Polypharmacy, cardiovascular-specific medication use, and comorbidity burden among elderly residents in care homes of Colombo District, Sri Lanka: A cross-sectional study

Polypharmacy, cardiovascular-specific medication use, and comorbidity burden among elderly residents in care homes of Colombo District, Sri Lanka: A cross-sectional 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 Polypharmacy, cardiovascular-specific medication use, and comorbidity burden among elderly residents in care homes of Colombo District, Sri Lanka: A cross-sectional study J.M.S.I. Madhushara, W.A.M. Indunil, S.B.M.D.P. Sanjeewanee, A.I. Sooriyaarachchi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8502816/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Multimorbidity is a significant contemporary public health challenge globally, with older adults being disproportionately affected. This vulnerability often necessitates long-term pharmacotherapy, increasing the risk of polypharmacy. The concurrent use of multiple medications can lead to adverse health outcomes. Elderly care home residents are particularly susceptible to this risk. This study aimed to determine the prevalence and associated factors of polypharmacy, with a specific emphasis on cardiovascular medication use, in relation to comorbidity burden among elderly residents in care homes in Colombo District, Sri Lanka. Methods A descriptive, cross-sectional study was conducted among 373 elderly residents (>60 years) from ten elderly care homes in Colombo District, Sri Lanka. Participants were recruited using proportionate stratified sampling. Data were collected via a validated interviewer-administered questionnaire and a structured medication checklist. Descriptive statistics were computed for demographic and clinical variables. Group differences were analyzed using t-test, ANOVA, and Chi-square test. Multivariate logistic regression was performed to identify factors associated with polypharmacy, with statistical significance set at p<0.05. Results The mean age of the participants was 74.74 ± 7.56 years, and 75.1% were female. Multimorbidity was reported by 79.4% of residents. The most prevalent chronic condition was hypertension (75.1%). The mean number of medications per resident was 5.50 ± 2.98. The prevalence of overall polypharmacy was 59.0%, while cardiovascular-specific polypharmacy was 20.9%. Multimorbidity (AOR: 2.44, 95% CI:1.03-5.78, p = 0.043), frequent contact with a general practitioner (AOR:2.17, 95% CI:1.04-4.52, p = 0.039), and a history of myocardial infarction (AOR:3.51, 95% CI:1.10-11.17, p = 0.033) were significantly associated with polypharmacy. Cardiovascular-specific polypharmacy was independently associated with frequent hospitalization (AOR:2.83, 95% CI:1.21-6.59, p = 0.016), a history of myocardial infarction (AOR:7.26, 95% CI:2.44-21.57, p<0.001) and a history of congestive heart failure (AOR:5.20, 95% CI:1.57-17.19, p = 0.007). Conclusion The high prevalence of both overall and cardiovascular-specific polypharmacy among elderly care home residents highlights an urgent need to implement structured deprescribing initiatives and integrated, multidisciplinary geriatric care models within care homes. Priority interventions should include strengthening medication review systems, establish coordinated care pathways, and deploy targeted strategies to minimize inappropriate medication use and its associated risks. risk factors elderly polypharmacy prevalence Sri Lanka Figures Figure 1 Introduction Population ageing represents one of the most significant demographic transitions of the twenty-first century. Projections indicate that by the late 2070s, the global population aged 65 years and older is projected to reach 2.2 billion, surpassing the number of children, while those aged 80 years and above are expected to outnumber infants by the mid-20230s( 1 ). This demographic shift is accompanied by a marked rise in chronic non-communicable diseases (NCDs), making multimorbidity a defining characteristic of aging populations( 2 ). Multimorbidity drives increased healthcare utilization and frequent necessities long-term pharmacotherapy, which in turn heightens the risk of medication-related harm among older adults ( 3 ). Polypharmacy, commonly defined as the concurrent use of five or more medications, represents a major challenge in geriatric care( 4 – 7 ). In clinical and research settings, polypharmacy is often categorized according to medication count such as non-polypharmacy (< 5 medications), Polypharmacy (5–9 medications) and excessive or hyper-polypharmacy (≥ 10 medications) ( 8 , 9 ). Conceptually, polypharmacy can also be classified as appropriate or inappropriate( 10 ). Appropriate polypharmacy refers to the evidenced-based and rational use of multiple medications to manage complex conditions and achieved therapeutic goals. Conversely, inappropriate polypharmacy involves unnecessary or irrational prescribing of medications, which elevates the risk of adverse outcomes( 11 ). Globally, polypharmacy is highly prevalent among older adults, with reported rates ranging from 5% to 78% across studies( 12 ). This prevalence rises substantially in elderly populations with multimorbidity( 13 ). Polypharmacy is associated with a wide range of adverse health outcomes in older adults. Systemic review evidence indicates positive associations between polypharmacy and frailty, malnutrition, functional decline, disability, and mortality, inappropriate prescribing, and medication non-adherence( 14 ). Age-related physiological changes, including altered pharmacokinetics and pharmacodynamics, such as altered drug metabolism and reduced renal clearance, further increase susceptibility to adverse drug reactions and toxicity in this population( 8 ). One study found that patients with polypharmacy are 5.1 times more likely to receive inappropriate prescriptions compared to those taking fewer than five medications( 15 ). Additionally, both cross-sectional and longitudinal analyses show that older adults exposed to polypharmacy have a significantly elevated risk of falls, highlighting its clinical importance in geriatric care( 16 ). The burden of polypharmacy is particularly high among institutionalized older adults. Compared to their community-dwelling counterparts, nursing home (NH) residents typically present with higher levels of functional dependence, cognitive impairment, and multimorbidity, rendering them especially vulnerable to drug-drug interactions (DDIs) and adverse drug events( 17 – 19 ). Large, multicentric studies conducted in long-term care settings consistently report high prevalence rates of polypharmacy, often exceeding 50%, with excessive polypharmacy affecting approximately one-quarter to one-third of residents( 9 , 10 , 18 – 21 ). These findings reflect the complex clinical profiles of institutionalized older adults and highlight the significant challenges in medication management within residential care environments. Polypharmacy in NHs is frequently accompanied by the use of potentially inappropriate medications (PIMs) and clinically significant potential drug-drug interactions (pDDIs). Evidence suggests that 23.7% to 70% of NH residents are exposed to at least one PIM, while 25.1% to 37.8% experience pDDIs, many of moderated to major severity ( 19 ). Such prescribing patterns contribute to prescribing cascades, adverse drug events, and medication-related harm, further increasing morbidity and mortality in this vulnerable population( 8 ). Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality in older adults, often necessitating long-term, multi-drug pharmacotherapy( 22 ). The coexistence of CVDs, frailty, and polypharmacy substantially amplifies the risk of adverse clinical outcomes, including cardiovascular complications, emergency admissions and premature death( 23 , 24 ). Older adults, particularly those aged 80 years and above, have a markedly higher likelihood of receiving higher number of cardiovascular-specific medications ( 25 ). Consequently, the majority of older patients with cardiovascular conditions meet criteria for polypharmacy( 26 ). Many cardiovascular agents, including statins, antihypertensives and anticoagulants, which dominate therapeutic regimens in older adults, have been linked to orthostatic hypotension and an increased risk of falls, which is major issue among care home residents( 27 , 28 ). Sri Lanka is undergoing rapid population ageing, with the elderly population is projected to double from 2.5 million in 2012 to 5.2 million by 2037 and to comprise nearly one-quarter of the total population by 2042( 29 ). Colombo District, the country’s most densely populated region, faces distinct challenges related to ageing, chronic disease management, and institutional elder care( 30 ). While previous studies in Sri Lanka have reported a high prevalence of multimorbidity and polypharmacy in tertiary care settings, along with strong associations between polypharmacy and pDDIs ( 2 , 31 , 32 ), evidence focusing specially on institutionalized elderly adults and cardiovascular-specific polypharmacy (CVSP) remains scarce. Therefore, this study aimed to determine the prevalence and associated factors of polypharmacy, with specific emphasis on cardiovascular medication use, in relation to comorbidity burden among among older adults residing in care homes in Colombo District, Sri Lanka. Materials and Methods Study design and setting A descriptive, cross-sectional study was conducted in ten selected care homes in Colombo District of Sri Lanka from July 2023 to March 2024. The study aimed to estimate the point prevalence of polypharmacy, cardiovascular specific polypharmacy, and identify the associated factors among institutionalized elderly care home residents. Study population The study population comprised all elderly residents (aged ≥ 60 years of age) of selected, elderly care homes in the Colombo District, Sri Lanka. All sampled homes were formally registered under the Social Service Department of Western Province, Sri Lanka. In accordance with the national policy for elders, and “elder” in Sri Lanka is defined as individual who has completed 60 years of age( 33 ). Inclusion criteria were residents who age 60 years or above, institutionalized for at least one month, with diagnosis of at least one chronic disease at least one month prior to data collection, and receiving ongoing pharmacotherapy via oral, inhalation, instillation, or tropical routes for at least one month prior to the data collection. Exclusion criteria were refused to participate in the study, critically or terminally ill individuals which would interfere with participation in the study and a clinically diagnosed cognitive impairment. Sample Size and Sampling Procedure The required sample size was calculated using Cochran’s formula. Based on a previously reported prevalence of polypharmacy (58.7%) among elderly population in Northen Sri Lanka( 2 ), with a 95% confidence level and a 5% margin of error, the minimum required sample size was 373 elderly residents. Participants recruitment followed a stratified quota sampling strategy. In the first stage, the total sample size was allocated across the ten participating care homes (strata) using proportionate allocation based on each home’s total resident population. In the second stage, eligible residents within each home were recruited using a purposive sampling approach to meet the pre-allocated quotas. Recruitment prioritized residents who were accessible at the time of data collection, willing to participate, and capable of completing the interviewer-administered instruments. This non-probability sampling strategy was adapted to accommodate practical constraints inherent to the institutional setting, including varying levels of cognitive capacity, resident availability, and consent capabilities. The final sample distribution is presented in Table 1 . Table 1 Final sample distribution across the selected care homes Identification number of care homes (total number of residents) Residents selected for study Number of residents (n) Elderly care home 01 (N = 18) 16 Elderly care home 02 (N = 47) 35 Elderly care home 03 (N = 144) 120 Elderly care home 04 (N = 35) 30 Elderly care home 05 (N = 62) 49 Elderly care home 06 (N = 96) 41 Elderly care home 07 (N = 18) 14 Elderly care home 08 (N = 10) 1 Elderly care home 09 (N = 47) 38 Elderly care home 10 (N = 37) 29 Total 373 Data Collection Tool Data were collected using a structured, interviewer-administered questionnaire and a checklist. Well-trained interviewers conducted the interviews in separate, private spaces within each care home, in the participant’s preferred language. The questionnaire comprised four sections: residential information, socio-demographic characteristics, clinical history and Charlson Comorbidity Index (CCI). The CCI, which was pre validated in the Sri Lankan context ( 34 ), was used to assess the overall level of multimorbidity among participants. The checklist captured medication-related data and anthropometric measurements. Medication information was collected and cross-verified through a review of medical records maintained by each care home. Each medication was classified according to the third level of the Anatomical Therapeutic Chemical (ATC) classification system( 35 ). Anthropometric assessment included weight, height, hip circumference, waist circumference, and chest circumference, providing comprehensive overview of each participant’s physical and nutritional health. Measurements were taken while participants wore lightweight clothing. Height was measured to the nearest 1.0 centimeter using a wall-mounted stadiometer, with participants standing upright in the Frankfort plan position. Body weight was measured to the nearest 1.0 kilogram using electronic weighing scale, which was calibrated by Ministry of Health, Sri Lanka. Hip, waist, and chest circumference were measured using a flexible, non-stretchable tape, following standardized protocols. Definitions of Polypharmacy, Cardiovascular-specific polypharmacy and associated factors