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. 2021 Mar 5;136(4):441–450. doi: 10.1177/0033354920979807

State-Level Health Care Expenditures Associated With Disability

Olga A Khavjou 1,✉, Wayne L Anderson 2, Amanda A Honeycutt 1, Laurel G Bates 1, NaTasha D Hollis 3, Scott D Grosse 3, Hilda Razzaghi 3
PMCID: PMC8203048  PMID: 33673781

Abstract

Objective

Given the growth in national disability-associated health care expenditures (DAHE) and the changes in health insurance–specific DAHE distribution, updated estimates of state-level DAHE are needed. The objective of this study was to update state-level estimates of DAHE.

Methods

We combined data from the 2013-2015 Medical Expenditure Panel Survey, 2013-2015 Behavioral Risk Factor Surveillance System, and 2014 National Health Expenditure Accounts to calculate state-level DAHE for US adults in total, per adult, and per (adult) person with disability (PWD). We adjusted expenditures to 2017 prices and assessed changes in DAHE from 2003 to 2015.

Results

In 2015, DAHE were $868 billion nationally (range, $1.4 billion in Wyoming to $102.8 billion in California) accounting for 36% of total health care expenditures (range, 29%-41%). From 2003 to 2015, total DAHE increased by 65% (range, 35%-125%). In 2015, DAHE per PWD were highest in the District of Columbia ($27 839) and lowest in Alabama ($12 603). From 2003 to 2015, per-PWD DAHE increased by 13% (range, −20% to 61%) and per-capita DAHE increased by 28% (range, 7%-84%). In 2015, Medicare DAHE per PWD ranged from $10 067 in Alaska to $18 768 in New Jersey. Medicaid DAHE per PWD ranged from $9825 in Nevada to $43 365 in the District of Columbia. Nonpublic–health insurer per-PWD DAHE ranged from $7641 in Arkansas to $18 796 in Alaska.

Conclusion

DAHE are substantial and vary by state. The public sector largely supports the health care costs of people with disabilities. State policy makers and other stakeholders can use these results to inform the development of public health programs that support and provide ongoing health care to people with disabilities.

Keywords: state estimates, disability costs, disability expenditures, health care, Medicare, Medicaid

People with disabilities face an array of health and support needs. 1 -3 Not only do they need more health care services than people without disabilities, they often require long-term services and supports to address functional limitations and actively participate in society. 4,5 Both access to care and supply of services and supports vary by state. In a recently updated analysis of disability-associated health care expenditures (DAHE), national DAHE, adjusted to 2017 prices, increased substantially from $527 billion in 2003 to $868 billion in 2015, increasing from 27% to 36% as a percentage of total health care spending. 6

Given the growth in national DAHE and the changes in health insurance–specific DAHE distribution, 6 updated estimates of state-level DAHE are needed. The burden of diseases, injuries, and risk factors, many of which contribute to disability, varies widely across states, and several factors drive these differences. 7 For example, differences in state-level prevalence and costs of chronic conditions, such as asthma, chronic obstructive pulmonary disease, diabetes, and obesity, can be explained by sociodemographic composition of the state population, general health status, and access to and quality of care. 8 -13 Consequently, state-level DAHE are also expected to vary widely.

The goal of this analysis was to update state-level estimates of DAHE generated more than a decade ago. 14 We calculated total and health insurance–specific DAHE by state. States and other stakeholders can use state-level DAHE estimates to inform the development of public health policies and programs that support and provide health care to people with disabilities.

Methods

For consistency with previously published state-level DAHE, we followed the methodology of Anderson et al. 14,15 Given a lack of data on health care expenditures and disability status at the state level from a single source, we combined 3 data sources. First, we used data from the 2013-2015 Medical Expenditure Panel Survey (MEPS) 16 to estimate national DAHE for noninstitutionalized adults. MEPS is administered by the Agency for Healthcare Research and Quality and is a nationally representative sample of medical expenditure data for the noninstitutionalized population. We used data from the MEPS Household Component, which contains payment data, including expenditures for outpatient and inpatient services, emergency department and physician office visits, and prescription medication. In MEPS, these expenditure data are obtained from the MEPS Medical Provider Component, where available, and imputed otherwise.

