Getting the Right Numbers for Native American Drug-Overdose Deaths

Have the numbers increased or dropped over the years? The IHS looks into the accuracy of the data.


More Native Americans have died of a drug overdose than members of any other racial or ethnic group in the US—which as a whole has seen drug overdose deaths triple since 1999. But little is known about the regional impact of opioids in tribal and urban American Indian/Alaska Native (AI/AN) communities, according to Indian Health Service (IHS) researchers from Portland, Oregon. They examined death records from the Washington State Center for Health Statistics to identify trends and disparities in drug, opioid-involved, and heroin-involved overdose deaths for AI/AN and non-Hispanic whites in Washington.

AI/AN and whites had similar overdose-related death rates during 1999 and 2001, but then the deaths began to multiply faster among Native Americans: Between 2013 and 2015, Native Americans were dying at nearly 3 times the rate of drug and opioid-related overdose. Heroin-related deaths were 4 times higher.

During 2013-2015, 184 AI/AN people died of a drug overdose in Washington; 126 from opioids. Most of the deaths were in urban communities (both Native American and white). Men were nearly twice as likely as women to die of overdose. Adults aged 25 to 54 years had the highest rates. Age-specific drug overdose mortality rates among AI/AN were almost twice those among whites.

The death rates are disturbing enough, but the researchers also found that death certificates that were not corrected for misclassification of AI/AN race underestimated the mortality rates among AI/AN by about 40%. For example, the uncorrected data showed 28.7 drug-overdose deaths in Washington. The corrected number was 40.9. In contrast, the uncorrected number for whites was 15.7, vs 15.1.

Even before correction for misclassification, AI/AN in Washington had higher drug-opioid-involved, and heroin-involved overdose mortality rates than did whites in Washington and AI/AN in the US.

The researchers note that misclassification of AI/AN in public health data can obscure the prevalence of disease and result in suppression of health statistics because of small numbers. That, in turn, can affect the ability of state and federal programs to direct resources needed for a “robust public health response to this epidemic.”

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