Mapping of long COVID conditions in India: A study protocol for systematic review and meta-analysis

doi:10.21203/rs.3.pex-2541/v1

Method Article

Mapping of long COVID conditions in India: A study protocol for systematic review and meta-analysis

https://doi.org/10.21203/rs.3.pex-2541/v1

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The COVID-19 pandemic has posed significant concern revolves around the prolonged effects experienced by a subset of individuals post-acute sequelae of SARS- COV-2 infection, termed long COVID. This study protocol outlines the methodology for a meta-analysis and systematic review with the objective of comprehensively assessing the prolonged effects of COVID-19 on the Indian population and determine the likely risk factors connected to the development and persistence of long COVID. A comprehensive search strategy will be employed across major databases. By combining data from various studies, the expected outcomes include a clearer understanding and documentation of long-term prevalence rates of COVID, symptom profiles, regional disparities, and potential determinants for long-term COVID conditions within the Indian population. The anticipated findings will contribute to a refined understanding of this condition, guiding healthcare interventions and future research endeavors aimed at mitigating the impact of the long COVID condition on the Indian population.

COVID-19

long COVID

prevalence

systematic review

meta-analysis

database.

Long COVID-19 is identified as collective signs, symptoms, or problems that persist after acute COVID-19 infection. It is frequently called the post-COVID condition, long COVID, and post-COVID syndrome [1]. Long COVID can include an extent of lingering problems that continue even after 3 months of SARS-CoV-2 infection, which might be new or returning, without any other explanation [2]. However, long COVID is a diversified and clustered term that includes a variety of symptoms that affect the physical, cognitive, and psychological health of an individual. Also, the type and acuteness of symptoms vary from population to population. While some people recover gradually, others struggle with a long-lasting and frequently unpredictable course of symptoms that interfere with their day-to-day activity. Physical symptoms generally linked to long COVID include shortness of breath, persistent fatigue, joint pain, chest pain, headaches, heart palpitations, dizziness, gastrointestinal problems, or loss of taste or smell [3-6]. Cognitive impairments such as memory problems, difficulty concentrating, mental fatigue, anxiety, depression, or mood swings collectively termed 'brain fog' can also be prevalent among those experiencing long COVID [7-9]. Global estimates suggested that approximately 10-20 % of the population infected with COVID-19 has long-lasting symptoms of COVID [10]. Like other countries, India is also facing a long COVID problem in a significant proportion of the population, imposing a huge burden on the healthcare system. With emerging evidence reporting an increasing burden of long COVID in Indians, it is imperative to 1) study, compile and document the variety and severity of long COVID conditions/symptoms reported in Indians 2) understand and estimate the true burden of these conditions and identify the most shared symptoms prevalent in Indian population 3) study the effect of individual-level factors such as sociodemographic, clinical, and economic factors on the same. Here, the authors present a protocol for a meta-analysis and systematic review aiming to reveal the long-term effects of COVID-19 and assess the burden of long COVID on Indians.

1. METHODS AND PLAN OF ANALYSIS

The study protocol presented here describes the methodology for a meta-analysis and systematic review conducted to represent the long-term effects of COVID-19 on the Indian population. The review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on 5 December 2023 (registration number: CRD42023480037).

1.1 Eligibility Criteria

1.1.1 Inclusion criteria

The articles to be included are (1) original research articles, (2) published in English only and (3) report empirical data [longitudinal or cohort studies (including short-term cohort studies) and, cross-sectional studies are included] (4) published between January 2020 and December 2023 and (5) Studies involving participants of Indian ethnicity with a confirmed history of COVID-19 infection will be selected. Any inconsistencies will be addressed through a group discussion among the study team.

1.1.2 Exclusion criteria

Individuals under 18 years of age, pregnant and lactating women will be excluded.

1.2 Search methods, information sources, study selection, and data management

A comprehensive electronic search will be developed and performed using medical subject headings (MeSH terms), entry terms, and text words related to long COVID or post-acute sequelae of COVID-19. The strategy for search will be developed and in total four databases will be explored, namely: PubMed, Cochrane, Ovid MEDLINE, and Google Scholar from January 2020 to December 2023. In addition, the grey literature will also be searched. Forward and backward citation searches of included studies will also be performed. Every database will have its own set of distinctive search techniques, and keywords will be customized correspondingly. Hand searching will be used besides electronic searches and will involve searching through the reference list. Based on eligibility criteria, domains will be set. These domains will be joined with the Boolean operators 'AND' and ‘OR’. The filters will be applied as dates between 2020/01/01 and 2023/12/30 (the last three years); longitudinal or cohort studies (also short-term cohort studies); and cross-sectional studies; humans; printed in English only. The search strategy will be developed by two independent investigators (NJ and RC) and an expert reviewer (KS) will peer-examine the comprehensiveness of the search approach. A six-member team, all with clinical and/or scientific expertise in chronic COVID conditions and evidence synthesis contributes to the study's selection for the review.