Second, we used the 2013-2015 Behavioral Risk Factor Surveillance System (BRFSS) 17 to obtain state-level data on disability and characteristics of people with disabilities. BRFSS is an annual, state-based, cross-sectional telephone interview survey conducted by the Centers for Disease Control and Prevention and state health departments. The survey represents the civilian noninstitutionalized adult population in the 50 states and the District of Columbia.

Third, we used the 2014 National Health Expenditure Accounts (NHEA) 18 to obtain data on health care expenditures not captured in MEPS and health care expenditures for adults in institutions (eg, nursing home residents). NHEA are produced by the Office of the Actuary at the Centers for Medicare & Medicaid Services. NHEA cover the entire US population and a full range of health care expenditures, making it the most comprehensive collection of data on health care expenditures.

In the first part of the analysis, we used MEPS data to estimate national-level DAHE for noninstitutionalized adults. In MEPS, disability was defined as receiving help with activities of daily living (eg, bathing) or instrumental activities of daily living (eg, shopping) or being limited in the ability to work, do housework, or go to school because of an impairment or a physical or mental health problem. We estimated a 2-part regression model of per-person DAHE using MEPS. 19,20 Our model is detailed elsewhere. 6 We controlled for sociodemographic characteristics but did not control for other health conditions to capture data on health care spending on diseases for which disability may be a risk factor. 21,22 We estimated DAHE per person with disability (PWD) using the counterfactual approach. 6 We also used MEPS data to estimate a multinomial logit model predicting health insurance category. We used the same control variables as in the expenditure regressions (plus health and employment status) to estimate health insurance category.

In the second part of the analysis, we combined results from the MEPS regressions with BRFSS data to generate state-level estimates of DAHE for noninstitutionalized adults. In BRFSS, disability was defined as being limited in any way in any activities because of physical, mental, or emotional problems. The BRFSS does not collect data on category of health insurance coverage. Thus, we used coefficients from the MEPS regression estimating health insurance category and covariates of BRFSS respondents to predict the probability of having each health insurance category for BRFSS respondents. We used these probabilities as indicators of health insurance category for BRFSS respondents.

To estimate DAHE for each BRFSS respondent, we applied coefficients from the MEPS expenditure regressions to covariates of each BRFSS respondent. We predicted total health care expenditures and DAHE for each BRFSS respondent and summed across respondents in each state to generate state-level total health care expenditures and DAHE for each health insurance category. We calculated DAHE as a percentage of total expenditures in each state for each health insurance category.

In the third part of the analysis, we used NHEA data to reconcile health care expenditures and include DAHE for institutionalized adults. We multiplied our estimated state-level DAHE percentages by state-level total health care expenditures for noninstitutionalized adults from NHEA. These estimates of state and health insurance–specific DAHE accounted for expenditures that were missing from MEPS but included in NHEA. 23 We also added state-level estimates of DAHE for institutionalized adults to estimate total DAHE in each state. 6

We adjusted state DAHE to sum to national DAHE that resulted from reconciliation of MEPS and NHEA expenditures. 6 We applied a ratio of the national health insurance–specific DAHE from MEPS/NHEA to the sum of state-level health insurance–specific DAHE from BRFSS/NHEA. We used the adjusted DAHE to calculate the DAHE percentage of state total expenditures by health insurer.

We estimated per-capita DAHE and DAHE per PWD in each state. We estimated the numbers of noninstitutionalized adults with and without disabilities and by health insurance category from the BRFSS. We estimated the numbers of institutionalized adults by state and by health insurance category using the Eiken 24 counts of enrollees in nursing homes for Medicaid, and for Medicare and nonpublic health insurers, using fractions of nursing home costs paid by each health insurer. 25

Following recommendations by Dunn et al, 26 we converted DAHE based on 2013-2015 data and 2003 DAHE generated by Anderson et al 14 to 2017 prices using the Personal Consumer Expenditures Health Component, 27 which adjusts for general medical price changes. We calculated percentage changes in state-level DAHE in total, per capita (adult population), and per PWD (adults only) and in prevalence of disability among adults from 2003 to 2015.

