Daikwon Han

Positional Accuracy of Geocoded Addresses in Epidemiologic Research

Background Geographic information systems (GIS) offer powerful techniques for epidemiologists. Geocoding is an important step in the use of GIS in epidemiologic research, and the validity of epidemiologic studies using this methodology depends, in part, on the positional accuracy of the geocoding process. Methods We conducted a study comparing the validity of positions geocoded with a commercially available program to positions determined by Global Positioning System (GPS) satellite receivers. Addresses (N = 200) were randomly selected from a recently completed case–control study in Western New York State. We geocoded addresses using ArcView 3.2 on the GDT Dynamap/2000 U.S. Street database. In addition, we measured the longitude and latitude of these addresses with a GPS receiver. The distance between the locations obtained by these two methods was calculated for all addresses. Results The distance between the geocoded point and the GPS point was within 100 m for the majority of subject addresses (79%), with only a small proportion (3%) having a distance greater than 800 m. The overall median distance between GPS points and geocoded points was 38 m (90% confidence interval [CI] = 34–46). Distances were not different for cases and controls. Urban addresses (median = 32 m; CI = 28–37) were slightly more accurate than nonurban addresses (median = 52 m; CI = 44–61). Conclusions. This study indicates that the suitability of geocoding for epidemiologic research depends on the level of spatial resolution required to assess exposure. Although sources of error in positional accuracy for geocoded addresses exist, geocoding of addresses is, for the most part, very accurate.

Association between neighborhood need and spatial access to food stores and fast food restaurants in neighborhoods of Colonias

ObjectiveTo determine the extent to which neighborhood needs (socioeconomic deprivation and vehicle availability) are associated with two criteria of food environment access: 1) distance to the nearest food store and fast food restaurant and 2) coverage (number) of food stores and fast food restaurants within a specified network distance of neighborhood areas of colonias, using ground-truthed methods.MethodsData included locational points for 315 food stores and 204 fast food restaurants, and neighborhood characteristics from the 2000 U.S. Census for the 197 census block group (CBG) study area. Neighborhood deprivation and vehicle availability were calculated for each CBG. Minimum distance was determined by calculating network distance from the population-weighted center of each CBG to the nearest supercenter, supermarket, grocery, convenience store, dollar store, mass merchandiser, and fast food restaurant. Coverage was determined by calculating the number of each type of food store and fast food restaurant within a network distance of 1, 3, and 5 miles of each population-weighted CBG center. Neighborhood need and access were examined using Spearman ranked correlations, spatial autocorrelation, and multivariate regression models that adjusted for population density.ResultsOverall, neighborhoods had best access to convenience stores, fast food restaurants, and dollar stores. After adjusting for population density, residents in neighborhoods with increased deprivation had to travel a significantly greater distance to the nearest supercenter or supermarket, grocery store, mass merchandiser, dollar store, and pharmacy for food items. The results were quite different for association of need with the number of stores within 1 mile. Deprivation was only associated with fast food restaurants; greater deprivation was associated with fewer fast food restaurants within 1 mile. CBG with greater lack of vehicle availability had slightly better access to more supercenters or supermarkets, grocery stores, or fast food restaurants. Increasing deprivation was associated with decreasing numbers of grocery stores, mass merchandisers, dollar stores, and fast food restaurants within 3 miles.ConclusionIt is important to understand not only the distance that people must travel to the nearest store to make a purchase, but also how many shopping opportunities they have in order to compare price, quality, and selection. Future research should examine how spatial access to the food environment influences the utilization of food stores and fast food restaurants, and the strategies used by low-income families to obtain food for the household.

Does Distance Matter? Distance to Mammography Facilities and Stage at Diagnosis of Breast Cancer in Kentucky.

