{"paper_id":"119d0efb-dec8-40b1-bab4-212a5b307135","body_text":"Quality of Life after Myocardial infarction in the Pakistani Population – Insights from a Single-Center Cohort Study | 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 Research Article Quality of Life after Myocardial infarction in the Pakistani Population – Insights from a Single-Center Cohort Study Javerya Hassan, Manzar Abbas, Hajra Arshad, Angelina Jessani, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4432059/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: Quality of life (QoL) assessment is essential for optimizing patient care, treatment adjustments, and medical decision-making, particularly in post-Myocardial Infarction (MI) patients, but limited data exists on QOL post MI from Pakistan. This study aimed to assess Quality of Life (QoL and its determinants in the Pakistani population. Methods: A single-center cross-sectional study was conducted at a tertiary care hospital in Karachi, Pakistan. Patients ≥ 18 years with a primary diagnosis of acute MI (ICD 9 codes: 410.0-410.9 and ICD-10 codes: 121.0-121.9) discharged from the Cardiology Service from January 2019 to December 2020 who could be contacted and consented to participate were included. Data was collected from electronic records, and patients were interviewed via phone calls using a validated Urdu version of the WHOQOL-BREF questionnaire. Statistical analysis was performed using non-parametric tests via RStudio (Version 1.4.1717). Results: The final study cohort was 440 patients with a median age of 63 (IQR: 56,72) years, with a male predominance (68.2%). Physical health was the most affected domain. Females, lower income individuals, and those with lower level of education had lower QoL scores in all domains. Diabetes and presence of multiple co-morbidities were associated with lower QoL. Marital and socioeconomic status, along with psychosocial factors were significantly associated with QoL scores. Notably, 62.0% of post-MI patients rated their overall QoL as good (scores of 4-5 on a Likert scale of 1-5). Cronbach's alpha values indicated good internal consistency, with an overall Cronbach's alpha of 0.902. Conclusion: Although a significant proportion of patients post MI in our cohort reported good QoL, several social factors were associated with lower QoL. These factors must be investigated further in discharge planning and post-discharge of patients with MI. Myocardial Infarction Health-related Quality of Life Patient-reported outcomes WHOQOL score Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Health-Related Quality of Life (HRQoL) can be defined as “How well a person functions in their life and his or her perceived wellbeing in physical, mental, and social domains of health” [ 1 ]. Understanding the QoL (Quality of Life) is pivotal for enhancing patient care, and rehabilitation. Patient-reported QoL issues can prompt treatment and care adjustments, contributing to therapy effectiveness. This assessment is essential for identifying ongoing problems in disease survivors which are usually overlooked. It is significant for medical decision-making and can predict treatment success [ 2 ]. Cardiovascular diseases, particularly myocardial infarction (MI), are among those diseases that most significantly impact the HRQoL, according to a study conducted using Behavioral Risk Factor Surveillance System (BRFSS) data from 2015. MI survivors were approximately 2.7 times more likely to report fair/poor general health compared to control (non-MI survivors) and 1.5 times more likely to report limitations to daily activities. Post-myocardial infarction, patients often require long-term changes in lifestyle and medical treatment, reducing the patient's quality of life. The onset of myocardial infarction, with its physical, psychological effects, and hospitalization stress, worsens HRQoL, leading to fear, reduced energy, and impaired daily functionality. Enhancing patient care and rehabilitation hinges on understanding Health-Related Quality of Life (HRQoL) [ 3 – 5 ]. In Pakistan, a LMIC, the rates of Ischemic Heart Disease (IHD) are rapidly rising [ 6 ] but little work as thus far been done to understand QoL outcomes in post MI patients. This knowledge gap needs to be filled in order to design contextual post MI care and the composition of teams required to take care of patients with MI and IHD [ 7 ].This study endeavors to bridge this gap by presenting an analysis of Health-Related Quality of Life (HRQoL) among individuals who are 2–4 years post-MI. Our primary objectives were to evaluate the association between various patient characteristics and HRQoL, and to provide comprehensive insights into HRQoL scores across the physical, social, psychological, and environmental domains. Methodology This is a single-center cross-sectional study conducted at a tertiary care center in Pakistan. The study included patients aged above 18 years who were discharged from the Cardiology Service with the diagnosis of initial episode of acute myocardial infarction from January 2019 to December 2020 and could be contacted and consented to participate. Patients who had died after discharge (n = 112, (16.02%)) or had a diagnosis of cancer within 2 to 4 years of MI were excluded from the research cohort as shown in Fig. 1. After approval from the Ethics Review Committee of Aga Khan University (AKU) (IRB committee), patient data was electronically collected from hospital records using ICD-9-CM codes (410.0-410.9) and ICD-10-CM codes (121.0-121.9) as shown in the supplementary Table. A standardized questionnaire, WHOQOL-BREF, was administered. To address language considerations, the validated Urdu version of WHOQOL-BREF was employed. Only one interviewer (SQ) was recruited and trained to conduct phone interviews with the patients and complete the forms, thus mitigating bias that could arise from having multiple interviewers involved. Privacy and confidentiality of patient information were ensured throughout the data collection process. Informed verbal consent was taken from the patients before implementing the questionnaires. Patient involvement: Patients with a primary diagnosis of acute MI, from January 2019 to December 2020, were identified electronically from the hospital records. Informed verbal consent was taken via telephonic calls followed by administration of a validated Urdu version of WHOQOL-BREF questionnaire to avoid language barrier. Only one interviewer (SQ) was trained to make these calls and complete the questionnaire to reduce interviewer bias. Privacy and confidentiality of patient information were ensured throughout the data collection process. The World Health Organization Quality of Life Brief Version (WHOQOL-BREF): We used a general Health-Related QoL questionnaire, ‘The WHOQOL-BREF' developed for cross-cultural comparisons [ 8 ] WHOQOL-BREF measures self-reported subjective quality of life, including satisfaction with the states, capacities, and functioning [ 9 ]. WHOQOL-BREF scale contains a total of 26 items: items 3–26 represent four domains (“Physical Health”—7 items (Pain and discomfort; Dependence on medicinal substances and medical aids; Energy and fatigue; Mobility; Sleep; Activities of daily living; Work capacity),“Psychological Health”—6 items (Positive feelings; Spirituality/personal beliefs; Thinking, learning, memory and concentration; Bodily image and appearance; Self-esteem; Negative feelings), “Social Relationships”—3 items (Personal relationships; Sexual activity; Social support), “Environment”—8 items (Freedom, physical safety, and Security; Physical environment (pollution/noise/traffic/climate); Financial resources; Opportunities for acquiring new information and skills; Participation in and opportunities for recreation/leisure activities; Home environment; Health and social care: accessibility and quality; Transport)) and, two items (1 and 2) that are examined separately and refer to an individual’s “Overall perception of quality of life” and an individual’s “Overall perception of health” [ 10 ]. A study by Mbakwem AC et al. showed the positive correlation between the Kansas City Cardiomyopathy Questionnaire (KCCQ) and the different domains of the WHOQOL-BREF [ 11 ]. Another study found that it’s a reliable scale as it had a Cronbach’s alpha value of 0.832 while SF-36 had 0.868 [ 12 ]. The 26 items are rated on a 5-point Likert scale where 1 is the lowest and 5 is the highest score. There are four domains: Physical, Psychological, Social, and Environment. The raw scores of each item were used to calculate the mean for each domain. The raw score is between 4 and 20. Each of these scores is multiplied by 4 to make it comparable to WHOQOL-100 (Transformed score) [ 13 ]. Statistical analysis: The team had access to the whole data set and conducted the analysis. The WHOQOL-BREF raw scores were calculated and converted into transformed score. Statistical analysis was performed using RStudio (Version 1.4.1717). All continuous variables were tested for normal distribution (Shapiro-Wilk test) and presented as median (interquartile range (IQR)) because all the data were not normally distributed. The non-parametric test, Mann-Whitney U test was used to compare the distribution of the 2 groups while Kruskal-Walli’s test was used to compare the distribution of multiple groups. Significantly different results were further analyzed using a post hoc Dunn-Bonferroni test to identify specific group differences. Non-parametric multivariate regression was used to adjust for other variables to find out the association of Tobacco use with QoL. Results From an initial N = 990 eligible cohort, N = 291 could not be contacted, N = 112 (14.4% males and 18.5% females) were found to have died on telephone follow up), N = 146 refused participation in the study, N = 1 had cancer. Finally, 440 patients formed our final study cohort (Fig. 1). Sociodemographic Characteristics of the Participants : As described in Table 1 , 68 .2% were males and the median age was 63.0 years (IQR: 56.0, 72.0). In terms of literacy, the majority had completed graduation. Most of the participants were married. Regarding the total monthly income of the household, 5.5% had an income of less than PKR 50,000 while 18.4% had an income greater than PKR 250,000. About 91.3% had health costs covered by family/self and the rest were covered by insurance or the employer. Table 1 Sociodemographic Characteristics Characteristics Categories Frequency Percentage Age (years)* - 63.0 56–72 Gender Males 300 68.2 Females 140 31.8 Education Primary (1 to 8th grade) 89 20.2 Matric/Intermediate 157 35.7 Graduation 179 40.7 Marital status Married 348 79.1 Unmarried/widow/widower/Separated 92 20.9 Total monthly income of the household < 50,000 24 5.5 > 50,000 to < 100,000 79.0 18.0 100,000–250,000 130 29.5 > 250,000 81 18.4 Unknown 126 28.6 Primary Payor Self/Family 402 91.3 Employee 16 3.6 Insurance 18 4.1 Unknown 4 0.9 Tobacco Use (Present use or History of Tobacco use) Yes 120 27.3 No 320 72.7 *Median and IQR are presented Morbidity: The study revealed that a majority of the participants had multiple comorbidities, with 28.2% (n = 124) having 1 comorbidity, 43.4% (n = 191) having 2 comorbidities, 20.9% (n = 92) having 3 comorbidities, and 4.6% (n = 20) having 4 or more comorbidities. Only 3.0% (n = 13) of the participants had ischemic heart disease (IHD), without any other comorbidities. The most common comorbidities are shown in Fig. 2 . QoL across different aspects of WHOQOL-BREF: The results showed significant differences in the QoL scores across different domains. The median scores out of 100 were 75.0 (IQR: 56.0, 88.0) for physical health, 81.0 (IQR: 