Author Response
<blockquote>
Reviewer #1 (Public Review):
</blockquote>
<blockquote>
Summary:
</blockquote>
<blockquote>
The current study aims to quantify associations between the regular use of proton-pump inhibitors (PPI) - defined as using PPI most days of the week during the last 4 weeks at one cross-section in time - with several respiratory outcomes up to several years later in time. There are 6 respiratory outcomes included: risk of influenza, pneumonia, COVID-19, other respiratory tract infections, as well as COVID-19 severity and mortality).
</blockquote>
<blockquote>
Strengths:
</blockquote>
<blockquote>
Several sensitivity analyses were performed, including i) estimation of the e-value to assess how strong unmeasured confounders should be to explain observed effects, ii) comparison with another drug with a similar indication to potentially reduce (but not eliminate) confounding by indication.
</blockquote>
Thank you for pointing out the strengths of our article. We also sincerely thank the reviewer for raising several concerns and providing significant suggestions to improve our manuscript. We will revise our manuscript according to our provisional responses.
<blockquote>
Weaknesses:
</blockquote>
<blockquote>
(1) The main exposure of interest seems to be only measured at one time-point in time (at study enrollment) while patients are considered many years at risk afterwards without knowing their exposure status at the time of experiencing the outcome. As indicated by the authors, PPI are sometimes used for only short amounts of time. It seems biologically implausible that an infection was caused by using PPI for a few weeks many years ago.
</blockquote>
We agree with the reviewer, and this is one of the limitations of the UK Biobank data. We might identify potential long-term PPI users by defining the users that have certain indications, since they tend to regularly take PPI for a long period rather than only short amounts of time. We will evaluate the effect modification for the subgroup of potential long-term PPI users.
<blockquote>
(2) Previous studies have shown that by focusing on prevalent users of drugs, one often induces several biases such as collider stratification bias, selection bias through depletion of susceptible, etc.
</blockquote>
Due to the limitations of the data from the UK Biobank, including the lack of information on the initiation of medications and close follow-up, we can only use prevalent user design to evaluate the associations between PPI use and respiratory outcomes. We will further discuss it in the limitation section.
<blockquote>
(3) It seems Kaplan Meier curves are not adjusted for confounding through e.g. inverse probability weighting. As such the KM curves are currently not informative (or the authors need to make clearer that curves are actually adjusted for measured confounding).
</blockquote>
We will provide Kaplan Meier curves adjusted for confounding by inverse probability weighting according to the reviewer’s suggestion.
<blockquote>
(4) Throughout the manuscript the authors seem to misuse the term multivariate (using one model with e.g. correlated error terms to assess multiple outcomes at once) when they seem to mean multivariable.
</blockquote>
We will correct the misused terms throughout the manuscript according to the reviewer’s suggestions.
<blockquote>
(5) Given multiple outcomes are assessed there is a clear argument for accounting for multiple testing, which following the logic of the authors used in terms of claiming there is no association when results are not significant may change their conclusions. More high-level, the authors should avoid the pitfall of stating there is evidence of absence if there is only an absence of evidence in a better way (no statistically significant association doesn't mean no relationship exists).
</blockquote>
We will revise our interpretation of the results, especially for those without statistically significant associations based on the reviewer’s advice.
<blockquote>
(6) While the authors claim that the quantitative bias analysis does show results are robust to unmeasured confounding, I would disagree with this. The e-values are around 2 and it is clearly not implausible that there are one or more unmeasured risk factors that together or alone would have such an effect size. Furthermore, if one would use the same (significance) criteria as used by the authors for determining whether an association exists, the required effect size for an unmeasured confounder to render effects 'statistically non-significant' would be even smaller.
</blockquote>
We agree with the reviewer that there might still exist one or more unmeasured risk factors that have effect sizes larger than 2. Therefore, we could not state that the results are robust to unmeasured confounding based on the current analysis, and this would be a limitation of our study. We will add the above information to the discussion section.
