Abstract
Background:
Post-coronavirus disease 2019 (COVID-19) condition, or long COVID, persists beyond the acute phase, impacting quality of life. This study estimated the incidence and determinants of post-COVID syndrome (PCS) in Puducherry, India.
Methods:
This ambispective cohort study from May to December 2022 included 1162 adults with COVID-19. Participants were stratified by disease severity (moderate/severe and mild) based on clinical parameters during acute illness. We conducted follow-ups at 1, 3, 6, and 12 months. PCS was defined as having ≥two of five common symptoms (fatigue, joint/muscle pain, breathing difficulty, loss of smell/taste, or sleep problems). Relative risks (RR) and 95% confidence intervals (CI) were estimated using binomial regression with a Poisson link, adjusting for socio-demographic and clinical factors.
Results:
Incidence of PCS was consistently higher in the moderate/severe group. At 1, 3, 6, and 12 months, incidence was 65.6%, 37.7%, 24.4%, and 34.4% for moderate/severe cases, and 24.3%, 11.0%, 7.1%, and 16.9% for mild cases, respectively. Moderate/severe cases had significantly higher RR of 2.70 at 1 month (95% CI: 2.31–3.15), 3.42 at 3 months (95% CI: 2.65–4.40), 3.46 at 6 months (95% CI: 2.49–4.80), and 2.04 at 12 months (95% CI: 1.65–2.52), all P < 0.001. Independent risk factors for PCS included rural residence, comorbidities, complications during hospitalization, and age over 30 years.
Conclusion:
Moderate/severe COVID-19 severity and specific socio-demographic and clinical factors were associated with an elevated risk of PCS. Early identification and targeted follow-up are recommended to mitigate long-term impacts.
Keywords: Ambispective cohort, COVID-19, incidence, post-COVID syndrome
INTRODUCTION
Post-coronavirus disease 2019 (COVID-19) condition, commonly known as long COVID, encompasses a range of symptoms persisting after the resolution of acute COVID-19 infection, often affecting physical, cognitive, and psychological health.[1,2] Typical manifestations include fatigue, shortness of breath, cognitive dysfunction, joint/muscle pain, and sleep disturbances, which can significantly impair quality of life.[3,4,5]
While most patients recover within 12 weeks, a substantial proportion experience prolonged symptoms extending months or even years.[1,6] Factors such as older age, comorbidities, female sex, and greater severity of the acute illness have been implicated as potential risk factors for long COVID.[7,8,9] Even individuals with initially mild infections may develop persistent symptoms.[1,10,11,12]
Prevalence estimates of long COVID vary widely. Global studies suggest persistent symptoms in 10% to 35% of cases, with rates exceeding 85% among hospitalized populations.[13,14] The U.S. Centers for Disease Control and Prevention (CDC) reports prevalence ranging from 5% to 30% in various settings.[15] Despite extensive international research, long-term studies from low and middle-income countries, including India, remain limited, particularly those with follow-up beyond 6 months.
A wide spectrum of symptoms has been assessed across studies. For this study, five symptoms, including fatigue, joint/muscle pain, breathing difficulty, loss of smell/taste, and sleep problems, were selected as they are consistently reported among the most common and impactful post-COVID symptoms globally. Validated tools were employed to ensure accurate measurement of these outcomes, balancing comprehensiveness with feasibility in the resource-constrained setting. Most existing studies are either hospital-based with limited generalizability, lack a defined comparison between severity groups, or have short follow-up durations. There is also a paucity of data from India evaluating both incidence and risk factors of post-COVID extending up to 12 months. This study was designed to address these gaps. This study aims to estimate the incidence and determinants of post-COVID syndrome (PCS) among adults with varying severity of COVID-19, followed for up to 12 months after infection.
METHODOLOGY
Study setting: The study was conducted at a designated tertiary care COVID-19 hospital providing comprehensive COVID-19 care services for patients from both Puducherry and Tamil Nadu. The hospital maintained a robust Hospital Management Information System (HMIS), which systematically recorded clinical data and facilitated longitudinal follow-up.
Study design: An ambispective cohort study was conducted between May and December 2022. Eligible participants were identified from hospital records (retrospective), and follow-up assessments were conducted prospectively at 1-, 3-, 6-, and 12-month post-COVID infection.
Study population and selection criteria
Adults (≥18 years) diagnosed with COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) or radiological findings were included. Patients were stratified into two groups based on disease severity at initial presentation:
Moderate/Severe group (Exposed cohort): Patients with respiratory rate >24/min, breathlessness, or oxygen saturation ≤93% on room air, or radiological infiltrates.
Mild group (Non-exposed cohort): Patients with upper respiratory tract symptoms or fever without hypoxia or breathlessness.
Patients with missing severity data or those who could not be contacted for follow-up were excluded.
