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.

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.

Introduction

Hypertension corresponds to a permanently raised blood pressure in arteries and arterioles [1]. It is defined as a systolic blood pressure equal or above 140 mmHg and /or a diastolic blood pressure above 90 mmHg [1, 2]. Hypertension is a threat to global public health [3] as it tires vessels, the heart and causes damage to artery walls [4, 5]. It is a major risk factor for cardiovascular diseases [6]with high morbidity and mortality rate [6]. If not identified and treated early, arterial hypertension may result in serious complications including strokes, coronary artery, kidney and hypertensive heart diseases [1, 7]. These complications are among the leading causes of mortality in the world. Approximately, cardiovascular diseases account for 17.8 million death in 2017 [8, 9], nearly 1/3 of total. More than three quarters were in low and middle-income, countries (LMICs) [9]. Hypertension complications, cardiovascular diseases account 9.4 million (52.8%) every year [10]. Hypertension is responsible for 45% deaths due to heart disease and 51% stroke related deaths [11, 12]. Premature death and health care expenditure for treatments due the hypertension puts an economic toll on families and pushes many into poverty [13, 14]. At the macro level, these high expenses and human losses significantly impacts on economic growth and reduces productivity [15, 16].

The prevalence of hypertension in adults was 40% only in 2015, with an estimated 1.13 billion people living with different forms of hypertension [17, 18]. Data from the World Health Organisation Global Health Observatory Repository [19] found the highest prevalence of hypertension in the Africa region (46%) followed by the America (35%) and other regions, majority of whom, were undiagnosed and untreated [20, 21]. In sub-Saharan Africa (SSA), as other settings, hypertension has been associated with lifestyles, diets, physical inactivity urbanization and socio economic status [22]. More than 125 million people with hypertension are expected by the year 2025 in SSA alone [23]. By year 2030, hypertension and other non-communicable diseases are projected to surpass communicable diseases as the top of mortality causes on the continent [24]. From 2011 to 2025, the cumulative lost output with non-communicable diseases is projected to be US$7.28 trillion in LMICs which is approximately a loss of US$500 billion per year [25]. Cardiovascular diseases including hypertension account for nearly half this cost [26]. Despite this, SSA faces a major problem of early screening, timely treatment and control of hypertension [27, 28]. Yet, studies to understand the epidemiology and associated risk factors of hypertension in the context of Burundi are lacking, prompting the conduct of this study. Therefore, in this study, we determined the overall prevalence of hypertension. We also evaluated its predictive risk factors and its occurrence probability based on risk factors. Knowing these factors could support effective public health planning and facilitate policy makers to formulate plausible policies towards the fight against hypertension and its complications.

Materials and methods

Ethics statement

This investigation is in accordance with Helsinki’s Declaration and approved by the institutional Research Ethics committees. Written permissions were acquired from ethical committee of the University teaching hospital of Kamenge and Kamenge military hospital to use retrospective data for this study. In effort to secure identity of patients, patient’s information were anonymised and replaced by a unique codes. Data are used for the unique objective of this study and will be destroyed after paper publication.

Study description

We employed a cross-sectional study. The study was conducted in two tertiary reference hospitals of Burundi: University teaching hospital of Kamenge and Kamenge Military hospital in different departments. Patients were hospitalized for different medicals diseases including infectious diseases, cardiovascular diseases and others. Based on socio-demographic and clinical characteristics associated with hypertension, the study used data from internal medicine services with or not haemodialysis sessions (for patients with kidney failure) where majority of patients with hypertension and others cardiovascular diseases are admitted Data were collected among patients hospitalized from January 2019 to December 2019 in these services. These hospital were firstly chosen because they are ranked as tertiary national reference and their receive patients from all over the country. Secondly, one is private hospital and another a public hospital.

Sampling methods

This study targeted a population of 4380 patients hospitalized in internal medicine services and intensive care unit (with chronic kidney failure) during the whole study period in the both hospitals. Majority of these patients are hospitalized there because this areas are located together with haemodialysis unit. Among them, a sample size of 353 patients have been selected. The inclusion probability were the same for patients admitted in the same service of the selected hospital and was calculated as ratio of admitted patients in the service over all admissions of all services. Patients were randomly selected in the service using their specific identifiers of admission. The minimal sample size was calculated based on the quintile of the normal distribution with at 95% of confidence (1.96), the population size, the prevalence of hypertension and the acceptable margin (5%) [28]. As the prevalence of hypertension is unknown in Burundi, a value of p = 0.5 was selected. According to these parameters, the minimal size of the sample was 353 patients. Basically, a respondent was selected if he had of the following criteria: Admitted in targeted service in the period of 2019, measured diastolic and systolic blood pressure three times.

Normally, after resting in a quiet environment for 5–10 minutes, the patient took a sitting position with his legs naturally flat and his right hand placed at heart height. An Omron HEM-705P arm-type electronic sphygmomanometer were used to measure the systolic blood pressure, diastolic blood pressure and resting heart rate of the brachial artery of the right upper arm at least twice, with an interval of no less than two minutes. The average of the two readings was taken.

