Insights into Medication Adherence among Jordanian Patients with Dyslipidemia: Evaluating Health Literacy, Well-being, and Doctor-Patient Communication 

Insights into Medication Adherence among Jordanian Patients with Dyslipidemia: Evaluating Health Literacy, Well-being, and Doctor-Patient Communication | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Insights into Medication Adherence among Jordanian Patients with Dyslipidemia: Evaluating Health Literacy, Well-being, and Doctor-Patient Communication Muna Barakat, Samar Thiab, Shaymaa B. Abdulrazzaq, Marah Al-Jamal, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4383265/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The study aimed to assess medication adherence among Jordanian patients with dyslipidemia, in addition to evaluating the impact of health literacy, health well-being, and doctor-patient communication on medication adherence in this population. Methods An observational cross-sectional study was conducted between March to July 2023. A convenient sample of adult Jordanians diagnosed with dyslipidemia was surveyed, by two trained researchers in a tertiary hospital. The study tool consisted of demographic data and several validated scales, including the Lebanese Medication Adherence Scale-14 (LMAS-14), the Doctor-Patient Communication Scale (DPC), the World Health Organization (WHO) well-being index, and the health literacy scale. Scores for each scale were computed, and associations were analyzed using bivariant analysis and linear regression models. Results A total of 410 participants were included in the study with a mean age of 58.62 ± 12.11 years. The sample mean score for LMAS-14 was 35.10, the DPC score was 55.77, the WHO-5 well-being index score was 47.53, and the health literacy score was 38.96. Linear regression models showed that older age ( B = 0.093, p = 0.049), university education ( B = 2.872, p = 0.017), prior surgery ( B = 2.317, p = 0.021), medium income level ( B = 3.605, p = 0.006), and higher doctor-patient communication scores ( B = 0.166, p = 0.003) were associated with higher medication adherence. Conversely, cigarette smoking ( B =-3.854, p = 0.001) and having health insurance ( B =-2.146, p = 0.039) were linked to lower adherence levels. Conclusion Results highlight the intricate interplay of various socio-demographic and clinical factors and their impact on medication adherence. Targeted public health interventions that address socio-demographic conditions, communication quality, and health literacy are pivotal to improved adherence and overall patient outcomes. Biological sciences/Psychology/Human behaviour Health sciences/Health care Health sciences/Medical research Dyslipidemia Medication Adherence Health literacy Doctor Patient Communication Jordan Figures Figure 1 Introduction Dyslipidemia, one of the most common chronic diseases, increases the risk of atherosclerotic and cardiovascular diseases (ASCVD) due to abnormal accumulation of lipids [ 1 ]. Dyslipidemia is divided into primary dyslipidemia triggered by genes, and secondary dyslipidemia developed due to environmental factors such as obesity and diabetes [ 2 ]. Despite the high prevalence of dyslipidemia worldwide, medication adherence is suboptimal, resulting in a massive economic impact of up to $ 19,000 per year per patient [ 3 , 4 ]. Medication adherence is defined by the World Health Organization (WHO) as the degree to which a patient’s behavior corresponds with the agreed recommendations provided by their healthcare provider (HCP) [ 5 , 6 ]. This concept signifies the collaborative effort between the patient and the HCP to enhance the quality of life and general health of the patient [ 7 , 8 ]. Despite the critical and vital role of long-term medication in treating dyslipidemia, adherence is a significant challenge [ 7 ]. Non-adherence to medications manifests in several forms, the most prominent of being non-fulfillment, non-persistence, and non-conformity [ 7 ]. Non-fulfillment includes the failure to start a prescribed treatment; non-persistence involves stopping treatment without medical advice; and non-conformity refers to taking the medication incorrectly, such as in terms of dosing [ 7 , 9 ]. The reasons behind this issue are multifaceted. Among the various factors influencing adherence, health literacy and the quality of the patient-doctor relationship are key determinants [ 10 ]. Adherence to medication among patients with dyslipidemia is strongly associated with a positive patient experience in addition to a good doctor-patient relationship according to a study conducted by Ho-Hyoun Yim et al. [ 11 ]. Health literacy is defined as the level in which a patient can obtain, process, and understand health information needed to make a decision. Health literacy is vital to increase the utilization of health services, decrease mortality, and reduce health costs. Low health literacy can lead to unhealthy lifestyle practice and low adherence to medications, which can subsequently increase the risk of dyslipidemia and cardiovascular diseases [ 12 ]. Doctor-patient communication (DPC) is a crucial component of a patient’s treatment journey and an essential skill for HCPs to possess [ 13 ]. In this process, a physician’s role extends to fostering positive motivations and engaging the patient in their treatments, which is crucial for patients who value a partnership approach and empathetic understanding from medical personnel [ 14 ]. An effective DPC must encompass core functions such as the exchange of information, support for the patient’s self-management, effective handling of uncertainties and emotions, facilitating decision-making, and fostering a robust doctor-patient relationship [ 9 ]. All of these functions are integral to enhancing both individualization and centeredness in patient care [ 9 ]. Actively engaging patients in the decision-making process can lead to less conflict and greater patient satisfaction [ 15 ]. Additionally, effective communication between physicians and patients is linked with improved psychological, somatic, and social health outcomes [ 14 ]. However, the dynamics of the doctor-patient relationship are influenced by a multitude of factors, making it challenging to assess and regulate [ 13 ]. Therefore, this study aims to assess medication adherence among Jordanian patients with dyslipidemia, in addition to evaluating the impact of health literacy, health well-being, and doctor-patient communication on medication adherence in this population. Materials and methods Study design and study period An observational cross-sectional study was conducted in Jordan, between March to July 2023, using an online survey. Eligibility criteria The inclusion criteria for this study encompassed adults aged 18 years and above. Participants had to have a physician diagnosis of dyslipidemia and treated with at least one prescribed medication for dyslipidemia management. Conversely, the exclusion criteria included patients who were unwilling to provide informed consent and those with cognitive impairment or health conditions that hindered their independent participation in the survey without assistance. Data collection procedure The survey was developed using Google Forms, and data was collected by two researchers at the University of Jordan Hospital. A web link to the survey was disseminated by the research team on different communication and social media platforms, including WhatsApp, Facebook, Twitter, and Instagram. The survey was in Arabic, the official language of Jordan. At the survey’s onset, participants were provided with a participant Information Statement (PIS) detailing the study’s primary goal and the anticipated time required to complete the survey (approximately 10 minutes). The survey also emphasized that participation was entirely voluntary. A snowball sampling technique was employed, encouraging respondents to participate and further share the survey with others. To reduce potential response biases, participants independently completed the survey without any assistance from the researchers. Sample size calculation Using the Epi info software, with a 95% confidence interval, a standard deviation of 0.5, a margin of error of 5%, and a 33.3% prevalence of dyslipidemia among the Jordanian population, the minimum sample size was calculated to be approximately 342 participants [ 16 , 17 ]. We targeted a larger sample to consider for missing and invalid data. Ethical consideration The Institutional Review Board (IRB) approval was obtained from the Faculty of Pharmacy-Applied Science Private University (Approval number: 2023-PHA-14). The ethical guidelines of the Declaration of Helsinki were adhered to in this investigation. All participants volunteered to be part of this study, and their responses were kept confidential. Prior to accessing the online survey, all participants provided written informed consent through an online consent form to participate in the research. Study Tool Socio-demographic and clinical characteristics The online survey covered the following criteria: Age, gender (male, female), body mass index (BMI, measured in kg/m 2 ), marital status (single, divorced, widowed, married), employment status (unemployed, student, retired, employed), work field (medical, non-medical), educational level (university degree or lower), and monthly income (No income, Less than 250 JOD, 251–500 JOD, 501–750 JOD, 751–1000 JOD, More than 1000 JOD). Additionally, it included household crowding index (HCI) which is calculated by dividing the total number of people living in a household by the total number of rooms available for accommodation, smoking status (cigarettes or e-cigarettes: yes, no; if yes, average number of cigarettes/e-cigarettes smoked per day and total duration of smoking in years), water-pipe smoking (Hookah: yes, no; if yes, average number of water-pipes smoked per day and total duration of smoking in years), history of any surgeries (yes, no), history of cardiovascular disease (yes, no), history of other diseases (yes, no), health insurance coverage (yes, no), and chronic medication use (yes, no). Adherence to medications The Lebanese Medication Adherence Scale-14 (LMAS-14), a scale validated in Arabic, was utilized in this study to assess adherence to dyslipidemia management [ 18 ]. LMAS-14 evaluates occupational factors, which include forgetfulness during busy periods (such as intensive work or travel), whether the patient is invited to lunch or dinner, prohibitions on certain food items during the treatment period due to potential food-medication interactions, and delays in purchasing a new pillbox when the old one is emptied. Additionally, LMAS-14 examines psychological factors, such as experiencing any secondary effects, feeling clinically better or worse, and behavioral changes that occur concurrently with improvements in laboratory exams. Factors of annoyance are also incorporated, including frustration overtaking numerous pills, the tedium of chronic treatment, and the experience of side effects. Lastly, LMAS-14 assesses economic factors, including the extent of health insurance coverage for medication costs and the expense of the medications themselves [ 19 ]. The scale comprised 14 items, each scored on a 4-point Likert scale ranging from zero (most of the time − indicating lower adherence) to three (Never − indicating higher adherence). The total LMAS-14 score was calculated by summing all the responses, ranging from 0 to 42 [ 20 ], with higher scores indicating higher medication adherence. The scale’s reliability in the study was high, as evidenced by Cronbach’s alpha of 0.98 (Supplementary Table S1). Doctor-patient communication The Doctor Patient Communication scale was used to quantitatively assess the quality and effectiveness of communication between doctors and patients [ 13 ]. It consists of 15 items, each offering four possible answers: ‘No’, ‘Possibly no’, ‘Possibly yes’, and ‘Yes.’ These responses are rated on a Likert-type scale ranging from 1 to 4 points, with higher scores indicating higher communication between the patient and the doctor. The scale’s reliability in the study was high, as evidenced by Cronbach’s alpha of 0.98. Quality of life The WHO-5 Well-being Index (WHO-5) was used to assess the level of subjective psychological well-being of patients over the past two weeks. It comprises 5 items, each scored on a 6-point Likert scale, where 0= ‘At no time’, 1= ‘Some of the time’, 2= ‘Less than half the time’, 3= ‘More than half the time’, 4= ‘Most of the time’, 5= ‘All of the time’. The raw score is calculated by totaling the scores of the five answers and ranges from 0 to 25, with 0 representing the worst possible and 25 representing the best possible quality of life. To obtain a percentage score, which ranges from 0 to 100, the raw score is multiplied by 4. On this scale, a percentage score of 0 indicates the worst possible quality of life, whereas a score of 100% indicates the best possible quality of life [ 21 ]. The scale’s reliability in the study was high, as evidenced by Cronbach’s alpha of 0.988. Health literacy The Health Literacy scale (HLS) is an assessment tool designed to evaluate three distinct levels of health literacy. Functional literacy, the basic level, emphasizes essential reading and writing skills necessary for effective daily functioning. The intermediate level, communicative literacy, involves more advanced abilities that enable active participation in daily activities, as well as understanding and interpreting various forms of communication and adapting to new information in changing environments. The highest level, critical literacy, entails advanced skills in critically analyzing information and applying this insight to control life events and situations more effectively. The HLS is composed of 16 items, categorized into three categories: functional, communicative, and critical literacy, all scored on a 5-point Likert scale (from 1: never to 4: always). The total score for each participant is calculated by summing the scores of these items, ranging from 16 to 64 points, with higher scores indicating better health literacy [ 22 ]. In this study, the HLS-14 demonstrated strong reliability, as indicated by Cronbach’s alpha value of 0.914. The questionnaires were initially in English and were then converted into Arabic, the commonly used local language, by proficient speakers in both English and Arabic. They were also adapted to be more applicable to the general populace. Statistical analysis All responses from the survey were downloaded from the Google Forms website and transferred to Microsoft Excel for organization. The data were summarized and presented as mean ± standard deviation for continuous variables and frequency (percentage) for categorical variables. First, Cronbach’s alpha coefficient was calculated to assess the internal consistency reliability of each of the four health-related scales used in this study. All assessed scales had an excellent internal consistency reliability score with a Cronbach’s alpha score greater than 0.9 (Supplementary Table S1). Bivariate associations were examined of each of the continuous outcome variables (LMAS-14 scores, DPC scores, WHO Well-being scores, and health literacy scores) with sociodemographic characteristics and medical history using t-tests, ANOVAs, and univariate linear regression models, as appropriate. Multiple linear regression models were conducted to assess the relationship of these predictors with each of the continuous outcome variables. Data cleaning and analyses were performed using Statistical Package for Social Sciences version 25.0. p < 0.05 indicates statistical significance. Results Sample socio demographic characteristics. A total of 410 individuals participated in the study. Their mean ± SD age was 58.62 ± 12.11 years and more than half of the sample were women (52.9%). The majority of participants were married (77.6%), employed in the non-medical field (92.2%), and had higher educational attainment, specifically a university degree (60.2%; Table 1 ). Table 1 Sample socio-demographics, medical history, and health-related measures. Characteristics (N = 410) Frequency n (%) or mean ± SD Socio-demographics Age*, in years 58.62 ± 12.11 Gender Male 193 (47.1) Female 217 (52.9) Body mass index (BMI)*, kg/m 2 30.20 ± 5.80 Marital status Single/divorced/widowed 92 (22.4) Married 318 (77.6) Employment status Unemployed/student/retired 264 (64.4) Employed 146 (35.6) Work field Medical field 32 (7.8) Non-medical field 378 (92.2) Educational level, university degree No 163 (39.8) Yes 247 (60.2) Monthly Income Low (no income/ 1,000 SAR) 31 (7.6) Household crowding index (HCI)* 1.00 ± 0.61 Lifestyle factors Smoker (cigarettes/E-cigarettes), yes 129 (31.5) Smoker waterpipe (Hookah), yes 47 (11.5) Alcohol intake, yes 32 (7.8) Medical history Any previous surgeries, yes 244 (59.5) History of cardiovascular disease, yes 170 (41.5) History of other chronic diseases, yes 365 (89) Health insurance, yes 262 (63.9) *Missing data for the following variables: age (n = 1), BMI (n = 5). Missing observations were not included in the percentages. JOD = Jordanian Dinar. Mean scores of the assessed health-related scales including LMAS-14, the DPC, WHO-5 Well-being index, and health literacy are presented in Fig. 1 . Bivariate associations LMAS-14 scale Older individuals and those with higher educational attainment had significantly higher mean LMAS-14 scores compared to their younger counterparts ( B = 0.088, p = 0.034) and those with lower educational levels (37.06 ± 8.18 vs 32.14 ± 11.91, p < 0.001; Table 2 ). Additionally, individuals who work in the medical field (p = 0.015), those without health insurance (p = 0.005), as well as those with medium monthly income (p < 0.001), had higher mean LMAS-14 scores than those in the non-medical work fields, with insurance coverage, and with low or high monthly incomes respectively. Higher mean DPC scores and health literacy scales were also significantly associated with higher mean LMAS-14 scores. On the contrary, a higher household crowding index was significantly linked with lower mean LMAS-14 scores ( B =-2.462, p = 0.003; Table 2 ). Table 2 Associations of LMAS-14 scores with socio-demographics, medical history and other health-related measures. LMAS-14 score Mean ± SD p-value Gender 0.994 Male 35.11 ± 10.46 Female 35.10 ± 9.82 Marital status 0.424 Single/divorced/widowed 35.85 ± 9.18 Married 34.89 ± 10.37 Employment status 0.167 Unemployed/student/retired 34.62 ± 10.71 Employed 35.99 ± 8.89 Work field 0.015 Medical field 38.47 ± 7.59 Non-medical field 34.82 ± 10.25 Educational level, university level < 0.001 No 32.14 ± 11.91 Yes 37.06 ± 8.18 Monthly Income < 0.001 Low 31.22 ± 11.90 Medium 36.62 ± 8.83 High 35.55 ± 10.46 Smoking (cigarettes or E-cigarettes) 0.059 No 35.78 ± 9.57 Yes 33.64 ± 11.09 Smoking water-pipe (Hookah) 0.127 No 34.90 ± 10.42 Yes 36.72 ± 7.21 Alcohol intake 0.832 No 35.07 ± 10.12 Yes 34.47 ± 10.23 Any previous surgeries 0.195 No 34.30 ± 11.08 Yes 35.66 ± 9.38 History of cardiovascular disease 0.178 No 35.67 ± 10.20 Yes 34.31 ± 9.96 History of other diseases 0.078 No 37.31 ± 8.55 Yes 34.83 ± 10.27 Health insurance 0.005 No 36.82 ± 8.24 Yes 34.14 ± 10.93 B (95% CI) p-value Age, in years 0.088 (0.007 0.169) 0.034 Body mass index (BMI), kg/m 2 -0.124 (-0.294 0.047) 0.156 Household crowding index (HCI) -2.462 (-4.056 -0.868) 0.003 Doctor-Patient Communication Scale 0.213 (0.100 0.325) < 0.001 WHO-5 well-being index, over 100 0.018 (-0.016 0.053) 0.305 Health literacy scale 0.143 (0.045 0.241) 0.005 Doctor-patient communication scale Individuals who work in the medical field vs non-medical field (57.94 ± 4.81 vs 55.58 ± 8.84, p = 0.018) and those with higher education vs low (56.50 ± 7.77 vs 54.65 ± 9.68, p = 0.042; Table 3 ) had significantly higher mean DPC scores. Having a history of cardiovascular diseases vs. no history (54.54 ± 10.11 vs 56.63 ± 7.27, p = 0.022) and a higher household crowding index ( B =-2.333, p = 0.001) were related to lower mean doctor-patient communication scores. Moreover, higher LMAS-14 ( B = 0.154, p < 0.001) and health literacy ( B = 0.113, p = 0.009) scores were significantly associated with higher doctor-patient communication mean scores (Table 3 ). Table 3 Associations of Doctor-Patient Communication scores with socio-demographics, medical history, and health-related measures. Doctor Patient Communication scale Mean ± SD p-value Gender 0.196 Male 55.17 ± 9.90 Female 56.29 ± 7.27 Marital status 0.579 Single/divorced/widowed 55.33 ± 10.19 Married 55.89 ± 8.12 Employment status 0.118 Unemployed/student/retired 55.31 ± 9.45 Employed 56.58 ± 6.81 Work field 0.018 Medical field 57.94 ± 4.81 Non-medical field 55.58 ± 8.84 Educational level, university level 0.042 No 54.65 ± 9.68 Yes 56.50 ± 7.77 Monthly Income 0.717 Low 55.27 ± 8.99 Medium 55.88 ± 8.76 High 56.55 ± 5.57 Smoking (cigarettes or E-cigarettes) 0.949 No 55.75 ± 8.45 Yes 55.81 ± 9.00 Smoking water-pipe (Hookah) 0.388 No 55.63 ± 8.85 Yes 56.79 ± 6.58 Alcohol intake 0.601 No 55.83 ± 8.32 Yes 55.00 ± 11.75 Any previous surgeries 0.623 No 55.51 ± 8.23 Yes 55.94 ± 8.88 History of cardiovascular disease No 56.63 ± 7.27 0.022 Yes 54.54 ± 10.11 History of other diseases 0.935 No 55.67 ± 6.68 Yes 55.78 ± 8.83 Health insurance 0.769 No 55.93 ± 8.03 Yes 55.67 ± 8.95 B (95% CI) p-value Age, in years -0.048 (-0.117 0.021) 0.175 Body mass index (BMI), kg/m 2 -0.032 (-0.178 0.114) 0.670 Household crowding index (HCI) -2.333 (-3.688 -0.979) 0.001 LMAS-14 scale 0.154 (0.073 0.236) < 0.001 WHO-5 well-being index, over 100 0.014 (-0.015 0.044) 0.339 Health literacy scale 0.113 (0.029 0.196) 0.009 WHO wellbeing index Participants with high versus low or medium income and those with health insurance versus no insurance had significantly higher mean WHO well-being scores (p = 0.047 and p = 0.008, respectively; Table 4 ). Older age ( B = 0.265, p = 0.023) and higher health literacy ( B = 0.269, p = 0.059) scores were significantly related to higher WHO well-being scores. Table 4 Associations of WHO-wellbeing index scores with socio-demographics, medical history, and health-related measures. WHO wellbeing index scale Mean ± SD p-value Gender 0.457 Male 48.64 ± 27.96 Female 46.54 ± 29.01 Marital status 0.152 Single/divorced/widowed 43.78 ± 30.60 Married 48.62 ± 27.83 Employment status 0.535 Unemployed/student/retired 48.18 ± 29.06 Employed 46.36 ± 27.53 Work field 0.791 Medical field 46.25 ± 30.48 Non-medical field 47.64 ± 28.37 Educational level, university level 0.575 No 46.53 ± 31.09 Yes 48.19 ± 26.11 Monthly Income 0.047 Low 44.40 ± 29.23 Medium 47.51 ± 28.16 High 58.71 ± 26.98 Smoking (cigarettes or E-cigarettes) 0.665 No 47.12 ± 28.41 Yes 48.43 ± 28.81 Smoking water-pipe (Hookah) 0.356 No 47.06 ± 28.44 Yes 51.15 ± 29.06 Alcohol intake 0.080 No 46.81 ± 28.46 Yes 56.00 ± 28.08 Any previous surgeries 0.588 No 48.46 ± 29.04 Yes 46.90 ± 28.18 History of cardiovascular disease 0.912 No 47.40 ± 27.38 Yes 47.72 ± 30.09 History of other diseases 0.385 No 51.02 ± 29.30 Yes 47.10 ± 28.42 Health insurance 0.008 No 42.73 ± 26.46 Yes 50.24 ± 29.30 B (95% CI) p-value Age, in years 0.265 (0.037 0.493) 0.023 Body mass index (BMI), kg/m 2 -0.370 (-0.849 0.108) 0.129 Household crowding index (HCI) -1.691 (-6.232 2.849) 0.464 LMAS-14 scale 0.143 (-0.131 0.417) 0.305 Doctor patient communication scale 0.157 (-0.165 0.478) 0.339 Health literacy scale 0.269 (-0.010 0.547) 0.059 Health literacy scale Individuals with higher education compared to lower levels (40.37 ± 9.39 vs 36.83 ± 10.34, p < 0.001), those with medium income compared to low or high income (39.88 ± 9.83 vs 36.61 ± 10.03 vs 39.29 ± 9.21, p = 0.014; Table 5 ), and those without health insurance compared to those with insurance coverage (40.27 ± 9.51 vs 38.23 ± 10.08, p = 0.045) had significantly higher health literacy scores. Higher LMAS-14, WHO well-being, and doctor-patient communication scores were significantly related to higher health literacy scores (p < 0.05 for all; Table 5 ). Table 5 Associations of health literacy scores with socio-demographics, medical history, and health-related measures. Health Literacy scale Mean ± SD p-value Gender 0.890 Male 38.89 ± 9.56 Female 39.03 ± 10.25 Marital status 0.138 Single/divorced/widowed 40.50 ± 11.71 Married 38.52 ± 9.31 Employment status 0.737 Unemployed/student/retired 38.84 ± 10.24 Employed 39.18 ± 9.35 Work field 0.132 Medical field 41.50 ± 10.98 Non-medical field 38.75 ± 9.81 Educational level, university level < 0.001 No 36.83 ± 10.34 Yes 40.37 ± 9.39 Monthly Income 0.014 Low 36.61 ± 10.03 Medium 39.88 ± 9.83 High 39.29 ± 9.21 Smoking (cigarettes or E-cigarettes) 0.150 No 39.44 ± 10.22 Yes 37.92 ± 9.17 Smoking water-pipe (Hookah) 0.917 No 38.94 ± 9.96 Yes 39.11 ± 9.68 Alcohol intake 0.793 No 38.93 ± 9.77 Yes 39.41 ± 11.73 Any previous surgeries 0.362 No 39.51 ± 9.58 Yes 38.59 ± 10.15 History of cardiovascular disease 0.090 No 39.65 ± 10.48 Yes 38.00 ± 9.01 