Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

M C Arjun; Arvind Kumar Singh; Debkumar Pal; Kajal Das; Alekhya G; Mahalingam Venkateshan; Baijayantimala Mishra; Binod Kumar Patro; Prasanta Raghab Mohapatra; Sonu Hangma Subba

doi:10.1371/journal.pone.0278825

. 2022 Dec 20;17(12):e0278825. doi: 10.1371/journal.pone.0278825

Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

M C Arjun ¹, Arvind Kumar Singh ^1,^*, Debkumar Pal ¹, Kajal Das ¹, Alekhya G ¹, Mahalingam Venkateshan ², Baijayantimala Mishra ³, Binod Kumar Patro ¹, Prasanta Raghab Mohapatra ⁴, Sonu Hangma Subba ¹

Editor: Vipa Thanachartwet⁵

PMCID: PMC9767341 PMID: 36538532

Abstract

Background

Long COVID or long-term symptoms after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe acute COVID-19 cases. We did this study to estimate the incidence and identify the characteristics and predictors of Long COVID among our patients.

Methodology

We recruited adult (≥18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks and six months of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristics of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID.

Results

We have analyzed 487 and 371 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47) and 223 days (IQR:195,251), respectively. Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) and 9.4% (95% CI: 6.7%,12.9%) of participants at four weeks and six months of follow-up, respectively. Incidence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n = 72) at four weeks of follow-up. At six months, the incidence among mild/moderate (n = 319) was 7.2% (95% CI:4.6%,10.6%) as compared to 23.1% (95% CI:12.5%,36.8%) in severe/critical (n = 52). The most common Long COVID symptom was fatigue. Statistically significant predictors of Long COVID at four weeks of follow-up were—Pre-existing medical conditions (Adjusted Odds ratio (aOR) = 2.00, 95% CI: 1.16,3.44), having a higher number of symptoms during acute phase of COVID-19 disease (aOR = 11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR = 2.32, 95% CI: 1.17,4.58), the severity of illness (aOR = 5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR) = 3.89, 95% CI: 2.49,6.08).

Conclusion

A considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers.

Introduction

COVID-19 was declared a pandemic in March 2020 [1]. Globally, 625 million people have been diagnosed, and around 6 million are reported dead due to COVID-19 [2]. Health systems worldwide are striving to stop the spread of the SAR-COV-2 virus and prevent death and complication due to COVID-19. Apart from acute illness, dialogues on the chronic effect of COVID-19 gained momentum as medical practitioners worldwide started reporting on post COVID complications even in mild cases [3]. It was observed that symptoms of COVID-19 either persist or new symptoms arise after a patient has recovered. Multiple nomenclatures began to appear to describe this condition which includes Long COVID, chronic COVID syndrome, long hauler COVID, post-acute sequelae of COVID-19, post-acute COVID19 syndrome etc [4,5].

Long COVID was discussed widely, and research was initiated to understand this phenomenon [6]. But there was no widely accepted definition for Long COVID, making it difficult to diagnose and treat the condition [7]. The National Institute for Health and Care Excellence (NICE) was among the first to come out with a rapid guideline to define Long COVID [8]. NICE defines Long COVID as signs and symptoms that continue or develop after acute COVID‑19, including both ongoing symptomatic COVID‑19 (from 4 to 12 weeks) and post‑COVID‑19 syndrome (12 weeks or more) [9]. Similarly, the Centers for Disease Control and Prevention (CDC) define Long COVID as a Post-COVID condition with a wide range of new, returning, or ongoing health problems people can experience four or more weeks after first being infected with the virus that causes COVID-19 [10]. WHO recently published a document defining the Long COVID based on the Delphi method [11].

Studies have shown that Long COVID can affect almost all systems in the body [12]. The most described in the literature are respiratory disorders, cardiovascular disorders, neurocognitive disorders, mental health disorders, metabolic disorders etc. Symptoms are in multitudes: including fatigue, breathlessness, cough, anxiety, depression, palpitation, chest main, myalgia, cognitive dysfunction (“brain fog”), loss of smell, etc. [12]. Newer symptoms are identified and included in the long COVID as evidence emerges [13]. Many initiatives have been launched to estimate the burden and characteristics of long COVID, especially in developed countries. The Office of National Statistics (ONS) in the United Kingdom gives an estimate of the prevalence and risk factors of long COVID using the national Coronavirus (COVID-19) Infection Survey (CIS) [14]. COVID Symptom study app is another data source [15]. National Institute of Health in the USA has also launched new initiatives to study Long COVID and is expected to bring out more evidence [6]. Independent researchers are also working on understanding this phenomenon.

In India, significantly less attention has been given to the burden of Long COVID [16]. During this study’s conception, there were no research papers available in peer-reviewed journals that measured the burden of Long COVID in India. As of today, India has more than 34 million cases of COVID-19 [17]. This can translate into a huge number of patients suffering from long COVID. Once the active cases come down, the already overstretched health systems can witness another public health crisis in the form of Long COVID. To mitigate this, we should have a clear idea about Long COVID to develop better management strategies. Post COVID care clinics are already functioning in some parts of the country [18]. The Government of India has also published a guideline for the management of post COVID sequelae [19]. But none of the systematic reviews on long COVID has included a study from India, and there is a wide evidence gap on this condition in India. Thus, it is pertinent that we undertake a study to measure the burden, the characteristics, and the predictors of long COVID in India to bring much-needed insight into this condition.

Methodology

We estimated the incidence, characteristics, and predictors of Long COVID by following up a cohort of patients who were Revere Transcription polymerase chain reaction (RTPCR) positive COVID-19 cases. The study was conducted at All India Institute of Medical Sciences (AIIMS) Bhubaneswar, a tertiary care government hospital and research institute. The study population included adult cases (age ≥18 years) of COVID-19 who were diagnosed with RTPCR test from AIIMS Bhubaneswar from April to September 2021. We did not test for the variant of COVID-19 but based on the Indian SARS-CoV-2 Genomics Consortium (INSACOG) data the predominant COVID-19 variant circulating in the community during the study period was Delta (B.1.617.2) [20]. Individuals less than 18 years and pregnant women were excluded.

