“Before Xpert I only had my expertise”: A qualitative study on the utilization and effects of Xpert technology among pediatricians in 4 Indian cities
Bibliographic record
Abstract
BACKGROUND: Diagnosing tuberculosis (TB) in children presents considerable challenges. Upfront testing on Xpert® MTB/RIF ('Xpert')-a rapid molecular assay with high sensitivity and specificity-for pediatric presumptive TB patients, as recommended by India's Revised National Tuberculosis Control Program (RNTCP), can pave the way for early TB diagnosis. As part of an ongoing project implemented by Foundation for Innovative New Diagnostics (FIND) dedicated to providing upfront free-of-cost (FOC) Xpert testing to children seeking care in the public and private sectors, a qualitative assessment was designed to understand how national guidelines on TB diagnosis and Xpert technology have been integrated into the pediatric TB care practices of different health providers. METHODS: We conducted semi-structured interviews with a sample of health providers from public and private sectors engaged in the ongoing pediatric project in 4 major cities of India. Providers were sampled from intervention data based on sector of practice, number of Xpert referrals, and TB detection rates amongst referrals. A total of 55 providers were interviewed with different levels of FOC Xpert testing uptake. Data were transcribed and analyzed inductively by a medical anthropologist using thematic content analysis and narrative analysis. RESULTS: It was observed that despite guidance from RNTCP on the use of Xpert and significant efforts by FIND and state authorities to disseminate these guidelines, there was notable diversity in their implementation by different health care providers. Xpert, apart from being utilized as intended, i.e. as a first diagnostic test for children, was utilized variably-as an initial screening test (to rule out TB), confirmatory test (once TB diagnosis is established based on antibiotic trial or clinically) and/or only for drug susceptibility testing after TB diagnosis was confirmed. Most providers who used Xpert frequently reported that Xpert was an important tool for managing pediatric TB cases, by reducing the proportion of cases diagnosed only on clinical suspicion and by providing upfront information on drug resistance, which is seldom suspected in children. Despite non-standard use, these results showed that Xpert access helped raise awareness, aided in antibiotic stewardship, and reduced dependence on clinical diagnosis among those who diagnose and treat TB in children. CONCLUSION: Access to free and rapid Xpert testing for all presumptive pediatric TB patients has had multiple positive effects on pediatricians' diagnosis and treatment of TB. It has important effects on speed of diagnosis, empirical treatment, and awareness of drug resistance among TB treatment naive children. In addition, our study shows that access to public sector Xpert machines may be an important way to encourage Public-Private integration and facilitate the movement of patients from the private to public sector for anti-TB treatment. Despite availability of rapid and free Xpert testing, our study showed an alarming diversity of Xpert utilization strategies across different providers who may be moving toward suggested practice over time. The degree of diversity in TB diagnostic approaches in children reported here highlights the urgent need for concerted efforts to place Xpert early in diagnostic algorithms to positively impact the pediatric TB care pathway. A positive change in diagnostic algorithms may be possible with continued advocacy, time, and increased access.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".