Prevalence and Predictors of Long COVID During the Omicron Variant Dominant Epidemic at 3 and 6 Months in Thailand

Chonlawat Chaichan; Sasinuch Rutjanawech; Sirinda Sritipsukho; Thammanard Charernboon; Paskorn Sritipsukho

doi:10.12688/f1000research.147565.1

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Research Article

Prevalence and Predictors of Long COVID During the Omicron Variant Dominant Epidemic at 3 and 6 Months in Thailand

[version 1; peer review: 1 approved with reservations]

Chonlawat Chaichan¹, Sasinuch Rutjanawech², Sirinda Sritipsukho³, Thammanard Charernboon^4,5, Paskorn Sritipsukho^4,6

Chonlawat Chaichan¹, Sasinuch Rutjanawech², [...] Sirinda Sritipsukho³, Thammanard Charernboon^4,5, Paskorn Sritipsukho^4,6

PUBLISHED 20 Jun 2025

Author details Author details

¹ Faculty of Medicine, Thammasat University, Pathumthani, 12120, Thailand
² Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathumthai, 1210, Thailand
³ Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
⁴ Center of Excellence in Applied Epidemiology, Research Group in Pediatrics Thammasat University, Pathumthani, 10120, Thailand
⁵ Psychiatry, Faculty of Medicine, Thammasat University, Pathumthani, 12120, Thailand
⁶ Department of Paediatrics, Faculty of Medicine, Thammasat University, Pathumthani, Thailand

Chonlawat Chaichan
Roles: Conceptualization, Data Curation, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Sasinuch Rutjanawech
Roles: Resources, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Sirinda Sritipsukho
Roles: Data Curation, Resources, Software

Thammanard Charernboon
Roles: Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Paskorn Sritipsukho
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Faculty of Medicine – Thammasat University collection.

This article is included in the Coronavirus (COVID-19) collection.

Abstract

Background

Long Covid is defined as conditions that continue or develop after acute COVID-19 infection more than 3 months. This study aimed to analyze the point prevalence and predictors of long Covid in the Omicron variant dominant epidemic at 3 and 6 months in Thailand.

Methods

Prospective cohort study on the COVID-19 cohort at Thammasat University Hospital (TUH) and Thammasat Field Hospital (TFH) during the Omicron variant dominant epidemic was performed. The phone interview for long Covid symptoms was performed among 1,388 patients at 3 months and 917 patients at 6 months after the infection. Clinical characteristics of all participants were retrieved from the database of the hospital.

Results

The point prevalence of long Covid was 52.59% at 3 months and 28.57% at 6 months. The most prevalent symptom group at both time points was fatigue and weakness, affecting 31.70% of patients at 3 months and 15.81% at 6 months. Three significant predictors of long Covid were identified at both 3 and 6 months: severe-to-critical illness during the acute phase, female sex, and healthcare worker occupation. The adjusted odds ratios (aOR) for severe-to-critical illness were 4.50 [95% CI, 1.59–12.75] at 3 months and 9.15 [95% CI, 1.35–62.10] at 6 months. Female sex was associated with aORs of 2.04 [95% CI, 1.61–2.58] at 3 months and 2.41 [95% CI, 1.71–3.34] at 6 months. Healthcare workers had aORs of 1.96 [95% CI, 1.39–2.78] at 3 months and 3.12 [95% CI, 1.85–5.27] at 6 months. Obesity was a significant predictor of long Covid only at 3 months.

Conclusions

The prevalence of long Covid remains substantial following the Omicron variant-dominant epidemic. Key predictors of long Covid at both 3 and 6 months include severe-to-critical illness during the acute phase, female sex, and healthcare worker occupation.

Keywords

Long COVID, Omicron, Covid-19, predictor, Thailand 

Corresponding author: Paskorn Sritipsukho

Competing interests: No competing interests were disclosed.

Grant information: This study was funded by the Faculty of Medicine, Thammasat University.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Chaichan C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Chaichan C, Rutjanawech S, Sritipsukho S et al. Prevalence and Predictors of Long COVID During the Omicron Variant Dominant Epidemic at 3 and 6 Months in Thailand [version 1; peer review: 1 approved with reservations]. F1000Research 2025, 14:607 (https://doi.org/10.12688/f1000research.147565.1) First published: 20 Jun 2025, 14:607 (https://doi.org/10.12688/f1000research.147565.1) Latest published: 20 Jun 2025, 14:607 (https://doi.org/10.12688/f1000research.147565.1)

Introduction

The COVID-19 pandemic has been present in the world for over four years, causing a significant burden on individuals known as “long Covid.” Long Covid poses significant challenges for those who have been infected with COVID-19 since it disrupts their everyday life in many ways. Furthermore, the government has faced difficulties in allocating resources, both in terms of funding and healthcare professionals, to adequately support these patients. The definition of long Covid was ambiguous in terms of patient monitoring, prompting various non-profit organizations, such as the World Health Organization (WHO), the National Institute for Health and Care Excellence (NICE), the Scottish Intercollegiate Guidelines Network (SIGN), and the Royal College of General Practitioners (RCGP), to attempt to define it through guideline development.¹ Hence, this term was succinctly defined as “signs and symptoms that develop during or following an infection consistent with COVID-19, continue for more than 12 weeks, and are not explained by an alternative diagnosis. The condition usually presents with clusters of symptoms, often overlapping, that can fluctuate and change over time and can affect any system in the body. Long Covid includes both ongoing symptomatic COVID-19 (from 4 to 12 weeks) and post-COVID-19 syndrome (12 weeks or more)”.

