Estimating childhood tuberculosis incidence and under-reporting in Gedeo Zone, Ethiopia: a Bayesian hidden Markov model
1
School of Public Health, Dilla University, Dilla, Ethiopia
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
OBJECTIVES:
To estimate the incidence and under-reported cases of childhood tuberculosis (TB) in rural Ethiopia.
METHODS:
We used a Bayesian hidden Markov model with Spatio-temporal random effects to analyze TB noti- fication data from rural areas between 2018 and 2023. Spatial dependence and HIV infection were included as covariates for estimating TB incidence, while the availability of diagnostic services informed the case detec- tion process. Sensitivity analysis were conducted to assess the robustness of the results with alternative prior distributions.
RESULTS:
Childhood TB incidence in the Gedeo Zone increased from 141 cases per 100,000 population (95% un- certainty interval: 96-193) in 2018 to 157 cases (95% uncertainty interval: 114-207) in 2023. Estimated case detection rates ranged from 56 cases per 100,000 in 2018 to 62 in 2023, indicating substantial under-reporting. Spatial lags of TB notifications predicted incidence in adjacent areas. Sensitivity analysis confirmed result robust- ness.
CONCLUSIONS:
The gap between estimated TB incidence and reported cases highlights the urgent need to strengthen TB surveillance in the study area.
To estimate the incidence and under-reported cases of childhood tuberculosis (TB) in rural Ethiopia.
METHODS:
We used a Bayesian hidden Markov model with Spatio-temporal random effects to analyze TB noti- fication data from rural areas between 2018 and 2023. Spatial dependence and HIV infection were included as covariates for estimating TB incidence, while the availability of diagnostic services informed the case detec- tion process. Sensitivity analysis were conducted to assess the robustness of the results with alternative prior distributions.
RESULTS:
Childhood TB incidence in the Gedeo Zone increased from 141 cases per 100,000 population (95% un- certainty interval: 96-193) in 2018 to 157 cases (95% uncertainty interval: 114-207) in 2023. Estimated case detection rates ranged from 56 cases per 100,000 in 2018 to 62 in 2023, indicating substantial under-reporting. Spatial lags of TB notifications predicted incidence in adjacent areas. Sensitivity analysis confirmed result robust- ness.
CONCLUSIONS:
The gap between estimated TB incidence and reported cases highlights the urgent need to strengthen TB surveillance in the study area.
| eISSN: | 2654-1459 |
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