ABSTRACT
Background and Aims
Older adults have higher rates of traumatic brain injuries (TBI) than other age groups, accounting for most TBI‐related hospitalizations and mortality. However, there is no up‐to‐date data on annual trends and regional differences in older Medicare beneficiaries. Here we estimate the incidence of TBI‐related hospitalizations and identify annual trends and regional differences.
Methods
This is a retrospective cohort analysis of fee‐for‐service Medicare claims data from 2010 to 2022. The study population consisted of beneficiaries with at least 1 year of continuous coverage including parts A and B. ICD codes were used to identify TBI‐related hospitalizations. We estimated generalized linear mixed Poisson regression models of the aggregated TBI counts and included age group, sex, race/ethnicity, a sex by race/ethnicity interaction, period, and a random state effect as covariates. Geographic variability was summarized by the state random effects.
Results
704,409 total observations met eligibility with 684,869 unique beneficiaries. Majority of beneficiaries were in the age ranges of 81–85 (22%) and 86–90 (22%), and 56% (n = 394,024) were female. Majority of beneficiaries were White (n = 610,688, 87%) and 5% (n = 31,723) were Hispanic. Incidence of TBI hospitalizations increased with age and were higher in females, particularly in the oldest age groups of 86–90 (7.15 [CI 7.06–7.25] in females vs. 5.49 [CI 5.41–5.56] in males) and > 91 (8.39 [CI 8.27–8.51] in females vs. 6.97 [CI 6.8–7.07] in males). Higher incidence of TBI hospitalizations were seen in the South, Northeast and West, with lower rates in the Midwest.
Conclusion
We demonstrate that over the years TBI‐related hospitalizations are consistently higher in the oldest‐old adults, and in females. There is regional variation with highest TBI‐related hospitalizations in the Northeast and South. Future studies aimed at identifying interactions between clinical and demographic factors are indicated to develop targeted public health interventions at mitigating TBI in older adults.
Keywords: epidemiology, medicare, older adults, TBI hospitalizations, traumatic brain injury
Summary
There is regional variation in traumatic brain injuries (TBI) incidence among older fee‐for‐service (FFS) Medicare beneficiaries.
TBI related hospitalizations are consistently higher in the oldest‐old adults.
Why does this paper matter?
This study provides an updated picture of regional and national incidence of Traumatic Brain related hospitalizations among older adults enrolled in FFS Medicare beneficiaries. It highlights the regional variation and need for continued work to further understand the relationships between clinical and demographic factors to improve clinical practice.
1. Introduction
Traumatic brain injuries (TBIs) are among the leading causes of hospitalization, disability, and death in the United States and globally [1, 2]. Older adults have higher rates of TBI than other age groups and account for a majority of TBI‐related hospitalizations and mortality [3, 4, 5]. Older adults also have the highest risk of post‐injury complications and morbidity, with outcomes varying by age, sex, and resources including access to trauma facilities [6]. Additionally, TBI‐related hospitalizations in older adults result in a projected $3 billion in annual Medicare spending [7].
Although TBI in older adults is a significant public health concern and priority with distinct causes and higher proportion of poor outcomes and complications, no up‐to‐date data or systematic disease surveillance system is focused on Medicare beneficiaries [6, 8]. There are gaps in data on the regional incidence and annual trends of TBI in Medicare beneficiaries [1, 9, 10]. Recent studies of national cohorts, including Health and Retirement Survey linked to Medicare claims, have examined TBI incidence in older adults and while they have identified demographic factors that may be associated with TBI, there has been limited evaluation of regional differences in incidence and annual trends [1, 9, 10]. The objective of this study is to report the incidence rates of TBI hospitalizations in this population over time and define regional differences. We also sought to identify any trends in TBI hospitalizations, discharge dispositions and in‐patient mortality with time.
2. Methods
2.1. Study Design, Setting and Population
This is a retrospective cohort analysis of fee‐for‐service (FFS) Medicare claims data from 2010 to 2022. The Medicare data supporting this study's findings are retrospective, routinely collected by the Centers for Medicare and Medicaid Services (CMS) for billing purposes, and made available without direct identifiers. These data are available under license and subject to restrictions. Data are available at data. cms. gov with the permission of CMS. The study population consisted of all beneficiaries with at least 1 year of continuous Medicare FFS coverage, including parts A and B. We excluded patients with any Health Maintenance Organization (HMO) or Medicare Advantage (MA) coverage during the study period due to data access limitations. The study was approved by the Institutional Review Boards at (2023P000114) Mass General Brigham and Harvard University. The requirement for informed consent was waived, as this research posed minimal risk to patients, would not affect clinical decisions about the individual's care, and could not be practicably carried out without the waiver of consent. Results are reported in accordance with the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines (Supporting Information Table S1) [11].
