Investigating Participant Characteristics and Scores in Hypertension Using Multi-Attribute Utility Theory Analysis on Hypertension Patients from Pakistan

Hypertension (HTN)

Hypertension (HTN) is a significant public health concern worldwide, affecting approximately a third of people during their lifetimes and diagnosed in an estimated 1.13 billion people as of 2021, according to the World Health Organization (WHO).¹ In 2000, about 26.4% of the adult population had HTN, with 26.6% of men and 26.1% of women affected; by 2025, it is projected that 29.2% of the population will have HTN, with 29.0% of men and 29.5% of women being affected.²

HTN in Pakistan

Developing countries exhibit inevitably increasing HTN prevalence, and earlier ages of cardiovascular disease and HTN onset and diagnosis.³ The second National Diabetes Survey of Pakistan (NDSP) reported the urgency of the alarmingly high occurrence of HTN in the country in 2016, with age-adjusted weighted prevalence of HTN of 46.2%, of whom 24.9% had longstanding diagnoses, while 21.3% were newly diagnosed.⁴ Perhaps counterintuitively, HTN prevalence was slightly higher in rural areas (46.8%) compared to urban areas (44.3%), despite common assumptions of healthier and more active lifestyles offsetting HTN in the former. The highest regional prevalence was in the Punjab province (49.2%), followed by Sindh (46.3%), Baluchistan (40.9%), and Khyber Pakhtunkhwa (33.3%). It is important to note that cardiovascular disease is continuously evolving and is projected to be the leading cause of death by 2030. HTN is often associated with cardiovascular disease, and various behavioral and socio-demographic factors are connected to both conditions.^5–7 In this context, studies of particular context can offer insights for healthcare professionals and public health systems to address widespread and systemic issues contributing to local and global care burdens.

HTN and lifestyle

HTN poses a major global health challenge, and innumerable studies have been conducted over many decades to understand HTN causes. Recent studies concur that lifestyle factors fundamentally influence HTN,^6–8 encompassing obesity, excessive fat and alcohol consumption, physical inactivity, smoking, and inadequate nutritional intake (e.g., fruits, vegetables, whole grains, and pulses).^8–10

HTN and socioeconomic problems

Socioeconomic challenges are intertwined with HTN; some socioeconomic factors are associated with HTN etiology, while HTN itself can result in socioeconomic issues (e.g., affecting employment status) and downstream health impacts for individuals, families, and communities. To help address health disparities globally and locally, the WHO-commissioned Centre for Urban Health created a document called “The Solid Facts” to raise awareness, encourage discussion, and inspire action on the social determinants of health. It identifies ten factors: social gradient, stress, early life experiences, social exclusion, employment, unemployment, social support, addiction, access to nutritious food, and transportation.¹¹

HTN and genetics

The genetic and environmental factors that make someone more likely to develop HTN are often shared among family members; thus, the family history of HTN is used as a predictor of individual risk, alongside other risk factors. A study conducted in Sri Lanka involving 5000 adults revealed that HTN prevalence was significantly higher in individuals with a family history (29.3%, n = 572 out of 1951) compared to those without (24.4%, n = 616 out of 2530) (p < 0.001). A family history significantly increases the risk of HTN (OR: 1.29; 95% CI: 1.13-1.47).¹²

HTN and medication (MS)

Lowering blood pressure (BP) is effective in reducing the risks of cardiovascular diseases. It is crucial to maintain a systolic blood pressure (SBP) below 130 mm Hg to prevent complications in patients with heart failure, diabetes, coronary artery disease, stroke, and other cardiovascular conditions. Therefore, the selection of appropriate antihypertensive medications is vital. Some common BP medicines include ACE inhibitors, angiotensin-2 receptor blockers, calcium channel blockers, diuretics, beta-blockers, alpha-blockers, and other diuretics.^13,14

HTN crisis, emergency and symptoms (SS)

HTN can cause fatigue, headaches, a heavy feeling in the neck, dizziness, blurred vision, palpitations, ringing in the ears, and nosebleeds. Patients with HTN who have poor lifestyle habits and coping skills are more likely to experience emotional disturbances, leading to feelings of tension, anxious thoughts, and physical changes such as increased BP and its associated impacts.^15,16

Hypertensive emergencies and crises are common medical emergencies, accounting for over 25% of all emergency cases.^17,18 Due to their high prevalence, all physicians need to be well-prepared in identifying and distinguishing between urgent and emergent HTN. A hypertensive crisis refers to a sudden increase in BP (≥ 180/120 mmHg) accompanied by symptoms that can range from mild, such as headache and dizziness, to severe, such as dyspnea, chest pain, coma, and even death. If the symptoms are mild and there is no acute target organ damage, it is considered a hypertensive crisis. However, if the symptoms pose a life-threatening risk to the patient and there is acute target organ damage, it is classified as a hypertensive emergency.¹⁷

