Introduction

F1000Research

2046-1402

F1000 Research Limited

London, UK

10.12688/f1000research.18353.1

Research Article

Articles

Are we missing ‘previously treated’ smear-positive pulmonary tuberculosis under programme settings in India? A cross-sectional study

[version 1; peer review: 2 approved with reservations]

Shewade

Hemant Deepak

Conceptualization Data Curation Formal Analysis Investigation Methodology Software Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-8242-1911 a 1 2 Gupta

Vivek

Conceptualization Formal Analysis Methodology Writing – Review & Editing 3 Satyanarayana

Srinath

Conceptualization Formal Analysis Methodology Writing – Review & Editing 1 Kharate

Atul

Conceptualization Project Administration Visualization Writing – Review & Editing 4 Murali

Lakshmi

Conceptualization Project Administration Visualization Writing – Review & Editing 5 Deshpande

Madhav

Conceptualization Project Administration Visualization Writing – Review & Editing 6 Kumar

Naresh

Conceptualization Project Administration Visualization Writing – Review & Editing 7 Pandey

Prabhat

Conceptualization Data Curation Investigation Supervision Writing – Review & Editing 2 Bajpai

U N

Conceptualization Data Curation Investigation Supervision Writing – Review & Editing 8 Tripathy

Jaya Prasad

Conceptualization Data Curation Investigation Supervision Writing – Review & Editing 1 2 Kathirvel

Soundappan

Conceptualization Data Curation Investigation Supervision Writing – Review & Editing 2 9 Pandurangan

Sripriya

Conceptualization Supervision Validation Writing – Review & Editing 2 Mohanty

Subrat

Conceptualization Project Administration Supervision Writing – Original Draft Preparation Writing – Review & Editing 2 Ghule

Vaibhav Haribhau

Conceptualization Supervision Writing – Original Draft Preparation Writing – Review & Editing 2 Sagili

Karuna D

Conceptualization Methodology Supervision Writing – Review & Editing 2 Prasad

Banuru Muralidhara

Conceptualization Supervision Writing – Review & Editing 2 Nath

Sudhi

Conceptualization Supervision Writing – Review & Editing 2 Singh

Priyanka

Data Curation Investigation Writing – Review & Editing 10 Singh

Kamlesh

Data Curation Investigation Writing – Review & Editing 11 Jayaraman

Gurukartick

Data Curation Investigation Supervision Writing – Review & Editing https://orcid.org/0000-0003-0642-8772 12 Rajeswaran

Data Curation Investigation Supervision Writing – Review & Editing 12 Srivastava

Binod Kumar

Data Curation Investigation Writing – Review & Editing 13 Biswas

Moumita

Data Curation Supervision Writing – Review & Editing 2 Mallick

Gayadhar

Data Curation Investigation Supervision Writing – Review & Editing 2 Bera

Om Prakash

Data Curation Investigation Supervision Writing – Review & Editing 2 Jaisingh

A James Jeyakumar

Data Curation Investigation Writing – Review & Editing 12 Naqvi

Ali Jafar

Data Curation Investigation Supervision Writing – Review & Editing https://orcid.org/0000-0001-9301-0985 10 Verma

Prafulla

Data Curation Investigation Writing – Review & Editing 10 Ansari

Mohammed Salauddin

Data Curation Investigation Writing – Review & Editing 13 Mishra

Prafulla C

Data Curation Investigation Writing – Review & Editing 14 Sumesh

Data Curation Investigation Writing – Review & Editing 12 Barik

Sanjeeb

Data Curation Investigation Writing – Review & Editing 15 Mathew

Vijesh

Data Curation Investigation Writing – Review & Editing 11 Lohar

Manas Ranjan Singh

Data Curation Investigation Writing – Review & Editing 15 Gaurkhede

Chandrashekhar S

Data Curation Investigation Methodology Writing – Review & Editing 11 Parate

Ganesh

Data Curation Investigation Writing – Review & Editing 10 Bale

Sharifa Yasin

Data Curation Investigation Writing – Review & Editing 11 Koli

Ishwar

Data Curation Investigation Writing – Review & Editing 11 Bharadwaj

Ashwin Kumar

Data Curation Investigation Writing – Review & Editing 11 Venkatraman

Data Curation Investigation Writing – Review & Editing 12 Sathiyanarayanan

Data Curation Investigation Writing – Review & Editing 12 Lal

Jinesh

Data Curation Investigation Supervision Writing – Review & Editing 11 Sharma

Ashwini Kumar

Data Curation Investigation Writing – Review & Editing 13 Kumar

Ajay MV

Conceptualization Formal Analysis Methodology Writing – Original Draft Preparation Writing – Review & Editing 1 2 16 Chadha

