Feasibility and limitations of using commercial databases to evaluate residential mobility in registry-based research on childhood cancer

BACKGROUND

Researchers have used commercial databases containing residential addresses to reduce exposure misclassification in case-control studies. Our objective is to evaluate the potential systematic bias regarding case status when reconstructing residential locations from commercial databases.

METHODS

Our study population of 3640 Colorado-born children includes 520 children diagnosed with acute lymphocytic leukemia between 2002 and 2019. We aligned addresses and date ranges obtained from LexisNexis with registry dates to determine three dichotomous outcomes: Found in LexisNexis, conception date found in LexisNexis, and reference date/diagnosis date found in LexisNexis. We applied logistic regression to determine whether outcomes differed by case status.

RESULTS

Mothers of cases were 39% more likely to be found in LexisNexis than mothers of controls (OR = 1.39, 95% CI: 0.97, 2). Of the mothers found in LexisNexis, a conception address was 33% more likely (OR= 1.33, 95% CI: 1.06, 1.66) and a reference/diagnosis address was 60% more likely (OR= 1.60, 95% CI: 1.21, 2.12) to be found for mothers of cases than mothers of controls.

CONCLUSION

This study indicates that use of commercial databases to reconstruct residential locations may systematically bias results in case-control studies of childhood cancers.

Prenatal exposure to per- and polyfluoroalkyl substances and early childhood adiposity and cardiometabolic health in the Healthy Start study

BACKGROUND/OBJECTIVES

Observational and experimental studies have suggested that prenatal exposure to per- and polyfluoroalkyl substances (PFAS) can increase childhood adiposity and cardiometabolic disruption. However, most previous studies have used weight-based measures that cannot distinguish between fat mass and lean mass. We evaluated associations of prenatal PFAS exposure with precisely measured body composition and cardiometabolic biomarkers in early childhood.

SUBJECTS

373 eligible mother-infant pairs in the Healthy Start longitudinal cohort.

METHODS

We used multiple linear regression and Bayesian kernel machine regression models to estimate associations between five PFAS in maternal mid-pregnancy serum, and early childhood adiposity via air displacement plethysmography. Secondary outcomes included body mass index, waist circumference, and fasting serum lipids, glucose, insulin and adipokines. Models were adjusted for potential confounders and effect modification by child sex was evaluated.

RESULTS

The median age of children at assessment was 4.6 years. Prenatal concentration of perfluorooctanoate (PFOA) was positively associated with percent fat mass (0.89% per log2-unit increase, 95% CI: 0.15, 1.64), while perfluorononanoate (PFNA) was positively associated with fat mass index and body mass index. Cardiometabolic markers in blood were generally not associated with prenatal PFAS in this population. Mixture models confirmed the importance of PFNA and PFOA in predicting percent fat mass, while PFNA was most important for fat mass index, body mass index, and waist circumference. There were no significant effects of the five PFAS as a mixture, potentially due to opposing effects of different PFAS.

CONCLUSIONS

Our results agree with previous studies showing that prenatal serum concentrations of certain PFAS are positively associated with early childhood adiposity. Notably, associations were stronger for measures incorporating precisely measured fat mass compared to measures of body size or weight. Early life increases in adiposity may precede the development of adverse cardiometabolic health outcomes in children exposed to PFAS during gestation.

Early-life exposure to residential black carbon and childhood cardiometabolic health

BACKGROUND

Early life exposure to air pollution, such as particulate matter ≤2.5 μm (PM2.5), may be associated with obesity and adverse cardiometabolic health outcomes in childhood. However, the toxicity of PM2.5 varies according to its chemical composition. Black carbon (BC) is a constituent of PM2.5, but few studies have examined its impact on childhood cardiometabolic health. Therefore, we examined relationships between prenatal and early childhood exposure to BC and markers of adiposity and cardiometabolic health in early childhood.

METHODS

This study included 578 mother-child pairs enrolled in the Healthy Start study (2009-2014) living in the Denver-metro area. Using a spatiotemporal prediction model, we assessed average residential black carbon levels during pregnancy and in the year prior to the early childhood follow-up visit at approximately 5 years old. We estimated associations between prenatal and early childhood BC and indicators of adiposity and cardiometabolic biomarkers in early childhood (mean 4.8 years; range, 4.0, 8.3), using linear regression.

RESULTS

We found higher early childhood BC was associated with higher percent fat mass, fat mass index, insulin, and homeostatic model assessment for insulin resistance (HOMA-IR), and lower leptin and waist circumference at approximately 5 years old, after adjusting for covariates. For example, per interquartile range (IQR) increase in early childhood BC (IQR, 0.49 μg/m3) there was 3.32% higher fat mass (95% CI; 2.05, 4.49). Generally, we did not find consistent evidence of associations between prenatal BC and cardiometabolic health outcomes in early childhood, except for an inverse association between prenatal BC and adiponectin, an adipocyte-secreted hormone typically inversely associated with adiposity.

