MedicinemedRxiv
Ranked by formulaVerdict: NotableIMPACT OF CENTRALIZED NIKSHAY ON TB NOTIFICATION AND DATA COMPLETENESS IN INDIGENOUS TRIBES: A 21-YEAR STUDY
Indigenous tribes face a disproportionately high tuberculosis (TB) burden due to deep-rooted socioeconomic disadvantage, highlighting the need for strengthened, culturally sensitive control strategies.
Key numbers
- 95% CI 1.70
- 95% CI 1.05
- 69.8% vs 87.2%
Caveats
- Not randomized: its findings are associations.
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
BACKGROUND: Indigenous tribes face a disproportionately high tuberculosis (TB) burden due to deep-rooted socioeconomic disadvantage, highlighting the need for strengthened, culturally sensitive control strategies. METHODS: We conducted a 21-year retrospective study (2005-2025) among tribal and neighboring non-tribal populations attending a tribal-serving hospital in Karnataka. Pre-Nikshay data were transcribed from paper registers, and post-Nikshay data were obtained from the digital system. We assessed negative binomial interrupted time-series regression model, data completeness, TB risk factors, and used multivariable logistic regression to identify determinants of poor treatment outcomes and household contact screening yield. RESULTS: We analyzed 1,512 TB cases (tribal = 1,021; non-tribal = 491). TB detection increased significantly over time, with a steeper rise among tribal populations (p<0.001). Nikshay implementation significantly improved data completeness (Wilcoxon W = 9900, p<0.001). Among tribal patients, males had higher odds of poor treatment outcome (aOR 2.81, 95% CI 1.70- 4.85; p<0.001). Diabetes (aOR 2.10, 95% CI 1.05- 4.03; p=0.029) was independently associated with poor treatment outcome. Among 503 index cases, 1,535 household contacts were identified and 1,161 (75.6%) screened; coverage was lower among tribal contacts (69.8% vs 87.2%; p<0.001). CONCLUSIONS: Nikshay strengthened TB surveillance and improved assessment of TB burden and risk factors in tribal communities.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ██░░░ 2 | 10% | Reusable within one subfield (a technique, dataset, or protocol a few groups will adopt). |
| Magnitude | ███░░ 3 | 20% | Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem. |
| Evidence | ███░░ 3 | 32% | Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data. |
| Novelty | ██░░░ 2 | 8% | A new combination of known ideas. |
| Trajectory | ██░░░ 2 | 5% | Some room to improve with obvious engineering. |
| Stakes | ███░░ 3 | 25% | Meaningful benefit to many people within a few years. |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: gains (relative gain, versus baseline); verification (confidence interval, p-value, independent replication); stakes (major disease, prevention or cure). Red flags: not randomized; weak evidence (single site).
How the score was computed
- Merit
- 5.5 / 10
- Adjusted merit
- 4.7 / 10
- Attention
- 0%
- Freshness
- 92%