Assessment of asthma-prone environment in Karachi, Pakistan using GIS modeling.

OBJECTIVE: To determine the association among number of factors influenced by asthma using geographic information system. METHODS: The cross-sectional study was conducted in Landhi and Korangi towns of Karachi from 2011 to 2013, and comprised ecological mapping and multi-criteria evaluation techniques to discover the relationship of local environmental settings with asthma. Additionally, exacerbating environment and the root causes within the local settings were assessed. Data was gathered using an extended version of the questionnaire developed by the International Union against Tuberculosis and Lung Disease. Data was analysed by using ArcGIS 10. RESULTS: The findings are very alarming as almost 40% (468,930 estimated pop 1998 census) of the study population lived in high asthma-prone environment, having a very high risk of respiratory disorders, including asthma. CONCLUSIONS: The integrated environmental effect in the form of respiratory disorders was appraised, focusing on asthma by using multi-criteria analysis.

Lifetime exposure to ambient air pollution and methylation of tumor suppressor genes in breast tumors.

BACKGROUND: We previously reported increased risk of breast cancer associated with early life exposure to two measures of air pollution exposure, total suspended particulates (TSP) and traffic emissions (TE), possible proxies for exposure to polycyclic aromatic hydrocarbons (PAHs). Exposure to PAHs has been shown to be associated with aberrant patterns of DNA methylation in peripheral blood of healthy individuals. Exposure to PAHs and methylation in breast tumor tissue has received little attention. We examined the association of early life exposure to TSP and TE with patterns of DNA methylation in breast tumors. METHODS: We conducted a study of women enrolled in the Western New York Exposures and Breast Cancer (WEB) Study. Methylation of nine genes (SFN, SCGB3A1, RARB, GSTP1, CDKN2A CCND2, BRCA1, FHIT, and SYK) was assessed using bisulfite-based pyrosequencing. TSP exposure at each woman's home address at birth, menarche, and when she had her first child was estimated. TE exposure was modeled for each woman's residence at menarche, her first birth, and twenty and ten years prior to diagnosis. Unconditional logistic regression was employed to estimate odds ratios (OR) of having methylation greater than the median value, adjusting for age, secondhand smoke exposure before age 20, current smoking status, and estrogen receptor status. RESULTS: Exposure to higher TSP at a woman's first birth was associated with lower methylation of SCGB3A1 (OR = 0.48, 95% CI: 0.23-0.99) and higher methylation of SYK (OR = 1.86, 95% CI: 1.03-3.35). TE at menarche was associated with increased methylation of SYK (OR = 2.37, 95% CI: 1.05-5.33). TE at first birth and ten years prior to diagnosis was associated with decreased methylation of CCND2 (OR ten years prior to diagnosis=0.48, 95% CI: 0.26-0.89). Although these associations were nominally significant, none were significant after adjustment for multiple comparisons (p < 0.01). CONCLUSIONS: We observed suggestive evidence that exposure to ambient air pollution throughout life, measured as TSP and TE, may be associated with DNA methylation of some tumor suppressor genes in breast tumor tissue. Future studies with a larger sample size that assess methylation of more sites are warranted.

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.

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

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.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 µm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.

Presumed Regional Incidence Rate of Out-of-Hospital Cardiac Arrest in Korea.

The regional incidence rates of out-of-hospital cardiac arrest (OHCA) were traditionally calculated with the residential population as the denominator. The aim of this study was to estimate the true incidence rate of OHCA and to investigate characteristics of regions with overestimated and underestimated OHCA incidence rates. We used the national OHCA database from 2006 to 2010. The nighttime residential and daytime transient populations were investigated from the 2010 Census. The daytime population was calculated by adding the daytime influx of population to, and subtracting the daytime outflow from, the nighttime residential population. Conventional age-standardized incidence rates (CASRs) and daytime corrected age-standardized incidence rates (DASRs) for OHCA per 100,000 person-years were calculated in each county. A total of 97,291 OHCAs were eligible. The age-standardized incidence rates of OHCAs per 100,000 person-years were 34.6 (95% CI: 34.3-35.0) in the daytime and 24.8 (95% CI: 24.5-25.1) in the nighttime among males, and 14.9 (95% CI: 14.7-15.1) in the daytime, and 10.4 (95% CI: 10.2-10.6) in the nighttime among females. The difference between the CASR and DASR ranged from 35.4 to -11.6 in males and from 6.1 to -1.0 in females. Through the Bland-Altman plot analysis, we found the difference between the CASR and DASR increased as the average CASR and DASR increased as well as with the larger daytime transient population. The conventional incidence rate was overestimated in counties with many OHCA cases and in metropolitan cities with large daytime population influx and nighttime outflow, while it was underestimated in residential counties around metropolitan cities.