Sarabjit S

Conceptualization Formal Analysis Methodology Project Administration Resources Supervision Writing – Review & Editing 2 1International Union Against Tuberculosis and Lung Disease (The Union), Paris, 75006, France 2International Union Against Tuberculosis and Lung Disease (The Union), South-East Asia Office, New Delhi, 110016, India 3All India Institute of Medical Sciences (AIIMS), New Delhi, 110029, India 4State TB Cell, Department of Health & Family Welfare, Government of Madhya Pradesh, Bhopal, 462004, India 5State TB Cell, Department of Health & Family Welfare, Government of Tamil Nadu, Chennai, 600006, India 6State TB Cell, Department of Health & Family Welfare, Government of Chattisgarh,, Raipur, 492002, India 7State TB Cell, Department of Health & Family Welfare, Government of Punjab, Chandigarh, 160022, India 8Voluntary Health Association of India (VHAI), New Delhi, 110016, India 9Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India 10MAMTA Health Institute for Mother and Child, New Delhi, 110048, India 11Catholic Health Association of India (CHAI), Secunderabad, 500009, India 12Resource Group for Education & Advocacy for Community Health (REACH), Chennai, 600014, India 13Population Services International (PSI),, New Delhi, 110019, India 14Catholic Bishops’ Conference of India-Coalition for AIDS and Related Diseases(CBCI-CARD), New Delhi, 110001, India 15Emmanuel Hospital Association (EHA), New Delhi, 110019, India 16Yenepoya Medical College, Yenepoya (Deemed to be University), Mangaluru, 575018, India

a hemantjipmer@gmail.com

^#AMVK and SSC contributed equally as senior authors

No competing interests were disclosed.

26 3 2019

2019

338

26 2 2019

2019

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: In 2007, a field observation from India reported 11% misclassification among ‘new’ patients registered under the revised national tuberculosis (TB) control programme. Ten years down the line, it is important to know what proportion of newly registered patients has a past history of TB treatment.

Methods: A study was conducted among new smear-positive pulmonary TB patients registered between March 2016 and February 2017 in 18 randomly selected districts to determine the effectiveness of an active case-finding strategy in marginalised and vulnerable populations. We included all patients detected through active case-finding. An equal number of randomly selected patients registered through passive case-finding from marginalised and vulnerable populations in the same districts were included. Before enrolment, we enquired about any history of previous TB treatment through interviews.

Results: Of 629 patients, we interviewed 521, of whom, 11% (n=56) had past history of TB treatment (public or private) for at least a month: 13% (34/268) among the active case-finding group and 9% (22/253) among the passive case-finding group (p=0.18). No factors were found to be significantly associated with misclassification.

Conclusion: Around one in every ten patients registered as ‘new’ had previous history of TB treatment. Corrective measures need to be implemented, followed by monitoring of any change in the proportion of ‘previously treated’ patients among all registered patients treated under the programme at national level.

Tuberculosis/classification Previously treated TB New TB Recurrent TB Vulnerable populations

The author(s) declared that no grants were involved in supporting this work.

Introduction

India has the highest tuberculosis (TB) burden in the world. The annual estimated TB incidence and deaths is 2.7 million and 0.4 million, respectively ¹. Of the patients receiving treatment under its revised national tuberculosis control programme (RNTCP), the proportion of ‘previously treated’ patients (received anti-TB drugs in the past for one month or more) was 19% in 2016 and 15% in 2017 ^{2,
3}. The national anti-tuberculosis drug resistance (2014–16) survey shows that ‘previously treated’ TB patients have four times higher prevalence of multidrug-resistant TB (MDR-TB) when compared to new patients (11.6% versus 2.8%) ⁴.

In 2007, Atre et al. ⁵ reported 11% misclassification among ‘new’ patients registered under the RNTCP. It is important to know how the programme is faring 10 years down the line. This study was carried out as a part of a larger study among new smear-positive pulmonary TB patients to determine the effectiveness of a community-based active case-finding (ACF) strategy when compared to passive case-finding (PCF) in 18 randomly selected districts of India ^{6,
7}. The ACF strategy was conducted as part of Project Axshya (meaning ‘free of TB’) whose focus was to increase detection of new smear-positive pulmonary TB patients among marginalised and vulnerable populations. Before enrolling the newly registered TB patients (both ACF and PCF patients) into our study, we enquired about their history of previous treatment. This provided us with a unique opportunity to document the proportion of newly registered smear-positive pulmonary TB patients that had previous history of TB treatment and were therefore misclassified.

Methods Study design and participants

This was a cross-sectional study involving new smear-positive pulmonary TB patients from marginalised and vulnerable populations that were registered for treatment under the RNTCP in India between March 2016 and February 2017.

Setting

National TB programme (2016–17): India’s RNTCP infrastructure included national, state, district and sub-district level administrative units (one for 250 000 to 500 000 population) and designated microscopy centres for sputum smear microscopy ⁸. Before starting TB treatment, the medical officer in the health facility classified the patients as ‘new’ or ‘previously treated’.

During the study period (March 2016 to February 2017), new patients received two months of Isoniazid, Rifampicin, Pyrazinamide and Ethambutol followed by four months of Isoniazid, Rifampicin and Ethambutol. ‘Previously treated’ patients received two months of Isoniazid, Rifampicin, Pyrazinamide, Ethambutol and Streptomycin, one month of Isoniazid, Rifampicin, Pyrazinamide and Ethambutol and five months of Isoniazid, Rifampicin and Ethambutol. Among TB patients, a subset of patients who were at high risk to have MDR-TB (presumptive MDR-TB patients) underwent genotypic drug susceptibility testing (DST). These included patients previously treated for TB, patients with a TB-HIV co-infection, patients who upon follow up during TB treatment were smear-positive and contacts of a confirmed MDR-TB patient.

