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. 2021 Jan 6;16(1):e0244785. doi: 10.1371/journal.pone.0244785

Implementation of the new integrated algorithm for diagnosis of drug-resistant tuberculosis in Karnataka State, India: How well are we doing?

Uma Shankar S 1,*, Ajay M V Kumar 2,3,4, Nikhil Srinivasapura Venkateshmurthy 5,6, Divya Nair 7, Reena Kingsbury 1, Padmesha R 1, Magesh Velu 1, Suganthi P 1, Joydev Gupta 1, Jameel Ahmed 1, Puttaswamy G 1, Somashekarayya Hiremath 1, Ravi K Jaiswal 1, Rony Jose Kokkad 1, Somashekar N 1
Editor: Igor Mokrousov8
PMCID: PMC7787455  PMID: 33406153

Abstract

Background

As per national policy, all diagnosed tuberculosis patients in India are to be tested using Xpert® MTB/RIF assay at the district level to diagnose rifampicin resistance. Regardless of the result, samples are transported to the reference laboratories for further testing: first-line Line Probe Assay (FL-LPA) for rifampicin-sensitive samples and second-line LPA(SL-LPA) for rifampicin-resistant samples. Based on the results, samples undergo culture and phenotypic drug susceptibility testing. We assessed among patients diagnosed with tuberculosis at 13 selected Xpert laboratories of Karnataka state, India, i) the proportion whose samples reached the reference laboratories and among them, proportion who completed the diagnostic algorithm ii) factors associated with non-reaching and non-completion and iii) the delays involved.

Methods

This was a cohort study involving review of programme records. For each TB patient diagnosed between 1st July and 31st August 2018 at the Xpert laboratory, we tracked the laboratory register at the linked reference laboratory until 30th September (censor date) using Nikshay ID (a unique patient identifier), phone number, name, age and sex.

Results

Of 1660 TB patients, 1208(73%) samples reached the reference laboratories and among those reached, 1124(93%) completed the algorithm. Of 1590 rifampicin-sensitive samples, 1170(74%) reached and 1104(94%) completed the algorithm. Of 64 rifampicin-resistant samples, only 35(55%) reached and 17(49%) completed the algorithm. Samples from rifampicin-resistant TB, extra-pulmonary TB and two districts were less likely to reach the reference laboratory. Non-completion was more likely among rifampicin-resistant TB and sputum-negative samples. The median time for conducting and reporting results of Xpert® MTB/RIF was one day, of FL-LPA 5 days and of SL-LPA16 days.

Conclusion

These findings are encouraging given the complexity of the algorithm. High non-reaching and non-completion rates in rifampicin-resistant patients is a major concern. Future research should focus on understanding the reasons for the gaps identified using qualitative research methods.

Introduction

Tuberculosis (TB), an ancient disease whose cause was discovered nearly two centuries ago, still kills more people in the world, than any other infectious disease. TB has received unprecedented global attention in recent times, rightly so, and the first-ever United Nations high-level meeting on TB was held in September 2018 [1]. As a global community, we have pledged to end TB globally by 2030, with India committing to achieve this goal by 2025 [2, 3]. This may not be possible unless we achieve universal access to prevention, diagnosis and treatment of all forms of TB including drug-resistant tuberculosis (DR-TB).

DR-TB has emerged as a major public health crisis globally as well as in India. According to the World Health Organization (WHO), in 2018 an estimated 130,000 people in India developed rifampicin-resistant TB (RR-TB) or multidrug-resistant TB (MDR-TB) (defined as resistance to at-least isoniazid (H) and rifampicin (R), the two most effective first-line drugs). This accounts for one-fourth of the global burden of MDR/RR-TB [4]. The national drug resistance survey conducted in India during 2014–16 showed that the overall prevalence of MDR-TB was 6.2% among all patients (2.8% among new and 11.6% among previously treated patients). Among MDR-TB patients, 25% had pre-extensively drug-resistant TB. i.e., resistance to either fluoroquinolone (FQ) or second-line injectables (SLI), but not both and 1.3% had extensively drug-resistant TB (XDR-TB), defined as additional resistance to both FQ and SLI [5].

India initiated the programmatic management of DR-TB in 2007 [6]. During the initial years, diagnosis of DR-TB was based on the time-consuming, culture and phenotypic drug susceptibility testing (CDST) methods. There has been great progress over the years with the scale-up of WHO-approved rapid molecular diagnostic technologies such as Xpert® MTB/RIF assay and Line Probe Assay (LPA) [7, 8]. Despite this, only 29% of all estimated RR/MDR-TB patients in 2017 were reported to be diagnosed in India [9]. However, of the patients diagnosed, it was encouraging to note that 92% were started on treatment. Similar findings were also reported in a systematic review and meta-analysis conducted on TB cascade of care in India [10]. These indicate relatively larger gaps in the diagnosis of DR-TB rather than treatment initiation. Such undiagnosed cases are likely to spread the disease and continue the chain of transmission in the community.

Another cause of concern has been treatment success, which remains poor at 46% for MDR-TB patients and 28% among XDR-TB patients in India [11]. The possible reasons for this might be related to delays in diagnosis of additional drug resistance [10, 12–14] and non-initiation of appropriate treatment based on the drug resistance patterns. This may lead to amplification of resistance and poorer treatment outcomes.

To address these concerns, several measures have been taken by the Government of India. Xpert® MTB/RIF assays were pilot-tested at 18 sites across India in 2012 and it was found that decentralized deployment was feasible [15]. Accordingly, Xpert labs were scaled up across the country and are now available at district and sub-district levels, while LPA and CDST services are available at state level (provincial) and sub-provincial reference laboratories. A policy of universal drug susceptibility testing (UDST) was introduced in 2017, wherein all diagnosed TB patients, whether microbiologically confirmed or clinically diagnosed are to be routinely offered Xpert® MTB/RIF assay to assess rifampicin resistance [16]. If MTB is detected, the samples are transported to the reference laboratories for further testing by LPA and liquid culture and DST methods (Fig 1).

Fig 1. The integrated drug resistant TB diagnostic algorithm as per the Programmatic Management of Drug Resistant TB Guidelines, Revised National TB Programme, India, 2017.

Fig 1

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aExtra-pulmonary TB bRifampicin resistant TB cRifampicin sensitive TB dFluoroquinolone eSecond-line injectables fFirst-line Line Probe Assay gSecond-line Line Probe Assay hIsoniazid iAll diagnosed TB patients (clinically diagnosed or microbiologically confirmed) were offered Xpert® MTB/Rif assay to know the rifampicin sensitivity status as per the Universal Drug Susceptibility policy since April 2018 in Karnataka state, India.

Since the launch of this new integrated diagnostic algorithm for DR-TB in India, there has not been any systematic assessment as to how well this is being implemented. Hence, we aimed to assess, among all the TB patients diagnosed at selected Xpert laboratories of Karnataka state, India, i) the proportion whose samples reached the reference laboratories and among them, the proportion who completed the diagnostic algorithm ii) demographic and clinical factors associated with non-reaching and non-completion of algorithm and iii) the delays involved at each step of the diagnostic cascade.

Methods

Study design

This was a cohort study involving analysis of routine programmatic data extracted from the laboratory registers maintained at the selected Xpert laboratories and their linked reference laboratories.

Setting

General setting. The study was conducted in Karnataka, one of the large states in South India. The state has a population of 61 million with about 60% living in rural areas [17]. The sex ratio is 979 females per 1,000 males and the literacy rates are 85% among men and 72% among women [17]. In Karnataka, the prevalence of TB is estimated to be 180 persons per 100,000 [17].

Specific setting. The general healthcare services are delivered through a network of government and private health facilities at primary, secondary and tertiary level. The Revised National Tuberculosis Programme (RNTCP) is implemented by the Karnataka State through 31 district TB centres, 188 sub-district level TB units and 688 sputum microscopy centres functioning under a system of quality assurance [18]. There are a total of 66 (65 permanent and 1 mobile) Xpert laboratories in the state with at-least one laboratory in each district. These have been linked to one of the four designated reference laboratories which include one National Reference Laboratory housed in National Tuberculosis Institute (NTI) and one Intermediate Reference Laboratory (IRL), both located in Bengaluru; and two other laboratories located at Hubli and Raichur. These laboratories are equipped and proficient to conduct first-line LPA (FL-LPA using GenoType MTBDRplus VER 2.0), second-line LPA (SL-LPA using GenoType MTBDRsl VER 2.0, Hain Lifescience GmbH) and liquid CDST using Mycobacterial Growth Indicator Tube 960 system (BD MGIT™). The samples are transported through the human carriers or more routinely, a courier agency. Human carriers are the employees of the general health system (multipurpose health workers, etc.) or programme staff (TB- Health Visitors) stationed at the facility. During the study period, a Non-Governmental Organization (NGO) with external funding was assisting in transportation of the samples between the laboratories in districts around Bengaluru city by deploying human carriers.

The integrated diagnostic algorithm

Karnataka adopted the policy of UDST in April 2018. As per the new algorithm, all the TB patients are offered Xpert® MTB/RIF assay. It is expected that two sputum samples are collected from the patients at the peripheral health institutions (including sputum microscopy centres) in conical tubes and transported in triple layered package in cool chain (temperature maintained at 8°C to 20°C) to the Xpert laboratories situated at district or sub-district level. One of the samples is used for Xpert testing and for all those diagnosed with TB, the other sample is transported to the linked reference laboratories.

Further testing at the reference laboratory depends on the Xpert® MTB/RIF assay results, which are expected to be available in one day. If rifampicin resistance is detected in a new patient, the second sample is used at the Xpert laboratory for a repeat test to confirm rifampicin resistance. If confirmed, the patients are requested to provide fresh sputum specimens to send to reference laboratories. In a previously treated patient, no such confirmatory testing is required, and the second sample is transported to the reference laboratory for SL-LPA to identify mutations in the MTb genes which could confer additional resistance to FQ and/or SLI class of drugs. In case any test was repeated more than once for any reason, then only the final test results were recorded. Awaiting results of SL-LPA, the patient is started on second-line treatment using the short course MDR-TB regimen. Generally, LPA results are expected within 72 hours. If SL-LPA shows no evidence of additional resistance, treatment with short course MDR-TB regimen is continued. If additional resistance is detected, Pre-XDR or XDR TB regimens are started based on resistance to either or both FQ and SLI and further testing is done using liquid CDST to test for resistance to individual or additional drugs (moxifloxacin, linezolid, kanamycin and capreomycin) and based on the final resistance pattern, an individualised DST-based treatment regimen is started.

If Xpert® MTB/RIF assay results show rifampicin sensitive TB, the patient is started on first line treatment while their second sample is sent for FL-LPA. If found sensitive to isoniazid, the patient is continued on first-line treatment; if resistant to isoniazid, regimen for isoniazid resistant TB (wherein resistance to the other first-line anti-TB drugs is not known) is initiated and SL-LPA test is conducted; further management is as described above (Fig 1).

If LPA is not able to provide a valid result or in case of smear-negative samples, the samples are cultured, and LPA is run on the culture isolate. When sputum sample is not of adequate quality or insufficient quantity to obtain a culture result, patients are contacted again for fresh samples. The laboratory turnaround times for Xpert® MTB/RIF is 2 hours, for both the LPAs is 72 hours and for liquid CDST is 42 days.

Study population and study period

All the TB patients diagnosed using Xpert® MTB/RIF assay at the 13 Xpert laboratories situated in ten selected districts between 1st July and 31st August 2018 were included. The districts were selected based on convenience and feasibility of data collection and all the Xpert laboratories within the selected districts are included. The districts were Bengaluru city, Bengaluru rural, Bengaluru urban, Chamarajanagar, Kodagu, Kolar, Mandya, Mysore, Ramanagara and Shivamogga. While the first three districts were linked to NTI, the rest were linked to IRL Bangalore, both located in South Karnataka. The data was extracted from laboratory registers between February and April 2019.

Data variables, sources of data and data collection

All the variables required were extracted from the laboratory registers maintained at the Xpert and the reference laboratories. These included dates of specimen collection, transportation, reporting the test results; type of sample, details of resistance detected and key population (which included high risk groups for TB like tobacco users, people living with HIV (PLHIV), diabetes etc.). To facilitate efficient tracking of patients between the registers, digitization of both the registers was done in quality-assured manner (double entry and validation was done for the data extracted from laboratory registers kept at Xpert laboratories) using EpiData software (v3.1, EpiData Association, Odense, Denmark) by trained data entry operators. We used password-protected electronic data capture systems which were installed on secure desktop systems with restricted access to study investigators only. In case multiple samples from the same patient were tested, the result of the latest sample was captured and used for analysis.

We first digitized the Xpert laboratory registers for the period 1st July to 31st August 2018 (database 1). We then digitized the laboratory register of the reference laboratories for the period 1st July to 30th September 2018 (database 2) to ensure that every patient found to be Mtb detected at the Xpert laboratory is tracked for a period of at-least one month in the reference laboratory register.

For each patient listed in database 1, we searched the database 2 to assess if the patient has reached the reference laboratory using Nikshay ID (a unique patient identification number generated on a case-based web-based TB notification and surveillance platform) as the primary tracking variable. If we did not find a match using Nikshay ID, we then used the mobile phone number. If we failed to get a match using mobile phone number, we used the combination of patient’s name, age and sex. If we did not find a match using any of the above variables, we considered that the sample had not reached the reference laboratory. A master database thus created was de-identified and used for analysis.

Analysis and statistics

Data analysis was done using EpiData (v 2.2.2.182) and STATA (v 12.1, Statacorp, Texas, USA) software. The key outcome variables were: i) proportion of TB patients diagnosed at Xpert laboratories whose samples did not reach the reference laboratory for further testing and among the samples reached, ii) proportion who did not complete the diagnostic algorithm. The median duration and interquartile range (IQR) between receipt of specimens and reporting of results at the two laboratories were calculated. The operational definitions of the outcome variables are described in Table 1.

