Prevalence and characteristics of long COVID in elderly patients: An observational cohort study of over 2 million adults in the US

Kin Wah Fung; Fitsum Baye; Seo H Baik; Zhaonian Zheng; Clement J McDonald

doi:10.1371/journal.pmed.1004194

. 2023 Apr 17;20(4):e1004194. doi: 10.1371/journal.pmed.1004194

Prevalence and characteristics of long COVID in elderly patients: An observational cohort study of over 2 million adults in the US

Kin Wah Fung ^1,^*, Fitsum Baye ¹, Seo H Baik ¹, Zhaonian Zheng ¹, Clement J McDonald ¹

PMCID: PMC10150975 PMID: 37068113

Abstract

Background

Incidence of long COVID in the elderly is difficult to estimate and can be underreported. While long COVID is sometimes considered a novel disease, many viral or bacterial infections have been known to cause prolonged illnesses. We postulate that some influenza patients might develop residual symptoms that would satisfy the diagnostic criteria for long COVID, a condition we call “long Flu.” In this study, we estimate the incidence of long COVID and long Flu among Medicare patients using the World Health Organization (WHO) consensus definition. We compare the incidence, symptomatology, and healthcare utilization between long COVID and long Flu patients.

Methods and findings

This is a cohort study of Medicare (the US federal health insurance program) beneficiaries over 65. ICD-10-CM codes were used to capture COVID-19, influenza, and residual symptoms. Long COVID was identified by (a) the designated long COVID code B94.8 (code-based definition), or (b) any of 11 symptoms identified in the WHO definition (symptom-based definition), from 1 to 3 months post-infection. A symptom would be excluded if it occurred in the year prior to infection. Long Flu was identified in influenza patients from the combined 2018 and 2019 Flu seasons by the same symptom-based definition for long COVID. Long COVID and long Flu were compared in 4 outcome measures: (a) hospitalization (any cause); (b) hospitalization (for long COVID symptom); (c) emergency department (ED) visit (for long COVID symptom); and (d) number of outpatient encounters (for long COVID symptom), adjusted for age, sex, race, region, Medicare-Medicaid dual eligibility status, prior-year hospitalization, and chronic comorbidities. Among 2,071,532 COVID-19 patients diagnosed between April 2020 and June 2021, symptom-based definition identified long COVID in 16.6% (246,154/1,479,183) and 29.2% (61,631/210,765) of outpatients and inpatients, respectively. The designated code gave much lower estimates (outpatients 0.49% (7,213/1,479,183), inpatients 2.6% (5,521/210,765)). Among 933,877 influenza patients, 17.0% (138,951/817,336) of outpatients and 24.6% (18,824/76,390) of inpatients fit the long Flu definition. Long COVID patients had higher incidence of dyspnea, fatigue, palpitations, loss of taste/smell, and neurocognitive symptoms compared to long Flu. Long COVID outpatients were more likely to have any-cause hospitalization (31.9% (74,854/234,688) versus 26.8% (33,140/123,736), odds ratio 1.06 (95% CI 1.05 to 1.08, p < 0.001)), and more outpatient visits than long Flu outpatients (mean 2.9(SD 3.4) versus 2.5(SD 2.7) visits, incidence rate ratio 1.09 (95% CI 1.08 to 1.10, p < 0.001)). There were less ED visits in long COVID patients, probably because of reduction in ED usage during the pandemic. The main limitation of our study is that the diagnosis of long COVID in is not independently verified.

Conclusions

Relying on specific long COVID diagnostic codes results in significant underreporting. We observed that about 30% of hospitalized COVID-19 patients developed long COVID. In a similar proportion of patients, long COVID-like symptoms (long Flu) can be observed after influenza, but there are notable differences in symptomatology between long COVID and long Flu. The impact of long COVID on healthcare utilization is higher than long Flu.

In a cohort study from the US including more than 2 million Medicare beneficiaries over the age of 65, Kin Wah Fung and colleagues explore the prevalence and characteristics of long COVID.

Author summary

Why was this study done?

The quoted incidence of long COVID varies widely because of differences in definition and measurement method. Long COVID in the elderly is likely to be underreported because they are less likely to respond to surveys, and symptoms may be confused with other chronic diseases.
Lingering ill health after infections is not limited to COVID-19. We postulate that some patients may fit the diagnostic criteria of long COVID after a bout of influenza. We call this condition “long Flu.” Comparing and contrasting long COVID and long Flu may shed light on the understanding of long COVID, a disease still shrouded in mystery.

What did the researchers do and find?

We used the World Health Organization long COVID definition on 2 million Medicare patients with COVID-19 between April 2020 and June 2021. We applied the same definition to almost 900,000 influenza patients during the 2018 and 2019 Flu seasons to identify long Flu.
Long COVID occurred in 16.6% of outpatients and 29.2% of inpatients. The corresponding rates for long Flu were 17% and 24.6%. Using only the designated long COVID code, the estimated rates would be 0.5% and 2.6%, way below the reported rates in most studies.
Despite the similar overall incidence rates, long COVID patients suffered with notably different symptoms compared to long Flu patients and were also more likely to access inpatient and outpatient healthcare services.

What do these findings mean?

The use of designated long COVID diagnostic codes alone is likely to result in gross underreporting of long COVID in this population.
Long COVID is associated with greater healthcare utilization than long Flu, suggesting a bigger impact on individual health and well-being, as well as on healthcare expenditure.

1. Introduction

Most Coronavirus Disease 2019 (COVID-19) patients recover completely after an infection with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus). However, a proportion suffer from persistent health issues after the acute phase of COVID-19 [1–7]. Various names have been used to describe this condition, including long COVID, long-haulers, long-term effects of COVID-19, post-COVID syndrome, chronic COVID syndrome, post-COVID conditions, and post-acute sequelae SARS-CoV-2 infection (PASC). We shall use long COVID in this report. Symptoms reported by long COVID patients range from fatigue, dyspnea, loss of smell to “brain fog.” The incidence of long COVID varies widely between studies, the majority are between 10% and 30% [8–19]. According to one estimation, up to 23 million people in the United States may have developed long COVID as of February 2022 [20]. Another study estimated that at least 3 to 5 million US adults have activity-limiting long COVID [21].

While it is known that elderly patients are more prone to develop severe COVID-19, some studies have identified age as a risk factor also for long COVID [22,23]. So far, relatively few long COVID studies have focused on the elderly. Long COVID can be underreported in the elderly population because they may not be as troubled by, or ready to report, the symptoms as in younger people [24]. They may also be less likely to participate in internet-based research or respond to questionnaires. Moreover, the long COVID symptoms may be masked by or attributed to existing chronic diseases. One study finds that almost a third of COVID-19 patients over 65 years developed one or more new or persistent clinical sequelae [25]. Another study reports significant deterioration in quality of life and functional decline in elderly patients 6 months after COVID-19 [26]. More information is needed to understand the impact of long COVID on the elderly population.

While long COVID is sometimes considered a novel disease, it is hardly a totally unexpected phenomenon. Many viral or bacterial infections have been known to cause prolonged illnesses in a subset of patients [27]. Rheumatic fever following infection by Streptococcus pyogenes is a well-known example. Herpesviruses and enteroviruses are implicated in the cause of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), characterized by fatigue, musculoskeletal pain, and post-exertional malaise [28]. First reports of prolonged symptoms after contracting Russian influenza dated back to the 19th century [10,29]. More recently, the SARS-CoV-1 and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) have been associated with post-acute phase persistent symptoms that affected approximately one-third of patients [30].

There are a lot of similarities between COVID-19 and influenza. Both diseases are caused by easily transmissible single-stranded RNA viruses primarily affecting the respiratory tract, with significant systemic manifestations. Both diseases affect millions of patients every year, presenting substantial medical and socioeconomic challenges. It is conceivable that, in some patients, a persistent state of ill health, similar to long COVID, can occur after influenza [31]. Given the similarities between COVID-19 and influenza, we postulate that there is considerable overlap in the symptomatology of the post-viral syndromes that they are associated with. This means that some patients would satisfy the diagnostic criteria for long COVID after an episode of influenza had the primary infection been COVID-19 instead. For lack of a better name, we shall call this condition “long Flu.” Conceptually, long Flu patients can serve as an “influenza comparator group” for long COVID. We think that comparing long COVID and long Flu would bring new insights to the understanding of long COVID.

