Household Clustering of High-Risk Contacts in Smear-Positive TB Patient Families: Evidence for Hotspot Households and Risk Stratification in Rural Eastern Cape

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Authors,

Your Original Research Article entitled "Household Clustering of High-Risk Contacts in Smear-Positive TB Patient Families: Evidence for Hotspot Households and Risk Stratification in Rural Eastern Cape" has been carefully reviewed.

This article deserves attention since it highlights on an important topic related to the possibility of application of AI and Machine Learning in Public Health issues, namely the prediction of high risk to develop TB in persons living with Positive TB cases.

The Article is well Written in English language, well presented and well designed.

Kindly find below my comments regarding this paper:

01- The list of keywords is somehow long try to remove some words from this list.

02- The Introduction section is short, try to make it longer.

03- In the Introduction section, When you started to define TB, you are invited to explain the disease and its causative agent "Mycobacterium tuberculosis" and you should talk a little bit about this bacterium. I invited you to use the following paper as reference for this point:

-- Mycobacterium tuberculosis: The Mechanism of Pathogenicity, Immune Responses, and Diagnostic Challenges

04- This study, since it works with humans, needs an IRB approval, did you  (or previous researchers) get and IRB approval for this study?

05- Figure 4 is not very suitable for publication please re-work on it.

06- In the Discussion section, you are kindly invited to talk about the importance of improving the knowledge, attitude and practice of persons towards TB. Starting with Healthcare workers to people in the community. You can refer to the following articles concerning this point:

-- Knowledge, Attitude, and Practices of Healthcare Workers Towards Tuberculosis, Multidrug-Resistant Tuberculosis, and Extensively Drug-Resistant Tuberculosis

-- Knowledge, attitude, and practice toward tuberculosis prevention and management among household contacts in Suzhou Hospital, Jiangsu province, China

 

Best Regards,

Author Response

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript written by the authors is good to identify hidden cases of TB in family suspects.  The title, "Household Clustering of High-Risk Contacts in Smear-Positive 2 TB Patient Families: Evidence for Hotspot Households and 3 Risk Stratification in Rural Eastern Cape" is good and well written. 

Please find below comments -

1. Introduction - Authors didn't mention why only smear positive method was selected for identifying patients in families. There are other ways to find out the PPE contacts and TST or Mantoux test. Apart from this, few IFN- gamma or IL-2 release assays are performed.
2. Method - Authors didn't take Gold Standard and compare the data of smear positive with it.
3. Results- In figure no. 3, how systematic 195 implementation of clinical governance and community-engaged education would sub-196 stantially reduce the burden of high-risk contacts was done.

Manuscript can be published with slight modifications.

 

Author Response

 

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,
The paper is very interesting and will be of great use to society.

I would like to make a few comments.

I would like you to detail what the gold standard is for using the predictive measures.
I would also like to know if an analysis of the sample size has been done. If so, it should be explained.
If any exclusion criteria were applied to define the study population, this should also be explained.
I would also like to make a few minor comments.
I had trouble understanding all the tables and figures on the first reading. I request that you include table captions or figure captions explaining everything indicated in the table or figure. Please also review the titles.
In Table 1, there is an asterisk (*) that is unnecessary and can be removed. In the age group, the total does not add up to 437; please check this data. I also recommend performing a Z-test for differences in proportions for each age category. For example, in the 45-64 age group, there are no differences in the percentages. This is the case in Figures 3 and 7.
The standard deviation is a measure of dispersion and not a measure of precision. Thus, the standard deviation should not be expressed with a ± sign. The measure of precision is the standard error (SE). For this reason, I recommend using mean (SD) or mean ± SE.

1. Altman DG, Gore SM, Gardner MJ, Pocock SJ. Statistical guidelines for contributors to medical journals. Br Med J 1983;286: 1489-1493
2. Bailar JC, Mosteller F. Guidelines for statistical reporting in articles for medical journals: amplifications and explanations. Ann Intern Med 1988;108: 266-273
3. Tobías A. [Mean ± SD, an incorrect expression]. Med Clin (Barc). 1998 Feb 7;110(4):157

Furthermore, I congratulate you on your work.

Author Response

 

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors,

The revised version of your article entitled "Household Clustering of High-Risk Contacts in Smear-Positive TB Patient Families: Evidence for Hotspot Households and Risk Stratification in Rural Eastern Cape" has been carefully reviewed,

The article is suitable for publication, thanks to the modifications you made,

Best Regards,

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,
Thank you so much for answering all my questions and comments.
I think the work has improved a lot.
Congratulations!