Assessment of Prevalence and Determinants Associated with Hypertension Among the Adult Population in Hawtat Bani Tamim Province

1. Introduction

According to the American Heart Association, Hypertension (HTN) is a medical condition classified by high blood pressure, which means the force of blood pushing against the walls of the arteries is regularly too high. This can lead to serious health difficulties such as heart disease, stroke, and kidney failure. [1]. According to the World Health Organization (WHO), 1.28 billion people have hypertension globally, and most of them (67%) are in countries that have lower and middle-income levels. [2]. In Asia, the percentage of people with hypertension was 27.2% overall. However, the prevalence varied widely by country, with the highest rates found in Central Asia and the lowest rates in South Asia. [3]. A 2023 study revealed that the hypertension prevalence in Saudi Arabia was 9.2% among people older than 15 years and 10% for women, compared to men 8.5% [4].

According to [5], the prevalence of HTN)was 6% among males compared to 4.2% for females. Overweight and obese were significantly associated with HTN. Studies revealed that HTN was positively related to weight, body mass index, and waist circumference. [6] . [7]disclosed that higher alcohol use, obesity, and older age were correlated with HTN [8]. Furthermore, it was observed that the prevalence of HTN was increasing with advancing age. It is also high among the rich and overweight/obese participants. [9]. The results from Ethiopia found that the prevalence of HTN was 44.91%. Hypertension was significantly associated with poor exercise, consuming cruddy oil, a family history of hypertension, and a history of diabetes. [10]. The study revealed a hypertension prevalence rate of 11.1%. Key factors linked to elevated hypertension risk included advancing age, unemployed status, insurance coverage, obesity, diabetes, cardiovascular conditions, and elevated cholesterol levels. [11]. Behavioral risk factors, such as alcohol consumption, being overweight, obesity, increased waist circumference, and high blood glucose levels, are positively associated with hypertension. [12]. It was found that the prevalence of hypertension was 40.8. There is no significant association between hypertension and education level, social status, and overweight. However, factors such as older age and obesity were positively related to hypertension. [13]. Many studies disclosed that older age, smoking, alcohol consumption, and being overweight were associated with hypertension. [7,14,15,16].

Previous studies revealed that factors such as Genetics, lifestyle factors, age, gender, and medical conditions were considered determinants of hypertension. [17,18,19,20,21,22,23], It is important to study the prevalence and determinants associated with hypertension because it helps to understand the burden of the disease in a population and identify the factors that contribute to its occurrence. This information can then be used to develop effective prevention and control strategies, as well as to target interventions for those who are at the highest risk. Additionally, understanding the determinants of hypertension can help to identify modifiable risk factors that individuals can address to reduce their risk of developing the condition. Subsequently, the study aims to assess the prevalence and its associated determinants of hypertension among the adult population in Hawtat Bani Tamim province.

2. Materials and Methods

2.1. Source of the Data

A cross-sectional study was carried out among adults in Hawtat Bani Tamim Province between November and December 2023. Researchers employed a multistage sampling approach to select participants. Initially, the province was divided into six residential neighborhoods—Birk, Al Hilwa, Elfara, El Hareeg, Al Hilah, and El Salamia—which served as geographic boundaries to ensure representation of diverse demographic and socioeconomic backgrounds. In the second stage, cluster sampling was used within each neighborhood to cover all residential areas. Finally, in the third stage, participants were selected from each cluster using simple random sampling.

2.2. Study Population and Sample Size

The study population consisted of adults from Hawtat Bani Tamim. The sample size was calculated using the formula. [24].

$$\mathrm{n}={\displaystyle \frac{Z2\mathrm{*}p\mathrm{*}(1-p)}{E2}}$$

where: Z = Z-score to the confidence level, which is 1.96 for 95% confidence

p: estimated proportion for the unknown; the researchers use 0.5 for maximum variability

E: margin of error, here was 0.05

By substituting the above values into the formula, the sample size was 384.

