COVID-19 mortality at a tertiary care center during the Second and Third Waves of the pandemic in Sri Lanka: a cross sectional analysis

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Sri Lanka reported 591 and 15,800 deaths during the Second and Third Waves, respectively. This study investigates COVID-19 mortality patterns at a major tertiary care hospital in Sri Lanka during the pandemic, providing crucial insights for resource-limited settings. Methods This descriptive cross-sectional study evaluated COVID-19 deaths at Colombo North Teaching Hospital, Ragama, Sri Lanka from November 2020 to December 2021. Data were sourced from hospital records, supplemented by telephone interviews with family members for missing information, following informed consent. Statistical analyses included Mann-Whitney U tests and Pearson's Chi-squared tests for group comparisons. Results The hospital mortality rate was 14.9% among 8,531 COVID-positive admissions. Among 1,004 recorded deaths, the median age was 72 years (IQR: 62–79, range: 19–99), with males constituting 57.2% of the cohort. Diabetes (61.6%), hypertension (36.9%), and coronary artery disease (14%) were the predominant comorbidities, with 37.2% having multiple comorbidities. COVID-19 pneumonia was the primary cause of death (83%). A majority (78.3%) of the deceased were unvaccinated, while 21.7% had received at least one vaccine dose. Unvaccinated individuals were older, had longer hospital stays, and exhibited more severe symptoms compared to their vaccinated counterparts. Conclusions The high prevalence of diabetes and hypertension among COVID-19 deaths mirrors patterns seen across South Asia, emphasizing the need for integrated management of non-communicable diseases during infectious disease outbreaks. The lower proportion of vaccinated individuals among the deceased suggests vaccine effectiveness, reinforcing the importance of prioritizing high-risk populations in vaccination campaigns. These findings provide guidance for strengthening pandemic preparedness in resource-constrained settings, with potential applications across similar healthcare systems globally. COVID-19 Deaths Vaccination Tertiary Referral Center Sri Lanka BACKGROUND The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, was first identified in Wuhan, China, in December 2019. Its rapid global spread led the World Health Organization (WHO) to declare COVID-19 a pandemic in March 2020 due to its extensive impact across more than 220 countries [ 1 ]. As of April 2022, over 520 million confirmed cases and more than 6.27 million deaths had been reported worldwide, resulting in significant strain on public health systems and profound socio-economic repercussions [ 2 ][ 3 ]. In Sri Lanka, the first case of COVID-19 was reported at the end of January 2020. By May 2022, the country had recorded over 664,000 confirmed cases and more than 16,000 deaths attributed to the virus, according to the Epidemiology Unit of the Ministry of Health [ 4 ]. The pandemic placed immense pressure on Sri Lanka's healthcare system and adversely affected the socio-economic status of its citizens due to prolonged lockdowns and economic disruptions [ 5 ]. Sri Lanka experienced three major waves of COVID-19. The First Wave, from January to September 2020, resulted in 13 deaths among 3,396 confirmed cases. The Second Wave, from October 2020 to April 2021, saw 591 deaths and 92,341 infections. The Third Wave, which began in April 2021, was particularly devastating, with over 565,000 confirmed cases and more than 15,800 fatalities, resulting in a case fatality rate of 2.8% [ 6 ][ 7 ]. While Sri Lanka managed the first two waves relatively well, the Third Wave posed unprecedented challenges, ultimately being controlled through stringent preventive measures, enhanced treatment facilities, and a nationwide vaccination campaign [ 8 ]. Most patients diagnosed with COVID-19 exhibit mild to moderate symptoms, including fever, cough, sore throat, fatigue, arthralgia, and myalgia, often recovering without specific treatments or hospitalization. However, some individuals develop severe illness, including viral pneumonia, which may require intensive care and mechanical ventilation. A minority may progress to critical conditions involving multi-organ failure and death [ 9 ]. During the Third Wave in Sri Lanka, the sheer volume of patients nearly overwhelmed the healthcare system, contributing to increased mortality rates. The challenge of treating a high number of critically ill patients was exacerbated by limited resources, particularly in intensive care units. Consequently, many patients with mild symptoms received home-based management, while those with moderate symptoms were treated in quarantine centers. Patients with significant comorbidities or severe symptoms were hospitalized, with transfers occurring as conditions deteriorated [ 8 ]. Extensive global research has established several key determinants of COVID-19 mortality. Advanced age (particularly > 65 years), male gender, and pre-existing conditions such as cardiovascular disease, diabetes mellitus, chronic respiratory disease, hypertension, obesity, and immunocompromised states are consistently associated with higher mortality risk [ 10 ]. The pathophysiology of severe COVID-19 involves direct viral damage to organs, dysregulated immune responses leading to cytokine storms, and thrombotic complications resulting in multi-organ failure [ 11 ]. Research has demonstrated that early intervention with appropriate respiratory support, judicious use of corticosteroids, anticoagulation, and targeted immunomodulatory therapies can improve survival outcomes [ 12 ]. However, significant variations exist in mortality patterns across different healthcare settings, geographical regions, and over time as virus variants evolved and clinical management protocols improved [ 13 ]. Globally, most COVID-19 deaths occurred among hospitalized patients, with causes including the progression of COVID-19 pneumonia, complications such as acute kidney injury and myocarditis, and exacerbation of pre-existing conditions like ischemic heart disease. Some patients died during transfers to hospitals, while others succumbed during home-based care or in quarantine centers [ 14 ]. The severity of illness, duration of disease, and type of treatment received were critical factors influencing mortality outcomes [ 15 ]. The implementation of global vaccination programs has significantly impacted morbidity and mortality rates. According to WHO data, as of April 2022, 66.3% of the global population had received at least one dose of a COVID-19 vaccine, and 60.2% were fully vaccinated. In Sri Lanka, 77.9% of the population had received at least one vaccine dose, with 66% fully vaccinated and 36.4% having received a booster [ 16 ]. The successful nationwide vaccination campaign in Sri Lanka, utilizing vaccines such as Sinopharm, Covishield, Pfizer, Moderna, and Sputnik V, was facilitated by the country's robust preventive healthcare infrastructure [ 17 ]. Vaccination has been pivotal in reducing COVID-19 severity and mortality worldwide. Numerous studies have confirmed that fully vaccinated individuals face substantially lower risks of hospitalization, ICU admission, and death compared to unvaccinated counterparts [ 18 ]. Booster doses have further enhanced protection, particularly against emerging variants like Delta and Omicron. Vaccine effectiveness studies have shown 90–95% reduction in mortality risk among fully vaccinated individuals, though effectiveness varies by vaccine type, age group, comorbidity status, and viral variants [ 19 ]. Despite breakthrough infections, vaccinated patients typically experience milder disease courses and better outcomes, underscoring vaccination's crucial role in pandemic control strategies [ 20 ]. The South Asian region, home to nearly a quarter of the world's population, faced unique challenges during the COVID-19 pandemic. India experienced one of the most severe outbreaks globally, particularly during its devastating second wave in April-May 2021, when daily cases exceeded 400,000 and overwhelmed healthcare systems [ 21 ]. Pakistan reported over 1.5 million cases and 30,000 deaths by April 2022, with major urban centers bearing the brunt of infections [ 22 ]. Bangladesh, despite early control measures, witnessed significant surges with limited healthcare resources, recording approximately 2 million cases and 29,000 deaths [ 23 ]. Nepal and the Maldives experienced severe strain on their healthcare systems during peak waves, despite having smaller populations [ 24 ]. Compared to its neighbors, Sri Lanka maintained lower case fatality rates than India (1.2%) and Pakistan (2.0%) during comparable periods, which can be attributed to its robust primary healthcare system and early implementation of public health measures [ 25 ]. However, vaccination rates varied significantly across the region, with the Maldives achieving the highest coverage (nearly 80% fully vaccinated) by early 2022, while Nepal and Bangladesh reported approximately 54% and 67% full vaccination rates, respectively [ 26 ]. These regional variations provide important context for understanding Sri Lanka's pandemic experience. Despite numerous global studies on COVID-19 mortality, there is limited information specifically regarding COVID-19 deaths in Sri Lanka, aside from data released by the Epidemiology Unit. According to WHO statistics, Sri Lanka ranks 77th in the world for total confirmed cases and 50th for total deaths [ 27 ]. The country boasts a high recovery rate of 97.36%, while its case fatality rate (CFR) of 2.5% is higher than many countries [ 28 ]. These statistics highlight the urgent need for comprehensive assessment and evaluation of COVID-19 mortality in Sri Lanka. This study aims to comprehensively investigate the demographic profile, pre-existing comorbidities, vaccination status, severity of illness, and duration of disease among patients who died from COVID-19 at a major tertiary referral center during the Second and Third Waves of the pandemic in Sri Lanka. By analyzing these factors, we aimed to identify patterns of mortality and assess the effectiveness of interventions, particularly vaccination, in preventing severe outcomes. The findings are expected to inform clinical management protocols and public health strategies for future pandemic responses in resource-limited settings. METHODS This descriptive cross-sectional study was conducted at Colombo North Teaching Hospital (CNTH) in Ragama, Sri Lanka. CNTH is the sole tertiary care facility in the Gampaha district, serving a vast catchment area that includes not only Gampaha but also neighboring districts such as Colombo, Puttalam, and Kurunegala. Its strategic location at a major transport hub facilitates access to the hospital, making it a critical resource for the densely populated Gampaha district, which is the most populated district in Sri Lanka. The hospital has a capacity of 1,749 beds and typically sees between 130,000 and 150,000 patient admissions annually [ 29 ]. The study population comprised all patients whose deaths were attributed to COVID-19 at CNTH during the period from November 2020 to December 2021, encompassing both the Second and Third Waves of the pandemic in Sri Lanka. Using the hospital's admission database and COVID-19 death registry maintained by the infection control unit, we identified all cases where COVID-19 was listed as the primary cause of death. We applied the WHO criteria for COVID-19 mortality, which defines a COVID-19 death as one resulting from a clinically compatible illness in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 disease [ 30 ]. No cases were excluded from the initial registry, ensuring a comprehensive analysis of all COVID-19 deaths at the facility during the study period. This complete enumeration approach was chosen rather than sampling to capture the full spectrum of mortality patterns and avoid selection bias. No formal power calculation was performed since this was a descriptive study analyzing all available cases. Data collection involved two complementary approaches: Medical Record Review : We systematically extracted information from the bed head tickets of deceased patients. This included demographic information (age, sex, residence), clinical data (comorbidities, symptoms, disease progression), laboratory and radiological findings, treatment details, and outcomes. A standardized data extraction form was developed and pilot-tested on 10 records before full implementation to ensure consistency and comprehensiveness. Telephone Interviews : To supplement the information from medical records and address missing data, an interviewer-administered questionnaire was conducted via telephone with a family member (preferably the next of kin or primary caregiver) of deceased patients. The questionnaire was developed based on WHO guidelines for COVID-19 mortality surveillance [ 31 ] and included structured questions covering: Sociodemographic details not available in medical records Pre-hospital illness course and symptoms Vaccination history (type, dates, number of doses) Healthcare-seeking behavior before admission Treatment received at home or intermediate centers Functional status before infection Living arrangements and possible exposure sources The questionnaire was translated into local languages (Sinhala and Tamil), back-translated to ensure accuracy, and pilot-tested with family members of 5 deceased patients not included in the study to assess clarity and cultural appropriateness. Three trained research assistants conducted the interviews using a standardized protocol. Prior to each interview, a brief explanation of the study was provided, and informed verbal consent was obtained from the family member. Interviews typically lasted 15–20 minutes. The cause of death was established through a systematic review process: Primary determination was based on the cause of death stated in the death certificate completed by the attending physician. For each case, two independent investigators reviewed the complete medical records, including clinical progression, laboratory data, and radiological findings to verify the cause of death. Cases were classified according to the WHO International Guidelines for Certification and Classification (Coding) of COVID-19 as Cause of Death [ 32 ], which distinguishes between: Death due to COVID-19 (directly caused by the disease) Death with COVID-19 (where SARS-CoV-2 contributed to but was not the underlying cause) Death unrelated to COVID-19 (where the virus was incidental) Only cases where COVID-19 was determined to be the direct or underlying cause