Data collection

In total, a population of 4380 patients stratified in 2 groups in both hospitals were targeted. A random sampling method have been used with proportional allocation to the admitted patients by service and by hospital. Data were collected using a standard-structured questionnaires based on hospital record. We collected socio-demographics, anthropometrics and clinical characteristics as summarized in . All patients with hypertension appeared before chronic kidney failure were excluded in the study. In this context, chronic kidney failure is predictor of hypertension.

Outcome and independent variables

In this study, hypertension was considered as outcome variable and was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg obtained after three successive measurements or patients on hypertension’s treatment. Pre-hypertension was defined as BP levels of 120 to 139/80 to 89 mm Hg [29]. Body mass index (BMI) was calculated as the weight in kilograms to square of height in meters and was categorized in into; underweight (BMI<18.5 kg/m2), normal (BMI: 18.5–24.9 kg/m2), overweight (BMI: 25.0–29.9 kg/m2) and obesity (BMI ≥ 30 kg/m2). During data analysis, BMI was categorized in three clusters (Under and normal weight, overweight, obesity). Others independents variables were classified as age group (in years) (15–39 years, 40–60 years; ≥60) sex (Men, Women), residence (Urban, Rural), educational level (Primary or less, Secondary, University), alcohol consumption (Yes/No), smoking (Yes/No), diabetes (Yes/No), existing cardiovascular impairment (Yes/No), familial history of hypertension (Yes/No) and chronic kidney failure (Yes/No). As chronic kidney failure can be predictor or consequence of hypertension, only patients with history of kidney failure before hypertension were included in the study.

Statistical analysis

Data analysis were undertaken in different steps: descriptive analysis, binary logistic modelling with fixed effects, power predictive evaluation of final (saturated) model and probabilities prediction. Hypertension associated risk factors were assessed using univariate and multivariate logistic regressions. We calculated the odds ratios (ORs) at 95% confidence level for each covariate to identify predictors of hypertension. Significant variables on 15% threshold were used in multivariable logistic modelling to determine a combined effect on the outcome. The likelihood ration test, the score test and the Wald test were used to determine significance of independent variables on the outcome [30]. A threshold of 5% have been used. To select the best model for this study, we use backward method with parsimony principle [31]. The Akaike Information Criterion (AIC) based on adjustment were used [32]. The best model is one with low AIC value.

The relevance of the final model to make prediction was assessed by Pearson residuals test. The Receiving Operating characteristics (ROC) and Area under Curve (AUC) were respectively used to compare and evaluate performance and predictive power of the model. Furthermore, the ROC was used to determine the discriminatory performance of the model, determining the false positive and false negative rates. The Mann Whitney statistics method showed that the two distributions were offset: normotensive people had an average higher scores than hypertensive people. Each individual’s score was ranked in ascending order. Thus, the AUC which determined the number of observations accurately predicted was calculated based on the number of normotensive patients, the number of hypertensive patients and the ranks of normotensive patients. If AUC = 0.5 no discrimination, 0.7≤AUC≤0.8 acceptable discrimination, 0.8≤AUC<0.9 excellent discrimination, AUC≥0.9 exceptional discrimination. To calculate the error of predictions, the cross validation with K -Fold method have been used. We used 10-folds. In validating internal performance of the model, a bootstrapping procedure with 2000 replications was done. Statistical significance was considered when p-value ≤ 0.05.

Influents points of the model were assessed using Hoaglin and Welsh criterion, methods based on model’s parameters and the sample size. The adjustment used was based on the residuals test. The Kolmogorov test was used to test the model adequation. For these test, a threshold of 5% was used. Then, the foreign, rpart and forest model packages were used to carry out results in this study [33]. Data processing and data analysis were done using R.3.5.0 software.

Results

In this study, the overall prevalence of hypertension was 16.7% (). Comparatively, men’s and in women’s prevalence were respectively 16.0% and 17.7% with insignificant difference (X2 = 0.0725, df = 1, p = 0.788). This prevalence was 2 times higher in overweight patients than normal and underweight patients and 3 times higher in diabetic patients than no diabetics patients. The pre hypertension was observed in 73.4% of patients. The high proportions above the overall prevalence were observed in people with existing cardiovascular impairment, married, patients aged between 40–60 years or 60 years and above and over, patients with chronic kidney failure, men patients, smokers patients, obese patients and patients with secondary or university level (). The table below showed descriptive analyses of patients’ characteristics.

Logistic regression modelling of predictive risk factors of hypertension

The Table 2 showed the univariate logistic regressions where hypertension was modelled by each explanatory variable.

Table 2

Univariates logistic regressions of hypertension’s factors.

