James Hibbert

Validation of 3 Food Outlet Databases: Completeness and Geospatial Accuracy in Rural and Urban Food Environments

Despite interest in the built food environment, little is known about the validity of commonly used secondary data. The authors conducted a comprehensive field census identifying the locations of all food outlets using a handheld global positioning system in 8 counties in South Carolina (2008–2009). Secondary data were obtained from 2 commercial companies, Dun & Bradstreet, Inc. (D&B) (Short Hills, New Jersey) and InfoUSA, Inc. (Omaha, Nebraska), and the South Carolina Department of Health and Environmental Control (DHEC). Sensitivity, positive predictive value, and geospatial accuracy were compared. The field census identified 2,208 food outlets, significantly more than the DHEC (n = 1,694), InfoUSA (n = 1,657), or D&B (n = 1,573). Sensitivities were moderate for DHEC (68%) and InfoUSA (65%) and fair for D&B (55%). Combining InfoUSA and D&B data would have increased sensitivity to 78%. Positive predictive values were very good for DHEC (89%) and InfoUSA (86%) and good for D&B (78%). Geospatial accuracy varied, depending on the scale: More than 80% of outlets were geocoded to the correct US Census tract, but only 29%–39% were correctly allocated within 100 m. This study suggests that the validity of common data sources used to characterize the food environment is limited. The marked undercount of food outlets and the geospatial inaccuracies observed have the potential to introduce bias into studies evaluating the impact of the built food environment.

Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study.

BackgroundEuropean ecologic studies suggest higher socioeconomic status is associated with higher incidence of type 1 diabetes. Using data from a case-control study of diabetes among racially/ethnically diverse youth in the United States (U.S.), we aimed to evaluate the independent impact of neighborhood characteristics on type 1 diabetes risk. Data were available for 507 youth with type 1 diabetes and 208 healthy controls aged 10-22 years recruited in South Carolina and Colorado in 2003-2006. Home addresses were used to identify Census tracts of residence. Neighborhood-level variables were obtained from 2000 U.S. Census. Multivariate generalized linear mixed models were applied.ResultsControlling for individual risk factors (age, gender, race/ethnicity, infant feeding, birth weight, maternal age, number of household residents, parental education, income, state), higher neighborhood household income (p = 0.005), proportion of population in managerial jobs (p = 0.02), with at least high school education (p = 0.005), working outside the county (p = 0.04) and vehicle ownership (p = 0.03) were each independently associated with increased odds of type 1 diabetes. Conversely, higher percent minority population (p = 0.0003), income from social security (p = 0.002), proportion of crowded households (0.0497) and poverty (p = 0.008) were associated with a decreased odds.ConclusionsOur study suggests that neighborhood characteristics related to greater affluence, occupation, and education are associated with higher type 1 diabetes risk. Further research is needed to understand mechanisms underlying the influence of neighborhood context.

Characterizing the food retail environment: Impact of count, type, and geospatial error in 2 secondary data sources

OBJECTIVE Commercial listings of food retail outlets are increasingly used by community members and food policy councils and in multilevel intervention research to identify areas with limited access to healthier food. This study quantified the amount of count, type, and geospatial error in 2 commercial data sources. METHODS InfoUSA and Dun and Bradstreet were compared with a validated field census and validity statistics were calculated. RESULTS Considering only completeness, Dun and Bradstreet data undercounted 24% of existing supermarkets and grocery stores, and InfoUSA, 29%. In addition, considering accuracy of outlet type assignment increased the undercount error to 42% and 39%, respectively. Marked overcount existed as well, and only 43% of existing supermarkets were correctly identified with respect to presence, outlet type, and location. CONCLUSIONS AND IMPLICATIONS Relying exclusively on secondary data to characterize the food environment will result in substantial error. Whereas extensive data cleaning can offset some error, verification of outlets with a field census is still the method of choice.

Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions

We evaluated geographic variation in type 1 and type 2 diabetes mellitus (T1DM, T2DM) in four regions of the United States. Data on 807 incident T1DM cases diabetes and 313 T2DM cases occurring in 2002-03 in South Carolina (SC) and Colorado (CO), 5 counties in Washington (WA), and an 8 county region around Cincinnati, Ohio (OH) among youth aged 10-19 years were obtained from the SEARCH for Diabetes in Youth Study. Geographic patterns were evaluated in a Bayesian framework. Incidence rates differed between the study regions, even within race/ethnic groups. Significant small-area variation within study region was observed for T1DM and T2DM. Evidence for joint spatial correlation between T1DM and T2DM was present at the county level for SC (r(SC)=0.31) and CO non-Hispanic Whites (r(CO)=0.40) and CO Hispanics (r(CO)=0.72). At the tract level, no evidence for meaningful joint spatial correlation was observed (r(SC)=-0.02; r(CO)=-0.02; r(OH)=0.03; and r(WA=)0.09). Our study provides evidence for the presence of both regional and small area, localized variation in type 1 and type 2 incidence among youth aged 10-19 years in the United States.

Adolescent self-defined neighborhoods and activity spaces: spatial overlap and relations to physical activity and obesity.

Defining the proper geographic scale for built environment exposures continues to present challenges. In this study, size attributes and exposure calculations from two commonly used neighborhood boundaries were compared to those from neighborhoods that were self-defined by a sample of 145 urban minority adolescents living in subsidized housing estates. Associations between five built environment exposures and physical activity, overweight and obesity were also examined across the three neighborhood definitions. Limited spatial overlap was observed across the various neighborhood definitions. Further, many places where adolescents were active were not within the participants׳ neighborhoods. No statistically significant associations were found between counts of facilities and the outcomes based on exposure calculations using the self-defined boundaries; however, a few associations were evident for exposures using the 0.75mile network buffer and census tract boundaries. Future investigation of the relationship between the built environment, physical activity and obesity will require practical and theoretically-based methods for capturing salient environmental exposures.

Environmental influences on fruit and vegetable intake: results from a path analytic model.

OBJECTIVE Fruit and vegetable (F&V) intake is influenced by behavioural and environmental factors, but these have rarely been assessed simultaneously. We aimed to quantify the relative influence of supermarket availability, perceptions of the food environment and shopping behaviour on F&V intake. DESIGN A cross-sectional study. SETTING Eight counties in South Carolina, USA, with verified locations of all supermarkets. SUBJECTS A telephone survey of 831 household food shoppers ascertained F&V intake with a seventeen-item screener, primary food store location, shopping frequency and perceptions of healthy food availability, and supermarket availability was calculated with a geographic information system. Path analysis was conducted. We report standardized beta coefficients on paths significant at the 0·05 level. RESULTS Frequency of grocery shopping at primary food store (β = 0·11) was the only factor exerting an independent, statistically significant direct effect on F&V intake. Supermarket availability was significantly associated with distance to utilized food store (β = -0·24) and shopping frequency (β = 0·10). Increased supermarket availability was significantly and positively related to perceived healthy food availability in the neighbourhood (β = 0·18) and ease of shopping access (β = 0·09). Collectively considering all model paths linked to perceived availability of healthy foods, this measure was the only other factor to have a significant total effect on F&V intake. CONCLUSIONS While the majority of the literature to date has suggested an independent and important role of supermarket availability for F&V intake, our study found only indirect effects of supermarket availability and suggests that food shopping frequency and perceptions of healthy food availability are two integral components of a network of influences on F&V intake.

Evaluating geographic imputation approaches for zip code level data: an application to a study of pediatric diabetes

BackgroundThere is increasing interest in the study of place effects on health, facilitated in part by geographic information systems. Incomplete or missing address information reduces geocoding success. Several geographic imputation methods have been suggested to overcome this limitation. Accuracy evaluation of these methods can be focused at the level of individuals and at higher group-levels (e.g., spatial distribution).MethodsWe evaluated the accuracy of eight geo-imputation methods for address allocation from ZIP codes to census tracts at the individual and group level. The spatial apportioning approaches underlying the imputation methods included four fixed (deterministic) and four random (stochastic) allocation methods using land area, total population, population under age 20, and race/ethnicity as weighting factors. Data included more than 2,000 geocoded cases of diabetes mellitus among youth aged 0-19 in four U.S. regions. The imputed distribution of cases across tracts was compared to the true distribution using a chi-squared statistic.ResultsAt the individual level, population-weighted (total or under age 20) fixed allocation showed the greatest level of accuracy, with correct census tract assignments averaging 30.01% across all regions, followed by the race/ethnicity-weighted random method (23.83%). The true distribution of cases across census tracts was that 58.2% of tracts exhibited no cases, 26.2% had one case, 9.5% had two cases, and less than 3% had three or more. This distribution was best captured by random allocation methods, with no significant differences (p-value > 0.90). However, significant differences in distributions based on fixed allocation methods were found (p-value < 0.0003).ConclusionFixed imputation methods seemed to yield greatest accuracy at the individual level, suggesting use for studies on area-level environmental exposures. Fixed methods result in artificial clusters in single census tracts. For studies focusing on spatial distribution of disease, random methods seemed superior, as they most closely replicated the true spatial distribution. When selecting an imputation approach, researchers should consider carefully the study aims.