Farm management, environment, and weather factors jointly affect the probability of spinach contamination by generic Escherichia coli at the preharvest stage.

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 farm's 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.

Does injury prevention education initiate household changes in a Spanish-speaking minority population?

Young children from low income families are among the most affected population of unintentional injury. This non-randomized longitudinal study examined knowledge for home and child safety with an injury prevention training offered to parents of children who reside in the Rio Grande Valley of Texas. Eighty eight parents received the training and pre-and post-test surveys were used to measure knowledge outcomes. A follow-up survey was conducted 2 months after the educational intervention to identify how many parents reported household and safety behavior changes as a result of the training. The most significant change in behavior, as it pertains to the household, was related to locking and storage of dangerous cleaning chemicals. Other significant changes in behavior were in areas that directly related to the child such as learning how to swim, use of sun block and fire safety in the home. This study suggests that tailored trainings can improve parent knowledge and change in behaviors for the promotion of safety activities to avoid risks for unintentional injuries. Further, the study identified certain at-risk areas that need to be addressed from an educational perspective. These areas include bicycle and water safety; specifically, the use of protective gear when bicycling; understanding and adhering to traffic rules when bicycling; and, the dangers of drowning in small quantities of water.

Relationship between low-level arsenic exposure in drinking water and kidney cancer risk in Texas.

Kidney cancer rates are increasing in the US and worldwide. Arsenic, a known human carcinogen, is a suspected contributor to this rise, particularly in areas with arsenic-rich groundwater. However, research on the connection between low-level arsenic in drinking water and kidney cancer is limited. In our ecological study, we assessed the association between county-level drinking water arsenic levels and kidney cancer incidences using data from 240 counties in Texas. The analysis included 28,896 cancer cases among adults aged ≥20 years and 101,776,294 person-years during the period 2016-2020. Spatial Poisson regression models estimated the risk ratio (RR) for incident kidney cancer based on drinking water arsenic levels, adjusting for demographic, socioeconomic, and other risk factors, as well as spatial factors. Population-weighted drinking water arsenic levels were calculated using data from water testing for both public water systems and private wells, adjusted for populations served from each source. After adjusting for spatial factors and covariates, we observed 6% and 22% higher incidence of cancer in the medium (1-5 ppb) (RR 1.06, 95% CI 1.01, 1.11) and high arsenic (>5 ppb) group counties (RR 1.22, 95% CI 1.12, 1.34) compared to the low arsenic level ones (<1 ppb), showing a dose-response relationship (p-trend <0.001). Additionally, when arsenic was treated as a continuous variable, the incidence increased by 4% for each doubling of drinking water arsenic level (RR 1.04, 95% CI 1.02, 1.07) when considering drinking water arsenic level as a continuous variable. Our study suggests that exposure to low-level drinking water arsenic may be associated with an increased risk of kidney cancer. Further prospective studies are required to confirm our findings.

Association of arsenic exposure with measles antibody titers in US children: Influence of sex and serum folate levels.

Exposure to arsenic during childhood is associated with various adverse health conditions. However, little is known about the effect of arsenic exposure on vaccine-related humoral immunity in children. We analyzed data from the National Health and Nutrition Examination Survey (2003-2004 and 2009-2010) to study the relationship between urinary arsenic and measles antibody levels in 476 US children aged 6-11. Multivariable linear regression was used to evaluate the association, adjusting for cycle, age, race, body mass index (BMI), serum cotinine, poverty index ratio, and vitamin B12 and selenium intakes. Stratified analyses were conducted by sex and serum folate levels using the median as cutoff (18.7 ng/mL). The measles antibody concentrations in the 3rd and 4th quartiles were found to have significantly decreased by 28.5 % (95 % Confidence Interval (CI) -47.6, -2.28) and 36.8 % (95 % CI -50.2, -19.5), compared to the lowest quartile among boys with serum folate levels lower than 18.7 ng/ml. The serum measles antibody titers significantly decreased by 16.7 % (95 %CI -25.0, -7.61) for each doubling of creatinine-corrected urinary total inorganic arsenic concentrations in the same group. No associations were found in boys with high serum folate levels or in girls. Further prospective studies are needed to validate these findings and develop interventions to protect children from infectious diseases.