We based this analysis on de-identified, publicly available data. As such, the study does not constitute human subjects research. We conducted analyses of MEPS and BRFSS data using Stata version 15 (StataCorp).

Results

In 2015, the DAHE percentage of total expenditures was 36% nationally, ranging from 29% in Utah to 41% in Arkansas and Kentucky (Table 1). Nationally, DAHE represented 54% of all Medicare expenditures, 72% of all Medicaid expenditures, and 19% of all expenditures paid by nonpublic sources. By state, the DAHE percentage of all Medicare expenditures ranged from 46% in Hawaii to 60% in Alabama; the DAHE percentage of all Medicaid expenditures ranged from 67% in Hawaii and Illinois to 79% in Alabama; and the DAHE percentage of all nonpublic health insurance expenditures ranged from 15% in the District of Columbia to 22% in Oregon and Washington.

Table 1.

Total disability-associated health care expenditures (DAHE), by health insurer and state, United States, 2015 a

State Total Medicare Medicaid Nonpublic sources
% of total expenditures b Expenditures, millions, $ % of total expenditures b Expenditures, millions $ % of total DAHE b % of total expenditures b Expenditures, millions $ % of total DAHE b % of total expenditures b Expenditures, millions $ % of total DAHE b
United States 36 868 037 54 324 709 37 72 277 183 32 19 266 145 31
Alabama 39 13 179 60 6053 46 79 3191 24 20 3935 30
Alaska 30 2247 55 428 19 71 736 33 19 1082 48
Arizona 37 14 849 52 5984 40 71 4746 32 18 4120 28
Arkansas 41 8616 59 3388 39 77 3037 35 19 2191 25
California 38 102 811 54 36 633 36 69 36 897 36 19 29 281 28
Colorado 35 12 013 53 3908 33 71 3802 32 20 4303 36
Connecticut 37 12 450 52 4052 33 72 4569 37 19 3830 31
Delaware 34 3131 52 1094 35 73 956 31 19 1080 35
District of Columbia 35 2654 55 587 22 72 1376 52 15 692 26
Florida 35 53 147 51 25 505 48 75 11 673 22 18 15 968 30
Georgia 32 20 298 55 8784 43 75 4622 23 17 6891 34
Hawaii 31 3035 46 982 32 67 1067 35 16 986 32
Idaho 33 3543 53 1303 37 73 895 25 19 1345 38
Illinois 33 32 751 52 12 225 37 67 9189 28 18 11 336 35
Indiana 36 18 659 55 6953 37 77 6200 33 18 5506 30
Iowa 35 8308 50 2718 33 70 2547 31 20 3042 37
Kansas 33 6874 52 2644 38 73 1411 21 20 2819 41
Kentucky 41 13 951 59 5377 39 77 4927 35 20 3648 26
Louisiana 38 13 315 57 5409 41 74 4478 34 18 3427 26
Maine 37 4490 53 1548 34 73 1516 34 20 1425 32
Maryland 35 16 965 51 5836 34 72 5598 33 18 5531 33
Massachusetts 37 24 841 53 7896 32 72 9376 38 19 7570 30
Michigan 37 28 174 56 12 180 43 75 8050 29 19 7943 28
Minnesota 34 15 457 48 4439 29 68 5780 37 19 5238 34
Mississippi 40 8695 58 3640 42 75 2875 33 19 2180 25
Missouri 39 17 830 56 6867 39 75 5391 30 21 5573 31
Montana 33 2581 53 893 35 73 581 23 20 1107 43
Nebraska 31 4628 51 1625 35 71 1045 23 19 1958 42
Nevada 34 5995 54 2635 44 70 1450 24 18 1911 32
New Hampshire 31 3740 52 1328 36 77 926 25 18 1486 40
New Jersey 33 24 784 51 9765 39 69 7502 30 17 7517 30
New Mexico 36 5070 55 1780 35 70 1745 34 19 1546 30
New York 39 71 811 53 21 984 31 70 33 829 47 17 15 998 22
North Carolina 36 25 033 56 10 249 41 73 7125 28 19 7659 31
North Dakota 30 2043 47 543 27 69 578 28 19 922 45
Ohio 37 35 021 53 12 997 37 73 11 617 33 19 10 408 30
Oklahoma 37 10 529 58 4213 40 77 2815 27 20 3501 33
Oregon 40 11 900 56 3791 32 72 4449 37 22 3660 31
Pennsylvania 37 41 527 52 15 175 37 75 13 997 34 19 12 355 30
Rhode Island 38 3670 54 1226 33 72 1437 39 19 1007 27
South Carolina 36 12 237 56 5453 45 75 2976 24 19 3808 31
South Dakota 31 2201 51 752 34 71 436 20 20 1013 46
Tennessee 38 17 509 58 7587 43 77 4718 27 19 5205 30
Texas 32 56 664 52 22 639 40 70 15 700 28 16 18 325 32
Utah 29 4833 54 1697 35 69 1018 21 18 2117 44
Vermont 36 2197 52 637 29 71 839 38 20 721 33
Virginia 31 18 359 52 6829 37 73 4200 23 18 7330 40
Washington 37 19 524 58 6212 32 73 5777 30 22 7535 39
West Virginia 40 6665 58 2547 38 77 2228 33 20 1891 28
Wisconsin 34 15 843 51 5260 33 73 4958 31 19 5625 36
Wyoming 31 1387 53 459 33 74 330 24 19 598 43
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aExpenditures based on 2013-2015 data were converted to 2017 medical prices. Data sources: 2013-2015 Medical Expenditure Panel Survey, 16 2013-2015 Behavioral Risk Factor Surveillance System, 17 and 2014 National Health Expenditure Accounts. 18