2.2. Cohort Derivation: Inclusion and Exclusion Criteria
Figure 1 shows our inclusion and exclusion flow chart. ICD‐9 and ICD‐10 codes (see Supporting Information Table S2) were used to identify hospitalizations with TBI as principal discharge diagnosis. The ICD‐9 and ICD‐10 codes used for TBI identification were derived through literature review of previously conducted studies and TBI coding guidance [12, 13, 14, 15, 16, 17]. ICD codes for comorbidities were taken from the Chronic Conditions Warehouse (CCW) algorithms; ICD codes were cross walked to ICD‐9 or ICD‐10 respectively when existing algorithms did not provide both versions (Supporting Information Table S3) [18, 19]. ICD code identification for some comorbidities (e.g., Acute Kidney Injury, Systemic Lupis Erythematosus, Age‐Related Macular Degeneration, Prior Cerebrovascular Accidents and Point Joint Replacement), along with motor vehicle accidents, fall related injuries were from other identification algorithms [20, 21, 22, 23, 24]. We included patients that were 66+ years with a discharge TBI Diagnoses in the Medical Provider Analysis and Review files from CMS. We excluded patients with TBI discharges (principal, secondary or any other position) in the prior 12 months. We restricted those whose original reason for Medicare coverage was old age and limited to beneficiaries in the U.S. States, Washington D.C. and Puerto Rico. Finally, we restricted to those that participated in FFS Medicare Parts A and B in year prior to index event and were community dwelling in 12 months prior to index event.
Figure 1.
Inclusion and exclusion flowchart. Flowchart outlines the inclusion and exclusion of patients to identify our sample for analysis. Dx, diagnosis; FFS, fee‐for‐service; MEDPAR, Medical Provider Analysis and Review; SNF, skilled nursing facility; TBI, traumatic brain injury.
2.3. Clinical and Demographic Variables
Demographics, including age, sex, race, and ethnicity, were extracted. Clinical characteristics were also collected to assess the severity of injury and post‐hospitalization outcomes. Major comorbid conditions, including Alzheimer's disease and related dementias (AD and ADRD), hypertension, diabetes, coronary artery disease and epilepsy were identified using ICD codes [18, 19]. Frailty index was used as a measure of frailty and baseline functionality [25, 26]. Hospitalization related metrics included encounter type, length of stay, and discharge disposition. We also evaluated indications for any other hospitalizations or skilled nursing facility admissions in the year prior to the index event.
2.4. Statistical Analysis
Descriptive statistics included frequency distribution of clinical and demographic variables, encounter level data and discharge disposition. Given the low number of TBIs, we aggregated counts by sex, race/ethnicity, 5‐year age groups, and 3 time periods (2010–2014, 2015–2019, 2020–2022) within each state. We estimated generalized linear mixed Poisson regression models of the aggregated TBI counts and included age group, sex, race/ethnicity, a sex by race/ethnicity interaction, period, and a random state effect as covariates. The number of Medicare beneficiaries meeting our inclusion criteria in each state sex‐race/ethnicity‐5‐year age group was included as an offset. The random effects were assumed to arise from a standard normal distribution. Incidence rate ratios [95% confidence intervals] were calculated to characterize associations of demographic covariates with TBI incidence. Geographic variability was summarized by the state random effects with positive (negative) values indicating states having higher (lower) incidence rate ratios than the national average over the observational period.
2.5. Missing Data
Observations missing race/ethnicity were assigned an “unknown” category. For location of residence, if missingness was small, we assumed the mechanism was missing completely at random and we eliminated the observations from the Poisson models; otherwise, we assumed missingness was at random. The Poisson mixed models were estimated using the SAS statistical software, version 9.4.