HTN and health status

HTN and its treatment with medication have been found to significantly impact reported health status. HTN is defined as having a SBP (≥140/90 mm Hg or ≥130/80 mm Hg); such elevated SBP has significant impacts, and both systolic and diastolic HTN independently influence the risk of adverse cardiovascular events, regardless of the HTN definition.¹⁹

Literature gap and study rationale

Based on the factors mentioned above that could influence people with HTN to develop cardiovascular disease, such as lifestyle, genetics, economics, overall health, and medications, the authors did not find any previous study employing Multi-Attribute Utility Theory (MAUT) with machine learning. The objective of this therefore to analyze data for HTN and HTN-CVD patients MAUT analysis, identifying attributes of lifestyle, symptoms, health status, genetic factors, social status, and medication status attributes that frequently appear and risk factors. By understanding these patterns and associations, we aim to enhance our knowledge of the relationship between risk groups and the presence of HTN, providing valuable insights for patient care.

Objective

Based on the above information, there is a sense of urgency to understand the characteristics of HTN patients in Pakistan; by using MAUT to analyze and evaluate different factors related to developing HTN, we can gain valuable insights into the characteristics of HTN patients in Pakistan. This analysis can help policymakers and healthcare professionals make evidence-based decisions on which policies and interventions would benefit HTN patients in Pakistan the most. This provides a systematic approach to understanding the complex interplay of various factors and can guide the development of targeted interventions and strategies.

Quantitative questionnaire

This study utilized a cross-sectional survey with 98 participants diagnosed with HTN and cardiovascular HTN (HTN-CVD). The variables of interest were participants’ characteristics and HTN scores. The characteristics of respondents comprised the attributes of lifestyle, socioeconomic factors, symptoms, genetics, medication, and health status. The questionnaire thus included sociodemographic items such as age, job, and income, and asked about health status, symptoms, hereditary factors, dietary habits, and age range (participants were aged from 19 to 60 years). Data analysis was undertaken using MAUT. The patients were briefed about the purpose and protocol of the study, and their written consent (in English/Urdu) was taken for inclusion in the study. The patients examined at Military Hospital (MH) Rawalpindi, Pakistan, and Fauji Foundation Hospital (FFH), Rawalpindi, Pakistan were selected for the current investigation.

Inclusion criteria

The male and female cardiovascular disease patients visiting MH and FFH Rawalpindi, Pakistan, with symptoms of high BP like headache, dizziness, blurred vision, chest pain, shortness of breath, nausea, sleep apnea was included in the present study.

Exclusion criteria

Patients with neurological diseases, chronic renal impairment, known psychological illnesses, asthma and pregnancy, alcoholics, advanced hepatic and renal insufficiency, and suffering from any other endocrinological disorder were excluded from the study.

Data preparation

The BP of hypertensive cardiovascular patients was measured by using a sphygmomanometer. Hypertensive patients were identified on the basis of three measured values of SBP higher than 140 mmHg and three measured values of diastolic blood pressure (DBP) higher than 90 mmHg, or those patients taking hypertensive medicines.

The height of hypertensive cardiovascular patients and normal subjects was measured using a Harpenden Stadiometer with a precision of 0.1 cm. The weight of the boys was determined through a digital weight scale as close as 0.1 Kg. The determination of BMI was carried out through Cole et al.’s equation²⁰:

Data analysis

Multi-Attribute Utility Theory (MAUT)

The MAUT approach involves assigning a weight to each dimension of an object and combining it with a relevant value to determine the overall evaluation; this evaluation is commonly referred to as the utility value. MAUT helps convert multiple interests into a numerical value on a scale of 0 to 1, whereby 0 represents the worst choice, and 1 represents the best. This allows for a direct comparison of different options. Ultimately, MAUT provides a ranking order of alternative evaluations that reflects the decision-maker’s choices.²¹

MAUT procedure

This analysis model consists of several steps: first, we identify relevant attributes for the analysis, such as lifestyle, economic factors, social status, genetics, symptoms of patients, and health status attributes, and assign a point of the score for each of these attribute’s variables based on several references from previous studies looking into the correlation between those attributes to HTN and the HTN-cardiovascular problem with a maximum of score one for the most support reasonable lifestyle/less risk genetic/most advantage socio-economic factors/no symptoms/good health status and minimum of score zero for the opposite condition. We then conducted a multi-variable regression analysis, Shapley additive explanation (SHAP), and feature selection technique for comparison, and assigned weights for those groups of attribute variables based on their contribution to or correlation with HTN and cardiovascular symptoms, with a maximum weight of 3 indicating the greatest correlation. Several attributes are not statistically significant, thus we hand pick those attributes based on discussion with an expert in HTN and HTN-cardiovascular patients.