Project Axshya : Project Axshya is implemented in India by the South-East Asia office of the International Union against Tuberculosis and Lung Disease (The Union) to enhance the reach and visibility of RNTCP services among marginalised and vulnerable populations and to mitigate the impact of TB on the country (see Box for criteria for marginalised and vulnerable populations). Axshya SAMVAD ( SAMVAD is an acronym for sensitization and advocacy in marginalised and vulnerable areas of the district) is the ACF strategy under the project. The word ‘ SAMVAD’ in Sanskrit language means ‘conversation’. In 2016–17, the project covered 285 districts spread across 19 states.

Box. Criteria used for marginalised and vulnerable populations in districts under Project <italic toggle="yes">Axshya*</italic>, India (2016–17).

1. Slums

2. Tribal areas

3. Marginalised communities as per the constitution of India

4. In pockets where occupational lung diseases are high

5. In pockets where there is high risk of acquiring TB like; stone crushing/mining/weaving industry/unorganized labour (construction workers etc)/homeless people

6. In pockets reported to have high HIV/ AIDS burden

7. In areas or communities where incidence of TB is high

8. Among household contacts of smear-positive pulmonary TB patients

9. Prisons

TB – tuberculosis; HIV – human immunodeficiency virus; AIDS – acquired immunodeficiency syndrome; *Project Axshya –implemented by The Union, South East Asia office, New Delhi, India, across 285 districts of India, to enhance the reach and visibility of national TB programme services among marginalised and vulnerable populations and to mitigate the impact of TB on the country. Axshya in Sanskrit means ‘free of TB’.

Axshya SAMVAD study: This study was conducted among new smear-positive pulmonary TB patients to determine the effectiveness of Axshya SAMVAD on diagnosis and treatment initiation delays, costs due to TB diagnosis and treatment outcomes ^{6,
7}. We included all new smear-positive pulmonary TB patients from marginalised and vulnerable populations that were detected through ACF and registered under the programme in the 18 randomly sampled Axshya districts (simple random sampling) during March 2016 to February 2017. Every month in the same districts, we randomly sampled an equal number of new smear-positive pulmonary TB patients registered through PCF from marginalised and vulnerable populations (simple random sampling) ^{6,
7}. Random numbers for simple random sampling were generated using Microsoft Excel.

Data collection

Under Axshya SAMVAD study, we collected data for each study participant through record review (age, gender, ACF/PCF status, residence (urban/rural), distance of residence from microscopy centre, sputum smear grade, weight, diabetes status and HIV status) and patient interviews at their residence. Patient interviews were set up during the review of the participant’s record. Before starting the patient interviews, we enquired about their past history of TB treatment for at least one month either from the public or private sector. Those with a past history of treatment were excluded from the Axshya SAMVAD study and referred to the programme for appropriate management. These constitute ‘misclassification’ for the purpose of present analysis.

Data analysis

We double entered and validated the data using EpiData Entry software ⁹ (version 3.1, EpiData Association, Odense Denmark). We analysed the data using STATA (version 12.1, copyright 1985–2011 StataCorp LP USA) ¹⁰. We used frequency and proportions (0.95 confidence intervals (CI)) to summarise (infer) the extent of misclassification. Adjusted analysis was done using log binomial regression to determine the factors associated with misclassification. Variables collected during record review (age, gender, ACF/PCF status, residence (urban/rural), distance of residence from microscopy centre and sputum smear grade) were included in the adjusted analysis. Baseline weight was missing in two-fifths of patients; baseline diabetes status was missing for more than three-fifths and only one patient was living with HIV. Hence, we excluded them from the adjusted analysis. The association was summarized (inferred) using adjusted prevalence ratios (0.95 CIs).

Ethics

The Axshya SAMVAD study was approved by the Ethics Advisory Group of The Union, Paris, France (EAG number 15/15, dated 28 September 2015). We conducted the study after receiving approvals from the State Tuberculosis Officers in the respective states (18 randomly sampled Axshya districts belonged to seven states). We obtained written informed consent for participation from all the study participants.

Results

Figure 1 depicts the misclassification of ‘previously treated’ smear-positive pulmonary TB patients as ‘new’. A total of 629 newly registered smear-positive pulmonary TB patients were enrolled for the Axshya SAMVAD study. We couldn’t contact 108 (17%) for interview as patients were not available at their residence during the visit (a maximum of two visits were made).

Figure 1. Flow chart depicting the misclassification of people with ‘previously treated’ smear-positive pulmonary TB as ‘new’ among the study participants enrolled in the <italic toggle="yes">Axshya SAMVAD</italic> study across 18 randomly sampled <italic toggle="yes">Axshya</italic> districts in India, March 2016-February 2017*.

TB – tuberculosis; SAMVAD – sensitization and advocacy in marginalised and vulnerable areas of the district; Axshya SAMVAD – an active case- finding strategy under project Axshya, implemented by The Union, South East Asia office, New Delhi, India, across 285 districts of India. *registered under programme between March 2016 and February 2017 for treatment after classification as ‘new’.

Of the 521 interviewed, 56 [10.8% (95% CI: 8.4%, 13.7%)] had a past history of TB treatment (public or private) for at least a month: 12.7% (34/268) among the ACF group and 8.7% (22/253) among the PCF group (p=0.18). No factors were found to be significantly associated with misclassification ( Table 1).

Table 1. Factors associated with the misclassification of ‘previously treated’ smear-positive pulmonary TB as ‘new’ among the new smear-positive pulmonary TB patients in the <italic toggle="yes">Axshya SAMVAD</italic> study across 18 randomly sampled <italic toggle="yes">Axshya</italic> districts in India, March 2016-February 2017 <xref ref-type="other" rid="FN2">*</xref>.

