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The incidence of tuberculosis (TB) has increased in Tigray, Ethiopia due to war and a crippled healthcare system. Although early detection and treatment are critical for TB control, over 30% of TB cases are missed using current diagnostic techniques. Thus, we developed and validated a risk prediction model for pulmonary TB in presumptive cases. In this multicenter cross-sectional study, we consecutively enrolled 907 respondents from primary healthcare facilities in Tigray, northern Ethiopia. We used least absolute shrinkage and selection operator regression to identify variables for the model. Risk scores were generated from the coefficients of multivariable logistic regression. We evaluated the model performance using the area under the curve and calibration plots, and clinical utility using decision curves. Among all respondents, 155 (17%) had GeneXpert-confirmed pulmonary TB. At an optimal cutoff value of 8.5, the model demonstrated a discrimination accuracy of 0.82 (95% CI: 0.78–0.85), a sensitivity of 82.6%, and a specificity of 68.9%. The model had a calibration slope of 0.98 and an intercept of 0.001. The model exhibits acceptable discrimination and calibration performance. Thus, it can be used for screening patients for pulmonary TB in primary healthcare settings where accurate diagnostic resources are limited.

Background Tuberculosis (TB) is Ethiopia’s leading infectious killer disease. The war in the Tigray region of Ethiopia has resulted in the disruption of TB care services. Prediction models are recommended to aid the diagnosis of TB in resource-limited settings. However, the development of such decision-support tools without the participation of end users may not be successful. To inform the tool development, we described barriers to diagnosing TB and identified applicable and desirable parameters for the proposed tool. Methods We conducted a qualitative study between February and June 2023 in two cities in Tigray, Northern Ethiopia. We conducted 12 in-depth interviews and four focus group discussions with healthcare workers (HCWs). Interviews were translated, coded, and analyzed to identify predefined and emergent themes during the thematic analysis. Results Healthcare workers used symptoms, risk factors, signs, and investigations to diagnose TB. However, failure to ask about antibiotic use, the absence and non-affordability of investigations, and patient load were barriers affecting the diagnosis of TB. Most of the classic TB symptoms and their duration were sorted as very important, simple, reliable, generalizable, and desirable indices. In addition, a trial of antibiotics, being chronically sick-looking, having HIV, having a contact history with a TB patient, and an erythrocyte sedimentation rate fulfilled the above criteria. Conclusions In the TB diagnostic process, HCWs account for a variety of data, but they prefer the classic symptoms of TB to heighten their clinical suspicion. Antibiotic trials and some risk factors were also considered reasonable. However, when HCWs have a heavy workload and a shortage of investigations, they experience a suboptimal TB diagnostic process. Hence, appropriate context consideration and care providers’ preferences for parameters will inform tool development.

Background: Although tuberculosis is highly prevalent in low- and middle-income countries, millions of cases remain undetected using current diagnostic methods. To address this problem, researchers have proposed prediction rules. Objective: We analyzed existing prediction rules for the diagnosis of pulmonary tuberculosis and identified factors with a moderate to high strength of association with the disease. Methods: We conducted a comprehensive search of relevant databases (MEDLINE/PubMed, Cochrane Library, Science Direct, Global Health for Reports, and Google Scholar) up to 14 November 2022. Studies that developed diagnostic algorithms for pulmonary tuberculosis in adults from low and middle-income countries were included. Two reviewers performed study screening, data extraction, and quality assessment. The study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. We performed a narrative synthesis. Results: Of the 26 articles selected, only half included human immune deficiency virus-positive patients. In symptomatic human immune deficiency virus patients, radiographic findings and body mass index were strong predictors of pulmonary tuberculosis, with an odds ratio of >4. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. In symptomatic human immune deficiency virus patients, a C-reactive protein level ⩾10 mg/L had a sensitivity and specificity of 93% and 40%, respectively, whereas a trial of antibiotics had a specificity of 86% and a sensitivity of 43%. In smear-negative patients, anti-tuberculosis treatment showed a sensitivity of 52% and a specificity of 63%. Conclusions: The performance of predictors and diagnostic algorithms differs among patient subgroups, such as in human immune deficiency virus-positive patients, radiographic findings, and body mass index were strong predictors of pulmonary tuberculosis. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. A few models have reached the World Health Organization’s recommendation. Therefore, more work should be done to strengthen the predictive models for tuberculosis screening in the future, and they should be developed rigorously, considering the heterogeneity of the population in clinical work.

