Determinants of and Trends in Total and Condition Specific Health Care Spending Per Privately Insured Adult.
- 1. Department of Health Policy and Management, Rollins School of Public Health Emory University, USA
Keywords
Multiple chronic conditions, Race and Ethnicity, Private Health Care Spending
CITATION
Thorpe KE (2023) Determinants of and Trends in Total and Condition Specific Health Care Spending Per Privately Insured Adult. J Chronic Dis Manag 7(1): 1032.
INTRODUCTION
This paper examines the key factors associated with the level and changes in total spending per privately insured person 18- 64 over the past decade. The analysis updates and expands upon previous work examining trends in chronic disease prevalence and spending [1-2]. We also examine changes in out of pocket spending over time. This will include estimating the difference in out of pocket spending by race, gender, ethnicity as well as the mix of medical conditions under treatment. Nearly 160 million Americans are covered through employer sponsored insurance and just less than 35 million through individual/non group plans [3]. Understanding trends in private insurance spending and the underlying factors associated with these trends provides important insights into the health care system overall. The analysis will highlight key medical conditions that policymakers could target to reduce the level and growth in health care spending.
The analysis starts by looking at trends in total per capita spending, total private insurance spending and out of pocket payments from 2010 through 2020. Then the analysis focuses on spending among patients with the most expensive medical conditions. This includes:
• Trauma
• Cancer
• Mental health disorders
• COPD/Asthma
• Heart Failure and Heart Disease
In addition, we examine the impact of multiple chronic conditions on the level and growth in private insurance spending over time treated conditions used in our analysis: diabetes, hypertension, hyperlipidemia, mental health disorders, cancer, trauma, heart disease, rheumatoid arthritis, asthma, chronic kidney disease (CKD), and chronic obstructive pulmonary disease (COPD). The regression models focused on the top five conditions, heart disease, cancer, mental disorders, trauma and COPD/asthma. MEPS respondents self-reported medical conditions that were then professionally coded into ICD-9-CM diagnosis codes for years 2010 to 2015 and ICD-10-CM codes for 2016 to 2020. Clinical classification software was then used to collapse the ICD-9-CM codes into mutually exclusive clinical classification categories (CCC)for 2010 to 2015 and refined clinical classification software was used to collapse the ICD-10-CM into mutually exclusive refined clinical classification categories (CCR) for 2018-2020. We defined each of the eleven conditions based on CCC (2010-2015), ICD-10-CM (2016-2017), and CCR (2018-2020) codes with one or more associated inpatient, outpatient, office-based, emergency department (ED), home health, or prescription medication health care event [Appendix A].
Appendix A
|
Condition |
2010 – 2015 Clinical Classifications Software Codes |
2016 – 2017 ICD-10-CM Codes |
2018 – 2020 Clinical Classifications Software Refined Codes |
|
Diabetes |
049 050 |
E08 E09 E10 E11 E13 |
END002 END003 END004 END005 END006 |
|
Hypertension |
098 099 |
I10 I11 I12 I13 I15 I16 |
CIR007 CIR008 |
|
Hyperlipidemia |
053 |
E78 |
END010 |
|
Mental Health |
650 651 652 653 654 655 656 657 658 659 660 661 662 663 |
F06 F07 F09 F10 F11 F12 F13 F14 F15 F16 F17 F18 F19 F20 F21 F22 F23 F24 F25 F28 F29 F30 F31 F32 F33 F34 F39 F40 F41 F42 F43 F44 F45 F48 F50 F52 F53 F54 F55 F59 F60 F63 F64 F65 F66 F68 F69 F70 F71 F72 F73 F78 F79 F80 F81 F82 F84 F88 F89 F90 F91 F93 F94 F95 F98 F99 K70 T36 T37 T38 T39 T40 T41 T42 T43 T44 T45 T46 T47 T48 T49 T50 T51 T52 T53 T54 T55 T56 T57 T58 T59 T60 T61 T62 T63 T64 T65 T71 X71 X72 X73 X74 X75 X76 X77 X78 X79 X80 X81 X82 X83 |
MBD001 MBD002 MBD003 MBD004 MBD005 MBD006 MBD007 MBD008 MBD009 MBD010 MBD011 MBD012 MBD013 MBD014 MBD017 MBD018 MBD019 MBD020 MBD021 MBD022 MBD023 MBD024 MBD025 MBD026 MBD027 MBD028 MBD029 MBD030 MBD031 MBD032 MBD033 MBD034 |
|
Cancer |
011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 |
First 2 characters: C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 DO |
NEO001 NEO002 NEO003 NEO004 NEO005 NEO006 NEO007 NEO008 NEO009 NEO010 NEO011 NEO012 NEO013 NEO014 NEO015 NEO016 NEO017 NEO018 NEO019 NEO020 NEO021 NEO022 NEO023 NEO024 NEO025 NEO026 NEO027 NEO028 NEO029 NEO030 NEO031 NEO032 NEO033 NEO034 NEO035 NEO036 NEO037 NEO038 NEO039 NEO040 NEO041 NEO042 NEO043 NEO044 NEO045 EO046 NEO047 NEO048 NEO049 NEO050 NEO051 NEO052 NEO053 NEO054 NEO055 NEO056 NEO057 NEO058 NEO059 NEO060 NEO061 NEO062 NEO063 NEO064 NEO065 NEO066 NEO067 NEO068 NEO069 NEO070 NEO071 |
|
Trauma |
225 226 227 228 229 230 231 232 233 234 235 236 239 240 244 |
T79 T76 T75 T74 T73 T71 T70 T69 T68 T67 T66 T34 T33 T32 T31 T30 T28 T27 T26 T25 T24 T23 T22 T21 T20 T19 T18 T17 T16 T15 T14 T07 S99 S98 S97 S96 S95 S94 S93 S92 S91 S90 S89 S88 S87 S86 S85 S84 S83 S82 S81 S80 S79 S78 S77 S76 S75 S74 S73 S72 S71 S70 S69 S68 S67 S66 S65 S64 S63 S62 S61 S60 S59 S58 S57 S56 S55 S54 S53 S52 S51 S50 S49 S48 S47 S46 S45 S44 S43 S42 S41 S40 S39 S38 S37 S36 S35 S34 S33 S32 S31 S30 S29 S28 S27 S26 S25 S24 S23 S22 S21 S20 S19 S17 S16 S15 S14 S13 S12 S11 S10 S09 S08 S07 S06 S05 S04 S03 S02 S01 S00 |
INJ001 INJ002 INJ003 INJ004 INJ005 INJ006 INJ007 INJ008 INJ009 INJ010 INJ011 INJ012 INJ013 INJ014 INJ015 INJ016 INJ017 INJ018 INJ019 INJ020 INJ021 INJ024 INJ025 INJ026 INJ027 INJ032 INJ038 INJ039 INJ040 INJ041 INJ042 INJ043 INJ044 INJ045 INJ046 INJ047 INJ048 INJ049 INJ050 INJ051 INJ052 INJ053 INJ054 INJ055 INJ056 INJ057 INJ058 INJ061 INJ062 INJ063 INJ064 INJ068 INJ073 INJ074 |
|
Heart Disease |
096 097 100 101 102 103 104 105 106 107 108 |
I09 I11 I13 I20 I21 I22 I23 I24 I25 I26 I27 I28 I44 I45 I46 I47 I48 I49 I50 I51 I52 I97 I30 I31 I32 I34 I35 I36 I37 I38 I39 I40 I41 I42 I43 |
CIR001 CIR002 CIR003 CIR004 CIR005 CIR006 CIR010 CIR011 CIR012 CIR014 CIR015 CIR016 CIR017 CIR018 CIR019 |
|
Rheumatoid Arthritis |
202 |
M05 M06 M45 |
MUS003 |
|
Asthma |
128 |
J45 |
RSP009 |
|
Chronic Kidney Disease |
158 |
N18 |
GEN003 |
|
COPD |
127 |
J40 J41 J42 J43 J44 J47 |
RSP008 |
Condition specific total spending included all spending on health care events that occurred during a given calendar year and were directly related to treating the condition. More specifically, we summed inpatient, outpatient, ED, office-based, home health, and prescription drug expenditures. When the health care event was associated with multiple conditions, the expenditures for that event were split evenly across the conditions.