We accessed the AIIMS Bhubaneswar COVID-19 screening OPD database and records of patients admitted due to COVID-19. The database was cleaned by removing individuals with missing phone numbers, patients who expired, and those less than 18 years. As per the operational definition based on NICE guidelines, these individuals were contacted through telephone after four weeks and six months from the date of their COVID-19 diagnosis. After taking verbal consent, a detailed telephonic interview was conducted to record the socio-demographic details, past medical history including chronic disease and substance use, acute manifestations of COVID-19, and the treatment received. Participants self-reported their height and weight, and Body Mass Index (BMI) was derived. BMI was classified based on World Health Organization criteria (W.H.O) [21]. Data on COVID-19 vaccination history was also collected. This was followed by self-reported Long COVID symptoms and their characteristics which included fatigue, cough, loss of taste and smell, cognitive dysfunction (Brain fog), etc., and an open-ended question. The interview questions were adapted from the W.H.O Global COVID-19 Clinical Platform Case Report Form (CRF) for Post COVID condition (Post COVID-19 CRF) [22]. All the data in this study was collected by the post-graduate student authors. Pre-testing of the questionnaire was done, and supervised calls were made before the beginning of actual data collection. The data collected during telephonic interviews were directly entered into EpiCollect5 app. An individual who could not be contacted after two attempts were excluded. The RTPCR cycle threshold (Ct) values during diagnosis of COVID-19 were retrieved from the hospital database to study its association with Long COVID symptoms.

Sample size and statistical analysis

The primary objective of this study was to estimate the percentage of participants who reported Long COVID symptoms. Apart from the overall percentage, we planned to estimate the percentage in the subgroups of COVID-19 patients separately, classified based on the severity of the acute COVID-19 disease. For mild/moderate acute COVID-19 subgroup, we assumed the percentage of Long COVID to be 20% based on previous literature [14]. A relative precision of 20% was used to derive a sample size of 400 for mild/moderate acute COVID-19 subgroup. Using the same approach, we also calculated the required sample size in severe/critical acute COVID-19 subgroup by estimating the prevalence of Long COVID to be 50%. The estimated sample size came to be 100 in the severe/critical subgroup. Thus, an overall 500 patients were targeted to be enrolled in the study.

Data was collected using EpiCollect5 and imported into Microsoft Excel for cleaning. The data was analyzed in statistical software R (version 3.6.3) and STATA version 16 (StataCorp, College Station, Texas 77845 USA). Incidence of Long COVID was determined by the number of participants who self-reported any of the Long COVID symptoms. The self-reported characteristics of symptoms were also given as proportions. The data were analyzed separately for mild to moderate and severe to critical acute COVID-19 patients. Logistics regression was used to find the predictive factors of Long COVID at the four weeks follow-up. Demographic variables, medical history including variables related to acute COVID-19 disease and COVID-19 vaccination were included in the logistic regression model based on the previous literature. Statistical significance for univariable analysis was set at p-value less than 0.05. Multivariable logistic regression was done to obtain an adjusted odds ratio with a 95% confidence interval. Clinically significant variables were added to the multivariable model.

Ethical issues

The institutional ethics committee (IEC) of AIIMS Bhubaneswar granted ethical approval before starting the study (IEC Number: T/IM-NF/CM&FM/21/37). The study was explained to each individual, and a telephonic verbal consent was taken before starting data collection. The consent process was approved by the Institutional Ethics Committee. After data collection, if the participant was found to have symptoms of Long COVID, they were referred to Long COVID OPD in the department of Pulmonary Medicine, AIIMS Bhubaneswar.

Results

We listed 698 COVID-19 RTPCR positive cases from April to September 2021, out of which 189 patients could not be contacted or enrolled. A total of 509 individuals were eligible to be included in the study. Consent was denied by nine participants, and thus a total of 500 interviews were conducted successfully at four weeks of follow-up. On the preliminary evaluation of data, thirteen entries had wrong dates and were dropped from the final analysis. A final sample of 487 individuals was analyzed at a median follow-up of 44 days (IQR = 39,47). The 487 participants were further followed up after six months. A total of 371 participants was successfully interviewed with a median follow-up of 223 days (IQR: 195,251) (Fig 1).

The mean age of the study participants was 39 years (SD = 15 years), ranging from 18 to 88 years. One hundred ninety-nine (40.9%) participants were female, and the majority were college graduates. Most of the participants were either unemployed or students or homemakers with no earnings. Thirty participants (6.2%) reported being in a job involving COVID-19 management. The majority of participants had normal BMI. (Table 1) Eighteen participants (3.7%) reported that they had COVID-19 before the current episode. Around 10% had a history of pre-existing Diabetes or Hypertension. Few participants gave the history of other comorbidities like asthma, tuberculosis, anxiety, cancer, or other chronic diseases, and none reported depression. A single question was used to record any type of self-reported substance use, and 54 (11.1%) participants gave the history of some form of substance use which included alcohol and tobacco. Two doses of vaccine were taken by 287 (58.9%) participants, one dose by 81 (16.6%), and there were 119 (24.5%) who had not been vaccinated at all. The majority of the sample had taken Covaxin. Very few participants reported having side effects post-vaccination.

Table 1. Sociodemographic characteristics, past medical history, and vaccination status of participants (n = 487).