3. Results
3.1. Study Cohort and Baseline Characteristics
A total of 704,409 observations met eligibility. The total number of unique beneficiaries was 684,869. We eliminated only 24 cases that were missing location of residence and thus assumed the missingness mechanism was completely at random. Race/ethnicity was missing for 3,883 (0.6%) observations over the study period and were assigned to the unknown category, although this percentage grew from 0.2% to 1.1% over the 3 time‐periods. Table 1 provides a summary of baseline demographic, clinical and hospital level variables. Majority of beneficiaries were in the age ranges of 81–85 (22%) and 86–90 (22%), and 56% (n = 394,024) were female. Majority of beneficiaries were White (n = 610,688, 87%) and 5% (n = 31,723) were Hispanic. Supporting Information Tables S4 shows the distribution of TBI ICD codes over the study period.
Table 1.
Cohort Demographic Characteristics.
| Age, n (%) | 2010–2014 | 2015–2019 | 2020–2022 | Total |
|---|---|---|---|---|
| 66–70 | 18,772 (7.7) | 25,984 (8.9) | 14,518 (8.7) | 59,274 (8.4) |
| 71–75 | 30,252 (12.3) | 39,384 (13.5) | 24,600 (14.7) | 94,236 (13.4) |
| 76–80 | 42,920 (17.5) | 51,919 (17.8) | 31,265 (18.7) | 126,104 (17.9) |
| 81–85 | 58,139 (23.7) | 62,619 (21.4) | 35,884 (21.5) | 156,642 (22.2) |
| 86–90 | 56,599 (23.1) | 63,168 (21.6) | 33,033 (19.8) | 152,800 (21.7) |
| 91+ | 38,474 (15.7) | 49,142 (16.8) | 27,737 (16.6) | 115,353 (16.4) |
| Total | 245,156 (100) | 292,216 (100) | 167,037 (100) | 704,409 (100) |
| Sex, n (%) | ||||
| Male | 100,853 (41.1) | 132,074 (45.2) | 77,458 (46.4) | 310,385 (44.1) |
| Female | 144,303 (58.9) | 160,142 (54.8) | 89,579 (53.6) | 394,024 (55.9) |
| Total | 245,156 (100) | 292,216 (100) | 16,7037 (100) | 704,409 (100) |
| Race RTI, n (%) | ||||
| White | 215,919 (88.1) | 251,493 (86.1) | 143,276 (85.8) | 610,688 (86.7) |
| Black | 9,974 (4.1) | 12,866 (4.4) | 6,608 (4) | 29,448 (4.2) |
| Asian/Pacific Islander | 6,143 (2.5) | 8,958 (3.1) | 5,495 (3.3) | 20,596 (2.9) |
| Hispanic | 10,537 (4.3) | 13,685 (4.7) | 7,501 (4.5) | 31,723 (4.5) |
| Native American | 950 (0.4) | 1,363 (0.5) | 780 (0.5) | 3,093 (0.4) |
| Other | 1,253 (0.5) | 2,182 (0.7) | 1,543 (0.9) | 4,978 (0.7) |
| Unknown | 380 (0.2) | 1,669 (0.6) | 1,834 (1.1) | 3,883 (0.6) |
| Total | 245,156 (100) | 292,216 (100) | 167,037 (100) | 704,409 (100) |
| Race, n (%) | ||||
| White | 215,919 (88.1) | 251,493 (86.1) | 143,276 (85.8) | 610,688 (86.7) |
| Black | 9,974 (4.1) | 12,866 (4.4) | 6,608 (4) | 29,448 (4.2) |
| Hispanic | 10,537 (4.3) | 13,685 (4.7) | 7,501 (4.5) | 31,723 (4.5) |
| Other/unknown | 8,726 (3.6) | 14,172 (4.8) | 9,652 (5.8) | 32,550 (4.6) |
| Total | 245,156 (100) | 292,216 (100) | 167,037 (100) | 704,409 (100) |
| Dual eligibility, n (%) | ||||
| Full dual | 33,147 (13.5) | 34,015 (11.6) | 17,060 (10.2) | 84,222 (12) |
| Total | 245,156 (100) | 292,216 (100) | 167,037 (100) | 704,409 (100) |
| Region, n (%) | ||||
| NorthEast | 53,456 (21.8) | 58,608 (20.1) | 31,569 (18.9) | 143,633 (20.4) |
| MidWest | 60,387 (24.6) | 66,486 (22.8) | 37,445 (22.4) | 164,318 (23.3) |
| West | 45,064 (18.4) | 59,582 (20.4) | 34,740 (20.8) | 139,386 (19.8) |