69.0, 94.0) for psychological health, 81.0 (IQR: 69.0, 100.0) for social, and 81.0 (IQR: 75.0, 88.0) for environment. Notably, the data for all QoL aspects were not normally distributed. Internal consistency was excellent for physical health (Cronbach’s alpha of 0.906), good for psychological and social (0.792 and 0.733, respectively) but slightly lower for environment (0.58). Overall Cronbach’s alpha was 0.902 depicting a reliable measure of QoL among the participants. Figure 3 depicts the varying distribution of overall QoL scores. The results show that 12.1% rated their QoL as low (scores of 1–2), 25.9% regarded it as moderate (score of 3), and 62.0% perceived their QoL as high with scores of 4–5 on the Likert scale. Relationship Between Participant Characteristics and Different Aspects of QoL: The relationships between participant’s sociodemographic and clinical characteristics and different aspects of QoL as described in WHOQOL-BREF were analyzed using Wilcox and Kruskal-Wallis tests as summarized in Table 2.1 and 2.2 . Significant associations emerged in several domains. Females displayed lower median scores for physical, psychological, and environmental QoL. Similar scores were noted for social QoL across males and females. Higher education levels and being married were linked to statistically significant higher median scores in physical, psychological, and environmental QoL. In terms of education, Graduation vs. Primary education showed pronounced variations in physical health (p < 0.001) and social factors (p < 0.0003) as shown in Table 3 . Higher median scores in physical and psychological QoL were observed in participants with a higher total monthly household income especially lowest income bracket (< 50,000) exhibited significant differences in psychological and social factors compared to highest income bracket (> 250,000). Participants whose employers paid for their health cost had higher median scores in physical, and social QoL. Those taking fewer medications (4 or less) reported higher median scores in physical, psychological, and social health QoL, particularly notable in cases of excessive polypharmacy (≥ 10 medications) compared to no polypharmacy (≤ 4 medications) (p < 0.001). No significant associations were found with rehabilitation program participation and the number of episodes of myocardial infarction with different aspects of QoL. Multiple comorbidities affected physical and psychological health more than other domains, especially in cases where there were three or more comorbidities compared to one or two. Among the comorbidities, diabetes affected all the domains of QoL, the most. Percutaneous transluminal coronary angioplasty (PTCA) was associated with higher physical QoL. Tobacco use was found to be significantly associated with better physical and psychological QoL without adjusting for other variables. Non-parametric linear regression was used to adjust for PTCA, Ejection Fraction, Age, Total No. of Comorbidities and Gender to find out the association of tobacco use with physical health (p = 0.183) and psychological health (p = 0.170). Table 2.1 Relationship Between Participant Characteristics and Different Aspects of Quality of Life (QoL) using Wilcoxon signed rank test & Kruskal Wallis test: Characteristics Physical Psychological Social Environment Median (IQR) P value Median P value Median P value Median P value Gender Females 63 (38,81) < 0.001 81 (56,94) < 0.001 81 (56,100) 0.005 78 (69,88) 0.002 Males 81 (63,94) 88 (75,94) 81 (69,100) 81 (75,94) Education Primary (1 to 8th grade) 63 (44,81) < 0.001 75 (56,88) < 0.001 75 (56,100) 0.011 75 (69,88) < 0.001 Matric/ Intermediate 75 (56,88) 81 (69,81) 81 (69,100) 81 (75,88) Graduation 88 (69,94) 88 (75,94) 94 (73.5,100) 88 (75,94) Marital status Married 81 (63,94) < 0.001 88 (75,94) < 0.001 81 (69,100) 0.146 75 (75,88) 0.03 Unmarried/ widow/ widower/ Separated 50 (31,69) 69 (50,81) 81 (62.5,100) 81 (69,88) Total monthly income of the household < 50,000 69 (56,88) 0.009 75 (56,81) < 0.001 69 (50,84.2) 0.003 63 (54.5, 76.5) < 0.001 > 50,000 to < 100,000 75 (51.5,88) 81 (69, 94) 81 (59.2, 100) 75 (69, 88) 100,000–250,000 75 (56,88) 81 (69, 94) 94 (75,100) 81 (75,88) > 250,000 88 (63,94) 94 (75,94) 94 (75,100) 88 (81,94) Primary Payor Family/ self 75 (56,88) 0.002 81 (69,94) 0.1377 81 (69,100) 0.028 81 (75,88) 0.71 Employer 88 (81,97) 94 (81,100) 100 (81, 100 ) 81 (75,91) Insurance 88 (81,88) 84 (66,94) 75 (70.5,90.8) 81 (70.5,86.2) No. of medication taken per day 4 or less (No polypharmacy) 88 (69,94) < 0.001 94 (75,94) < 0.001 94 (75,100) 0.029 81 (69,88) 0.256 5 to 9 (Polypharmacy) 75 (56,88) 81 (69,94) 81 (69,100) 81 (75,88) 10 or more (Excessive/ major polypharmacy) 50 (26.5, 63) 59.5 (56,81) 81 (69,94) 75 (70.5,88) Rehabilitation program No 69 (56,88) 0.115 81 (69,94) 0.232 81 (69,100) 0.355 81 (75,88) 0.828 Yes 81 (63,94) 88 (75,94) 81 (75,100) 81 (75,88) PTCA (Percutaneous transluminal coronary angioplasty) Yes 75 (63,94) < 0.001 81 (69, 94) 0.051 81 (69,100) 0.107 81 (75, 88) 0.706 No 75 (50, 82.8) 81 (63,82.8) 81 (69,100) 81 (75,88) More than 1 MI No 75 (56,88) 0.18 81 (69,94) 0.227 81 (69,100) 0.388 81 (75,88) 0.659 Once 69 (56,81) 81 (69,92.5 78 (69,100) 81 (75,88) More than once 81 (63,94) 81 (56,94) 94 (69,100) 81 (63,94) Tobacco use Yes 75 (50,88) < 0.001 81 (69,94) < 0.001 81 (69,100) 0.357 81 (75,88) 0.518 No 75 (50,88) 81 (69,94) 81 (69,100) 81 (75,88) Table 2.2 Relationship Between Participant Comorbidities and Different Aspects of Quality of Life (QoL) using Wilcoxon signed rank test & Kruskal Wallis test: Comorbidities and their association with QoL Median (IQR) P value Median P value Median P value Median P value No. of comorbidities 1–2 81 (69,94) < 0.001 88 (75,94) < 0.001 81 (69, 100) 0.431 81 (75,91) 0.380 3–4 69 (53,88) 81 (69, 94) 81 (69, 100) 81 (75,88) ≥ 5 50 (31,64.5) 62.5 (53,81) 81 (69,95.5) 75 (75,88) Diabetes Yes 69 (50,88) < 0.001 81(69,94) 0.002 81 (69,100) 0.050 81 (75,88) 0.019 No 88 (69,94) 88 (75,94) 94 (75,100) 88 (75,94) HTN Yes 75 (56,88) 0.127 81 (69,94) 0.274 81 (69,100) 0.262 81 (75,88) 0.298 No 81 (56,94) 81 (69,94) 81 (75,100) 81 (75,88) Asthma Yes 56 (44,72) < 0.001 75 (56,81) 0.001 81 (62.5,100) 0.051 81 (69,88) 0.205 No 81 (63,94) 81 (69,94) 75 (69,100) 81 (75,88) CKD Yes 38 (25,63) < 0.001 63 (44,81) < 0.001 81 (69,100) 0.286 75 (75,81) 0.155 No 81 (63,88) 81 (69,94) 94 (75,100) 81 (75,88) Table 3 Dunn's test (P-values): Variable Physical Psychological Social Environmental Education Graduation & Matric/intermediate 0.0003 0.079 - 0.022 Graduation & Primary (1 to 8th grade) < 0.001 < 0.002 - 0.0003 Matric/Intermediate & Primary (1 to 8th grade) 0.023 0.091 - 0.708 Total monthly income of the household < 50,000 & >250,000 0.651 0.002 0.032 < 0.001 < 50,000 & >50,000 to < 100,000 1.000 0.402 0.769 0.076 > 250,000 & >50,000 to < 100,000 0.023 0.196 0.866 < 0.002 < 50,000 &100,000 to 250,000 1.000 0.125 0.008 < 0.006 > 250,000 & 100,000 to 250,000 0.348 0.381 1.000 0.010 > 50,000 to < 100,000 &100,000 to 250,000 1.000 1.000 0.230 0.007 Primary payor Employee & Family/self 0.008 0.150 0.332 - Employee & Insurance 1.000 1.000 0.183 - Family/self & Insurance 0.223 1.000 1.000 - No. of medication taken per day 10 or more (Excessive/ major polypharmacy) & 4 or less (No polypharmacy) 0.008 < 0.001 0.026 - 10 or more (Excessive/ major polypharmacy) & 5 to 9 (Polypharmacy) < 0.001 < 0.001 0.258 - 4 or less (No polypharmacy) & 5 to 9 (Polypharmacy) 0.002 < 0.001 0.363 - Number of comorbidities 1–2 & 3–4 < 0.001 0.004 - - 1–2 & ≥5 < 0.001 < 0.001 - - 3–4 & ≥5 < 0.001 0.002 - - Relationship of QoL with Length of stay (LOS), Ejection Fraction (EF) and Age: For the Physical dimension, we observed a negative correlation with LOS (-0.114, (p = 0.016)) and a positive correlation with EF (0.112, (p = 0.019)) as shown in Table 4 . Additionally, there was a strong negative correlation with Age (-0.443, (p < 0.001)). In the Psychological dimension, there was a negative correlation with LOS (-0.086, (p = 0.071)), no significant correlation with EF (0.021, (p = 0.657)), and a negative correlation with Age (-0.288, (p < 0.001)). The Environmental dimension showed a negligible correlation with LOS (0.005, (p = 0.909)), a positive correlation with EF (0.027, (p = 0.572)), and a non-significant correlation with Age (0.035, (p = 0.464)). Lastly, the social dimension exhibited non-significant weak negative correlation with LOS (--0.012, (p = 0.804)) and Age (-0.058, (p = 0.224)) and a weak positive correlation with EF (0.039 (p = 0.411)). Table 4 Correlation of QoL with LOS, EF, and Age: LOS EF Age Physical -0.114 (p = 0.016) 0.112 (p = 0.019) -0.443 (p < 0.001) Psychological -0.086 (p = 0.071) 0.021 (p = 0.657) -0.288 (p < 0.001) Environmental 0.005 (p = 0.909) 0.027 (p = 0.572) 0.035 (p = 0.464) Social -0.012 (p = 0.804) 0.039 (p = 0.411) -0.058 (p = 0.224) Discussion This study is one of the first QoL investigations of its kind in Pakistan, our findings not only illuminate the multifaceted challenges faced by post-MI individuals but also underscore the critical need for tailored interventions to address these complexities effectively. Our main findings can be summarized as follows: 1. There was a high mortality rate and high event rate among survivors discovered on follow up. 2. Over all QoL was good among survivors but the physical health domain scored the worst among all components and in general approximately 10% had low QoL. 3. Several factors were associated with poorer QoL including diabetes, female sex, age, multiple co-morbidities, and low education and income level. One notable revelation pertains to an unusually high mortality rate i.e. 16.02% post-discharge among MI patients, 14.4% males and 18.05% females. There is a possibility that it is an underestimation as many patient households could not be contacted. A study conducted in Pakistan showed that upon follow-up (average duration: 16.43 ± 7.40 months) of 211 female patients (55.8%), the cumulative mortality rate was 20.3%, slightly higher than our results [ 14 ]. This statistic draws attention to the severe impact of MI and its potential to lead to fatal outcomes. The study’s findings not only contribute to a better understanding of the magnitude of the issue but also emphasize the need for early intervention, robust risk assessment, and preventive measures to mitigate mortality risks associated with MI through comprehensive secondary prevention programs. This alarming statistic emphasizes the urgency for healthcare providers and policymakers to prioritize strategies that enhance both survival rates and post-MI quality of life. Among the post-MI patients, 62.0% reported their overall QoL as good with scores of 4–5 on a Likert scale of 1–5. The high overall QoL scores among post-MI patients in this study were inconsistent with studies from the Republic of China [ 15 ], Ethiopia [ 16 ], the United States of America [ 17 ], and Hong Kong [ 18 ] where the QoL scores were generally lower. These variations might be attributed to differences in data collection instrument which included generic (SF-36) and disease-specific (MIDAS) and Hospital Anxiety and Depression Scale (HADS), sample sizes, sociocultural factors, and participants' perceptions of QoL. Intriguingly, our results align with those of a study conducted in Myanmar and another in Singapore, both of which reported good QoL scores among their respective post-MI populations [ 15 , 19 ]. This suggests that certain cultural or contextual factors may exert a powerful influence on QoL perceptions. Recognizing and harnessing these cultural subtleties can potentially catalyze the development of more effective tailored interventions