Variable	Total	Misclassification		PR (0.95 CI)	aPR ^@ (0.95 CI)
	N	n	(%)
Total	521 **	56	(11)	-	-
Exposure
Axshya SAMVAD	268	34	(13)	1.5 (0.9, 2.4)	1.3 (0.7, 2.1)
Passive case finding	253	22	(9)	Ref	Ref
Age categories in years
15–44	276	25	(9)	Ref	Ref
45–64	185	22	(12)	1.3 (0.8, 2.3)	1.1 (0.6, 1.9)
≥65	59	9	(15)	1.7 (0.8, 3.4)	1.5 (0.7, 3.1)
Missing	1	0	(0)	-	-
Gender
Male	346	39	(11)	1.2 (0.7, 2.0)	1.3 (0.7, 2.2)
Female	174	17	(10)	Ref	Ref
Missing	1	0	(0)	-	-
Residence
Urban	59	1	(2)	Ref	Ref
Rural	457	55	(12)	7.1 (1.0, 50.4)	6.4 (0.9, 48.2)
Missing	5	0	(0)	-	-
Distance from DMC in km
≤5	128	10	(8)	Ref	Ref
6–10	161	17	(11)	1.4 (0.6, 2.8)	1.0 (0.5, 2.0)
11–15	118	11	(9)	1.2 (0.5, 2.7)	0.8 (0.4, 1.9)
>15	113	17	(15)	1.9 (0.9, 4.0)	1.4 (0.7, 2.9)
Missing	1	1	(100)	-	-
Sputum smear grading
3+	90	7	(8)	Ref	Ref
1+/2+	413	48	(12)	1.5 (0.7, 3.2)	2.4 (0.3, 16.2)
Positive not quantified	18	1	(6)	-	-

TB – tuberculosis; SAMVAD – sensitization and advocacy in marginalised and vulnerable areas of the district; Axshya SAMVAD – an active case-finding strategy under project Axshya implemented by The Union, South East Asia office, New Delhi, India, across 285 districts of India; aPR – adjusted prevalence ratio; CI – confidence interval.

*registered under programme between March 2016 and February 2017 for treatment after classification as ‘new’; **Total 661 were enrolled, 32 were later excluded as they did not fit the operational definition of study participant based on information obtained from record review. Among 629 eligible for patient interviews, 521 study participants could be contacted; ^@log binomial regression.

Discussion Key findings

About one in ten ‘new’ TB patients had a past history of TB treatment. This misclassification meant that these patients received the wrong treatment regimen as per the national guidelines at the time. This is similar to the RNTCP report of 2018 ³ and previous documentation in 2007 ⁵. The misclassification among new smear-positive TB patients was two times higher than the 4.5% reported from Malawi in 2000 ¹¹.

One possible reason for this might be a lack of attention on the part of the medical officer to enquire for previous history of TB before starting treatment. Ambiguity in classification when there was a large gap between previous and current treatment, absence of treatment records and patients’ reluctance to disclose previous treatment details due to possible stigma (fear of being seen as a ‘problem patient’) could be the other reasons ⁵.

Limitations

This study has some limitations. First, this programmatically relevant finding was incidental and part of a larger study ( Axshya SAMVAD study) and hence, we did not systematically record the details of past TB treatment (when, duration of treatment, whether under programme or in private sector) and the reasons for misclassification. Secondly, as patients with misclassification were excluded from the Axshya SAMVAD study, we do not know what happened to them, including their treatment outcomes. Thirdly, we did not include smear-negative pulmonary TB and extrapulmonary TB patients as they were not part of the Axshya SAMVAD study. In Malawi (2000) ¹¹, they had a higher risk of misclassification when compared to smear-positive pulmonary TB patients. Finally, non-response was a limitation. However, in a best-case scenario (assuming all 108 non-responders did not have previous history of TB treatment), the proportion of misclassification would have been 8.9% (56/629) which is still programmatically significant.

Implications for the TB programme

Limitations notwithstanding, our study has programme implications. Of the new smear-positive pulmonary TB patients registered in India in 2016, 21% had an unfavourable outcome ³. Some of these unfavourable outcomes can be explained by wrong management – patients getting an inferior treatment regimen (previously treated patients being treated with a regimen meant for new cases) and missing an opportunity for drug susceptibility testing (as previously treated patients were eligible for DST at the time).

India has recently adopted the World Health Organization (WHO) recommendation that the category II regimen (for ‘previously treated’ patients) ‘ should no longer be prescribed and drug susceptibility testing should be conducted to inform the choice of treatment regimen’ ¹². To make this a reality, India now recommends universal DST, meaning all diagnosed TB patients are eligible for testing via the Xpert MTB/RIF assay ^®(Cepheid Sunnyvale USA) followed by first-line (if rifampicin susceptible) or second-line line probe assay (if rifampicin resistant) ^{4,
13}. This further means that both new and previously treated patients are treated with the same regimen ^{4,
8,
14}. Hence, in the present scenario, the impact of misclassification on individual patient management is minimal. This was not the case at the time of conduct of this study. Despite these developments, we think asking for previous treatment history is still relevant for two reasons. First, the information on the proportion of previously treated patients is epidemiologically an important piece of information and is regularly reported to the WHO for monitoring the global TB epidemic. Second, the universal DST is not a reality in every part of the country and in such instances, prioritizing previously treated patients for DST is a better strategy, given the higher prevalence of drug-resistant TB among them.

