Quantifying Prescribed-Fire Smoke Exposure Using Low-Cost Sensors and Satellites: Springtime Burning in Eastern Kansas.

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March-May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low-cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hr median PM2.5 to increase by 3.0-5.3 µg m-3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite-derived product aligned with our ground-based measurements. Satellite-derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates.

Developing a National-Scale Exposure Index for Combined Environmental Hazards and Social Stressors and Applications to the Environmental Influences on Child Health Outcomes (ECHO) Cohort.

Tools for assessing multiple exposures across several domains (e.g., physical, chemical, and social) are of growing importance in social and environmental epidemiology because of their value in uncovering disparities and their impact on health outcomes. Here we describe work done within the Environmental influences on Child Health Outcomes (ECHO)-wide Cohort Study to build a combined exposure index. Our index considered both environmental hazards and social stressors simultaneously with national coverage for a 10-year period. Our goal was to build this index and demonstrate its utility for assessing differences in exposure for pregnancies enrolled in the ECHO-wide Cohort Study. Our unitless combined exposure index, which collapses census-tract level data into a single relative measure of exposure ranging from 0-1 (where higher values indicate higher exposure to hazards), includes indicators for major air pollutants and air toxics, features of the built environment, traffic exposures, and social determinants of health (e.g., lower educational attainment) drawn from existing data sources. We observed temporal and geographic variations in index values, with exposures being highest among participants living in the West and Northeast regions. Pregnant people who identified as Black or Hispanic (of any race) were at higher risk of living in a "high" exposure census tract (defined as an index value above 0.5) relative to those who identified as White or non-Hispanic. Index values were also higher for pregnant people with lower educational attainment. Several recommendations follow from our work, including that environmental and social stressor datasets with higher spatial and temporal resolutions are needed to ensure index-based tools fully capture the total environmental context.

Factors affecting urinary organophosphate pesticide metabolite levels among Californian agricultural community members.

Organophosphate (OP) pesticides are widely used in California for agricultural pest and weed control despite their well-documented adverse health effects among infants, children, and adults. We sought to identify factors affecting urinary OP metabolites among families living in high-exposure communities. Our study included 80 children and adults who lived within 61 m (200 ft) of agricultural fields in the Central Valley of California in January and June 2019, which are pesticide non-spraying and spraying seasons, respectively. We collected one urine sample per participant during each visit to measure dialkyl phosphate (DAP) metabolites; these were coupled with in-person surveys to identify health, household, sociodemographic, pesticide exposure, and occupational risk factors. We used a data-driven, best subsets regression approach to identify key factors that influenced urinary DAPs. Participants were mostly Hispanic/Latino(a) (97.5 %), over half were female (57.5 %), and most households reported having a member who worked in agriculture (70.6 %). Among the 149 urine samples suitable for analysis, DAP metabolites were detected in 48.0 % and 40.5 % of samples during January and June, respectively. Total diethyl alkylphosphates (EDE) were only detected in 4.7 % (n = 7) of samples, but total dimethyl alkylphosphates (EDM) were detected in 41.6 % (n = 62) of samples. No differences were observed in urinary DAP levels by visit month or by occupational exposure to pesticides. Best subsets regression identified several individual- and household-level variables that influenced both urinary EDM and total DAPs: the number of years spent living at the current address, household use of chemical products to control mice/rodents, and seasonal employment status. Among adults only, we identified educational attainment (for total DAPs) and age category (for EDM) as significant factors. Our study found consistent urinary DAP metabolites among participants, regardless of spraying season, and identified potential mitigating factors that members of vulnerable populations can implement to protect their health against OP exposure.

Disparities in Air Pollutants Across Racial, Ethnic, and Poverty Groups at US Public Schools.