In this study, we develop a pragmatic algorithm to identify long COVID patients based on the clinical definition proposed by the World Health Organization (WHO). Furthermore, we apply the same algorithm to identify patients who may be suffering from long Flu in 2 previous influenza seasons (2018 and 2019) and compare them with long COVID patients in 3 aspects: incidence, symptomatology, and impact on healthcare utilization.

2. Materials and methods

2.1 Study population

The primary cohort of this study was all Medicare patients over 65 diagnosed to have COVID-19 between April 2020 and June 2021. A control cohort of non-COVID-19 patients was identified by 1 to 5 matching for the same period. An influenza comparator cohort was identified from 2 pre-pandemic influenza seasons in 2018 and 2019. Through the Virtual Research Data Center (VRDC) [32] of the Centers for Medicare and Medicaid Services (CMS), we accessed de-identified encounter data of all Medicare beneficiaries from 2016 to 2021. Medicare is the US federal government’s health insurance program that primarily covers people 65 and older, and certain younger people with disabilities or kidney failure. Most individuals become eligible for Medicare when they reach 65 [33]. By one estimate, almost all (93%) of noninstitutionalized persons 65 and over, about 52 million in 2017, are covered by Medicare [34]. We focused our analysis only on Medicare beneficiaries aged ≥65, since younger Medicare beneficiaries are not representative of the general population aged <65 as they need qualifying disability conditions to enroll. To ensure we have sufficient data for symptom look-up (see below for method to identify long COVID and long Flu), we excluded patients (a) with less than 1 year of Medicare coverage, (b) with no encounters in a year prior to COVID-19 or influenza diagnosis, and (c) who were continuously enrolled in Medicare Advantage plans, mostly private health maintenance organization (HMO) plans that are not original Medicare fee-for-service (FFS) plans in the period between 1 year before and 12 weeks after the COVID-19 or influenza diagnosis. The last exclusion is necessary because Medicare claims data are potentially incomplete for patients enrolled in non-FFS plans. This study was declared not human subject research by the Office of Human Research Protection at the National Institutes of Health and by the CMS’s Privacy Board. There was no prospective analysis protocol submitted before the commencement of this study.

2.2 Identifying long COVID and long Flu

2.2.1 Long COVID

We identified COVID-19 patients based on the International Classification of Disease-10th Version-Clinical Modification (ICD-10-CM) code U07.1 COVID-19, in either inpatient or outpatient claims between April 1, 2020, and June 30, 2021. We stopped at June 2021 to ensure that we have acquired complete data for long COVID analysis because of several months’ lag for claim maturity. We separated COVID-19 patients into 2 mutually exclusive groups: outpatient and inpatient. For outpatients, the first COVID-19 diagnosis must be an outpatient coding, and the patient must not be admitted to an inpatient facility (acute care hospital or skilled nursing facility (SNF)) for COVID-19 within 4 weeks of COVID-19 diagnosis. An SNF is an inpatient rehabilitation and medical treatment center that provides a wide range of medical care including physical therapy, intravenous therapy, injections, monitoring of vital signs, and medical equipment. For inpatients, (a) the first COVID-19 diagnosis must be an inpatient coding, or (b) the first COVID-19 diagnosis is an outpatient coding, but the patient must be admitted to an inpatient facility for COVID-19 within 4 weeks of the COVID-19 diagnosis.

For identification of long COVID, we tried 2 approaches. The first was based on the recommended ICD-10-CM code for long COVID, B94.8 Sequelae of other specified infectious and parasitic diseases, during our study period (code-based definition). B94.8 can potentially be used in non-COVID-19 infections, but significant use of this code in Medicare data occurred only after April 2020 (usage increased over 20-fold), making it reasonably specific for long COVID. Note that after our study, a new specific code for long COVID, U09.9 Post COVID-19 condition, became available from October 2021. During peer review of this paper, additional analysis was suggested to study the impact of the new long COVID code on the diagnosis of long COVID. This was done using additional data from September to December 2021, which had become available after our study was concluded. The second approach was based on a constellation of symptoms (symptom-based definition). We followed the WHO’s clinical definition that was developed through a consensus process involving over 200 experts, researchers and patients [35]:

“Post COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include fatigue, shortness of breath, cognitive dysfunction but also others which generally have an impact on everyday functioning. Symptoms may be new onset, following initial recovery from an acute COVID-19 episode, or persist from the initial illness. Symptoms may also fluctuate or relapse over time.”

We used ICD-10-CM codes to identify the 11 symptoms that at least 50% of the participants in the WHO’s consensus building process thought were critical to include (see S1 Table for their incidence and temporal trend). Long COVID was defined as presence of any of the 11 symptoms unless they were excluded (see below for exclusion criteria). We also did a sensitivity analysis using only the top 3 symptoms (fatigue, shortness of breath, cognitive dysfunction) that reached 70% agreement in the WHO’s consensus building. For outpatients, we looked for symptoms from 4 to 12 weeks after the COVID-19 diagnosis. For inpatients, we started looking for long COVID symptoms after they were discharged to their original place of residence, following the recommendation from Amenta and colleagues [36]. The observation period for inpatients was from 2 to 10 weeks after discharge to compensate for the median hospitalization period of 2 weeks.

We did not include SNF patients (9% of all COVID-19 patients) in the inpatient group because the proportion of COVID-19 patients admitted to SNF was unusually high (32.6%) compared to influenza patients in 2 previous Flu seasons (4.2%). More importantly, 88.6% of SNF COVID-19 patients were not discharged home but transferred to different inpatient facilities at the end of our study period. Two CMS policy changes in response to the pandemic may explain these phenomena: (a) waiving of the 3-day requirement of prior hospital stay for admission to SNF; and (b) the extension of SNF coverage for an additional 100 days [37]. Since our identification of long COVID for inpatients started after their discharge, most of SNF patients did not satisfy the inclusion criteria.

To satisfy the requirement that “[the symptom] cannot be explained by an alternative diagnosis,” we tried 2 approaches:

Exclusion by history—the long COVID symptom must not be present from 2 weeks to 1 year before the COVID-19 diagnosis. We started the look-back period 2 weeks prior to COVID-19 diagnosis because COVID-19-related symptoms started to increase from 2 weeks before the COVID-19 diagnosis, indicating a lag in diagnosis reporting (see S1 Table). Note that this exclusion only applied to individual symptoms, not to the patient as a whole. For example, if a patient had dyspnea 6 weeks after COVID-19 diagnosis, but the patient also complained of dyspnea 6 months before COVID-19 diagnosis, then dyspnea would not be counted as a long COVID symptom. However, the patient was not excluded and might still be identified as long COVID due to other symptoms.
Exclusion by history and comorbidities—in addition to 1 above, we excluded symptoms that could be explained by a known comorbidity, for example, dyspnea excluded in the presence of chronic obstructive pulmonary disease (see S2 Table for excluded symptoms). We used the chronic condition onset dates in the Medicare database to identify comorbidities [38].

To estimate the false positive rate of the symptom-based definition, we matched each outpatient COVID-19 case to 5 controls (never had the code U07.1 COVID-19 or Z86.16 Personal history of COVID-19) on age in years, race, sex, dual eligibility status (a surrogate for income), geographic region, and Charlson comorbidity index [39] (within a range of +/− 2). We applied the same symptom-based definition on controls to assess the proportion of patients who would be falsely identified as long COVID.

2.2.2 Long Flu

Since the incidence of influenza diminished significantly during the COVID pandemic, we used data from 2 pre-pandemic Flu seasons, October 2017 to May 2018 (2018 season) and October 2018 to May 2019 (2019 season) to estimate the incidence of the postulated long Flu. We identified influenza patients using the ICD-10-CM codes J09, J10, and J11. Similar to COVID-19, we separated influenza patients into outpatient and inpatient groups. We used the same list of long COVID symptoms and symptom exclusion criteria to identify long Flu. The observation period was the same as for long COVID, i.e., from 4 to 12 weeks after influenza diagnosis for outpatients, and 2 to 10 weeks after discharge for inpatients. We excluded all SNF influenza inpatients (0.5% of total) from the long Flu cohort to be comparable with the long COVID cohort.

