Response to 'Was the rate of Long Covid as high as 45%–a scary report with flaw'

We would like to thank Dr Yao and colleagues for their interest in our systematic review and for taking the time to express their concerns, which were listed in four key questions. In relation to point one, the concern related to the calculation of a different and lower prevalence estimate than reported in our systematic review based on re-calculation of data presented in the supplementary material (Table S3). As stated in the methods section of our paper, symptom prevalence data from individual studies were pooled and meta-analysed using a random effects model. When calculating an average this method weights the pooled estimate by study size, and therefore different prevalence estimates will be attained by simply totalling numbers across studies to calculate a simple percentage (as per the data provided by Yao and colleagues). When calculating a pooled effect size, random effects meta-analysis assigns larger studies with more precise effect size estimates greater weight than smaller studies with less reliable estimates.1Borenstein M. Hedges L.V. Higgins J.P.T. Rothstein H.R. Introduction to meta-analysis. John Wiley & Sons, 2021Crossref Google Scholar The formula for random effects models can be found in full in a 2010 paper by Borenstein et al.2Borenstein M. Hedges L.V. Higgins J.P.T. Rothstein H.R. A basic introduction to fixed-effect and random-effects models for meta-analysis.Res Synth Methods. 2010; 1: 97-111Crossref PubMed Scopus (3126) Google Scholar When we report in our study "With an average study follow-up time of 126 days, the pooled prevalence of COVID-19 survivors experiencing at least one unresolved symptom, regardless of hospitalisation status, was 45%"? We have used random effects meta-analyses to calculate the pooled prevalence of experiencing at least one resolved symptom across all studies, and then we have calculated the mean follow-up time across all studies. In addition to this, the data used as the basis of Yao and colleagues' re-analysis – ‘at least one or more symptom’ was not presented in Table S3. When creating this descriptive table, we provided symptom specific data in the table. Following their comment, we have amended Table S3 to remove this column to avoid any confusion. To provide further clarity we have presented the data extracted and used in our review (i.e. study values) for the three exemplar studies identified by Yao and colleagues (Table 1). Two of the three papers (O'Keefe et al. and Tessitore et al.) did not report ‘at least one symptom at follow up’ the data presented by Yao and colleagues in their Table 1 relates to individual symptoms by the corresponding studies and therefore would not have been extracted. We would like to thank the authors for highlighting that Venturelli et al., did report data for ‘At Least 1 Symptom at Follow-up’ (i.e., “51.4% still complained of symptoms). This symptom was not extracted in our original analysis. Adding this data point did not change the associated pooled estimates, but the CI changed marginally; 52.6 (95% CI: 43.5, 61.6) to 52.6 (95% CI: 43.6 to 61.5). We would like to take this opportunity to highlight that a 10% random sample of the extracted data was cross-checked by a second reviewer.Table 1Data extraction for three exemplar studies.AuthorTotalWith symptomMales (%)AgeHospital statusTime to FUFU time pointContinentSymptomO'Keefe (A)aConverted from % to absolute values.3041631.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaCoughO'Keefe (A)304731.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaHeadache/MigraineO'Keefe (A)3041031.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaExertional BreathlessnessO'Keefe (A)304431.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaSore ThroatO'Keefe (A)304731.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaChest Pain/TightnessO'Keefe (A)304431.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaAbdominal PainO'Keefe (A)304731.