High rates of undiagnosed diabetes mellitus among patients with active tuberculosis in Addis Ababa, Ethiopia. (May 2022) - Jerene, Degu; Muleta, Chaltu; Ahmed, Abdurezak; Tarekegn, Getahun; Haile, Tewodros; Bedru, Ahmed; Gebhard, Agnes; Wares, Fraser
Abstract: Background: Tuberculosis (TB) and diabetes mellitus (DM) have negative synergistic impact on each other. Global guidelines recommend collaborative efforts to address this synergy, but implementation has been slow. Part of the reason is lack of adequate evidence on the operational feasibility of existing tools and mechanisms of collaboration. The objective of this study was to assess the yield of DM screening among TB patients using risk scoring tools combined with blood tests as a feasible strategy for early detection to improve TB/DM treatment outcomes. Methods: Between September 2020 and December 2021, we conducted a cross-sectional study among patients receiving TB treatment in public health facilities in Addis Ababa, Ethiopia. Trained health workers collected data on symptoms and risk scoring checklists before testing for random and fasting blood glucose levels. We used logistic regression analyses techniques to determine factors associated with increased DM detection. A receiver-operating characteristic curve was constructed to determine the performance of the risk scoring checklist. Results: Of 2381 TB patients screened, 197 (8.3%) had DM of which 48.7% were newly diagnosed. Having a family history of DM predicted DM with Odds Ratio (OR) of 5.36 (95% Confidence Interval, [3.67, 7.83]) followed by age ≥ 45 years (OR = 4.64, [3.18, 6.76]). Having one or more "symptoms" of DM was a significant predictor (OR 2.88, 95% CI, 2.06–4.01). Combining risk scores with symptoms predicted DM diagnosis with sensitivity of 94.7%, but specificity was low at 29.4%. In patients with known treatment outcome status, death rate was high. Conclusions: Almost a half of TB patients with DM did not know their status. A simple tool that combined risk factors with symptoms accurately predicted a subsequent diagnosis of DM. Such tools can help avoid high rates of death among TB patients suffering from DM through early detection.