| South | 86,249 (35.2) | 107,540 (36.8) | 63,283 (37.9) | 257,072 (36.5) |
| Total | 245,156 (100) | 292,216 (100) | 167,037 (100) | 704,409 (100) |
| Frailty index, n (%) | ||||
| Robust ( < 0.15) | 33,258 (13.6) | 41,741 (14.3) | 22,326 (13.4) | 97,325 (13.8) |
| Pre‐frail (0.15–0.25) | 132,735 (54.1) | 157,772 (54) | 92,076 (55.1) | 382,583 (54.3) |
| Mildly frail (0.25–0.35) | 72,274 (29.5) | 85,244 (29.2) | 48,573 (29.1) | 206,091 (29.3) |
| Mod to severe frailty ( > 0.35) | 6,889 (2.8) | 7,459 (2.6) | 4,062 (2.4) | 18,410 (2.6) |
| ADRD (claims‐based definition), n (%) | ||||
| Claims‐based approach | 7,762 (3.2) | 16,481 (5.6) | 10,365 (6.2) | 34,608 (4.9) |
| Any dementia diagnosis | 28,082 (11.5) | 49,383 (16.9) | 29,501 (17.70 | 106,966 (15.2) |
| Recent SNF admission a , n (%) | 29,703 (12.1) | 34,767 (11.9) | 16,935 (10.1) | 81,405 (11.6) |
| Recent hospitalization a (%) | 87,324 (35.6) | 101,365 (34.7) | 52,771 (31.6) | 241,460 (34.3) |
| Length of stay (days), n (%) | ||||
| 1 | 45,050 (18.4) | 55,808 (19.1) | 33,562 (20.1) | 134,420 (19.1) |
| 2 | 38,689 (15.8) | 46,199 (15.8) | 27,831 (16.7) | 112,719 (16) |
| 3 | 45,732 (18.7) | 52,318 (17.9) | 23,282 (13.9) | 121,332 (17.2) |
| 4 | 29,361 (12) | 33,576 (11.5) | 17,015 (10.2) | 79,952 (11.4) |
| 5 to 7 | 44,546 (18.2) | 50,960 (17.4) | 28,815 (17.3) | 124,321 (17.6) |
| 8 to 14 | 26,676 (10.9) | 34,929 (12) | 22,310 (13.4) | 83,915 (11.9) |
| 15 to 29 | 10,975 (4.5) | 14,544 (5) | 10,602 (6.3) | 36,121 (5.1) |
| 30+ | 4,127 (1.7) | 3,882 (1.3) | 3,620 (2.2) | 11,629 (1.7) |
| Discharge destination, n (%) | ||||
| Home/self‐care | 75,813 (30.9) | 83,374 (28.5) | 47,833 (28.6) | 207,020 (29.4) |
| Short term gen. hospital | 4,807 (2) | 5,665 (1.9) | 3,480 (2.1) | 13,952 (2.0) |
| SNF | 69,551 (28.4) | 77,881 (26.7) | 34,947 (20.9) | 182,379 (25.9) |
| Intermed. care facility | 4,225 (1.7) | 3,995 (1.4) | 2,097 (1.3) | 10,317 (1.5) |
| Psychiatric hosp | 1,018 (0.4) | 853 (0.3) | 388 (0.2) | 2,259 (0.3) |
| Home health care org | 32,863 (13.4) | 42,743 (14.6) | 30,734 (18.4) | 106,340 (15.1) |
| Left against advice | 893 (0.4) | 1,290 (0.4) | 1,024 (0.6) | 3,207 (0.5) |
| Expired | 14,440 (5.9) | 20,466 (7) | 12,101 (7.2) | 47,007 (6.7) |
| Still patient | 1,125 (0.5) | 1,050 (0.4) | 1,457 (0.9) | 3,632 (0.5) |
| Hospice‐home | 2,464 (1) | 4,795 (1.6) | 4,086 (2.4) | 11,345 (1.6) |
| Hospice‐facility | 5,292 (2.2) | 9,466 (3.2) | 6,285 (3.8) | 21,043 (3.0) |
| Within institution | 2,171 (0.9) | 1,864 (0.6) | 952 (0.6) | 4,987 (0.7) |
| Inpat rehab facility | 26,418 (10.8) | 33,876 (11.6) | 19,375 (11.6) | 79,669 (11.3) |
| Long term care | 2,995 (1.2) | 3,291 (1.1) | 1,536 (0.9) | 7,822 (1.1) |
| Medicaid nursing facility | 354 (0.1) | 420 (0.1) | 184 (0.1) | 958 (0.1) |
| Other with acute care re‐admit plan | 127 (0.1) | 506 (0.2) | 250 (0.1) | 883 (0.1) |
| Other type institution | 600 (0.2) | 681 (0.2) | 308 (0.2) | 1589 (0.2) |
| Encounter types, n (%) | ||||
| Emergency | 128,148 (52.3) | 174,718 (59.8) | 90,370 (54.1) | 393,236 (55.8) |
| Urgent | 24,533 (10) | 28,722 (9.8) | 13,553 (8.1) | 66,808 (9.5) |
