Levels and Determinants of DDT and DDE Exposure in the VHEMBE Cohort.

BackgroundAlthough indoor residual spraying (IRS) is an effective tool for malaria control, its use contributes to high insecticide exposure in sprayed communities and raises concerns about possible unintended health effects.ObjectiveThe Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) is a birth cohort study initiated in 2012 to characterize prenatal exposure to IRS insecticides and exposures' impacts on child health and development in rural South Africa.MethodsIn this report, we describe the VHEMBE cohort and dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyldichloroethylene (DDE) serum concentrations measured in VHEMBE mothers when they presented for delivery. In addition, we applied a causal inference framework to estimate the potential reduction in population-level p,p'-DDT and p,p'-DDE serum concentrations under five hypothetical interventions. A total of 751 mothers were enrolled.ResultsSerum concentrations of p,p' isomers of DDT and DDE were above the limit of detection (LOD) in ≥98% of the samples, whereas the o,p' isomers were above the LOD in at least 80% of the samples. Median (interquartile range) p,p'-DDT and p,p'-DDE serum concentrations for VHEMBE cohort participants were 55.3 (19.0-259.3) and 242.2 (91.8-878.7) ng/g-lipid, respectively. Mothers reporting to have lived in a home sprayed with DDT for malaria control had ~5-7 times higher p,p'-DDT and p,p'-DDE serum concentrations than those who never lived in a home sprayed with DDT. Of the five potential interventions tested, we found increasing access to water significantly reduced p,p'-DDT exposure and increasing the frequency of household wet mopping significantly reduced p,p'-DDT and p,p'-DDE exposure.ConclusionOur findings suggest that several intervention approaches may reduce DDT/DDE exposure in pregnant women living in IRS communities. https://doi.org/10.1289/EHP353

Bayesian hierarchical modeling and analysis for physical activity trajectories using actigraph data

Rapid developments in streaming data technologies are continuing to generate increased interest in monitoring human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy), have become prevalent. An actigraph unit continually records the activity level of an individual, producing a very large amount of data at a high-resolution that can be immediately downloaded and analyzed. While this kind of \textit{big data} includes both spatial and temporal information, the variation in such data seems to be more appropriately modeled by considering stochastic evolution through time while accounting for spatial information separately. We propose a comprehensive Bayesian hierarchical modeling and inferential framework for actigraphy data reckoning with the massive sizes of such databases while attempting to offer full inference. Building upon recent developments in this field, we construct Nearest Neighbour Gaussian Processes (NNGPs) for actigraphy data to compute at large temporal scales. More specifically, we construct a temporal NNGP and we focus on the optimized implementation of the collapsed algorithm in this specific context. This approach permits improved model scaling while also offering full inference. We test and validate our methods on simulated data and subsequently apply and verify their predictive ability on an original dataset concerning a health study conducted by the Fielding School of Public Health of the University of California, Los Angeles

mSpray: A mobile phone technology to improve malaria control efforts and monitor human exposure to malaria control pesticides in Limpopo, South Africa

Recent estimates indicate that malaria has led to over half a million deaths worldwide, mostly to African children. Indoor residual spraying (IRS) of insecticides is one of the primary vector control interventions. However, current reporting systems do not obtain precise location of IRS events in relation to malaria cases, which poses challenges for effective and efficient malaria control. This information is also critical to avoid unnecessary human exposure to IRS insecticides. We developed and piloted a mobile-based application (mSpray) to collect comprehensive information on IRS spray events. We assessed the utility, acceptability and feasibility of using mSpray to gather improved homestead- and chemical-level IRS coverage data. We installed mSpray on 10 cell phones with data bundles, and pilot tested it with 13 users in Limpopo, South Africa. Users completed basic information (number of rooms/shelters sprayed; chemical used, etc.) on spray events. Upon submission, this information as well as geographic positioning system coordinates and time/date stamp were uploaded to a Google Drive Spreadsheet to be viewed in real time. We administered questionnaires, conducted focus groups, and interviewed key informants to evaluate the utility of the app. The low-cost, cell phone-based “mSpray” app was learned quickly by users, well accepted and preferred to the current paper-based method. We recorded 2865 entries (99.1% had a GPS accuracy of 20 m or less) and identified areas of improvement including increased battery life. We also identified a number of logistic and user problems (e.g., cost of cell phones and cellular bundles, battery life, obtaining accurate GPS measures, user errors, etc.) that would need to be overcome before full deployment. Use of cell phone technology could increase the efficiency of IRS malaria control efforts by mapping spray events in relation to malaria cases, resulting in more judicious use of chemicals that are potentially harmful to humans and the environment.This publication was supported by grant numbers: R01 ES020360 and R01 ES020360-S1 from the National Institute of Environmental Health Sciences (NIEHS). We would also like to thank Zinto Corporation, South Africa for donating Smartphones

mSpray : a mobile phone technology to improve malaria control efforts and monitor human exposure to malaria control pesticides in Limpopo, South Africa