The total attribute scores and weight scores were calculated with the formula:

Where V(χ) is the overall value of each of the HTN or HTN cardiovascular patients, Wj is the weight of the criteria, and Xi is the value of the attribute score.²²

We calculated the overall score by multiplying each attribute score by its corresponding weight and summing it up to get each case’s overall score. This enables comparison and ranking of cases based on their scores. Finally, we interpreted the results by analyzing the scores and identifying patterns, trends, or relationships between the attributes and the presence of HTN or cardiovascular disease. This helps understand the factors that contribute to these conditions. Figure 1 displays a summary the MAUT procedure used in this study.

Figure 1. MAUT analysis procedure in this study.

Analytical tools

The MAUT analysis was developed using Python 3.7 (Python Software Foundation, Wilmington, DE, USA). We used libraries “pandas”, “numpy”, “statsmodels” and”shap”. The calculation was performed, and the data was visualized using the “seaborn" and “matplotlib” libraries.

Statistical analysis

In the statistical analysis, we presented demographic data as counts (percentages). Age data had two missing values and two outliers. We filled the missing values with median values and addressed outliers using the normal data range. Python version 3.7 was utilized for data preparation and statistical analysis. In our study for the MAUT analysis, the sample size of participants allows us to assess their suitability by considering the complexity of our model and the attributes we are analyzing. It is crucial to focus on the quality and relevance of these attributes rather than just the number of participants. Additionally, we employed feature selection techniques to enhance the robustness of our results with this sample size.

Ethical permission

The study was approved by the institutional board of Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Ethics Committee (No. PMAS-AAUR/1406)

Implications of the study

Studies have shown that patients with complications of HTN cardiovascular have higher MAUT scores in lifestyle, social status, health status, and economic status. This indicates that individuals with CVD tend to have higher scores in these areas. These findings emphasize the importance of educating individuals about the risks, symptoms, and the significance of adopting healthy behaviors. It also highlights the need to pay more attention to individuals who may be disadvantaged by socioeconomic factors. Early detection and management of HTN and CVD can be facilitated through BP monitoring, while access to affordable and quality healthcare services is crucial.¹¹ Our study revealed that cardiovascular disease can begin as early as 13 years old, and genetic factors play a significant role in HTN risk.³⁵ Lifestyle modifications, such as promoting healthy choices through public health initiatives, encourage individuals to follow a balanced diet, engage in regular physical activity, and avoid or quit tobacco consumption. These modifications can help prevent and manage HTN and CVD.³⁶

In terms of policy, the findings from this study suggest the importance of implementing strategies that address the identified attributes with stronger correlations to HTN and HTN-CVD. Policies could focus on promoting healthy eating habits, providing resources for physical activity, implementing smoking cessation programs, considering the physical demands of specific jobs, and ensuring access to appropriate treatment and healthcare services.³⁷ By incorporating these measures into policies, we can work towards reducing the prevalence and impact of HTN and HTN-CVD in the population.

Summary

The MAUT scores were higher for patients with HTN-CVD (373) than for those with HTN alone (270), and there is mean age of HTN patients is higher than HTN-CVD, almost all attributes of 10 highest MAUT score of HTN-CVD higher than HTN patients except for attribute of medication status.

Patients with HTN cardiovascular complications have higher MAUT scores for lifestyle, social status, health status, and economic status. This emphasizes the importance of educating patients (and indeed the general public) about risks, symptoms, and adopting healthy behaviors. Early detection and management are facilitated through BP monitoring and access to quality healthcare. Lifestyle changes like promoting healthy choices can prevent and manage HTN and CVD. Health policies should focus on promoting healthy eating, physical activity, smoking cessation, and ensuring access to treatment and healthcare services.

Author contribution

All authors contributed to the manuscript. SR collected data, curated data, wrote, reviewed, and edited. DN curated data, conducted analysis, interpreted data, wrote results, reviewed, and edited. HW curated data, wrote drafts, interpreted data, reviewed, and edited. MZ: handled project administration, curated data, and edited manuscripts. LH: wrote, supervised, reviewed, and edited. OO, AA, and SSR also wrote, supervised, reviewed, and edited. All authors read and approved the final version of the manuscript.