Community-based prevalence and factors associated with hypertension in adults in the rural Nyiragongo Health Zone, North Kivu Province, Democratic Republic of the Congo: a cross-sectional study

Abstract

Introduction

Hypertension is a growing public health concern in sub-Saharan Africa, contributing significantly to cardiovascular morbidity and mortality. In conflict-affected regions such as eastern Democratic Republic of the Congo (DRC), fragile health systems and recurrent insecurity further exacerbate this burden. This study aimed to determine the prevalence of hypertension and identify associated factors among adults in the Nyiragongo Health Zone, North Kivu, DRC.

Methods

A cross-sectional survey was conducted among 786 adults (527 women, 259 men). Sociodemographic, clinical and lifestyle data were collected using standardised questionnaires. Blood pressure (BP) measurements followed international guidelines. Bivariate and multivariate logistic regression analyses were performed to identify factors associated with hypertension.

Results

The mean age of participants was 36.2±14.8 years (range: 18–78 years), with women slightly younger than men (35.3±14.2 vs 38.1±15.6 years; p=0.017). The overall prevalence of hypertension was 36.1% (n=284), with 2.4% of hypertensive participants receiving treatment. BP increased with age for both sexes, with systolic and diastolic pressures higher in women than in men after 50 years. Multivariate analysis identified age >40 years (adjusted OR (aOR)=2.92 (95% CI 2.01 to 4.25); p<0.0001), alcohol consumption (aOR=1.69 (95% CI 1.09 to 2.63); p=0.020), suboptimal consumption of vegetables (aOR=1.50 (95% CI 1.07 to 2.10); p=0.019), family history of hypertension (aOR=1.71 (95% CI 1.26 to 2.35); p<0.0001) and obesity (aOR=1.72 (95% CI 1.02 to 2.91); p=0.043) as independently associated factors.

Conclusions

Hypertension is highly prevalent in the Nyiragongo Health Zone, with a substantial proportion of cases remaining untreated. Targeted community interventions promoting lifestyle modification, early detection and management are urgently needed to mitigate the cardiovascular burden in this population.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Hypertension is a major contributor to cardiovascular morbidity and mortality in sub-Saharan Africa. Its prevalence is rising, but few community-based studies have been conducted in conflict-affected rural areas such as eastern Democratic Republic of the Congo.

WHAT THIS STUDY ADDS

• This study shows that more than one in three adults in the Nyiragongo Health Zone is hypertensive, with alarmingly low treatment rates. It identifies age, obesity, alcohol consumption, suboptimal vegetable intake and family history as significantly associated factors.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• The findings highlight the urgent need for community-based strategies to improve prevention, early detection and management of hypertension in resource-limited and conflict-affected settings. They also provide evidence to guide tailored interventions and inform local health policy.

Introduction

Hypertension is a major global public health challenge and one of the leading causes of cardiovascular morbidity and mortality, responsible for over 10 million deaths annually.¹ Currently, more than 1.13 billion people worldwide live with hypertension, two-thirds of whom reside in low- and middle-income countries.¹ Sub-Saharan Africa (SSA) now records some of the highest age-standardised prevalence rates of hypertension globally, with estimates ranging between 20% and 40% among adults, depending on the subregion and population studied.^{2 3} In SSA, the burden of hypertension is compounded by rapid urbanisation, demographic transitions and lifestyle changes, creating an ‘epidemic in silence’ that is often undiagnosed and untreated.⁴

Multiple factors influence the occurrence of hypertension. Non-modifiable factors include advanced age⁵ and family history/genetic susceptibility.⁶ Modifiable factors comprise overweight and obesity,⁷ diabetes mellitus,⁷ physical inactivity,⁸ tobacco use and excessive alcohol consumption,⁹ psychological stress¹⁰ and unhealthy dietary habits, including high salt and saturated fat intake and suboptimal fruit and vegetable consumption.¹¹ In the Democratic Republic of the Congo (DRC), particularly in North Kivu Province, data on community-level hypertension remain limited. A study conducted in Goma reported a prevalence of 20% and identified factors such as age >35 years, obesity, marital status, higher education, family history of hypertension, unemployment and physical inactivity.¹² In another recent community-based study conducted in Lubumbashi, an overall prevalence of 33.6% was reported, with women being more affected than men (34.5% vs 31.7%; p=0.024); independent predictors of hypertension included age >50 years, overweight, obesity, central obesity, diabetes mellitus, alcohol consumption, suboptimal vegetable intake and history of stroke.¹³

The substantial burden of hypertension in SSA is compounded by the high proportion of individuals who remain undiagnosed, untreated or inadequately managed, increasing their risk of preventable complications such as stroke, heart disease and other cardiovascular events. Understanding the prevalence and determinants of hypertension in rural settings is therefore essential. In the DRC, most studies to date have focused on urban populations, leaving a critical knowledge gap in rural areas. This knowledge gap is particularly concerning in conflict-affected rural areas such as eastern DRC, where fragile health systems and recurrent insecurity further hinder prevention and management of hypertension. Generating updated, region-specific data is vital for informing targeted prevention, screening and management strategies. This study aimed to estimate the prevalence of hypertension and identify associated factors in adults residing in the rural Nyiragongo Health Zone, Nyiragongo Territory, North Kivu Province, DRC, thereby providing evidence to support local health planners, policymakers and communities in mitigating the rising burden of hypertension.