of death were included in the final analysis. Discrepancies in classification were resolved through discussion with a senior consultant physician with expertise in COVID-19 management. Data were entered into an SPSS database using double-entry verification to minimize transcription errors. Logical and random checks were performed for accuracy, and inconsistencies were resolved by referring back to the original records. Missing data were categorized and analyzed according to the pattern of missingness. For variables with less than 5% missing data, complete case analysis was performed. For variables with 5–20% missing data, multiple imputation using chained equations was employed to generate five imputed datasets, with results pooled according to Rubin's rules [ 33 ]. Variables with more than 20% missing data were reported with the actual number of available observations and analyzed separately with appropriate caveats. Statistical analysis was carried out using SPSS version 29. Continuous data were described using means and standard deviations for normally distributed variables, and medians with interquartile ranges for non-normally distributed variables. Normality was assessed using the Shapiro-Wilk test and visual inspection of histograms. Categorical data were expressed as frequencies and percentages. Parametric tests (Student's t-test, ANOVA) were applied to normally distributed data, and non-parametric tests (Mann-Whitney U, Kruskal-Wallis) were used for non-normally distributed data to conduct group comparisons. Chi-square or Fisher's exact tests were used to compare categorical variables. For all analyses, a significance level was set at p < 0.05. Ethical clearance for the study was obtained from the Ethics Review Committee of the Faculty of Medicine, University of Kelaniya (Reference P33/06/2022). Ethical aspects of the study were in accordance with the Declaration of Helsinki. Given the retrospective nature of the study and the challenges of obtaining written consent from bereaved families during the pandemic, the Ethics Committee approved the use of verbal consent for telephone interviews. All data were de-identified before analysis to ensure confidentiality, and data security measures were implemented to protect sensitive information. RESULTS There were 108,025 admissions to CNTH during the study period of November-2020 to December-2021. Of them 8,531 persons were COVID positive by PCR and/or RAT (7.9% of all admissions). A total of 1269 persons died due to COVID-19, giving a mortality rate of 14.9%. Complete data was available for 1004 of these deaths (79.1%). The characteristics of the study population are given in Table 1 . The deceased patients had a median age of 72 years (IQR: 62–79), with ages ranging from 19 to 99 years. Males were slightly older than females at the time of death, with a mean age of 71 years compared to 68 years for females (p = 0.008). Most of the deceased were Sinhalese (93%), married (78%), and had an educational level up to GCE O/L (66.3%). Most were unemployed (66%) and had a monthly family income between LKR 35,001–75,000 (62%). Comorbidities were highly prevalent among the deceased patients. Diabetes was the most common affecting 61.6% of the cohort, followed by hypertension (36.9%) and coronary artery disease (14%). A significant proportion (37.2%) had multiple comorbidities, while only 10.7% had no underlying health conditions. The majority (78.3%) were unvaccinated, while 21.7% had received at least one dose of a COVID-19 vaccine. Only 7.8% had received two doses, and 0.8% had received a booster dose. Sinopharm was the most administered vaccine (18%), since Sinopharm was the predominant vaccine used in the national vaccination programme. Table 1 Characteristics of Deceased Persons Characteristic Overall, N = 1,004 1 Male, N = 574 1 Female, N = 430 1 p-value 2 Patient age 0.008 Mean (SD) 70 (13) 71 (13) 68 (15) Median (IQR) 72 (16) 72 (16) 70 (19) Range 19–99 19–99 24–95 Ethnicity 0.005 Sinhalese 937 (93%) 526 (92%) 411 (96%) Tamil 43 (4.3%) 28 (4.9%) 15 (3.5%) Muslims 17 (1.7%) 16 (2.8%) 1 (0.2%) Burger 7 (0.7%) 4 (0.7%) 3 (0.7%) Marital status 0.89 Married 784 (78%) 452 (79%) 332 (77%) Widowed 212 (21%) 118 (21%) 94 (22%) Single 6 (0.6%) 3 (0.5%) 3 (0.7%) Divorced 2 (0.2%) 1 (0.2%) 1 (0.2%) Level of Education 0.16 Qualified GCE O/L 666 (66%) 379 (66%) 287 (67%) Grade 6–11 215 (21%) 118 (21%) 97 (23%) Qualified GCE A/L 73 (7.3%) 50 (8.7%) 23 (5.3%) Grade 1–5 33 (3.3%) 17 (3.0%) 16 (3.7%) Graduated/Diploma 9 (0.9%) 7 (1.2%) 2 (0.5%) No formal schooling 5 (0.5%) 1 (0.2%) 4 (0.9%) Post-graduate 2 (0.2%) 1 (0.2%) 1 (0.2%) Other 1 (< 0.1%) 1 (0.2%) 0 (0%) Employment Status < 0.001 Unemployed 663 (66%) 340 (59%) 323 (75%) Employed 341 (34%) 234 (41%) 107 (25%) Monthly Family Income LKR 0.088 55,001–75,000 323 (32%) 176 (31%) 147 (34%) 35,001–55,000 304 (30%) 194 (34%) 110 (26%) 15,001–35,000 167 (17%) 89 (16%) 78 (18%) 75,001–95,000 118 (12%) 59 (10%) 59 (14%) 95,001-115,000 39 (3.9%) 22 (3.8%) 17 (4.0%) >115,001 33 (3.3%) 21 (3.7%) 12 (2.8%) <15,000 20 (2.0%) 13 (2.3%) 7 (1.6%) Comorbidities Diabetes 618 (62%) 346 (60%) 272 (63%) 0.34 Hypertension 370 (37%) 190 (33%) 180 (42%) 0.004 Coronary artery disease 144 (14%) 84 (15%) 60 (14%) 0.76 Chronic kidney disease 109 (11%) 59 (10%) 50 (12%) 0.50 Stroke 25 (2.5%) 13 (2.3%) 12 (2.8%) 0.60 Respiratory illness 49 (4.9%) 31 (5.4%) 18 (4.2%) 0.38 Chronic liver disease 35 (3.5%) 27 (4.7%) 8 (1.9%) 0.015 Malignancy 33 (3.3%) 21 (3.7%) 12 (2.8%) 0.45 Multiple comorbidities 373 (37%) 203 (35%) 170 (40%) 0.18 No comorbidities 107 (10.7%) 75 (13%) 32 (7.4%) > 0.99 Vaccination against COVID-19 0.50 Not received 786 (78%) 445 (78%) 341 (79%) Received 218 (22%) 129 (22%) 89 (21%) Type of Vaccine 0.33 None 786 (78%) 445 (78%) 341 (79%) Sinopharm 181 (18%) 108 (19%) 73 (17%) Covishield 30 (3.0%) 15 (2.6%) 15 (3.5%) Pfizer 7 (0.7%) 6 (1.0%) 1 (0.2%) 1st dose received 218 (22%) 129 (22%) 89 (21%) 0.50 2nd dose received 79 (7.9%) 49 (8.5%) 30 (7.0%) 0.37 3rd dose received 8 (0.8%) 5 (0.9%) 3 (0.7%) > 0.99 Hospital admission 564 (56%) 336 (59%) 228 (53%) 0.081 Home-based care 280 (28%) 150 (26%) 130 (30%) 0.15 1 n (%) 2 Welch Two Sample t-test; Fisher's exact test; Pearson's Chi-squared test A detailed comparison of characteristics between those who received and did not receive any form of vaccination against COVID-19 is given in Table 2 . Compared to vaccinated individuals, unvaccinated patients were older (mean age: 71 vs. 68 years, p = 0.008), and more likely to be unemployed (70% vs. 52%, p < 0.001). Table 2 Characteristics of Vaccinated and Non-vaccinated Deaths Characteristic Overall, N = 1,004 1 Not vaccinated, N = 786 1 Vaccinated, N = 218 1 p-value 2 Patient age 0.008 Mean (SD) 70 (13) 71 (13) 68 (15) Median (IQR) 72 (16) 72 (16) 70 (19) Range 19–99 19–99 24–95 Gender 0.50 Male 574 (57%) 445 (57%) 129 (59%) Female 430 (43%) 341 (43%) 89 (41%) Ethnicity 0.13 Sinhalese 937 (93%) 730 (93%) 207 (95%) Tamil 43 (4.3%) 39 (5.0%) 4 (1.8%) Muslims 17 (1.7%) 12 (1.5%) 5 (2.3%) Burger 7 (0.7%) 5 (0.6%) 2 (0.9%) Other 0 (0%) 0 (0%) 0 (0%) Marital status 0.023 Married 784 (78%) 613 (78%) 171 (78%) Widowed 212 (21%) 170 (22%) 42 (19%) Single 6 (0.6%) 3 (0.4%) 3 (1.4%) Divorced 2 (0.2%) 0 (0%) 2 (0.9%) Separated 0 (0%) 0 (0%) 0 (0%) Level of Education 0.14 Qualified GCE O/L 666 (66%) 520 (66%) 146 (67%) Grade 6–11 215 (21%) 178 (23%) 37 (17%) Qualified GCE A/L 73 (7.3%) 52 (6.6%) 21 (9.6%) Grade 1–5 33 (3.3%) 22 (2.8%) 11 (5.0%) Graduated/Diploma 9 (0.9%) 7 (0.9%) 2 (0.9%) No formal schooling 5 (0.5%) 5 (0.6%) 0 (0%) Post-graduate 2 (0.2%) 1 (0.1%) 1 (0.5%) Other 1 (< 0.1%) 1 (0.1%) 0 (0%) Employment Status < 0.001 Unemployed 663 (66%) 549 (70%) 114 (52%) Employed 341 (34%) 237 (30%) 104 (48%) Previous COVID 19 infection 20 (2.0%) 10 (1.3%) 10 (4.6%) 0.004 Co-morbidities Diabetes 618 (62%) 486 (62%) 132 (61%) 0.73 Hypertension 370 (37%) 279 (35%) 91 (42%) 0.091 Coronary artery disease 144 (14%) 108 (14%) 36 (17%) 0.30 Chronic kidney disease 109 (11%) 87 (11%) 22 (10%) 0.68 Stroke 25 (2.5%) 19 (2.4%) 6 (2.8%) 0.78 Respiratory illness 49 (4.9%) 36 (4.6%) 13 (6.0%) 0.40 Chronic liver disease 35 (3.5%) 24 (3.1%) 11 (5.0%) 0.16 Malignancy 33 (3.3%) 28 (3.6%) 5 (2.3%) 0.35 Multiple co-morbidities 373 (37%) 291 (37%) 82 (38%) 0.87 No co-morbidities 107 (100%) 95 (100%) 12 (100%) > 0.99 Smoking 181 (18%) 114 (15%) 67 (31%) < 0.001 Alcohol consumption 141 (14%) 85 (11%) 56 (26%) < 0.001 1 n (%) 2 Welch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test There were no clear differences in symptom profile, treatment modalities including oxygen requirement and cause of death among the vaccinated and the unvaccinated, except those who were vaccinated had lesser arthralgia and/or myalgia and more fatigue (Table 3 ). The unvaccinated had longer hospital stays where 85% stayed more than 48 hours compared to 76% among the vaccinated (P = 0.021). The primary cause of death was COVID-19 pneumonia (83%), followed by complications of COVID-19 (5.8%) and acute myocardial infarction with COVID-19 (4.7%). Table 3 Disease characteristics among Vaccinated and Non-vaccinated Deaths Symptoms and Characteristics Overall, N = 1,004 1 (%) Not vaccinated N = 786 1 (%) Vaccinated N = 218 1 (%) P value 2 Fever 628 (63%) 489 (62%) 139 (64%) 0.68 Arthralgia and/or Myalgia 217 (22%) 184 (23%) 33 (15%) 0.009 Cough 502 (50%) 390 (50%) 112 (51%) 0.65 Shortness of breath 442 (44%) 351 (45%) 91 (42%) 0.44 Runny nose 44 (4.4%) 33 (4.2%) 11 (5.0%) 0.59 Fatigue 97 (9.7%) 65 (8.3%) 32 (15%) 0.004 Nausea 257 (26%) 196 (25%) 61 (28%) 0.36 Vomiting 65 (6.5%) 47 (6.0%) 18 (8.3%) 0.23 Loss of taste 409 (41%) 326 (41%) 83 (38%) 0.37 Sore throat 318 (32%) 254 (32%) 64 (29%) 0.41 Loss of smell 245 (24%) 202 (26%) 43 (20%) 0.069 Headache 275 (27%) 213 (27%) 62 (28%) 0.69 Developed complications < 0.001 None 845 (84%) 691 (88%) 154 (71%) Other 92 (9.2%) 57 (7.3%) 35 (16%) Acute coronary syndrome 53 (5.3%) 31 (3.9%) 22 (10%) Stroke 14 (1.4%) 7 (0.9%) 7 (3.2%) Needed oxygen 556 (55%) 438 (56%) 118 (54%) 0.67 Mode of oxygenation 0.62 None 448 (45%) 349 (44%) 99 (45%) HFNO 394 (39%) 312 (40%) 82 (38%) NIV 105 (10%) 84 (11%) 21 (9.6%) Intubation & ventilation 57 (5.7%) 41 (5.2%) 16 (7.3%) Dialysis 20 (2.0%) 15 (1.9%) 5 (2.3%) 0.78 Tozlucimab given 15 (1.5%) 9 (1.1%) 6 (2.8%) 0.11 Hospitalization > 48 hours 469 (83%) 376 (85%) 93 (76%) 0.021 Cause of death COVID-19 Pneumonia 834 (83%) 659 (84%) 175 (80%) Complications of COVID-19 58 (5.8%) 47 (6.0%) 11 (5.0%) Acute myocardial infarction with COVID-19 47 (4.7%) 32 (4.1%) 15 (6.9%) Acute kidney injury with COVID-19 23 (2.3%) 17 (2.2%) 6 (2.8%) Acute respiratory distress syndrome 12 (1.2%) 9 (1.1%) 3 (1.4%) Natural death 12 (1.2%) 11 (1.4%) 1 (0.5%) Complications of cirrhosis 11 (1.1%) 8 (1.0%) 3 (1.4%) Other 6 (0.6%) 3 (0.4%) 3 (1.4%) Diabetic ketoacidosis 1 (< 0.1%) 0 (0%) 1 (0.5%) 1 n 2 Welch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test HFNO – high flow nasal oxygen, NIV – non invasive ventilation Most patients (56%) were hospitalized, while 28% received home-based care (Table 4 ). Patients who received home-based care were relatively older (mean age: 72 vs. 69 years, p = 0.020) and more likely to have respiratory illnesses and a history of COVID-19 contact. The main reasons for not hospitalizing patients were that they were not perceived as very ill (74%) and patient or family choice (23%). Table 4 Deaths by treatment setting: Home-based care vs Hospital and Intermediate Center care Characteristic Overall, N = 1,004 1 Hospital & Intermediate Center N = 724 1 Home-based N = 280 1 p-value 2 Patient age 0.020 Mean (SD) 70 (13) 69 (13) 72 (14) Median (IQR) 72 (16) 71 (16) 75 (17) Range 19–99 19–99 22–98 Gender 0.15 Male 574 (57%) 424 (59%) 150 (54%) Female 430 (43%) 300 (41%) 130 (46%) Ethnicity 0.49 Sinhalese 937 (93%) 679 (94%) 258 (92%) Tamil 43 (4.3%) 27 (3.7%) 16 (5.7%) Muslims 17 (1.7%) 12 (1.7%) 5 (1.8%) Burger 7 (0.7%) 6 (0.8%) 1 (0.4%) Other 0 (0%) 0 (0%) 0 (0%) Marital status 0.98 Married 784 (78%) 563 (78%) 221 (79%) Widowed 212 (21%) 154 (21%) 58 (21%) Single 6 (0.6%) 5 (0.7%) 1 (0.4%) Divorced 2 (0.2%) 2 (0.3%) 0 (0%) Separated 0 (0%) 0 (0%) 0 (0%) Level of Education 0.50 Qualified GCE O/L 666 (66%) 474 (65%) 192 (69%) Grade 6–11 215 (21%) 158 (22%) 57 (20%) Qualified GCE A/L 73 (7.3%) 51 (7.0%) 22 (7.9%) Grade 1–5 33 (3.3%) 28 (3.9%) 5 (1.8%) Graduated/Diploma 9 (0.9%) 8 (1.1%) 1 (0.4%) No formal schooling 5 (0.5%) 3 (0.4%) 2 (0.7%) Post-graduate 2 (0.2%) 1 (0.1%) 1 (0.4%) Other 1 (< 0.1%) 1 (0.1%) 0 (0%) Employment Status 0.76 Unemployed 663 (66%) 476 (66%) 187 (67%) Employed 341 (34%) 248 (34%) 93 (33%) Comorbidities Diabetes 618 (62%) 447 (62%) 171 (61%) 0.85 Hypertension 370 (37%) 275 (38%) 95 (34%) 0.23 Coronary artery disease 144 (14%) 105 (15%) 39 (14%) 0.82 Chronic kidney disease 109 (11%) 85 (12%) 24 (8.6%) 0.15 Stroke 25 (2.5%) 17 (2.3%) 8 (2.9%) 0.64 Respiratory illness 49 (4.9%) 23 (3.2%) 26 (9.3%) < 0.001 Chronic liver disease 35 (3.5%) 25 (3.5%) 10 (3.6%) 0.93 Malignancy 33 (3.3%) 27 (3.7%) 6 (2.1%) 0.21 Multiple comorbidities 373 (37%) 280 (39%) 93 (33%) 0.11 No comorbidities 107 (100%) 81 (100%) 26 (100%) > 0.99 Smoking 181 (18%) 126 (17%) 55 (20%) 0.41 Alcohol consumption 141 (14%) 97 (13%) 44 (16%) 0.36 Vaccination against COVID-19 218 (22%) 154 (21%) 64 (23%) 0.58 1st dose received 218 (22%) 154 (21%) 64 (23%) 0.58 2nd dose received 79 (7.9%) 59 (8.2%) 20 (7.1%) 0.59 3rd dose received 8 (0.8%) 6 (0.8%) 2 (0.7%) > 0.99 Contact history of COVID-19 712 (71%) 500 (69%) 212 (76%) 0.044 1 n (%) 2 Welch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test DISCUSSION This study conducted at Colombo North Teaching Hospital (CNTH) provides insights into the demographic and clinical characteristics of COVID-19-related deaths during the Second and Third Waves of the pandemic in Sri Lanka. Our analysis of 1,004 deaths revealed a predominance of male mortality (57%) with a mean age of 70 years. Males were significantly older than females at the time of death (71 vs. 68 years, p = 0.008). The overall mortality rate among COVID-19 patients admitted to CNTH was 14.9%, considerably higher than the national average of 2.5% [ 34 ]. A substantial proportion of deceased individuals presented with multiple comorbidities (37%), with diabetes and hypertension being the most prevalent underlying conditions. Only 22% of the deceased had received any form of COVID-19 vaccination, with the majority having received the Sinopharm vaccine. Notably, vaccinated individuals who died were generally older and more likely to be employed compared to unvaccinated deceased individuals, suggesting potential differential access to vaccination during the