Our analyses were limited to adults with 12 months of private insurance, ages 18 to 64 years old. Any respondent with a missing survey weight or missing values in any of the model covariates were excluded resulting in an analytic sample of 92,792.
DISCUSSION
We used generalized linear model (GLM) with gamma distribution and log-link function to predict three types of annual expenditures (total amount for all health care utilization, total amount paid by private insurance, and total out-of-pocket) in three time periods: 2010 – 2013, 2014 – 2017, and 2018 – 2020. We then estimated counterfactuals for the latter two time periods by calculating predicted spending using the characteristics of the 2010 – 2013 patients with the regression coefficients for each of the respective latter two time periods.
In each of our models, we controlled for patient characteristics, including age, sex, race/ethnicity, education, region, health status, income level, smoking, and total number of treated conditions.
We used Stata, version 17.0, for data analysis [5]. Sample weights and survey estimation commands were used to adjust for the complex survey design of MEPS. All spending amounts are presented in 2020 dollars, using the GDP implicit price deflator [6].
Findings
Table 1 presents 10-year trends in average private insurance and out-of-pocket spending, as well as trends in chronic disease prevalence between 2010 and 2020. Real per insured private insurance spending increased from $3,540 in 2010 to $4,967 by 2020, an average annual increase of 3.4 percent. These results may have been impacted by the COVID-19 pandemic. The Centers for Medicare and Medicaid Services (CMS) reported that private health spending declined by 1.2 percent in 2020 [7]. However, CMS reports that the decline was largely due to a reduction in private insurance enrollment. We report average annual increases per insured and the results are virtually identical. The average increase between 2000 and 2009 was 3.47 percent and between 2000 and 2010 was 3.45 percent. We also did the analysis dropping the year 2020 using 2018-2019 as the third time period. The regression results were virtually identical to the results reported here.
Table 1: Averages Among Those with Private Health Insurance, Age 18-64, 2010-2020
|
Year |
2010 |
2011 |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
|
Real Private Spending |
$3,540 |
$4,193 |
$3,659 |
$3,463 |
$3,639 |
$4,119 |
$3,678 |
$4,049 |
$4,901 |
$4,813 |
$4,967 |
|
Real Out of Pocket |
$830 |
$823 |
$751 |
$795 |
$709 |
$791 |
$811 |
$789 |
$984 |
$993 |
$897 |
|
Total Chronic Conditions |
|
|
|
|
|
|
|
|
|
|
|
|
0 |
52.0% |
51.6% |
54.8% |
54.3% |
53.7% |
53.1% |
56.9% |
57.9% |
57.4% |
57.2% |
56.8% |
|
1 |
26.2% |
26.5% |
25.3% |
25.3% |
26.6% |
25.1% |
25.5% |
24.7% |
24.3% |
25.3% |
25.0% |
|
2 |
12.5% |
12.1% |
10.8% |
11.6% |
11.1% |
12.3% |
10.1% |
10.3% |
11.0% |
10.9% |
11.2% |
|
3 |
6.1% |
6.3% |
5.9% |
5.4% |
5.5% |
5.4% |
4.8% |
4.7% |
4.8% |
4.3% |
4.8% |
|
4+ |
3.3% |
3.5% |
3.1% |
3.5% |
3.1% |
4.0% |
2.7% |
2.3% |
2.5% |
2.3% |
2.2% |
|
Treated Prevalence |
|
|
|
|
|
|
|
|
|
|
|
|
Diabetes |
6.2% |
6.8% |
6.1% |
6.6% |
6.1% |
6.8% |
6.2% |
6.1% |
5.9% |
5.8% |
6.2% |
|
Kidney Disease |
0.0% |
0.0% |
0.0% |
0.0% |
0.0% |
0.0% |
0.1% |
0.2% |
0.1% |
0.1% |
0.1% |
|
Hypertension |
18.5% |
18.5% |
17.3% |
17.4% |
16.9% |
17.7% |
16.8% |
16.3% |
16.5% |
15.7% |
15.3% |
|
Hyperlipidemia |
15.2% |
14.7% |
14.1% |
14.0% |
13.1% |
13.4% |
12.7% |
11.7% |
11.5% |
10.9% |
11.2% |
|
COPD |
3.3% |
3.2% |
2.5% |
2.9% |
2.5% |
3.0% |
3.1% |
2.8% |
0.7% |
0.6% |
0.6% |
|
arthritis |
0.9% |
1.0% |
1.0% |
0.8% |
1.0% |
1.0% |
0.0% |
1.0% |
1.0% |
0.8% |
0.8% |
|
Asthma |
4.3% |
4.1% |
4.1% |
4.2% |
4.1% |
4.5% |
3.8% |
3.9% |
5.1% |
4.8% |
4.7% |
|
Depression |
8.0% |
8.0% |
7.3% |
7.9% |
7.5% |
7.6% |
6.7% |
6.2% |
6.9% |
7.5% |
8.1% |
|
Heart Disease |
5.1% |
5.2% |
4.4% |
4.6% |
4.4% |
4.8% |
3.1% |
2.5% |
3.4% |
3.3% |
2.9% |
|
Trauma |
12.3% |
12.1% |
11.3% |
11.3% |
11.4% |
12.6% |
9.4% |
9.2% |
9.0% |
8.6% |
8.8% |
|
Mental Disorders |
13.8% |
14.6% |
13.8% |
14.3% |
15.5% |
15.4% |
14.9% |
14.1% |
15.5% |
15.9% |
17.8% |
|
Cancer |
4.2% |
4.7% |
3.7% |
3.9% |
4.4% |
4.6% |
2.2% |
1.9% |
3.1% |
3.2% |
2.9% |
The average annual increase in spending between 2000 and 2009 Out-of-pocket spending among privately insured adult increased from $703 in 2010 to $897 by 2020, an average annual increase of 2.5 percent.