More than 65 million people worldwide have been afflicted with long Covid for a period of three years since the COVID-19 pandemic started.² The symptoms endured in a chronic manner for a significant period, coinciding with various systemic manifestations such as cognitive decline, fatigue, lethargy, dyspnea, and reduced functional capacity. These manifestations persisted for several months. Individual symptoms were analyzed in systematic reviews and meta-analyses,³ with the aggregate prevalence of these symptoms being subject to considerable variation across studies and temporal variation in symptom patterns. One of the symptoms that is frequently reported is fatigue. It has been detected in patients ranging from 21.59 to 39.45%. Cardiopulmonary complications, including symptoms such as cough, sore throat, chest pain, and palpitations, are reported in approximately 12.21% to 18.41% of cases. Approximately 11.65-16.58% of the population reported neurological issues, which included headaches, cognitive impairment, memory deficits, difficulties with attention, dizziness, loss of scent and taste, and hearing problems. Anxiety, post-traumatic stress disorder (PTSD), sleep disturbances, and depression were reported by approximately 16.81% to 26.00% of the population. Thirteen symptoms were documented in the southern region of Thailand according to a cross-sectional study. The study identified a prevalence of long Covid in approximately 79% of all cases that were infected with the virus. The most frequently reported symptoms were lethargy (72.9%), cough (66.0%), and muscle pain (54.1%).⁴

The characteristics of COVID-19 patients have exhibited dynamic shifts across different waves of the pandemic. In the initial wave, older adults, especially those aged 60 and above, were notably vulnerable to severe outcomes, with a higher prevalence of comorbidities such as hypertension and cardiovascular diseases.⁵ Age demographics shifted during subsequent outbreaks, as infections increased among younger individuals. The emergence of variants, such as the B.1.1.7 lineage, introduced uncertainties in transmission dynamics.⁶ The Omicron variant, unlike previous waves, had a significant impact on real-world vaccine implementation. Most individuals in this cohort have received a minimum of two doses of the COVID vaccination. Thus, it is possible that everyone has already attained herd immunity or population immunity. Patients who have previously been vaccinated experience a decrease in mortality rates, and the severity of cases is significantly diminished.^7–9 Nevertheless, different SARS-CoV-2 virus variants may reduce the efficacy of vaccines.¹⁰

Our previous study examined the vaccine effectiveness (VE) status in Thailand during the Omicron variant dominant epidemic. In Thailand, we are administering a mixture of various vaccine types and brands, including CoronaVac, ChAdOx1, and n-CoV-19-BNT162b2. The administration of 4 doses of CoronaVac-CoronaVac-ChAdOx1 nCoV-19-BNT162b2 demonstrated the highest vaccine effectiveness (VE). This vaccine regimen was able to prevent infection by 65% and reduce the occurrence of moderate-to-critical diseases by 82%.¹¹ Hence, reducing the severity of acute illness resulted in a decrease in the occurrence of long Covid, which served as a potential predictor. However, in COVID-19, different mutational variants change properties as they confer immune escape and influence vaccine efficacy. Different vaccine durations revealed different ranges of protection before infection.

While effective vaccination may decrease the severity of acute illness, long Covid is influenced by various factors including sex (specifically females), smoking, weight, and comorbidities.^12–19 These variables also contributed to an increased risk of long Covid. As follows, Omicron might be a difference in long Covid prevalence. So, the aims of this study were to find the overall prevalence, systematic symptoms prevalence, and prognostic factors of long Covid during the Omicron variant dominant epidemic at 3 and 6 months in Thailand.

Methods

Study setting

The COVID-19 cohort at Thammasat University Hospital (TUH) and Thammasat Field Hospital (TFH) was followed for 3 and 6 months to determine long Covid at 3 and 6 months respectively. The COVID-19 cases were recruited when the Omicron variant dominant epidemic in Thailand. The TUH is a 700-bed tertiary-care academic medical facility that serves population in northern Bangkok and central Thailand while TFH is a 490-bed field hospital branch of TUH, established specifically to provide comprehensive care to asymptomatic and mild COVID-19 patients. The COVID-19 patients provided inform consent at first visit.

Study population

The study population included all COVID-19 cases, aged ≥18 years old, who were diagnosed with COVID-19 infection by positive nasopharyngeal RT-PCR test for SARS-CoV-2 at TUH and TFH.¹¹ During the study period, 363 adults were sampled for variant testing to confirm Omicron dominant pandemics, with 97.8% Omicron. All listed individuals were asked to participate by telephone interview at 3 and 6 months after diagnosis. Patients who were unable to communicate in the Thai language, disinclined to consent by phone, and unable to be contacted by phone by three times were excluded from the study. Additionally, patient mortality or loss of follow-up within three months or six months of infection were also excluded.