We investigate socioeconomic disparities in air quality at public schools in the contiguous US using high resolution estimates of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations. We find that schools with higher proportions of people of color (POC) and students eligible for the federal free or reduced lunch program, a proxy for poverty level, are associated with higher pollutant concentrations. For example, we find that the median annual NO2 concentration for White students, nationally, was 7.7 ppbv, compared to 9.2 ppbv for Black and African American students. Statewide and regional disparities in pollutant concentrations across racial, ethnic, and poverty groups are consistent with nationwide results, where elevated NO2 concentrations were associated with schools with higher proportions of POC and higher levels of poverty. Similar, though smaller, differences were found in PM2.5 across racial and ethnic groups in most states. Racial, ethnic, and economic segregation across the rural-urban divide is likely an important factor in pollution disparities at US public schools. We identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we highlight that disparities exist not only across urban and non-urban lines but also within urban environments.

Using non-parametric Bayes shrinkage to assess relationships between multiple environmental and social stressors and neonatal size and body composition in the Healthy Start cohort.

BACKGROUND: Both environmental and social factors have been linked to birth weight and adiposity at birth, but few studies consider the effects of exposure mixtures. Our objective was to identify which components of a mixture of neighborhood-level environmental and social exposures were driving associations with birth weight and adiposity at birth in the Healthy Start cohort. METHODS: Exposures were assessed at the census tract level and included air pollution, built environment characteristics, and socioeconomic status. Prenatal exposures were assigned based on address at enrollment. Birth weight was measured at delivery and adiposity was measured using air displacement plethysmography within three days. We used non-parametric Bayes shrinkage (NPB) to identify exposures that were associated with our outcomes of interest. NPB models were compared to single-predictor linear regression. We also included generalized additive models (GAM) to assess nonlinear relationships. All regression models were adjusted for individual-level covariates, including maternal age, pre-pregnancy BMI, and smoking. RESULTS: Results from NPB models showed most exposures were negatively associated with birth weight, though credible intervals were wide and generally contained zero. However, the NPB model identified an interaction between ozone and temperature on birth weight, and the GAM suggested potential non-linear relationships. For associations between ozone or temperature with birth weight, we observed effect modification by maternal race/ethnicity, where effects were stronger for mothers who identified as a race or ethnicity other than non-Hispanic White. No associations with adiposity at birth were observed. CONCLUSIONS: NPB identified prenatal exposures to ozone and temperature as predictors of birth weight, and mothers who identify as a race or ethnicity other than non-Hispanic White might be disproportionately impacted. However, NPB models may have limited applicability when non-linear effects are present. Future work should consider a two-stage approach where NPB is used to reduce dimensionality and alternative approaches examine non-linear effects.

Tropical Cyclone Exposures and Risks of Emergency Medicare Hospital Admission for Cardiorespiratory Diseases in 175 Urban United States Counties, 1999-2010.

BACKGROUND: Although injuries experienced during hurricanes and other tropical cyclones have been relatively well-characterized through traditional surveillance, less is known about tropical cyclones' impacts on noninjury morbidity, which can be triggered through pathways that include psychosocial stress or interruption in medical treatment. METHODS: We investigated daily emergency Medicare hospitalizations (1999-2010) in 180 US counties, drawing on an existing cohort of high-population counties. We classified counties as exposed to tropical cyclones when storm-associated peak sustained winds were ≥21 m/s at the county center; secondary analyses considered other wind thresholds and hazards. We matched storm-exposed days to unexposed days by county and seasonality. We estimated change in tropical cyclone-associated hospitalizations over a storm period from 2 days before to 7 days after the storm's closest approach, compared to unexposed days, using generalized linear mixed-effect models. RESULTS: For 1999-2010, 175 study counties had at least one tropical cyclone exposure. Cardiovascular hospitalizations decreased on the storm day, then increased following the storm, while respiratory hospitalizations were elevated throughout the storm period. Over the 10-day storm period, cardiovascular hospitalizations increased 3% (95% confidence interval = 2%, 5%) and respiratory hospitalizations increased 16% (95% confidence interval = 13%, 20%) compared to matched unexposed periods. Relative risks varied across tropical cyclone exposures, with strongest association for the most restrictive wind-based exposure metric. CONCLUSIONS: In this study, tropical cyclone exposures were associated with a short-term increase in cardiorespiratory hospitalization risk among the elderly, based on a multi-year/multi-site investigation of US Medicare beneficiaries ≥65 years.

