Feasibility and acceptability of monitoring personal air pollution exposure with sensors for asthma self-management.

BACKGROUND: Exposure to fine particulate matter (PM2.5) increases the risk of asthma exacerbations, and thus, monitoring personal exposure to PM2.5 may aid in disease self-management. Low-cost, portable air pollution sensors offer a convenient way to measure personal pollution exposure directly and may improve personalized monitoring compared with traditional methods that rely on stationary monitoring stations. We aimed to understand whether adults with asthma would be willing to use personal sensors to monitor their exposure to air pollution and to assess the feasibility of using sensors to measure real-time PM2.5 exposure. METHODS: We conducted semi-structured interviews with 15 adults with asthma to understand their willingness to use a personal pollution sensor and their privacy preferences with regard to sensor data. Student research assistants used HabitatMap AirBeam devices to take PM2.5 measurements at 1-s intervals while walking in Philadelphia neighborhoods in May-August 2018. AirBeam PM2.5 measurements were compared to concurrent measurements taken by three nearby regulatory monitors. RESULTS: All interview participants stated that they would use a personal air pollution sensor, though the consensus was that devices should be small (watch- or palm-sized) and light. Patients were generally unconcerned about privacy or sharing their GPS location, with only two stating they would not share their GPS location under any circumstances. PM2.5 measurements were taken using AirBeam sensors on 34 walks that extended through five Philadelphia neighborhoods. The range of sensor PM2.5 measurements was 0.6-97.6 µg/mL (mean 6.8 µg/mL), compared to 0-22.6 µg/mL (mean 9.0 µg/mL) measured by nearby regulatory monitors. Compared to stationary measurements, which were only available as 1-h integrated averages at discrete monitoring sites, sensor measurements permitted characterization of fine-scale fluctuations in PM2.5 levels over time and space. CONCLUSIONS: Patients were generally interested in using sensors to monitor their personal exposure to PM2.5 and willing to share personal sensor data with health care providers and researchers. Compared to traditional methods of personal exposure assessment, sensors captured personalized air quality information at higher spatiotemporal resolution. Improvements to currently available sensors, including more reliable Bluetooth connectivity, increased portability, and longer battery life would facilitate their use in a general patient population.

Consolidated Environmental and Social Data Facilitates Neighborhood-Level Health Studies in Philadelphia.

A wide range of datasets containing geographically distributed measures of the environment and social factors is currently available, and as low-cost sensors and other devices become increasingly used, the volume of these data will continue to grow. Because such factors influence many health outcomes, researchers with varied interests often repeat tasks related to gathering and preparing these data for studies. We created Sensor-based Analysis of Pollution in the Philadelphia Region with Information on Neighborhoods and the Environment (SAPPHIRINE), offered as a web application and R package, to integrate pollution, crime, social disadvantage, and traffic data relevant to investigators, citizen scientists, and policy makers in the Greater Philadelphia Area. SAPPHIRINE's capabilities include providing interactive maps and customizable data retrieval to aid in the visual identification of pollution and other factor hotspots, as well as hypothesis generation regarding relationships among these factors and health outcomes.

Impact of Individual versus Geographic-Area Measures of Socioeconomic Status on Health Associations Observed in the Behavioral Risk Factor Surveillance System.

Efforts to enhance Electronic Health Record (EHR) data for the study of conditions in which social and economic variables play a prominent role include linking clinical data to sources of external information via patient-specific geocodes. This approach is convenient, but whether geographic-area-level information from secondary sources is adequate as a surrogate of individual-level information is not fully understood. We used Behavioral Risk Factor Surveillance System (BRFSS) epidemiologic data to compare associations of individual income, median aggregate income, and Area Deprivation Index (ADI)-a validated score of U.S. socioeconomic deprivation-with various health outcomes. Median income and ADI assigned according to respondent area of residence were significantly associated with various health outcomes, but with substantially lower effect sizes than those of individual income. Our results show the limited ability of median income and ADI at the level of metropolitan/micropolitan statistical areas versus individual income for use as measures of socioeconomic status.

Facilitating Inclusion of Geocoded Pollution Data into Health Studies.

Exposure to pollutants impacts health and has been associated with a range of diseases, including respiratory and heart diseases, as well as all-cause mortality. Because taking exposure measures for individual studies is costly and impractical, most rely on data from sources such as the Environmental Protection Agency (EPA), which provides a wealth of publicly available pollution measures taken at over two thousand monitoring sites across the United States. While EPA data is readily available, estimating pollution exposure at a given latitude-longitude location remains computationally intensive. We developed Pollution-Associated Risk Geospatial Analysis SITE (PARGASITE), an online web-application and R package, that can be used to estimate levels of pollutants in the U.S. for 2005 through 2017 at user-defined geographic locations and time ranges. We demonstrate how PARGASITE can facilitate the study of associations between exposures and health outcomes using as an example an analysis of asthma risk factors among adults.

Factors associated with exacerbations among adults with asthma according to electronic health record data.

BACKGROUND: Asthma is a chronic inflammatory lung disease that affects 18.7 million U.S. adults. Electronic health records (EHRs) are a unique source of information that can be leveraged to understand factors associated with asthma in real-life populations. In this study, we identify demographic factors and comorbidities associated with asthma exacerbations among adults according to EHR-derived data and compare these findings to those of epidemiological studies. METHODS: We obtained University of Pennsylvania Hospital System EHR-derived data for asthma encounters occurring between 2011 and 2014. Regression analyses were performed to model asthma exacerbation frequency as explained by age, sex, race/ethnicity, health insurance type, smoking status, body mass index (BMI) and various comorbidities. We analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2012 to compare findings with those from the EHR-derived data. RESULTS: Based on data from 9068 adult patients with asthma, 33.37% had at least one exacerbation over the four-year study period. In a proportional odds logistic regression predicting number of exacerbations during the study period (levels: 0, 1-2, 3-4, 5+ exacerbations), after controlling for age, race/ethnicity, sex, health insurance type, and smoking status, the highest odds ratios (ORs) of significantly associated factors were: chronic bronchitis (2.70), sinusitis (1.50), emphysema (1.39), fluid and electrolyte disorders (1.35), class 3 obesity (1.32), and diabetes (1.28). An analysis of NHANES data showed associations for class 3 obesity, anemia and chronic bronchitis with exacerbation frequency in an adjusted model controlling for age, race/ethnicity, sex, financial class and smoking status. CONCLUSIONS: EHR-derived data is helpful to understand exacerbations in real-life asthma patients, facilitating design of detailed studies and interventions tailored for specific populations.