Trends in the prevalence of chronic disease among privately insured adults were relatively stable over time. Fifty two percent adults had no treated chronic conditions in 2010 compared to 57 percent by 2020. The percent of adults with 3 or more chronic conditions treated decreased slightly over time, falling from 9.4 percent in 2010 to 7 percent in 2020.
Table 2 examines changes in real per insured spending by the most expensive and prevalent chronic conditions. Between 2010 and 2020 private insurance spending per adult rose from $3,716 to $4,893 a 31.7 percent increase. The 2020 spending level however was most likely impacted by COVID. The pattern of spending changes varied widely across chronic condition. Per insured spending on hyperlipidemia declined nearly 50 percent per person over the ten-year period, falling from $735 to $371. This decline reflects a dramatic change in the mix of statins with a large rise in the use of generic statins and a 90 percent reduction over a ten-year period for brand name statins [8]. Similarly, increased use of generic drugs to treat hypertension (generic Lipitor) rose over the ten-year period resulting in 16 percent decrease in spending.
Table 2: Mean Real Private Health Insurance Spending per Capital by Medical Conditions, 2010-2013, 2018-2020
|
|
2010-2013 |
2018-2020 |
% Change |
|
Diabetes |
$ 2,380 |
$ 4,875 |
104.8% |
|
Hypertension |
$ 546. |
$ 459. |
-16% |
|
Hyperlipidemia |
$735. |
$371. |
-49.5% |
|
Mental Disorders |
$1,400 |
$1,786 |
23.3% |
|
Heart Disease |
$6,997 |
$8,960 |
28.1% |
|
Cancer |
$2,423 |
$3,023 |
24.8% |
|
Total |
$3,716 |
$4,893 |
31.7% |
|
Source: Tabulations from MEPS-HC, 2010-2020 |
|||
In contrast, spending on other chronic conditions increased sharply. Spending to treat diabetes more than doubled, rising from $2,380 in 2010 to $4,875 by 2020. Cancer spending showed the next largest rise, from $6,997 in 2010 to $8,960 by 2020, over a 28 percent increase. Spending on trauma cases and mental disorders increased by roughly the same amount 25 and 23 percent respectfully over the ten-year period.
Table 3 presents the full regression results for private insurance spending focusing on the number of chronic conditions treated. We focus on the results from 2010-2013 and 2018- 2020 through the 2014-2017 results are reported as well. In both periods, women incurred higher expenses (about $2,000) than men. Self-reported health status also impacted spending, private insurance spending for those that reported poor health were over $15,000 higher compared to those reporting excellent health (results similar in both periods).
Table 3: Real (2020$) Total Private Health Expenditures marginal effects, 18-64, 2010-2013
|
Average marginal effects |
|
Number of strata = 660 Number of obs = 40,057 |
|
Number of PSUs = 1,453 Population size = 41,127,025 |
|
Subpop. no. obs = 36,537 |
|
| Linearized |
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|
---------------+---------------------------------------------------------------- |
|
1.female | 1995.323 163.5247 12.20 0.000 1674.331 2316.315 |
|
hlthstat | |
|
very_good | 713.9643 156.4876 4.56 0.000 406.7854 1021.143 |
|
good | 1888.794 188.5281 10.02 0.000 1518.721 2258.867 |
|
fair | 5425.748 482.1349 11.25 0.000 4479.336 6372.159 |
|
poor | 15240.91 2080.745 7.32 0.000 11156.5 19325.33 |
|
racethx | |
|
Hisp | -508.158 249.1425 -2.04 0.042 -997.2149 -19.10123 |
|
NHblack | -713.4314 214.595 -3.32 0.001 -1134.673 -292.19 |
|
NHother | -906.8461 199.5752 -4.54 0.000 -1298.604 -515.088 |
|
agegrp | |
|
35-49 | -188.8206 224.927 -0.84 0.401 -630.3433 252.7021 |
|
50-64 | 455.5581 259.2237 1.76 0.079 -53.28777 964.4039 |
|
povcat | |
|
100-199% | 73.18823 330.0251 0.22 0.825 -574.6378 721.0143 |
|
200-399% | 526.6362 316.809 1.66 0.097 -95.24728 1148.52 |
|
400%+ | 1090.472 316.4289 3.45 0.001 469.3346 1711.609 |
|
region | |
|
Midwest | 454.8001 316.2648 1.44 0.151 -166.0151 1075.615 |
|
South | -614.3952 210.0326 -2.93 0.004 -1026.681 -202.1096 |
|
West | -239.53 222.9592 -1.07 0.283 -677.19 198.13 |
|
educgrp | |
|
HSgrad | 420.7144 280.017 1.50 0.133 -128.9478 970.3765 |
|
SomeColl_Assc | 505.604 269.37 1.88 0.061 -23.15861 1034.367 |
|
CollegeGrad | 1468.191 310.1386 4.73 0.000 859.4016 2076.981 |
|
|
|
1.smoker | -706.1203 174.4024 -4.05 0.000 -1048.465 -363.7755 |
|
totcond | |
|
1_cond | 1983.512 132.2196 15.00 0.000 1723.97 2243.054 |
|
2_cond | 3616.264 318.1501 11.37 0.000 2991.748 4240.78 |
|
3_cond | 5432.958 394.8348 13.76 0.000 4657.913 6208.003 |
|
4+_cond | 7658.692 661.9313 11.57 0.000 6359.347 8958.036 |
|
|
|
year | |
|
2011 | 627.3848 259.531 2.42 0.016 117.9358 1136.834 |
|
2012 | -8.824805 236.4594 -0.04 0.970 -472.9851 455.3355 |
|
2013 | 90.87363 239.4017 0.38 0.704 -379.0622 560.8095 |
|
-------------------------------------------------------------------------------- |
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|
|
|
Real (2020$) Total Private Health Expendtures marginal effects, 18-64, 2014-2017 |
|
Average marginal effects |
|
|
|
Number of strata = 777 Number of obs = 39,737 |