Socio-demographic characteristics
Variable				n (%)
Age (Mean (SD); Range)				39 (15); 18 to 88
Females				199 (40.9)
Education	Illiterate/No formal education			24 (4.9)
	Studied up to 10 std or below			116 (23.8)
	Higher secondary			75 (15.4)
	College Graduate			218 (44.8)
	Post-graduate and above			54 (11.1)
Current Occupation	Unemployed/Student/Homemaker			205 (42.1)
	Professionals/Technical/Administrators			149 (30.6)
	Skilled and Unskilled Manual laborer			54 (11.1)
	Retired			11 (2.3)
	Other			68 (13.9)
Occupation involving COVID-19 management				30 (6.2)
BMI (n = 484)		Underweight (<18.5)		24 (5)
		Normal (18.5–24.9)		284 (58.7)
		Overweight (25.0–29.9)		153 (31.6)
		Obese (≥30.0)		23 (4.7)
Past Medical History
History of COVID-19 before the current episode				18 (3.7)
Diagnosed to have Diabetes				58 (11.9)
Diagnosed to have Hypertension				51 (10.5)
Diagnosed to have Anxiety				3 (0.6)
Diagnosed to have Asthma				15 (3.1)
Diagnosed to have Tuberculosis				5 (1)
Diagnosed to have Cancer				19 (3.9)
Diagnosed to have other medical condition				53 (10.9)
Participants who gave history of substance use				54 (11.1)
Smoking status			Current smoker	16 (3.3)
			Former (Not smoked more than one year)	7 (1.4)
Chewable tobacco				32 (6.6)
Alcohol use				19 (3.9)
COVID-19 Vaccination status
Participants who received two doses of COVID-19 vaccine				287 (58.9)
Participants who received one dose of COVID-19 vaccine				81 (16.6)
Participants who did not receive COVID-19 vaccine				119 (24.5)

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Clinical features of the participants revealed that majority of them had 1 to 4 symptoms during the acute phase, and the most common symptoms were Fever and Cough. According to W.H.O Post COVID-19 Case Report Form criteria, 415 (85.2%) had mild to moderate and 68 (14%) had severe disease, and four participants (0.8%) had become critical. Most of the participants, 377 (77.4%), underwent home-isolation and were treated as Outpatient and 110 (22.6%) were hospitalized (Table 2).

Table 2. Clinical features and management of acute illness of COVID-19 among participants (n = 487).

Total number of symptoms reported by each participant			n (%)
		No symptoms	111 (22.8)
		1 to 4 symptoms	312 (64.1)
		5 or more symptoms	64 (13.1)
Most common symptoms reported
	Fever		316 (64.9)
	Cough		221 (45.4)
	Body ache		89 (18.3)
	Breathing difficulty		78 (16)
	Loss of smell		63 (12.9)
	Loss of taste		60 (12.3)
Severity of acute illness	Mild/Moderate–Did not receive oxygen		415 (85.2)
	Severe- Required oxygen or was told you required oxygen		68 (14)
	Critical–Received invasive ventilation t		4 (0.8)
Care received during acute illness	Home isolation		377 (77.4)
Care received during acute illness	Admitted to hospital		110 (22.6)

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At four weeks of follow-up, the overall incidence of Long COVID was 29.2% (95% CI: 25.3%,33.4%) with 142 individuals reporting it. Subgroup analysis revealed that incidence of Long COVID was 62.5% (95% CI: 50.7%,73%) among severe/critical cases (n = 72), which was significantly higher than among mild/moderate cases (n = 415) at 23.4% (95% CI: 19.5%,27.7%). (Fig 2) Among participants who were asymptomatic during the acute phase of COVID-19 (n = 111), only six reported Long COVID symptoms.

At four weeks of follow-up the most common symptom reported was Fatigue 92 (64.8%), followed by Cough 46 (32.4%). Only three participants reported cognitive dysfunction or Brain fog. Limitation of daily activity following Long COVID was not reported by the majority, but 41 (28.9%) participants reported having some activity limitation. Out of the 142 participants who self-reported Long COVID, 131 (92.3%) perceived the symptoms to be not severe, whereas 11 (7.7%) experienced the symptoms a lot. Health care practitioners were consulted for Long COVID by 49 (34.5%) participants (Table 3).

Table 3. Self-reported Long COVID symptoms and its features at four weeks and six months follow-up.

Variable		At 4 weeks (N = 487) n (%)	At 6 months (N = 371) n (%)
Self-reported Long COVID symptoms		n = 142 (29.2)	n = 35 (9.4)
Most common Self-reported Long COVID symptoms	Fatigue	92 (64.8)	19 (54.3)
	Cough	46 (32.4)	6 (17.1)
	Breathing difficulty	24 (16.9)	10 (28.6)
	Chest pain	12 (8.4)	2 (5.7)
	Loss of taste	6 (4.2)	0
	Loss of smell	4 (2.8)	1 (2.9)
	Brain fog	3 (2.1)	0
	Palpitation	3 (2.1)	1 (2.9)
	Anxiety	3 (2.1)	1 (2.9)
	Depression	0	4 (11.4)
Limitation of activity	Activity limited a lot	10 (7)	0
	Activity limited a little	41 (28.9)	16 (45.7)
	No activity limitation	91 (64.1)	19 (54.3)
Perceived severity of Long COVID symptoms	Not severe	131 (92.3)	33 (94.3)
Perceived severity of Long COVID symptoms	Severe	11 (7.7)	2 (5.7)
Consulted a health care practitioner		49 (34.5)	8 (22.9)

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At six months, we followed up 371 out of 487 participants (Lost to follow-up 23.8%). The incidence of Long COVID reported was 9.4% (95% CI: 6.7%,12.9%) with a median follow-up of 223 days (IQR: 195,251). The incidence among mild/moderate (n = 319) was 7.2% (95% CI:4.6%,10.6%) as compared to 23.1% (95% CI:12.5%,36.8%) in severe/critical (n = 52). (Fig 2) Fatigue was the most common symptom. (Table 3) Between the first and second follow-up, 15 participants newly reported Long COVID symptoms, 151 participants received additional vaccination, and 5 participants were newly diagnosed with diabetes. During the follow-up period, seventeen participants were again diagnosed with COVID-19, which was mild/moderate in severity, out of which only one participant reported Long COVID.