bPercentages of total expenditures and of total DAHE were calculated using expenditures rounded to the nearest dollar and, as a result, may be different from percentages calculated based on the expenditures presented in the table that are expressed in millions $.

In 2015, DAHE for all US adults were $868 billion (at 2017 prices) and state-level expenditures ranged from $1.4 billion in Wyoming to $102.8 billion in California, with a median of $12.2 billion in South Carolina (Table 1). Public sources paid for 69% of DAHE, ranging from 52% in Alaska to 78% in New York, but the main DAHE payer varied across states. The highest portion of DAHE paid by Medicare was in Florida (48%), by Medicaid was in the District of Columbia (52%), and by nonpublic sources was in Alaska (48%). Medicare DAHE ranged from $428 million in Alaska to $36.6 billion in California, with a median of $4.2 billion in Oklahoma. Medicaid DAHE ranged from $330 million in Wyoming to $36.9 billion in California, with a median of $3.8 billion in Colorado. Nonpublic health insurance DAHE ranged from $598 million in Wyoming to $29.3 billion in California, with a median of $3.7 billion in Oregon.

The mean state-level DAHE per PWD in 2015 was $17 431 and the median was $16 489 in South Dakota (Figure). The highest mean per-PWD DAHE (District of Columbia, $27 839) was more than double the lowest mean per-PWD DAHE (Alabama, $12 603). States in the Northeast and Midwest, along with California and Alaska, were in the top third of the per-PWD DAHE distribution (DAHE >$18 000), whereas most states in the West and Southeast were in the bottom third (DAHE <$15 000).

Figure.

Figure

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Mean disability-associated health care expenditures (DAHE) per person with a disability (2017 prices), United States, 2015. Mean DAHE for United States is $17 431. The median DAHE is $16 489 (South Dakota). Expenditures based on 2013-2015 and 2002-2003 data were converted to 2017 medical prices. Data sources: 2013-2015 Medical Expenditure Panel Survey, 16 2013-2015 Behavioral Risk Factor Surveillance System, 17 and 2014 National Health Expenditure Accounts. 18

DAHE per PWD paid by Medicare in 2015 ranged from $10 067 in Alaska to $18 768 in New Jersey, with a median of $12 918 in Virginia (Table 2). Per-PWD DAHE paid by Medicaid ranged from $9825 in Nevada to $43 365 in the District of Columbia, with a median of $17 155 in Maine. Nonpublic health insurance DAHE per PWD ranged from $7641 in Arkansas to $18 796 in Alaska, with a median of $11 630 in Virginia.