CONCLUSIONS

Higher early childhood, but not in utero, ambient concentrations of black carbon, a component of air pollution, were associated with greater adiposity and altered insulin homeostasis at approximately 5 years old. Future studies should examine whether these changes persist later in life.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination

BACKGROUND

Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence.

METHODS

In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data.

RESULTS

The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen's kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways.

CONCLUSION

Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

Health Symptoms and Proximity to Active Multi-Well Unconventional Oil and Gas Development Sites in the City and County of Broomfield, Colorado

City and County of Broomfield (CCOB) residents reported over 500 health concerns between January 2020 and December 2021. Our objective was to determine if CCOB residents living within 1 mile of multi-well unconventional oil and gas development (UOGD) sites reported more frequent health symptoms than residents living > 2 miles away. We invited 3993 randomly selected households to participate in a health survey. We applied linear regression to test associations between distance to UOGD and summed Likert scores for health symptom categories. After covariate adjustment, respondents living within 1 mile of one of CCOB's UOGD sites tended to report higher frequencies of upper respiratory, lower respiratory, gastrointestinal and acute symptoms than respondents living more than 2 miles from the sites, with the largest differences for upper respiratory and acute symptoms. For upper respiratory and acute symptoms, scores differed by 0.81 (95% CI: 0.06, 2.58) and 0.75 (95% CI: 0.004, 1.99), respectively. Scores for adults most concerned about air pollution, noise and odors trended higher within 1 mile for all symptom categories, while scores among adults least concerned trended lower. Scores trended higher for lower respiratory, gastrointestinal and acute symptoms in children living within 2 miles of UOGD, after covariate adjustment. We did not observe any difference in the frequency of symptoms reported in unadjusted results. Additional study is necessary to understand relationships between proximity to UOGD and health symptoms.

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.

Ambient air pollution during pregnancy and DNA methylation in umbilical cord blood, with potential mediation of associations with infant adiposity: The Healthy Start study

BACKGROUND

Prenatal exposure to ambient air pollution has been associated with adverse offspring health outcomes. Childhood health effects of prenatal exposures may be mediated through changes to DNA methylation detectable at birth.

METHODS

Among 429 non-smoking women in a cohort study of mother-infant pairs in Colorado, USA, we estimated associations between prenatal exposure to ambient fine particulate matter (PM2.5) and ozone (O3), and epigenome-wide DNA methylation of umbilical cord blood cells at delivery (2010-2014). We calculated average PM2.5 and O3 in each trimester of pregnancy and the full pregnancy using inverse-distance-weighted interpolation. We fit linear regression models adjusted for potential confounders and cell proportions to estimate associations between air pollutants and methylation at each of 432,943 CpGs. Differentially methylated regions (DMRs) were identified using comb-p. Previously in this cohort, we reported positive associations between 3rd trimester O3 exposure and infant adiposity at 5 months of age. Here, we quantified the potential for mediation of that association by changes in DNA methylation in cord blood.

RESULTS

We identified several DMRs for each pollutant and period of pregnancy. The greatest number of significant DMRs were associated with third trimester PM2.5 (21 DMRs). No single CpGs were associated with air pollutants at a false discovery rate <0.05. We found that up to 8% of the effect of 3rd trimester O3 on 5-month adiposity may be mediated by locus-specific methylation changes, but mediation estimates were not statistically significant.

CONCLUSIONS

Differentially methylated regions in cord blood were identified in association with maternal exposure to PM2.5 and O3. Genes annotated to the significant sites played roles in cardiometabolic disease, immune function and inflammation, and neurologic disorders. We found limited evidence of mediation by DNA methylation of associations between third trimester O3 exposure and 5-month infant adiposity.

Characterization of Colorado residents and radon reduction behaviors through latent class analysis and path models

Radon is a naturally occurring radioactive gas that enters homes through cracks in the foundation where accumulated levels can cause lung cancer. Within the United States (U.S.), state level radon reduction strategies rely on education and outreach to motivate people to test and mitigate their home. Only about 5% of the housing units in Colorado, U.S. have been tested for radon. This study looks at the 2012 Behavioral Risk Factors Surveillance System (BRFSS) in Colorado to identify distinct groups of people using Latent Class Analysis, and compares radon awareness, testing, and mitigation to understand underlying differences of radon reduction behaviors using path models. Five classes were identified: 1) Wealthy Young Families, 2) Older Singles, 3) Empty Nesters, 4) Smokers, and 5) Struggling Young Families. Significant differences in responses to radon survey questions existed across groups in which Struggling Young Families were the least likely to be aware of radon, have tested their home for radon, and have their home mitigated. Average radon awareness, testing, and mitigation appeared to be influenced by financial stress. Results from this study can be used to tailor future radon interventions and policy initiatives to enhance equity of radon reduction behaviors including legal framework to ensure radon mitigation takes place in rental properties.