History of other diseases 0.878 No 39.18 ± 9.32 Yes 38.94 ± 10.00 Health insurance 0.045 No 40.27 ± 9.51 Yes 38.23 ± 10.08 B (95% CI) p-value Age*, in years 0.058 (-0.022 0.137) 0.157 Body mass index (BMI)*, kg/m 2 0.051 (-0.115 0.218) 0.545 Household crowding index (HCI)* -1.232 (-2.808 0.345) 0.125 LMAS-14 scale 0.137 (0.043 0.232) 0.005 WHO-5 well-being index, over 100 0.033 (-0.001 0.066) 0.059 Doctor patient communication scale 0.149 (0.038 0.260) 0.009 Linear regression Table 6 represents the models of linear regression. In the first model, advanced age ( B = 0.093, p = 0.049), university education ( B = 2.872, p = 0.017), a history of prior surgery ( B = 2.317, p = 0.021), and a medium income level ( B = 3.605, p = 0.006) were significantly associated with higher adherence to lipid-lowering medications, as indicated by the LMAS-14 scale. A higher doctor-patient communication score was related to higher adherence ( B = 0.166, p = 0.003), while cigarette smoking ( B =-3.854, p = 0.001) and having health insurance ( B =-2.146, p = 0.039) showed lower overall adherence. Table 6. Linear regression. Model 1: Taking LMAS-14 mean scores as the dependent variable. LMAS-14 score Unstandardized B p-value 95% CI Age 0.093 0.049 0.000 0.186 Educational level (university degree vs no*) 2.872 0.017 0.508 5.236 Monthly income (Medium vs. low*) 3.605 0.006 1.027 6.182 Cigarette smoking (yes vs. no*) -3.854 0.001 -6.084 -1.624 Health insurance (yes vs. no*) -2.146 0.039 -4.184 -0.107 Doctor-Patient Communication score 0.166 0.003 0.056 0.276 Previous surgery (yes vs, no*) 2.317 0.021 0.349 4.284 Variables entered: socio-demographics (age, BMI, household crowding index, employment status, work field, education, monthly income, cigarette smoking, waterpipe smoking), medical history (previous surgeries, history of cardiovascular diseases, history of other diseases, health insurance), Doctor-Patient Communication score, and Health literacy scale score. *stands for the reference group. Model 2: Taking the Doctor-patient communication scale mean score as the dependent variable. Doctor patient communication scores Unstandardized B p-value 95% CI Age, in years -0.074 0.069 -0.154 0.006 Gender (females vs males*) 1.635 0.064 -0.097 3.367 Household crowding index -2.204 0.002 -3.599 -0.809 LMAS-14 score 0.119 0.006 0.034 0.203 Health literacy scale score 0.079 0.065 -0.005 0.163 Variables entered: socio-demographics (age, gender, employment status, work field, education, household crowding index), medical history (history of cardiovascular diseases), LMAS-14 scores, Health literacy scale. *stands for reference group. Model 3: Taking the WHO wellbeing index mean scores as the dependent variable. WHO wellbeing scores Unstandardized B p-value 95% CI Age, in years 0.278 0.017 0.049 0.506 Marital status (married vs single*) 6.737 0.047 0.085 13.389 Health insurance (yes vs. no*) 8.080 0.006 2.356 13.804 Health literacy scale score 0.330 0.021 0.050 0.610 Variables entered: socio-demographics (age, marital status, monthly income, BMI, alcohol intake), medical history (health insurance), and Health literacy scale score. *stands for reference group. Model 4: Taking the Health literacy scale mean scores as the dependent variable. Health Literacy scores Unstandardized B p-value 95% CI Marital status (married vs. single/divorced/widowed) -2.271 0.057 -4.606 0.065 Educational level (university degree vs no*) 2.237 0.065 -0.136 4.609 Health insurance (yes vs. no*) -1.784 0.096 -3.887 0.318 Cigarette smoking (yes vs. no*) -2.023 0.070 -4.209 0.164 Doctor-Patient Communication score 0.117 0.042 0.004 0.231 WHO-5 well-being index score 0.032 0.062 -0.002 0.066 Variables entered: sociodemographic (age, marital status, work field, education, monthly income, smoking cigarette, household crowding index), medical history (history of cardiovascular disease, health insurance), LMAS-14 score, Doctor-Patient Communication score, WHO-5 well-being index. *stands for reference group. Higher LMAS-14 adherence scores ( B = 0.119, p = 0.006) were significantly related to a higher doctor-patient mean scores, whereas a higher household crowding index ( B =-2.204, p = 0.002) was significantly related to lower doctor-patient communication scores (Model 2). Model 3 showed that older age ( B = 0.278, p = 0.017), being married (B = 6.737, p = 0.047), having health insurance ( B = 8.080, p = 0.006), and having higher health literacy scores ( B = 0.330, p = 0.021) were all significantly associated with higher WHO-wellbeing scores. Model 4 shows that married individuals had significantly lower health literacy scores than single ones ( B =-2.271, p = 0.05). Having a higher doctor-patient communication score was significantly related to higher health literacy ( B = 0.117, p = 0.042). Discussion Main findings This study investigated medication adherence and the role of various socio-demographic and clinical factors on adherence levels in Jordanian patients with dyslipidemia. In particular, our study sheds light on the role of health literacy, health well-being, and doctor-patient communication in shaping medication adherence patterns, thereby contributing to a comprehensive understanding of adherence behaviors in this context. Results showed that older age, higher educational levels, prior surgery history, medium income levels, and better doctor-patient communication were associated with higher medication adherence. Conversely, cigarette smoking and access to health insurance were linked to lower medication adherence levels. These findings highlight the need for tailored interventions that take into account sociodemographic characteristics and the dynamics of physician-patient communication in an effort to improve medication adherence in patients with dyslipidemia. Sociodemographic factors Age was found to have a significant influence on medication adherence. In this study, older patients were more likely to adhere to medications, which is consistent with the findings of a study conducted in the United States (US) on patients with coronary heart disease [ 23 ]. Similarly, older patients were more adherent to taking and refilling their medications than younger patients in two other US studies [ 24 , 25 ]. A systematic review also concluded that older age was associated with better adherence to dyslipidemia medications [ 26 ]. In a published overview of systematic reviews, adherence was found to be the lowest in very young and very old people [ 27 ]. Patients with higher levels of education showed better medication adherence. This finding is in accordance with the results of several studies where proper patient education about their health condition was found to have a positive impact on medication adherence [ 28 – 30 ]. Patients with better education and knowledge about their disease were found to be more adherent to their medications in a study conducted in China for patients with coronary heart disease [ 28 ] and another similar study targeting Chinese patients who are prescribed antihypertensive medications [ 31 ]. Similar findings were observed in a randomized controlled trial conducted in Thailand targeting patients with rheumatoid arthritis [ 29 ]. Moreover, a systematic review and meta-analysis was carried out to evaluate the effectiveness of education on medication adherence for patients with hypertension, hyperlipidemia, and diabetes and concluded that education improves health literacy, and as a result, improves medication adherence [ 30 ]. Medium compared to low-income levels was associated with higher medication adherence in this study. Yet, some research in this area has found no association between different levels of income and medication adherence, highlighting the complexity and variability of this relationship across different populations [ 32 – 35 ]. A possible explanation for our findings could be that individuals with medium income levels tend to have more financial resources compared to those with low incomes. This may translate into better access to healthcare services, including medications, which can contribute to improved adherence. Interestingly, better doctor-patient communication was a predictor for higher adherence in this study. This is consistent with the findings of several studies[ 26 , 36 ]. Generally, patients who communicate effectively with their doctors tend to better comprehend their therapy, adhere more to medications, and place greater faith and trust in their doctors, especially when their doctor demonstrates good communication skills, such as using simple language instead of complicated terms and showing more compassion by listening patiently and attentively [ 37 ]. This highlights the need for interventions that enhance doctor-patient communication, such as training healthcare providers in effective communication skills, promoting patient-centered care approaches, and utilizing communication tools like clear medication instructions and follow-up reminders. These interventions can play a crucial role in improving medication adherence and overall patient outcomes. Cigarette smoking and access to health insurance were potential barriers to medication adherence in our sample. A recent systematic review on the determinant of non-adherence to medications for dyslipidemia also reported higher non-adherence among current smokers[ 26 ]. Contrary to our findings, this review found that medication adherence was higher among patients with health insurance[ 26 ]. Regarding the WHO wellbeing index, patients with higher health literacy showed higher wellbeing scores. Indeed, patients who possess a better understanding of their condition and treatment options may feel more confident in managing their health and making informed decisions, consequently experiencing a higher quality of life [ 38 ]. Overall, these findings underscore the need for holistic approaches to dyslipidemia management that not only focus on medical interventions but also address the psychosocial and educational needs of patients to optimize their overall well-being [ 39 ]. Limitations This study is subject to a few key limitations. Firstly, its cross-sectional design, utilizing a convenience sample, limits the ability to infer causality from the associations observed and affects the generalizability of the findings. Additionally, there is a possibility of information bias since the data was self-reported by participants. This data collection method can lead to overestimation of adherence and recall bias. Finally, the study’s focus on dyslipidemia patients in Jordan, despite the demographic diversity in Jordan, might limit the applicability of its findings to other population groups within the country. Conclusion The findings of this research highlight a multifaceted relationship between various socio demographic and clinical factors and their impact on medication adherence. This research emphasizes the need for comprehensive and individualized strategies in the management of dyslipidemia and adherence to dyslipidemia medication, considering elements such as socio-economic conditions, the quality of communication between doctor and patient, and health literacy levels. These insights are valuable for shaping healthcare strategies and interventions designed to enhance medication adherence and improve patient outcomes in Jordan. Declarations Author contributions DM and MB: conceptualization. DM and MB: investigation. DM, MB, MAJ, FAH, and RA: methodology. RA: project administration. DM and MB: supervision: DM, MB, BH, SH, and HH. Formal analysis: DM and SM. writing— original draft preparation: ST, SHB. SM, DM and MB. writing—reviewing and editing: DM, MB, SM, ST, SH and HH. All authors read and approved the final version of the manuscript. Conflict of interest None Funding NA Data availability statement The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Berberich, A.J. and R.A. Hegele, A modern approach to dyslipidemia. Endocrine Reviews, 2022. 43 (4): p. 611-653. Yuan, Y., et al., Dyslipidemia: Causes, symptoms and treatment. International Journal of Trend in Scientific Research and Development 2021. 5 (2): p. 1013-6. Cutler, R.L., et al., Economic impact of medication non-adherence by disease groups: a systematic review. BMJ open, 2018. 8 (1). Naderi, S.H., J.P. Bestwick, and D.S. Wald, Adherence to drugs that prevent cardiovascular disease: meta-analysis on 376,162 patients. The American journal of medicine, 2012. 