More information about text formats
We read with great interest the article titled ‘Colchicine prophylaxis is associated with fewer gout flares after COVID-19 vaccination’ by Lu et al.1. The authors found that COVID-19 vaccination was associated with increased odds of gout flare and was negatively associated with colchicine prophylaxis. However, we would like to draw attention to some concerns.
First, it is problematic to present multiple (over eight covariates) adjusted effect estimates from a single model in Table 3. The findings present in Table 3 may mix direct and total effect of each variable and make interpretation difficult2. Specifically, in current study, the authors aimed to assess the total effect of COVID-19 vaccination (the primary exposure) on the odds of gout flare while adjusting for other potential confounders3. Thus, the odds ratios for other covariates (e.g. colchicine prophylaxis treatment) are likely to reflect the direct effect of each covariate rather than total effect, and such a direct effect may be biased due to potential selection bias 4. For example, the odds ratio of sex increased dramatically from 0.82 in univariate analysis to 4.33 in model 2 analysis, which was biologically controversial.
Second, the calculation of odds ratios for vaccination and its subgroups in Table 3 seems questionable. Using the same reference group (no vaccination), the odds ratio of gout flares for any COVID-19 vaccines was 4.57 (p< 0.001), for subgroup of Sinovac Life vaccine was 2....
Second, the calculation of odds ratios for vaccination and its subgroups in Table 3 seems questionable. Using the same reference group (no vaccination), the odds ratio of gout flares for any COVID-19 vaccines was 4.57 (p< 0.001), for subgroup of Sinovac Life vaccine was 2.90 (p= 0.011), and for Sinopharm BIBP vaccine and other vaccines were 0.55 (p= 0.09) and 0.70 (p= 0.32), respectively. Of note, Sinopharm BIBP and other vaccines cases accounted for 45.9% of all COVID-19 vaccines assessed in this study as shown in Table 1. One would expect that the odds ratio of gout flare for any COVID-19 vaccines should be less than 2.90 (i..e, the odds ratio of gout flares for Sinovac Life vaccine). Using results provided in Table 1 and Table 2, we recalculated the crude odds ratio of gout flares for any COVID-19 vaccines (i.e., Sinovac Life vaccine, Sinopharm BIBP vaccine, and other vaccines combined), the odds ratio of gout flares was 1.65, not 4.57, suggesting either effect estimate for any COVID-19 vaccines or effect estimates for subgroups of vaccines may be incorrect.
To sum up, the concerns regarding “Table 2 Fallacy” and miscalculations of effect sizes in Table 3 should be taken into consideration to make this study more convincing and validating. We appreciate the work done by the authors and are looking forward to their responses.
Ethics statements: Not required.
Patient consent for publication: Not required.
Conflict of Interest Disclosures: None reported.
Contributors: TF, PC and ZZ drafted this correspondence. YL, MZ and SC were involved in revising and editing the correspondence.
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; internally peer reviewed
1. Lu J, He Y, Terkeltaub R, et al. Colchicine prophylaxis is associated with fewer gout flares after COVID-19 vaccination. Ann Rheum Dis 2022 doi: 10.1136/annrheumdis-2022-222199 [published Online First: 2022/03/13]
2. Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013;177(4):292-8. doi: 10.1093/aje/kws412 [published Online First: 2013/02/02]
3. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology 1992;3(2):143-55. doi: 10.1097/00001648-199203000-00013 [published Online First: 1992/03/01]
4. Hernan MA, Hernandez-Diaz S, Werler MM, et al. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol 2002;155(2):176-84. doi: 10.1093/aje/155.2.176 [published Online First: 2002/01/16]
5. Susser ES, S.; Morabia, A,; Bromet, E, J. . Psychiatric epidemiology 2006
6. Szklo M. Population-based cohort studies. Epidemiol Rev 1998;20(1):81-90. doi: 10.1093/oxfordjournals.epirev.a017974 [published Online First: 1998/10/08]
7. Schwartz S, Campbell UB, Gatto NM, et al. Toward a Clarification of the Taxonomy of "Bias" in Epidemiology Textbooks. Epidemiology 2015;26(2):216-22. doi: 10.1097/Ede.0000000000000224