Variables	Category	OR	95% CI	p
Educational	Primary or less	Reference
level	Secondary	1.9	[1.0–3.9]	0.054
	University	3.7	[1.6–8.4]	0.002
Smoking	No	Reference
	Yes	2.1	[0.9–05.0]	0.085
Diabetes	No	Reference
	Yes	6.7	[3.7–12.3]	<0.001
Age group	15–39 years	Reference
	40–59 years	12.2	[4.2–52.1]	<0.001
	≥60 years	22.5	[17.6–96.8]	<0.001
Currently working	No	Reference
	Yes	0.9	[0.6–1.7]	0.944
Residence	Rural	Reference
	Urban	1.1	[0.6–1.8]	0.868
Body mass index	Underweight and normal	Reference
	Overweight	2.5	[1.3–4.9]	0.007
	Obesity	4.5	[1.7–10.9]	0.001
Sex	Women	Reference
	Men	0.8	[0.6–1.9]	0.451
Marital status	Unmarried	Reference
	Married	2.4	[1.2–5.7]	0.027
Existing cardiovascular impairment	No	Reference
	Yes	1.2	[0.5–4.3]	0.720
Alcohol	No	Reference
	Yes	1.1	[0.6–1.9]	0.794
Chronic Kidney	No	Reference
failure	Yes	13.6	[5.8–39.9]	<0.001
Familial History	No	Reference
of Hypertension	Yes	9.7	[5.2–18.4]	<0.001

OR: Odd ratio, CI: Confident interval, p: p-value.

Eight variables were significantly associated with hypertension in univariate regression (). Those variables were Educational level{University (OR: 3.7, 95% CI: 1.6–8.4, p<0.002); Secondary (OR: 1.9, 95% CI: 1.1–3.9, p: 0.045), Diabetes (OR: 6.7, 95% CI: 3.37–12.3, p<0.001), Age {(40–59 years (OR: 12.2, 95% CI: 4.2–22.1, p<0.001); 60 and above (22.5, 95% CI: 7.6–26.8, <0.001)}, Body mass index {Overweight (OR: 2.5, 95% CI: 1.3, 4.9, p: 0.007); Obesity (OR: 4.5, 95% CI: 1.7–10.9, p: 0.001)}, Marital status (OR: 2.4, 95% CI: 1.2–5.7, p: 0.027), Chronic kidney failure (OR: 13.6, 95% CI: 5.8–19.9, p<0.001) and Family hypertension history (OR: 9.7, 95% CI: 5.2–18.4, p<0.001). All these variables were introduced in a multivariate analysis. In addition, other relevant variables were introduced in the multivariate model as well as sex, smoking, alcohol and existing cardiovascular impairment. The identification of variables based on AIC and backward selection methods showed six variables significantly associated with hypertension.

Then, the equation of saturated model derived from the above multivariate model include the Educational level, Body mass index, Chronic kidney failure and familial history of hypertension. After controlling the cofounding variables as indicated above, adjusted odds ratio and their lower and upper bound along with a corresponding p-value were derived. Table 4 detailed these results.

Table 4

Predicted probabilities.

Ind.	Education level	Smoking	Age group	BMI	CKF	FHH	p
1*	Primary or less	no	15–39 years	Normal	no	no	0.002
2	Primary or less	yes	15–39 years	Normal	no	no	0.005
3	Primary or less	no	40–59 years	Normal	no	no	0.124
4	Primary or less	no	≥60 years	Normal	no	no	0.916
5	Primary or less	no	15–39 years	Overweight	no	no	0.016
6	Primary or less	no	15–39 years	Obesity	no	no	0.131
7	Primary or less	no	15–39 years	Normal	yes	no	0.005
8	Primary or less	no	15–39 years	Normal	no	yes	0.009
9	Primary or less	yes	40–59 years	Normal	no	no	0.291
10	Primary or less	yes	40–59 years	Overweight	no	no	0.789
11	Primary or less	yes	≥60 years	Overweight	no	no	0.997
12	Primary or less	no	≥60 years	Overweight	yes	yes	0.999
13	Secondary	yes	40–59 years	Obesity	yes	yes	0.999
14	Secondary	yes	≥60 years	Obesity	yes	yes	0.999
15	University	yes	≥60 years	Overweight	yes	yes	0.999
16	University	yes	≥60 years	Obesity	yes	yes	≈1,00

Ind.: individual, BMI: Body Mass Index, CKF: Chronic Kidney Failure, FHH: Familial hypertension

*Reference individual, p: probability of becoming hypertensive based on combination of risk factors.