Correction: Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors.

The original version of this Article featured an incorrect supplementary figure file. This error has been rectified in the PDF and HTML versions of this Article.

Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors.

Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.

Combined environmental and social exposures during pregnancy and associations with neonatal size and body composition: the Healthy Start study.

BACKGROUND: Prenatal environmental and social exposures have been associated with decreased birth weight. However, the effects of combined exposures in these domains are not fully understood. Here we assessed multi-domain exposures for participants in the Healthy Start study (Denver, CO) and tested associations with neonatal size and body composition. METHODS: In separate linear regression models, we tested associations between neonatal outcomes and three indices for exposures. Two indices were developed to describe exposures to environmental hazards (ENV) and social determinants of health (SOC). A third index combined exposures in both domains (CE = ENV/10 × SOC/10). Index scores were assigned to mothers based on address at enrollment. Birth weight and length were measured at delivery, and weight-for-length z-scores were calculated using a reference distribution. Percent fat mass was obtained by air displacement plethysmography. RESULTS: Complete data were available for 897 (64%) participants. Median (range) ENV, SOC, and CE values were 31.9 (7.1-63.2), 36.0 (2.8-75.0), and 10.9 (0.4-45.7), respectively. After adjusting for potential confounders, 10-point increases in SOC and CE were associated with 27.7 g (95%CI: 12.4 - 42.9 g) and 56.3 g (19.4 - 93.2 g) decreases in birth weight, respectively. SOC and CE were also associated with decreases in % fat mass. CONCLUSIONS: Combined exposures during pregnancy were associated with lower birth weight and % fat mass. Evidence of a potential synergistic effect between ENV and SOC suggests a need to more fully consider neighborhood exposures when assessing neonatal outcomes.

A national burden assessment of estimated pediatric asthma emergency department visits that may be attributed to elevated ozone levels associated with the presence of smoke.

Asthma is the most common pediatric disease in the USA. It has been consistently demonstrated that asthma symptoms are exacerbated by exposure to ozone. Ozone (O3) is a secondary pollutant produced when volatile organic compounds (VOCs) are oxidized in the atmosphere in the presence of nitrogen oxides (NOx). At ground level, elevated ozone is typically formed as a result of human activities. However, wildfires represent an additional source of ozone precursors. Recent evidence suggests that smoke can increase ozone concentrations. We estimated the number of excess asthma-related emergency department (ED) visits in children with asthma that may be attributed to elevated ozone associated with smoke (EOAS) in the USA. We conducted a quantitative burden assessment (BA) using a Monte Carlo approach to estimate the median number of excess pediatric asthma ED visits that may be attributed to EOAS among children with asthma in the continental USA between 2005 and 2014, as well as 95% confidence bounds (95% CB). We estimated that a median of 2403 (95% CB 235-5382) pediatric asthma ED visits could be attributed to EOAS exposure between 2005 and 2014 in the continental USA. Furthermore, the impact of EOAS on estimated asthma ED visits was greatest in the eastern half of the continental USA. We found a significant increase in pediatric asthma ED visits that may be attributed to exposure to EOAS. EOAS may have a measurable negative impact on children with asthma in the USA.

Health and Environmental Justice Implications of Retiring Two Coal-Fired Power Plants in the Southern Front Range Region of Colorado.

Despite improvements in air quality over the past 50 years, ambient air pollution remains an important public health issue in the United States. In particular, emissions from coal-fired power plants still have a substantial impact on both nearby and regional populations. Of particular concern is the potential for this impact to fall disproportionately on low-income communities and communities of color. We conducted a quantitative health impact assessment to estimate the health benefits of the proposed decommissioning of two coal-fired electricity generating stations in the Southern Front Range region of Colorado. We estimated changes in exposures to fine particulate matter and ozone using the Community Multiscale Air Quality model and predicted avoided health impacts and related economic values. We also quantitatively assessed the distribution of these benefits by population-level socioeconomic status. Across the study area, decommissioning the power plants would result in 2 (95% CI: 1-3) avoided premature deaths each year due to reduced PM2.5 exposures and greater reductions in hospitalizations and other morbidities. Health benefits resulting from the modeled shutdowns were greatest in areas with lower educational attainment and other economic indicators. Our results suggest that decommissioning these power plants and replacing them with zero-emissions sources could have broad public health benefits for residents of Colorado, with larger benefits for those that are socially disadvantaged. Our results also suggested that researchers and decision makers need to consider the unique demographics of their study areas to ensure that important opportunities to reduce health disparities associated with point-source pollution.