Table 2.

Disability-associated health care expenditures per person with disability, by health insurance category and state, United States, 2015 a

State Medicare expenditures, $ Medicaid expenditures, $ Nonpublic expenditures, $
United States 14 063 17 887 11 289
Alabama 11 624 11 137 7995
Alaska 10 067 21 751 18 796
Arizona 12 487 13 178 8828
Arkansas 10 976 17 660 7641
California 16 170 19 774 12 468
Colorado 12 731 17 452 10 765
Connecticut 16 489 22 896 14 693
Delaware 15 481 27 268 13 698
District of Columbia 13 970 43 365 15 243
Florida 14 755 13 740 9709
Georgia 12 355 10 895 9554
Hawaii 12 775 17 565 12 242
Idaho 11 532 11 387 11 119
Illinois 15 637 17 576 12 933
Indiana 14 241 17 003 10 770
Iowa 12 440 20 282 12 667
Kansas 13 197 11 434 12 671
Kentucky 12 354 20 690 8071
Louisiana 13 966 19 270 9311
Maine 12 881 17 155 13 453
Maryland 15 917 32 170 12 513
Massachusetts 16 424 21 830 15 430
Michigan 14 616 14 173 9481
Minnesota 14 393 30 684 13 152
Mississippi 12 994 15 954 9001
Missouri 12 711 14 456 10 164
Montana 10 080 9993 12 320
Nebraska 13 275 14 710 14 173
Nevada 13 509 9825 10 113
New Hampshire 13 836 12 717 15 050
New Jersey 18 768 17 980 14 459
New Mexico 11 212 12 716 10 554
New York 15 613 26 164 12 935
North Carolina 12 373 16 936 9649
North Dakota 11 897 21 875 17 279
Ohio 14 692 17 782 11 295
Oklahoma 11 335 14 391 9456
Oregon 11 042 18 857 10 668
Pennsylvania 15 332 16 947 13 843
Rhode Island 14 969 20 341 13 284
South Carolina 12 402 12 608 8875
South Dakota 12 185 12 401 14 396
Tennessee 12 339 13 823 8674
Texas 14 532 18 369 11 026
Utah 12 187 10 649 11 577
Vermont 13 067 22 928 14 175
Virginia 12 918 15 727 11 630
Washington 10 888 15 023 11 497
West Virginia 11 697 20 194 9308
Wisconsin 13 742 19 344 12 860
Wyoming 11 158 11 398 13 149
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aExpenditures are converted to 2017 medical prices. Data sources: 2013-2015 Medical Expenditure Panel Survey, 16 2013-2015 Behavioral Risk Factor Surveillance System, 17 and 2014 National Health Expenditure Accounts. 18

From 2003 to 2015, total DAHE increased by an average of 65%, ranging from 35% in New York to 125% in Hawaii and a median of 68% in Indiana (Table 3). DAHE per capita, which represents DAHE spread across the entire state population, increased by an average of 28%, ranging from 7% in Illinois to 84% in California, and a median increase of 29% in Alaska, Virginia, West Virginia, and Wisconsin. Both DAHE per PWD and prevalence of disability among adults increased by an average of 13% from 2003 to 2015. Change in per-PWD DAHE ranged from a 20% decrease in Tennessee to a 61% increase in California, with a median increase of 12% in Delaware, Montana, and New Jersey. Change in disability prevalence ranged from a 13% decrease in Minnesota to a 38% increase in Tennessee, with a median increase of 15% in Kentucky, Maine, Ohio, Pennsylvania, and Vermont.

Table 3.