Sample size estimation
The sample size was calculated based on the expected difference in the prevalence of residual dyspnea between severity groups. Assuming the least prevalence of 10%,[11] with 80% power and a 95% confidence level, the required sample was 581 participants per group (total N = 1162) using nMaster 2.0 software. This ensured sufficient power to detect clinically meaningful differences in post-COVID outcomes, using dyspnea as the most conservative estimate due to its relatively low prevalence among long COVID symptoms.
Sampling technique: A line list of eligible patients was generated from HMIS records. Simple random sampling using OpenEpi software was applied to select participants, ensuring representation across severity strata.
Study procedure: The study, approved by the Scientific and the Institutional Ethics Committee, began in May 2022. Data were collected from the Medical Records department, stratifying patients into exposed (moderate to severe cases) and non-exposed (mild cases) cohorts based on oxygen saturation values. Using simple random sampling, 581 patients were selected for each group. Follow-ups were conducted at 1-, 3-, 6-, and 12-month post-infection to assess post-COVID syndrome.
Patient details were retrieved from hospital registers, and phone numbers from the HMIS facilitated pre-visit calls. Home visits by research assistants involved collecting socio-demographic data (age, education, religion, residence, family income, family size, family type, and housing), clinical details (COVID-19 diagnosis date, hospital stay duration, comorbidities, clinical manifestations, and post-COVID complications), and symptoms (dyspnea, fatigue, loss of smell/taste, stomach pain, chest pain, etc.). Hospital records and personal interviews were used to gather missing information, minimizing recall bias.
A structured questionnaire was employed to assess post-COVID symptoms at each follow-up. Fatigue was measured with the Fatigue Assessment Scale (FAS), shortness of breath with the UCSD Medical Center Pulmonary Rehabilitation Program scale, depression with the Patient Health Questionnaire 9 (PHQ 9), insomnia with the Insomnia Severity Index (ISI), and anxiety with the Generalized Anxiety Disorder Assessment (GAD7). Validated cut-offs were used to define the presence of symptoms: fatigue (FAS ≥22), anxiety (GAD-7 ≥10), depression (PHQ-9 ≥10), and insomnia (ISI ≥15). These thresholds correspond to clinically significant symptom levels as per standard validation studies. Symptom data were supplemented with clinical records where available to minimize recall bias.
Research and field assistants received training from the principal investigator and medical experts. Data collection was monitored regularly to ensure quality. Biomedical waste, including glucose strips and lancets, was disposed of following protocols, with strips in a red bag and lancets in a puncture-proof sharps box. Data were stored electronically to minimize environmental impact. To minimize recall bias, patient-reported information was cross-verified with hospital records wherever available. Standardized questionnaires and trained interviewers were used to ensure consistency in data collection.
Operational definitions
In this study, Post-COVID Syndrome is defined as the presence of any two or more of the following five symptoms persisting after the acute COVID-19 illness: fatigue, joint/muscle pain, breathing difficulty, loss of smell/taste, or sleep problems. The dependent variables in this study include:
Acute COVID syndrome: This refers to the persistence of the above-mentioned COVID-19 symptoms beyond four weeks, which is known as acute post-COVID syndrome.[16]
Post-COVID syndrome: Defined as the presence of the above-mentioned symptoms that develop during or after COVID-19 and continue for more than 3 months and 6 months, without any alternative explanations.[2,17]
Persistent post-COVID symptoms: This category includes individuals who continue to experience the above-mentioned symptoms even after 12 months following the initial COVID-19 infection.[2,17,18]
Statistical analysis
The statistical analysis was done using STATA version 14 software. In the statistical analysis, descriptive methods were employed to summarize the socio-demographic characteristics and clinical details of the study participants, including age, education, comorbidities, and symptoms. The incidence of acute PCS, PCS, and persistent post-COVID was calculated at each follow-up point. Relative risk (RR) with 95% confidence intervals (CI) was estimated to compare incidence between the groups. Given the absence of matching at the design stage, potential confounding due to baseline differences between severity groups was addressed using multivariable regression models. Univariate analyses identified potential risk factors associated with PCS. Multivariable binomial regression models with a Poisson link and robust error variance were used to estimate adjusted relative risks (aRR), adjusting for age, gender, residence, comorbidities, hospitalization complications, and other relevant variables. A P value of < 0.05 was considered statistically significant. No imputation was performed for missing data.
Ethical considerations
The study strictly adheres to the ICMR (Indian Council of Medical Research) ethical guidelines, which emphasize the importance of minimizing risks and ensuring participant well-being. The potential risks involved in this study are considered minimal or involve only a minor increase over minimal risk. The study was approved by the Institutional Ethics Committee of Jawaharlal Institute of Postgraduate Medical Education & Research (JIPMER). Written informed consent was obtained from all participants prior to data collection. Confidentiality of participant information was maintained throughout the study.
