Chapter 8.1 · The twilight

Disability, retirement and Medicare

Adult disability rises from 23.3% to 42.9% across tenths of tracts by deprivation and the disabled-worker share of Social Security beneficiaries from 7.2% to 16.8% across fifths of ZIP codes, while Medicare payments per fee-for-service beneficiary show no detectable difference between the least and most deprived fifths of counties and, weighted by residents, county values of expected age at death at 40 spread more for the poorest income quartile than for the richest.

Author: Vishal Singh
Affiliation: NYU Stern
Published: 9 October 2026

The share of adults with any disability, as CDC PLACES models it, was 42.9% (95% CI 42.5% to 43.2%) in the most deprived tenth of census tracts and 23.3% (95% CI 22.3% to 24.1%) in the least deprived, and the share of Social Security beneficiaries who were disabled workers in December 2024 was 16.8% (95% CI 16.4% to 17.3%) in the most deprived fifth of ZIP codes and 7.2% (95% CI 6.9% to 7.5%) in the least deprived. This chapter follows those two gradients into the programs of old age. It asks whether Medicare payments per beneficiary and the expected age at death of the poorest income quartile also move with deprivation, at the grain of tracts, ZIP codes and counties.

Deprivation is measured by the Area Deprivation Index (ADI), a national percentile rank on which a higher value means a more deprived neighborhood.[1] The sources differ in kind. The Social Security and Medicare files are counts and payments from administrative records, while PLACES and the Health Inequality Project report modeled estimates. Every figure names its unit and its weights.

Disability rises with neighborhood deprivation, and age structure explains little of it

CDC PLACES estimates, for every census tract, the share of adults aged 18 and over who report difficulty with hearing, vision, thinking, walking, self-care or living independently, using survey responses from 2022 in its 2024 release.[2] Across 82,779 tracts, weighted by adult population, the share with any disability was 23.3% (95% CI 22.3% to 24.1%) in the least deprived tenth of tracts and 42.9% (95% CI 42.5% to 43.2%) in the most deprived, 1.8 (95% CI 1.8 to 1.9) times as high (Figure 1). Among adults aged 65 and over, the share who have lost all their teeth was 6.9% (95% CI 6.2% to 7.9%) and 25.1% (95% CI 24.7% to 25.6%), 3.6 (95% CI 3.2 to 4.0) times as high. The means of both measures rise at every step across the tenths, though the intervals of some neighboring tenths overlap, and tooth loss rises fastest at the deprived end. Across tracts, disability correlates +0.69 (95% CI 0.65 to 0.73) with the ADI rank.

PLACES does not count anyone. It fits a model to survey responses and applies it to each tract using census and American Community Survey inputs, and the ADI is built from American Community Survey data as well, so part of these gradients is built in. The tract file carries no intervals of its own, and the intervals here come from resampling counties.

A person has any disability if they report difficulty in at least one of the six areas. Values are crude prevalence, not adjusted for age, so a tract with more older residents would have a higher value if age mattered much.

The age of residents does not account for the gradient. Residents aged 65 and over were 17.3% (95% CI 16.6% to 18.0%) of the population in the least deprived tenth of tracts and 16.6% (95% CI 16.1% to 17.0%) in the most deprived. Across tracts, disability correlates only +0.07 (95% CI 0.04 to 0.10) with the share of older residents. Disability was 30.5% (95% CI 29.6% to 31.3%) in the tenth of tracts with the fewest older residents (5.9% of residents) and 32.3% (95% CI 31.8% to 32.7%) in the tenth with the most (35.8%), a difference of 1.8 (95% CI 0.9 to 2.7) points. Each ten-point step in the ADI rank goes with 2.0 (95% CI 1.8 to 2.1) points more disability, and holding the older share fixed leaves the slope at 2.0 (95% CI 1.8 to 2.1).

Figure 1

Adult disability is 23.3% in the least deprived tenth of census tracts and 42.9% in the most deprived, and it differs by 1.8 points between tenths of tracts with few and many older residents

Percent of adults with any disability and percent of older adults who have lost all their teeth, by tenth of the Area Deprivation Index, and disability against the share of older residents, census tracts, 2024 PLACES release

Disability and tooth loss both rise across tenths of tracts by deprivation, tooth loss from 6.9% to 25.1% and disability from 23.3% to 42.9%

Tooth loss is 3.6 times as common in the most deprived tenth as in the least deprived, and disability is 1.8 times as common. Each point is a weighted mean of tract values with a 95% interval from a bootstrap over counties.

Show data

Data for Adult disability is 23.3% in the least deprived tenth of census tracts and 42.9% in the most deprived, and it differs by 1.8 points between tenths of tracts with few and many older residents, By deprivation
conf_high	conf_low	decile	estimate	n	series
24.07	22.30	1	23.28	8,274	Any disability among adults
27.45	24.50	2	25.97	8,118	Any disability among adults
27.99	26.08	3	27	8,509	Any disability among adults
28.73	27.60	4	28.17	8,261	Any disability among adults
30.32	29.39	5	29.84	8,189	Any disability among adults
32.06	31.21	6	31.62	8,318	Any disability among adults
33.88	33.16	7	33.53	8,305	Any disability among adults
35.81	35.15	8	35.48	8,238	Any disability among adults
38.39	37.74	9	38.06	8,354	Any disability among adults
43.21	42.50	10	42.86	8,213	Any disability among adults
7.91	6.24	1	6.94	8,258	All teeth lost among older adults
9.17	7.72	2	8.48	8,111	All teeth lost among older adults
9.65	8.65	3	9.12	8,498	All teeth lost among older adults
10.29	9.66	4	9.97	8,251	All teeth lost among older adults
11.53	10.90	5	11.20	8,179	All teeth lost among older adults
12.90	12.32	6	12.61	8,299	All teeth lost among older adults
14.49	13.95	7	14.22	8,298	All teeth lost among older adults
16.51	15.96	8	16.23	8,214	All teeth lost among older adults
19.15	18.58	9	18.87	8,341	All teeth lost among older adults
25.56	24.65	10	25.10	8,213	All teeth lost among older adults

Both sets of means rise at every step from the least to the most deprived tenth, though intervals of some neighboring tenths overlap, and tooth loss rises more steeply, while disability in tenths of tracts by age structure rises by only 1.8 points. The first view plots the weighted mean of tract values in each tenth with 95% intervals from a bootstrap over counties; the second plots disability against the share of residents aged 65 and over, with no fitted line, for a random sample of 2,000 tracts, while 398 tracts fall outside the plotted range.Note: PLACES values are modeled estimates built from survey responses with census and ACS inputs that overlap the Area Deprivation Index, so part of each gradient is built in. Values are crude prevalence among adults, not adjusted for age. The tract file has no intervals, and the bands and whiskers come from resampling counties.Note: Any disability is difficulty with hearing, vision, thinking, walking, self-care or independent living reported by adults aged 18 and over, and older adults are those aged 65 and over. Tenths hold equal numbers of tracts, and a tract with no tooth-loss value is left out of that line.Note: The dots in the second view are a random sample of 2,000 tracts inside the plotted range, drawn with a fixed seed (stated in the methods note); 398 tracts fall outside that range, and every estimate uses all tracts.Source: CDC PLACES 2024 release (BRFSS 2022), ACS 2019-2023 and Area Deprivation Index 2023; modeled estimates, weighted by adult population