BACKGROUND National and regional data indicate that breast cancer early detection is low in Kentucky, especially rural regions, perhaps because access to mammography services can be problematic. OBJECTIVE This study examined the distance between residences of women diagnosed with breast cancer and the nearest mammography facility, as a risk factor for advanced stage diagnosis in rural populations. METHODS 1999-2003 Kentucky Cancer Registry data were used for this study. A total of 12,322 women, aged 40 and older at diagnosis, with no previous history of cancer, and with known cancer stage were included. Travel distance was obtained using a geographic information system (GIS). Hierarchical logistic regression models were used to analyze the relationship between travel distance and advanced stage diagnosis. RESULTS Advanced diagnoses had longer average travel distances than early stage diagnoses (P < 0.01). After adjusting for age, race, insurance, and education at census tract level, the odds of advanced diagnosis were significantly greater for women residing over 15 miles from a facility, compared to those living within 5 miles (adjusted OR = 1.50, 95% CI = 1.25-1.80). CONCLUSION Although socioeconomic status, race, and age may help explain advanced diagnoses, longer travel distance also adversely affects early detection for rural populations. Accurate measurement of spatial accessibility indicators, such as travel distance, facilitates identification of at-risk groups so that interventions can be developed to reduce this disease.

Lifetime adult weight gain, central adiposity, and the risk of pre- and postmenopausal breast cancer in the Western New York exposures and breast cancer study

While there are quite consistent data regarding associations of body weight and postmenopausal breast cancer, there are now accumulating data that would indicate that weight gain in adult life is more predictive of risk than absolute body weight. There is, however, little known about the relative impact of timing of weight gain in adult life as well as other characteristics of the weight and breast cancer association that might provide insight into the mechanism of the observation. We conducted a population‐based case control study of breast cancer (1996–2001), the Western New York Exposures and Breast Cancer Study. Included were 1,166 women with primary, histologically confirmed, incident breast cancer and 2,105 controls frequency‐matched on age, race and county of residence. Unconditional logistic regression was used to estimate odds ratios and 95% confidence intervals. We found increased risk of breast cancer associated with lifetime adult weight gain among post‐ but not premenopausal women, and there was a 4% increase in risk for each 5 kg increase in adult weight. Further there was a tendency toward a stronger association for those with higher waist circumference and those with positive estrogen or progesterone status, and who had never used HRT. We also found an association with risk for weight gain since first pregnancy and for weight gain between the time of the first pregnancy and menopause, independent of body mass index and lifetime adult weight gain. Our results suggest that there are time periods of weight gain that have greater impact on risk, and that central body fat, receptor status and hormone replacement therapy may all affect the observed association.

The effects of migration on the detection of geographic differences in disease risk

Human migration can make it more difficult to detect geographic differences in disease risk because of the spatial diffusion of people originally exposed in a given geographic area. There are also situations where migration can facilitate the detection of disease attributable to environmental hazards. This paper assesses the effects that migration has on the ability to detect regional variability in disease risk. Several characteristics of migration are discussed, including some that are not widely known. Because of regional variations in mobility rates and other characteristics of the migration process, there is substantial regional variation in the ability to detect spatial variation in risk.

Assessing spatio-temporal variability of risk surfaces using residential history data in a case control study of breast cancer.

BackgroundMost analyses of spatial clustering of disease have been based on either residence at the time of diagnosis or current residence. An underlying assumption in these analyses is that residence can be used as a proxy for environmental exposure. However, exposures earlier in life and not just those in the most recent period may be of significance. In breast cancer, there is accumulating evidence that early life exposures may contribute to risk. We explored spatio-temporal patterns of risk surfaces using data on lifetime residential history in a case control study of breast cancer, and identified elevated areas of risk and areas potentially having more exposure opportunities, defined as risk surfaces in this study. This approach may be more relevant in understanding the environmental etiology of breast cancer, since lifetime cumulative exposures or exposures at critical times may be more strongly associated with risk for breast cancer than exposures from the recent period.ResultsA GIS-based exploratory spatial analysis was applied, and spatio-temporal variability of those risk surfaces was evaluated using the standardized difference in density surfaces between cases and controls. The significance of the resulting risk surfaces was tested and reported as p-values. These surfaces were compared for premenopausal and postmenopausal women, and were obtained for each decade, from the 1940s to 1990s. We found strong evidence of clustering of lifetime residence for premenopausal women (for cases relative to controls), and a less strong suggestion of such clustering for postmenopausal women, and identified a substantial degree of temporal variability of the risk surfaces.ConclusionWe were able to pinpoint geographic areas with higher risk through exploratory spatial analyses, and to assess temporal variability of the risk surfaces, thus providing a working hypothesis on breast cancer and environmental exposures. Geographic areas with higher case densities need further epidemiologic investigation for potential relationships between lifetime environmental exposures and breast cancer risk. Examination of lifetime residential history provided additional information on geographic areas associated with higher risk; limiting exploration of chronic disease clustering to current residence may neglect important relationships between location and disease.