Future research

Our findings were based on patients from marginalised and vulnerable populations and this limits our generalisability to TB patients registered from the general population. The programme should consider replicating similar studies among patients from the general population.

Since 2017, the revised laboratory register at the level of designated microscopy centres under the RNTCP (one per 50 000 to 100 000 population) also captures this information of previous treatment ⁸. Hence, complete filling of the revised laboratory register at microscopy centres should be closely monitored by the programme and future operational research should focus on this.

Systematic qualitative enquiry is recommended to understand the ‘why’ (why does it happen) and ‘how’ (how can it be addressed) of misclassification.

Conclusions

This study demonstrated that ‘previously treated’ patients were being missed and were being registered as ‘new’ patients under the RNTCP in India. Corrective measures need to be implemented, followed by monitoring any change in the proportion of ‘previously treated’ patients among all registered patients treated under the programme at national level.

Data availability Underlying data

figshare: Underlying data. https://doi.org/10.6084/m9.figshare.7756688 ¹⁵

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Extended data

figshare: Questionnaire Axshya SAMVAD study. https://doi.org/10.6084/m9.figshare.7768589 ¹⁶

This project contains the following extended data:

S2 Annex.pdf (Part I of the questionnaire – record review)

S3 Annex.pdf (Part II of the questionnaire – patient interview)

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Consent

Written informed consent for publication of the patients’ details was obtained from the patients.

Acknowledgements

The authors would like to acknowledge funding support for Project Axshya from The Global Fund TB grant to India. The Project is implemented by the Project Management Unit of The Union South East Asia Office since 2010 till date with support of the sub-recipient partners (in alphabetical order): The Catholic Bishops’ Conference of India-Coalition for AIDS and Related Diseases (CBCI-CARD); The Catholic Health Association of India (CHAI); Emmanuel Hospital Association (EHA); MAMTA Health Institute for Mother and Child; Population Services International (PSI); Resource Group for Education and Advocacy for Community Health (REACH); and Voluntary Health Association of India (VHAI). Publication fee for this study were covered by the Department for International Development (DFID), UK and La Fondation Veuve Emile Metz-Tesch (Luxembourg).

We thank the following for their support in data collection: Robinson Robert, Madhu Nema, Yashpal Singh Rajput. We would also like to thank other Project Axshya staff: Anand Das, Ganesh M, A Mary Mamatha, Antony Santhappan, Prabhat Kumar Singh, Deepak Tigga and Khumanthem Jayanta Kumar Singh, Kamlesh Kumar and Ranjan Singh who participated in the initial training, planning and/or questionnaire development. We would also like to thank the RNTCP staff in the study districts that supported the District Coordinators and Interpersonal Communication Coordinator in study participant enrolment and record review. We thank the Department for International Development (DFD), UK, for funding the Global Operational Research Fellowship Programme at the International Union Against Tuberculosis and Lung Disease (The Union), Paris, France in which HDS and JPT work as a senior operational research fellow.

Disclaimer: The contents of this paper do not necessarily reflect the views of the Government or Non-Governmental Organizations or The Union

World Health Organization (WHO): Global tuberculosis report 2018. Geneva, Switzerland;2018. Reference Source

Revised National Tuberculosis Control Programme (RNTCP): TB India 2017. Annual status report. New Delhi India;2017. Reference Source

Revised National Tuberculosis Control Programme (RNTCP): TB India 2018. Annual status report. New Delhi, India;2018. Reference Source

Central TB Division: Directorate General of Health Services. Ministry of Health & Family Welfare. Government of India. Guidelines on Programmatic Management of Drug Resistant TB (PMDT) in India. New Delhi, India;2017. Reference Source

Atre

D’Souza

Dholakia

: Observations on categorisation of new TB cases: implications for controlling drug resistance. Int J Tuberc Lung Dis. 2007;11(10):1152–3. 17966562

2488406

Axshya SAMVAD Study Group, Shewade

Chadha

: Data collection using open access technology in multicentre operational research involving patient interviews. Public Health Action. 2017;7(1):74–77. 28744430

10.5588/pha.15.0079

5515255

Shewade

Gupta

Satyanarayana

: Active case finding among marginalised and vulnerable populations reduces catastrophic costs due to tuberculosis diagnosis. Glob Health Action. 2018;11(1): 1494897. 30173603

10.1080/16549716.2018.1494897

6129780

Revised National Tuberculosis Control Programme (RNTCP); Central TB Division. Ministry of Health and Family Welfare. Government of India. Technical and operational guidelines for tuberculosis control in India.New Delhi India;2016. Reference Source

Lauritsen

Bruus

: EpiData Entry (version 3.1). A comprehensive tool for validated entry and documentation of data. The EpiData Association, Odense Denmark,2004. Reference Source

StataCorp: Stata Statistical Software: Release 12.College Station, TX: StataCorp LP.2011.