Polypharmacy was defined as use of five or more medications daily. When using medication ten or more routinely, it was defined as excessive polypharmacy (≥ 10 medications). Cardiovascular-specific polypharmacy was defined as the routine use of five or more medications prescribed for the management of CVDs. Participants’ socio-demographic characteristics, clinical characteristics and anthropometric measurements were considered as factors associated with polypharmacy and cardiovascular-specific polypharmacy. CCI score was categorized into three categories such as low (0–2), moderate ( 3 – 4 ), and severe (≤ 5)( 36 ). Comorbidity burden was assessed using comorbidity-polypharmacy score (CPS), calculated by summing the number of chronic conditions and the total number of medications used. Data analysis Statistical analyses were performed using Statistical Package for Social Sciences (SPSS) (Version 27.0). Descriptive statistics were used to summarize socio-demographic characteristics, clinical characteristics and medication patterns. Continuous variables were presented as means and standard deviations while categorical variables were reported as frequencies and percentage with 95% confidence intervals. Inferential statistical analyses were used to examine associations of polypharmacy. Pearson’s chi-square test, student t-test and one-way ANOVA test employed to assess associations of polypharmacy with socio-demographic and clinical characteristics. When significant differences were found in the one-way ANOVA, Turky’s post hoc analysis was applied to identify subgroup analysis. Multivariable regression analysis was conducted to determine the independent predictors associated with polypharmacy. A two-tailed p value < 0.05 was considered as statistically significant. Ethical Considerations The study was approved by the Ethic Review Committee of the Faculty of Medicine, University of Colombo (Approval No. EC-2023-128; approved on November 16, 2023). Permission was obtained from Social Service Department, Western Province and administrators of participating in care homes before data collection. Data were collected following obtaining written informed consent from the individual participants. Participant confidentiality and data anonymity were maintained throughout the study, in accordance with the Declaration of Helsinki. Results Scio-demographic Characteristics of participants A total of 373 elderly residents participated in the study. Mean age of the participant was 74.74 ± 7.56 (range = 60 years to 103 years) with majority (55%) belonging to “elderly” age category (60–75 years). Most participants were female (75.1%). Educational attainment varied: 11.3% had no formal schooling, 18.5% had completed primary education (1–5 grade), 26.5% secondary (6–9 grade), 21.4% ordinary level (10–11 grade), 18% advanced level (12–13 grade) and 4.3% held a diploma or higher qualification. Most participants (71.6%) reported no proper income level. Mean duration of institutionalization was 5.24 ± 5.33 years with most participants stay less than 5 years (59.8%) (Table 2 ). Table 2 Socio-demographic characteristics of the elderly residing in selected care homes of Colombo district, Sri Lanka (N = 373) Socio-demographic variable Frequency n(N = 373) % Gender Female 280 75.1% Male 93 24.9% Age categories Elderly (60–75 years) 205 55% Senile (75–90 years) 157 42.1% Long livers (90 + years) 11 2.9% Educational Attainment No schooling 42 11.3% Primary (1–5 grade) 69 18.5% Secondary (6–9 grade) 99 26.5% Ordinary level (10–11 grade) 80 21.4% Advanced level (12–13 grade) 67 18% Diploma or higher 16 4.3% Income Level No income 267 71.6% Low income (< 25 000 LKR) 73 19.6% Lower middle income (25000–100000 LKR) 28 7.5% Upper middle income (100000–300000 LKR) 5 1.3% Length of institutionalization 15 years 23 6.2% Clinical Characteristics of participants The mean Body Mass Index (BMI) of the participants was 22.54 ± 4.76 kg/m 2 , with 56.3% falling within the normal BMI category. Most residents (84.5%) had consulted a general practitioner (GP) within the previous two months, while 30.6% had been hospitalized in the past year. Regarding past surgical history, 26.8% of participants had undergone eye surgery. The most prevalent medical condition was hypertension, affecting approximately three-quarters (75.1%) of total sample, followed by dyslipidemia (58.2%), diabetes mellitus (DM) (39.1%), and respiratory diseases (27.1%). A total of 17.7% of participants reported history of “other” medical conditions, including thyroid disorders, epilepsy, Parkinson’s disease, anemia and nervous system conditions. Overall, 79.4% of participants had two or more coexisting medical conditions. In terms of family history on NCDs, 26.8% reported history of CVD, followed by 23.3% with a family history of metabolic diseases. Table 3 Clinical characteristics among the elderly residing in selected care homes, Colombo District, Sri Lanka Clinical variable Frequency n (N = 373) % BMI category Underweight (≤ 18.49 kg/m2) 69 18.5% Healthy (18.5-24.99 kg/m2) 210 56.3% Overweight (25.0-29.9 kg/m2) 69 18.5% Obese (≥ 30 kg/m2) 25 6.7% Contact with GP in the past 2 months Yes 315 84.5% No 58 15.5% Hospitalization in the past 12 months Yes 114 30.6% No 259 69.4% Past surgical history No reported history 166 44.5% Eye surgery 100 26.8% Orthopedic surgery 44 11.8% Surgeries in bowel 15 4% Lower segment cesarian section 14 3.8% Other 65 17.4% Past medical history Hypertension 280 75.1% Dyslipidemia 217 58.2% Diabetes mellitus 146 39.1% Respiratory illness 101 27.1% Other 66 17.7% Number of chronic illnesses 1 disease 77 20.6% 2 diseases 99 26.5% 3 diseases 93 24.9% 4 diseases 59 15.8% ≥ 5 diseases 45 12.1% Family history of NCDs No family history 198 53% Cardiovascular diseases 100 26.8% Metabolic diseases 87 23.3% Cancers 26 7% Chronic kidney disease 8 2.1% others 13 3.5% CCI score Mild ( 1 – 2 ) 204 54.7% Moderate ( 3 – 4 ) 105 28.2% Severe (≥ 5) 64 17.1% Medication-related characteristics of participants The mean number of medications per residents was 5.50 ± 2.98, while the mean number of cardiovascular-specific medications per prescription was 2.71 ± 2.03. According to ATC classification system (Level III), a total of 80 distinct pharmacological subgroups were identified. Among these, 15 subgroups were prescribed to more than 10% of participants. The most frequently prescribed subgroup was blood glucose-lowering drugs (excluding insulin) (A10B), used by 60.9% of participants. This was followed by lipid-modifying agents (C10A) (58.2%), angiotensin-converting enzyme (ACE) (C09C) (48.0%), antithrombotic agents (B01A) (43.7%), and vitamin B complex, including combinations (A11E) (43.7%) (Fig. 1 ). Prevalence of polypharmacy and cardiovascular polypharmacy The overall prevalence of polypharmacy in the study population was 59.0%, with 10.5% of residents meeting the criteria for excessive polypharmacy (≥ 10 medications). Polypharmacy was more prevalent among males (65.6%) than females (56.4%). Prevalence of polypharmacy decreased with increasing age, being highest among the “elderly” (62.4%), and lowest among those aged 90 years and older (36.4%). A higher prevalence was observed among participants who had consulted a GP in the past two months (63.1%) compared to those who had not (36.2%). Similarly, polypharmacy was more common among participants hospitalized in the past 12 months (76.7%) than among those who had not been hospitalized (59.9%). Among participants with multimorbidity (≥ 2 conditions), 72.9% reported polypharmacy. A higher prevalence was also observed with a past surgical history (66.1%) and a family history of NCDs (67.4%) (Table 4 ). The prevalence of CVSP was 20.9%. The prevalence was higher among males (24.7%) and was most common in the “senile” age group (75–90 years) at 25.5%. Residents categorized as overweight (25.0-29.9 kg/m2) had highest prevalence (33.3%) compared to other BMI categories. Contact with a GP in the past two months was common among participants with CVSP. The majority of participants with CVSP (89.7%) had consulted a GP within that period. The highest prevalence of CVSP was observed among participants hospitalized in the past 12 months (55.1%). All participants with multimorbidity presented with CVSP. Furthermore, 14.6% of participants with family history of NCDs had CVSP while 30% of participants with a family history of CVDs (Table 6 ). Associations of polypharmacy, cardiovascular polypharmacy Based on Pearson’s chi-square test, no statistically significant associations were observed between polypharmacy or CVSP and socio-demographic characteristics such as gender, age group, income level, length of institutionalization. Polypharmacy was significantly associated with several clinical characteristics, including contact with a GP within past two months (p < 0.001), hospitalization in the past year (p < 0.001), present of multimorbidity (p < 0.001), history of past surgery (p < 0.05), family history of NCDs (p < 0.05), family history of CVDs (p < 0.05). One-way ANOVA test revealed significant differences of mean number of medical conditions (p < 0.001), mean number of CCI score (p < 0.001), mean number of total medications (p < 0.001), and mean number of CPS (p < 0.001) (Table 5 ). Post-hoc analysis confirmed that all pairwise comparisons between non-polypharmacy, polypharmacy, and excessive polypharmacy groups for aforementioned variables were statistically significant (p < 0.001). Furthermore, polypharmacy was significantly associated with the presence of several chronic health conditions such as hypertension (p < 0.001), dyslipidemia (p < 0.001), DM (p < 0.001) and congestive heart failure (p < 0.001). Table 4 Distribution of polypharmacy among elderly, residing in elderly care homes of Colombo District (N = 373) Variable Polypharmacy Categories Chi-squared P- Value (0–4 Drugs) (5–9 Drugs) (≥ 10 Drugs) Total sample 153(41%) 181(48.5%) 39(10.5%) Gender 0.097 Female 121(43.2%) 127(45.4%) 32(11.4%) Male 32(34.4%) 54(58.1%) 7(7.5%) Age categories 0.054 Elderly Age 77(37.6%) 111(54.1%) 17(8.3%) Senile Age 69(43.9%) 66(42%) 22(14%) Long-livers 7(63.6%) 4(36.4%) 0(0%) Length of stay 0.612 15 years 7(30.4%) 12(52.2%) 4(11.7%) Contact with GP in the past 2 months < 0.001** Yes 116(36.2%) 162(51.4%) 37(11.7%) No 37(63.8%) 19(32.8%) 2(3.4%) Hospitalization in the past 12 months 0.014* Yes 26(22.8%) 69(60.5%) 19(16.7%) No 127(49%) 112(43.2%) 20(16.7%) Multimorbidity < 0.001** Yes 89 (30.1%) 168(56.8%) 39 (13.1%) No 64 (83.1%) 13(16.9%) 0 (0%) Past surgical history 0.004* Yes 70(33.8%) 110(53.1%) 27(13.0%) No 83(50.0%) 71(42.8%) 12(7.2%) Family History of NCDs 0.008* Yes 57 (32.6%) 97 (55.4%) 21(12%) No 96 (48.5%) 84 (42.4%) 18(9.1%) Family History of CVDs 0.011* Yes 34 (34%) 48 (48%) 18 (18%) No 119 (43.6%) 133 (48.8%) 21 (7.6%) *Significant associations were taken when p < 0.05 **Significant associations were taken when p < 0.001 Table 5 One-way ANOVA test of polypharmacy among different clinical characteristics of elderly residents (N = 373) Variable Mean ± SD Polypharmacy categories p-value 0–4 drugs 5–9 drugs ≥ 10 drugs Age 74.74 ± 7.56 75.50 ± 8.18 74.08 ± 7.08 74.85 ± 7.07 0.234 Length of stay 5.25 ± 5.34 4.73 ± 5.04 5.42 ± 5.50 6.40 ± 5.54 0.176 BMI 22.54 ± 4.76 22.08 ± 4.88 22.93 ± 4.75 22.48 ± 4.19 0.259 Waist to Hip ratio 0.91 ± 0.091 0.89 ± 0.09 0.91 ± 0.08 0.92 ± 0.09 0.138 No. medical conditions 2.80 ± 1.49 1.91 ± 1.00 3.17 ± 1.26 4.58 ± 1.64 < 0.001* CCI score 2.77 ± 1.85 1.85 ± 1.40 3.20 ± 1.75 4.41 ± 1.99 < 0.001* No. of total medications 5.50 ± 2.98 2.86 ± 0.95 6.39 ± 1.28 11.66 ± 2.11 < 0.001* CPS 8.30 ± 4.03 4.78 ± 1.52 9.57 ± 2.08 16.25 ± 3.29 < 0.001* *Significant associations were taken when p < 0.05 Similar to polypharmacy, CVSP showed no statistically significant association with socio-demographic characteristics. However, it was significantly associated with clinical variables including BMI category (p < 0.05), hospitalization within the past 12 months (p < 0.001), multimorbidity (p < 0.001), past surgical history (p < 0.05), family history of NCDs (p < 0.05), family history of CVDs (p < 0.05) (Table 6 ). Independent student t-test further revealed significant differences between participants with and without CVSP. Those with CVSP has a significantly higher mean number of chronic medical conditions (p < 0.001), CCI score (p < 0.001), total number of medications (p < 0.001), CPS (p < 0.001) (Table 7 ). Table 6 Distribution of cardiovascular-specific polypharmacy with participant characteristics of elderly residents (N = 373) Variable Cardiovascular-specific Polypharmacy Chi-squared P- Value No Yes Total sample 295(79.1%) 78(20.9%) Gender 0.305 Female 225(80.4%) 55(19.6%) Male 70(75.3%) 23(24.7%) Age categories 0.061 Elderly Age 167(81.5%) 38(18.5%) Senile Age 117(74.5%) 40(25.5%) Long-livers 11(100%) 0 (0%) Length of stay 0.130 15 years 19(82.6%) 4(17.4%) BMI categories 0.018* Underweight 58(84.1%) 11(15.9%) Healthy 168(80%) 42(20%) Overweight 46(66.7%) 23(33.3%) Obese 23(92%) 2(8%) Contact with GP within the past 2 months 0.098 Yes 245(83.1%) 70(89.7%) No 50(16.9%) 8(10.3%) Multimorbidity < 0.001** Yes 218 (73.6%) 78 (26.4%) No 77 (100%) 0(0%) Hospitalization within the past 12 months < 0.001** Yes 71(24.1%) 43(55.1%) No 224(75.9%) 35(44.9%) Family History of NCDs 0.002* Yes 169 (85.4%) 29 (14.6%) No 126 (72%) 49 (28%) Family History of CVDs 0.014* Yes 70(70%) 30(30%) No 225(82.4%) 48(17.6%) *Significant associations were taken when p < 0.05 **Significant associations were taken when p < 0.001 Table 7 The student t-test of cardiovascular-specific polypharmacy among different characteristics of the elderly residents. (N = 373) Variable (Mean ± SD) CVSP (Mean ± SD) P-Value Yes No Age of the person 74.74 ± 7.56 74.76 ± 7.66 74.67 ± 7.22 0.918 Length of stay 5.25 ± 5.34 5.16 ± 5.46 5.53 ± 4.88 0.567 BMI 22.54 ± 4.76 22.50 ± 4.89 22.67 ± 4.25 0.761 Waist to Hip ratio 0.91 ± 0.091 0.90 ± 0.088 0.93 ± 0.102 0.057 Number of medical conditions 2.80 ± 1.49 2.43 ± 1.26 4.18 ± 1.49 < 0.001* CCI score 2.77 ± 1.85 2.44 ± 1.71 4.01 ± 1.83 < 