Old-Age, Survivors and Disability Insurance pays benefits to retired workers, to disabled workers, to survivors (widows, widowers and parents of deceased workers), and to spouses and children. The Social Security Administration counts its beneficiaries by ZIP code. In December 2024 the 31,815 ZIP codes in the states and the District of Columbia that match a ZIP code area with an ADI rank held 65.9 million beneficiaries. In the least deprived fifth of these ZIP codes, 80.5% (95% CI 79.9% to 81.1%) of beneficiaries were retired workers and 7.2% (95% CI 6.9% to 7.5%) were disabled workers. In the most deprived fifth, 65.9% (95% CI 65.3% to 66.4%) were retired workers and 16.8% (95% CI 16.4% to 17.3%) were disabled workers (Figure 2). The shares of widows, widowers and parents and of spouses and children are also larger in the most deprived fifth, 6.6% against 4.8% and 10.7% against 7.5%.

A disabled worker's benefit becomes a retired worker's benefit at full retirement age. The retired-worker share therefore includes people who first qualified through disability, and the disabled-worker share counts only those still below full retirement age.

Figure 2

Disabled workers are 16.8% of Social Security beneficiaries in the most deprived fifth of ZIP codes and 7.2% in the least deprived, and retired workers are 65.9% against 80.5%

Share of OASDI beneficiaries by type, percent, ZIP codes in fifths of the Area Deprivation Index national rank, December 2024

Fifth of ZIP codes by ADI

Retired workers
Disabled workers
Widowed
Spouses and children

Show data

Data for Disabled workers are 16.8% of Social Security beneficiaries in the most deprived fifth of ZIP codes and 7.2% in the least deprived, and retired workers are 65.9% against 80.5%
Fifth of ZIP codes by ADI	Type of beneficiary	Share of beneficiaries (%)	Lower limit of the 95% interval	Upper limit of the 95% interval	ZIP codes	Beneficiaries
1 (least deprived)	Retired workers	80.5	79.9	81.0	6,363	23,011,380
1 (least deprived)	Disabled workers	7.2	6.9	7.5	6,363	23,011,380
1 (least deprived)	Widowed	4.8	4.7	4.9	6,363	23,011,380
1 (least deprived)	Spouses and children	7.5	7.2	7.8	6,363	23,011,380
2	Retired workers	77.2	76.9	77.6	6,363	17,037,990
2	Disabled workers	10.1	9.8	10.3	6,363	17,037,990
2	Widowed	5.1	5.0	5.1	6,363	17,037,990
2	Spouses and children	7.6	7.5	7.8	6,363	17,037,990
3	Retired workers	73.9	73.6	74.3	6,363	11,839,725
3	Disabled workers	12.3	12.1	12.5	6,363	11,839,725
3	Widowed	5.5	5.5	5.6	6,363	11,839,725
3	Spouses and children	8.3	8.1	8.4	6,363	11,839,725
4	Retired workers	70.4	70.0	70.7	6,363	8,506,535
4	Disabled workers	14.4	14.2	14.7	6,363	8,506,535
4	Widowed	6.1	6.0	6.2	6,363	8,506,535
4	Spouses and children	9.1	8.9	9.2	6,363	8,506,535
5 (most deprived)	Retired workers	65.8	65.3	66.4	6,363	5,518,365
5 (most deprived)	Disabled workers	16.8	16.4	17.3	6,363	5,518,365
5 (most deprived)	Widowed	6.6	6.5	6.8	6,363	5,518,365
5 (most deprived)	Spouses and children	10.7	10.4	10.9	6,363	5,518,365

The disabled-worker segment lengthens and the retired-worker segment shortens at every step toward the most deprived fifth. Each bar covers the beneficiaries in the ZIP codes of a single fifth and sums to the whole; the fifths hold equal numbers of ZIP codes but different numbers of beneficiaries, and the table gives 95% intervals from a bootstrap over counties.Note: Types are the groups SSA reports: retired workers, disabled workers, widows, widowers and parents (survivors), and spouses and children. Disabled-worker benefits become retired-worker benefits at full retirement age, so former disabled workers are counted as retired workers after it.Note: The figure covers 31,815 ZIP codes and 65.9 million beneficiaries that match a ZCTA with an ADI rank. A ZIP code is matched to the ZCTA of the same number (a ZIP code is not a ZCTA), and ZIP codes with no ZCTA, such as post office boxes, are left out. Connecticut ZIP codes are included. SSA rounds its counts and does not list ZIP codes below a disclosure threshold.Source: SSA OASDI beneficiaries by ZIP code, December 2024, and Area Deprivation Index 2023; full counts, pooled over ZIP codes, intervals from a bootstrap over counties

Across ZIP codes, the disabled-worker share correlates +0.68 (95% CI 0.64 to 0.71) with the ADI rank when ZIP codes are weighted by population, and +0.52 (95% CI 0.50 to 0.54) when each counts once. Small ZIP codes, whose shares rest on few beneficiaries and counts that SSA rounds, count for more in the second correlation. Among the 1,838 counties with at least 5,000 beneficiaries in their ZIP codes, the disabled-worker share is highest in Pike County, Kentucky (27.2%). The 12 counties with the highest shares, down to 23.0%, are all in Kentucky, Virginia and West Virginia. The ranking depends on the cutoff: with a minimum of 10,000 beneficiaries, the 12 highest counties include counties in Alabama, Arkansas and Oklahoma as well as Kentucky and Virginia.

Deprivation and income carry separate information about the share. In a regression across ZIP codes with the ADI rank, median household income and the share of residents aged 65 and over, each in standard-deviation units, weighted by population, the disabled-worker share has a coefficient of 0.46 (95% CI 0.40 to 0.52) on the ADI rank and −0.36 (95% CI −0.42 to −0.30) on income.

The size of a retired worker's benefit follows ZIP code income

The Social Security benefit formula starts from a worker's earnings over a career, so the average benefit in a ZIP code should rise with the incomes of its residents. The average monthly retired-worker benefit was $1,554 (95% CI $1,523 to $1,586) in the tenth of ZIP codes with the lowest median household income and $2,370 (95% CI $2,354 to $2,386) in the tenth with the highest (Figure 3). Across 28,547 ZIP codes with at least 50 retired workers, the correlation between the average benefit and median household income is +0.84 (95% CI 0.83 to 0.85) weighted by population and +0.80 (95% CI 0.79 to 0.82) counting each ZIP code once.

