Clinical profile and short-term outcomes of RT-PCR positive COVID-19 patients in a tertiary care hospital in Dhaka, Bangladesh

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Still, there is a dearth of original research exploring the attributes of COVID-19 patients from lower-and middle-income countries, such as Bangladesh. Based on a case series from a tertiary healthcare center, this observational study has explored the epidemiological and clinical profile of COVID-19 patients in Dhaka, Bangladesh. A total of 422 COVID-19 confirmed patients (via Reverse transcription-polymerase chain reaction test) were enrolled in this study. We have compiled patients' medical records and reported their demographic, socioeconomic, and clinical features, treatment history, health outcome, and post-discharge complications descriptively. Result: Patients were predominantly male (64%), between 35 to 49 years (28%), with at least one comorbidity (52%), and had COVID-19 symptoms for one week before hospitalization (66%). A significantly higher proportion (P<0.05) of male patients had diabetes, hypertension, and ischemic heart disease, while females had a significantly higher proportion (P<0.05) of asthma. The most common symptoms were fever (80%), cough (60%), dyspnea (41%), and sore throat (21%). Most patients received antibiotics (77%) and anticoagulant therapy (56%) and stayed in the hospital for an average of 12 days. Over 90% of patients were successfully weaned, while 3% died from COVID-19, and 41% reported complications after discharge. Conclusion: The diversity of clinical and epidemiological characteristics and health outcomes of COVID-19 patients across age groups and gender is noteworthy. Our result will inform the clinicians and epidemiologists of Bangladesh of their COVID-19 mitigation effort. Virology Health Economics & Outcomes Research COVID-19 Clinical Characteristics Post-COVID-19 Complications Post-COVID-19 Fatigue Syndrome Oxygen Saturation Bangladesh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Globally, over the past year, the Coronavirus disease (COVID-19) pandemic has escalated and continues to threaten the health and wellbeing of the population. The virus has already transmitted to more than 75 million people from over 213 countries and territories, with nearly 1.6 million deaths as of 20 December 2020, indicating an overall death rate per number of diagnosed cases as 2.27% [1]. Early epidemiological studies on COVID-19 from Wuhan, China, reveals infection predominantly resulted in acute respiratory illness. However, the clinical spectrum ranged from asymptomatic or mild upper respiratory tract illness to severe viral Pneumonia with respiratory failure and even death [2,3]. Roughly 20% of cases lead to clinically complex and severe conditions. The most vulnerable group were adults older than 60 years of age with comorbid conditions, including diabetes, hypertension, and cardiovascular disease [2–4]. Recent studies have also indicated that COVID-19's clinical spectrum may vary across diverse ethnic backgrounds and geographic locations worldwide [5]. In the months following the emergence of the pandemic, health systems were overwhelmed due to the sheer number of the cases, partially attributed to the comparatively high "basic reproductive number" (R0) of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) – ranged between 2.24 and 3.58 [6,7]. The high transmissibility of SARS-CoV-2 is particularly perilous for the densely populated countries [8–10], specifically in Southeast Asia [11,12]. Therefore, the 162.6 million people of Bangladesh, one of the most densely populated nations, are especially vulnerable to this raging virus. The first confirmed case of SARS-CoV-2 infection was reported in Dhaka, the capital of Bangladesh, on 8 March 2020 [13], which was followed by a nationwide lockdown from 26 March 2020 to mitigate the transmission of the virus and allow the healthcare system to prepare itself from the onslaught of the COVID-19 cases [14]. However, during the lockdown's initial days, there was a mass exodus of 11 million residents from Dhaka who took this opportunity to wait out the lockdown period in their home districts or villages, which likely only expedited the spread of the disease. On 25 April 2020, the lockdown was partially lifted to restart the economy by allowing workers to return to their station in ready-made garment factories, industries, and private offices. The migrating workforce with limited awareness and opportunity of social distancing and safe hygienic practices ultimately led to millions of additional viral transmissions [15]. After a series of extensions, the 'partial lockdown' was lifted, but cases had already been identified in all 64 districts nationwide [16]. By 11 January 2021, Bangladesh had reached 522,453 confirmed cases of COVID-19, with 566,801 recovered patients and 7,781 deaths. Though the country observed a decreasing number of weekly cases, the number of weekly deaths is gradually increasing [16]. Beyond the difficulties of enforcing the nationwide lockdown and promoting social distancing norms, the healthcare system of Bangladesh was also underprepared to handle such a large-scale pandemic [17]. At the start of the pandemic, only 1,169 Intensive Care Unit beds were available in the entire country, the majority (737 beds) in private hospitals. Besides, there was an insufficient supply of high-quality personal protective equipment, confirmative tests, medications, and logistics [18,19]. However, dealing with a healthcare emergency of this scale has left much room for research and examination of the system's effectiveness in treating patients. Although previous studies detailed the clinical presentation of hospital-admitted Reverse transcription-polymerase chain reaction (RT-PCR) positive COVID-19 patients, very few of these originate from an LMIC, and fewer still from Bangladesh [20,21]. To our knowledge, no previous study from Bangladesh has been able to present a comprehensive clinical profile of CVOID-19 patients by examining the full suite of patients' characteristics, clinical presentation, diagnostic test results, treatment regiment, health outcomes, and any reported complications during the follow-up. This study describes the epidemiological and clinical features, as well as health outcomes of COVID‐19 patients admitted to a tertiary health facility in Dhaka, Bangladesh. 2. Methodology 2.1 Study setting and design It is a single-center, retrospective, observational case series study conducted at a Government tertiary health facility in Dhaka, Bangladesh. The healthcare facility was declared a COVID-19 dedicated hospital during the early stage of the pandemic and operated its COVID-19 inpatient service between 16 April to 5 September 2020. Information of all admitted confirmed COVID-19 patients in this facility were included in this study. 2.2 Data source During the study period – between16 April to 5 September 2020 – a total of 442 suspected COVID-19 patients were admitted to the study hospital. Among them, 422 patients had confirmed the SARS-CoV-2 infection by RT-PCR test, who were considered as the analytical sample for our study. We have retrospectively compiled the medical records of the hospitalized COVID-19 confirmed patients, which includes demographic information, presented signs and symptoms, self-reported comorbidity of the patients during the initial consultation, the result of diagnostic tests, and medications provided to the patients. As part of the facility's clinical care protocol, the discharged patients were reached out for follow-up via teleconsultation. We have also included information related to the persistent complications of COVID-19 among the discharged patients as part of the analysis. The sample size of the different components of the study is presented in Figure 1. The information collected from the patients and their medical record were entered in a Microsoft EXCEL workbook by two researchers, followed by double-checking the record for any inconsistency. One senior researcher re-assessed the data quality by reviewing the raw data and made any necessary corrections. The clean data were imported into statistical software for analysis. We used Stata, version 15.1 [22], to perform the data management and statistical analysis, and to develop the data visualizations, we have used R software, version 4.0.1 [23]. 2.3 Variables and definitions All demographic covariates of the patients were recoded into categorical variables. Patients were stratified into age categories of – less than 19 years, 19-24 years, 25-34 years, 35-49 years, 50-59 years, and more than 59 years, which corresponds to child and adolescents, younger adults, adults, older adults, seniors, and older seniors. While providing their demographic information, the patients self-reported their monthly income in four categories – 5-10 thousand, 10-30 thousand, 30-50 thousand, and more than 50 thousand. Bangladeshi Taka (BDT). During the initial consultation, the patients also provided information related to their smoking habits, existing comorbidities (such as Hypertension, Diabetes Mellitus. Asthma, chronic heart, and kidney diseases, etc.), history of contact with any confirmed