to elevate QoL outcomes in specific populations. According to a previous study conducted in 2007 in another tertiary hospital in Pakistan, in women, the raw WHOQOL scores were in the range of 10.09–14.62 which are equivalent to transformed scores of 40.36–58.48 [ 20 ]. These scores notably lag behind those observed in our study. Such a discrepancy suggests an improvement in treatment outcomes over time. Physical health was found to be the most affected domain, with the worst median score observed. After the discharge, the physical functioning, general health and vitality of health are the most affected [ 21 ]. With low EF and high rates of complications in post-MI patients, their ability to perform regular physical activity is limited, which can be a potential target for interventions to improve QoL. 61.8% of participants were male and 31.2% were females, with a median age of 63 years indicating a connection between MI and advancing age. Moreover, females reported lower median scores in physical, psychological, and environmental health domains compared to males, highlighting the influence of gender on QoL. This aligns with findings from a similar study in England, which found that women had lower health-related quality of life (HRQoL) compared to men among a large cohort of MI survivors, both at baseline and during the 12-month follow-up. Another study utilizing EuroQOL five dimensions (EQ-5D-3L) questionnaire reported that women had higher impairment levels in mobility, self-care, usual activities, pain/discomfort, and anxiety/depression at each time point [ 22 ]. Studies done in India and Ethiopia, also showed that females have been shown to experience lower QoL after MI compared to their male counterparts [ 23 ]. This gender-specific variation in HRQoL after MI echoes the importance of exploring factors and health disparities contributing to lower QoL in females. The QoL depends on various factors, including differences in healthcare-seeking behaviors, social support systems, and coping mechanisms. Females may face unique challenges in managing their health and juggling multiple roles, which can impact their overall well-being. Moreover, specific rehabilitation programs for women, however, need to be further explored [ 24 ]. Additionally, higher education levels, being married, and higher income levels were associated with better QoL scores, suggesting the potential influence of social support, access to resources, and overall lifestyle on post-MI well-being. Other studies have also shown that higher education, marriage and good financial status are associated with the highest assessments of quality of life and perceived health condition [ 25 ] as this supports the social capital theory, which suggests that social connections, networks, and norms are valuable resources that can help people and communities do better. This also decreases the degree of depression [ 26 , 27 ]. According to a meta-analysis and systemic review, married/partnered patients reported a higher HRQoL compared to their unpartnered counterparts. However, no association was found between marital status and improved functional status, symptoms of anxiety and depression [ 28 ]. Furthermore, a study conducted in the Netherlands found that spirituality positively contributed to QoL [ 29 ]. These findings align with the importance of addressing psychosocial and financial factors and providing holistic care to enhance the overall well-being of post-MI patients in diverse cultural settings. The results from the present study also highlight the substantial burden of concurrent health conditions among post-MI patients, with a majority having two or more comorbidities. This is congruent with the findings from the UK multicenter longitudinal study, which emphasized the prevalence of multiple comorbidities in MI survivors, and its impact on HRQoL [ 30 ]. This highlights the complexity of managing health conditions in this population, which negatively impacted QoL in this study. The comprehensive breakdown of comorbidities provides valuable information for healthcare providers and policymakers to tailor interventions that account for these concurrent health challenges. This study serves as a crucial reminder of the dearth of information regarding post-MI management strategies in Pakistan. The scarcity of research on this topic is indicative of the broader challenge in addressing the holistic needs of post-MI patients beyond the acute phase. This gap highlights the importance of not only investigating QoL outcomes but also delving into the intricacies of post-MI care. Improved management strategies encompassing both acute and long-term care are essential to mitigate the impact of MI on QoL and overall mortality. Strengths and Limitations: One of the study's key strengths was using a standardized tool (WHOQOL-BREF) validated for both developed and developing countries, ensuring the QoL assessment's reliability. The study's results were based on robust internal consistency measures, with good Cronbach's alpha scores for physical and psychological health domains. In the present study, Cronbach's alpha scores for physical and psychological health were 0.906 and 0.792, respectively. The slightly lower Cronbach's alpha scores for social relationship (0.733) and environment (0.58) also lie within acceptable range. The overall Cronbach's alpha score of 0.902 indicates a reliable measure of QoL among the participants. Alpha values are described in Fig. 4.0 [ 30 ]. This indicates that the WHOQOL-BREF instrument is a reliable measure for assessing physical and psychological health aspects in the Pakistani MI population. However, several limitations should be considered when interpreting these findings. The cross-sectional design limited the ability to establish causal relationships between participant characteristics and QoL outcomes. Additionally, data collection through phone calls may not be the best approach for such a sensitive topic and may decrease the quality of interaction with the patients who are less likely to give appropriate answers. The study's single-center nature might limit the generalizability of results to other regions in Pakistan with different sociodemographic characteristics and healthcare systems. A multi-center approach involving diverse regions could have enhanced the external validity of the findings. Future research could benefit from a longitudinal design to better understand the dynamic nature of QoL in myocardial infarction patients. Conclusion The study's revelations regarding the dearth of post-myocardial infarction (MI) quality of life data in Pakistan highlighting important points that need to be advocated by healthcare providers and policymakers. Physicians who know the impact of participants' characteristics on quality of life (QoL) should focus on the specific needs of subgroups, including women and people from low economic backgrounds in rehabilitation programs. Using strategies aimed at strengthening social support, improving coping mechanisms, and improving access to healthcare can improve people's quality of life and thereby reduce mortality rates. Furthermore, the substantial burden of concurrent health conditions among post-MI patients emphasizes the necessity for comprehensive healthcare management and tailored interventions to mitigate the impact of comorbidities on QoL. Notably, there is an urgent call to enhance rehabilitation programs to better serve the post-MI population and improve their overall well-being. A holistic treatment approach with multidisciplinary teams providing medical and psychosocial support is needed to improve quality of life. Further studies are needed to find out the causal relationship between the above-mentioned factors and poor quality of life to better understand the relationship. Declarations Ethics approval and consent to participate Ethical approval was taken from the Ethics Review Committee (ERC) of Aga Khan University (AKU); ERC reference number: 2023-7040-23790. Patient data was then electronically collected from hospital records. Informed verbal consent was taken from patients via telephonic calls before administration of the WHOQOL-BREF questionnaire. Availability of Data and Materials The datasets generated and analysed during the current study are not publicly available due to institutional policies, but can be made available from the corresponding author on reasonable request. Competing Interests Authors do not have any competing interests. Disclosure Authors don’t have any conflict of interest. Funding Source Brain and Mind Institute, Aga Khan University Authorship Contribution Statement J.H.: Conceptualization, Methodology, Supervision, Writing- Reviewing and Editing, Analysis, Project administration ; M.A.: Conceptualization, Methodology, Analysis, Supervision, Writing- Reviewing and Editing, Project administration, Supervision; H.A.: Conceptualization, Methodology, Writing- Original draft preparation, Visualization ; A.J. : Writing- Original draft preparation, Writing- Reviewing and Editing, Visualization, I.T. : Conceptualization, Methodology, Writing- Reviewing and Editing, Submission ; S.Q.: Data Curation ; J.S.: Writing-Review and Editing ; Z.M. : Conceptualization, Writing-Review and Editing, Supervision, Project administration. Z.A. : Conceptualization, Writing-Review and Editing, Supervision, Project administration. References Karimi M, Brazier J, Health. Health-Related Quality of Life, and Quality of Life: What is the Difference? Pharmacoeconomics 2016;34. https://doi.org/10.1007/s40273-016-0389-9 . Fayers P, Machin D. Quality of Life: The assessment, analysis and interpretation of patient-reported outcomes. Wiley; 2007. Kang K, Gholizadeh L, Inglis SC, Han HR. Correlates of health-related quality of life in patients with myocardial infarction: A literature review. Int J Nurs Stud. 2017;73:1–16. https://doi.org/10.1016/J.IJNURSTU.2017.04.010 . Endalew HL, Liyew B, Kassew T, Tarekegn GE, Tilahun AD, Alamneh TS. Health-Related Quality of Life Among Myocardial Infarction Survivors: Structural Equation Modeling Approach. J Multidiscip Healthc. 2021;14:1543. https://doi.org/10.2147/JMDH.S296064 . Mollon L, Bhattacharjee S. Health related quality of life among myocardial infarction survivors in the United States: A propensity score matched analysis. Health Qual Life Outcomes. 2017;15:1–10. https://doi.org/10.1186/S12955-017-0809-3/TABLES/3 . Global health estimates. Leading causes of DALYs n.d. https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/global-health-estimates-leading-causes-of-dalys (accessed March 24, 2024). Jafary MH, Samad A, Ishaq M, Jawaid SA, Ahmad M, Vohra EA. Profile of Acute Myocardial Infarction (AMI) in Pakistan. Pak J Med Sci 2007;23. Vahedi S, World Health Organization Quality-of-Life Scale (WHOQOL-BREF). : Analyses of Their Item Response Theory Properties Based on the Graded Responses Model. Iran J Psychiatry 2010;5. Huang IC, Wu AW, Frangakis C. Do the SF-36 and WHOQOL-BREF measure the same constructs? Evidence from the taiwan population. Qual Life Res. 2006;15. https://doi.org/10.1007/s11136-005-8486-9 . Ilić I, Šipetić-Grujičić S, Grujičić J, Živanović Mačužić I, Kocić S, Ilić M. Psychometric Properties of the World Health Organization’s Quality of Life (WHOQOL-BREF) Questionnaire in Medical Students. Medicina. 2019;55(12):772. Mbakwem AC, Aina FO, Amadi CE, Akinbode AA, Mokwunyei J. Comparative analysis of the quality of life of heart failure patients in South Western Nigeria. World J Cardiovasc Dis. 2013;03. https://doi.org/10.4236/wjcd.2013.31a021 . Castro PC, Driusso P, Oishi J. Convergent validity between SF-36 and WHOQOL-BREF in older adults. Rev Saude Publica. 