0.001* Number of total medications 5.50 ± 2.98 4.57 ± 2.24 9.00 ± 2.82 < 0.001* CPS 8.30 ± 4.03 7.02 ± 3.06 13.17 ± 3.79 < 0.001* *Significant associations were taken when p < 0.05 In the multivariable logistic regression analysis, several factors demonstrated significant independent associations with polypharmacy (Table 8 ). The most substantial associations were observed with measures of comorbidity burden. Compared to patients with a lower CCI score (≤ 1), those having moderate ( 2 – 3 ) had 3.96 times higher adjusted odds of polypharmacy (95% CI:1.84–8.53, p < 0.001), while those in CCI category 2 had 6.32 times higher odds (95% CI:2.38–16.82, p < 0.001). Similarly, patients with multimorbidity had 2.44 times higher odds of polypharmacy compared to those without multimorbidity (95% CI:1.03–5.78, p = 0.043). Recent healthcare utilization was also significantly associated with polypharmacy. Patients who had contacted their GP within the past two months had 2.17 times higher odds of polypharmacy (95% CI:1.04–4.52, p = 0.039). A history of myocardial infarction conferred 3.51 times higher odds (95% CI:1.10-11.17, p = 0.033). All other individual comorbidities, including CHF, DM and hypertension demonstrated no independent association with polypharmacy. Hospitalization within the past 12 months approached but did not reach statistical significance (AOR = 1.81, 95% CI:0.97–3.37, p = 0.062) and the senile age group showed a trend toward lower odds of polypharmacy compared to the elderly age category (AOR = 0.58, 95% CI: 0.33–1.02, p < 0.060). Gender of the residents (AOR = 1.58, 95% CI:0.82–3.05, p = 0.171), surgical history (AOR = 1.42, 95% CI:0.83–2.43, p = 0.205), family history of NCDs (AOR = 1.21, 95% CI: 0.71–2.05, p = 0.483) and BMI value (AOR = 0.91, 95% CI:0.54–1.54, p = 0.717) were not significantly associated with polypharmacy. CVSP was associated with frequent hospitalization within past 12 months (AOR:2.83, 95% CI:1.21–6.59, p = 0.016), while past history of myocardial infarction (AOR:7.26, 95% CI:2.44–21.57, p < 0.001) and congestive heart failure (AOR:5.20, 95% CI:1.57–17.19, p = 0.007) were associated with CVSP. However, all other socio-demographic and clinical variables were not significantly associated with CVSP. Table 8 Multivariable logistic regression of factors associated with polypharmacy among elderly residents (n = 373) Factor Category AOR 95% CI p-value Gender Male (Ref) 1.00 - - Female 1.58 0.82–3.05 0.171 Age groups 0.166 Elderly (Ref) 1.00 - - Senile 0.58 0.33–1.02 0.060 Long livers 0.63 0.14–2.82 0.541 BMI category Healthy BMI (Ref) 1.00 - - Abnormal BMI 0.91 0.54–1.54 0.717 Contact with GP, past 2 months No (Ref) 1.00 - - Yes 2.17 1.04–4.52 0.039* Hospitalization, past 12 months No (Ref) 1.00 - - Yes 1.81 0.97–3.37 0.062 Surgical history No (Ref) 1.00 - - Yes 1.42 0.83–2.43 0.205 Family history No (Ref) 1.00 - - Yes 1.21 0.71–2.05 0.483 Multimorbidity No (Ref) 1.00 - - Yes 2.44 1.03–5.78 0.043* CCI score < 0.001** Lower (≤ 1) (Ref) 1.00 - - Moderate ( 2 – 3 ) 3.96 1.84–8.53 < 0.001** High (≥ 4) 6.32 2.38–16.82 < 0.001** MI No (Ref) 1.00 - - Yes 3.51 1.10–11.17 0.033* CHF No (Ref) 1.00 - - Yes 1.54 0.54–4.42 0.424 Hypertension No (Ref) 1.00 - - Yes 1.48 0.78–2.82 0.229 **Significant associations were taken when p < 0.05 Discussion This study assessed the prevalence and associated factors of polypharmacy, cardiovascular-specific polypharmacy, and burden of multimorbidity among elderly residents in care homes in the Colombo district of Sri Lanka. These findings offer valuable insights into medication-use patterns and prescribing practices within long-term care facilities in low-and middle-income countries (LMICs). The World Health Organization (WHO) defines polypharmacy as the concurrent use of multiple medications( 37 ), though it does not specify a numerical threshold, which contributes to variability in reported prevalence rates among older adults. Based on the commonly applied definition of five or more routine medications, the current study observed a high prevalence of polypharmacy (59.0%) among elderly care home residents, with 10.5% experiencing excessive polypharmacy (use of ≥ 10 medications) (Table 4 ). These findings are consistent with prior studies conducted in the NHs and long-term care facilities worldwide, which consistently report higher polypharmacy rates among institutionalized older adults compared to those living in the community ( 4 , 8 – 10 , 18 , 19 , 38 – 42 ). The prevalence observed in this study is higher than the 51.2% reported among community-dwelling older adults in Sri Lanka ( 32 ), highlighting the vulnerability of care home residents to polypharmacy and its potential adverse outcomes. Compared to community-dwelling elders, elderly care home residents often have limited access to non-pharmacological interventions, which may lead clinicians to rely more heavily on pharmacotherapy. This tendency, coupled with fragmented care from multiple prescribers, contributes significantly to the accumulation of medications( 43 ). Previous studies confirm that prescriber-related issues are prevalent and directly contribute to the existence of polypharmacy among institutionalized older adults ( 14 ). The elevated prevalence observed in this study highlight polypharmacy as a significant public health challenged within long-term care facilities in LMICs. Approximately one in five elderly care home residents in this study experienced CVSP. This prevalence in higher than the 13.7% reported among hospitalized patients in Ethiopia( 7 ), reflecting a greater burden of CVDs among care home residents (Table 6 ). CVDs are a primary contributor to multimorbidity in this setting, with numerous studies reporting a higher prevalence of conditions like hypertension, ischemic heart disease, and other chronic CVDs among older adults in care facilities( 4 , 9 , 10 , 19 , 41 , 44 ). In alignment with this, the current study found a high prevalence of hypertension (75.1%) and dyslipidemia (58.2%) (Table 3 ), exceeding rates reported for community-dwelling older adults in Sri Lanka ( 2 ). While guideline-based prescribing of multiple cardiovascular agents may be clinically justified, it simultaneously elevates the risk of adverse drug reactions, DDIs, and medication non-adherence. This concern is supported by evidence from care homes indicating high rates of medications errors, which may contribute to pDDIs and pIMs( 44 ). The risk of CVSP is likely exacerbated by the limited availability of geriatric specialist and irregular medication review. Although hypertension was the most common medical condition, the most frequently prescribed pharmacological subgroup (ATC Level III) was oral blood glucose-lowering agents (Fig. 1 ). This finding contrasts with the previous study conducted in Northern Sri Lanka, where antihypertensive medications were predominant( 2 ). Differences in urbanization, lifestyle factors, and healthcare access in Colombo District may explain this variation. Furthermore, while antidiabetic medications were the most prescribed at the ATC Level III subgroup, cardiovascular system specific medications (ATC Level I) constituted for most prescribed category overall, highlighting the significant burden of CVSP. This pattern suggests that while diabetes management is prioritized through specific pharmacological agents, cardiovascular medications remain a dominant component of the overall prescription burden in this population( 8 , 38 , 40 – 42 , 45 ). Multimorbidity emerged as a significant independent predictor of polypharmacy in the population (AOR: 2.44, 95% CI:1.03–5.78, p = 0.043) (Table 8 ). This relationship is consistent with previous studies, which identify multimorbidity as a strong predictor of increased number of medication prescriptions among institutionalized residents ( 9 , 18 , 20 , 21 , 38 , 39 , 41 , 42 ). The high prevalence of multimorbidity (79.4%) in this population (Table 3 ) was greater than that reported among community-dwelling older adults in Sri Lanka ( 2 , 32 ). This clinical complexity is further reflected in the elevated Charlson Comorbidity Index (CCI) scores, which were significantly higher among residents with polypharmacy (3.20 ± 1.75) and CVSP (4.01 ± 1.83) compared to the overall cohort mean (2.77 ± 1.85) and to score reported in previous studies of hospitalized patients. Furthermore, the history of MI was independently associated with polypharmacy (AOR: 3.51, 95% CI:1.10-11.17, p = 0.033). While polypharmacy is often a necessary and appropriate response to complex conditions like MI( 46 ), as supported by clinical guidelines, the high prevalence highlights the critical need to distinguish between appropriate and inappropriate prescribing. Systematic, regular medication reviews are essential to minimize DDIs, reduce pIMs, and lower the risk of adverse drug events in this vulnerable population. Socio-demographic factors were not significantly associated with polypharmacy (Table 4 ), excessive polypharmacy or CVSP (Table 6 ), aligning with several previous studies ( 39 – 41 ). While many studies identify advancing age as risk factor [37,38], this study found that residents in the “senile” age (75–90 years) had lower odds of polypharmacy compared to the “elderly” age (60–74 years), through these results was of borderline statistical significance (AOR = 0.58, 95% CI: 0.33–1.02, p < 0.060). This pattern is supported by a Polish study suggesting that younger institutionalized elderly may be more susceptible to polypharmacy( 42 ). This observed trend may be partly attributable to deprescribing practices in very old and frail individuals, whereby clinicians deliberately discontinue preventive or long-term medications to reduce the risk of adverse drug events and treatment burden. Furthermore, evidence indicates that most older adults are receptive to deprescribing when it is recommended by healthcare providers, with a considerable proportion expressing a desire to reduce the number of medications they take( 47 ). In contrast, clinical factors were strong predictors. Polypharmacy and CVSP were significantly associated with a higher number of chronic conditions, frequent healthcare utilization, and specific comorbidities (Tables 6 & 8 ). Frequent contact with a GP within the past two months was also a key predictor of polypharmacy. Residents who had consulted a GP within the past two months had significantly higher odds of polypharmacy (AOR:2.17, 95% CI:1.04–4.52, p = 0.039) (Table 8 ). This aligns with evidence linking continuous care to increased medication use, highlighting the need for optimized guideline-based management to ensure appropriateness( 48 ). A history of hospitalization within the past 12 months showed a borderline association with polypharmacy (AOR = 1.81, 95% CI:0.97–3.37, p = 0.062) in multivariate analysis but was a significant independent predictor of CVSP (AOR:2.83, 95% CI:1.21–6.59, p = 0.016) (Table 8 ). This pattern is consistent with the well-documented increase in medication burden following hospital discharge. A study conducted in Northen Sri Lanka supports this finding by demonstrating that polypharmacy prevalence rises after hospitalization, with an average of two medication added per hospital stay( 2 ). For residents with CVDs, this period often involves the initiation or intensification of guideline-directed multidrug regimens, directly leading to CVSP. While healthcare utilization is necessary and increases appropriate polypharmacy for managing complex multimorbidity, it also elevates the risk of inappropriate polypharmacy. Therefore, a careful review of prescriptions, using explicit criteria for deprescribing, prioritizing medication safety during prescribing, and conducting regular medication reconciliation, is essential to mitigate the adverse effects of inappropriate polypharmacy in this vulnerable population. In addition to the primary predictors, univariate analysis identified several other factors associated with polypharmacy and CVSP. These included a history of surgical procedures, family history of NCDs and a family history of CVDs. The association between surgical history and polypharmacy (Table 4 ) may reflect the intensive medical management and hospitalization often required for such interventions, periods during which medication burden typically increases. Surgical procedures may require hospitalization( 49 ). Similarly, a family history of NCDs and CVDs likely signals a highest underlying genetic and clinical risk for chronic conditions, contributing to greater medication use. For example, an Ethiopian study found that individuals with a family history of CVD were more than twice as likely to experience polypharmacy( 7 ). An abnormal BMI was specifically associated with CVSP in univariate analysis. This aligns with findings from Ethiopia, where abnormal BMI predicated CVSP among hospitalized patients( 7 ), likely because it is a well-established risk factors for CVDs, often necessitating more intensive pharmacotherapy( 50 ). However, in the final multivariate model, factors such as surgical history, abnormal BMI, and family history of NCDs/CVD did not retain independent statistical significance either polypharmacy or CVSP. This suggests that their influence is likely mediated through or confounded by stronger, more direct clinical variables, such as the presence and severity of multimorbidity. The high prevalence of polypharmacy and excessive polypharmacy observed in this study carries significant clinical and public health implications. Polypharmacy is strongly associated with adverse drug reactions, falls, cognitive impairment, hospitalization, and increase healthcare costs, risks that are amplified in frail, institutionalized populations. The findings emphasize the need for regular medication review programs, incorporated of evidence-based prescribing tools such as STOPP/START criteria and Beers Criteria, and greater involvement of multidisciplinary teams including nurses and pharmacists( 43 ). From policy perspective, integrating geriatric pharmacotherapy guidelines into elder care services and