Abstract
Introduction:
COVID-19 survivors are at greater risk for new medical conditions. Among older adults, where multimorbidity and functional impairment are common, frailty measurement provides a tool for understanding how infection impacts future health beyond a one-disease-at-a-time approach. We investigated whether COVID-19 was associated with change in frailty among older Veterans.
Methods:
Data were from the Veterans Affairs (VA) COVID-19 Observational Research Collaboratory, which extracted VA medical record data. We included Veterans who had COVID-19 from March 1, 2020 to April 30, 2021 and matched, uninfected controls. We excluded those <50 years at index or did not survive 12 months after. Frailty was assessed at the index date and 12 months using the VA Frailty Index (VA-FI).
We assessed the number of new VA-FI deficits over 12 months. Analysis was by negative binomial regression adjusted for age, gender, race, ethnicity, and BMI. Coefficients are given as the ratio of the mean number of new deficits in COVID-19 cases vs. controls during follow-up.
Results:
We identified 91,338 COVID-19 infected Veterans and an equal number of matched controls. Median (IQR) age was 68.9 years (60.3–74.2), 5% were female, 71% were White, and baseline VA-FI was 0.16 (0.10, 0.26). Median (IQR) number of new deficits at one year was 1 (0–2) for infected and 0 (0–1) for uninfected controls. After adjustment, those with COVID-19 accrued 1.54 (95% CI 1.52–1.56) times more deficits than those who did not. The five most common new deficits were fatigue (9.7%), anemia (6.8%), muscle atrophy (6.5%), gait abnormality (6.2%), and arthritis (5.8%).
Discussion:
We found a greater increase in frailty among older Veterans with COVID-19 compared to matched uninfected controls, suggesting that COVID-19 infection has long-term implications for vulnerability and disability among older adults. Functional impairments such as fatigue, impaired mobility and joint pain may may warrant specific attention in this population.
Introduction
COVID-19 has infected more than half of adults in the United States, with many of those reporting persistent symptoms or new health problems following the resolution of their acute infection.1–3 However, longer-term health implications of this are less clear.4,5 Older adults ages 65 and older are a key group with regard to the burden of COVID-19. Despite comprising only 15% of the US population, they make up over 45% of all hospital and ICU admissions and 75% of all deaths due to COVID-19.6 For older adults, the overall burden of comorbid conditions, functional impairments, cognitive and mental health changes after COVID-19 infection may matter more than any one specific condition both as a predictor of future health outcomes and as an outcome in its own right.7,8
Frailty is defined as the impaired ability to recover from a physiologic stressor, such as an illness or invasive procedure.9 Importantly, frailty provides an integrative framework for studying multiple conditions in older adults.9,10 Different measures of frailty have been associated with a wide array of poor health outcomes for older adults, complications from surgery, all-cause mortality, and mortality following COVID-19 infection.11–20 Further, increasing levels of frailty over time has been associated with increased mortality.8,21 Studies of frailty and COVID-19 infection have either focused on smaller cohorts, hospitalized patients only or those with non-severe infections or who are robust at baseline.22,23 The implications of all levels COVID-19 infection severity and in people of all baseline frailty levels on subsequent frailty and frailty-associated deficits, however, are less clear and may elucidate important longer-term health outcomes.
In this study we assessed the change in frailty in a large cohort of older adults following COVID-19 infection, comparing individuals with infection versus matched controls in order to better understand the sequelae of COVID-19 infection. The goal of this analysis was to better understand changes in frailty as well as which new frailty deficits occurred among older adults infected with COVID-19.