2014;48. https://doi.org/10.1590/S0034-8910.2014048004783 . WHO Quality of Life-BREF (WHOQOL-BREF). | RehabMeasures Database n.d. https://www.sralab.org/rehabilitation-measures/who-quality-life-bref-whoqol-bref (accessed January 26, 2024). Furnaz S, Karim M, Ashraf T, Ali S, Shahid I, Ali S, et al. Performance of the TIMI risk score in predicting mortality after primary percutaneous coronary intervention in elderly women: Results from a developing country. PLoS ONE. 2019;14. https://doi.org/10.1371/JOURNAL.PONE.0220289 . Wang W, Thompson DR, Ski CF, Liu M. Health-related quality of life and its associated factors in Chinese myocardial infarction patients. Eur J Prev Cardiol. 2014;21. https://doi.org/10.1177/2047487312454757 . Endalew HL, Liyew B, Baye Z, Tarekegn GE. Health-related quality of life and associated factors among myocardial infarction patients at cardiac center, ethiopia. Biomed Res Int 2021;2021. https://doi.org/10.1155/2021/6675267 . Gandek B, Sinclair SJ, Kosinski M, Ware JE. Psychometric evaluation of the SF-36® health survey in medicare managed care. Health Care Financ Rev 2004;25. Yu DSF, Thompson DR, Yu C, man, Oldridge NB. Assessing HRQL among Chinese patients with coronary heart disease: Angina, myocardial infarction and heart failure. Int J Cardiol. 2009;131. https://doi.org/10.1016/j.ijcard.2007.10.043 . Kalayar Hlaing S, Sriyuktasuth A, Wattanakitkrileart D. Related Quality of Life among Patients with Myocardial Infarction in Myanmar*. vol. 36. n.d. Qaiser S. Quality of Life (QoL) among Post Myocardial Infarction (MI) Women in Karachi, Pakistan. International Journal of Innovative Research and Development; 2017. Hawkes AL, Patrao TA, Ware R, Atherton JJ, Taylor CB, Oldenburg BF. Predictors of physical and mental health-related quality of life outcomes among myocardial infarction patients. BMC Cardiovasc Disord. 2013;13. https://doi.org/10.1186/1471-2261-13-69 . Dondo TB, Munyombwe T, Hall M, Hurdus B, Soloveva A, Oliver G, et al. Sex differences in health-related quality of life trajectories following myocardial infarction: national longitudinal cohort study. BMJ Open. 2022;12. https://doi.org/10.1136/bmjopen-2022-062508 . Huffman MD, Mohanan PP, Devarajan R, Baldridge AS, Kondal D, Zhao L, et al. Health-Related Quality of Life at 30 Days among Indian Patients with Acute Myocardial Infarction: Results from the ACS QUIK Trial. Circ Cardiovasc Qual Outcomes. 2019;12. https://doi.org/10.1161/CIRCOUTCOMES.118.004980 . Fridlund B. Self-rated health in women after their first myocardial infarction: a 12-month comparison between participation and nonparticipation in a cardiac rehabilitation progamme. Health Care Women Int. 2000;21:727–38. https://doi.org/10.1080/073993300300340547 . Puciato D, Rozpara M, Bugdol M, Mróz-Gorgoń B. Socio-economic correlates of quality of life in single and married urban individuals: a Polish case study. Health Qual Life Outcomes. 2022;20:1–16. https://doi.org/10.1186/S12955-022-01966-2/TABLES/5 . Du W, Luo M, Zhou Z. A Study on the Relationship Between Marital Socioeconomic Status, Marital Satisfaction, and Depression: Analysis Based on Actor–Partner Interdependence Model (APIM). Appl Res Qual Life. 2022;17:1477–99. https://doi.org/10.1007/S11482-021-09975-X . Kim JH, Park EC. Impact of socioeconomic status and subjective social class on overall and health-related quality of life. BMC Public Health. 2015;15:1–15. https://doi.org/10.1186/S12889-015-2014-9/TABLES/7 . Zhu C, Tran PM, Leifheit EC, Spatz ES, Dreyer RP, Nyhan K, et al. Association of marital/partner status and patient-reported outcomes following myocardial infarction: A systematic review and meta-Analysis. Eur Heart J Open. 2023;3. https://doi.org/10.1093/ehjopen/oead018 . Wachelder EM, Moulaert VRMP, van Heugten C, Gorgels T, Wade DT, Verbunt JA. Dealing with a life changing event: The influence of spirituality and coping style on quality of life after survival of a cardiac arrest or myocardial infarction. Resuscitation 2016;109. Munyombwe T, Dondo TB, Aktaa S, Wilkinson C, Hall M, Hurdus B, et al. Association of multimorbidity and changes in health-related quality of life following myocardial infarction: a UK multicentre longitudinal patient-reported outcomes study. BMC Med. 2021;19. https://doi.org/10.1186/s12916-021-02098-y . Additional Declarations No competing interests reported. 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Single-Center Cohort Study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eHealth-Related Quality of Life (HRQoL) can be defined as \\u0026ldquo;How well a person functions in their life and his or her perceived wellbeing in physical, mental, and social domains of health\\u0026rdquo; [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Understanding the QoL (Quality of Life) is pivotal for enhancing patient care, and rehabilitation. Patient-reported QoL issues can prompt treatment and care adjustments, contributing to therapy effectiveness. This assessment is essential for identifying ongoing problems in disease survivors which are usually overlooked. It is significant for medical decision-making and can predict treatment success [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCardiovascular diseases, particularly myocardial infarction (MI), are among those diseases that most significantly impact the HRQoL, according to a study conducted using Behavioral Risk Factor Surveillance System (BRFSS) data from 2015. MI survivors were approximately 2.7 times more likely to report fair/poor general health compared to control (non-MI survivors) and 1.5 times more likely to report limitations to daily activities. Post-myocardial infarction, patients often require long-term changes in lifestyle and medical treatment, reducing the patient's quality of life. The onset of myocardial infarction, with its physical, psychological effects, and hospitalization stress, worsens HRQoL, leading to fear, reduced energy, and impaired daily functionality. Enhancing patient care and rehabilitation hinges on understanding Health-Related Quality of Life (HRQoL) [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. In Pakistan, a LMIC, the rates of Ischemic Heart Disease (IHD) are rapidly rising [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] but little work as thus far been done to understand QoL outcomes in post MI patients. This knowledge gap needs to be filled in order to design contextual post MI care and the composition of teams required to take care of patients with MI and IHD [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].This study endeavors to bridge this gap by presenting an analysis of Health-Related Quality of Life (HRQoL) among individuals who are 2\\u0026ndash;4 years post-MI. Our primary objectives were to evaluate the association between various patient characteristics and HRQoL, and to provide comprehensive insights into HRQoL scores across the physical, social, psychological, and environmental domains.\\u003c/p\\u003e\"},{\"header\":\"Methodology\",\"content\":\"\\u003cp\\u003eThis is a single-center cross-sectional study conducted at a tertiary care center in Pakistan. The study included patients aged above 18 years who were discharged from the Cardiology Service with the diagnosis of initial episode of acute myocardial infarction from January 2019 to December 2020 and could be contacted and consented to participate. Patients who had died after discharge (n\\u0026thinsp;=\\u0026thinsp;112, (16.02%)) or had a diagnosis of cancer within 2 to 4 years of MI were excluded from the research cohort as shown in \\u003cem\\u003eFig.\\u0026nbsp;1.\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003eAfter approval from the Ethics Review Committee of Aga Khan University (AKU) (IRB committee), patient data was electronically collected from hospital records using ICD-9-CM codes (410.0-410.9) and ICD-10-CM codes (121.0-121.9) as shown in the supplementary Table. A standardized questionnaire, WHOQOL-BREF, was administered. To address language considerations, the validated Urdu version of WHOQOL-BREF was employed. Only one interviewer (SQ) was recruited and trained to conduct phone interviews with the patients and complete the forms, thus mitigating bias that could arise from having multiple interviewers involved. Privacy and confidentiality of patient information were ensured throughout the data collection process. Informed verbal consent was taken from the patients before implementing the questionnaires.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient involvement:\\u003c/h2\\u003e \\u003cp\\u003ePatients with a primary diagnosis of acute MI, from January 2019 to December 2020, were identified electronically from the hospital records. Informed verbal consent was taken via telephonic calls followed by administration of a validated Urdu version of WHOQOL-BREF questionnaire to avoid language barrier. Only one interviewer (SQ) was trained to make these calls and complete the questionnaire to reduce interviewer bias. Privacy and confidentiality of patient information were ensured throughout the data collection process.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eThe World Health Organization Quality of Life Brief Version (WHOQOL-BREF):\\u003c/h2\\u003e \\u003cp\\u003eWe used a general Health-Related QoL questionnaire, \\u0026lsquo;The WHOQOL-BREF' developed for cross-cultural comparisons [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e] WHOQOL-BREF measures self-reported subjective quality of life, including satisfaction with the states, capacities, and functioning [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. WHOQOL-BREF scale contains a total of 26 items: items 3\\u0026ndash;26 represent four domains (\\u0026ldquo;Physical Health\\u0026rdquo;\\u0026mdash;7 items (Pain and discomfort; Dependence on medicinal substances and medical aids; Energy and fatigue; Mobility; Sleep; Activities of daily living; Work capacity),\\u0026ldquo;Psychological Health\\u0026rdquo;\\u0026mdash;6 items (Positive feelings; Spirituality/personal beliefs; Thinking, learning, memory and concentration; Bodily image and appearance; Self-esteem; Negative feelings), \\u0026ldquo;Social Relationships\\u0026rdquo;\\u0026mdash;3 items (Personal relationships; Sexual activity; Social support), \\u0026ldquo;Environment\\u0026rdquo;\\u0026mdash;8 items (Freedom, physical safety, and Security; Physical environment (pollution/noise/traffic/climate); Financial resources; Opportunities for acquiring new information and skills; Participation in and opportunities for recreation/leisure activities; Home environment; Health and social care: accessibility and quality; Transport)) and, two items (1 and 2) that are examined separately and refer to an individual\\u0026rsquo;s \\u0026ldquo;Overall perception of quality of life\\u0026rdquo; and an individual\\u0026rsquo;s \\u0026ldquo;Overall perception of health\\u0026rdquo; [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. A study by Mbakwem AC et al. showed the positive correlation between the Kansas City Cardiomyopathy Questionnaire (KCCQ) and the different domains of the WHOQOL-BREF [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Another study found that it\\u0026rsquo;s a reliable scale as it had a Cronbach\\u0026rsquo;s alpha value of 0.832 while SF-36 had 0.868 [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. The 26 items are rated on a 5-point Likert scale where 1 is the lowest and 5 is the highest score. There are four domains: Physical, Psychological, Social, and Environment. The raw scores of each item were used to calculate the mean for each domain. The raw score is between 4 and 20. Each of these scores is multiplied by 4 to make it comparable to WHOQOL-100 (Transformed score) [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis:\\u003c/h2\\u003e \\u003cp\\u003eThe team had access to the whole data set and conducted the analysis. The WHOQOL-BREF raw scores were calculated and converted into transformed score. Statistical analysis was performed using RStudio (Version 1.4.1717). All continuous variables were tested for normal distribution (Shapiro-Wilk test) and presented as median (interquartile range (IQR)) because all the data were not normally distributed. The non-parametric test, Mann-Whitney U test was used to compare the distribution of the 2 groups while Kruskal-Walli\\u0026rsquo;s test was used to compare the distribution of multiple groups. Significantly different results were further analyzed using a post hoc Dunn-Bonferroni test to identify specific group differences. Non-parametric multivariate regression was used to adjust for other variables to find out the association of Tobacco use with QoL.