Harries

Hargreaves

Kwanjana

: Relapse and recurrent tuberculosis in the context of a national tuberculosis control programme. Trans R Soc Trop Med Hyg. 2000;94(3):247–9. 10974988

10.1016/S0035-9203(00)90306-7

World Health Organization (WHO): Treatment of tuberculosis. Guidelines for treatment of drug susceptible tuberculosis and patient care. 2017 update.Geneva, Switzerland;2017. Reference Source

Central TB Division, Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India. Guidelines on Programmatic Management of Drug Resistant TB (PMDT) in India.New Delhi, India;2012. Reference Source

Revised National Tuberculosis Control Programme (RNTCP); Central TB Division: National strategic plan for TB elimination 2017–25.New Delhi, India;2017. Reference Source

Shewade

: Underlying data. figshare;2019. Reference Source

Shewade

: Questionnaire Axshya SAMVAD study. figshare;2019. Reference Source

10.5256/f1000research.20077.r46257

Reviewer response for version 1

Ranzani

Otavio T.

1 2 Referee https://orcid.org/0000-0002-4677-6862 1Pulmonary Division, Heart Institute (InCor), Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, University of São Paulo, São Paulo, Brazil 2Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain

Competing interests: No competing interests were disclosed.

12 4 2019

2019

This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

recommendation

approve-with-reservations

The authors conducted a secondary analysis on the data originated from the Axshya SAMVAD study aiming to describe the proportion of patients with smear-positive pulmonary tuberculosis labelled as "new" (regarding antibiotic treatment), but that actually have had tuberculosis before. The research question is of public health importance, it is not widely reported in the literature, and makes this short article interesting to the literature. The authors should be commended. I have some comments below aiming to improve the manuscript.

The authors should clarify to the reader on abstract that "new" refers to previous TB treatment for at least 30 consecutive days.

The authors should revise the entire manuscript, particularly Abstract and Introduction, to clarify about what "missclassification" refers to. Misclassification is an epidemiological term, but it can refer to different variables depending on the context. And during the read, it became clear only after going to the results section. Example: (abstract) "reported 11% misclassification among ‘new’ patients". Maybe the authors could rephrase as:"reported that 11% of patients with tuberculosis were misclassified as new patients regarding previous treatment history" or "reported that 11% of patients with tuberculosis were misclassified as new patients despite their previous TB treatment" or "reported that 11% of patients with tuberculosis were misclassified as new patients despite previously treated"

Regarding the inclusion criteria, did it only include adults?

Regarding Table 1: Why did you categorize age? And why in only 3 categories merging different age profile? For instance, the range 15-44 covers different population. You might have loose power and contrast. You should use age as continuous and/or open the category 15-44. Do the authors have converge problems with the log-binomial model? The authors should re-phrase their statements on the prevalence ratio associated factors on Table 1. For instance, it is clear that Residence (rural/urban) is an important factor, with a big point estimate, but wide confidence interval (6.4 (0.9, 48.2)). Likely with a bigger sample size and/or higher number of events, you would have better precision and would achieve "significancy" or "no include 1".

How is it possible to have such low prevalence of positive HIV status in a vulnerable population? How was the HIV testing coverage?

The discussion section must be improved: Although recommended and ideal, DST will not be available for all. Even if available, the previous history treatment is fundamental. The authors should propose solutions for the problem, such as linkage deduplication, linked records form the national system, etc. Please, better discuss the potential selection bias and representative of the included population regarding India population.

Minor:

Maybe better to use the notation of 95% CI rather than 0.95

Figure 1. I think the first box at left has a wrong "the" : as AN error

Change gender to sex

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Epidemiology, Infection, Tuberculosis, Pneumonia, Air Pollution

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Shewade

Hemant Deepak

International Union Against Tuberculosis and Lung Disease, India

Competing interests: There are no competing interest to declare

7 5 2019

REVIEWER #2

Otavio T. Ranzani, University of São Paulo, Brazil; Barcelona Institute for Global Health (ISGlobal), Spain

REVIEWER COMMENT

AUTHOR RESPONSE

Thank you very much for the constructive comments.

REVIEWER COMMENT

The authors should clarify to the reader on abstract that "new" refers to previous TB treatment for at least 30 consecutive days.

AUTHOR RESPONSE

We are not clear here what the reviewer intends to mean. ‘New’ refers patients who have not been previously treated for TB (at least for one month).

REVIEWER COMMENT

The authors should revise the entire manuscript, particularly Abstract and Introduction, to clarify about what "misclassification" refers to. Misclassification is an epidemiological term, but it can refer to different variables depending on the context. And during the read, it became clear only after going to the results section. Example: (abstract) "reported 11% misclassification among ‘new’ patients". Maybe the authors could rephrase as: "reported that 11% of patients with tuberculosis were misclassified as new patients regarding previous treatment history" or "reported that 11% of patients with tuberculosis were misclassified as new patients despite their previous TB treatment" or "reported that 11% of patients with tuberculosis were misclassified as new patients despite previously treated"

AUTHOR RESPONSE

Thank you for the comment. In the revised manuscript, both at beginning of abstract and main text introduction, we have clarified as to what we mean by ‘misclassification’ (reproduced below)

Abstract

“In 2007, a field observation from India reported 11% misclassification among ‘new’ patients registered under the revised national tuberculosis (TB) control programme. Ten years down the line, it is important to know what proportion of newly registered patients has a past history of TB treatment for at least one month (henceforth called as ‘misclassification’).”

Introduction (main text)

“This provided us with a unique opportunity to document the proportion of newly registered smear-positive pulmonary TB patients that had previous history of TB treatment and were therefore misclassified (henceforth called as ‘misclassification’).”