study period. Our findings align with global trends indicating that older adults and those with pre-existing health conditions are at heightened risk for severe COVID-19 outcomes and mortality. A comprehensive meta-analysis by Dessie and Zewotir (2021) examining 42 studies across multiple countries found similar patterns of increased mortality risk among males and elderly patients, with pooled odds ratios of 1.45 (95% CI: 1.37–1.54) for male sex and 3.61 (95% CI: 3.21–4.04) for ages above 65 years [ 35 ]. The predominance of diabetes and hypertension among deceased patients in our study mirrors findings from a systematic review by Singh et al. (2021), which indicated that these comorbidities significantly increased the risk of COVID-19 mortality, with pooled hazard ratios of 1.81 (95% CI: 1.52–2.16) for diabetes and 1.74 (95% CI: 1.46–2.08) for hypertension [ 36 ]. The low vaccination rate (22%) among deceased patients in our study is comparable to findings from early vaccination impact studies in other middle-income countries [ 37 ]. However, our figure is lower than those reported in studies from high-income countries during similar time periods, such as the UK, where Hippisley-Cox et al. (2021) found that approximately 37% of COVID-19 deaths had received at least one vaccine dose [ 38 ], reflecting disparities in vaccine access and rollout timing. The hospital mortality rate of 14.9% in our study is comparable to reports from tertiary care centers in Pakistan (13.8%) [ 38 ] during similar pandemic phases, but lower than reported rates from Bangladesh (16.5%) [ 39 ]. These variations likely reflect differences in healthcare infrastructure, admission criteria, and case severity across these settings. The predominance of unvaccinated individuals (78%) among COVID-19 deaths in our study warrants careful interpretation. This finding should be contextualized within Sri Lanka's vaccination timeline, where mass vaccination began in late January 2021 but faced initial supply constraints, with widespread availability only achieved by mid-2021 [ 40 ]. The timing of our study period (November 2020 to December 2021) means many deaths occurred before vaccines were widely accessible, particularly during the deadly Third Wave that began in April 2021. Nevertheless, the lower proportion of vaccinated individuals among the deceased strongly suggests a protective effect of vaccination. The observation that vaccinated individuals who died were generally older suggests that age-related immunosenescence may have reduced vaccine effectiveness in this vulnerable group, a phenomenon documented in immunological studies of COVID-19 vaccines [ 41 ]. Additionally, the higher employment rate among vaccinated deceased patients may reflect prioritization of working-age adults in early vaccination campaigns, leaving some vulnerable elderly populations inadequately protected during critical periods. The high prevalence of multiple comorbidities (37%) among deceased patients has important implications for risk stratification and clinical management. The synergistic effect of diabetes and hypertension, the two most common comorbidities in our cohort, likely contributed to poorer outcomes through multiple pathophysiological mechanisms. Diabetes has been shown to impair immune function and increase ACE2 receptor expression, facilitating SARS-CoV-2 cellular entry [ 42 ]. Similarly, hypertension-associated endothelial dysfunction may exacerbate COVID-19's thromboinflammatory complications [ 43 , 44 ]. The comorbidity profile observed in our study reflects Sri Lanka's ongoing epidemiological transition, with a rising burden of non-communicable diseases alongside persistent infectious disease challenges. This "double burden" creates unique vulnerabilities during pandemics, as healthcare systems must simultaneously manage acute infectious crises while maintaining care for chronic conditions. Although not explicitly measured, our findings indirectly reflect challenges in Sri Lanka's healthcare system during the pandemic. The high hospital mortality rate (14.9%) compared to the national case fatality rate (2.5%) suggests that CNTH, as a tertiary referral center, received a disproportionate share of severe cases, many likely referred when already in critical condition. This pattern of late presentation was observed across South Asia, where healthcare-seeking behaviors were influenced by fear of infection, transportation limitations during lockdowns, and financial constraints. Our findings strongly support prioritizing vaccination for elderly individuals with comorbidities, who constituted the majority of deaths in this study. Sri Lanka's initial age-based rollout strategy, while logistically efficient, may have benefited from earlier integration of comorbidity status in prioritization schemes. Moving forward, continuous vaccination coverage monitoring among high-risk groups is essential, with targeted outreach for under-vaccinated vulnerable populations. For future vaccination campaigns, our data suggest that healthcare authorities should: Develop more robust systems for identifying and reaching homebound elderly with comorbidities Establish mobile vaccination units for remote communities Create accelerated pathways for individuals with multiple high-risk comorbidities Implement recall systems for booster doses prioritizing elderly with diabetes and hypertension The prominent role of comorbidities in COVID-19 mortality highlights the need for integrated care approaches. During pandemic surges, maintaining continuity of care for chronic disease management presented significant challenges. Our findings suggest that greater integration of COVID-19 and non-communicable disease care could improve outcomes through: Telehealth consultations specifically targeting comorbidity management among COVID-19 patients Medication delivery services for chronic disease medications during isolation periods Home-based monitoring protocols that include both COVID-19 symptoms and comorbidity parameters Post-COVID follow-up pathways with enhanced screening for comorbidity complications The high mortality observed during the Third Wave highlights the need for more resilient health system capacity. Sri Lanka's relatively strong primary healthcare infrastructure could be further leveraged through: Strengthening intermediate care facilities to reduce tertiary hospital burden Developing clear clinical pathways with standardized transfer criteria between levels of care Expanding critical care capacity at district hospitals to manage surges locally Training primary care providers in early identification and management of high-risk patients [58] This study has several limitations. As a single-center study conducted at a tertiary referral hospital our findings may not be generalizable to other regions or healthcare settings in Sri Lanka, particularly rural areas with different demographic and comorbidity profiles. The patient population at CNTH likely represents more severe cases, potentially overestimating mortality rates compared to the general infected population. The retrospective nature of the study introduces potential for information bias, particularly regarding vaccination status and comorbidity documentation. Although we supplemented hospital records with family interviews, recall bias may have affected the accuracy of pre-hospital information, especially for patients who died shortly after admission with limited documentation. Our study period encompassed different phases of the pandemic with evolving viral variants, changing clinical protocols, and varying resource availability, which may have influenced mortality patterns independently of patient characteristics. We were unable to perform consistent viral genomic sequencing to correlate outcomes with specific variants. Limited data on socioeconomic status, education level, and healthcare access barriers prevented more nuanced analysis of social determinants influencing COVID-19 mortality. Additionally, detailed analysis of treatment modalities and their impact on outcomes was beyond the scope of this study. The cross-sectional design precludes establishing causal relationships between observed factors and mortality outcomes. Prospective studies would better elucidate the complex interactions between comorbidities, vaccination status, and COVID-19 mortality. This study highlights several critical areas for future research. Longitudinal studies investigating the long-term effects of COVID-19 on survivors, particularly those with comorbidities, are essential to understand the full impact of the pandemic and inform rehabilitation services. Integration of genomic surveillance with clinical outcomes research would enhance understanding of variant-specific mortality risks and vaccine effectiveness. More comprehensive evaluation of Sri Lanka's vaccination campaign effectiveness across different population segments would identify remaining gaps and inform targeted interventions. Implementation research on optimizing home-based care models for high-risk COVID-19 patients could reduce mortality during future surges. Health systems research examining hospital capacity, resource allocation, and clinical pathways during pandemic surges would strengthen preparedness planning. Also, comparative studies across different regions of Sri Lanka and neighboring South Asian countries would identify transferable best practices for pandemic response in resource-constrained settings. Conclusion The findings of this study underscore the critical public health implications of COVID-19 mortality in Sri Lanka. The high prevalence of comorbidities among deceased patients highlights the need for targeted health interventions aimed at managing chronic diseases, particularly in older adults. The significantly lower vaccination rates among the deceased emphasize the protective effect of vaccines and the importance of prioritizing vulnerable d The insights gained from this study provide valuable guidance for strengthening pandemic preparedness, optimizing COVID-19 management protocols, and enhancing healthcare system resilience in Sri Lanka and similar resource-constrained settings. By addressing the identified gaps in comorbidity management, vaccination coverage, and healthcare delivery, public health authorities can better prepare for future pandemic threats and improve outcomes for vulnerable populations. Abbreviations COVID 19–Corona Virus Disease 2019 SARS CoV–2–severe acute respiratory syndrome coronavirus 2 WHO World Health Organization CNTH Colombo North Teaching Hospital SPSS Statistical Package for Social Sciences PCR polymerace chain reaction RAT rapid antigen test IQR Inter quantile range GCE (O/L) General Certificate of Examination Ordinary Level LKR Lankan Rupee CI confidence interval Declarations Ethics approval and consent to participate - ethical approval was obtained from the Ethics Review Committee, Faculty of Medicine, University of Kelaniya. Informed verbal consent was obtained from all participants. Ethical aspects of the study were in accordance with the Declaration of Helsinki. (Approval No – P/ P33/06/2022) Availability of data and material - The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Author Contribution - SDS and RL conceptualized and designed the study. NE, TS and KP collected data with assistance from AW. RL and AW assisted in verifying data. DE analyzed the data. SDS prepared and revised the manuscript together with DE. All authors read and agreed to the final version of the manuscript. Acknowledgments - We gratefully acknowledge the assistance given by the Director, Colombo North Teaching Hospital and the staff of the hospital Records Unit in conducting this study. We especially thank the family member of the deceased patients for their cooperation. Consent for publication – not applicable Funding – this study was self-funded. Competing interests – none Clinical trial number - not applicable References World Health Organization. (2020). "WHO Timeline - COVID-19." World Health Organization. (2022). "COVID-19 Dashboard." Johns Hopkins University. (2022). "COVID-19 Map." Epidemiology Unit, Ministry of Health, Sri Lanka. (2022). "COVID-19 Situation Report." Fernando S. COVID-19: Sri Lanka’s Moral Test. 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Downey, Thomas Gadsden, Victor Del Rio Vilas, David Peiris, Stephen Jan, The impact of COVID-19 on essential health service provision for endemic infectious diseases in the South-East Asia region: A systematic review, The Lancet Regional Health - Southeast Asia, Volume 1, 2022 https://doi.org/10.1016/j.lansea.2022.04.007. Deepankar Basu, Priyanka Srivastava. COVID-19 in South Asia: Health, economics and politics. Inv. Econ vol.81 no.319 Ciudad de México ene./mar. 2022 https://doi.org/10.22201/fe.01851667p.2022.319.79762 UNICEF. (2022). "COVID-19 Vaccine Market Dashboard: South Asia Region." World Health Organization. (2022). "COVID-19 Situation in Sri Lanka." https://www.who.int/countries/lka Sri Lanka: Coronavirus Pandemic Country Profile. Our World in Data https://ourworldindata.org/coronavirus/country/sri-lanka Ministry of Health Sri Lanka. (2021). "Annual Health Statistics 2020 - Sri Lanka." 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Diabetes Obes Metab. 2020 Oct 1;22(10):1915–24. Solís Arce JS, Warren SS, Meriggi NF, Scacco A, McMurry N, Voors M, Syunyaev G, Malik AA, Aboutajdine S, Adeojo O, Anigo D, Armand A, Asad S, Atyera M, Augsburg B, Awasthi M, Ayesiga GE, Bancalari A, Björkman Nyqvist M, Borisova E, Bosancianu CM, Cabra García MR, Cheema A, Collins E, Cuccaro F, Farooqi AZ, Fatima T, Fracchia M, Galindo Soria ML, Guariso A, Hasanain A, Jaramillo S, Kallon S, Kamwesigye A, Kharel A, Kreps S, Levine M, Littman R, Malik M, Manirabaruta G, Mfura JLH, Momoh F, Mucauque A, Mussa I, Nsabimana JA, Obara I, Otálora MJ, Ouédraogo BW, Pare TB, Platas MR, Polanco L, Qureshi JA, Raheem M, Ramakrishna V, Rendrá I, Shah T, Shaked SE, Shapiro JN, Svensson J, Tariq A, Tchibozo AM, Tiwana HA, Trivedi B, Vernot C, Vicente PC, Weissinger LB, Zafar B, Zhang B, Karlan D, Callen M, Teachout M, Humphreys M, Mobarak AM, Omer SB. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat Med. 2021 Aug;27(8):1385-1394. doi: 10.1038/s41591-021-01454-y Nasir N, Habib K, Khanum I, Khan N, Muhammad ZA, Mahmood SF. Clinical characteristics and outcomes of COVID-19: Experience at a major tertiary care center in Pakistan. J Infect Dev Ctries. 