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eFrom an initial N\\u0026thinsp;=\\u0026thinsp;990 eligible cohort, N\\u0026thinsp;=\\u0026thinsp;291 could not be contacted, N\\u0026thinsp;=\\u0026thinsp;112 (14.4% males and 18.5% females) were found to have died on telephone follow up), N\\u0026thinsp;=\\u0026thinsp;146 refused participation in the study, N\\u0026thinsp;=\\u0026thinsp;1 had cancer. Finally, 440 patients formed our final study cohort (Fig.\\u0026nbsp;1).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSociodemographic Characteristics of the Participants\\u003c/b\\u003e:\\u003c/h2\\u003e \\u003cp\\u003eAs described in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cb\\u003e68\\u003c/b\\u003e.2% were males and the median age was 63.0 years (IQR: 56.0, 72.0). In terms of literacy, the majority had completed graduation. Most of the participants were married. Regarding the total monthly income of the household, 5.5% had an income of less than PKR 50,000 while 18.4% had an income greater than PKR 250,000. About 91.3% had health costs covered by family/self and the rest were covered by insurance or the employer.\\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\\u003eSociodemographic Characteristics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCategories\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFrequency\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePercentage\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56\\u0026ndash;72\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eGender\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMales\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e300\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e68.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFemales\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e140\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eEducation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimary (1 to 8th grade)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMatric/Intermediate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e157\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGraduation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e179\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e40.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMarital status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMarried\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e348\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e79.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnmarried/widow/widower/Separated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e92\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003eTotal monthly income of the household\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;50,000 to \\u0026lt;\\u0026thinsp;100,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100,000\\u0026ndash;250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e130\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e29.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e126\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003ePrimary Payor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSelf/Family\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e402\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e91.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEmployee\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eInsurance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTobacco Use\\u003c/p\\u003e \\u003cp\\u003e(Present use or History of Tobacco use)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e120\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e*Median and IQR are presented\\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=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMorbidity:\\u003c/h2\\u003e \\u003cp\\u003eThe study revealed that a majority of the participants had multiple comorbidities, with 28.2% (n\\u0026thinsp;=\\u0026thinsp;124) having 1 comorbidity, 43.4% (n\\u0026thinsp;=\\u0026thinsp;191) having 2 comorbidities, 20.9% (n\\u0026thinsp;=\\u0026thinsp;92) having 3 comorbidities, and 4.6% (n\\u0026thinsp;=\\u0026thinsp;20) having 4 or more comorbidities. Only 3.0% (n\\u0026thinsp;=\\u0026thinsp;13) of the participants had ischemic heart disease (IHD), without any other comorbidities. The most common comorbidities are shown in \\u003cem\\u003eFig.\\u0026nbsp;2\\u003c/em\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eQoL across different aspects of WHOQOL-BREF:\\u003c/h2\\u003e \\u003cp\\u003eThe results showed significant differences in the QoL scores across different domains. The median scores out of 100 were 75.0 (IQR: 56.0, 88.0) for physical health, 81.0 (IQR: 69.0, 94.0) for psychological health, 81.0 (IQR: 69.0, 100.0) for social, and 81.0 (IQR: 75.0, 88.0) for environment. Notably, the data for all QoL aspects were not normally distributed. Internal consistency was excellent for physical health (Cronbach\\u0026rsquo;s alpha of 0.906), good for psychological and social (0.792 and 0.733, respectively) but slightly lower for environment (0.58). Overall Cronbach\\u0026rsquo;s alpha was 0.902 depicting a reliable measure of QoL among the participants. Figure\\u0026nbsp;3 depicts the varying distribution of overall QoL scores. The results show that 12.1% rated their QoL as low (scores of 1\\u0026ndash;2), 25.9% regarded it as moderate (score of 3), and 62.0% perceived their QoL as high with scores of 4\\u0026ndash;5 on the Likert scale.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRelationship Between Participant Characteristics and Different Aspects of QoL:\\u003c/h2\\u003e \\u003cp\\u003eThe relationships between participant\\u0026rsquo;s sociodemographic and clinical characteristics and different aspects of QoL as described in WHOQOL-BREF were analyzed using Wilcox and Kruskal-Wallis tests as summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2.1\\u003c/span\\u003e \\u003cb\\u003eand 2.2\\u003c/b\\u003e. Significant associations emerged in several domains. Females displayed lower median scores for physical, psychological, and environmental QoL. Similar scores were noted for social QoL across males and females. Higher education levels and being married were linked to statistically significant higher median scores in physical, psychological, and environmental QoL. In terms of education, Graduation vs. Primary education showed pronounced variations in physical health (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and social factors (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0003) as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Higher median scores in physical and psychological QoL were observed in participants with a higher total monthly household income especially lowest income bracket (\\u0026lt;\\u0026thinsp;50,000) exhibited significant differences in psychological and social factors compared to highest income bracket (\\u0026gt;\\u0026thinsp;250,000). Participants whose employers paid for their health cost had higher median scores in physical, and social QoL. Those taking fewer medications (4 or less) reported higher median scores in physical, psychological, and social health QoL, particularly notable in cases of excessive polypharmacy (\\u0026ge;\\u0026thinsp;10 medications) compared to no polypharmacy (\\u0026le;\\u0026thinsp;4 medications) (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). No significant associations were found with rehabilitation program participation and the number of episodes of myocardial infarction with different aspects of QoL. Multiple comorbidities affected physical and psychological health more than other domains, especially in cases where there were three or more comorbidities compared to one or two. Among the comorbidities, diabetes affected all the domains of QoL, the most. Percutaneous transluminal coronary angioplasty (PTCA) was associated with higher physical QoL. Tobacco use was found to be significantly associated with better physical and psychological QoL without adjusting for other variables. Non-parametric linear regression was used to adjust for PTCA, Ejection Fraction, Age, Total No. of Comorbidities and Gender to find out the association of tobacco use with physical health (p\\u0026thinsp;=\\u0026thinsp;0.183) and psychological health (p\\u0026thinsp;=\\u0026thinsp;0.170).\\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 2.1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eRelationship Between Participant Characteristics and Different Aspects of Quality of Life (QoL) using Wilcoxon signed rank test \\u0026amp; Kruskal Wallis test:\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"10\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003ePhysical\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003ePsychological\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eSocial\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eEnvironment\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMedian (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eP value\\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 \\u003cp\\u003eFemales\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63 (38,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (56,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(56,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.005\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e78 (69,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\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\\u003eMales\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eEducation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimary (1 to 8th grade)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63 (44,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e75 (56,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.011\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003cp\\u003e(69,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\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=\\\"c2\\\"\\u003e \\u003cp\\u003eMatric/\\u003c/p\\u003e \\u003cp\\u003eIntermediate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGraduation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (73.5,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMarital status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMarried\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.146\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e75 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.03\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnmarried/ widow/ widower/ Separated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50 (31,69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e69 (50,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (62.5,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (69,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eTotal monthly income of the household\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.009\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75 (56,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e69\\u003c/p\\u003e \\u003cp\\u003e(50,84.