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IntroductionAcross a variety of settings, women in tenuous financial circumstances are drawn to community health work as a way to advance themselves in the context of limited employment options. Female Community Health Workers (CHWs) are often preferred because they can more easily access mothers and children; at the same time, gender norms are at the heart of many of the challenges and inequities that these workers encounter. Here, we explore how these gender roles and a lack of formal worker protections leave CHWs vulnerable to violence and sexual harassment, common occurrences that are frequently downplayed or silenced.MethodsWe are a group of researchers who work on CHW programmes in a variety of contexts globally. The examples here are drawn from our ethnographic research (participant observation and in-depth interviews).ResultsCHW work creates job opportunities for women in contexts where such opportunities are extremely rare. These jobs can be a lifeline for women with few other options. Yet the threat of violence can be very real: women may face violence from the community, and some experience harassment from supervisors within health programmes.ConclusionTaking gendered harassment and violence seriously in CHW programmes is critical for research and practice. Fulfilling CHWs’ vision of health programmes that value them, support them and give them opportunities may be a way for CHW programmes to lead the way in gender-transformative labour practices.

Background Successful delivery and completion of tuberculosis preventive treatment are necessary for tuberculosis elimination. Shorter preventive treatment regimens currently have higher medication costs, but patients spend less time in care and are more likely to complete treatment. It is unknown how economic costs of successful delivery differ between longer and shorter regimens in high-tuberculosis-burden settings. Methods We developed survey instruments to collect costs from program and patient sources, considering costs incurred from when household contacts first entered the health system. We compared the cost per completed course of preventive treatment with either 6 months of daily isoniazid (6H) or 3 months of weekly isoniazid and rifapentine (3HP), delivered by the Indus Health Network tuberculosis program in Karachi, Pakistan, between October 2016 and February 2018. Results During this period, 459 individuals initiated 6H and 643 initiated 3HP; 39% and 61% completed treatment, respectively. Considering costs to both the program and care recipients, the cost per completed course was 394 US dollars (USD) for 6H and 333 USD for 3HP. Using a new 2020 price for rifapentine reduced the cost per completed course of 3HP to 290 USD. Under varying assumptions about drug prices and costs incurred by care recipients, the cost per completed course was lower for 3HP in all scenarios, and the largest cost drivers were the salaries of clinical staff. Conclusions In a high-burden setting, the cost of successful delivery of 3HP was lower than that of 6H, driven by higher completion., In a tuberculosis-preventive treatment program in Karachi, Pakistan, the cost per completed course was lower for 12-dose isoniazid and rifapentine than for 6 months of daily isoniazid.

BACKGROUND Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to determine whether commercially available chest x-ray analysis software meet WHO recommendations for minimal sensitivity and specificity as pulmonary tuberculosis triage tests. METHODS We recruited symptomatic adults at the Indus Hospital, Karachi, Pakistan. We compared two software, qXR version 2.0 (qXRv2) and CAD4TB version 6.0 (CAD4TBv6), with a reference of mycobacterial culture of two sputa. We assessed qXRv2 using its manufacturer prespecified threshold score for chest x-ray classification as tuberculosis present versus not present. For CAD4TBv6, we used a data-derived threshold, because it does not have a prespecified one. We tested for non-inferiority to preset WHO recommendations (0·90 for sensitivity, 0·70 for specificity) using a non-inferiority limit of 0·05. We identified factors associated with accuracy by stratification and logistic regression. FINDINGS We included 2198 (92·7%) of 2370 enrolled participants. 2187 (99·5%) of 2198 were HIV-negative, and 272 (12·4%) had culture-confirmed pulmonary tuberculosis. For both software, accuracy was non-inferior to WHO-recommended minimum values (qXRv2 sensitivity 0·93 [95% CI 0·89-0·95], non-inferiority p=0·0002; CAD4TBv6 sensitivity 0·93 [0·90-0·96], p

The practice of assigning grades is a universal quality of educational institutions, but the effects of biases in grading as well as its potential to distract from actual learning can make grading particularly problematic in the context of global health education. Ungrading is an anti-oppressive strategy to teaching which seeks to promote students as the experts of their own learning. It de-emphasizes grades and focuses on improved student engagement over time facilitated through thoughtful instructor comments and ongoing conversations. We implemented ungrading in two graduate-level global health courses at the Johns Hopkins Bloomberg School of Public Health; this manuscript documents the implementation process, the teaching teams’ experience, and student evaluations related to ungrading. Overall, quantitative course reviews were much improved from previous year(s). Qualitative responses revealed that students across both courses felt that ungrading improved their ability to focus on learning course material without anxiety about GPAs. Ungrading also encouraged students to embrace comments for learning, take risks, and leverage their lived experiences in responses. The teaching team felt students put in more effort, not less, and enjoyed a transformation in interactions with students—away from grades and toward content. Ungrading is a promising approach in graduate global health education that can facilitate inclusive and reflexive learning spaces.