RESULTS
Figure 1 shows the flow diagram of the ambispective cohort study with 1162 eligible study participants, with 581 in the exposed group and 581 in the non-exposed group. All 1162 participants were successfully followed up at 1, 3, 6, and 12 months, with no loss to follow-up.
Figure 1.
Flow diagram of the ambispective cohort study
Table 1 shows the distribution of socio-demographic and clinical characteristics. It revealed differences between moderate to severe and mild COVID-19 cases. Moderate to severe cases had a higher proportion of males (55.94%), individuals over 30 years (93.46%), urban residents (63.51%), and unskilled workers (53.18%). In contrast, mild cases had more females (51.12%), younger individuals ≤30 years (26.68%), rural residents (54.04%), and unskilled workers (67.13%). Both groups were mostly literate, non-vegetarian, and from nuclear families with ≤4 members. Notably, moderate to severe cases had higher rates of comorbidities (65.92%) and hospitalization complications (87.36%).
Table 1.
Baseline socio-demographic and clinical characteristics of study participants by COVID-19 severity (n=1162)
| Variables | Moderate/Severe cases n=581 (%) | Mild cases n=581 (%) | ||||
|---|---|---|---|---|---|---|
| Gender | Male | 325 (55.94) | 284 (48.88) | |||
| Female | 256 (44.06) | 297 (51.12) | ||||
| Age (years) | ≤ 30 | 38 (6.54) | 155 (26.68) | |||
| > 30 | 543 (93.46) | 426 (73.32) | ||||
| Education | Illiterate | 82 (14.11) | 57 (9.81) | |||
| Literate | 499 (85.89) | 524 (90.19) | ||||
| Residence | Urban | 369 (63.51) | 267 (45.96) | |||
| Rural | 212 (36.49) | 314 (54.04) | ||||
| Occupation | Skilled worker | 272 (46.82) | 191 (32.87) | |||
| Unskilled worker | 309 (53.18) | 390 (67.13) | ||||
| No. of family members | ≤ 4 | 374 (64.37) | 408 (70.22) | |||
| >4 | 207 (35.63) | 173 (29.78) | ||||
| Type of family | Nuclear family | 478 (82.27) | 505 (86.92) | |||
| Joint family | 103 (17.73) | 76 (13.08) | ||||
| Diet | Vegetarian | 54 (9.29) | 21 (3.61) | |||
| Non-vegetarian | 527 (90.71) | 560 (96.39) | ||||
| Housing type | Pucca | 518 (89.16) | 509 (87.61) | |||
| Semi-pucca/Khutcha | 63 (10.84) | 72 (12.39) | ||||
| Smoking status | Smoker | 21 (3.61) | 24 (4.13) | |||
| Non-smoker | 557 (95.87) | 560 (96.39) | ||||
| Alcohol intake | Yes | 65 (11.19) | 77 (13.25) | |||
| No | 516 (88.81) | 504 (86.75) | ||||
| No. of family members affected | ≤ 1 | 435 (74.87) | 432 (74.35) | |||
| >1 | 146 (25.13) | 149 (25.65) | ||||
| Complication during hospitalization | Yes | 242 (87.36) | 35 (6.02) | |||
| No | 339 (58.35) | 546 (93.98) | ||||
| Testing Center | Study site | 297 (51.12) | 171 (29.43) | |||
| Others (Govt./Pvt.) | 237 (48.88) | 380 (70.56) | ||||
| Comorbidities | Present | 383 (65.92) | 201 (34.60) | |||
| Absent | 198 (34.08) | 380 (65.40) | ||||
COVID-19: Coronavirus disease 2019
Table 2 shows the incidence of PCS among the study participants. Among moderate to severe COVID-19 cases, 65.6% developed acute PCS by the end of the first month compared to 24.3% of mild cases (RR: 5.94, 95% CI: 4.60-7.67, P < 0.001). At 3 months, 37.7% had PCS versus 11% of mild cases (RR: 4.88, 95% CI: 3.58-6.66, P < 0.001). By 6 months, 24.4% were affected versus 7.1% of mild cases (RR: 4.26, 95% CI: 2.94-6.16, P < 0.001). At 12 months, 34.4% had PCS compared to 16.9% of mild cases (RR: 2.58, 95% CI: 1.96-3.41, P < 0.001). These findings, as indicated in Figure 2, also show a significantly higher risk of developing post-COVID syndrome among moderate to severe cases at all observed intervals.
Table 2.