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e63 (54.5, 76.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\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=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;50,000 to \\u0026lt;\\u0026thinsp;100,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (51.5,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69, 94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (59.2, 100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e75 (69, 88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100,000\\u0026ndash;250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69, 94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (75,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88 (63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (75,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e88 (81,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003ePrimary Payor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFamily/\\u003c/p\\u003e \\u003cp\\u003eself\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.1377\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.028\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEmployer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88 (81,97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94 (81,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e100 (81, 100 )\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eInsurance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88 (81,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e84 (66,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e75 (70.5,90.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (70.5,86.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eNo. of medication taken per day\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 or less (No polypharmacy)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94\\u003c/p\\u003e \\u003cp\\u003e(75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94\\u003c/p\\u003e \\u003cp\\u003e(75,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.029\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (69,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.256\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 to 9 (Polypharmacy)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10 or more (Excessive/ major polypharmacy)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50 (26.5, 63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e59.5 (56,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e75 (70.5,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eRehabilitation program\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.115\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.232\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.355\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.828\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (75,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePTCA\\u003c/p\\u003e \\u003cp\\u003e(Percutaneous transluminal coronary angioplasty)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003cp\\u003e(63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69, 94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.107\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75, 88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.706\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (50, 82.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(63,82.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMore than 1 MI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.227\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.388\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.659\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOnce\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (56,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,92.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e78 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMore than once\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (56,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTobacco use\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003cp\\u003e(50,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.357\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.518\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003cp\\u003e(50,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\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 2.2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eRelationship Between Participant Comorbidities and Different Aspects of Quality of Life (QoL) using Wilcoxon signed rank test \\u0026amp; Kruskal Wallis test:\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"10\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"10\\\" nameend=\\\"c10\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eComorbidities and their association with QoL\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMedian (IQR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eNo. of comorbidities\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69, 100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.431\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(75,91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e0.380\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u0026ndash;4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69\\u003c/p\\u003e \\u003cp\\u003e(53,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69, 94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69, 100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003cp\\u003e(31,64.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e62.5 (53,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(69,95.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003cp\\u003e(75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDiabetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69 (50,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81(69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.050\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.019\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (75,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e88 (75,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eHTN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75 (56,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.127\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.274\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.262\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.298\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (56,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (75,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eAsthma\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e56 (44,72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75 (56,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81\\u003c/p\\u003e \\u003cp\\u003e(62.5,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (69,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.205\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (63,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003cp\\u003e(69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCKD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38 (25,63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e63 (44,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e81 (69,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.286\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e75 (75,81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.155\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81 (63,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81 (69,94)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e94 (75,100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e81 (75,88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\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 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDunn's test (P-values):\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePhysical\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePsychological\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSocial\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eEnvironmental\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eEducation\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGraduation \\u0026amp; Matric/intermediate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.079\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003eGraduation \\u0026amp; Primary (1 to 8th grade)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMatric/Intermediate \\u0026amp; Primary (1 to 8th grade)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.023\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.091\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.708\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal monthly income of the household\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50,000 \\u0026amp; \\u0026gt;250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.651\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.032\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\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\\u003e\\u0026lt;\\u0026thinsp;50,000 \\u0026amp; \\u0026gt;50,000 to \\u0026lt;\\u0026thinsp;100,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.402\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.769\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.076\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;250,000 \\u0026amp; \\u0026gt;50,000 to \\u0026lt;\\u0026thinsp;100,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.023\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.196\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.866\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;50,000 \\u0026amp;100,000 to 250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.125\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.008\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.006\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;250,000 \\u0026amp; 100,000 to 250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.348\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.381\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.010\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;50,000 to \\u0026lt;\\u0026thinsp;100,000 \\u0026amp;100,000 to 250,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.230\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003ePrimary payor\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEmployee \\u0026amp; Family/self\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.008\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.150\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.332\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEmployee \\u0026amp; Insurance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFamily/self \\u0026amp; Insurance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.223\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eNo. of medication taken per day\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10 or more (Excessive/ major polypharmacy) \\u0026amp; 4 or less (No polypharmacy)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.008\\u003c/b\\u003e\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.026\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10 or more (Excessive/ major polypharmacy) \\u0026amp; 5 to 9 (Polypharmacy)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.258\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4 or less (No polypharmacy) \\u0026amp; 5 to 9 (Polypharmacy)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.363\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eNumber of comorbidities\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;2 \\u0026amp; 3\\u0026ndash;4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;2 \\u0026amp; \\u0026ge;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\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 \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u0026ndash;4 \\u0026amp; \\u0026ge;5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\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=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRelationship of QoL with Length of stay (LOS), Ejection Fraction (EF) and Age:\\u003c/h2\\u003e \\u003cp\\u003eFor the Physical dimension, we observed a negative correlation with LOS (-0.114, (p\\u0026thinsp;=\\u0026thinsp;0.016)) and a positive correlation with EF (0.112, (p\\u0026thinsp;=\\u0026thinsp;0.019)) as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. Additionally, there was a strong negative correlation with Age (-0.443, (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001)). In the Psychological dimension, there was a negative correlation with LOS (-0.086, (p\\u0026thinsp;=\\u0026thinsp;0.071)), no significant correlation with EF (0.021, (p\\u0026thinsp;=\\u0026thinsp;0.657)), and a negative correlation with Age (-0.288, (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001)). The Environmental dimension showed a negligible correlation with LOS (0.005, (p\\u0026thinsp;=\\u0026thinsp;0.909)), a positive correlation with EF (0.027, (p\\u0026thinsp;=\\u0026thinsp;0.572)), and a non-significant correlation with Age (0.035, (p\\u0026thinsp;=\\u0026thinsp;0.464)). Lastly, the social dimension exhibited non-significant weak negative correlation with LOS (--0.012, (p\\u0026thinsp;=\\u0026thinsp;0.804)) and Age (-0.058, (p\\u0026thinsp;=\\u0026thinsp;0.224)) and a weak positive correlation with EF (0.039 (p\\u0026thinsp;=\\u0026thinsp;0.411)).\\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 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCorrelation of QoL with LOS, EF, and Age:\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\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\\u003eLOS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEF\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePhysical\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.114 (p\\u0026thinsp;=\\u0026thinsp;0.016)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.112 (p\\u0026thinsp;=\\u0026thinsp;0.019)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.443 (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePsychological\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.086 (p\\u0026thinsp;=\\u0026thinsp;0.071)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.021 (p\\u0026thinsp;=\\u0026thinsp;0.657)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.288 (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEnvironmental\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.005 (p\\u0026thinsp;=\\u0026thinsp;0.909)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.027 (p\\u0026thinsp;=\\u0026thinsp;0.572)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.035 (p\\u0026thinsp;=\\u0026thinsp;0.464)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSocial\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.012 (p\\u0026thinsp;=\\u0026thinsp;0.804)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.039 (p\\u0026thinsp;=\\u0026thinsp;0.411)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.058 (p\\u0026thinsp;=\\u0026thinsp;0.224)\\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\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study is one of the first QoL investigations of its kind in Pakistan, our findings not only illuminate the multifaceted challenges faced by post-MI individuals but also underscore the critical need for tailored interventions to address these complexities effectively. Our main findings can be summarized as follows: 1. There was a high mortality rate and high event rate among survivors discovered on follow up. 2. Over all QoL was good among survivors but the physical health domain scored the worst among all components and in general approximately 10% had low QoL. 3. Several factors were associated with poorer QoL including diabetes, female sex, age, multiple co-morbidities, and low education and income level.\\u003c/p\\u003e \\u003cp\\u003eOne notable revelation pertains to an unusually high mortality rate i.e. 16.02% post-discharge among MI patients, 14.4% males and 18.05% females. There is a possibility that it is an underestimation as many patient households could not be contacted. A study conducted in Pakistan showed that upon follow-up (average duration: 16.43\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.40 months) of 211 female patients (55.8%), the cumulative mortality rate was 20.3%, slightly higher than our results [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. This statistic draws attention to the severe impact of MI and its potential to lead to fatal outcomes. The study\\u0026rsquo;s findings not only contribute to a better understanding of the magnitude of the issue but also emphasize the need for early intervention, robust risk assessment, and preventive measures to mitigate mortality risks associated with MI through comprehensive secondary prevention programs. This alarming statistic emphasizes the urgency for healthcare providers and policymakers to prioritize strategies that enhance both survival rates and post-MI quality of life.\\u003c/p\\u003e \\u003cp\\u003eAmong the post-MI patients, 62.0% reported their overall QoL as good with scores of 4\\u0026ndash;5 on a Likert scale of 1\\u0026ndash;5. The high overall QoL scores among post-MI patients in this study were inconsistent with studies from the Republic of China [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], Ethiopia [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], the United States of America [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], and Hong Kong [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e] where the QoL scores were generally lower. These variations might be attributed to differences in data collection instrument which included generic (SF-36) and disease-specific (MIDAS) and Hospital Anxiety and Depression Scale (HADS), sample sizes, sociocultural factors, and participants' perceptions of QoL. Intriguingly, our results align with those of a study conducted in Myanmar and another in Singapore, both of which reported good QoL scores among their respective post-MI populations [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. This suggests that certain cultural or contextual factors may exert a powerful influence on QoL perceptions. Recognizing and harnessing these cultural subtleties can potentially catalyze the development of more effective tailored interventions to elevate QoL outcomes in specific populations. According to a previous study conducted in 2007 in another tertiary hospital in Pakistan, in women, the raw WHOQOL scores were in the range of 10.09\\u0026ndash;14.62 which are equivalent to transformed scores of 40.36\\u0026ndash;58.48 [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. These scores notably lag behind those observed in our study. Such a discrepancy suggests an improvement in treatment outcomes over time.\\u003c/p\\u003e \\u003cp\\u003ePhysical health was found to be the most affected domain, with the worst median score observed. After the discharge, the physical functioning, general health and vitality of health are the most affected [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. With low EF and high rates of complications in post-MI patients, their ability to perform regular physical activity is limited, which can be a potential target for interventions to improve QoL.\\u003c/p\\u003e \\u003cp\\u003e61.8% of participants were male and 31.2% were females, with a median age of 63 years indicating a connection between MI and advancing age. Moreover, females reported lower median scores in physical, psychological, and environmental health domains compared to males, highlighting the influence of gender on QoL. This aligns with findings from a similar study in England, which found that women had lower health-related quality of life (HRQoL) compared to men among a large cohort of MI survivors, both at baseline and during the 12-month follow-up. Another study utilizing EuroQOL five dimensions (EQ-5D-3L) questionnaire reported that women had higher impairment levels in mobility, self-care, usual activities, pain/discomfort, and anxiety/depression at each time point [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Studies done in India and Ethiopia, also showed that females have been shown to experience lower QoL after MI compared to their male counterparts [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. This gender-specific variation in HRQoL after MI echoes the importance of exploring factors and health disparities contributing to lower QoL in females. The QoL depends on various factors, including differences in healthcare-seeking behaviors, social support systems, and coping mechanisms. Females may face unique challenges in managing their health and juggling multiple roles, which can impact their overall well-being. Moreover, specific rehabilitation programs for women, however, need to be further explored [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAdditionally, higher education levels, being married, and higher income levels were associated with better QoL scores, suggesting the potential influence of social support, access to resources, and overall lifestyle on post-MI well-being. Other studies have also shown that higher education, marriage and good financial status are associated with the highest assessments of quality of life and perceived health condition [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] as this supports the social capital theory, which suggests that social connections, networks, and norms are valuable resources that can help people and communities do better. This also decreases the degree of depression [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. According to a meta-analysis and systemic review, married/partnered patients reported a higher HRQoL compared to their unpartnered counterparts. However, no association was found between marital status and improved functional status, symptoms of anxiety and depression [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Furthermore, a study conducted in the Netherlands found that spirituality positively contributed to QoL [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. These findings align with the importance of addressing psychosocial and financial factors and providing holistic care to enhance the overall well-being of post-MI patients in diverse cultural settings.