2.3 Comparing long COVID and long Flu

We compared the incidence and the distribution of symptoms between long COVID and long Flu patients. To estimate the impact of long COVID or long Flu on healthcare utilization, we analyzed 4 outcomes: (a) hospitalization (any cause); (b) hospitalization (due to any long COVID symptom); (c) emergency department (ED) visit (due to any long COVID symptom); and (d) number of outpatient (excluding ED) encounters (due to any long COVID symptom). We ran analyses of each of 4 outcomes separately for outpatients and hospitalized patients. For outpatients, we observed the outcomes for the period 4 to 12 weeks post-COVID-19 or influenza diagnosis. For inpatients, the observation period was 2 to 10 weeks post-discharge.

2.4 Statistical analysis

We included all patients with COVID-19 or influenza, including those diagnosed with both. When we had to compare COVID-19 and influenza patients statistically, we excluded patients who had both conditions to ensure independence between groups. To test the difference in incidence of each specific symptom between long COVID and long Flu patients, we first implemented a two-by-two contingency chi-squared test [40] and used Hochberg method to control for familywise error rate from multiple hypotheses testing by decreasing the number of false positives [41]. We then used a multiple logistic regression model [42] to compare them further controlling for age, sex, race, region, dual eligibility, and Charlson comorbidity index and reported the adjusted odds ratios. We did not have to deal with missing data as demographics and socioeconomic data were always present.

To test the difference in the first 3 outcomes of healthcare utilization (hospitalization any cause, hospitalization with long COVID symptoms, ED visit with long COVID symptoms), we implemented a generalized linear model with logit link adjusting for all available patient characteristics (age, sex, race, geographical region, dual eligibility status, history of any hospitalization in prior year, and 55 chronic conditions) as covariates. We adjusted for these covariates because demographics, comorbidities, and socioeconomic factors are known to affect healthcare utilization. To compare the number of outpatient visits with long COVID symptoms, we used a generalized linear model with a log link function (i.e., Poisson regression or log-linear regression analysis) with the same set of covariates as adjusters. We used the generalized estimating equations (GEEs) method to account for overdispersion in the Poisson regression model.

The primary goal of this study is to estimate the incidence of long COVID in the elderly by various approaches (code-based and symptom-based). Furthermore, we aim to compare the incidence, symptomatology, and healthcare utilization of long COVID with the hypothetical condition of long Flu. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

3. Results

3.1 Patient characteristics

We began with 2,434,154 COVID-19, 1,033,515 influenza, and 8,755,799 control Medicare beneficiaries aged ≥65. After applying exclusion criteria discussed in 2.1., we were left with 2,071,532 COVID-19 patients of whom 1,479,183 (71.4%) were outpatients (Fig 1). We combined data from the 2 Flu seasons (2018 and 2019) because they were quite similar in terms of patients’ health status and demographics (see S3 Table). There were 933,877 influenza patients of whom 817,336 (87.5%) were outpatients. All cases and controls were followed-up for 2 months to identify long COVID or long Flu, making a total follow-up time of 1.5 million patient years.

Table 1 shows the characteristics of the COVID-19 and influenza patients. Note that in comparing 2 groups, 91,796 COVID-19 patients who also had influenza in previous 2 Flu seasons were excluded (3% of total). Among the hospitalized patients, influenza patients were older and sicker (higher prior hospitalization rate and Charlson comorbidity index) than COVID-19 patients, but the difference was much smaller among outpatients.

Table 1. Characteristics of COVID-19 and influenza patients (CCI, Charlson comorbidity index, IQR, interquartile range; LIS, low-income subsidy; SD, standard deviation).

	Outpatient			Inpatient (hospitalized)
	COVID-19	Influenza^a	P value^b	COVID-19	Influenza^a	P value^b
N (%)	1,479,183 (100)	817,336 (100)		399,124 (100)	111,721 (100)
Any prior hospitalization^c	349,688(23.6)	174,024(21.3)	<0.001	121,372(30.4)	42,868(38.4)	<0.001
CCI, mean ± SD	1.9±2.3	1.9±2.2	<0.001	2.6±2.6	3.0±2.6	<0.001
Age, median (IQR)	75.0(70.0–82.0)	74.0(70.0–81.0)	<0.001	78.0(72.0–85.0)	81.0(74.0–88.0)	<0.001
65–69	313,613(21.2)	185,573(22.7)	<0.001	58,643(14.7)	11,826(10.6)	<0.001
70–74	389,872(26.4)	225,954(27.6)	<0.001	86,543(21.7)	18,726(16.8)	<0.001
75–79	277,075(18.7)	160,188(19.6)	<0.001	79,671(20.0)	20,232(18.1)	<0.001
80–84	205,435(13.9)	110,568(13.5)	<0.001	70,961(17.8)	20,733(18.6)	<0.001
85+	293,188(19.8)	135,053(16.5)	<0.001	103,306(25.9)	40,204(36.0)	<0.001
Female	853,157(57.7)	491,113(60.1)	<0.001	201,712(50.5)	65,020(58.2)	<0.001
Race: White	1,165,494(78.8)	664,540(81.3)	<0.001	296,000(74.2)	92,339(82.7)	<0.001
Black	117,273(7.9)	55,488(6.8)	<0.001	46,349(11.6)	8,637(7.7)	<0.001
Hispanic	111,488(7.5)	50,594(6.2)	<0.001	34,782(8.7)	6,176(5.5)	<0.001
Asian	37,698(2.5)	24,401(3.0)	<0.001	9,870(2.5)	2,448(2.2)	<0.001
Other	47,230(3.2)	22,313(2.7)	<0.001	12,123(3.0)	2,121(1.9)	<0.001
Region: Northeast	322,838(21.8)	131,762(16.1)	<0.001	76,301(19.1)	22,251(19.9)	<0.001
Midwest	309,689(20.9)	160,077(19.6)	<0.001	97,444(24.4)	30,757(27.5)	<0.001
South	566,699(38.3)	384,311(47.0)	<0.001	164,849(41.3)	38,622(34.6)	<0.001
West	265,878(18.0)	129,759(15.9)	<0.001	57,326(14.4)	18,873(16.9)	<0.001
Income: Ever Dual	367,459(24.8)	156,074(19.1)	<0.001	108,094(27.1)	28,562(25.6)	<0.001
Non-Dual LIS	21,666(1.5)	14,397(1.8)	<0.001	8,806(2.2)	2,466(2.2)	<0.001
Non-Dual Non-LIS	1,090,058(73.7)	646,865(79.1)	<0.001	282,224(70.7)	80,693(72.2)	<0.001

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^aCombined 2018 and 2019 Flu seasons.

^bExcluding 91,796 patients with both COVID-19 and influenza.

^cAny prior hospitalization within 1 year of COVID-19 or influenza diagnosis.

Each COVID-19 outpatient was matched to 5 non-COVID-19 patients. Overall, there were 6,286,633 controls with unmatched rate (COVID-19 patients unable to be matched) of 1.1%. The cases and controls were generally quite well matched in terms of demographics and Charlson comorbidity index. Due to the large sample size, most differences between cases and controls were statistically significant, but the absolute standardized difference <0.25 indicated good balance between them (S4 Table) [43].

3.2 Incidence of long COVID and long Flu

Among the hospitalized COVID-19 and influenza patients, 52.8% (210,765/399,124) and 68.4% (76,390/111,721), respectively, were discharged to their original place of residence by the end of our study period. These patients were used to estimate the incidence of long COVID and long Flu in inpatients. Based on the ICD-10-CM diagnosis code of B94.8 (i.e., the code-based definition), only 0.49% of outpatients and 2.6% of hospitalized patients were identified to develop long COVID (Table 2), way lower than most published reports. Using the symptom-based definition, the estimated incidence of long COVID was closest to other studies when we applied the definition of “any of 11 symptoms with history exclusion only.” By this definition, the incidence of long COVID was 16.6% and 29.2% in outpatients and hospitalized patients, respectively. If we added the comorbidity exclusion to this definition, the rates would drop to 5.7% (outpatient) and 8.8% (inpatient). We shall use the “any of 11 symptoms with history exclusion only” definition as our main result in subsequent discussion.

