Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut during a week in July, 2016, in conjunction with an ozone pollution model, and compare individual ozone exposure assuming static versus mobile scenarios. Our results show that exposure models not taking mobility into account often provide poor estimates of individuals commuting into and out of urban areas: the average 8-hour maximum difference between these estimates can exceed 80 parts per billion (ppb). However, for most of the population, the difference in exposure assignment between the two models is small, thereby validating many current epidemiologic studies focusing on exposure to ozone

The Need to Incorporate Communities in Compartmental Models

Tian et al. provide a framework for assessing population- level interventions of disease outbreaks through the construction of counterfactuals in a large-scale, natural experiment assessing the efficacy of mild, but early interventions compared to delayed interventions. The technique is applied to the recent SARS-CoV-2 outbreak with the population of Shenzhen, China acting as the mild-but-early treatment group and a combination of several US counties resembling Shenzhen but enacting a delayed intervention acting as the control. To help further the development of this framework and identify an avenue for further enhancement, we focus on the use and potential limitations of compartmental mod- els. In particular, compartmental models make assumptions about the communicability of a disease that may not per- form well when they are used for large areas with multiple communities where movement is restricted. To illustrate this phenomena, we provide a simulation of a directed percolation (outbreak) process on a simple stochastic block model with two blocks. The simulations show that when transmissibility between two communities is severely restricted an outbreak in two communities resembles a primary and secondary outbreak potentially causing policy and decision makers to mistake effective intervention strategies with non- compliance or inefficacy of an intervention

Distribution of human exposure to ozone during commuting hours in Connecticut using the cellular device network

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school, or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut, USA during a week in July 2016, in conjunction with an ozone pollution model, and compare individual ozone exposure assuming static versus mobile scenarios. Our results show that exposure models not taking mobility into account often provide poor estimates of individuals commuting into and out of urban areas: the average 8-h maximum difference between these estimates can exceed 80 parts per billion (ppb). However, for most of the population, the difference in exposure assignment between the two models is small, thereby validating many current epidemiologic studies focusing on exposure to ozone

Spatiotemporal Calibration of Atmospheric Nitrogen Dioxide Concentration Estimates From an Air Quality Model for Connecticut

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (NO$_2$) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on NO$_2$ that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the NO$_2$ monitor sites. The final model was used to predict the daily concentration of ambient NO$_2$ over the entire state of Connecticut on a grid with pixels of size 300 x 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient NO$_2$ concentration stands out. An animation was also provided to show the change in the concentration of ambient NO$_2$ over space and time for 1994 and 1995.Comment: 23 pages, 8 figures, supplementary materia

Enhancing models and measurements of traffic-related air pollutants for health studies using dispersion modeling and Bayesian data fusion

Research Report 202 describes a study led by Dr. Stuart Batterman at the University of Michigan, Ann Arbor and colleagues. The investigators evaluated the ability to predict traffic-related air pollution using a variety of methods and models, including a line source air pollution dispersion model and sophisticated spatiotemporal Bayesian data fusion methods. Exposure assessment for traffic-related air pollution is challenging because the pollutants are a complex mixture and vary greatly over space and time. Because extensive direct monitoring is difficult and expensive, a number of modeling approaches have been developed, but each model has its own limitations and errors. Dr. Batterman and colleagues sought to improve model estimations by applying and systematically comparing the performance of different statistical models. The study made extensive use of data collected in the Near-road EXposures and effects of Urban air pollutants Study (NEXUS), a cohort study designed to examine the relationship between near-roadway pollutant exposures and respiratory outcomes in children with asthma who live close to major roadways in Detroit, Michigan

Evaluating clinical and demographic influences on health perception: A translation of the SF-12 for use with NHANES

Improving public health depends on an intricate understanding of the factors that influence how individuals perceive and self-report their personal health. Self-perceived health is an independent predictor of future health-related outcomes, but capturing self-perception of health is complex due to the intricate relationship between clinical and perceived health. A commonly used measure of self-perceived health is the Short Form 12 (SF-12), developed in the 1990s. In this study, we aim to evaluate clinical and demographic influences on self-perceived health among American adults using the National Health and Nutrition Examination Survey (NHANES). While NHANES captures information on a number of domains of health, including clinical assessments, it does not include SF-12 items necessary to measure self-perceived health. Therefore, to assess self-perceived health for our study, we constructed and validated a novel SF-12-equivalent measure for use with NHANES using analogous items from the 2015–2016 NHANES interview questionnaires. The developed measure reflects established knowledge of population health patterns and closely parallels the behavior of the original SF-12. An analysis of the clinical and demographic influences on this novel measure of health perception revealed that both clinical and demographic factors, such as depression status and race, influence how healthy individuals perceive themselves to be. Importantly, our analysis indicated that among American adults, while controlling for clinical and demographic covariates, an increase in low-density lipoprotein (i.e., “bad”) cholesterol level was associated with an improvement in self-perceived health. This study contributes significantly in two domains: it provides a novel measure of self-perceived health compatible for use with the widely used NHANES data (as well as details on how the process was developed), and it identifies a critical area in need of improved clinical education regarding the apparent confusion around cholesterol health

Nonstationary Spatiotemporal Bayesian Data Fusion for Pollutants in the Near-Road Environment