Methods
Cohort
Details regarding this matched cohort have been previously published.24 Briefly, Veterans with documented COVID-19 positivity from March 1, 2020 through April 30, 2021 both from within and outside the VA testing laboratories were included as COVID-19 positive cases. Only those assigned to a VA primary care team or having had at least one VA primary care clinic visit within two years before the infection were included in the cohort. Other exclusion criteria were implausible age, height and weight; residing outside the United States, and death before index date. Cohorts were identified sequentially on a monthly basis, with assignment to a particular month for cases based on the date of documented positivity. Veterans without documented positivity prior to or during the month who met the same inclusion criteria were considered uninfected potential comparators for that month. Fourteen separate monthly patient cohorts were developed— one for each month from March 2020-April 2021 —for the purpose of defining index dates and matching covariates. To minimize immortal time bias, the index date was defined as the date of the earliest positive test for SARS-CoV-2- infected Veterans and as the 1st day of the relevant month for uninfected Veterans. Each COVID-19 positive case was matched to a single uninfected control, first by exact matching based on index month, sex, immunosuppressive medication use (binary), state of residence, and COVID-19 vaccination status. Among exact matched persons, we then executed a propensity score matching step, that included a total of 37 covariates that were strongly associated with infection or adverse outcomes in the propensity score model to identify the single best-matching uninfected person for each infected case. We outline this in the Supplementary Methods. Cases and controls were matched 1:1 rather than 1:5 based on the best propensity score match. All variables were matched using this process resulting in non-exact age matching. Cases and controls were deleted if they had a documented infection before the window of interest or had non-unique identifiers. These exclusions were the same as used in a previous study.25 This matching resulted in 198,938 matched infected-uninfected pairs.
To focus on older adults, we excluded those cased-control pairs where either were younger than 50 years of age (88,944 pairs) and those who died within a year of infection and thus lacked 12 months of follow up data (18,656 pairs) (Figure 1). The final cohort included 91,338 matched pairs (n=182,676 total).
Figure 1.
Flow diagram depicting the cohort composition after exclusion criteria were applied.
Frailty
Frailty was assessed using the Veterans Affairs Frailty Index (VA-FI),19,20 which uses diagnosis and procedure codes from the VA electronic medical record to assess 31 deficits spanning physical health, functional impairment, cognitive and mental health limitations, and undernutrition. The VA-FI has been associated with an array of poor health outcomes, including all-cause and cardiovascular mortality and incident cardiovascular events.19,26 The VA-FI is typically represented as a ratio of the number of deficits identified to the total deficits assessed, with a range of 0 to 1. In this study, we assessed baseline frailty using data from the 36 months preceding the index date. To assess new deficits, we then evaluated the number of new VA-FI related deficits accumulated within the 12 months following the index date both to capture longer-term sequelae of infection and reduce ascertainment bias by providing time for routine care to occur. We outline this approach in Supplementary Figure 1. We did not have data from Medicare to assess non-VA care.
Covariates
Age was categorized into ten-year age groups from ages 50–59, 60–69, 70–79 and ≥80. BMI was divided into standard Centers for Disease Control categories: ≤18.5 kg/m2, >18.5 to 25, >25 to 30, >30 to 35, >35 to 40, and >40.27 Smoking status and 12-month hospitalization and mortality VA Care Assessment Need (CAN) score were also assessed to describe the cohort.28 COVID-19 severity was calculated using a previously-validated measure based on VA data,29 that classifies severity among survivors of infection as mild (not hospitalized or hospitalized for less than 24 hours), moderate (hospitalized for more than 24 hours with or without low-flow supplemental oxygen), and severe (hospitalized for more than 24 hours with non-invasive positive pressure ventilation, intubation, mechanical ventilation, vasopressors, extracorporeal membrane oxygenation, or dialysis).
Analyses
We compared patient characteristics between infected and uninfected Veterans.
We first evaluated the association of COVID-19 infection with the number of new VA-FI deficits accumulated over 12 months using negative binomial regression. In the multivariable negative binomial regression, we also controlled for age, female sex, race, ethnicity, body mass index (BMI), and baseline VA-FI deficit count. In a second analysis we assessed whether or not COVID-19 severity was associated with greater deficit accumulation among infected individuals using a previously-validated measure of COVID-19 severity in VA data adjusting for the same covariates as in our primary model. Finally, to evaluate whether most new deficits arose during the period of acute infection, we assessed proportion of new deficits that were identified within 30 days following index date versus days 31–365.
To assess which deficits changed among individuals with COVID-19, we took two approaches. First, we measured the most common new deficits among COVID-19 infected individuals. Second, we looked at which new deficits had the largest difference in incidence between infected and uninfected individuals.
As a sensitivity analysis to ensure inclusion of individuals with greater degrees of frailty at baseline, who may not survive 12 months, we repeated the above steps using individuals who had survived at least thirty days past their index date regardless of total duration of follow-up. For those who died prior to twelve months, we considered the number of deficits accumulated up to their date of death.
