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.

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.

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.

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.

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.

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.

Smartphone-assisted spatial data collection improves geographic information quality: pilot study using a birth records dataset.

It is well known that the conventional, automated geocoding method based on self-reported residential addresses has many issues. We developed a smartphone-assisted aerial image-based method, which uses the Google Maps application programming interface as a spatial data collection tool during the birth registration process. In this pilot study, we have tested whether the smartphone-assisted method provides more accurate geographic information than the automated geocoding method in the scenario when both methods can get the address geocodes. We randomly selected 100 well-geocoded addresses among women who gave birth in Alachua county, Florida in 2012. We compared geocodes generated from three geocoding methods: i) the smartphone-assisted aerial image-based method; ii) the conventional, automated geocoding method; and iii) the global positioning system (GPS). We used the GPS data as the reference method. The automated geocoding method yielded positional errors larger than 100 m among 29.3% of addresses, while all addresses geocoded by the smartphoneassisted method had errors less than 100 m. The positional errors of the automated geocoding method were greater for apartment/condominiums compared with other dwellings and also for rural addresses compared with urban ones. We conclude that the smartphone-assisted method is a promising method for perspective spatial data collection by improving positional accuracy.

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination.

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

Pesticide-Related Hospitalizations Among Children and Teenagers in Texas, 2004-2013.

OBJECTIVE: Acute exposure to pesticides is associated with nausea, headaches, rashes, eye irritation, seizures, and, in severe cases, death. We characterized pesticide-related hospitalizations in Texas among children and teenagers for 2004-2013 to characterize exposures in this population, which is less well understood than pesticide exposure among adults. METHODS: We abstracted information on pesticide-related hospitalizations from hospitalization data using pesticide-related International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and E-codes. We calculated the prevalence of pesticide-related hospitalizations among children and teenagers aged #19 years for all hospitalizations, unintentional exposures, intentional exposures, pesticide classifications, and illness severity. We also calculated age- and sex-specific prevalence of pesticide-related hospitalizations among children. RESULTS: The prevalence of pesticide-related hospitalizations among children and teenagers was 2.1 per 100,000 population. The prevalence of pesticide-related hospitalizations per 100,000 population was 2.7 for boys and 1.5 for girls. The age-specific prevalence per 100,000 population was 5.3 for children aged 0-4 years, 0.3 for children and teenagers aged 5-14 years, and 2.3 for teenagers aged 15-19 years. Children aged 0-4 years had the highest prevalence of unintentional exposures, whereas teenagers aged 15-19 years had the highest prevalence of intentional exposures. Commonly reported pesticide categories were organophosphates/carbamates, disinfectants, rodenticides, and other pesticides (e.g., pyrethrins, pyrethroids). Of the 158 pesticide-related hospitalizations, most were coded as having minor (n=86) or moderate (n=40) illness severity. CONCLUSION: Characterizing the prevalence of pesticide-related hospitalizations among children and teenagers leads to a better understanding of the burden of pesticide exposures, including the type of pesticides used and the severity of potential health effects. This study found differences in the frequency of pesticide-related hospitalizations by sex, age, and intent (e.g., unintentional vs. intentional).

Pesticide-related poison center exposures in children and adolescents aged ≤19 years in Texas, 2000-2013.

CONTEXT: Although national poison center data show that pesticides were the 8th most commonly reported substance category (3.27%) for children aged ≤5 years in 2014, there is limited information on childhood and adolescent pesticide exposures. OBJECTIVE: This study assessed pesticide-related poison center exposures in children and adolescents aged ≤19 years from 2000-2013 in Texas to characterize the potential burden of pesticides. MATERIALS AND METHODS: Pesticide-related poison center exposures among children and adolescents aged ≤19 years reported to Texas poison centers were identified. The distribution of exposures was estimated by gender, age category, medical outcome, management site, exposure route, and pesticide category. RESULTS: From 2000 to 2013, there were 61,147 pesticide-related poison center exposures in children and adolescents aged ≤19 years. The prevalence was highest among males at 864.24 per 100,000 population. The prevalence of unintentional exposures was highest among children aged ≤5 years at 2310.69 per 100,000 population, whereas the prevalence of intentional exposures was highest among adolescents aged 13-19 years at 13.82 per 100,000 population. A majority of medical outcomes reported were classified as having no effect (30.24%) and not followed, but minimal clinical effects possible (42.74%). Of all the exposures, 81.24% were managed on site. However, 57% of intentional exposures were referred to or treated at a health-care facility. The most common routes of exposure were ingestion (80.83%) and dermal (17.21%). The most common pesticide categories included rodenticides (30.02%), pyrethrins/pyrethroids (20.69%), and other and unspecified insecticides (18.14%). DISCUSSION: The study found differences in the frequency of exposures by intent for sex and age categories, and identified the most common medical outcomes, management site, exposure route, and pesticide category. CONCLUSION: Through characterizing pesticide-related poison center exposures, future interventions can be designed to address groups with higher prevalence of exposure.

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.

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.

Impacting Environmental and Public Health through the Use of Dual Targeted and Tailored Asthma Educational Interventions.

Home-based asthma environmental education for parents of asthmatic children is needed since many health professionals lack the time to offer it. However, developing targeted and tailored education is important in order to address the individual needs of participants. This nonrandomized longitudinal study examined knowledge on asthma with an Asthma and Healthy Homes educational intervention training offered to parents of children from low income families who reside in the Rio Grande Valley of Texas. Eighty-nine parents received the training and pre- and posttest surveys were used to measure knowledge outcomes. A standardized assessment on asthma triggers was used to identify the different triggers each child was exposed to, and a follow-up survey was conducted 6 months after the educational intervention to identify how many parents reported household and behavior changes as a result of the training. Results showed significant changes in behavior by participants as a result of the training received. This study suggests that these behavioral changes are attributed to the dual "targeted" and "tailored" educational interventions delivered to parents which resulted in a greater understanding of how to manage asthma by eliminating asthma triggers in their respective homes.

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.

Evaluating the effects of a large increase in off-sale alcohol outlets on motor vehicle crashes: a time-series analysis.

This study examined the effects on motor vehicle crashes of a policy change that led to the introduction of a very large number of off-sale alcohol outlets in Lubbock, Texas. Times-series analysis of total crashes and single-vehicle nighttime (SVN) crashes was used to compare the periods before and after the policy change in Lubbock and in a comparison area. The results of the analysis revealed some weak effects on total crashes, but no statistically significant effects were found for SVN crashes. Possible reasons for the essentially null findings of the current study regarding the effects of the policy change on motor vehicle crashes are discussed. These include the fact that there were a small number of off-sale outlets already present in the community and that motor vehicle travel immediately following alcohol consumption is less likely to occur with alcohol purchased from an off-sale outlet compared to an on-sale outlet.

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.