Changes in disability-associated health care expenditures (DAHE) and prevalence of disability by state, United States, 2003-2015 a

State Total DAHE DAHE per capita DAHE per person with disability Prevalence of disability, %
2015, millions $ 2003, millions $ Change, % b 2015, $ 2003, $ Change, % b 2015, $ 2003, $ Change, % b 2015 2003 Change b
United States 868 037 527 112 65 3716 2902 28 17 431 15 422 13 21 19 13
Alabama 13 179 8191 61 3605 2859 26 12 603 12 619 0 29 23 26
Alaska 2247 1282 75 4183 3241 29 20 497 17 237 19 20 19 9
Arizona 14 849 6769 119 3042 1987 53 14 326 10 569 36 21 19 13
Arkansas 8616 4868 77 3915 2781 41 13 955 12 617 11 28 22 27
California 102 811 50 679 103 4044 2200 84 19 949 12 414 61 20 18 14
Colorado 12 013 5815 107 3217 1973 63 15 935 11 415 40 20 17 17
Connecticut 12 450 8403 48 4509 3886 16 21 927 22 574 −3 21 17 19
Delaware 3131 1621 93 4378 3360 30 20 889 18 643 12 21 18 16
District of Columbia 2654 1658 60 5078 4453 14 27 839 29 810 −7 18 15 22
Florida 53 147 30 674 73 3491 2822 24 15 811 14 209 11 22 20 11
Georgia 20 298 13 453 51 2788 2498 12 13 543 13 841 −2 21 18 14
Hawaii 3035 1349 125 2845 2252 26 17 721 17 296 2 16 13 23
Idaho 3543 1983 79 3048 2304 32 14 059 11 295 24 22 20 6
Illinois 32 751 21 216 54 3457 3223 7 18 881 19 680 −4 18 16 12
Indiana 18 659 11 081 68 3806 2820 35 16 973 15 476 10 22 18 23
Iowa 8308 5198 60 3571 2693 33 17 819 15 753 13 20 17 17
Kansas 6874 4484 53 3293 2634 25 15 746 14 852 6 21 18 18
Kentucky 13 951 7713 81 4220 3233 31 15 177 13 362 14 28 24 15
Louisiana 13 315 8738 52 3986 3355 19 16 752 18 559 −10 24 18 32
Maine 4490 3204 40 4309 3722 16 18 219 18 023 1 24 21 15
Maryland 16 965 9648 76 3773 2803 35 21 118 16 604 27 18 17 6
Massachusetts 24 841 15 451 61 4800 3825 25 22 017 21 091 4 22 18 20
Michigan 28 174 18 058 56 3736 2883 30 15 868 13 937 14 24 21 14
Minnesota 15 457 10 343 49 3791 3138 21 21 267 15 318 39 18 20 −13
Mississippi 8695 6025 44 4044 3451 17 15 483 15 328 <1 26 23 16
Missouri 17 830 11 796 51 3887 3176 22 15 359 14 746 4 25 22 18
Montana 2581 1442 79 3283 2493 32 13 638 12 136 12 24 21 17
Nebraska 4628 3101 49 3387 2835 19 17 500 16 025 9 19 18 9
Nevada 5995 2775 116 2855 1946 47 14 225 10 381 37 20 19 7
New Hampshire 3740 2193 71 3620 2646 37 17 463 14 344 22 21 18 12
New Jersey 24 784 16 477 50 3738 3127 20 21 415 19 200 12 17 16 7
New Mexico 5070 2865 77 3371 2433 39 14 518 12 249 19 23 20 17
New York 71 811 53 166 35 4786 4416 8 23 101 24 747 −7 21 18 16
North Carolina 25 033 14 863 68 3393 2908 17 15 230 16 185 −6 22 18 24
North Dakota 2043 1152 77 3635 2755 32 20 352 17 132 19 18 16 11
Ohio 35 021 25 110 39 3980 3431 16 17 732 17 528 1 22 20 15
Oklahoma 10 529 6230 69 3724 2800 33 13 946 12 207 14 27 23 16
Oregon 11 900 5670 110 3989 2429 64 16 127 10 424 55 25 23 6
Pennsylvania 41 527 27 192 53 4207 3371 25 19 241 17 799 8 22 19 15
Rhode Island 3670 2368 55 4454 3473 28 20 174 19 677 3 22 18 25
South Carolina 12 237 7335 67 3349 2807 19 13 807 15 010 −8 24 19 30
South Dakota 2201 1346 63 3458 2711 28 16 489 14 196 16 21 19 10
Tennessee 17 509 11 675 50 3653 3334 10 13 925 17 496 −20 26 19 38
Texas 56 664 31 883 78 2972 2400 24 17 189 13 553 27 17 18 −2
Utah 4833 2658 82 2562 1913 34 14 112 10 430 35 18 18 −1
Vermont 2197 1248 76 4449 3036 47 20 096 15 715 28 22 19 15
Virginia 18 359 10 571 74 2928 2271 29 15 766 12 595 25 19 18 3
Washington 19 524 10 719 82 3671 2712 35 15 068 11 766 28 24 23 6
West Virginia 6665 4306 55 4555 3536 29 15 494 13 211 17 29 27 10
Wisconsin 15 843 10 272 54 3728 2882 29 18 320 16 041 14 20 18 13
Wyoming 1387 793 75 3158 2398 32 14 821 12 726 16 21 19 13
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aExpenditures based on 2013-2015 and 2002-2003 data were converted to 2017 medical prices. Data sources: 2013-2015 Medical Expenditure Panel Survey, 16 2013-2015 Behavioral Risk Factor Surveillance System, 17 and 2014 National Health Expenditure Accounts. 18