\\u003c/p\\u003e \\u003cp\\u003eThe results from the present study also highlight the substantial burden of concurrent health conditions among post-MI patients, with a majority having two or more comorbidities. This is congruent with the findings from the UK multicenter longitudinal study, which emphasized the prevalence of multiple comorbidities in MI survivors, and its impact on HRQoL [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. This highlights the complexity of managing health conditions in this population, which negatively impacted QoL in this study. The comprehensive breakdown of comorbidities provides valuable information for healthcare providers and policymakers to tailor interventions that account for these concurrent health challenges.\\u003c/p\\u003e \\u003cp\\u003eThis study serves as a crucial reminder of the dearth of information regarding post-MI management strategies in Pakistan. The scarcity of research on this topic is indicative of the broader challenge in addressing the holistic needs of post-MI patients beyond the acute phase. This gap highlights the importance of not only investigating QoL outcomes but also delving into the intricacies of post-MI care. Improved management strategies encompassing both acute and long-term care are essential to mitigate the impact of MI on QoL and overall mortality.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrengths and Limitations:\\u003c/h2\\u003e \\u003cp\\u003eOne of the study's key strengths was using a standardized tool (WHOQOL-BREF) validated for both developed and developing countries, ensuring the QoL assessment's reliability. The study's results were based on robust internal consistency measures, with good Cronbach's alpha scores for physical and psychological health domains. In the present study, Cronbach's alpha scores for physical and psychological health were 0.906 and 0.792, respectively. The slightly lower Cronbach's alpha scores for social relationship (0.733) and environment (0.58) also lie within acceptable range. The overall Cronbach's alpha score of 0.902 indicates a reliable measure of QoL among the participants. Alpha values are described in \\u003cem\\u003eFig.\\u0026nbsp;4.0\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. This indicates that the WHOQOL-BREF instrument is a reliable measure for assessing physical and psychological health aspects in the Pakistani MI population.\\u003c/p\\u003e \\u003cp\\u003eHowever, several limitations should be considered when interpreting these findings. The cross-sectional design limited the ability to establish causal relationships between participant characteristics and QoL outcomes. Additionally, data collection through phone calls may not be the best approach for such a sensitive topic and may decrease the quality of interaction with the patients who are less likely to give appropriate answers. The study's single-center nature might limit the generalizability of results to other regions in Pakistan with different sociodemographic characteristics and healthcare systems. A multi-center approach involving diverse regions could have enhanced the external validity of the findings. Future research could benefit from a longitudinal design to better understand the dynamic nature of QoL in myocardial infarction patients.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe study's revelations regarding the dearth of post-myocardial infarction (MI) quality of life data in Pakistan highlighting important points that need to be advocated by healthcare providers and policymakers. Physicians who know the impact of participants' characteristics on quality of life (QoL) should focus on the specific needs of subgroups, including women and people from low economic backgrounds in rehabilitation programs. Using strategies aimed at strengthening social support, improving coping mechanisms, and improving access to healthcare can improve people's quality of life and thereby reduce mortality rates. Furthermore, the substantial burden of concurrent health conditions among post-MI patients emphasizes the necessity for comprehensive healthcare management and tailored interventions to mitigate the impact of comorbidities on QoL. Notably, there is an urgent call to enhance rehabilitation programs to better serve the post-MI population and improve their overall well-being. A holistic treatment approach with multidisciplinary teams providing medical and psychosocial support is needed to improve quality of life.\\u003c/p\\u003e \\u003cp\\u003eFurther studies are needed to find out the causal relationship between the above-mentioned factors and poor quality of life to better understand the relationship.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval was taken from the Ethics Review Committee (ERC) of Aga Khan University (AKU); ERC reference number: 2023-7040-23790. Patient data was then electronically collected from hospital records. Informed verbal consent was taken from patients via telephonic calls before administration of the WHOQOL-BREF questionnaire.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of Data and Materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and analysed during the current study are not publicly available due to institutional policies, but can be made available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors do not have any competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDisclosure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors don\\u0026rsquo;t have any conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding Source\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBrain and Mind Institute, Aga Khan University\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthorship Contribution Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJ.H.: Conceptualization, Methodology, Supervision, Writing- Reviewing and Editing, Analysis, Project administration\\u003cstrong\\u003e; M.A.:\\u003c/strong\\u003e Conceptualization, Methodology, Analysis, Supervision, Writing- Reviewing and Editing, Project administration, Supervision; \\u003cstrong\\u003eH.A.:\\u003c/strong\\u003e Conceptualization, Methodology, Writing- Original draft preparation, Visualization\\u003cstrong\\u003e; A.J.\\u003c/strong\\u003e: Writing- Original draft preparation, Writing- Reviewing and Editing, Visualization, \\u003cstrong\\u003eI.T.\\u003c/strong\\u003e: Conceptualization, Methodology, Writing- Reviewing and Editing, Submission\\u003cstrong\\u003e; S.Q.:\\u0026nbsp;\\u003c/strong\\u003eData Curation \\u003cstrong\\u003e; J.S.:\\u003c/strong\\u003e Writing-Review and Editing\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e;\\u0026nbsp;\\u003cstrong\\u003eZ.M.\\u003c/strong\\u003e: Conceptualization, Writing-Review and Editing, Supervision, Project administration. \\u003cstrong\\u003eZ.A.\\u003c/strong\\u003e: Conceptualization, Writing-Review and Editing, Supervision, Project administration.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eKarimi M, Brazier J, Health. 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BMC Public Health. 2015;15:1\\u0026ndash;15. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/S12889-015-2014-9/TABLES/7\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/S12889-015-2014-9/TABLES/7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhu C, Tran PM, Leifheit EC, Spatz ES, Dreyer RP, Nyhan K, et al. Association of marital/partner status and patient-reported outcomes following myocardial infarction: A systematic review and meta-Analysis. Eur Heart J Open. 2023;3. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/ehjopen/oead018\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/ehjopen/oead018\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWachelder EM, Moulaert VRMP, van Heugten C, Gorgels T, Wade DT, Verbunt JA. Dealing with a life changing event: The influence of spirituality and coping style on quality of life after survival of a cardiac arrest or myocardial infarction. Resuscitation 2016;109.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMunyombwe T, Dondo TB, Aktaa S, Wilkinson C, Hall M, Hurdus B, et al. Association of multimorbidity and changes in health-related quality of life following myocardial infarction: a UK multicentre longitudinal patient-reported outcomes study. BMC Med. 2021;19. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12916-021-02098-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12916-021-02098-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\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\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Myocardial Infarction, Health-related Quality of Life, Patient-reported outcomes, WHOQOL score\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4432059/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4432059/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e Quality of life (QoL) assessment is essential for optimizing patient care, treatment adjustments, and medical decision-making, particularly in post-Myocardial Infarction (MI) patients, but limited data exists on QOL post MI from Pakistan. This study aimed to assess Quality of Life (QoL and its determinants in the Pakistani population.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e A single-center cross-sectional study was conducted at a tertiary care hospital in Karachi, Pakistan. Patients ≥ 18 years with a primary diagnosis of acute MI (ICD 9 codes: 410.0-410.9 and ICD-10 codes: 121.0-121.9) discharged from the Cardiology Service from January 2019 to December 2020 who could be contacted and consented to participate were included. Data was collected from electronic records, and patients were interviewed via phone calls using a validated Urdu version of the WHOQOL-BREF questionnaire. Statistical analysis was performed using non-parametric tests via RStudio (Version 1.4.1717).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e The final study cohort was 440 patients with a median age of 63 (IQR: 56,72) years, with a male predominance (68.2%). Physical health was the most affected domain. Females, lower income individuals, and those with lower level of education had lower QoL scores in all domains. Diabetes and presence of multiple co-morbidities were associated with lower QoL.\\u003c/p\\u003e\\n\\u003cp\\u003eMarital and socioeconomic status, along with psychosocial factors were significantly associated with QoL scores. Notably, 62.0% of post-MI patients rated their overall QoL as good (scores of 4-5 on a Likert scale of 1-5). Cronbach's alpha values indicated good internal consistency, with an overall Cronbach's alpha of 0.902.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough a significant proportion of patients post MI in our cohort reported good QoL, several social factors were associated with lower QoL. These factors must be investigated further in discharge planning and post-discharge of patients with MI.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Quality of Life after Myocardial infarction in the Pakistani Population – Insights from a Single-Center Cohort Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-06-05 17:36:22\",\"doi\":\"10.21203/rs.3.rs-4432059/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"cb765614-2d0e-4a1a-95e5-69324333fa09\",\"owner\":[],\"postedDate\":\"June 5th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-09-23T11:38:33+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-06-05 17:36:22\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4432059\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4432059\",\"identity\":\"rs-4432059\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}