Opioid, maternal and newborn hospital burden by state (AHRQ HCUP Fast Stats)
Agency for Healthcare Research and Quality (HCUP)
In a RepVector build, this dataset quantifies clinical need and utilisation across the population.
| Measure | State rate | Unit | National rate | vs national (%) | Rank among reporting states |
|---|---|---|---|---|---|
| Opioid-related hospital use | — | Rate per 100,000 population | 258.3 | — | — |
| Neonatal abstinence syndrome | — | Rate per 1,000 newborn hospitalizations | 5.7 | — | — |
| Severe maternal morbidity | — | Rate per 10,000 in-hospital deliveries | 98.8 | — | — |
What this dataset is
State-level, all-payer hospital burden rates from AHRQ HCUP Fast Stats: opioid-related inpatient stays and emergency department visits per 100,000 residents, neonatal abstinence syndrome per 1,000 newborn stays, and severe maternal morbidity per 10,000 in-hospital deliveries. Every measure is reported next to the national rate so a state can be read as above or below the country as a whole. Because HCUP draws on state all-payer discharge data, these rates cover commercial, Medicaid, Medicare and self-pay encounters alike. It is published by Agency for Healthcare Research and Quality (HCUP) and can be queried at state level, refreshed annual, released with a one to two year lag. Each row is an aggregate statistic for a geography, so results describe a market as a whole rather than a single account.
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 Opioid, maternal and newborn hospital burden by state (AHRQ HCUP Fast Stats) 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 Agency for Healthcare Research and Quality (HCUP) line up with every other source in the same dashboard instead of sitting in an isolated table.
- Quality checks score coverage and recency. If Agency for Healthcare Research and Quality (HCUP) 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 |
|---|---|---|
| Measure | measure | string |
| State rate | rate | number |
| Unit | unit | string |
| National rate | nationalRate | number |
| vs national (%) | vsNational | percent |
| Rank among reporting states | rank | number |
| States reporting | statesReporting | number |
| Year | year | string |
| Coverage | available | string |
| State | state | geo |
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 Opioid, maternal and newborn hospital burden by state (AHRQ HCUP Fast Stats):
Other health datasets
Common questions
What does Opioid, maternal and newborn hospital burden by state (AHRQ HCUP Fast Stats) contain?
State-level, all-payer hospital burden rates from AHRQ HCUP Fast Stats: opioid-related inpatient stays and emergency department visits per 100,000 residents, neonatal abstinence syndrome per 1,000 newborn stays, and severe maternal morbidity per 10,000 in-hospital deliveries. Every measure is reported next to the national rate so a state can be read as above or below the country as a whole. Because HCUP draws on state all-payer discharge data, these rates cover commercial, Medicaid, Medicare and self-pay encounters alike. It is published by Agency for Healthcare Research and Quality (HCUP) at state grain.
How often is it refreshed?
Agency for Healthcare Research and Quality (HCUP) publishes on a annual, released with a one to two year lag cycle. RepVector re-reads the source on that cadence and keeps every prior capture, so you can see what changed.
Do I need an account to use it?
No. Browsing the catalog and the free lookup tools needs no account. Running a full market build across every county in a territory is the paid product.
Build the list for one county and see whether the names are right.
Ask in plain language. RepVector finds the universe, enriches each target from its own website, scores it 0–100, and hands you a ranked call list with phones, addresses and named contacts.
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