In the regression above, with the average retired-worker benefit as the outcome, the coefficient on income is 0.89 (95% CI 0.80 to 0.98), the coefficient on the share of residents aged 65 and over is 0.16 (95% CI 0.14 to 0.18) and the coefficient on the ADI rank is 0.08 (95% CI −0.02 to 0.18), an interval that includes zero. The benefit amount moves with income and not with the ADI rank, while the disabled-worker share moves with both.

Figure 3

The average monthly retired-worker benefit rises with ZIP code income, from $1,554 in the lowest tenth of ZIP codes to $2,370 in the highest, a correlation of +0.84

Average monthly OASDI retired-worker benefit in dollars against median household income in dollars (log scale), one dot per sampled ZIP code area, with means for tenths of ZIP codes by income

Loading data

Dots drift up from left to right and the markers for tenths of ZIP codes by income rise at every step, with intervals that do not overlap between neighboring tenths. Dots are a random sample of 2,000 of the 28,547 ZIP codes with at least 50 retired workers, and 102 ZIP codes fall outside the plotted range; markers are retired-worker-weighted means with 95% intervals from a bootstrap over counties.Note: The average is the monthly benefit dollars paid to retired workers in a ZIP code divided by its retired workers, for December 2024. SSA reports benefit totals in thousands of dollars, converted here, and rounds them. ZIP code income is the ACS 2019-2023 median household income of the matching ZCTA, which the Census Bureau tops out at a ceiling, and ZIP codes without an income are left out.Note: The dots are drawn at random from the ZIP codes inside the plotted range with a fixed seed (stated in the methods note); every estimate uses all ZIP codes. The correlation weights ZIP codes by population; counting each ZIP code once gives +0.80.Source: SSA OASDI beneficiaries by ZIP code, December 2024, and ACS 2019-2023 ZCTA median household income; full counts of benefits, weighted by retired workers, intervals from a bootstrap over counties

Social Security is one layer of retirement income. Taxable pensions and annuities reported on tax returns for 2022 include pensions from private and public employers. They were 41.7% (95% CI 41.0% to 42.4%) of the sum of those pensions and annualized Social Security benefits in the least deprived fifth of ZIP codes and 28.0% (95% CI 27.5% to 28.4%) in the most deprived. The two sources cover different years and different people, tax filers and beneficiaries, so the share is a rough index of how much retirement income comes from outside Social Security.

Medicare payments show no detectable difference across county deprivation, but their mix differs

The Medicare Geographic Variation file reports, for each county, Part A and Part B payments per beneficiary with local prices removed, called price-standardized payments.[3] The payments are per beneficiary enrolled in Original Medicare, the fee-for-service program, with both parts. Medicare Advantage enrollees, who were 54.5% of beneficiaries with both parts in these counties, are not in them. The file counts beneficiaries of all ages, including people under 65 who qualify through disability, and standardization does not adjust for how sick beneficiaries are. Across 3,128 counties with an ADI rank, covering 27.6 million fee-for-service beneficiaries, payments averaged $12,585 (95% CI $12,125 to $12,987) per beneficiary in 2024.

Ranking counties on a county ADI, the mean of its tracts' ranks weighted by population, payments were $12,680 (95% CI $11,997 to $13,194) in the least deprived fifth of counties and $13,176 (95% CI $12,517 to $13,785) in the most deprived. The difference of $496 (95% CI −$290 to $1406) has an interval that includes zero, so the data show no detectable difference, though the interval is wide (Figure 4). The correlation between a county's payments and its ADI is −0.04 (95% CI −0.19 to 0.21) weighted by fee-for-service beneficiaries and +0.30 (95% CI 0.20 to 0.39) counting each county once.

The fifths are different kinds of counties. 87.3% of the beneficiaries in the most deprived fifth live in counties with fewer than 5,000 fee-for-service beneficiaries, against 3.1% in the least deprived fifth, so the comparison of fifth means sets mostly small counties against mostly large ones. Among counties below that size, payments rise from $10,893 (95% CI $10,526 to $11,270) in the least deprived fifth to $13,295 (95% CI $12,601 to $13,903) in the most deprived, and the intervals do not overlap. Among larger counties they go from $12,736 (95% CI $12,046 to $13,250) to $12,359 (95% CI $11,830 to $12,916), and the intervals overlap. Small counties make up 97% of the counties in the most deprived fifth and carry little weight in the weighted correlation, which is part of why the two correlations disagree.

The total hides changes in its parts. Between the least and the most deprived fifth, payments for procedures, tests, imaging and treatments differ by −$738 (95% CI −$971 to −$475), from $2,508 to $1,770, and payments for evaluation and management visits, the category CMS uses for clinician visits in offices, hospitals and other settings, differ by −$430 (95% CI −$543 to −$296), from $1,333 to $903, which is 32% (95% CI 24% to 39%) lower. Payments for skilled nursing facilities are $437 (95% CI $214 to $653) higher, from $865 to $1,302, for hospital outpatient departments and surgery centers $410 (95% CI $206 to $601) higher and for all other services $480 (95% CI $243 to $736) higher. The differences for inpatient hospital stays ($134 (95% CI −$12 to $315)) and for home health and hospice ($224 (95% CI −$56 to $511)) have intervals that include zero. Emergency department visits are higher in the more deprived fifths, 571 (95% CI 556 to 584) per thousand beneficiaries in the least deprived and 677 (95% CI 656 to 694) in the most deprived, and a county's visit rate correlates +0.37 (95% CI 0.26 to 0.47) with its ADI. The means rise at every step, though the intervals of neighboring fifths overlap. Chapter 5.1, Health behaviors and the supply of care reports the supply of primary care physicians and the rate of preventable hospital stays by county deprivation.

Figure 4

Price-standardized Medicare payments are $12,680 per fee-for-service beneficiary in the least deprived fifth of counties and $13,176 in the most deprived, a difference whose interval includes zero

Medicare Part A and Part B payments per Original Medicare beneficiary with local prices removed, by fifth of the county Area Deprivation Index, service lines, emergency department visits and county life expectancy, 2024

Payments for procedures, tests, imaging and treatments fall from $2,508 to $1,770 across fifths of counties while payments for skilled nursing facilities rise from $865 to $1,302

Price-standardized payments per fee-for-service beneficiary by service line, dollars per year, fifths of counties by the county Area Deprivation Index

Fifth of counties by ADI

Inpatient hospital stays
Hospital outpatient and surgery centers
Skilled nursing facilities
Home health and hospice
Evaluation and management visits
Procedures, tests, imaging and treatments
All other services

The totals are close. Differences between the most and the least deprived fifth, in dollars per beneficiary, are −$738 for procedures, tests, imaging and treatments, −$430 for evaluation and management visits, $437 for skilled nursing facilities, $410 for hospital outpatient departments and surgery centers and $480 for all other services; the intervals for inpatient hospital stays ($134) and for home health and hospice ($224) include zero. Each segment is a mean of county payments weighted by fee-for-service beneficiaries.