At level one, articles that fit in the search terms will be selected based on abstract and title assessment by two independent investigators (NJ and RC). At level second, the remaining articles will be screened by abstract by three researchers separately (NJ, RC, and PA). At level third, the remaining articles will be selected by full text in terms of eligibility criteria and study objective relevance by three researchers (NJ, RC, and PA). At level four, three researchers (AG, KS, and DS) will finally evaluate all articles to ensure their relevance concerning the review objective and eligibility criteria. AG, KS, and DS will carry out cross-checks of the eligibility of articles. Missing values of the comprised studies will be requested from the concerned author via email or telephone.

All disagreements will be discussed with the full review team. The authors of the included studies will be contacted if eligibility criteria remain elusive after a review by the full review team. The final inclusion of the study, accompanied by reasons for exclusion, will be presented in a PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) flow chart. The Microsoft Excel and EndNote 21 reference software will be used to record the data. Search results are deduplicated manually and with the assistance of EndNote 21 software.

1.3 Data collection process and data items

The following data will be included: data associated with the article; sociodemographic data of the population; study design and setting; clinical history of COVID-19 infection and vaccination; associated comorbidities and various symptoms of long COVID. A coding sheet will be developed to transform the described data into categorical data. The data extraction sheet will be pilot-tested in the first five studies. Two reviewers (NJ and RC) will extract data separately from all the included articles and cross-compare them at review completion. In the event of discrepancies, an expert (KS) will be consulted.

1.4 Bias assessment in individual studies

Three or more experienced independent investigators will conduct the quality assessment, the critical appraisal of bias assessment, and the search for appropriate studies. For cross-sectional and longitudinal or cohort studies the Newcastle- Ottawa Scale will be used to evaluate the risk of bias at the study level [11, 12]. The narrative synthesis will include an overview of the bias risk. The value of the evidence will be summarised using the GRADE (Grading of Recommendation Assessment, Development, and Evaluation) approach. Considering the GRADE evaluation, the characteristics of data will be considered high, moderate, low, or very low. Several GRADE domains (e.g., risk of bias, imprecision, inconsistency, publication bias, effect size) will be considered, and based on these, evidence may be downgraded or upgraded. The entire review group will contribute to the quality of the evidence. There will be a summarized presentation of the findings [13].

1.5 Bias in studies

Publication bias will be estimated using Begg's test and Egger’s test.

1.6 Data synthesis and meta-analysis estimation

All included articles will be studied in a narrative synthesis. Outcome data will be comprehensively collected from each study and presented in tabular form. Significant clinical and nonclinical confounders will be documented as descriptive summaries in the results section. Review Manager software (RevMan) V.5.4 (Cochrane Collaboration, Copenhagen, Denmark) will be employed to combine the results of individual studies. The upper limit of statistical significance will be set at p=0.05. Heterogeneity between studies will be estimated and reported using the I² index. Further, meta-analysis will be performed to calculate the pooled prevalence of the common long COVID condition and map the symptoms of the long COVID condition using quantitative data provided by individual studies.

Expected outcome

The primary aim of this research is to determine the frequency of long COVID conditions in the Indian population. This requires a thorough examination of the symptoms and problems associated with long COVID, with a special emphasis on presenting the severity and frequency of these outcomes. Furthermore, the study intends to explore broader aspects by investigating the link between long COVID and numerous demographic factors (age and sex). Furthermore, the study will explore potential correlations with accompanying comorbidities to gain a more detailed understanding of the varied impact of extended COVID on different demographic groups and health conditions in India.