COVID-19 patients, duration of symptoms, the outcome of the hospitalization. The symptoms include body temperature more than 38°C, cough, difficulty in breathing (dyspnea), sore throat, bodily discomfort (malaise), diarrhea, headache, weakness, runny nose, loss of taste, loss of sense of smell (anosmia), vomiting, vertigo, and abdominal pain. We have also compiled the clinical history of the patients, which includes medication provided to the patients in response to the illness resulted from COVID-19, and performed diagnostic tests. The medication history was clustered as antibiotics for secondary infections, Hydroxychloroquine, Anticoagulants, Glucocorticoid therapy, oxygen support, and other medications. However, we did not present the frequency or the doses of the medication. We have categorized the result of the diagnostic tests performed into “Good” or “Poor” prognostic values as follows: X-ray finding suggestive of pneumonia: as present or absent based on radiologist’s report; serum creatinine level: poor = >1.2mg/dL and good = ≤1.2mg/dL, serum glutamic pyruvic transaminase (SGPT): poor = >40 U/L and good = ≤40 U/L; serum c- reactive protein: poor = ≥6 mg/L and good = 500 ng/mL and good = ≤500 ng/mL; blood hemoglobin: poor = <10 gm/dL and good = ≥10 gm/dL; total count of White blood cell (WBC): poor = <4000/μL and good = ≥ 4000/μL and 3.5 and good = ≤3.5; differential count of Monocytes: poor = >8% and good = 2-8%; differential count of Eosinophils: poor = >4% and good = 1-4%; and blood Platelet level: poor = <150000/μL and good = ≥ 150000/μL. During the follow-up via teleconsultation, the patients reported a wide range of persistent symptoms as self-reported complications. We have documented responses from the patients' medical records and aggregated them into broad categories of respiratory, cardiovascular, abdominal, otolaryngologic (ears, nose, and throat), musculoskeletal, febrile, post-COVID fatigue syndrome, other miscellaneous, death, and none as reported complications. 2.4 Statistical Analysis As the case series of COVID-19 patients presented some selection bias– the patients' sample was not selected by random sampling – we have only focused on descriptive analysis of information derived from the COVID-19 patients. To describe the characteristics of the study sample, we have categorized their demographic information, health status, and signs and symptoms associated with COVID-19, which were summarized as counts and percentages. These attributes of the sample were further disaggregated across gender and age categories to assess their association using χ2 test at the significance level of P < 0.05 (in case of small sample size, less than 5, in the bivariate cells Fisher's exact test was used). Next, considering the lowest recorded Oxygen Saturation (SpO2) of the patient as a cardinal prognostic factor, we have explored the statistical association between a binary indicator of SpO2 (> 93% as good, and ≤ 93% as poor) and key demographic and health-related indicators of the patients. We have also investigated a similar association between the result of the diagnostic tests of the patients and their SpO2 to identify any significant association which may provide further insight on the hematological correlates of COVID-19. To understand the patients' treatment regimen pattern, we have presented descriptive statistics of the medication provided to different age groups and genders. Lastly, we have attempted to visualize the persistent complications self-reported by the patients during the follow-up. As presented in Figure 1, the different components of the analysis had a varying number of samples due to the limited availability of the data. However, during the analysis, we did not impute any missing data. 2.5 Compliance with ethical standards The result of this retrospective study was based on the data obtained during the clinical provision of care. During the compilation of the data, all medical records were anonymized to protect the confidentiality of the patients. The institutional review board of the James P. Grant School of Public Health, BRAC University, has provided the ethical approval of the study protocol (Ref. No: IRB-7 December'20-054). 