Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis.
BACKGROUND: Reliable and detailed data on the prevalence of tuberculosis (TB) with sub-national estimates are scarce in Ethiopia. We address this knowledge gap by spatially predicting the national, sub-national and local prevalence of TB, and identifying drivers of TB prevalence across the country....
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2023
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| Online Access: | http://purl.org/au-research/grants/nhmrc/1196549 http://hdl.handle.net/20.500.11937/92301 |
| _version_ | 1848765631588990976 |
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| author | Alene, Kefyalew Python, Andre Weiss, Daniel J Elagali, Ahmed Wagaw, Zeleke Alebachew Kumsa, Andargachew Gething, Peter W Clements, Archie CA |
| author_facet | Alene, Kefyalew Python, Andre Weiss, Daniel J Elagali, Ahmed Wagaw, Zeleke Alebachew Kumsa, Andargachew Gething, Peter W Clements, Archie CA |
| author_sort | Alene, Kefyalew |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | BACKGROUND: Reliable and detailed data on the prevalence of tuberculosis (TB) with sub-national estimates are scarce in Ethiopia. We address this knowledge gap by spatially predicting the national, sub-national and local prevalence of TB, and identifying drivers of TB prevalence across the country. METHODS: TB prevalence data were obtained from the Ethiopia national TB prevalence survey and from a comprehensive review of published reports. Geospatial covariates were obtained from publicly available sources. A random effects meta-analysis was used to estimate a pooled prevalence of TB at the national level, and model-based geostatistics were used to estimate the spatial variation of TB prevalence at sub-national and local levels. Within the MBG Plugin Framework, a logistic regression model was fitted to TB prevalence data using both fixed covariate effects and spatial random effects to identify drivers of TB and to predict the prevalence of TB. RESULTS: The overall pooled prevalence of TB in Ethiopia was 0.19% [95% confidence intervals (CI): 0.12%-0.28%]. There was a high degree of heterogeneity in the prevalence of TB (I2 96.4%, P <0.001), which varied by geographical locations, data collection periods and diagnostic methods. The highest prevalence of TB was observed in Dire Dawa (0.96%), Gambela (0.88%), Somali (0.42%), Addis Ababa (0.28%) and Afar (0.24%) regions. Nationally, there was a decline in TB prevalence from 0.18% in 2001 to 0.04% in 2009. However, prevalence increased back to 0.29% in 2014. Substantial spatial variation of TB prevalence was observed at a regional level, with a higher prevalence observed in the border regions, and at a local level within regions. The spatial distribution of TB prevalence was positively associated with population density. CONCLUSION: The results of this study showed that TB prevalence varied substantially at sub-national and local levels in Ethiopia. Spatial patterns were associated with population density. These results suggest that targeted interventions in high-risk areas may reduce the burden of TB in Ethiopia and additional data collection would be required to make further inferences on TB prevalence in areas that lack data. |
| first_indexed | 2025-11-14T11:38:19Z |
| format | Journal Article |
| id | curtin-20.500.11937-92301 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | eng |
| last_indexed | 2025-11-14T11:38:19Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-923012023-10-04T06:33:01Z Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. Alene, Kefyalew Python, Andre Weiss, Daniel J Elagali, Ahmed Wagaw, Zeleke Alebachew Kumsa, Andargachew Gething, Peter W Clements, Archie CA Ethiopia Tuberculosis geospatial meta-analysis mapping prevalence BACKGROUND: Reliable and detailed data on the prevalence of tuberculosis (TB) with sub-national estimates are scarce in Ethiopia. We address this knowledge gap by spatially predicting the national, sub-national and local prevalence of TB, and identifying drivers of TB prevalence across the country. METHODS: TB prevalence data were obtained from the Ethiopia national TB prevalence survey and from a comprehensive review of published reports. Geospatial covariates were obtained from publicly available sources. A random effects meta-analysis was used to estimate a pooled prevalence of TB at the national level, and model-based geostatistics were used to estimate the spatial variation of TB prevalence at sub-national and local levels. Within the MBG Plugin Framework, a logistic regression model was fitted to TB prevalence data using both fixed covariate effects and spatial random effects to identify drivers of TB and to predict the prevalence of TB. RESULTS: The overall pooled prevalence of TB in Ethiopia was 0.19% [95% confidence intervals (CI): 0.12%-0.28%]. There was a high degree of heterogeneity in the prevalence of TB (I2 96.4%, P <0.001), which varied by geographical locations, data collection periods and diagnostic methods. The highest prevalence of TB was observed in Dire Dawa (0.96%), Gambela (0.88%), Somali (0.42%), Addis Ababa (0.28%) and Afar (0.24%) regions. Nationally, there was a decline in TB prevalence from 0.18% in 2001 to 0.04% in 2009. However, prevalence increased back to 0.29% in 2014. Substantial spatial variation of TB prevalence was observed at a regional level, with a higher prevalence observed in the border regions, and at a local level within regions. The spatial distribution of TB prevalence was positively associated with population density. CONCLUSION: The results of this study showed that TB prevalence varied substantially at sub-national and local levels in Ethiopia. Spatial patterns were associated with population density. These results suggest that targeted interventions in high-risk areas may reduce the burden of TB in Ethiopia and additional data collection would be required to make further inferences on TB prevalence in areas that lack data. 2023 Journal Article http://hdl.handle.net/20.500.11937/92301 10.1093/ije/dyad052 eng http://purl.org/au-research/grants/nhmrc/1196549 restricted |
| spellingShingle | Ethiopia Tuberculosis geospatial meta-analysis mapping prevalence Alene, Kefyalew Python, Andre Weiss, Daniel J Elagali, Ahmed Wagaw, Zeleke Alebachew Kumsa, Andargachew Gething, Peter W Clements, Archie CA Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. |
| title | Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. |
| title_full | Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. |
| title_fullStr | Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. |
| title_full_unstemmed | Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. |
| title_short | Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis. |
| title_sort | mapping tuberculosis prevalence in ethiopia using geospatial meta-analysis. |
| topic | Ethiopia Tuberculosis geospatial meta-analysis mapping prevalence |
| url | http://purl.org/au-research/grants/nhmrc/1196549 http://hdl.handle.net/20.500.11937/92301 |