Coroner county systems are associated with a higher likelihood of unclassified drug overdoses compared to medical examiner county systems.

Background: There is a striking geographic variation in drug overdose deaths without a specific drug recorded, many of which likely involve opioids. Knowledge of the reasons underlying this variation is limited.Objectives: We sought to understand the role of medicolegal death investigation (MDI) systems in unclassified drug overdose mortality.Methods: This is an observational study of 2014 and 2018 fatal drug overdoses and U.S. county-level MDI system type (coroner vs medical examiner). Mortality data are from the CDC's National Center for Health Statistics. We estimated multivariable logistic regressions to quantify associations between MDI system type and several outcome variables: whether the drug overdose was unclassified and whether involvement of any opioid, synthetic opioid, methadone, and heroin was recorded (vs unclassified), for 2014 (N = 46,996) and 2018 (N = 67,359).Results: In 2018, drug overdose deaths occurring in coroner counties were almost four times more likely to be unclassified (OR 3.87, 95% CI 2.32, 6.46) compared to medical examiner counties. These odds ratios are twice as large as in 2014 (difference statistically significant, P < .001), indicating that medical examiner counties are improving identification of opioids in drug overdoses faster than coroner counties.Conclusions: Accurate reporting of drug overdose deaths depends on MDI systems. When developing state policies and local interventions aimed to decrease opioid overdose mortality, decision-makers should understand the role their MDI system is playing in underestimating the extent of the opioid overdose crisis. Improvements to state and county MDI systems are desirable if accurate reporting and appropriate policy response are to be achieved.

Mortality during Resource Booms and Busts.

Using national data on county-level mortality, coal mining, and shale development, we examine the effects of resource booms and busts on mortality in the United States. We find evidence that decreases in operating coal mines increased total all-cause mortality, non-drug mortality, and opioid overdose mortality, especially for counties with greater than 10 operating coal mines in 2000. Our model results for drug overdose mortality and opioid overdose mortality are sensitive to the panel's start year. For shale development, the shale boom is associated with increases in non-drug suicides but otherwise had little impact on mortality. Our findings suggest a potential role for job-training programs and the cultivation of local healthcare resources in regions suffering coal busts and suicide prevention in areas with shale development.

Associations between Residential Proximity to Oil and Gas Drilling and Term Birth Weight and Small-for-Gestational-Age Infants in Texas: A Difference-in-Differences Analysis.

BACKGROUND: Oil and natural gas extraction may produce environmental pollution at levels that affect reproductive health of nearby populations. Available studies have primarily focused on unconventional gas drilling and have not accounted for local population changes that can coincide with drilling activity. OBJECTIVE: Our study sought to examine associations between residential proximity to oil and gas drilling and adverse term birth outcomes using a difference-in-differences study design. METHODS: We created a retrospective population-based term birth cohort in Texas between 1996 and 2009 composed of mother-infant dyads (n=2,598,025) living <10km from an oil or gas site. We implemented a difference-in-differences approach to estimate associations between drilling activities and infant health: term birth weight and term small for gestational age (SGA). Using linear and logistic regression, we modeled interactions between births before (unexposed) or during (exposed) drilling activity and residential proximity near (0-1, 1-2, or 2-3km) or far (3-10km) from an active or future drilling site, adjusting for individual- and neighborhood-level characteristics. RESULTS: The adjusted mean difference in term birth weight for mothers living 0-1 vs. 3-10km from a current or future drilling site was -7.3g [95% confidence interval (CI): -11.6, -3.0] for births during active vs. future drilling. The corresponding adjusted odds ratio for SGA was 1.02 (95% CI: 0.98, 1.06). Negative associations with term birth weight were observed for the 1-2 and 2-3km near groups, and no consistent differences were identified by type of drilling activity. Larger, though imprecise, adverse associations were found for infants born to Hispanic women, women with the lowest educational attainment, and women living in cities. CONCLUSIONS: Residing near oil and gas drilling sites during pregnancy was associated with a small reduction in term birth weight but not SGA, with some evidence of environmental injustices. Additional work is needed to investigate specific drilling-related exposures that might explain these associations. https://doi.org/10.1289/EHP7678.

Fatal opioid overdoses in the U.S. declined more than reported between 2017 and 2018.

Background: In U.S. death records, many drug overdoses do not have classified drug involvement, which challenges surveillance of opioid overdoses across time and space.Objective: To estimate the 2017-2018 change in opioid overdose deaths that accounts for probable opioid involvement in unclassified drug overdose deaths.Methods: In this retrospective design study, data on all drug overdose decedents from 2017-2018 in the U.S. were used to calculate the year-to-year change in known opioid overdoses, predict opioid involvement in unclassified drug overdoses, and estimate the year-to-year change in corrected opioid overdoses, which include both known and predicted opioid deaths. We used the Multiple Cause of Death (MCOD) data from CDC.Results: We estimated that the decrease in the age-adjusted opioid overdose death rate from 2017-2018 was 7.0%. There is a striking variation across states. Age-adjusted opioid overdose death rates decreased by 9.9% in Ohio and more than 5.0% in other Appalachian states (Pennsylvania, West Virginia, Kentucky), while they increased by 6.8% in Delaware.Conclusions: Our models suggest that opioid overdose-related mortality declined from 2017 to 2018 at a higher rate than reported (7.0% versus than the reported 2.0%), potentially indicating that clinical efforts and federal, state, and local government policies designed to control the epidemic have been effective in most states. Our local area estimates can be used by researchers, policy-makers and public health officials to assess effectiveness of state policies and interventions in smaller jurisdictions implemented in response to the crisis.

