Leading causes of death by state
CDC / National Center for Health Statistics
In a RepVector build, this dataset quantifies clinical need and utilisation across the population.
| Cause of death | Deaths | Age-adjusted rate / 100k | State | Year |
|---|---|---|---|---|
| Heart disease | 28,008 | 186.2 | Ohio | 2,017 |
| Cancer | 25,643 | 171.2 | Ohio | 2,017 |
| Unintentional injuries | 8,971 | 75.1 | Ohio | 2,017 |
| CLRD | 7,312 | 48.5 | Ohio | 2,017 |
| Stroke | 6,425 | 42.8 | Ohio | 2,017 |
| Alzheimer's disease | 5,117 | 33.6 | Ohio | 2,017 |
| Diabetes | 3,740 | 25.2 | Ohio | 2,017 |
| Influenza and pneumonia | 2,243 | 14.9 | Ohio | 2,017 |
What this dataset is
Annual death counts and age-adjusted death rates for each leading cause of death, by state, from national vital statistics. Covers heart disease, cancer, stroke, unintentional injury, diabetes, kidney disease, suicide and others. It is published by CDC / National Center for Health Statistics and can be queried at state and nation level, refreshed annual. Each row is a period observation, so results show direction and rate of change rather than a single snapshot.
Why it is useful
- Separates markets with real clinical demand from markets that only have supply.
- Grounds a territory case in published prevalence and utilisation instead of assumption.
- Highlights where need outruns available capacity — usually the fastest conversations.
Questions it helps answer
- Where is the underlying clinical need highest relative to available capacity?
- How does utilisation here compare with the state and national picture?
- Which measures are suppressed or unreported for this geography?
How different verticals use it
RepVector is vertical-agnostic: this dataset is selected by what your question asks for, not by industry. These are common ways teams put it to work.
Prioritises territories where clinical need outruns available capacity.
Quantifies prevalence and utilisation behind a programme or referral case.
Informs risk and benefit design with published utilisation patterns.
Shows community health pressures that shape support-service demand.
How RepVector uses it
- You describe a market in plain language. The geography resolver turns the place you named into the exact identifiers this dataset needs — city to county to state, plus DMA where relevant — so a query at state runs without you knowing any codes.
- The planner selects Leading causes of death by state only when your question matches what it actually reports. It is never included to pad a result.
- Returned rows are normalised into the shared metric layer, so figures from CDC / National Center for Health Statistics line up with every other source in the same dashboard instead of sitting in an isolated table.
- Quality checks score coverage and recency. If CDC / National Center for Health Statistics suppresses or omits a value for your geography, the gap is reported as a gap — nothing is estimated or filled in.
- The result renders as charts, KPIs and, where the rows are entities, prioritised targets you can export to PDF, Excel, CSV or JSON.
What it does not tell you
- Aggregated and often suppressed at small geographies to protect privacy; gaps are reported rather than filled in.
| Field | Key | Type |
|---|---|---|
| Cause of death | cause | string |
| Deaths | deaths | number |
| Age-adjusted rate / 100k | ageAdjustedRate | number |
| State | state | geo |
| Year | year | number |
How it fits with the rest of the catalog
Need data explains why a territory matters; provider and facility data explains who you visit once you get there. RepVector queries every selected dataset against the same resolved geography and time window, then normalises the results into one metric layer so they can be charted side by side. These datasets are the most common companions to Leading causes of death by state:
Other health datasets
Common questions
Which causes of death are included?
The CDC's leading-cause list: heart disease, cancer, stroke, chronic lower respiratory disease, unintentional injury, Alzheimer's, diabetes, influenza and pneumonia, kidney disease and suicide.
What is an age-adjusted rate?
Deaths per 100,000 people standardised to a reference population, so two states with different age profiles can be compared directly.
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