Materials and methods

Study setting

This study was conducted in the rural Nyiragongo Health Zone, located within the Nyiragongo Territory, North Kivu Province, DRC. The Nyiragongo Health Zone is one of the 34 health zones in the North Kivu region (figure 1). It comprises 11 health areas (HAs) covering a total area of 333 km², of which 170 km² is largely occupied by the Virunga National Park, while the remaining 163 km² is inhabited. The population of the health zone was estimated at 362 000 in 2023, corresponding to a population density of approximately 1087 inhabitants per km².

Figure 1. Geographical map of the Democratic Republic of the Congo showing the location of North Kivu Province (highlighted in red) and Nyiragongo Territory (highlighted in blue). This map was created by the study team for this study. All geographical boundaries and locations are original and not copied from any copyrighted source.

Study design, period and population

This was a community-based analytical cross-sectional study conducted from 1 to 31 December 2023 in the rural Nyiragongo Health Zone, Nyiragongo Territory, North Kivu Province, DRC. The study targeted adults aged 18 years and older who voluntarily agreed to participate and provided written informed consent.

Participants were required to be free from conditions that could influence blood pressure (BP) measurements at the time of the survey, including recent physical activity (within the last 30 min) and intake of caffeine or other stimulants.

Exclusion criteria included: individuals under 18 years, pregnant women, persons with diagnosed mental disorders, individuals with hearing or speech impairments preventing coherent responses, and internally displaced persons from other health zones residing temporarily in the area.

Sample size calculation and sampling technique

The minimum sample size was calculated using the formula: n=Z²×p×q/d²

where n is the sample size, p the estimated prevalence (50%, corresponding to maximum variability), q=1 − p, d the margin of error (0.05) and Z the standard normal deviate corresponding to a 95% confidence level. This yielded a minimum sample size of 384 households. To account for the design effect of cluster sampling, the sample size was doubled, resulting in a total of 768 households. Anticipating a 10% non-response rate, the target sample size was 853 participants. The final sample included 786 participants, corresponding to a participation rate of approximately 92%, slightly lower than the planned sample size.

Sampling method

A multistage random sampling procedure was employed to select study participants. In the first stage, all 11 HAs of the Nyiragongo Health Zone were enumerated and assigned numerical identifiers. Six HAs were randomly selected using the random number generator function of the Emergency Nutrition Assessment (ENA) software. The selected HAs were Turunga, Kibati, Munigi, Ngangi III, Rusayo and Kanyaruchinya.

In the second stage, a cluster sampling approach was applied within each selected HA, with villages serving as the primary sampling units (clusters). The allocation of clusters across HAs was performed proportionally to population size using the ENA software cluster allocation table. Following WHO recommendations for cluster surveys, a total of 30 clusters were selected. The number of households to be surveyed per cluster was calculated by dividing the required sample size (n=768) by the number of clusters, resulting in 26 households per cluster.

In the third stage, households within each selected village were randomly chosen using standard cluster procedures. The first household was identified using a random direction method, and subsequent households were selected systematically until the required number was reached. Within each selected household, one eligible adult was randomly invited to participate, ensuring that all households had an equal probability of selection.

Data collection and study variables

This study was not conducted as an official WHO STEPwise approach to non-communicable disease risk factor surveillance (WHO STEPS) survey. However, it adopted and adapted the WHO STEPS (V.3.2) as a standardised methodological framework for data collection. The questionnaire content and physical measurement protocols were derived from the WHO STEPS instrument to ensure consistency and comparability. Following authorisation from local administrative authorities, data were collected using a structured, interviewer-administered questionnaire implemented on KoboCollect. The instrument comprised closed-ended questions covering sociodemographic characteristics, behavioural factors and relevant medical history.

Following authorisation from local administrative authorities, data were collected using a structured, interviewer-administered questionnaire implemented on KoboCollect. The instrument comprised closed-ended questions covering sociodemographic characteristics, behavioural factors and relevant medical history. Hypertension was the primary outcome variable, while independent variables included socio-demographic characteristics, family history, behavioural and lifestyle factors, and other associated factors.

The survey employed the standardised WHO STEPS (V.3.2),¹⁴ which integrates structured questionnaires with physical measurements including BP, heart rate (HR), height and weight. The questionnaire was developed in English and translated into the local language (Kiswahili) to ensure comprehension and cultural relevance.