Data collection

Our study was collecting data from two data sources. First, baseline characteristics data such as age, gender, body weight, comorbidities such as cardiovascular diseases (CVD), chronic lung diseases, diabetes mellitus, chronic kidney disease (CKD), stroke, and cancer, history of COVID-19 vaccination and presenting symptoms and peak severity during patient acute illness were retrieved from the previous study on vaccine effectiveness. Asymptomatic, mild, moderate, severe, and critical ailing were the classifications of disease severity according to the National Institutes of Health (NIH). Symptoms during acute patient illness were categorized by the National Institute for Health and Care Excellence (NICE) guideline including respiratory symptoms (cough, sore throat, rhinorrhea, sputum production, dyspnea), neurological symptoms (headache), musculoskeletal symptoms (myalgia), gastrointestinal symptoms (diarrhea), ear, nose, and throat symptoms (loss of smell, loss of taste), and dermatological symptoms (rash, red eye). All cases of acute illness of COVID-19 were categorized into asymptomatic, mild, moderate, severe, and critical diseases according to the World Health Organization criteria. These parts were collected and published.¹¹

Second, symptoms based structured questionnaire developed by the Thai Ministry of Public Health (MOPH), was used to determine long Covid by telephone interview with a well-trained interviewer. The structured questionnaire for long Covid symptoms in this study consisted of 21 symptoms asking for new or persistent symptoms at 3 and 6 months after diagnosis to determine the point prevalence of long Covid at 3 and 6 months respectively. The symptoms included dyspnea, cough, chest tightness, palpitation, headache, attention deficit, memory loss, insomnia, diarrhea, myalgia, arthralgia, dizziness, loss of appetite, anosmia, ageusia, rash, alopecia, depression, stress, fatigue, and weakness. Answer can choose with 3 choices among of “yes”, “no” or “exacerbation”. Questions were asked by research assistants who were trained to ensure the accuracy of the information.

Statistical analysis

To describe patient characteristics, we used descriptive statistics. We displayed categorical variables and point prevalence in the form of frequencies and percentages. We conducted hypothesis testing using the Chi-square or Fisher’s exact test to compare two categorical variables with and without long Covid, using appropriate data. The analysis of symptoms classified the participant’s answers into two classes: “yes & exacerbation” and “no”. Point prevalence was reported not only for at least one symptom but also categorized by NICE, including respiratory symptoms (dyspnea, cough), cardiovascular symptoms (chest tightness, palpitation), neurological symptoms (headache, attention deficit, memory loss, insomnia), gastrointestinal symptoms (diarrhea), musculoskeletal symptoms (myalgia, arthralgia), ear, nose, and throat symptoms (dizziness, loss of appetite, anosmia, ageusia), dermatological symptoms (rash, alopecia), psychiatric symptoms (depression, stress), and fatigue & weakness. In predictor analysis, we used the crude odds ratio (cOR) and its 95% confidence intervals (CI) to determine a factor’s effect on long Covid through univariable logistic regression. All factors with previous evident relevance were performed to explore significant prognostic factors by multivariable logistic regression model to adjust for confounding variables reported by adjusted odds ratio (aOR) and 95%CI.

Results

Baseline characteristics of long Covid patients

A total of 1,388 individuals participated in the follow-up telephone survey at 3 months, with 917 responding at 6 months. Table 1 provides an overview of the demographic and clinical characteristics of the participants at both time points. In the 3-month follow-up, 730 individuals (52.59%) were identified as having long Covid, compared to 658 individuals (47.41%) without long Covid. A higher proportion of females was observed among those with long Covid compared to those without (68.90% vs. 51.06%). Additionally, there was a greater percentage of healthcare workers (20.41% vs. 9.88%), individuals who had not been vaccinated (5.62% vs. 3.19%), and those who experienced severe or critical acute illness (4.38% vs. 1.98%) in the long Covid group. No significant differences were found between the two groups with respect to the proportion of older adults (aged >60 years), education levels, or symptoms during the acute illness phase.

Table 1. Patient characteristics for long Covid follow-up at 3 and 6 months after Omicron variant COVID infection.