| Elective | 11,347 (4.6) | 18,247 (6.2) | 11,914 (7.1) | 41,508 (5.9) |
| Trauma center | 31,585 (12.9) | 69,809 (23.9) | 50,796 (30.4) | 152,190 (21.6) |
| Unknown/missing | 1,140 (0.5) | 720 (0.2) | 404 (0.2) | 2,264 (0.3) |
| Mechanism of injury, n (%) | ||||
| Fall related | 6,271 (2.6) | 6,936 (2.4) | 3,065 (1.8) | 16,272 (2.3) |
| Motor vehicle accident | 584 (0.2) | 252 (0.1) | 84 (0.1) | 920 (0.1) |
Note: Table shows breakdown of our cohort's baseline demographic, clinical, and hospital level characteristics.
Abbreviations: ADRD, Alzheimer's disease and related dementia; Gen., general; Inpat, inpatient; Mod., moderate; SNF, skilled nursing facility; TBI, traumatic brain injury.
In year prior to TBI.
Most beneficiaries had a baseline frailty‐index of either pre‐frail (n = 382,583, 54%) or mildly frail (n = 206,091, 29%). At baseline, 5% (n = 34,608) beneficiaries met the criteria for claims‐based definition of dementia, and 11% (n = 81,405) had a skilled nursing facility admission in the year prior to the TBI. The most common comorbid conditions (shown in Table 2) included hypertension, diabetes mellitus, coronary artery disease, rheumatoid/osteoarthritis and prior cerebrovascular accident.
Table 2.
Baseline Comorbidity Incidence.
| Comorbidity | 2010–2014 | 2015–2019 | 2020–2022 |
|---|---|---|---|
| Hypertension | 216,288 (88.2) | 257,333 (88.1) | 146,549 (87.7) |
| Diabetes mellitus | 76,630 (31.3) | 94631 (32.4) | 53,935 (32.3) |
| Coronary artery disease | 96,422 (39.3) | 113,030 (38.7) | 63,110 (37.8) |
| Epilepsy | 11,396 (4.6) | 16,871 (5.8) | 9,656 (5.8) |
| Rheumatoid arthritis/osteoarthritis | 95,973 (39.1) | 113,815 (38.9) | 63,984 (38.3) |
| Systemic lupus erythematosus | 913 (0.4) | 1,056 (0.4) | 616 (0.4) |
| Osteoarthritis (without fracture) | − (0) | 32,398 (11.1) | 22,106 (13.2) |
| Peripheral vascular disease | 33,210 (13.5) | 43,057 (14.7) | 25,427 (15.2) |
| Prior cerebral vascular accident | 88,791 (36.2) | 103,803 (35.5) | 57,207 (34.2) |
| Cancer | 40,107 (16.4) | 51,467 (17.6) | 29,701 (17.8) |
| Renal: chronic kidney disease | 54,334 (22.2) | 77,229 (26.4) | 47,948 (28.7) |
| Renal: acute kidney injury | 34,272 (14) | 56,123 (19.2) | 36,337 (21.8) |
| Cataracts | 22,196 (9.1) | 22,378 (7.7) | 12,063 (7.2) |
| Age‐related macular degeneration | 7,098 (2.9) | 8,750 (3.0) | 4,980 (3.0) |
Note: The table describes the incidence of comorbidities in our sample at baseline in the grouped timeframes. Cancer includes diagnoses Breast, Colorectal, Endometrial, Lung, Prostate, and Urologic.
With regards to the TBI hospitalizations, majority were emergency (n = 393,236, 56%) or trauma center encounters (n = 152,190, 22%). Approximately 2% (n = 16,272) of injuries were fall related. The most frequent discharge dispositions were to home (29% to home/self‐care, 15% to home health) or skilled nursing facilities (26%), with a lower proportion of 11% being discharged to inpatient rehabilitation. Supporting Information Table S5 provides additional hospital level details. Majority of the hospitals were urban (94%, = 662,110), non‐profit (71%, n = 499,001) and with bed side of 200–399 beds (31%, n = 216,142) or 400–599 (23%, n = 162,055).
