Original Research

Assessment of tuberculosis case notification rate: spatial mapping of hotspot, coverage and diagnostics in Katsina State, north-western Nigeria

Makplang Milaham, Margo van Gurp, Oluwafemi J. Adewusi, Oluwaseun Chidera Okonuga, Hermen Ormel, Tristan Bayly, Solomon Adeojo, Abdulrasheed Yusuf, Mustapha Gidado
Journal of Public Health in Africa | Vol 13, No 3 | a427 | DOI: https://doi.org/10.4081/jphia.2022.2040 | © 2024 Makplang Milaham, Margo van Gurp, Oluwafemi J. Adewusi, Oluwaseun Chidera Okonuga, Hermen Ormel, Tristan Bayly, Solomon Adeojo, Abdulrasheed Yusuf, Mustapha Gidado | This work is licensed under CC Attribution 4.0
Submitted: 11 April 2024 | Published: 07 September 2022

About the author(s)

Makplang Milaham, Institute of Human Virology, Abuja, Nigeria
Margo van Gurp, KIT Royal Tropical Institute, Amsterdam, Netherlands
Oluwafemi J. Adewusi, KIT Royal Tropical Institute, Amsterdam, Netherlands
Oluwaseun Chidera Okonuga, Institute of Human Virology, Abuja, Nigeria
Hermen Ormel, KIT Royal Tropical Institute, Amsterdam, Netherlands
Tristan Bayly, KIT Royal Tropical Institute, Amsterdam, Netherlands
Solomon Adeojo, KIT Royal Tropical Institute, Amsterdam, Netherlands
Abdulrasheed Yusuf, Katsina STBLCP, Katsina, Nigeria
Mustapha Gidado, KNCV, Hague, Netherlands

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Abstract

Tuberculosis (TB) is prevalent in Nigeria, and Katsina, along with other 12 states in the country, accounts for a high proportion of unnotified TB cases: constituting the high priority-intervention States in the country. Interventions focused on TB detection and coverage in the state could benefit from a better understanding of hotspot Local Government Areas (LGAs) that trigger and sustain the disease. Therefore, this study investigated the spatial distribution of TB Case Notification Rates (CNRs), diagnostics and coverage across the LGAs. Using 2017 to 2019 TB case finding data, the geocoordinates of diagnostic facilities and shapefiles, a retrospective ecological study was conducted. The data were analysed with QGIS and GeoDa. Moran’s I and LISA were used to locate and quantify hotspots. The coverage of microscopy and GeneXpert facilities was assessed on QGIS using a 5 km and 20 km radius, respectively. The CNR in the state, and 29 of the 34 LGAs, increased steadily from 2017 to 2019. Hotspots of high CNRs were also identified in 2017 (Moran’s I=0.106, p-value=0.090) and 2018 (Moran’s I=-0.020, p-value=0.370). While CNRs increased along with presumptive TB rates across most LGAs over the years, the positivity yield and bacteriological and Xpert diagnostic rates decreased. Bacteriological and GeneXpert coverage were 78% and 49% respectively. Additionally, only 51% of the state’s population lived within 20km of a GeneXpert facility. These results suggest that TB program interventions had some positive impact on the CNR, however, diagnostic facilities need to be equitably distributed and more innovative approaches need to be explored to find the missing cases.


Keywords

Tuberculosis; Case Notification Rates; Spatial Analysis; Hotspot; Katsina State; Nigeria

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