Table 2. Incidence of long COVID and long Flu by various definitions.

Long COVID definition	Outpatient		Inpatient (hospitalized)		Control
Long COVID definition	COVID-19	Influenza	COVID-19	Influenza	Matched with COVID-19 outpatient
N(%)	1,479,183(100)	817,336(100)	210,765(100)	76,390(100)	6,286,633(100)
Code-based^a	7,213(0.49)	39(0.005)	5,521(2.6)	11(0.01)	264 (0.004)
Symptom-based
Any of 11 symptoms with history exclusion only	246,154(16.6)	138,951(17.0)	61,631(29.2)	18,824(24.6)	658,229(10.5)
Any of 11 symptoms with history and comorbidity exclusion	84,774(5.7)	50,616(6.2)	18,486(8.8)	4,804(6.3)	220,751(3.5)
Any of 3 main symptoms with history exclusion only	142,175(9.6)	70,680(8.7)	42,690(20.3)	10,907(14.3)	357,915(5.7)
Any of 3 main symptoms with history and comorbidity exclusion	54,754(3.7)	30,053(3.7)	13,531(6.4)	3,302(4.3)	148,251(2.4)

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^aUsing code B94.8.

3.3 Difference in symptomatology of long COVID and long Flu

After excluding 91,796 patients with both COVID-19 and influenza (3% of all COVID-19 and influenza patients), we had 293,172 long COVID patients (outpatient 234,688, inpatient 58,484) and 140,697 long Flu patients (outpatient 123,736, inpatient 16,961) (Table 3). Rates of dyspnea, fatigue, palpitations, and loss of taste/smell were significantly higher in long COVID than long Flu, among both outpatients and hospitalized patients. In contrast, cough, chest pain, headache, and muscle/joint pain were more frequent in long Flu in both outpatient and inpatient groups. The incidence of memory problem, cognitive impairment, and sleep disturbance were significantly higher in long COVID among outpatients only.

Table 3. Symptoms in long COVID and long Flu patients (CI, confidence interval; OR, odds ratio of developing each specific symptom for long COVID compared to long Flu).

Symptoms	Long COVID (%)	Long Flu (%)	P value		OR (95% CI)
Symptoms	Long COVID (%)	Long Flu (%)	Unadjusted	Adjusted^a	Unadjusted	Adjusted^b
Outpatient	N = 234,688(100)	N = 123,736(100)
dyspnea	66,562(28)	29,689(24)	<0.001	<0.001	1.25(1.23,1.27)	1.25(1.23,1.27)
fatigue/malaise/weakness	76,881(33)	36,547(30)	<0.001	<0.001	1.16(1.15,1.18)	1.15(1.13,1.16)
cough	45,776(20)	32,192(26)	<0.001	<0.001	0.69(0.68,0.70)	0.69(0.67,0.70)
chest pain	41,697(18)	22,538(18)	0.001	0.002	0.97(0.95,0.99)	0.97(0.95,0.99)
palpitations	31,120(13)	13,940(11)	<0.001	<0.001	1.20(1.18,1.23)	1.20(1.18,1.23)
headache	17,950(8)	10,637(9)	<0.001	<0.001	0.88(0.86,0.90)	0.89(0.87,0.91)
muscle/joint pain	14,097(6)	8,332(7)	<0.001	<0.001	0.89(0.86,0.91)	0.91(0.88,0.93)
memory problem	8,261(4)	4,097(3)	0.001	0.002	1.07(1.03,1.11)	1.09(1.05,1.13)
cognitive impairment	6,698(3)	2,671(2)	<0.001	<0.001	1.33(1.27,1.39)	1.29(1.23,1.35)
sleep disturbance	2,360(1)	1,148(0.9)	0.024	0.024	1.08(1.01,1.16)	1.10(1.02,1.18)
loss of taste/smell	1,537(0.7)	414(0.3)	<0.001	<0.001	1.96(1.76,2.19)	2.02(1.81,2.26)
Inpatient (hospitalized)	N = 58,484(100)	N = 16,961(100)
dyspnea	24,915(43)	5,186(31)	<0.001	<0.001	1.69(1.62,1.75)	1.51(1.45,1.57)
fatigue/malaise/weakness	20,500(35)	5,424(32)	<0.001	<0.001	1.15(1.11,1.19)	1.25(1.21,1.30)
cough	11,425(20)	4,247(25)	<0.001	<0.001	0.73(0.70,0.76)	0.69(0.66,0.72)
chest pain	9,773(17)	3,065(18)	<0.001	<0.001	0.91(0.87,0.95)	0.91(0.87,0.95)
palpitations	8,467(14)	2,054(12)	<0.001	<0.001	1.23(1.17,1.29)	1.22(1.15,1.28)
headache	3,026(5)	1,288(8)	<0.001	<0.001	0.66(0.62,0.71)	0.71(0.66,0.76)
muscle/joint pain	2,103(4)	715(4)	<0.001	<0.001	0.85(0.78,0.92)	0.86(0.79,0.94)
memory problem	1,849(3)	648(4)	<0.001	0.034	0.82(0.75,0.90)	0.91(0.83,1.00)
cognitive impairment	1,066(2)	401(2)	<0.001	<0.001	0.77(0.68,0.86)	0.89(0.79,1.01)
sleep disturbance	508(0.9)	137(0.8)	0.448	0.448	1.08(0.89,1.30)	1.09(0.90,1.33)
loss of taste/smell	288(0.5)	42(0.3)	<0.001	<0.001	1.99(1.44,2.76)	2.04(1.47,2.83)

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^aAdjusted for multiple hypothesis testing from chi-squared test.

^bAdjusted for age, sex, race, region, dual eligibility, and Charlson comorbidity index.

3.4 Difference in healthcare utilization in long COVID and long Flu

For both outpatients and hospitalized patients, long COVID was associated with significantly higher chance of any hospitalizations and more outpatient visits than long Flu (Table 4). The difference is especially notable among hospitalized patients, even though hospitalized influenza patients, at the baseline, were sicker and older than hospitalized COVID-19 patients, which should normally translate into more healthcare utilization. In contrast, the likelihood of an ED visit was significantly higher among long Flu patients.

Table 4. Healthcare utilization of long COVID and long Flu patients (CI, confidence interval; ED, emergency department; IRR, incidence rate ratio; OR, odds ratio; SD, standard deviation).

	Healthcare utilization
	Any hospitalization (%)	Symptom-specific^a hospitalization (%)	Symptom-specific^a ED visit (%)	Number of symptom-specific^a outpatient visits, mean (SD)
Outpatient
Long COVID, N = 234,688	74,854(31.9)	36,721(15.6)	25,131(10.7)	2.9(3.4)
Long Flu, N = 123,736	33,140(26.8)	16,065(13.0)	15,566 (12.6)	2.5(2.7)
Inpatient (hospitalized)
Long COVID, N = 58,484	20,182(34.5)	8,286(14.2)	7,816(13.4)	2.9(3.2)
Long Flu, N = 16,961	6,245(36.8)	2,593(15.3)	2,749(16.2)	2.8(3.0)
Comparative risk in long COVID compared to long Flu	OR (95%CI, p-value)	OR (95%CI, p-value)	OR (95%CI, p-value)	IRR (95%CI, p-value)
Unadjusted
Outpatient	1.28(1.26,1.30, <0.001)	1.24(1.22,1.27, <0.001)	0.83(0.82,0.85, <0.001)	1.16(1.15,1.16, <0.001)
Inpatient (hospitalized)	0.90(0.87,0.94, <0.001)	0.91(0.87,0.96, <0.001)	0.80(0.76,0.84, <0.001)	1.02(1.01,1.04, 0.009)
Adjusted^b
Outpatient	1.06(1.05,1.08, <0.001)	1.00(0.98,1.02, 0.979)	0.80(0.78,0.82, <0.001)	1.09(1.08,1.10, <0.001)
Inpatient (hospitalized)	1.24(1.19,1.29, <0.001)	1.20(1.14,1.27, <0.001)	0.90(0.86,0.95, <0.001)	1.09(1.07,1.11, <0.001)

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^aWith any of the 11 symptoms of long COVID.