Participants were interviewed regarding sociodemographic characteristics (age, sex, marital status, education level, occupation), behavioural factors (physical activity, cigarette smoking, alcohol consumption, fruit and vegetable consumption) and family history of hypertension. According to WHO recommendations, a healthy diet should include more than 400 g of fruits and vegetables per day. In this study, suboptimal fruit and vegetable consumption was defined as consuming fewer than three servings of fruits and vegetables (excluding gherkins) per day. Fruit and vegetable intake was assessed using frequency-based self-report and was not quantified in grams; therefore, this operational definition does not correspond directly to the WHO threshold of ≥400 g/day. Anthropometric measurements were conducted according to WHO STEPS guidelines using calibrated, clinically validated instruments. Weight was measured to the nearest 0.1 kg, and height to the nearest 0.1 cm. Body mass index (BMI) was calculated, with overweight and obesity defined as BMI 25–29.9 kg/m² and ≥30 kg/m², respectively.^{15 16}

BP and HR measurements were performed by clinicians using an automated, clinically validated Omron IT oscillometric BP monitor. Participants rested for 30 min in a seated position, with back support, feet flat on the floor, legs uncrossed and the right arm resting comfortably on a table at heart level. Participants were required to have an empty bladder and to refrain from smoking, caffeine or stimulants before measurement. The upper arm was exposed approximately two inches above the elbow crease, and the cuff was applied to the brachial artery.

Three consecutive BP measurements and HR readings were taken at 1 min intervals. Systolic and diastolic BP (SBP, DBP, in mm Hg) and HR (in beats per minute) were recorded. The mean of the last two of the three readings, taken 3 min apart, was used for analysis. Hypertension was defined as SBP ≥140 mm Hg and/or DBP ≥90 mm Hg, or current use of antihypertensive medication.¹³

Before the main survey, the questionnaire underwent a pilot test in a cell not included in the study to evaluate content, construct and face validity. Feedback from participants and expert review guided refinements to ensure accuracy, reliability and suitability for the study population. Data collection was conducted by two trained and supervised health workers at selected health centres, combining questionnaire administration with BP, HR and anthropometric measurements.

Data management and statistical analysis

All data collected via KoboCollect were exported to Microsoft Excel for cleaning and verification, with no manual double-entry performed, as electronic capture minimised data entry errors. Consistency checks and range validations were applied to ensure data integrity. The dataset was then exported to Stata V.16 for statistical analysis. Quantitative variables were summarised as mean±SD, while qualitative variables were presented as frequencies and percentages. Comparisons of proportions between groups were conducted using Pearson’s χ² test, and comparisons of means between two groups (eg, male vs female) were performed using Student’s t-test. To examine factors associated with hypertension, bivariate analyses were first conducted to explore the relationships between potential factors and hypertension. All variables deemed relevant based on prior evidence and conceptual considerations were subsequently included in a multivariable logistic regression model to estimate adjusted ORs (aORs) with 95% CIs, allowing identification of factors independently associated with hypertension. A p value <0.05 was considered statistically significant. Model fit and assumptions were assessed to ensure the validity and reliability of the results. Although the sample size calculation accounted for the multistage cluster sampling design through the application of a design effect, the regression analyses were performed without survey-weighted procedures or design-based variance estimation. As a result, SEs and CIs may be underestimated, and the magnitude and precision of effect estimates should be interpreted with caution.

Results

Sociodemographic and clinical characteristics of the study population

Table 1 summarises the sociodemographic and clinical characteristics of the study participants. The mean age of all participants was 36.2±14.8 years, with females being slightly younger than males (35.3±14.2 vs 38.1±15.6 years; p=0.017). Regarding educational level, a higher proportion of females had no formal education compared with males (38.5% vs 17.8%), whereas males were more likely to have attained secondary or university education (50.2% vs 35.9% for secondary; 8.9% vs 5.3% for university; p<0.0001). In terms of marital status, more females were married compared with males (68.5% vs 56.8%), while males had higher proportions of being single (32.8% vs 21.4%; p=0.002). Occupation differed significantly between sexes (p<0.0001), with a greater proportion of females being unemployed (56.0% vs 42.9%) and males more represented among civil servants (19.3% vs 7.8%) and cultivators (16.2% vs 7.4%). Traders accounted for 28.8% of females and 21.6% of males.

Table 1. Sociodemographic and clinical characteristics of the study population (n=786).