strengthening primary care coordination are critical to reducing inappropriate polypharmacy and optimizing medication safety. This study was the first attempt to determine the prevalence and determinants of polypharmacy and CVSP among the older adults residing in care home facilities in Sri Lanka, as per our knowledge. The use of standardized classification systems enhances comparability with international literature. Furthermore, to reduce recall bias, clinical records of individual participants and their medications were thoroughly reviewed during this study. However, the cross-sectional design limits causal inference, and medication appropriateness was not directly assessed. The use of purposive sampling within care homes may have introduced selection bias, as residents who were more accessible or communicative were more likely to be recruited than those who were bedbound or less socially active. Additionally, findings may not be generalizable to community-dwelling older adults. Furthermore, the exclusion of complementary or ayurvedic medicines may have underestimated the true nature of polypharmacy among the elderly care home residents. Also, this study did not evaluate the pDDI and pIMs among the elderly residents. Despite these limitations, the study provides valuable baseline evidence for future research and intervention development. Conclusion Polypharmacy is highly prevalent among elderly residents in care homes in Sri Lanka and is strongly associated with multimorbidity and cardiovascular disease burden. Excessive polypharmacy and CVSP are also common, highlighting the vulnerability of this population to adverse drug events. These findings emphasize the need for targeted interventions, such as systematic medication review, multidisciplinary management, and policy attention to optimization of medication prescribing practices. Further longitudinal research is warranted to evaluate and optimize medication use and improve health outcomes among institutionalized older adults. Abbreviations NCDs non-communicable diseases NH Nursing Home DDI Drug-drug Interaction pIMs Potential Inappropriate Medications pDDIs Potential Drug-drug Interactions CVDs Cardiovascular Diseases CVSP Cardiovascular Specific Polypharmacy CCI Charlson Comorbidity Index ATC Anatomical Therapeutic Classification CPS Comorbidity-polypharmacy score SPSS Statistical Package for Social Sciences ANOVA analysis of variance BMI Body mass index GP General Practitioner DM Diabetes Mellitus AOR Adjusted Odd Ratio CI Confidence Interval MI Myocardial infarction CHF Congestive heart Failure PVD Peripheral Vascular Diseases LMIC Low-and middle-income country WHO world health organizations STOPP/START Screening tool for older person’s prescriptions/ Screening tool to alert to right treatment Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Ethic Review Committee of Faculty of Medicine of University of Colombo (Ethical Approval No: EC-23-128; Approval Date: 16 th November 2023). All participants were informed well and written volunteer informed consent obtained prior data collection. All participants’ data were anonymized by removing any identification details to ensure the confidentiality of the data. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article. The raw datasets supporting the findings will be made available by the corresponding author upon reasonable request, without undue reservation, to any qualified researcher. Completing Interests The authors declare no conflict of interests. Funding Not applicable. Authors’ contributions Conceptualization, D.A.S and T.A.; Methodology, J.M.S.I. and S.B.M.D.P.S.; Investigation, L.K.V., W.A.M.I., S.B.M.D.P.S., A.I.S., J.K.M.S.P., U.L.A., and I.L.M.F.; Data Curation, J.M.S.I., and S.B.M.D.P.S.; Formal Analysis, J.M.S.I; Writing – Original Draft Preparation, J.M.S.I.; Writing – Review & Editing, D.A.S., T.A.; Supervision, D.A.S . All authors have read and agreed to the published version of the manuscript. Acknowledgements The authors thank all the residents at elderly care homes who participate in this study and their management for approving permission and support to conduct this study. References United Nations Department of Economic and Social Affairs. World Population Prospects 2024: Summary of Results. 2024. Thiyahiny SN, Kumanan T, Surenthirakumaran R. Chronic illnesses and polypharmacy in elderly patients: A hospital-based study in Northern Sri Lanka. Jaffna Med J. 2021;33(1):14–22. Hosseini S, Zabihi A, Jafarian Amiri S, Bijani A. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8502816","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570037031,"identity":"b9254201-4eb3-4919-a36f-c69e35406b56","order_by":0,"name":"J.M.S.I. 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Venuri","email":"","orcid":"","institution":"University of Colombo","correspondingAuthor":false,"prefix":"","firstName":"L.K.","middleName":"","lastName":"Venuri","suffix":""},{"id":570037036,"identity":"5730bb39-2eaa-42d3-ab96-e32720d5607a","order_by":5,"name":"J.K.M.S. Perera","email":"","orcid":"","institution":"University of Colombo","correspondingAuthor":false,"prefix":"","firstName":"J.K.M.S.","middleName":"","lastName":"Perera","suffix":""},{"id":570037037,"identity":"08a8f380-e473-4b99-ba50-337a5ffbc036","order_by":6,"name":"U.L. Atheep","email":"","orcid":"","institution":"University of Colombo","correspondingAuthor":false,"prefix":"","firstName":"U.L.","middleName":"","lastName":"Atheep","suffix":""},{"id":570037038,"identity":"0e285bd4-e05e-4a2c-a222-e63e8244f938","order_by":7,"name":"I.L.M. Fazil","email":"","orcid":"","institution":"University of Colombo","correspondingAuthor":false,"prefix":"","firstName":"I.L.M.","middleName":"","lastName":"Fazil","suffix":""},{"id":570037039,"identity":"60a768dd-d99a-4000-8a1d-66c94fdccdd5","order_by":8,"name":"D.A.S. Elvitigala","email":"","orcid":"","institution":"University of Colombo","correspondingAuthor":false,"prefix":"","firstName":"D.A.S.","middleName":"","lastName":"Elvitigala","suffix":""},{"id":570037040,"identity":"d062de31-a99c-4f5b-bfb8-89f82d43ee86","order_by":9,"name":"T.S. Amarasinghe","email":"","orcid":"","institution":"University of Colombo","correspondingAuthor":false,"prefix":"","firstName":"T.S.","middleName":"","lastName":"Amarasinghe","suffix":""}],"badges":[],"createdAt":"2026-01-02 18:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8502816/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8502816/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99746801,"identity":"b4947cff-3f03-489a-b4e2-0b8008a9173f","added_by":"auto","created_at":"2026-01-08 02:30:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":491200,"visible":true,"origin":"","legend":"","description":"","filename":"PolypharmacycardiovascularmedicationuseamongcarehomeresidentsVersion1.0.docx","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/599308c1070b5b8ad0736792.docx"},{"id":99746804,"identity":"4e1daf78-0ad6-4ea0-bc20-1ceb7120a716","added_by":"auto","created_at":"2026-01-08 02:30:50","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11937,"visible":true,"origin":"","legend":"","description":"","filename":"734a249b40bb4aa7980769aef808f2cc.json","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/afdbd03f582285dde82e4902.json"},{"id":99797683,"identity":"53e7a377-a858-4114-bd98-c38639473dd8","added_by":"auto","created_at":"2026-01-08 13:46:19","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200444,"visible":true,"origin":"","legend":"","description":"","filename":"734a249b40bb4aa7980769aef808f2cc1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/d30d67a006a94f872506c1d9.xml"},{"id":99746803,"identity":"d0032468-0d65-4d13-825f-36e443c234b0","added_by":"auto","created_at":"2026-01-08 02:30:50","extension":"eps","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":407,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage2.eps","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/c5debd0a3936db554f250c4b.eps"},{"id":99746805,"identity":"adadee85-70dc-4757-b474-b63ab921e2ee","added_by":"auto","created_at":"2026-01-08 02:30:51","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194284,"visible":true,"origin":"","legend":"","description":"","filename":"734a249b40bb4aa7980769aef808f2cc1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/4ebce5e9e852b7c86e347101.xml"},{"id":99746806,"identity":"d42fa0cd-e42e-4ebf-9f2d-3b4bd5eda745","added_by":"auto","created_at":"2026-01-08 02:30:51","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":211906,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/1001de02c069f5dd347167af.html"},{"id":99797429,"identity":"ee9dbd6d-dc8d-4c87-ba5d-f2af180197c0","added_by":"auto","created_at":"2026-01-08 13:45:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31134,"visible":true,"origin":"","legend":"\u003cp\u003ePrescribed Medications distribution among elderly residing in elderly care homes of Colombo District (N=373)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/0b402844cac238885a130993.png"},{"id":99805152,"identity":"659d04a3-bde9-4ae9-8375-60f5bd613963","added_by":"auto","created_at":"2026-01-08 14:15:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2119444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8502816/v1/df803601-6687-4786-858b-cfabbb629dba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Polypharmacy, cardiovascular-specific medication use, and comorbidity burden among elderly residents in care homes of Colombo District, Sri Lanka: A cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation ageing represents one of the most significant demographic transitions of the twenty-first century. Projections indicate that by the late 2070s, the global population aged 65 years and older is projected to reach 2.2\u0026nbsp;billion, surpassing the number of children, while those aged 80 years and above are expected to outnumber infants by the mid-20230s(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This demographic shift is accompanied by a marked rise in chronic non-communicable diseases (NCDs), making multimorbidity a defining characteristic of aging populations(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Multimorbidity drives increased healthcare utilization and frequent necessities long-term pharmacotherapy, which in turn heightens the risk of medication-related harm among older adults (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolypharmacy, commonly defined as the concurrent use of five or more medications, represents a major challenge in geriatric care(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In clinical and research settings, polypharmacy is often categorized according to medication count such as non-polypharmacy (\u0026lt;\u0026thinsp;5 medications), Polypharmacy (5\u0026ndash;9 medications) and excessive or hyper-polypharmacy (\u0026ge;\u0026thinsp;10 medications) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Conceptually, polypharmacy can also be classified as appropriate or inappropriate(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Appropriate polypharmacy refers to the evidenced-based and rational use of multiple medications to manage complex conditions and achieved therapeutic goals. Conversely, inappropriate polypharmacy involves unnecessary or irrational prescribing of medications, which elevates the risk of adverse outcomes(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Globally, polypharmacy is highly prevalent among older adults, with reported rates ranging from 5% to 78% across studies(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This prevalence rises substantially in elderly populations with multimorbidity(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolypharmacy is associated with a wide range of adverse health outcomes in older adults. Systemic review evidence indicates positive associations between polypharmacy and frailty, malnutrition, functional decline, disability, and mortality, inappropriate prescribing, and medication non-adherence(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Age-related physiological changes, including altered pharmacokinetics and pharmacodynamics, such as altered drug metabolism and reduced renal clearance, further increase susceptibility to adverse drug reactions and toxicity in this population(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). One study found that patients with polypharmacy are 5.1 times more likely to receive inappropriate prescriptions compared to those taking fewer than five medications(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Additionally, both cross-sectional and longitudinal analyses show that older adults exposed to polypharmacy have a significantly elevated risk of falls, highlighting its clinical importance in geriatric care(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe burden of polypharmacy is particularly high among institutionalized older adults. Compared to their community-dwelling counterparts, nursing home (NH) residents typically present with higher levels of functional dependence, cognitive impairment, and multimorbidity, rendering them especially vulnerable to drug-drug interactions (DDIs) and adverse drug events(\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Large, multicentric studies conducted in long-term care settings consistently report high prevalence rates of polypharmacy, often exceeding 50%, with excessive polypharmacy affecting approximately one-quarter to one-third of residents(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These findings reflect the complex clinical profiles of institutionalized older adults and highlight the significant challenges in medication management within residential care environments.