Results
Characteristics of study population
The COVID-19+ cohort and matched uninfected cohort were similar in demographic and baseline health characteristics (Table 1). The overall, median (IQR) age was 69.5 (60.7–74.6) years, 5.4% of subjects were female, 71% were White, and 6.5% were Hispanic. Infected and uninfected individuals had similar median (IQR) VA-FI of 0.16 (0.10 – 0.26) at index date for each group. They were also similar with respect to BMI, history of smoking, and CAN score category. Of the controls, 4,533 (5%) developed COVID-19 over the 12-month follow-up period and their follow-up was not censored. Standardized mean differences between the COVID-19+ and uninfected cohorts were all less than 0.1 other than age, which was 0.106.
Table 1.
Baseline cohort demographics and health characteristics.
| Overall N = 182,676 | COVID-19 Infection N = 91,338 | Matched Uninfected Comparators N = 91,338 | SMD | |
|---|---|---|---|---|
| Index Date VA-FI - Median (IQR) | 0.16 (0.10, 0.26) | 0.16 (0.10, 0.26) | 0.16 (0.10, 0.26) | 0.01 |
| Index Date VA-FI Severity - N (%) | 0.031 | |||
| Robust (0 – <0.1) | 51,843 (28%) | 26,041 (29%) | 25,802 (28%) | |
| Pre-Frail (0.1 − <0.2) | 62,032 (34%) | 31,035 (34%) | 30,997 (34%) | |
| Mildly Frail (0.2 − <0.3) | 37,522 (21%) | 18,474 (20%) | 19,048 (21%) | |
| Moderately Frail (0.3 − <0.4) | 18,238 (10%) | 8,960 (9.8%) | 9,278 (10%) | |
| Severely Frail (>= 0.4) | 13,041 (7.1%) | 6,828 (7.5%) | 6,213 (6.8%) | |
| Age - Median (IQR) | 68.95 (60.31, 74.20) | 68.20 (59.61, 73.94) | 69.57 (61.07, 74.48) | −0.106 |
| Age Category - N (%) | 0.106 | |||
| 50 – 59 | 38,961 (21%) | 21,177 (23%) | 17,784 (19%) | |
| 60 – 64 | 26,288 (14%) | 13,590 (15%) | 12,698 (14%) | |
| 65 – 74 | 69,804 (38%) | 34,064 (37%) | 35,740 (39%) | |
| 75 – 110 | 47,623 (26%) | 22,507 (25%) | 25,116 (27%) | |
| Female - N (%) | 9,818 (5.4%) | 4,911 (5.4%) | 4,907 (5.4%) | 0.000 |
| Race - N (%) | 0.027 | |||
| White | 129,601 (71%) | 64,638 (71%) | 64,963 (71%) | |
| American Indian/Alaska Native | 1,482 (0.8%) | 787 (0.9%) | 695 (0.8%) | |
| Asian | 1,164 (0.6%) | 540 (0.6%) | 624 (0.7%) | |
| Black | 42,527 (23%) | 21,228 (23%) | 21,299 (23%) | |
| Native Hawaiian/PI | 1,459 (0.8%) | 742 (0.8%) | 717 (0.8%) | |
| More than one race | 1,452 (0.8%) | 752 (0.8%) | 700 (0.8%) | |
| Missing | 4,991 (2.7%) | 2,651 (2.9%) | 2,340 (2.6%) | |
| Ethnicity - N (%) | 0.006 | |||
| Not Hispanic | 165,189 (90%) | 82,612 (90%) | 82,577 (90%) | |
| Hispanic | 11,936 (6.5%) | 5,994 (6.6%) | 5,942 (6.5%) | |
| Missing | 5,551 (3.0%) | 2,732 (3.0%) | 2,819 (3.1%) | |
| Rural - N (%) | 60,717 (33%) | 30,418 (33%) | 30,299 (33%) | 0.003 |
| BMI - Median (IQR) | 30.45 (26.88, 34.72) | 30.59 (27.09, 34.72) | 30.32 (26.69, 34.72) | 0.019 |
| Smoking Status - N (%) | ||||
| Never | 67,488 (37%) | 33,739 (37%) | 33,749 (37%) | 0.021 |
| Current | 22,156 (12%) | 10,932 (12%) | 11,224 (12%) | |
| Former | 85,091 (47%) | 42,518 (47%) | 42,573 (47%) | |
| Missing | 7,941 (4.3%) | 4,149 (4.5%) | 3,792 (4.2%) | |
| Elixhauser Index - Median (IQR) | 0.00 (0.00, 5.00) | 0.00 (0.00, 5.00) | 0.00 (0.00, 5.00) | -0.041 |