We analyzed the predictors for Long COVID at 4 weeks of follow-up. Analysis revealed that age, sex, occupation, BMI, history of substance use, and Cycle threshold (Ct) values were not significantly associated with Long COVID. Pre-existing medical conditions (adjusted Odds ratio (aOR) = 2.00 (95% CI: 1.16,3.44)), receiving two doses of COVID-19 vaccination (aOR = 2.32 (95% CI: 1.17,4.58)), having more severe COVID-19 disease (aOR = 5.71 (95% CI: 3.00,10.89)) and having a greater number of symptoms during acute phase of COVID-19 disease were significantly associated with Long COVID. Admission to hospital during the acute phase of disease was significantly associated with Long COVID (Odds ratio = 3.89 (95% CI: 2.49,6.08)); however, this variable was not included in the multivariable model due to the colinear relation with the severity of the disease. (Table 4) The odds ratio of Long COVID for acute COVID-19 severity remained similar for subgroups based on COVID-19 vaccination status. For two doses of COVID-19 vaccination, the odds ratio of Long COVID for acute COVID-19 severity was 7.5 (95% CI: 3.1,18.4), and for zero to one dose of COVID-19 vaccination, the odds ratio was 7.4 (95% CI: 3.5,15.8). This analysis rules out the possibility of interaction between COVID-19 vaccination and acute COVID-19 severity.

Table 4. Predictors of self-reported Long COVID symptoms at four weeks follow-up.

Variable		Univariable logistic regression		Multivariable logistic regression
Variable		Odds Ratio (95% CI)	p value	Adjusted Odds Ratio (95% CI)	p value
Age Categories	18 to 45 years	Reference	-	Reference	-
	46 to 59 years	1.46 (0.91,2.36)	0.12	1.24 (0.68,2.29)	0.48
	60 years & above	1.46 (0.79,2.67)	0.22	1.08 (0.48,2.43)	0.86
Sex	Male	Reference	-	Reference	-
Sex	Female	1.33 (0.89,1.97)	0.16	1.29 (0.74,2.25)	0.36
Occupation	Unemployed/Student/Homemaker	Reference		Reference
	Professional/Technical/ Administrative/Managerial	1.32 (0.84,2.08)	0.23	1.79 (0.96,3.33)	0.06
	Skilled/Unskilled manual	0.65 (0.31,1.34)	0.24	0.82 (0.32,2.09)	0.68
	Other	0.98 (0.55,1.74)	0.94	1.15 (0.53,2.48)	0.73
BMI	Underweight (<18.5)	Reference	-	Reference	-
	Normal or lean (18.5–24.9)	2.13 (0.71,6.44)	0.18	1.58 (0.39,6.47)	0.52
	Overweight (25.0–29.9)	2.35 (0.76,7.26)	0.14	1.49 (0.35,6.26)	0.58
	Obese (≥30.0)	1.05 (0.23,4.82)	0.95	0.56 (0.09,3.44)	0.53
History of substance use		0.75 (0.39,1,44)	0.38	0.95 (0.41,2.16)	0.89
Past history of COVID-19		0.93 (0.33,2.66)	0.90	0.66 (0.20,2.15)	0.49
Pre-existing medical condition		1.69 (1.12,2.55)	0.01	2.00 (1.16,3.44)	0.01
COVID-19 vaccination	Not vaccinated	Reference	-	Reference	-
	Completed 1 dose	1.30 (0.66,2.55)	0.45	1.88 (0.84,4.22)	0.13
	Completed 2 doses	2.05 (1.23,3.42)	0.01	2.32 (1.17,4.58)	0.01
Number of COVID-19 symptoms	No symptoms	Reference	-	Reference	-
	1 to 4 symptoms	9.40 (3.99,22.09)	<0.001	6.88 (2.74,17.23)	<0.001
	5 or more symptoms	12.77 (4.89,33.37)	<0.001	11.24 (4.00,31.51)	<0.001
Severity of COVID-19 disease	Mild/Moderate	Reference	-	Reference
Severity of COVID-19 disease	Severe/Critical	5.46 (3.22,9.27)	<0.001	5.71 (3.00,10.89)	<0.001
Care received during COVID-19 disease	Home Isolation	Reference	-	-	-
Care received during COVID-19 disease	Admitted to hospital	3.89 (2.49,6.08)	<0.001	-	-
Cycle threshold	E Gene/N Gene (n = 442)	0.98 (0.94,1.02)	0.38	-	-
Cycle threshold	ORF1a/ORF1b/N/N2 Gene (n = 378)	0.99 (0.95,1.03)	0.53	-	-

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Discussion

Long COVID is studied extensively all around the world, but research from India is limited. A recently published living systematic review has identified important research gaps, which includes paucity of evidence from low to middle-income countries and in people who were not hospitalized [23]. Both these research gaps are addressed in our study.

The overall incidence of Long COVID in our study was 29.2%, with a median follow-up period of 44 days. This is comparable to the Office of National Statistics (UK) estimates based on their National Coronavirus (COVID-19) Infection Survey. The survey estimates that around 1 in 5 respondents testing positive for COVID-19 exhibit symptoms for five weeks or longer, i.e., 21% (CI: 19.9,22.1) [14]. In mild to moderate cases, the symptoms of Long COVID were 23.4% in our study, after four weeks of COVID-19 infection. A study from India reported Long COVID symptoms in mild COVID-19 to be 22.6% (prevalence of fatigue), albeit with a low sample size [24]. Similarly, another study from Northern India, which followed up the patients from a tertiary care hospital, estimates that 22% had Long COVID [25]. In severe to critical cases with a sample size of 72, our study estimated an incidence of 62.5%. These estimates are similar to a study from India published in pre-print server, which reported dyspnea in 74.3% and fatigue and disturbed sleep in more than 50% of patients after 30 to 40 days of recovery [26]. Another study in pre-print, which estimated Long COVID in hospitalized patients of North India, gave an estimate of 40.3% after 4 to 6 weeks follow-up [27]. High prevalence of Long COVID symptoms in severe and hospitalized cases are reported from multiple studies from all over the world [28,29].