2021 Apr 1;15(4):480–9. Parvin S, Islam MS, Majumdar TK, Ahmed F. Clinicodemographic profile, intensive care unit utilization and mortality rate among COVID-19 patients admitted during the second wave in Bangladesh. IJID Reg. 2022 Mar;2:55-59. doi: 10.1016/j.ijregi.2021.11.011. Performance and Progress Report 2021 Ministry of Health Sri Lanka Collier DA, Ferreira IATM, Kotagiri P, Datir RP, Lim EY, Touizer E, et al. Age-related immune response heterogeneity to SARS-CoV-2 vaccine BNT162b2. Nature. 2021 Aug 19;596(7872):417–22. Muniangi-Muhitu H, Akalestou E, Salem V, Misra S, Oliver NS, Rutter GA. Covid-19 and Diabetes: A Complex Bidirectional Relationship. Vol. 11, Frontiers in Endocrinology. Frontiers Media S.A.; 2020. Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Vol. 17, Nature Reviews Cardiology. Nature Research; 2020. p. 543–58. Hippisley-Cox J, Young D, Coupland C, Channon KM, Tan PS, Harrison DA, et al. Risk of severe COVID-19 disease with ACE inhibitors and angiotensin receptor blockers: Cohort study including 8.3 million people. Heart. 2020 Oct 1;106(19):1503–11. Additional Declarations No competing interests reported. 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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-6674012","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471475570,"identity":"c9ef1423-4c66-4053-9aaf-3814330339d6","order_by":0,"name":"Shamila De Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCSBOKGBgbGBvYzwAFmFnI0aLAVALzzEGiBZmYrQwgLRIpBGpRX5289MNDwzsZLdLPks4XMBgJw/UkoBXi8GdY2Y3EgySjXfOTjtweAZDsmEDM9sB/FokEkBamBM33E5vOMzDwJzAwMzegN9hM9K/AbXUJ264eRykpZ6wFoYbOSBbDiduuMF2AKjlMFALIYfdyCkDajluvLMnLeEwj8FxwzZC3gc6bNvNHxXVstvZjxk+5qmoludnbzPA7zC4dTCSUESiaxkFo2AUjIJRgAUAAG8hRfIkfLx5AAAAAElFTkSuQmCC","orcid":"","institution":"Faculty of Medicine, University of Kelaniya,","correspondingAuthor":true,"prefix":"","firstName":"Shamila","middleName":"","lastName":"De Silva","suffix":""},{"id":471475571,"identity":"f58a465f-714c-480d-8bc3-b45b46508c20","order_by":1,"name":"Dileepa Ediriweera","email":"","orcid":"","institution":"Faculty of Medicine, University of Kelaniya,","correspondingAuthor":false,"prefix":"","firstName":"Dileepa","middleName":"","lastName":"Ediriweera","suffix":""},{"id":471475572,"identity":"24d55d23-24fc-44ae-82fe-e6f9704b1e8e","order_by":2,"name":"Nimasha Ekanayake","email":"","orcid":"","institution":"Faculty of Medicine, University of Kelaniya,","correspondingAuthor":false,"prefix":"","firstName":"Nimasha","middleName":"","lastName":"Ekanayake","suffix":""},{"id":471475573,"identity":"e07cb605-88a6-4e7d-b67f-4858ff21cc3b","order_by":3,"name":"Tharini Solangaarachchi","email":"","orcid":"","institution":"Faculty of Medicine, University of Kelaniya,","correspondingAuthor":false,"prefix":"","firstName":"Tharini","middleName":"","lastName":"Solangaarachchi","suffix":""},{"id":471475574,"identity":"b0a7c3df-9ae6-4732-961c-00d1957a7bd8","order_by":4,"name":"Kavinda Perera","email":"","orcid":"","institution":"Faculty of Medicine, University of Kelaniya,","correspondingAuthor":false,"prefix":"","firstName":"Kavinda","middleName":"","lastName":"Perera","suffix":""},{"id":471475575,"identity":"cf24055c-c4a6-46f5-a1f5-6924c5e4fb3c","order_by":5,"name":"Asanga Wickremasinghe","email":"","orcid":"","institution":"Colombo North Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Asanga","middleName":"","lastName":"Wickremasinghe","suffix":""},{"id":471475576,"identity":"29b6f58b-6875-4c98-9ad5-62b0cfd64f14","order_by":6,"name":"Rasika Liyanage","email":"","orcid":"","institution":"Colombo North Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rasika","middleName":"","lastName":"Liyanage","suffix":""}],"badges":[],"createdAt":"2025-05-15 15:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6674012/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6674012/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-026-13469-2","type":"published","date":"2026-04-30T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":108437623,"identity":"6f5da660-7911-4a44-b2cf-7cafc5f8e443","added_by":"auto","created_at":"2026-05-04 16:00:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830921,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6674012/v1/d21f7695-f738-4c73-8ecd-a85c496d0838.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"COVID-19 mortality at a tertiary care center during the Second and Third Waves of the pandemic in Sri Lanka: a cross sectional analysis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, was first identified in Wuhan, China, in December 2019. Its rapid global spread led the World Health Organization (WHO) to declare COVID-19 a pandemic in March 2020 due to its extensive impact across more than 220 countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As of April 2022, over 520\u0026nbsp;million confirmed cases and more than 6.27\u0026nbsp;million deaths had been reported worldwide, resulting in significant strain on public health systems and profound socio-economic repercussions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Sri Lanka, the first case of COVID-19 was reported at the end of January 2020. By May 2022, the country had recorded over 664,000 confirmed cases and more than 16,000 deaths attributed to the virus, according to the Epidemiology Unit of the Ministry of Health [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The pandemic placed immense pressure on Sri Lanka's healthcare system and adversely affected the socio-economic status of its citizens due to prolonged lockdowns and economic disruptions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSri Lanka experienced three major waves of COVID-19. The First Wave, from January to September 2020, resulted in 13 deaths among 3,396 confirmed cases. The Second Wave, from October 2020 to April 2021, saw 591 deaths and 92,341 infections. The Third Wave, which began in April 2021, was particularly devastating, with over 565,000 confirmed cases and more than 15,800 fatalities, resulting in a case fatality rate of 2.8% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While Sri Lanka managed the first two waves relatively well, the Third Wave posed unprecedented challenges, ultimately being controlled through stringent preventive measures, enhanced treatment facilities, and a nationwide vaccination campaign [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost patients diagnosed with COVID-19 exhibit mild to moderate symptoms, including fever, cough, sore throat, fatigue, arthralgia, and myalgia, often recovering without specific treatments or hospitalization. However, some individuals develop severe illness, including viral pneumonia, which may require intensive care and mechanical ventilation. A minority may progress to critical conditions involving multi-organ failure and death [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the Third Wave in Sri Lanka, the sheer volume of patients nearly overwhelmed the healthcare system, contributing to increased mortality rates. The challenge of treating a high number of critically ill patients was exacerbated by limited resources, particularly in intensive care units. Consequently, many patients with mild symptoms received home-based management, while those with moderate symptoms were treated in quarantine centers. Patients with significant comorbidities or severe symptoms were hospitalized, with transfers occurring as conditions deteriorated [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive global research has established several key determinants of COVID-19 mortality. Advanced age (particularly\u0026thinsp;\u0026gt;\u0026thinsp;65 years), male gender, and pre-existing conditions such as cardiovascular disease, diabetes mellitus, chronic respiratory disease, hypertension, obesity, and immunocompromised states are consistently associated with higher mortality risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The pathophysiology of severe COVID-19 involves direct viral damage to organs, dysregulated immune responses leading to cytokine storms, and thrombotic complications resulting in multi-organ failure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Research has demonstrated that early intervention with appropriate respiratory support, judicious use of corticosteroids, anticoagulation, and targeted immunomodulatory therapies can improve survival outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, significant variations exist in mortality patterns across different healthcare settings, geographical regions, and over time as virus variants evolved and clinical management protocols improved [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, most COVID-19 deaths occurred among hospitalized patients, with causes including the progression of COVID-19 pneumonia, complications such as acute kidney injury and myocarditis, and exacerbation of pre-existing conditions like ischemic heart disease. Some patients died during transfers to hospitals, while others succumbed during home-based care or in quarantine centers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The severity of illness, duration of disease, and type of treatment received were critical factors influencing mortality outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe implementation of global vaccination programs has significantly impacted morbidity and mortality rates. According to WHO data, as of April 2022, 66.3% of the global population had received at least one dose of a COVID-19 vaccine, and 60.2% were fully vaccinated. In Sri Lanka, 77.9% of the population had received at least one vaccine dose, with 66% fully vaccinated and 36.4% having received a booster [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The successful nationwide vaccination campaign in Sri Lanka, utilizing vaccines such as Sinopharm, Covishield, Pfizer, Moderna, and Sputnik V, was facilitated by the country's robust preventive healthcare infrastructure [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVaccination has been pivotal in reducing COVID-19 severity and mortality worldwide. Numerous studies have confirmed that fully vaccinated individuals face substantially lower risks of hospitalization, ICU admission, and death compared to unvaccinated counterparts [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Booster doses have further enhanced protection, particularly against emerging variants like Delta and Omicron. Vaccine effectiveness studies have shown 90\u0026ndash;95% reduction in mortality risk among fully vaccinated individuals, though effectiveness varies by vaccine type, age group, comorbidity status, and viral variants [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite breakthrough infections, vaccinated patients typically experience milder disease courses and better outcomes, underscoring vaccination's crucial role in pandemic control strategies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe South Asian region, home to nearly a quarter of the world's population, faced unique challenges during the COVID-19 pandemic. India experienced one of the most severe outbreaks globally, particularly during its devastating second wave in April-May 2021, when daily cases exceeded 400,000 and overwhelmed healthcare systems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Pakistan reported over 1.5\u0026nbsp;million cases and 30,000 deaths by April 2022, with major urban centers bearing the brunt of infections [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Bangladesh, despite early control measures, witnessed significant surges with limited healthcare resources, recording approximately 2\u0026nbsp;million cases and 29,000 deaths [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nepal and the Maldives experienced severe strain on their healthcare systems during peak waves, despite having smaller populations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCompared to its neighbors, Sri Lanka maintained lower case fatality rates than India (1.2%) and Pakistan (2.0%) during comparable periods, which can be attributed to its robust primary healthcare system and early implementation of public health measures [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, vaccination rates varied significantly across the region, with the Maldives achieving the highest coverage (nearly 80% fully vaccinated) by early 2022, while Nepal and Bangladesh reported approximately 54% and 67% full vaccination rates, respectively [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These regional variations provide important context for understanding Sri Lanka's pandemic experience.\u003c/p\u003e \u003cp\u003eDespite numerous global studies on COVID-19 mortality, there is limited information specifically regarding COVID-19 deaths in Sri Lanka, aside from data released by the Epidemiology Unit. According to WHO statistics, Sri Lanka ranks 77th in the world for total confirmed cases and 50th for total deaths [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The country boasts a high recovery rate of 97.36%, while its case fatality rate (CFR) of 2.5% is higher than many countries [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These statistics highlight the urgent need for comprehensive assessment and evaluation of COVID-19 mortality in Sri Lanka.\u003c/p\u003e \u003cp\u003eThis study aims to comprehensively investigate the demographic profile, pre-existing comorbidities, vaccination status, severity of illness, and duration of disease among patients who died from COVID-19 at a major tertiary referral center during the Second and Third Waves of the pandemic in Sri Lanka. By analyzing these factors, we aimed to identify patterns of mortality and assess the effectiveness of interventions, particularly vaccination, in preventing severe outcomes. The findings are expected to inform clinical management protocols and public health strategies for future pandemic responses in resource-limited settings.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis descriptive cross-sectional study was conducted at Colombo North Teaching Hospital (CNTH) in Ragama, Sri Lanka. CNTH is the sole tertiary care facility in the Gampaha district, serving a vast catchment area that includes not only Gampaha but also neighboring districts such as Colombo, Puttalam, and Kurunegala. Its strategic location at a major transport hub facilitates access to the hospital, making it a critical resource for the densely populated Gampaha district, which is the most populated district in Sri Lanka. The hospital has a capacity of 1,749 beds and typically sees between 130,000 and 150,000 patient admissions annually [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study population comprised all patients whose deaths were attributed to COVID-19 at CNTH during the period from November 2020 to December 2021, encompassing both the Second and Third Waves of the pandemic in Sri Lanka. Using the hospital's admission database and COVID-19 death registry maintained by the infection control unit, we identified all cases where COVID-19 was listed as the primary cause of death. We applied the WHO criteria for COVID-19 mortality, which defines a COVID-19 death as one resulting from a clinically compatible illness in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 disease [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNo cases were excluded from the initial registry, ensuring a comprehensive analysis of all COVID-19 deaths at the facility during the study period. This complete enumeration approach was chosen rather than sampling to capture the full spectrum of mortality patterns and avoid selection bias. No formal power calculation was performed since this was a descriptive study analyzing all available cases.