Critical Illness and Dread Disease Rates
This dataset provides comprehensive incidence rates for major critical illnesses and dread diseases, segmented by age, gender, risk factors, country, and year. It is ideal for actuaries, insurers, and healthcare analysts seeking granular data to inform critical illness insurance pricing, risk modeling, and public health research.
Sample rows
preview · 8 of 600 rows · all 9 columns| record_idstring | disease_typestring | ageinteger | sourcestring | genderstring | incidence_ratefloat | countrystring | data_yearinteger | risk_factorsstring |
|---|---|---|---|---|---|---|---|---|
| REC1001 | other | 0 | national registry | female | 7.5 | Germany | 1978 | {"birth_complications":true,"family_history":false} |
| REC1002 | other | 5 | insurance study | male | 4.3 | Japan | 1984 | {"vaccination_status":"complete","athlete":false} |
| REC1003 | other | 8 | national registry | female | 6.1 | Nigeria | 1993 | {"vaccination_status":"partial","family_history":true} |
| REC1004 | other | 14 | insurance study | other | 9.4 | United States | 1900 | {"athlete":true,"stress_level":2} |
| REC1005 | other | 17 | national registry | male | 8.2 | Italy | 2001 | {"vaccination_status":"complete","athlete":true} |
| REC1006 | cancer | 23 | WHO | female | 38.6 | Brazil | 2015 | {"smoking_status":"never","BMI":22.5,"family_history":true} |
| REC1007 | stroke | 25 | insurance study | male | 27.4 | France | 2003 | {"smoking_status":"current","BMI":26.1,"hypertension":false,"family_history":false} |
| REC1008 | heart_attack | 26 | WHO | female | 24.3 | Russia | 2011 | {"smoking_status":"never","BMI":21.8,"diabetes":false} |
| REC1009 | organ_failure | 32 | national registry | female | 48.6 | Australia | 1998 | {"hypertension":true,"diabetes":false,"stress_level":3} |
| REC1010 | cancer | 35 | insurance study | male | 65.3 | Canada | 2017 | {"smoking_status":"current","BMI":27.9,"family_history":true,"chemical_exposure":true} |
| REC1011 | stroke | 37 | WHO | female | 59.8 | South Africa | 2008 | {"smoking_status":"former","BMI":23.4,"hypertension":true} |
| REC1012 | heart_attack | 39 | insurance study | other | 51.1 | India | 2012 | {"smoking_status":"never","family_history":true,"diabetes":false} |
| REC1013 | organ_failure | 41 | WHO | male | 77.2 | China | 2022 | {"hypertension":true,"alcohol_use":true,"stress_level":4,"BMI":30.2} |
| REC1014 | stroke | 44 | national registry | female | 93.5 | Ecuador | 2010 | {"hypertension":true,"smoking_status":"never","BMI":24.6} |
| REC1015 | heart_attack | 46 | insurance study | male | 118.7 | Spain | 2023 | {"smoking_status":"current","diabetes":true,"alcohol_use":true,"stress_level":5} |
| REC1016 | cancer | 53 | WHO | female | 195.3 | United Kingdom | 2020 | {"smoking_status":"former","BMI":29.2,"family_history":true,"chemical_exposure":false} |
| REC1017 | organ_failure | 55 | national registry | male | 261.8 | Turkey | 1995 | {"hypertension":true,"diabetes":true,"alcohol_use":false} |
| REC1018 | stroke | 58 | insurance study | female | 303.2 | Poland | 2018 | {"smoking_status":"never","BMI":28.1,"hypertension":true} |
| REC1019 | heart_attack | 61 | WHO | male | 352.9 | Mexico | 2006 | {"smoking_status":"former","BMI":31.4,"diabetes":true,"alcohol_use":true,"stress_level":3} |
| REC1020 | organ_failure | 64 | insurance study | female | 429.7 | Norway | 2016 | {"hypertension":true,"diabetes":true,"alcohol_use":false,"stress_level":2} |
What the 600 rows show
from the 600-row sampleOther (disease type) stands out: mean age is 14.3, against 67.6 for the rest.
- 56median age
- 3genders
- 82countries
- 223.0median incidence_
rate
Median 56, from 0 to 120.
- string 6
- integer 2
- float 1
Columns
9 columns in two groups| column | type | description | example |
|---|---|---|---|
| Text 6 columns | |||
record_id | string | Unique identifier for each incidence rate recordunique | REC1001 |
gender | string | Gender of the individual or groupmale · female · other | female |
disease_type | string | Type of critical illness or dread disease (e.g., cancer, heart attack, stroke, organ failure)cancer · heart_attack · stroke · organ_failure · other | other |
risk_factors | string | JSON object containing relevant risk factors (e.g., smoking status, BMI, family history, hypertension, diabetes)optional | {"birth_complications":tr… |
country | string | Country or region where the incidence rate data was collected | Germany |
source | string | Source or reference for the incidence rate data (e.g., WHO, national registry, insurance study)4 sources · optional | national registry |
| Numbers 3 columns | |||
age | integer | Age of the individual or age group for which the incidence rate applies0 to 120 | 0 |
incidence_rate | float | Incidence rate per 100,000 population for the specified disease, age, gender, and risk profile0 or more | 7.5 |
data_year | integer | Year in which the incidence rate data was collected or published1,900 to 2,100 | 1978 |
Use it for
An insurance dashboard
Age by disease_
type and a breakdown of source. Excel, Power BI or Tableau. Why do the 108 other rows have a mean age of 14.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records600REC10010otherREC10025otherREC10038other
A software demo
Believable records with age, gender and disease_
type to fill a screen in front of a buyer.
This dataset600 rows9 columns
Yours10,000 rows9 columnscountry: UK only
blueprint · critical-illness-and-dread-disease-rates
Behind this dataset
Same schema. As many rows as you need.
These 600 rows came out of a blueprint — 9 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Critical illness types: cancer, heart attack, stroke, renal failure
- Incidence rates by age and gender
- Survival rates post-diagnosis
- Family history risk factors
- Lifestyle factors: smoking, obesity, diabetes
- Waiting period for coverage
- Lump sum benefit on diagnosis
- Recurrence provisions
Open the blueprint in Data Factory
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- critical-illness-and-dread-disease-rates