Variable	Female, n=527 (67.1%)	Male, n=259 (32.9%)	Total, N=786	P value
Age (years), mean (SD)	35.3 (14.2)	38.1 (15.6)	36.2 (14.8)	0.017
Educational level				<0.0001
None, n (%)	203 (38.5)	46 (17.8)	249 (31.7)
Primary, n (%)	107 (20.3)	60 (23.2)	167 (21.3)
Secondary, n (%)	189 (35.9)	130 (50.2)	319 (40.6)
University, n (%)	28 (5.3)	23 (8.9)	51 (6.5)
Marital status				0.002
Married, n (%)	361 (68.5)	147 (56.8)	508 (64.6)
Single, n (%)	113 (21.4)	85 (32.8)	198 (25.2)
Divorced, n (%)	53 (10.1)	27 (10.4)	80 (10.2)
Occupation				<0.0001
Unemployed, n (%)	295 (56.0)	111 (42.9)	406 (51.7)
Trader, n (%)	152 (28.8)	56 (21.6)	208 (26.5)
Cultivator, n (%)	39 (7.4)	42 (16.2)	81 (10.3)
Civil servant	41 (7.8)	50 (19.3)	91 (11.6)
Weight (kg), mean (SD)	61.4 (12.1)	62.1 (10.1)	61.7 (11.5)	0.393
Height (cm), mean (SD)	159.2 (11.6)	163.1 (8.9)	160.5 (10.9)	<0.0001
BMI (kg/m²), mean (SD)	24.4 (5.0)	23.4 (3.9)	24.1 (4.7)	0.004
SBP (mm Hg), mean (SD)	124.1 (20.1)	128.9 (18.9)	125.7 (19.8)	0.001
DBP (mm Hg), mean (SD)	82.5 (11.8)	85.5 (11.9)	83.5 (11.9)	0.001
HR (bpm), mean (SD)	81.3 (12.0)	80.1 (12.8)	80.9 (12.3)	0.227
Alcohol consumption, n (%)	55 (10.4)	71 (27.4)	126 (16.0)	<0.0001
Cigarette smoking, n (%)	7 (1.3)	27 (10.4)	34 (4.3)	<0.0001
Adequate vegetable consumption, n (%)	319 (60.5)	158 (61.0)	477 (60.7)	0.960
Adequate fruit consumption, n (%)	120 (22.8)	65 (25.1)	185 (23.5)	0.5266
Physical activity, n (%)	110 (20.9)	124 (47.9)	234 (29.8)	<0.0001
History of stroke, n (%)	17 (0.32)	9 (3.5)	26 (3.3)	1.000
Family history of hypertension, n (%)	216 (41.0)	108 (41.7)	324 (41.2)	0.909

BMI, body mass index; DBP, diastolic blood pressure; HR, heart rate; SBP, systolic blood pressure.

Mean SBP and DBP were 125.7±19.8 mm Hg and 83.5±11.9 mm Hg, respectively, while the mean HR was 80.9±12.3 bpm. Males had significantly higher SBP and DBP than females (128.9±18.9 vs 124.1±20.1 mm Hg, p=0.001; 85.5±11.9 vs 82.5±11.8 mm Hg, p=0.001), whereas females had a higher BMI than males (24.4±5.0 vs 23.4±3.9 kg/m²; p=0.004). Height was significantly greater in males than in females (163.1±8.9 vs 159.2±11.6 cm; p<0.0001), while weight did not differ significantly (62.1±10.1 vs 61.4±12.1 kg; p=0.393).

Regarding lifestyle factors, males reported higher proportions of alcohol consumption (27.4% vs 10.4%; p<0.0001), cigarette smoking (10.4% vs 1.3%; p<0.0001) and physical activity (47.9% vs 20.9%; p<0.0001). Vegetable and fruit consumption did not differ significantly between sexes. Among men and women, 60.5% and 61.0%, respectively, had adequate vegetable consumption, while 22.8% of men and 25.1% of women had adequate fruit consumption (p=0.960 and p=0.527, respectively).

Family history of hypertension (41.0% vs 41.7%; p=0.909) and history of stroke (3.2% vs 3.5%; p=1.000) were similar between females and males.

Systolic and diastolic blood pressure across age and sex

SBP and DBP generally increased with age in both sexes (figure 2). SBP rose steadily across age groups for men and women, with women surpassing men from around 51 years of age (eg, 134.1 vs 136.7 mm Hg at 51–60 years; 139.9 vs 139.4 mm Hg at >60 years). DBP showed a less linear pattern, with an overall increase until 51–60 years (highest levels: 89.8 mm Hg in men and 88.7 mm Hg in women), followed by a slight decrease in participants aged >60 years (87.5 mm Hg in men; 86.6 mm Hg in women). Up to age 50, DBP was consistently higher in men than in women, while SBP in women exceeded that of men in older age groups.

Figure 2. Age group-specific and sex-specific trends in systolic and diastolic blood pressure. DBP, diastolic blood pressure; SBP, systolic blood pressure.

