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Prevalence and Predictors of Long COVID-19 and the Average Time to Diagnosis in the General Population: A Systematic Review, Meta-Analysis and Meta-Regression

Authors :
John Kyalo Muthuka
Japeth Mativo Nzioki
Jack Oluoch Kelly
Everlyn Nyamai Musangi
Lucy Chepkemei Chebungei
Rosemary Nabaweesi
Michael Kibet Kiptoo
Source :
COVID, Vol 4, Iss 7, Pp 968-981 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Objectives: We aimed to assess the prevalence of long COVID-19 and estimate the average time to its diagnosis and meta-regression for covariates. Methods: We conducted a systematic review, meta-analysis, and meta-regression from 43 studies (367,236 patients) (June 2020–August 2022). With the random-effects model, the pooled prevalence of long COVID-19 was measured. Publication bias was ascertained, and meta-regression analysis was performed on predetermined covariates. The trial was registered with PROSPERO (CRD42022328509). Results: The pooled prevalence of long COVID-19 was 42.5% (95% CI 36% to 49.3%), with 25% and 66% at four and two months, respectively. Mostly, long COVID-19 signs and symptoms occurred at three (54.3%) to six (57%) months (p < 0.0001), further increasing at 12 months (57.9%, p = 0.0148). Hypertension was significantly associated with long COVID-19 at 32% (0.322 (95% CI 0.166, 0.532) (p < 0.001) and hospital re-admission contributed to 17% (Q = 8.70, df = 1, p = 0.0032) (R2 = 0.17). All the covariates explained at least some of the variance in effect size on long COVID-19 at 53% (Q = 38.81, df = 19, p = 0.0047) (R2 analog = 0.53). Conclusion: The prevalence of long COVID-19 was 42.5% when linked with a cardiovascular disorder. Hospital re-admission majorly predicted the incidence of long COVID-19. Clinical and methodological characteristics in a specific study contributed to over 50% of long COVID-19 events, with most signs and symptoms occurring between 3 and 6 months and increasing at 12 months.

Details

Language :
English
ISSN :
26738112
Volume :
4
Issue :
7
Database :
Directory of Open Access Journals
Journal :
COVID
Publication Type :
Academic Journal
Accession number :
edsdoj.1eef31a318d04057adcfa021450699f1
Document Type :
article
Full Text :
https://doi.org/10.3390/covid4070067