| CAN Score – Median (IQR) | 65.00 (40.00, 85.00) | 65.00 (40.00, 85.00) | 70.00 (45.00, 85.00) | −0.035 |
| COVID-19 Severity | ||||
| Mild | -- | 75,326 (85%) | -- | |
| Moderate | -- | 10,072 (11%) | -- | |
| Severe | -- | 3,009 (3.4%) | -- | |
| Missing | -- | 2,931 (3.2%) | -- |
Association between COVID-19 infection and new deficits
At 12 months of follow-up, the median (IQR) number of new deficits was 1 (0–2) among infected individuals and 0 (0–1) among uninfected individuals.. The mean number of new deficits increased by COVID-19 severity, 0.82 (0.81–0.83) for uninfected, 1.00 (0.99–1.01) for those with mild infection, 2.50 (2.46–2.55) for those with moderate infection and 3.51 (3.42–3.61) for those with severe infection. At every level of baseline frailty, COVID-19+ individuals accumulated a greater number of deficits than controls (Supplementary Table 1). Within the moderate and severe COVID-19 categories, those with moderate and severe baseline frailty levels accumulated fewer deficits on average than those with lower baseline levels of frailty (Figure 2, Supplementary Table 1). In the mild COVID-19 severity category, only those with severe frailty at baseline showed an attenuation in frailty deficit accumulation.
Figure 2.
Mean number of new frailty-related deficits at 12 months following COVID-19 infection by disease severity and baseline frailty. Horizontal black bars indicate mean increase in deficits at each level of COVID-19 severity.
Multivariable negative binomial regression estimated that those with COVID-19 infection had 1.54 (95% CI 1.52–1.56) times more new frailty related deficits than their matched uninfected comparators (Table 2). Additionally, among those with COVID-19, we found that higher COVID-19 severity was associated with increased risk of new frailty deficits with the following mean ratios relative to mild infection: moderate infection 2.34 (95% CI 2.29–2.39) and severe infection 3.32 (3.20–3.44) (Table 3). Results were similar in sensitivity analysis of individuals who had survived at least 30 days following index date but may have died before 1 year (Supplementary Tables 2 and 3)
Table 2.
Negative binomial regression of number of new frailty-related deficits at 12 months following COVID-19 test.
| Variable | Mean Ratio (95% CI) | p-value |
|---|---|---|
| COVID-19 Infection (vs. no infection) | 1.54 (1.52 – 1.56) | < 0.0001 |
| Baseline VA-FI (deficit count) | 1.04 (1.04 – 1.04) | < 0.0001 |
| Female | 1.01 (0.98 – 1.04) | 0.688 |
| Age Group | ||
| Age 50–59 | 1 | Ref |
| Age 60–69 | 1.20 (1.18 – 1.23) | < 0.0001 |
| Age 70–79 | 1.35 (1.32 – 1.37) | < 0.0001 |
| Age 80+ | 1.58 (1.54 – 1.62) | < 0.0001 |
| Race | ||
| White | 1 | Ref |
| Black | 1.08 (1.07 – 1.10) | < 0.0001 |
| American Indian/ Asian/Native Hawaiian | 1.04 (0.99–1.08) | 0.127 |
| Missing/Multiple Races | 0.99 (0.96–1.03) | 0.763 |
| Hispanic | 1.02 (0.99 – 1.05) | 0.140 |
| BMI | ||
| <18.5 | 1.15 (1.06 – 1.25) | 0.001 |
| 18.5 – 25 | 1 | Ref |
| >25 – 30 | 0.93 (0.91 – 0.95) | < 0.0001 |
| >30 – 35 | 0.97 (0.95 – 1.00) | 0.018 |
| >35 – 40 | 1.02 (1.00 – 1.05) | 0.091 |
| >40 | 1.11 (1.08 – 1.14) | < 0.0001 |
Table 3.