We followed the same cohort of 487 individuals for a median of 223 days. There were 23.8% lost to follow-up. The Long COVID incidence reported in the six months follow-up was considerably low at 9.4% compared to the 29.2% reported at four weeks. At the six months follow-up, many participants did not report the symptoms of Long COVID that they reported during the four weeks follow-up. The incidence of 9.4% of Long COVID at six months is also very low when compared to other cohort studies from different parts of the world [30–33].

The most common Long COVID symptoms found in our study was fatigue. This is similar to other studies from India [24–26,34]. The Self-reported symptoms in the COVID Symptom Study app and the National Coronavirus (COVID-19) Infection Survey (CIS) from the United Kingdom has also recorded that fatigue is the most common symptom reported [14,15]. Multiple systematic reviews and meta-analysis on Long COVID have listed fatigue as the most common or among the first three Long COVID symptoms [12,23,35–37]. A recent study from India reported fatigue to be present even after three months of recovery from COVID-19 [38]. Although fatigue was self-reported in this study, a consistent finding in multiple studies indicates that fatigue is, in fact, the most common of Long COVID symptoms [39].

Predictors of Long COVID are important because it helps to prioritize the at-risk population and design interventions. In our study, one of the strongest predictors of Long COVID was the severity of COVID-19 disease and hospital admission. This is intuitive because the chances of having persistent symptoms after four weeks post-infection can be higher if the disease is severe. This is backed up by a systematic review which found that hospitalization during the acute infection (odds ratio [OR] 2·9, 95% CI 1·3–6·9) was the most significant predictor of developing the post-COVID syndrome [36]. Similar to the severity of the COVID-19 disease, having more than one symptom during the acute phase of COVID-19 disease was associated with Long COVID. This finding is similar to the COVID symptoms app study based on self-reported symptoms [15]. Another important predictor of Long COVID was the presence of pre-existing conditions like diabetes and hypertension. A study from India and a systematic review on this topic has found a similar and strong association between the pre-existing condition and Long COVID [26,35]. Age and sex, which was commonly found to be associated with Long COVID was not a significant predictor in our study. Cycle threshold (Ct) values of two genes were also not a significant predictor of Long COVID.

An observational paradox in our study was that the participants who took two doses of COVID-19 vaccination had higher odds of developing Long COVID. It could be due to better survival in vaccinated individuals who may continue to exhibit symptoms of COVID-19 disease. We could not find any interaction effect of COVID-19 vaccination and acute COVID-19 severity on causing Long COVID. This association might have also arisen due to Collider bias [40]. The Collider bias might have operated in this case since the sample included only COVID-19 positive tested patients who accessed the hospital (healthcare workers included) making the sample inherently biased to derive such conclusions. A rapid review by UK Health Security Agency has concluded that vaccinated people are less likely to report Long COVID symptoms [41]. Although most studies show a negative association of COVID-19 vaccination and Long COVID, a recent study of 13 million people has reported that Long COVID risk falls only slightly after vaccination [42]. The negative association of COVID-19 vaccination and development of Long COVID is reiterated with the recent systematic review which concluded with low level of evidence that vaccination before SARS-CoV-2 infection could lower the risk for development of Long COVID [43].

The strength of this study is that all the cases of COVID-19 were diagnosed with RTPCR, and there is minimal risk of misclassification. The questionnaire used to capture the Long COVID was adapted from the standard case reporting format recommended by W.H.O. The data were collected by doctors involved in patient care and which improves the validity of the findings. Our study also had limitations. The Long COVID symptoms were all self-reported, and thus objective assessment of symptoms like fatigue was not done. Telephonic interviews precluded us from collecting additional information like clinical and radiological examination for correlating with the findings. Telephonic interview can also introduce recall bias especially with collecting information regarding confounding variables like severity of COVID-19 disease, vaccination status etc. The cause of death of fifty-two individuals who were not alive during the time of data collection was not enquired, and their death may have been related to Long covid complications. Also, we could not compare the baseline characteristics of participants with non-responders who were not included in the study, to assess for response bias.

In developed countries, many large-scale cohort studies are undertaken to understand this phenomenon [33,44]. Similar studies on Long COVID are lacking in India, and our research community should bridge this gap. We need more research into Long COVID to objectively assess the symptoms, to monitor the symptoms for a longer duration, and to study the biological and radiological markers, which can lead to better treatment guidelines and comprehensive management of COVID-19 disease.

Supporting information

S1 Checklist. STROBE statement—Checklist of items that should be included in reports of cross-sectional studies.

(DOCX)

Click here for additional data file.^{(31.3KB, docx)}

Data Availability

The anonymized data set has been uploaded to the public repository (figshare). The dataset can be accessed using the DOI: 10.6084/m9.figshare.21665618

Funding Statement

The authors received no specific funding for this work.

References

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PLoS One. doi: 10.1371/journal.pone.0278825.r001

Decision Letter 0

Vipa Thanachartwet

7 Oct 2022

PONE-D-22-01446Prevalence, characteristics, and predictors of Long COVID among diagnosed cases of COVID-19PLOS ONE

Dear Dr. Arvind Kumar Singh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. We appreciate your study which is an interesting study. However, the manuscript does not meet the publication criteria as the study design and statistical analysis should be appropriate and data should be described in sufficient detail. Please carefully consider and respond all of the reviewers’ comments, criticisms and suggestions. Please submit your revised manuscript by Oct 17 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:

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Additional Editor Comments (if provided):

There are some important points raised as follows:

This study aims to estimate the prevalence and identify the characteristics and predictors of Long COVID among patients with acute COVID-19. Long COVID occur following acute COVID-19 and this condition develop during a particular time period, i.e., a new case, therefore, this study should determine the incidence rather than the prevalence. The median follow-up time of participants was 44 days in the results (page 7), however the follow-up time was too short for determining the occurrence of Long COVID.
In methodology (page 5), “these individuals were contacted through telephone after four weeks from the date of their COVID-19 diagnosis. After taking verbal consent, a detailed telephonic interview was conducted to record the socio-demographic details, past medical history including chronic disease and substance use, acute manifestations of COVID-19, and the treatment received.” The recall bias might occur in this study and should be addressed.
In methodology (page 5), “Pre-testing of the questionnaire was done, and supervised calls were made before the beginning of actual data collection.” Is there any validation of the questionnaire prior to the initiation of the study?
The sample size for the study was calculated separately for mild to moderate cases and severe cases. Based on previous estimates, a 20% prevalence was taken for mild to moderate cases and the required sample size was 400. (14) For severe cases we assumed a 50% prevalence of Long COVID, and the sample size was calculated to be 100. A final sample of 487 individuals was analyzed (Figure 1) in the results (page 7). The sample size estimation was confusing to the readers and the occurrence of Long COVID for different severity was not specified. The data should be described in sufficient detail in Tables 1-3. The authors should present the possible associated factors for the occurrence of Long COVID prior to analysis using a logistic regression model.

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Reviewer #1: I Don't Know

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Reviewer #1: No

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Reviewer #1: Yes

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Reviewer #1: In this cross-sectional single-site study, Singh et al describe the prevalence of persistent symptoms 4 weeks after COVID-19 diagnosis as determined by patient report on a telephone survey. While as the authors note this report adds to the minimal reporting of Long COVID in India to date, the findings are not novel, though they agree with prior reports. The study is limited by response bias and short follow-up time. Further, the authors do not specify the time period of data collection and when the patients were infected with SARS-CoV-2; given the different clinical outcomes of different variants this is important to know.

Specific comments:

Abstract:

Results section of abstract: I would change “significant number” to “higher number” as this is confusing with statistical significance.

Introduction:

Update numbers – 263 cases and 5 million deaths are no longer accurate numbers.

Methodology:

You specify that cases were diagnosed between April to September – but of what year? Can you describe the surges occurring at this time and the most common variants?

You mention that participants with missing phone numbers were scrubbed from the dataset. This is a source of bias and should be addressed. What was different about patients without phone numbers? Or those who refused to participate? Response bias should be noted.

Why was follow up done only at 4 weeks and not further out? Are there plans for more long term follow up? 4 weeks is quite short and many of these patients may recover in the following weeks.

Why was sample size included, if the goal was to simply describe the prevalence?

Results:

Why were pregnant women excluded? There is no need given this is an observational study.

It is interesting that the previously twice vaccinated patients had more Long COVID at 4 weeks. You report prior infections and vaccination, but how many of these participants had both prior infection and vaccination? How long ago were the vaccinations? Of those who had prior COVID-19, did they have long covid after that infection too? What was different about participants who were vaccinated? Were participants with more comorbidities or older age more likely to be vaccinated, and that’s why they also were more likely to have long COVID?

The sentence reading “Females were 199…” should be re-written. “One hundred ninety nine (40.9%) participants were female, and the majority were… [specify graduates of what? College graduates? High school graduates?]”

**********

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Reviewer #1: No

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PLoS One. 2022 Dec 20;17(12):e0278825. doi: 10.1371/journal.pone.0278825.r002

Author response to Decision Letter 0

6 Nov 2022

Old title: Prevalence, characteristics, and predictors of Long COVID among diagnosed cases of COVID-19

New title: Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

Additional Editor Comments and Response :

Comment: This study aims to estimate the prevalence and identify the characteristics and predictors of Long COVID among patients with acute COVID-19. Long COVID occur following acute COVID-19 and this condition develop during a particular time period, i.e., a new case, therefore, this study should determine the incidence rather than the prevalence. The median follow-up time of participants was 44 days in the results (page 7), however the follow-up time was too short for determining the occurrence of Long COVID.

Reply: 1 This study aims to estimate the prevalence and identify the characteristics and predictors of Long COVID among patients with acute COVID-19. Long COVID occur following acute COVID-19 and this condition develop during a particular time period, i.e., a new case, therefore, this study should determine the incidence rather than the prevalence. The median follow-up time of participants was 44 days in the results (page 7), however the follow-up time was too short for determining the occurrence of Long COVID. Thank you for the comments. We have removed the word “prevalence” from the manuscript and has edited the title also.

The follow-up period was decided based on the definition of Long COVID given by National Institute for Health and Care Excellence (NICE) UK. The same definition is used by Govt. of India. According to NICE UK definition, Long COVID is classified as signs and symptoms that continue or develop after acute COVID‑19, including both ongoing symptomatic COVID‑19 (from 4 to 12 weeks) and post‑COVID‑19 syndrome (12 weeks or more).

We have followed up this cohort at 6 months and this data was not available at the time of submission to journal. In response to Editor and reviewer comments, we have added the 6 months follow-up data to this manuscript. (“Manuscript” file without track changes: (Results at Page 12, Lines:213-222 and Table 3) Thank you.

Comment 2: 2 In methodology (page 5), “these individuals were contacted through telephone after four weeks from the date of their COVID-19 diagnosis. After taking verbal consent, a detailed telephonic interview was conducted to record the socio-demographic details, past medical history including chronic disease and substance use, acute manifestations of COVID-19, and the treatment received.” The recall bias might occur in this study and should be addressed. Thank you for pointing this out. We have now discussed this possibility of Recall Bias in the limitation paragraph of the revised manuscript (“Manuscript” file without track changes: Page 19, Lines: 328-330)

Since the data on COVID-19 diagnosis was taken from the hospital records, we do not expect any bias in estimating our primary objective. We also believe that since COVID-19 diagnosis was a potential life changing diagnosis, the patients might recall much of information, although the possibility of recall bias still exist.