^bAdjusted for age, sex, race, geographical region, dual eligibility status, history of any hospitalization in prior year, and chronic comorbidities.

4. Discussion

Using Medicare data and a symptom-based definition, we estimate that long COVID happens in 16.6% of outpatients and 29.2% of hospitalized patients that are over 65. We find notable differences between the pattern of symptoms of long COVID and the hypothetical condition of long Flu. Furthermore, long COVID patients utilize more healthcare services than long Flu.

One major hurdle in long COVID research is the difficulty in identifying long COVID cases. Due to the wide range of definitions, the results of studies often cannot be compared or generalized [12]. The development of a consensus on clinical definition by WHO partially fills this gap [35]. Our study has developed a method to operationalize this clinical definition. The WHO definition depends on the presence of a constellation of symptoms but requires that the symptoms do not have an alternative explanation. Identifying such explanations can be done in observational studies. We compared 2 approaches—exclusion by history (the symptom did not occur within the previous year) and exclusion by history and comorbidities. The use of exclusion by history alone yields estimates of long COVID of 16.6% in outpatients and 29.2% in hospitalized patients, which are close to many published results. A recent large meta-analysis covering 194 studies shows that on average, at least 45% of COVID-19 survivors (nonhospitalized patients 34.5%, hospitalized patients 52.6%) continue to experience at least one unresolved symptom [44]. This means that our results are likely to be an underestimation. One potential problem of exclusion of individual symptoms based on history is that common symptoms are more likely to be excluded, even if they are indeed related to long COVID. In our study, the incidence of cognitive impairment and memory problem among long COVID patients is lower than that reported in the literature [44]. Since these problems are generally more common among elderly patients, they are more likely to be excluded by coincidence. Another factor that can be at play is that the threshold of seeking help for these problems may be higher in elderly patients. Adding the exclusion based on comorbidities cuts the estimates significantly to 5.7% for outpatients and 8.8% for hospitalized patients. We speculate that the comorbidity exclusion is probably too strict because some comorbidities are very prevalent in our study population of elderly Medicare beneficiaries. For example, high prevalence of heart failure (43%), chronic obstructive pulmonary disease (41%), and fibromyalgia chronic pain and fatigue (54%) would lead to exclusion of cough, dyspnea, and fatigue.

A recent study shows promise in the use of machine learning to identify long COVID [45]. However, the model in Pfaff and colleagues was only built on a very small, 0.6%, subset of 97,995 COVID-19 patients attending a long COVID clinic, highlighting the difficulty of identifying all patients suffering from long COVID. For machine learning methods to be effective, a large number of cases along with high-quality data is required. Achieving this goal can be difficult because long COVID tends to be underreported and undercoded [46]. In our study, code-based identification of long COVID yields an estimation of only 0.49% in outpatients and 2.6% in hospitalized patients, way below published results. A specific long COVID code, U09.9, was delivered in October 2021; however, healthcare providers were slow to take advantage of this code [47]. Based on additional data from September to December 2021, which became available after our study concluded, we found that the new long COVID code (U09.9) was used more often than the old one (B94.8), whose usage dropped off significantly. Using the new code in our code-based definition, 2.0% (11,424/573,965) of outpatients and 9.2% (6,253/68,030) of hospitalized patients developed long COVID. This is still considerably lower than most reports in the literature. Researchers should be aware of the potential underreporting if they rely solely on specific codes to identify long COVID.

We postulate the existence of “long Flu” based on reports of post-infectious sequelae after influenza [29]. Long COVID has attracted special attention because of the pandemic, but the possible occurrence of prolonged symptoms from influenza should not be overlooked. Long COVID is still a poorly understood disease. Comparing and contrasting long COVID with long Flu may offer new insights into its pathogenesis and treatment. The pathogenesis of long COVID is likely to be complex and more than one mechanism may be implicated in some clinical manifestations [28,48]. Evidence suggests that prolonged inflammation probably plays a key role. In addition, it is known that, like other coronaviruses, SARS-CoV-2 can invade the blood–brain barrier and access the central nervous system through peripheral or olfactory neurons [49,50]. This could explain the greater incidence of psychoneurological symptoms (for example, cognitive impairment, loss of taste or smell, memory problem, and sleep disturbance) in long COVID compared to long Flu in our study. Another special feature of COVID-19 is the high incidence of thromboembolism, probably as a result of endothelial injury and heightened inflammation, which can lead to organ or tissue injury [51,52]. Investigators have found high incidence of significant radiological and functional abnormalities indicative of lung parenchymal and small airway disease after the acute phase of COVID-19, which could give rise to dyspnea and easy fatigue [53].

Based on our estimation, the incidence of long COVID and long Flu is comparable among outpatients (16.6% versus 17.0%) and slightly higher for long COVID in inpatients (29.2% versus 24.6%). But incidence alone does not tell the whole story about the impact of the 2 diseases. Our model on healthcare utilization shows that long COVID patients are more likely to seek outpatient care and be hospitalized, after controlling for demographics, socioeconomic factors, and comorbidities. This suggests that long COVID is a more serious illness than long Flu and has greater societal impact. One unexpected finding is the reduced ED visits in long COVID patients. This is probably an anomaly and could be explained by the fact that overall, ED visits shrunk during the pandemic, possibly because patients fearing exposure to COVID-19 avoided the ED for conditions for which they otherwise would have sought emergency care. Long Flu data came from 2017 to 2019, and we observed that the overall usage of ED dropped by 18% in 2020 compared with 2017 to 2019 among Medicare senior beneficiaries.

The primary strength of our study is the sample size. We have 2 million COVID-19 patients over 65, which is considerably more than most published studies. We recognize the following limitations. Based on our exclusion criteria, 15% of COVID-19 patients, 10% of influenza patients, and 28% of matched controls were excluded, which may affect the generalizability of our findings. Not all COVID-19 diagnoses are captured in Medicare claims data. Our previous study showed that up to one-third of COVID-19 cases could be missed [54]. Medicare claims-based data may miss services or treatment paid for by private insurance or other means. The code-based definition of long COVID is based on the recommended code available for the study period, which may not be specific for long COVID. The symptom-based definition relies on the symptoms reported as “diagnosis” at the healthcare encounter and may not be sensitive because providers may not routinely code all symptoms. Using claims data, we cannot easily ascertain the duration of the symptoms as stipulated in the WHO definition. There is no independent confirmation that patients identified by our method are indeed suffering from long COVID. However, we can venture some estimation of our error rates. False positive rate can be estimated by the positive rate in controls (10.5%). Among the 12,734 patients specifically coded as long COVID (code-based definition), 8,239 (64.7%) patients were identified as long COVID by the symptom-based definition, so the false negative rate can be estimated to be about 35%. If we adjust our results by these estimated error rates, the incidence of long COVID would be 9% for outpatients and 28% for inpatients, still not far from other studies. Our observation period ends at 12 weeks after the COVID-19 or influenza diagnosis. Some patients may present with long COVID or long Flu after that period. Among the inpatients, we exclude SNF patients because a significant proportion of them are not discharged within the study period. We assume that long Flu is similar to long COVID and use the same symptomatic definition. There may be symptoms in post-influenza syndrome that are not common in long COVID, and patients with those symptoms may not be identified as long Flu in our study.

Based on a constellation of symptoms identified in the WHO’s consensus definition, we estimate that long COVID occurs in 16.6% and 29.2% of elderly COVID-19 outpatients and inpatients, respectively. The corresponding incidence for long Flu, identified by the same constellation of symptoms for 2 pre-pandemic influenza seasons, is about the same (17% and 24.6%). Long COVID patients have significantly higher incidence of dyspnea, fatigue, palpitations, loss of taste or smell, and neurocognitive symptoms. Compared to long Flu, patients with long COVID are hospitalized more often and have more outpatient visits, suggesting that it is a more serious illness and has higher societal impact.

Supporting information

S1 Checklist. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline checklist.

(DOCX)

Click here for additional data file.^{(34KB, docx)}

S1 Table. Long COVID symptoms and temporal trends.