125 (9): p. 882-887. e1. Al Qasem, A., F. Smith, and S. Clifford, Adherence to medication among chronic patients in Middle Eastern countries: review of studies. EMHJ-Eastern Mediterranean Health Journal, 17 (4), 356-363, 2011, 2011. Alvi, Y., et al., World Health Organization dimensions of adherence to antiretroviral therapy: A study at antiretroviral therapy centre, Aligarh. Indian journal of community medicine: official publication of Indian Association of Preventive & Social Medicine, 2019. 44 (2): p. 118. Jimmy, B. and J. Jose, Patient medication adherence: measures in daily practice. Oman medical journal, 2011. 26 (3): p. 155. Gellad, W.F., J.L. Grenard, and E.A. McGlynn, A review of barriers to medication adherence: a framework for driving policy options. 2009. Zill, J.M., et al., Measurement of physician-patient communication—a systematic review. PLoS one, 2014. 9 (12): p. e112637. Al-Noumani, H., et al., Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study. Scientific Reports, 2023. 13 (1): p. 7067. Yim, H.-H., et al., Association between Patient Experience and Medication Compliance of Dyslipidemia: Using Korea National Health and Nutrition Examination Survey (2015). Korean Journal of Family Medicine, 2021. 42 (2): p. 116. Gurgel do Amaral, M., et al., Do uncontrolled hypertension, diabetes, dyslipidemia, and obesity mediate the relationship between health literacy and chronic kidney disease complications? International journal of environmental research public health, 2021. 18 (10): p. 5235. Sustersic, M., et al., A scale assessing doctor-patient communication in a context of acute conditions based on a systematic review. PLoS One, 2018. 13 (2): p. e0192306. Świątoniowska-Lonc, N., et al., Impact of satisfaction with physician–patient communication on self-care and adherence in patients with hypertension: cross-sectional study. BMC health services research, 2020. 20 (1): p. 1-9. Thomson, R., M. Murtagh, and F.M. Khaw, Tensions in public health policy: patient engagement, evidence-based public health and health inequalities. BMJ Quality & Safety, 2005. 14 (6): p. 398-400. Suresh, K. and S. Chandrashekara, Sample size estimation and power analysis for clinical research studies. Journal of human reproductive sciences, 2012. 5 (1): p. 7. AlMuhaidib, S., et al., Prevalence and factors associated with dyslipidemia among adolescents in Saudi Arabia. Scientific Reports, 2022. 12 (1): p. 16888. Bou Serhal, R., et al., A new Lebanese medication adherence scale: validation in Lebanese hypertensive adults. International journal of hypertension, 2018. 2018 . Mroueh, L., et al., Evaluation of medication adherence among Lebanese diabetic patients. Pharmacy Practice (Granada), 2018. 16 (4). Hallit, S., et al., Medication adherence among Lebanese adult patients with hypothyroidism: Validation of the Lebanese Medication Adherence Scale and correlates. Clinical Epidemiology and Global Health, 2021. 9 : p. 196-201. Omani-Samani, R., et al., The WHO-5 well-being index: A validation study in people with infertility. Iranian journal of public health, 2019. 48 (11): p. 2058. Aoki, T. and M. Inoue, Association between health literacy and patient experience of primary care attributes: a cross-sectional study in Japan. PLoS One, 2017. 12 (9): p. e0184565. Kripalani, S., M.E. Gatti, and T.A. Jacobson, Association of age, health literacy, and medication management strategies with cardiovascular medication adherence. Patient education and counseling, 2010. 81 (2): p. 177-181. Cohen, M.J., et al., Predictors of medication adherence postdischarge: the impact of patient age, insurance status, and prior adherence. Journal of hospital medicine, 2012. 7 (6): p. 470-475. Rolnick, S.J., et al., Patient characteristics associated with medication adherence. Clinical medicine & research, 2013. 11 (2): p. 54-65. Lopes, J. and P. Santos, Determinants of non-adherence to the medications for dyslipidemia: a systematic review. Patient preference and adherence, 2021: p. 1853-1871. Gast, A. and T. Mathes, Medication adherence influencing factors—an (updated) overview of systematic reviews. Systematic reviews, 2019. 8 : p. 1-17. Zhao, S., et al., Education is critical for medication adherence in patients with coronary heart disease. Acta cardiologica, 2015. 70 (2): p. 197-204. Taibanguay, N., et al., Effect of patient education on medication adherence of patients with rheumatoid arthritis: a randomized controlled trial. Patient preference and adherence, 2019: p. 119-129. Tan, J.P., K.K.F. Cheng, and R.C.J. Siah, A systematic review and meta‐analysis on the effectiveness of education on medication adherence for patients with hypertension, hyperlipidaemia and diabetes. Journal of advanced nursing, 2019. 75 (11): p. 2478-2494. Lee, G.K., et al., Determinants of medication adherence to antihypertensive medications among a Chinese population using Morisky Medication Adherence Scale. PloS one, 2013. 8 (4): p. e62775. Aravindakshan, R., S.B. Abraham, and R. Aiyappan, Medication adherence to oral hypoglycemic drugs among individuals with Type 2 diabetes mellitus–A community study. Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine, 2021. 46 (3): p. 503. Bonger, Z., S. Shiferaw, and E.Z. Tariku, Adherence to diabetic self-care practices and its associated factors among patients with type 2 diabetes in Addis Ababa, Ethiopia. Patient preference and adherence, 2018: p. 963-970. Trief, P.M., et al., Medication adherence in young adults with youth-onset type 2 diabetes: iCount, an observational study. Diabetes research and clinical practice, 2022. 184 : p. 109216. Saraiva, E.M.S., et al., Medication non-adherence in patients with type 2 diabetes mellitus with full access to medicines. Journal of Diabetes & Metabolic Disorders, 2020. 19 : p. 1105-1113. Casula, M., E. Tragni, and A.L. Catapano, Adherence to lipid-lowering treatment: the patient perspective. Patient preference and adherence, 2012. 6 (2012): p. 805-814. Wu, D., et al., Patient Trust in Physicians Matters—Understanding the Role of a Mobile Patient Education System and Patient-Physician Communication in Improving Patient Adherence Behavior: Field Study. Journal of Medical Internet Research, 2022. 24 (12): p. e42941. Streja, E. and D. Streja. Management of Dyslipidemia in the Elderly . 2020. Cho, S.M.J., et al., Associations between age and dyslipidemia are differed by education level: The Cardiovascular and Metabolic Diseases Etiology Research Center (CMERC) cohort. Lipids in Health Disease, 2020. 19 : p. 1-12. Additional Declarations No competing interests reported. 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the sample\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4383265/v1/29c6551d34573870a29ed2ab.png"},{"id":57905731,"identity":"5e8ff575-e54f-4b32-8ca3-0954ab323308","added_by":"auto","created_at":"2024-06-07 09:47:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1294781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4383265/v1/748c635d-bfae-47fe-83db-96912859981d.pdf"},{"id":56888989,"identity":"0a711779-5f1b-44a5-9981-d3b9a8d7d560","added_by":"auto","created_at":"2024-05-21 19:05:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14780,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4383265/v1/79b90d4944b4ef76c568951d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Insights into Medication Adherence among Jordanian Patients with Dyslipidemia: Evaluating Health Literacy, Well-being, and Doctor-Patient Communication ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDyslipidemia, one of the most common chronic diseases, increases the risk of atherosclerotic and cardiovascular diseases (ASCVD) due to abnormal accumulation of lipids [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Dyslipidemia is divided into primary dyslipidemia triggered by genes, and secondary dyslipidemia developed due to environmental factors such as obesity and diabetes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite the high prevalence of dyslipidemia worldwide, medication adherence is suboptimal, resulting in a massive economic impact of up to \u003cspan\u003e$\u003c/span\u003e19,000 per year per patient [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMedication adherence is defined by the World Health Organization (WHO) as the degree to which a patient\u0026rsquo;s behavior corresponds with the agreed recommendations provided by their healthcare provider (HCP) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This concept signifies the collaborative effort between the patient and the HCP to enhance the quality of life and general health of the patient [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite the critical and vital role of long-term medication in treating dyslipidemia, adherence is a significant challenge [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Non-adherence to medications manifests in several forms, the most prominent of being non-fulfillment, non-persistence, and non-conformity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Non-fulfillment includes the failure to start a prescribed treatment; non-persistence involves stopping treatment without medical advice; and non-conformity refers to taking the medication incorrectly, such as in terms of dosing [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The reasons behind this issue are multifaceted. Among the various factors influencing adherence, health literacy and the quality of the patient-doctor relationship are key determinants [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Adherence to medication among patients with dyslipidemia is strongly associated with a positive patient experience in addition to a good doctor-patient relationship according to a study conducted by Ho-Hyoun Yim et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealth literacy is defined as the level in which a patient can obtain, process, and understand health information needed to make a decision. Health literacy is vital to increase the utilization of health services, decrease mortality, and reduce health costs. Low health literacy can lead to unhealthy lifestyle practice and low adherence to medications, which can subsequently increase the risk of dyslipidemia and cardiovascular diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDoctor-patient communication (DPC) is a crucial component of a patient\u0026rsquo;s treatment journey and an essential skill for HCPs to possess [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this process, a physician\u0026rsquo;s role extends to fostering positive motivations and engaging the patient in their treatments, which is crucial for patients who value a partnership approach and empathetic understanding from medical personnel [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. An effective DPC must encompass core functions such as the exchange of information, support for the patient\u0026rsquo;s self-management, effective handling of uncertainties and emotions, facilitating decision-making, and fostering a robust doctor-patient relationship [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. All of these functions are integral to enhancing both individualization and centeredness in patient care [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Actively engaging patients in the decision-making process can lead to less conflict and greater patient satisfaction [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, effective communication between physicians and patients is linked with improved psychological, somatic, and social health outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the dynamics of the doctor-patient relationship are influenced by a multitude of factors, making it challenging to assess and regulate [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, this study aims to assess medication adherence among Jordanian patients with dyslipidemia, in addition to evaluating the impact of health literacy, health well-being, and doctor-patient communication on medication adherence in this population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study period\u003c/h2\u003e \u003cp\u003eAn observational cross-sectional study was conducted in Jordan, between March to July 2023, using an online survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria for this study encompassed adults aged 18 years and above. Participants had to have a physician diagnosis of dyslipidemia and treated with at least one prescribed medication for dyslipidemia management. Conversely, the exclusion criteria included patients who were unwilling to