	3 months (N = 1388)			6 months (N = 917)
	Long Covid (N = 730)	No long Covid (N = 658)	p-value^a	Long Covid (N = 262)	No long Covid (N = 655)	p-value^b
Female sex – no (%)	503 (68.90)	336 (51.06)	<0.001^*	189 (72.14)	336 (51.30)	<0.001^*
Age – no (%)
18–64	656 (89.86)	576 (87.54)	0.171	243 (92.75)	608 (92.82)	0.968
>65	74 (10.14)	82 (12.46)		19 (7.25)	47 (7.18)
Obesity (BMI > 25 kg/m²)	282 (38.63)	211 (32.07)	0.011^*	100 (38.17)	241 (36.79)	0.709
Occupational – no (%)
Healthcare	149 (20.41)	65 (9.88)	<0.001^*	39 (14.89)	34 (5.19)	<0.001^*
Non-healthcare	581 (79.59)	593 (90.12)		223 (85.11)	621 (94.81)
Educational level – no (%)
Primary & secondary	343 (46.99)	278 (42.55)	0.074	102 (38.93)	285 (43.51)	0.205
Bachelor & post-graduate	379 (51.92)	373 (56.69)		370 (56.49)	160 (61.07)
Vaccine doses – no (%)
No vaccine	41 (5.62)	21 (3.19)	0.001*	7 (2.67)	17 (2.60)	0.614
1 dose	6 (0.82)	12 (1.82)		2 (0.76)	13 (1.98)
2 doses	239 (32.74)	273 (41.49)		117 (44.66)	295 (45.04)
$\geq$ 3 doses	444 (60.82)	352 (53.50)		136 (51.91)	330 (50.38)
Comorbidities – no (%)
Cardiovascular diseases	122 (16.71)	106 (16.11)	0.762	35 (13.36)	72 (10.99)	0.313
Chronic lung diseases	29 (3.97)	20 (3.04)	0.347	7 (2.67)	15 (2.29)	0.733
Diabetes mellitus	67 (9.18)	61 (9.27)	0.953	17 (6.49)	43 (6.56)	0.966
Chronic kidney disease	24 (3.29)	18 (2.74)	0.549	5 (1.91)	9 (1.37)	0.557
Stroke	17 (2.33)	15 (2.28)	0.951	3 (1.15)	4 (0.61)	0.414
Cancer	14 (1.92)	9 (1.37)	0.423	2 (0.76)	8 (1.22)	0.733
Severity at acute illness – no (%)
Asymptomatic	23 (3.15)	36 (5.47)	0.023^*	6 (2.29)	37 (5.65)	0.032^*
Mild	607 (83.15)	546 (82.98)		224 (85.50)	559 (85.34)
Moderate	68 (9.32)	63 (9.57)		28 (10.69)	56 (8.55)
Severe-to-critical	32 (4.38)	13 (1.98)		4 (1.53)	3 (0.46)
Symptoms during acute illness – no (%)
At least 1 symptom	674 (92.33)	593 (90.12)	0.146	242 (92.37)	593 (90.53)	0.380
Respiratory	600 (82.19)	519 (78.88)	0.119	219 (83.59)	517 (78.93)	0.110
Cough	413 (56.58)	349 (53.04)	0.186	150 (57.25)	333 (50.84)	0.079
Sore throat	400 (54.79)	348 (52.89)	0.477	139 (53.05)	349 (53.28)	0.950
Rhinorrhea	159 (21.78)	132 (20.06)	0.432	51 (19.47)	133 (20.31)	0.774
Sputum production	84 (11.51)	80 (12.16)	0.707	31 (11.83)	76 (11.60)	0.922
Dyspnea	59 (8.08)	33 (5.02)	0.022^*	20 (7.63)	32 (4.89)	0.104
Musculoskeletal	63 (8.63)	49 (7.45)	0.419	18 (6.87)	59 (9.01)	0.292
Myalgia	63 (8.63)	49 (7.45)	0.419	18 (6.87)	59 (9.01)	0.292
Neurological	146 (20.00)	124 (18.84)	0.587	59 (22.52)	123 (18.78)	0.200
Headache	146 (20.00)	124 (18.84)	0.587	59 (22.52)	123 (18.78)	0.200
Gastrointestinal	17 (2.33)	13 (1.98)	0.651	11 (4.20)	10 (1.53)	0.015^*
Diarrhea	17 (2.33)	13 (1.98)	0.651	11 (4.20)	10 (1.53)	0.015^*
Ear, nose, and throat	9 (1.23)	7 (1.06)	0.768	1 (0.38)	9 (1.37)	0.191
Loss of smell	8 (1.10)	5 (0.76)	0.516	1 (0.38)	7 (1.07)	0.312
Loss of taste	1(0.14)	5 (0.76)	0.077	0 (0.00)	4 (0.61)	0.205
Dermatological	2 (0.27)	1 (0.15)	0.625	0 (0.00)	1 (0.15)	1.000
Rash	1 (0.14)	1 (0.15)	1.000	0 (0.00)	0 (0.00)	1.000
Red eye	1(0.14)	0 (0.00)	1.000	0 (0.00)	1 (0.15)	0.527

a Comparison between long Covid and no long Covid at 3 months.

b Comparison between long Covid and no long Covid at 6 months.

* Statistical significance.

At the 6-month follow-up, 28.57% of respondents were still affected by long Covid. A similar trend was noted, with females comprising 72.14% of those with long Covid compared to 51.30% of those without. Healthcare workers also represented a larger proportion of the long Covid group (14.89%) compared to those without long Covid (5.19%). Among those with long Covid, 1.53% had experienced severe or critical acute illness, whereas this was the case for only 0.46% of those without long Covid.

Point prevalence of long Covid

Table 2 illustrates the point prevalence of long Covid at 3 and 6 months. At the 3-month mark, 730 out of 1,388 individuals (52.59%) experienced long Covid. The five most prevalent symptom clusters were: fatigue and weakness (31.70%), neurological symptoms (31.05%), respiratory symptoms (23.85%), dermatological manifestations (17.94%), and ear, nose, and throat (ENT) symptoms (16.71%). By the 6-month follow-up, 262 out of 917 individuals (28.57%) continued to report long Covid symptoms. The most common symptom clusters at this stage were: fatigue and weakness (15.81%), neurological symptoms (12.87%), respiratory symptoms (10.81%), dermatological manifestations (7.74%), and musculoskeletal symptoms (4.03%).

Table 2. Point prevalence of individual symptoms of long Covid follow-up at 3 and 6 months after Omicron variant COVID infection.

	At 3 months (N = 1388)	At 6 months (N = 917)
At least 1 symptom – no (%)	730 (52.59)	262 (28.57)
Respiratory symptoms	33 (23.85)	99 (10.81)
Dyspnea	207 (14.91)	66 (7.21)
Cough	185 (13.33)	47 (5.13)
Cardiovascular symptoms	163 (11.74)	36 (3.93)
Chest tightness	77 (5.55)	19 (2.07)
Palpitation	105 (7.56)	23 (2.51)
Neurological symptoms	431 (31.05)	118 (12.87)
Headache	126 (9.08)	25 (2.73)
Attention deficit	159 (11.46)	20 (2.18)
Memory loss	271 (19.52)	70 (7.63)
Insomnia	196 (14.12)	57 (6.22)
Gastrointestinal symptoms	60 (4.32)	9 (0.98)
Diarrhea	60 (4.32)	9 (0.98)
Musculoskeletal symptoms	190 (13.69)	37 (4.03)
Myalgia	141 (10.16)	29 (3.16)
Arthralgia	118 (8.50)	15 (1.64)
Ear, nose, and throat symptoms	232 (16.71)	27 (2.94)
Dizziness	130 (9.37)	15 (1.74)
Loss of appetite	109 (7.85)	13 (1.42)
Anosmia	23 (1.66)	2 (0.22)
Ageusia	26 (1.87)	2 (0.22)
Dermatological symptoms	249 (17.94)	71 (7.74)
Rash	63 (4.54)	11 (1.20)
Alopecia	203 (14.63)	63 (6.87)
Psychological/psychiatric symptoms	110 (7.93)	14 (1.53)
Depression	42 (3.03)	7 (0.76)
Stress	95 (6.84)	12 (1.31)
Fatigue & weakness symptoms	440 (31.70)	145 (15.81)
Fatigue	192 (13.83)	43 (4.69)
Weakness	407 (29.32)	138 (15.05)