The Table 3 showed that advanced age and education level were the most associated risk factor of hypertension. Chronic kidney failure, overweight, obesity, familial hypertension history were significantly associated with hypertension. Patients with secondary level had near five times higher (AOR: 4.9, 95% CI: 2.1–12.4, p<0.001) the risk of hypertension occurrence than patients with primary or less educational level. That risk was more than 8 times higher (AOR: 8.6, 95% CI: 2.9–27.3, p<0.001) in patients with university level than patients with none or primary level. Even if smokers had more than two times high the risk (AOR: 2.9, 95% CI: 0.8–9.3, p: 0.073) to become hypertensive than non-smokers, tobacco consumption were not significantly associated with hypertension. Tobacco consumption remained in saturated model because it minimized the AIC criterion than the model with no tobacco use as explanatory variable. Patients aged between 40–59 years had more than 5 times (AOR: 5.9, 95% CI: 1.9–26.8, p: 0.007) the risk to become hypertensive than patients aged under 40 years. That risk was more than 12 times higher (AOR: 12.9, 95% CI: 3.4–65.8, p<0.001) in patients aged over 60 years than young patients under 40 years. Patients with family history of hypertension had near three times higher (AOR: 2.9, 95% CI: 1.3–6.7, p: 0.007) the risk to become hypertensive than patients with non familial hypertension history. This risk was more than two times (AOR: 2.5, 95% CI: 1.1–5.7, p: 0.037) in overweight patients than normal and underweight patients. That risk of hypertension was more than 3 times higher (OR: 3.7, 95% CI: 1.2–11.5, p: 0.023) among obese patients than normal and underweight. Patients with chronic kidney failure had approximately 5 times (AOR: 4.9, 95% CI: 1.8–15.6, p: 0.003) the risk of hypertension than normal patients. The Wald test (X2 = 86.7, df = 8, p<0.001) rejected the null hypothesis and therefore to confirm the alternative hypothesis stating that there is at least one estimate significantly different to zero. This suggests an overall significance of the model. Pearson residuals test of (X2 = 266.17, df = 344, p = 0.99) was done and showed the model was well adjusted on the observations. A McFadden statistic (R2: 0.4) also indicated that this model had a good fit.

Table 3

Results of multivariate logistic regression.

Variables	Category	N	H-	H+	AOR	95% CI	p
Education level	Primary or less	137	123	14	Reference
	Secondary	165	130	35	4.6	[1.9–11.5]	<0.001
	University	51	36	15	8.5	[2.9–27.2]	<0.001
Smoking	No	325	274	51	Reference
	Yes	28	20	8	2.9	[0.9–9.3]	0.073
Age group	15–39 years	138	135	3	Reference
	40–59 years	131	103	28	5.9	[1.8–26.8]	0.007
	≥60 years	84	56	28	12.9	[3.4–65.8]	<0.001
Body mass	Underweight and normal	270	236	34	Reference
index	Overweight	60	44	16	2.5	[1.1–5.7]	0.037
	Obesity	23	14	9	3.7	[1.2–11.5]	0.023
CKF	No	169	164	5	Reference
	Yes	184	130	54	4.9	[1.8–15.6]	0.003
FHH	No	289	262	27	Reference
	Yes	64	32	32	2.9	[1.3–6.7]	0.014

H-: Normotensive people, H+: Hypertensive people, AOR: Adjusted Odd ratio, CI: Confidence interval, p: p-value, CKF: Chronic kidney failure, FHH: Familial history of hypertension.

The influential points’ analysis based on Hoaglin and Welsh criterion showed that only 9 points were influential. Also, Cook’s distance showed that only 3 points (108, 114 and 199) were outliers, which means the influential points were not numerous. Studentized residues analysis (Fig 1) showed that 97% (343/353) were between -2 and 2. Observations with residues (Fig 2) greater than 2 were ten (9, 34, 105, 108, 114, 199, 212, 232, 265, 273). Then, any observation with studentized residue were less than -2, indicating that the number of outliers were negligible.

Fig 1

Studentized residuals.

Fig 2

Welsh-Kuh’s distance.

Cross validation and probabilities predictions

The Fig 3 showed the ROC curve and area under curve (AUC). A bootstrap method, with 2000 replications, was used to determine an AUC of 88.6% (95% CI: 84.1%-92.3%) which suggested an excellent discrimination (Fig 3). This implies that saturated model had an excellent predictive power and probabilities accurately determine patients with hypertension based on identified characteristics. In supervised learning, the resubstitution error rate was 13.3%. This error rare is supposed to indicate the performance of the model when it is employed on the population. However, we know, being estimated on learning data, it was biased, and under estimating true error rate using cross-validation method which is the best quality estimator than the substitution method. The cross-validation error rate was 16.1%.

Fig 3

Area under curve.

The table showed the predicted probabilities of becoming hypertensive based on different scenarios of having either a risk factor or a combination of risk factors. The first individual was the one without risk factors. He was considered as the reference individual. Sixteen predictions were generated from reference individual to whom with all hypertension risk factors. The table below showed increasing probabilities predictions from zero factors to all factors.

In absence of the risk factors highlighted above, the reference individual have 2 per 1000 the chance to become hypertensive. This probability goes from simple to more than double among smokers and 6 times higher among adult patients aged between 40 and 60 years. Then, no smokers patients with normal weight, aged between 40 and 59 years and the young obese patients (19–40 years) had probability to become hypertensive between 12% and 15%. Comparatively to the reference patient, this probability was more than 40 times higher among patients aged between 60 years and over. If the individual aged between 40 and 60 was also smoker, his probability to become hypertensive increased near 15 times (29.1%). If that patient was overweight, that probability increased from 29.1% to 78.6%. Furthermore, if the overweight patients aged between 60 and over were also smoked, their chance to develop hypertension was 99.7%. That probability was very high among patients with coexistence of more than three risk factors (99%).