DISCUSSION

Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection, is a complex and multifaceted condition that continues to affect individuals long after their initial COVID-19 disease [14]. The condition's unpredictability and the wide spectrum of symptoms pose significant challenges in diagnosis, treatment, and treatment. However, the situation and understanding of long COVID are continuously evolving, and for the most precise and up-to-date information, it’s critical to stay current with the most recent information from dependable sources. The mapping of long COVID in India involves several crucial aspects. To start, understanding the prevalence and distribution of long-term COVID-19 symptoms at different ages and sex is fundamental. The current meta-analysis and systematic review will emphasize providing updated information on the prevalence of long COVID conditions in India and identifying factors such as age, gender, preexisting conditions, and vaccination status that could play a role in understanding susceptibility and severity. Furthermore, this meta-analysis and systematic review will list all the post-infection effects of COVID-19 infection. This review will use a detailed methodology and examine a wide range of variables, also considering several secondary outcomes, in addition to mapping the prevalence of long COVID conditions as a primary outcome.

REFERENCES

1. Long Covid or post-covid conditions (no date) Centers for Disease Control and Prevention. Available at: https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/index.html#:~:text=Some%20people%2C%20especially%20those%20who,kidney%2C%20skin%2C%20and%20brain. (Accessed: 07 November 2023).

2. Post covid-19 condition (long covid) (no date) World Health Organization. Available at: https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition#:~:text=It%20is%20defined%20as%20the,months%20with%20no%20other%20explanation. (Accessed: 26 December 2023).

3. Aiyegbusi, O.L. et al. TLC Study Group. Symptoms, complications, and management of long COVID: a review. J Royal Soc Med. 114(9), 428–2 (2021). doi.org/10.1177/01410768211032850

4. Raveendran, A.V., Jayadevan, R., Sashidharan, S. Long COVID: An overview. Diabetes Metab Syndr. 15(3), 869–75 (2021). doi.org/10.1016/j.dsx.2021.04.007

5. Davis, H.E., McCorkell, L., Vogel, J.M., Topol, E.J. Long COVID: major findings, mechanisms, and recommendations. Nat Rev Microbiol. 21, 133–46 (2023). doi.org/10.1038/s41579-022-00846-2

6. Yong, S.J. Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments. Infect dis (Lond.). 53(10), 737–54 (2021). doi.org/10.1080/23744235.2021.1924397

7. Orfei, M.D., Porcari, D.E., D'Arcangelo, S., Maggi, F., Russignaga, D., Ricciardi, E. A. New Look on Long-COVID Effects: The Functional Brain Fog Syndrome. J Clin Med. 11(19), 5529 (2022). doi.org/10.3390/jcm11195529

8. De Luca, R., Bonanno, M., Calabrò, R.S. Psychological and Cognitive Effects of Long COVID: A Narrative Review Focusing on the Assessment and Rehabilitative Approach. J Clin Med. 11(21), 6554 (2022). doi.org/10.3390/jcm11216554

9. Zawilska, J.B., Kuczyńska, K. Psychiatric and neurological complications of long COVID. J Psychiatr Res. 156, 349-60 (2022). doi.org/10.1016/j.jpsychires.2022.10.045

10. Covid-19 cases | WHO COVID-19 Dashboard (no date) World Health Organization. Available at: https://data.who.int/dashboards/covid19/cases?n=c (Accessed: 26 December 2023).

11. Higgins, J.P.T. et al. The Cochrane collaboration's tool for assessing risk of bias in randomised trials. BMJ. 343, d5928 (2011). doi: 10.1136/bmj.d5928.

12. Wells, G., Shea, B., O’Connell, D., Peterson, Je., Welch, V., Tugwell, P. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 3rd Symposium on Systematic Reviews: Beyond the Basics. Jul 3-5 (2000). Oxford, UK.

13. Schünemann, H.J. et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ. 336(7653), 1106–10 (2008). doi: 10.1136/bmj.39500.677199.AE.

14. Raman, B., Bluemke, D.A., Lüscher, T.F., Neubauer, S. Long COVID: post-acute sequelae of COVID-19 with a cardiovascular focus. Eur heart J. 43(11), 1157–72 (2022). doi.org/10.1093/eurheartj/ehac031

CREDIT AUTHOR STATEMENT

NJ: conception, procedure, data collection, preparation of an original writing draft, visualization, review, and editing; KS: conception, procedure, data collection, visualization, supervision, review and editing; RC: conception, procedure, data collection, preparation of an original writing draft; PA: data collection, preparation of an original writing draft; AG: supervision, review and editing; DS: Supervision.

CONFLICT OF INTEREST

None.

SOURCE OF FUNDING

This study is funded and supported by “The Indian Council of Medical Research (ICMR), New Delhi, 'Indo-Swedish Research on Ageing and Health' (project sanctioned no NCD / NTF / 25/2022-23).” The sponsor has no role in study design, data analysis, and interpretation of data; in writing the article, and in the decision to submit the article for publication.

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