Table 1. Operational definitions of the outcome variables used to assess the reach, completion, dates of collection, receipt and reporting of results for various diagnostic tests used for diagnosis of DR-TB in the integrated DR-TB diagnostic algorithm.

Indicator Operational Definition
Reaching the reference laboratory A patient found to be Mtb detected in Xpert laboratory, who was documented to have received services in the reference laboratory was considered as having reached. If a match was found by Nikshay IDa, mobile phone number or name-age-sex, then it was considered that the patient’s sample had reached the reference laboratory.
Patients completing the diagnostic algorithm This is a composite indicator and was calculated among patients whose samples reached the reference laboratory. Patients fulfilling the following criteria was considered as having completed the diagnostic algorithm.
• Rifampicin sensitive on Xpert® MTB/Rif and Isoniazid and Rifampicin sensitive on FL-LPAb at reference laboratory
• Rifampicin sensitive on Xpert® MTB/Rif and resistance to Isoniazid and/or Rifampicin on FL-LPA and sensitive to second-line drugs on SL-LPAc
• Rifampicin sensitive on Xpert® MTB/Rif and resistance to Isoniazid and/or Rifampicin on FL-LPA and resistant on SL-LPA and culture done
• Rifampicin resistant on Xpert® MTB/Rif and sensitive on SL-LPA
• Rifampicin resistant on Xpert® MTB/Rif and resistant on SL-LPA and culture done
• Culture done on samples with indeterminate or no results on either FL-LPA or SL-LPA
Date of specimen collection This is the date of specimen collection as documented in the laboratory register maintained at Xpert laboratory.
Date of specimen receipt This is the date of specimen receipt as documented in the laboratory register maintained at Xpert laboratory or reference laboratory.
Date of reporting results This is the date of reporting results as documented in the laboratory register at the Xpert laboratory or reference laboratory for a diagnostic technology.
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a Unique patient identifier generated on the case-based web-based TB notification platform b First line—Line Probe Assay c Second line–Line Probe Assay

Association between the outcomes (non-reaching and non-completion of diagnostic algorithm) and various demographic and clinical factors were examined using chi-square test and measured using unadjusted relative risk (RR) with 95% confidence intervals (CI). We used poisson regression to calculate adjusted relative risks and 95% CI. We used an exploratory approach and included all the variables in adjusted analysis in the multivariable model. A p-value of ≤0.05 were considered statistically significant.

Ethics

Ethics approval was obtained from the Institutional Ethics Committee of the NTI, Bangalore, India, and the Ethics Advisory Group of the International Union Against Tuberculosis and Lung Disease, Paris, France (Number 128/18). Since the study involved review of existing programme records without any direct interaction with human participants, the need for individual informed consent was exempted by the ethics committees. Permission to conduct the study was obtained from the director of NTI and the State TB Officer of Karnataka State.

Results

Demographic and clinical profile

There were a total of 1660 TB patients tested by Xpert® MTB/Rif during the study period. Of them, 1199 (72%) were males and the mean (SD) age was 42 (16) years. Most of the patients (1431, 86%) had been referred from the government health facilities for Xpert MTB/Rif testing and sputum was most common specimen tested (1449, 87%). Those who did not belong to any key population group were 928 (56%) and among the key population 218 (13.1%) were tobacco users and 208 (13%) were PLHIV. Among those documented, 1135 (83%) were newly diagnosed cases. Of the total TB patients, 1590 (96%) were rifampicin sensitive, 64 (4%) were rifampicin resistant, four patients had indeterminate results and two did not have any information on the rifampicin resistance status. (Table 2)

Table 2. Demographic and clinical profile of TB patients diagnosed by Xpert® MTB/Rif assay between 1st July and 31st August 2018 at t13 selected Xpert laboratories in Karnataka, India.

Variable Number (%)
Total 1660 (100)
Age (years)
    ≤ 14 20 (1.2)
    15–29 396 (23.9)
    30–44 523 (31.5)
    45–59 429 (25.8)
    ≥ 60 284 (17.1)
    Not recorded 8 (0.5)
Sex
    Male 1199 (72.2)
    Female 457 (27.5)
    Not recorded 4 (0.2)
Key population
    Contact of TB patient 148 (8.9)
    Tobacco user 218 (13.1)
    Urban slum dweller 135 (8.1)
    People living with HIV 208 (12.5)
    Othersa 23 (1.4)
    non-key population 928 (55.9)
Type of referring health facility
    Government health facility 1431 (86.2)
    Private health facility 215 (13.0)
    Not recorded 14 (0.8)
Type of TB patient
    New 1135 (68.4)
    Previously treated 236 (14.2)
    Not recorded 289 (17.4)
Type of specimen
    Pulmonary 1449 (87.3)
    Extra-pulmonary 66 (4.0)
    Not recorded 145 (8.7)
Specimen condition on receipt at Xpert lab
    Mucopurulentb 1019 (61.4)
    Blood Stainedb 3 (0.2)
    Salivab 403 (24.3)
    Not recorded 235 (14.2)
Rifampicin resistance results at Xpert lab
    Rifampicin Sensitive 1590 (95.8)
    Rifampicin Resistant 64 (3.9)
    Rifampicin Indeterminate 4 (0.2)
    Not recorded 2 (0.1)
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a Others include people with diabetes, migrants, miners, prisoners etc.

b these conditions apply only for sputum samples.

Samples reaching and completing the diagnostic algorithm at the reference laboratories

Out of the total 1660 TB patients tested by Xpert® MTB/Rif six were not included in the analysis for samples reaching and completing the diagnostic algorithm (R resistance reports were not available for 2 and were indeterminate for 4). Of the 1590 rifampicin-sensitive patient samples detected at Xpert laboratory, 1170 (74%) reached the reference laboratory and all were eligible for testing with FL-LPA. Among them, FL-LPA was done for 1131 (97%) and 51 (5%) were diagnosed to have isoniazid resistance and three had rifampicin resistance. Thus, among the 54 eligible samples (with resistance to isoniazid or rifampicin), SL-LPA was done for 11(20%). Culture was done on 41/107 (38%) samples whenever either FL-LPA or SL-LPA was not done or did not yield a result. Thus, a total of 1104 (94%) of rifampicin-sensitive TB patients whose samples were successfully received at the reference laboratory were considered to have completed the diagnostic algorithm (Fig 2).

Fig 2. Cascade of integrated DR-TB diagnostic algorithm for Rifampicin sensitive TB patients (RS-TB) diagnosed at the 13 selected Xpert laboratories between 1st July and 31st August 2018 in Karnataka, India.

Fig 2

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a First line–Line Probe Assay b Isoniazid c Rifampicin d Two were H resistant and one was H sensitive e Second line–Line Probe Assay f Fluoroquinolone g Second line injectable.

Of the 64 rifampicin-resistant patient samples detected at Xpert laboratory, 35 (55%) reached the reference laboratory and all were eligible for testing with SL-LPA. Among them, SL-LPA was done for 14 (40%) and nine samples were sensitive to FQ and SLI. Culture was done on eight samples where SL-LPA results were resistant or unknown. Thus, a total of 17 (49%) of rifampicin-resistant TB patients were considered to have completed the diagnostic algorithm (Fig 3).

Fig 3. Cascade of integrated DR-TB diagnostic algorithm for Rifampicin resistant TB (RR-TB) patients diagnosed at the 13 Xpert laboratories between 1st July and 31st August 2018 in Karnataka, India.

Fig 3

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a Second line–Line Probe Assay b Fluoroquinolone c Second line injectable.

Overall, out of the total 1660 samples, 452 (27%) samples did not reach the reference laboratories and among those reached, 84 (7%) did not complete the DR-TB diagnostic algorithm.

Delays in the diagnostic pathway

Dates to calculate the delays were not consistently recorded in the laboratory registers. A total of 1250/1554 (80%) samples collected at the microscopy centres were transported within a day to the Xpert laboratory and the results were available within a median duration of one day. The median time duration between receipt of sample at the Xpert laboratory to receipt at the linked reference laboratory after transportation was 5 (IQR 3–7) days. On sample receipt at the reference laboratory, the median time taken for conducting and reporting the result of FL-LPA was 5 (IQR 4–6) days (among rifampicin-sensitive TB patients), of SL-LPA was 16 (IQR 8–30) days (among rifampicin-resistant TB patients) and 18 (IQR 11–37) days (among rifampicin-sensitive TB patients who underwent FL-LPA first and then SL-LPA). (Table 3)

Table 3. Timing of specimen collection, receipt and reporting of results of TB patients diagnosed between 1st July and 31st August 2018 at the 13 selected Xpert laboratories and their two linked reference laboratories in Karnataka, India.

Time durations between various laboratory steps No. eligible No. with valid dates (%) Median (IQR)
Specimen collection at DMCa and receipt at Xpert site 1660 1554 (93.6) 0 (0–1)
Specimen receipt and reporting of results at Xpert site 1660 1066 (64.2) 1 (0–2)
Specimen receipt at Xpert site and receipt at reference laboratory 1205 1126 (93.4) 5 (3–7)
Specimen receipt at reference laboratory and FL-LPAb result reporting (for RS-TBc patients) 1170 1116 (95.4) 5 (4–6)
Specimen receipt at reference laboratory and SL-LPAd result reporting (for RR-TBe patients) 35 32 (9.1) 16 (8–30)
Specimen receipt at reference laboratory and SL-LPA reporting (for RS-TB patients whose FL-LPA result was resistant) 54 10 (18.5) 18 (11–37)
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a Designated microscopy center

b First line–Line probe assay

c Rifampicin sensitive tuberculosis

d Second line–Line probe assay

e Rifampicin resistant tuberculosis

Factors associated with non-reach and non-completion

Factors associated with samples not reaching the reference laboratories are shown in Table 4. On adjusted analysis, we found that rifampicin-resistant TB samples, extra-pulmonary samples and samples from two districts (Chamarajnagara and Mysore) were less likely to reach the reference laboratory.

Table 4. Factors associated with samples not reaching the linked reference laboratories from patients diagnosed with TB at the 13 selected Xpert laboratories between 1st July and 31st August 2018 in Karnataka, India.

Variable Total N (%) RRb (95% CI) aRRd (95% CIe)
Total 1660 452 (27.2) 
Health Facility type
    Government 1431 377 (26.3)  Refc Ref
    Private 215 70 (32.6)  1.24 (1.00–1.53) 1.04 (0.84–1.28)
Specimen type
    Sputum 1449 360 (24.8)  Ref Ref
    Extra-Pulmonary 66 40 (60.6)  2.45 (1.97–3.02) 2.52 (1.98–3.21)
    Not recorded 145 52 (35.9)  1.44 (1.14–1.83) 1.58 (0.89–2.81)
District name
    Chamarajanagar 129 48 (37.2)  2.07 (1.48–2.91) 2.19 (1.57–3.08)
    Bengaluru city 273 49 (17.9)  Ref Ref
    Bengaluru rural 120 22 (18.3)  1.02 (0.65–1.61) 0.94 (0.59–1.51)
    Bengaluru urban 207 38 (18.4)  1.02 (0.70–1.50) 1.01 (0.69–1.48)
    Kodagu 52 9 (17.3)  0.96 (0.51–1.84) 1.00 (0.51–1.93)
    Kolar 143 48 (33.6)  1.87 (1.32–2.63) 1.30 (0.69–2.43)
    Mandya 214 54 (25.2)  1.41 (1.00–1.99) 1.38 (0.98–1.93)
    Mysore 224 129 (57.6)  3.21 (2.43–4.23) 3.31 (2.51–4.38)
    Ramanagara 116 21 (18.1)  1.01 (0.63–1.60) 1.11 (0.70–1.75)
    Shivamogga 182 34 (18.7)  1.04 (0.70–1.55) 1.07 (0.73–1.57)
Ra resistance Results
    R Sensitive 1590 420 (26.4)  Ref Ref
    R Resistant 64 29 (45.3)  1.72 (1.29–2.27) 1.77 (1.32–2.37)
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N = Samples not reaching the linked reference laboratories for further testing from the Xpert labs.

a Rifampicin

b Relative risk

c Reference

d Adjusted relative risk

e Confidence Interval

Factors with relative risk in bold font are statistically significant (p value <0.05)

Factors associated with non-completion of the DR-TB diagnostic algorithm are shown in Table 5. On adjusted analysis, we found that rifampicin-resistant TB samples, and sputum microscopy negative samples were less likely to complete the diagnostic algorithm. Patient level variables like age, sex, key population and type of patient were not included in the Tables 4 and 5, considering these variables did not have any effect on the outcomes. Statistical output sheets for Tables 4 and 5 are provided as S1 File.

Table 5. Factors associated with non-completion of the DR-TB diagnostic algorithm among the samples reaching the two linked reference labs from those diagnosed with TB at the 13 selected Xpert laboratories between 1st July and 31st August 2018 in Karnataka, India.