Incidence of Post-COVID Syndrome (PCS) at different follow-up intervals by disease severity (n=1162)
| Time Point | Disease Severity | PCS Present n (%) | PCS Absent n (%) | Total | P # | RR (95% CI) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Acute Post-COVID Syndrome (1 month) | Moderate/Severe | 381 (65.6) | 200 (34.4) | 581 | <0.001 | 2.70 (2.31–3.15) | ||||||
| Mild | 141 (24.3) | 440 (75.7) | 581 | |||||||||
| Post-COVID Syndrome (3 months) | Moderate/Severe | 219 (37.7) | 362 (62.3) | 581 | <0.001 | 3.42 (2.65–4.40) | ||||||
| Mild | 64 (11.0) | 517 (89.0) | 581 | |||||||||
| Post-COVID Syndrome (6 months) | Moderate/Severe | 142 (24.4) | 439 (75.6) | 581 | <0.001 | 3.46 (2.49–4.80) | ||||||
| Mild | 41 (7.1) | 540 (92.9) | 581 | |||||||||
| Persistent Post-COVID Syndrome (12 months) | Moderate/Severe | 200 (34.4) | 381 (65.6) | 581 | <0.001 | 2.04 (1.65–2.52) | ||||||
| Mild | 98 (16.9) | 483 (83.1) | 581 |
# Chi-squared test; RR: Relative Risk; CI: Confidence Interval; COVID: Coronavirus disease
Figure 2.
Incidence of post COVID syndrome over time
Table 3 shows the factors associated with the incidence of PCS among the study participants at various time points of follow-ups.
Table 3.
Factors associated with Post-COVID Syndrome at various time points following COVID-19 (n=1162)
| Variable | PCS Present | Total | Unadjusted RR (95% CI) | Adjusted RR (95% CI) | P # | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Acute Post-COVID Syndrome (PCS) | ||||||||||
| Residence | ||||||||||
| Rural | 258 (49) | 526 | 1.36 (1.07-1.71) | 1.65 (1.28-2.13) | <0.001 | |||||
| Urban | 264 (41.5) | 636 | 1 | 1 | ||||||
| Comorbidities | ||||||||||
| Present | 320 (54.8) | 584 | 2.25 (1.78-2.86) | 1.91 (1.47-2.47) | <0.001 | |||||
| Absent | 202 (34.9) | 578 | 1 | 1 | ||||||
| Complications during hospitalization | ||||||||||
| Present | 160 (57.8) | 277 | 1.77 (1.32-2.37) | 1.97 (1.50-2.59) | <0.001 | |||||
| Absent | 362 (40.9) | 885 | 1 | 1 | ||||||
| Post-COVID Syndrome (3 months) | ||||||||||
| Residence | ||||||||||
| Rural | 164 (31.2) | 526 | 1.97 (1.50-2.58) | 2.65 (1.97-3.56) | <0.001 | |||||
| Urban | 119 (18.7) | 636 | 1 | 1 | ||||||
| Age | ||||||||||
| ≤30 | 19 (9.8) | 193 | 1 | 1 | 0.001 | |||||
| >30 | 264 (27.2) | 969 | 3.43 (2.09-5.62) | 2.51 (1.48-4.27) | ||||||
| Comorbidities | ||||||||||
| Present | 175 (30) | 584 | 1.86 (1.42-2.44) | 1.52 (1.12-2.05) | 0.007 | |||||
| Absent | 108 (18.7) | 578 | 1 | 1 | ||||||
| Complications during hospitalization | ||||||||||
| Present | 99 (35.7) | 277 | 2.12 (1.57-2.84) | 2.14 (1.55-2.93) | <0.001 | |||||
| Absent | 184 (20.8) | 885 | 1 | 1 | ||||||
| Post-COVID Syndrome (6 months) | ||||||||||
| Gender | ||||||||||
| Male | 83 (13.6) | 609 | 1.39 (1.02-1.92) | 1.49 (1.05-2.13) | 0.02 | |||||
| Female | 100 (18.1) | 553 | 1 | 1 | ||||||
| Residence | ||||||||||
| Rural | 109 (20.7) | 526 | 1.98 (1.44-2.73) | 2.72 (1.92-3.85) | <0.001 | |||||
| Urban | 74 (11.6) | 636 | 1 | 1 | ||||||
| Comorbidities | ||||||||||
| Present | 123 (21.1) | 584 | 2.30 (1.65-3.21) | 2.02 (1.41-2.91) | <0.001 | |||||
| Absent | 60 (10.4) | 578 | 1 | 1 | ||||||
| Complications during hospitalization | ||||||||||
| Present | 67 (24.2) | 277 | 2.12 (1.51-2.96) | 2.06 (1.43-2.95) | <0.001 | |||||
| Absent | 116 (13.1) | 885 | 1 | 1 | ||||||
| Persistent post-COVID syndrome (12 months) | ||||||||||
| Residence | ||||||||||
| Rural | 85 (16.2) | 526 | 1 | 1 | <0.001 | |||||
| Urban | 213 (33.5) | 636 | 2.61 (1.96-3.47) | 2.57 (1.89-3.47) | ||||||
| Comorbidities | ||||||||||
| Present | 196 (33.6) | 584 | 2.36 (1.79-3.10) | 1.66 (1.23-2.24) | 0.001 | |||||
| Absent | 102 (17.6) | 578 | 1 | 1 |
#P-value for adjusted analysis; RR: Relative Risk; CI: Confidence Interval; COVID: Coronavirus disease
Acute Post-COVID Syndrome: Rural residents (adjusted RR 1.65, 95% CI: 1.28-2.13, P < 0.001), those living in pucca houses (adjusted RR 1.57, 95% CI: 1.07-2.30, P = 0.02), families with four or fewer members (adjusted RR 1.37, 95% CI: 1.06-1.78, P = 0.016), individuals with alcohol intake (adjusted RR 1.50, 95% CI: 1.03-2.19, P = 0.04), the presence of comorbidities (adjusted RR 1.91, 95% CI: 1.47-2.47, P < 0.001), and complications during hospitalization (adjusted RR 1.97, 95% CI: 1.50-2.59, P < 0.001) were all associated with a higher risk of acute PCS.
