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Thank you for addressing the reviewers' previous comments in an appropriate way.
Please address each point raised by the reviewers in your response. In particular - and as pointed out by Reviewer #2 - I recommend you discuss the issue of sampling bias and provide accession numbers for the analysed sequences.
The authors find out that the drug resistance with non-B subtypes and recombinants has been growing and they pointed out that transmitted drug resistance seems to be stable over time. They not only verify an increase in drug resistance along with antiretroviral therapy (ART) being accessible in resource constrained areas, but also disclose the plateau effect on decreasing drug resistance in areas covered by the latest drug regimens.
This paper studies the development of HIV-1 drug resistance in the past 20 years and estimates the global burden of drug resistance with data from two HIV sequence repositories Los Alamos and Stanford HIVdb. The formal one is used to derive the worldwide prevalence, time trends and geodemographic predictors of HIV drug resistance while the latter is used for sensitivity analysis. Three available algorithms ANRS, Rega and HIV db are applied to figure out resistance to reverse transcriptase as well as protease inhibitors (NRTIs, NNTRIs and PIs). Moreover, country-level sociodemographic and health indicators and the list of surveillance drug resistance mutations are obtained from World Bank and WHO, respectively, for the analyses of country-specific resistance and transmitted drug resistance.
However, two major limitations of the study are the lack of information on resistance to integrase inhibitors or rare recombinants and the inconsistency of data from different sources, which may affect the validity the of the results.
Aside from the above issues, I also have some minor remarks on the manuscript for further consideration.
- Line 166 (…has been decreasing by 3%...): I checked the row of Calendar Year in Table 2 and could not find out how the authors got the lower bound 3%.
- Line 178-179 (…the kappa agreement between B subtype and antiretroviral-naïve was -0.06.): It would be more readable if the authors mention how the get the value -0.06.
- Line 184-185 (All results are provided in the supplementary materials.): The authors should have specified the corresponding data set for results in the current paragraph. If those results are concluded from Supplementary Table S2, they had better point out the data attributes for reference, say, the sex M is compared with sex F and the B subtype with non-B subtype, as they did for Table 2 in the manuscript.
- Line 188-189 (In relation to latest generation PI/NNRTI drugs, specifically the PI darunavir (DRV) and the NNRTIs etravirine (ETR) and rilpivirine (RPV).): the sentence appears to be incomplete.
- Line 201 (…higher literacy rates, and public health expenditure.): From Figure 3, it seems that another factor “adjusted net national income” should be added, which is also related to a higher prevalence of B subtype (correlation value 0.63).
I would like the authors to revise and resubmit their manuscript according to my review given above.
See general comments
See general comments
See general comments
The present manuscript derives trends and patterns of antiretroviral resistance from published nucleotide sequences extracted from the Los Alamos and Stanford databases. These analyses consider either all available sequences or sequences from treatment naive patients. Based on this, the authors derive time trends for different subtypes, levels of resistance for different countries, and consider associations between resistance and demographic/socio-economic characteristics (of different countries). Overall this is a well written paper presenting an interesting analysis on an important subject.
I have several comments that need to be addressed :
* I think that the limitations of the study approach and their impact on the results should be discussed and emphasised more. This applies in particular to sampling bias, i.e. the fact that different types of patients are sampled for different dates and countries (which could result in some of the differences presented here) and also the fact that some countries are strongly overrepresented in these databases.This concern applies in particular to sequences from treatment experienced patients (for which treatment history plays a key role in the observed resistance patterns) but to a lesser extent also for treatment naive sequences. I guess for many sequences no information on prior treatment is available, which can result in an additional bias. Generally, it would be very useful if the authors could provide a detailed discussion of the challenges associated with interpreting sequence data for which no or only very limited demographic and clinical data is available. As stated above, I think that this work is despite these limitations a valuable analysis, but the limitations should be emphasised more than in the current version (in lines 265-267).
* On a related note, it would be useful if the authors could provide a discussion of the pros and cons of the present approach compared to the more traditional systematic-review approach (e.g. Rhee et al. Plos Med 2015).
* It is unclear what the correlations summarised in lines 200-205 mean: For example the correlation of subtype with sanitation, physicians, public health expenditure, etc. just reflects the known fact that subtype B is the predominant subtype in the US and western Europe. Similarly, it is unclear to what extent confounding was taken into account in these analyses.
*Given that this work is based on publicly available sequences, the accession numbers of the analysed sequences (by country and category[naive, all] ) should be provided to ensure the reproducibility of the results.
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