Variable Total N (%) RRd (95% CI) aRRf (95% CIg)
Total records 1208 84 (7.0) 
Health Facility type
    Government 1054 68 (6.5)  Refe Ref
    Private 145 14 (9.7)  1.50 (0.86–2.59) 1.64 (0.95–2.82)
Specimen type
    Sputum 1089 80 (7.3)  Ref Ref
    Extra-Pulmonary 26 1 (3.8)  0.52 (0.08–3.62) 0.53 (0.07–4.04)
    Not recorded 93 3 (3.2)  0.44 (0.14–1.36) 0.35 (0.09–1.42)
Ra resistance Results
    Ra Sensitive 1170 64 (5.5)  Ref Ref
    Ra Resistant 35 17 (48.6)  8.88 (5.86–13.46) 8.47 (5.36–13.39)
Reference laboratory (RL)
    NTIb Bengaluru 491 38 (7.7)  1.21 (0.80–1.83) 0.68 (0.42–1.12)
    IRLc Bengaluru 717 46 (6.4)  Ref Ref
Smear microscopy results at RL
    Negative 100 16 (16.0)  2.67 (1.61–4.44) 2.69 (1.42–5.12)
    Positive 1069 64 (6.0)  Ref Ref
    Not recorded 39 4 (10.3)  1.71 (0.66–4.47) 1.45 (0.48–4.43)
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N = non-completion of the DR-TB diagnostic algorithm among the specimens reaching the two linked reference labs from the Xpert labs

Not recorded- Not recorded were excluded from the model for age group, sex, health facility type, R resistance results and smear microscopy results at RL.

a Rifampicin

b National Tuberculosis Institute

c Intermediate reference laboratory

d Relative risk

e Reference

f Adjusted relative risk

g Confidence interval

Factors with relative risk in bold font are statistically significant (p value <0.05)

Discussion

This is the first study from India to assess the gaps and delays in implementation of an integrated DR-TB diagnostic algorithm under programme settings. We found that about three-fourths of the samples reached the reference laboratory and of them, more than 90% completed the diagnostic algorithm. No gross delays were observed in the diagnostic cascade except for the pathway involving SL-LPA testing. These findings are encouraging given the complexity of the algorithm, involving several tests to be administered in a specific sequence, involving transportation of samples between the Xpert and reference laboratories located far away from each other (distance ranging from 1 to 280 kilometres).

However, there were some gaps too. Approximately a quarter of the samples did not reach the reference laboratories. This was more likely with extra-pulmonary samples, RR-TB samples and samples from some districts. The challenges associated with obtaining adequate sample volume, transportation and processing of the extra-pulmonary samples have been well documented in previous studies [19–21]. In one of the earlier studies for diagnosis of MDR-TB, patients with extra pulmonary TB had 50% higher risk of not getting tested when compared to patient with pulmonary TB [22]. This might be due to inadequate capacity of people involved in the collection of extra-pulmonary specimens, the methods and volume/size of the sample required, non-availability of mechanisms for early transportation of these samples and non-availability of concentration methods or testing capacity for such samples at all the Xpert laboratories.

We were surprised by the gaps in the transport of RR-TB samples because they are generally accorded higher priority by the RNTCP. We speculate that this might be due to the practice of requesting for additional specimens from the patients after being diagnosed as RR-TB at Xpert laboratories especially for new cases (diagnosed with TB first time) and not receiving them in time or due to non-transportation of the samples from the Xpert laboratories. This needs further investigation.

The samples reaching the reference laboratory from Xpert laboratories situated in districts closer to Bangalore city was better. This may be due to the presence of system of human carriers for sample transport in these districts, who were deployed by a non-governmental organization with the support of external funding. A higher proportion of specimens from a couple of districts located farther away did not reach the reference laboratories. This calls for a review of training of key health personnel and sputum collection and transportation mechanisms in these districts.

Completion rates were excellent (95% among rifampicin-sensitive) once the samples reached the reference laboratories, except in sputum microscopy negative and RR-TB samples (49%). This may be explained by the fact that smear microscopy negative samples must be cultured before testing by LPA and if cultures did not yield growth, it required recollection of specimens from the patients–these might lead to non-completion of the diagnostic algorithm or delays in the process. The non-completion rates in RR-TB samples might be related to limitations of the first-generation SL-LPA technology with high rates of indeterminate or invalid results leading to non-availability of results, which could be as high as 44% when tested indirectly on culture isolates in smear microscopy negative samples as per the WHO policy guidance document on the usage of LPA [8]. As a result, the specimens needed to be re-tested multiple times before obtaining a valid result. This may explain why delays with SL-LPA were three times more when compared to FL-LPA, though valid dates of receipt and reporting of SL-LPA results were not available for all samples. This needs to be addressed on priority and requires further investigation A second-generation SL-LPA technology has been validated and recommended for usage by WHO, which may resolve these challenges [8].

Strengths

First, we performed a rigorous assessment and took special efforts to ensure quality of data which included double entry and validation, wherever possible. Second, we had a large sample size which helped in performing a robust analysis and minimize the effect of random variation. Since this was an operational research done using the programme data, it reflects the field realities and actual field performance of different diagnostic tests in providing early and accurate diagnosis of DR-TB.

Limitations

One of the major limitations was that the Nikshay ID was not documented for all TB patients. This made the tracking process challenging and we had to rely on using other variables such as mobile phone numbers, name, age and sex. As a result, we may have underestimated the proportion of samples reached. We have not examined for any interactions, given the fewer variables and low statistical power. We conducted the study in laboratories located in the selected districts situated in southern part of Karnataka state, thus limiting the generalizability of findings to other parts of the state.

Programme implications

There are several implications of the study findings. First, we need to assess the reasons for gaps in the diagnostic cascade using qualitative research methods such as key informant interviews and a bottleneck analysis of the laboratory networks. This will inform specific measures that are required to address the issue. Second, comprehensive measures need to be put in place to improve specimen collection and transportation to reduce the delays involved. Third, the RNTCP needs to strengthen the documentation of Nikshay ID in all documents used along the diagnostic cascade, especially in the laboratory requisition forms filled at the time of sample collection which acts as a source document for updating the entries into the laboratory registers maintained at Xpert and reference laboratories. Steps must be taken to strengthen the real-time updating of results of FL-LPA, SL-LPA and CDST in laboratory registers and online in Nikshay for easy tracking and monitoring. This will also enable cohort-wise analysis and periodic review. Finally, we recommend repeating similar studies in other areas of the state and the country.

In conclusion, we found that approximately 75% of the samples of TB patients diagnosed in the Xpert laboratories of the southern part of Karnataka State reached the reference laboratory and of them, more than 90% completed the diagnostic algorithm. Some gaps were noted, wherein 25% of the specimens were not transported to the reference laboratories for further testing and non-completion of diagnostic algorithm, especially with respect to extra-pulmonary, sputum smear-negative and RR-TB samples. We appreciate the performance of the RNTCP in implementing such a complex diagnostic algorithm in an efficient manner, but there is scope for further improvement. Further research involving Xpert sites representing the whole state and also exploring the reasons for delays and non-completion is necessary to provide constructive feedback to programme managers for reducing the time and improving the efficiency in completion of the integrated DR-TB diagnostic algorithm.

Supporting information

S1 File

(DOCX)

Click here for additional data file. (20.6KB, docx)

Acknowledgments

This research was conducted through the Structured Operational Research and Training Initiative (SORT IT), a global partnership led by the Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR). The model is based on a course developed jointly by the International Union Against Tuberculosis and Lung Disease (The Union) and Medécins sans Frontières (MSF/Doctors Without Borders). The specific SORT IT programme which resulted in this publication was jointly developed and implemented by: The Union South-East Asia Office, New Delhi, India; the Centre for Operational Research, The Union, Paris, France; Medécins sans Frontières (MSF/Doctors Without Borders), India; Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India; Department of Community Medicine, All India Institute of Medical Sciences, Nagpur, India; Department of Community Medicine, ESIC Medical College and PGIMSR, Bengaluru, India; Department of Community Medicine, Sri Manakula Vinayagar Medical College and Hospital, Puducherry, India; Karuna Trust, Bangalore, India; Public Health Foundation of India, Gurgaon, India; The INCLEN Trust International, New Delhi, India; Indian Council of Medical Research (ICMR), Department of Health Research, Ministry of Health and Family Welfare, New Delhi, India; Department of Community Medicine, Sri Devraj Urs Medical College, Kolar, India; and Department of Community Medicine, Yenepoya Medical College, Mangalore, India. Further, the support of all the Xpert and reference laboratory technicians and microbiologists, District TB Officers and the State TB Officer is highly valued.

Data Availability

All relevant data are within the manuscript and the tables and figures. The raw data is the routine programme data from National TB Elimination Programme (NTEP), Ministry of Health & Family Welfare, Government of India. The sharing such programme data requires permission of the Deputy Director General of NTEP.

Funding Statement

US, the corresponding author received the fund. The training program, within which this paper was developed, was funded by the Department for International Development (DFID), UK. In addition, funding for field data collection was provided by National Tuberculosis Institute, Bengaluru. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Igor Mokrousov

29 Jan 2020

PONE-D-19-25977

Implementation of the new integrated algorithm for diagnosis of drug resistant tuberculosis in Karnataka State, India: How well are we doing?

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Reviewer #1: Summary:

This manuscript examines a new diagnostic algorithm in Karnataka state for the identification of drug resistant TB. The authors have presented results for 1660 patients diagnosed over a two-month period in 2018, and indicate that the while there are some specimens that did not reach the second testing lab or complete the testing algorithm, overall they are encouraged by the proportion that made it through the complex algorithm. Factors associated with specimens not making it through the testing process were examined, and the turn-around-times were reported.

I enjoyed reading this manuscript and found it to be generally clear despite the challenges of describing complicated algorithms. There are however some concerns with the statistical methods used to examine factors that may be associated specimens reaching the reference labs and not completing the testing algorithm. There are some minor issues related to clarity of message and grammar/wording which I will offer suggestions as PLoS ONE does not copyedit accepted manuscripts.

Major issues:

Line 290. Unless I am misunderstanding the data, a poisson regression model would be the incorrect choice given the binary outcome variable (Not reaching vs reaching; completion vs non-completion). The measure of the association would be an odds ratio. Please revise accordingly.

Line 291. An exploratory data analysis is often necessary to conduct initially to understand the dataset and variables; however, for reporting results all variables included in a model should make sense. They should either be chosen a priori based on literature and experience, and/or because an association was observed on bivariate analysis. Could you please elaborate on what support there would be for an association between not reaching or not completing, for variables related to the patient such as age, sex and key population? Do the laboratory personnel processing specimens have access to this information and would make a choice not to send or process specimens and samples based on this information? It seems more likely that an association is possible with respect to variables related to the specimens themselves or facilities. If there is no statistical, literature, or common knowledge that would indicate that patient-level variables be considered in the multivariable model please update the analysis without these and revise accordingly. If there is clear rationale to support their inclusion please update the methods to include this information.

Table 4. Analysis for non-completion. Kolar district is no longer significant when covariates are added to the model. Did you investigate why this might be? What is the relevant confounder to the association? Given my concerns regarding the covariates included this warrants further investigation.

It does not appear there was an assessment of model fit, and there may be issues related to overfitting. There are quite a few variables included, and a some of these have many levels. Cell sizes were quite low in some instances in the non-completion model. It is recommended that the model building approach and assessment of fit be carefully considered.

Minor issues:

Abstract:

1. Line 65. Capitalize Line Probe Assay for consistency with the manuscript text.

2. Line 67. Insert the word ‘phenotypic’ before drug susceptibility testing

3. Line 75. Please change ‘till’ to ‘until’ (more formal for scientific writing)

4. Line 78. Were there any duplicate patients in the 1660 (more than one specimen/patient) if so then patients should be updated to ‘specimens’ here, and throughout the manuscript as necessary. It would also be helpful to indicate in the methods or results if each patient is represented by a single specimen, or if not please report the number of patients with multiple specimens and an indication of how many specimens/patient were represented in this study.

5. While I agree that putting this complex algorithm into place is encouraging and having a considerable proportion of specimens make it through testing, I am also concerned that only about a quarter of RIF resistant TB completed the testing algorithm – which is the TB that is most in need of full testing. It would be beneficial to include a few words in the abstract and again in the manuscript indicating that while encouraging there is also significant concern about this.

Introduction:

1. Line 113. Please add the word ‘phenotypic’ in front of drug susceptibility

2. Line 118. A better word for ‘heartening’ in scientific writing may be ‘encouraging’

3. Line 126. Can you clarify here if by non-diagnosis it is meant that resistance was not tested/diagnosed? If patients weren’t diagnosed at all they wouldn’t be treated and therefore not part of the statistic for treatment failure.

4. Line 184. Reference #15 does not link to the pdf for the correct province, please update.

5. Line 185. The reference #15 does not appear to contain the statistic for prevalence, please update the reference to the correct one and ensure that the statistic reported is indeed prevalence rather than incidence.

Methods:

1. Line 195-196. The company that produces the products listed should be included, Hain Lifescience, BD Biosciences BACTEC, and which version of the line probe assays were used.

2. Line 199. Could you elaborate on the human carriers and how this differs from a courier service?

3. Line 215. It may be better to specify ‘one day’ rather than ‘a day’.

4. Line 219. Perhaps reword to read: ‘testing is required, and the second sample…’

5. Line 224. Please specify in the methods or introduction which second line drugs are being tested, not all readers may be familiar with the Hain assay and the version number was not indicated.

6. Line 224. I assume that further testing is for those that were resistant to second line drugs in the SL-LPA? Perhaps clarify this in the sentence.

7. Line 229. It appears that a word is missing. Either ‘a sample’ or ‘their sample’

8. Line 230. Could you clarify the use of poly resistant treatment? As I understand the sentence these cases are RIF-S and INH-R, so initiation of poly-resistant treatment would be indicated by FL-LPA indicating resistance to EMB or PZA.

9. Line 245. I assume the data was collected at the time of specimen collection and testing, and here you mean the data was extracted between February and April 2019 from the laboratory registers?

10. Line 274-275. Quotation marks are not needed here.

11. How were the laboratories selected for this study? Was there specific criteria?

Results:

1. Line 309. This is the first time “key population group” has been introduced and does not include an explanation of what this represents. Information regarding this should be included in the methods as it is not a standard variable.

2. Line 310. In the context of this sentence, it should be ‘persons living with HIV’

3. It is not clear what happened to the specimens with indeterminant or no information? – Where do they fit in the algorithm? What was the outcome? Indicate in the methods sections how these were handled for the analysis.

4. Line 347. It is stated that culture was done for 41 samples. however, in Figure 2 it indicates culture was done for 43 samples?

5. Lines 361-363. This sentence is a bit hard to follow. Perhaps something along the lines of

“on eight samples, under the following SL-LPA conditions: (i) resistance, (ii) was not done, or (iii) no result.”

6. Line 373. Based on Figs 2 and 3, the number that reached the laboratory was 1170+35 = 1205 and therefore n=455 did not reach the laboratory? which also affects the calculation of those that completed the algorithm. Please clarify.

