Data on drug overdose death rates, by drug type and selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System, numerator data from annual public-use Mortality Files; denominator data from U.S. Census Bureau national population estimates; and Murphy SL, Xu JQ, Kochanek KD, Arias E, Tejada-Vera B. Deaths: Final data for 2018. National Vital Statistics Reports; vol 69 no 13. Hyattsville, MD: National Center for Health Statistics.2021. Available from: https://www.cdc.gov/nchs/products/nvsr.htm. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

Opioid Data Description

Land Area of County: factfinder.census.gov 2010 Census Summary 1890 counties are taken under consideration

Year: 2011- 2017

Population: https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html#par_textimage_70769902 Annual Estimates of the Resident Population for Counties: April 1, 2010 to July 1, 2018

Death by Opioid Type: https://wonder.cdc.gov/ The mortality data are based on information from all death certificates filed in the fifty states all sub-national data representing zero to nine (0-9) deaths are suppressed.

601 counties had the minimum mortality rate to be represented for analysis and were pulled from the WONDER database. These were the recommended codes to use when relating to Opioid deaths provided by the CDC.

Type of death: T40.0 (Opium) – No county reached the number of deaths above 9 per year to not be suppressed when finding specific cause T40.1 (Heroin) T40.2 (Other opioids) T40.3 (Methadone) T40.4 (Other synthetic narcotics) From the CDC Wonder Database. Type of death by county will not add up to total mortality due to the fact that low death rate of a county was withheld from data to protect privacy of individuals.

Non-US Born: factfinder.census.gov American Community Survey 5-Year Estimates The total number of Non-Us born citizens that reside in each county

Education: factfinder.census.gov American Community Survey 5-Year Estimates Categories Consist of: Less Than High School Degree Some College or Associate’s Degree Bachelor’s Degree Graduate or Professional Degree

Income by Household: factfinder.census.gov American Community Survey 5-Year Estimates Incomes given by the mean household income in that county

Transportation: Percentage of County that uses these means of transportation to get to work. American Community Survey 5-Year Estimates Categories Consist of: Commute Alone to work by driving Carpool Walk Public Transit Bike

Unemployment Rate by county collected from: https://catalog.data.gov/dataset?tags=unemployment-rate

GDP by county in regards to funds spent on healthcare, education, and social assistance as well as overall GDP collected from: https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areas

Objective: A one third reduction of premature deaths from non-communicable diseases by 2030 is a target of the United Nations Sustainable Development Goal for Health. Unlike in other developed nations, premature mortality in the United States (US) is increasing. The state of Oklahoma suffers some of the greatest rates in the US of both all-cause mortality and overdose deaths. Medicaid opioids are associated with overdose death at the patient level, but the impact of this exposure on population all-cause mortality is unknown. The objective of this study was to look for an association between Medicaid spending, as proxy measure for Medicaid opioid exposure, and all-cause mortality rates in the 45–54-year-old American Indian/Alaska Native (AI/AN45-54) and non-Hispanic white (NHW45-54) populations.Methods: All-cause mortality rates were collected from the US Centers for Disease Control & Prevention Wonder Detailed Mortality database. Annual per capita (APC) Medicaid spending, and APC Medicare opioid claims, smoking, obesity, and poverty data were also collected from existing databases. County-level multiple linear regression (MLR) analyses were performed. American Indian mortality misclassification at death is known to be common, and sparse populations are present in certain counties; therefore, the two populations were examined as a combined population (AI/NHW45-54), with results being compared to NHW45-54 alone.Results: State-level simple linear regressions of AI/NHW45-54 mortality and APC Medicaid spending show strong, linear correlations: females, coefficient 0.168, (R2 0.956; P < 0.0001; CI95 0.15, 0.19); and males, coefficient 0.139 (R2 0.746; P < 0.0001; CI95 0.10, 0.18). County-level regression models reveal that AI/NHW45-54 mortality is strongly associated with APC Medicaid spending, adjusting for Medicare opioid claims, smoking, obesity, and poverty. In females: [R2 0.545; (F)P < 0.0001; Medicaid spending coefficient 0.137; P < 0.004; 95% CI 0.05, 0.23]. In males: [R2 0.719; (F)P < 0.0001; Medicaid spending coefficient 0.330; P < 0.001; 95% CI 0.21, 0.45].Conclusions: In Oklahoma, per capita Medicaid spending is a very strong risk factor for all-cause mortality in the combined AI/NHW45-54 population, after controlling for Medicare opioid claims, smoking, obesity, and poverty.

The characteristics of people on opioids by jurisdiction.