|
Number of PSUs = 1,707 Population size = 43,554,880 |
|
Subpop. no. obs = 34,682 |
|
-------------------------------------------------------------------------------- |
|
| Linearized |
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|
2011 | 627.3848 259.531 2.42 0.016 117.9358 1136.834 |
6.725614 |
33.23019 0.20 0.840 -58.50392 71.95514
|
|||
|
2012 | -8.824805 236.4594 -0.04 0.970 -472.9851 455.3355 |
|
||||
|
2013 | 90.87363 239.4017 0.38 0.704 -379.0622 560.8095 |
-20.46487 |
35.06566 -0.58 0.560 -89.29736 48.36761 |
|||
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|||||
|
Real (2020$) Total out-of-pocket spending marginal effects, 18-64, 2014-2017 |
|||||
|
Average marginal effects |
|||||
|
Number of strata = 777 Number of obs = 39,737 |
|||||
|
Number of PSUs = 1,707 Population size = 43,554,880 |
|||||
|
Subpop. no. obs = 34,682 |
|||||
|
| Linearized |
|||||
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|||||
|
1.female | 353.6019 25.19495 14.03 0.000 304.1563 403.0474 |
|
||||
|
hlthstat | |
|
||||
|
very_good | 87.16979 30.03582 2.90 0.004 28.22395 146.1156 |
|||||
|
good | 163.1072 34.02968 4.79 0.000 96.32337 229.8911 |
|||||
|
fair | 516.5662 66.59355 7.76 0.000 385.8751 647.2572 |
|||||
|
poor | 783.4051 170.7659 4.59 0.000 448.274 1118.536 |
|||||
|
racethx | |
|
||||
|
Hisp | -282.2319 34.66339 -8.14 0.000 -350.2594 -214.2043 |
|||||
|
NHblack | -367.6007 30.27213 -12.14 0.000 -427.0103 -308.1911 |
|||||
|
NHother | -321.3087 30.96833 -10.38 0.000 -382.0846 -260.5328 |
|||||
|
agegrp | |
|
||||
|
35-49 | -188.8206 224.927 -0.84 0.401 -630.3433 52.7021 |
38.62786 |
30.06198 1.28 0.199 -20.36931 97.62503 |
|||
|
50-64 | 455.5581 259.2237 1.76 0.079 -53.28777 4.4039 |
201.4485 |
30.81292 6.54 0.000 140.9776 261.9194 |
|||
|
povcat | |
|
||||
|
100-199% | -76.40574 63.93363 -1.20 0.232 -201.8766 49.06516 |
|||||
|
200-399% | 26.11468 61.71868 0.42 0.672 -95.00935 147.2387 |
|||||
|
400%+ | 231.7134 61.12645 3.79 0.000 111.7517 351.6752 |
|||||
|
region | |
|
||||
|
Midwest | 109.5119 41.26408 2.65 0.008 28.53035 190.4934 |
|||||
|
South | 37.97976 39.93753 0.95 0.342 -40.39836 116.3579 |
|||||
|
West | 110.9818 42.98803 2.58 0.010 26.61703 195.3466 |
|||||
|
educgrp | |
|
||||
|
HSgrad | -72.68969 91.82671 -0.79 0.429 -252.9013 107.5219 |
|||||
|
SomeColl_Assc | -.6659862 93.48409 -0.01 0.994 -184.1302 182.7982 |
|||||
|
CollegeGrad | 230.8402 94.17284 2.45 0.014 46.02427 415.6561 |
|||||
|
1.smoker | -706.1203 174.4024 -4.05 0.000 -1048.465 -363.7755 |
|
41.26408 2.65 0.008 28.53035 190.4934 |
|||
|
totcond | |
|
||||
|
1_cond | 452.5405 27.85817 16.24 0.000 397.8683 507.2126 |
|||||
|
2_cond | 644.4768 34.52524 18.67 0.000 576.7204 712.2333 |
|||||
|
3_cond | 1056.148 67.54476 15.64 0.000 923.5905 1188.706 |
|||||
|
4+_cond | 1184.645 78.21692 15.15 0.000 1031.143 1338.147 |
|||||
|
year | |
|
||||
|
2015 | 627.3848 259.531 2.42 0.016 117.9358 1136.834 |
|
3 3 . 2 3 0 1 9 0.20 0.840 - 5 8 . 5 0 3 9 2 71.95514 |
|||
|
2016 | -8.824805 236.4594 -0.04 0.970 -472.9851 455.3355 |
|
||||
|
2017 | 90.87363 239.4017 0.38 0.704 -379.0622 560.8095 |
|
35.06566 -0.58 0.560 -89.29736 48.36761 |
|||
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|||||
|
|
|||||
|
Real (2020$) Total out-of-pocket spending marginal effects, 18-64, 2018-2020 |
|||||
|
Average marginal effects |
|||||
|
2011 | 627.3848 259.531 2.42 0.016 117.9358 1136.834 |
6.725614 |
33.23019 0.20 0.840 -58.50392 71.95514 |
|||
|
2012 | -8.824805 236.4594 -0.04 0.970 -472.9851 455.3355 |
|
||||
|
2013 | 90.87363 239.4017 0.38 0.704 -379.0622 560.8095 |
-20.46487 |
35.06566 -0.58 0.560 -89.29736 48.36761 |
|||
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|||||
|
Real (2020$) Total out-of-pocket spending marginal effects, 18-64, 2014-2017 |
|||||
|
Average marginal effects |
|||||
|
Number of strata = 777 Number of obs = 39,737 |
|||||
|
Number of PSUs = 1,707 Population size = 43,554,880 |
|||||
|
Subpop. no. obs = 34,682 |
|||||
|
| Linearized |
|||||
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|||||
|
1.female | 353.6019 25.19495 14.03 0.000 304.1563 403.0474 |
|
||||
|
hlthstat | |
|
||||
|
very_good | 87.16979 30.03582 2.90 0.004 28.22395 146.1156 |
|||||
|
good | 163.1072 34.02968 4.79 0.000 96.32337 229.8911 |
|||||
|
fair | 516.5662 66.59355 7.76 0.000 385.8751 647.2572 |
|||||
|
poor | 783.4051 170.7659 4.59 0.000 448.274 1118.536 |
|||||
|
racethx | |
|
||||
|
Hisp | -282.2319 34.66339 -8.14 0.000 -350.2594 -214.2043 |
|||||
|
NHblack | -367.6007 30.27213 -12.14 0.000 -427.0103 -308.1911 |
|||||
|
NHother | -321.3087 30.96833 -10.38 0.000 -382.0846 -260.5328 |
|||||
|
agegrp | |
|
||||
|
35-49 | -188.8206 224.927 -0.84 0.401 -630.3433 52.7021 |
38.62786 |
30.06198 1.28 0.199 -20.36931 97.62503 |
|||
|
50-64 | 455.5581 259.2237 1.76 0.079 -53.28777 4.4039 |
201.4485 |
30.81292 6.54 0.000 140.9776 261.9194 |
|||
|
povcat | |
|
||||
|
100-199% | -76.40574 63.93363 -1.20 0.232 -201.8766 49.06516 |
|||||
|
200-399% | 26.11468 61.71868 0.42 0.672 -95.00935 147.2387 |
|||||
|
