4.6 Article

Opioid-related Mortality in United States Death Certificate Data A Quantitative Bias Analysis With Expert Elicitation of Bias Parameters

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EPIDEMIOLOGY
卷 34, 期 3, 页码 421-429

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LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/EDE.0000000000001600

关键词

Sensitivity and specificity; Data interpretation; Statistical; Misclassification; Opioid overdose; Opioid epidemic; Death certificates; Cause of death

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This study examines the potential bias in opioid-related mortality estimates due to misclassification in death certificates. By eliciting the opinions of primary care physicians and applying probabilistic bias analysis, the study finds that opioid-related mortality rates may be substantially underestimated, particularly among older adults. The findings highlight the importance of addressing misclassification in cause-of-death data for accurate public health assessments.
Background: Opioid-related mortality is an important public health problem in the United States. Incidence estimates rely on death certificate data generated by health care providers and medical examiners. Opioid overdoses may be underreported when other causes of death appear plausible. We applied physician-elicited death certificate bias parameters to quantitative bias analyses assessing potential age-related differential misclassification in US opioid-related mortality estimates. Methods: We obtained cause-of-death data (US, 2017) from the National Center for Health Statistics and calculated crude opioid-related outpatient death counts by age category (25-54, 55-64, 65+). We elicited beliefs from 10 primary care physicians on sensitivity of opioid-related death classification from death certificates. We summarized elicited sensitivity estimates, calculated plausible specificity values, and applied resulting parameters in a probabilistic bias analysis. Results: Physicians estimated wide sensitivity ranges for classification of opioid-related mortality by death certificates, with lower estimated sensitivities among older age groups. Probabilistic bias analyses adjusting for physician-estimated misclassification indicated 3.1 times more (95% uncertainty interval: 1.2-23.5) opioid-related deaths than the observed death count in the 65+ age group. All age groups had substantial increases in bias-adjusted death counts. Conclusions: We developed and implemented a feasible method of eliciting physician expert opinion on bias parameters for sensitivity of a medical record-based death indicator and applied findings in quantitative bias analyses adjusting for differential misclassification. Our findings are consistent with the hypothesis that opioid-related mortality rates may be substantially underestimated, particularly among older adults, due to misclassification in cause-of-death data from death certificates.

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