Negative binomial regression evaluating the impact of COVID-19 severity on the number of new deficits at 12 months in Veterans with positive COVID-19 test. (N = 91,338)
| Variable | Mean Ratio (95% CI) | p-value |
|---|---|---|
| COVID-19 Severity | ||
| Mild | 1 | Ref |
| Moderate | 2.34 (2.29–2.39) | < 0.001 |
| Severe | 3.32 (3.2–3.44) | < 0.001 |
| VA-FI deficits at baseline | 1.01 (1.01–1.01) | < 0.001 |
| Female | 0.98 (0.95–1.02) | 0.317 |
| Age Group | ||
| Age 50–59 | 1 | Ref |
| Age 60–69 | 1.19 (1.16–1.22) | < 0.001 |
| Age 70–79 | 1.35 (1.32–1.38) | < 0.001 |
| Age 80+ | 1.6 (1.55–1.65) | < 0.001 |
| Race | ||
| White | 1 | Ref |
| Black | 1.04 (1.02–1.07) | < 0.001 |
| American Indian/ Asian/Native Hawaiian | 1.01 (0.95–1.06) | 0.781 |
| Missing/Multiple Races | 1.02 (0.97–1.07) | 0.443 |
| Hispanic | 0.99 (0.96–1.03) | 0.663 |
| BMI | ||
| <18.5 | 1.05 (0.95–1.16) | 0.371 |
| 18.5 – 25 | 1 | Ref |
| >25 – 30 | 0.95 (0.93–0.98) | 0.0002 |
| >30 – 35 | 0.99 (0.96–1.01) | 0.342 |
| >35 – 40 | 1.03 (1–1.06) | 0.059 |
| >40 | 1.14 (1.1–1.18) | < 0.001 |
Distribution of frailty-related deficits
A total of 114,190 new deficits arose among infected individuals. Of these, 33,926 (29.7%) were identified in the 30 days following the index date, while the remaining 80,264 (70.3%) were identified between days 31 and 365 (Supplementary Table 4). Table 4 shows both the ten most common new deficits among infected individuals and the ten deficits with the biggest difference in new onset among infected versus uninfected controls. Four items that appear on both lists relate primarily to function: fatigue, muscle atrophy and cachexia, gait abnormality, and chronic pain. Similarly, two mental/cognitive health conditions appear on both lists: anxiety and dementia.
Table 4.
Top ten new deficits by A. incidence among COVID-19 infected individuals and by B. incidence difference between infected individuals and uninfected controls.
| A. Incidence, New VA-FI Deficits Among COVID-19 Infected | B. Incidence Difference, New Deficits, COVID-19 Infected vs Uninfected | ||
|---|---|---|---|
| New Deficit | Incidence | New Deficit | Incidence Difference |
| Fatigue | 8,818 (9.7%) | Fatigue | 5.7% (5.4–5.9) |
| Anemia | 6,174 (6.8%) | Muscle Atrophy & Cachexia | 3.7% (3.5–3.9) |
| Muscle Atrophy & Cachexia | 5,899 (6.5%) | Lung Disease | 3.1% (2.9–3.2) |
| Gait Abnormality | 5,689 (6.2%) | Anemia | 2.3% (2.1–2.5) |
| Arthritis | 5,285 (5.8%) | Gait Abnormality | 2.0% (1.8–2.2) |
| Lung Disease | 5,193 (5.7%) | Dementia | 1.9% (1.7–2.1) |
| Chronic Pain | 4,777 (5.2%) | Chronic Pain | 1.6% (1.4–1.8) |
| Durable Medical Equipment | 4,711 (5.2%) | Anxiety | 1.6% (1.5–1.8) |
| Dementia | 4,444 (4.9%) | Peripheral Vascular Disease | 1.6% (1.4–1.7) |
| Anxiety | 4,405 (4.8%) | Depression & Bipolar Disorder | 1.6% (1.4–1.7) |
Discussion
In a national cohort of older US Veterans, we found a greater accumulation of new frailty deficits in COVID-19 infected individuals compared to matched uninfected controls. The majority of new deficits were identified after the acute phase of illness (i.e. 31–365 days after infection), suggesting they are longer-term sequelae of disease that may affect health and well-being into the future. Finally, we found that increase in frailty was associated with both baseline frailty and with SARS-CoV-2 infection severity, findings that highlight the importance of prevention and prompt treatment in older individuals. These findings were stable in our sensitivity analysis where we included individuals who did not survive the full 12 months of follow-up. This reduces ceiling effects from highly frail individuals potentially being excluded from the main analysis due to earlier mortality.
