Spatial Clustering of Tuberculosis-HIV Coinfection in Ethiopia at Districts Level

Background. Tuberculosis (TB) is a preventable and treatable disease but it is the leading cause of death among people living with HIV (PLHIV). In addition, the emergence of the HIV pandemic has also had a major impact on TB incidence rates. There are studies in spatial patterns of TB and HIV separately in Ethiopia; there is, however, no information on spatial patterns of TB-HIV coinfection in the country at the districts level at least using yearly data. This paper, therefore, aimed at determining the spatial clustering of TB-HIV coinfection prevalence rates in the country at the districts level on an annual basis over a four-year period, 2015–2018. Methods. District-level aggregated data on the number of TB-HIV infections were obtained from the Ethiopian Federal Ministry of Health for 2015 to 2018. The univariate and bivariate global Moran’s index, Getis-Ord <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:msubsup> <a:mi>G</a:mi> <a:mi>i</a:mi> <a:mi>∗</a:mi> </a:msubsup> </a:math> local statistic, a chi-square test, and a modified <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>t</c:mi> </c:math> -test statistic for Spearman’s correlation coefficient were used to evaluate the spatial clustering and spatial heterogeneity of TB among PLHIV and HIV among TB patients prevalence rates. Results. The district-level prevalence rate of HIV among TB patients was positively and significantly spatially autocorrelated with global Moran’s <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>I</e:mi> </e:math> values range between 0.021 and 0.134 ( <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>p</g:mi> </g:math> value <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mo>&lt;</i:mo> <i:mn>0.001</i:mn> </i:math> ); however, the prevalence of TB among PLHIV was significant only for 2015 and 2017 ( <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" id="M6"> <k:mi>p</k:mi> </k:math> value <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" id="M7"> <m:mo>&lt;</m:mo> <m:mn>0.001</m:mn> </m:math> ). Spearman’s correlation also shows there was a strong positive association between the two prevalence rates over the study period. The local indicators of spatial analysis using the Getis–Ord statistic revealed that hot-spots for TB among PLHIV and HIV among TB patients have appeared in districts of various regions and the two city administrations in the country over the study period; however, the geographical distribution of hotspots varies over the study period. Similar trends were also observed for the cold-spots except for 2017 and 2018 where there were no cold-spots for TB among PLHIV. Conclusions. The study presents detailed knowledge about the spatial clustering of TB-HIV coinfection in Ethiopia at the districts level, and the results could provide information for planning coordinated district-specific interventions to jointly control both diseases in Ethiopia.