\u003c/p\u003e \u003cp\u003ePolypharmacy in NHs is frequently accompanied by the use of potentially inappropriate medications (PIMs) and clinically significant potential drug-drug interactions (pDDIs). Evidence suggests that 23.7% to 70% of NH residents are exposed to at least one PIM, while 25.1% to 37.8% experience pDDIs, many of moderated to major severity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Such prescribing patterns contribute to prescribing cascades, adverse drug events, and medication-related harm, further increasing morbidity and mortality in this vulnerable population(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCardiovascular diseases (CVDs) are a leading cause of morbidity and mortality in older adults, often necessitating long-term, multi-drug pharmacotherapy(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The coexistence of CVDs, frailty, and polypharmacy substantially amplifies the risk of adverse clinical outcomes, including cardiovascular complications, emergency admissions and premature death(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Older adults, particularly those aged 80 years and above, have a markedly higher likelihood of receiving higher number of cardiovascular-specific medications (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Consequently, the majority of older patients with cardiovascular conditions meet criteria for polypharmacy(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Many cardiovascular agents, including statins, antihypertensives and anticoagulants, which dominate therapeutic regimens in older adults, have been linked to orthostatic hypotension and an increased risk of falls, which is major issue among care home residents(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSri Lanka is undergoing rapid population ageing, with the elderly population is projected to double from 2.5\u0026nbsp;million in 2012 to 5.2\u0026nbsp;million by 2037 and to comprise nearly one-quarter of the total population by 2042(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Colombo District, the country\u0026rsquo;s most densely populated region, faces distinct challenges related to ageing, chronic disease management, and institutional elder care(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). While previous studies in Sri Lanka have reported a high prevalence of multimorbidity and polypharmacy in tertiary care settings, along with strong associations between polypharmacy and pDDIs (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), evidence focusing specially on institutionalized elderly adults and cardiovascular-specific polypharmacy (CVSP) remains scarce. Therefore, this study aimed to determine the prevalence and associated factors of polypharmacy, with specific emphasis on cardiovascular medication use, in relation to comorbidity burden among among older adults residing in care homes in Colombo District, Sri Lanka.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eA descriptive, cross-sectional study was conducted in ten selected care homes in Colombo District of Sri Lanka from July 2023 to March 2024. The study aimed to estimate the point prevalence of polypharmacy, cardiovascular specific polypharmacy, and identify the associated factors among institutionalized elderly care home residents.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population comprised all elderly residents (aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years of age) of selected, elderly care homes in the Colombo District, Sri Lanka. All sampled homes were formally registered under the Social Service Department of Western Province, Sri Lanka. In accordance with the national policy for elders, and \u0026ldquo;elder\u0026rdquo; in Sri Lanka is defined as individual who has completed 60 years of age(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Inclusion criteria were residents who age 60 years or above, institutionalized for at least one month, with diagnosis of at least one chronic disease at least one month prior to data collection, and receiving ongoing pharmacotherapy via oral, inhalation, instillation, or tropical routes for at least one month prior to the data collection. Exclusion criteria were refused to participate in the study, critically or terminally ill individuals which would interfere with participation in the study and a clinically diagnosed cognitive impairment.\u003c/p\u003e\n\u003ch3\u003eSample Size and Sampling Procedure\u003c/h3\u003e\n\u003cp\u003eThe required sample size was calculated using Cochran\u0026rsquo;s formula. Based on a previously reported prevalence of polypharmacy (58.7%) among elderly population in Northen Sri Lanka(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), with a 95% confidence level and a 5% margin of error, the minimum required sample size was 373 elderly residents. Participants recruitment followed a stratified quota sampling strategy. In the first stage, the total sample size was allocated across the ten participating care homes (strata) using proportionate allocation based on each home\u0026rsquo;s total resident population.\u003c/p\u003e \u003cp\u003eIn the second stage, eligible residents within each home were recruited using a purposive sampling approach to meet the pre-allocated quotas. Recruitment prioritized residents who were accessible at the time of data collection, willing to participate, and capable of completing the interviewer-administered instruments. This non-probability sampling strategy was adapted to accommodate practical constraints inherent to the institutional setting, including varying levels of cognitive capacity, resident availability, and consent capabilities. The final sample distribution is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal sample distribution across the selected care homes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification number of care homes (total number of residents)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidents selected for study\u003c/p\u003e \u003cp\u003eNumber of residents (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 01 (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 02 (N\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 03 (N\u0026thinsp;=\u0026thinsp;144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 04 (N\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 05 (N\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 06 (N\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 07 (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 08 (N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 09 (N\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly care home 10 (N\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e373\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eData Collection Tool\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured, interviewer-administered questionnaire and a checklist. Well-trained interviewers conducted the interviews in separate, private spaces within each care home, in the participant\u0026rsquo;s preferred language. The questionnaire comprised four sections: residential information, socio-demographic characteristics, clinical history and Charlson Comorbidity Index (CCI). The CCI, which was pre validated in the Sri Lankan context (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), was used to assess the overall level of multimorbidity among participants. The checklist captured medication-related data and anthropometric measurements. Medication information was collected and cross-verified through a review of medical records maintained by each care home. Each medication was classified according to the third level of the Anatomical Therapeutic Chemical (ATC) classification system(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnthropometric assessment included weight, height, hip circumference, waist circumference, and chest circumference, providing comprehensive overview of each participant\u0026rsquo;s physical and nutritional health. Measurements were taken while participants wore lightweight clothing. Height was measured to the nearest 1.0 centimeter using a wall-mounted stadiometer, with participants standing upright in the Frankfort plan position. Body weight was measured to the nearest 1.0 kilogram using electronic weighing scale, which was calibrated by Ministry of Health, Sri Lanka. Hip, waist, and chest circumference were measured using a flexible, non-stretchable tape, following standardized protocols.\u003c/p\u003e\n\u003ch3\u003eDefinitions of Polypharmacy, Cardiovascular-specific polypharmacy and associated factors\u003c/h3\u003e\n\u003cp\u003ePolypharmacy was defined as use of five or more medications daily. When using medication ten or more routinely, it was defined as excessive polypharmacy (\u0026ge;\u0026thinsp;10 medications). Cardiovascular-specific polypharmacy was defined as the routine use of five or more medications prescribed for the management of CVDs.\u003c/p\u003e \u003cp\u003eParticipants\u0026rsquo; socio-demographic characteristics, clinical characteristics and anthropometric measurements were considered as factors associated with polypharmacy and cardiovascular-specific polypharmacy. CCI score was categorized into three categories such as low (0\u0026ndash;2), moderate (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), and severe (\u0026le;\u0026thinsp;5)(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Comorbidity burden was assessed using comorbidity-polypharmacy score (CPS), calculated by summing the number of chronic conditions and the total number of medications used.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using Statistical Package for Social Sciences (SPSS) (Version 27.0). Descriptive statistics were used to summarize socio-demographic characteristics, clinical characteristics and medication patterns. Continuous variables were presented as means and standard deviations while categorical variables were reported as frequencies and percentage with 95% confidence intervals.\u003c/p\u003e \u003cp\u003eInferential statistical analyses were used to examine associations of polypharmacy. Pearson\u0026rsquo;s chi-square test, student t-test and one-way ANOVA test employed to assess associations of polypharmacy with socio-demographic and clinical characteristics. When significant differences were found in the one-way ANOVA, Turky\u0026rsquo;s post hoc analysis was applied to identify subgroup analysis. Multivariable regression analysis was conducted to determine the independent predictors associated with polypharmacy. A two-tailed p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study was approved by the Ethic Review Committee of the Faculty of Medicine, University of Colombo (Approval No. EC-2023-128; approved on November 16, 2023). Permission was obtained from Social Service Department, Western Province and administrators of participating in care homes before data collection. Data were collected following obtaining written informed consent from the individual participants. Participant confidentiality and data anonymity were maintained throughout the study, in accordance with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eScio-demographic Characteristics of participants\u003c/h2\u003e \u003cp\u003eA total of 373 elderly residents participated in the study. Mean age of the participant was 74.74\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56 (range\u0026thinsp;=\u0026thinsp;60 years to 103 years) with majority (55%) belonging to \u0026ldquo;elderly\u0026rdquo; age category (60\u0026ndash;75 years). Most participants were female (75.1%). Educational attainment varied: 11.3% had no formal schooling, 18.5% had completed primary education (1\u0026ndash;5 grade), 26.5% secondary (6\u0026ndash;9 grade), 21.4% ordinary level (10\u0026ndash;11 grade), 18% advanced level (12\u0026ndash;13 grade) and 4.3% held a diploma or higher qualification. Most participants (71.6%) reported no proper income level. Mean duration of institutionalization was 5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.33 years with most participants stay less than 5 years (59.8%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of the elderly residing in selected care homes of Colombo district, Sri Lanka (N\u0026thinsp;=\u0026thinsp;373)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en(N\u0026thinsp;=\u0026thinsp;373)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly (60\u0026ndash;75 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenile (75\u0026ndash;90 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong livers (90\u0026thinsp;+\u0026thinsp;years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Attainment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary (1\u0026ndash;5 grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary (6\u0026ndash;9 grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrdinary level (10\u0026ndash;11 grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced level (12\u0026ndash;13 grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income (\u0026lt;\u0026thinsp;25 000 LKR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower middle income (25000\u0026ndash;100000 LKR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper middle income (100000\u0026ndash;300000 LKR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of institutionalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2%\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics of participants\u003c/h2\u003e \u003cp\u003eThe mean Body Mass Index (BMI) of the participants was 22.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76 kg/m\u003csup\u003e2\u003c/sup\u003e, with 56.3% falling within the normal BMI category. Most residents (84.5%) had consulted a general practitioner (GP) within the previous two months, while 30.6% had been hospitalized in the past year. Regarding past surgical history, 26.8% of participants had undergone eye surgery.