A mixed-methods analysis of logging injuries in Montana and Idaho.

BACKGROUND: Despite advances in mechanization, logging continues to be one of the most dangerous occupations in the United States. Logging in the Intermountain West region (Montana and Idaho) is especially hazardous due to steep terrain, extreme weather, and remote work locations. METHODS: We implemented a mixed-methods approach combining analyses of workers' compensation claims and focus groups to identify factors associated with injuries and fatalities in the logging industry. RESULTS: Inexperienced workers (>6 months experience) accounted for over 25% of claims. Sprain/strain injuries were the most common, accounting for 36% of claims, while fatalities had the highest median claim cost ($274 411). Focus groups identified job tasks involving felling trees, skidding, and truck driving as having highest risk. CONCLUSIONS: Injury prevention efforts should focus on training related to safe work methods (especially for inexperienced workers), the development of a safety culture and safety leadership, as well as implementation of engineering controls.

Ozone-Related Respiratory Morbidity in a Low-Pollution Region.

OBJECTIVE: We evaluated the effects of ozone on respiratory-related hospital admissions in three counties in Washington State from 1990 to 2006. We further examined vulnerability to ozone by key demographic factors. METHOD: Using linked hospital admission and ambient monitoring data, we estimated the age-, sex-, and health insurance-stratified associations between ozone (0 to 3 days' lag) and respiratory-related hospital admissions in King, Spokane, and Clark County, Washington. RESULTS: The adjusted relative risk (RR) for a 10 ppb increase in ozone at 3 days' lag was 1.04 (95% confidence interval [CI]: 1.02, 1.07) for Clark County, 1.03 (95% CI: 1.01, 1.05) for Spokane County, and 1.02 (95% CI: 1.01, 1.03) for King County. There was consistent evidence of effect modification by age. CONCLUSION: Ozone at levels below federal standards contributes to respiratory morbidity among high-risk groups in Washington.

Data for Community Health Assessment in Rural Colorado: A Comparison of Electronic Health Records to Public Health Surveys to Describe Childhood Obesity.

CONTEXT: Community-level data are necessary to inform community health assessments and to plan for appropriate interventions. However, data derived from public health surveys may be limited or unavailable in rural locations. OBJECTIVE: We compared 2 sources of data for community health assessment in rural Colorado, electronic health records (EHRs) and routine public health surveys. DESIGN: Comparison of cross-sectional measures of childhood/youth obesity prevalence and data quality. SETTING: Two rural Colorado counties, La Plata and Prowers. PARTICIPANTS: The EHR cohort comprised patients 2 to 19 years of age who underwent a visit with the largest health care provider in each county. These data included sex, age, weight, height, race, ethnicity, and insurance status. Public health survey data were obtained from 2 surveys, the Colorado Child Health Survey (2-14 years of age) and the Healthy Kids Colorado Survey (15-19 years of age) and included caregiver and self-reported height and weight estimates. MAIN OUTCOME MEASURES: We calculated body mass index percentile for each patient and survey respondent and determined overweight/obesity prevalence by county. We evaluated data source quality indicators according to a rubric developed for this analysis. RESULTS: The EHR sample captured approximately 35% (n = 3965) and 70% (n = 2219) of all children living in La Plata and Prowers Counties, respectively. The EHR prevalence estimates of overweight/obesity were greater in precision than survey data in both counties among children 2 to 14 years of age. In addition, the EHR data were more timely and geographically representative than survey data and provided directly measured height and weight. Conversely, survey data were easier to access and more demographically representative of the overall population. CONCLUSIONS: Electronic health records describing the prevalence of obesity among children/youth living in rural Colorado may complement public health survey data for community health assessment and health improvement planning.