(XLSX)

Click here for additional data file.^{(110.6KB, xlsx)}

S2 Table. Exclusion of symptoms by comorbidities.

(DOCX)

Click here for additional data file.^{(22.5KB, docx)}

S3 Table. Patient characteristics in the 2018 and 2019 Flu seasons.

(DOCX)

Click here for additional data file.^{(14.1KB, docx)}

S4 Table. COVID-19 outpatient cases and controls.

(DOCX)

Click here for additional data file.^{(15.7KB, docx)}

S5 Table. Detailed statistical data of the logistic and Poisson regression models for healthcare utilization.

(XLSX)

Click here for additional data file.^{(46.8KB, xlsx)}

Abbreviations

CMS: Centers for Medicare and Medicaid Services
COVID-19: Coronavirus Disease 2019
ED: emergency department
FFS: fee-for-service
GEE: generalized estimating equation
HMO: health maintenance organization
ICD-10-CM: International Classification of Disease-10th Version-Clinical Modification
ME/CFS: myalgic encephalomyelitis/chronic fatigue syndrome
MERS-CoV: Middle East Respiratory Syndrome Coronavirus
PASC: post-acute sequelae SARS-CoV-2 infection
SARS-CoV-2: Severe Acute Respiratory Syndrome Coronavirus 2
SNF: skilled nursing facility
VRDC: Virtual Research Data Center
WHO: World Health Organization

Data Availability

Concerning data availability, the minimal data set is included in the Supporting information. All data in Supporting information can be used without restriction. This now includes the precise values used to build the long COVID symptom trend graphs (S1 Table) and the detailed statistical data obtained in the logistic and Poisson regressions (S5 Table), from which the odds ratios and incidence rate ratios can be derived. As for raw data, CMS does not let us download (or distribute) any patient level data. The data stay on their machine, and we analyze it with software they provide on their machine. If researchers wish to access the raw data, they can contact the CMS Virtual Research Data Center https://resdac.org/cms-virtual-research-data-center-vrdc. However, data access requires the payment of a fee.

Funding Statement

This research was supported by the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health. All authors were full time employees of NLM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1004194.r001

Decision Letter 0

Philippa Dodd

15 Sep 2022

Dear Dr Fung,

Thank you for submitting your manuscript entitled "Long COVID in Elderly Patients: An Epidemiologic Exploration Using Medicare Data" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Sep 19 2022 11:59PM.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Sincerely,

Philippa Dodd, MBBS MRCP PhD

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004194.r002

Decision Letter 1

Philippa Dodd

3 Jan 2023

Dear Dr. Fung,

Thank you very much for submitting your manuscript "Long COVID in Elderly Patients: An Epidemiologic Exploration Using Medicare Data" (PMEDICINE-D-22-03023R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by Jan 24 2023 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Philippa Dodd, MBBS MRCP PhD

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

GENERAL

Please respond to all editor and reviewer comments detailed below in full.

Please insert line numbers throughout the manuscript starting with 1 on page 2 at “Abstract” and in continuous sequence thereafter

Please ensure that the study is reported according to the STROBE guideline (we note the inclusion of a CONSORT diagram which is has greater relevance to RCTs) and include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/

When completing the checklist, please use section and paragraph numbers, rather than page numbers.

* We agree with the reviewers and the academic editor that further details regarding Medicare eligibility and the study population would be helpful

** In light of reviewer #1 comments (below) regarding the use of the term “long-flu”, please consider how you frame the use of this term and in the context of this study and its aims

COMMENTS FROM THE ACADEMIC EDITOR

I think this is an interesting paper. I would recommend to ask the authors for a major revision to accommodate the comments of the reviewers. They should describe in more detail who is eligible for Medicare and what the characteristics are of the Medicare population, as this is not common knowledge outside the USA.

DATA AVAILABILITY STATEMENT

Thank you for including a statement regarding your data availability. The Data Availability Statement (DAS) requires revision.

PLOS Medicine requires that the de-identified data underlying the specific results in a published article be made available, without restrictions on access, in a public repository or as Supporting Information at the time of article publication, provided it is legal and ethical to do so. Please see the policy at

http://journals.plos.org/plosmedicine/s/data-availability

and FAQs at

http://journals.plos.org/plosmedicine/s/data-availability#loc-faqs-for-data-policy

For each data source used in your study:

a) If the data are freely or publicly available, note this and state the location of the data: within the paper, in Supporting Information files, or in a public repository (include the DOI or accession number).

b) If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data.

c) If the data are not freely available, please describe briefly the ethical, legal, or contractual restriction that prevents you from sharing it. Please also include an appropriate contact (web or email address) for inquiries (again, this cannot be a study author).

ABSTRACT

Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions).

Abstract Background: please remove “and importance”. The final sentence of the background section should clearly state the study question.

Abstract Methods and Findings:

Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

Please include additional details regarding the medicare population and the study setting - rural, urban, pan-US or restricted to certain regions, total number of participants included in total and in each of the long covid and long-flu cohorts.

You state “Long Flu was identified by the same symptom-based definition” – were these the same symptoms as used to define long-covid or a different set devised around the same concept? Please clarify. Please include further details of the long-flu cohort – over which years were cases of flu identified, was the time frame for identification of long-flu the same as for covid ? And so on...

Please clearly define the main outcome measures.

Please include any important dependent variables that are adjusted for in the analyses.

Please ensure percentages are quantified with numerators and denominators

Please include the actual amounts and/or absolute risk(s) of relevant outcomes (including NNT or NNH where appropriate), not just relative risks or correlation coefficients. (example for absolute risks: PMID: 28399126).

Please quantify the main results with 95% CIs and p values.

In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

Abstract Conclusions:

Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful.

Please interpret the study based on the results presented in the abstract, emphasizing what is new without overstating your conclusions.

Please avoid vague statements such as "these results have major implications for policy/clinical care". Mention only specific implications substantiated by the results.

Please avoid assertions of primacy ("We report for the first time....")

AUTHOR SUMMARY

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

METHODS and RESULTS

We agree with the reviewer (please see below) and with the academic editor (see above) that some additional detail regarding Medicare, its coverage and the population included would be helpful to the reader, please revise accordingly

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

For all observational studies, in the manuscript text, please indicate:

(1) the specific hypotheses you intended to test,

(2) the analytical methods by which you planned to test them,

(3) the analyses you actually performed, and

(4) when reported analyses differ from those that were planned, transparent explanations for differences that affect the reliability of the study's results. If a reported analysis was performed based on an interesting but unanticipated pattern in the data, please be clear that the analysis was data-driven.

Section 3.4, page 12 – the second half of this paragraph, sentence beginning “This is probably an anomaly…” should be moved to the relevant part of the discussion, suggest paragraph 2 of page 14 where healthcare utilization estimates are mentioned.

TABLES and FIGURES

Please ensure that all figures and tables have appropriate titles and captions which clearly describe the content without the need to refer to the text

Figure 1 – the inclusion of the figure is a helpful one but as CONSORT is designed to report RCTs and that we request reporting by STROBE guidelines for this observational study, suggest altering the title of the figure to “Participant inclusion, exclusion and matching” or something similar

Tables – we note the statistical reviewer’s comments (below) regarding the tables which we agree with.

Currently it is a journal requirement to present both 95% CIs and p-values when reporting main outcome measures and we therefore ask that you please report both rather than the use of bold and/or italic fonts to indicate significance

Table 3 and 4 require revision for the purpose of improved accessibility – please apply the following to all tables presented in the main manuscript and as supporting information

- Please revise to ensure that whole words are not split across lines – you may need to create the tables in landscape format to ensure that you have room, or the current tables can be split into more than 1

- Please ensure that confidence limits are not split across separate lines

- Please do not report p 0.000; report as p<0.001

- In an appropriate caption, please indicate which factors are adjusted for

- To help facilitate transparency of data reporting, please also provide the unadjusted analyses for comparison

DISCUSSION

Please remove the sub-heading “conclusions” form the end of the main manuscript such that the discussion reads as a single continuous piece of prose ending in a one paragraph conclusion

Please remove the funding statement, COI statement form the end of the main manuscript and include only in the manuscript submission form

REFERENCES

For in-text reference call-outs, citations should be placed within square brackets preceding punctuation as follows: “…reference [1,2].” Please note the absence of a space between citations.