bPercentage changes for total DAHE were calculated using expenditures rounded to the nearest dollar and, as a result, may be different from percentage changes calculated based on the expenditures presented in the table that are expressed in millions $. Similarly, percentage changes for disability prevalence were calculated using prevalence rounded to one decimal point and, as a result, may be different from percentage changes calculated based on the prevalence presented in the table and rounded to the nearest percentage point.

Discussion

Changes in total DAHE from 2003 to 2015 reflect changes in the number of people with disabilities, which is a function of total population and disability prevalence, and changes in per-PWD DAHE. On average, both prevalence of disability and per-PWD DAHE increased from 2003 to 2015; however, disability prevalence decreased in 3 states and per-PWD DAHE decreased in 10 states during this period. None of the states had a reduction in both prevalence and per-PWD DAHE, and total DAHE increased in every state. In general, states with relatively low growth in total DAHE had low growth in both per-PWD DAHE and prevalence. However, in some states, growth in total DAHE was relatively low even if one of these components increased at a relatively high rate because the other component had a relatively low increase or even a decrease. For example, Minnesota had a relatively low increase in total DAHE even with a relatively high increase in per-PWD DAHE, because its disability prevalence decreased from 2003 to 2015. In Tennessee, total DAHE also increased at a relatively low rate even with a high increase in disability prevalence, because per-PWD DAHE in this state decreased from 2003 to 2015. States with relatively high growth in total DAHE had high growth in either or both disability prevalence and DAHE per PWD.

Per-PWD health care spending is determined by the prices of services and the number of services used. Both are likely to vary across states and over time, thus contributing to variation in spending and growth across states. 28 Differences in sociodemographic composition across states explain some of this variation in prices and utilization and their changes over time. For example, because per-PWD DAHE increase with age, states with higher proportions of older adults will have higher per-PWD DAHE. Similarly, states that have higher growth in the proportion of older adults may also have faster growth in per-PWD DAHE.

Beyond sociodemographic characteristics, however, differences in health status, access to health care, quality of care, and health care systems and payment structures likely contribute to state-level differences in DAHE and its growth. 8 -11 These factors vary substantially across states and over time and may drive DAHE in different directions. Thus, increased DAHE are not necessarily undesirable, and caution should be used when comparing expenditures and increases across states. For example, improved access to and quality of preventive care for people with disabilities may increase health care use and, as a result, DAHE. 29 Because growth in prevention spending may improve access to and quality of care for people with disabilities, 29 it can offset downstream medical spending on treatment and care. Lower-than-average DAHE may also reflect barriers to care and poor quality of preventive care and indicate that resources spent on people with disabilities are inadequate. Higher-than-average DAHE could reflect higher-than-average severity of disability or higher-than-average prevalence of other chronic conditions, such as diabetes, among people with disabilities. Our findings demonstrate that nursing home care is also an important contributor to DAHE. In states with high nursing home costs, providing access to less expensive nursing home care, such as home- and community-based services for long-term services and supports, may reduce these costs. Receipt of more targeted, timely, and efficient care to reduce inpatient readmission and emergency department visits for ambulatory care–sensitive conditions may also reduce DAHE. 30