REVIEWER COMMENT

Regarding the inclusion criteria, did it only include adults?

AUTHOR RESPONSE

Thank you for pointing this out. Yes it included only adults (≥ 15 y). We have clarified this under study population and titles of tables and figures.

REVIEWER COMMENT

AUTHOR RESPONSE

Thank you for the comment. We categorized aged, because we wanted to explore whether misclassification was significantly higher among certain meaningful sub-groups. Regarding opening up the category 15-44 y, we decided not to do this as there are only 25 events of interest (=misclassification) in this sub-group. If we further divide this sub-group, the event of interest could get reduced and this would have further widened the 95% CI for the adjusted prevalence ratios.

As per reviewer comments, we have added a line under results narrative in the revised manuscript to discuss at adjusted prevalence ratio for the variable ‘residence’ (reproduced below).

“Patients belonging to rural areas had higher prevalence of misclassification when compared to urban areas (12% vs 2%), but this difference was not statistically significant probably due to small sample size.”

REVIEWER COMMENT

How is it possible to have such low prevalence of positive HIV status in a vulnerable population? How was the HIV testing coverage?

AUTHOR RESPONSE

The HIV percentage among all TB patients in India is around 3%. In our study, HIV status was missing in records for two-fifth patients and among those whose test results were recorded (n=288), one was positive.

REVIEWER COMMENT

AUTHOR RESPONSE

Thank you very much. We have reviewed and revised the discussion section as per the comments.

We agree that DST may not be available for all. Hence, in the “Implications for the TB programme” sub-section under discussion section we have included this this. We are reproducing it below:

“Second, the universal DST is not a reality in every part of the country and in such instances, prioritizing previously treated patients for DST is a better strategy, given the higher prevalence of drug-resistant TB among them.”

Regarding DST availability, we are reproducing the relevant discussion lines below

“Hence, in the present scenario, the impact of misclassification on individual patient management is minimal. This was not the case at the time of conduct of this study. Despite these developments, we think asking for previous treatment history is still relevant for two reasons. First, the information on the proportion of previously treated patients is epidemiologically an important piece of information and is regularly reported to the WHO for monitoring the global TB epidemic. Second, the universal DST is not a reality in every part of the country and in such instances, prioritizing previously treated patients for DST is a better strategy, given the higher prevalence of drug-resistant TB among them” (second paragraph under ‘implications for TB programme’ sub-section of discussion)

Regarding potential selection bias and representativeness, we are reproducing the relevant discussion lines below:

“Our findings were based on patients from marginalised and vulnerable populations and this limits our generalisability to TB patients registered from the general population. The programme should consider replicating similar studies among patients from the general population with a possible sub-group to look for rural-urban differences.” (First paragraph under ‘recommendations’ sub-section of discussion)

Regarding proposing solutions, we have revised it as per reviewer suggestions under recommendations subsection of discussion (reproducing below)

“Since 2017, the revised laboratory register at the level of designated microscopy centres under the RNTCP (one per 50 000 to 100 000 population) also captures this information of previous treatment ⁸ . RNTCP staff needs to be re-sensitized to “ask” for previous history of TB treatment. Hence, complete filling of the revised laboratory register at microscopy centres should be closely monitored by the programme and future operational research should focus on this.

Systematic qualitative enquiry is recommended to understand the ‘why’ (why does it happen) and ‘how’ (how can it be addressed) of misclassification. In the national case-based TB notification software ( NIKSHAY), record linkage and deduplication using key attributes may be considered to identify repeat notification of the same person separated by a time period.”

REVIEWER COMMENT

Minor:

Maybe better to use the notation of 95% CI rather than 0.95

AUTHOR RESPONSE

We have revised throughout the manuscript as per reviewer suggestion

REVIEWER COMMENT

Figure 1. I think the first box at left has a wrong "the" : as AN error

AUTHOR RESPONSE

We are not clear as to what the reviewer means here. We did not find this error in the first left box of figure 1. However, we re-checked figure 1 and found it no grammatical errors.

REVIEWER COMMENT

Change gender to sex

AUTHOR RESPONSE

We have revised throughout the manuscript as per reviewer suggestion

10.5256/f1000research.20077.r46256

Reviewer response for version 1

Atre

Sachin

1 2 Referee https://orcid.org/0000-0003-2148-465X 1Dr. D.Y. Patil Medical College, Hospital and Research Centre Pune, Pimpri-Chinchwad, India 2Center for Clinical Global Health Education (CCGHE), Johns Hopkins University, Baltimore, MD, USA

Competing interests: No competing interests were disclosed.

9 4 2019

2019

recommendation

approve-with-reservations

The manuscript addresses one of the crucial problems with in India’s Revised National TB Control Program. Interestingly, the study is based on our prior study (in which I was a lead author) in 2007, which was conducted in Mumbai and rural areas of Pune district. Given the research context and the data, I feel this manuscript should go as a brief communication or notes from the field rather than a full original research article. It does not make any novel contribution, but just confirms the earlier research finding. Even to make it a brief communication, I feel some points need to be provocatively addressed. My comments are as below.

Authors have provided details of their larger study, of which the current study is only a part. I feel that level of details is unnecessary here. On the other hand, unfortunately despite having a large team (as seen from the long list of authors), they did not do in-depth inquiry into the reasons for an erroneous categorization of cases, which is actually the main aim of the manuscript. They just mentioned the same reasons for erroneous categorization by providing a reference to our 2007 article without making any new contribution. Identifying the most prominent reasons would have been helpful to identify as a focus area for the policy makers to make some action plan for operational implementation of the program. In my opinion, there is not much substance to publish it as an original research article.