In the bibliography, for each reference listed, please list up to, but no more than, 6 author names followed by et al, where more than 6 authors contribute to the published article.

Journal name abbreviations should be those found in the National Center for Biotechnology Information (NCBI) databases.

Please see our website for other reference guidelines: https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

SUPPORTING INFORMATION

Please provide titles and legends for each individual table and figure in the Supporting Information.

eFigure 1 – please consider avoiding the use of green (or red) to improve accessibility to those with colour blindness

eTable 1 – as for the main manuscript tables, please ensure that words are not split across lines – see the column headers. Please also ensure that any and all abbreviations are defined in an appropriate caption (COPD, AMI, for example)

eTable 2 – we agree with the reviewer that modification of this table consistent with table 1 of the main manuscript

Comments from the reviewers:

Reviewer #1: Overall Impression:

This manuscript addresses an important gap in the scientific literature on the topic of long COVID, specifically among the elderly population. The authors accurately describe the challenges of diagnosing long COVID and identifying it in medical administrative data and highlight challenges among the elderly. The authors use methods similar to other studies of analyzing ICD-10 codes in medical claims data to identify possible cases of long COVID but apply them to a Medicare dataset and also include an influenza comparator group. The manuscript would advance our knowledge in ways to identify long COVID cases among this Medicare population and would be of interest to clinicians caring for this patient population. Overall, the conclusions are supported by the findings and in line with what has been previously reported in the general population.

Major issues:

The use of the term "long flu" is a bit of a distraction. What the authors are trying to do with the influenza comparator group is valid and informative, but it seems less about trying to identify "long flu" (which also seems to have little scientific literature supporting it) and more about comparing the post-influenza time period with the post-COVID time period with respect to diagnoses/symptoms and healthcare utilization. I recommend the authors re-frame how they describe this aspect of the analysis and use a term like "influenza comparator group" or something similar.

Additionally, I would like to see more in the discussion about how this analysis might change if done with more recent data after the introduction of the new Post-COVID condition ICD-10 code U09.9. While long COVID is still likely underdiagnosed, the introduction of this code will improve the coding of the diagnosis compared to when only B94.8 was available.

Minor issues:

In the introduction, 3rd sentence, please include post-COVID condition or post-COVID conditions as these are the terms used by the U.S. Centers for Disease Control and Prevention and WHO.

At the end of the first paragraph of the intro, suggest addition of the following reference: 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 | The Journal of Infectious Diseases | Oxford Academic (oup.com)

In the results, there are several sentences throughout that deviate from a strict reporting of results and that some would consider more appropriate for the discussion. For example, in section 3.2 with the sentence "We speculate…" and in 3.4 "This is probably an anomaly….".

For Table 2, suggest specifying "any of 11 symptoms" and "any of 3 main symptoms" so reader is not confused that you must have all 11 or all 3. This is explained in the methods, but readers might appreciate a reminder when reviewing the table.

Reviewer #2: Thanks for the opportunity to review your manuscript. My role is as a statistical reviewer, so my review concentrates on the study design, data, and analysis that are presented. I have put general questions first, followed by queries relevant to a specific section of the manuscript (with a page/paragraph reference).

The rationale behind the work is interesting. People are (rightfully IMO) worried about long COVID symptoms and there has been research which has attempted to objectively look at what symptoms are more common following acute COVID infection. Longer-term effects from viral infection aren't unknown, but there hasn't (to my knowledge) been any attempts to understand which symptoms are also more common after influenza infection in the same rigorous way that COVID has been examined. Data from the USA Medicare program was taken from 2016-2021, limited to those >65 years, with enough data for a look-back period for pre-existing symptoms, not from a skilled nursing facility, and without a diagnosis for both COVID and influenza. Covid exposure was defined as unexposed, out-patient, and in-patient. Examination of long-covid was based on a specific ICD10 code, and the WHO symptom-based definition (based on newly reported symptoms that were unrelated to a recorded comorbidity). The symptom based definition was assessed by comparing patients exposed to COVID-19 to those without a record of exposure. The 'long COVID' symptom based definition was applied to patients with a diagnosis of influenza over the 2017/2018 flu seasons. Use of healthcare services was compared between patients with 'long COVID' and 'long Flu'. A sensitivity analysis using only the three most common long-COVID symptoms was also included.

With the inclusion/exclusion criteria, and the temporal window of the data the results are specific to older people using Medicare, without records of both influenza and COVID. Some of the language in the conclusions is fairly general ('elderly COVID-10 outpatients and inpatients'), I wanted to check whether the exclusions necessary to create an appropriate cohort for this analysis might limit the generalisability of the results.

P5, Paragraph. Given that PLOS Medicine has a global audience, an extra sentence or two describing the US Medicare system would be helpful, i.e. who is eligible to be enrolled, what does it cover?

Do all >65 year olds use Medicare and have their health services recorded in the database? Or do some in this age group pay for healthcare through other means (private insurance, out of pocket) that means part of the >65 year old population has no or incomplete records of health service use?

P6, Paragraph 2. I wasn't sure how a SNFs differ from a hospital, or an aged care facility (nursing home/facility). Would this be a facility that provides care to less complex patients (i.e. a lower level hospital)?

P8, Paragraph 5. Was testing in the US at this sufficiently complete, i.e. were all COVID cases likely to have the diagnosis recorded in the Medicare records? Or could the 'unexposed' include patients who had COVID but didn't receive a diagnosis?

P10, Paragraph 2. How were these covariates selected to include in the adjusted logistic regression model?

P10, Paragraph 2. How was overdispersion in the Poisson model checked? Was the form of the residuals checked and ok?

P11, Paragraph 2/Supp e Table 3. I wonder if standardised difference (often used with propensity score matching) would be an alternative to p-values here given the generous sample size you have.

Also, to clarify does the unmatched rate refer to COVID patients unable to be matched?

P11, Paragraph 3. Just wondering if use of the B94.8 changed over time (as long COVID become more recognised)? No need to run additional analyses, just curious and would be interested to see if you have the output already.

P12, Paragraph 2. The ED result is interesting, I would move the explanation to the discussion rather than having it in the results.

Table 3. Rather than using a significance level (bolding) it would be fine to just have the p-values and ORs displayed here (and in other tables). I would also modify the p-values listed as "0.000" to be "<0.001". With the sample size, reporting the percent with the symptom to two decimal places is probably more than needed as well, reporting to just percent (or one decimal place where the % is <1) would be fine here.

eFigure 1. I would update the Y-axis to remove the extra 00s in percent, i.e. 25% instead of 25.00% The dots also overlap and it is difficult to see the individual series in the neuropsychiatric graph, I would suggest expanding the Y scale and maybe also using lines instead of dots so there is less overlap.

Etable 2. I would modify the table so it's clear which categories belong to each variable, e.g. it wasn't clear that 'other' and 'northeast' belonged to separate variables initially. The way it is laid out in Table 1 (main manuscript) is good.

Reviewer #3: Publications on COVID-19 sequelae in the elderly population is largely lacking, and the authors of this manuscript are commended for focusing on this age group. The authors utilize Medicare data, and compare post-covid and post-flu registered diagnoses and symptoms captured in medical records during the period April 2020 to June 2021. Diagnosis-based approach yielded low estimated incidence, while the symptom approach gave higher estimates. This concurs with previous reports showing that specific diagnosis-based estimates likely underestimate true prevalence to a large extent. While the same is likely true also for specific symptom reporting, this paper reports as high as 17% post infection symptoms both after COVID and influenza in outpatient settings and 25-29% in hospitalized settings.

Some questions remain:

1. Comorbidities are known risk factors for long COVID. Still, the authors excluded patients with encounters during the past year. What encounters are considered here? If it means any contact with the health services, this seems too strict and will exclude many individuals with risk of prolonged symptoms. Did you look at data without this exclusion criteria?

2. In table 3 individual symptoms are compared. Dyspnoea and fatigue are as expected high post COVID, while memory or cognitive symptoms are at a lower frequency that have previously been reported in prospective cohorts of younger age. At the same time increased incidence of dementia has been reported in this age group, as well as MRI changes in COVID patients. Could the threshold for patients, as well as doctors, reporting of this symptom be higher, for various reasons, and therefore the approach in this study for cognitive symptoms be less sensitive?