We also found that distribution of DAHE and per-PWD DAHE across health insurers varied substantially by state. National DAHE per PWD was the highest for Medicaid. Across states, Medicaid DAHE varied more than Medicare or nonpublic health insurer DAHE, which possibly reflects state policy differences in Medicaid generosity. Differences in Medicaid enrollment and coverage may explain the difference in the mix of DAHE payers across states, because Medicaid policies vary by state. The timing and implementation of state-level health care policies, such as Medicaid expansion and Medicaid payment policies, may also contribute to varying changes in DAHE over time. 31 -34

Our estimates may be helpful for informing discussions of public health policies, programs, and resources needed to provide ongoing quality health care and improve quality of life for people with disabilities. Our results highlight the need for interventions that aim to improve health behaviors, prevent secondary conditions, and provide ongoing quality health care to people with disabilities. The Medicaid expenditure estimates, in particular, may provide valuable information for Medicaid health care resource planning and program evaluation. For example, 67% to 79% of all Medicaid expenditures in each state were DAHE. These results reinforce the importance of implementing public programs and policies that support this vulnerable population and help people with disabilities avoid complications and associated health care expenditures.

Several factors could explain differences in total, per-PWD, and per-capita DAHE across states and over time, including access to and quality of care, severity of disability, prevalence of chronic conditions, and coverage and payment policies. Although our results alone cannot be used to interpret and identify factors driving changes in DAHE in each state, they provide a starting point for further research in trying to understand those factors and develop policies to address them. Our results highlight another important topic for future research, which is assessing whether DAHE increases were associated with improvements in health or survival for people with disabilities.

Limitations

Our study had several limitations. First, because we lacked state-level data on both disability status and health care expenditures from a single source, we combined data from multiple sources. Specifically, we predicted health insurance category and health care expenditures by applying coefficients from an analysis using national MEPS data to state-level BRFSS data. Because of the differences between MEPS and BRFSS survey designs and the questions used to define disability, the prevalence of disability was higher in BRFSS than in MEPS. Some of the characteristics of the disability samples from the 2 surveys were also different. For example, BRFSS respondents with disabilities were younger, more educated, and more likely to be married than adults with disabilities responding to MEPS. However, reconciling the sum of state DAHE with national DAHE largely eliminated differences in the survey populations.

Our definition of disability was similar to the one used in the analysis by Anderson et al, 14,15 because our goal was to produce DAHE estimates that could be compared with previous estimates. Our definition of disability assessed deficits in activities of daily living, instrumental activities of daily living, and general activity limitations. Although parts of this definition have been frequently used in many other studies, a second limitation of our analysis is that a different definition or data might produce different results. Third, our definition of disability was based on self-reported data and is subject to self-report bias; however, self-reported data are routinely used to assess disability at the national and state levels. 35

Finally, we did not assess age of onset, severity, permanence, duration, or underlying health conditions or causes of reported disability and the extent to which those factors may explain DAHE increases. We intentionally did not control for health conditions when estimating DAHE because we wanted to capture downstream cost effects of those conditions among people with disabilities, who may be at a higher risk of developing certain chronic conditions. 21,22

Conclusions

Results of this study provide policy makers, health insurers, and public health officials with an updated analysis of state-level health care expenditures associated with disability. Results indicate that DAHE were substantial, varied extensively across states, and changed at varying rates from 2003 to 2015. The public sector largely supports the costs of health care for people with disabilities. States and other stakeholders may use these state-level estimates to better design public health policies and planning efforts that support people with disabilities and provide ongoing, accessible, and quality health care to this vulnerable population.

Footnotes

Authors’ Note: This article was prepared by RTI, under contract to the Centers for Disease Control and Prevention (CDC). The findings and conclusions of this article are those of the authors and do not necessarily represent the official position of CDC.

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors declared the following financial support with respect to the research, authorship, and/or publication of this article: This article was developed by RTI International under contract number 200-2013-M-53964B from CDC.

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