Geographic clustering of residence in early life and subsequent risk of breast cancer (United States)

ObjectiveThis study focused on geographic clustering of breast cancer based on residence in early life and identified spatio-temporal clustering of cases and controls. Methods: Data were drawn from the WEB study (Western New York Exposures and Breast Cancer Study), a population-based case–control study of incident, pathologically confirmed breast cancer (1996–2001) in Erie and Niagara counties. Controls were frequency-matched to cases on age, race, and county of residence. All cases and controls used in the study provided lifetime residential histories. The k-function difference between cases and controls was used to identify spatial clustering patterns of residence in early life. Results: We found that the evidence for clustered residences at birth and at menarche was stronger than that for first birth or other time periods in adult life. Residences for pre-menopausal cases were more clustered than for controls at the time of birth and menarche. We also identified the size and geographic location of birth and menarche clusters in the study area, and found increased breast cancer risk for pre-menopausal women whose residence was within the cluster compared to those living elsewhere at the time of birth. Conclusion: This study provides evidence that early environmental exposures may be related to breast cancer risk, especially for pre-menopausal women.

Farm Management, Environment, and Weather Factors Jointly Affect the Probability of Spinach Contamination by Generic Escherichia coli at the Preharvest Stage

ABSTRACT The National Resources Information (NRI) databases provide underutilized information on the local farm conditions that may predict microbial contamination of leafy greens at preharvest. Our objective was to identify NRI weather and landscape factors affecting spinach contamination with generic Escherichia coli individually and jointly with farm management and environmental factors. For each of the 955 georeferenced spinach samples (including 63 positive samples) collected between 2010 and 2012 on 12 farms in Colorado and Texas, we extracted variables describing the local weather (ambient temperature, precipitation, and wind speed) and landscape (soil characteristics and proximity to roads and water bodies) from NRI databases. Variables describing farm management and environment were obtained from a survey of the enrolled farms. The variables were evaluated using a mixed-effect logistic regression model with random effects for farm and date. The model identified precipitation as a single NRI predictor of spinach contamination with generic E. coli, indicating that the contamination probability increases with an increasing mean amount of rain (mm) in the past 29 days (odds ratio [OR] = 3.5). The model also identified the farms hygiene practices as a protective factor (OR = 0.06) and manure application (OR = 52.2) and state (OR = 108.1) as risk factors. In cross-validation, the model showed a solid predictive performance, with an area under the receiver operating characteristic (ROC) curve of 81%. Overall, the findings highlighted the utility of NRI precipitation data in predicting contamination and demonstrated that farm management, environment, and weather factors should be considered jointly in development of good agricultural practices and measures to reduce produce contamination.

Multifactorial effects of ambient temperature, precipitation, farm management, and environmental factors determine the level of generic Escherichia coli contamination on preharvested spinach.

ABSTRACT A repeated cross-sectional study was conducted to identify farm management, environment, weather, and landscape factors that predict the count of generic Escherichia coli on spinach at the preharvest level. E. coli was enumerated for 955 spinach samples collected on 12 farms in Texas and Colorado between 2010 and 2012. Farm management and environmental characteristics were surveyed using a questionnaire. Weather and landscape data were obtained from National Resources Information databases. A two-part mixed-effect negative binomial hurdle model, consisting of a logistic and zero-truncated negative binomial part with farm and date as random effects, was used to identify factors affecting E. coli counts on spinach. Results indicated that the odds of a contamination event (non-zero versus zero counts) vary by state (odds ratio [OR] = 108.1). Odds of contamination decreased with implementation of hygiene practices (OR = 0.06) and increased with an increasing average precipitation amount (mm) in the past 29 days (OR = 3.5) and the application of manure (OR = 52.2). On contaminated spinach, E. coli counts increased with the average precipitation amount over the past 29 days. The relationship between E. coli count and the average maximum daily temperature over the 9 days prior to sampling followed a quadratic function with the highest bacterial count at around 24°C. These findings indicate that the odds of a contamination event in spinach are determined by farm management, environment, and weather factors. However, once the contamination event has occurred, the count of E. coli on spinach is determined by weather only.

Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach

There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters.