🩺 CDC WONDER Mortality Data Scraper

🚀 Export U.S. mortality records in seconds. Pull underlying and multiple cause-of-death counts, crude rates, and age-adjusted rates by year, state, age group, sex, and ICD-10 chapter from the CDC's official National Vital Statistics System. No survey wrangling, no SAS code.

🕒 Last updated: 2026-05-27 · 📊 16 fields per record · 🇺🇸 50 states + DC · 📅 1999 - present · 🏥 6 mortality databases

The CDC WONDER Mortality Scraper queries the Centers for Disease Control's WONDER public-health data warehouse and returns 16 structured fields per grouping, including deaths, exposed population, crude rate per 100,000, age-adjusted rate, standard error, and the 95 percent confidence interval. The underlying data comes from the National Vital Statistics System and is the same source cited in CDC fact sheets, JAMA, the NEJM, and major U.S. newsrooms.

The catalog covers all 50 states, DC, six mortality databases (underlying cause, multiple cause, infant deaths, linked birth/infant death, provisional), and the full ICD-10 chapter hierarchy. This Actor lets you choose the database, group results by year, state, sex, age group, or ICD-10 chapter, and downloads the result as CSV, Excel, JSON, or XML.

🎯 Target Audience	💡 Primary Use Cases
Epidemiologists, public-health analysts, biostatisticians, health journalists, policy researchers, pharma teams, civic-tech projects	Mortality dashboards, cause-of-death trend analysis, age-adjusted rate comparisons, state benchmarks, longitudinal studies, equity research

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📋 What the CDC WONDER Mortality Scraper does

Six dataset choices and seven grouping presets in a single run:

• 📘 Underlying Cause of Death (D76). 1999-current, the workhorse for cause-of-death trend analysis.
• 📙 Multiple Cause of Death (D77). All contributing causes per death record.
• 👶 Infant Deaths (D157) and Linked Birth / Infant Death (D158) for perinatal research.
• 🕒 Provisional Mortality (D159, D176) for the most recent records before final tabulation.
• 🧩 Group by year, state, year+state, year+state+sex, year+state+age, year+ICD-10 chapter, or state+sex+age.
• 🩻 Filter by ICD-10 chapter (A00-B99 infectious, C00-D48 neoplasms, I00-I99 circulatory, etc.) and a single state FIPS code.

Each grouping ships back the deaths count, exposed population, crude rate per 100k, the age-adjusted rate, its standard error, and the 95 percent confidence interval. Suppressed cells (count < 10 per CDC privacy policy) come back as null.

💡 Why it matters: mortality data is the gold standard for measuring population health, drug-overdose epidemics, suicide trends, maternal mortality, and the effect of policy on outcomes. Building it yourself means parsing CDC WONDER's quirky XML response format and respecting the public-data-use agreement. This Actor turns that into a one-click data pull.

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🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing how to go from sign-up to a downloaded mortality dataset.

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⚙️ Input

Input	Type	Default	Behavior
maxItems	integer	10	Records to return. Free plan caps at 10, paid plan at 1,000,000.
database	string	"D76"	One of six mortality datasets. D76 is the underlying-cause workhorse.
groupBy	string	"year-state"	One of seven grouping presets.
yearFrom	integer	2020	Inclusive start year (1999+).
yearTo	integer	2020	Inclusive end year.
state	string	""	Single U.S. state FIPS code (e.g. `06` California). Empty = all states.
icd10Chapter	string	""	One ICD-10 chapter range. Empty = all causes.

Example: 2020 deaths by state, all causes.

{
    "maxItems": 60,
    "database": "D76",
    "groupBy": "year-state",
    "yearFrom": 2020,
    "yearTo": 2020
}

Example: California cardiovascular deaths by year and sex, 2018-2022.

{
    "maxItems": 50,
    "database": "D76",
    "groupBy": "year-state-sex",
    "yearFrom": 2018,
    "yearTo": 2022,
    "state": "06",
    "icd10Chapter": "I00-I99"
}

⚠️ Good to Know: the CDC suppresses any grouping with fewer than 10 deaths under its data-use restrictions. Suppressed cells return as null. The age-adjusted rate is flagged "Unreliable" by CDC when the underlying count is below 20; the Actor surfaces that as null for the rate field while keeping the raw death count.

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📊 Output

Each grouping record contains 16 fields. Download the dataset as CSV, Excel, JSON, or XML.

🧾 Schema

Field	Type	Example
📚 database	string	"D76"
🧩 groupBy	string	"year-state"
📅 yearFrom	integer	2020
📅 yearTo	integer	2020
🏷️ groupLabels	string[]	["2020", "California"]
🔢 groupCodes	string[]	["2020", "06"]
📆 year	string | null	"2020"
🇺🇸 state	string | null	"California"
🧷 additionalLabel	string | null	"Female"
⚰️ deaths	number | null	321428
👥 population	number | null	39538223
📉 crudeRatePer100k	number | null	812.9
📊 ageAdjustedRate	number | null	659.4
± ageAdjustedRateStdErr	number | null	1.2
▼ ageAdjustedRateLowerCI	number | null	657.0
▲ ageAdjustedRateUpperCI	number | null	661.7
🩻 icd10Filter	string | null	"I00-I99"
🔗 sourceUrl	string	"https://wonder.cdc.gov/ucd-icd10.html"
🕒 scrapedAt	ISO 8601	"2026-05-27T00:00:00.000Z"

📦 Sample records

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✨ Why choose this Actor

	Capability
🏛️	Official source. CDC WONDER is the national vital-statistics system. Same numbers cited in JAMA and NEJM.
📅	24+ years of history. Records from 1999 through the latest provisional release.
🧩	Seven grouping presets. Year, state, sex, age, or ICD-10 chapter, in the combinations researchers actually need.
📊	Adjusted rates included. Crude and age-adjusted rates with confidence intervals out of the box.
🚫	Honors CDC suppression. Cells below 10 deaths are returned as null, matching CDC privacy policy.
🔁	Always fresh. Every run hits the live WONDER service for the most current release.
🌐	No login. Uses public mortality data only. No account or API key needed.

📊 Population-scale mortality data is the foundation of every public-health program, policy evaluation, and epidemiologic study in the United States.

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📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Filters	Setup
⭐ CDC WONDER Scraper (this Actor)	$5 free credit, then pay-per-use	All 50 states + DC, 1999-current	Live per run	year, state, sex, age, ICD-10	⚡ 2 min
Direct CDC WONDER portal	Free	Same	Live	Many, but UI-driven	🐢 Manual export per query
Academic vital-stats datasets	Free	Often dated	Annual	Limited	🐢 Hours
Commercial health-data vendors	$5,000+/year	Same + extras	Quarterly	Many	⏳ Sales cycle

Pick this Actor when you want the official CDC numbers in a clean JSON feed with zero query-builder clicks.

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🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the CDC WONDER Mortality Scraper page on the Apify Store.
3. 🎯 Set input. Pick a database, grouping preset, year range, and optional filters.
4. 🚀 Run it. Click Start and let the Actor query CDC WONDER.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to downloaded dataset: 3-5 minutes. No coding required.

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💼 Business use cases

💊 Pharma & life sciences Disease-burden baselines for therapeutic-area planning Age-adjusted comparisons across state markets Pre-launch demand modeling for new indications Real-world-evidence anchors for safety analyses	🏥 Provider & payer analytics Mortality benchmarks for quality scorecards State-level cause-of-death trend dashboards Risk-stratification inputs for population health Outcomes baselines for CMS reporting
📰 Health journalism Story-ready charts on opioid, suicide, and maternal mortality State-by-state rankings with confidence intervals Year-over-year change visualizations Quick "is this surprising?" fact-checks vs. national average	🏛️ Policy & government Evidence base for state legislation Public-health-program evaluation pre/post intervention Equity dashboards by race, age, geography Federal-funding allocation models

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🔌 Automating CDC WONDER Scraper

Control the scraper programmatically for scheduled runs and pipeline integrations:

• 🟢 Node.js. Install the apify-client NPM package.
• 🐍 Python. Use the apify-client PyPI package.
• 📚 See the Apify API documentation for full details.

The Apify Schedules feature lets you trigger this Actor on any cron interval. A monthly refresh on the provisional dataset (D159) keeps a "what's happening now in U.S. mortality" dashboard always current.

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🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🎓 Research and academia Epidemiology coursework with real CDC data Biostatistics theses on age-adjusted rate methodology Reproducible studies citing versioned WONDER pulls Equity research across race, geography, and time	🎨 Personal and creative Data-journalism portfolio pieces Personal blogs on long-running mortality trends Visualizations that make population health tangible Hobbyist explorations of public-health history
🤝 Non-profit and civic Harm-reduction orgs tracking overdose deaths by state Maternal-health non-profits monitoring outcomes Mental-health advocacy with suicide-trend data Community-health needs assessments	🧪 Experimentation Train forecast models on mortality time series Benchmark anomaly-detection systems with real spikes Prototype public-health agent pipelines Test dashboard concepts with live CDC feeds

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🤖 Ask an AI assistant about this scraper

Open a ready-to-send prompt about this ParseForge actor in the AI of your choice:

• 💬 ChatGPT
• 🧠 Claude
• 🔍 Perplexity
• 🅒 Copilot

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❓ Frequently Asked Questions

🧩 How does it work?

You pick a mortality database, a grouping preset, a year range, and optional state or ICD-10 filters. The Actor submits the query to CDC WONDER, waits for the server to compute the table, parses the response, and returns one clean record per grouping.

📚 Which databases are supported?

All six WONDER mortality databases: D76 (Underlying Cause 1999-current), D77 (Multiple Cause 1999-current), D157 (Infant Deaths 2007-current), D158 (Linked Birth/Infant Death 2017-current), D159 (Provisional Mortality), and D176 (Underlying Cause Provisional).

🚫 Why are some fields null?

CDC suppresses any grouping with fewer than 10 deaths under its data-use policy. The age-adjusted rate is also flagged "Unreliable" when the underlying count is below 20. Both come back as null. This is the same behavior you'd see on the official portal.

🩻 How do I filter by cause of death?

Pass the ICD-10 chapter range (e.g. I00-I99 for circulatory diseases, V01-Y98 for external causes). For all causes, leave the field empty.

📅 How recent is the data?

The final-tabulation databases (D76, D77) typically lag by 12-18 months. The provisional databases (D159, D176) update within weeks of new deaths being filed.

🕒 Why does the query take so long?

CDC WONDER assembles each query server-side. Queries spanning many years and groupings take 30-60 seconds; some can take longer. The Actor allows up to three minutes per query.

⏰ Can I schedule regular runs?

Yes. Use Apify Schedules to run this Actor on any cron interval (daily, weekly, monthly) and keep a downstream database in sync.

⚖️ Is this data legal to use?

Yes. CDC WONDER data is published by the U.S. federal government and is freely usable for research, commercial analytics, journalism, and policy work. You must accept the data-use restrictions on each query (the Actor does this automatically).

💳 Do I need a paid Apify plan?

No. The free Apify plan is enough for testing and small runs (10 records). A paid plan lifts the limit and gives you scheduling, higher concurrency, and larger datasets.

🔁 What happens if a run fails?

CDC WONDER is occasionally flaky during peak hours. The Actor retries transient failures. If a run still fails, the dataset contains a clean { error: ... } record explaining the issue.

🆘 What if I need help?

Contact us through the Apify platform or use the Tally form linked below.

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🔌 Integrate with any app

CDC WONDER Mortality Scraper connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• Airbyte - Pipe mortality data into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a run finishes. Push fresh mortality data into your BI tool, or alert your team in Slack when a state's age-adjusted rate crosses a threshold.

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🆘 Need Help? Open our contact form to request a new scraper, propose a custom data project, or report an issue.

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⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the U.S. Centers for Disease Control and Prevention or the National Center for Health Statistics. All trademarks mentioned are the property of their respective owners. Only publicly available mortality data is collected, subject to the CDC's standard data-use restrictions.