\u003c/p\u003e \u003cp\u003eData collection involved two complementary approaches:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMedical Record Review\u003c/b\u003e: We systematically extracted information from the bed head tickets of deceased patients. This included demographic information (age, sex, residence), clinical data (comorbidities, symptoms, disease progression), laboratory and radiological findings, treatment details, and outcomes. A standardized data extraction form was developed and pilot-tested on 10 records before full implementation to ensure consistency and comprehensiveness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTelephone Interviews\u003c/b\u003e: To supplement the information from medical records and address missing data, an interviewer-administered questionnaire was conducted via telephone with a family member (preferably the next of kin or primary caregiver) of deceased patients. The questionnaire was developed based on WHO guidelines for COVID-19 mortality surveillance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and included structured questions covering:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSociodemographic details not available in medical records\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePre-hospital illness course and symptoms\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVaccination history (type, dates, number of doses)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHealthcare-seeking behavior before admission\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTreatment received at home or intermediate centers\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFunctional status before infection\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLiving arrangements and possible exposure sources\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe questionnaire was translated into local languages (Sinhala and Tamil), back-translated to ensure accuracy, and pilot-tested with family members of 5 deceased patients not included in the study to assess clarity and cultural appropriateness. Three trained research assistants conducted the interviews using a standardized protocol. Prior to each interview, a brief explanation of the study was provided, and informed verbal consent was obtained from the family member. Interviews typically lasted 15\u0026ndash;20 minutes.\u003c/p\u003e \u003cp\u003eThe cause of death was established through a systematic review process:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePrimary determination was based on the cause of death stated in the death certificate completed by the attending physician.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFor each case, two independent investigators reviewed the complete medical records, including clinical progression, laboratory data, and radiological findings to verify the cause of death.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCases were classified according to the WHO International Guidelines for Certification and Classification (Coding) of COVID-19 as Cause of Death [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which distinguishes between:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDeath due to COVID-19 (directly caused by the disease)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeath with COVID-19 (where SARS-CoV-2 contributed to but was not the underlying cause)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeath unrelated to COVID-19 (where the virus was incidental)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOnly cases where COVID-19 was determined to be the direct or underlying cause of death were included in the final analysis. Discrepancies in classification were resolved through discussion with a senior consultant physician with expertise in COVID-19 management.\u003c/p\u003e \u003cp\u003eData were entered into an SPSS database using double-entry verification to minimize transcription errors. Logical and random checks were performed for accuracy, and inconsistencies were resolved by referring back to the original records. Missing data were categorized and analyzed according to the pattern of missingness. For variables with less than 5% missing data, complete case analysis was performed. For variables with 5\u0026ndash;20% missing data, multiple imputation using chained equations was employed to generate five imputed datasets, with results pooled according to Rubin's rules [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Variables with more than 20% missing data were reported with the actual number of available observations and analyzed separately with appropriate caveats.\u003c/p\u003e \u003cp\u003eStatistical analysis was carried out using SPSS version 29. Continuous data were described using means and standard deviations for normally distributed variables, and medians with interquartile ranges for non-normally distributed variables. Normality was assessed using the Shapiro-Wilk test and visual inspection of histograms. Categorical data were expressed as frequencies and percentages. Parametric tests (Student's t-test, ANOVA) were applied to normally distributed data, and non-parametric tests (Mann-Whitney U, Kruskal-Wallis) were used for non-normally distributed data to conduct group comparisons. Chi-square or Fisher's exact tests were used to compare categorical variables. For all analyses, a significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e Ethical clearance for the study was obtained from the Ethics Review Committee of the Faculty of Medicine, University of Kelaniya (Reference P33/06/2022). Ethical aspects of the study were in accordance with the Declaration of Helsinki. Given the retrospective nature of the study and the challenges of obtaining written consent from bereaved families during the pandemic, the Ethics Committee approved the use of verbal consent for telephone interviews. All data were de-identified before analysis to ensure confidentiality, and data security measures were implemented to protect sensitive information.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThere were 108,025 admissions to CNTH during the study period of November-2020 to December-2021. Of them 8,531 persons were COVID positive by PCR and/or RAT (7.9% of all admissions). A total of 1269 persons died due to COVID-19, giving a mortality rate of 14.9%. Complete data was available for 1004 of these deaths (79.1%).\u003c/p\u003e \u003cp\u003eThe characteristics of the study population are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The deceased patients had a median age of 72 years (IQR: 62\u0026ndash;79), with ages ranging from 19 to 99 years. Males were slightly older than females at the time of death, with a mean age of 71 years compared to 68 years for females (p\u0026thinsp;=\u0026thinsp;0.008). Most of the deceased were Sinhalese (93%), married (78%), and had an educational level up to GCE O/L (66.3%). Most were unemployed (66%) and had a monthly family income between LKR 35,001\u0026ndash;75,000 (62%).\u003c/p\u003e \u003cp\u003eComorbidities were highly prevalent among the deceased patients. Diabetes was the most common affecting 61.6% of the cohort, followed by hypertension (36.9%) and coronary artery disease (14%). A significant proportion (37.2%) had multiple comorbidities, while only 10.7% had no underlying health conditions.\u003c/p\u003e \u003cp\u003eThe majority (78.3%) were unvaccinated, while 21.7% had received at least one dose of a COVID-19 vaccine. Only 7.8% had received two doses, and 0.8% had received a booster dose. Sinopharm was the most administered vaccine (18%), since Sinopharm was the predominant vaccine used in the national vaccination programme.\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\u003eCharacteristics of Deceased Persons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,004\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;574\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;430\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient age\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\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (15)\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\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (19)\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\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u0026ndash;95\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\u003eEthnicity\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\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinhalese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e937 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e526 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e411 (96%)\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\u003eTamil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (3.5%)\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\u003eMuslims\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.7%)\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\u003eMarital status\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\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e784 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332 (77%)\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\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (22%)\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\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.7%)\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\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Education\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\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQualified GCE O/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e666 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e287 (67%)\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\u003eGrade 6\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (23%)\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\u003eQualified GCE A/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (5.3%)\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\u003eGrade 1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (3.7%)\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\u003eGraduated/Diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.5%)\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\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.9%)\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\u003ePost-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\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\u003eEmployment Status\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\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e663 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323 (75%)\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\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (25%)\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\u003eMonthly Family Income LKR\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\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55,001\u0026ndash;75,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147 (34%)\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\u003e35,001\u0026ndash;55,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (26%)\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\u003e15,001\u0026ndash;35,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (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\u003e75,001\u0026ndash;95,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (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\u003e95,001-115,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (4.0%)\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\u0026gt;115,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (2.8%)\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\u0026lt;15,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.6%)\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\u003eComorbidities\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 \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\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 \u003cp\u003e370 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (42%)\u003c/p\u003e \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\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccination against COVID-19\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\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e786 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (79%)\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\u003eReceived\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (21%)\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\u003eType of Vaccine\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\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e786 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (79%)\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\u003eSinopharm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (17%)\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\u003eCovishield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (3.5%)\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\u003ePfizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st dose received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd dose received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd dose received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e564 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e336 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome-based care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWelch Two Sample t-test; Fisher's exact test; Pearson's Chi-squared test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA detailed