Show data

Data for Price-standardized Medicare payments are $12,680 per fee-for-service beneficiary in the least deprived fifth of counties and $13,176 in the most deprived, a difference whose interval includes zero, Service lines
Fifth of counties by ADI	Service line	Dollars per beneficiary	Lower limit of the 95% interval	Upper limit of the 95% interval	Counties
1 (least deprived)	Inpatient hospital stays	2,871	2,712	3,001	605
2	Inpatient hospital stays	2,869	2,765	2,963	585
3	Inpatient hospital stays	2,913	2,812	2,993	571
4	Inpatient hospital stays	2,955	2,858	3,036	565
5 (most deprived)	Inpatient hospital stays	3,005	2,919	3,073	541
1 (least deprived)	Hospital outpatient and surgery centers	2,516	2,401	2,684	605
2	Hospital outpatient and surgery centers	2,780	2,618	2,953	585
3	Hospital outpatient and surgery centers	2,864	2,708	3,035	571
4	Hospital outpatient and surgery centers	3,002	2,793	3,201	565
5 (most deprived)	Hospital outpatient and surgery centers	2,926	2,768	3,098	541
1 (least deprived)	Skilled nursing facilities	865	712	986	605
2	Skilled nursing facilities	771	721	829	585
3	Skilled nursing facilities	879	831	928	571
4	Skilled nursing facilities	1,004	925	1,081	565
5 (most deprived)	Skilled nursing facilities	1,302	1,122	1,462	541
1 (least deprived)	Home health and hospice	1,010	839	1,148	605
2	Home health and hospice	962	853	1,066	585
3	Home health and hospice	1,018	864	1,187	571
4	Home health and hospice	1,060	896	1,228	565
5 (most deprived)	Home health and hospice	1,234	975	1,460	541
1 (least deprived)	Evaluation and management visits	1,333	1,218	1,426	605
2	Evaluation and management visits	1,146	1,062	1,219	585
3	Evaluation and management visits	1,054	992	1,102	571
4	Evaluation and management visits	967	902	1,028	565
5 (most deprived)	Evaluation and management visits	903	831	967	541
1 (least deprived)	Procedures, tests, imaging and treatments	2,508	2,275	2,723	605
2	Procedures, tests, imaging and treatments	2,204	2,011	2,399	585
3	Procedures, tests, imaging and treatments	1,938	1,808	2,073	571
4	Procedures, tests, imaging and treatments	1,786	1,655	1,936	565
5 (most deprived)	Procedures, tests, imaging and treatments	1,770	1,635	1,884	541
1 (least deprived)	All other services	1,578	1,375	1,745	605
2	All other services	1,639	1,456	1,830	585
3	All other services	1,724	1,544	1,922	571
4	All other services	1,776	1,622	1,931	565
5 (most deprived)	All other services	2,058	1,863	2,226	541

The five totals are close and the difference between the ends is not distinguishable from zero, but the fifths are different kinds of counties: 87.3% of the most deprived fifth's beneficiaries live in counties with fewer than 5,000 fee-for-service beneficiaries, against 3.1% in the least deprived fifth. The first view splits each total by service line, the second plots county life expectancy against payments, and the third plots emergency department visits. Medicare Advantage enrollees are not in these figures, and 34 counties fall outside the plotted range of the second view.Note: Payments are CMS price-standardized Part A and Part B payments per capita for beneficiaries enrolled in Original Medicare with both parts, so Medicare Advantage enrollees, who are 54.5% of beneficiaries with both parts in these counties, are not in them. Standardization removes local price differences and does not adjust for how sick beneficiaries are. The file counts beneficiaries of all ages, including those under 65 who qualify through disability.Note: A fifth holds equal numbers of counties, ranked on the county Area Deprivation Index (the mean of its tracts' ranks, weighted by population). Intervals resample whole states. Connecticut is by planning region. The service lines view leaves out counties for which CMS blanks a line, and its last segment is the total minus the lines shown. Evaluation and management visits are CMS's category for visits to physicians and other clinicians in many settings, among them offices, hospitals, nursing facilities, homes and emergency departments.Note: Life expectancy is the County Health Rankings 2024 estimate computed from NCHS death counts. Connecticut is left out of the second view because that file uses its legacy counties.Source: CMS Medicare Geographic Variation, county, 2024, and Area Deprivation Index 2023; price-standardized payments per fee-for-service beneficiary, weighted by fee-for-service beneficiaries, full counts (life expectancy from County Health Rankings 2024, deaths 2019-2021, full counts)

Payments vary across counties far more than they vary across the fifths. Weighted by beneficiaries, the county at the 90th percentile of payments, $14,770, is at 1.42 (95% CI 1.34 to 1.49) times the county at the 10th percentile, $10,433. Fisher and colleagues compared Medicare beneficiaries across regions grouped by spending in a cohort study and found that those in higher-spending regions received more care without better health outcomes.[4] Across the 3,057 counties that also have a life expectancy at birth in County Health Rankings, for deaths in 2019 to 2021, the correlation between county payments and life expectancy is −0.07 (95% CI −0.27 to 0.05) weighted by beneficiaries, with a slope of −0.12 (95% CI −0.51 to 0.08) years per thousand dollars, and −0.35 (95% CI −0.43 to −0.25) counting each county once. Holding the county ADI fixed, the slope is −0.17 (95% CI −0.33 to −0.01) years per thousand dollars weighted by beneficiaries and −0.28 (95% CI −0.40 to −0.16) counting each county once, so at a given level of deprivation life expectancy is slightly lower where payments are higher. The county ADI correlates −0.82 (95% CI −0.85 to −0.77) with life expectancy.

Weighted by residents, the poorest income quartile's expected age at death varies more across counties

The Health Inequality Project linked federal tax records to Social Security death records to estimate expected age at death for people who reach age 40, by household income, sex and county.[5] The file used here gives the estimate for each quartile of the national distribution of household income, from the poorest to the richest, in 1,559 larger counties, for 2001 to 2014, adjusted to the national racial composition. The file does not state its size threshold; its smallest county has 15,304 residents and its median county 69,380, in the County Health Rankings 2024 release (a Census Bureau estimate for 2022). The chapter averages the estimates for women and men. Weighted by county residents, the poorest quartile's expected age at death averaged 79.5 (95% CI 79.1 to 79.9) across counties and the richest quartile's 86.3 (95% CI 86.1 to 86.4), a difference of 6.7 (95% CI 6.3 to 7.2) years.