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaJoint PainO'Keefe (A)3041031.845.7Non-hospitalised≥30Symptom OnsetNorth AmericaNasal SymptomsTessitore165106258Hospitalised365DischargeEuropeMuscles Weakness/PainTessitore16516258Hospitalised365DischargeEuropeFeverTessitore16546258Hospitalised365DischargeEuropeGastro-intestinal ProblemsTessitore165456258Hospitalised365DischargeEuropeFatigueTessitore16516258Hospitalised365DischargeEuropeSputumTessitore16586258Hospitalised365DischargeEuropeHeadache/MigraineTessitore165236258Hospitalised365DischargeEuropeBreathlessnessTessitore16516258Hospitalised365DischargeEuropeNasal SymptomsTessitore16556258Hospitalised365DischargeEuropeCoughVenturelli76739567.163Hospitalised81DischargeEuropeAt Least 1 Symptom at Follow-upbNot extracted in the original data extraction but added now. Converted from %.Venturelli7673967.163Hospitalised81DischargeEuropeArrhythmiaVenturelli7672367.163Hospitalised81DischargeEuropeCoughVenturelli7672467.163Hospitalised81DischargeEuropeChest Pain/TightnessVenturelli7671667.163Hospitalised81DischargeEuropeImpaired Kidney FunctionVenturelli767467.163Hospitalised81DischargeEuropeHeadache/MigraineVenturelli7673367.163Hospitalised81DischargeEuropeDepressionVenturelli76722267.163Hospitalised81DischargeEuropePTSDVenturelli767467.163Hospitalised81DischargeEuropeFeverVenturelli767267.163Hospitalised81DischargeEuropeCognitive DysfunctionVenturelli76712167.163Hospitalised81DischargeEuropeImpaired Usual ActivityVenturelli76722867.163Hospitalised81DischargeEuropeBreathlessnessVenturelli7673067.163Hospitalised81DischargeEuropePalpitationsVenturelli76718667.163Hospitalised81DischargeEuropeFatigueVenturelli7672367.163Hospitalised81DischargeEuropeConfusion/Brain FogVenturelli7678267.163Hospitalised81DischargeEuropeAnxietyNote: Since all data related to the prevalence of people, when the calculated n value was not a whole number it was rounded up to the next highest whole number.a Converted from % to absolute values.b Not extracted in the original data extraction but added now. Converted from %. Open table in a new tab Note: Since all data related to the prevalence of people, when the calculated n value was not a whole number it was rounded up to the next highest whole number. We would also like to note that although Dr Yao and colleagues suggest our prevalence estimates differ from a credible recent publication, the publication that they reference is not a meta-analysis. When compared to other meta-analyses, our prevalence estimates are similar.3Chen C. Haupert S.R. Zimmermann L. Shi X. Fritsche L.G. Mukherjee B. Global prevalence of post-Coronavirus disease 2019 (COVID-19) condition or Long COVID: a meta-analysis and systematic review.J Infect Dis. 2022; 226: 1593-1607Crossref PubMed Scopus (260) Google Scholar,4Groff D. Sun A. Ssentongo A.E. et al.Short-term and long-term rates of postacute sequelae of SARS-CoV-2 infection: a systematic review.JAMA Netw Open. 2021; 4e2128568Crossref PubMed Scopus (388) Google Scholar Specifically, the study by Chen and colleagues found a pooled prevalence of 49% at 120 days which very similar to our finding of 45% at a mean follow-up 126 days.3Chen C. Haupert S.R. Zimmermann L. Shi X. Fritsche L.G. Mukherjee B. Global prevalence of post-Coronavirus disease 2019 (COVID-19) condition or Long COVID: a meta-analysis and systematic review.J Infect Dis. 2022; 226: 1593-1607Crossref PubMed Scopus (260) Google Scholar Additionally, as the authors acknowledge in their paper, a key limitation of the study referred to is that coded symptom data in primary records is likely to underrepresent the true symptom burden experienced by individuals with persistent symptoms. Moreover, the authors explicitly state that “the symptom data we used for the study thus cannot be used to make inferences about the absolute prevalence of these symptoms”, as has been done by Yao and colleagues. In point two, Yao and colleagues discussed the impact of follow-up duration on prevalence estimates. As stated above, a meta-analysis was used which weights study estimates depending on sample size. A