Rapid Assessment of Existing HIV Prevention Programming in a Community Mental Health Center

In preparation for implementation of a comprehensive HIV prevention program in a Community Mental Health Center for persons with mental illness who are also abusing substances, a rapid assessment procedure (RAP) of existing prevention services that may have developed in the setting over time was undertaken at baseline. In addition to an ecological assessment of the availability of HIV-related information that was available on-site, in-depth interviews and focus groups were conducted with Center administrators, direct-care staff, and mental health consumers. Results indicated that responses regarding available services differed depending upon type of respondent, with administration reporting greater availability of preventive programs and educational materials than did direct-care staff or mental health consumers themselves. But overall, formalized training on HIV prevention by case managers is extremely rare. Case managers felt that other providers, such as doctors or nurses, were more appropriate to deliver an HIV prevention intervention

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

Hershey Medical Center Technical Workshop Report: Optimizing the design and interpretation of epidemiologic studies for assessing neurodevelopmental effects from in utero chemical exposure

Neurodevelopmental disabilities affect 3-8% of the 4 million babies born each year in the U.S. alone, with known etiology for less than 25% of those disabilities. Numerous investigations have sought to determine the role of environmental exposures in the etiology of a variety of human neurodevelopmental disorders (e.g., learning disabilities, attention deficit-hyperactivity disorder, intellectual disabilities) that are manifested in childhood, adolescence, and young adulthood. A comprehensive critical examination and discussion of the various methodologies commonly used in investigations is needed. The Hershey Medical Center Technical Workshop: Optimizing the design and interpretation of epidemiologic studies for assessing neurodevelopmental effects from in utero chemical exposure provided such a forum for examining these methodologies. The objective of the Workshop was to develop scientific consensus on the key principles and considerations for optimizing the design and interpretation of epidemiologic studies of in utero exposure to environmental chemicals and subsequent neurodevelopmental effects. (The Panel recognized that the nervous system develops post-natally and that critical periods of exposure can span several developmental life stages.) Discussions from the Workshop Panel generated 17 summary points representing key tenets of work in this field. These points stressed the importance of: a well-defined, biologically plausible hypothesis as the foundation of in utero studies for assessing neurodevelopmental outcomes; understanding of the exposure to the environmental chemical(s) of interest, underlying mechanisms of toxicity, and anticipated outcomes; the use of a prospective, longitudinal cohort design that, when possible, runs for periods of 2-5 years, and possibly even longer, in an effort to assess functions at key developmental epochs; measuring potentially confounding variables at regular, fixed time intervals; including measures of specific cognitive and social-emotional domains along with non-cognitive competence in young children, as well as comprehensive measures of health; consistency of research design protocols across studies (i.e., tests, covariates, and analysis styles) in an effort to improve interstudy comparisons; emphasis on design features that minimize introduction of systematic error at all stages of investigation: participant selection, data collection and analysis, and interpretation of results; these would include (but not be limited to) reducing selection bias, using double-blind designs, and avoiding post hoc formulation of hypotheses; a priori data analysis strategies tied to hypotheses and the overall research design, particularly for methods used to characterize and address confounders in any neurodevelopmental study; actual quantitative measurements of exposure, even if indirect, rather than methods based on subject recall; careful examination of standard test batteries to ensure that the battery is tailored to the age group as well as what is known about the specific neurotoxic effects on the developing nervous system; establishment of a system for neurodevelopmental surveillance for tracking the outcomes from in utero exposure across early developmental time periods to determine whether central nervous system injuries may be lying silent until developmentally challenged; ongoing exploration of computerized measures that are culturally and linguistically sensitive, and span the age range from birth into the adolescent years; routine incorporation of narrative in manuscripts concerning the possibility of spurious (i.e., false positive and false negative) test results in all research reportage (this can be facilitated by detailed, transparent reporting of design, covariates, and analyses so that others can attempt to replicate the study); forthright, disciplined, and intellectually honest treatment of the extent to which results of any study are conclusive--that is, how generalizable the results of the study are in terms of the implications for the individual study participants, the community studied, and human health overall; confinement of reporting to the actual research questions, how they were tested, and what the study found, and avoiding, or at least keeping to a minimum, any opinions or speculation concerning public health implications; education of clinicians and policymakers to critically read scientific reports, and to interpret study findings and conclusions appropriately; and recognition by investigators of their ethical duty to report negative as well as positive findings, and the importance of neither minimizing nor exaggerating these findings

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