Prevalence of hypertension

The overall prevalence of hypertension in the study population was 36.1% (284 out of 786; 95% CI 32.8% to 39.6%). Among these participants, 19 (2.4%; 95% CI 1.6% to 3.7%) reported being on antihypertensive treatment. Importantly, all 19 individuals were hypertensive during the survey, suggesting that none had controlled hypertension at the time of measurement. A much larger proportion (33.7%; 265/786; 95% CI 30.5% to 37.1%) were hypertensive but untreated. In contrast, 63.9% (502/786; 95% CI 60.5% to 67.2%) of participants were normotensive.

Age-specific and sex-specific prevalence of hypertension

Hypertension prevalence varied by age and sex (figure 3). Among participants ≤20 years, males had a higher prevalence (21.9%) than females (16.5%), while from age 31 years onwards, prevalence was generally higher in women. Peaks were observed in the 51–60 year group (66.7% men; 58.7% women), with a slight decline in men >60 years (42.3%) but persistence in women (50.0%). Cross-tabulation confirmed that hypertension prevalence increased with age, from 18.0% in the youngest group to 62.0% in the 51–60 year group. χ² analysis showed a significant association between age and hypertension (p<0.001).

Figure 3. Age group-specific and sex-specific prevalence of hypertension.

Factors associated with hypertension

Table 2 presents the results of both bivariate and multivariate logistic regression analyses examining correlates of hypertension among study participants.

Table 2. Bivariate and multiple logistic regression analyses of factors associated with hypertension among 786 study participants.

Variable	Hypertensive participants (n=284)		Normotensive participants (n=502)		Crude OR (95% CI)	P value	Adjusted OR (95% CI)	P value
Age
≤40 years	156	(29.1%)	380	(70.9%)	1.00		1.00
>40 years	128	(51.2%)	122	(48.8%)	2.56 (1.87 to 3.49)	<0.0001	2.92 (2.01 to 4.25)	<0.0001
Sex
Male	104	(40.2%)	155	(59.8%)	1.29 (0.95 to 1.76)	0.099	1.14 (0.78 to 1.65)	0.499
Female	180	(34.2%)	347	(65.8%)	1.00		1.00
Marital status
Married	191	(37.6%)	317	(62.4%)	1.57 (1.09 to 2.24)	0.014	1.25 (0.81 to 1.92)	0.310
Single	55	(27.8%)	143	(72.2%)	1.00		1.00
Divorced	38	(47.5%)	42	(52.5%)	2.35 (1.37 to 4.03)	0.002	1.24 (0.66 to 2.34)	0.507
Educational level
None	91	(36.6%)	158	(63.4%)	1.00		1.00
Primary	65	(38.9%)	102	(61.1%)	1.11 (0.74 to 1.66)	0.624	1.04 (0.67 to 1.62)	0.848
Secondary	105	(32.9%)	214	(67.1%)	0.85 (0.60 to 1.21)	0.366	1.09 (0.72 to 1.66)	0.673
University	23	(45.1%)	28	(54.9%)	1.43 (0.78 to 2.62)	0.252	1.65 (0.82 to 3.31)	0.158
Occupation
Unemployed	131	(32.3%)	275	(67.7%)	1.00		1.00
Trader	94	(45.2%)	114	(54.8%)	1.73 (1.23 to 2.44)	0.002	1.36 (0.92 to 1.99)	0.123
Cultivator	30	(37.0%)	51	(63.0%)	1.23 (0.75 to 2.03)	0.405	0.86 (0.49 to 1.51)	0.600
Civil servant	29	(31.9%)	62	(68.1%)	0.98 (0.60 to 1.60)	0.941	0.64 (0.36 to 1.14)	0.132
Alcohol consumption
Yes	59	(46.8%)	67	(53.2%)	1.70 (1.16 to 2.50)	0.006	1.69 (1.09 to 2.63)	0.020
No	225	(34.1%)	435	(65.9%)	1.00		1.00
Cigarette smoking
Yes	21	(61.8%)	13	(38.2%)	3.00 (1.48 to 6.09)	0.001	1.92 (0.87 to 4.24)	0.107
No	263	(35.0%)	489	(65.0%)	1.00		1.00
Fruit consumption
Optimal	75	(40.5%)	110	(59.5%)	1.00		1.00
Suboptimal	209	(34.8%)	392	(65.2%)	0.78 (0.56 to 1.10)	0.153	0.84 (0.57 to 1.22)	0.356
Vegetable consumption
Optimal	157	(32.9%)	320	(67.1%)	1.00		1.00
Suboptimal	127	(41.1%)	182	(58.9%)	1.42 (1.06 to 1.91)	0.020	1.50 (1.07 to 2.10)	0.019
Physical activity
Yes	88	(37.6%)	146	(62.4%)	1.00		1.00
No	196	(35.5%)	356	(64.5%)	0.91 (0.66 to 1.25)	0.575	0.89 (0.61 to 1.29)	0.532
Family history of hypertension
Present	143	(44.1%)	181	(55.9%)	1.80 (1.34 to 2.42)	<0.0001	1.71 (1.26 to 2.35)	<0.0001
Absent	141	(30.5%)	321	(69.5%)	1.00		1.00
Body mass index
Underweight	17	(31.5%)	37	(68.5%)	0.90 (0.49 to 1.65)	0.738	0.91 (0.47 to 1.74)	0.770
Normal	166	(33.7%)	326	(66.3%)	1.00		1.00
Overweight	64	(39.0%)	100	(61.0%)	1.26 (0.87 to 1.81)	0.219	1.23 (0.83 to 1.83)	0.305
Obesity	37	(48.7%)	39	(51.3%)	1.86 (1.14 to 3.03)	0.011	1.72 (1.02 to 2.91)	0.043