comparison of characteristics between those who received and did not receive any form of vaccination against COVID-19 is given in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared to vaccinated individuals, unvaccinated patients were older (mean age: 71 vs. 68 years, p\u0026thinsp;=\u0026thinsp;0.008), and more likely to be unemployed (70% vs. 52%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eCharacteristics of Vaccinated and Non-vaccinated Deaths\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,004\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot vaccinated,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;786\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccinated,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;218\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient age\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\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (15)\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\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (19)\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\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u0026ndash;95\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\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 \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e574 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129 (59%)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e341 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (41%)\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\u003eEthnicity\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\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinhalese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e937 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e730 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (95%)\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\u003eTamil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.8%)\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\u003eMuslims\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2.3%)\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\u003eBurger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.9%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\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 \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\u003eMarital status\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\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e784 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e613 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (78%)\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\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (19%)\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\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.4%)\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\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2%)\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 \u003cp\u003e2 (0.9%)\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\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\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 \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\u003eLevel of Education\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\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQualified GCE O/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e666 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (67%)\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\u003eGrade 6\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (17%)\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\u003eQualified GCE A/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (9.6%)\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\u003eGrade 1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (5.0%)\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\u003eGraduated/Diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.9%)\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 formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.6%)\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\u003ePost-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1%)\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\u003eEmployment Status\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\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e663 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (52%)\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\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (48%)\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\u003ePrevious COVID 19 infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (4.6%)\u003c/p\u003e \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\u003eCo-morbidities\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 \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e486 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\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 \u003cp\u003e370 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple co-morbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo co-morbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (31%)\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\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (26%)\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWelch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were no clear differences in symptom profile, treatment modalities including oxygen requirement and cause of death among the vaccinated and the unvaccinated, except those who were vaccinated had lesser arthralgia and/or myalgia and more fatigue (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The unvaccinated had longer hospital stays where 85% stayed more than 48 hours compared to 76% among the vaccinated (P\u0026thinsp;=\u0026thinsp;0.021). The primary cause of death was COVID-19 pneumonia (83%), followed by complications of COVID-19 (5.8%) and acute myocardial infarction with COVID-19 (4.7%).\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\u003eDisease characteristics among Vaccinated and Non-vaccinated Deaths\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms and Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,004\u003csup\u003e1\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot vaccinated\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;786\u003csup\u003e1\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaccinated\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;218\u003csup\u003e1\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e628 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e489 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthralgia and/or Myalgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e502 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShortness of breath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e442 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRunny nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (15%)\u003c/p\u003e \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\u003eNausea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of taste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSore throat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss of smell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDeveloped complications\u003c/em\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\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e845 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e691 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154 (71%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (16%)\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\u003eAcute coronary syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (10%)\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\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNeeded oxygen\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e556 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMode of oxygenation\u003c/em\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\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e448 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (45%)\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\u003eHFNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e394 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (38%)\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\u003eNIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (9.6%)\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\u003eIntubation \u0026amp; ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (7.3%)\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\u003e\u003cem\u003eDialysis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTozlucimab given\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHospitalization\u0026thinsp;\u0026gt;\u0026thinsp;48 hours\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCause of death\u003c/em\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19 Pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e834 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e659 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175 (80%)\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\u003eComplications of COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (5.0%)\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\u003eAcute myocardial infarction with COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (6.9%)\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\u003eAcute kidney injury with COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.8%)\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\u003eAcute respiratory distress syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.4%)\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\u003eNatural death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5%)\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\u003eComplications of cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.4%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.4%)\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\u003eDiabetic ketoacidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\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 \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en \u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWelch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHFNO \u0026ndash; high flow nasal oxygen, NIV \u0026ndash; non invasive ventilation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMost patients (56%) were hospitalized, while 28% received home-based care (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Patients who received home-based care were relatively older (mean age: 72 vs. 69 years, p\u0026thinsp;=\u0026thinsp;0.020) and more likely to have respiratory illnesses and a history of COVID-19 contact. The main reasons for not hospitalizing patients were that they were not perceived as very ill (74%) and patient or family choice (23%).\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\u003eDeaths by treatment setting: Home-based care vs Hospital and Intermediate Center care\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,004\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospital \u0026amp; Intermediate Center N\u0026thinsp;=\u0026thinsp;724\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHome-based\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;280\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient age\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\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (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\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (17)\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\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u0026ndash;98\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\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 \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e574 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e424 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (54%)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (46%)\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\u003eEthnicity\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\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinhalese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e937 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e679 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258 (92%)\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\u003eTamil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (5.7%)\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\u003eMuslims\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.8%)\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\u003eBurger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.4%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\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 \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\u003eMarital status\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\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e784 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e563 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (79%)\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\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (21%)\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\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.4%)\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\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.3%)\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\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\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 \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\u003eLevel of Education\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\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQualified GCE O/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e666 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e474 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192 (69%)\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\u003eGrade 6\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (20%)\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\u003eQualified GCE A/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (7.9%)\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\u003eGrade 