Predictors of long Covid

Table 3 presents the univariate analysis and multivariable logistic regression findings. At the 3-month follow-up, significant factors identified in the univariate analysis included female sex, healthcare worker status, obesity, and severe-to-critical acute illness. The multivariable logistic regression model identified key prognostic factors for long Covid: severe-to-critical acute illness was associated with an adjusted odds ratio (aOR) of 4.50 [95% CI, 1.59-12.75], female sex with an aOR of 2.04 [95% CI, 1.61-2.58], healthcare worker occupation with an aOR of 1.96 [95% CI, 1.39-2.78], and obesity with an aOR of 1.39 [95% CI, 1.09-1.77]. Additionally, having a postgraduate education was found to be protective, reducing the likelihood of long Covid with an aOR of 0.53 [95% CI, 0.30-0.96].

Table 3. Predictors of long Covid at 3 and 6 months after Omicron variant COVID infection.

	3 months				6 months
	cOR [95%CI]	p-value^a	aOR [95%CI]^†	p-value^a	cOR [95%CI]	p-value^b	aOR [95%CI]^†	p-value^b
Female sex	2.12 [1.71-2.64]	<0.001^*	2.04 [1.61-2.58]	<0.001^*	2.46 [1.80-3.35]	<0.001^*	2.41 [1.74-3.34]	<0.001^*
Age ≥ 65 years	0.79 [0.57-1.11]	0.172	0.66 [0.41-1.06]	0.083	1.01 [0.58-1.76]	0.968	1.02 [0.51-2.07]	0.940
Healthcare worker	2.34 [1.71-3.20]	<0.001^*	1.96 [1.39-2.78]	<0.001^*	3.19 [1.97-5.19]	<0.001^*	3.12 [1.85-5.27]	<0.001^*
Obesity (BMI > 25 kg/m²)	1.33 [1.07-1.66]	0.012^*	1.39 [1.09-1.77]	0.008^*	1.06 [0.79-1.42]	0.709	1.02 [0.74-1.42]	0.894
Education level		0.093		0.028^*		0.277		0.364
Primary	Ref		Ref		Ref		Ref
Secondary	1.14 [0.82-1.59]		1.15 [0.77-1.70]		1.06 [0.65-1.72]		1.20 [0.68-2.10]
Bachelor	0.93 [0.69-1.27]		0.91 [0.62-1.33]		1.27 [0.81-1.99]		1.38 [0.81-2.35]
Postgraduate	0.64 [0.37-1.09]		0.53 [0.30-0.96]		1.09 [0.50-2.37]		1.16 [0.49-2.72]
Vaccine doses		0.380		0.618		0.591		0.966
No vaccine	Ref		Ref		Ref		Ref
1 dose	0.26 [0.84-0.78]		0.32 [0.10-1.04]		0.37 [0.07-2.11]		0.44 [0.08-2.65]
2 doses	0.45 [0.26-0.78]		0.54 [0.28-1.03]		0.96 [0.39-2.38]		1.20 [0.46-3.15]
$\geq$ 3 doses	0.65 [0.38-1.11]		0.69 [0.36-1.32]		1.00 [0.41-2.47]		1.06 [0.40-2.80]
Comorbidities
Cardiovascular diseases	1.05 [0.79-1.39]	0.762	1.10 [0.75-1.62]	0.628	1.25 [0.81-1.92]	0.314	1.48 [0.84-2.60]	0.171
Chronic lung diseases	1.32 [0.74-2.36]	0.348	1.31 [0.68-2.53]	0.424	1.17 [0.47-2.91]	0.733	0.99 [0.35-2.75]	0.977
Diabetes mellitus	0.99 [0.69-1.42]	0.953	0.89 [0.57-1.38]	0.598	0.99 [0.55-1.77]	0.966	0.77 [0.38-1.57]	0.473
Chronic kidney diseases	1.21 [0.65-2.25]	0.549	1.02 [0.48-2.16]	0.964	1.40 [0.46-4.21]	0.553	1.15 [0.32-4.19]	0.831
Stroke	1.02 [0.51-2.06]	0.951	0.94 [0.41-2.16]	0.890	1.89 [0.42-8.48]	0.409	1.73 [0.34-8.86]	0.509
Cancer	1.41 [0.61-3.28]	0.425	1.17 [0.43-3.15]	0.761	0.62 [1.13-2.95]	0.550	0.42 [0.80-2.17]	0.301
Severity levels at acute illness		0.012^*		0.020^*		0.011^*		0.062
Asymptomatic	Ref		Ref		Ref		Ref
Mild	1.74 [1.02-2.97]		1.23 [0.68-2.24]		2.47 [1.03-5.94]		1.63 [0.63-4.17]
Moderate	1.69 [0.90-3.16]		1.21 [0.60-2.41]		3.08 [1.16-8.17]		1.92 [0.67-5.49]
Severe-to-critical	3.85 [1.68-8.84]		4.50 [1.59-12.75]		8.22 [1.46-46.27]		9.15 [1.35-62.10]
Respiratory	1.24 [0.96-1.61]	0.119	1.17 [0.86-1.59]	0.309	1.36 [0.93-1.98]	0.110	1.21 [0.78-1.85]	0.396
Ear, nose, and throat	1.16 [0.43-3.13]	0.768	1.07 [0.38-2.99]	0.905	0.28 [0.04-2.18]	0.222	0.26 [0.03-2.19]	0.217
Gastrointestinal	1.18 [0.57-2.45]	0.652	1.09 [0.50-2.35]	0.834	2.83 [1.19-6.74]	0.019^*	3.04 [1.21-7.64]	0.018^*
Musculoskeletal	1.17 [0.80-1.73]	0.419	1.20 [0.79-1.81]	0.397	0.75 [0.43-1.29]	0.293	0.68 [0.38-1.22]	0.200
Neurological	1.08 [0.83-1.41]	0.587	1.01 [0.75-1.34]	0.975	1.26 [0.89-1.78]	0.200	1.25 [0.86-1.82]	0.246
Dermatological	1.80 [0.16-19.95]	0.630	1.17 [0.10-13.78]	0.902	1 (omitted)	-	1 (omitted)	-