simple percentage based on total numbers of patients across studies will result in a different estimate. In addition, the prevalence estimates presented in our paper relate to the average follow-up time of all studies. We did not limit the analysis to studies with follow-up longer than 111 days. As all studies covered a period of time, limiting the analysis to studies >4 months (or 111 days) would not provide a prevalence estimate for an average of 4 months. Rather it would provide an approximate prevalence estimate across studies with a follow-up of at least 4 months, resulting in an average follow-up that is necessarily longer than 4 months. It is reasonable to expect that symptom prevalence will reduce over time since acute infection, as some patients’ symptoms will resolve (either spontaneously or with intervention from healthcare services). Thus, by only including studies with follow-up longer than 4 months, it is not surprising that Yao and colleagues produced a prevalence estimate that is lower than our own. However, we do agree that the variability in prevalence estimates is high and previously acknowledged this within the manuscript on Page 5 “Between studies heterogeneity was high for the majority of meta-analyses, ranging from 2 to 99.9%.” We also provide confidence intervals for the pooled prevalence of any symptom across all groups 44.8% (95% CI 38.6%–51.2%). In point three, Yao and colleagues discuss the accuracy of data extraction. We do appreciate it is difficult to identify the correct corresponding publication based on the names of the author alone. Therefore, we have now included full references in our corrigendum (and in Table 2 below) for all studies included in the analysis. With regards to the three papers that were crosschecked by the authors for accuracy,5Venturelli S. Benatti S.V. Casati M. et al.Surviving COVID-19 in Bergamo province: a post-acute outpatient re-evaluation.Epidemiol Infect. 2021; 149: e32Crossref PubMed Scopus (93) Google Scholar as discussed above, the data included in Table S3 was not specific to at least one symptom at follow up, rather it was a range of varying symptom codes. We appreciate that this may cause confusion and as mentioned previously, we have removed the column for clarity.Table 2List of included study references.Lead AuthorFull ReferenceAhmedAhmed GK, Khedr EM, Hamad DA, Meshref TS, Hashem MM, Aly MM. Long term impact of Covid-19 infection on sleep and mental health: A cross-sectional study. Psychiatry Res. 2021;305:114243.AkterAkter F, Mannan A, Mehedi HMH, Rob MA, Ahmed S, Salauddin A, et al. Clinical characteristics and short term outcomes after recovery from COVID-19 in patients with and without diabetes in Bangladesh. Diabetes Metab Syndr. 2020;14(6):2031-8.ArmangeArmange L, Bénézit F, Picard L, Pronier C, Guillot S, Lentz PA, et al. Prevalence and characteristics of persistent symptoms after non-severe COVID-19: a prospective cohort study. Eur J Clin Microbiol Infect Dis. 2021;40(11):2421-5.ArnoldArnold DT, Hamilton FW, Milne A, Morley AJ, Viner J, Attwood M, et al. Patient outcomes after hospitalisation with COVID-19 and implications for follow-up: results from a prospective UK cohort. Thorax. 2021;76(4):399-401.Asadi-PooyaAsadi-Pooya AA, Akbari A, Emami A, Lotfi M, Rostamihosseinkhani M, Nemati H, et al. Risk Factors Associated with Long COVID Syndrome: A Retrospective Study. Iran J Med Sci. 2021;46(6):428-36.AugustinAugustin M, Schommers P, Stecher M, Dewald F, Gieselmann L, Gruell H, et al. Post-COVID syndrome in non-hospitalised patients with COVID-19: a longitudinal prospective cohort study. Lancet Reg Health Eur. 2021;6:100122.AulAul DR, Gates DJ, Draper DA, Dunleavy DA, Ruickbie DS, Meredith DH, et al. Complications after discharge with COVID-19 infection and risk factors associated with development of post-COVID pulmonary fibrosis. Respir Med. 2021;188:106602.AydinAydin S, Unver E, Karavas E, Yalcin S, Kantarci M. Computed tomography at every step: Long coronavirus disease. 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