Post-COVID Syndrome (3 Months): Rural residents (adjusted RR 2.65, 95% CI: 1.97-3.56, P < 0.001), those aged 30 and above (adjusted RR 2.51, 95% CI: 1.48-4.27, P = 0.001), the presence of comorbidities (adjusted RR 1.52, 95% CI: 1.12-2.05, P = 0.007), and complications during hospitalization (adjusted RR 2.14, 95% CI: 1.55-2.93, P < 0.001) were all associated with a higher risk of developing PCS at 3 months.
Post-COVID Syndrome (6 Months): Male gender (adjusted RR 1.49, 95% CI: 1.05-2.13, P = 0.02), rural residence (adjusted RR 2.72, 95% CI: 1.92-3.85, P < 0.001), age over 30 (adjusted RR 2.53, 95% CI: 1.28-4.98, P = 0.007), the presence of comorbidities (adjusted RR 2.02, 95% CI: 1.41-2.91, P < 0.001), and complications during hospitalization (adjusted RR 2.06, 95% CI: 1.43-2.95, P < 0.001) were all associated with a higher risk of PCS at 6 months.
Persistent Post-COVID Syndrome: Rural residents had a higher risk of persistent post-COVID syndrome at 12 months compared to urban residents, with an aRR of 2.57 (95% CI: 1.89-3.47, P < 0.001). Unskilled workers had a higher risk compared to skilled workers, with an aRR of 1.58 (95% CI: 1.13-2.12, P = 0.007). Individuals with an education level of middle school or above had a higher risk compared to those with higher education, with an adjusted RR of 1.63 (95% CI: 1.18-2.24, P = 0.003). The presence of comorbidities was a significant risk factor, with an aRR of 1.66 (95% CI: 1.23-2.24, P = 0.001).
DISCUSSION
This ambispective cohort study aimed to investigate the occurrence and factors contributing to PCS in mild compared to moderate and severe COVID cases. A total of 1,162 participants were enrolled in the study. The definition of PCS used in this study differs from the World Health Organization (WHO) definition. While WHO defines PCS as symptoms persisting for at least 2 months beyond 3 months of infection without an alternative explanation, our study used an operational definition based on the presence of two or more selected symptoms at predefined follow-up intervals. This approach was adopted to ensure feasibility and capture clinically meaningful symptom burden; however, it may limit comparability with studies using standardized WHO criteria.
In our study, participants with moderate to severe COVID-19 cases were mostly males, while mild cases were mostly females, aligning with previous research indicating that males have a higher risk of developing severe disease and mortality due to biological, lifestyle, and genetic factors.[14,15,19,20,21] The majority of participants in both groups were over 30 years old, emphasizing the increased vulnerability of middle-aged and older individuals to severe COVID-19 outcomes.[22,23]
In the first and second follow-ups (end of 1 month and 3 months), approximately two-third of moderate to severe cases experienced acute PCS compared to only one-fourth of mild cases at 1 month, and more than one-third of moderate to severe cases compared to one-tenth of mild cases at 3 months (RR: 5.94, 95% CI: 4.60-7.67, P < 0.001; RR: 4.88, 95% CI: 3.58-6.66, P < 0.001). These results align closely with studies conducted in other countries, where the reported proportions of PCS ranged from 55% to 87.6%.[24] The prevalence of PCS varies widely across studies, but recent research provides a range of estimates. Differences in prevalence rates from other studies could be due to variations in cohort inclusion and symptom assessment.[25,26,27]
At 6 and 12 months, 24.4% and 34.4% of moderate to severe cases experienced PCS compared to 7.1% and 16.9% of mild cases, respectively (RR: 4.26, 95% CI: 2.94-6.16, P < 0.001; RR: 2.58, 95% CI: 1.96-3.41, P < 0.001). These findings are consistent with other studies showing a higher prevalence of long-term symptoms in severe COVID-19 cases.[24,25,26,27] For example, Emecen et al.[28] reported symptom frequencies decreasing from 37.2% at the first month to 18.2% at the sixth month, while Peter et al.[29] found 28.5% of adults aged 18-65 experienced persistent symptoms six to 12 months post-infection.