3. The data on flu are an important addition to the present literature on flu complications.

4. In the discussion it is stated that long COVID symptoms in 16% of outpatients and 29% of hospitalised patients is similar to published data. Even though there is a large literature here, and perhaps hard to keep an overview, the references given (refs 8 through 19), should have been penetrated more in order to support the claim. The authors have not attempted to discriminate between hospitalised and non-hospitalised cohorts, and even included population-based health record data, based on similar approaches as the diagnosis-based which the authors show underestimate prevalence in this manuscript. 40% to 70% post COVID symptoms would be more appropriate when scrutinizing existing literature, particularly after hospitalisations. This means that even the approach in this manuscript under-estimates the incidence of long COVID symptoms. This needs to be acknowledged in the discussion. It doesn't make the data less interesting, since this manuscript likely shows an approach which is better suited than previous diagnosis-based approaches, and can be seen as an improved long COVID estimate based on large populations.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. doi: 10.1371/journal.pmed.1004194.r003

Decision Letter 2

Philippa Dodd

3 Mar 2023

Dear Dr. Fung,

Thank you very much for re-submitting your manuscript "Long COVID in Elderly Patients: An Epidemiologic Exploration Using a Medicare Cohort" (PMEDICINE-D-22-03023R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by 2 reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Mar 24 2023 11:59PM.

Sincerely,

Philippa Dodd, MBBS MRCP PhD

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

GENERAL

Thank you for considered and detailed responses to editor and reviewer comments.

Please see below for further minor points that we request you respond to in full.

DATA AVAILABILITY STATEMENT

Thank you for your detailed statement. Please include a URL or contact email address for the CMS Virtual Research Data Center.

TITLE

We wonder if your title could better reflect your study design and size. Suggest revising in line with our guidance. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

INTRODUCTION

Thank you for modifying your introduction to define long-flu more clearly.

Line 118 suggest, “This means that some patients would satisfy the diagnostic criteria for long COVID after an episode of influenza had the primary infection been COVID-19 instead. For lack of a better name, we shall call this condition “long Flu”.

ABSTRACT

Throughout you report long flu in different ways “long Flu, long flu and long-flu”

Similarly “long-COVID Vs long COVID” is also written please amend for consistency, including throughout the rest of the manuscript.

AUTHOR SUMMARY

This reads very nicely but is a little too long. Ideally each sub-heading should contain 2-3 single sentence, concise bullet points. Please revise including the most salient points from your study.

TABLES

Table 1: We previously requested that the presentation of p values in the tables were revised but in my version they appear unchanged. Please report p as p<0.001 or where higher as p=0.002, for example (not p<.0…)

Table 2: as above, please revise the presentation of p values. Please also report Please ensure that numerical values contained in brackets are clearly defined for the reader to ensure accessibility – this is not evident in some cases i.e., where (presumably) p values are presented).

Table 3: Thank you for indicating that your analyses are adjusted, we previously asked you to include unadjusted analyses for comparison but could not find these. For the purpose of transparent data reporting please include the unadjusted analyses. If not please clearly state the reasons why not.

Table 4: Thank you for indicating that your analyses are adjusted, we previously asked you to include unadjusted analyses for comparison but could not find these. For the purpose of transparent data reporting please include the unadjusted analyses. If not please clearly state the reasons why not.

Row header: “Comparative risk of long COVID patients relative to long Flu patients” – we wonder whether a different header could better describe these data? Suggest “Comparative risk in long COVID compared to long flu” or similar?

DISCUSSION

Please ensure that you present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

REFERENCES

For in-text reference callouts, please remove spaces from between citations. For example line 93 should read “[22,23].”

SOCIAL MEDIA

To help us extend the reach of your research, please provide any Twitter handle(s) that would be appropriate to tag, including your own, your co-authors’, your institution, funder, or lab. Please detail any handles you wish to be included when we tweet this paper, in the manuscript submission form when you re-submit the manuscript.

Comments from Reviewers:

Reviewer #2: Thanks for the revised manuscript and responses to my review. The revision resolves the queries from my first review. The Medicare population is clearly explained, and I think the limitations of using this data are articulated in the discussion.

Reviewer #3: The authors have responded well to all my queries, and I think the manuscript can be published.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. doi: 10.1371/journal.pmed.1004194.r004

Decision Letter 3

Philippa Dodd

14 Mar 2023

Dear Dr Fung,

On behalf of my colleagues and the Academic Editor, Professor Mirjam Kretzschmar, I am pleased to inform you that we have agreed to publish your manuscript "Prevalence and Characteristics of Long COVID in Elderly Patients: An Epidemiologic Exploration Using a Retrospective Medicare Cohort" (PMEDICINE-D-22-03023R3) in PLOS Medicine.

Before we can publish your manuscript we request that you make the following changes:

1) TITLE - suggest the following, or similar:

Prevalence and characteristics of long COVID in elderly patients: An observational cohort study of over 2 million adults in the US

2) AUTHOR SUMMARY

Suggest combining points on lines 60 and 63 into one to read as follows:

“Despite the similar overall incidence rates, long COVID patients suffered with notably different symptoms compared to long Flu patients and were also more likely to access inpatient and outpatient healthcare services.”

Line 66: suggest “The use of designated long COVID diagnostic codes alone is likely to result in gross under reporting of long COVID in this population."

Line 68: please remove this statement as it repeats the findings listed above

Line 71: suggest “Long COVID is associated with greater healthcare utilization than long Flu, suggesting a bigger impact on individual health and well-being, as well as on healthcare expenditure.

3) DISCUSSION

Line 422: “However, we can venture…” suggest making this a separate paragraph

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Best wishes,

Pippa

Philippa Dodd, MBBS MRCP PhD

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline checklist.

(DOCX)

Click here for additional data file.^{(34KB, docx)}

S1 Table. Long COVID symptoms and temporal trends.

(XLSX)

Click here for additional data file.^{(110.6KB, xlsx)}

S2 Table. Exclusion of symptoms by comorbidities.

(DOCX)

Click here for additional data file.^{(22.5KB, docx)}

S3 Table. Patient characteristics in the 2018 and 2019 Flu seasons.

(DOCX)

Click here for additional data file.^{(14.1KB, docx)}

S4 Table. COVID-19 outpatient cases and controls.

(DOCX)

Click here for additional data file.^{(15.7KB, docx)}

S5 Table. Detailed statistical data of the logistic and Poisson regression models for healthcare utilization.

(XLSX)

Click here for additional data file.^{(46.8KB, xlsx)}

Data Availability Statement

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PERMALINK

Prevalence and characteristics of long COVID in elderly patients: An observational cohort study of over 2 million adults in the US

Kin Wah Fung

Fitsum Baye

Seo H Baik

Zhaonian Zheng

Clement J McDonald

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

1. Introduction

2. Materials and methods

2.1 Study population

2.2 Identifying long COVID and long Flu

2.2.1 Long COVID

2.2.2 Long Flu

2.3 Comparing long COVID and long Flu

2.4 Statistical analysis

3. Results

3.1 Patient characteristics

Fig 1. Participant inclusion, exclusion, and matching.

Table 1. Characteristics of COVID-19 and influenza patients (CCI, Charlson comorbidity index, IQR, interquartile range; LIS, low-income subsidy; SD, standard deviation).

3.2 Incidence of long COVID and long Flu

Table 2. Incidence of long COVID and long Flu by various definitions.

3.3 Difference in symptomatology of long COVID and long Flu

Table 3. Symptoms in long COVID and long Flu patients (CI, confidence interval; OR, odds ratio of developing each specific symptom for long COVID compared to long Flu).

3.4 Difference in healthcare utilization in long COVID and long Flu

Table 4. Healthcare utilization of long COVID and long Flu patients (CI, confidence interval; ED, emergency department; IRR, incidence rate ratio; OR, odds ratio; SD, standard deviation).

4. Discussion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Philippa Dodd

Roles

Decision Letter 1

Philippa Dodd

Roles

Decision Letter 2

Philippa Dodd

Roles

Decision Letter 3

Philippa Dodd

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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