Recent estimates indicate thatmalaria has led to over half amillion deathsworldwide,mostly to African children. Indoor residual spraying (IRS) of insecticides is one of the primary vector control interventions. However, current reporting systems do not obtain precise location of IRS events in relation tomalaria cases,which poses challenges for effective and efficient malaria control. This information is also critical to avoid unnecessary human exposure to IRS insecticides. We developed and piloted a mobile-based application (mSpray) to collect comprehensive information on IRS spray events. We assessed the utility, acceptability and feasibility of using mSpray to gather improved homestead- and chemical-level IRS coverage data. We installed mSpray on 10 cell phones with data bundles, and pilot tested it with 13 users in Limpopo, South Africa. Users completed basic information (number of rooms/shelters sprayed; chemical used, etc.) on spray events. Upon submission, this information as well as geographic positioning system coordinates and time/date stamp were uploaded to a Google Drive Spreadsheet to be viewed in real time. We administered questionnaires, conducted focus groups, and interviewed key informants to evaluate the utility of the app. The low-cost, cell phone-based “mSpray” app was learned quickly by users, well accepted and preferred to the current paper-based method. We recorded 2865 entries (99.1% had a GPS accuracy of 20 m or less) and identified areas of improvement including increased battery life. We also identified a number of logistic and user problems (e.g., cost of cell phones and cellular bundles, battery life, obtaining accurate GPS measures, user errors, etc.) that would need to be overcome before full deployment. Use of cell phone technology could increase the efficiency of IRSmalaria control efforts by mapping spray events in relation to malaria cases, resulting in more judicious use of chemicals that are potentially harmful to humans and the environment.This publication was supported by grant numbers: R01 ES020360 and R01 ES020360-S1 from the National Institute of Environmental Health Sciences (NIEHS)http://www.elsevier.com/ locate/envinthb201

Geographic Information Systems (GIS) for Misaligned Variables: Advances in Small-Area Analyses for Environmental Health Sciences

Geospatial methods are increasingly used to evaluate environmental exposures in epidemiologic studies. Geospatial data are often acquired from different sources and time periods and at different spatial resolutions. This dissertation presents methods for combining datasets that do not overlap in space and, or time and are essential for the accurate quantification of metrics used in Environmental Health Sciences. In three chapters, we demonstrated and compared advances in small-area geospatial methods used to reduce misalignment and misclassification of key variables (e.g., exposure).First, we showed how small-area analyses of COVID-19 can benefit from spatial aggregation to account for areal misalignment. In an analysis of the association between air pollution levels and COVID-19 incidence and mortality, we used residential building footprints to combine misaligned COVID-19 outcomes recorded at the neighborhood level, population demographics recorded at the census-tract level, and NO2 interpolated surfaces recorded at 30-meter grids. We found NO2 to be positively associated with COVID-19 incidence and mortality for neighborhoods in Los Angeles.Second, we used data acquired through the PASTA-LA study to attribute daily green space exposure for physically-active spaces (PASs) where activity, location, and green space data were misaligned. We used tracking data from accelerometers and smartphones to attribute green-space exposure using 21 geospatial methods. We found that exposures attributed to home-address buffers, a commonly used method, can result in exposure misclassification. We also found a large range in correlation depending on tracking device used and the way physical activity was categorized across the 21 methods, suggesting that the method selected is key to the findings.Finally, third, also using PASTA-LA data, we studied the association between heat exposure and physical activity in which temperature, green space, and participant-level covariates were misaligned. We showed that heat exposure was associated with increased physical activity and that exposure to green space modified this association in only some models. In this chapter, and in all chapters of this dissertation, we demonstrated how choice in geospatial approach to spatiotemporal misalignment can yield different study results

The association of green space, tree canopy and parks with life expectancy in neighborhoods of Los Angeles

Substantial evidence suggests that access to urban green spaces and parks is associated with positive health outcomes, including decreased mortality. Few existing studies have investigated the association between green spaces and life expectancy (LE), and none have used small-area data in the U.S. Here we used the recently released U.S. Small-Area Life Expectancy Estimates Project data to quantify the relationship between LE and green space in Los Angeles County, a large diverse region with inequities in park access. We developed a model to quantify the association between green space and LE at the census tract level. We evaluated three green space metrics: normalized difference vegetation index (NDVI, 0.6-meter scale), percent tree canopy cover, and accessible park acres. We statistically adjusted for 15 other determinants of LE. We also developed conditional autoregressive models to account for spatial dependence. Tree canopy and NDVI were both significantly associated with higher LE. For an interquartile range (IQR) increase in each metric respectively, the spatial models demonstrated a 0.24 to 0.33-year increase in LE. Tree canopy and NDVI also modified the effect of park acreage on LE. ln areas with tree canopy levels below the county median, an IQR increase in park acreage was associated with an increase of 0.12 years. Although on an individual level these effects were modest, we predicted 155,300 years of LE gains across the population in LA County if all areas below median tree canopy were brought to the county median of park acres. If tree canopy or NDVI were brought to median levels, between 570,300 and 908,800 years of LE could be gained. The majority of potential gains are in areas with predominantly Hispanic/Latinx and Black populations. These findings suggest that equitable access to green spaces could result in substantial population health benefits

