Challenges and solutions by intervention phase.

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.

移动健康筛查项目（Wellness on Wheels，简称WoW）是一种针对高风险人群的移动式系统化肺结核（tuberculosis, TB）筛查模式，该模式将数字化胸部X光摄影与计算机辅助自动检测（computer-aided automated detection, CAD）、慢性咳嗽筛查相结合，用于在尼日利亚的社区、医疗机构及监狱中识别疑似肺结核患者。该模式持续迭代演进，以应对技术、政策及可持续性层面的挑战。研究团队对筛查方法进行了反复优化，以在异质性人群中平衡肺结核检出率与筛查可行性，并对不同时期的性能指标开展对比分析，涵盖筛查总量、风险构成、需筛查人数（number needed to screen, NNS）、需检测人数（number needed to test, NNT）、样本丢失率、肺结核治疗启动率及治疗结局。研究还追踪了为减少诊断流程链中患者流失所采取的各项措施。对于CAD4TB评分（CAD4TB score）≥80分且单次即时GeneXpert检测结果为阴性的人群，研究团队对其开展了为期6个月的随访以评估肺结核感染状态。一种实验性校准方法成功确立了适用的CAD检测阈值。项目团队动员高风险人群及关键利益相关方参与项目，并针对实际运行中出现的问题实时调整运营方案。研究记录了多项指标的逐步改善：日均接诊量从128例提升至140例，目标人群覆盖率从92%升至93%，现场检测比例从84%提升至86%，肺结核治疗启动率从87%升至91%，肺结核治疗成功率从71%提升至85%。针对最高风险人群的重点关注进一步提升了筛查效率：需检测人数（NNT）从8.2±SD8.2降至7.6±SD7.7。在对CAD4TB评分≥80分且初始随机痰涂片结果为阴性的人群开展随访后，研究团队新增了临床诊断路径，在121人年的随访周期内额外检出11例肺结核患者，占比6.3%。基于性能指标的迭代优化，推动了尼日利亚地区兼具可行性、可接受性与高效性的肺结核病例发现工作。较高的CAD评分可在短期内识别亚临床肺结核患者，以及进展为病原学确诊肺结核的高风险人群。