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

Autor:	Ogbudebe C; Technical Division, KNCV Tuberculosis Foundation, Abuja, Nigeria., Jeong D; University of Texas, Dallas, TX, United States., Odume B; Technical Division, KNCV Tuberculosis Foundation, Abuja, Nigeria., Chukwuogo O; Technical Division, KNCV Tuberculosis Foundation, Abuja, Nigeria., Dim C; College of Medicine, University of Nigeria Teaching Hospital, Enugu, Nigeria., Useni S; Technical Division, KNCV Tuberculosis Foundation, Abuja, Nigeria., Okuzu O; InStrat Global Health Solutions, Abuja, Nigeria., Malolan C; University of Texas Southwestern Medical Center, Dallas, TX, United States., Kim D; University of Texas, Dallas, TX, United States., Nwariaku F; University of Texas Southwestern Medical Center, Dallas, TX, United States., Nwokoye N; Technical Division, KNCV Tuberculosis Foundation, Abuja, Nigeria., Gande S; Technical Division, KNCV Tuberculosis Foundation, Abuja, Nigeria., Nongo D; Office of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome and Tuberculosis, United States Agency for International Development, Abuja, Nigeria., Eneogu R; Office of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome and Tuberculosis, United States Agency for International Development, Abuja, Nigeria., Odusote T; Office of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome and Tuberculosis, United States Agency for International Development, Abuja, Nigeria., Oyelaran S; Office of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome and Tuberculosis, United States Agency for International Development, Abuja, Nigeria., Chijioke-Akaniro O; National Tuberculosis, Leprosy and Buruli Ulcer Control Program, Federal Ministry of Health, Abuja, Nigeria., Nihalani N; Plus91 Technologies, Delhi, India., Anyaike C; National Tuberculosis, Leprosy and Buruli Ulcer Control Program, Federal Ministry of Health, Abuja, Nigeria., Gidado M; Program Management Unit, KNCV Tuberculosis Foundation, Hague, Netherlands.
Jazyk:	angličtina
Zdroj:	JMIR public health and surveillance [JMIR Public Health Surveill] 2023 Feb 08; Vol. 9, pp. e40311. Date of Electronic Publication: 2023 Feb 08.
DOI:	10.2196/40311
Abstrakt:	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. (©Chidubem Ogbudebe, Dohyo Jeong, Bethrand Odume, Ogoamaka Chukwuogo, Cyril Dim, Sani Useni, Okey Okuzu, Chenchita Malolan, Dohyeong Kim, Fiemu Nwariaku, Nkiru Nwokoye, Stephanie Gande, Debby Nongo, Rupert Eneogu, Temitayo Odusote, Salewa Oyelaran, Obioma Chijioke-Akaniro, Nrip Nihalani, Chukwuma Anyaike, Mustapha Gidado. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 08.02.2023.)
Databáze:	MEDLINE
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