The results demonstrate a clear disparity in PCS incidence between moderate to severe and mild cases over time, highlighting the increased risk of prolonged symptoms in more severe cases. While mild and asymptomatic cases can develop PCS, the risk is significantly lower.[28,29] These findings align with other studies showing a higher incidence of PCS in individuals with severe disease.[29,30] The CDC also notes that post-COVID conditions are more common among those with severe illness.[1]
The mechanisms behind the increased risk of long-term complications in severe COVID-19 cases are not fully understood but may involve persistent inflammation, endothelial dysfunction, autoimmune dysregulation, and direct viral damage.[1,24] An increase in the proportion of PCS observed at 12 months compared to 6 months in both severity groups suggests a non-linear trajectory of post-COVID symptoms.[31,32] This may be explained by the fluctuating, delayed onset of certain symptoms and improved recognition of symptoms over time.[33,34] Additionally, the use of a composite definition requiring the presence of multiple symptoms may have contributed to this pattern. Similar fluctuations in symptom prevalence over time have been reported in longitudinal studies of post-COVID condition.[35,36,37] However, this finding should be interpreted cautiously, and further research is needed to elucidate long-term symptom dynamics.
Socio-demographic factors such as older age and rural residence are significant risk factors for PCS.[38] Individuals over 30 years, especially those aged 30-49, are at a significantly higher risk of developing long-term symptoms, necessitating extensive healthcare monitoring and support for this demographic.[6] Clinical factors such as pre-existing conditions and initial disease severity also significantly influence the risk of developing PCS. The presence of comorbidities increased the risk, with an aRR of 1.91 (95% CI: 1.47-2.47, P < 0.001).[39,40]
These findings are consistent with other studies identifying comorbidities and severe illness as significant risk factors for PCS.[41] Addressing these factors through targeted public health interventions could help mitigate the long-term impacts of COVID-19.
This study possesses several notable strengths, including its large, systematically sampled cohort followed for 12 months, which effectively addresses the scarcity of long-term data on PCS in India. The internal validity was further enhanced by stratifying participants based on objectively measured disease severity, using parameters like oxygen saturation and respiratory rate. The reliability of outcome measurement was also improved through the utilization of standardized, validated tools for symptom assessment. However, the study also has limitations that warrant consideration. The assessment of PCS was restricted to only five symptoms, which might lead to an underestimation of the true burden compared to studies employing more extensive symptom lists. Some data were collected through patient interviews, which may be subject to recall bias. Although efforts were made to verify information with hospital records, incomplete documentation may have affected data accuracy. The single tertiary care center setting of the study may limit the generalizability of the findings to community settings or non-hospitalized populations. Finally, despite multivariable adjustment, the possibility of residual confounding cannot be entirely ruled out.
CONCLUSION
PCS remains a significant health concern up to 12 months after acute infection, particularly among individuals with moderate to severe disease. In addition to severity, socio-demographic and clinical factors such as rural residence, older age, comorbidities, and hospitalization complications independently contributed to the risk of PCS. Early identification of high-risk groups and implementation of long-term follow-up strategies are essential to mitigate the impact of PCS and improve patient outcomes.
Conflicts of interest
There are no conflicts of interest.
Acknowledgment
The authors acknowledge the contribution of the study participants for their cooperation in conducting the research.
Funding Statement
This work was supported by the ICMR grant (P-53)/ Epidemiology and Communicable Division (ECD)/Adhoc/66/2021-22.
