Automated analysis · v01 · 2026-08-12

Ohio Opioid Overdose Trend Since 2015 Using Rolling CDC Provisional Data

This analysis examined whether opioid overdose deaths in Ohio have declined since 2015 using CDC's Provisional Drug Overdose Death Counts, a rolling 12-month series that extends the requested 2020-2023 window because that was the data available. Because the dataset used has no county field and reports raw counts rather than age-adjusted rates, the county-by-county comparison asked for in the question could not be produced, and the trend below describes counts, not rates. The fitted linear trend across the ordered rolling series pointed downward and was statistically significant, though it explained only a small share of the month-to-month variation, and the raw count fell from the start to the end of the series. This is an automated, ecological analysis of aggregate counts and cannot establish what drove the change.

Results

After filtering and exclusions, 120 rows remained, covering the rolling 12-month period ending April 2015 through the rolling 12-month period ending February 2026. The rolling death count began at 2335.0 in the first period and ended at 1522.0 in the last period, an absolute change of -813.0 over the full series; across the series the value ranged from a minimum of 1522.0 to a maximum of 4702.0. A linear trend fitted to this ordered series was decreasing and statistically significant, though the model explained only a small share of the overall variation in the series, consistent with a rolling measure that fluctuates over many overlapping windows even as its longer-run trajectory moves downward. No county-level breakdown could be generated because the underlying dataset does not contain a county field, and because the values are raw death counts rather than age-adjusted rates, this result speaks to the trend in counts over time rather than to age-adjusted rate change.

Background

Opioid overdose mortality has been a persistent and evolving public health concern in Ohio, with prior work documenting shifts toward synthetic opioids, geographic variation in impact, and community-level social determinants tied to fatal overdose risk [PMID 40657589] [PMID 37866438] [PMID 36846577]. Statistical modeling of Ohio-specific overdose patterns has previously identified spatiotemporal change points in the state's opioid mortality trajectory [PMID 34721750], and national analyses have described a broader 'fourth wave' of overdose deaths tied to shifting drug supply and diminished prescription access [PMID 35322965]. Other work has cautioned that provisional overdose counts can understate the true decline or rise once data are finalized [PMID 34107224], and that opioid-related mortality intersects with other health complications such as gastrointestinal disease [PMID 40693060]. Community institutions, including public libraries, have also been documented as responding to the opioid crisis in Ohio [PMID 38760681]. Against this backdrop, the current question asked whether age-adjusted opioid overdose death rates in Ohio, overall and by county, have declined from 2020 through the most recent finalized year.

Methods

This analysis used CDC's VSRR Provisional Drug Overdose Death Counts dataset (data-cdc-gov, dataset ID xkb8-kh2a, vintage 2026-07-15), which is a rolling 12-month-ending series rather than discrete calendar-year totals. The data were filtered to the indicator 'Opioids (T40.0-T40.4,T40.6)' to restrict to the overall opioid overdose death category rather than pooling with cocaine, methadone, or other drug-specific indicators; this filter kept 134 of 1,608 input rows. Rows with reporting under 100% complete were excluded (11 rows dropped), and three additional rows with suppressed or missing values were excluded and were not imputed or treated as zero. This left 120 rows analyzed, spanning from April 2015 through February 2026. Because the series is a rolling 12-month total, values were analyzed as levels over time (never summed), and a single linear trend was fit to the ordered series (year and month as the time axis, the death count as the outcome) to test for directional change; no grouping variable (such as county) was available in this dataset, so no breakdown by county could be produced.

Limitations

This dataset (CDC VSRR provisional counts) contains no county column, so a county-by-county breakdown as asked in the question could not be produced from this data \u2014 only a single statewide Ohio series is available
The outcome here is a raw death count, not an age-adjusted rate; no population denominator is present, so age-adjustment and true rate comparisons over time are not possible with this data
Rows are 12-month-ending rolling windows, so consecutive monthly points are highly autocorrelated and represent overlapping periods, not independent annual observations; a fitted slope describes a smoothed multi-year trend, not year-to-year change
Recent periods are provisional (98-100% complete) and will be revised upward, which can distort the estimated trend at the most recent end of the series
This is an ecological, aggregate time-series design; it cannot establish causal drivers (e.g., policy effects or fentanyl-specific mechanisms) behind any observed trend
Percent-complete varies by row but no explicit incomplete/partial flag column exists to exclude on beyond this percentage field
The requested 2020-2023 timeframe could not be isolated; the available rolling series extends well before and after that window, and results describe the entire available series rather than a 2020-onward comparison specifically
134 of 1,608 rows were retained after restricting to the overall opioid indicator, and additional rows were excluded for incomplete reporting or suppression, so the analyzed series reflects a reduced and filtered subset of the original data

What the analysis excluded

Restricted to indicator='Opioids (T40.0-T40.4,T40.6)' (Restrict to the overall opioid overdose death category rather than pooling with cocaine, methadone, or other drug-specific indicators); 134 of 1608 rows.
3 row(s) had no usable value (suppressed or missing) and were excluded from the analysis. They were not imputed or treated as zero.
11 row(s) with reporting under 100% complete were excluded.
Series is rolling: each value is a trailing-window total. Values are analysed as levels over time and are never summed.