400%+ | 231.7134 61.12645 3.79 0.000 111.7517 351.6752 |
|||||
|
region | |
|
||||
|
Midwest | 109.5119 41.26408 2.65 0.008 28.53035 190.4934 |
|||||
|
South | 37.97976 39.93753 0.95 0.342 -40.39836 116.3579 |
|||||
|
West | 110.9818 42.98803 2.58 0.010 26.61703 195.3466 |
|||||
|
educgrp | |
|
||||
|
HSgrad | -72.68969 91.82671 -0.79 0.429 -252.9013 107.5219 |
|||||
|
SomeColl_Assc | -.6659862 93.48409 -0.01 0.994 -184.1302 182.7982 |
|||||
|
CollegeGrad | 230.8402 94.17284 2.45 0.014 46.02427 415.6561 |
|||||
|
1.smoker | -706.1203 174.4024 -4.05 0.000 -1048.465 -363.7755 |
|
41.26408 2.65 0.008 28.53035 190.4934 |
|||
|
totcond | |
|
||||
|
1_cond | 452.5405 27.85817 16.24 0.000 397.8683 507.2126 |
|||||
|
2_cond | 644.4768 34.52524 18.67 0.000 576.7204 712.2333 |
|||||
|
3_cond | 1056.148 67.54476 15.64 0.000 923.5905 1188.706 |
|||||
|
4+_cond | 1184.645 78.21692 15.15 0.000 1031.143 1338.147 |
|||||
|
year | |
|
||||
|
2015 | 627.3848 259.531 2.42 0.016 117.9358 1136.834 |
|
3 3 . 2 3 0 1 9 0.20 0.840 - 5 8 . 5 0 3 9 2 71.95514 |
|||
|
2016 | -8.824805 236.4594 -0.04 0.970 -472.9851 455.3355 |
|
||||
|
2017 | 90.87363 239.4017 0.38 0.704 -379.0622 560.8095 |
|
35.06566 -0.58 0.560 -89.29736 48.36761 |
|||
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|||||
|
|
|||||
|
Real (2020$) Total out-of-pocket spending marginal effects, 18-64, 2018-2020 |
|||||
|
Average marginal effects |
|||||
|
|
|
|||
|
Number of strata = 327 Number of obs = 27,761 |
|
|||
|
Number of PSUs = 819 Population size = 33,951,518 |
|
|||
|
Subpop. no. obs = 21,573 |
|
|||
|
| Linearized |
||||
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|
|||
|
---------------+---------------------------------------------------------------- |
|
|||
|
1.female | 385.9663 42.42095 9.10 0.000 302.6177 469.3148 |
|
|
||
|
hlthstat | |
|
|||
|
very_good | 118.9276 42.41185 2.80 0.005 35.59689 202.2583 |
|
|||
|
good | 313.2483 62.04145 5.05 0.000 191.3495 435.1472 |
|
|||
|
fair | 719.5106 132.062 5.45 0.000 460.0355 978.9857 |
|
|||
|
poor | 1071.651 269.8622 3.97 0.000 541.4261 1601.875 |
|
|||
|
racethx | |
|
|||
|
Hisp | -347.0093 45.61811 -7.61 0.000 -436.6397 -257.379 |
|
|||
|
NHblack | -418.7708 80.21721 -5.22 0.000 -576.3814 -261.1603 |
|
|||
|
NHother | -355.9838 58.22933 -6.11 0.000 -470.3927 -241.575 |
|
|||
|
agegrp | |
|
|||
|
35-49 | -188.8206 224.927 -0.84 0.401 -630.3433 252.7021 |
-3.611623 |
51.87324 -0.07 0.945 -105.532 98.30879 |
|
|
|
50-64 | 455.5581 259.2237 1.76 0.079 -53.28777 64.4039 |
|
|
||
|
povcat | |
|
|||
|
100-199%| 107.4156 109.222 0.98 0.326 -107.1835 322.0147 |
|
|||
|
200-399% | 135.055 92.21461 1.46 0.144 -46.12805 316.238 |
|
|||
|
400%+ | 339.5286 91.83284 3.70 0.000 159.0956 519.9615 |
|
|||
|
region | |
|
|||
|
Midwest | 110.2337 60.56904 1.82 0.069 -8.772207 229.2396 |
|
|||
|
South | 23.35172 62.21167 0.38 0.708 -98.8816 145.585 |
|
|||
|
West | 19.37645 57.57367 0.34 0.737 -93.74414 132.497 |
|
|||
|
educgrp | |
|
|||
|
HSgrad | -17.13086 75.19877 -0.23 0.820 -164.8812 130.6195 |
||||
|
SomeColl_Assc | 170.0514 77.38554 2.20 0.028 18.00452 322.0983 |
||||
|
CollegeGrad | 400.1915 82.03416 4.88 0.000 239.011 561.372 |
||||
|
1.smoker | -161.2051 49.75993 -3.24 0.001 -258.9733 -63.43694 |
|
|||
|
1_cond | 627.0462 44.77842 14.00 0.000 539.0657 715.0268 |
||||
|
2_cond | 769.5137 67.18709 11.45 0.000 637.5046 901.5227 |
||||
|
3_cond | 1175.277 115.7188 10.16 0.000 947.9132 1402.641 |
||||
|
4+_cond | 1235.621 123.4258 10.01 0.000 993.1142 1478.128 |
||||
|
year | |
|
|||
|
2019 | -12.07607 46.67606 -0.26 0.796 -103.7851 79.63294 |
||||
|
2020 | -117.8587 51.86508 -2.27 0.023 -219.763 -15.95429 |
||||
|
Note: dy/dx for factor levels is the discrete change from the base level. |
||||
.Private insurance spending was considerably lower for racial and ethnic minorities. In 2010-2013 and 2018-2020 spending was approximately $900 lower for Non-Hispanic other adults compared to Non-Hispanic White adults. Insurance spending was $713 lower in 2010-2013 and $619 lower in 2018-2020 for NonHispanic Black adults compared to Non-Hispanic White adults. Finally, spending on Hispanic adults was over $500 lower in the earlier period, and $213 lower in the latter period compared to non-Hispanic White adults.
In both time periods, higher levels of education were associated with higher private insurance spending. In the 2018- 2020 time period, adults with a college degree spending $2,700 more than adults with no degree.
Somewhat surprisingly smokers incurred lower health care spending in both periods, ranging from $700 to over $840 lower per year.
Finally, even when controlling for self-reported health status, the number of chronic conditions treated has a substantial impact on private insurance spending.
The incremental spending for each additional condition was about $2,000 in 2010-2013 and was similar in 2018-2020. The major difference was the incremental spending associated with moving from 3 to 4 or more conditions treated in 2018-2020. In this case, the additional spending was over $4,200 higher (p< 10).