The most common new deficits of those with COVID-19 infection were largely related to functional issues such as fatigue and musculoskeletal complaints, rather than common, chronic medical conditions such as hypertension or diabetes that have also been attributed to COVID-19.4 This suggests the need for a particular focus on rehabilitation and physical activity following infection in older adults. Fatigue was noted as the most frequent new deficit in nearly 10% of COVID-19 infected individuals. Although long-COVID has many symptoms, fatigue is a predominant symptom occurring in approximately 32% of people with long-covid and can be debilitating.30 The links between frailty and long-covid as an outcome of COVID-19 infection among older adults warrants further study.31 We believe our findings are important because functional independence is often a key component of what matters most to older individuals. There is evidence to show that increasing in-hospital mobility may improve longer-term functional outcomes, but data on those who are not hospitalized are lacking.32 Early intervention to maintain mobility and independence should be considered by clinicians and studied as potential interventions to improve outcomes for older adults infected with COVID-19.
While many studies have considered frailty as a prognostic factor for older adults infected with COVID-19, less have considered it as an outcome following infection. One recent publication by Resendes et al. detailed frailty onset over a 36-month period in Veterans after COVID-19 infection and found a 66% increased risk of becoming frail over that time frame.23 Similar to our study, they also evaluated underlying frailty deficits, with some similarities but did not find a significant association with COVID-19 infection and lung disease, chronic pain or anxiety as our group has. This may be related to their exclusion of severe COVID-19 infection as well as the exclusion of those with underlying psychosis. Additionally, Resendes et al only included robust older adults and those with non-severe COVID-19 infection and required only a negative COVID-19 test to be included in their matching cohort. An additional large study that focused on hospitalized patients with COVID-19 found that frailty rates did improve between 5 months and 1 year after COVID-19 hospitalization and that age and COVID-19 severity were predictors of frailty.22 Additionally, three smaller, uncontrolled studies conducted over shorter time frames found worsening frailty following COVID-19. 33–35 Our study builds on these with the largest cohort to-date, drawn from a nation-wide health care system, including all levels of baseline frailty and COVID-19 infection with well-matched uninfected controls observed over 12 months after infection. While the sample is overwhelmingly male, the large sample size allows for meaningful inferences for female COVID-19 survivors as well. However, we are limited by assessing frailty through usual medical care: deficits may have been missed and time of onset is difficult to determine. There may have been closer follow-up of those who had COVID-19, leading to differences in ascertainment; we attempted to reduce this bias by looking over one year. We were also unable to consider SARS-CoV-2 variants and vaccination during the time frame we studied and these should be considered in future work. We also could not consider home COVID-19 testing, though this was not available for much of the time period considered. Among individuals with more advanced frailty and more severe SARS-CoV-2 infection, the increase in frailty attenuates. This is likely a ceiling effect, where those at the highest levels of frailty cannot sustain more deficits and die during acute infection, excluding them from our analysis.36 Sensitivity analysis using at least 30 days of survival had similar results, supporting this interpretation.
In conclusion, COVID-19 infection accelerates progression of frailty, particularly functional impairment, among older adults. Whether this accelerated progression is sustained, what it means for the prognosis of older COVID-19 survivors, and how to prevent it are important topics for future work.
Supplementary Material
Key points:
COVID-19 infection leads to a significant increase in frailty over 12 months compared to uninfected individuals.
Greater COVID-19 severity and baseline frailty are associated with greater increase in frailty
The majority of new frailty deficits are related to function and mobility rather than chronic diseases.
Why does this paper matter?
COVID-19 has been linked to new onset of many individual illnesses. Among older adults where multimorbidity is the norm and functional outcomes may be of equal or greater importance, change in frailty may be a more useful way to understand the long-term health effects of COVID-19. Our findings show that COVID-19 is associated with progression of frailty using a cumulative deficit model. We find that frailty deficit accumulation is associated with greater COVID-19 severity, particularly hospitalization, and that the most common new deficits are related to function and mobility. These findings support ongoing efforts to minimize COVID-19 infection and severity is important to prevent frailty progression and that efforts towards mobility maintenance and recovery may be particularly important for older individuals infected with COVID-19.
Acknowledgments
We would like to thank Mazhgan Rowneki of VA Portland HCS for her work in developing the original CORC cohort.
Sponsor’s Role:
This publication does not represent the views of the Department of Veterans Affairs nor the United States Government.