The Tigray region of Ethiopia, which has been affected by civil war from 2020 to 2022, is facing an increase in tuberculosis in the damaged health system. Our study employed mathematical modeling to predict the incidence of tuberculosis and its trends during the war and in the post-conflict setting of Tigray, Northern Ethiopia. We predicted the incidence of tuberculosis from 2020 to 2025 in Tigray using the SEIRD model in the context of the recent war and compared it with its counterfactual trend in the absence of war. The counterfactual trend was forecasted using an autoregressive integrated moving average (ARIMA) model for stationary time-series data. We performed rolling origin cross-validation for ARIMA and sensitivity analysis for the SEIRD model. The initial tuberculosis data and model parameters were obtained from the Institute for Health Metrics and Evaluation and the literature, respectively. Between 2000 and 2017, the incidence of tuberculosis in Tigray decreased at an annual rate of 3.0%. Shortly before the war, the incidence of tuberculosis in the region was 178 per 100,000 people. In a counterfactual scenario where there was no war, the incidence was projected to decrease to 144.3 in 2022 and 126.3 in 2025. However, owing to the war and siege, the SEIRD-projected incidence of tuberculosis would have increased to 965.5 (95% CI: 958.5-972.7) in 2022 and 372.4 (95% CI: 367.7-376.6) in 2025. Over 800 cases of tuberculosis per 100,000 people were attributed to the war in 2022. In the postwar period, the incidence is projected to decrease by 30% by 2023. The Tigray War reversed a two-decade decline in tuberculosis cases, causing a five-fold increase compared to the no-war scenario. Urgent interventions are needed to support tuberculosis prevention, testing, and treatment, particularly in key and vulnerable populations.

Background More than 70% of the health facilities in Tigray, northern Ethiopia, have been totally or partially destroyed by the recent war in the region. Diagnosis and management of tuberculosis were among many health services that suffered. In this study we assess the status of tuberculosis care in health facilities of Tigray during the recent war and compare it with the immediate pre-war state. Methods Using sequential mixed method, we analyzed and compared the availability of diagnostic services in 69 health facilities and the utilization of tuberculosis care in 50 of them immediately before the war (September-October 2020) and during the war (November-July 2021). TB focal persons in each selected health facility were interviewed to evaluate the status of diagnostic services. Patient service utilization was assessed using health facility registrations. We also compared the average monthly case detection rate of multidrug resistant tuberculosis in the region before and during the war. We computed summary statistics and performed comparisons using t-tests. Finally, existing challenges related to tuberculosis care in the region were explored via in-depth interviews. Two investigators openly coded and analyzed the qualitative data independently via thematic analysis. Results Among the 69 health facilities randomly selected, the registers of 19 facilities were destroyed by the war; data from the remaining 50 facilities were included in the TB service utilization analysis. In the first month of the war (November 2021) the number of tuberculosis patients visiting health facilities fell 34%. Subsequently the visitation rate improved steadily, but not to pre-war rates. This reduction was significant in northwest, central and eastern zones. Tuberculosis care in rural areas was hit hardest. Prior to the war 60% of tuberculosis patients were served in rural clinics; this number dropped to an average of 17% during the war. Health facilities were systematically looted. Of the 69 institutions assessed, over 69% of the microscopes in health centers, 87.5% of the microscopes in primary hospitals, and 68% of the microscopes in general hospitals were stolen or damaged. Two GeneXpert nucleic acid amplification machines were also taken from general hospitals. Regarding drug resistant TB, the average number of multidrug resistant tuberculosis (MDR TB) cases detected per month was reduced by 41% during the war with p-value < 0.001. In-depth interviews with eight health care workers indicated that the main factors affecting tuberculosis care in the area were lack of security, health facility destruction, theft of essential equipment, and drug supply disruption. Conclusion and recommendation Many tuberculosis patients failed to visit health facilities during the war. There was substantial physical damage to health care facilities and systematic looting of diagnostic equipment. Restoring basic public services and revitalizing clinical care for tuberculosis need urgent consideration.