\u003c/p\u003e \u003cp\u003eThe most prevalent medical condition was hypertension, affecting approximately three-quarters (75.1%) of total sample, followed by dyslipidemia (58.2%), diabetes mellitus (DM) (39.1%), and respiratory diseases (27.1%). A total of 17.7% of participants reported history of \u0026ldquo;other\u0026rdquo; medical conditions, including thyroid disorders, epilepsy, Parkinson\u0026rsquo;s disease, anemia and nervous system conditions. Overall, 79.4% of participants had two or more coexisting medical conditions. In terms of family history on NCDs, 26.8% reported history of CVD, followed by 23.3% with a family history of metabolic diseases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics among the elderly residing in selected care homes, Colombo District, Sri Lanka\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (N\u0026thinsp;=\u0026thinsp;373)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026le;\u0026thinsp;18.49 kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy (18.5-24.99 kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25.0-29.9 kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30 kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContact with GP in the past 2 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization in the past 12 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePast surgical history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo reported history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrthopedic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgeries in bowel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower segment cesarian section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePast medical history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of chronic illnesses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of NCDs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo family history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere (\u0026ge;\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.1%\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMedication-related characteristics of participants\u003c/h2\u003e \u003cp\u003eThe mean number of medications per residents was 5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98, while the mean number of cardiovascular-specific medications per prescription was 2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03. According to ATC classification system (Level III), a total of 80 distinct pharmacological subgroups were identified. Among these, 15 subgroups were prescribed to more than 10% of participants. The most frequently prescribed subgroup was blood glucose-lowering drugs (excluding insulin) (A10B), used by 60.9% of participants. This was followed by lipid-modifying agents (C10A) (58.2%), angiotensin-converting enzyme (ACE) (C09C) (48.0%), antithrombotic agents (B01A) (43.7%), and vitamin B complex, including combinations (A11E) (43.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of polypharmacy and cardiovascular polypharmacy\u003c/h2\u003e \u003cp\u003eThe overall prevalence of polypharmacy in the study population was 59.0%, with 10.5% of residents meeting the criteria for excessive polypharmacy (\u0026ge;\u0026thinsp;10 medications). Polypharmacy was more prevalent among males (65.6%) than females (56.4%). Prevalence of polypharmacy decreased with increasing age, being highest among the \u0026ldquo;elderly\u0026rdquo; (62.4%), and lowest among those aged 90 years and older (36.4%). A higher prevalence was observed among participants who had consulted a GP in the past two months (63.1%) compared to those who had not (36.2%). Similarly, polypharmacy was more common among participants hospitalized in the past 12 months (76.7%) than among those who had not been hospitalized (59.9%). Among participants with multimorbidity (\u0026ge;\u0026thinsp;2 conditions), 72.9% reported polypharmacy. A higher prevalence was also observed with a past surgical history (66.1%) and a family history of NCDs (67.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevalence of CVSP was 20.9%. The prevalence was higher among males (24.7%) and was most common in the \u0026ldquo;senile\u0026rdquo; age group (75\u0026ndash;90 years) at 25.5%. Residents categorized as overweight (25.0-29.9 kg/m2) had highest prevalence (33.3%) compared to other BMI categories. Contact with a GP in the past two months was common among participants with CVSP. The majority of participants with CVSP (89.7%) had consulted a GP within that period. The highest prevalence of CVSP was observed among participants hospitalized in the past 12 months (55.1%). All participants with multimorbidity presented with CVSP. Furthermore, 14.6% of participants with family history of NCDs had CVSP while 30% of participants with a family history of CVDs (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of polypharmacy, cardiovascular polypharmacy\u003c/h2\u003e \u003cp\u003eBased on Pearson\u0026rsquo;s chi-square test, no statistically significant associations were observed between polypharmacy or CVSP and socio-demographic characteristics such as gender, age group, income level, length of institutionalization.\u003c/p\u003e \u003cp\u003ePolypharmacy was significantly associated with several clinical characteristics, including contact with a GP within past two months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hospitalization in the past year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), present of multimorbidity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), history of past surgery (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), family history of NCDs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), family history of CVDs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOne-way ANOVA test revealed significant differences of mean number of medical conditions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mean number of CCI score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mean number of total medications (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and mean number of CPS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Post-hoc analysis confirmed that all pairwise comparisons between non-polypharmacy, polypharmacy, and excessive polypharmacy groups for aforementioned variables were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFurthermore, polypharmacy was significantly associated with the presence of several chronic health conditions such as hypertension (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), dyslipidemia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and congestive heart failure (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of polypharmacy among elderly, residing in elderly care homes of Colombo District (N\u0026thinsp;=\u0026thinsp;373)\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePolypharmacy Categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChi-squared P- Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0\u0026ndash;4 Drugs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5\u0026ndash;9 Drugs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;10 Drugs)\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\u003eTotal sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153(41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181(48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenile Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-livers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106(47.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;15 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContact with GP in the past 2 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116(36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(63.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization in the past 12 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112(43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultimorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168(56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePast surgical history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110(53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History of NCDs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History of CVDs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e*Significant associations were taken when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e**Significant associations were taken when p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way ANOVA test of polypharmacy among different clinical characteristics of elderly residents (N\u0026thinsp;=\u0026thinsp;373)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePolypharmacy categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;4 drugs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;9 drugs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10 drugs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e74.74\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e75.50\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e74.08\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e74.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist to Hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. medical conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of total medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.30\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e16.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e*Significant associations were taken when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/h2\u003e \u003cp\u003eSimilar to polypharmacy, CVSP showed no statistically significant association with socio-demographic characteristics. However, it was significantly associated with clinical variables including BMI category (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), hospitalization within the past 12 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), multimorbidity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), past surgical history (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), family history of NCDs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), family history of CVDs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndependent student t-test further revealed significant differences between participants with and without CVSP. Those with CVSP has a significantly higher mean number of chronic medical conditions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CCI score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total number of medications (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CPS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of cardiovascular-specific polypharmacy with participant characteristics of elderly residents (N\u0026thinsp;=\u0026thinsp;373)\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCardiovascular-specific Polypharmacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChi-squared\u003c/p\u003e \u003cp\u003eP- Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\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\u003eTotal sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295(79.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225(80.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElderly Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167(81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenile Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117(74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-livers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183(82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(69.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(79.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(84.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168(80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContact with GP within the past 2 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245(83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(89.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultimorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (73.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization within the past 12 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224(75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History of NCDs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History of CVDs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225(82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e*Significant associations were taken when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e**Significant associations were taken when p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe student t-test of cardiovascular-specific polypharmacy among different characteristics of the elderly residents. (N\u0026thinsp;=\u0026thinsp;373)\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCVSP (Mean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of the person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e74.74\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e74.76\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e74.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist to Hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medical conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of total medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.30\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e*Significant associations were taken when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/h2\u003e \u003cp\u003eIn the multivariable logistic regression analysis, several factors demonstrated significant independent associations with polypharmacy (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The most substantial associations were observed with measures of comorbidity burden. Compared to patients with a lower CCI score (\u0026le;\u0026thinsp;1), those having moderate (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) had 3.96 times higher adjusted odds of polypharmacy (95% CI:1.84\u0026ndash;8.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while those in CCI category 2 had 6.32 times higher odds (95% CI:2.38\u0026ndash;16.