The highest probability was observed among patients who were at the same time with university education level smokers, chronic kidney failure and born in the hypertensive family. The 11th, 12th, 13th, 14th, 15th and 16th patient were respectively at 99.7%, 99.9%, 99.9%, 99.9% and 100% risk of hypertension ().

Discussion

In this study, we determined the prevalence of hypertension, identified principal predictive risk factors of hypertension and predictive probabilities to become hypertensive based on risk factors. Overall, the prevalence of hypertension was relatively high Considering women only, the prevalence was also high. That prevalence was similar to results of a recent study conducted in Lesotho which showed a relatively high prevalence (17.3%) among women [34]. These results were also in the line with previous study recently conducted in US among adults which showed a high prevalence among men than woman [35]. In another study conducted in Saudi Arabia, the prevalence was 15.2% among those aged 15 years old and above had different levels of hypertension [36].

This study did not showed the significant difference of the prevalence between women and men [36]. This finding was consistent with a studies conducted in Benin where there was no significant difference between men [37]. The highest prevalence of hypertension observed in diabetic patients and the lowest in young patients aged under 40 years [37]. This study showed that the hypertension’s prevalence was less than SSA’s hypertension prevalence. The pre hypertension was estimated on high proportion.

Literature on marital status and hypertension was inconclusive and mostly compared never married to currently married persons [38]. In congruence to this, our study did not show association between marital status and hypertension [38]. These findings were contrary to what had previously reported on this association [39]. After adjusting hypertension on other covariates using logistic regression model, high educational level, smoking, advanced age, overweight, obesity, chronic kidney failure and familial history of hypertension were significantly associated with hypertension. Similar findings were found in a previous study conducted in Malawi, Kenya and Bangladesh which showed that that the factors associated with hypertension were overweight, smoking, education level and older age [40-42]. Similar findings were also reported in recent study conducted in Nepal which showed that being overweight, obese and with hypertension’s family history were associated with hypertension [43]. The association between advanced age and high risk of hypertension could be due to the biological effects of increased arterial resistance which increases with old age [44]. This study didn’t find the association between residence and marital status. Furthermore, as in this study, alcohol was also not associated with hypertension. These findings are in line with results reported in two studies conducted in Benin and some Europeans countries [34, 37].

This study showed that predicted probabilities to become hypertensive was low in young patients aged under 40 years. High probabilities were observed in patients with advanced age. It was also observed in patients with coexistence of risk factors. The highest probabilities (≥90%) were observed among old patients with at least two additional risk factors.

One strength of our study was the ability to study hypertensive and normotensive people at the same time, combining descriptive and inferential analysis (logistic regression with fixed effects, Wald test, deviance test) to build the ROC curve. Other strengths of the study were the ability to estimate area under curve, to build bootstrap AUC interval confidence using Bootstrap method with 2000 replicates, model’s residuals analysis using Welsh-Kuh’s distance, predicting probabilities of becoming hypertensive given a combination of risk factors. Lastly, even if the sample was not nationally representative to some extent, all patients were from all over the country towards these two hospitals (tertiary referral hospitals).

However, despite these strengths, some limitations should be noted during interpretation and policy formulation. First of all, this study was a cross-sectional study, which could only provide clues to the etiology, and further exploration was needed to prove the causal relationship; Secondly, our study used secondary data and as such, we were unable to measure quantities and type of alcohol and tobacco consumed as well as obtain information on physical activities which have been found to be associated with hypertension. Lastly, the sample size was relatively not large and caution should be taken when generalizing findings on high blood pressure as data used were only reported from two hospitals.

To validate findings, additional studies should be conducted in other hospitals in the country and take into others characteristics including more biomarkers. Score based models and nomograms should be used to support clinical implementation of risk models. A random effect logistic regression or Bayesian regression based on Markov chain Hamiltonian Monte Carlo simulations and Langevin algorithms could give precision in the estimation of model’s parameters. Bayesian credibility intervals as such these methods are recommended for future research. The main interest of this study was to identify predictive risk factors of hypertension which allowed prediction of hypertension’s occurrence controlling possible cofounders.

Conclusion

This study showed that the hypertension prevalence was relatively high. Hypertension’s prevalence was not significantly different in men and women. Predictive risk factors of hypertension were advanced age smoking, presence of chronic kidney failure, existing cardiovascular impairment, educational level and body mass index.

The lowest predicted probability of hypertension was observed in young patients with no risk factors. High predicted probabilities to become hypertensive were observed in patients with coexistence of two or more risk factors. Resources in Burundi are scare, therefore, the tackling the high burden of cardiovascular diseases should be based on instituting systems for early detection and prompt treatment especially those identified as high risks.

