Drug Poisoning Mortality in the United States, 2003-2020
County-level Drug Overdose Mortality in the United States, 2003-2020
These figures present drug overdose death rates at the county level. The three dashboards depict heat maps, grids, and trend-lines of model-based county estimates of drug overdose mortality beginning in 2003. Hierarchical Bayesian models with spatial and temporal random effects were used to generate these county-level estimates (see Technical Notes for details).
Select a dashboard from the drop-down menu, then click on “Update Dashboard” to navigate through different graphics.
- The first dashboard is a heat map of county estimates, showing model-based crude death rates for drug overdose per 100,000 population by county and year. The color scale indicates the magnitude of the estimated county-level death rates. Use the arrows or the slider to select a year. Click on any state to zoom into it on the map. Click outside the state to zoom back out to the map of the U.S. Users may click on the gray “home” icon in the upper left-hand corner of the map to reset the view, if necessary.
- The second dashboard features a county grid showing the change in estimated drug overdose death rates rate by year using the same color scale as the county heat map. Click on a state in the map to display the counties for that state in the grid.
- The third dashboard displays county-level trends in drug overdose mortality by urban-rural classification. Select one or more states from the drop-down menu at the top to compare county-level trends in drug overdose death rates across urban-rural categories.
Download datasets in CSV format by clicking on the link for the desired dataset under “CSV Format” link. Additional file formats are available for download for each dataset at Data.CDC.Gov. National and state-level data can be obtained from CDC WONDER (https://wonder.cdc.gov/ucd-icd10.html). Additional provisional drug overdose data can be found here: https://www.cdc.gov/nchs/nvss/vsrr/provisional-drug-overdose.htm.
Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug overdose deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Populations used for computing death rates for 2011–2018 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.
Death rates for some states/counties and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states or counties. Drug overdose death rates may be underestimated in those instances.
County-level estimates were generated using Hierarchical Bayesian models with spatial and temporal random effects using the INLA package for R (3–5). These models borrow strength over time and across neighboring counties to produce stable estimates of drug overdose death rates by county and year.
Annual county-level drug overdose deaths were modeled as a function of:
- An overall intercept;
- A fixed effect for year;
- A spatial random effect, which accounts for clustering of drug overdose death rates;
- A non-spatial random effect, which accounts for any residual county-level variation;
- A temporal random effect, which accounts for nonlinearities over time by allowing the value in any given year to depend on the value in a prior year, plus an error term;
- A space-time interaction term (i.e., a county- and year-specific random effect), accounting for any residual spatiotemporal variation.
Drug overdose death counts at the county-level are not normally distributed, as they are highly zero-inflated and right-skewed. To account for this, models used a zero-inflated binomial distribution. Models were also fit using zero-inflated Poisson and zero-inflated negative binomial distributions. Models were compared using the Deviance Information Criterion (DIC) where lower values are preferred (6), and indicated that the zero-inflated binomial models offered the best fit.
Posterior predicted median death rates were obtained from the best fitting model. These model-based estimates are provided to show which counties have higher or lower drug overdose death rates, and how these rates have changed over time. Model-based estimates may over- or under-estimate true drug overdose death rates, and may not match drug overdose death rates obtained from CDC WONDER. Bayesian credible intervals are provided, which show a range of values within which there is a 95% probability that the true drug overdose death rate will fall, based on the observed death rates and the model. The updated estimates may differ from the previous model-based estimates (7–10) for several reasons, and should not be considered directly comparable. Previously published estimates can be found here (9–10). County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with some counties merged with adjacent counties in cases where county boundaries changed over time (11). County urban-rural classification is based on the 2013 National Center for Health Statistics (NCHS) Urban–Rural Classification Scheme for Counties (12).
NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm).
- National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.
- CDC. CDC Wonder: Underlying cause of death 1999–2019. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.
- Khan D, Rossen LM, Hedegaard H, Warner M. A Bayesian spatial and temporal modeling approach to mapping geographic variation in mortality rates for subnational areas with R-INLA. J Data Sci. 2018;18: 147-182.
- Bivand RS, Rubio-Gomez V, Rue H. Spatial data analysis with R-INLA with some extensions. J Stat Softw. 2015;63(20).
- Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc Series B Stat Methodol. 2009;71:319-392.
- Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit (with discussion). J R Stat Soc Series B Stat Methodol. 2002;64(4):583-639.
- Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19-25. 2013.
- Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.
- Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2016. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/p56q-jrxg.
- Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2018. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/rpvx-m2md.
- National Center for Health Statistics. County geography changes: 1990–present. Available from: https://www.cdc.gov/nchs/data/nvss/bridged_race/County-Geography-Changes-1990-present.pdfpdf icon.
- Ingram DD, Franco SJ. 2013 NCHS urban–rural classification scheme for counties. National Center for Health Statistics. Vital Health Stat 2(166). 2014.
Rossen LM, Bastian B, Warner M, Khan D, Chong Y. County-level Drug Overdose Mortality: United States, 2003–2020. National Center for Health Statistics. 2022.
Designed by B Bastian, L Rossen, JM Keralis, and Y Chong: CDC/National Center for Health Statistics.