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Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges. Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Efforts to mitigate losses along the diagnostic cascade were tracked. Persons with high CAD4TB score (≥80), who tested negative on a single spot GeneXpert were followed-up to assess TB status at six months. An experimental calibration method achieved a viable CAD threshold for testing. High risk groups and key stakeholders were engaged. Operations evolved in real time to fix problems. Incremental improvements in mean client volumes (128 to 140/day), target group inclusion (92% to 93%), on-site testing (84% to 86%), TB treatment initiation (87% to 91%), and TB treatment success (71% to 85%) were recorded. Attention to those as highest risk boosted efficiency (the NNT declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added after follow-up among those with ≥ 80 CAD scores and initially spot -sputum negative found 11 additional TB cases (6.3%) after 121 person-years of follow-up. Iterative adaptation in response to performance metrics foster feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD scores can identify subclinical TB and those at risk of progression to bacteriologically-confirmed TB disease in the near term.
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SETTING: Nigeria adopted GeneXpert MTB Rif as a primary diagnostic tool were available and accessible since 2016. The current geographical coverage of GeneXpert machines by LGAs stands at 48%, with a varied access and utilization. OBJECTIVES: To assess the association between the type and level of health facilities implementing GeneXpert MTB/Rif and performance outcome of the machines in Nigeria. STUDY DESIGN: Retrospective secondary data analysis of GeneXpert performance for 2017 from GXAlert database. The independent variables were type and levels of health care facilities, and dependent variables were GeneXpert performance (utilization, successful test, error rates, MTB detected, and Rifampicin resistance detected). RESULTS: Only 366 health care facilities are currently implementing and reporting GeneXpert performance, the distribution is 86.9% and 13.1% public and private health care facilities respectively, and only 6.3% of the facilities are primary health care. Of 354,321 test conducted in 2017, 91.5% were successful, and among unsuccessful test 6.8% were errors. The yield was 16.8% MTB detected (54,713) among which 6.8% had Rif resistance. The GeneXpert utilization rate was higher among private health care facilities (55.8%) compared to 33.3% among public health care facilities. There was a statistically significant difference in the number of successful test between public and private health facility-based machines as determined by one-way ANOVA (F(1,2) = 21.81, P = 0.02) and between primary, secondary and tertiary level health facility-based machines (F(1,2) = 41.24, P < 0.01). CONCLUSION: Nigeria with very low TB coverage should rapidly scale-up and decentralize GeneXpert services to the private sector.
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BACKGROUND: Drug-Resistant tuberculosis (DR-TB) is estimated to cause about 10% of all TB related deaths. There is dearth of data on determinants of DR-TB mortality in Nigeria. Death among DR-TB treated cohorts in Nigeria from 2010 to 2013 was 30%, 29%, 15% and 13% respectively. Our objective was to identify factors affecting survival among DR-TB patients in northern Nigeria. METHODS: Demographic and clinical data of all DR-TB patients enrolled in Kano, Katsina and Bauchi states of Nigeria between 1st February 2015 and 30th November 2016 was used. Survival analysis was done using Kaplan-Meier and multiple regression with Cox proportional hazard modeling. RESULTS: Mean time to death during treatment is 19.2 weeks and 3.9 weeks among those awaiting treatment. Death was recorded among 38 of the 147 DR-TB patients assessed. HIV co-infection significantly increased probability of mortality, with an adjusted hazard ratio (aHR) of 2.35, 95% CI: 1.05-5.29, p = 0.038. Treatment delay showed significant negative association with survival (p = 0.000), not starting treatment significantly reduced probability of survival with an aHR of 7.98, 95% CI: 2.83-22.51, p = 0.000. Adjusted hazard ratios for patients started on treatment more than eight weeks after detection or within two to four weeks after detection, was beneficial though not statistically significant with respective p-values of 0.056 and 0.092. The model of care (facility vs. community-based) did not significantly influence survival. CONCLUSION: Both HIV co-infected DR-TB patients and DR-TB patients that fail to start treatment immediately after diagnosis are at significant risk of mortality. Our study showed no significant difference in mortality based on models of care. The study highlights the need to address programmatic and operational issues pertaining to treatment delays and strengthening DR-TB/HIV co-management as key strategies to reduce mortality.