Background Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH). Methods We collected CXRs and individual patient data (IPD) from studies evaluating CAD in patients self-referring for tuberculosis symptoms with culture or nucleic acid amplification testing as the reference. We reanalyzed CXRs with three CAD programs (CAD4TB version (v) 6, Lunit v3.1.0.0, and qXR v2). We estimated sensitivity and specificity within each study and pooled using IPD meta-analysis. We used multivariable meta-regression to identify characteristics modifying accuracy. Results We included CXRs and IPD of 3727/3967 participants from 4/7 eligible studies. 17% (621/3727) were PLWH. 17% (645/3727) had microbiologically confirmed tuberculosis. Despite using the same threshold score for classifying CXR in every study, sensitivity and specificity varied from study to study. The software had similar unadjusted accuracy (at 90% pooled sensitivity, pooled specificities were: CAD4TBv6, 56.9% [95% confidence interval {CI}: 51.7–61.9]; Lunit, 54.1% [95% CI: 44.6–63.3]; qXRv2, 60.5% [95% CI: 51.7–68.6]). Adjusted absolute differences in pooled sensitivity between PLWH and HIV-uninfected participants were: CAD4TBv6, −13.4% [−21.1, −6.9]; Lunit, +2.2% [−3.6, +6.3]; qXRv2: −13.4% [−21.5, −6.6]; between smear-negative and smear-positive tuberculosis was: were CAD4TBv6, −12.3% [−19.5, −6.1]; Lunit, −17.2% [−24.6, −10.5]; qXRv2, −16.6% [−24.4, −9.9]. Accuracy was similar to human readers. Conclusions For CAD CXR analysis to be implemented as a high-sensitivity tuberculosis rule-out test, users will need threshold scores identified from their own patient populations and stratified by HIV and smear status.

Background In settings without access to rapid expert radiographic interpretation, artificial intelligence (AI)–based chest radiograph (CXR) analysis can triage persons presenting with possible tuberculosis (TB) symptoms, to identify those who require additional microbiological testing. However, there is limited evidence of the cost-effectiveness of this technology as a triage tool. Methods A decision analysis model was developed to evaluate the cost-effectiveness of triage strategies with AI-based CXR analysis for patients presenting with symptoms suggestive of pulmonary TB in Karachi, Pakistan. These strategies were compared to the current standard of care using microbiological testing with smear microscopy or GeneXpert, without prior triage. Positive triage CXRs were considered to improve referral success for microbiologic testing, from 91% to 100% for eligible persons. Software diagnostic accuracy was based on a prospective field study in Karachi. Other inputs were obtained from the Pakistan TB Program. The analysis was conducted from the healthcare provider perspective, and costs were expressed in 2020 US dollars. Results Compared to upfront smear microscopy for all persons with presumptive TB, triage strategies with AI-based CXR analysis were projected to lower costs by 19%, from $23233 per 1000 persons, and avert 3%–4% disability-adjusted life-years (DALYs), from 372 DALYs. Compared to upfront GeneXpert, AI-based triage strategies lowered projected costs by 37%, from $34346 and averted 4% additional DALYs, from 369 DALYs. Reinforced follow-up for persons with positive triage CXRs but negative microbiologic tests was particularly cost-effective. Conclusions In lower-resource settings, the addition of AI-based CXR triage before microbiologic testing for persons with possible TB symptoms can reduce costs, avert additional DALYs, and improve TB detection., We evaluated the cost-effectiveness of artificial intelligence–based chest radiograph analysis for tuberculosis detection, based on a trial in Pakistan. Compared to upfront sputum testing, use of this technology to triage people with symptoms reduced costs and averted disability-adjusted life-years.

Background Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records by using data quality assessment framework. Methodology We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled TB clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n=59; 59 out of 117 TB01 cards) were digitized. There were 1,239 comparable data fields, out of which 65% (n=803) were correctly matched between paper based and digital records. However, 35% of the data fields (n=436) had anomalies, either in paper-based records or in digital records. 1.9 data quality issues were calculated per digital patient record, whereas it was 2.1 issues per record for paper-based record. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n=123) than in digital records (n=110). Conclusion There were fewer data quality issues in digital records as compared to the corresponding paper-based records. Greater use of mobile data capture and continued use of the data quality assessment framework can deliver more meaningful information for decision making.