BackgroundPretreatment loss to follow-up (PTLFU) is a barrier to tuberculosis (TB) control in India’s Revised National TB Control Programme (RNTCP). PTLFU studies have not been conducted in India’s mega-cities, where patient mobility may complicate linkage to care.MethodsWe collected data from patient registries for May 2015 from 22 RNTCP designated microscopy centers (DMCs) in Chennai and audited addresses and phone numbers for patients evaluated for suspected TB to understand how missing contact information may contribute to PTLFU. From November 2015 to June 2016, we audited one month of records from each of these 22 DMCs and tracked newly diagnosed smear-positive patients using RNTCP records, phone calls, and home visits. We defined PTLFU cases as including: (1) patients who did not start TB therapy within 14 days and (2) patients who started TB therapy but were lost to follow-up or died before official RNTCP registration. We used multivariate logistic regression to identify factors associated with PTLFU.ResultsIn the audit of May 2015 DMC registries, out of 3696 patients evaluated for TB, 1273 (34.4%) had addresses and phone numbers that were illegible or missing. Out of 344 smear-positive patients tracked from November 2015 to June 2016, 40 (11.6%) did not start TB therapy within 14 days and 36 (10.5%) started therapy but were lost to follow-up or died before official RNTCP registration, for an overall PTLFU rate of 22.1% (95%CI: 17.8%—26.4%). Of all PTLFU patients, 55 (72.4%) were lost to follow-up and 21 (27.6%) died before starting treatment or before RNTCP registration. In the regression analysis, age > 50 years (OR 2.9, 95%CI 1.4—6.5), history of prior TB (OR 3.9, 95%CI 2.2—7.1), evaluation at a high patient volume DMC (OR 3.2, 95% CI 1.7—6.3), and absence of legible patient contact information (OR 4.5, 95%CI 1.3—15.1) were significantly associated with PTLFU.ConclusionsIn an Indian mega-city, we found a high PTLFU rate, especially in patients with a prior TB history, who are at greater risk for having drug-resistance. Enhancing quality of care and health system transparency is critical for improving linkage of newly diagnosed patients to TB care in urban India.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3039-3) contains supplementary material, which is available to authorized users.

Background Patients with multidrug-resistant tuberculosis (MDR-TB) face challenges adhering to medications, given that treatment is prolonged and has a high rate of adverse effects. The Medication Event Reminder Monitor (MERM) is a digital pillbox that provides pill-taking reminders and facilitates the remote monitoring of medication adherence. Objective This study aims to assess the MERM’s acceptability to patients and health care providers (HCPs) during pilot implementation in India’s public sector MDR-TB program. Methods From October 2017 to September 2018, we conducted qualitative interviews with patients who were undergoing MDR-TB therapy and were being monitored with the MERM and HCPs in the government program in Chennai and Mumbai. Interview transcripts were independently coded by 2 researchers and analyzed to identify the emergent themes. We organized findings by using the Unified Theory of Acceptance and Use of Technology (UTAUT), which outlines 4 constructs that predict technology acceptance—performance expectancy, effort expectancy, social influence, and facilitating conditions. Results We interviewed 65 patients with MDR-TB and 10 HCPs. In patient interviews, greater acceptance of the MERM was related to perceptions that the audible and visual reminders improved medication adherence and that remote monitoring reduced the frequency of clinic visits (performance expectancy), that the device’s organization and labeling of medications made it easier to take them correctly (effort expectancy), that the device facilitated positive family involvement in the patient’s care (social influences), and that remote monitoring made patients feel more cared for by the health system (facilitating conditions). Lower patient acceptance was related to problems with the durability of the MERM’s cardboard construction and difficulties with portability and storage because of its large size (effort expectancy), concerns regarding stigma and the disclosure of patients’ MDR-TB diagnoses (social influences), and the incorrect understanding of the MERM because of suboptimal counseling (facilitating conditions). In their interviews, HCPs reported that MERM implementation resulted in fewer in-person interactions with patients and thus allowed HCPs to dedicate more time to other tasks, which improved job satisfaction. Conclusions Several features of the MERM support its acceptability among patients with MDR-TB and HCPs, and some barriers to patient use could be addressed by improving the design of the device. However, some barriers, such as disease-related stigma, are more difficult to modify and may limit use of the MERM among some patients with MDR-TB. Further research is needed to assess the accuracy of MERM for measuring adherence, its effectiveness for improving treatment outcomes, and patients’ sustained use of the device in larger scale implementation.