Table 4 presents the same set of results for out-of-pocket spending trends over time. Like total spending, females spent approximately $380 more out of pocket compared to males in both time periods. Reported health status also influenced spending as those reporting in poor health spent $1,000 to $1,200 more out-of-pocket in the two time periods. Racial minorities spent less out-of-pocket compared to non-Hispanic White adults. In the latest period, out-of-pocket spending was approximately $350 to $419 lower per year for racial and ethnic minorities.
|
Average marginal effects |
|
||
|
Number of strata = 660 Number of obs = 40,057 |
|
||
|
Number of PSUs = 1,453 Population size = 41,127,025 |
|
||
|
Subpop. no. obs = 36,537 |
|
||
|
| Linearized |
|
||
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|
||
|
1.female | 373.6866 23.3429 16.01 0.000 327.8654 419.5077 |
|
||
|
hlthstat | |
|
||
|
very_good | 76.86804 25.31669 3.04 0.002 27.17239 126.5637 |
|
||
|
good | 217.1564 35.60013 6.10 0.000 147.2747 287.038 |
|
||
|
fair | 574.4637 68.27431 8.41 0.000 440.444 708.4834 |
|
||
|
poor | 1214.077 178.1707 6.81 0.000 864.3351 1563.819 |
|
||
|
racethx | |
|
||
|
Hisp | -291.2019 27.56302 -10.56 0.000 -345.307 -237.0968 |
|
||
|
NHblack | -391.8161 25.75718 -15.21 0.000 -442.3765 -341.2558 |
|
||
|
NHother | -171.8883 47.62473 -3.61 0.000 -265.3737 -78.40284 |
|
||
|
agegrp | |
|
||
|
35-49 | -188.8206 224.927 -0.84 0.401 -630.3433 252.7021 |
22.598 |
28.30305 0.80 0.425 -32.95976 78.15575 |
|
|
50-64 | 455.5581 259.2237 1.76 0.079 -53.28777 964.4039 |
262.5501 |
34.33086 7.65 0.000 195.16 329.9402 |
|
|
povcat | |
|
||
|
100-199% | 175.9181 79.20286 -2.22 0.027 -331.3901 -20.44604 |
|
||
|
200-399% | -75.09815 76.81253 -0.98 0.329 -225.8781 75.68177 |
|
||
|
400%+ | 35.10354 77.28725 0.45 0.650 -116.6082 186.8153 |
|
||
|
region | |
|
||
|
Midwest | 196.8679 31.58485 6.23 0.000 134.8681 258.8677 |
|
||
|
South | 146.7547 34.18499 4.29 0.000 79.65089 213.8584 |
|
||
|
West | 238.5506 39.18327 6.09 0.000 161.6354 315.4658 |
|
||
|
educgrp | |
|
||
|
HSgrad | 35.13912 38.89257 0.90 0.367 -41.20543 111.4837 |
|
||
|
SomeColl_Assc | 152.8567 35.29145 4.33 0.000 83.58104 222.1324 |
|
||
|
CollegeGrad | 338.6766 38.36374 8.83 0.000 263.3701 413.9831 |
|
||
|
1.smoker | 373.6866 23.3429 16.01 0.000 327.8654 419.5077 |
|
|
|
|
totcond | |
|
|
|
|
1_cond | 415.2717 23.75265 17.48 0.000 368.6463 461.8972 |
|
||
|
2_cond | 773.8486 44.10294 17.55 0.000 687.2763 860.4209 |
|
||
|
3_cond | 946.4466 50.92685 18.58 0.000 846.4793 1046.414 |
|
||
|
4+_cond | 1293.191 85.96375 15.04 0.000 1124.448 1461.935 |
|
||
|
year | |
|
|
|
|
2011 | 627.3848 259.531 2.42 0.016 117.9358 1136.834 |
6.725614 |
33.23019 0.20 0.840 -58.50392 71.95514 |
|
|
2012 | -8.824805 236.4594 -0.04 0.970 -472.9851 455.3355 |
|
|
|
|
2013 | 90.87363 239.4017 0.38 0.704 -379.0622560.8095 |
-20.46487 |
35.06566 -0.58 0.560 -89.29736 48.36761 |
|
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|
||
|
Real (2020$) Total out-of-pocket spending marginal effects, 18-64, 2014-2017 |
|
||
|
Average marginal effects |
|
||
|
Number of strata = 777 Number of obs = 39,737 |
|
||
|
Number of PSUs = 1,707 Population size = 43,554,880 |
|
||
|
Subpop. no. obs = 34,682 |
|
||
|
| Linearized |
|
||
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|
||
|
1.female | 353.6019 25.19495 14.03 0.000 304.1563 403.0474 |
|
|
|
|
hlthstat | |
|
|
|
|
very_good | 87.16979 30.03582 2.90 0.004 28.22395 146.1156 |
|
||
|
good | 163.1072 34.02968 4.79 0.000 96.32337 229.8911 |
|
||||
|
fair | 516.5662 66.59355 7.76 0.000 385.8751 647.2572 |
|
||||
|
poor | 783.4051 170.7659 4.59 0.000 448.274 1118.536 |
|
||||
|
racethx | |
|
|
|||
|
Hisp | -282.2319 34.66339 -8.14 0.000 -350.2594 -214.2043 |
|
||||
|
NHblack | -367.6007 30.27213 -12.14 0.000 -427.0103 -308.1911 |
|
||||
|
NHother | -321.3087 30.96833 -10.38 0.000 -382.0846 -260.5328 |
|
||||
|
agegrp | |
|
|
|||
|
35-49 | -188.8206 224.927 -0.84 0.401 -630.3433 52.7021 |
38.62786 |
30.06198 1.28 0.199 -20.36931 97.62503 |
|
||
|
50-64 | 455.5581 259.2237 1.76 0.079 -53.28777 4.4039 |
201.4485 |
30.81292 6.54 0.000 140.9776 261.9194 |
|
||
|
povcat | |
|
|
|||
|
100-199% | -76.40574 63.93363 -1.20 0.232 -201.8766 49.06516 |
|
||||
|
200-399% | 26.11468 61.71868 0.42 0.672 -95.00935 147.2387 |
|
||||
|
400%+ | 231.7134 61.12645 3.79 0.000 111.7517 351.6752 |
|
||||
|
region | |
|
|
|||
|
Midwest | 109.5119 41.26408 2.65 0.008 28.53035 190.4934 |
|
||||
|
South | 37.97976 39.93753 0.95 0.342 -40.39836 116.3579 |
|
||||
|
West | 110.9818 42.98803 2.58 0.010 26.61703 195.3466 |
|
||||
|
educgrp | |
|
|
|||
|
HSgrad | -72.68969 91.82671 -0.79 0.429 -252.9013 107.5219 |
|
||||
|
SomeColl_Assc | -.6659862 93.48409 -0.01 0.994 -184.1302 182.7982 |
|
||||
|
CollegeGrad | 230.8402 94.17284 2.45 0.014 46.02427 415.6561 |
|
||||
|
1.smoker | -706.1203 174.4024 -4.05 0.000 -1048.465 -363.7755 |
|
41.26408 2.65 0.008 28.53035 190.4934 |
|
||
|
totcond | |
|
|
|||
|
1_cond | 452.5405 27.85817 16.24 0.000 397.8683 507.2126 |
|
||||
|
2_cond | 644.4768 34.52524 18.67 0.000 576.7204 712.2333 |
|
||||
|
3_cond | 1056.148 67.54476 15.64 0.000 923.5905 1188.706 |
|
||||
|
4+_cond | 1184.645 78.21692 15.15 0.000 1031.143 1338.147 |
|
||||
|
year | |
|
|
|||
|
2015 | 627.3848 259.531 2.42 0.016 117.9358 1136.834 |
|
33.23019 0.20 0.840 -58.50392 71.95514 |