Bayesian hierarchical modeling and analysis for physical activity trajectories using actigraph data

The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems, such as diabetes, hypertension, and heart diseases. This has generated substantial interest in monitoring human activity to gear interventions toward environmental features that may relate to higher physical activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraph units) continuously record the activity levels of a subject, producing massive amounts of high-resolution measurements. Analyzing actigraph data needs to account for spatial and temporal information on trajectories or paths traversed by subjects wearing such devices. Inferential objectives include estimating a subject’s physical activity levels along a given trajectory, identifying trajectories that are more likely to produce higher levels of physical activity for a given subject, and predicting expected levels of physical activity in any proposed new trajectory for a given set of health attributes. Here, we devise a Bayesian hierarchical modeling framework for spatial-temporal actigraphy data to deliver fully model-based inference on trajectories while accounting for subject-level health attributes and spatial-temporal dependencies. We undertake a comprehensive analysis of an original dataset from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study to ascertain spatial zones and trajectories exhibiting significantly higher levels of physical activity while accounting for various sources of heterogeneity

Open Access Cyclist route choice, traffic-related air pollution, and lung function: a scripted exposure study

Background: A travel mode shift to active transportation such as bicycling would help reduce traffic volume and related air pollution emissions as well as promote increased physical activity level. Cyclists, however, are at risk for exposure to vehicle-related air pollutants due to their proximity to vehicle traffic and elevated respiratory rates. To promote safe bicycle commuting, the City of Berkeley, California, has designated a network of residential streets as “Bicycle Boulevards. ” We hypothesized that cyclist exposure to air pollution would be lower on these Bicycle Boulevards when compared to busier roads and this elevated exposure may result in reduced lung function. Methods: We recruited 15 healthy adults to cycle on two routes – a low-traffic Bicycle Boulevard route and a high-traffic route. Each participant cycled on the low-traffic route once and the high-traffic route once. We mounted pollutant monitors and a global positioning system (GPS) on the bicycles. The monitors were all synced to GPS time so pollutant measurements could be spatially plotted. We measured lung function using spirometry before and after each bike ride. Results: We found that fine and ultrafine particulate matter, carbon monoxide, and black carbon were all elevated on the high-traffic route compared to the low-traffic route. There were no corresponding changes in the lung function of healthy non-asthmatic study subjects. We also found that wind-speed affected pollution concentrations

Levels and Determinants of DDT and DDE Exposure in the VHEMBE Cohort.

BackgroundAlthough indoor residual spraying (IRS) is an effective tool for malaria control, its use contributes to high insecticide exposure in sprayed communities and raises concerns about possible unintended health effects.ObjectiveThe Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) is a birth cohort study initiated in 2012 to characterize prenatal exposure to IRS insecticides and exposures' impacts on child health and development in rural South Africa.MethodsIn this report, we describe the VHEMBE cohort and dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyldichloroethylene (DDE) serum concentrations measured in VHEMBE mothers when they presented for delivery. In addition, we applied a causal inference framework to estimate the potential reduction in population-level p,p'-DDT and p,p'-DDE serum concentrations under five hypothetical interventions. A total of 751 mothers were enrolled.ResultsSerum concentrations of p,p' isomers of DDT and DDE were above the limit of detection (LOD) in ≥98% of the samples, whereas the o,p' isomers were above the LOD in at least 80% of the samples. Median (interquartile range) p,p'-DDT and p,p'-DDE serum concentrations for VHEMBE cohort participants were 55.3 (19.0-259.3) and 242.2 (91.8-878.7) ng/g-lipid, respectively. Mothers reporting to have lived in a home sprayed with DDT for malaria control had ~5-7 times higher p,p'-DDT and p,p'-DDE serum concentrations than those who never lived in a home sprayed with DDT. Of the five potential interventions tested, we found increasing access to water significantly reduced p,p'-DDT exposure and increasing the frequency of household wet mopping significantly reduced p,p'-DDT and p,p'-DDE exposure.ConclusionOur findings suggest that several intervention approaches may reduce DDT/DDE exposure in pregnant women living in IRS communities. https://doi.org/10.1289/EHP353