provide informed consent and those with cognitive impairment or health conditions that hindered their independent participation in the survey without assistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection procedure\u003c/h2\u003e \u003cp\u003eThe survey was developed using Google Forms, and data was collected by two researchers at the University of Jordan Hospital. A web link to the survey was disseminated by the research team on different communication and social media platforms, including WhatsApp, Facebook, Twitter, and Instagram. The survey was in Arabic, the official language of Jordan. At the survey\u0026rsquo;s onset, participants were provided with a participant Information Statement (PIS) detailing the study\u0026rsquo;s primary goal and the anticipated time required to complete the survey (approximately 10 minutes). The survey also emphasized that participation was entirely voluntary. A snowball sampling technique was employed, encouraging respondents to participate and further share the survey with others. To reduce potential response biases, participants independently completed the survey without any assistance from the researchers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculation\u003c/h2\u003e \u003cp\u003eUsing the Epi info software, with a 95% confidence interval, a standard deviation of 0.5, a margin of error of 5%, and a 33.3% prevalence of dyslipidemia among the Jordanian population, the minimum sample size was calculated to be approximately 342 participants [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We targeted a larger sample to consider for missing and invalid data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEthical consideration\u003c/h2\u003e \u003cp\u003e The Institutional Review Board (IRB) approval was obtained from the Faculty of Pharmacy-Applied Science Private University (Approval number: 2023-PHA-14). The ethical guidelines of the Declaration of Helsinki were adhered to in this investigation. All participants volunteered to be part of this study, and their responses were kept confidential. Prior to accessing the online survey, all participants provided written informed consent through an online consent form to participate in the research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Tool\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSocio-demographic and clinical characteristics\u003c/h2\u003e \u003cp\u003eThe online survey covered the following criteria: Age, gender (male, female), body mass index (BMI, measured in kg/m\u003csup\u003e2\u003c/sup\u003e), marital status (single, divorced, widowed, married), employment status (unemployed, student, retired, employed), work field (medical, non-medical), educational level (university degree or lower), and monthly income (No income, Less than 250 JOD, 251\u0026ndash;500 JOD, 501\u0026ndash;750 JOD, 751\u0026ndash;1000 JOD, More than 1000 JOD). Additionally, it included household crowding index (HCI) which is calculated by dividing the total number of people living in a household by the total number of rooms available for accommodation, smoking status (cigarettes or e-cigarettes: yes, no; if yes, average number of cigarettes/e-cigarettes smoked per day and total duration of smoking in years), water-pipe smoking (Hookah: yes, no; if yes, average number of water-pipes smoked per day and total duration of smoking in years), history of any surgeries (yes, no), history of cardiovascular disease (yes, no), history of other diseases (yes, no), health insurance coverage (yes, no), and chronic medication use (yes, no).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAdherence to medications\u003c/h2\u003e \u003cp\u003eThe Lebanese Medication Adherence Scale-14 (LMAS-14), a scale validated in Arabic, was utilized in this study to assess adherence to dyslipidemia management [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. LMAS-14 evaluates occupational factors, which include forgetfulness during busy periods (such as intensive work or travel), whether the patient is invited to lunch or dinner, prohibitions on certain food items during the treatment period due to potential food-medication interactions, and delays in purchasing a new pillbox when the old one is emptied. Additionally, LMAS-14 examines psychological factors, such as experiencing any secondary effects, feeling clinically better or worse, and behavioral changes that occur concurrently with improvements in laboratory exams. Factors of annoyance are also incorporated, including frustration overtaking numerous pills, the tedium of chronic treatment, and the experience of side effects. Lastly, LMAS-14 assesses economic factors, including the extent of health insurance coverage for medication costs and the expense of the medications themselves [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The scale comprised 14 items, each scored on a 4-point Likert scale ranging from zero (most of the time\u0026thinsp;\u0026minus;\u0026thinsp;indicating lower adherence) to three (Never\u0026thinsp;\u0026minus;\u0026thinsp;indicating higher adherence). The total LMAS-14 score was calculated by summing all the responses, ranging from 0 to 42 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with higher scores indicating higher medication adherence. The scale\u0026rsquo;s reliability in the study was high, as evidenced by Cronbach\u0026rsquo;s alpha of 0.98 (Supplementary Table S1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDoctor-patient communication\u003c/h2\u003e \u003cp\u003eThe Doctor Patient Communication scale was used to quantitatively assess the quality and effectiveness of communication between doctors and patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It consists of 15 items, each offering four possible answers: \u0026lsquo;No\u0026rsquo;, \u0026lsquo;Possibly no\u0026rsquo;, \u0026lsquo;Possibly yes\u0026rsquo;, and \u0026lsquo;Yes.\u0026rsquo; These responses are rated on a Likert-type scale ranging from 1 to 4 points, with higher scores indicating higher communication between the patient and the doctor. The scale\u0026rsquo;s reliability in the study was high, as evidenced by Cronbach\u0026rsquo;s alpha of 0.98.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuality of life\u003c/h2\u003e \u003cp\u003eThe WHO-5 Well-being Index (WHO-5) was used to assess the level of subjective psychological well-being of patients over the past two weeks. It comprises 5 items, each scored on a 6-point Likert scale, where 0= \u0026lsquo;At no time\u0026rsquo;, 1= \u0026lsquo;Some of the time\u0026rsquo;, 2= \u0026lsquo;Less than half the time\u0026rsquo;, 3= \u0026lsquo;More than half the time\u0026rsquo;, 4= \u0026lsquo;Most of the time\u0026rsquo;, 5= \u0026lsquo;All of the time\u0026rsquo;. The raw score is calculated by totaling the scores of the five answers and ranges from 0 to 25, with 0 representing the worst possible and 25 representing the best possible quality of life. To obtain a percentage score, which ranges from 0 to 100, the raw score is multiplied by 4. On this scale, a percentage score of 0 indicates the worst possible quality of life, whereas a score of 100% indicates the best possible quality of life [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The scale\u0026rsquo;s reliability in the study was high, as evidenced by Cronbach\u0026rsquo;s alpha of 0.988.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHealth literacy\u003c/h2\u003e \u003cp\u003eThe Health Literacy scale (HLS) is an assessment tool designed to evaluate three distinct levels of health literacy. Functional literacy, the basic level, emphasizes essential reading and writing skills necessary for effective daily functioning. The intermediate level, communicative literacy, involves more advanced abilities that enable active participation in daily activities, as well as understanding and interpreting various forms of communication and adapting to new information in changing environments. The highest level, critical literacy, entails advanced skills in critically analyzing information and applying this insight to control life events and situations more effectively. The HLS is composed of 16 items, categorized into three categories: functional, communicative, and critical literacy, all scored on a 5-point Likert scale (from 1: never to 4: always). The total score for each participant is calculated by summing the scores of these items, ranging from 16 to 64 points, with higher scores indicating better health literacy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, the HLS-14 demonstrated strong reliability, as indicated by Cronbach\u0026rsquo;s alpha value of 0.914. The questionnaires were initially in English and were then converted into Arabic, the commonly used local language, by proficient speakers in both English and Arabic. They were also adapted to be more applicable to the general populace.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll responses from the survey were downloaded from the Google Forms website and transferred to Microsoft Excel for organization. The data were summarized and presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and frequency (percentage) for categorical variables. First, Cronbach\u0026rsquo;s alpha coefficient was calculated to assess the internal consistency reliability of each of the four health-related scales used in this study. All assessed scales had an excellent internal consistency reliability score with a Cronbach\u0026rsquo;s alpha score greater than 0.9 (Supplementary Table S1). Bivariate associations were examined of each of the continuous outcome variables (LMAS-14 scores, DPC scores, WHO Well-being scores, and health literacy scores) with sociodemographic characteristics and medical history using t-tests, ANOVAs, and univariate linear regression models, as appropriate. Multiple linear regression models were conducted to assess the relationship of these predictors with each of the continuous outcome variables. Data cleaning and analyses were performed using Statistical Package for Social Sciences version 25.0. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSample socio demographic characteristics.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 410 individuals participated in the study. Their mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age was 58.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11 years and more than half of the sample were women (52.9%). The majority of participants were married (77.6%), employed in the non-medical field (92.2%), and had higher educational attainment, specifically a university degree (60.2%; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample socio-demographics, medical history, and health-related measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics (N\u0026thinsp;=\u0026thinsp;410)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency n (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocio-demographics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge*, in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (47.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (52.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI)*, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.20\u0026thinsp;\u0026plusmn;\u0026thinsp;5.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318 (77.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed/student/retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e264 (64.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (35.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-medical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378 (92.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level, university degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (39.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247 (60.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (no income/ \u0026lt;250 JOD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (26.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium (250-1,000 SAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270 (65.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;1,000 SAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (7.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold crowding index (HCI)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLifestyle factors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker (cigarettes/E-cigarettes), yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (31.