An evaluation of edge effects in nutritional accessibility and availability measures: a simulation study

BackgroundThis paper addresses the statistical use of accessibility and availability indices and the effect of study boundaries on these measures. The measures are evaluated via an extensive simulation based on cluster models for local outlet density. We define outlet to mean either food retail store (convenience store, supermarket, gas station) or restaurant (limited service or full service restaurants). We designed a simulation whereby a cluster outlet model is assumed in a large study window and an internal subset of that window is constructed. We performed simulations on various criteria including one scenario representing an urban area with 2000 outlets as well as a non-urban area simulated with only 300 outlets. A comparison is made between estimates obtained with the full study area and estimates using only the subset area. This allows the study of the effect of edge censoring on accessibility measures.ResultsThe results suggest that considerable bias is found at the edges of study regions in particular for accessibility measures. Edge effects are smaller for availability measures (when not smoothed) and also for short range accessibilityConclusionsIt is recommended that any study utilizing these measures should correct for edge effects. The use of edge correction via guard areas is recommended and the avoidance of large range distance-based accessibility measures is also proposed.

Spatial accessibility and availability measures and statistical properties in the food environment.

Spatial accessibility is of increasing interest in the health sciences. This paper addresses the statistical use of spatial accessibility and availability indices. These measures are evaluated via an extensive simulation based on cluster models for local food outlet density. We derived Monte Carlo critical values for several statistical tests based on the indices. In particular we are interested in the ability to make inferential comparisons between different study areas where indices of accessibility and availability are to be calculated. We derive tests of mean difference as well as tests for differences in Morans I for spatial correlation for each of the accessibility and availability indices. We also apply these new statistical tests to a data example based on two counties in South Carolina for various accessibility and availability measures calculated for food outlets, stores, and restaurants.

Travel distance and sociodemographic correlates of potentially avoidable emergency department visits in California, 2006–2010: an observational study

IntroductionUse of the hospital emergency department (ED) for medical conditions not likely to require immediate treatment is a controversial topic. It has been faulted for ED overcrowding, increased expenditures, and decreased quality of care. On the other hand, such avoidable ED utilization may be a manifestation of barriers to primary care access.MethodsA random 10% subsample of all ED visits with unmasked variables, or approximately 7.2% of all ED visits in California between 2006 and 2010 are used in the analysis. Using panel data methods, we employ linear probability and fractional probit models with hospital fixed effects to analyze the associations between avoidable ED utilization in California and observable patient characteristics. We also test whether shorter estimated road distances to the hospital ED are correlated with non-urgent ED utilization, as defined by the New York University ED Algorithm. We then investigate whether proximity of a Federally Qualified Health Center (FQHC) is correlated with reductions in non-urgent ED utilization among Medicaid patients.ResultsWe find that relative to the reference group of adults aged 35–64, younger patients generally have higher scores for non-urgent conditions and lower scores for urgent conditions. However, elderly patients (≥65) use the ED for conditions more likely to be urgent. Relative to male and white patients, respectively, female patients and all identified racial and ethnic minorities use the ED for conditions more likely to be non-urgent. Patients with non-commercial insurance coverage also use the ED for conditions more likely to be non-urgent. Medicare and Medicaid patients who live closer to the hospital ED have higher probability scores for non-emergent visits. However, among Medicaid enrollees, those who live in zip codes with an FQHC within 0.5 mile of the zip code population centroid visit the ED for medical conditions less likely to be non-emergent.ConclusionsThese patterns of ED utilization point to potential barriers to care among historically vulnerable groups, observable even when using rough estimates of travel distances and avoidable ED utilization.