Mapping tuberculosis prevalence in Ethiopia: protocol for a geospatial meta-analysis. Issue 5 (25th May 2020)

• Record Type: Journal Article
• Authors: Alene, Kefyalew Addis; Wagaw, Zeleke Alebachew; Clements, Archie C A
• Is Part Of: BMJ open. Volume 10:Issue 5(2020)
• Publication Date: 2020-05-25
• Subjects: tuberculosis -- public health -- epidemiology; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2019-034704
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: Tuberculosis (TB), a major public health concern in Ethiopia, is distributed heterogeneously across the country. Mapping TB prevalence at national and subnational levels can provide information for designing and implementing control strategies. Data for spatial analysis can be obtained through systematic review of the literature, and spatial prediction can be done by meta-analysis of published data (geospatial meta-analysis). Geospatial meta-analysis can increase the power of spatial analytic models by making use of all available data. It can also provide a means for spatial prediction where new survey data in a given area are sparse or not available. In this report, we present a protocol for a geospatial meta-analysis to investigate the spatial patterns of TB prevalence in Ethiopia. Methods and analysis: To conduct this study, a national TB prevalence survey, supplemented with data from a systematic review of published reports, will be used as the source of TB prevalence data. Systematic searching will be conducted in PubMed, Scopus and Web of Science for studies published up to 15 April 2020 to identify all potential publications reporting TB prevalence in Ethiopia. Data for covariates for multivariable analysis will be obtained from different, readily available sources. Extracted TB survey and covariate data will be georeferenced to specific locations or the centroids of small administrative areas. A binomial logistic regression model will be fitted to TB prevalence data using both fixed covariate effects and random geostatistical effects based on the approach of model-based geostatistics. Markov Chain Monte Carlo simulation will be conducted to obtained posterior parameter estimations, including spatially predicted prevalence. Ethics and dissemination: Ethical approval will not be required for this study as it will be based on deidentified, aggregate published data. The final report of this review will be disseminated through publication in a peer-reviewed scientific journal and will also be presented at relevant conferences.