The researchers sent out 384 questionnaires via email to the selected participants, resulting in a sample size of 384 individuals. Data were collected using a questionnaire developed after a thorough review of relevant prior studies. The survey was created electronically using Google Forms and distributed to the chosen respondents. Ethical standards were maintained, with both verbal and written consent obtained from all participants.

2.3. Study Variables

The dependent variable in this analysis is hypertension status, a binary outcome coded as 1 if hypertension is present and 0 if not. The independent variables include age, gender, place of residence, marital status, education level, BMI, physical activity, stress, smoking status, and relative infection of hypertension. The selection of these covariates was guided by recommendations from numerous previous studies. [25,26,27,28,29,30,31,32]. The variable BMI is calculated using the formula. [33].

$$\mathrm{BMI}={\displaystyle \frac{\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\left(\mathrm{k}\mathrm{g}\right)}{\left[\mathrm{h}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\right(\mathrm{m}\left)\right]2}}$$

The Body Mass Index (BMI) was categorized into four groups: underweight, normal weight, overweight, and obese.

2.4. Statistical Model

Binary logistic regression is a statistical method that models the relationship between a binary (dichotomous) dependent variable and one or more independent variables. It is commonly used when the dependent variable is categorical and has two possible outcomes. [34,35,36,37,38].

The logistic regression model is formulated as follows.

Let $P(Y=1)$ Denote the probability of the binary outcomes being 1.

$\mathit{X}$The vector of the independent variables.

${\beta }_0,{\beta }_1,\dots \dots \dots ,{\beta }_k$ Represent the coefficients associated with the intercept and the independent variables, respectively.

The binary logistic regression model can be represented as follows:

$$P\left(Y=1\right)={\displaystyle \frac{1}{1+e^{-({\beta }_0+{\beta }_1{x}_1+\dots \dots .+{\beta }_k{x}_k)}}}$$

Where,

${\beta }_0$ Is the intercept of the model.

${\beta }_1,{\beta }_2,\dots \dots \dots ,{\beta }_k$ Are the coefficients associated with the response variable.

Maximum Likelihood Estimation (MLE) is used to estimate the unknown parameter of binary logistic regression. [39,40,41,42,43]. For more details about the theory and applications of logistic regression, see [42,44,45,46,47,48,49,50]

3. Results

Table 1 provides the characteristics of the participants. In this sample, hypertension affected 25.5% of the participants. The highest proportions were in the 31–40 and 20–30 age brackets, at 24.7% and 24.5%, respectively. Rural residents made up most of the sample (68.8%), and there was a higher proportion of females (59.6%) compared to males (40.4%).

Regarding education, the majority had a university degree (71.1%), while only 12.2% held postgraduate qualifications. BMI classifications showed that the categories were the most common (35.7%), and the obese category (28.9%) was the most common.

Occupationally, public sector employees represented nearly half of the sample (48.2%), whereas students comprised only 8.9%. Most participants were single (69.0%).

Regarding physical activity, over half (52.1%) were moderately active, but only 3.1% reported being highly active. The majority (64.3%) had a relative with hypertension.

Most participants did not smoke (88.8%), and a little over half (56.3%) reported experiencing no stress

Table 2 presents the association between covariates and hypertension. Urban residents showed a higher prevalence of hypertension (58 cases, 15%) compared to those in rural areas (40 cases, 10.4%). Hypertension was more frequent in older age groups, with the highest rates observed in individuals over 60 years old (20 cases, 5.2%). Higher BMI was associated with an increased prevalence of hypertension, notably among overweight and obese individuals (32 and 42 cases, respectively).

Public sector employees (56 cases, 14.6%) and those with freelance jobs (16 cases, 4.2%) exhibited higher rates of hypertension compared to students (7 cases, 1.8%).

Educational attainment also appeared to play a role, with those holding a bachelor’s degree showing the highest prevalence (65 cases, 16.9%).

Regarding marital status, single individuals had slightly higher rates of hypertension (54 cases, 14.1%) than their married counterparts (44 cases, 11.5%).