Conclusion

In this automated analysis of CDC's rolling provisional opioid overdose death series for Ohio, the statewide count of opioid overdose deaths was associated with a downward trend from 2015 through the most recent available period, and the count in the most recent period was lower than in the earliest period, though the linear trend explained only a small part of the variation over time. Because the underlying data lack a county field and report raw counts rather than age-adjusted rates, this analysis cannot address whether age-adjusted rates have declined statewide or determine whether any decline is uniform or uneven across Ohio counties, and the finding should be read as an association in aggregate provisional counts rather than a confirmed, finalized, or causal trend.

Versions: v01 · v02

Findings

Series	Estimate	Precision
overall	slope -5.051 per period (95% CI -9.554 to -0.549)	p = 0.0282, R² = 0.040

Automated review

Before publication this analysis was reviewed by an adversarial critic whose job is to find what is wrong with it. Verdict: acquire-more.

The plan and results answer a materially different question than the one asked. The question requires age-adjusted rates by county for 2020 through the latest finalized year, with fentanyl-subtype decomposition; the analysis instead fits a single unweighted statewide trend on raw provisional death counts spanning 2015-2026, with no county breakdown, no age-adjustment, no population denominator, and no subtype stratification. These are not fixable by re-analyzing the same VSRR file — county-level, age-adjusted, finalized mortality data (e.g., CDC WONDER Underlying/Multiple Cause of Death, finalized years) must be fetched.

fatal scope-mismatch — Requested window was 2020 through the latest finalized year, but the series analyzed runs from 2015-April to 2026-February, mixing pre-period years and provisional 2025-2026 data far outside the finalized 2020-2023 request. Remedy: Restrict the analysis window to 2020-01 through the last finalized year (per CDC finalization schedule, likely 2022 or 2023), dropping both pre-2020 and provisional recent points.
fatal multiplicity — The question explicitly asks for results 'overall and by county,' and one hypothesis concerns non-uniform county trends, but the analysis produces only a single statewide series with no county grouping at all. Remedy: Acquire county-level mortality/population data (e.g., CDC WONDER county-level underlying cause of death) and fit the trend separately by county, or at minimum by urbanicity strata.
fatal denominator — The outcome analyzed is a raw death count, not an age-adjusted rate; no population denominator or age distribution was used, so the core requested measure (age-adjusted rate) was never computed and county comparisons would be invalid using counts alone. Remedy: Acquire age-adjusted rate data or population-by-age-and-county data and compute age-adjusted rates per the planned covariate before trend-fitting.
major unanswered — The third hypothesis about fentanyl/synthetic-opioid subtype driving the overall trend is dropped entirely; no subtype stratifier was used despite being planned and fetched conceptually. Remedy: Re-run the trend separately for fentanyl-specific (T40.4) and other opioid subtype indicators and compare slopes to the overall trend.
major model-shape — A single linear trend is fit to a 120-point series with r-squared of 0.04, indicating the straight line explains almost none of the variance; a series spanning 11 years likely rises then falls (consistent with known Ohio overdose patterns), which a single slope cannot represent, and the marginal p-value is not a reliable signal here. Remedy: Use a model that can capture non-monotonic shape (e.g., piecewise/segmented regression with a breakpoint, or year-over-year comparison of annual rates) rather than one global linear slope.
major rolling-window — Overlapping 12-month rolling windows are treated as approximately independent observations in an OLS trend/CI/p-value calculation, which will understate standard errors and overstate the precision and significance of the slope. Remedy: Either use non-overlapping annual (or finalized calendar-year) data points, or fit the trend with an autocorrelation-robust method (e.g., Newey-West standard errors) appropriate for the rolling series.
major provisional-data — Despite an 'exclude_incomplete' filter, the series still extends to 2026-February, which cannot be finalized data and is almost certainly provisional/undercount-prone, potentially exaggerating the apparent decline at the recent end. Remedy: Cap the series at the last year CDC/Ohio vital statistics designates as finalized and drop all rows still subject to revision, not just those below a completeness percentage threshold.

Provenance

drug/opioid subtype involved (e.g., fentanyl, heroin, prescription opioids) stratifier

Source: data-cdc-gov
Dataset: xkb8-kh2a
Query: state = OH
Rows: 1608 returned, 27 suppressed and left missing
Fetched: 2026-08-11T01:03:23
Substitution: dataset xkb8-kh2a was pinned by the caller; the Scout's hint for 'drug/opioid subtype involved (e.g., fentanyl, heroin, prescription opioids)' was 'VSRR Provisional Drug Overdose Death Counts, by drug category (T40.x groupings)'

opioid overdose deaths

Source: data-cdc-gov
Dataset: gb4e-yj24
Query: state_name = Ohio
Rows: 6336 returned, 1572 suppressed and left missing
Fetched: 2026-08-10T01:48:48

Suppressed cells are never imputed or treated as zero. Raw outputs: results.json · data.csv

This is an automated analysis of publicly available, aggregate data. It is not peer-reviewed research, not a clinical guideline, and not medical advice. Findings describe associations in published data, never causes. Figures are produced by executed code and every number on this page is checked against that code's output before publication.