1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.8%)\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\u003eGraduated/Diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.4%)\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\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.7%)\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\u003ePost-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.4%)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.1%)\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\u003eEmployment Status\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\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e663 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (67%)\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\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (33%)\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\u003eComorbidities\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 \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\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 \u003cp\u003e370 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (9.3%)\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\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccination against COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st dose received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd dose received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd dose received\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContact history of COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e712 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWelch Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study conducted at Colombo North Teaching Hospital (CNTH) provides insights into the demographic and clinical characteristics of COVID-19-related deaths during the Second and Third Waves of the pandemic in Sri Lanka. Our analysis of 1,004 deaths revealed a predominance of male mortality (57%) with a mean age of 70 years. Males were significantly older than females at the time of death (71 vs. 68 years, p\u0026thinsp;=\u0026thinsp;0.008). The overall mortality rate among COVID-19 patients admitted to CNTH was 14.9%, considerably higher than the national average of 2.5% [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A substantial proportion of deceased individuals presented with multiple comorbidities (37%), with diabetes and hypertension being the most prevalent underlying conditions. Only 22% of the deceased had received any form of COVID-19 vaccination, with the majority having received the Sinopharm vaccine. Notably, vaccinated individuals who died were generally older and more likely to be employed compared to unvaccinated deceased individuals, suggesting potential differential access to vaccination during the study period.\u003c/p\u003e \u003cp\u003eOur findings align with global trends indicating that older adults and those with pre-existing health conditions are at heightened risk for severe COVID-19 outcomes and mortality. A comprehensive meta-analysis by Dessie and Zewotir (2021) examining 42 studies across multiple countries found similar patterns of increased mortality risk among males and elderly patients, with pooled odds ratios of 1.45 (95% CI: 1.37\u0026ndash;1.54) for male sex and 3.61 (95% CI: 3.21\u0026ndash;4.04) for ages above 65 years [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The predominance of diabetes and hypertension among deceased patients in our study mirrors findings from a systematic review by Singh et al. (2021), which indicated that these comorbidities significantly increased the risk of COVID-19 mortality, with pooled hazard ratios of 1.81 (95% CI: 1.52\u0026ndash;2.16) for diabetes and 1.74 (95% CI: 1.46\u0026ndash;2.08) for hypertension [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe low vaccination rate (22%) among deceased patients in our study is comparable to findings from early vaccination impact studies in other middle-income countries [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, our figure is lower than those reported in studies from high-income countries during similar time periods, such as the UK, where Hippisley-Cox et al. (2021) found that approximately 37% of COVID-19 deaths had received at least one vaccine dose [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], reflecting disparities in vaccine access and rollout timing.\u003c/p\u003e \u003cp\u003eThe hospital mortality rate of 14.9% in our study is comparable to reports from tertiary care centers in Pakistan (13.8%) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] during similar pandemic phases, but lower than reported rates from Bangladesh (16.5%) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These variations likely reflect differences in healthcare infrastructure, admission criteria, and case severity across these settings.\u003c/p\u003e \u003cp\u003eThe predominance of unvaccinated individuals (78%) among COVID-19 deaths in our study warrants careful interpretation. This finding should be contextualized within Sri Lanka's vaccination timeline, where mass vaccination began in late January 2021 but faced initial supply constraints, with widespread availability only achieved by mid-2021 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The timing of our study period (November 2020 to December 2021) means many deaths occurred before vaccines were widely accessible, particularly during the deadly Third Wave that began in April 2021.\u003c/p\u003e \u003cp\u003eNevertheless, the lower proportion of vaccinated individuals among the deceased strongly suggests a protective effect of vaccination. The observation that vaccinated individuals who died were generally older suggests that age-related immunosenescence may have reduced vaccine effectiveness in this vulnerable group, a phenomenon documented in immunological studies of COVID-19 vaccines [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, the higher employment rate among vaccinated deceased patients may reflect prioritization of working-age adults in early vaccination campaigns, leaving some vulnerable elderly populations inadequately protected during critical periods.\u003c/p\u003e \u003cp\u003eThe high prevalence of multiple comorbidities (37%) among deceased patients has important implications for risk stratification and clinical management. The synergistic effect of diabetes and hypertension, the two most common comorbidities in our cohort, likely contributed to poorer outcomes through multiple pathophysiological mechanisms. Diabetes has been shown to impair immune function and increase ACE2 receptor expression, facilitating SARS-CoV-2 cellular entry [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Similarly, hypertension-associated endothelial dysfunction may exacerbate COVID-19's thromboinflammatory complications [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe comorbidity profile observed in our study reflects Sri Lanka's ongoing epidemiological transition, with a rising burden of non-communicable diseases alongside persistent infectious disease challenges. This \"double burden\" creates unique vulnerabilities during pandemics, as healthcare systems must simultaneously manage acute infectious crises while maintaining care for chronic conditions.\u003c/p\u003e \u003cp\u003eAlthough not explicitly measured, our findings indirectly reflect challenges in Sri Lanka's healthcare system during the pandemic. The high hospital mortality rate (14.9%) compared to the national case fatality rate (2.5%) suggests that CNTH, as a tertiary referral center, received a disproportionate share of severe cases, many likely referred when already in critical condition. This pattern of late presentation was observed across South Asia, where healthcare-seeking behaviors were influenced by fear of infection, transportation limitations during lockdowns, and financial constraints.\u003c/p\u003e \u003cp\u003eOur findings strongly support prioritizing vaccination for elderly individuals with comorbidities, who constituted the majority of deaths in this study. Sri Lanka's initial age-based rollout strategy, while logistically efficient, may have benefited from earlier integration of comorbidity status in prioritization schemes. Moving forward, continuous vaccination coverage monitoring among high-risk groups is essential, with targeted outreach for under-vaccinated vulnerable populations.\u003c/p\u003e \u003cp\u003eFor future vaccination campaigns, our data suggest that healthcare authorities should:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDevelop more robust systems for identifying and reaching homebound elderly with comorbidities\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEstablish mobile vaccination units for remote communities\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCreate accelerated pathways for individuals with multiple high-risk comorbidities\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImplement recall systems for booster doses prioritizing elderly with diabetes and hypertension\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe prominent role of comorbidities in COVID-19 mortality highlights the need for integrated care approaches. During pandemic surges, maintaining continuity of care for chronic disease management presented significant challenges. Our findings suggest that greater integration of COVID-19 and non-communicable disease care could improve outcomes through:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTelehealth consultations specifically targeting comorbidity management among COVID-19 patients\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMedication delivery services for chronic disease medications during isolation periods\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHome-based monitoring protocols that include both COVID-19 symptoms and comorbidity parameters\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePost-COVID follow-up pathways with enhanced screening for comorbidity complications\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe high mortality observed during the Third Wave highlights the need for more resilient health system capacity. Sri Lanka's relatively strong primary healthcare infrastructure could be further leveraged through:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStrengthening intermediate care facilities to reduce tertiary hospital burden\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDeveloping clear clinical pathways with standardized transfer criteria between levels of care\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExpanding critical care capacity at district hospitals to manage surges locally\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTraining primary care providers in early identification and management of high-risk patients [58]\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study has several limitations. As a single-center study conducted at a tertiary referral hospital our findings may not be generalizable to other regions or healthcare settings in Sri Lanka, particularly rural areas with different demographic and comorbidity profiles. The patient population at CNTH likely represents more severe cases, potentially overestimating mortality rates compared to the general infected population.\u003c/p\u003e \u003cp\u003eThe retrospective nature of the study introduces potential for information bias, particularly regarding vaccination status and comorbidity documentation. Although we supplemented hospital records with family interviews, recall bias may have affected the accuracy of pre-hospital information, especially for patients who died shortly after admission with limited documentation.\u003c/p\u003e \u003cp\u003eOur study period encompassed different phases of the pandemic with evolving viral variants, changing clinical protocols, and varying resource availability, which may have influenced mortality patterns independently of patient characteristics. We were unable to perform consistent viral genomic sequencing to correlate outcomes with specific variants.\u003c/p\u003e \u003cp\u003eLimited data on socioeconomic status, education level, and healthcare access barriers prevented more nuanced analysis of social determinants influencing COVID-19 mortality. Additionally, detailed analysis of treatment modalities and their impact on outcomes was beyond the scope of this study.\u003c/p\u003e \u003cp\u003eThe cross-sectional design precludes establishing causal relationships between observed factors and mortality outcomes. Prospective studies would better elucidate the complex interactions between comorbidities, vaccination status, and COVID-19 mortality.\u003c/p\u003e \u003cp\u003eThis study highlights several critical areas for future research. Longitudinal studies investigating the long-term effects of COVID-19 on survivors, particularly those with comorbidities, are essential to understand the full impact of the pandemic and inform rehabilitation services. Integration of genomic surveillance with clinical outcomes research would enhance understanding of variant-specific mortality risks and vaccine effectiveness.\u003c/p\u003e \u003cp\u003eMore comprehensive evaluation of Sri Lanka's vaccination campaign effectiveness across different population segments would identify remaining gaps and inform targeted interventions. Implementation research on optimizing home-based care models for high-risk COVID-19 patients could reduce mortality during future surges.