a Comparison between long Covid and no long Covid at 3 months.

b Comparison between long Covid and no long Covid at 6 months.

† Adjusted odds ratio at 3 and 6 months by female sex, age, healthcare worker, obesity, education level, vaccine doses, CVD, chronic lung diseases, CKD, stroke, cancer, severity and symptoms during acute illness.

* Statistical significance.

At the 6-month follow-up, univariate analysis identified healthcare worker status, severe-to-critical acute illness, and the presence of diarrhea during the acute phase as significant factors. After adjusting for confounding variables, the significant prognostic factors at 6 months included female sex (aOR 2.41 [95% CI, 1.74-3.34]), healthcare worker occupation (aOR 3.12 [95% CI, 1.85-5.27]), and gastrointestinal symptoms during the acute illness (aOR 3.04 [95% CI, 1.21-7.64]). Although the severity of illness during the acute phase remained an important factor, its significance at 6 months approached the threshold (p=0.062).

Discussion

This study investigates the point prevalence of long Covid in adult patients (aged 18 years and older) during the Omicron variant-dominant period of the COVID-19 pandemic. We further analyze the symptom-based prevalence and predictors associated with long Covid at 3- and 6-months post-infection, utilizing data from patients at Thammasat University Hospital (TUH) and Thammasat Field Hospital (TFH) in Thailand. Our findings reveal significant point prevalence rates of long Covid, with 52.59% of patients affected at 3 months and 28.57% at 6 months. The data underscore that the prevalence of long Covid was notably higher at 3 months, nearly double that observed at 6 months, reflecting the evolving clinical trajectory over time.

The point prevalence of prior research on extended COVID exhibited significant variation, encompassing a range of symptoms. Systematic reviews and meta-analysis (SR/MA) gathered publications on extended COVID from January 1, 2020 to November 2, 2021. They reported a combined prevalence of 13.6% (ranging from 1.2% to 68.0%) according to health records.²⁰ LUO et al. conducted a study in Hong Kong during the Omicron variant dominant epidemic to investigate the frequency of long Covid. They identified a total of forty-three symptoms via telephone follow-up and determined that 55.0% of patients experienced at least one persistent long Covid symptom for a duration of at least 6 months but no longer than 12 months.²¹ The authors noted that there was a significant variation and likelihood of bias in the study due to the absence of consistent criteria, as well as issues related to recall, nonresponse, and differences in follow-up.²²

Many studies reported individual symptoms such as fatigue and weakness, memory impairment, insomnia, cough, and headache.^15,23–27 Systematic reviews and meta-analysis by Natarajan et al. categorized phenotype of symptoms. To compare their reviews and our study on classification, their study showed only five groupings. Unlike our study, overall mental disorders (i.e., sleep disturbance, post-traumatic stress disorder (PTSD), anxiety, depression, anxiety/depression) had the highest pooled prevalence with 21.3%. Overall cardiopulmonary problems (i.e., chest pain, sore throat, dyspnea, palpitations, cough), overall general problem (i.e., mobility dysfunction, fatigue, general pain, joint pain, muscle pain, hairfall, skin rash, weight loss, fever), overall neurological problems (i.e., headache, cognitive impairment, memory deficit, attention/concentration deficit, dizziness, smell loss, taste loss, hearing problem) showed pooled prevalence of 14.9%, 14.4% and 13.9%, respectively. Overall gestational problems (i.e., poor appetite, diarrhea, nausea, vomiting, abdominal pain) had the lowest pooled prevalence at 6.2%. However, most of this SR/MA were reported high of heterogeneity (I²) more than 90% in each single criterion.