Quantile regression in environmental health: Early life lead exposure and end-of-grade exams.

Conditional means regression, including ordinary least squares (OLS), provides an incomplete picture of exposure-response relationships particularly if the primary interest resides in the tail ends of the distribution of the outcome. Quantile regression (QR) offers an alternative methodological approach in which the influence of independent covariates on the outcome can be specified at any location along the distribution of the outcome. We implemented QR to examine heterogeneity in the influence of early childhood lead exposure on reading and math standardized fourth grade tests. In children from two urban school districts (n=1,076), lead exposure was associated with an 18.00 point decrease (95% CI: -48.72, -3.32) at the 10th quantile of reading scores, and a 7.50 point decrease (95% CI: -15.58, 2.07) at the 90th quantile. Wald tests indicated significant heterogeneity of the coefficients across the distribution of quantiles. Math scores did not show heterogeneity of coefficients, but there was a significant difference in the lead effect at the 10th (ß=-17.00, 95% CI: -32.13, -3.27) versus 90th (ß=-4.50, 95% CI: -10.55, 4.50) quantiles. Our results indicate that lead exposure has a greater effect for children in the lower tail of exam scores, a result that is masked by conditional means approaches.

Examining household asthma management behavior through a microeconomic framework.

National guidelines on the effective management of pediatric asthma have been promoted for over 20 years, yet asthma-related morbidity among low-income children remains disproportionately high. To date, household and clinical interventions designed to remediate these differences have been informed largely by a health behavior framework. However, these programs have not resulted in consistent sustained improvements in targeted populations. The continued funding and implementation of programs based on the health behavior framework leads us to question if traditional behavioral models are sufficient to understand and promote adaptation of positive health management behaviors. We introduce the application of the microeconomic framework to investigate potential mechanisms that can lead to positive management behaviors to improve asthma-related morbidity. We provide examples from the literature on health production, preferences, trade-offs and time horizons to illustrate how economic constructs can potentially add to understanding of disease management. The economic framework, which can be empirically observed, tested, and quantified, can explicate the engagement in household-level activities that would affect health and well-being. The inclusion of a microeconomic perspective in intervention research may lead to identification of mechanisms that lead to household decisions with regard to asthma management strategies and behavior. The inclusion of the microeconomic framework to understand the production of health may provide a novel theoretical framework to investigate the underlying causal behavioral mechanisms related to asthma management and control. Adaptation of an economic perspective may provide new insight into the design and implementation of interventions to improve asthma-related morbidity in susceptible populations.

Moderate lead exposure and elementary school end-of-grade examination performance.

PURPOSE: This study investigated the association between moderate lead poisoning in early childhood with performance on a comprehensive set of end-of-grade examinations at the elementary school level in two urban school districts. METHODS: Children born between 1996 and 2000 who resided in Milwaukee or Racine, WI, with a record of a blood lead test before the age of 3 years were considered for the analysis. Children were defined as exposed (blood lead level ≥10 and <20 µg/dL) or not exposed (BLL < 5 µg/dL). Parents of eligible children were mailed surveys to consent to participation and elicit information on potential confounders. On consent, children were matched to educational records for fourth grade Wisconsin Knowledge and Concepts Examinations. Seemingly unrelated regression was used to evaluate the relation between scaled scores on all sections of the examination (math, reading, language arts, science, and social studies) with exposure status, controlling for demographics, social status indicators, health indicators, and district-based poverty indicators. RESULTS: A total of 1133 families responded to the survey and consented to have educational records released; 43% of children were considered exposed. After controlling for demographic and socioeconomic covariates, lead exposure was associated with significantly lower scores in all sections of the Wisconsin Knowledge and Concepts Examinations (range: science, ß = -5.21, P = .01; reading, ß = -8.91, P = .003). Children who were black, had a parent with less than a high-school education, and were classified by parents as having less than excellent health had significantly lower performance on all examination components. CONCLUSIONS: Children with moderate lead poisoning in early childhood performed significantly lower on all components of elementary school end-of-grade examinations compared with unexposed children. Household level social status and childhood health indicators partially explain decreased examination scores.