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, patients with multimorbidity had 2.44 times higher odds of polypharmacy compared to those without multimorbidity (95% CI:1.03\u0026ndash;5.78, p\u0026thinsp;=\u0026thinsp;0.043). Recent healthcare utilization was also significantly associated with polypharmacy. Patients who had contacted their GP within the past two months had 2.17 times higher odds of polypharmacy (95% CI:1.04\u0026ndash;4.52, p\u0026thinsp;=\u0026thinsp;0.039). A history of myocardial infarction conferred 3.51 times higher odds (95% CI:1.10-11.17, p\u0026thinsp;=\u0026thinsp;0.033). All other individual comorbidities, including CHF, DM and hypertension demonstrated no independent association with polypharmacy.\u003c/p\u003e \u003cp\u003eHospitalization within the past 12 months approached but did not reach statistical significance (AOR\u0026thinsp;=\u0026thinsp;1.81, 95% CI:0.97\u0026ndash;3.37, p\u0026thinsp;=\u0026thinsp;0.062) and the senile age group showed a trend toward lower odds of polypharmacy compared to the elderly age category (AOR\u0026thinsp;=\u0026thinsp;0.58, 95% CI: 0.33\u0026ndash;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.060).\u003c/p\u003e \u003cp\u003eGender of the residents (AOR\u0026thinsp;=\u0026thinsp;1.58, 95% CI:0.82\u0026ndash;3.05, p\u0026thinsp;=\u0026thinsp;0.171), surgical history (AOR\u0026thinsp;=\u0026thinsp;1.42, 95% CI:0.83\u0026ndash;2.43, p\u0026thinsp;=\u0026thinsp;0.205), family history of NCDs (AOR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 0.71\u0026ndash;2.05, p\u0026thinsp;=\u0026thinsp;0.483) and BMI value (AOR\u0026thinsp;=\u0026thinsp;0.91, 95% CI:0.54\u0026ndash;1.54, p\u0026thinsp;=\u0026thinsp;0.717) were not significantly associated with polypharmacy.\u003c/p\u003e \u003cp\u003eCVSP was associated with frequent hospitalization within past 12 months (AOR:2.83, 95% CI:1.21\u0026ndash;6.59, p\u0026thinsp;=\u0026thinsp;0.016), while past history of myocardial infarction (AOR:7.26, 95% CI:2.44\u0026ndash;21.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and congestive heart failure (AOR:5.20, 95% CI:1.57\u0026ndash;17.19, p\u0026thinsp;=\u0026thinsp;0.007) were associated with CVSP. However, all other socio-demographic and clinical variables were not significantly associated with CVSP.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression of factors associated with polypharmacy among elderly residents (n\u0026thinsp;=\u0026thinsp;373)\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=\"char\" char=\".\" 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\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u0026ndash;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166\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\u003eElderly (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\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\u003eLong livers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026ndash;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthy BMI (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContact with GP, past 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u0026ndash;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization, past 12 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u0026ndash;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u0026ndash;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u0026ndash;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026ndash;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\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\u003eLower (\u0026le;\u0026thinsp;1) (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84\u0026ndash;8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\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\u003eHigh (\u0026ge;\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38\u0026ndash;16.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u0026ndash;11.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u0026ndash;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u0026ndash;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.229\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=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e**Significant associations were taken when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/h2\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed the prevalence and associated factors of polypharmacy, cardiovascular-specific polypharmacy, and burden of multimorbidity among elderly residents in care homes in the Colombo district of Sri Lanka. These findings offer valuable insights into medication-use patterns and prescribing practices within long-term care facilities in low-and middle-income countries (LMICs).\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) defines polypharmacy as the concurrent use of multiple medications(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), though it does not specify a numerical threshold, which contributes to variability in reported prevalence rates among older adults. Based on the commonly applied definition of five or more routine medications, the current study observed a high prevalence of polypharmacy (59.0%) among elderly care home residents, with 10.5% experiencing excessive polypharmacy (use of \u0026ge;\u0026thinsp;10 medications) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings are consistent with prior studies conducted in the NHs and long-term care facilities worldwide, which consistently report higher polypharmacy rates among institutionalized older adults compared to those living in the community (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The prevalence observed in this study is higher than the 51.2% reported among community-dwelling older adults in Sri Lanka (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), highlighting the vulnerability of care home residents to polypharmacy and its potential adverse outcomes.\u003c/p\u003e \u003cp\u003eCompared to community-dwelling elders, elderly care home residents often have limited access to non-pharmacological interventions, which may lead clinicians to rely more heavily on pharmacotherapy. This tendency, coupled with fragmented care from multiple prescribers, contributes significantly to the accumulation of medications(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Previous studies confirm that prescriber-related issues are prevalent and directly contribute to the existence of polypharmacy among institutionalized older adults (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The elevated prevalence observed in this study highlight polypharmacy as a significant public health challenged within long-term care facilities in LMICs.\u003c/p\u003e \u003cp\u003eApproximately one in five elderly care home residents in this study experienced CVSP. This prevalence in higher than the 13.7% reported among hospitalized patients in Ethiopia(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), reflecting a greater burden of CVDs among care home residents (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). CVDs are a primary contributor to multimorbidity in this setting, with numerous studies reporting a higher prevalence of conditions like hypertension, ischemic heart disease, and other chronic CVDs among older adults in care facilities(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In alignment with this, the current study found a high prevalence of hypertension (75.1%) and dyslipidemia (58.2%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), exceeding rates reported for community-dwelling older adults in Sri Lanka (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). While guideline-based prescribing of multiple cardiovascular agents may be clinically justified, it simultaneously elevates the risk of adverse drug reactions, DDIs, and medication non-adherence. This concern is supported by evidence from care homes indicating high rates of medications errors, which may contribute to pDDIs and pIMs(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The risk of CVSP is likely exacerbated by the limited availability of geriatric specialist and irregular medication review.\u003c/p\u003e \u003cp\u003eAlthough hypertension was the most common medical condition, the most frequently prescribed pharmacological subgroup (ATC Level III) was oral blood glucose-lowering agents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This finding contrasts with the previous study conducted in Northern Sri Lanka, where antihypertensive medications were predominant(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Differences in urbanization, lifestyle factors, and healthcare access in Colombo District may explain this variation. Furthermore, while antidiabetic medications were the most prescribed at the ATC Level III subgroup, cardiovascular system specific medications (ATC Level I) constituted for most prescribed category overall, highlighting the significant burden of CVSP. This pattern suggests that while diabetes management is prioritized through specific pharmacological agents, cardiovascular medications remain a dominant component of the overall prescription burden in this population(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultimorbidity emerged as a significant independent predictor of polypharmacy in the population (AOR: 2.44, 95% CI:1.03\u0026ndash;5.78, p\u0026thinsp;=\u0026thinsp;0.043) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This relationship is consistent with previous studies, which identify multimorbidity as a strong predictor of increased number of medication prescriptions among institutionalized residents (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The high prevalence of multimorbidity (79.4%) in this population (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was greater than that reported among community-dwelling older adults in Sri Lanka (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This clinical complexity is further reflected in the elevated Charlson Comorbidity Index (CCI) scores, which were significantly higher among residents with polypharmacy (3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75) and CVSP (4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83) compared to the overall cohort mean (2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85) and to score reported in previous studies of hospitalized patients. Furthermore, the history of MI was independently associated with polypharmacy (AOR: 3.51, 95% CI:1.10-11.17, p\u0026thinsp;=\u0026thinsp;0.033). While polypharmacy is often a necessary and appropriate response to complex conditions like MI(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), as supported by clinical guidelines, the high prevalence highlights the critical need to distinguish between appropriate and inappropriate prescribing. Systematic, regular medication reviews are essential to minimize DDIs, reduce pIMs, and lower the risk of adverse drug events in this vulnerable population.\u003c/p\u003e \u003cp\u003eSocio-demographic factors were not significantly associated with polypharmacy (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), excessive polypharmacy or CVSP (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), aligning with several previous studies (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). While many studies identify advancing age as risk factor [37,38], this study found that residents in the \u0026ldquo;senile\u0026rdquo; age (75\u0026ndash;90 years) had lower odds of polypharmacy compared to the \u0026ldquo;elderly\u0026rdquo; age (60\u0026ndash;74 years), through these results was of borderline statistical significance (AOR\u0026thinsp;=\u0026thinsp;0.58, 95% CI: 0.33\u0026ndash;1.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.060). This pattern is supported by a Polish study suggesting that younger institutionalized elderly may be more susceptible to polypharmacy(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). This observed trend may be partly attributable to deprescribing practices in very old and frail individuals, whereby clinicians deliberately discontinue preventive or long-term medications to reduce the risk of adverse drug events and treatment burden. Furthermore, evidence indicates that most older adults are receptive to deprescribing when it is recommended by healthcare providers, with a considerable proportion expressing a desire to reduce the number of medications they take(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, clinical factors were strong predictors. Polypharmacy and CVSP were significantly associated with a higher number of chronic conditions, frequent healthcare utilization, and specific comorbidities (Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Frequent contact with a GP within the past two months was also a key predictor of polypharmacy. Residents who had consulted a GP within the past two months had significantly higher odds of polypharmacy (AOR:2.17, 95% CI:1.04\u0026ndash;4.52, p\u0026thinsp;=\u0026thinsp;0.039) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This aligns with evidence linking continuous care to increased medication use, highlighting the need for optimized guideline-based management to ensure appropriateness(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). A history of hospitalization within the past 12 months showed a borderline association with polypharmacy (AOR\u0026thinsp;=\u0026thinsp;1.81, 95% CI:0.97\u0026ndash;3.37, p\u0026thinsp;=\u0026thinsp;0.062) in multivariate analysis but was a significant independent predictor of CVSP (AOR:2.83, 95% CI:1.21\u0026ndash;6.59, p\u0026thinsp;=\u0026thinsp;0.016) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This pattern is consistent with the well-documented increase in medication burden following hospital discharge. A study conducted in Northen Sri Lanka supports this finding by demonstrating that polypharmacy prevalence rises after hospitalization, with an average of two medication added per hospital stay(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For residents with CVDs, this period often involves the initiation or intensification of guideline-directed multidrug regimens, directly leading to CVSP. While healthcare utilization is necessary and increases appropriate polypharmacy for managing complex multimorbidity, it also elevates the risk of inappropriate polypharmacy. Therefore, a careful review of prescriptions, using explicit criteria for deprescribing, prioritizing medication safety during prescribing, and conducting regular medication reconciliation, is essential to mitigate the adverse effects of inappropriate polypharmacy in this vulnerable population.