When we analyzed prognostic factors for long Covid by multivariable logistic regression, we found four predictors of long Covid: severity during acute illness, healthcare worker occupation, female sex, and obesity with a BMI greater than 25 kg/m². Many publications reported severe & critical acute illness as precipitating long Covid because the damage occurred to the host varied depending on disease severity.^15,17 However, studies have shown that some asymptomatic COVID patients who were not hospitalized still developed long Covid,^28–31 but our study had a different result. Healthcare workers who have recovered from a COVID-19 infection may nevertheless come into frequent contact with COVID patients and hard work,^32–34 and some studies reported that healthcare workers were more frequently reinfected with COVID.³⁵ Therefore, this component exhibited a second-order level of strength in our study. Female was a strong associated factor with long Covid at 3 and 6 months, suggesting that fluctuations in hormonal regulation in females might influence a robust immune response against viral infection.^36–40 Almost all studies suggested that females were more likely to develop long Covid than males.^15–19,41 As for obesity, every 1 kg/m² of BMI increase in patients was shown to elevate the risk of long Covid. Also, obesity triggered severity during acute illness, which led to more potential of having long Covid.⁴² Obesity was found in Vimercati, L et al. and Subramanian, A et al. which studied long Covid. They found that overweight (BMI > 25 kg/m²) people was more likely to develop long Covid.^19,42 Moreover, we found higher education levels represented a higher level of knowledge seeking and self-care recovery after COVID infection to protect against long Covid persistence.

This study did not report some predictors that were found in other studies, such as vaccine dose-related long Covid cause of type of vaccine, older adults, comorbidities including chronic obstructive pulmonary disease, diabetes mellitus, chronic kidney disease, cancer, and symptoms at acute illness.^{17–19,42–46} The data showed a significant p-value, but the 95% confidence interval had no impact on the effect. However, the study did not explore treatment during acute illness such as treatment satisfaction, early or late treatment, and traditional Thai medicinal usage.

Our study highlights a large sample size of long Covid evidence particularly in the dominant wave of Omicron variant dominant epidemic in Thailand by telephone survey. As the Omicron variant dominant epidemic was announced by WHO from November 26^th, 2021, our data seemed to contain less contamination from other variants.^9,47–51 Furthermore, we used RT-PCR technique which had high specificity to COVID. Consequently, false positives were extremely rare in this research. However, there were three limitations to this study. The non-standardization of the definition and criteria for long Covid remained unclear. Therefore, the evidence was difficult to compare and represent true prevalence. Since the data was gathered through telephone interviews, physical and laboratory examinations were not provided for the investigation of long Covid. Inter-rater reliability might affect the consistency of results. Lastly, test-negative COVID infection and population were not considered for the control group in this study,⁸ leading to increased noise for the true prevalence of long Covid studies.

Ethical considerations

This study was approved by the Human Research Ethics Committee of Thammasat University (Medicine), ethical approval number MTU-EC-PE-1-280/65 dated 12 January 2023. Verbal consent has been obtained from all participants via telephone that was approved by the ethics committee.

Data availability

Repository name: Mendeley Data

This project contains the following underlying data and documents

1. Data name: long covid data, DOI:10.17632/zs4rzwf6c8.1
2. Files: long covid final.xls

https://data.mendeley.com/datasets/zs4rzwf6c8/1⁵²

Description: The dataset of long Covid for analysis in Thailand

Detail of licence: CC by 4.0 “The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence”

3. Document name: longcovid questionnaire.docx, DOI: 10.17632/mrf4b2xxk3.1
4. Longcovid questionnaire.docx
https://data.mendeley.com/datasets/mrf4b2xxk3/1⁵³
Description: Symptoms based structured questionnaire to determine longcovid condition
Detail of licence: CC by 4.0 “The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence”

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¹ Faculty of Medicine, Thammasat University, Pathumthani, 12120, Thailand
² Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathumthai, 1210, Thailand
³ Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
⁴ Center of Excellence in Applied Epidemiology, Research Group in Pediatrics Thammasat University, Pathumthani, 10120, Thailand
⁵ Psychiatry, Faculty of Medicine, Thammasat University, Pathumthani, 12120, Thailand
⁶ Department of Paediatrics, Faculty of Medicine, Thammasat University, Pathumthani, Thailand

Chonlawat Chaichan
Roles: Conceptualization, Data Curation, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing

Sasinuch Rutjanawech
Roles: Resources, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Sirinda Sritipsukho
Roles: Data Curation, Resources, Software

Thammanard Charernboon
Roles: Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Paskorn Sritipsukho
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This study was funded by the Faculty of Medicine, Thammasat University.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Chaichan C, Rutjanawech S, Sritipsukho S et al. Prevalence and Predictors of Long COVID During the Omicron Variant Dominant Epidemic at 3 and 6 Months in Thailand [version 1; peer review: 1 approved with reservations]. F1000Research 2025, 14:607 (https://doi.org/10.12688/f1000research.147565.1)

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Reviewer Report 04 Aug 2025

Engin Berber, Cleveland Clinic, Cleveland, Ohio, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.161772.r393771

This manuscript presents a prospective cohort study examining the prevalence and predictors of long COVID at 3 and 6 months following infection during the Omicron-dominant period in Thailand. The authors utilized telephone interviews with a follow-up questionnaire among individuals who ... Continue reading

This manuscript presents a prospective cohort study examining the prevalence and predictors of long COVID at 3 and 6 months following infection during the Omicron-dominant period in Thailand. The authors utilized telephone interviews with a follow-up questionnaire among individuals who had RT-PCR–confirmed COVID-19, and employed multivariable logistic regression to identify key risk factors.
The methodology is generally appropriate, and the structure is sound. However, I have identified several issues that require clarification, correction, or elaboration prior to consideration.