\u003c/p\u003e \u003cp\u003eHealth systems research examining hospital capacity, resource allocation, and clinical pathways during pandemic surges would strengthen preparedness planning. Also, comparative studies across different regions of Sri Lanka and neighboring South Asian countries would identify transferable best practices for pandemic response in resource-constrained settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study underscore the critical public health implications of COVID-19 mortality in Sri Lanka. The high prevalence of comorbidities among deceased patients highlights the need for targeted health interventions aimed at managing chronic diseases, particularly in older adults. The significantly lower vaccination rates among the deceased emphasize the protective effect of vaccines and the importance of prioritizing vulnerable d\u003c/p\u003e \u003cp\u003eThe insights gained from this study provide valuable guidance for strengthening pandemic preparedness, optimizing COVID-19 management protocols, and enhancing healthcare system resilience in Sri Lanka and similar resource-constrained settings. By addressing the identified gaps in comorbidity management, vaccination coverage, and healthcare delivery, public health authorities can better prepare for future pandemic threats and improve outcomes for vulnerable populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e19\u0026ndash;Corona Virus Disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSARS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoV\u0026ndash;2\u0026ndash;severe acute respiratory syndrome coronavirus 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNTH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColombo North Teaching Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\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\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolymerace chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erapid antigen test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInter quantile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCE (O/L)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Certificate of Examination Ordinary Level\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLKR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLankan Rupee\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\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 - ethical approval was obtained from the Ethics Review Committee, Faculty of Medicine, University of Kelaniya. Informed verbal consent was obtained from all participants. Ethical aspects of the study were in accordance with the Declaration of Helsinki. (Approval No \u0026ndash; P/ P33/06/2022)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e - The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e - SDS and RL conceptualized and designed the study. NE, TS and KP collected data with assistance from AW. RL and AW assisted in verifying data. DE analyzed the data. SDS prepared and revised the manuscript together with DE. All authors read and agreed to the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e - We gratefully acknowledge the assistance given by the Director, Colombo North Teaching Hospital and the staff of the hospital Records Unit in conducting this study. We especially thank the family member of the deceased patients for their cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u0026ndash; not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e \u0026ndash; this study was self-funded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u0026ndash; none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e - not applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. (2020). \u0026quot;WHO Timeline - COVID-19.\u0026quot;\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2022). \u0026quot;COVID-19 Dashboard.\u0026quot;\u003c/li\u003e\n\u003cli\u003eJohns Hopkins University. 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BMJ Open. 2024 Nov 28;14(11):e090341. doi: 10.1136/bmjopen-2024-090341.\u003c/li\u003e\n\u003cli\u003eHuang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 2020 Feb 15;395(10223):497\u0026ndash;506\u003c/li\u003e\n\u003cli\u003eZhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020 Mar 28;395(10229):1054\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eWiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. Vol. 324, JAMA - Journal of the American Medical Association. American Medical Association; 2020. p. 782\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eDexamethasone in Hospitalized Patients with Covid-19. New England Journal of Medicine [Internet]. 2021 Feb 25;384(8):693\u0026ndash;704. Available from: http://www.nejm.org/doi/10.1056/NEJMoa2021436\u003c/li\u003e\n\u003cli\u003eSorci G, Faivre B, Morand S. Explaining among-country variation in COVID-19 case fatality rate. Sci Rep. 2020 Dec 1;10(1).\u003c/li\u003e\n\u003cli\u003elhazzani W, M\u0026oslash;ller MH, Arabi YM, Loeb M, Gong MN, Fan E, et al. Surviving Sepsis Campaign: guidelines on the management of critically ill adults with Coronavirus Disease 2019 (COVID-19). Intensive Care Med. 2020 May 1;46(5):854\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eYang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020 May 1;8(5):475\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. 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The Impact of COVID-19 on Healthcare Services in Bangladesh: A Qualitative Study on Healthcare Providers\u0026apos; Perspectives. J Prev Med Public Health. 2024 Jul;57(4):356-369. doi: 10.3961/jpmph.24.081. \u003c/li\u003e\n\u003cli\u003eLaura E. Downey, Thomas Gadsden, Victor Del Rio Vilas, David Peiris, Stephen Jan, The impact of COVID-19 on essential health service provision for endemic infectious diseases in the South-East Asia region: A systematic review, The Lancet Regional Health - Southeast Asia, Volume 1, 2022 https://doi.org/10.1016/j.lansea.2022.04.007. \u003c/li\u003e\n\u003cli\u003eDeepankar Basu, Priyanka Srivastava. COVID-19 in South Asia: Health, economics and politics. Inv. Econ vol.81 no.319 Ciudad de M\u0026eacute;xico ene./mar. 2022 https://doi.org/10.22201/fe.01851667p.2022.319.79762 \u003c/li\u003e\n\u003cli\u003eUNICEF. (2022). \u0026quot;COVID-19 Vaccine Market Dashboard: South Asia Region.\u0026quot;\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2022). \u0026quot;COVID-19 Situation in Sri Lanka.\u0026quot; https://www.who.int/countries/lka\u003c/li\u003e\n\u003cli\u003eSri Lanka: Coronavirus Pandemic Country Profile. Our World in Data https://ourworldindata.org/coronavirus/country/sri-lanka\u003c/li\u003e\n\u003cli\u003eMinistry of Health Sri Lanka. (2021). \u0026quot;Annual Health Statistics 2020 - Sri Lanka.\u0026quot;\u003c/li\u003e\n\u003cli\u003eInternational guidelines for certification and classification (coding) of COVID-19 as cause of death based on ICD International Statistical Classification of Diseases.\u003c/li\u003e\n\u003cli\u003eSurveillance protocol for SARS-CoV-2 infection among health workers [Internet]. 2020. Available from: https://apps.who.int/iris/bitstream/handle/10665/331340/WHO-2019-nCov-\u003c/li\u003e\n\u003cli\u003eMedical certification, ICD mortality coding, and reporting mortality associated with COVID-19. 2020. \u003c/li\u003e\n\u003cli\u003eRubin DB. Multiple imputation for nonresponse in surveys. Wiley; 1987. 258 p.\u003c/li\u003e\n\u003cli\u003eEpidemiology Unit, Ministry of Health, Sri Lanka. (2022). \u0026quot;COVID-19 Situation Report.\u0026quot;\u003c/li\u003e\n\u003cli\u003eDessie ZG, Zewotir T. Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients. BMC Infect Dis. 2021 Dec 1;21(1).\u003c/li\u003e\n\u003cli\u003eSingh AK, Gillies CL, Singh R, Singh A, Chudasama Y, Coles B, et al. Prevalence of co-morbidities and their association with mortality in patients with COVID-19: A systematic review and meta-analysis. Diabetes Obes Metab. 2020 Oct 1;22(10):1915\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eSol\u0026iacute;s Arce JS, Warren SS, Meriggi NF, Scacco A, McMurry N, Voors M, Syunyaev G, Malik AA, Aboutajdine S, Adeojo O, Anigo D, Armand A, Asad S, Atyera M, Augsburg B, Awasthi M, Ayesiga GE, Bancalari A, Bj\u0026ouml;rkman Nyqvist M, Borisova E, Bosancianu CM, Cabra Garc\u0026iacute;a MR, Cheema A, Collins E, Cuccaro F, Farooqi AZ, Fatima T, Fracchia M, Galindo Soria ML, Guariso A, Hasanain A, Jaramillo S, Kallon S, Kamwesigye A, Kharel A, Kreps S, Levine M, Littman R, Malik M, Manirabaruta G, Mfura JLH, Momoh F, Mucauque A, Mussa I, Nsabimana JA, Obara I, Ot\u0026aacute;lora MJ, Ou\u0026eacute;draogo BW, Pare TB, Platas MR, Polanco L, Qureshi JA, Raheem M, Ramakrishna V, Rendr\u0026aacute; I, Shah T, Shaked SE, Shapiro JN, Svensson J, Tariq A, Tchibozo AM, Tiwana HA, Trivedi B, Vernot C, Vicente PC, Weissinger LB, Zafar B, Zhang B, Karlan D, Callen M, Teachout M, Humphreys M, Mobarak AM, Omer SB. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat Med. 2021 Aug;27(8):1385-1394. doi: 10.1038/s41591-021-01454-y\u003c/li\u003e\n\u003cli\u003eNasir N, Habib K, Khanum I, Khan N, Muhammad ZA, Mahmood SF. Clinical characteristics and outcomes of COVID-19: Experience at a major tertiary care center in Pakistan. J Infect Dev Ctries. 2021 Apr 1;15(4):480\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eParvin S, Islam MS, Majumdar TK, Ahmed F. Clinicodemographic profile, intensive care unit utilization and mortality rate among COVID-19 patients admitted during the second wave in Bangladesh. IJID Reg. 2022 Mar;2:55-59. doi: 10.1016/j.ijregi.2021.11.011. \u003c/li\u003e\n\u003cli\u003ePerformance and Progress Report 2021 Ministry of Health Sri Lanka\u003c/li\u003e\n\u003cli\u003eCollier DA, Ferreira IATM, Kotagiri P, Datir RP, Lim EY, Touizer E, et al. Age-related immune response heterogeneity to SARS-CoV-2 vaccine BNT162b2. Nature. 2021 Aug 19;596(7872):417\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eMuniangi-Muhitu H, Akalestou E, Salem V, Misra S, Oliver NS, Rutter GA. Covid-19 and Diabetes: A Complex Bidirectional Relationship. Vol. 11, Frontiers in Endocrinology. Frontiers Media S.A.; 2020.\u003c/li\u003e\n\u003cli\u003eNishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Vol. 17, Nature Reviews Cardiology. Nature Research; 2020. p. 543\u0026ndash;58. \u003c/li\u003e\n\u003cli\u003eHippisley-Cox J, Young D, Coupland C, Channon KM, Tan PS, Harrison DA, et al. Risk of severe COVID-19 disease with ACE inhibitors and angiotensin receptor blockers: Cohort study including 8.3 million people. Heart. 2020 Oct 1;106(19):1503\u0026ndash;11.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Deaths, Vaccination, Tertiary Referral Center, Sri Lanka","lastPublishedDoi":"10.21203/rs.3.rs-6674012/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6674012/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction \u0026amp; Objectives\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic has claimed over 6.8\u0026nbsp;million lives globally, with varying impacts across different healthcare systems. Sri Lanka reported 591 and 15,800 deaths during the Second and Third Waves, respectively. This study investigates COVID-19 mortality patterns at a major tertiary care hospital in Sri Lanka during the pandemic, providing crucial insights for resource-limited settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis descriptive cross-sectional study evaluated COVID-19 deaths at Colombo North Teaching Hospital, Ragama, Sri Lanka from November 2020 to December 2021. Data were sourced from hospital records, supplemented by telephone interviews with family members for missing information, following informed consent. Statistical analyses included Mann-Whitney U tests and Pearson's Chi-squared tests for group comparisons.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe hospital mortality rate was 14.9% among 8,531 COVID-positive admissions. Among 1,004 recorded deaths, the median age was 72 years (IQR: 62\u0026ndash;79, range: 19\u0026ndash;99), with males constituting 57.2% of the cohort. Diabetes (61.6%), hypertension (36.9%), and coronary artery disease (14%) were the predominant comorbidities, with 37.2% having multiple comorbidities. COVID-19 pneumonia was the primary cause of death (83%). A majority (78.3%) of the deceased were unvaccinated, while 21.7% had received at least one vaccine dose. Unvaccinated individuals were older, had longer hospital stays, and exhibited more severe symptoms compared to their vaccinated counterparts.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe high prevalence of diabetes and hypertension among COVID-19 deaths mirrors patterns seen across South Asia, emphasizing the need for integrated management of non-communicable diseases during infectious disease outbreaks. The lower proportion of vaccinated individuals among the deceased suggests vaccine effectiveness, reinforcing the importance of prioritizing high-risk populations in vaccination campaigns. These findings provide guidance for strengthening pandemic preparedness in resource-constrained settings, with potential applications across similar healthcare systems globally.\u003c/p\u003e","manuscriptTitle":"COVID-19 mortality at a tertiary care center during the Second and Third Waves of the pandemic in Sri Lanka: a cross sectional analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 09:19:49","doi":"10.21203/rs.3.rs-6674012/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T13:51:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T06:40:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-10-24T14:14:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112175809173984419724274864705045951592","date":"2025-09-16T02:23:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T18:47:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291511571731618910735744923343226922027","date":"2025-09-12T17:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148604248210583881109703002252998221734","date":"2025-09-12T16:49:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T23:01:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24582825585299453566750909966820245771","date":"2025-07-02T17:52:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121340889615323226173550910301711278116","date":"2025-06-25T12:22:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T12:13:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123579331495519388539922118237057984188","date":"2025-06-15T12:10:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308252705534985196405236305013537674092","date":"2025-06-14T03:27:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305236011816164121190942148484034309461","date":"2025-06-13T16:48:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T09:55:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-21T19:33:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T05:28:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T05:25:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-05-15T15:18:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21cff8bc-5ede-41e6-9252-a535319b773e","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:59:50+00:00","versionOfRecord":{"articleIdentity":"rs-6674012","link":"https://doi.org/10.1186/s12879-026-13469-2","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2026-04-30 15:57:45","publishedOnDateReadable":"April 30th, 2026"},"versionCreatedAt":"2025-06-17 09:19:49","video":"","vorDoi":"10.1186/s12879-026-13469-2","vorDoiUrl":"https://doi.org/10.1186/s12879-026-13469-2","workflowStages":[]},"version":"v1","identity":"rs-6674012","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6674012","identity":"rs-6674012","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}