Ethanol as a potential mosquito sample storage medium for RNA preservation

Sample storage for downstream RNA analysis can be challenging in some field settings, especially where access to cryogenic materials or refrigeration/freezer facilities are limited. This has limited RNA-based studies on African malaria vectors collected in the field. We evaluated RNA quality after storing mosquito samples in three different sample preservation media over a 4-week period. Storing mosquito specimens in cold (4°C) media significantly improved yields of intact RNA. Our results indicate commercially available products perform well in keeping RNA integrity as advertised. Moreover, absolute ethanol may be an economical alternative for sample preservation that can be utilized in some resource-limited settings.

Short-term exposure to ambient ozone and stroke hospital admission: A case-crossover analysis

We evaluated the association between short-term exposure to ambient ozone air pollution and stroke hospital admissions among adult residents of South Carolina (SC). Data on all incident stroke hospitalizations from 2002 to 2006 were obtained from the SC Office of Research and Statistics. Ozone exposure data were obtained from the US Environmental Protection Agency's Hierarchical Bayesian Model. A semi-symmetric bidirectional case-crossover design was used to examine the association between ozone exposure on lag days 0-2 (0 to 2 days before admission) and stroke hospitalization. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). No significant associations were observed between short-term ozone exposure and hospitalization for all stroke (e.g., lag day 0: OR=0.98; 95% CI=0.96, 1.00) or ischemic stroke (lag day 0: OR=0.98; 95% CI=0.96, 1.01). Risk of hospitalization for hemorrhagic stroke appeared to be higher among African Americans than European Americans; however, the majority of these associations did not reach statistical significance. Among adults in SC from 2002 to 2006, there was no evidence of an association between ozone exposure and risk of hospitalization for all stroke or ischemic stroke; however, African Americans may have an increased risk of hemorrhagic stroke.

Scale effects in food environment research: Implications from assessing socioeconomic dimensions of supermarket accessibility in an eight-county region of South Carolina

Choice of neighborhood scale affects associations between environmental attributes and health-related outcomes. This phenomenon, a part of the modifiable areal unit problem, has been described fully in geography but not as it relates to food environment research. Using two administrative-based geographic boundaries (census tracts and block groups), supermarket geographic measures (density, cumulative opportunity and distance to nearest) were created to examine differences by scale and associations between three common U.S. Census-based socioeconomic status (SES) characteristics (median household income, percentage of population living below poverty and percentage of population with at least a high school education) and a summary neighborhood SES z-score in an eight-county region of South Carolina. General linear mixed-models were used. Overall, both supermarket density and cumulative opportunity were higher when using census tract boundaries compared to block groups. In analytic models, higher median household income was significantly associated with lower neighborhood supermarket density and lower cumulative opportunity using either the census tract or block group boundaries, and neighborhood poverty was positively associated with supermarket density and cumulative opportunity. Both median household income and percent high school education were positively associated with distance to nearest supermarket using either boundary definition, whereas neighborhood poverty had an inverse association. Findings from this study support the premise that supermarket measures can differ by choice of geographic scale and can influence associations between measures. Researchers should consider the most appropriate geographic scale carefully when conducting food environment studies.

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.

Where are the food deserts? An evaluation of policy-relevant measures of community food access in South Carolina

Several recent United States (US) policies target spatial access to healthier food retailers. We evaluated two measures of community food access developed by two different agencies, using a 2009 food environment validation study in South Carolina as a reference. While the US Department of Agriculture Economic Research Service's (USDA ERS) measure designated 22.5% of census tracts as food deserts, the Centers for Disease Control and Prevention's (CDC) measure designated 29.0% as non-healthier retail tracts; 71% of tracts were designated consistently between USDA ERS and CDC. Our findings suggest a need for greater harmonization of these measures of community food access.

Variation in low food access areas due to data source inaccuracies

Several spatial measures of community food access identifying so called "food deserts" have been developed based on geospatial information and commercially-available, secondary data listings of food retail outlets. It is not known how data inaccuracies influence the designation of Census tracts as areas of low access. This study replicated the U.S. Department of Agriculture Economic Research Service (USDA ERS) food desert measure and the Centers for Disease Control and Prevention (CDC) non-healthier food retail tract measure in two secondary data sources (InfoUSA and Dun & Bradstreet) and reference data from an eight-county field census covering169 Census tracts in South Carolina. For the USDA ERS food deserts measure accuracy statistics for secondary data sources were 94% concordance, 50-65% sensitivity, and 60-64% positive predictive value (PPV). Based on the CDC non-healthier food retail tracts both secondary data demonstrated 88-91% concordance, 80-86% sensitivity and 78-82% PPV. While inaccuracies in secondary data sources used to identify low food access areas may be acceptable for large-scale surveillance, verification with field work is advisable for local community efforts aimed at identifying and improving food access.

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.

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

BACKGROUND

European 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.

RESULTS

Controlling 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.

CONCLUSIONS

Our 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.

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.

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.

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

BACKGROUND

There 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).

METHODS

We 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.

RESULTS

At 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).

CONCLUSION

Fixed 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.