20 research outputs found

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

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    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

    Truck and Multivehicle Truck Accidents with Injuries Near Colorado Oil and Gas Operations

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    Unconventional and conventional oil and gas (O&G) operations raise public health concerns, such as the potential impacts from trucking activity in communities that host these operations. In this work, we used two approaches to evaluate accidents in relation to O&G activities in the State of Colorado. First, we calculated the rate of truck accidents by computing the ratio of accident count and county population. When comparing counties with increased O&G operations to counties with less activity, we found that counties with more activity have greater rates of truck traffic accidents per capita (Rate Ratio = 1.07, p < 0.05, 95% CI: 1.01–1.13). Second, we laid a grid over the eleven counties of interest and counted, for each cell, the number of truck accidents, the number of multivehicle accidents with injuries, the number of homes, and the number of O&G wells. We then applied hurdle count models, using the accident counts as the outcomes and the number of homes and number of wells as independent variables. We found that both independent variables are significant predictors of truck accidents and multivehicle truck accidents. These accidents are of concern since they can have an impact on the people who live near O&G operations

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

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    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

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    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

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

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    Abstract 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

    Childhood hematologic cancer and residential proximity to oil and gas development

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    <div><p>Background</p><p>Oil and gas development emits known hematological carcinogens, such as benzene, and increasingly occurs in residential areas. We explored whether residential proximity to oil and gas development was associated with risk for hematologic cancers using a registry-based case-control study design.</p><p>Methods</p><p>Participants were 0–24 years old, living in rural Colorado, and diagnosed with cancer between 2001–2013. For each child in our study, we calculated inverse distance weighted (IDW) oil and gas well counts within a 16.1-kilometer radius of residence at cancer diagnosis for each year in a 10 year latency period to estimate density of oil and gas development. Logistic regression, adjusted for age, race, gender, income, and elevation was used to estimate associations across IDW well count tertiles for 87 acute lymphocytic leukemia (ALL) cases and 50 non-Hodgkin lymphoma (NHL) cases, compared to 528 controls with non-hematologic cancers.</p><p>Findings</p><p>Overall, ALL cases 0–24 years old were more likely to live in the highest IDW well count tertiles compared to controls, but findings differed substantially by age. For ages 5–24, ALL cases were 4.3 times as likely to live in the highest tertile, compared to controls (95% CI: 1.1 to 16), with a monotonic increase in risk across tertiles (trend p-value = 0.035). Further adjustment for year of diagnosis increased the association. No association was found between ALL for children aged 0–4 years or NHL and IDW well counts. While our study benefited from the ability to select cases and controls from the same population, use of cancer-controls, the limited number of ALL and NHL cases, and aggregation of ages into five year ranges, may have biased our associations toward the null. In addition, absence of information on O&G well activities, meteorology, and topography likely reduced temporal and spatial specificity in IDW well counts.</p><p>Conclusion</p><p>Because oil and gas development has potential to expose a large population to known hematologic carcinogens, further study is clearly needed to substantiate both our positive and negative findings. Future studies should incorporate information on oil and gas development activities and production levels, as well as levels of specific pollutants of interest (e.g. benzene) near homes, schools, and day care centers; provide age-specific residential histories; compare cases to controls without cancer; and address other potential confounders, and environmental stressors.</p></div

    Study population characteristics for children residing in rural Colorado with a geocoded address and diagnosed with cancer between 2001 and 2013 by exposure group.

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    <p>Study population characteristics for children residing in rural Colorado with a geocoded address and diagnosed with cancer between 2001 and 2013 by exposure group.</p

    Number of oil and gas wells in 16.1-kilometer radius from a child’s home versus the minimum distance of an oil and gas well from the child’s home for children with at least one oil and gas well within the 16.1-kilometer radius.

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    <p>Number of oil and gas wells in 16.1-kilometer radius from a child’s home versus the minimum distance of an oil and gas well from the child’s home for children with at least one oil and gas well within the 16.1-kilometer radius.</p
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