Mapping of long COVID condition in India: a study protocol for systematic review and meta-analysis.

Nidhi Jain, Komal Shah, Roshani Chauhan, Abhishek Gupta, Priyanka Arora, Deepak Saxena, Dileep Mavalankar
Author Information
  1. Nidhi Jain: Department of Public Health Science, Indian Institute of Public Health, Gandhinagar, India.
  2. Komal Shah: Department of Public Health Science, Indian Institute of Public Health, Gandhinagar, India.
  3. Roshani Chauhan: Department of Public Health Science, Indian Institute of Public Health, Gandhinagar, India.
  4. Abhishek Gupta: Department of Public Health Science, Indian Institute of Public Health, Gandhinagar, India.
  5. Priyanka Arora: Department of Public Health Science, Indian Institute of Public Health, Gandhinagar, India.
  6. Deepak Saxena: Department of Public Health Science, Indian Institute of Public Health, Gandhinagar, India.
  7. Dileep Mavalankar: Department of Public Health Science, Indian Institute of Public Health, Gandhinagar, India.

Abstract

Background: The COVID-19 pandemic has reported significant alarming aftereffects experienced by some individuals following acute sequelae of SARS-CoV-2 infection, commonly referred to as long COVID. Long COVID is a set of symptoms that remain for weeks or months, after the initial phase of COVID-19 infection is ended.
Objective: This study protocol outlines the methodology of a systematic review followed by a meta-analysis to comprehensively assess the chronic effects of COVID-19 infection on the Indian population and determine the likely risk factors connected to the development and persistence of long COVID.
Methodology: This study will employ comprehensive search through a custom-made search strategy across significant databases (PubMed, MEDLINE etc.) and grey literature to identify related literature from January 2020 to December 2023. A systematic review and meta-analysis will be conducted to synthesize data from various studies. The data synthesis will involve a comprehensive narrative and tabular presentation of outcome data from included studies, focusing on long-term effects of COVID-19 infection in Indian population. A meta-analysis will be conducted contingent upon the availability and suitability of data. If sufficient and comparable quantitative data are identified across the included studies, statistical synthesis will be undertaken. Subgroup and sensitivity analyses will manage confounders, while MedCalc software will facilitate a meta-analysis to assess pooled data. Publication bias will be evaluated using statistical tests to ensure the integrity of the findings. In the absence of adequate data, a narrative synthesis will be performed to summarize the findings systematically and transparently.
Conclusion: The anticipated findings will contribute to a refined understanding of this condition and its lingering symptoms, guiding healthcare interventions and future research endeavors to mitigate the impact of long COVID in the Indian population.

Keywords

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