The poorest quartile's values run from 77.5 in the county at the 5th percentile to 81.8 in the county at the 95th (Figure 5). Population-weighted state averages of the county values are lowest in Oklahoma, Nevada, Indiana, Wyoming, Kentucky, Michigan and Arkansas and highest in New York, Montana, California, Vermont, Massachusetts, Maine and Idaho, and Nevada and Wyoming have few counties in the file. The Project estimated only larger counties, 1,559 of them on this map, so the map is gray for the rest.

Figure 5

Expected age at death at 40 for the poorest income quartile runs from 77.5 in the county at the fifth percentile to 81.8 in the county at the ninety-fifth

Expected age at death in years for people who reach age 40 in the lowest quartile of national household income, 2001 to 2014, counties in the Health Inequality Project file

Loading county values

Expected age at death at age 40, poorest income quartile (years)

Lower expected age at deathHigher expected age at death

Values outside 76.5 to 82.5 take the end colors.

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Hover or tap a county for its value, or search by name. Zoom with the buttons on the map, then drag to move it.

Population-weighted state averages of the county values are lowest in Oklahoma, Nevada, Indiana, Wyoming, Kentucky, Michigan and Arkansas and highest in New York, Montana, California, Vermont, Massachusetts, Maine and Idaho, and Nevada and Wyoming have few counties in the file. Gray counties are not in the file. The map plots each county's value, the average of the estimates for women and men, adjusted to the national racial composition, on a scale centered on the population-weighted mean of 79.5.Note: The Project estimated only the larger counties, so 1,559 counties are drawn with a value and the rest are gray with the reason in the tooltip. A gray county was not estimated; it is not a county with a low value.Note: Each value comes from a bootstrap estimate with a standard error given in the tooltip, and small counties are noisy. Connecticut is shown by its eight legacy counties, as in the Project's file. Puerto Rico is not drawn.Source: Health Inequality Project county expected age at death, 2001-2014 (Online Data Table 11); modeled estimates, race-adjusted, average of women and men; county residents from the County Health Rankings 2024 release

The richest quartile's expected age at death is higher than the poorest quartile's in 1,558 of the 1,559 counties (Figure 6), but the poorest quartile's values vary more from county to county. Weighted by residents, the range from the 5th to the 95th percentile of counties is 4.3 (95% CI 3.7 to 4.9) years for the poorest quartile and 3.0 (95% CI 2.7 to 3.6) for the richest, a difference of 1.3 (95% CI 0.3 to 1.9) years whose interval excludes zero. Among the 283 counties with at least 250,000 residents, the ranges are 4.2 (95% CI 3.5 to 5.0) and 2.6 (95% CI 2.3 to 3.4), a difference of 1.6 (95% CI 0.7 to 2.5) years.

Figure 6

Weighted by residents, county values of expected age at death at 40 span 4.3 years between the 5th and 95th percentiles for the poorest income quartile and 3.0 for the richest

Expected age at death in years for people who reach age 40 in the poorest quartile against the richest quartile of national household income, one dot per county, dot area is county residents, 2001 to 2014

Loading data

The richest quartile reaches a higher expected age at death than the poorest in 1,558 of 1,559 counties, so every dot but one lies below the diagonal, which marks equal values. Weighted by county residents, the poorest quartile's values run from 77.5 to 81.8 between the fifth and ninety-fifth percentiles of counties and the richest quartile's from 84.7 to 87.7. The four labeled counties are the two with the highest and the two with the lowest value for the poorest quartile among counties of at least 250,000 residents.Note: The richest quartile's estimates rest on fewer deaths in small counties and carry larger standard errors: the mean standard error of a county's value is 0.9 years for the richest quartile and 0.5 years for the poorest. Counting each county once, the range is 4.0 years for the poorest quartile and 4.3 for the richest, and among counties of at least 250,000 residents it is 4.2 against 2.6.Note: Values are adjusted to the national racial composition and averaged over women and men. Without the race adjustment, the range across counties is 6.4 years for the poorest quartile and 3.0 for the richest. The ranges run from the 5th to the 95th percentile of counties, weighted by county residents, and their intervals resample whole states.Note: Weights and dot areas are county residents in the County Health Rankings 2024 release, a Census Bureau estimate for 2022 and the nearest population available, while the estimates are for 2001 to 2014. The file covers 1,559 larger counties.Source: Health Inequality Project county expected age at death, 2001-2014 (Online Data Table 11); modeled estimates, race-adjusted, average of women and men; weighted by county residents (County Health Rankings 2024 release)

The comparison depends on the weights. Counting each county once, the ranges are 4.0 (95% CI 3.7 to 4.4) years for the poorest quartile and 4.3 (95% CI 3.9 to 4.6) for the richest, and the difference of −0.3 (95% CI −0.7 to 0.2) includes zero. Estimates for the richest quartile rest on fewer deaths in small counties and are less precise, with a mean standard error of 0.9 years against 0.5 for the poorest quartile. Removing the mean squared standard error from the variance of the county values, and counting each county once, leaves a standard deviation of 1.13 (95% CI 1.00 to 1.24) years for the poorest quartile and 0.91 (95% CI 0.77 to 1.02) for the richest, a difference of 0.22 (95% CI 0.08 to 0.38) years whose interval excludes zero. Without the race adjustment, the ranges are 6.4 (95% CI 4.7 to 6.7) years for the poorest quartile and 3.0 (95% CI 2.7 to 3.4) for the richest, so the adjustment shortens the poorest quartile's range and leaves the richest quartile's nearly unchanged.

The figure below sets expected age at death beside 15 county measures, the county ADI and measures from County Health Rankings (Figure 7). Weighted by residents, the poorest quartile's expected age at death correlates −0.67 (95% CI −0.74 to −0.51) with the county ADI, −0.64 (95% CI −0.71 to −0.51) with the share of adults with obesity, −0.58 (95% CI −0.67 to −0.45) with the smoking rate and +0.48 (95% CI 0.38 to 0.55) with median household income, and +0.32 (95% CI 0.25 to 0.40) with the share of adults with some college. Counting each county once, the correlations are −0.44 (95% CI −0.53 to −0.34) with the ADI, −0.45 (95% CI −0.54 to −0.34) with obesity and −0.34 (95% CI −0.44 to −0.24) with smoking. The share of people under 65 without health insurance correlates −0.15 (95% CI −0.28 to 0.06) weighted and −0.16 (95% CI −0.29 to 0.01) counting each county once, intervals that include zero, and primary care physicians per hundred thousand residents correlate +0.28 (95% CI 0.20 to 0.35). For the richest quartile, the county ADI correlates −0.22 (95% CI −0.43 to −0.07), the uninsured share −0.27 (95% CI −0.37 to −0.18) and physical inactivity −0.50 (95% CI −0.60 to −0.41). The Project's own analysis of local areas reported a correlation of −0.69 between life expectancy in the lowest income quartile and smoking, and no significant correlation with access to medical care.[5]