A slightly higher prevalence of hypertension was noted among males (52 cases, 13.5%) compared to females (46 cases, 12%).

No substantial pattern was observed about having a relative infected, engaging in physical activities, or smoking status.

Table 3 displays the findings from the multivariate logistic regression. Age is significantly linked to hypertension (p-value=0.007). Compared to those over 60, younger age groups have substantially lower odds of hypertension. For example, individuals aged 20–30 have 82% lower odds (OR=0.181, 95% CI: 0.067–0.485, p-value=0.001). Regarding gender, males have significantly reduced odds of hypertension relative to females (OR=0.423, 95% CI: 0.192–0.932, p-value=0.033). Education is also significantly associated (p=0.014). Individuals with secondary education or less (OR=0.315, 95% CI: 0.118–0.844, p-value=0.022) and those with a bachelor’s degree (OR=0.294, 95% CI: 0.127–0.679, p-value=0.004) have lower odds of hypertension than those with postgraduate qualifications. In addition, BMI shows a strong association with hypertension (p-value=0.0001). Compared to obese individuals, those with normal BMI (OR=0.262, 95% CI: 0.126–0.544, p-value=0.0002) and those overweight (OR=0.421, 95% CI: 0.220–0.805, p-value=0.009) have significantly lower odds of hypertension. Furthermore, no significant difference in hypertension risk was observed between urban and rural residents (p-value=0.273). There is no significant association between having an infected relative and the risk of hypertension (p-value=0.104). Physical activity level does not show a significant relationship with hypertension (p-value=0.237). Married individuals are significantly more likely to have hypertension compared to singles (OR=3.222, 95% CI: 1.807–6.110, p-value=0.0001).

4. Discussion

This research investigated the prevalence of hypertension and its related factors among adults, with particular attention to demographic, socioeconomic, and lifestyle variables. The multivariate analysis identified several significant relationships, highlighting the intricate interactions between various risk factors that contribute to the development of hypertension.

Excessive hypertension remains a worldwide health concern. 10.2 million deaths and 208 million years of life with a disability were attributed to it each year. According to a Ministry of Health report from 2021, two out of every five adults in the Middle East have hypertension. Nevertheless, many previous studies conducted in Saudi Arabia revealed that the prevalence of HTN in adults was approximately 49%. It is imperative to eradicate this high rate of hypertension in both adults and adolescents. [51,52]. Furthermore, adult hypertension and other chronic diseases are caused by childhood hypertension that is not treated. Because of this, measuring blood pressure, identifying hypertension HTN, and preventing it in kids and teenagers have become international priorities.

The prevalence of hypertension in the current study (25.5%) was lower than the overall prevalence of hypertension in Asia (27.2%). However, when compared to the prevalence found in other studies, the prevalence of hypertension in this study was higher. [4,5,8,53]. Few studies have published findings that were consistent with this investigation.

In this study, no significant association was found between place of residence and hypertension, which is consistent with the results reported in several other studies. [54,55,56,57]. Hypertension was more common in urban areas (15%) compared to rural areas (10%). However, this difference indicates that urban or rural residence may not play a significant role in determining hypertension risk in this population, possibly because both groups share similar lifestyles or have comparable access to healthcare services.

Our research revealed a significant relationship between marital status and hypertension, a finding that is consistent with the results of numerous previous studies. [58,59]. Hypertension was found in 14.5% of single participants, compared to 11.2% among married individuals. This difference may be explained by a mix of social, psychological, and lifestyle influences. Studies have shown that unmarried people, particularly men, face a notably higher risk of developing hypertension, even when factors like age, body mass index, and smoking are taken into consideration.

Furthermore, there was a significant relationship observed between gender and hypertension, a result that aligns with the findings of numerous other studies. [5,18,19]. The overall prevalence was 13.5%, with rates of 12% among males and females, respectively. The occurrence of hypertension varies between males and females, influenced by a combination of biological and social factors. Typically, men exhibit a higher prevalence of hypertension than women during early and middle adulthood. However, as people age, this trend may shift, with older women sometimes experiencing rates of uncontrolled hypertension that are comparable to or even exceed those seen in men.