Exposure to ambient air pollution during pregnancy and inflammatory biomarkers in maternal and umbilical cord blood: The Healthy Start study

BACKGROUND

Air pollution exposure during pregnancy has been associated with adverse pregnancy and birth outcomes. Inflammation has been proposed as a potential link. We estimated associations between air pollution exposure during pregnancy and inflammatory biomarkers in maternal and cord blood. We evaluated whether maternal inflammation was associated with infant outcomes.

METHODS

Among 515 mother-infant dyads in the Healthy Start study (2009-2014), trimester-long, 7- and 30-day average concentrations of particulate matter ≤2.5 μm (PM_2.5) and ozone (O₃) during pregnancy were estimated, using inverse-distance-weighted interpolation. Inflammatory biomarkers were measured in maternal blood in mid-pregnancy (C-reactive protein [CRP], Interleukin [IL]-6, and tumor necrosis factor-α [TNFα]) and in cord blood at delivery (CRP, IL-6, IL-8, IL-10, monocyte chemoattractant protein-1 [MCP-1], and TNFα). We used linear regression to estimate associations between pollutants and inflammatory biomarkers and maternal inflammatory biomarkers and infant weight and body composition.

RESULTS

There were positive associations between PM_2.5 during certain exposure periods and maternal IL-6 and TNFα. There were negative associations between recent O₃ and maternal CRP, IL-6, and TNFα and positive associations between trimester-long O₃ exposure and maternal inflammatory biomarkers, though some 95% confidence intervals included the null. Patterns were inconsistent for associations between PM_2.5 and O₃ and cord blood inflammatory biomarkers. No consistent associations between maternal inflammatory biomarkers and infant outcomes were identified.

CONCLUSIONS

Air pollution exposure during pregnancy may impact maternal inflammation. Further investigations should examine the health consequences for women and infants of elevated inflammatory biomarkers associated with air pollution exposure during pregnancy.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R² of 0.83 and a root-mean-square error of 0.15 μg/m³ for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

Prenatal exposure to traffic and ambient air pollution and infant weight and adiposity: The Healthy Start study

BACKGROUND

Prenatal exposures to ambient air pollution and traffic have been associated with adverse birth outcomes, and may also lead to an increased risk of obesity. Obesity risk may be reflected in changes in body composition in infancy.

OBJECTIVE

To estimate associations between prenatal ambient air pollution and traffic exposure, and infant weight and adiposity in a Colorado-based prospective cohort study.

METHODS

Participants were 1125 mother-infant pairs with term births. Birth weight was recorded from medical records and body composition measures (fat mass, fat-free mass, and adiposity [percent fat mass]) were evaluated via air displacement plethysmography at birth (n = 951) and at ~5 months (n = 574). Maternal residential address was used to calculate distance to nearest roadway, traffic density, and ambient concentrations of fine particulate matter (PM_2.5) and ozone (O₃) via inverse-distance weighted interpolation of stationary monitoring data, averaged by trimester and throughout pregnancy. Adjusted linear regression models estimated associations between exposures and infant weight and body composition.

RESULTS

Participants were urban residents and diverse in race/ethnicity and socioeconomic status. Average ambient air pollutant concentrations were generally low; the median, interquartile range (IQR), and range of third trimester concentrations were 7.3 μg/m³ (IQR: 1.3, range: 3.3-12.7) for PM_2.5 and 46.3 ppb (IQR: 18.4, range: 21.7-63.2) for 8-h maximum O_3. Overall there were few associations between traffic and air pollution exposures and infant outcomes. Third trimester O₃ was associated with greater adiposity at follow-up (2.2% per IQR, 95% CI 0.1, 4.3), and with greater rates of change in fat mass (1.8 g/day, 95% CI 0.5, 3.2) and adiposity (2.1%/100 days, 95% CI 0.4, 3.7) from birth to follow-up.

CONCLUSIONS

We found limited evidence of an association between prenatal traffic and ambient air pollution exposure and infant body composition. Suggestive associations between prenatal ozone exposure and early postnatal changes in body composition merit further investigation.