In the bivariate analysis, the likelihood of hypertension was significantly higher in participants aged >40 years (crude OR (cOR)=2.56 (95% CI 1.87 to 3.49); p<0.0001), divorced individuals (cOR=2.35 (95% CI 1.37 to 4.03); p=0.002), traders (cOR=1.73 (95% CI 1.23 to 2.44); p=0.002), those consuming alcohol (cOR=1.70 95% CI 1.16 to 2.50); p=0.006), suboptimal vegetable consumers (cOR=1.42 (95% CI 1.06 to 1.91); p=0.020), individuals with a family history of hypertension (cOR=1.80 (95% CI 1.34 to 2.42); p<0.0001) and participants with obesity (cOR=1.86 (95% CI 1.14 to 3.03); p=0.011).

In multivariate analysis, factors independently associated with hypertension included age >40 years (aOR=2.92 (95% CI 2.01 to 4.25); p<0.0001), alcohol consumption (aOR=1.69 (95% CI 1.09 to 2.63); p=0.020), suboptimal consumption of vegetables (aOR=1.50 (95% CI 1.07 to 2.10); p=0.019), family history of hypertension (aOR=1.71 (95% CI 1.26 to 2.35); p<0.0001) and obesity (aOR=1.72 (95% CI 1.02 to 2.91); p=0.043). Other factors, including sex, marital status, educational level, occupation, cigarette smoking, fruit consumption and physical activity, were not significantly associated with hypertension after adjustment.

Discussion

The overall prevalence of hypertension in the rural Nyiragongo Health Zone was 36.1%, highlighting the substantial burden of this condition even in rural settings. Within the DRC, this prevalence is slightly higher than those reported in urban areas such as Goma (20%),¹² Kisangani (28.3%)¹⁷ and Kinshasa (30.9%),¹⁸ comparable to Lubumbashi (33.6%),¹³ but remains lower than prevalences observed in Bukavu (41.4%)¹⁹ and Kinshasa (41.9%).²⁰ When compared with other rural populations in SSA, the prevalence in Nyiragongo is similar to that reported in Angola (34.5%),²¹ higher than in rural Ghana (18.4%),²² rural Ethiopia (18.5%)²³ and rural Zambia (25.9%),²⁴ yet lower than in rural Nigeria (45.6%).²⁵ Variations observed between studies may be attributable to differences in measurement methods, such as the number of BP readings taken, the type of device used and sample size, as well as differences in population characteristics. Key factors likely significantly associated with hypertension prevalence in this population include demographic variables (age, sex), socioeconomic status, ethnic background and environmental exposures. Additionally, lifestyle-related correlates, such as dietary patterns, physical inactivity, alcohol and tobacco use, alongside limited access to healthcare services and hypertension screening programmes, may significantly influence individual risk. By specifying these methodological, demographic and behavioural factors, the sources of variation in hypertension prevalence can be better understood and contextualised. These findings underscore the importance of up-to-date, locally generated data in rural areas to inform tailored prevention, screening and management strategies for hypertension.

A particularly striking finding of this study is the extremely low proportion of participants receiving antihypertensive treatment (2.4%), and all of them were uncontrolled, highlighting a major gap in access to hypertension care in the Nyiragongo Health Zone. Importantly, this treatment gap may reflect both a low level of prior diagnosis (awareness) among hypertensive individuals and limited access to sustained pharmacological management among those already diagnosed. In rural and conflict-affected settings, underdiagnosis due to limited screening opportunities may coexist with structural barriers to treatment. This treatment gap likely reflects multiple, interrelated barriers common in rural and conflict-affected settings. These include limited availability and affordability of antihypertensive medications, inadequate primary healthcare infrastructure, shortages of trained healthcare personnel and restricted access to health facilities due to insecurity and population displacement. These findings underscore the urgent need for context-appropriate policy responses, including the integration of hypertension screening and management into existing primary healthcare and community-based health programmes. Strengthening task-shifting approaches, ensuring a reliable supply of essential antihypertensive medicines and improving community awareness through health education could substantially improve treatment coverage. Given the chronic nature of hypertension and the fragile health system context, decentralised and community-oriented models of care are likely to be particularly relevant for improving hypertension control in similar rural and conflict-affected settings.

