A study on tuberculosis disease disclosure patterns and its associated factors: Findings from a prospective observational study in Chennai.

BACKGROUND: Disclosure of tuberculosis (TB) status by patients is a critical step in their treatment cascade of care. There is a lack of systematic assessment of TB disclosure patterns and its positive outcomes which happens dynamically over the disease period of individual patients with their family and wider social network relations. METHODS: This prospective observational study was conducted in Chennai Corporation treatment units during 2019-2021. TB patients were recruited and followed-up from treatment initiation to completion. Information on disease disclosures made to different social members at different time points, and outcomes were collected and compared. Bivariate and multi variate analysis were used to identify the patients and contact characteristics predictive of TB disclosure status. RESULTS: A total of 466 TB patients were followed-up, who listed a total of 4039 family, extra familial and social network contacts of them. Maximum disclosures were made with family members (93%) and half of the relatives, occupational contacts and friendship contacts (44-58%) were disclosed within 15 days of treatment initiation. Incremental disclosures made during the 150-180 days of treatment were highest among neighbourhood contacts (12%), and was significantly different between treatment initiation and completion period. Middle aged TB patients (31 years and 46-55 years) were found less likely to disclose (AOR 0.56 and 0.46 respectively; p<0.05) and illiterates were found more likely to disclose their TB status (AOR 3.91; p<0.05). Post the disclosure, family contacts have mostly provided resource support (44.90%) and two third of all disclosed contacts have provided emotional support for TB patients (>71%). CONCLUSION: Findings explain that family level disclosures were predominant and disclosures made to extra familial network contacts significantly increased during the latter part of treatment. Emotional support was predominantly received by TB patients from all their contacts post disclosure. Findings could inform in developing interventions to facilitate disclosure of disease status in a beneficial way for TB patients.

Diagnostic accuracy of mercurial versus digital blood pressure measurement devices: a systematic review and meta-analysis.

This study aims to systematically review the diagnostic accuracy of a digital blood pressure measurement device compared to the gold standard mercury sphygmomanometer in published studies. Searches were conducted in PubMed, Cochrane, EBSCO, EMBASE and Google Scholar host databases using the specific search strategy and filters from 1st January 2000 to 3rd April 2021. We included studies reporting data on the sensitivity or specificity of blood pressure measured by digital devices and mercury sphygmomanometer used as the reference standard. Studies conducted among children, special populations, and specific disease groups were excluded. We considered published manuscripts in the English language only. The risk of bias and applicability concerns were assessed based on the author's judgment using the QUADAS2 manual measurement evaluation tool. Based on the screening, four studies were included in the final analysis. Sensitivity, specificity, diagnostic odds ratio (DOR), and 95% confidence interval were estimated. The digital blood pressure monitoring has a moderate level of accuracy and the device can correctly distinguish hypertension with a pooled estimate sensitivity of 65.7% and specificity of 95.9%. After removing one study, which had very low sensitivity and very high specificity, the pooled sensitivity estimate was 79%, and the specificity was 91%. The meta-analysis of DOR suggests that the digital blood pressure monitor had moderate accuracy with a mercury sphygmomanometer. This will provide the clinician and patients with accurate information on blood pressure with which diagnostic and treatment decisions could be made.

Tracing the potential extra-household contacts of TB patients: findings from a personal social network survey in a high TB burden setting in India.

BACKGROUND: Evidence on the extra-household contacts of TB patients who drive disease transmission is scarce. METHODS: We conducted a cross-sectional personal social network survey among 300 newly diagnosed index pulmonary TB patients to identify their first-degree extra-household contacts. RESULTS: A significantly higher proportion of neighbourhood (3.5; 95% CI 1.3 to 7.5), occupational (3.2; 95% CI 1.3 to 9.2) and friendship contacts (2.2; 95% CI 0.8 to 4.5) developed TB within 1 y of the index patient's diagnosis than their household contacts (0.7; 95% CI 0.3 to 1.3). Similarly, a higher proportion of extra-household contacts had TB at different time points before the index patient was diagnosed. CONCLUSION: Extra-household contacts of TB patients could be a potential source of TB or could be at increased risk of TB.

Social network analysis methods for exploring SARS-CoV-2 contact tracing data.

BACKGROUND: Contact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels. METHODS: Standard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as "nodes" and their epidemiological contact as "edges" was created. Directed network perspective was utilized in which directionality of infection emanated from a "source patient" towards a "target patient". Network measures of " degree centrality" and "betweenness centrality" were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers. RESULTS: Out-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (> = 10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient's which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network. CONCLUSIONS: Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.

Understanding pretreatment loss to follow-up of tuberculosis patients: an explanatory qualitative study in Chennai, India.

Introduction: Pretreatment 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. Methods: We 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. Results: We 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. Conclusion: Health 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.

Correction: Tuberculosis patients in an Indian mega-city: Where do they live and where are they diagnosed?

[This corrects the article DOI: 10.1371/journal.pone.0183240.].

Pretreatment loss to follow-up of tuberculosis patients in Chennai, India: a cohort study with implications for health systems strengthening.

BACKGROUND: Pretreatment 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. METHODS: We 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. RESULTS: In 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. CONCLUSIONS: In 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.

Tuberculosis patients in an Indian mega-city: Where do they live and where are they diagnosed?

OBJECTIVE: Tuberculosis (TB) is a major source of mortality in urban India, with many structural challenges to optimal care delivery. In the government TB program in Chennai, India's fourth most populous city, there is a 49% gap between the official number of smear-positive TB patients diagnosed and the official number registered in TB treatment within the city in 2014. We hypothesize that this "urban registration gap" is partly due to rural patients temporarily visiting the city for diagnostic evaluation. METHODS: We collected data for one month (May 2015) from 22 government designated microscopy centers (DMCs) in Chennai where 90% of smear-positive TB patients are diagnosed and coded patient addresses by location. We also analyzed the distribution of chest symptomatics (i.e., patients screened for TB because of pulmonary symptoms) and diagnosed smear-positive TB patients for all of Chennai's 54 DMCs in 2014. RESULTS: At 22 DMCs in May 2015, 565 of 3,543 (15.9%) chest symptomatics and 71 of 412 (17.2%) diagnosed smear-positive patients had an address outside of Chennai. At the city's four high patient volume DMCs, 54 of 270 (20.0%) smear-positive patients lived out-of-city. At one of these high-volume DMCs, 31 of 59 (52.5%) smear-positive patients lived out-of-city. Out of 6,135 smear-positive patients diagnosed in Chennai in 2014, 3,498 (57%) were diagnosed at the four high-volume DMCs. The 32 DMCs with the lowest patient volume diagnosed 10% of all smear-positive patients. CONCLUSIONS: TB case detection in Chennai is centralized, with four high-volume DMCs making most diagnoses. One-sixth of patients are from outside the city, most of whom get evaluated at these high-volume DMCs. This calls for better coordination between high-volume city DMCs and rural TB units where many patients may take TB treatment. Patient mobility only partly explains Chennai's urban registration gap, suggesting that pretreatment loss to follow-up of patients who live within the city may also be a major problem.