Our research identified a strong association between BMI and hypertension, a result that is consistent with the findings of numerous other studies. [10,25,30]. The highest prevalence rate was observed among participants who were obese, at 10%. Individuals with obesity are more likely to have hypertension because of various related physiological processes. Increased body fat, particularly around the abdomen, stimulates both the sympathetic nervous system and the renin-angiotensin-aldosterone system (RAAS), resulting in elevated blood pressure.

Furthermore, our study found a significant association between age and hypertension. Such a finding is consistent with many previous studies. [25,28,30].It is noteworthy because age is typically a strong predictor of hypertension. due to Smaller sample sizes, self-reported physical activity measures, or age categorization could explain weaker statistical power. The likelihood of developing hypertension increases with age due to a combination of physiological and lifestyle factors. As people get older, their arteries tend to become stiffer and less flexible, which raises blood pressure. This is mainly because the arterial walls thicken and lose elasticity over time, making it more difficult for blood vessels to handle changes in blood flow. Additionally, aging affects the body’s ability to regulate blood pressure through changes in the nervous system, hormone levels, and kidney function. Reduced physical activity, weight gain, and the presence of other health conditions like diabetes or kidney disease are also more common in older adults, further increasing the risk of hypertension. Because of these factors, a large proportion of people over the age of 60 develop high blood pressure.

The study revealed that there was a significant association between education level and hypertension. Such a result agreed with many previous studies. [10,28,32]. The findings showed that participants with postgraduate education had a higher prevalence (19%)of hypertension compared to other education levels. This finding is contrary to the general expectation that higher education is protective, suggesting the need for further investigation into occupational stress or lifestyle factors among highly educated individuals in this population.

Interestingly, the study found that physical activity was not associated with hypertension; such results align with many studies. [10,30]. This may reflect limitations in how physical activity was measured or reported, or it may indicate that other factors, such as diet or genetic predisposition, play a more prominent role in this population.

The present study found no significant link between smoking and hypertension, which contrasts with the results of several previous studies. [10,28]. This counterintuitive result may be due to confounding factors, such as the “healthy smoker” effect, or underreporting of smoking status among hypertensive individuals.

4.1. Limitations of the Study

The study has several potential limitations. Firstly, the sample size may be relatively small, which could affect the ability to generalize the findings to the entire adult population in the province. Secondly, the data on hypertension prevalence and associated factors could be subject to recall bias or underreporting, especially if the study relies on self-reported information. Thirdly, there may be other important factors, such as lifestyle, diet, physical activity, or access to healthcare, that were not adequately captured or controlled for in the study and could influence the prevalence of hypertension. Fourthly, the findings may be specific to the Hawtat Bani Tamim province and may not be easily applicable to other regions or populations, particularly if there are significant differences in socioeconomic, cultural, or healthcare-related factors. Finally, the absence of longitudinal data could limit the ability to understand the dynamic changes in hypertension prevalence and the long-term impact of the identified determinants.

4.2. Future Direction of the Study

• Increasing the sample size and ensuring better representation of the target population
• Implementing more robust data collection methods to minimize bias and improve data quality.
• Accounting for a wider range of potential confounding factors
• Conducting a longitudinal study to better understand the temporal relationships between determinants and hypertension development.
• Validating the findings in other geographic regions to assess the generalizability of the results.

5. Conclusion

The study identifies age, gender, education level, BMI, marital status, and smoking as key factors associated with hypertension in this population. Notably, some results, such as the reduced risk observed in males and smokers, and the increased risk among those with higher education and married individuals, differ from what is commonly reported in the literature and suggest the need for further research. These findings highlight the importance of considering local circumstances and possible confounding variables when analyzing epidemiological data on hypertension.