Spatio-Temporal Analysis of Tuberculosis Case Notifications in Gombe State

Abstract

Tuberculosis (TB) still constitutes a critical public health issue in Nigeria, especially in the Northeastern part of the country due to persistent socio-economic, environmental and healthcare factors. Spatio-temporal distribution of TB is vital to improve the quality of surveillance and intervention strategies. In this study, the spatio-temporal distribution of TB was evaluated by analyzing annual number of cases notified in Gombe State, Nigeria for five years (from 2020 to 2024). Choropleth mapping in R was used to create visual maps while global spatial autocorrelation of annual TB data was assessed using Global Moran’s I with a distance-based Gaussian kernel weight function. The findings of this study revealed an increasing trend in the number of cases reported during the period under review. Yamaltu/Deba and Akko LGAs had higher figures than other local government areas. Global Moran’s I did not identify any statistical significance in spatial autocorrelation in the years 2020, 2021, 2022 and 2023. However, in the year 2024, there was a significant positive spatial autocorrelation with Moran’s I = 0.0923 and p-value = 0.024. Thus, there is the likelihood that TB case notifications have become geographically clustered in the local government area as at 2024. The implication these findings demand the need to incorporate spatial intelligence in active case finding strategy.