\u003c/p\u003e \u003cp\u003eIn addition to the primary predictors, univariate analysis identified several other factors associated with polypharmacy and CVSP. These included a history of surgical procedures, family history of NCDs and a family history of CVDs. The association between surgical history and polypharmacy (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) may reflect the intensive medical management and hospitalization often required for such interventions, periods during which medication burden typically increases. Surgical procedures may require hospitalization(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Similarly, a family history of NCDs and CVDs likely signals a highest underlying genetic and clinical risk for chronic conditions, contributing to greater medication use. For example, an Ethiopian study found that individuals with a family history of CVD were more than twice as likely to experience polypharmacy(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). An abnormal BMI was specifically associated with CVSP in univariate analysis. This aligns with findings from Ethiopia, where abnormal BMI predicated CVSP among hospitalized patients(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), likely because it is a well-established risk factors for CVDs, often necessitating more intensive pharmacotherapy(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). However, in the final multivariate model, factors such as surgical history, abnormal BMI, and family history of NCDs/CVD did not retain independent statistical significance either polypharmacy or CVSP. This suggests that their influence is likely mediated through or confounded by stronger, more direct clinical variables, such as the presence and severity of multimorbidity.\u003c/p\u003e \u003cp\u003eThe high prevalence of polypharmacy and excessive polypharmacy observed in this study carries significant clinical and public health implications. Polypharmacy is strongly associated with adverse drug reactions, falls, cognitive impairment, hospitalization, and increase healthcare costs, risks that are amplified in frail, institutionalized populations. The findings emphasize the need for regular medication review programs, incorporated of evidence-based prescribing tools such as STOPP/START criteria and Beers Criteria, and greater involvement of multidisciplinary teams including nurses and pharmacists(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). From policy perspective, integrating geriatric pharmacotherapy guidelines into elder care services and strengthening primary care coordination are critical to reducing inappropriate polypharmacy and optimizing medication safety.\u003c/p\u003e \u003cp\u003eThis study was the first attempt to determine the prevalence and determinants of polypharmacy and CVSP among the older adults residing in care home facilities in Sri Lanka, as per our knowledge. The use of standardized classification systems enhances comparability with international literature. Furthermore, to reduce recall bias, clinical records of individual participants and their medications were thoroughly reviewed during this study.\u003c/p\u003e \u003cp\u003eHowever, the cross-sectional design limits causal inference, and medication appropriateness was not directly assessed. The use of purposive sampling within care homes may have introduced selection bias, as residents who were more accessible or communicative were more likely to be recruited than those who were bedbound or less socially active. Additionally, findings may not be generalizable to community-dwelling older adults. Furthermore, the exclusion of complementary or ayurvedic medicines may have underestimated the true nature of polypharmacy among the elderly care home residents. Also, this study did not evaluate the pDDI and pIMs among the elderly residents. Despite these limitations, the study provides valuable baseline evidence for future research and intervention development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePolypharmacy is highly prevalent among elderly residents in care homes in Sri Lanka and is strongly associated with multimorbidity and cardiovascular disease burden. Excessive polypharmacy and CVSP are also common, highlighting the vulnerability of this population to adverse drug events. These findings emphasize the need for targeted interventions, such as systematic medication review, multidisciplinary management, and policy attention to optimization of medication prescribing practices. Further longitudinal research is warranted to evaluate and optimize medication use and improve health outcomes among institutionalized older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNCDs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-communicable diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNursing Home\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDDI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug-drug Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003epIMs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePotential Inappropriate Medications\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003epDDIs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePotential Drug-drug Interactions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVDs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVSP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Specific Polypharmacy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eATC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnatomical Therapeutic Classification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCPS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComorbidity-polypharmacy score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Practitioner\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Odd Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCHF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCongestive heart Failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePVD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeripheral Vascular Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLMIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-and middle-income country\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eworld health organizations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTOPP/START\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScreening tool for older person\u0026rsquo;s prescriptions/ Screening tool to alert to right treatment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Ethic Review Committee of Faculty of Medicine of University of Colombo (Ethical Approval No: EC-23-128; Approval Date: 16\u003csup\u003eth\u003c/sup\u003e November 2023). All participants were informed well and written volunteer informed consent obtained prior data collection. All participants’ data were anonymized by removing any identification details to ensure the confidentiality of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. The raw datasets supporting the findings will be made available by the corresponding author upon reasonable request, without undue reservation, to any qualified researcher.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompleting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, D.A.S and T.A.; Methodology, J.M.S.I. and S.B.M.D.P.S.; Investigation, L.K.V., W.A.M.I., S.B.M.D.P.S., A.I.S., J.K.M.S.P., U.L.A., and I.L.M.F.; Data Curation, J.M.S.I., and S.B.M.D.P.S.; Formal Analysis, J.M.S.I; Writing – Original Draft Preparation, J.M.S.I.; Writing – Review \u0026amp; Editing, D.A.S., T.A.; Supervision, D.A.S . All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the residents at elderly care homes who participate in this study and their management for approving permission and support to conduct this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Department of Economic and Social Affairs. World Population Prospects 2024: Summary of Results. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThiyahiny SN, Kumanan T, Surenthirakumaran R. Chronic illnesses and polypharmacy in elderly patients: A hospital-based study in Northern Sri Lanka. Jaffna Med J. 2021;33(1):14\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosseini S, Zabihi A, Jafarian Amiri S, Bijani A. Polypharmacy among the elderly. 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A Scoping Review on the Prevalence of Potentially Inappropriate Medication and Polypharmacy among Older People in a Lower-middle Income Country. JMA J. 2025;8(4):1055\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasanna SMS, Cader TSB, Sabalingam S, Shanika LGT, Samaranayake NR. Are medications safely used by residents in elderly care homes? \u0026ndash; A multi-centre observational study from Sri Lanka. PLoS ONE. 2020;15(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo HW, Goh LC, Tan JYC, Chia D, Sim JLM, Soong JL. Prevalence of polypharmacy: a cross-sectional study in Singapore public healthcare institutions. Singap Med J. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha T, Soliman-Aboumarie H. Review of Current Management of Myocardial Infarction. Journal of Clinical Medicine. Volume 14. 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J Am Heart Assoc. 2022;11(3).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"risk factors, elderly, polypharmacy, prevalence, Sri Lanka","lastPublishedDoi":"10.21203/rs.3.rs-8502816/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8502816/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultimorbidity is a significant contemporary public health challenge globally, with older adults being disproportionately affected. This vulnerability often necessitates long-term pharmacotherapy, increasing the risk of polypharmacy. The concurrent use of multiple medications can lead to adverse health outcomes. Elderly care home residents are particularly susceptible to this risk. This study aimed to determine the prevalence and associated factors of polypharmacy, with a specific emphasis on cardiovascular medication use, in relation to comorbidity burden among elderly residents in care homes in Colombo District, Sri Lanka.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA descriptive, cross-sectional study was conducted among 373 elderly residents (\u0026gt;60 years) from ten elderly care homes in Colombo District, Sri Lanka. Participants were recruited using proportionate stratified sampling. Data were collected via a validated interviewer-administered questionnaire and a structured medication checklist. Descriptive statistics were computed for demographic and clinical variables. Group differences were analyzed using t-test, ANOVA, and Chi-square test. Multivariate logistic regression was performed to identify factors associated with polypharmacy, with statistical significance set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age of the participants was 74.74 ± 7.56 years, and 75.1% were female. Multimorbidity was reported by 79.4% of residents. The most prevalent chronic condition was hypertension (75.1%). The mean number of medications per resident was 5.50 ± 2.98. The prevalence of overall polypharmacy was 59.0%, while cardiovascular-specific polypharmacy was 20.9%.\u003c/p\u003e\n\u003cp\u003eMultimorbidity (AOR: 2.44, 95% CI:1.03-5.78, p = 0.043), frequent contact with a general practitioner (AOR:2.17, 95% CI:1.04-4.52, p = 0.039), and a history of myocardial infarction (AOR:3.51, 95% CI:1.10-11.17, p = 0.033) were significantly associated with polypharmacy. Cardiovascular-specific polypharmacy was independently associated with frequent hospitalization (AOR:2.83, 95% CI:1.21-6.59, p = 0.016), a history of myocardial infarction (AOR:7.26, 95% CI:2.44-21.57, p\u0026lt;0.001) and a history of congestive heart failure (AOR:5.20, 95% CI:1.57-17.19, p = 0.007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe high prevalence of both overall and cardiovascular-specific polypharmacy among elderly care home residents highlights an urgent need to implement structured deprescribing initiatives and integrated, multidisciplinary geriatric care models within care homes. Priority interventions should include strengthening medication review systems, establish coordinated care pathways, and deploy targeted strategies to minimize inappropriate medication use and its associated risks.\u003c/p\u003e","manuscriptTitle":"Polypharmacy, cardiovascular-specific medication use, and comorbidity burden among elderly residents in care homes of Colombo District, Sri Lanka: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 02:30:40","doi":"10.21203/rs.3.rs-8502816/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"78857932633154904537633283697077618352","date":"2026-02-07T21:02:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21634963017686417493738778417468341309","date":"2026-02-04T20:23:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T09:02:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-07T06:40:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T08:05:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T08:02:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-02T18:29:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df782ea2-0406-4009-8bf7-0284474e44da","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-30T09:09:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 02:30:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8502816","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8502816","identity":"rs-8502816","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}