REFERENCES
- 1.CDC. Centers for Disease Control and Prevention. Post-COVID Conditions. 2024. Available from: https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html. [Last accessed on 2024 May 23]
- 2.WHO. Coronavirus disease (COVID-19): Post COVID-19 condition. Available from: https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-(covid-19)-post-covid-19-condition. [Last accessed on 2024 May 23]
- 3.Sree Sudha TY, Sasanka KSBSK, Thangaraju P, Kuotsu R, William AG. Identification of spectrum of persistent post-COVID-19 symptoms and their duration in Central India: A pilot study. J Family Med Prim Care. 2022;11:7850–6. doi: 10.4103/jfmpc.jfmpc_801_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kunal S, Madan M, Tarke C, Gautam DK, Kinkar JS, Gupta K, et al. Emerging spectrum of post-COVID-19 syndrome. Postgrad Med J. 2022;98:633–43. doi: 10.1136/postgradmedj-2020-139585. [DOI] [PubMed] [Google Scholar]
- 5.Wesselingh R. Prevalence, pathogenesis and spectrum of neurological symptoms in COVID-19 and post-COVID-19 syndrome: A narrative review. Med J Aust. 2023;219:230–6. doi: 10.5694/mja2.52063. [DOI] [PubMed] [Google Scholar]
- 6.Lopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo PA, Cuapio A, et al. More than 50 long-term effects of COVID-19: A systematic review and meta-analysis. Sci Rep. 2021;11:16144. doi: 10.1038/s41598-021-95565-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Bai F, Tomasoni D, Falcinella C, Barbanotti D, Castoldi R, Mulè G, et al. Female gender is associated with long COVID syndrome: A prospective cohort study. Clin Microbiol Infect. 2022;28:611. doi: 10.1016/j.cmi.2021.11.002. e9-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Subramanian A, Nirantharakumar K, Hughes S, Myles P, Williams T, Gokhale KM, et al. Symptoms and risk factors for long COVID in non-hospitalized adults. Nat Med. 2022;28:1706–14. doi: 10.1038/s41591-022-01909-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Long COVID: Long-term effects of COVID-19. 2024. Available from: https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/covid-long-haulers-long-term-effects-of-covid19. [Last accessed on 2024 Sep 05]
- 10.Alghamdi SA, Alfares MA, Alsulami RA, Alghamdi AF, Almalawi AM, Alghamdi MS, et al. Post-COVID-19 syndrome: Incidence, risk factor, and the most common persisting symptoms. Cureus. 2022;14:e32058. doi: 10.7759/cureus.32058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Pavli A, Theodoridou M, Maltezou HC. Post-COVID syndrome: Incidence, clinical spectrum, and challenges for primary healthcare professionals. Arch Med Res. 2021;52:575–81. doi: 10.1016/j.arcmed.2021.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Prevalence of post COVID-19 condition symptoms: A systematic review and meta-analysis of cohort study data, stratified by recruitment setting. Available from: https://www.ecdc.europa.eu/sites/default/files/documents/Prevalence-post-COVID-19-condition-symptoms.pdf. [Last accessed on 2024 May 24]
- 13.Bisht R, Saharia R, Sarma J. COVID-19 and the burden of ill-health: A double crisis of disruptions and inequalities. J Soc Econ Dev. 2021;23(Suppl 2):342–56. doi: 10.1007/s40847-020-00117-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Jin JM, Bai P, He W, Wu F, Liu XF, Han DM, et al. Gender differences in patients with COVID-19: Focus on severity and mortality. Front Public Health. 2020;8:152. doi: 10.3389/fpubh.2020.00152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Twitchell DK, Christensen MB, Hackett G, Morgentaler A, Saad F, Pastuszak AW. Examining male predominance of severe COVID-19 outcomes: A systematic review. Androg Clin Res Ther. 2022;3:41–53. doi: 10.1089/andro.2022.0006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27:601–15. doi: 10.1038/s41591-021-01283-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hallek M, Adorjan K, Behrends U, Ertl G, Suttorp N, Lehmann C. Post-COVID syndrome. Dtsch Arztebl Int. 2023;120:48–55. doi: 10.3238/arztebl.m2022.0409. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Suri TM, Srivastava G, Kumar S, Surendranath A, Shaji S, Mittal S, et al. Persistent pulmonary impairment after 2 years of COVID-19 infection: An observational study. Lung India. 2024;41:405–10. doi: 10.4103/lungindia.lungindia_87_24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ranjan J, Ravindra A, Mishra B. Gender and genetic factors impacting COVID-19 severity. J Family Med Prim Care. 2021;10:3956–63. doi: 10.4103/jfmpc.jfmpc_769_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Fabião J, Sassi B, Pedrollo EF, Gerchman F, Kramer CK, Leitão CB, et al. Why do men have worse COVID-19-related outcomes? A systematic review and meta-analysis with sex adjusted for age. Braz J Med Biol Res. 2022;55:e11711. doi: 10.1590/1414-431X2021e11711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhang JJ, Dong X, Liu GH, Gao YD. Risk and protective factors for COVID-19 morbidity, severity, and mortality. Clin Rev Allerg Immunol. 2023;64:90–107. doi: 10.1007/s12016-022-08921-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Millet C, Narvaneni S, Roman S, Horani G, Chaudhry S, Michael P, et al. Symptoms persist in patients two years after COVID-19 infection: A prospective follow-up study. Clin Microbiol Infect. 2022;28:1505–7. doi: 10.1016/j.cmi.2022.