Figure 7

Across counties weighted by residents, the poorest quartile's expected age at death correlates −0.67 with the county ADI, −0.64 with obesity and −0.58 with smoking, and −0.15 with the uninsured share

Correlation across counties of expected age at death at age 40 in the poorest and the richest income quartile with 15 county measures, 2001 to 2014 deaths and 2018 to 2023 measures, with 95% intervals

Poorest quartile
Richest quartile

Show data

Data for Across counties weighted by residents, the poorest quartile's expected age at death correlates −0.67 with the county ADI, −0.64 with obesity and −0.58 with smoking, and −0.15 with the uninsured share
conf_high	conf_low	estimate	measure	n	quartile	weighting
0.547	0.376	0.478	Median household income	1,559	Poorest quartile	Population-weighted
0.366	0.197	0.290	Median household income	1,559	Poorest quartile	Each county once
0.382	0.171	0.266	Median household income	1,559	Richest quartile	Population-weighted
0.355	0.255	0.308	Median household income	1,559	Richest quartile	Each county once
0.403	0.247	0.324	Adults with some college	1,559	Poorest quartile	Population-weighted
0.305	0.156	0.235	Adults with some college	1,559	Poorest quartile	Each county once
0.527	0.380	0.456	Adults with some college	1,559	Richest quartile	Population-weighted
0.453	0.356	0.407	Adults with some college	1,559	Richest quartile	Each county once
0.346	0.205	0.277	Primary care physicians per 100,000 residents	1,559	Poorest quartile	Population-weighted
0.330	0.199	0.264	Primary care physicians per 100,000 residents	1,559	Poorest quartile	Each county once
0.491	0.339	0.418	Primary care physicians per 100,000 residents	1,559	Richest quartile	Population-weighted
0.407	0.317	0.363	Primary care physicians per 100,000 residents	1,559	Richest quartile	Each county once
0.468	0.025	0.273	Residents who are Hispanic	1,559	Poorest quartile	Population-weighted
0.250	−0.138	0.032	Residents who are Hispanic	1,559	Poorest quartile	Each county once
−0.117	−0.404	−0.300	Residents who are Hispanic	1,559	Richest quartile	Population-weighted
0.040	−0.270	−0.125	Residents who are Hispanic	1,559	Richest quartile	Each county once
0.321	−0.010	0.195	Ratio of 80th to 20th percentile household income	1,545	Poorest quartile	Population-weighted
0.098	−0.094	0	Ratio of 80th to 20th percentile household income	1,545	Poorest quartile	Each county once
−0.008	−0.216	−0.125	Ratio of 80th to 20th percentile household income	1,545	Richest quartile	Population-weighted
−0.051	−0.222	−0.145	Ratio of 80th to 20th percentile household income	1,545	Richest quartile	Each county once
0.169	−0.198	0.025	Unemployment rate	1,559	Poorest quartile	Population-weighted
0.086	−0.147	−0.023	Unemployment rate	1,559	Poorest quartile	Each county once
−0.232	−0.443	−0.343	Unemployment rate	1,559	Richest quartile	Population-weighted
−0.135	−0.366	−0.249	Unemployment rate	1,559	Richest quartile	Each county once
0.026	−0.271	−0.126	Residents who are non-Hispanic Black	1,559	Poorest quartile	Population-weighted
−0.005	−0.179	−0.084	Residents who are non-Hispanic Black	1,559	Poorest quartile	Each county once
−0.038	−0.248	−0.130	Residents who are non-Hispanic Black	1,559	Richest quartile	Population-weighted
−0.075	−0.255	−0.162	Residents who are non-Hispanic Black	1,559	Richest quartile	Each county once
0.057	−0.280	−0.151	People under 65 without health insurance	1,559	Poorest quartile	Population-weighted
0.011	−0.290	−0.157	People under 65 without health insurance	1,559	Poorest quartile	Each county once
−0.179	−0.369	−0.272	People under 65 without health insurance	1,559	Richest quartile	Population-weighted
−0.165	−0.363	−0.271	People under 65 without health insurance	1,559	Richest quartile	Each county once
−0.050	−0.316	−0.218	Residents living in rural areas	1,559	Poorest quartile	Population-weighted
0.133	−0.076	0.023	Residents living in rural areas	1,559	Poorest quartile	Each county once
0.060	−0.158	−0.046	Residents living in rural areas	1,559	Richest quartile	Population-weighted
−0.068	−0.204	−0.136	Residents living in rural areas	1,559	Richest quartile	Each county once
−0.122	−0.358	−0.241	Children in poverty	1,559	Poorest quartile	Population-weighted
−0.138	−0.287	−0.211	Children in poverty	1,559	Poorest quartile	Each county once
−0.309	−0.454	−0.383	Children in poverty	1,559	Richest quartile	Population-weighted
−0.335	−0.450	−0.395	Children in poverty	1,559	Richest quartile	Each county once
−0.155	−0.532	−0.355	Adults with no leisure-time physical activity	1,559	Poorest quartile	Population-weighted
−0.311	−0.478	−0.401	Adults with no leisure-time physical activity	1,559	Poorest quartile	Each county once
−0.412	−0.603	−0.504	Adults with no leisure-time physical activity	1,559	Richest quartile	Population-weighted
−0.428	−0.533	−0.485	Adults with no leisure-time physical activity	1,559	Richest quartile	Each county once
−0.223	−0.511	−0.379	Preventable hospital stays per 100,000 Medicare enrollees	1,557	Poorest quartile	Population-weighted
−0.281	−0.439	−0.360	Preventable hospital stays per 100,000 Medicare enrollees	1,557	Poorest quartile	Each county once
−0.279	−0.485	−0.375	Preventable hospital stays per 100,000 Medicare enrollees	1,557	Richest quartile	Population-weighted
−0.274	−0.387	−0.333	Preventable hospital stays per 100,000 Medicare enrollees	1,557	Richest quartile	Each county once
−0.451	−0.670	−0.576	Adults who smoke	1,559	Poorest quartile	Population-weighted
−0.243	−0.436	−0.343	Adults who smoke	1,559	Poorest quartile	Each county once
−0.137	−0.453	−0.273	Adults who smoke	1,559	Richest quartile	Population-weighted
−0.298	−0.442	−0.372	Adults who smoke	1,559	Richest quartile	Each county once
−0.510	−0.707	−0.642	Adults with obesity	1,559	Poorest quartile	Population-weighted
−0.340	−0.536	−0.446	Adults with obesity	1,559	Poorest quartile	Each county once
−0.204	−0.476	−0.320	Adults with obesity	1,559	Richest quartile	Population-weighted
−0.320	−0.449	−0.388	Adults with obesity	1,559	Richest quartile	Each county once
−0.510	−0.744	−0.670	Area Deprivation Index of the county (national rank)	1,559	Poorest quartile	Population-weighted
−0.338	−0.532	−0.442	Area Deprivation Index of the county (national rank)	1,559	Poorest quartile	Each county once
−0.075	−0.427	−0.218	Area Deprivation Index of the county (national rank)	1,559	Richest quartile	Population-weighted
−0.277	−0.414	−0.350	Area Deprivation Index of the county (national rank)	1,559	Richest quartile	Each county once