Clustering of place of birth for women with breast cancer: differences by tumor characteristics.

PURPOSE: There is increasing evidence that exposures in early life affect breast cancer risk, and that breast cancer etiology differs by tumor subtype. If environmental exposures in early life contribute to risk, it is expected that there would be clustering of women with breast cancer by their place of birth, and that clustering might differ by subtype. We examined spatial associations between place of birth and breast cancer by subtype, using hormone receptor status and molecular profiles of breast tumors. METHODS: Data were drawn from the 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. Included were women born in the study area (579 cases and 931 controls). Clustering of breast cancer subgroups relative to controls was examined by the k-function method in groups stratified by estrogen receptor (ER), progesterone receptor (PR), and HER2 status, and by DNA methylation status and p53 mutation status, and the k-function difference was used to compare relative spatial aggregation and spatial range of the difference between case subgroups and controls. RESULTS: We found a tendency to cluster among ER positive, PR positive, and HER2 negative cases (i.e., luminal A subtype), especially among premenopausal women, but not among the other groups defined by hormonal receptor status, or by either methylation or p53 mutation status. CONCLUSIONS: While our findings cannot rule out clustering of cases by birth place because of shared behaviors related to residence location, they also suggest that early life environmental exposures may affect subsequent breast cancer risk, and that premenopausal breast tumors of the luminal A subtype may be more affected by these early life exposures than other subtypes.

Evaluating the effects of the introduction of off-sale alcohol outlets on violent crime.

AIMS: To examine the effects on violence of a policy change that ended prohibition of off-sale alcohol outlets in Lubbock, Texas. METHODS: Time-series analysis of violent crime data from police records comparing the periods before and after the policy change. RESULTS: The effect of the policy change on both total violent crime and aggregated assault was small and did not approach statistical significance. CONCLUSIONS: Increased availability of alcohol through off-sale premises may not influence the type of violence reported to the police in Lubbock, Texas.

Characterization of Arsenic and Atrazine Contaminations in Drinking Water in Iowa: A Public Health Concern.

Arsenic and atrazine are two water contaminants of high public health concern in Iowa. The occurrence of arsenic and atrazine in drinking water from Iowa's private wells and public water systems was investigated over several decades. In this study, the percentages of detection and violation of regulations were compared over region, season, and water source, and factors affecting the detection and concentration of arsenic and atrazine were analyzed using a mixed-effects model. Atrazine contamination in drinking water was found to vary by region, depending on agricultural usage patterns and hydrogeological features. The annual median atrazine levels of all public water systems were below the drinking water standard of 3 ppb in 2001-2014. Around 40% of public water systems contained arsenic at levels > 1 ppb in 2014, with 13.8% containing arsenic at levels of 5-10 ppb and 2.6% exceeding 10 ppb. This unexpected result highlights the ongoing public health threat posed by arsenic in drinking water in Iowa, emphasizing the need for continued monitoring and mitigation efforts to reduce exposure and associated health risks. Additionally, an atrazine metabolite, desethylatrazine, should be monitored to obtain a complete account of atrazine exposure and possible health effects.

Optimizing Disaster Preparedness Planning for Minority Older Adults: One Size Does Not Fit All.

By 2050, one in five Americans will be 65 years and older. The growing proportion of older adults in the U.S. population has implications for many aspects of health including disaster preparedness. This study assessed correlates of disaster preparedness among community-dwelling minority older adults and explored unique differences for African American and Hispanic older adults. An electronic survey was disseminated to older minority adults 55+, between November 2020 and January 2021 (n = 522). An empirical framework was used to contextualize 12 disaster-related activities into survival an0000000d planning actions. Multivariate logistic regression models were stratified by race/ethnicity to examine the correlates of survival and planning actions in African American and Hispanic older adults, separately. We found that approximately 6 in 10 older minority adults did not perceive themselves to be disaster prepared. Medicare coverage was positively associated with survival and planning actions. Income level and prior experience with disaster were related to survival actions in the African American population. In conclusion, recognizing the gaps in disaster-preparedness in elderly minority communities can inform culturally sensitive interventions to improve disaster preparedness and recovery.

