Methodology

How OpiEye collects, processes, and presents drug overdose data.

Data last refreshed: April 2026. CDC provisional data is updated quarterly. OpiEye refreshes its data within 30 days of each CDC release. Coverage: 3,147 counties, 42 quarterly periods (2015–2025).

What This Data Covers

OpiEye shows all drug overdose deaths at the county level. According to 2024 CDC data (NCHS Data Brief 549), 68% of drug overdose deaths involve opioids and 60% involve fentanyl or other synthetic opioids specifically. The county-level data from the CDC does not break out by drug type, so the map shows total drug overdose deaths.

Why Our Numbers May Differ from Other Sources

If you look up overdose statistics and find different numbers, common reasons include:

United States Data Sources

2020 onward: CDC Vital Statistics Rapid Release (VSRR) Provisional County-Level Drug Overdose Death Counts (dataset gb4e-yj24). These are 12-month rolling counts converted to quarterly estimates. Counties with fewer than 10 deaths are suppressed by the CDC for privacy.

2015–2019: National Center for Health Statistics model-based estimates (dataset rpvx-m2md). These use a Bayesian spatial-temporal model that smooths rates across neighboring counties. Individual county figures are approximate — the model pulls small counties toward their neighbors' averages. Annual estimates are divided by four for quarterly display.

Population: Census Bureau Population Estimates Program, year-specific county estimates. The 2010–2019 intercensal series (co-est2019-alldata.csv) provides estimates revised after the 2020 Census. The 2020–2024 postcensal series (co-est2024-alldata.csv) provides current estimates. Each quarter uses the population estimate for its corresponding year, so per capita rates accurately reflect the population at the time of the deaths. The 2025 estimate uses the 2024 figure as a proxy until Census releases official 2025 estimates.

Emergency department visits: Estimated at 3–6 times the death count based on published epidemiological ratios. The primary source is Casillas et al., “Estimating the ratio of fatal to non-fatal overdoses,” Injury Prevention 2024;30(2):114-124 (PMC10958315), which found opioid nonfatal-to-fatal ratios ranging from ~6:1 (2010) to ~4:1 (2020). Supporting data from CDC MMWR (Vivolo-Kantor et al. 2020) showed ~6.4:1 for opioids in 2017. These are not from real hospital records. Actual hospital discharge data (HCUP) is restricted-access. Note: for synthetic opioids (fentanyl), the ratio inverts — fatal overdoses exceed nonfatal ED visits, likely because fentanyl kills before EMS arrives. The 3–6x estimate should not be cited as measured data.

CDC VSRR: data.cdc.gov/NCHS/VSRR-Provisional-County-Level-Drug-Overdose-Death-/gb4e-yj24
NCHS Mortality: data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/rpvx-m2md

Opioid and Fentanyl Breakdown

While the map shows all drug overdose deaths, opioids are the primary driver of the crisis. According to 2024 CDC data:

Opioid-involved deaths include heroin (T40.1), fentanyl and synthetic opioids (T40.4), prescription opioids like oxycodone and hydrocodone (T40.2), methadone (T40.3), opium (T40.0), and other unspecified opioids (T40.6). The remaining 32% of drug overdose deaths involve substances like cocaine, methamphetamine, and benzodiazepines without opioid involvement.

Source: NCHS Data Brief 549, "Drug Overdose Deaths in the United States, 2023–2024"

International Comparisons

International data comes from each country's national health authority. These comparisons are approximate because countries define and count opioid deaths differently:

FactorHow It Varies
What counts as an opioidSome countries count only heroin; others include methadone, prescription opioids, and fentanyl analogues
Cause of death attributionSome require opioids as the underlying cause; others count any mention on the death certificate
IntentSome include suicides involving opioids; others count only accidental poisonings
Reporting systemMedical examiner/coroner (United States, Canada, Australia), police records (Germany, Italy), national drug monitoring (France), or statistical estimates (European Union)
CompletenessSome systems are acknowledged as non-exhaustive (France DRAMES system). Some use minimum counts (Italy police-sourced)
Example: Canada reports 20.0 opioid deaths per 100,000 versus the United States at 24.0. But Canada counts "apparent opioid toxicity deaths" (confirmed and probable), while the United States counts deaths with specific ICD-10 opioid codes as a contributing cause. The definitions are close but not identical, so the exact gap between countries is uncertain even though the general pattern is reliable.

European opioid counts are derived from the European Union Drugs Agency (EUDA) by multiplying total drug-induced deaths by the opioid involvement percentage. This introduces rounding and assumes the percentage is consistent across regions within each country.

When you click a country on the map, OpiEye shows the specific definition used for that country so you can judge the comparison yourself.

Data Quality Tiers

LabelTime PeriodWhat It Means
CDC Provisional2024 onwardDeath certificates still being processed. Counts will increase as investigations complete. Expect 5–15% undercount.
CDC Estimated2020–2023Derived from 12-month rolling counts divided by four. Seasonal variation is smoothed out. Most recent revisions incorporated.
NCHS Modeled2015–2019Statistical estimates that smooth rates across neighboring counties. Good for geographic patterns; less reliable for individual small counties.

Suppressed counties (fewer than 10 deaths reported to the CDC) are filled with the most recent available modeled estimate. Approximately 462 counties are affected. These appear on the map but their counts are approximate.

Known Limitations

How to Cite This Data

OpiEye aggregates and visualizes public data. When citing specific numbers, reference the original source:

OpiEye is a public health tool, not a primary data source. Our methodology is transparent so you can evaluate the data yourself.