7. Line 379. Please report the precise number of samples that reached within one day for the results section.

8. Lines 380 and 383. IQR should be included with the median.

Discussion:

1. Line 451. It may be helpful to provide a reference here regarding the standard or recommended turn around times to support this statement that there were no major delays.

2. Line 452. Please expand briefly on the SL-LPA testing delays.

3. Line 455. How far away?

4. Line 457. One-fourth is not commonly used in this context. ‘Approximately 25%’ or ‘approximately one quarter’

5. Lines 457-463. What are the reasons that extrapulmonary specimens do not get tested? Do you have any recommendations on how to improve this?

6. Line 465. Intrigued is an interesting choice of word here. I would have thought surprised or dismayed.

7. Lines 465-470. Is this similar or different to the findings of other studies? A common issue that RIF-R samples are not submitted for further testing? This result is a major finding and should be discussed further and stressed as important. Instead of ‘This needs further investigation’ at the very least something like ‘This is an important issue and requires further investigation’.

8. Lines 472. How was is better?

9. Line 473. Please reword to: “This may be due to the presence of a system of…”

10. Line 475. Instead of gaps, “proportion of specimens that did not reach”

11. Line 479. You may want to include the percentage here to highlight how excellent they were.

12. Line 480-483. Could you please clarify why delays related to culture would result in non-completion of the algorithm?

13. Line 480. If evidence of this was not provided as a result for why there were delays than this is speculation? If you do not have results demonstrating this please rephrase to “This may be explained”

14. Line 481. Correct wording to ‘…must be cultured before’ or ‘…require culture before’

15. Line 489. See previous comment. If evidence is not presented in the results, “This may explain” as it is an assumption.

16. Lines 501-503. Following STROBE is not a strength of the study, but of the manuscript writing. Please remove.

17. Line 511. Briefly expand on this, and how it limits your ability to recommend changes/improvements. What information would be needed to inform these gaps and how would you propose to gather it?

18. Lines 515-519. Here you have given specific examples of things to improve; however, there were no results proving that these were the specific issues to be addressed. Please revise.

19. Line 529. ‘Approximately’ is a preferred word to ‘about’. This sentence could be more concise.

20. Line 531. ‘gaps’ should be expanded here – delays? not reaching the reference laboratory? not completing the algorithm?

21. Lines 532-534. This should be included in the acknowledgements section rather than conclusions.

Tables

Please be consistent across tables with capitalization and variable names. e.g. Table 2 non-key population; Table 4 Not Key population. Similarly, use consistent term for unavailable data for each variable. ‘Missing’ or ‘Not recorded’ or ‘Not available’. Also, with the site and laboratory.

Table 2.

1. Under key population: reword to ‘persons living with HIV’

2. Under key population: Footnote should indicate what population is represented in ‘Others’

3. For Specimen condition of receipt at Xpert lab – these categories only apply to sputum specimens? The table should reflect this.

Table 3.

1. The last line FL-LPA result was resistant – to any first line drug?

Table 4.

1. Male reference is not indicated in aRR column

2. There is no footnote for the abbreviation PLHIV

3. The N and (%) columns presumably refer the those that did not reach the laboratory. Please clarify this in the column header.

Table 5.

1. As in Table 4, please be specific for the column header N (%)

2. Age 0-14 is not an appropriate reference given that the N = 0 for non-completion.

3. Please indicate what the NA values represent.

4. Under Specimen type, remove the word sample for Extra-pulmonary

Figures

Figure 2.

1. The denominator for culture done does not appear to add up. 39+21+43+4 = 107

2. For the 3 Xpert RIF-S samples that were then RIF-R as the reference laboratory what was the pattern of INH resistant? This could be included as a footnote.

Reviewer #2: Overall comments: Thank you for the opportunity to review this manuscript. It is a well written manuscript that addresses a topic that is of fundamental importance to TB care in India.

Abstract: No major comments

Introduction:

• Line 106: Is this prevalence of MDR-TB among all cases, if so I think specify.

• Line 108: Spell out XDR TB at first use

• Line 112: I think it would be good to have a brief explanation of what PMDT is.

• Line 116-117: is this statistic of 29% from India? Pls kindly clarify so that the context is clear.

• Line 126: You talk about non diagnosis as being one of the reasons for low treatment success but treatment success is really only measured for diagnosed cases, pls clarify.

• Paragraph starting at line 130: I would have liked to know about more about the rollout of Xpert in India, can you provide a couple more sentences about this including the dates of rollout and how quickly it happened?

• Line 133: When did the policy of universal DST start? A date would be helpful.

Methods:

• Line 183: I think this sentence about the population size needs a reference.

• You mention the human carriers or couriers in lines 199-200 and then again in lines 208-209 which I think is repetitious.

• Line 216-217: Is this second sample also tested using Xpert or LPA?

• Line 223: I think it would be better to say “If additional resistance” rather than “If resistant” as I think this is what is meant, i.e. if there is additional resistance then a pre XDR or XDR regimen is started.

• I was wondering why a mobile phone number was used as the second method of identifying people rather than the name-age-sex combination which may be more unique. How well does a mobile phone number identify the user? Has this method been previously validated for matching people in population based studies? I think this needs further discussion and justification.

• In Table 1 I think some additional clarity is needed, i.e. for the third to fifth bullet points what is the resistance or sensitivity to? I think some additional detail is needed here. It should also be clear why completion of the diagnostic algorithm was constructed the way it was including having the denominator start at the reference laboratory as the diagnostic algorithm actually seems to start before then, i.e. in the Xpert laboratory.

Results:

Overall the results section was well constructed and clear. My main comment relates to the numbers and Figures 2 and 3 and the definition of having completed the diagnostic algorithm. For Figure 2 I am not 100% sure how you got the figure of 103 in the culture done box, should this be 107 (i.e. 4 plus 43 plus 21 plus 39)? If I follow the lines on all of the boxes that lead the culture done box I get 107 instead of 103. And I wondered why the people who are susceptible or who had culture are the only ones who are deemed eligible to have completed the algorithm? If there is resistance on FL LPA and then that person goes on to have the appropriate tests, they have also completed the algorithm haven’t they? In Figure 3 should there be a lone from the box results not available to the box culture done so that the total is 26 and not 24? I also wondered if your denominators should really be 1590 and 64 rather than the denominators that you have as this is where the algorithm starts. For Figure 3 I also wondered if the people who completed the algorithm should be the 14 who had SL LPA and then any additional people who had culture when it was indicated. I think the 9 people who were FQ and SLI susceptible are include in the numerator of 17 but if you are resistant doesn’t it also mean that you have completed the algorithm?

Discussion:

• Line 461: I think you should reference the “previous studies” referred to here and as a general comment I think there could be more use of other studies in the Discussion section as it mainly focuses on the findings of the study rather than comparing and contrasting with other literature from India, the region or elsewhere. There is one study mentioned in lines 461-463 but it is not clear what date this was and it is a study on EPTB so may not be directly comparable to your overall sample as the majority of your sample were PTB (although admittedly it does seem that EPTB samples were less likely to be referred to the reference laboratory).

• I think it could be emphasized a bit more the loss of specimens going from the Xpert lab to the reference lab and the implications of this. I think you could also emphasise the losses for the RR cases as well as these are the very cases that you would want to know have completed the diagnostic algorithm.

• Under the section on Strengths you talk about sensitivity and specificity of individual tests but you did not do this so I would recommend leaving this out

Reviewer #3: Comments:

The subject of the manuscript has merit and describes important findings related to Drugs resistant TB diagnostic algorithm under routine programme settings in India. The authors may address following queries to strengthen the manuscript.

Major concerns:

1) Introduction- Introduction may need restructuring.

- The first paragraph seems general and it discusses about prevention, diagnosis and treatment while the manuscript is only about diagnostic cascade.

- Line 124-128: It describes about treatment success and its linkage to delay in diagnosis. This section could be mentioned in the discussion.

2) Methods:

- Line 241: How were these ten districts selected for the study? Please provide some information.

- The study mentions about gap in UDST, however the last steps of the algorithm is considered as Culture done. I wonder if it must end at DST level (for how many had DST was done). Though the operational definition mentions the algorithm finishes at culture done, the authors may want to describe about this concern in the manuscript.

3) Results:

- Line 347-348: Please check if the total eligible for culture was 107 instead of 103.

4) Discussion:

- Line 448-449: Since the integrated DR-TB diagnostic algorithm is specific for India, you may tone down the first statement.

- Line 457-459 seems a repeat of the results, please review.

- Line 489: when the authors mention about the delays, they may consider that availability of dates for SL-LPA was quite low. The claims could be toned down.

Minor concerns:

- Please update the references. For example, Line 105 must include the recent literature (Global TB Report 2019).

- Line 167: no systematic assessment.. do we mean .. in India? If yes, you may want to mention this.

- Line 199: change ‘…were transported..’ to ‘..are transported..’

- Line 200-202: This should be mentioned under the Programme implementation part of the Methods section.

- Line 204-237: The integrated diagnostic algorithm: Can the authors summarize the section, as the same is described in the Figure 1.

- Line 251- double entry and validation, wherever possible: please explain where it was carried out and where it was not possible.

- Line 309: Were the key population mutually exclusive group. If someone was urban slum dweller and PLHIV, in which category they were considered?

- Table 2: It is good to mention in the title of the table that 13 Xpert laboratories were included in the study

- Line 461: Please add reference to the statement.

- Line 474: In Methods it was mentioned that the NGO was working in all selected districts of study, please review and change the statement.

END

Reviewer #4: I wish to congratulate the authors on this very clear and helpful paper. It is a transparent analysis of an operational challenge in TB control, which will be of benefit to others working in the same field. I however do have a small number of concerns that I would suggest the authors address, before recommending this manuscript for publication:

1) This study on the performance of the UDST in Karnataka was conducted only a few months after its implementation (data from July-August, for a system implemented in April). Could the authors comment on whether the results are likely to be affected by the study being conducted in this early phase? Could there be "teething troubles" with the UDST, or conversely could there be an ambitious start, which then deteriorates over time?

2) In the same vein: the study was conducted only over 2 months (July-August) - do the authors anticipate any seasonality in the performance of the UDST?

3) The study hinges to a large extent on the matching algorithm that was used between database 1 and database 2, relying on the Nikshay number, phone number, and a name/age/sex match. This is a commendable effort, but it is not without risk. I would recommend that the authors report on how well the matching worked (which proportion was matched on Nikshay, which on phone number, etc.); possibly as supplemental material. Additionally, if I understand well, all entries in database 2 should have a corresponding match in database 1 (as no patients would end up directly at the reference lab) - any "unmatched" individuals in database 2 would therefore represent a measure of how many incorrect matchings resulted from the algorithm, and this may be worth reporting on.

4) Line 290-291: I am not entirely clear on the "exploratory approach" applied, and/or why *all* factors were included in the adjusted analysis.

5) While I appreciate the various ethics reviews done, I would suggest to expand on how patient confidentiality was protected, given the extensive use of phone numbers and names in this study.

6) Line 348-350 ("Thus, a total of 1106 (95%) of rifampicin-sensitive TB patients were considered to have completed the diagnostic algorithm.") is perhaps phrased a bit too optimistically, given that a large proportion of RS TB samples did not even show up at the reference lab. I would suggest correcting to "(...) of rifampicin-sensitive TB patients whose samples were successfully received at the reference laboratory were considered to have completed the diagnostic algorithm." Same comment for line 363-364.

7) On a minor note: one of the percentages in table 3 is incorrect (9.1 should read 91.4).

8) In the methods section the authors refer to "selected districts", and in the limitations to "selected laboratories". Could they clarify the selection process, and which criteria were used?

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PLoS One. 2021 Jan 6;16(1):e0244785. doi: 10.1371/journal.pone.0244785.r002

Author response to Decision Letter 0

31 Aug 2020

28th July, 2020

Bangalore, India

To

The Editor,

PLoS ONE

Dear Editor,

We thank you and the reviewers for taking time to review our paper and provide constructive comments and suggestions. We have been through the comments and provide here a point-by-point response. We have revised the paper accordingly and submit two versions – one with track changes reflecting all the changes and another, a clean version. We have also used the opportunity to read the paper in its entirety for editorial corrections, as and where required. As a result of this process, the manuscript has improved considerably in clarity and readability.

I hope this version meets your satisfaction. If there are any further comments, we will be happy to address them.

Best wishes,

Dr Uma Shankar S, on behalf of the co-authors

Reviewer #1: Summary:

This manuscript examines a new diagnostic algorithm in Karnataka state for the identification of drug resistant TB. The authors have presented results for 1660 patients diagnosed over a two-month period in 2018, and indicate that the while there are some specimens that did not reach the second testing lab or complete the testing algorithm, overall they are encouraged by the proportion that made it through the complex algorithm. Factors associated with specimens not making it through the testing process were examined, and the turn-around-times were reported.

I enjoyed reading this manuscript and found it to be generally clear despite the challenges of describing complicated algorithms. There are however some concerns with the statistical methods used to examine factors that may be associated specimens reaching the reference labs and not completing the testing algorithm. There are some minor issues related to clarity of message and grammar/wording which I will offer suggestions as PLoS ONE does not copyedit accepted manuscripts.

Response to the reviewer: Thank you for the appreciation. We have addressed all the comments below, point-by-point.

Major issues:

1. Line 290. Unless I am misunderstanding the data, a poisson regression model would be the incorrect choice given the binary outcome variable (Not reaching vs reaching; completion vs non-completion). The measure of the association would be an odds ratio. Please revise accordingly.

Response to the reviewer: We thank the reviewer for the comment. This is a cohort study and it enables direct calculation of risk ratio, the most preferred effect measure in epidemiology. While odds ratios can also be calculated, they often overestimate associations, especially when the outcomes are common (as is the case in this study with 27% non-reaching). We also think relative risk (compared to odds ratio) would be easy to communicate to policy makers and program managers and also for them to understand. (1,2). There are multiple ways to arrive at adjusted relative risk and we have opted for the Poisson model as no convergence was obtained in the log binomial model. We have provided references in support of our stance here.