|
||
|
2016 | -8.824805 236.4594 -0.04 0.970 -472.9851 455.3355 |
|
|
|||
|
2017 | 90.87363 239.4017 0.38 0.704 -379.0622 560.8095 |
|
35.06566 -0.58 0.560 -89.29736 48.36761 |
|
||
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|
||||
|
Real (2020$) Total out-of-pocket spending marginal effects, 18-64, 2018-2020 |
|||||
|
Average marginal effects |
|
||||
|
Number of strata = 327 Number of obs = 27,761 |
|
|
|||
|
Number of PSUs = 819 Population size = 33,951,518 |
|
|
|||
|
Subpop. no. obs = 21,573 |
|
|
|||
|
| Linearized |
|
||||
|
| dy/dx std. err. t P>|t| [95% conf. interval] |
|
|
|||
|
---------------+---------------------------------------------------------------- |
|
|
|||
|
1.female | 385.9663 42.42095 9.10 0.000 302.6177 469.3148 |
|
|
|
||
|
hlthstat | |
|
|
|||
|
very_good | 118.9276 42.41185 2.80 0.005 35.59689 202.2583 |
|
|
|||
|
good | 313.2483 62.04145 5.05 0.000 191.3495 435.1472 |
|
|
|||
|
fair | 719.5106 132.062 5.45 0.000 460.0355 978.9857 |
|
|
|||
|
poor | 1071.651 269.8622 3.97 0.000 541.4261 1601.875 |
|
|
|||
|
racethx | |
|
|
|||
|
Hisp | -347.0093 45.61811 -7.61 0.000 -436.6397 -257.379 |
|
|
|||
|
NHblack | -418.7708 80.21721 -5.22 0.000 -576.3814 -261.1603 |
|
|
|||
|
NHother | -355.9838 58.22933 -6.11 0.000 -470.3927 -241.575 |
|
|
|||
|
agegrp | |
|
|
|||
|
35-49 | -188.8206 224.927 -0.84 0.401 -630.3433 252.7021 |
-3.611623 |
51.87324 -0.07 0.945 -105.532 98.30879 |
|
|
|
|
50-64 | 455.5581 259.2237 1.76 0.079 -53.28777 64.4039 |
|
|
|
|
|
povcat | |
|
|
||
|
100-199%| 107.4156 109.222 0.98 0.326 -107.1835 322.0147 |
|
|
||
|
200-399% | 135.055 92.21461 1.46 0.144 -46.12805 316.238 |
|
|
||
|
400%+ | 339.5286 91.83284 3.70 0.000 159.0956 519.9615 |
|
|
||
|
region | |
|
|
||
|
Midwest | 110.2337 60.56904 1.82 0.069 -8.772207 229.2396 |
|
|
||
|
South | 23.35172 62.21167 0.38 0.708 -98.8816 145.585 |
|
|
||
|
West | 19.37645 57.57367 0.34 0.737 -93.74414 132.497 |
|
|
||
|
educgrp | |
|
|
||
|
HSgrad | -17.13086 75.19877 -0.23 0.820 -164.8812 130.6195 |
|
|||
|
SomeColl_Assc | 170.0514 77.38554 2.20 0.028 18.00452 322.0983 |
|
|||
|
CollegeGrad | 400.1915 82.03416 4.88 0.000 239.011 561.372 |
|
|||
|
1.smoker | -161.2051 49.75993 -3.24 0.001 -258.9733 -63.43694 |
|
|
||
|
1_cond | 627.0462 44.77842 14.00 0.000 539.0657 715.0268 |
|
|||
|
2_cond | 769.5137 67.18709 11.45 0.000 637.5046 901.5227 |
|
|||
|
3_cond | 1175.277 115.7188 10.16 0.000 947.9132 1402.641 |
|
|||
|
4+_cond | 1235.621 123.4258 10.01 0.000 993.1142 1478.128 |
|
|||
|
year | |
|
|
||
|
2019 | -12.07607 46.67606 -0.26 0.796 -103.7851 79.63294 |
|
|||
|
2020 | -117.8587 51.86508 -2.27 0.023 -219.763 -15.95429 |
|
|||
|
Note: dy/dx for factor levels is the discrete change from the base level. |
|
|||
Out-of-pocket was over $200 higher for adults aged 50 to 64 compared to those under 50. Out-of-pocket spending also increased with higher levels of education. Adults with a college degree spent $340 to $400 more per year out-of-pocket compared to those without a high school diploma. As before smokers spend less out-of-pocket ($120 to $160) compared to non-smokers.
As before, even accounting for self-reported health status, out-of-pocket spending increased sharply with the number of chronic conditions treated. In both time periods examined, outof-pocket spending was $1,200 to nearly $1,300 more per year for those with 4 or more conditions compared to those with no chronic conditions.
The results in [Table 5], estimates the marginal impact of race and ethnicity and the most expensive and prevalent chronic conditions on private insurance spending in two time periods (2010-2013 and 2018 and 2020) as well as over the two periods. Other demographic results were similar in these models that were reported above and therefore are not shown.
Table 5: Marginal Effects on Private Health Insurance Spending by Numbers of Chronic Conditions 10-2013, 2018-2020
|
Variable |
2010-2013 |
2018-2020 |
|
Female |
$1,995* |
$ 2,764* |
|
Relative to Non-Hispanic White |
|
|
|
Hispanic |
-$508* |
- $212* |
|
Non-Hispanic Black |
- $713* |
-$619* |
|
Non-Hispanic Other |
-$907* |
-$905* |
|
Number of Chronic Conditions |
|
|
|
1 |
$1,983* |
$3,175* |
|
2 |
$3,616* |
$ 5,330* |
|
3 |
$5,432* |
$ 7,950* |
|
4+ |
$7,659* |
$12,197* |
|
SOURCE: Analysis from MEPS-HC |
||
|
*Significant different from Fewer conditions p< .05 (4 vs 3, 3 vs. 2, 2 vs. 1) |
||
|
Significant different p< .05 across the two time periods. |
||
In both time periods, health spending was approximately $2,000 higher for females. Spending on racial and ethnic minorities in the 2010-2013 time period were uniformly lower compared to non-Hispanic White adults. This ranged from $508 per year lower for Hispanic Adults to over $900 per year for nonHispanic other adults.
Spending increased sharply with the number of chronic conditions treated. In 2010-2013 incremental spending was $1,983 higher and those with 4 or more conditions $7,659 higher for those with 4 or more conditions compared to those with no chronic conditions. Spending on chronic disease increased sharply over the ten-year period. Private insurance spending for each category of the number of chronic conditions increased by nearly $1,200 (for those with one condition) to over $4,500 more in 2018-2020 for those with 4 or more conditions. These changes within each category were all statistically significant (p<05).
