Identifying impacts of air pollution on subacute asthma symptoms using digital medication sensors.

BACKGROUND: Objective tracking of asthma medication use and exposure in real-time and space has not been feasible previously. Exposure assessments have typically been tied to residential locations, which ignore exposure within patterns of daily activities. METHODS: We investigated the associations of exposure to multiple air pollutants, derived from nearest air quality monitors, with space-time asthma rescue inhaler use captured by digital sensors, in Jefferson County, Kentucky. A generalized linear mixed model, capable of accounting for repeated measures, over-dispersion and excessive zeros, was used in our analysis. A secondary analysis was done through the random forest machine learning technique. RESULTS: The 1039 participants enrolled were 63.4% female, 77.3% adult (>18) and 46.8% White. Digital sensors monitored the time and location of over 286 980 asthma rescue medication uses and associated air pollution exposures over 193 697 patient-days, creating a rich spatiotemporal dataset of over 10 905 240 data elements. In the generalized linear mixed model, an interquartile range (IQR) increase in pollutant exposure was associated with a mean rescue medication use increase per person per day of 0.201 [95% confidence interval (CI): 0.189-0.214], 0.153 (95% CI: 0.136-0.171), 0.131 (95% CI: 0.115-0.147) and 0.113 (95% CI: 0.097-0.129), for sulphur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3), respectively. Similar effect sizes were identified with the random forest model. Time-lagged exposure effects of 0-3 days were observed. CONCLUSIONS: Daily exposure to multiple pollutants was associated with increases in daily asthma rescue medication use for same day and lagged exposures up to 3 days. Associations were consistent when evaluated with the random forest modelling approach.

COPD exacerbations associated with the modified Medical Research Council scale and COPD assessment test among Humana Medicare members.

BACKGROUND: The Global initiative for chronic Obstructive Lung Disease guidelines recommend assessment of COPD severity, which includes symptomatology using the modified Medical Research Council (mMRC) or COPD assessment test (CAT) score in addition to the degree of airflow obstruction and exacerbation history. While there is great interest in incorporating symptomatology, little is known about how patient reported symptoms are associated with future exacerbations and exacerbation-related costs. METHODS: The mMRC and CAT were mailed to a randomly selected sample of 4,000 Medicare members aged >40 years, diagnosed with COPD (≥2 encounters with International Classification of Dis eases-9th Edition Clinical Modification: 491.xx, 492.xx, 496.xx, ≥30 days apart). The exacerbations and exacerbation-related costs were collected from claims data during 365-day post-survey after exclusion of members lost to follow-up or with cancer, organ transplant, or pregnancy. A logistic regression model estimated the predictive value of exacerbation history and symptomatology on exacerbations during follow-up, and a generalized linear model with log link and gamma distribution estimated the predictive value of exacerbation history and symptomatology on exacerbation-related costs. RESULTS: Among a total of 1,159 members who returned the survey, a 66% (765) completion rate was observed. Mean (standard deviation) age among survey completers was 72.0 (8.3), 53.7% female and 91.2% white. Odds ratios for having post-index exacerbations were 3.06, 4.55, and 16.28 times for members with 1, 2, and ≥3 pre-index exacerbations, respectively, relative to members with 0 pre-index exacerbations (P<0.001 for all). The odds ratio for high vs low symptoms using CAT was 2.51 (P<0.001). Similarly, exacerbation-related costs were 73% higher with each incremental pre-index exacerbation, and over four fold higher for high-vs low-symptom patients using CAT (each P<0.001). The symptoms using mMRC were not statistically significant in either model (P>0.10). CONCLUSION: The patient-reported symptoms contribute important information related to future COPD exacerbations and exacerbation-related costs beyond that explained by exacerbation history.