Correlates of Social Isolation Among Community-Dwelling Older Adults During the COVID-19 Pandemic.

Background: The past year has severely curtailed social interactions among older adults given their high rates of COVID-19 morbidity and mortality. This study examined social, behavioral, and medical correlates of social isolation among community-dwelling older adults during the COVID-19 pandemic and stratified findings to explore unique differences in two typically neglected populations, African American and Hispanic older adults. Methods: Working with community-based organizations and senior living centers, the research team administered a survey to older adults 55 years of age and older (n = 575). The survey assessed COVID-19 prevention behaviors, medical conditions, and lived experiences, including feelings of social isolation, in the target population. Responses to a previously validated social isolation question informed a dichotomous social isolation dependent variable. Multivariable logistic regression was used to adjust for sociodemographic characteristics, medical conditions, unmet caregiving needs, and COVID-19 prevention behaviors. Results from the regression model were stratified by race/ethnicity to examine correlates of social isolation in African American and Hispanic older adults, separately. Results: Overall, female sex and a higher level of education were also positively associated with social isolation (OR = 2.46, p = 0.04; OR = 5.49, p = 0.02) while having insurance exhibited an inverse relationship (OR = 0.25, p = 0.03). Unmet caregiving needs were strongly associated with social isolation (OR = 6.41, p < 0.001) as was having any chronic conditions (OR = 2.99, p = 0.02). Diabetes was the single strongest chronic condition predictor of social isolation. Among minority older adults, a different pattern emerged. For Hispanic older adults, language, unmet caregiving needs, and social distancing were strongly associated with social isolation; while unmet caregiving needs, having 1+ chronic conditions and adhering to social distancing guidelines were significant predictors in African American older adults. Conclusion: These findings suggest that social isolation affects older adults in a myriad of ways and support the need for culturally sensitive initiatives to mitigate the effect of social isolation in these vulnerable populations.

Health Disparities and Climate Change: The Intersection of Three Disaster Events on Vulnerable Communities in Houston, Texas.

Although evidence suggests that successive climate disasters are on the rise, few studies have documented the disproportionate impacts on communities of color. Through the unique lens of successive disaster events (Hurricane Harvey and Winter Storm Uri) coupled with the COVID-19 pandemic, we assessed disaster exposure in minority communities in Harris County, Texas. A mixed methods approach employing qualitative and quantitative designs was used to examine the relationships between successive disasters (and the role of climate change), population geography, race, and health disparities-related outcomes. This study identified four communities in the greater Houston area with predominantly non-Hispanic African American residents. We used data chronicling the local community and environment to build base maps and conducted spatial analyses using Geographic Information System (GIS) mapping. We complemented these data with focus groups to assess participants' experiences in disaster planning and recovery, as well as community resilience. Thematic analysis was used to identify key patterns. Across all four communities, we observed significant Hurricane Harvey flooding and significantly greater exposure to 10 of the 11 COVID-19 risk factors examined, compared to the rest of the county. Spatial analyses reveal higher disease burden, greater social vulnerability, and significantly higher community-level risk factors for both pandemics and disaster events in the four communities, compared to all other communities in Harris County. Two themes emerged from thematic data analysis: (1) Prior disaster exposure prepared minority populations in Harris County to better handle subsequent disaster suggesting enhanced disaster resilience, and (2) social connectedness was key to disaster resiliency. Long-standing disparities make people of color at greater risk for social vulnerability. Addressing climate change offers the potential to alleviate these health disparities.

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

OBJECTIVE: To 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. METHODS: Data 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. RESULTS: Overall, 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. CONCLUSION: It 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.

Social segregation of ecosystem services delivery in the San Antonio region, Texas, through 2050.