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker waterpipe (Hookah), yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMedical history\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny previous surgeries, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (59.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cardiovascular disease, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (41.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of other chronic diseases, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365 (89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262 (63.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e*Missing data for the following variables: age (n\u0026thinsp;=\u0026thinsp;1), BMI (n\u0026thinsp;=\u0026thinsp;5).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eMissing observations were \u003cb\u003enot included\u003c/b\u003e in the percentages.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eJOD\u0026thinsp;=\u0026thinsp;Jordanian Dinar.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean scores of the assessed health-related scales including LMAS-14, the DPC, WHO-5 Well-being index, and health literacy are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBivariate associations\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eLMAS-14 scale\u003c/h2\u003e \u003cp\u003eOlder individuals and those with higher educational attainment had significantly higher mean LMAS-14 scores compared to their younger counterparts (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.088, p\u0026thinsp;=\u0026thinsp;0.034) and those with lower educational levels (37.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18 vs 32.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, individuals who work in the medical field (p\u0026thinsp;=\u0026thinsp;0.015), those without health insurance (p\u0026thinsp;=\u0026thinsp;0.005), as well as those with medium monthly income (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had higher mean LMAS-14 scores than those in the non-medical work fields, with insurance coverage, and with low or high monthly incomes respectively. Higher mean DPC scores and health literacy scales were also significantly associated with higher mean LMAS-14 scores. On the contrary, a higher household crowding index was significantly linked with lower mean LMAS-14 scores (\u003cem\u003eB\u003c/em\u003e=-2.462, p\u0026thinsp;=\u0026thinsp;0.003; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of LMAS-14 scores with socio-demographics, medical history and other health-related measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMAS-14 score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.11\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.10\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed/student/retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-medical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level, university level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.14\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.62\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (cigarettes or E-cigarettes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking water-pipe (Hookah)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.90\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.07\u0026thinsp;\u0026plusmn;\u0026thinsp;10.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny previous surgeries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.30\u0026thinsp;\u0026plusmn;\u0026thinsp;11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.66\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of other diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.31\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.82\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.14\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e \u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.088 (0.007 0.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.124 (-0.294 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold crowding index (HCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.462 (-4.056 -0.868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor-Patient Communication Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.213 (0.100 0.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO-5 well-being index, over 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018 (-0.016 0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth literacy scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.143 (0.045 0.241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDoctor-patient communication scale\u003c/h2\u003e \u003cp\u003eIndividuals who work in the medical field vs non-medical field (57.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81 vs 55.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84, p\u0026thinsp;=\u0026thinsp;0.018) and those with higher education vs low (56.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77 vs 54.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68, p\u0026thinsp;=\u0026thinsp;0.042; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) had significantly higher mean DPC scores. Having a history of cardiovascular diseases vs. no history (54.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11 vs 56.63\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27, p\u0026thinsp;=\u0026thinsp;0.022) and a higher household crowding index (\u003cem\u003eB\u003c/em\u003e=-2.333, p\u0026thinsp;=\u0026thinsp;0.001) were related to lower mean doctor-patient communication scores. Moreover, higher LMAS-14 (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.154, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and health literacy (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.113, p\u0026thinsp;=\u0026thinsp;0.009) scores were significantly associated with higher doctor-patient communication mean scores (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of Doctor-Patient Communication scores with socio-demographics, medical history, and health-related measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor Patient Communication scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.33\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed/student/retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-medical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level, university level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.55\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (cigarettes or E-cigarettes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.81\u0026thinsp;\u0026plusmn;\u0026thinsp;9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking water-pipe (Hookah)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.79\u0026thinsp;\u0026plusmn;\u0026thinsp;6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.83\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny previous surgeries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.51\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.63\u0026thinsp;\u0026plusmn;\u0026thinsp;7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of other diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.78\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e \u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.048 (-0.117 0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.032 (-0.178 0.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold crowding index (HCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.333 (-3.688 -0.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMAS-14 scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.154 (0.073 0.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO-5 well-being index, over 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014 (-0.015 0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth literacy scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.113 (0.029 0.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eWHO wellbeing index\u003c/h2\u003e \u003cp\u003eParticipants with high versus low or medium income and those with health insurance versus no insurance had significantly higher mean WHO well-being scores (p\u0026thinsp;=\u0026thinsp;0.047 and p\u0026thinsp;=\u0026thinsp;0.008, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Older age (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.265, p\u0026thinsp;=\u0026thinsp;0.023) and higher health literacy (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.269, p\u0026thinsp;=\u0026thinsp;0.059) scores were significantly related to higher WHO well-being scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of WHO-wellbeing index scores with socio-demographics, medical history, and health-related measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHO wellbeing index scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.64\u0026thinsp;\u0026plusmn;\u0026thinsp;27.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.54\u0026thinsp;\u0026plusmn;\u0026thinsp;29.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.78\u0026thinsp;\u0026plusmn;\u0026thinsp;30.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.62\u0026thinsp;\u0026plusmn;\u0026thinsp;27.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed/student/retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.18\u0026thinsp;\u0026plusmn;\u0026thinsp;29.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.36\u0026thinsp;\u0026plusmn;\u0026thinsp;27.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.25\u0026thinsp;\u0026plusmn;\u0026thinsp;30.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-medical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.64\u0026thinsp;\u0026plusmn;\u0026thinsp;28.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level, university level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.53\u0026thinsp;\u0026plusmn;\u0026thinsp;31.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.19\u0026thinsp;\u0026plusmn;\u0026thinsp;26.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.40\u0026thinsp;\u0026plusmn;\u0026thinsp;29.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.51\u0026thinsp;\u0026plusmn;\u0026thinsp;28.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.71\u0026thinsp;\u0026plusmn;\u0026thinsp;26.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (cigarettes or E-cigarettes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.12\u0026thinsp;\u0026plusmn;\u0026thinsp;28.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.43\u0026thinsp;\u0026plusmn;\u0026thinsp;28.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking water-pipe (Hookah)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.06\u0026thinsp;\u0026plusmn;\u0026thinsp;28.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.15\u0026thinsp;\u0026plusmn;\u0026thinsp;29.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.81\u0026thinsp;\u0026plusmn;\u0026thinsp;28.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.00\u0026thinsp;\u0026plusmn;\u0026thinsp;28.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny previous surgeries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.46\u0026thinsp;\u0026plusmn;\u0026thinsp;29.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.90\u0026thinsp;\u0026plusmn;\u0026thinsp;28.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.40\u0026thinsp;\u0026plusmn;\u0026thinsp;27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.72\u0026thinsp;\u0026plusmn;\u0026thinsp;30.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of other diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.02\u0026thinsp;\u0026plusmn;\u0026thinsp;29.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.10\u0026thinsp;\u0026plusmn;\u0026thinsp;28.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.73\u0026thinsp;\u0026plusmn;\u0026thinsp;26.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.24\u0026thinsp;\u0026plusmn;\u0026thinsp;29.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e \u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.265 (0.037 0.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.370 (-0.849 0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold crowding index (HCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.691 (-6.232 2.849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMAS-14 scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.143 (-0.131 0.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor patient communication scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157 (-0.165 0.478)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth literacy scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.269 (-0.010 0.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eHealth literacy scale\u003c/h2\u003e \u003cp\u003eIndividuals with higher education compared to lower levels (40.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39 vs 36.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), those with medium income compared to low or high income (39.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83 vs 36.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03 vs 39.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21, p\u0026thinsp;=\u0026thinsp;0.014; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and those without health insurance compared to those with insurance coverage (40.27\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51 vs 38.23\u0026thinsp;\u0026plusmn;\u0026thinsp;10.08, p\u0026thinsp;=\u0026thinsp;0.045) had significantly higher health literacy scores. Higher LMAS-14, WHO well-being, and doctor-patient communication scores were significantly related to higher health literacy scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of health literacy scores with socio-demographics, medical history, and health-related measures.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Literacy scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.50\u0026thinsp;\u0026plusmn;\u0026thinsp;11.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.52\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed/student/retired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-medical field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.75\u0026thinsp;\u0026plusmn;\u0026thinsp;9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level, university level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (cigarettes or E-cigarettes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.44\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.92\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking water-pipe (Hookah)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.11\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.93\u0026thinsp;\u0026plusmn;\u0026thinsp;9.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.41\u0026thinsp;\u0026plusmn;\u0026thinsp;11.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny previous surgeries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.59\u0026thinsp;\u0026plusmn;\u0026thinsp;10.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of other diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.27\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.23\u0026thinsp;\u0026plusmn;\u0026thinsp;10.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e \u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge*, in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058 (-0.022 0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI)*, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051 (-0.115 0.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold crowding index (HCI)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.232 (-2.808 0.345)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMAS-14 scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.137 (0.043 0.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO-5 well-being index, over 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033 (-0.001 0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor patient communication scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.149 (0.038 0.260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLinear regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e represents the models of linear regression. In the first model, advanced age (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.093, p\u0026thinsp;=\u0026thinsp;0.049), university education (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.872, p\u0026thinsp;=\u0026thinsp;0.017), a history of prior surgery (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.317, p\u0026thinsp;=\u0026thinsp;0.021), and a medium income level (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.605, p\u0026thinsp;=\u0026thinsp;0.006) were significantly associated with higher adherence to lipid-lowering medications, as indicated by the LMAS-14 scale. A higher doctor-patient communication score was related to higher adherence (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.166, p\u0026thinsp;=\u0026thinsp;0.003), while cigarette smoking (\u003cem\u003eB\u003c/em\u003e=-3.854, p\u0026thinsp;=\u0026thinsp;0.001) and having health insurance (\u003cem\u003eB\u003c/em\u003e=-2.146, p\u0026thinsp;=\u0026thinsp;0.039) showed lower overall adherence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e Linear regression.\u003c/p\u003e\n\u003cp\u003eModel 1: Taking LMAS-14 mean scores as the dependent variable.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.892857142857146%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLMAS-14 score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized \u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.000 0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eEducational level (university degree vs no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e2.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.508 5.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eMonthly income (Medium vs. low*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e3.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e1.027 6.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eCigarette smoking (yes vs. no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-3.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-6.084 -1.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eHealth insurance (yes vs. no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-2.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-4.184 -0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eDoctor-Patient Communication\u0026nbsp;score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.056 0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003ePrevious surgery (yes vs, no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e2.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.349 4.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eVariables entered: socio-demographics (age, BMI, household crowding index, employment status, work field, education, monthly income, cigarette smoking, waterpipe smoking), medical history (previous surgeries, history of cardiovascular diseases, history of other diseases, health insurance), Doctor-Patient Communication score,\u0026nbsp;and Health literacy scale score.\u003c/p\u003e\n\u003cp\u003e*stands for the reference group.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 2: Taking the Doctor-patient communication scale mean score as the dependent variable.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.892857142857146%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDoctor patient communication scores\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized \u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eAge, in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.154 0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eGender (females vs males*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e1.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.097 3.367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eHousehold crowding index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-2.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-3.599 -0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eLMAS-14 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.034 0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eHealth literacy scale score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005 0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eVariables entered: socio-demographics (age, gender, employment status, work field, education, household crowding index), medical history (history of cardiovascular diseases), LMAS-14 scores,\u0026nbsp;Health literacy scale.\u003c/p\u003e\n\u003cp\u003e*stands for reference group.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 3: Taking the WHO wellbeing index mean scores as the dependent variable.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.892857142857146%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eWHO wellbeing scores\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized \u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eAge, in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.049 0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status (married vs single*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e6.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.085 13.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eHealth insurance (yes vs. no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e8.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e2.356 13.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eHealth literacy scale score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.050 0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eVariables entered: socio-demographics (age, marital status, monthly income, BMI, alcohol intake), medical history (health insurance), and Health literacy scale score.\u003c/p\u003e\n\u003cp\u003e*stands for reference group.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 4: Taking the Health literacy scale mean scores as the dependent variable.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.892857142857146%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHealth Literacy scores\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized \u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status (married vs. single/divorced/widowed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-2.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.057\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-4.606 0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eEducational level (university degree vs no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e2.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.136 4.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eHealth insurance (yes vs. no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-1.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-3.887 0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eCigarette smoking (yes vs. no*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-2.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-4.209 0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eDoctor-Patient Communication\u0026nbsp;score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.004 0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.107142857142854%\" valign=\"top\"\u003e\n \u003cp\u003eWHO-5 well-being index score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.002 0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eVariables entered: sociodemographic (age, marital status, work field, education, monthly income, smoking cigarette, household crowding index), medical history (history of cardiovascular disease, health insurance), LMAS-14 score, Doctor-Patient Communication score, WHO-5 well-being index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*stands for reference group.\u003c/p\u003e\u003cp\u003eHigher LMAS-14 adherence scores (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.119, p\u0026thinsp;=\u0026thinsp;0.006) were significantly related to a higher doctor-patient mean scores, whereas a higher household crowding index (\u003cem\u003eB\u003c/em\u003e=-2.204, p\u0026thinsp;=\u0026thinsp;0.002) was significantly related to lower doctor-patient communication scores (Model 2).\u003c/p\u003e \u003cp\u003eModel 3 showed that older age (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.278, p\u0026thinsp;=\u0026thinsp;0.017), being married (B\u0026thinsp;=\u0026thinsp;6.737, p\u0026thinsp;=\u0026thinsp;0.047), having health insurance (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.080, p\u0026thinsp;=\u0026thinsp;0.006), and having higher health literacy scores (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.330, p\u0026thinsp;=\u0026thinsp;0.021) were all significantly associated with higher WHO-wellbeing scores.