Reply: 2 In methodology (page 5), “these individuals were contacted through telephone after four weeks from the date of their COVID-19 diagnosis. After taking verbal consent, a detailed telephonic interview was conducted to record the socio-demographic details, past medical history including chronic disease and substance use, acute manifestations of COVID-19, and the treatment received.” The recall bias might occur in this study and should be addressed. Thank you for pointing this out. We have now discussed this possibility of Recall Bias in the limitation paragraph of the revised manuscript (“Manuscript” file without track changes: Page 19, Lines: 328-330)

Comment 3: In methodology (page 5), “Pre-testing of the questionnaire was done, and supervised calls were made before the beginning of actual data collection.” Is there any validation of the questionnaire prior to the initiation of the study?

Reply 3: The questionnaire was adapted from the W.H.O Global COVID-19 Clinical Platform Case Report Form (CRF) for Post COVID condition (Post COVID-19 CRF). Thus, we did not do a separate validation. We have pre-tested the questionnaire and trained the data collectors for standardization and accuracy.

Comment 4: 4 The sample size for the study was calculated separately for mild to moderate cases and severe cases. Based on previous estimates, a 20% prevalence was taken for mild to moderate cases and the required sample size was 400. (14) For severe cases we assumed a 50% prevalence of Long COVID, and the sample size was calculated to be 100. A final sample of 487 individuals was analyzed (Figure 1) in the results (page 7). The sample size estimation was confusing to the readers and the occurrence of Long COVID for different severity was not specified. The data should be described in sufficient detail in Tables 1-3. The authors should present the possible associated factors for the occurrence of Long COVID prior to analysis using a logistic regression model. Thank you for helping us improve the manuscript. We have rewritten the sample size paragraph with more clarity. (Page 6, Lines: 125-135)

The primary objective was to estimate the proportion of COVID-19 patients who report the symptoms of Long COVID. We also planned to estimate this proportion separately for different severity of acute COVID-19. Hence, we did separate sample size estimation.

The results are now enriched with the addition of 6 months data. Figure 2 has been edited to highlight the finding of our primary objective.

The variables added to the logistic regression model was taken from the W.H.O Global COVID-19 Clinical Platform Case Report Form (CRF) for Post COVID condition (Post COVID-19 CRF). This was the reason we did not mention the variables separately in the original manuscript. We have now mentioned the choice of variables in Methodology section of the revised manuscript. (Page 6, Lines:143-146)

Reply 4: Thank you for helping us improve the manuscript. We have rewritten the sample size paragraph with more clarity. (Page 6, Lines: 125-135)

The results are now enriched with the addition of 6 months data. Figure 2 has been edited to highlight the finding of our primary objective.

Reviewer #1 Comments and Response

Comment 1: Reviewer #1: In this cross-sectional single-site study, Singh et al describe the prevalence of persistent symptoms 4 weeks after COVID-19 diagnosis as determined by patient report on a telephone survey. While as the authors note this report adds to the minimal reporting of Long COVID in India to date, the findings are not novel, though they agree with prior reports. The study is limited by response bias and short follow-up time. Further, the authors do not specify the time period of data collection and when the patients were infected with SARS-CoV-2; given the different clinical outcomes of different variants this is important to know.

Reply: 1 Reviewer #1: In this cross-sectional single-site study, Singh et al describe the prevalence of persistent symptoms 4 weeks after COVID-19 diagnosis as determined by patient report on a telephone survey. While as the authors note this report adds to the minimal reporting of Long COVID in India to date, the findings are not novel, though they agree with prior reports. The study is limited by response bias and short follow-up time. Further, the authors do not specify the time period of data collection and when the patients were infected with SARS-CoV-2; given the different clinical outcomes of different variants this is important to know. Thank you for the comments. Long COVID is now a research priority all over the world especially with the release of National Research Action Plan on Long COVID by the US Department of Health

and Human Services. The plan recognizes the need to have more studies on Long COVID and its risk factors from different geographical regions of the world. During the submission of this manuscript, there was hardly any well conducted study from India which used standard definitions and questionnaire. We believe the novelty in our research is that we bridge this evidence gap.

The response bias is now discussed in the limitation section of the revised manuscript. (“Manuscript” file without track changes Page 19, Lines:332-334). Thank you for the insights.

We now have the 6 months follow up data of this cohort which was not available during the original submission to journal. We have added the 6-month data and revised the manuscript.

The missing time period (year) was a typographical error, and this is corrected in the revised manuscript. Thank you for pointing this out. We have also added the reference to data on genetic variants of COVID-19 predominant in the community at the time conducting our study (Page 5, Lines:97-100, Reference No 20).

Comment 2: 2 Abstract:

Background section of abstract has some awkward language. I suggest defining Long COVID as “long-term symptoms after COVID-19”. I would specify that by “severe cases” you mean severe acute COVID-19, not severe long COVID. Results section of abstract: I would change “significant number” to “higher number” as this is confusing with statistical significance. Thank you for the valuable inputs to improve our manuscript. We have incorporated all the suggestions in the revised manuscript.

Reply 2: Thank you for the valuable inputs to improve our manuscript. We have incorporated all the suggestions in the revised manuscript.

Comment 3: Introduction:

Update numbers – 263 cases and 5 million deaths are no longer accurate numbers.

Reply 3: The numbers are updated. Thank you.

Comment 4: Methodology:

You specify that cases were diagnosed between April to September – but of what year? Can you describe the surges occurring at this time and the most common variants?

Reply 4:Thank you for pointing out this mistake. We have added the year 2021. The surge occurring at this time was due to Delta variant (B.1.617.2) and we have described and added reference for the same in the methodology. (Page 5, Lines:97-100, Reference No 20).