Inequality in access to ecosystem services is inextricably linked with environmental justice in socially heterogeneous urban settings. Historically, San Antonio has been the gateway to Mexico and is strategically located along the North American Free Trade Agreement (NAFTA) corridor. It is also characterized by some of the most distinct residential segregation among U.S. cities. However, little is understood about the ways in which historically institutionalized residential segregation initiated by the Home Owners' Loan Corporation (HOLC) and NAFTA have affected socio-ecological outcomes. Here, this paper presents a novel empirical study of racial residential segregation. The study utilizes quantitative and spatially explicit estimates of regulating ecosystem services and biodiversity, and links the supply of ecosystem services to the distribution of human well-being within a heterogeneous social-ecological system. Specifically, the paper employed 1930s HOLC redlining maps and applied the ceteris paribus approach for racial concentrations to reflect a historical legacy and path dependence by institutional inertia. The results point to the social-ecological divide in that Hispanic and African American minorities derive fewer ecosystem benefits and face greater health risks and socio-economic disadvantages (p < 0.01). Notably, NAFTA corridor-related health risks are the most significant for the Hispanic population (p < 0.01). These patterns are likely to persist and may be amplified by 2050 (adjusted R2 = 0.646). The findings highlight that institutional transformations are essential for the greater social-ecological equity in the San Antonio region under NAFTA and, potentially, new United States-Mexico-Canada Agreement. Additionally, by assessing the EJ implications of spatially heterogeneous distribution of ecosystem services supply, the paper provides methodology that enhances science-based planning and better environmental decision-making to avoid or mitigate social-ecological divides in rapidly urbanizing regions both in the U.S. and around the world.

Exploring Demographic and Environmental Factors Related to Unintentional Pesticide Poisonings in Children and Adolescents in Texas.

There is limited literature on the frequency and distribution of pesticide exposures, specifically with respect to demographic and environmental factors in the United States. The purpose of this exploratory study was to investigate geographic trends and factors associated with unintentional pesticide exposures in children and adolescents in Texas. The study used an ecological design with secondary data. A spatial scan statistic, based on a Poisson regression model, was employed to identify spatial clusters of unintentional pesticide-related poison center exposures. Next, logistic regression models were constructed to identify potential demographic and environmental factors associated with unintentional pesticide-related poison center exposures. There were 59,477 unintentional pesticide-related poison center exposures from 2000 to 2013. The spatial scan statistic found a change in the number of counties in the identified clusters (e.g. , aggregation of counties with higher than expected exposures) for two time periods (2000-2006; 2007-2013). Based on the logistic regression models, factors associated with unintentional pesticide-related poison center exposures were percent black or African American population, year structure built, and percent moved in the past 12 months. In conclusion, this study found certain demographic and environmental factors may be associated with unintentional pesticide-related poison center exposures. Through understanding trends and associated factors, public health professionals can design interventions for populations at higher risk of pesticide exposures. This study also supports the use of spatial methods being utilized to expand upon current analysis of poison center data. Future research should confirm and build upon these findings.

Violent crime redistribution in a city following a substantial increase in the number of off-sale alcohol outlets: A Bayesian analysis.

INTRODUCTION AND AIMS: This study examined whether the introduction of a large number of off-premise alcohol outlets into a city over a brief period of time could affect rates of violent crime. DESIGN AND METHODS: The study analysed annual counts of violent crime across 172 US Census block groups in Lubbock, Texas from 2006 through 2011. Spatial Poisson models related annual violent crime counts within each block group to off-premise and on-premise alcohol outlets active during this time period as well as neighbourhood socio-demographic characteristics. The effects of alcohol outlets were assessed both within block groups and across adjacent block groups. RESULTS: On-premise outlets had a small, significant positive association with violence within a given block group. A similar well-supported local effect for off-premise outlets was not found. However, the spatially lagged effect for off-sale premises was well-supported, indicating that greater densities of these outlets were related to greater rates of violent crime in adjacent areas. DISCUSSION AND CONCLUSIONS: While these analyses confirmed a previous time-series analysis in finding no city-wide effect of the increase in off-premise outlets, they do suggest that such outlets in a local area may be related to violence in nearby geographic areas. They indicate the importance of examining neighbourhood-specific effects of alcohol outlets on violence in addition to the city-wide effects. They also present further evidence supporting the need to examine the differential effects of on-sale and off-sale premises.