The data stating that "28.57% of respondents were still affected by long COVID at 6 months" is located in Table 2, not Table 1. Table references throughout the manuscript should be reviewed and corrected for accuracy.
The manuscript refers to older adults as “aged >60 years”; however, Table 1 uses the category of ≥65 years. This inconsistency should be corrected to ensure alignment across the text and tables.
While comparisons for female sex and healthcare worker status show statistical significance between long COVID and non-long COVID groups at both 3 and 6 months, the same does not hold true for severe-to-critical illness at 6 months, which only approaches significance (p = 0.062). The authors should clearly indicate which comparisons reached statistical significance in both univariate and multivariable models, and distinguish between statistically significant and borderline results.
The inclusion of dermatological symptoms (e.g., rash, alopecia) appears of limited relevance given their low prevalence in this study. In contrast, gastrointestinal symptoms specifically diarrhea should be given greater emphasis in the results and highlighted in the discussion.
Obesity was a significant predictor of long COVID at 3 months; however, the potential confounding or modifying effects of age on BMI are not discussed. Since BMI and age are physiologically interrelated, particularly in the context of metabolic and immune function, the authors should include a nuanced discussion of how age-related changes in BMI might influence long COVID risk or symptom persistence.
The manuscript reports that having a postgraduate education is protective against long COVID (aOR 0.53). However, the discussion includes an ambiguous rationale suggesting that higher education might increase the incidence of long COVID due to enhanced health-seeking behavior. This contradiction requires clarification. If higher education contributes to improved self-management and prevention, this hypothesis should be clearly articulated and supported by the data. This interpretation should also be critically questioned within the discussion.
The referenced questionnaire is not entirely presented in English. To ensure transparency and reproducibility, a fully translated version of the structured long COVID symptom questionnaire should be included as supplementary material.
Lastly, the attached long COVID dataset lists 3,173 participants, whereas the manuscript states that 1,388 individuals participated in the study. This discrepancy should be cross-checked, and the number of participants confirmed to align with the data presented in the manuscript and tables.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: My research is focused on viral immunology, with a particular emphasis on humoral and cellular immune responses to viral infections, especially SARS-CoV-2 (COVID-19), influenza, HSV-1, and hemorrhagic fever infections.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Engin Berber, Cleveland Clinic, Cleveland, Ohio, USA

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Approved With Reservations

This manuscript presents a prospective cohort study examining the prevalence and predictors of long COVID at 3 and 6 months following infection during the Omicron-dominant period in Thailand. The authors utilized telephone interviews with a follow-up questionnaire among individuals who had RT-PCR–confirmed COVID-19, and employed multivariable logistic regression to identify key risk factors.
The methodology is generally appropriate, and the structure is sound. However, I have identified several issues that require clarification, correction, or elaboration prior to consideration.

The data stating that "28.57% of respondents were still affected by long COVID at 6 months" is located in Table 2, not Table 1. Table references throughout the manuscript should be reviewed and corrected for accuracy.
The manuscript refers to older adults as “aged >60 years”; however, Table 1 uses the category of ≥65 years. This inconsistency should be corrected to ensure alignment across the text and tables.
While comparisons for female sex and healthcare worker status show statistical significance between long COVID and non-long COVID groups at both 3 and 6 months, the same does not hold true for severe-to-critical illness at 6 months, which only approaches significance (p = 0.062). The authors should clearly indicate which comparisons reached statistical significance in both univariate and multivariable models, and distinguish between statistically significant and borderline results.
The inclusion of dermatological symptoms (e.g., rash, alopecia) appears of limited relevance given their low prevalence in this study. In contrast, gastrointestinal symptoms specifically diarrhea should be given greater emphasis in the results and highlighted in the discussion.
Obesity was a significant predictor of long COVID at 3 months; however, the potential confounding or modifying effects of age on BMI are not discussed. Since BMI and age are physiologically interrelated, particularly in the context of metabolic and immune function, the authors should include a nuanced discussion of how age-related changes in BMI might influence long COVID risk or symptom persistence.
The manuscript reports that having a postgraduate education is protective against long COVID (aOR 0.53). However, the discussion includes an ambiguous rationale suggesting that higher education might increase the incidence of long COVID due to enhanced health-seeking behavior. This contradiction requires clarification. If higher education contributes to improved self-management and prevention, this hypothesis should be clearly articulated and supported by the data. This interpretation should also be critically questioned within the discussion.
The referenced questionnaire is not entirely presented in English. To ensure transparency and reproducibility, a fully translated version of the structured long COVID symptom questionnaire should be included as supplementary material.
Lastly, the attached long COVID dataset lists 3,173 participants, whereas the manuscript states that 1,388 individuals participated in the study. This discrepancy should be cross-checked, and the number of participants confirmed to align with the data presented in the manuscript and tables.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

My research is focused on viral immunology, with a particular emphasis on humoral and cellular immune responses to viral infections, especially SARS-CoV-2 (COVID-19), influenza, HSV-1, and hemorrhagic fever infections.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Prevalence and Predictors of Long COVID During the Omicron Variant Dominant Epidemic at 3 and 6 Months in Thailand

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Study setting

Study population

Data collection

Statistical analysis

Results

Baseline characteristics of long Covid patients

Table 1. Patient characteristics for long Covid follow-up at 3 and 6 months after Omicron variant COVID infection.

Point prevalence of long Covid

Table 2. Point prevalence of individual symptoms of long Covid follow-up at 3 and 6 months after Omicron variant COVID infection.

Predictors of long Covid

Table 3. Predictors of long Covid at 3 and 6 months after Omicron variant COVID infection.

Discussion

Ethical considerations

Data availability

References

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