Opioid overdose deaths and potentially inappropriate opioid prescribing practices (PIP): A spatial epidemiological study.

INTRODUCTION: Opioid overdose deaths quintupled in Massachusetts between 2000 and 2016. Potentially inappropriate opioid prescribing practices (PIP) are associated with increases in overdoses. The purpose of this study was to conduct spatial epidemiological analyses of novel comprehensively linked data to identify overdose and PIP hotspots. METHODS: Sixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering >98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: ≥100 morphine milligram equivalents (MMEs), co-prescription of benzodiazepines and opioids, cash purchases of opioid prescriptions, opioid prescriptions without a recorded pain diagnosis, and opioid prescriptions through multiple prescribers or pharmacies. Using spatial autocorrelation and cluster analyses, overdose and PIP hotspots were identified among 538 ZIP codes. RESULTS: More than half of the adult population (n = 3,143,817, ages 18 and older) were prescribed opioids. Nearly all ZIP codes showed increasing rates of overdose over time. Overdose clusters were identified in Worcester, Northampton, Lee/Tyringham, Wareham/Bourne, Lynn, and Revere/Chelsea (Getis-Ord Gi*; p < 0.05). Large PIP clusters for ≥100 MMEs and prescription without pain diagnosis were identified in Western Massachusetts; and smaller clusters for multiple prescribers in Nantucket, Berkshire, and Hampden Counties (p < 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p < 0.05). Overlap in PIP and overdose clusters was identified in Cape Cod and Berkshire County. However, we also found contradictory patterns in overdose and PIP hotspots. CONCLUSIONS: Overdose and PIP hotspots were identified, as well as regions where the two overlapped, and where they diverged. Results indicate that PIP clustering alone does not explain overdose clustering patterns. Our findings can inform public health policy decisions at the local level, which include a focus on PIP and misuse of heroin and fentanyl that aim to curb opioid overdoses.

A field study to estimate inhalation rates for use in a particle inhalation rate exposure metric.

Particle inhalation rate (PIR) is an air pollution exposure metric that relies on age-, sex-, and physical activity-specific estimates of minute respiratory volume (MRV; L/min-kg) to account for personal inhalation patterns. United States Environmental Protection Agency (USEPA)-generated MRV estimates derive primarily from relatively homogenous populations without substantial cardiorespiratory challenges. To determine if these MRV estimates are relevant to populations in generally poor cardiorespiratory health (e.g., the Boston Puerto Rican Health Study (BPRHS) population) or whether population-specific estimates are needed, we 1) estimated population-specific MRVs and compared them to USEPA MRV estimates, and 2) compared exposure distributions and health effect estimates using PIR with population-specific MRVs, PIR with USEPA MRVs, and ambient particle number concentration (PNC). We recruited 40 adults (80% Puerto Rican, mean age = 60.2 years) in Boston with health characteristics similar to the BPRHS population. We measured pulse, oxygen saturation, respiration rate, and inspiratory volume while participants walked, stood, sat, and lay down. Pulse, respiration rate, inspiratory volume, and MRV were greater when participants were walking/standing compared to sitting or lying down. We then calculated MRVs adjusted for age, sex, measured body weight, and physical activity using data from 19 Puerto Rican participants who wore a nose clip or held their nostrils closed. We applied the population-specific and USEPA MRVs to estimate ultrafine particle exposure for participants in the BPRHS (n = 781). We compared exposure distributions and health effect estimates using the PIR with population-specific MRV estimates, PIR with USEPA MRV estimates, and ambient concentrations. We found that while population-specific MRVs differed from USEPA MRVs, particularly for unhealthy participants, PIR exposure distributions and health effect estimates were similar using each exposure metric. Confidence intervals were narrower using the PIR metrics than ambient PNC, suggesting increased statistical efficiency. Even in our understudied population, using USEPA MRVs did not meaningfully change PIR estimates.