The results presented in [Table 6], estimate the incremental out-of-pocket expenditures by race and ethnicity and number of chronic conditions treated. Out-of-pocket spending increased sharply as the number of chronic conditions treated increased. Relative to adults with no chronic conditions, out-of-pocket spending was $415 higher for those with 1 condition rising to nearly $1,300 higher for those with 4 or more conditions treated. As before, out-of-pocket spending for racial and ethnic minorities were lower than from Non-Hispanic White adults.
Table 6: Marginal Effects on Out-Of-Pocket Health Insurance Spending, By Number of Chronic Conditions, 2010-2013, 2018-2022
|
Variable |
2010-2013 |
2018-2020 |
|
Female |
$374* |
$ 385* |
|
Relative to Non-Hispanic White |
|
|
|
Hispanic |
-$291* |
- $347* |
|
Non-Hispanic Black |
- $392* |
-$419* |
|
Non-Hispanic Other |
-$172* |
-$356* |
|
Number of Chronic Conditions |
|
|
|
1 |
$415* |
$627* |
|
2 |
$774* |
$ 770* |
|
3 |
$946* |
$ 1,175* |
|
4+ |
$1,293* |
$1,235* |
|
Source Analysis from MEPS-HC *Significant different p< .05 Significant different p< .05 across the two time periods. |
||
We next estimate the change in private insurance spending for five of the most expensive chronic conditions over time. Using a Wald Chi-Square test we compare the impact of changes in chronic care spending on total private insurance spending [Table 7], and out-of-pocket spending [Table 8], for three time periods; 2014-2017 and 2010-2013 and 2018-2020 and 2010-2013.
Table 7: Fully Interacted Model Impact of Key Chronic Disease on Level 1 Private Insurance Spending, 2010-2020
|
Conditions |
(2014-2017 vs 2010-2013) |
(2010-2013 vs 2018-2020) |
|
Heart Disease |
$2,592* |
$3,417* |
|
Trauma |
$630 |
$1,300* |
|
Cancer |
$417 |
$4,282* |
|
Mental Disease |
$190 |
$965* |
|
COPD/Asthma |
$502 |
$510 |
|
Source: Analysis from MEPS-HC ·Significantly different from zero, p<.05 |
||
Spending for four of the five chronic conditions examined increased significantly between 2018-2020 and 2010-2013. Cancer spending was $4,282 higher in the latter period compared to the earlier period. Similarly spending to treat heart disease was $3,417 higher, trauma spending $1.300 and treatment of mental disorders $965 higher in 2018-2020 compared to 2010- 2013. Spending to treat heart disease was also higher ($2,592) in 2014-2017 compared to 2010-2013.
Out-of-pocket spending to treat two chronic conditions also increased over time (Table 8). Spending to treat trauma patients was $201 higher in 2018-2020 compared to 2010-2013. Out-ofpocket spending for patients treated for mental disorders was $280 in 2018-2020 compared to 2010-2013.
Table 8: Fully Interacted Model Impact of Key Chronic Conditions on Out-of-Pocket Spending Privately Insurance Audits, 2010-2020
|
Conditions |
(2014-2017 vs 2010-2013) |
(2010-2013 vs 2018-2020) |
|
Heart Disease |
-$75 |
$102 |
|
Trauma |
$67 |
$201* |
|
Cancer |
$7 |
$180 |
|
Mental Disease |
$8 |
$280* |
|
COPD/Asthma |
-$65 |
-$61 |
|
Source: Analysis from MEPS-HC · Significantly different from zero, p<.05 |
||
CONCLUSIONS, LIMITATIONS AND RECOMMENDATIONS
Real per capita private insurance spending increased an average of 3.4 percent per year between 2010 and 2020. Out-ofpocket spending also increased over this period but by a lower amount of 2.5 percent per year. The analysis highlights several important demographic differences in the level of both total insurance spending and out-of-pocket spending over this period. First, health care spending, even controlling for reported health status and chronic health conditions, were lower for racial and ethnic minorities compared to non-Hispanic White adults. Out-ofpocket spending was also lower for racial and ethnic minorities. These differences however have not increased over the tenyear period examined. The result also show that spending rises with educational attainment and age. Total and out-of-pocket spending were also higher for women.
As may be expected the most important determinants of total and out-of-pocket were self-reported health status and the number of chronic health care conditions treated. Total spending on those with self-reported poor health was over $15,000 higher compared to those reporting excellent health. The significantly higher spending among those with fair or poor health compared to patients with excellent health was observed even when controlling for the number of chronic health care conditions treated.
The analysis also highlighted the substantial increase in total and out-of-pocket spending among patients a greater number of chronic conditions treated. Private insurance spending for adults under treatment for 4 or more chronic conditions were over $12,000 higher compared to adults with no chronic conditions treated. Even among chronically ill adults, spending increased substantially for each additional chronic condition treated. A similar result was found for out-of-pocket spending for chronically ill adults. These high levels of out-of-pocket spending for chronically ill patients are a concern if they deter patients from adhering to medications and seeking timely treatment. One approach would be to lower or eliminate cost sharing on clinically important medications used to treat patients with highly prevalent chronic conditions.
The analysis highlights several areas of interest. The lower spending overall on racial minorities even after accounting for health status raises some issues that require additional study. The analysis also highlighted that higher spending on several chronic conditions were an important factor accounting for the growth in total private insurance spending. Additional treatment costs of cancer and heart disease were the two leading conditions accounting for the increased spending. The increased spending was not associated with higher prevalence of the conditions, they were relatively stable over the ten- year period. Higher treatment costs per case then accounted for the rise.
The analysis showed that the treatment costs of some conditions like hyperlipidemia and hypertension actually declined over time. The lower level of spending is linked to the increased use of generic medications to treat both conditions. A limitation is understanding the factors associated with the growth in spending on other conditions. These factors could include changes in the intensity of treatment, or changes in the mix of services provided to treat.
The results provide important information for capitated health insurance plans in general (Medicare, Medicaid, and private insurance). These plans must predict forward looking treatment costs in setting premiums or negotiation per capita payments. The fact that both reported health status and the number and mix of chronic conditions (which are used in risk adjusting per capita rates for Medicare Advantage plans) are highly predictive of spending highlights the important role that health risk assessments in addition to clinical data in predicting levels of spending. Relying solely on risk adjusting simply using claims data on clinical data could result in underpredicting next year’s spending levels. Both risk assessment information and the clinical data are two of the most important determinants of the level and change in health care spending.
Finally, the results allow policymakers to target chronic conditions with the highest level and growth in spending. Approaches for reducing the level and growth in spending include effective chronic care management (transitional care, education, medication therapy management). Effective medication therapy management programs have been founds to reduce disease specific health care spending and should be more broadly expanded and used [9].
REFERENCES
3. American Community Survey. Health insurance coverage of the nonelderly. 0–64.
6. StataCorp. 2021. Stata Statistical Software: Release 17. College Station, TX: StataCorp LLC.
7. National Health Spending in 2020 Increases due to Impact of COVID-19 Pandemic.