Changes to the manuscript: No changes made to the manuscript

References:

1. UCLA Institute for Digital Research and Education. How can I estimate relative risk using glm for common outcomes in cohort studies? | stata FAQ [Internet]. Institute for Digital Research and Education. [cited 2020 June 18]. Available from: https://stats.idre.ucla.edu/stata/ faq/how-can-i-estimate-relative-risk-using-glm-forcommon-outcomes-in-cohort-studies/.

2. McNutt L-A, Wu C, Xue X, et al. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol [Internet].2003;157:940–943. [cited 2020 June 18].

2. Line 291. An exploratory data analysis is often necessary to conduct initially to understand the dataset and variables; however, for reporting results all variables included in a model should make sense. They should either be chosen a priori based on literature and experience, and/or because an association was observed on bivariate analysis. Could you please elaborate on what support there would be for an association between not reaching or not completing, for variables related to the patient such as age, sex and key population? Do the laboratory personnel processing specimens have access to this information and would make a choice not to send or process specimens and samples based on this information? It seems more likely that an association is possible with respect to variables related to the specimens themselves or facilities. If there is no statistical, literature, or common knowledge that would indicate that patient-level variables be considered in the multivariable model please update the analysis without these and revise accordingly. If there is clear rationale to support their inclusion, please update the methods to include this information.

Response to the reviewer:

We thank the reviewer for the comment. Regarding association of age with completion of diagnostic algorithm, there could be a significant association due to the variations in quality and quantity of the sample being collected. For e.g., it is difficult to collect mucopurulent sample from children, which needs to be obtained after deep breathing and intense coughing and the quantity can also be less when collected from children. Similarly, key population (tobacco users, PLHIV etc.), are relevant because the policies for TB management identifies them so. We would like to submit that these are standard variables which could act as confounders in the analysis. Confounders may not be significant in unadjusted analysis, but they may be unmasked during multivariable analysis. We agree that associations may or may not be present but exploring these variables help in identifying population groups which may require intervention.

Changes made in the manuscript: No changes were done in the manuscript

3. Table 4. Analysis for non-completion. Kolar district is no longer significant when covariates are added to the model. Did you investigate why this might be? What is the relevant confounder to the association? Given my concerns regarding the covariates included this warrants further investigation.

Response to the reviewer: We thank the reviewer for the comment. We wish to clarify that Kolar district was significantly associated with samples ‘not reaching’ the reference laboratory and not with non-completion of the diagnostic algorithm. There might be many reasons for this including differences in distributions of other key variables such as the proportion of rifampicin resistant samples, EP TB samples or absence of efficient sample transportation mechanisms. Since we did not study the reasons, we are not able to postulate why Kolar was not significant in adjusted analysis.

Changes made in the manuscript: No changes were done in the manuscript

4. It does not appear there was an assessment of model fit, and there may be issues related to overfitting. There are quite a few variables included, and a some of these have many levels. Cell sizes were quite low in some instances in the non-completion model. It is recommended that the model building approach and assessment of fit be carefully considered.

Response to the reviewer: We thank the reviewer for the comment. The approach to analysis was exploratory and we wanted to identify as many factors as possible and also identify such factors which would help guide the programme managers to take informed decisions wherever required. Since there are no studies from India and really very few from elsewhere in the world which explored the completion of a complex diagnostic algorithm having a cascade of tests to be done at two different laboratories, a statistical approach of exploring the goodness of fit was not done initially. We however ran the goodness of fit test (estat gof command in Stata) for our model and found that the model fits well (p value 0.957)

Changes in the manuscript: No changes were done in the manuscript

Minor issues:

Abstract:

1. Line 65. Capitalize Line Probe Assay for consistency with the manuscript text.

Response to the reviewer: We thank the reviewer for the suggestion. We have made the correction.

Changes in the manuscript: Please refer to lines 64, 84 and 119 of ‘Revised Manuscript with Track Changes’

2. Line 67. Insert the word ‘phenotypic’ before drug susceptibility testing

Response to the reviewer: We thank the reviewer for the suggestion. We have made the correction.

Changes in the manuscript: Please refer to the line no. 66 of ‘Revised Manuscript with Track Changes’

3. Line 75. Please change ‘till’ to ‘until’ (more formal for scientific writing)

Response to the reviewer: We thank the reviewer for the suggestion. We have made the correction.

Changes in the manuscript: Correction reflected in Line 74 of ‘Revised Manuscript with Track Changes’

4. Line 78. Were there any duplicate patients in the 1660 (more than one specimen/patient) if so then patients should be updated to ‘specimens’ here, and throughout the manuscript as necessary. It would also be helpful to indicate in the methods or results if each patient is represented by a single specimen, or if not please report the number of patients with multiple specimens and an indication of how many specimens/patient were represented in this study.

Response to the reviewer: We thank the reviewer for the suggestion. The number 1660 represents the number of patients. Rarely, multiple specimens were collected from the same patient, say whenever a new TB patient was diagnosed with rifampicin resistance (refer to specific setting in the paper) or whenever a sample container was damaged/leaking on receipt at the laboratories, a second sample was requested. However, we considered only the latest results for a given patient, even if there were more than one sample tested. Hence, there were 1660 unique patients.

Changes to the manuscript: None.

5. While I agree that putting this complex algorithm into place is encouraging and having a considerable proportion of specimens make it through testing, I am also concerned that only about a quarter of RIF resistant TB completed the testing algorithm – which is the TB that is most in need of full testing. It would be beneficial to include a few words in the abstract and again in the manuscript indicating that while encouraging there is also significant concern about this.

Response to the reviewer: We thank the reviewer for the suggestion. We have made the correction.

Changes in the manuscript: Please see the changes made in Lines 87-88 and 551-553 of ‘Revised Manuscript with Track Changes’

Introduction:

1. Line 113. Please add the word ‘phenotypic’ in front of drug susceptibility

Response to the reviewer: We thank the reviewer for the suggestion. We have made the correction.

Correction to the manuscript. Please see the changes made in line no. 66 and 116

Line 118. A better word for ‘heartening’ in scientific writing may be ‘encouraging’

Response to the reviewer: We thank the reviewer for the suggestion. We have made the correction.

Changes in the manuscript: Edits reflected in Line 124 of ‘Revised Manuscript with Track Changes’

2. Line 126. Can you clarify here if by non-diagnosis it is meant that resistance was not tested/diagnosed? If patients weren’t diagnosed at all they wouldn’t be treated and therefore not part of the statistic for treatment failure.

Response to the reviewer: We thank the reviewer for the suggestion. “Non-diagnosis” has been removed. What we meant was delays in diagnosis and treatment leading to poor outcomes.

Changes in the manuscript: Edits reflected in line 134 of ‘Revised Manuscript with Track Changes’.

4. Reference #15 does not link to the pdf for the correct province, please update.

Response to the reviewer: We thank the reviewer for the suggestion. We have made the correction.

Changes in the manuscript: Reference Number 15 updated.

5. Line 185. The reference #15 does not appear to contain the statistic for prevalence, please update the reference to the correct one and ensure that the statistic reported is indeed prevalence rather than incidence.

Response to the reviewer: We thank the reviewer for the suggestion. The prevalence of TB according to NFHS-4 is 180/100,000 population in Karnataka. This figure is reported in Table 77, Page 130 of the referenced document.

Changes in the manuscript: Reference Number 15 updated.

Methods:

1. Line 195-196. The company that produces the products listed should be included, Hain Lifescience, BD Biosciences BACTEC, and which version of the line probe assays were used.

Response to the reviewer: We thank the reviewer for the suggestion.

Changes in the manuscript: Edits reflected in Line 246-250 of ‘Revised Manuscript with Track Changes’

2. Line 199. Could you elaborate on the human carriers and how this differs from a courier service?

Response to the reviewer: We thank the reviewer for the comment and the suggestion. Human carriers are the employees of the health facility or programme staff stationed at the facility. They may be multipurpose health workers, TB-Health Visitors, etc. who might be entrusted with specimen transport on days that they are able to visit the reference laboratory during their routine work-related trips. As a routine, courier services specialising in transport of samples are used. This has been explained in the manuscript.

Changes in the manuscript: Edits reflected in Line 250-253 of ‘Revised Manuscript with Track Changes’.

3. Line 215. It may be better to specify ‘one day’ rather than ‘a day’.

Response to the reviewer: We thank the reviewer for the suggestion. We have made the change in the manuscript.

Changes in the manuscript: Edits reflected in Line 268 of ‘Revised Manuscript with Track Changes’.

4. Line 219. Perhaps reword to read: ‘testing is required, and the second sample…’

Response to the reviewer: We thank the reviewer for the suggestion. We have made the change in the manuscript.

Changes in the manuscript: Edits reflected in Line 272 of ‘Revised Manuscript with Track Changes’

5. Line 224. Please specify in the methods or introduction which second line drugs are being tested, not all readers may be familiar with the Hain assay and the version number was not indicated.

Response to the reviewer: We thank the reviewer for the suggestion. We have added he names of drugs. SL-LPA tests for resistance to fluoroquinolone or second line class of drugs. Liquid CDST tests for resistance to levofloxacin, moxifloxacin, linezolid, kanamycin, amikacin. Version number has now been indicated along with the brand name in the previous section.

Changes in the manuscript: Edits reflected in Lines 281-282 of ‘Revised Manuscript with Track Changes’

6. Line 224. I assume that further testing is for those that were resistant to second line drugs in the SL-LPA? Perhaps clarify this in the sentence.

Response to the reviewer: We thank the reviewer for the suggestion. Yes this assumption is correct and changes have been made to make this clear.

Changes in the manuscript: Edit reflected in Line 288 of ‘Revised Manuscript with Track Changes’

7. Line 229. It appears that a word is missing. Either ‘a sample’ or ‘their sample’

Response to the reviewer: Thanks for the reviewing and suggesting. Correction done.

Changes in the manuscript: Edit reflected in Line 286 of ‘Revised Manuscript with Track Changes’

8. Line 230. Could you clarify the use of poly resistant treatment? As I understand the sentence these cases are RIF-S and INH-R, so initiation of poly-resistant treatment would be indicated by FL-LPA indicating resistance to EMB or PZA.

Response to the reviewer: We thank the reviewer for the suggestion. A Poly drug resistant TB patient is one whose biological specimen is resistant to more than one first-line anti-TB drug, other than both INH and RIF. In operational terms based on the FL drugs for which susceptibility testing is carried out, this translates to patients who are RIF susceptible, INH resistant and whose susceptibility to streptomycin, ethambutol and pyrazinamide is not known. These patients are treated using the same regimen as INH mono resistant TB as per the RNTCP guidelines.

A phrase is added- “…for isoniazid resistant TB (Wherein resistance to the other first-line anti-TB drugs is not done/not known) to clarify the term “poly resistant TB.

Changes in the manuscript: Edit reflected in Lines 288 of ‘Revised Manuscript with Track Changes’

9. Line 245. I assume the data was collected at the time of specimen collection and testing, and here you mean the data was extracted between February and April 2019 from the laboratory registers?

Response to the reviewer: We thank the reviewer for the suggestion. Yes, the data was extracted between February and April 2019 from the laboratory registers. Change made in the manuscript to this effect.

Changes in the manuscript: Edit reflected in Line 304-305 of ‘Revised Manuscript with Track Changes’

10. Line 274-275. Quotation marks are not needed here.

Response to the reviewer: We thank the reviewer for the suggestion. We have made the change in the manuscript.

Changes in the manuscript: Edits reflected in Lines 340-343 of ‘Revised Manuscript with Track Changes’

11. How were the laboratories selected for this study? Was there specific criteria?

Response to the reviewer: The districts were selected conveniently based on feasibility of data collection. Within a district all the Xpert/CBNAAT sites were included.

Changes to the manuscript: Edits reflected in Lines 299-301 of ‘Revised Manuscript with Track Changes’

Results:

1. Line 309. This is the first time “key population group” has been introduced and does not include an explanation of what this represents. Information regarding this should be included in the methods as it is not a standard variable.

Response to the reviewer: We thank the reviewer for the suggestion. Data captured in the laboratory registers kept at the Xpert and reference laboratories, including key population is described briefly at the end of the section on ‘The integrated diagnostic algorithm’. Key population is the term used in RNTCP for specific population groups which are known to be at high risk for TB- tobacco users, prisoners, migrants, PLHIV etc. This field is included in all RNTCP documents like patients’ treatment card and different laboratory registers.

Changes in the manuscript: Edits reflected in Lines 310-312 of ‘Revised Manuscript with Track Changes’

2. Line 310. In the context of this sentence, it should be ‘persons living with HIV’

Response to the reviewer: We thank the reviewer for the suggestion. The national programme documents in India (http://naco.gov.in/documents/annual-reports Page no 335 of the Annual report 2015-16 of the National AIDS Control Organization of India) mention ‘people living with HIV’ and not “persons living with HIV”. Hence, we would like to use ‘people living with HIV’ because this is also the term used in many international documents including those by the WHO (https://www.who.int/news-room/fact-sheets/detail/hiv-aids).

Changes in the manuscript: None

3. It is not clear what happened to the specimens with indeterminant or no information? – Where do they fit in the algorithm? What was the outcome? Indicate in the methods sections how these were handled for the analysis.

Response to the reviewer: We thank the reviewer for the suggestion. We had designed the data capturing tool to collect information on the final test results in case tests were performed more than once using a diagnostic technology at the same laboratory. So, indeterminate and no results mean that this was the final result for a given test whether it was tested once or more, and no further information was available. The outcome has been defined in the section, Patients completing the diagnostic algorithm, of the Operational definitions table.

Changes in the manuscript: Edits reflected in Lines 274-275 of ‘Revised Manuscript with Track Changes’.

4. Line 347. It is stated that culture was done for 41 samples. however, in Figure 2 it indicates culture was done for 43 samples?