3.2. Incidence Rate of TBI Hospitalizations
Table 3 shows the incidence rate for TBI hospitalizations adjusted for age and sex. Incidence of TBI hospitalizations increased with age, and were consistently higher in females, particularly in the oldest age groups of 86‐90 (7.15 [CI 7.06–7.25] in females vs. 5.49 [CI 5.41–5.56] in males) and > 91 (8.39 [CI 8.27–8.51] in females vs. 6.97 [CI 6.8–7.07] in males). Supporting Information Table S6 shows the incidence rate for TBI hospitalizations adjusted for race. Blacks and Hispanics had lower TBI hospitalizations compared with Whites. Supporting Information Table S7 shows the incidence of TBI hospitalizations over time. There was a modest reduction in TBI hospitalizations between 2015 and 19 and 2020–2022. Figure 2 shows the regional distribution of TBI hospitalizations. Supporting Information Table S8 shows the full result of state level random effects. Higher incidence of TBI hospitalizations were seen in the South, Northeast and West, with lower rates in the Midwest. The highest rates of TBI hospitalizations were seen in Pennsylvania (1.26), Florida (1.23), and also in the District of Columbia (1.44). Supporting Information Figure S1 shows all level 1 trauma centers overlaid on the regional distribution of TBI hospitalizations.
Table 3.
Fixed Effect Estimates for Age‐Sex Combinations.
| Female | Estimate | CI (95%) |
|---|---|---|
| 66–70 | Ref | |
| 71–75 | 1.58 | 1.55–1.60 |
| 76–80 | 2.89 | 2.85–2.93 |
| 81–85 | 4.94 | 4.87–5.00 |
| 86–90 | 7.15 | 7.05–7.25 |
| 91 + | 8.39 | 8.27–8.51 |
| Male | ||
| 66–70 | Ref | |
| 71–75 | 1.35 | 1.33–1.42 |
| 76–80 | 2.27 | 2.24–2.30 |
| 81–85 | 3.69 | 3.64–3.74 |
| 86–90 | 5.49 | 5.41–5.56 |
| 91 + | 6.97 | 6.87–7.07 |
Note: The table shows the incidence rate for TBI hospitalizations adjusted for age and sex groups.
Abbreviations: CI, confidence interval; Ref, reference.
Figure 2.
State Level Random Effects. U.S. map of states showing regional distribution of TBI hospitalizations based on their incident rate ratio. IRR, incidence rate ratio; TBI, Traumatic Brain Injury.
4. Discussion
In this cohort study of Medicare beneficiaries, we found that overall incidence of TBI hospitalizations is highest in the oldest older adults, and higher in females. There is regional variation in the incidence of TBI hospitalizations, with higher incidence in the south and northeast. Hospital level characteristics remained relatively consistent with majority being in urban settings (94%) over the study period. These demographic and regional differences highlight the need for larger public health studies investigating the causes for these differences and identifying opportunities to improve outcomes.
The general population is aging with a shift towards an increasing number of the oldest‐old patients in developed countries [27, 28]. This demographic change has implications for the epidemiology of TBI, particularly in light of our data. The leading cause of TBI in this population is falls [29]. Up to a quarter of patients aged 65 and older that present with falls are hospitalized, and post hospitalization are at higher risk of additional falls both during and after the hospitalization [9, 30]. In the U.S. age‐adjusted incidence rate of hospitalization from fall‐related TBI increases with age and adults aged 75 and older have three times as high a rate compared to other age groups [10]. Interestingly, we found that patients with the highest proportion of TBIs were among patients that were either pre‐frail or frail. Robust patients and those with severe frailty had lower incidence of TBI hospitalizations. This again, may be explained by the higher risk of falls among patients that are pre‐frail or frail. Those with severe frailty, may be more bed bound or dependent on others for activities of daily living and therefore less predisposed to falls [1, 31]. Future directions and public health initiatives that include a focus on fall prevention in older adults are an important next step to minimize TBI and TBI related admissions for this population.