The key findings of our study revealed that several sociodemographic, behavioural, dietary and familial factors were independently associated with hypertension in the rural Nyiragongo Health Zone. Participants aged over 40 years had a significantly higher likelihood of being hypertensive. Other independent predictors included alcohol consumption, non-consumption of vegetables, a family history of hypertension and obesity. Conversely, other factors such as sex, marital status, educational attainment, occupation, smoking habits, fruit/vegetable consumption and engagement in physical activity did not demonstrate a statistically significant association with hypertension following multivariate adjustment. This underscores the complex interplay of biological, genetic and behavioural correlates in hypertension aetiology, where certain established associated factors maintain their predictive power even after accounting for various confounders.

Our study identified advanced age as a significant independent predictor of hypertension in the rural Nyiragongo Health Zone. Participants aged over 40 years were substantially more likely to be hypertensive compared with younger adults (≤40 years). This finding is consistent with evidence from other African contexts,^{13 17 18} where increasing age has been repeatedly associated with higher hypertension prevalence. A systematic review and meta-analysis reported an estimated prevalence of 57% among adults aged 50 years and older, highlighting the contribution of population ageing to the overall burden of hypertension in SSA.²⁶ Similarly, studies in semiurban Cameroon identified age above 40 years as a key risk factor for hypertension,²⁷ and in rural Rwanda (Ndera sector, Gasabo District), older age (≥55 years) was strongly associated with hypertension.²⁸ This association between advanced age and hypertension can be attributed to structural and functional arterial changes related to ageing. With age, arteries stiffen due to elastin loss, increased collagen and vascular wall calcification, leading to elevated systolic and pulse pressures and increased load on the heart and target organs.²⁹ This ‘vascular ageing’ is an independent predictor of cardiovascular events, even in young adults. Accelerated vascular ageing, marked by increased arterial stiffness, is therefore linked to a higher incidence of cardiovascular disease, independent of traditional risk factors.³⁰ These consistent observations underscore the importance of targeting older adults in rural health programmes for hypertension prevention, early detection and management.

As reported in previous studies,^{27 28 31} our findings indicate that alcohol consumption is a significant predictor of hypertension. Alcohol is a major modifiable risk factor for hypertension, even at moderate levels.^{9 32} A 2023 meta-analysis examined the impact of alcohol on BP across different populations and showed that even moderate drinking (1–2 drinks per day) was associated with higher odds of hypertension in both men and women,³³ challenging earlier reports suggesting a protective effect at low consumption levels.³⁴ The risk is dose-dependent, with higher intake associated with greater increases in BP.³³ Mechanistically, alcohol elevates BP through several pathways. It activates the sympathetic nervous system, promoting vasoconstriction and increasing cardiac output.³⁵ Additionally, alcohol disrupts the renin–angiotensin–aldosterone system, further significantly associated with elevated BP.³⁶ These observations underscore the importance of targeted prevention strategies in rural populations, where alcohol consumption may be underestimated and the burden of hypertension is often higher due to limited access to healthcare services.

In the present study, participants who reported not regularly consuming vegetables had a significantly higher prevalence of hypertension compared with those who consumed them. Previous studies reported that increased fruit and vegetable intake reduced the likelihood of hypertension among adults.^{13 37} Similarly, meta-analyses have confirmed that higher consumption of fruits and vegetables is associated with a lower risk of developing hypertension.^{38 39} However, our questionnaire did not allow quantification of fruit and vegetable intake relative to recommended daily levels of four to five servings.⁴⁰ Insufficient vegetable consumption represents a key nutritional factor significantly associated with hypertension. Vegetables are rich in fibre, potassium, magnesium and antioxidants, which regulate BP through mechanisms such as vasodilation, reduction of peripheral vascular resistance and modulation of systemic inflammation.⁴¹ Suboptimal vegetable intake has been linked to higher hypertension prevalence in SSA, where diets are often low in fruits and vegetables and high in salt and ultraprocessed foods.^{31 42} These findings highlight the importance of promoting vegetable-rich diets in hypertension prevention programmes, particularly in rural populations with limited access to diverse foods.

Consistent with our findings, studies conducted in Cameroon and Ethiopia have confirmed that individuals with a family history of hypertension are at significantly higher risk of developing the condition.^{27 43} Family history is a well-established risk factor, reflecting the combined influence of shared genetic predisposition and environmental exposures.⁴⁴ Genetic research has further identified multiple loci involved in BP regulation, highlighting the role of hereditary susceptibility in the early onset of hypertension.⁴⁵ In SSA settings, adults with a positive family history are consistently shown to have a markedly greater likelihood of developing hypertension, regardless of age or sex.⁴⁶ These observations emphasise the importance of early identification of at-risk individuals within families, coupled with targeted awareness, regular BP screening and preventive strategies such as healthy lifestyle promotion and tailored educational interventions.