Prenatal Exposure to Tobacco and Offspring Neurocognitive Development in the Healthy Start Study

OBJECTIVE

To explore the associations between prenatal exposure to tobacco and neurocognitive development, in the absence of prematurity or low birth weight.

STUDY DESIGN

We followed mother-child pairs within Healthy Start through 6 years of age. Children were born at ≥37 weeks of gestation with a birth weight of ≥2500 g. Parents completed the Third Edition Ages and Stages Questionnaire (n = 246) and children completed a subset of the National Institutes of Health Toolbox Cognition Battery (n = 200). The Ages and Stages Questionnaire domains were dichotomized as fail/monitor and pass. Maternal urinary cotinine was measured at approximately 27 weeks of gestation. Separate logistic regression models estimated associations between prenatal exposure to tobacco (cotinine below vs above the limit of detection) and the Ages and Stages Questionnaire domains. Separate linear regression models estimated associations between prenatal exposure to tobacco and fully corrected T-scores for inhibitory control, cognitive flexibility, and receptive language, as assessed by the National Institutes of Health Toolbox. A priori covariates included sex, maternal age, maternal education, daily caloric intake during pregnancy, race/ethnicity, household income, maternal psychiatric disorders, and, in secondary models, postnatal exposure to tobacco.

RESULTS

Compared with unexposed offspring, exposed offspring were more likely to receive a fail/monitor score for fine motor skills (OR, 3.9; 95% CI, 1.5-10.3) and decreased inhibitory control (B: -3.0; 95% CI, -6.1 to -0.7). After adjusting for postnatal exposure, only the association with fine motor skills persisted.

CONCLUSIONS

Prenatal and postnatal exposures to tobacco may influence neurocognitive development, in the absence of preterm delivery or low birth weight. Increased developmental screening may be warranted for exposed children.

Community Noise and Air Pollution Exposure During the Development of a Multi-Well Oil and Gas Pad

Unconventional oil and gas development (UOGD) in the United States is increasingly being conducted on multiwell pads (MWPs) and in residential areas. We measured air pollution, noise, and truck traffic during four distinct phases of UOGD: drilling, hydraulic fracturing, flowback, and production. We monitored particulate matter (PM_2.5), black carbon (BC), A-weighted (dBA), and C-weighted (dBC) noise using real-time instruments on 1 and 5 min time scales, and truck traffic for 4-7 days per phase at a large 22-well pad sited in a residential area of Weld County, Colorado. Hydraulic fracturing, which requires frequent truck trips to move supplies and diesel engines to power the process, had the highest median air pollution levels of PM_2.5 and BC and experienced the greatest number of heavy trucks per hour compared to other phases. Median air pollution was lowest during drilling at this MWP, possibly because an electric drill rig was used. The equivalent continuous noise level ( L_eq) exceeded guidelines of 50 dBA and 65 dBC for A-weighted and C-weighted noise, respectively, during all development phases. Our data show that these multiple stressors are present around the clock at these sites, and this work provides baseline measurements on likely human exposure levels near similarly sized MWPs.

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.

Relationships between indicators of cardiovascular disease and intensity of oil and natural gas activity in Northeastern Colorado

BACKGROUND

Oil and natural gas (O&G) extraction emits pollutants that are associated with cardiovascular disease, the leading cause of mortality in the United States.

OBJECTIVE

We evaluated associations between intensity of O&G activity and cardiovascular disease indicators.

METHODS

Between October 2015 and May 2016, we conducted a cross-sectional study of 97 adults living in Northeastern Colorado. For each participant, we collected 1-3 measurements of augmentation index, systolic and diastolic blood pressure (SBP and DBP), and plasma concentrations of interleukin (IL)- 1β, IL-6, IL-8 and tumor necrosis factor alpha (TNF-α). We modelled the intensity of O&G activity by weighting O&G well counts within 16 km of a participant's home by intensity and distance. We used linear models accounting for repeated measures within person to evaluate associations.

RESULTS

Adjusted mean augmentation index differed by 6.0% (95% CI: 0.6, 11.4%) and 5.1% (95%CI: -0.1, 10.4%) between high and medium, respectively, and low exposure tertiles. The greatest mean IL-1β, and α-TNF plasma concentrations were observed for participants in the highest exposure tertile. IL-6 and IL-8 results were consistent with a null result. For participants not taking prescription medications, the adjusted mean SBP differed by 6 and 1 mm Hg (95% CIs: 0.1, 13 mm Hg and -6, 8 mm Hg) between the high and medium, respectively, and low exposure tertiles. DBP results were similar. For participants taking prescription medications, SBP and DBP results were consistent with a null result.