Concentrations of near-road air pollutants (NRAPs) have increased to very high levels in many urban centers around the world, particularly in developing countries. The adverse health effects of exposure to NRAPs are greater when the exposure occurs in the near-road environment as compared to background levels of pollutant concentration. Therefore, there is increasing interest in monitoring pollutant concentrations in the near-road environment. However, due to various practical limitations, monitoring pollutant concentrations near roadways and traffic sources is generally rather difficult and expensive. As an alternative, various deterministic computer models that provide predictions of pollutant concentrations in the near-road environment, such as the research line-source dispersion model (RLINE), have been developed. A common feature of these models is that their outputs typically display systematic biases and need to be calibrated in space and time using observed pollutant data. In this paper, we present a nonstationary Bayesian data fusion model that uses a novel data set on monitored pollutant concentrations (nitrogen oxides or NOx and fine particulate matter or PM2.5) in the near-road environment and, combining it with the RLINE model output, provides predictions at unsampled locations. The model can also be used to evaluate whether including the RLINE model output leads to improved pollutant concentration predictions and whether the RLINE model output captures the spatial dependence structure of NRAP concentrations in the near-road environment. A defining characteristic of the proposed model is that we model the nonstationarity in the pollutant concentrations by using a recently developed approach that includes covariates, postulated to be the driving force behind the nonstationary behavior, in the covariance function

Patient-centered medical home exposure and its impact on PA career intentions

BACKGROUND AND OBJECTIVES: The transformation of primary care (PC) training sites into patient-centered medical homes (PCMH) has implications for the education of health professionals. This study investigates the extent to which physician assistant (PA) students report learning about the PCMH model and how clinical exposure to PCMH might impact their interest in a primary care career. METHODS: An electronic survey was distributed to second-year PA students who had recently completed their PC rotation from 12 PA programs. Descriptive statistics and ordered logistic regression analyses were used to characterize the results. RESULTS: A total of 202 second-year PA students completed the survey. When asked about their knowledge of the new health care delivery models, 30% of the students responded they had received instruction about the PCMH. Twenty- five percent of respondents stated they were oriented to new payment structures proposed in the Affordable Care Act and quality improvement principles. Based on their experiences in the primary care clerkship, 64% stated they were likely to pursue a career in primary care, 13% were not likely, and 23% were unsure. Predictors of interest in a primary care career included: (1) age greater than 35 years, (2) being a recipient of a NHSC scholarship, (3) clerkship site setting in an urban cluster of 2,500 to 50,000 people, (4) number of PCMH elements offered at site, and (4) positive impression of team-based care. CONCLUSIONS: PA students lack adequate instruction related to the new health care delivery models. Students whose clerkship sites offered greater number of PCMH elements were more interested in pursuing a career in primary care

Primary Care Teams, Composition, Roles, and Satisfaction of PA Students During Primary Care Rotations.

PurposeThe goal of t his study was to describe the characteristics of primary care teams, activities, and ro les of physician assistant (PA) students as they encounter various primary care sites.MethodsAn electronic survey was distributed to second year PA students in 12 programs who had completed at least 4 weeks in a primary care rotation.ResultsOf the 179 students who responded (response rate 41 %), 88% had completed their primary care rotations in urban settings, mostly in private practices (53%). Physician assistant students reported encountering many types of health care providers on their teams, and the 2 most favored features of the rotations were the interactions with their supervising clinicians and clinical responsibilities. About 68% interacted with other health profession students during their rotation(interprofessional experiential learning). Almost all students completed histories, physical examinations, and treatment plans, but less than 30% reported involvement in billing or care coordination and less than 10% participated in quality improvement projects. More than 60% were satisfied with team-based and interprofessional practices encountered during their primary care rotations, and 39% were more than likely to pursue primary care careers.ConclusionsTeam-based prima ry ca re had a positive impact on students, but more exposure to underserved clinical settings, care coordination, quality improvement, and billing is needed to prepare PA students for the practice of the future. This study is t he first of its kind to explore the relationship between primary care sites and PA training in the era of health care reform

Patient-Centered Medical Home Exposure and Its Impact on PA Career Intentions.

Background and objectivesThe transformation of primary care (PC) training sites into patient-centered medical homes (PCMH) has implications for the education of health professionals. This study investigates the extent to which physician assistant (PA) students report learning about the PCMH model and how clinical exposure to PCMH might impact their interest in a primary care career.MethodsAn electronic survey was distributed to second-year PA students who had recently completed their PC rotation from 12 PA programs. Descriptive statistics and ordered logistic regression analyses were used to characterize the results.ResultsA total of 202 second-year PA students completed the survey. When asked about their knowledge of the new health care delivery models, 30% of the students responded they had received instruction about the PCMH. Twenty- five percent of respondents stated they were oriented to new payment structures proposed in the Affordable Care Act and quality improvement principles. Based on their experiences in the primary care clerkship, 64% stated they were likely to pursue a career in primary care, 13% were not likely, and 23% were unsure. Predictors of interest in a primary care career included: (1) age greater than 35 years, (2) being a recipient of a NHSC scholarship, (3) clerkship site setting in an urban cluster of 2,500 to 50,000 people, (4) number of PCMH elements offered at site, and (4) positive impression of team-based care.ConclusionsPA students lack adequate instruction related to the new health care delivery models. Students whose clerkship sites offered greater number of PCMH elements were more interested in pursuing a career in primary care