A significant association was observed between obesity and hypertension. This finding aligns with numerous previous studies demonstrating that obesity is a major and consistent factor associated with hypertension across diverse populations.1317,19 21 27 Obesity was associated with hypertension through well-established mechanisms, including increased peripheral vascular resistance, sympathetic nervous system activation, endothelial dysfunction and systemic inflammation.⁴⁷ These data underscore the need to incorporate weight management and obesity prevention into hypertension control strategies in SSA.

These findings carry important public health implications, highlighting the need for targeted screening of high-risk groups such as older adults, individuals with obesity, those who consume alcohol, have suboptimal vegetable intake or have a family history of hypertension. At the same time, community-based interventions should focus on modifiable factors, including unhealthy diets, excess weight and harmful alcohol use, to effectively reduce the burden of hypertension. Strengthening primary healthcare systems to integrate routine BP monitoring, patient education and affordable access to antihypertensive treatment is essential, particularly in low-resource settings where undiagnosed and uncontrolled hypertension remains prevalent.^{2 48} In addition, innovative tools such as digital health and remote monitoring offer promising opportunities, but their deployment must be adapted to local contexts to ensure equity, affordability and data protection.⁴⁹

This study has some limitations that should be acknowledged. Its cross-sectional design prevents establishing causal relationships. Although BP was measured three consecutive times and the mean of the last two readings was used, some degree of misclassification of hypertension status remains possible. BP was measured during a single visit, which may have influenced prevalence estimates, particularly in a population exposed to psychosocial stress and insecurity. Some variables, including family history and lifestyle behaviours, were self-reported and may therefore be subject to recall or reporting bias. Fruit and vegetable intake was assessed using self-reported frequency of consumption rather than quantified intake in grams, and therefore does not directly correspond to the WHO recommendation of ≥400 g/day. This methodological difference should be considered when comparing our findings with studies using standardised quantitative dietary assessments and may have introduced some degree of exposure misclassification. The inability to assess other relevant factors, such as a history of kidney disease, hyperuricaemia, hypercholesterolaemia or the use of medications including corticosteroids, further limits interpretation of the findings.

Although regression analyses were not survey-weighted despite the cluster sampling design, we carefully considered the potential implications of this analytical choice. Given the relatively homogeneous cluster structure and the primary focus on estimating associations rather than deriving population-level weighted parameters, this limitation is more likely to affect the precision of variance estimates (and consequently CIs) rather than the direction of the observed associations. While SEs may therefore be modestly underestimated, the overall interpretation and directionality of the identified relationships are unlikely to be substantially altered.

Regarding representativeness, the age and sex distribution of the study participants was broadly consistent with the general adult population structure of the Nyiragongo Health Zone, based on available local administrative and health zone data. However, the absence of recent population census data at the health zone level limits the precision of these comparisons. As with most community-based surveys, some degree of selection bias cannot be excluded, particularly in a conflict-affected setting where population mobility, insecurity and temporary displacement may influence participation. Consequently, while the findings are likely representative of adults living in similar rural and low-resource settings, caution is warranted when extrapolating the results to other populations or geographical contexts. Our findings are most directly applicable to rural, low-resource and conflict-affected settings with similar healthcare infrastructure and population characteristics. Caution is therefore warranted when extrapolating these results to urban or higher-resource contexts, where health system capacity and epidemiological profiles may differ substantially.

Despite these limitations, our findings highlight critical gaps in hypertension awareness, treatment and control in the Nyiragongo Health Zone. While the policy implications, such as integrating hypertension management into primary healthcare and community-based programmes, are broadly relevant, tailored strategies that consider local health system capacity, accessibility and population needs are essential when applying these findings to other settings.

Conclusion

This study highlights a high prevalence of hypertension and its significant associations with older age, obesity, alcohol consumption, suboptimal vegetable consumption and a family history of hypertension. These findings suggest that both non-modifiable factors and modifiable lifestyle-related factors are associated with hypertension in this rural population.

Addressing this growing public health challenge in SSA requires integrated approaches that combine community-based prevention, early detection and improved access to hypertension care within strengthened primary healthcare systems. Interventions focusing on modifiable behaviours, such as reducing harmful alcohol consumption and promoting healthy diets rich in vegetables, may help inform strategies to reduce the burden of hypertension and its related complications. Future longitudinal and interventional studies are needed to clarify causal pathways and to guide context-specific prevention and control strategies.

Data availability statement

Data are available upon reasonable request.