Identifying Hot Spots of Tuberculosis in Nigeria Using an Early Warning Outbreak Recognition System: Retrospective Analysis of Implications for Active Case Finding Interventions.

BACKGROUND: Undiagnosed tuberculosis (TB) cases are the major challenge to TB control in Nigeria. An early warning outbreak recognition system (EWORS) is a system that is primarily used to detect infectious disease outbreaks; this system can be used as a case-based geospatial tool for the real-time identification of hot spot areas with clusters of TB patients. TB screening targeted at such hot spots should yield more TB cases than screening targeted at non-hot spots. OBJECTIVE: We aimed to demonstrate the effectiveness of an EWORS for TB hot spot mapping as a tool for detecting areas with increased TB case yields in high TB-burden states of Nigeria. METHODS: KNCV Tuberculosis Foundation Nigeria deployed an EWORS to 14 high-burden states in Nigeria. The system used an advanced surveillance mechanism to identify TB patients' residences in clusters, enabling it to predict areas with elevated disease spread (ie, hot spots) at the ward level. TB screening outreach using the World Health Organization 4-symptom screening method was conducted in 121 hot spot wards and 213 non-hot spot wards selected from the same communities. Presumptive cases identified were evaluated for TB using the GeneXpert instrument or chest X-ray. Confirmed TB cases from both areas were linked to treatment. Data from the hot spot and non-hot spot wards were analyzed retrospectively for this study. RESULTS: During the 16-month intervention, a total of 1,962,042 persons (n=734,384, 37.4% male, n=1,227,658, 62.6% female) and 2,025,286 persons (n=701,103, 34.6% male, n=1,324,183, 65.4% female) participated in the community TB screening outreaches in the hot spot and non-hot spot areas, respectively. Presumptive cases among all patients screened were 268,264 (N=3,987,328, 6.7%) and confirmed TB cases were 22,618 (N=222,270, 10.1%). The number needed to screen to diagnose a TB case in the hot spot and non-hot spot areas was 146 and 193 per 10,000 people, respectively. CONCLUSIONS: Active TB case finding in EWORS-mapped hot spot areas yielded higher TB cases than the non-hot spot areas in the 14 high-burden states of Nigeria. With the application of EWORS, the precision of diagnosing TB among presumptive cases increased from 0.077 to 0.103, and the number of presumptive cases needed to diagnose a TB case decreased from 14.047 to 10.255 per 10,000 people.

Training health workers at scale in Nigeria to fight COVID-19 using the InStrat COVID-19 tutorial app: an e-health interventional study.

BACKGROUND: Health worker training is an essential component of epidemic control; rapid delivery of such training is possible in low-middle income countries with digital platforms. METHODS: Based on prior experience with the Ebola outbreak, we developed and deployed a bespoke InStrat COVID-19 tutorial app, to deliver accurate and regularly updated information about COVID-19 to frontline health workers and epidemic response officers across 25 states of Nigeria. The potential effectiveness of this app in training frontline health workers was assessed through online pre- and post-tests and a survey. RESULTS: A total of 1051 health workers from 25 states across Nigeria undertook the e-learning on the InStrat COVID-19 training app. Of these, 627 (57%) completed both the pre- and post-tests in addition to completing the training modules. Overall, there were statistically significant differences between pre- and post-tests knowledge scores (54 increasing to 74). There were also differences in the subcategories of sex, region and cadre. There were higher post-test scores in males compared with females, younger versus older and southern compared with northern Nigeria. A total of 65 (50%) of the participants reported that the app increased their understanding of COVID-19, while 69 (53%) stated that they had applied the knowledge and skills learnt at work. Overall, the functionality and usability of the app were satisfactory. CONCLUSION: Capacity building for epidemic control using e-health applications is potentially effective, can be delivered at minimal cost and service disruption and can serve as a tool for capacity building in similar contexts.