We found that the rate of adjusted TBI hospitalizations were consistently higher in females, particularly in the oldest age groups of 86–90. Prior studies have also demonstrated that while males have a higher incidence of TBI in older adults, in the older trauma population, females are most likely to be hospitalized [9]. While there are variable reports of differences in TBI related outcomes between males and females, most larger studies report better outcomes in females [32, 33]. Further studies are indicated to determine whether the differences in TBI incidence and hospitalizations are the results of biological effects of sex versus other factors such as differences in comorbid conditions or extracerebral injuries.
When examining regional differences, we found a higher incidence of TBI hospitalizations in the south and northeast. While there is no established TBI belt in the United States, prior work has shown variable distribution of TBI related hospitalizations and death. A study of the CDC demonstrated the highest TBI‐related hospitalization rate (98.9) in Pennsylvania and the lowest in Ohio (55.1) [34]. Our study has consistent findings with higher rate of hospitalizations in Pennsylvania, and also in Florida. One possible explanation for the high rates of TBI hospitalizations in states such as Pennsylvania and Florida is their older population. These states have some of the largest proportions, and total numbers of, residents aged 65 years or older in the United States [34, 35, 36, 37]. These states are also among those with the highest total Medicare enrollment and the largest number of Medicare beneficiaries [38]. Future public health studies should be aimed at determining what the region variation in TBI incidence is, and regional access to trauma care. Akin to the stroke belt, identifying a TBI belt can enable targeted public and population health policies to mitigate risk for TBI, and optimize access to care and outcomes.
When examining discharge patterns among Medicare beneficiaries with TBI, we observed that patients were more often discharged to skilled nursing facilities (SNFs; 25.9%) than to inpatient rehabilitation facilities (IRFs; 11.3%) over the study period. Across year groupings, SNF discharges declined from 28.4% in 2010%–2014% to 20.9% in 2020–2022, while IRF discharges increased modestly from 10.8% to 11.6%. This pattern is notable, as IRFs have been associated with substantial functional gains, higher rates of home discharge, and more healthy days at home among older Medicare beneficiaries with TBI [39, 40, 41]. However, discharge destination is shaped by multiple patient‐level factors, including comorbidity burden, pre‐injury functional status, and functional ability at hospital or IRF discharge. Federal policies, including the Improving Medicare Post‑Acute Care Transformation (IMPACT) Act of 2014 and the subsequent CMS discharge planning rule, now require hospitals to use standardized post‑acute assessment and quality data and to engage patients in shared decision‑making about post‑acute care, with the aim of aligning setting selection, such as SNF versus IRF, with beneficiaries' goals, treatment preferences, and clinical needs [41, 42, 43, 44, 45, 46, 47] Additional research is needed to determine the extent to which these federal policies are implemented in discharge planning for Medicare beneficiaries with TBI.
With regards to comorbid conditions, hypertension, coronary artery disease, diabetes, prior stroke and arthritis were the most frequently encountered. These chronic conditions increase frailty and increase risk for falls [48]. Interestingly, AD/ADRD diagnosis remained steady over the years of the study with only a modest increase. Therefore, future work aimed at mitigation of falls and TBI in the older population, also needs to take into consideration the common comorbid conditions and their impact on frailty and fall risk. Reflecting the multifactorial nature of this risk, fall prevention after traumatic brain injury has been proposed to require a comprehensive, multidisciplinary, and patient‐centered approach [42]. Interventions should be prioritized through shared decision‐making and tailored to the patient's needs and capacity to engage in them. Key strategies include reviewing medications to reduce polypharmacy and the use of medications that increase fall risk, implement structured exercise programs that emphasize balance and mobility, and evaluation of the home environment to address modifiable hazards. Additional components include vision assessment and correction, routine cognitive screening, and targeted cardiovascular evaluation when syncope or unexplained falls are suspected. Together, these measures address the multifactorial contributors to fall risk in this population [42].
5. Limitations
First, our reliance on a retrospective cohort of FFS Medicare claims from 2010 to 2022 limits the generalizability of these findings to the broader older adult population. We did not capture uninsured individuals ‐ who may bear a greater burden of TBI and whose geographic distribution could differ ‐ and we excluded those with private insurance, who may access care more readily (albeit often for less severe injuries) [49, 50, 51]. Although FFS beneficiaries still represent a substantial share of U.S. adults aged ≥ 65, our incidence estimates may under‐ or overstate true rates when compared to a fully representative sample of all older adults. We also had lower representation of rural hospitals limiting the generalizability.