CONCLUSIONS

Despite limitations, our results support associations between O&G activity and augmentation index, SBP, DBP, IL-1β, and TNF-α. Our study was not able to elucidate possible mechanisms or environmental stressors, such as air pollution and noise.

Ambient Nonmethane Hydrocarbon Levels Along Colorado's Northern Front Range: Acute and Chronic Health Risks

Oil and gas (O&G) facilities emit air pollutants that are potentially a major health risk for nearby populations. We characterized prenatal through adult health risks for acute (1 h) and chronic (30 year) residential inhalation exposure scenarios to nonmethane hydrocarbons (NMHCs) for these populations. We used ambient air sample results to estimate and compare risks for four residential scenarios. We found that air pollutant concentrations increased with proximity to an O&G facility, as did health risks. Acute hazard indices for neurological (18), hematological (15), and developmental (15) health effects indicate that populations living within 152 m of an O&G facility could experience these health effects from inhalation exposures to benzene and alkanes. Lifetime excess cancer risks exceeded 1 in a million for all scenarios. The cancer risk estimate of 8.3 per 10 000 for populations living within 152 m of an O&G facility exceeded the United States Environmental Protection Agency's 1 in 10 000 upper threshold. These findings indicate that state and federal regulatory policies may not be protective of health for populations residing near O&G facilities. Health risk assessment results can be used for informing policies and studies aimed at reducing and understanding health effects associated with air pollutants emitted from O&G facilities.

Spatiotemporal Industrial Activity Model for Estimating the Intensity of Oil and Gas Operations in Colorado

Oil and gas (O&G) production in the United States has increased in the last 15 years, and operations, which are trending toward large multiwell pads, release hazardous air pollutants. Health studies have relied on proximity to O&G wells as an exposure metric, typically using an inverse distance-weighting (IDW) approach. Because O&G emissions are dependent on multiple factors, a dynamic model is needed to describe the variability in air pollution emissions over space and time. We used information on Colorado O&G activities, production volumes, and air pollutant emission rates from two Colorado basins to create a spatiotemporal industrial activity model to develop an intensity-adjusted IDW well-count metric. The Spearman correlation coefficient between this metric and measured pollutant concentrations was 0.74. We applied our model to households in Greeley, Colorado, which is in the middle of the densely developed Denver-Julesburg basin. Our intensity-adjusted IDW increased the unadjusted IDW dynamic range by a factor of 19 and distinguishes high-intensity events, such as hydraulic fracturing and flowback, from lower-intensity events, such as production at single-well pads. As the frequency of multiwell pads increases, it will become increasingly important to characterize the range of intensities at O&G sites when conducting epidemiological studies.

Population Size, Growth, and Environmental Justice Near Oil and Gas Wells in Colorado

We evaluated population size and factors influencing environmental justice near oil and gas (O&G) wells. We mapped nearest O&G well to residential properties to evaluate population size, temporal relationships between housing and O&G development, and 2012 housing market value distributions in three major Colorado O&G basins. We reviewed land use, building, real estate, and state O&G regulations to evaluate distributive and participatory justice. We found that by 2012 at least 378,000 Coloradans lived within 1 mile of an active O&G well, and this population was growing at a faster rate than the overall population. In the Denver Julesburg and San Juan basins, which experienced substantial O&G development prior to 2000, we observed a larger proportion of lower value homes within 500 feet of an O&G well and that most O&G wells predated houses. In the Piceance Basin, which had not experienced substantial prior O&G development, we observed a larger proportion of high value homes within 500 feet of an O&G well and that most houses predated O&G wells. We observed economic, rural, participatory, and/or distributive injustices that could contribute to health risk vulnerabilities in populations near O&G wells. We encourage policy makers to consider measures to reduce these injustices.

Mapping health data: improved privacy protection with donut method geomasking

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.

Geomasking sensitive health data and privacy protection: an evaluation using an E911 database

Geomasking is used to provide privacy protection for individual address information while maintaining spatial resolution for mapping purposes. Donut geomasking and other random perturbation geomasking algorithms rely on the assumption of a homogeneously distributed population to calculate displacement distances, leading to possible under-protection of individuals when this condition is not met. Using household data from 2007, we evaluated the performance of donut geomasking in Orange County, North Carolina. We calculated the estimated k-anonymity for every household based on the assumption of uniform household distribution. We then determined the actual k-anonymity by revealing household locations contained in the county E911 database. Census block groups in mixed-use areas with high population distribution heterogeneity were the most likely to have privacy protection below selected criteria. For heterogeneous populations, we suggest tripling the minimum displacement area in the donut to protect privacy with a less than 1% error rate.