3. Results 3.1 Demographic and clinical characteristics of the patients Of the 442 patients admitted to the hospital, 422 had SARS-CoV-2 infection confirmed by the RT-PCR test. Among the confirmed COVID-19 patients, 64% (n = 271) were male, and 36% (n = 150) were female, and among them four female patients were pregnant. The demographic and health characteristics of the patients are presented in Tables 1 and 2, disaggregated according to their gender and age. Among the admitted COVID-19 patients, the majority of patients ( 28%, n = 120) were 35-49 years old. While most of the patients (38%, n = 159) were service holders, the study sample consisted of 17% (n = 71) healthcare workers. Around 41% (n = 168) of the patients could not recall any contact history with previously confirmed COVID-19 patients, and an additional 40% (n = 164) patients provided positive contact history. Before admitting to the hospital, almost two-third (66%, n = 277) of the patients had the symptoms of COVID-19 for one week. Half of the patients (52%, n = 154) reported having at least one underlying comorbidity. Male patients reported a higher proportion of comorbidity than females (69% vs. 41%), though it was not statistically significant. However, looking into individual type of comorbidity, significantly higher proportion of male patients had Diabetes (P < 0.001), Hypertension (P < 0.001), Ischemic heart disease (P = 0.048) compared to the female patients (Figure 2), while 30% (n = 22) females presented with Asthma, compared to 14% (n = 16) men and this difference is statistically significant (P = 0.027). At triage in the hospital, out of 422 patients, 379 presented any clinical feature of COVID-19, and the most common symptom (80%) was fever (Figure 3). Consequently, the next three most frequent symptoms were associated with respiratory systems, which were cough (60%, n = 227), dyspnea (41%, n = 155), and sore throat (21%, n = 81). The clinical record showed the blood SpO2 level of 304 patients (72% of the study sample). A lower SpO2 level is a critical factor indicating the severity of COVID-19, and Table 3 presents the SpO2 level (≤ 93% vs.> 93%) according to the patients' characteristics. The SpO2 level of the patients presented significant association with their age (P < 0.001), occupation (P = 0.002), and presence of underlying comorbidity (P < 0.001). We have observed that lower SpO2 levels were reported for older patients. Similarly, almost twice as many patients with lower SpO2 levels reported comorbidity (69% vs. 31%), indicating a strong association between the underlying health condition and the severity of the disease among confirmed COVID-19 patients. 3.2 Radiological and laboratory findings Of the confirmed patients, 274 had their chest X-ray available, while computed tomography (C.T.) of the chest was not conducted due to resource constraints. Table 4 shows the radiological findings and the result of laboratory investigations during hospitalization. X-ray finding suggestive of Pneumonia was observed among 39 % (n = 107) of the patients, indicated by mixed inhomogeneous opacity in the posterior-anterior (P.A.) view of lung X-ray. Laboratory findings suggests, only 3% (n = 2) and 10% (n = 7) patients presented leukopenia and thrombocytopenia accordingly. However, 29% (25 out of 85) patients had their Neutrophil Lymphocyte Ratio elevated more than 3.5 times. Among other findings, elevated level of SGPT (58.54%, n = 144), C-reactive Protein (37%, n = 90), serum creatinine (21%, n = 53), and D-dimer (22%, n – 32) were observed. SpO2 level was significantly associated with radiological findings of Pneumonia (P < 0.001), serum Creatinine (P < 0.011), serum C-reactive Protein (P = 0.004), and Neutrophil Lymphocyte Ratio (P = 0.001). The patients with lower SpO2 level had the poor prognostic values of the radiological and laboratory findings. 3.3 Treatment and medications All patients (n = 422) received symptomatic medication for their illness during hospitalization. Table 5 presents the treatment and medication given to patients. Majority of the patients received antibiotics (77%, n = 318), and anticoagulants therapy (56%, n = 232). These proportions were even higher among the patients with lower SpO2 levels – 93% and 87% for antibiotic and anticoagulants therapy accordingly. Overall, 63% (n = 262) patients received oxygen supplementation. Except for one patient, everyone presented with a SpO2 level ≤ 93% received oxygen supplement, and more than half of the patients (59%, n = 114) received oxygen supplement despite having their SpO2 level > 93%. Most of the older adults and senior patients received anticoagulant and glucocorticoid medications. Among all patients, 29% (n = 121) received Hydroxychloroquine and 6% (n = 27) received Ivermectin. More than 95% of these patients receiving these therapies were between 19 and 49 years. Moreover, a negligible number of patients with low SpO2 level received Hydroxychloroquine (14%, n = 15) and Ivermectin (5%, n = 6) therapies. The clinical record showed that 7.43% of the patients (n = 31) received antiviral medications. Only ten patients received injectable Remdesivir, and 21 patients received oral antiviral Favipiravir therapy. Among other drugs, four patients received Convalescent Plasma therapy, and three patients received injectable Tocilizumab (Interleukin-6 inhibitor). 