Response to the reviewer: We thank the reviewer for the suggestion. The actual figure is 41 out of 107 and the same has been updated in the fig 2.

Changes in the manuscript: Edits reflected in Lines 418 of ‘Revised Manuscript with Track Changes’

5. Lines 361-363. This sentence is a bit hard to follow. Perhaps something along the lines of “on eight samples, under the following SL-LPA conditions: (i) resistance, (ii) was not done, or (iii) no result.”

Response to the reviewer: We thank the reviewer for the suggestion. The sentence has been modified.

Changes in the manuscript: Edits reflected in Lines 437-438 of ‘Revised Manuscript with Track Changes’

6. Line 373. Based on Figs 2 and 3, the number that reached the laboratory was 1170+35 = 1205 and therefore n=455 did not reach the laboratory? which also affects the calculation of those that completed the algorithm. Please clarify.

Response to the reviewer: We thank the reviewer for the suggestion. Please note that 6 patients results were neither sensitive nor resistant, of which 4 had indeterminate results and 2 had no results. Of the six specimens, three reached the reference laboratories. Therefore, the number non-reaching was 452 and not 455. Operational definitions specify that culture done on samples with indeterminate or no results on either FL-LPA or SL-LPA will be considered as completed the algorithm.

Changes to the manuscript: None.

7. Line 379. Please report the precise number of samples that reached within one day for the results section.

Response to the reviewer: Thanks for the reviewing and suggesting. A total of 1250/1554 (80.4%) samples reached the laboratory within one day.

Changes to the manuscript: Edits reflected in Lines 454 of ‘Revised Manuscript with Track Changes’

8. Lines 380 and 383. IQR should be included with the median.

Response to the reviewer: We thank the reviewer for the suggestion. IQR has been added.

Changes in the manuscript: Edits reflected in Lines 457,459-461 of ‘Revised Manuscript with Track Changes’

Discussion:

1. Line 451. It may be helpful to provide a reference here regarding the standard or recommended turnaround times to support this statement that there were no major delays.

Response to the reviewer: We thank the reviewer for the suggestion. There are no reference standards for turnaround times at every step of the cascade, except for the turnaround time at the laboratory that the results should be reported within two hours for Xpert® MTB/RIF, 72 hours for LPAs and within 42 days for liquid culture and phenotypic DST by BD MGIT™. We also cannot compare with other studies, as there are no studies done in India on implementation of this integrated algorithm. The authors feel that the delays noted in this study are not “major” based on their experience of working with the TB programme in India for more than 15 years.

Changes in the manuscript: Edits reflected in Lines 294-295 of ‘Revised Manuscript with Track Changes’

2. Line 452. Please expand briefly on the SL-LPA testing delays.

Response to the reviewer: We thank the reviewer for the suggestion. SL-LPA testing delays may be related to limitations of the first-generation SL-LPA technology with high rates of indeterminate or invalid results leading to non-availability of results, requiring the specimens to be re-tested multiple times before obtaining a valid result. Since no exploration of the reasons were done, we are not in a position to offer an in-depth explanation for the delays.

Changes in the manuscript: Reasons are discussed in Lines 590-593 of ‘Revised Manuscript with Track Changes’

3. Line 455. How far away?

Response to the reviewer: We thank the reviewer for the suggestion. A line explaining the same has been added. The distance between the Xpert laboratory and the reference laboratory ranged from as close as 1 km to as far as 280 kilometres.

Changes in the manuscript: Edits reflected in Lines 539-540 of ‘Revised Manuscript with Track Changes’

4. Line 457. One-fourth is not commonly used in this context. ‘Approximately 25%’ or ‘approximately one quarter’

Response to the reviewer: We thank the reviewer for the suggestion. We have made the change in the manuscript.

Changes in the manuscript: Edits reflected in Line 551-552 of ‘Revised Manuscript with Track Changes’

5. Lines 457-463. What are the reasons that extrapulmonary specimens do not get tested? Do you have any recommendations on how to improve this?

Response to the reviewer: We thank the reviewer for the suggestion. Adequate sensitization of the surgeons involved in the collection of extra-pulmonary specimens on the methods and volume/size of the sample required, mechanisms for early transportation and concentration of the specimen could help increase the testing rates. Recommendations added.

Changes in the manuscript: Edits reflected in Lines 552-557 of ‘Revised Manuscript with Track Changes’

6. Line 465. Intrigued is an interesting choice of word here. I would have thought surprised or dismayed.

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript.

Changes in the manuscript: Edits reflected in Line 563 of ‘Revised Manuscript with Track Changes’

7. Lines 465-470. Is this similar or different to the findings of other studies? A common issue that RIF-R samples are not submitted for further testing? This result is a major finding and should be discussed further and stressed as important. Instead of ‘This needs further investigation’ at the very least something like ‘This is an important issue and requires further investigation’.

Response to the reviewer: We thank the reviewer for the suggestion. The possible reasons for this finding are discussed and a statement “This needs to be addressed on priority and requires further investigation.” has been added

Changes in the manuscript: Edits reflected in Line 592-593 of ‘Revised Manuscript with Track Changes’

8. Lines 472. How was is better?

Response to the reviewer: We thank the reviewer for the suggestion. The specimens reaching the reference laboratory from Xpert laboratories situated in districts closer to Bangalore city were better, probably due to reduced distance and sample transportation services provided by an NGO.

Changes in the manuscript: Edits reflected in Line 571-573 of ‘Revised Manuscript with Track Changes’

9. Line 473. Please reword to: “This may be due to the presence of a system of…”

Response to the reviewer: We thank the reviewer for the suggestion. Correction may not be required as one reason for better transportation is added in the earlier line.

Changes in the manuscript: None

10. Line 475. Instead of gaps, “proportion of specimens that did not reach”

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript.

Changes in the manuscript: Edits reflected in Line 573-574 of ‘Revised Manuscript with Track Changes’

11. Line 479. You may want to include the percentage here to highlight how excellent they were.

Response to the reviewer: We thank the reviewer for the suggestion. Percentages have been included.

Changes in the manuscript: Edits reflected in Line 578 of ‘Revised Manuscript with Track Changes’

12. Line 480-483. Could you please clarify why delays related to culture would result in non-completion of the algorithm?

Response to the reviewer: We thank the reviewer for the suggestion. Smear microscopy negative specimens must be cultured before testing by LPA. All cultures may not yield growth which may require recollection of samples, leading to non-completion of the diagnostic algorithm. We did collect data from the reference labs for a period of one month from the last date of the sample received at the Xpert labs. If culture was not done within that period, we assumed that it was not done at all. It is possible that some cultures might have been conducted beyond the one-month cut-off we had in the study. We though think (based on our programme experience) that such instances are very rare.

Changes in the manuscript: Edits reflected in Lines 580-585 of ‘Revised Manuscript with Track Changes’

13. Line 480. If evidence of this was not provided as a result for why there were delays than this is speculation? If you do not have results demonstrating this please rephrase to “This may be explained”

Response to the reviewer: We thank the reviewer for the suggestion.

A change has been made in the manuscript Changes in the manuscript: Edits reflected in Lines 590 of ‘Revised Manuscript with Track Changes’

14. Line 481. Correct wording to ‘…must be cultured before’ or ‘…require culture before’

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript

Changes in the manuscript: Edits reflected in Lines 580-581 of ‘Revised Manuscript with Track Changes’

15. Line 489. See previous comment. If evidence is not presented in the results, “This may explain” as it is an assumption.

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript

Changes in the manuscript: Edits reflected in Line 590 of ‘Revised Manuscript with Track Changes’

16. Lines 501-503. Following STROBE is not a strength of the study, but of the manuscript writing. Please remove.

Response to the reviewer: We thank the reviewer for the suggestion. The statement is deleted.

Changes in the manuscript: Edits reflected in Lines 604-606 of ‘Revised Manuscript with Track Changes’

17. Line 511. Briefly expand on this, and how it limits your ability to recommend changes/improvements. What information would be needed to inform these gaps and how would you propose to gather it?

Response to the reviewer: We thank the reviewer for the suggestion. Further information could be gathered using qualitative techniques, bottle neck analysis and inclusion of the entire lab network in a similar analysis.

Changes in the manuscript: Edits reflected in Lines 620-623 of ‘Revised Manuscript with Track Changes’

18. Lines 515-519. Here you have given specific examples of things to improve; however, there were no results proving that these were the specific issues to be addressed. Please revise.

Response to the reviewer: We thank the reviewer for the suggestion. We have rephrased this section.

19. Line 529. ‘Approximately’ is a preferred word to ‘about’. This sentence could be more concise.

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript

Changes in the manuscript: Edits reflected in Line 639 of ‘Revised Manuscript with Track Changes’

20. Line 531. ‘gaps’ should be expanded here – delays? not reaching the reference laboratory? not completing the algorithm?

Response to the reviewer: We thank the reviewer for the suggestion. Gaps have been expanded.

Changes in the manuscript: Edits reflected in Line 646-650 of ‘Revised Manuscript with Track Changes’

21. Lines 532-534. This should be included in the acknowledgements section rather than conclusions.

Response to the reviewer: We thank the reviewer for the suggestion. We beg to differ as we are not acknowledging the RNTCP staff for supporting our research but are just appreciating their routine performance based on the study findings.

Changes in the manuscript: None

Tables:

Please be consistent across tables with capitalization and variable names. e.g. Table 2 non-key population; Table 4 Not Key population. Similarly, use consistent term for unavailable data for each variable. ‘Missing’ or ‘Not recorded’ or ‘Not available’. Also, with the site and laboratory.

Response to the reviewer: We thank the reviewer for the suggestion.

Changes in the manuscript: A change has been made in the table of ‘Revised Manuscript with Track Changes’.

Table 2.

1. Under key population: reword to ‘persons living with HIV’

Response to the reviewer: Thanks for the reviewing and suggesting. As mentioned earlier, PLHIV refers to “people living with HIV” as per WHO and National programme documents and therefore we would retain this expression.

Changes in the manuscript: None

2. Under key population: Footnote should indicate what population is represented in ‘Others’

Response to the reviewer: We thank the reviewer for the suggestion Footnote added under Table 2.

Changes in the manuscript: Footnote added under Table 2.

3. For Specimen condition of receipt at Xpert lab – these categories only apply to sputum specimens? The table should reflect this.

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript Changes in the manuscript: Footnote added under Table 2.

Table 3.

1. The last line FL-LPA result was resistant – to any first line drug?

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript

Changes in the manuscript: Changes reflected in the last line of table 3.

Table 4.

1. Male reference is not indicated in aRR column

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript

2. There is no footnote for the abbreviation PLHIV

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript 3. The N and (%) columns presumably refer the those that did not reach the laboratory. Please clarify this in the column header.

Response to the reviewer: We thank the reviewer for the suggestion. N explained in the footer.

Table 5.

1. As in Table 4, please be specific for the column header N (%)

Response to the reviewer: We thank the reviewer for the suggestion. N explained in the footer.

2. Age 0-14 is not an appropriate reference given that the N = 0 for non-completion.

Response to the reviewer: Thanks for the reviewing and suggesting. The Reference here is actually 60 and above and the changes are done accordingly.

Changes to the manuscript: We have made changes to the table.

3. Please indicate what the NA values represent.

Response to the reviewer: We thank the reviewer for the suggestion. A footnote is added.

4. Under Specimen type, remove the word sample for Extra-pulmonary

Response to the reviewer: We thank the reviewer for the suggestion.

Changes to the manuscript: A change has been made in the table

Figures:

Figure 2.

1. The denominator for culture done does not appear to add up. 39+21+43+4 = 107

Response to the reviewer: Thanks for the reviewing and suggesting.

Corrections made in Figure 2. The denominator for culture done is 107. The edits are reflected in the figure 2

2. For the 3 Xpert RIF-S samples that were then RIF-R as the reference laboratory what was the pattern of INH resistant? This could be included as a footnote.

Response to the reviewer: Thanks for the reviewing and suggesting.

Changes to the manuscript: A footnote depicting the same is reflected.

___________________________________________________________________________

Reviewer #2: Overall comments: Thank you for the opportunity to review this manuscript. It is a well written manuscript that addresses a topic that is of fundamental importance to TB care in India.

Abstract: No major comments

Introduction:

1. Line 106: Is this prevalence of MDR-TB among all cases, if so I think specify.

Response to the reviewer: We thank the reviewer for the suggestion. The prevalence of MDR-TB is 6.2% among all cases.

Changes in the manuscript: Edits reflected in Lines 110 of ‘Revised Manuscript with Track Changes’

2. Line 108: Spell out XDR TB at first use

Response to the reviewer: We thank the reviewer for the suggestion. XDR expanded.

Changes in the manuscript: Edits reflected in Lines 111-113 of ‘Revised Manuscript with Track Changes’

3. Line 112: I think it would be good to have a brief explanation of what PMDT is.

Response to the reviewer: We thank the reviewer for the suggestion. PMDT provides guidelines for the integration of management of DR-TB with the existing National TB programme activities

Changes in the manuscript: Edits reflected in Lines 116-120 of ‘Revised Manuscript with Track Changes’

4. Line 116-117: is this statistic of 29% from India? Pls kindly clarify so that the context is clear.

Response to the reviewer: We thank the reviewer for the suggestion. This statistic is from India.

Changes in the manuscript: Edits reflected in Line 122-123 of ‘Revised Manuscript with Track Changes’

5. Line 126: You talk about non diagnosis as being one of the reasons for low treatment success but treatment success is really only measured for diagnosed cases, pls clarify.

Response to the reviewer: We thank the reviewer for the suggestion. Non-diagnosis has been removed.

Changes in the manuscript: Edits reflected in Line 133-135 of ‘Revised Manuscript with Track Changes’

6. Paragraph starting at line 130: I would have liked to know about more about the rollout of Xpert in India, can you provide a couple more sentences about this including the dates of rollout and how quickly it happened?

Response to the reviewer: We thank the reviewer for the suggestion. The feasibility study and scaling up details added.

