Dowlingbell2476

Disease surveillance data are important for monitoring disease burden and occurrence, and for informing a wide range of efforts to improve population health. Surveillance for infectious diseases may be conducted passively, relying on reports from healthcare facilities, or actively, involving surveys of the population at risk. Passive surveillance typically provides wide spatial coverage, but is subject to biases arising from differences in care-seeking behavior, diagnostic practices, and under-reporting. Active surveillance minimizes these biases, but is typically constrained to small areas and subpopulations due to resource limitations. Methods based on linkage of individual records between passive and active surveillance datasets provide a means to estimate and correct for the biases of each system, leveraging the size and coverage of passive surveillance and the quality of data in active surveillance. We develop a spatial Bayesian hierarchical model for bias-correcting data from both systems to yield an improved estimate of disease measures after adjusting for under-ascertainment. We apply the framework to data from a passive and an active surveillance system for pulmonary tuberculosis (PTB) in Sichuan, China, and estimate the average sensitivity of the active surveillance system at 70% (95% credible interval 62%, 78%), and the passive system at 30% (95% CI 24%, 35%). Passive surveillance sensitivity exhibited considerable spatial variability, and was positively associated with a site's gross domestic product per capita. Bias-corrected estimates of county-level PTB prevalence in the province in 2010 identified regions in the southeast with the highest PTB burden, yielding different geographic priorities than previous reports.This study used spatiotemporal hot-spot analysis to characterize physical activity on the childcare center playground. Preschool-aged children (N = 34) wore a GPS and accelerometer during 2-3 outdoor periods on one day. A spatiotemporal weights matrix was generated so that points within a specified distance in meters (space) and 3 min (time) were considered neighbors. The Getis-Ord G* statistic was calculated to detect locations of significant hot/cold spots in vector magnitude counts/15‑sec. Hot/cold spots changed within a single outdoor period and between outdoor periods, highlighting the importance of time. This approach can be used to identify points of intervention during provided outdoor time.The effect that traffic congestion has on the service areas of stroke centers has received scarce attention. We aimed to determine the effect of traffic conditions on the characteristics of service areas of stroke centers in Bogotá, Colombia. Using a webservice, we sampled travel times from a set of census blocks to medical centers offering stroke management in the city. We obtained 179.340 transport times under different conditions. The size of service areas was reduced significantly with congestion (up to 94.83%). Overlap in the locations of centers led to large areas covered by only five centers. We identified areas with transport times to the closest center consistently exceeding 30-minutes to 1-hour in the west and south-west. Traffic conditions in Bogotá significantly affect service areas of centers capable of offering comprehensive stroke care. Spatial overlap of centers led to small catchment areas.Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address "unknown denominator problems" ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.Although heat exposure is the leading cause of mortality for undocumented immigrants attempting to traverse the Mexico-U.S. border, there has been little work in quantifying risk. Therefore, our study aims to develop a methodology projecting increase in core temperature over time and space for migrants in Southern Arizona using spatial analysis and remote sensing in combination with the heat balance equation-adapting physiological formulae to a multi-step geospatial model using local climate conditions, terrain, and body specifics. We sought to quantitatively compare the results by demographic categories of age and sex and qualitatively compare them to known terrestrial conditions and prior studies of those conditions. We demonstrated a more detailed measure of risk for migrants than those used most recently energy expenditure and terrain ruggedness. Our study not only gives a better understanding of the 'funnel effect' mechanisms, but also provides an opportunity for relief and rescue operations.Baylisascaris procyonis, the raccoon roundworm, is a parasite found throughout North America and parts of Europe. More than 150 species of mammals and birds including humans can develop neurological disease following infection with the larval stage of this parasite. To investigate whether B. procyonis infections in raccoons cluster in space, time, or space-time, we used data from 1353 Ontario raccoons submitted to the Canadian Wildlife Health Cooperative between 2013 and 2016. SB939 We identified a significant spatial cluster of increased infection prevalence in southern Ontario centered over a major metropolitan area, as well as a significant cluster of decreased infection prevalence in a primarily agricultural region in southwestern Ontario. Furthermore, we identified statistically significant temporal clusters in the fall in annual scans of data from 2014, 2015 and 2016. Examination of both Bernoulli and space-time permutation models for space-time analysis suggested that the purely spatial and temporal clusters were not explained by relatively short and spatially discrete events in space-time.