PONE-D-21-03916

Epidemiology of Clostridioides difficile in South Africa

PLOS ONE

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Additional Editor Comments:

Epidemiology of Clostridioides difficille in South Africa

PONE-D-21-03916

This work is about the incidence of clostridioides difficile infection (CDI) in South Africa (SA). Current knowledge, mostly derived from high income countries, suggests that clostridioides difficile (CD) is a major cause of healthcare associated diarrhea and it is increasingly present in the community. This manuscript contributes to raise clinical awareness of its existence in SA and, I dare say, to the all African continent.

To estimate the incidence, as numerator, the authors took the [newly?] number of cases diagnosed through the National Health Laboratory Service (NHLS) between 1 July 2016 and 30 June 2017. According to the authors the NHLS has a national database of all processed laboratory results in the South Africa public sector [the public health sector which covers 84% of South Africa population]. For the denominator they took the overall mid-year population of South Africa in 2016. Therefore, the CDI incidence is underestimated for both underestimated numerator and large denominator, but I agree with them that it is useful estimate.

In addition to the incidence estimation, the authors produce some measure of association using as exposures (the call risk factors) age, gender, level of care and ICU/HC, and outcomes the state of positive or negative to diagnosis of CDI. Although the negatives here are not good controls it is OK to this exercise.

Few issues:

In the next version please add line numbering. That helps to track and indicate the place of an issue.

I. Introduction is adequate and quite instructive.

II. Methods:

Why the study of risk factors ignore incidence rates? The authors proceed as if they built a case-control with OR estimation. I guess you can obtain population per age and gender. That could be used on a Poisson regression which gives IRR/RR. Just a clarification.

1. Somewhere in the first paragraph there is an indication that a STROBE form was filled. I did not find the attached form. Please add this in the supplements.

2. Data management and analysis

- For the mid-year population conceptually it represents the number of people living in a specific geographic area on 1st July (others would say 30th of June). That is too simplistic. It is more useful to think of it as the total of the fractions year that each individual in South Africa lived. Eg: some lived the full year so their fraction is 1; others just the first quarter so 0.25; and others up to 3rd quarter so 0.75 and so on. So it is a person-years contribution to one year. So, why the authors here decided to mid-year population of 2016 and the numerator covers 2 years? The mid-year population between 1st July 2016 and 30th June 2017 is something close to 1st January of 2017. Just a clarification please.

- State in the methods how the CID incidence confidence intervals were estimated.

- The statement starting as “The NHLS only test specimens for CD that conform to the shape of the container”. This is unclear and confusing. Please clarify.

- Figure 1 is important but it needs changes to 1) to separate clearly exclusions and 2) distinguish sample count from patient counts. Otherwise we will be confused on the calculations as one of the reviewers rightly asks. A sort of suggestion is attached as picture [pardon me for lack of technological sophistication, see the photo of a drawing attached].

- This paragraph related to figure 1 has a lot of information that belongs to results section. Please review.

- Be careful when interpreting odds-ratios as risk-ratios as in, for example, “receiving care in a specialized TB hospital resulted in a more than 11-fold increase in the risk of CDI”. This is incorrect. An example: the proportions of positivity among males and females are 19.12% and 18.28%, respectively. So the is [19.12/(100 – 19.12)]/[18.28/(100 – 18.28)] = 1.06 which is not a ratio of proportion 19.12/18.28 = 1.05. The difference between these two gets worse for higher OR.

III. Results

1. Table 1.

* The percentages cause confusion. I propose these changes

- Please add a row of totals

- For the 3 columns ‘n(%)’, ‘CDI’, and ‘non-CID’ make them the percentages be in columns

- Add one column named ‘CD positivity rate’ so the current percentage calculated in CDI would move to a column dedicated for row percentage.

- Why we need p-values here? Those p-values inform less than the ones on table 2.

* One of the reviewers complains about the numbers. Please re-verify the calculations.

- Eg: The positivity frequency for females 974/5,329 = 18.28% that is OK. But for males should be 756/3,953 = 19.12%.

* Why province is not here?

2. Table 2 –

* Why province is not here?

3. Table 3 needs similar changes as table 1.

4. Table 4 – please what is the unity of measurement of the incidence.

Remove the positivity rate. That is fine on table one.

Three columns are missing. One for the population and the numerators. That will be important in a future meta-analysis.

Add a row for the total. This is important to understand the 0.101 described in the results.

5. Table 5 – the same comments as for table 4.

be careful with abbreviation of CD.

6. The first paragraph of results ends with something as “On univariate analysis only level of patient care was statistically significantly associated with CDI (p < 0.001) (table 1)” Please do not use p-values as evidence of associations. For that, first describe the association measure.

7. If the p-value is below the 0.001 please write “p-value < 0.001” not “p = 0.000” as in “81% (OR 1.81 CI95% 1.51 – 2.17, p = 0.000)”.

8. All figures are numbered figure 1. Please correct this.

9. I am not sure about the seasonality based just with one year of data. Please note this limitation in your discussion

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: This is a very interesting and informative article. This is the first study to determine the incidence of CDI in the South African public sector. All of data are well presented. Considering this, I suggest to accept this article

Reviewer #2: The authors of this paper have addressed key health concerns unique to African continent and a growing problem globally. The paper is well written, organized and easy to read. The English language is from a first speaking point and the paper reads well overall.

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Reviewer #1: Yes: Fernando Gil

Reviewer #2: No

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Attachments

Attachment

Submitted filename: CD Manuscript Review comments-PONE-D-21-03916.docx

Epidemiology of Clostridioides difficile in South Africa

PONE-D-21-03916R1

Dear Dr. de Jager,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Orvalho Augusto, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The issues raised on the previous round have been address properly.

As previously noted, this is an important contribution for the body of literature on this not that rare infectious condition in times of a “pandemic” of antimicrobial resistance. I applaud the authors for this exercise.

Few more:

1. Please correct multivariate to multivariable in the whole document. Multivariate is usually reserved for situations simultaneous assessment of multiple outcomes, whereas multivariable is for situations of one outcome and multiple predictors as is the case here.

2. Good that the authors do present the association analysis through Poisson regressions (a tricky to approximate the log-binomial regression which is so prone to fail to converge). However, let’s be careful with the interpretation of exponentiated coefficients here. Because you do not use the SA population (as an offset) in the model, then you are not modelling the incidence rate in the same spirit of the incidence definition (as in the current table 4). These models are about the proportion of positivity so the coefficients are multiplicative changes of such proportions. Can you make a note on those lines, please? (or may use some other designation eg: ratio of positivity for table 2).

3. The current table 3 is built as in table 1. But the table 1, is supplemented with the current table 2 presenting an unadjusted and adjusted analysis. I would suggest to make an unadjusted and adjusted as well for this.

4. Line 203: please put citation for Stata 16.

5. The presentation of the results is curious. Please, start with the measures of frequency (the incidence) and its geographic distribution. Then move to the analysis of factors.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The previous review comments have been adequately addressed. The paper addresses key concerns about infection prevention and control mechanisms in health-care environment especially Clostridium difficile infections as a potential emerging global public health problem that needs urgent attention.

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Reviewer #2: No