To our knowledge, no study combining the predictive risk factors analysis and probabilities predictions have been carried in Burundi. At the community level, efforts should be channelled towards intensifying innovative and inclusive health promotion aimed at behaviour change. At the health system, creating a risk-based nomogram based on these identified risks factors could allow those at high risks to be identified early and well-targeted with the needed treatment.

Finally, provision of long term care for those identified cases will depend on not just consistent treatment but also on the overall health systems’ strengthening. This will ensure sustainability and effectiveness of public health interventions aimed at chronic diseases tackling along with other high burden infectious diseases.

(SAV)

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23 Jul 2021

PONE-D-21-17418

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

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Reviewer #2: N/A

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: The manuscript about Prevalence and Predictive Risk Factors of Hypertension in patients hospitalized in Kamenge Military hospital and Kamenge University teaching hospital is well written with detailed descriptions of methodology and results. Numerous advanced statistical methods have been applied. The Table and Figures are quite detailed. Both the topic of the study as well as the findings are interesting. However, I have some comments about the manuscript, as described below.

1. The attached database has 304 respondents, while the manuscript shows 353. It is not possible to repeat the analyzes listed in the paper. A proper database needs to be attached.

2. The population of respondents is not precisely explained through the structure of wards and patients in hospitals. What are the primary diagnoses for which they were admitted to the hospitals? How was the randomization done in relation to the hospital ward?

3. When calculating the sample size, the prevalence of hypertension was taken to be 0.5, which is a large value.

4. The incidence of Chronic kidney failure is 52.1% (184/353 * 100), which is a high incidence. Additionally, whether Chronic kidney failure is a predictor or a consequence of hypertension. Can these patients be regarded as predictors of hypertension in the study?

5. It is not explained how and based on what data was the General cardiovascular risk calculated and what it refers to.

6. More than 50% of references are older than 5 years. There are newer publications for this topic, so I suggest adding them.

7. How the results obtained on the basis of hospital patients can be generalized to the population of Burundi?

8. Include BMI in the model as an ordinal variable with all categories or compare Overweight and Obesity in relation to Underweight and Normal.

9. Is cardiovascular comorbidity a predictor or a consequence of hypertension?

10. In logistic regression, the odds ratio is obtained, so the results should be interpreted in that way.

11. The paper does not state the level of significance.

12. Delete AIC values from tables with univariate analyzes because they do not contribute to informativeness.

13. In the attached database it is necessary to check the values for: age (range 15-107 years) and BMI (minimum value is 6.72)

14. In Table 3 the p-value for Smoking is 0.081, which is not statistically significant.

15. In Table 1 the sum of all categories in column N is 353. In cell named Total, the value is 309.

16. All numeric values in tables and text should be set to one decimal place.

I suggest a revision of the manuscript followed by second round of review.

Reviewer #2: The topic of the paper is interesting, always actual and always need new information-up to date. The topic is also relevant in the application of appropriate statistical modeling to identify risk factors.

In order for the paper to be publish, it is necessary to change the way of describing the used statistical methods and presenting the results. In the material and methods, it is described how the required sample size was calculated. It only needs to be describe, without writing formulas.

In the part of statistical analysis, it should be described why certain statistical tests and analyzes were used. Describe the tests used in the different data collected and analyzed. It is not necessary to show the formulas of the regression analysis used. This is unnecessary if the topic of the paper is hypertension in a certain patient population. This way of writing the methodology as well as presenting the results is matching with the paper whose topic is logistic regression analysis, with example of a study to identify risk factors for the occurrence of hypertension in the observed patient population.

The results are needs to be completely changed. Show the tables so that the results are clear to the doctor who reading the paper.

Table 1: What 95% CI refers. Table 2: explain what AIC is. Is table 3 required?

Fig. 1 and Fig. 2, and Fig. 4 are unclear. Describe the adequacy of the model, and the tests used for testing in the methodology. I don’t think they need to be shown in the results.

**********

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Reviewer #2: No

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21 Sep 2021

Reviewer 1

Comment 1: The attached database has 304 respondents, while the manuscript shows 353. It is not possible to repeat the analyzes listed in the paper. A proper database needs to be attached.

Response 1: Thank you for the comment. A dataset of 353 respondents have been attached.

Comment 2: The population of respondents is not precisely explained through the structure of wards and patients in hospitals. What are the primary diagnoses for which they were admitted to the hospitals? How was the randomization done in relation to the hospital ward?

Response 2: Thank you for the comments. Description have been made in the manuscript

Comment 3: When calculating the sample size, the prevalence of hypertension was taken to be 0.5, which is a large value.

Response 3: Yet, studies to understand the epidemiology and associated in the context of Burundi are lacking. Without prevalence of hypertension in previous studies conducted in Burundi, we used p=0.5 as recommended in literature on sampling method (Giezendanner, François Daniel. "Taille d’un échantillon aléatoire et marge d’erreur." Instruction Publique, Culture et Sport (2012): 7.) .

