Insurance
10 columns
800 rows
8 formats

Morbidity Tables and Disability Rates

This dataset provides comprehensive morbidity and disability rate tables for insurance pricing and valuation, detailing incidence and termination rates by age, occupation class, elimination period, gender, and year. It enables actuaries and analysts to accurately model disability risk, set premiums, and calculate reserves for disability and long-term care insurance products.

last updated 2 Nov 2025
by GoMask

The brief that made it

Disability insurance pricing and product development

Sample rows

preview · 8 of 800 rows · all 10 columns

record_idinteger	occupation_classstring	disability_incidence_ratefloat	genderstring	ageinteger	elimination_period_daysinteger	disability_termination_ratefloat	rate_yearinteger	source_tablestring	notesstring
1	professional	1.56	male	29	60	568.3	2016	MDT_2016P	Standard rate for urban professionals, mid-career, male insured.
2	professional	1.71	female	31	90	602.7	2022	MDT_2022P	Mid-career professional female, standard elimination period.
3	executive	5.06	male	52	180	266.1	2034	MDT_2034E	Older executive male, projected rates for future year.
4	executive	7.49	female	60	180	191.4	2042	MDT_2042E	Senior executive female, higher incidence due to age.
5	skilled_labor	1.08	male	24	0	689.2	2008	MDT_2008S	Immediate benefit start for young skilled labor, male insured.
6	skilled_labor	2.37	female	35	30	602.9	2023	MDT_2023S	Mid-age skilled labor, female, short elimination period.
7	clerical	5.13	female	57	90	312.7	2001	MDT_2001C	Senior clerical worker, female, moderate termination rate.
8	clerical	7.81	male	65	365	189.6	2028	MDT_2028C	Maximum elimination period for senior clerical, male insured.
9	skilled_labor	2.15	male	38	30	614.1	2012	MDT_2012S	Experienced skilled labor, immediate benefit eligibility.
10	other	0.04	unknown	0	0	0.02	2100	MDT_2100O	Boundary age case for newborn; removal of all periods.
11	other	0.08	other	120	30	0.09	2100	MDT_2100O	Maximum age outlier, rare occupation, termination nearly zero.
12	clerical	4.22	female	43	90	472.5	1999	MDT_1999C	Historic clerical rate, female, moderate age.
13	executive	5.82	male	55	180	308.4	2019	MDT_2019E	Older executive, increased incidence due to age.
14	skilled_labor	0.83	female	20	0	705.8	2011	MDT_2011S	Young skilled labor, immediate benefit, high recovery rate.
15	clerical	8.02	female	68	365	148.7	2030	MDT_2030C	Senior clerical, future projection, female insured.
16	professional	1.92	female	33	60	630.4	2018	MDT_2018P	Professional female, mid-career, moderate incidence.
17	executive	4.72	male	47	180	415.9	2025	MDT_2025E	Younger executive, male insured, shorter duration.
18	clerical	7.21	female	62	180	210.6	2022	MDT_2022C	Senior clerical, female, moderate elimination period.
19	professional	2.48	male	41	90	585.2	2020	MDT_2020P	Professional, male, higher age bracket mid-career.
20	skilled_labor	1.24	unknown	27	0	700.1	2003	MDT_2003S	Young skilled labor, unknown gender, high termination.

What the 800 rows show

from the 800-row sample

Executive (occupation class) stands out: mean disability_incidence_rate is 7.0, against 3.0 for the rest.

2.7median disability_incidence_rate
195source tables
44median age
90median elimination_period_days
431.1median disability_termination_rate

Mean disability_incidence_rate by occupation_class800 rows

disability_incidence_rate800 rows, in bands of 2

Median 2.7, from 0.01 to 11.8.

gender800 rows · 4 values

female404
male323
unknown46
other27

10 columns by typefrom the column list below

string 4
integer 4
float 2

Columns

10 columns in two groups

blueprint · 10 columns
column	type	description	example
Text 4 columns
`occupation_class`	string	Occupation classification or risk class (e.g., professional, skilled labor, clerical)6 values	professional
`gender`	string	Gender of the insured individual (if applicable)male · female · other · unknown · optional	male
`source_table`	string	Name or code of the morbidity/disability table used for the ratesoptional	MDT_2016P
`notes`	string	Additional notes or comments regarding the rate recordoptional	Standard rate for urban p…
Numbers 6 columns
`record_id`	integer	Unique identifier for each morbidity/disability rate recordunique · 1 or more	1
`age`	integer	Age of the insured individual at the time of rate application0 to 120	29
`elimination_period_days`	integer	Elimination period in days before disability benefits begin0 to 365	60
`disability_incidence_rate`	float	Annual rate of new disability claims per 1,000 insureds for this age, occupation, and elimination period0 or more	1.56
`disability_termination_rate`	float	Annual rate at which existing disability claims terminate (recoveries, deaths, etc.) per 1,000 disabled for this age, occupation, and elimination period0 or more	568.3
`rate_year`	integer	Year for which the rates are applicable or published1,900 to 2,100	2016

Use it for

An insurance dashboard
Disability_incidence_rate by occupation_class and a breakdown of gender. Excel, Power BI or Tableau.
Why do the 174 executive rows have a mean disability_incidence_rate of 7.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Rows800
11.56professi…
21.71professi…
35.06executive
A software demo
Believable rows with age, occupation_class and elimination_period_days to fill a screen in front of a buyer.

This dataset800 rows10 columns

Yours10,000 rows10 columns

blueprint · morbidity-tables-and-disability-rates

Behind this dataset

Same schema. As many rows as you need.

These 800 rows came out of a blueprint — 10 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

Incidence rate: new disabilities per 1,000 lives
Termination rate: recovery or death from disabled state
Occupation class: white collar, blue collar, professional
Elimination period: 30, 60, 90, 180 days
Benefit period: 2-year, 5-year, to age 65, lifetime
Own occupation vs any occupation definitions
Mental/nervous limitation periods
Claim costs vary significantly by occupation

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

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: morbidity-tables-and-disability-rates

What should your data show?

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Sample rows

What the 800 rows show

Columns

Use it for

An insurance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Insurance

What should your data show?