Prevalence and predictive risk factors of hypertension in patients hospitalized in Kamenge Military hospital and Kamenge University teaching hospital in 2019: A fixed effect modelling study in Burundi

Abstract

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.

Un miembro del personal de la Building Foundation for Development ayuda a preparar suministros para su entrega a instalaciones sanitarias y campos de desplazados en Al Jazirah (Sudán), que actualmente acoge a desplazados internos que huyen del conflicto en Jartum (Sudán).

“…For instance, one study primarily focused on investigating the prevalence of hypertension among medical in-patients, but it also discovered that out of the 59 patients with hypertension, 33 of them were also diagnosed with diabetes. 66 We also found high prevalence of acute decompensated presentations. We observed increased chronic disease prevalence within hospitals compared to community settings.…”

“…The pooled prevalence of diabetes in medical settings was 11.9% (95% CI: 9.9–14.3; 15 studies; Table 2 ). 39 , 49 , 50 , 52 , 53 , 62 , 64 , 66 , 69 – 71 , 79 , 84 , 85 , 92 In emergency departments, we found an overlapping prevalence estimate of 9.3% (95% CI: 5.1–16.2; three studies). 58 , 75 , 80 A high degree of heterogeneity of estimates in both settings is noted (medical wards 95% PI: 5.4–24.3; I 2 : 97.6% and emergency departments I 2 : 98.5%; < 5 studies).…”

“…Fourteen studies reported comorbid chronic diseases alongside a primary condition (online repository). 20 Hypertension and diabetes co-prevalence was reported in three studies: 3790/108 357 (3.5%); 64 33/353 (9.3%); 66 and 273/3961 (6.9%). 58 HIV and hypertension co-prevalence was reported in three studies: 665/108 357 (0.6%); 64 6/972 (0.6%); 48 and 140/2316 (6.0%).…”

“…We estimated the prevalence of hypertension from 16 studies. Pooled prevalence was 24.4% (95% CI: 16.7–34.2; 14 studies) 39 , 49 , 50 , 52 , 55 , 64 , 66 , 67 , 69 – 71 , 79 , 84 , 85 in medical wards, and 31.5% (95% CI: 26.4–37.1; two studies) 58 , 80 in emergency departments. A high degree of heterogeneity in prevalence estimates was observed in the medical setting (95% PI: 4.1–71.1; I 2 : 99.7%).…”

Spatial distribution and predictive factors of antenatal care in Burundi: A spatial and multilevel baseline analysis for the third burundian demographic and health survey

“…We manually selected the final model using the backward step-by-step selection method with parsimony principle [ 34 , 35 ]. The Bayesian Information Criterion (BIC) based on adjustment were used [ 36 , 37 ]. The best model is one with low BIC value.…”