\u003c/p\u003e \u003cp\u003eModel 4 shows that married individuals had significantly lower health literacy scores than single ones (\u003cem\u003eB\u003c/em\u003e=-2.271, p\u0026thinsp;=\u0026thinsp;0.05). Having a higher doctor-patient communication score was significantly related to higher health literacy (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.117, p\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eThis study investigated medication adherence and the role of various socio-demographic and clinical factors on adherence levels in Jordanian patients with dyslipidemia. In particular, our study sheds light on the role of health literacy, health well-being, and doctor-patient communication in shaping medication adherence patterns, thereby contributing to a comprehensive understanding of adherence behaviors in this context. Results showed that older age, higher educational levels, prior surgery history, medium income levels, and better doctor-patient communication were associated with higher medication adherence. Conversely, cigarette smoking and access to health insurance were linked to lower medication adherence levels. These findings highlight the need for tailored interventions that take into account sociodemographic characteristics and the dynamics of physician-patient communication in an effort to improve medication adherence in patients with dyslipidemia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic factors\u003c/h2\u003e \u003cp\u003eAge was found to have a significant influence on medication adherence. In this study, older patients were more likely to adhere to medications, which is consistent with the findings of a study conducted in the United States (US) on patients with coronary heart disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, older patients were more adherent to taking and refilling their medications than younger patients in two other US studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A systematic review also concluded that older age was associated with better adherence to dyslipidemia medications [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In a published overview of systematic reviews, adherence was found to be the lowest in very young and very old people [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with higher levels of education showed better medication adherence. This finding is in accordance with the results of several studies where proper patient education about their health condition was found to have a positive impact on medication adherence [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Patients with better education and knowledge about their disease were found to be more adherent to their medications in a study conducted in China for patients with coronary heart disease [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and another similar study targeting Chinese patients who are prescribed antihypertensive medications [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similar findings were observed in a randomized controlled trial conducted in Thailand targeting patients with rheumatoid arthritis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, a systematic review and meta-analysis was carried out to evaluate the effectiveness of education on medication adherence for patients with hypertension, hyperlipidemia, and diabetes and concluded that education improves health literacy, and as a result, improves medication adherence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMedium compared to low-income levels was associated with higher medication adherence in this study. Yet, some research in this area has found no association between different levels of income and medication adherence, highlighting the complexity and variability of this relationship across different populations [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A possible explanation for our findings could be that individuals with medium income levels tend to have more financial resources compared to those with low incomes. This may translate into better access to healthcare services, including medications, which can contribute to improved adherence.\u003c/p\u003e \u003cp\u003eInterestingly, better doctor-patient communication was a predictor for higher adherence in this study. This is consistent with the findings of several studies[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Generally, patients who communicate effectively with their doctors tend to better comprehend their therapy, adhere more to medications, and place greater faith and trust in their doctors, especially when their doctor demonstrates good communication skills, such as using simple language instead of complicated terms and showing more compassion by listening patiently and attentively [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This highlights the need for interventions that enhance doctor-patient communication, such as training healthcare providers in effective communication skills, promoting patient-centered care approaches, and utilizing communication tools like clear medication instructions and follow-up reminders. These interventions can play a crucial role in improving medication adherence and overall patient outcomes.\u003c/p\u003e \u003cp\u003eCigarette smoking and access to health insurance were potential barriers to medication adherence in our sample. A recent systematic review on the determinant of non-adherence to medications for dyslipidemia also reported higher non-adherence among current smokers[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Contrary to our findings, this review found that medication adherence was higher among patients with health insurance[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the WHO wellbeing index, patients with higher health literacy showed higher wellbeing scores. Indeed, patients who possess a better understanding of their condition and treatment options may feel more confident in managing their health and making informed decisions, consequently experiencing a higher quality of life [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, these findings underscore the need for holistic approaches to dyslipidemia management that not only focus on medical interventions but also address the psychosocial and educational needs of patients to optimize their overall well-being [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study is subject to a few key limitations. Firstly, its cross-sectional design, utilizing a convenience sample, limits the ability to infer causality from the associations observed and affects the generalizability of the findings. Additionally, there is a possibility of information bias since the data was self-reported by participants. This data collection method can lead to overestimation of adherence and recall bias. Finally, the study\u0026rsquo;s focus on dyslipidemia patients in Jordan, despite the demographic diversity in Jordan, might limit the applicability of its findings to other population groups within the country.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this research highlight a multifaceted relationship between various socio demographic and clinical factors and their impact on medication adherence. This research emphasizes the need for comprehensive and individualized strategies in the management of dyslipidemia and adherence to dyslipidemia medication, considering elements such as socio-economic conditions, the quality of communication between doctor and patient, and health literacy levels. These insights are valuable for shaping healthcare strategies and interventions designed to enhance medication adherence and improve patient outcomes in Jordan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDM and MB: conceptualization. DM and MB: investigation. DM, MB, MAJ, FAH, and RA: methodology. RA: project administration. DM and MB: supervision: DM, MB, BH, SH, and HH. Formal analysis: DM and SM. writing\u0026mdash; original draft preparation: ST, SHB. SM, DM and MB. writing\u0026mdash;reviewing and editing: DM, MB, SM, ST, SH and HH. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerberich, A.J. and R.A. 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Streja. \u003cem\u003eManagement of Dyslipidemia in the Elderly\u003c/em\u003e. 2020.\u003c/li\u003e\n\u003cli\u003eCho, S.M.J., et al., \u003cem\u003eAssociations between age and dyslipidemia are differed by education level: The Cardiovascular and Metabolic Diseases Etiology Research Center (CMERC) cohort.\u003c/em\u003e Lipids in Health Disease, 2020. \u003cstrong\u003e19\u003c/strong\u003e: p. 1-12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dyslipidemia, Medication Adherence, Health literacy, Doctor Patient Communication, Jordan","lastPublishedDoi":"10.21203/rs.3.rs-4383265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4383265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe study aimed to assess medication adherence among Jordanian patients with dyslipidemia, in addition to evaluating the impact of health literacy, health well-being, and doctor-patient communication on medication adherence in this population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn observational cross-sectional study was conducted between March to July 2023. A convenient sample of adult Jordanians diagnosed with dyslipidemia was surveyed, by two trained researchers in a tertiary hospital. The study tool consisted of demographic data and several validated scales, including the Lebanese Medication Adherence Scale-14 (LMAS-14), the Doctor-Patient Communication Scale (DPC), the World Health Organization (WHO) well-being index, and the health literacy scale. Scores for each scale were computed, and associations were analyzed using bivariant analysis and linear regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 410 participants were included in the study with a mean age of 58.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11 years. The sample mean score for LMAS-14 was 35.10, the DPC score was 55.77, the WHO-5 well-being index score was 47.53, and the health literacy score was 38.96. Linear regression models showed that older age (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.093, p\u0026thinsp;=\u0026thinsp;0.049), university education (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.872, p\u0026thinsp;=\u0026thinsp;0.017), prior surgery (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.317, p\u0026thinsp;=\u0026thinsp;0.021), medium income level (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.605, p\u0026thinsp;=\u0026thinsp;0.006), and higher doctor-patient communication scores (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.166, p\u0026thinsp;=\u0026thinsp;0.003) were associated with higher medication adherence. Conversely, cigarette smoking (\u003cem\u003eB\u003c/em\u003e=-3.854, p\u0026thinsp;=\u0026thinsp;0.001) and having health insurance (\u003cem\u003eB\u003c/em\u003e=-2.146, p\u0026thinsp;=\u0026thinsp;0.039) were linked to lower adherence levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eResults highlight the intricate interplay of various socio-demographic and clinical factors and their impact on medication adherence. Targeted public health interventions that address socio-demographic conditions, communication quality, and health literacy are pivotal to improved adherence and overall patient outcomes.\u003c/p\u003e","manuscriptTitle":"Insights into Medication Adherence among Jordanian Patients with Dyslipidemia: Evaluating Health Literacy, Well-being, and Doctor-Patient Communication ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-21 19:04:59","doi":"10.21203/rs.3.rs-4383265/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d46d6c7-bd6d-4b57-8f57-d6e45e62a7a5","owner":[],"postedDate":"May 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32171322,"name":"Biological sciences/Psychology/Human behaviour"},{"id":32171323,"name":"Health sciences/Health care"},{"id":32171324,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-06-07T09:39:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-21 19:04:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4383265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4383265","identity":"rs-4383265","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}