Changes in the manuscript: Edits reflected in Lines 171-176 of ‘Revised Manuscript with Track Changes’

7. Line 133: When did the policy of universal DST start? A date would be helpful.

Response to the reviewer: We thank the reviewer for the suggestion. Start date of universal DST has been added.

Changes in the manuscript: Edits reflected in Line 176-179 of ‘Revised Manuscript with Track Changes’

Methods:

1. Line 183: I think this sentence about the population size needs a reference.

Response to the reviewer: We thank the reviewer for the suggestion. The reference added.

2. You mention the human carriers or couriers in lines 199-200 and then again in lines 208-209 which I think is repetitious.

Response to the reviewer: We thank the reviewer for the suggestion. The second reference to couriers and human carriers has been removed.

Changes in the manuscript: Edits made in line 261-262 of ‘Revised Manuscript with Track Changes’

3. Line 216-217: Is this second sample also tested using Xpert or LPA?

Response to the reviewer: We thank the reviewer for the query. If rifampicin resistance is detected in a new patient, the second sample is used at the Xpert laboratory for a repeat Xpert® MTB/RIF test to confirm rifampicin resistance.

Changes in the manuscript: None

4. Line 223: I think it would be better to say “If additional resistance” rather than “If resistant” as I think this is what is meant, i.e. if there is additional resistance then a pre XDR or XDR regimen is started.

Response to the reviewer: We thank the reviewer for the suggestion. A change has been made in the manuscript

Changes in the manuscript: Edits made in line 279 of ‘Revised Manuscript with Track Changes’

5. I was wondering why a mobile phone number was used as the second method of identifying people rather than the name-age-sex combination which may be more unique. How well does a mobile phone number identify the user? Has this method been previously validated for matching people in population based studies? I think this needs further discussion and justification.

Response to the reviewer: We thank the reviewer for the query and the suggestion. The primary tracking was done with NIKSHAY Id. The mobile number was not used for identifying people. The mobile no. was used to match the records from two different lab registers. Name-age-sex being non-numerical variable may not provide a better match.

Changes in the manuscript: No changes done

6. In Table 1 I think some additional clarity is needed, i.e. for the third to fifth bullet points what is the resistance or sensitivity to? I think some additional detail is needed here. It should also be clear why completion of the diagnostic algorithm was constructed the way it was including having the denominator start at the reference laboratory as the diagnostic algorithm actually seems to start before then, i.e. in the Xpert laboratory.

Response to the reviewer: We thank the reviewer for the query and the suggestion. We have reviewed this and have made small edits to improve clarity. In the 3rd to 5th bullets, resistance on SL-LPA/FL-LPA refers to resistance to the SL-LPA/FL-LPA class of drugs. Since LPA reports resistance to a class of drugs and not to specific drugs per se. There were two key steps to the cascade we were assessing: 1) samples not-reaching the reference laboratory 2) not completing the algorithm after reaching the reference laboratory. The issues related to each of these steps are different and hence, we kept the analysis separate and used different denominators (total number of patients when assessing non-reaching and only those who reached for assessing non-completion). Using the total number as denominator for assessing non-completion would have mixed up issues of both non-reaching and non-completion, thus creating confusion. I hope this clarifies.

Changes in the manuscript: No changes done.

Results:

Overall the results section was well constructed and clear.

1. My main comment relates to the numbers and Figures 2 and 3 and the definition of having completed the diagnostic algorithm. For Figure 2 I am not 100% sure how you got the figure of 103 in the culture done box, should this be 107 (i.e. 4 plus 43 plus 21 plus 39)? If I follow the lines on all of the boxes that lead the culture done box I get 107 instead of 103.

Response to the reviewer: We thank the reviewer for the comment. Yes, there was an error in the calculation in figure 2. The relevant corrections have been made in figure 2 and some details are added in figure 3. The numbers/numerators in the greyed-out boxes add up to total completing the diagnostic algorithm in both figure 2 and 3.

2. And I wondered why the people who are susceptible or who had culture are the only ones who are deemed eligible to have completed the algorithm? If there is resistance on FL LPA and then that person goes on to have the appropriate tests, they have also completed the algorithm haven’t they?

Response to reviewer: We thank the reviewer for the comment. We would like to clarify that those resistant on FL-LPA are considered to have completed the diagnostic algorithm if the appropriate tests are done. Those resistant on FL-LPA are required to undergo SL-LPA and then culture DST depending on the results of the SL-LPA. This is reflected in the following categories of patients completing diagnostic algorithm (Table 1):

• Rifampicin sensitive on Xpert® MTB/Rif and resistance to Isoniazid and/or Rifampicin on FL-LPA and sensitive to second-line drugs on SL-LPA

• Rifampicin sensitive on Xpert® MTB/Rif and resistance to Isoniazid and/or Rifampicin on FL-LPA and resistant on SL-LPA and culture done

3. In Figure 3 should there be a line from the box results not available to the box culture done so that the total is 26 and not 24?

Response to reviewer: We thank the reviewer for the suggestion. Samples in whom no results were available on SL-LPA could be invalid results and they should ideally be subjected to culture and DST to complete the algorithm (Refer to Category “Culture done on specimens with indeterminate or no results on either FL-LPA or SL-LPA” in Table 1) Therefore, the line linking “No results available’ to the ‘Culture done box’ has been drawn. We have also made the corrections to the total as 26.

4. I also wondered if your denominators should really be 1590 and 64 rather than the denominators that you have as this is where the algorithm starts.

Response to reviewer: We thank the reviewer for the comment. As explained earlier, there were two key steps to the cascade: 1) samples not-reaching the reference laboratory 2) not completing the algorithm after reaching the reference laboratory. The issues related to each of these steps are different and hence, we kept the analysis separate and used different denominators (total number of patients when assessing non-reaching and only those who reached for assessing non-completion). Using the total number as denominator for assessing non-completion would have mixed up issues of both non-reaching and non-completion, thus creating confusion. I hope this clarifies.

5. For Figure 3 I also wondered if the people who completed the algorithm should be the 14 who had SL LPA and then any additional people who had culture when it was indicated. I think the 9 people who were FQ and SLI susceptible are include in the numerator of 17 but if you are resistant doesn’t it also mean that you have completed the algorithm?

Response to reviewer: We thank the reviewer for the comment. You are right that the 9 people who were FQ and SLI susceptible are included in the numerator of 17. We would like to clarify that detection of resistance on SL-LPA does not indicate completion, they would still need to undergo culture DST so that resistance patterns to individual drugs can be identified.

As shown in Fig 3 and definitions of Table 1, the following is the distribution of the 17 who were labelled as having completed the diagnostic algorithm:

• FQ and SLI sensitive box �9 individuals

• Culture done when it was indicated (Rifampicin resistant on Xpert® MTB/Rif and resistant on SL-LPA, specimens with indeterminate or no results on either FL-LPA or SL-LPA)� 8 individuals

Discussion:

1. Line 461: I think you should reference the “previous studies” referred to here and as a general comment I think there could be more use of other studies in the Discussion section as it mainly focuses on the findings of the study rather than comparing and contrasting with other literature from India, the region or elsewhere. There is one study mentioned in lines 461-463 but it is not clear what date this was and it is a study on EPTB so may not be directly comparable to your overall sample as the majority of your sample were PTB (although admittedly it does seem that EPTB samples were less likely to be referred to the reference laboratory).

Response to reviewer: We thank the reviewer for the comment. The one study referenced was not only EPTB but also had PTB samples. Since the integrated algorithm was introduced in India, this is the first assessment of the complete integrated diagnostic algorithm. Recommended reference standards are not available for the processes involved in the completion of the diagnostic algorithm, the time for taken for specimen collection, transportation and reporting of the results, except for turnaround time for testing. Hence, we could not compare with other studies.

2. I think it could be emphasized a bit more the loss of specimens going from the Xpert lab to the reference lab and the implications of this. I think you could also emphasise the losses for the RR cases as well as these are the very cases that you would want to know have completed the diagnostic algorithm.

Response to reviewer: We thank the reviewer for the comment. We have made changes to the manuscript.

Changes in the manuscript: Edits made in lines 551-561 of ‘Revised Manuscript with Track Changes’

3. Under the section on Strengths you talk about sensitivity and specificity of individual tests but you did not do this so I would recommend leaving this out.

Response to reviewer: Thank for the comments. The line on sensitivity and specificity has been removed.

Changes in manuscript: Edits made in line numbers 601-602 of ‘Revised Manuscript with Track Changes’

Reviewer #3: Comments:

The subject of the manuscript has merit and describes important findings related to Drugs resistant TB diagnostic algorithm under routine programme settings in India. The authors may address following queries to strengthen the manuscript.

Major concerns:

Introduction- Introduction may need restructuring.

1. The first paragraph seems general and it discusses about prevention, diagnosis and treatment while the manuscript is only about diagnostic cascade.

Response to reviewer: We thank the reviewer for the comment. This is to provide general information about the TB, globally and in India to all those who read, and more so to the general readership of PLoS ONE who may need more context to gain a comprehensive understanding of the programme in India.

Changes in the manuscript: None

2. Line 124-128: It describes about treatment success and its linkage to delay in diagnosis. This section could be mentioned in the discussion.

Response to the reviewer: We thank the reviewer for the comment. We feel this is best placed in the introduction because we want to emphasize upfront the importance of early and accurate diagnosis for successful treatment of TB, especially DR-TB.

Changes in the manuscript: None

Methods:

1. Line 241: How were these ten districts selected for the study? Please provide some information.

Response to the reviewer: We thank the reviewer for the query. The districts were selected conveniently based on feasibility of data collection (and all laboratories within a district were included).

2. The study mentions about gap in UDST, however the last steps of the algorithm is considered as Culture done. I wonder if it must end at DST level (for how many had DST was done). Though the operational definition mentions the algorithm finishes at culture done, the authors may want to describe about this concern in the manuscript.

Response to the reviewer: We thank the reviewer for this suggestion. We agree that ideally the algorithm should have ended with samples in which the results of culture DST were available. Since culture DST takes time, we might not have been able to extract results of culture and DST within the timeframe of the study. Hence, we operationally defined completion if the samples were subjected to culture and DST.

Changes in the manuscript: Edits made in lines 179-180 of ‘Revised Manuscript with Track Changes’

Results:

1. Line 347-348: Please check if the total eligible for culture was 107 instead of 103.

Response to the reviewer: Thanks for the observation. The relevant corrections have been made in figure 2

Discussion:

1. Line 448-449: Since the integrated DR-TB diagnostic algorithm is specific for India, you may tone down the first statement.

Response to the reviewer: We thank the reviewer for the comment. We have made changes in the manuscript.

Changes in the manuscript: correction done in line 532 of ‘Revised Manuscript with Track Changes’

2. Line 457-459 seems a repeat of the results, please review.

Response to the reviewer: We thank the reviewer for the suggestion. We have tried to summarise key results in the discussion section for ease of reading and comprehension.

3. Line 489: when the authors mention about the delays, they may consider that availability of dates for SL-LPA was quite low. The claims could be toned down.

Response to the reviewer: We thank the reviewer for the suggestion. We have rephrased this statement to “This may explain why delays with SL-LPA were three times more when compared to FL-LPA, though valid dates of receipt and reporting of SL-LPA samples were found for few samples.”

Changes in the manuscript: Edits reflected in lines 590-592 of ‘Revised Manuscript with Track Changes’

Minor concerns:

1. Please update the references. For example, Line 105 must include the recent literature (Global TB Report 2019).

Response to the reviewer: We thank the reviewer for the suggestion.

Changes in the manuscript: Edits reflected in lines 105 & 108 of ‘Revised Manuscript with Track Changes’

2. Line 167: no systematic assessment. do we mean. in India? If yes, you may want to mention this.

Response to the reviewer: We thank the reviewer for the comment. We have made this change.

Changes in the manuscript: Edits reflected in line 210 of ‘Revised Manuscript with Track Changes’

3. Line 199: change ‘…were transported.’ to ‘...are transported.’

Response to the reviewer: We agree to this suggestion and have made this change.

Changes in the manuscript: Edits reflected in line 250 of ‘Revised Manuscript with Track Changes’

4. Line 200-202: This should be mentioned under the Programme implementation part of the Methods section.

Response to the reviewer: We thank the reviewer for this suggestion. However, the support provided by the NGO is specific to Bengaluru city and adjacent districts and not a routine feature in the programme. Therefore, it has been mentioned in the “Specific Setting” section.

5. Line 204-237: The integrated diagnostic algorithm: Can the authors summarize the section, as the same is described in the Figure 1.

Response to the reviewer: We thank the reviewer for this suggestion. There have been comments from other reviewers requesting additional details in this write up, so that readers who are not familiar with the algorithms followed in India may be able to understand it better. Therefore, we would like to retain this.

6. Line 251- double entry and validation, wherever possible: please explain where it was carried out and where it was not possible.

Response to the reviewer: We thank the reviewer for the comment. Double entry and validation were done for the data entered from the laboratory registers of Xpert lab and not for the data entered from the lab registers of reference laboratories.

Changes in the manuscript: Edits reflected in lines 313 - 315 of ‘Revised Manuscript with Track Changes’

7. Line 309: Were the key population mutually exclusive group. If someone was urban slum dweller and PLHIV, in which category they were considered?

Response to the reviewer: We agree that these categories may not be mutually exclusive, but the registers maintained at the laboratories allow entry of only one option and hence whatever was recorded was considered.

8. Table 2: It is good to mention in the title of the table that 13 Xpert laboratories were included in the study

Response to the reviewer: We agree to this suggestion and have made this change in the Title of Table 2.

Changes in the manuscript: Edits reflected in line 405 of ‘Revised Manuscript with Track Changes’

9. Line 461: Please add reference to the statement.

Response to the reviewer: Thanks for the suggestions. I have added three references.

Changes in the manuscript: Edits reflected in line 555 of ‘Revised Manuscript with Track Changes’

10. Line 474: In Methods it was mentioned that the NGO was working in all selected districts of study, please review and change the statement.

