Title: Risk of cardiometabolic outcomes among women with a history of pelvic inflammatory
disease: A retrospective matched cohort study from the UK
Version: 2 Date: 04 Oct 2022
Reviewer's report:
This study on the association between PID and CVD is interesting and extends our knowledge of
PID. Yet, major imprecisions in methodology prevent me from being totally confident about the
results, and I would recommend the authors strengthen their methodology by using the STROBE
guidelines and including sensitivity analyses.
My primary concern with this study is that they do not follow the STROBE or similar guidelines
in the reporting.
In this study, the author uses data from the Health Improvement Network (THIN), which I
assume is a database with prospectively collected data. Furthermore, they use a Cox proportional
hazard model to analyse the data, so I do not understand why the author characterises the study
design as retrospective. However, irrespective the study design is prospective or retrospective,
the word is redundant and should be avoided1.
1. Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of
Observational Studies in Epidemiology (STROBE): explanation and elaboration. Epidemiology
2007;18(6):805-35. doi: 10.1097/EDE.0b013e3181577511
It isn't easy to follow which population the study population derive from and which steps have
been taken. It would be informative if the authors included a flow chart.
Are the prevalence and incidence studies based on the whole background cohort or the matched
study cohort?
Please inform us how many PID-cases had fewer than four matches and if these PID cases with
less than four matches were included in the Cox proportional hazards model?
When reviewing Table 1, it seems that non-PID have more missing in potential confounders.
Also, a history of PID is associated with a diagnosis of polycystic ovary syndrome,
endometriosis, and having a current prescription for combined oral contraceptive pills. This
could indicate surveillance bias. Surveillance bias occurs when one group is followed up more
closely than others. A common way to take this phenomenon into account is to adjust for the
number of healthcare visits or recordings in the database during follow-up. Also, a sensitivity
analysis with a lag period after PID diagnosis could inform whether the two diseases are
diagnosed within a short time and, in addition, inform us about the risk of reverse causality.
Another suggestion is to stratify the analysis by follow-up time.
How were possible cases of PID handled? Please inform us of this in the methods. When reading
the discussion, it seems that possible PID cases are included in the comparison matches, which
is a conservative strategy, that may weaken the association. I recommend doing a sensitivity
analysis where you include the possible cases among PID cases or that you exclude them from
the comparison matches.
Can PID cases be a match to another PID case before diagnosis (and excluded at the date of
PID diagnosis)?
The study is based on a primary care database, and the main outcome, CVD, is often diagnosed
and treated in more specialised care. Do the authors know the validity of the CVD diagnosis?
In cohort studies with a matched design, the data is often analysed with a stratified or marginal
cox model that accounts for dependence between subjects within a pair or cluster. Is there
accounted for the matched design in the cox model in this study? I notice that age and BMI are
used as matching factors and are also included in the model as potential confounders. Why is
general practice not included as a potential confounder as well?
How were potential covariates selected in the final model? Did the authors have any strategy to
prevent overadjustment by selecting which factors were important to include and which were
not?
It would be valuable to briefly mention and discuss that because your study population is young,
the low number of CVD prevents a strong conclusion.
Minor comments:
Abstract: Inconsistencies in thousands separator.
Line 122: patients with a record of, type 2 diabetes or hypertension at baseline were excluded
from the respective analyses.
Line 91: The parenthesis (a seems to miss an end.