Background Despite the World Health Organization efforts to expand access to the tuberculosis treatment, multidrug resistant tuberculosis (MDR-TB) remains a major threat. MDR-TB represents a challenge for clinicians and staff operating in national tuberculosis (TB) programmes/centres. In sub-Saharan African countries including Burundi, MDR-TB coexists with high burden of other communicable and non-communicable diseases, creating a complex public health situation which is difficult to address. Tackling this will require targeted public health intervention based on evidence which well defines the at-risk population. In this study, using data from two referral anti-tuberculosis in Burundi, we model the key factors associated with MDR-TB in Burundi. Methods A case-control study was conducted from 1stAugust 2019 to 15th January 2020 in Kibumbu Sanatorium and Bujumbura anti-tuberculosis centres for cases and controls respectively. In all, 180 TB patients were selected, comprising of 60 cases and 120 controls using incidence density selection method. The associated factors were carried out by mixed effect logistic regression. Model performance was assessed by the Area under Curve (AUC). Model was internally validated via bootstrapping with 2000 replications. All analysis were done using R Statistical 3.5.0. Results MDR-TB was more identified among patients who lived in rural areas (51.3%), in patients’ residence (69.2%) and among those with a household size of six or more family members (59.5%). Most of the MDR-TB cases had already been under TB treatment (86.4%), had previous contact with an MDR-TR case (85.0%), consumed tobacco (55.5%) and were diabetic (66.6 %). HIV prevalence was 32.3 % in controls and 67.7 % among cases. After modelling using mixed effects, Residence of patients (aOR= 1.31, 95%C: 1.12-1.80), living in houses with more than 6 family members (aOR= 4.15, 95% C: 3.06-5.39), previous close contact with MDR-TB (aOR= 6.03, 95% C: 4.01-8.12), history of TB treatment (aOR= 2.16, 95% C: 1.06-3.42), tobacco consumption (aOR = 3.17 ,95% C: 2.06-5.45) and underlying diabetes’ ( aOR= 4.09,95% CI = 2.01-16.79) were significantly associated with MDR-TB. With 2000 stratified bootstrap replicates, the model had an excellent predictive performance, accurately predicting 88.15% (95% C: 82.06%-92.8%) of all observations. The coexistence of risk factors to the same patients increases the risk of MDR-TB occurrence. TB patients with no any risk factors had 17.6% of risk to become MDR-TB. That probability was respectively three times and five times higher among diabetic and close contact MDR-TB patients. Conclusion The relatively high TB’s prevalence and MDR-TB occurrence in Burundi raises a cause for concern especially in this context where there exist an equally high burden of chronic diseases including malnutrition. Targeting interventions based on these identified risk factors will allow judicious channel of resources and effective public health planning.

Introduction Hypertension is a major threat to public health globally. Especially in sub-Saharan African countries, this coexists with high burden of other infectious diseases, creating a complex public health situation which is difficult to address. Tackling this will require targeted public health intervention based on evidence that well defines the at risk population. In this study, using retrospective data from two referral hospitals in Burundi, we model the risk factors of hypertension in Burundi. Materials and methods Retrospective data of a sample of 353 randomly selected from a population of 4,380 patients admitted in 2019 in two referral hospitals in Burundi: Military and University teaching hospital of Kamenge. The predictive risk factors were carried out by fixed effect logistic regression. Model performance was assessed with Area under Curve (AUC) method. Model was internally validated using bootstrapping method with 2000 replications. Both data processing and data analysis were done using R software. Results Overall, 16.7% of the patients were found to be hypertensive. This study didn’t showed any significant difference of hypertension’s prevalences among women (16%) and men (17.7%). After adjustment of the model for cofounding covariates, associated risk factors found were advanced age (40–59 years) and above 60 years, high education level, chronic kidney failure, high body mass index, familial history of hypertension. In absence of these highlighted risk factors, the risk of hypertension occurrence was about 2 per 1000 persons. This probability is more than 90% in patients with more than three risk factors. Conclusion The relatively high prevalence and associated risk factors of hypertension in Burundi raises a call for concern especially in this context where there exist an equally high burden of infectious diseases, other chronic diseases including chronic malnutrition. Targeting interventions based on these identified risk factors will allow judicious channel of resources and effective public health planning.

BackgroundTuberculosis is a serious global public health problem, and it is in the top 10 causes of mortality in low and middle income countries. MDR-TB and XDR-TB still being a challenge for clinicians and staff operating in national TB programs .Particularly in sub-Saharan African countries, it particularly coexists with high burden of other infectious and no communicable diseases, creating a complex public health situation which is difficult to address. Tackling this will require targeted public health intervention based on evidence well defines the at risk population. In this study, using data from two referral anti tuberculosis in Burundi, we model the determinants factors associated with MDR-TB in Burundi.MethodsProspective data of a sample of 180 tuberculosis randomly selected from a population of patients admitted in 2019 in two referral anti tuberculosis centres in Burundi: Kibumbu Sanatorium Centre and Bujumbura anti-tuberculosis Center. The associated factors were carried out by fixed and random effect logistic regression. Model performance was assessed by Area under Curve (AUC). Model was internally validated via bootstrapping with 1000 replications. All analysis were conducted in R 3.5.0.ResultsOver 180 participants of the study, 60 patients of them were MDR-TB and 120 were Drug Susceptible. High MDR-TB is observed in patients who lives in rural zone (51,3%),in collective residence (69,2%) ,in house with more than six people (59,5%), many people who live in the same room(70,0%) ,in patients with TB treatment history(86.4%) and in diabetics people(66.6%).HIV was 32.3% and 67.7% positive respectively in MDR-TB patients and Drug susceptible patients. More than half of cases (75%) and controls (73.3%) belonged to the age group of ≤ 45 years.The Pearson's Chi-squared test with Yates' continuity correction showed the house’s rooms (p = 0,010), People by house (p < 0,001), currently workers (p = 0,019), MDR-TB close contact (p < < 0.001), Collective residence (p = 0,004), Residence area (p = 0,007) and tobacco consumption (< 0.001) were not independent with MDR-TB.After modelling using fixed and random effects, Residence (AOR: 1.31, 95%CI: 1.12–1.80), People by house (AOR: 4.15, 95% CI: 3.06–5.39), MDR-TB close contact (AOR: 6.03, 95% CI: 4.01–8.12), History TB treatment (AOR: 2.16, 95% CI: 1.06–3.42), Tobacco consumption (AOR : 3.17 ,95% CI: 2.06–5.45) and Diabetes( AOR: 4.09,95% CI : 2.01–16.79) were statistically associated with MDR-TBs. With 2000 stratified bootstrap replicates, the model had an excellent predictive performance (AUC), accurately predicting 88.15%(95% CI: 82.06%-92.8%) of all observations. Drug susceptible patients with no close contact had the low probability around 10% to develop MDR-TB.ConclusionThe relatively high prevalence of tuberculosis and associated factors of MDR-TB in Burundi raises a call for concern especially in this context where there exist an equally high burden of chronic diseases, chronic malnutrition, HIV/SIDA and others infectious diseases. Targeting interventions based on these identified factors will allow judicious channel of resources and effective public health planning.