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    Researchers find that more people died from opioid deaths than reported - The Washington Post
    ▻https://www.washingtonpost.com/health/2020/02/28/opioid-deaths

    Opioid-related overdoses could be 28 percent higher than reported because of incomplete death records, researchers found in a study published Thursday.

    More than 400,000 people in the United States have died of opioid overdoses since the turn of the century, a quarter of them in just the past six years. But University of Rochester researchers found that between 1999 and 2016, about 100,000 more people died from opioids who were not accounted for — potentially obscuring the scope of the opioid epidemic and affecting funding for government programs intended to confront it, Elaine Hill, an economist and senior author of the study, told The Washington Post.

    The discrepancies were most pronounced in several states, including Alabama, Mississippi, Pennsylvania, Louisiana and Indiana.
    “We thought we would find underreporting, but we were definitely not prepared to find how spatially determined it is,” Hill said.

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      The researchers found that the records were least consistent in poorer communities. On average, the people whose records were not counted were white females in the 30 to 60 age range.
      The incorrect records could be attributed to several factors, Hill said. Limited resources in counties can delay toxicology reports, limit drug testing and even prevent the completion of autopsies.

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      Seul le résumé est accessible

      Using contributing causes of death improves prediction of opioid involvement in unclassified drug overdoses in US death records - Boslett - - Addiction - Wiley Online Library
      ▻https://onlinelibrary.wiley.com/doi/10.1111/add.14943

      https://onlinelibrary.wiley.com/cms/asset/d733d1f7-ce12-4a61-bd66-555fe94cec82/add.v115.3.cover.gif

      Abstract
      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.

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