Lead exposure and educational proficiency: moderate lead exposure and educational proficiency on end-of-grade examinations.

PURPOSE: To investigate and quantify the impact of moderate lead exposure on students' ability to score at the "proficient" level on end-of-grade standardized tests. METHODS: We compared the scores of 3757 fourth grade students from Milwaukee, Wisconsin, on the Wisconsin Knowledge and Concepts Exam (WKCE). The sample consisted of children with a blood lead test before age 3 years that was either unquantifiable at the time of testing (<5 µg/dL) or in the range of moderate exposure (10-19 µg/dL). RESULTS: After controlling for gender, poverty, English language learner status, race/ethnicity, school disciplinary actions, and attendance percentage, results showed a significant negative effect of moderate lead exposure on academic achievement for all 5 subtests of the WKCE. Test score deficits owing to lead exposure were equal to 22% of the interval between student categorization at the "proficient" or "basic" levels in Reading, and 42% of the interval in Mathematics. CONCLUSIONS: Children exposed to amounts of lead before age 3 years that are insufficient to trigger intervention under current policies in many states are nonetheless at a considerable educational disadvantage compared with their unexposed peers 7 to 8 years later. Exposed students are at greater risk of scoring below the proficient level, an outcome with serious negative consequences for both the student and the school.

Understanding socioeconomic and racial differences in adult lung function.

RATIONALE: The contribution of socioeconomic factors to racial differences in the distribution of lung function is not well understood. OBJECTIVES: We investigated the contribution of socioeconomic factors to racial differences in FEV1 using statistical tools that allow for examination across the population distribution of FEV1. METHODS: We compared FEV1 for white and African-American participants (aged 20-80 yr) in NHANES III with greater than or equal to two acceptable maneuvers to a restricted sample following the routine exclusion criteria used to derive population reference equations. Ordinary least squares and quantile regression analyses using spirometric, anthropometric, and socioeconomic data (high school completion) were performed separately by sex for both data sets. MEASUREMENTS AND MAIN RESULTS: In the entire sample with acceptable spirometry (n » 9,658), high school completion was associated with a mean 69.13-ml increase in FEV1 for males (P , 0.05) and a mean 50.75-ml increase in FEV1 for females (P , 0.01). In quantile regression analysis, we observed a significant racial difference in the association of high school completion with FEV1 among both sexes that varied across the distribution; college completion was associated with an additional increase in FEV1 for white males (70.36-250.76 ml) and white females (57.87-317.77 ml). Routine exclusion criteria differentially excluded individuals by age, race, and education. In the restricted sample (n » 2,638), the association with high school completion was not significant. CONCLUSIONS: High school completion is associated with racially patterned improvements in the FEV1 of adults in the general population. The application of routine exclusion criteria leads to underestimates of the role of high school completion on FEV1.

Crime, neighborhood deprivation, and asthma: a GIS approach to define and assess neighborhoods.

Exposure to neighborhood factors remains difficult to quantify when neighborhoods are often predefined and imprecisely measured. This study examines the association between neighborhood deprivation and participation in a community-based asthma case management (CM) program in Oakland, CA. We estimated neighborhoods by calculating walking distances of , and miles around each child's (n=2892) residence. The model assesses deprivation by the addition of weighted factors within a child's neighborhood-crime rates, alcohol outlets, and eight 2000 US Census characteristics. The results illustrate that neighborhood deprivation is weakly associated with greater levels of program participation, but neighborhood education level, measured by percentage of residents with less than a high school education, is strongly associated with greater program participation (OR: 4.43, 95% CI: [1.23, 15.99]). Neighborhood deprivation factors were significantly different between neighborhoods defined by walking distances and census blockgroups (Wilcoxon-signed rank test: p<0.05).