3.4 Clinical outcomes and persistent complications The median duration of hospital stay for the patients was 12 days; mean 12.36 days, standard deviation [SD] 6.51 days, and range 1 to 32 days. Across the age groups, significant variability of hospitalization duration (P = 0.012) (Figure 4). While the elderly patients (59+ years) had the most variability of hospital stay (S.D. = 7.30-day, range 1 to 31), their average hospitalization duration was 11 days. In contrast, younger patients (19-24 years) had, on average, the most prolonged hospital stay (mean 14.11 days, SD 5.92, and range 3 to 27 days). More than 90% (n = 381) patients successfully weaned from SARS-CoV-2 infection (Table 1). During their hospital stay, 13 patients (3%) died due to COVID-19, 18 patients (4%) were referred out to other facilities, and eight patients (2%) were discharged voluntarily after signing risk bonds. After discharge, the hospital was able to conduct a teleconsultation to follow up on 399 patients. An additional eight deaths (2%) were reported during the follow-up teleconsultation (Figure 5). The follow-up was conducted on average, 66 days after the discharge of the patients (range 1 to 129 days). Out of 399 patients, 164 patients (41%) reported experiencing complications after hospital discharge. Among them, 84 patients (51%) reported one, 50 reported two (37%), 30 reported three compilations (22%). During the follow-up period, additional eight deaths were reported. The majority of the patients' complications were associated with respiratory systems, consisting of around 62% (n = 52) of the first and 44% (n = 22) of the second complications. The type of respiratory complications consisted of cough and cold, chest heaviness, shortness of breath, and pain during breathing. Among other frequently reported complications were post-COVID fatigue syndrome, fever, and musculoskeletal pain. 4. Discussion This retrospective, observational case series study analyzed the clinical and epidemiological characteristics, and outcomes of RT-PCR positive COVID-19 patients admitted in a tertiary hospital in Dhaka, Bangladesh. Among the admitted patients, one-third were female, and one-third were service holders, 17% were healthcare providers and more than half of the patients presented with underlying comorbidity. Significantly, a higher number of male COVID-19 patients presented with diabetes, hypertension, and ischemic heart disease, whereas asthma was significantly prevalent among women patients. Several meta-analyses also indicated a higher disease burden of COVID-19 among male smokers and patients with comorbid conditions such as cardiovascular disease, hypertension, and diabetes [24–26]. Around 9.35% of patients included in this study were asymptomatic during the initial assessment. Fever and respiratory symptoms were most common among the patients, reported in numerous other studies [21,27,28]. While several studies from China [28,29], Uzbekistan [30], and Brazil [31] reported weakness or fatigue as a common symptom, only 8% (n = 31 out of 377) of the patients reported that they experienced weakness during initial triage at the hospital. Our result showed a SpO2 level of more than 36% (110 out of 304) hospitalized patients dropped below 94% – which is an indicator of severe illness [32,33]. The lower SpO2 level or severe illness was significantly associated with older age and the presence of any comorbidity. Besides, the severe illness was significantly associated with radiological findings of Pneumonia [34], higher creatinine [27,35], c-reactive protein [20,35], and Neutrophil Lymphocyte ratio [36–38]. A higher proportion of patients with low SpO2 levels frequently received antibiotics, anticoagulants, and glucocorticoid therapy. Among the patient pool, a total of 21 deaths were reported – 13 during the hospitalization period. Eight deaths were reported after discharge, which resulted in a total case-fatality ratio of 5.45% (21 out of 422), which is significantly higher than the national average case-fatality ratio of Bangladesh which is 1.47% [39]. We followed up with 399 patients to investigate their post-discharge complications, and around 41.10% reported at least one complication. Respiratory complications were reported most frequently as persistent symptoms