Rural Light Pollution from Shale Gas Development and Associated Sleep and Subjective Well-Being.

The shale oil and gas boom has had large economic, environmental, and social impacts on rural communities in the United States. This study provides novel estimates of the impacts of shale oil and gas development on light pollution in rural areas of the United States. Using nationwide, time-calibrated DMSP-OLS database from 2000 to 2012, we find robust evidence that the shale oil and gas boom significantly increased light pollution in rural areas. We then assess associations between horizontal drilling and subjective self-rated health using nationwide data from the Behavioral Risk Factor Surveillance System (BRFSS) from 2000 to 2012. Our findings suggest that insufficient sleep and poor health (physical or mental) are associated with increased drilling in rural areas. These results provide support for drilling-related light pollution as an additional environmental pathway of concern for public health beyond the mechanisms of air or water pollution.

Using contributing causes of death improves prediction of opioid involvement in unclassified drug overdoses in US death records.

BACKGROUND AND AIMS: A substantial share of fatal drug overdoses is missing information on specific drug involvement, leading to under-reporting of opioid-related death rates and a misrepresentation of the extent of the opioid epidemic. We aimed to compare methodological approaches to predicting opioid involvement in unclassified drug overdoses in US death records and to estimate the number of fatal opioid overdoses from 1999 to 2016 using the best-performing method. DESIGN: This was a secondary data analysis of the universe of drug overdoses in 1999-2016 obtained from the National Center for Health Statistics Detailed Multiple Cause of Death records. SETTING: United States. CASES: A total of 632 331 drug overdose decedents. Drug overdoses with known drug classification comprised 78.2% of the cases (n = 494 316) and unclassified drug overdoses (ICD-10 T50.9) comprised 21.8% (n = 138 015). MEASUREMENTS: Known opioid involvement was defined using ICD-10 codes T40.0-40.4 and T40.6, recorded in the set of contributing causes. Opioid involvement in unclassified drug overdoses was predicted using multiple methodological approaches: logistic regression and machine learning techniques, inclusion/exclusion of contributing causes of death and inclusion/exclusion of county-level characteristics. Having selected the model with the highest predictive ability, we calculated corrected estimates of opioid-related mortality. FINDINGS: Logistic regression and random forest models performed similarly. Including contributing causes substantially improved predictive accuracy, while including county characteristics did not. Using a superior prediction model, we found that 71.8% of unclassified drug overdoses in 1999-2016 involved opioids, translating into 99 160 additional opioid-related deaths, or approximately 28% more than reported. Importantly, there was a striking geographic variation in undercounting of opioid overdoses. CONCLUSIONS: In modeling opioid involvement in unclassified drug overdoses, highest predictive accuracy is achieved using a statistical model-either logistic regression or a random forest ensemble-with decedent characteristics and contributing causes of death as predictors.

Unclassified drug overdose deaths in the opioid crisis: emerging patterns of inequity.

OBJECTIVE: Examine whether individual, geographic, and economic phenotypes predict missing data on specific drug involvement in overdose deaths, manifesting inequities in overdose mortality data, which is a key data source used in measuring the opioid epidemic. MATERIALS AND METHODS: We combined national data sources (mortality, demographic, economic, and geographic) from 2014-2016 in a multi-method analysis of missing drug classification in the overdose mortality records (as defined by the use of ICD-10 T50.9 on death certificates). We examined individual disparities in decedent-level multivariate logistic regression models, geographic disparities in spatial analysis (heat maps), and economic disparities in a combination of temporal trend analyses (descriptive statistics) and both decedent- and county-level multivariate logistic regression models. RESULTS: Our analyses consistently found higher rates of unclassified overdoses in decedents of female gender, White race, non-Hispanic ethnicity, with college education, aged 30-59 and those from poorer counties. Despite the fact that unclassified drug overdose death rates have reduced over time, gaps persist between the richest and poorest counties. There are also striking geographic differences both across and within states. DISCUSSION: Given the essential role of mortality data in measuring the scale of the opioid epidemic, it is important to understand the individual and community inequities underlying the missing data on specific drug involvements. Knowledge of these inequities could enhance our understanding of the opioid crisis and inform data-driven interventions and policies with more equitable resource allocations. CONCLUSION: Multiple individual, geographic, and economic disparities underlie unclassified overdose deaths, with important implications for public health informatics and addressing the opioid crisis.

Shale gas transmission and housing prices.

In this study, we exploit residential property sales data in New York to value the external environmental costs of the proposed Constitution Pipeline, a high-capacity transmission pipeline designed to transport hydraulically-fractured natural gas in Pennsylvania to large northeastern markets. Results from difference-in-differences models suggest post-announcement price declines of 9% (~$12,000) for those properties located within three kilometers of the pipeline. These results are strongly robust to different specifications and subsets of the data, as well as falsification testing. Additionally, we find some evidence of attenuation in our treatment effect over time, which is indicative of either declining salience or expectations of the pipeline over time. Our results suggest that homebuyer expectations of the environmental externalities of natural gas pipeline construction and operations are large and negative.