in the BRFSS provided geocoded data at subcounty levels. The Bayesian model provided estimation with improved precision at the census tract and HRA levels. Multiple imputation can be used to account for missing geographic data. Small-area estimation, which has been used for King County public health programs, has increasingly become a useful tool to meet the demand of presenting data at more granular levels.

Effective community health assessments in King County, Washington.

BACKGROUND: Community health assessment (CHA) is widely practiced in public health, but its effectiveness has seldom been evaluated. METHOD: We present three examples of successful CHAs, carried out by Public Health-Seattle & King County, with diverse strategies: a quantitative assessment of asthma hospitalizations; Communities Count, a set of social and health indicators paired with qualitative data; and Growing Up Healthy, an assessment using qualitative methods to provide guidance for a statewide media campaign on youth sexual abstinence. FINDINGS: These assessments were successful in attracting new resources, forming and sustaining new partnerships, and/or providing guidance or resources for program and policy development. They also illustrate the difficulties of evaluating the effects of CHA in at least three ways: untangling its effects from other important community and political factors; documenting outcomes that are distant in time from and indirectly related to the assessment; and cultural or political restrictions on collecting sensitive evaluation data. We suggest common characteristics of an effective assessment, potential effectiveness indicators, and evaluation strategies. CONCLUSIONS: Despite barriers to documenting the relative contribution of a CHA, a set of rigorous evaluation methods needs to be developed and tested to document the benefits of a CHA in a competitive funding environment.

Disparities in obesity rates: analysis by ZIP code area.

Obesity in the United States has been linked to individual income and education. Less is known about its geographic distribution. The goal of this study was to determine whether obesity rates in King County, Washington State, at the ZIP code scale were associated with area-based measures of socioeconomic status and wealth. Data from the Behavioral Risk Factor Surveillance System were analyzed. At the ZIP code scale, crude obesity rates varied six-fold. In a model adjusting for covariates and spatial dependence, property values were the strongest predictor of the area-based smoothed obesity prevalence. Geocoding of health data provides new insights into the nature of social determinants of health. Disparities in obesity rates by ZIP code area were greater than disparities associated with individual income or race/ethnicity.

Menu labeling regulations and calories purchased at chain restaurants.

BACKGROUND: The federal menu labeling law will require chain restaurants to post caloric information on menus, but the impact of labeling is uncertain. PURPOSE: The goal of the current study was to examine the effect of menu labeling on calories purchased, and secondarily, to assess self-reported awareness and use of labels. DESIGN: Single-community pre-post-post cross-sectional study. Data were collected in 2008-2010 and analyzed in 2011-2012. SETTING/PARTICIPANTS: 50 sites from 10 chain restaurants in King County, Washington, selected through stratified, two-stage cluster random sampling. A total of 7325 customers participated. Eligibility criteria were: being an English speaker, aged ≥ 14 years, and having an itemized receipt. The study population was 59% male, 76% white non-Hispanic, and 53% aged<40 years. INTERVENTION: A regulation requiring chain restaurants to post calorie information on menus or menu boards was implemented. MAIN OUTCOME MEASURES: Mean number of calories purchased. RESULTS: No significant changes occurred between baseline and 4-6 months postregulation. Mean calories per purchase decreased from 908.5 to 870.4 at 18 months post-implementation (38 kcal, 95% CI=-76.9, 0.8, p=0.06) in food chains and from 154.3 to 132.1 (22 kcal, 95% CI=-35.8, -8.5, p=0.002) in coffee chains. Calories decreased in taco and coffee chains, but not in burger and sandwich establishments. They decreased more among women than men in coffee chains. Awareness of labels increased from 18.8% to 61.7% in food chains and from 4.4% to 30.0% in coffee chains (both p<0.001). Among customers seeing calorie information, the proportion using it (about one third) did not change substantially over time. After implementation, food chain customers using information purchased on average fewer calories compared to those seeing but not using (difference=143.2 kcal, p<0.001) and those not seeing (difference=135.5 kcal, p<0.001) such information. CONCLUSIONS: Mean calories per purchase decreased 18 months after implementation of menu labeling in some restaurant chains and among women but not men.

Innovations in user-defined analysis: dynamic grouping and customized user datasets in VistaPHw.

Flexible, ready access to community health assessment data is a feature of innovative Web-based data query systems. An example is VistaPHw, which provides access to Washington state data and statistics used in community health assessment. Because of its flexible analysis options, VistaPHw customizes local, population-based results to be relevant to public health decision-making. The advantages of two innovations, dynamic grouping and the Custom Data Module, are described. Dynamic grouping permits the creation of user-defined aggregations of geographic areas, age groups, race categories, and years. Standard VistaPHw measures such as rates, confidence intervals, and other statistics may then be calculated for the new groups. Dynamic grouping has provided data for major, successful grant proposals, building partnerships with local governments and organizations, and informing program planning for community organizations. The Custom Data Module allows users to prepare virtually any dataset so it may be analyzed in VistaPHw. Uses for this module may include datasets too sensitive to be placed on a Web server or datasets that are not standardized across the state. Limitations and other system needs are also discussed.