The poorest quartile's dots are furthest from zero for the county ADI, obesity, smoking and median household income, and the uninsured share is close to zero with an interval that includes it. The richest quartile's smoking dot is closer to zero and its physical inactivity dot further from it. Intervals come from a bootstrap over states.Note: The measures are for 2018 to 2023, after the 2001 to 2014 window of the life-expectancy estimates, and smoking, obesity and physical inactivity are County Health Rankings' modeled estimates for all adults, not for the poorest quartile. The county ADI is the mean of its tracts' ranks (2023), and part of the other measures overlaps its inputs. A correlation across counties does not say that smoking or obesity shortens the lives of the poor.Note: Expected age at death is adjusted to the national racial composition and averaged over women and men. The two views use the same 1,559 counties wherever a measure exists.Source: Health Inequality Project county expected age at death, 2001-2014 (Online Data Table 11); modeled estimates, race-adjusted, average of women and men; county measures from County Health Rankings 2024; population-weighted or unweighted as labeled

Causal evidenceRegression discontinuity; instrumental variables (examiner assignment); difference-in-differences

What eligibility rules and cash benefits change for the people they reach

The comparisons above are between places. Four studies ask what Medicare and Social Security change for the people who receive them, each with its own design.

Card, Dobkin and Maestas compared patients just below and just above age 65, when most people become eligible for Medicare. The patients were admitted to California hospitals through the emergency department with conditions whose admission rates did not differ between weekdays and weekends. In this regression discontinuity design, patients just past the threshold received more treatment and had lower mortality in the days after admission.[6]

Maestas, Mullen and Strand used the assignment of disability insurance applications to examiners who differ in how often they allow claims. An examiner's allowance rate served as an instrument for whether an applicant received benefits, an instrumental variables design, and applicants on the margin of allowance would have worked more had they not received benefits.[7] Deshpande, Gross and Su used two age thresholds in the disability determination rules at which the chance of allowance rises, in a design similar to a regression discontinuity, and found that allowance lowered the chances of bankruptcy and foreclosure for the applicants the thresholds affected.[8]

Snyder and Evans used the Social Security benefit notch, a change in the benefit formula that gave people born just before January 1917 larger benefits than people born just after it. They compared mortality across those birth cohorts, with other cohorts as controls, in a difference-in-differences design that the published paper pairs with a regression discontinuity. The cohort with the larger benefits had higher mortality, and the authors link the lower mortality of the smaller-benefit cohort to more work after retirement.[9]

These designs estimate what a program changes for the people it reaches at a threshold or on the margin of a decision. None of them estimates whether the programs would narrow the differences between places in this chapter.

What these data cannot show

Every pattern here describes tracts, ZIP codes or counties, and a pattern across places need not hold for the people inside them (ecological inference). A ZIP code with a high disabled-worker share says nothing about whether any resident should receive a disability benefit, and a county's expected age at death for the poorest quartile is an estimate for a group of people who share an income rank, not a forecast for one of them.

The measures differ in vintage and in how they are made. PLACES values are modeled from 2022 survey responses, and their link with the ADI is partly built in. The Social Security counts are for December 2024 and are rounded by SSA, and the ZIP code is matched to the ZIP code area of the same number, which leaves out ZIP codes such as post office boxes. The Medicare payments are for 2024, cover only fee-for-service beneficiaries, and so leave out the Medicare Advantage enrollees, who are 54.5% of beneficiaries with both parts in these counties. They are not adjusted for the health of the beneficiaries, so equal payments do not mean equal care or equal need. The life-expectancy estimates describe deaths from 2001 to 2014 and end before the later years of this book, and household income is measured on a national scale that does not adjust for the cost of living where a household lives.

The correlates are measured years after the deaths, and smoking and obesity are County Health Rankings' modeled estimates for all adults, not for the poorest quartile. The Project's authors found that the share of immigrants was among the strongest correlates of low-income longevity across local areas, a pattern that tract and county measures of this kind cannot separate from place itself (see Chapter 9.3, Immigrants and the gradient (planned)). Weights matter for several results here. The Medicare correlations with deprivation and with life expectancy and the spread of the richest quartile's expected age at death all change when each county counts once, and both versions are reported.

None of the gradients tells what would move a number. The causal evidence above concerns eligibility rules, examiner decisions and a benefit notch, and the places in this chapter differ in many ways that those studies do not address.

Data sources

CDC PLACESAny disability and all teeth lost among adults, census tracts, 2024 release (BRFSS 2022 responses)Years: 2024 release (tracts); 2025 release (ZCTAs, 2023 data year) · Weights: adult population of the tract; estimated residents aged 65 and over for tooth loss · License: Public domain (CDC, U.S. Department of Health and Human Services)
Area Deprivation IndexArea Deprivation Index national rank, 2023, tracts and ZCTAsYears: 2023 release (version 4.0.1; built on ACS 2019-2023) · Weights: none (index); the county rank is the population-weighted mean of tract ranks · License: Free for non-commercial use; cite the Neighborhood Atlas and Kind and Buckingham
American Community SurveyACS 2019-2023 tract and ZCTA population, share aged 65 and over and median household incomeYears: 2019-2023 (5-year) · Weights: ACS population · License: Public domain (U.S. Census Bureau)
SSA beneficiaries by ZIP codeOASDI beneficiaries and benefits by ZIP code, December 2024Years: December 2024 · Weights: none (full counts); beneficiaries or retired workers where pooled · License: Public domain (U.S. federal)
IRS Statistics of IncomeTaxable pensions and annuities by ZIP code, tax year 2022Years: tax year 2022 · Weights: none (full counts); dollars where pooled · License: Public domain (IRS Statistics of Income)
Medicare Geographic VariationPrice-standardized Medicare payments, emergency department visits and service lines per Original Medicare beneficiary, county, 2024Years: 2024 (from the 2014-2024 file) · Weights: fee-for-service beneficiaries · License: Public domain (CMS, U.S. federal government work)
County Health RankingsCounty Health Rankings 2024: life expectancy (deaths 2019-2021), population and county measuresYears: 2024 release · Weights: county residents; each county once where stated · License: CC BY 4.0 (University of Wisconsin Population Health Institute)
Health Inequality ProjectExpected age at death at age 40 by national household-income quartile and sex, county, 2001-2014 (Online Data Table 11)Years: 2001-2014 (pooled) · Weights: county residents (County Health Rankings 2024) · License: Open (cite Chetty and coauthors, 2016)

All data sources

Methods

This chapter compares tracts, ZIP codes and counties across tenths or fifths of the Area Deprivation Index (2023 national rank) on disability, tooth loss, Social Security beneficiaries and benefits, pensions, Medicare payments and expected age at death at 40. The sources differ in kind and period: PLACES prevalence is modeled with census and ACS inputs from BRFSS 2022 responses, the Social Security counts are for December 2024, pensions are for tax year 2022, and Medicare payments for 2024 are for Original Medicare beneficiaries with both parts. ZIP codes with no ZCTA are left out, and intervals come from a bootstrap over whole counties or states.