Comment 4: The incidence of Chronic kidney failure is 52.1% (184/353 * 100), which is a high incidence. Additionally, whether chronic kidney failure is a predictor or a consequence of hypertension. Can these patients be regarded as predictors of hypertension in the study?

Response 4: This incidence is relatively high because the study included all patients from intensive care unit with chronic kidney failure. Majority of these patients are hospitalized there because this areas are located together with haemodialysis unit. Therefore, these patients can be regarded as predictors as the Renin-Angiotensin-Aldosterone System participate in the development of hypertension from chronic kidney failure.

Comment 5: It is not explained how and based on what data was the General cardiovascular risk calculated and what it refers to.

Response 5: The cardiovascular risk was based on both blood pressure level, risk factors and clinical symptoms. The increase of blood pressure level associated with increase of risk factors (them founded among our patients) increase the risk. In the data set, we scored each risk factors by one 1 point. For each patients, calculated the total of the points and we classified it in four classes: Zero risk factor,1-2 Risk factors, 3 and more risk factors and 3 risk factors associated with clinical blood pressure.

Therefore, as this evaluation is not linked with title and specific objectives of the study, the table 5 have been removed in the manuscript.

Comment 6: More than 50% of references are older than 5 years. There are newer publications for this topic, so I suggest adding them.

Response 6: New publications are added.

Comment 7: How the results obtained on the basis of hospital patients can be generalized to the population of Burundi?

Response 7: Partially, we generalized these findings because these hospitals are the tertiary national reference hospitals which receive patients from across the country. On other side, these findings could not be generalized because the sample is not nationally representative. We highlighted it is as limit of the study, prompt to conduct others study on the national level. In conclusion, these findings cannot be generalized and suggest to conduct another study in most of Burundian hospitals.

Comment 8: Include BMI in the model as an ordinal variable with all categories or compare Overweight and Obesity in relation to Underweight and Normal.

Response 8: BMI have been included in the model. It has been categorized as suggested

Comment 9: Is cardiovascular comorbidity a predictor or a consequence of hypertension?

Response 9: By mention of cardiovascular comorbidities, we implied the presence of an existing cardiovascular impairment apart from hypertension. We have now clarified that throughout the manuscript. Thank you.

Comment 10: In logistic regression, the odds ratio is obtained, so the results should be interpreted in that way.

Response 10: Results have been presented using odds ratios and adjusted odds across the manuscript. Interpretation follows same

Comment 11: The paper does not state the level of significance.

Response 11: Level of significance has been stated on page 6 line 7.

Comment 12: Delete AIC values from tables with univariate analyzes because they do not contribute to informativeness.

Response 12: Changes have been made in the manuscript

Comment 13: In the attached database it is necessary to check the values for: age (range 15-107 years) and BMI (minimum value is 6.72)

Response 13: These values are correct even if they seems to outliers.

Comment 14: In Table 3 the p-value for Smoking is 0.081, which is not statistically significant.

Response 14: Even if the table 3 have been removed in manuscript, the selection of significant variables was based on AIC and the best model is the one with low AIC. As we used the backward method, when we remove tobacco consumption, the AIC increases which is not recommended in modelling. Beside the Akaike criterion, the model without tobacco decrease the AUC, and consequently the predictive power. All these augments allowed us to include tobacco use in the model.

Comment 15: In Table 1 the sum of all categories in column N is 353. In cell named Total, the value is 309.

Response15: Changes have been made in the manuscript

Comment 16: All numeric values in tables and text should be set to one decimal place.

Response 16: Changes have been made in the manuscript

Reviewer 2

The topic of the paper is interesting, always actual and always need new information-up to date. The topic is also relevant in the application of appropriate statistical modelling to identify risk factors.

Comment 1: In order for the paper to be publish, it is necessary to change the way of describing the used statistical methods and presenting the results. In the material and methods, it is described how the required sample size was calculated. It only needs to be describe, without writing formulas.

Response 1: Thank you for the comment. All those changes have been made in the manuscript.

Comment 2: In the part of statistical analysis, it should be described why certain statistical tests and analyzes were used. Describe the tests used in the different data collected and analyzed

Response 2: Changes have been made in the manuscript

Comment 3: It is not necessary to show the formulas of the regression analysis used. This is unnecessary if the topic of the paper is hypertension in a certain patient population.

Response 3: Changes have been made in the manuscript

Comment 4: This way of writing the methodology as well as presenting the results is matching with the paper whose topic is logistic regression analysis, with example of a study to identify risk factors for the occurrence of hypertension in the observed patient population.

Response 4: changes have been made in the manuscript

Comment 5: Table 1: What 95% CI refers.

Response 5: 95% CI refers to proportion hypertension ‘’P+ ‘’

Comment 6: Table 2: explain what AIC is.

Response 6 : AIC have been removed in the Table 3 but is very well explained in manuscript as Akaike Information Criterion used to choose best model.

Comment 7: Is table 3 required?