<blockquote>
(7) Some patients are excluded due to the absence of follow-up, but it is unclear how that is determined. Is there potentially some selection bias underlying this where those who are less healthy stop participating in the UK biobank?
</blockquote>
We will provide the details for the determination of absence of follow-up in the UK Biobank and illustrate whether it potentially induced selection bias.
<blockquote>
(8) Given that the exposure is based on self-report how certain can we be that patients e.g. do know that their branded over-the-counter drugs are PPI (e.g. guardium tablets)? Some discussion around this potential issue is lacking.
</blockquote>
In the data collection of the UK Biobank, the participants can enter the generic or trade name of the treatment on the touchscreen to match the medications they used. We will discuss this important issue in the discussion section.
<blockquote>
(9) Details about the deprivation index are needed in the main text as this is a UK-specific variable that will be unfamiliar to most readers.
</blockquote>
We will provide details about the deprivation index in the manuscript.
<blockquote>
(10) It is unclear how variables were coded/incorporated from the main text. More details are required, e.g. was age included as a continuous variable and if so was non-linearity considered and how?
</blockquote>
Age was included as a continuous variable. We will provide information on whether non-linearity was considered in our manuscript.
<blockquote>
(11) The authors state that Schoenfeld residuals were tested, but don't report the test statistics. Could they please provide these, e.g. it would already be informative if they report that all p-values are above a certain value.
</blockquote>
We will provide the test statistics for the Schoenfeld residuals.
<blockquote>
(12) The authors would ideally extend their discussion around unmeasured confounding, e.g. using the DAGs provided in <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832226/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832226/</a>, in particular (but not limited to) around severity and not just presence/absence of comorbidities.
</blockquote>
We will use the DAGs provided by the article (PMC7832226) to extend our discussion around unmeasured confounding, especially the severity of comorbidities.
<blockquote>
(13) The UK biobank is known to be highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. The potential problems this might create in terms of collider stratification bias - as highlighted here for example: <a href="https://www.nature.com/articles/s41467-020-19478-2">https://www.nature.com/articles/s41467-020-19478-2</a> - should be discussed in greater detail and also appreciated more when providing conclusions.
</blockquote>
We agree with the reviewer that the highly selective nature of the UK Biobank might create collider stratification bias for the evaluation of COVID-19-related outcomes. We will further discuss this in detail and be cautious when generating conclusions.
<blockquote>
Reviewer #2 (Public Review):
</blockquote>
<blockquote>
Summary:
</blockquote>
<blockquote>
Zeng et al investigate in an observational population-based cohort study whether the use of proton pump inhibitors (PPIs) is associated with an increased risk of several respiratory infections among which are influenza, pneumonia, and COVID-19. They conclude that compared to non-users, people regularly taking PPIs have increased susceptibility to influenza, pneumonia, as well as COVID-19 severity and mortality. By performing several different statistical analyses, they try to reduce bias as much as possible, to end up with robust estimates of the association.
</blockquote>
<blockquote>
Strengths:
</blockquote>
<blockquote>
The study comprehensively adjusts for a variety of critical covariates and by using different statistical analyses, including propensity-score-matched analyses and quantitative bias analysis, the estimates of the associations can be considered robust.
</blockquote>
We thank the reviewer for demonstrating the strengths of our articles. We will further revise our manuscript according to the reviewer’s suggestions.
<blockquote>
Weaknesses:
</blockquote>
<blockquote>
As it is an observational cohort study there still might be bias. Information on the dose or duration of acid suppressant use was not available, but might be of influence on the results. The outcome of interest was obtained from primary care data, suggesting that only infections as diagnosed by a physician are taken into account. Due to the self-limiting nature of the outcome, differences in health-seeking behavior might affect the results.
</blockquote>
We will try to adjust or provide discussions about the above factors, including the dose/duration of PPI use, outcome assessment, and health-seeking behavior.