after discharge [40]. Approximately, 31% of patients reported post-COVID fatigue syndrome as complications reported during the telemedicine consultation. Similar to other post-viral infections that cause Chronic Fatigue Syndrome (similar to Myalgic Encephalomyelitis), prolonged fatigue is commonly reported after COVID-19 infection [41,42]. Using a comprehensive set of medical records is the core strength of the study. The clinical and follow-up data quality originated from the tertiary care hospital is exceptionally vigorous, making our result robust and reliable. However, we have to acknowledge a few limitations of this study. First, the result of this study cannot be generalized for the national context of Bangladesh. We have included confirmed COVID-19 cases admitted to the hospital, which can result in selection bias. It is also indicated by the high level of case-fatality ratio identified in the study [43]. Generally, more severe cases of COVID-19 were admitted to the hospital, which resulted from a non-randomized nature of sample recruitment in our study due to the sampling bias [44]. However, we are not exploring any inferential statistics – just defining feature of COVID-19 cases – the selection bias is inconsequential for our study. Secondly, the completeness of the data is a common complication while using medical records [45]. We decided not to impute any data point to account for the missingness. Instead, we wanted to be transparent [46] and explicitly report the data's missingness for each study component (Figure 1). 5. Conclusion In conclusion, the clinical and epidemiological characteristics and health outcomes of COVID-19 patients are significantly different across the patients' age groups and gender. Our study has also identified several significant associations with SpO2 level and several patient attributes, hematological correlations, and medication regimen. While we recommend multicenter studies with a larger patient cohort, this study has broken new ground in Bangladesh for clinical research on COVID-19. The result of this study will inform clinicians, public health researchers, and policymakers regarding the nature of COVID-19 in Dhaka, Bangladesh, which became an epicenter of the pandemic. 6. Declarations Acknowledgments: We want to acknowledge the Director of the Maternal and Child Health Training Institute (MCHTI), Dr. Md. Shamsul Karim for his invaluable assistance. We also thank the health care providers of MCHTI for their dedication and relentless determination to deliver care to the patients infected with SARS-CoV-2. Lastly, we humbly acknowledge the patients admitted to the MCHTI hospital. Without their information, this paper could not be developed. 6.1 Funding No funding was received for conducting this study. 6.2 Conflicts of interest The authors have no conflicts of interest to declare that are relevant to the content of this article. 6.3 Availability of data and material The data supporting the finding of this study will be made available to any qualified researcher by the authors upon reasonable request. 6.4 Ethical Review The institutional review board of the James P. Grant School of Public Health, BRAC University, has provided the ethical approval of the study protocol (Ref. No: IRB-7 December'20-054). 6.5 Authors' contributions All authors of this study contributed to the design and conceptualization. Establishing institutional collaboration and data acquisition was led by Nirmol Kumar Biswas, Juli Chowdhury, and Ahmad Monjurul Aziz. The analytical plan was developed collaboratively by Md Zabir Hasan and Malabika Sarker. The Analysis was performed by Md Zabir Hasan. The first draft of the manuscript was prepared by Md Zabir Hasan with the support of Shams Shabab Haider and with the supervision of Malabika Sarker and Nirmol Kumar Biswas. All authors reviewed the content of the manuscript and provided their critical comments. The final version of the manuscript was read approved by all authors. References World Health Organization. COVID-19 Weekly Epidemiological Update [Internet]. World Health Organization; 2020 Dec. 