BS is supported by VA CSR&D CDA-2 Award IK2-CX002648 and NIA GEMSSTAR Award R03-AG082989
KDW is supported by VA CSR&D CDA-2 Award IK2-CX002351
This work was supported by U.S. Department of Veterans Affairs Health Services Research & Development (HSR&D) grant C19 21-278 to Drs. Boyko, Ioannou, Osborne, and Maciejewski; HSR&D grant C19 21-279 to Drs. Hynes.
Footnotes
Conflict of Interest Statement:
The authors have no conflicts of interest to report. The sponsors played no role in the design, methods, analysis, or preparation of the manuscript.
Works Cited
- 1.Schulman Jonathan, Lazer David, Perlis Roy H., Baum Matthew A., Cadenasso Samantha, Ognyanova Katherine, Quintana Alexi, Uslu Ata, Druckman James, Santillana Mauricio, Green Jon, Safarpour Alauna C., Kristin Lunz Trujillo Hong Qu. State of the COVID-19 Pandemic [Internet]. United States of America: The COVID States Project; 2022. Available from: https://www.covidstates.org/reports/state-of-the-covid-19-pandemic [Google Scholar]
- 2.Wanga V. Long-Term Symptoms Among Adults Tested for SARS-CoV-2 — United States, January 2020–April 2021. MMWR Morb Mortal Wkly Rep [Internet] 2021. [cited 2023 Feb 9];70. Available from: https://www.cdc.gov/mmwr/volumes/70/wr/mm7036a1.htm [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Tenforde MW, Devine OJ, Reese HE, et al. Point Prevalence Estimates of Activity-Limiting Long-term Symptoms Among United States Adults ≥1 Month After Reported Severe Acute Respiratory Syndrome Coronavirus 2 Infection, 1 November 2021. J Infect Dis 2022;jiac281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature 2021;594(7862):259–64. [DOI] [PubMed] [Google Scholar]
- 5.Xie Y, Xu E, Bowe B, Al-Aly Z. Long-term cardiovascular outcomes of COVID-19. Nat Med 2022;28(3):583–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Centers for Disease Control and Prevention. COVID-NET: COVID-19 Associated Hospitalization Surveillance Network [Internet] [cited 2021 Jan 14];Available from: https://gis.cdc.gov/grasp/COVIDNet/COVID19_5.html
- 7.Wen Y-C, Chen L-K, Hsiao F-Y. Predicting mortality and hospitalization of older adults by the multimorbidity frailty index. PLOS ONE 2017;12(11):e0187825. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Stolz E, Hoogendijk EO, Mayerl H, Freidl W. Frailty Changes Predict Mortality in 4 Longitudinal Studies of Aging. J Gerontol Ser A 2021;76(9):1619–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: Implications for Clinical Practice and Public Health. Lancet 2019;394(10206):1365–75. [DOI] [PubMed] [Google Scholar]
- 10.Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A Standard Procedure for Creating a Frailty Index. BMC Geriatr 2008;8(1):24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Fried LP, Tangen CM, Walston J, et al. Frailty in Older Adults: Evidence for a Phenotype. J Gerontol A Biol Sci Med Sci 2001;56(3):M146–57. [DOI] [PubMed] [Google Scholar]
- 12.Rockwood K, Mitnitski A. Frailty in Relation to the Accumulation of Deficits. J Gerontol Ser A 2007;62(7):722–7. [DOI] [PubMed] [Google Scholar]
- 13.Panayi AC, Orkaby AR, Sakthivel D, et al. Impact of frailty on outcomes in surgical patients: A systematic review and meta-analysis. Am J Surg 2019;218(2):393–400. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.George EL, Hall DE, Youk A, et al. Association Between Patient Frailty and Postoperative Mortality Across Multiple Noncardiac Surgical Specialties. JAMA Surg 2021;156(1):e205152–e205152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hewitt J, Carter B, Vilches-Moraga A, et al. The effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort study. Lancet Public Health 2020;5(8):e444–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Aliberti MJR, Szlejf C, Avelino-Silva VI, et al. COVID-19 is not over and age is not enough: Using frailty for prognostication in hospitalized patients. J Am Geriatr Soc 2021;69(5):1116–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Petermann-Rocha F, Hanlon P, Gray SR, et al. Comparison of two different frailty measurements and risk of hospitalisation or death from COVID-19: findings from UK Biobank. BMC Med 2020;18(1):355. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Seligman B, Charest B, Ho Y-L, et al. 30-day mortality following COVID-19 and influenza hospitalization among US veterans aged 65 and older. J Am Geriatr Soc n/a(n/a):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Orkaby AR, Nussbaum L, Ho Y-L, et al. The Burden of Frailty Among U.S. Veterans and Its Association With Mortality, 2002–2012. J Gerontol Ser A 2018;74(8):1257–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Cheng D, DuMontier C, Yildirim C, et al. Updating and Validating the Veterans Affairs Frailty Index: Transitioning from ICD-9 to ICD-10. J Gerontol Ser A 2021;76(7):1318–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Shi SM, Olivieri-Mui B, McCarthy EP, Kim DH. Changes in a Frailty Index and Association with Mortality. J Am Geriatr Soc 2021;69(4):1057–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.McAuley HJC, Evans RA, Bolton CE, et al. Prevalence of physical frailty, including risk factors, up to 1 year after hospitalisation for COVID-19 in the UK: a multicentre, longitudinal cohort study. EClinicalMedicine 2023;57:101896. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Resendes NM, Bradley J, Tang F, Hammel IS, Ruiz JG. The association of non-severe COVID-19 infection and progression to frailty among robust older veterans. J Nutr Health Aging 2024;28(8):100296. [DOI] [PubMed] [Google Scholar]
- 24.Smith VA, Berkowitz TSZ, Hebert P, et al. Design and analysis of outcomes following SARS-CoV-2 infection in veterans. BMC Med Res Methodol 2023;23:81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hynes DM, Niederhausen M, Chen JI, et al. Risk of Suicide-Related Outcomes After SARS-COV-2 Infection: Results from a Nationwide Observational Matched Cohort of US Veterans. J Gen Intern Med [Internet] 2023. [cited 2023 Dec 7];Available from: 10.1007/s11606-023-08440-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Shrauner W, Lord EM, Nguyen XMT, et al. Frailty and cardiovascular mortality in more than 3 million US Veterans. Eur Heart J 2022;43(8):818–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Centers for Disease Control and Prevention. Defining Adult Overweight and Obesity [Internet]. Cent. Dis. Control Prev 2022. [cited 2023 Jun 14];Available from: https://www.cdc.gov/obesity/basics/adult-defining.html [Google Scholar]
- 28.Wang L, Porter B, Maynard C, et al. Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med Care 2013;51(4):368–73. [DOI] [PubMed] [Google Scholar]
- 29.Galloway A, Park Y, Tanukonda V, et al. Impact of Coronavirus Disease 2019 (COVID-19) Severity on Long-term Events in United States Veterans Using the Veterans Affairs Severity Index for COVID-19 (VASIC). J Infect Dis 2022;jiac182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol 2023;21(3):133–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Xie Y, Choi T, Al-Aly Z. Postacute Sequelae of SARS-CoV-2 Infection in the Pre-Delta, Delta, and Omicron Eras. N Engl J Med [Internet] 2024. [cited 2024 Jul 31];0(0). Available from: https://www.nejm.org/doi/full/10.1056/NEJMoa2403211 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Hastings SN, Sloane R, Morey MC, Pavon JM, Hoenig H. Assisted Early Mobility for Hospitalized Older Veterans: Preliminary Data from the STRIDE Program. J Am Geriatr Soc 2014;62(11):2180–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Okoye C, Calsolaro V, Calabrese AM, et al. Determinants of Cause-Specific Mortality and Loss of Independence in Older Patients following Hospitalization for COVID-19: The GeroCovid Outcomes Study. J Clin Med 2022;11(19):5578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Ochiai GS, de Godoy CG, Silva ÉCGE, et al. Functional impact on adults and older people after hospitalization by Covid-19. Physiother Res Int J Res Clin Phys Ther 2023;28(2):e1983. [DOI] [PubMed] [Google Scholar]
- 35.Prampart S, Le Gentil S, Bureau ML, et al. Functional decline, long term symptoms and course of frailty at 3-months follow-up in COVID-19 older survivors, a prospective observational cohort study. BMC Geriatr 2022;22:542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Rockwood K, Mitnitski A. Limits to deficit accumulation in elderly people. Mech Ageing Dev 2006;127(5):494–6. [DOI] [PubMed] [Google Scholar]
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