Marc Bonten

Jos van der Meer

eLife assessment
This useful study aims to quantify associations between regular use of proton-pump inhibitors (PPI) - defined as using PPI most days of the week during the last 4 weeks at one cross-section in time - with several respiratory outcomes (6 different outcomes) up to several years later in time. Weaknesses were identified in the design of the study, such as the measurement of the primary outcome and also the potential of bias which is inherent to the study design, which means the manuscript provides incomplete evidence.

anonymous

Reviewer #1 (Public Review):
Summary:
The current study aims to quantify associations between the regular use of proton-pump inhibitors (PPI) - defined as using PPI most days of the week during the last 4 weeks at one cross-section in time - with several respiratory outcomes up to several years later in time. There are 6 respiratory outcomes included: risk of influenza, pneumonia, COVID-19, other respiratory tract infections, as well as COVID-19 severity and mortality).
Strengths:
Several sensitivity analyses were performed, including i) estimation of the e-value to assess how strong unmeasured confounders should be to explain observed effects, ii) comparison with another drug with a similar indication to potentially reduce (but not eliminate) confounding by indication.
Weaknesses:
(1) The main exposure of interest seems to be only measured at one time-point in time (at study enrollment) while patients are considered many years at risk afterwards without knowing their exposure status at the time of experiencing the outcome. As indicated by the authors, PPI are sometimes used for only short amounts of time. It seems biologically implausible that an infection was caused by using PPI for a few weeks many years ago.
(2) Previous studies have shown that by focusing on prevalent users of drugs, one often induces several biases such as collider stratification bias, selection bias through depletion of susceptible, etc.
(3) It seems Kaplan Meier curves are not adjusted for confounding through e.g. inverse probability weighting. As such the KM curves are currently not informative (or the authors need to make clearer that curves are actually adjusted for measured confounding).
(4) Throughout the manuscript the authors seem to misuse the term multivariate (using one model with e.g. correlated error terms to assess multiple outcomes at once) when they seem to mean multivariable.
(5) Given multiple outcomes are assessed there is a clear argument for accounting for multiple testing, which following the logic of the authors used in terms of claiming there is no association when results are not significant may change their conclusions. More high-level, the authors should avoid the pitfall of stating there is evidence of absence if there is only an absence of evidence in a better way (no statistically significant association doesn't mean no relationship exists).
(6) While the authors claim that the quantitative bias analysis does show results are robust to unmeasured confounding, I would disagree with this. The e-values are around 2 and it is clearly not implausible that there are one or more unmeasured risk factors that together or alone would have such an effect size. Furthermore, if one would use the same (significance) criteria as used by the authors for determining whether an association exists, the required effect size for an unmeasured confounder to render effects 'statistically non-significant' would be even smaller.
(7) Some patients are excluded due to the absence of follow-up, but it is unclear how that is determined. Is there potentially some selection bias underlying this where those who are less healthy stop participating in the UK biobank?
(8) Given that the exposure is based on self-report how certain can we be that patients e.g. do know that their branded over-the-counter drugs are PPI (e.g. guardium tablets)? Some discussion around this potential issue is lacking.
(9) Details about the deprivation index are needed in the main text as this is a UK-specific variable that will be unfamiliar to most readers.
(10) It is unclear how variables were coded/incorporated from the main text. More details are required, e.g. was age included as a continuous variable and if so was non-linearity considered and how?
(11) The authors state that Schoenfeld residuals were tested, but don't report the test statistics. Could they please provide these, e.g. it would already be informative if they report that all p-values are above a certain value.
(12) The authors would ideally extend their discussion around unmeasured confounding, e.g. using the DAGs provided in <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832226/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832226/</a>, in particular (but not limited to) around severity and not just presence/absence of comorbidities.
(13) The UK biobank is known to be highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. The potential problems this might create in terms of collider stratification bias - as highlighted here for example: <a href="https://www.nature.com/articles/s41467-020-19478-2">https://www.nature.com/articles/s41467-020-19478-2</a> - should be discussed in greater detail and also appreciated more when providing conclusions.

Reviewer #2 (Public Review):
Summary:
Zeng et al investigate in an observational population-based cohort study whether the use of proton pump inhibitors (PPIs) is associated with an increased risk of several respiratory infections among which are influenza, pneumonia, and COVID-19. They conclude that compared to non-users, people regularly taking PPIs have increased susceptibility to influenza, pneumonia, as well as COVID-19 severity and mortality. By performing several different statistical analyses, they try to reduce bias as much as possible, to end up with robust estimates of the association.
Strengths:
The study comprehensively adjusts for a variety of critical covariates and by using different statistical analyses, including propensity-score-matched analyses and quantitative bias analysis, the estimates of the associations can be considered robust.
Weaknesses:
As it is an observational cohort study there still might be bias. Information on the dose or duration of acid suppressant use was not available, but might be of influence on the results. The outcome of interest was obtained from primary care data, suggesting that only infections as diagnosed by a physician are taken into account. Due to the self-limiting nature of the outcome, differences in health-seeking behavior might affect the results.

Associations of proton pump inhibitors with susceptibility to influenza, pneumonia, and COVID-19: Evidence from a large population based cohort study

Figures and data

Flow diagram of eligible participants selection.

Baseline characteristics of the included participants.

Associations of PPI use with the susceptibility to pneumonia, influenza, COVID-19 positivity, and other respiratory infections.

Stratified analysis of regular proton pump inhibitor (PPI) users and the risk of influenza, pneumonia, and COVID-19 infection.

Comparisons of the risks of influenza, pneumonia, and COVID-19 between proton pump inhibitor (PPI) and histamine-2 receptor antagonist (H2RA) users.

Be the first to read new articles from eLife