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Tables Table 1: Characteristics of the COVID-19 positive patients disaggregated by their gender Patient's Characteristics (Reported Sample Size) Male (n = 271) Female (n = 150) All Patients (n = 421) N (Col%) N (Col%) P-values N (Col%) Patient Age Category in Years (n = 420) Less than 19 Years 7 (2.59) 9 (6.00) 0.478 16 (3.81) 19-24 Years 30 (11.11) 17 (11.33) 47 (11.19) 25-34 Years 71 (26.30) 43 (28.67) 114 (27.14) 35-49 Years 80 (29.63) 40 (26.67) 120 (28.57) 50-59 Years 41 (15.19) 24 (16.00) 65 (15.48) More than 59 Years 41 (15.19) 17 (11.33) 58 (13.81) Patient Occupation (n = 415) Wage Earner 12 (4.53) 4 (2.67) < 0.001 16 (3.86) Business 51 (19.25) 1 (0.67) 52 (12.53) Service 131 (49.43) 28 (18.67) 159 (38.31) Healthcare Worker 31 (11.70) 40 (26.67) 71 (17.11) Housewife 0 (0.00) 62 (41.33) 62 (14.94) Student 18 (6.79) 12 (8.00) 30 (7.23) Unemployed 22 (8.30) 3 (2.00) 25 (6.02) Monthly Income in BDT (n = 411) 5,000-10,000 138 (52.87) 70 (46.67) 0.409 208 (50.61) 10,000-30,000 76 (29.12) 45 (30.00) 121 (29.44) 30,000-50,000 12 (4.60) 12 (8.00) 24 (5.84) More than 50,0000 35 (13.41) 23 (15.33) 58 (14.11) Smoking Status of Patient (n = 411) Non-smoker 206 (78.93) 148 (99.33) < 0.001 354 (86.34) Smoker 55 (21.07) 1 (0.67) 56 (13.66) Presence of Any Comorbidity (n = 412) No 149 (57.09) 77 (51.33) 0.259 226 (54.99) Yes 112 (42.91) 73 (48.67) 185 (45.01) History of Contact with COVID-19 Case (n = 411) No 63 (24.14) 16 (10.67) < 0.001 79 (19.22) Yes 85 (32.57) 79 (52.67) 164 (39.90) Unknown 113 (43.30) 55 (36.67) 168 (40.88) Duration of Symptoms during Initial Assessment (n = 418) Asymptomatic during initial assessment 25 (9.36) 14 (9.33) 0.827 39 (9.35) 1-7 Days 173 (64.79) 104 (69.33) 277 (66.43) 8-14 Days 56 (20.97) 26 (17.33) 82 (19.66) 15-21 Days 9 (3.37) 5 (3.33) 14 (3.36) More than 21 Days 4 (1.50) 1 (0.67) 5 (1.20) Outcome of Hospitalization (n = 421) Death 10 (3.70) 3 (2.00) 0.681 13 (3.10) Recovery 242 (89.63) 139 (92.67) 381 (90.71) Referred to other facilities 13 (4.81) 5 (3.33) 18 (4.29) Discharge on risk bond 5 (1.85) 3 (2.00) 8 (1.90) Reported any Complication during Follow-up (n = 399) No 154 (61.11) 81 (55.10) 0.239 235 (58.90) Yes 98 (38.89) 66 (44.90) 164 (41.10) Note: Col% = Column Percentage Table 2: Characteristics of the COVID-19 positive patients disaggregated by their age category Patient's Characteristics (Reported Sample Size) 59 Years All Patients (n = 16) (n = 47) (n = 114) (n = 120) (n = 65) (n = 58) (n = 420) N (Col%) N (Col%) N (Col%) N (Col%) N (Col%) N (Col%) P-Values N (Col%) Patient Gender (n = 420) Male 7 (43.75) 30 (63.83) 71 (62.28) 80 (66.67) 41 (63.08) 41 (70.69) 0.478 270 (64.29) Female 9 (56.25) 17 (36.17) 43 (37.72) 40 (33.33) 24 (36.92) 17 (29.31) 150 (35.71) Patient Occupation (n = 415) Wage Earner 0 (0.00) 1 (2.22) 4 (3.54) 9 (7.63) 2 (3.08) 0 (0.00) < 0.001 16 (3.86) Business 0 (0.00) 0 (0) 8 (7.08) 22 (18.64) 12 (18.46) 10 (17.24) 52 (12.53) Service 0 (0.00) 20 (44.44) 53 (46.90) 50 (42.37) 24 (36.92) 12 (20.69) 159 (38.31) Healthcare Worker 0 (0.00) 13 (28.89) 32 (28.32) 17 (14.41) 8 (12.31) 1 (1.72) 71 (17.11) Housewife 0 (0.00) 1 (2.22) 8 (7.08) 20 (16.95) 19 (29.23) 14 (24.14) 62 (14.94) Student 14 (87.50) 9 (20) 7 (6.19) 0 (0.00) 0 (0.00) 0 (0.00) 30 (7.23) Unemployed 2 (12.50) 1 (2.22) 1 (0.88) 0 (0.00) 0 (0.00) 21 (36.21) 25 (6.02) Monthly Income in BDT (n = 411) 5,000-10,000 5 (31.25) 32 (71.11) 65 (58.56) 64 (54.70) 21 (32.31) 21 (36.84) < 0.001 208 (50.61) 10,000-30,000 6 (37.50) 3 (6.67) 39 (35.14) 32 (27.35) 21 (32.31) 20 (35.09) 121 (29.44) 30,000-50,000 2 (12.50) 3 (6.67) 4 (3.60) 6 (5.13) 5 (7.69) 4 (7.02) 24 (5.84) More than 50,0000 3 (18.75) 7 (15.56) 3 (2.70) 15 (12.82) 18 (27.69) 12 (21.05) 58 (14.11) Smoking Status of Patient (n = 411) Non-smoker 16 (100) 38 (84.44) 95 (85.59) 103 (88.03) 57 (87.69) 45 (80.36) 0.440 354 (86.34) Smoker 0 (0.00) 7 (15.56) 16 (14.41) 14 (11.97) 8 (12.31) 11 (19.64) 56 (13.66) Presence of Any Comorbidity (n = 412) No 15 (93.75) 39 (86.67) 89 (80.18) 65 (55.56) 10 (15.38) 8 (14.04) < 0.001 226 (54.99) Yes 1 (6.25) 6 (13.33) 22 (19.82) 52 (44.44) 55 (84.62) 49 (85.96) 185 (45.01) History of Contact with COVID-19 Case (n = 411) No 3 (18.75) 7 (15.56) 16 (14.41) 28 (23.93) 13 (20.00) 12 (21.05) 0.109 79 (19.22) Yes 7 (43.75) 18 (40.00) 56 (50.45) 42 (35.90) 28 (43.08) 13 (22.81) 164 (39.90) Unknown 6 (37.50) 20 (44.44) 39 (35.14) 47 (40.17) 24 (36.92) 32 (56.14) 168 (40.88) Duration of Symptoms during Initial Assessment (n = 418) Asymptomatic during initial assessment 2 (12.50) 11 (23.40) 14 (12.39) 10 (8.47) 1 (1.54) 1 (1.75) 0.005 39 (9.38) 1-7 Days 11 (68.75) 29 (61.70) 75 (66.37) 76 (64.41) 45 (69.23) 40 (70.18) 276 (66.35) 8-14 Days 2 (12.50) 2 (4.26) 20 (17.70) 29 (24.58) 18 (27.69) 11 (19.30) 82 (19.71) 15-21 Days 1 (6.25) 4 (8.51) 3 (2.65) 2 (1.69) 0 (0.00) 4 (7.02) 14 (3.37) More than 21 Days 0 (0.00) 1 (2.13) 1 (0.88) 1 (0.85) 1 (1.54) 1 (1.75) 5 (1.20) Outcome of Hospitalization (n = 421) Death 1 (6.25) 0 (0) 0 (0) 3 (2.5) 5 (7.69) 4 (6.90) 0.008 13 (3.10) Recovery 15 (93.75) 46 (97.87) 106 (93.81) 112 (93.33) 56 (86.15) 