Authors nowhere discussed the major implication of their observation that even after 10 years, there remains a big disconnect between the operational research and the program implementation, which is really unfortunate. This finding has another major implication that because of erroneous categorization, previously treated cases are being treated with first-line regimen, which results in amplification of resistance in those cases who may have primary or acquired drug resistance (from prior treatment) and/MDR-TB. This has been happening for over 10 years so one can see why India now faces the serious problem of drug resistant TB.

I am not convinced with the factor analysis in Table 1 because the detailed inquiry was not made into reasons for misclassification/erroneous categorization which should have been the main focus.

Authors state in discussion that as per the WHO recommendation, universal DST will be done for all TB cases. This is an ideal situation. The scale up of GeneXpert even remains questionable. The WHO global report 2018 showed that only 40% of TB cases in India were subjected to GeneXert in 2017. There are problems of shortage of cartridges, falcon tubes, power outages etc. Given the glacial speed of translation of findings from operational research in the actual RNTCP implementation, India’s claim of TB elimination by 2025 looks really questionable.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Operational research on TB and MDR-TB in India

Shewade

Hemant Deepak

International Union Against Tuberculosis and Lung Disease, India

Competing interests: There are no competing interests to declare

7 5 2019

REVIEWER #1

Sachin R. Atre, Johns Hopkins University, USA; Dr. D.Y. Patil Medical College, Hospital and Research Centre Pune, India

REVIEWER COMMENT

AUTHOR RESPONSE

Thank you for the comment. F1000Research does not have the article types as suggested: brief communication or notes from the field. ‘Research notes’ is the closest option, but here too there is no specific word count mentioned. Considering our manuscript is around 1800 words only, we are fine if F1000Research agrees to consider this as a research note.

We agree the findings are not novel, but are timely considering 10 years down the line, the issue remains as it is and this is important to highlight.

We have addressed your comments, details below.

REVIEWER COMMENT

AUTHOR RESPONSE

The actual study was done across 18 randomly spread districts of the country (Axshya SAMVAD study – Axshya SAMVAD is an ACF strategy for detecting TB and is implemented by The Union South East Asia). As mentioned, the findings were accidental (not intended) and we thought it is important to report this and look for differences in rates of misclassification across various patient sub-groups.

The Axshya SAMVAD study was conducted by project Axshya staff in operational setting without any additional funding for the study itself. The research team in the field (project Axshya staff) were not trained and did not have the required capacity to conduct qualitative research. Hence, we could not go in-depth into the reasons. But, one key point is that our study was conducted in randomly selected 18 districts of the country and hence, the findings are representative.

We agree the findings are not novel, but are timely considering 10 years down the line, the issue remains as it is and this is important to highlight.

Considering our manuscript is around 1800 words only, we are fine if F1000Research agrees to consider this as a research note.

REVIEWER COMMENT

AUTHOR RESPONSE

Thank you very much for your comment. We agree with the reviewer. We have included the above point in the revised manuscript as suggested by the reviewer. We have discussed the implications in the first paragraph of ‘Implications for the TB programme’ in the revised manuscript (reproduced below):

“Limitations notwithstanding, our study has programme implications. Of the new smear-positive pulmonary TB patients registered in India in 2016, 21% had an unfavourable outcome ³ . Some of these unfavourable outcomes can be explained by wrong management – patients getting an inferior treatment regimen (previously treated patients being treated with a regimen meant for new cases) and missing an opportunity for drug susceptibility testing (as previously treated patients were eligible for DST at the time). Inferior regimen might have also contributed to amplification of resistance in those who may have primary or acquired drug resistance (from prior treatment) and MDR-TB. This has been happening for over 10 years so one can see why India now faces the serious problem of drug resistant TB.”

REVIEWER COMMENT

I am not convinced with the factor analysis in Table 1 because the detailed inquiry was not made into reasons for misclassification/erroneous categorization which should have been the main focus.

AUTHOR RESPONSE

Thank you for the comment. In Table 1, we are quantitatively looking for patient sub-groups who have higher prevalence of misclassification. Though not statistically significant, programmatically significant differences were observed in misclassification in urban and rural areas (12% in rural and 2% in urban areas). We agree that we have not looked into the ‘why’ and ‘how’ of misclassification (limitations). But, based on our programme experiences made some useful recommendations in the revised manuscript (reproduced below)

Since 2017, the revised laboratory register at the level of designated microscopy centres under the RNTCP (one per 50 000 to 100 000 population) also captures this information of previous treatment ⁸ . RNTCP staff needs to be re-sensitized to “ask” for previous history of TB treatment. Hence, complete filling of the revised laboratory register at microscopy centres should be closely monitored by the programme and future operational research should focus on this.

REVIEWER COMMENT

AUTHOR RESPONSE

Thank you for the comment. We agree with the reviewer. We agree that DST for all TB patients is an ideal situation. Therefore, we have also stated that in a non-ideal situation (DST not possible for all TB patients), DST may be focussed on those with previous history of TB treatment. Therefore, correct classification as new or previously treated is still relevant. We have mentioned this in the last three lines of the section ‘implications for the TB programme’ (reproduced below):