Response to the reviewer: We would like to clarify that the NGO was working only in few districts.

Please refer to the Revised manuscript (Line 220-222) under the specific settings section. “During the study period, a Non-Governmental Organization (NGO) was assisting in transportation of the samples between the laboratories in districts around Bengaluru city….”

___________________________________________________________________________

Reviewer #4: I wish to congratulate the authors on this very clear and helpful paper. It is a transparent analysis of an operational challenge in TB control, which will be of benefit to others working in the same field. I however do have a small number of concerns that I would suggest the authors address, before recommending this manuscript for publication:

1. This study on the performance of the UDST in Karnataka was conducted only a few months after its implementation (data from July-August, for a system implemented in April). Could the authors comment on whether the results are likely to be affected by the study being conducted in this early phase? Could there be "teething troubles" with the UDST, or conversely could there be an ambitious start, which then deteriorates over time?

Response to the reviewer: We thank you for this suggestion. The guidelines for the implementation of UDST were given by the Government of India in 2017, while Karnataka had all its districts covered under UDST by April 2018. While the overall results are encouraging, there are some gaps that are worrisome. We are not able to comment on whether this could be attributed to teething troubles or ambitious start. Such an assertion would probably best be made after conducting a detailed situational analysis as mentioned in the conclusion section of the manuscript.

Changes in the manuscript: Edits reflected in line 176-179 of ‘Revised Manuscript with Track Changes’

2. In the same vein: the study was conducted only over 2 months (July-August) - do the authors anticipate any seasonality in the performance of the UDST?

Response to the reviewer: To the best of our knowledge, it is unlikely that seasonality has played a role in the performance of UDST. However, to be able to validate this assumption, we would need to look at the indicators for at least a year, which was outside the scope of this study.

3. The study hinges to a large extent on the matching algorithm that was used between database 1 and database 2, relying on the Nikshay number, phone number, and a name/age/sex match. This is a commendable effort, but it is not without risk. I would recommend that the authors report on how well the matching worked (which proportion was matched on Nikshay, which on phone number, etc.); possibly as supplemental material. Additionally, if I understand well, all entries in database 2 should have a corresponding match in database 1 (as no patients would end up directly at the reference lab) - any "unmatched" individuals in database 2 would therefore represent a measure of how many incorrect matchings resulted from the algorithm, and this may be worth reporting on.

Response to the reviewer: Thanks for this insightful comment. Unfortunately, we did not keep a record of the number of matches obtained by each of the identifiers (such as Nikshay ID, phone number and name-age-sex). Hence, we will not be able to provide this information. However, we would like to clarify that unmatched individuals in database 2 do not necessarily represent a measure of incorrect matchings because, the reference laboratory also received samples from other districts (not included in the study) and, sometimes also received direct samples from private providers without routing through the Xpert laboratories located in the districts.

4. Line 290-291: I am not entirely clear on the "exploratory approach" applied, and/or why *all* factors were included in the adjusted analysis.

Response to the reviewer: Linked to 1st Reviewer comment on use of gender as a factor

5. While I appreciate the various ethics reviews done, I would suggest to expand on how patient confidentiality was protected, given the extensive use of phone numbers and names in this study.

Response to the reviewer: We highly appreciate the concern regarding patient confidentiality. As noted, certain identifiers had to be extracted from records to enable linkage across databases. The data was entered on to a restricted access password protected electronic data capture system (EpiData) which was installed on secure desktop systems in the laboratories. Only the investigators had access to the data with identifiers required for linking databases. Once linked, the final dataset was stripped of all identifiers prior to further analysis.

Changes in the manuscript: Edits reflected in line nos. 316-318 of ‘Revised Manuscript with Track Changes’

6. Line 348-350 ("Thus, a total of 1106 (95%) of rifampicin-sensitive TB patients were considered to have completed the diagnostic algorithm.") is perhaps phrased a bit too optimistically, given that a large proportion of RS TB samples did not even show up at the reference lab. I would suggest correcting to "(...) of rifampicin-sensitive TB patients whose samples were successfully received at the reference laboratory were considered to have completed the diagnostic algorithm." Same comment for line 363-364.

Response to the reviewer: We agree and the statement has been rephrased.

Changes in the manuscript: Edits reflected in line 420-421 of ‘Revised Manuscript with Track Changes’

7. On a minor note: one of the percentages in table 3 is incorrect (9.1 should read 91.4).

Response to the reviewer: Thank you for bringing this to our notice. We have made the correction in Table 3

Changes in the manuscript: Edits reflected in Table 3 of ‘Revised Manuscript with Track Changes’

8. In the methods section the authors refer to "selected districts” and in the limitations to "selected laboratories". Could they clarify the selection process, and which criteria were used?

Response to the reviewer: The districts selected conveniently based on feasibility of data collection, but all the Xpert/CBNAAT sites within the selected districts were included.

Changes in the manuscript: Edits reflected in line 614-615 of ‘Revised Manuscript with Track Changes’

Attachment

Submitted filename: Response to Reviewers 1.docx

Click here for additional data file. (76.9KB, docx)

Decision Letter 1

Igor Mokrousov

8 Oct 2020

PONE-D-19-25977R1

Implementation of the new integrated algorithm for diagnosis of drug resistant tuberculosis in Karnataka State, India: How well are we doing?

PLOS ONE

Dear Dr. Shankar S,

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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 #1: (No Response)

**********

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 #1: Partly

**********

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

Reviewer #1: I Don't Know

**********

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 #1: Yes

**********

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 #1: Yes

**********

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 #1: Comments: Thank you for the opportunity to review this revised manuscript. The authors have made changes to clarify statements in the paper and fix errors as recommended by the reviewers. However, the authors have declined to address issues raised by reviewers that would allow the reader to properly assess their findings and that would improve the manuscript overall, particularly in the methods, results and discussion.

Major issues:

1. The rationale for including patient-level variables in the model was not included in the methods section as requested. As the manuscript is currently written, it remains unclear to the reader why these variables would confound the association between specimen-specific variables and ‘not reaching’. If they are being used as proxies for something related to the specimen then this information should be included in the methods section. If for example, the authors in their response to reviewers have identified that specimens are inherently different when collected from children and this may be associated with ‘not reaching’, then perhaps use broader age categories that have only 2 or 3 levels (children, adult, older adult age groups).

Same for key population. These populations are not mutually exclusive and treating them as discrete populations is likely to create noise in the model and not provide a meaningful result. I suspect there are individuals that are HIV positive, reside in a slum and use tobacco. It is recommended that this variable be aggregated to a simple Yes/No of belonging to a key population. Please include in the methods the justification for including patient variables. It is understandable why key population would be associated with being tested, but once a specimen is collected the reason the specimen wouldn’t make it through the algorithm is not clearly outlined in the manuscript with respect to key population or other patient-centered variables. Please support where possible with references and explanation. While this may be the first such TB study in India there are many studies of other similar algorithms, diseases and/or populations from which to draw information from.

Without clear rationale it will be assumed that this was the data available to the authors and so it was included in the model without any consideration of how to best incorporate it (appropriate levels within variables) and why it should be.

2. It was requested that model fitting information be reported. The authors declined to do this.

Key practices of model building and validation, and reporting of results are required for reviewers and readers to assess the findings. It will be necessary to include:

• Standard (ANOVA table) model output, either in the body of the paper or as supplementary materials. All statistics packages produces this as output. A table for each of the final models showing the estimate, SE, df, test statistic and p value for each term in the model.

• Tests of model assumptions (e.g. tested for overdispersion and found…)

3. Typically, calculation of RR from a Poisson model should use robust error variance as the standard confidence intervals are not valid. Based on the information available regarding the models it is not clear if this was done. Please ensure the appropriate measure of variation was used and detail what was done in the methods section.

4. The authors should also address limitations of the models in the Discussion (e.g., no examination of interaction terms, possibility of low statistical power for some tests)

Minor:

1. It was requested that the authors indicate if multiple specimens were included for individuals. Information regarding this was provided in the response but not incorporated into the manuscript. Please include this in the methods section to provide the reader with this pertinent info.

2. The number not reaching was not clear based on the figures and clarification of why this is was provided in the response to reviewers but not incorporated into the manuscript. A separate figure or inclusion of another cascade in either figure 2 or 3 would be helpful to make this clear to readers. In addition details regarding these 6 negative/indeterminate should be included prior to the “Overall, out of the total 1660 samples,” otherwise it is not clear why the numbers from the RS and RR do not add to the overall numbers.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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PLoS One. 2021 Jan 6;16(1):e0244785. doi: 10.1371/journal.pone.0244785.r004

Author response to Decision Letter 1

22 Nov 2020

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 #1: Comments: Thank you for the opportunity to review this revised manuscript. The authors have made changes to clarify statements in the paper and fix errors as recommended by the reviewers. However, the authors have declined to address issues raised by reviewers that would allow the reader to properly assess their findings and that would improve the manuscript overall, particularly in the methods, results and discussion.

Major issues:

1. The rationale for including patient-level variables in the model was not included in the methods section as requested. As the manuscript is currently written, it remains unclear to the reader why these variables would confound the association between specimen-specific variables and ‘not reaching’. If they are being used as proxies for something related to the specimen then this information should be included in the methods section. If for example, the authors in their response to reviewers have identified that specimens are inherently different when collected from children and this may be associated with ‘not reaching’, then perhaps use broader age categories that have only 2 or 3 levels (children, adult, older adult age groups).

Ans. We thank the reviewer for the comment. We accept that it is not justifiable to include patient level variables and hence have decided to do away with inclusion of the patient level variables both for non- reach and non-completion tables.

Same for key population. These populations are not mutually exclusive and treating them as discrete populations is likely to create noise in the model and not provide a meaningful result. I suspect there are individuals that are HIV positive, reside in a slum and use tobacco. It is recommended that this variable be aggregated to a simple Yes/No of belonging to a key population. Please include in the methods the justification for including patient variables. It is understandable why key population would be associated with being tested, but once a specimen is collected the reason the specimen wouldn’t make it through the algorithm is not clearly outlined in the manuscript with respect to key population or other patient-centered variables. Please support where possible with references and explanation. While this may be the first such TB study in India there are many studies of other similar algorithms, diseases and/or populations from which to draw information from.

Without clear rationale it will be assumed that this was the data available to the authors and so it was included in the model without any consideration of how to best incorporate it (appropriate levels within variables) and why it should be.

Ans. We accept that key population too constitute patient level data, hence we have decided to exclude this variable too from both the tables of non-reach and non-completion.

2. It was requested that model fitting information be reported. The authors declined to do this.

Key practices of model building and validation, and reporting of results are required for reviewers and readers to assess the findings. It will be necessary to include:

• Standard (ANOVA table) model output, either in the body of the paper or as supplementary materials. All statistics packages produces this as output. A table for each of the final models showing the estimate, SE, df, test statistic and p value for each term in the model.

• Tests of model assumptions (e.g. tested for overdispersion and found…)

Ans. The final model output table will be attached as a supplementary file as suggested.

3. Typically, calculation of RR from a Poisson model should use robust error variance as the standard confidence intervals are not valid. Based on the information available regarding the models it is not clear if this was done. Please ensure the appropriate measure of variation was used and detail what was done in the methods section.

Ans. Yes robust error variance was done. We will attach the final model output sheet and table depicting the same as a supplementary file.

4. The authors should also address limitations of the models in the Discussion (e.g., no examination of interaction terms, possibility of low statistical power for some tests)

Ans. We have not examined any interactions, due to the fewer variables we had and the low statistical power. The same will be mentioned in line nos. 498,499.

Minor:

1. It was requested that the authors indicate if multiple specimens were included for individuals. Information regarding this was provided in the response but not incorporated into the manuscript. Please include this in the methods section to provide the reader with this pertinent info.

Ans. Thanks for this valuable observation. We have mentioned about the same in line number 272 and 273 of the revised manuscript.

2. The number not reaching was not clear based on the figures and clarification of why this is was provided in the response to reviewers but not incorporated into the manuscript. A separate figure or inclusion of another cascade in either figure 2 or 3 would be helpful to make this clear to readers. In addition details regarding these 6 negative/indeterminate should be included prior to the “Overall, out of the total 1660 samples,” otherwise it is not clear why the numbers from the RS and RR do not add to the overall numbers.

Ans. Thanks for the suggestion, the description about the 6-rifampicin resistance reports not available/indeterminate has been mentioned at two appropriate paragraphs, one in line nos. 331,332 and another in line nos. 344,345.

Attachment

Submitted filename: Response to reviewers 22 Nov 20.docx

Click here for additional data file. (15.3KB, docx)

Decision Letter 2

Igor Mokrousov

17 Dec 2020

Implementation of the new integrated algorithm for diagnosis of drug resistant tuberculosis in Karnataka State, India: How well are we doing?

PONE-D-19-25977R2

Dear Dr. Shankar S,

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.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Igor Mokrousov, Ph.D., D.Sc.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #1: All comments have been addressed

**********

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 #1: Yes

**********

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

Reviewer #1: 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 #1: Yes

**********

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 #1: Yes

**********

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 #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Acceptance letter

Igor Mokrousov

23 Dec 2020

PONE-D-19-25977R2

Implementation of the new integrated algorithm for diagnosis of drug-resistant tuberculosis in Karnataka State, India: How well are we doing?

Dear Dr. Shankar S:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr Igor Mokrousov

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File

    (DOCX)

    Click here for additional data file. (20.6KB, docx)
    Attachment

    Submitted filename: Response to Reviewers 1.docx

    Click here for additional data file. (76.9KB, docx)
    Attachment

    Submitted filename: Response to reviewers 22 Nov 20.docx

    Click here for additional data file. (15.3KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and the tables and figures. The raw data is the routine programme data from National TB Elimination Programme (NTEP), Ministry of Health & Family Welfare, Government of India. The sharing such programme data requires permission of the Deputy Director General of NTEP.

    Articles from PLoS ONE are provided here courtesy of PLOS

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