Second, by design we excluded beneficiaries enrolled in MA plans, who grew from under 30% to over 40% of Medicare enrollees during our study window [52]. MA members may experience different patterns of care use—potentially better preventive services and care coordination—which could influence both fall risk and hospitalization thresholds. We elected this exclusion to ensure complete encounter capture and consistent coding practices across settings, aligning with standard claims‐based epidemiology methods; however, it also restricts the applicability of our results to fee‐for‐service populations only.
Third, our focus on community‐dwelling beneficiaries in the 12 months preceding their index TBI hospitalization further narrows our sample to relatively healthier older adults. Those discharged from institutional settings outside this window ‐ such as long‐term care facilities ‐ were not included, yet they may face high fall and TBI risk. Conversely, severely frail or bed‐bound individuals likely to remain institutionalized were also omitted, meaning we did not assess in‐facility TBIs, which represent a nontrivial share of injuries in this age group [53, 54].
Fourth, our identification of TBI hospitalizations and comorbid conditions depends entirely on administrative ICD‐9 and ICD‐10 codes, which can be prone to misclassification and temporal shifts in coding fidelity. Although we employed published code sets, it remains possible that coding changes (e.g., the ICD‐9 to ICD‐10 transition) and variable documentation practices ‐ especially during the COVID‐19 pandemic ‐ introduced information bias into our incidence and comorbidity estimates [12, 13, 14, 15, 16, 17].
Finally, our analysis extends only through 2022 and may not reflect more recent developments in health care delivery, Medicare policy, or regional resource allocation that could affect TBI risk and hospitalization patterns. As quality‐improvement initiatives, equity‐focused funding, and facility capacities evolve, ongoing surveillance and updated claims‐based studies will be essential to understand current trends and to inform timely public health and policy interventions.
6. Conclusion
Over the years, TBI‐related hospitalizations have consistently been higher in the oldest‐old adults and in females. There is regional variation, with the highest TBI‐related hospitalizations in the northeast and the South. Higher frailty likely predisposes to TBI. Future studies aimed at examining more recent trends among Medicare beneficiaries and identifying the interactions between demographic factors and comorbid conditions are indicated to develop targeted public health interventions to mitigate TBI in older adults.
Author Contributions
Lidia MVR Moura: conceptualization, methodology, writing – original draft, writing – review and editing, supervision, funding acquisition, visualization. Lawrence Zaborski: data curation, formal analysis, software. Aidan McDonald Wojciechowski: writing – review and editing, writing – original draft. John Hsu: conceptualization, methodology. David C. Grabowski: conceptualization, methodology, review for critical content, Sharon‐Lise T. Normand: conceptualization, methodology, data curation, formal analysis, review for critical content. Sahar F. Zafar: conceptualization, methodology, supervision, writing – original draft, writing – review and editing, funding acquisition.
Conflicts of Interest
L.M.V.R.M. receives support from the NIH, CDC, and Epilepsy Foundation of America, and reports no conflict of interest. SFZ received speaker honoraria from Marinus, royalties from Springer publication, and from Walters Kluwer and reports no conflict of interest. DCG received personal fees outside the submitted work from the Analysis Group, EntityRisk, GRAIL LLC, and the University of Southern California and reports no conflict of interest. S.‐L.N. receives funding from NIH and reports no conflict of interest. J.H. receives personal fees outside of the area of the submitted work from Brandeis University and Cambridge Health Alliance and reports no conflicts of interest. A.M.W. reports no conflicts of interest. L.Z. reports no conflicts of interest.
Transparency Statement
The lead author Sahar F. Zafar affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Supporting information
Supplement Material 3.13.26.
Acknowledgments
Dr. Sahar Zafar affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. All authors have read and approved the final version of the manuscript. Dr. Sharon‐Lise Normand has full access to all data in this study. Drs. Zafar, Moura and Normand take full responsibility for the integrity and accuracy of the data analysis. This study was funded by the NIH grant R01AG082693. The NIH grant describes a predetermined plan for this work. Other financial relationships were not involved in the study design.
Data Availability Statement
The data that support the findings of this study are available from Centers for Medicare & Medicaid Services. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from https://www.cms.gov/data-research/cms-data/data-available-researchers with the permission of Centers for Medicare & Medicaid Services.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplement Material 3.13.26.
Data Availability Statement
The data that support the findings of this study are available from Centers for Medicare & Medicaid Services. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from https://www.cms.gov/data-research/cms-data/data-available-researchers with the permission of Centers for Medicare & Medicaid Services.