Background Poor adherence to tuberculosis (TB) treatment is associated with disease recurrence and death. Little research has been conducted in India to understand TB medication non-adherence. Methods We enrolled adult drug-susceptible TB patients, about half of whom were people living with HIV (PLHIV), in Chennai, Vellore, and Mumbai. We conducted a single unannounced home visit to administer a survey assessing reasons for non-adherence and collect a urine sample that was tested for isoniazid content. We described patient-reported reasons for non-adherence and identified factors associated with non-adherence (i.e., negative urine test) using multivariable logistic regression. We also assessed the association between non-adherence and treatment outcomes. Results Of 650 participants in the cohort, 77 (11.8%) had a negative urine test. Non-adherence was independently associated with daily wage labor (aOR 2.7, CI: 1.1—6.5, p=0.03), the late continuation treatment phase (aOR 2.0, CI:1.1—3.9, p=0.03), smear-positive pulmonary disease (aOR 2.1, CI: 1.1—3.9, p=0.03), alcohol use (aOR 2.5, CI: 1.2—5.2, p=0.01), and spending >=30 minutes collecting medication refills (aOR 6.6, CI: 1.5—29.5, p=0.01). PLHIV reported greater barriers to collecting medications than non-PLHIV. Among 167 patients reporting missing doses, reported reasons included traveling from home, forgetting, feeling depressed, and running out of pills. The odds of unfavorable treatment outcomes were 4.0 (CI: 2.1—7.6) times higher among patients with non-adherence (p<0.0001). Conclusion Addressing structural and psychosocial barriers will be critical to improve TB treatment adherence in India. Urine isoniazid testing may help identify non-adherent patients to facilitate early intervention during treatment.

IntroductionPretreatment loss to follow-up (PTLFU)—dropout of patients after diagnosis but before treatment registration—is a major gap in tuberculosis (TB) care in India and globally. Patient and healthcare worker (HCW) perspectives are critical for developing interventions to reduce PTLFU.MethodsWe tracked smear-positive TB patients diagnosed via sputum microscopy from 22 diagnostic centres in Chennai, one of India’s largest cities. Patients who did not start therapy within 14 days, or who died or were lost to follow-up before official treatment registration, were classified as PTLFU cases. We conducted qualitative interviews with trackable patients, or family members of patients who had died. We conducted focus group discussions (FGDs) with HCWs involved in TB care. Interview and FGD transcripts were coded and analysed with Dedoose software to identify key themes. We created categories into which themes clustered and identified relationships among thematic categories to develop an explanatory model for PTLFU.ResultsWe conducted six FGDs comprising 53 HCWs and 33 individual patient or family member interviews. Themes clustered into five categories. Examining relationships among categories revealed two pathways leading to PTLFU as part of an explanatory model. In the first pathway, administrative and organisational health system barriers—including the complexity of navigating the system, healthcare worker absenteeism and infrastructure failures—resulted in patients feeling frustration or resignation, leading to disengagement from care. In turn, HCWs faced work constraints that contributed to many of these health system barriers for patients. In the second pathway, negative HCW attitudes and behaviours contributed to patients distrusting the health system, resulting in refusal of care.ConclusionHealth system barriers contribute to PTLFU directly and by amplifying patient-related challenges to engaging in care. Interventions should focus on removing administrative hurdles patients face in the health system, improving quality of the HCW-patient interaction and alleviating constraints preventing HCWs from providing patient-centred care.

Introduction: Pretreatment loss to follow-up (PTLFU)--dropout of patients after diagnosis but before registration in treatment--is a major gap in TB care delivery in India and globally. Patient and healthcare worker (HCW) perspectives are critical for developing interventions to address this problem. Methods: We prospectively tracked newly diagnosed smear-positive TB patients from 22 TB diagnostic centers in Chennai, one of the largest cities in India. Patients who did not start therapy within 14 days, or who died or were lost to follow-up before official registration in treatment, were classified as PTLFU cases. We conducted qualitative interviews with all trackable PTLFU patients, or family members of patients who had died. We conducted focus group discussions (FGDs) with three types of HCWs involved in TB care. Interview and FGD recordings were transcribed, coded, and analyzed with the support of Dedoose 8.0.35 software to identify key themes. We created categories into which these themes clustered, identified relationships among thematic categories, and assembled findings into a broader explanatory model for PTLFU. Results: We conducted six FGDs comprising 53 HCWs and 33 individual patient or family member interviews. Themes clustered into five categories. Examining relationships among these categories revealed two pathways leading to PTLFU as part of a broader explanatory model. In the first pathway, administrative and organizational health system barriers--including the complexity of navigating the system, healthcare worker absenteeism, and infrastructure failures--resulted in patients feeling frustration or resignation, leading to disengagement from care. Health system barriers experienced by patients were in turn shaped by constraints that made it difficult for HCWs to do their jobs effectively. In the second pathway, negative or judgmental HCW attitudes and behaviors towards patients contributed to patient distrust of the health system, resulting in refusal of further care. Conclusion: Health system barriers contribute substantially to PTLFU directly and by amplifying patient-related challenges to engaging in care. Interventions should focus on removing administrative hurdles patients face in the health system, improving the quality of the HCW-patient interaction, and alleviating constraints HCWs face in being able to provide optimal patient-centered care.