Response 7: No it is not required and we removed it in the manuscript

Comment 8: Fig. 1 and Fig. 2, and Fig. 4 are unclear. Describe the adequacy of the model, and the tests used for testing in the methodology.

Response 8: Changes have been made in the manuscript.

Submitted filename: Response to Reviewers1.docx

Click here for additional data file.

19 Oct 2021

PONE-D-21-17418R1Prevalence 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 BurundiPLOS ONE

Dear Dr. Iradukunda,

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I suggest that the manuscript can be accepted for publication after a minor revision:

1. The attached database does not have a variable BMI divided to categories (Underweight and healthy, Overweight, Obese)

2. Check all p-values (for example, the p-value for EL in Table 2 is different from the p-value obtained after analysis from the database)

3. Check and correct all errors in the Tables (one of the examples [2.1-12.4-11.15])

4. Equalize decimal places for numbers – number of decimal places should be consistent throughout the paper

5. Correct typing mistakes, sometimes there is no break, and sometimes it is in the wrong place.

Reviewer #2: (No Response)

**********

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Reviewer #1: No

Reviewer #2: No

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28 Oct 2021

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response: We confirm the reference list is complete and accurately reflects all the in-text citations. There are no cited papers which have been retracted and the list as it stands need no further changes. Thank you.

Reviewer comments and their respective responses

Reviewer #1: I suggest that the manuscript can be accepted for publication after a minor revision:

Comment 1. The attached database does not have a variable BMI divided to categories (Underweight and healthy, Overweight, Obese)

Response: Thank you for the comment. As we used R software, we coded directly the BMI’s categories in R: These are the code used to carry out the BMI and its categories:

a. BMI<-(WEIGHT/ (HEIGHT^2)) #Body mass index calculation

b. BMI cat<- ifelse (BMI<25,"Underweight/Normal", (BMI<30,"Overweight", "Obesity")) #Body mass index categorization.

Coment.2. Check all p-values (for example, the p-value for EL in Table 2 is different from the p-value obtained after analysis from the database)

Response 2: Thank you for your comments. All p value were have been well checked.

Comment 3. Check and correct all errors in the Tables (one of the examples [2.1-12.4-11.15])

Response 3: Thank you for the comment. Change have been made in the main manuscript

Comment. 4. Equalize decimal places for numbers – number of decimal places should be consistent throughout the paper

Response 4: Changes are made in the main manuscript

Comment. 5. Correct typing mistakes, sometimes there is no break, and sometimes it is in the wrong place.

Response 5: Changes are made in the main manuscript

Reviewer #2: (No Response)

Kind regards,

Arnaud IRADUKUNDA (Corresponding author)

Submitted filename: Response to Reviewers1.docx

Click here for additional data file.

5 Nov 2021

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

PONE-D-21-17418R2

Dear Dr. Iradukunda,

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22 Nov 2021

PONE-D-21-17418R2

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

Dear Dr. Iradukunda:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Table 1

Descriptive analysis of patients’ characteristics.

Individual characteristics	Category	N	H-	H+	P+	95% CI of P+
Sex	Men	206	173	33	16.0	[11.3–21.8]
	Women	147	121	26	17.7	[11.9–24.8]
Age group	15–39 years	138	135	3	2.2	[0.5–6.2]
	40–59 years	131	103	28	21.4	[14.7–29.4]
	≥60 years	84	56	28	33.3	[23.4–44.5]
Marital status	Married	89	81	8	9.0	[39.0–16.9]
	Unmarried	264	213	51	19.3	[14.7–24.6]
Educational level	Primary or less	137	123	14	10.2	[5.7–16.6]
	Secondary	165	135	30	18.2	[12.6–24.9]
	University	51	36	15	29.4	[17.5–43.8]
Alcohol	No	173	145	28	16.2	[11.1–22.5]
	Yes	180	149	31	17.2	[12.0–23.6]
Smoking	No	325	274	51	15.7	[11.9–20.1]
	Yes	28	20	8	28.6	[13.2–48.7]
Existing cardiovascular impairment	No	28	24	4	14.3	[4.0–32.7]
	Yes	325	270	55	16.9	[13.0–21.5]
Family History	No	289	262	27	9.3	[6.3–13.3]
with Hypertension	Yes	64	32	32	50.0	[37.2–62.7]
Diabetes	No	273	247	26	9.5	[6.3–13.6]
	Yes	80	47	33	41.3	[30.4–52.8]
Chronic kidney failure	No	169	164	5	3.0	[1.0–6.7]
	Yes	184	130	54	29.3	[22.9–36.5]
Body mass index	Underweight and normal	270	236	34	12.6	[8.9–17.3]
	Overweight	60	44	16	26.7	[16.5–39.9]
	Obesity	23	14	9	39.1	[20.5–61.2]
Overweight	No	271	237	34	12.5	[8.9–17.1]
	Yes	82	57	25	30.5	[20.8–41.7]
Total		353	294	59	16.7	[15.6–25.1]

N: Category’s total, H-: Normotensive people, H+: Hypertensive people, P+: Hypertensive people proportion