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Starke KR, Reissig D, Petereit-Haack G, Schmauder S, Nienhaus A, Seidler A. The isolated effect of age on the risk of COVID-19 severe outcomes: A systematic review with meta-analysis. BMJ Glob Health. 2021;6:e006434. doi: 10.1136/bmjgh-2021-006434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Sk Abd Razak R, Ismail A, Abdul Aziz AF, Suddin LS, Azzeri A, Sha’ari NI. Post-COVID syndrome prevalence: A systematic review and meta-analysis. BMC Public Health. 2024;24:1785. doi: 10.1186/s12889-024-19264-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Montoy JCC. Prevalence of symptoms ≤12 months after acute illness, by COVID-19 testing status among adults — United States, December 2020–March 2023. MMWR Morb Mortal Wkly Rep. 2023;72:859–65. doi: 10.15585/mmwr.mm7232a2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Anjana NKN, Annie TT, Siba S, Meenu MS, Chintha S, Anish TSN. Manifestations and risk factors of post COVID syndrome among COVID-19 patients presented with minimal symptoms – A study from Kerala, India. J Family Med Prim Care. 2021;10:4023–9. doi: 10.4103/jfmpc.jfmpc_851_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Nair CV, Moni M, Edathadathil F, A A, Prasanna P, Raghavan RP, et al. Incidence and characterization of Post-COVID-19 symptoms in hospitalized COVID-19 survivors to recognize syndemic connotations in India: Single-center prospective observational cohort study. JMIR Form Res. 2023;7:e40028. doi: 10.2196/40028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Emecen AN, Keskin S, Turunc O, Suner AF, Siyve N, Basoglu Sensoy E, et al. The presence of symptoms within 6 months after COVID-19: A single-center longitudinal study. Ir J Med Sci. 2023;192:741–50. doi: 10.1007/s11845-022-03072-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Peter RS, Nieters A, Kräusslich HG, Brockmann SO, Göpel S, Kindle G, et al. Post-acute sequelae of covid-19 six to 12 months after infection: population based study. BMJ. 2022;379:e071050. doi: 10.1136/bmj-2022-071050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Salmon D, Slama D, Linard F, Dumesges N, Baut VL, Hakim F, et al. Patients with Long COVID continue to experience significant symptoms at 12 months and factors associated with improvement: A prospective cohort study in France (PERSICOR) Int J Infect Dis. 2024;140:9–16. doi: 10.1016/j.ijid.2023.11.038. [DOI] [PubMed] [Google Scholar]
- 31.Ziauddeen N, Pantelic M, O’Hara ME, Hastie C, Alwan NA. Symptom patterns, recovery, and impact of long COVID: Findings from a longitudinal survey. Open Forum Infect Dis. 2026;13:ofag040. doi: 10.1093/ofid/ofag040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Servier C, Porcher R, Pane I, Ravaud P, Tran VT. Trajectories of the evolution of post-COVID-19 condition, up to two years after symptoms onset. Int J Infect Dis. 2023;133:67–74. doi: 10.1016/j.ijid.2023.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ballouz T, Menges D, Anagnostopoulos A, Domenghino A, Aschmann HE, Frei A, et al. Recovery and symptom trajectories up to two years after SARS-CoV-2 infection: Population based, longitudinal cohort study. BMJ. 2023;381:e074425. doi: 10.1136/bmj-2022-074425. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Fernández-de-Las-Peñas C. Are Patients Exhibiting Post-Coronavirus Disease (COVID) Symptoms at 12 months the Same at 5 or 9 Months? The Fluctuating Nature of Post-COVID. Clin Infect Dis. 2022;75:e1208. doi: 10.1093/cid/ciac007. [doi: 10.1093/cid/ciac007] [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Peter RS, Sedelmaier L, Nieters A, Brockmann SO, Göpel S, Merle U, et al. Symptom burden and post-COVID-19 syndrome 24 months following SARS-CoV-2 infection: Longitudinal population-based study. J Infect. 2025;90:106500. doi: 10.1016/j.jinf.2025.106500. [DOI] [PubMed] [Google Scholar]
- 36.Fischer A, Zhang L, Elbéji A, Wilmes P, Oustric P, Staub T, et al. Long COVID symptomatology after 12 months and its impact on quality of life according to initial coronavirus disease 2019 disease severity. Open Forum Infect Dis. 2022;9:ofac397. doi: 10.1093/ofid/ofac397. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Gutzeit J, Weiß M, Bahmer T, Lieb W, Schreiber S, Vehreschild JJ, et al. Long-term trends in Post-COVID severity: A machine learning analysis from the POP/COVIDOM cohort of the German NAPKON Cohort Network. eClinicalMedicine. 2026;93:103822. doi: 10.1016/j.eclinm.2026.103822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Bek LM, Berentschot JC, Heijenbrok-Kal MH, Huijts S, van Genderen ME, Vlake JH, et al. Symptoms persisting after hospitalisation for COVID-19: 12 months interim results of the CO-FLOW study. ERJ Open Res. 2022;8:00355–2022. doi: 10.1183/23120541.00355-2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Dotan A, Shoenfeld Y. Editorial for “Post COVID-19 syndrome in patients with asymptomatic/mild form. Pathogens. 2023;12:167. doi: 10.3390/pathogens12020167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: Major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21:133–46. doi: 10.1038/s41579-022-00846-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Zadeh FH, Wilson DR, Agrawal DK. Long COVID: Complications, underlying mechanisms, and treatment strategies. Arch Microbiol Immunol. 2023;7:36–61. [PMC free article] [PubMed] [Google Scholar]