How disability and tooth loss are measured

Tract disability and tooth loss are CDC PLACES crude prevalence among adults (2024 release, BRFSS 2022 responses, modeled with census and ACS inputs), weighted by adult population (disability) and by estimated residents aged 65 and over (tooth loss).

Figure explained here: Figure 1

How places are ranked and grouped by deprivation

Tenths and fifths hold equal numbers of tracts, ZIP codes or counties, ranked on the Area Deprivation Index national rank (2023). A county's rank is the mean of its tracts' ranks weighted by ACS population.

Social Security counts and benefits are SSA's December 2024 ZIP code table for the 50 states and DC: ZIP codes that cross a state line are summed, benefit totals in thousands of dollars are converted to dollars, and a ZIP code is matched to the ZCTA of the same number in the project's ZCTA atlas (ZIP codes with no ZCTA are left out; Connecticut's are matched like the rest).

Pensions are taxable pensions and annuities from IRS Statistics of Income for tax year 2022 (thousands of dollars, converted), set against 12 times the December 2024 monthly benefits.

Figures explained here: Figure 2 and Figure 3

What the Medicare payments cover

Medicare payments are CMS price-standardized Part A and Part B payments per capita for Original Medicare beneficiaries with both parts, 2024; the file has one row per county, and Connecticut appears by planning region.

Counties with a blank service line are left out of the stacked bars and the last segment is the total minus the lines shown.

Figure explained here: Figure 4

How expected age at death is measured and weighted

Expected age at death at 40 is the Health Inequality Project's county estimate by national household-income quartile; the chapter uses the race-adjusted column, averaged over women and men, and weights counties by their residents in the County Health Rankings 2024 release (a Census Bureau estimate for 2022).

Figures explained here: Figure 5, Figure 6 and Figure 7

How intervals, ranges and plotted samples are computed

Intervals are 95% percentile intervals from 2,000 draws of whole clusters with replacement (seed 12345): counties for tract and ZIP code estimates and states for county estimates, computed by the same routine for means, shares, ratios, differences, correlations and slopes; the regressions use cluster-robust errors on county.

The width of the range of county values is the distance from the 5th to the 95th weighted percentile.

Scatter plots show random samples (tracts, ZIP codes) inside the plotted range with the same seed, or every county.

References

Kind, Amy J. H.; Buckingham, William R. (2018). Making Neighborhood-Disadvantage Metrics Accessible: The Neighborhood Atlas. New England Journal of Medicine 378(26): 2456-2458. doi:10.1056/NEJMp1802313
Greenlund, Kurt J.; Lu, Hua; Wang, Yan; Matthews, Kevin A.; LeClercq, Jennifer M.; Lee, Benjamin; Carlson, Susan A. (2022). PLACES: Local Data for Better Health. Preventing Chronic Disease 19: 210459. doi:10.5888/pcd19.210459
Centers for Medicare & Medicaid Services (2026). Medicare Data for the Original Medicare Geographic Variation Public Use File: A Methodological Overview (May 2026 update). CMS Office of Enterprise Data and Analytics. data.cms.gov/sites/default/files/2026-05/Original%20Medicare%20Geographic%20Variation%20Public%20Use%20File%20Methods%20Paper.pdf
Fisher, Elliott S.; Wennberg, David E.; Stukel, Therese A.; Gottlieb, Daniel J.; Lucas, F. L.; Pinder, Etoile L. (2003). The Implications of Regional Variations in Medicare Spending. Part 2: Health Outcomes and Satisfaction with Care. Annals of Internal Medicine 138(4): 288-298. doi:10.7326/0003-4819-138-4-200302180-00007
Chetty, Raj; Stepner, Michael; Abraham, Sarah; Lin, Shelby; Scuderi, Benjamin; Turner, Nicholas; Bergeron, Augustin; Cutler, David (2016). The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA 315(16): 1750-1766. doi:10.1001/jama.2016.4226
Card, David; Dobkin, Carlos; Maestas, Nicole (2009). Does Medicare Save Lives?. The Quarterly Journal of Economics 124(2): 597-636. doi:10.1162/qjec.2009.124.2.597
Maestas, Nicole; Mullen, Kathleen J.; Strand, Alexander (2013). Does Disability Insurance Receipt Discourage Work? Using Examiner Assignment to Estimate Causal Effects of SSDI Receipt. American Economic Review 103(5): 1797-1829. doi:10.1257/aer.103.5.1797
Deshpande, Manasi; Gross, Tal; Su, Yalun (2021). Disability and Distress: The Effect of Disability Programs on Financial Outcomes. American Economic Journal: Applied Economics 13(2): 151-178. doi:10.1257/app.20190709
Snyder, Stephen E.; Evans, William N. (2006). The Effect of Income on Mortality: Evidence from the Social Security Notch. The Review of Economics and Statistics 88(3): 482-495. doi:10.1162/rest.88.3.482

How to cite

@misc{singh2026disabilityretirementandmedicare,
  author = {Singh, Vishal},
  title = {Disability, retirement and Medicare},
  year = {2026},
  url = {https://vishalsingh.org/zipcode/disability-retirement-and-medicare},
  note = {In ZIP Code Destiny. Published 9 October 2026.}
}

Disability rises with neighborhood deprivation, and age structure explains little of it

Disabled workers are a larger share of beneficiaries in deprived ZIP codes

The size of a retired worker's benefit follows ZIP code income

Medicare payments show no detectable difference across county deprivation, but their mix differs

Weighted by residents, the poorest income quartile's expected age at death varies more across counties

What these data cannot show

Data sources

Methods

How disability and tooth loss are measured

How places are ranked and grouped by deprivation

How Social Security and pension data are matched to ZIP codes

What the Medicare payments cover

How expected age at death is measured and weighted

How intervals, ranges and plotted samples are computed

References

How to cite

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