Comment 5: Methodology:

Reply 5: We agree with reviewer that there is a possibility of response bias. Since the COVID-19 hospital database we accessed did not have baseline characteristics of the patients and all data in the dataset came after the telephonic interview, we don’t have any meaningful data to make the comparison. We have discussed the same in limitations. (Page 19, Lines:332-334)

Comment 6: 6 Methodology:

Why was follow up done only at 4 weeks and not further out? Are there plans for more long term follow up? 4 weeks is quite short and many of these patients may recover in the following weeks. The 4 weeks follow-up was chosen based on the Long COVID definition given by National Institute for Health and Care Excellence (NICE) UK. The same definition is used by Govt. of India. (Reference 19)

During submission we did not had data on further follow-up. But now we are ready with the 6 months follow-up data and the same is added to the manuscript. (Page 12, Lines:213-222, Table 3) The choice of 6 months follow-up is also based on Long COVID definition by NICE.

Reply 6: The 4 weeks follow-up was chosen based on the Long COVID definition given by National Institute for Health and Care Excellence (NICE) UK. The same definition is used by Govt. of India. (Reference 19)

Comment 7: Methodology:

Why was sample size included if the goal was to simply describe the prevalence?

Reply 7: 7 Methodology:

Why was sample size included if the goal was to simply describe the prevalence? Thank you for the comment. We needed a rough estimate on how many participants to be followed up to get a meaningful estimate of incidence. The process of sample size calculation is rewritten based on comments from the Editor of the journal. (Page 6, Lines: 125-135)

Comment 8: Methodology:

Reply 8: 8 Methodology:

I’m confused about the use of multivariate logistic regression. The methods say that only the variables with p value <0.2 were included but Table 4 looks like all variables were included? Also, was occupation run as categorical or ordinal variable? Seems hard to assign an order to these categories and being unemployed versus a student seems like very different backgrounds so it is unclear why these were lumped together. Finally, why was number of COVID-19 symptoms included as categorical variable instead of continuous? It seems that there is a huge difference in severity between having 1 versus 4 symptoms but these are lumped into same group. Thank you for the comments. The plan was to use both statistics and clinical significance for including the variables in the multivariable logistic regression. Since most of the variables were clinically significant, they were retained except in case of collinearity. The line on p value < 0.2 is dropped to avoid confusion.

Occupation was run as categorical variable. They were lumped together to avoid excess subcategories and resultant decrease in statistical power.

The categorization of COVID-19 symptoms was based on previous literature. The grouping of the symptoms ensured that enough sample size is available in each category and adequate statistical power is available for analysis.

Comment 9: Results:

Why were pregnant women excluded? There is no need given this is an observational study.

Reply 9: Thank you for the comment. We agree that Pregnant women need not be excluded in this observational study. Since common Long COVID symptoms like fatigue is common in pregnancy, we took a decision on excluding pregnant women. Only 4 participants were excluded due to this reason.

Comment 10: Results:

Reply 10: Only 16 participants had prior infection and at least one dose of vaccination.

The date of last vaccination was collected but many of the participants could not report the exact date and due to high number of missing data the variable was dropped from analysis. But since COVID-19 positive patients cannot receive vaccination for 3 months post-infections, all the participants received vaccination prior to infection with COVID-19.

Only 18 participants had history of past history of COVID-19. We did not have a variable asking these participants for Long COVID after the past infection. Since the numbers were small, we did not explore this further.

It is likely that vaccinated individuals are different from unvaccinated with regards to age and comorbidities, but these variables were added and adjusted in the multivariable logistic regression to remove the confounding effects.

We thank you for the comments. Since the finding of vaccination increasing the odds of Long COVID was unusual, we have discussed this in detail by adding latest articles on this question, as well as discussed other possibilities of bias (Collider bias). We also checked for interaction of acute COVID-19 severity and COVID-19 vaccination on causing Long COVID and found no interaction. (Page 14, Lines:238-244) The same is reported with Odds ratio in the result section. In the Discussion section a full paragraph is dedicated to the discussion on these points and literature. (Page 18, Lines:304-319)

Comment 11: Results:

Reply 11: Thank you for the comments. We have edited these in the revised manuscript. (Page 8, Lines: 172-173)

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PLoS One. doi: 10.1371/journal.pone.0278825.r003

Decision Letter 1

Vipa Thanachartwet

24 Nov 2022

Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

PONE-D-22-01446R1

Dear Dr. Singh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Additional Editor Comments (optional):

We appreciate your efforts for the study and the authors have made a careful revision to the manuscript. All issues were revised according to the comments and suggestions.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0278825.r004

Acceptance letter

Vipa Thanachartwet

12 Dec 2022

PONE-D-22-01446R1

Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

Dear Dr. Singh:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Associated Data

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

Supplementary Materials

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Data Availability Statement

The anonymized data set has been uploaded to the public repository (figshare). The dataset can be accessed using the DOI: 10.6084/m9.figshare.21665618

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PERMALINK

Characteristics and predictors of Long COVID among diagnosed cases of COVID-19

M C Arjun

Arvind Kumar Singh

Debkumar Pal

Kajal Das

Alekhya G

Mahalingam Venkateshan

Baijayantimala Mishra

Binod Kumar Patro

Prasanta Raghab Mohapatra

Sonu Hangma Subba

Roles

Abstract

Background

Methodology

Results

Conclusion

Introduction

Methodology

Sample size and statistical analysis

Ethical issues

Results

Fig 1. Flow chart showing the selection of study participants and follow-up at four weeks and six months.

Table 1. Sociodemographic characteristics, past medical history, and vaccination status of participants (n = 487).

Table 2. Clinical features and management of acute illness of COVID-19 among participants (n = 487).

Fig 2. Percentage of self-reported Long COVID symptoms at four weeks and six months of follow-up.

Table 3. Self-reported Long COVID symptoms and its features at four weeks and six months follow-up.

Table 4. Predictors of self-reported Long COVID symptoms at four weeks follow-up.

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Vipa Thanachartwet

Roles

Author response to Decision Letter 0

Decision Letter 1

Vipa Thanachartwet

Roles

Acceptance letter

Vipa Thanachartwet

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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