45 (77.59) 380 (90.69) Referred to other facilities 0 (0.00) 0 (0) 5 (4.42) 3 (2.5) 2 (3.08) 8 (13.79) 18 (4.30) Discharge on risk bond 0 (0.00) 1 (2.13) 2 (1.77) 2 (1.67) 2 (3.08) 1 (1.72) 8 (1.91) Reported any Complication during Follow-up (n = 399) No 10 (66.67) 35 (79.55) 73 (65.77) 59 (51.75) 27 (44.26) 31 (57.41) 0.003 235 (58.90) Yes 5 (33.33) 9 (20.45) 38 (34.23) 55 (48.25) 34 (55.74) 23 (42.59) 164 (41.10) Note: Col% = Column percentage Table 3: Presentation of blood oxygen saturation level (SpO2: ≤ 93% vs. > 93%) according to their characteristics of the COVID-19 positive patients Patient's Characteristics (Reported Sample Size) SpO2 Level ≤ 93% SpO2 Level > 93% Patients with reported SpO2 (n = 110) (n = 194) (n = 304) N (Col%) N (Col%) P-Values N (Col%) Patient Age Category in Years (n = 420) Less than 19 Years 2 (1.83) 6 (3.11) < 0.001 8 (2.65) 19-24 Years 6 (5.50) 16 (8.29) 22 (7.28) 25-34 Years 11 (10.09) 70 (36.27) 81 (26.82) 35-49 Years 27 (24.77) 58 (30.05) 85 (28.15) 50-59 Years 29 (26.61) 26 (13.47) 55 (18.21) More than 59 Years 34 (31.19) 17 (8.81) 51 (16.89) Patient Gender (n = 421) Male 70 (64.22) 118 (60.82) 0.559 188 (62.05) Female 39 (35.78) 76 (39.18) 115 (37.95) Patient Occupation (n = 415) Wage Earner 5 (4.59) 8 (4.19) 0.002 13 (4.33) Business 20 (18.35) 18 (9.42) 38 (12.67) Service 33 (30.28) 79 (41.36) 112 (37.33) Healthcare Worker 12 (11.01) 35 (18.32) 47 (15.67) Housewife 25 (22.94) 26 (13.61) 51 (17.00) Student 2 (1.83) 16 (8.38) 18 (6.00) Unemployed 12 (11.01) 9 (4.71) 21 (7.00) Monthly Income in BDT (n = 411) 5,000-10,000 50 (45.87) 91 (48.40) 0.886 141 (47.47) 10,000-30,000 35 (32.11) 53 (28.19) 88 (29.63) 30,000-50,000 7 (6.42) 11 (5.85) 18 (6.06) More than 50,0000 17 (15.60) 33 (17.55) 50 (16.84) Smoking Status of Patient (n = 411) Non-smoker 97 (88.99) 165 (87.77) 0.752 262 (88.22) Smoker 12 (11.01) 23 (12.23) 35 (11.78) Presence of Any Comorbidity (n = 412) No 34 (30.91) 110 (58.51) < 0.001 144 (48.32) Yes 76 (69.09) 78 (41.49) 154 (51.68) History of Contact with COVID-19 Case (n = 411) No 21 (19.27) 36 (19.15) 0.225 57 (19.19) Yes 35 (32.11) 78 (41.49) 113 (38.05) Unknown 53 (48.62) 74 (39.36) 127 (42.76) Duration of Symptoms during Initial Assessment (n = 418) Asymptomatic during initial assessment 2 (1.82) 12 (6.25) 0.146 14 (4.64) 1-7 Days 71 (64.55) 132 (68.75) 203 (67.22) 8-14 Days 30 (27.27) 41 (21.35) 71 (23.51) 15-21 Days 4 (3.64) 6 (3.13) 10 (3.31) More than 21 Days 3 (2.73) 1 (0.52) 4 (1.32) Outcome of Hospitalization (n = 421) Death 9 (8.18) 1 (0.52) < 0.001 10 (3.30) Recovery 88 (80.00) 185 (95.85) 273 (90.10) Referred to other facilities 11 (10.00) 3 (1.55) 14 (4.62) Discharge on risk bond 2 (1.82) 4 (2.07) 6 (1.98) Reported any Complication during Follow-up (n = 399) No 46 (46.00) 108 (57.75) 0.057 154 (53.66) Yes 54 (54.00) 79 (42.25) 133 (46.34) Note: Oxygen saturation above 93% is considered a good prognostic indicator, and a saturation below or equal to 93% is considered poor; Col% = Column percentage Table 4: Radiological and laboratory findings of the COVID-19 positive patients during hospitalization and the association with their blood oxygen saturation level measured during the initial examination Patient's Characteristics (Reported Sample Size) SpO2 Level ≤ 93% SpO2 Level > 93% All Patients (n = 110) (n = 194) N (Col%) N (Col%) P-Values N (Col%) X-ray Finding Suggestive of Pneumonia (n = 274) Absent 24 (28.24) 96 (72.73) 1.2mg/dl 23 (26.44) 16 (12.7) 0.011 53 (20.54) Good: ≤ 1.2mg/dl 64 (73.56) 110 (87.3) 205 (79.46) SGPT Level (n = 246) Poor: > 40 U/L 49 (59.76) 67 (55.37) 0.536 144 (58.54) Good: ≤ 40 U/L 33 (40.24) 54 (44.63) 102 (41.46) C-reactive Protein Test (n = 244) Poor: ≥ 6 mg/L 43 (52.44) 38 (31.93) 0.004 90 (36.89) Good: 500 ng/mL 20 (28.99) 9 (15) 0.058 32 (22.38) Good: ≤ 500 ng/mL 49 (71.01) 51 (85) 111 (77.62) Blood Hemoglobin Level (n = 87) Poor: < 10 gm/dl 11 (40.74) 11 (28.21) 0.288 28 (32.18) Good: ≥ 10 gm/dl 16 (59.26) 28 (71.79) 59 (67.82) WBC Total Count (n = 79) Poor: < 4000/μL 0 (0) 1 (2.86) 0.404 2 (2.53) Good: ≥ 4000/μL and 3.5 15 (55.56) 6 (15.38) 0.001 25 (29.07) Good: ≤ 3.5 12 (44.44) 33 (84.62) 61 (70.93) Monocytes Differential Count (n = 86) Poor: > 8% 21 (31.82) 21 (31.82) 0.091 22 (25.58) Good: 2-8% 45 (68.18) 45 (68.18) 64 (74.42) Eosinophils Differential Count (n = 86) Poor: > 4% 3 (11.11) 4 (10.26) 0.912 8 (9.30) Good: 1-4% 24 (88.89) 35 (89.74) 78 (90.70) Platelet Level (n = 71) Poor: < 150000/μL 2 (9.09) 5 (14.71) 0.535 7 (9.86) Good: ≥ 150000/μL 20 (90.91) 29 (85.29) 64 (90.14) Note: Oxygen saturation above 93% is considered a good prognostic indicator, and a saturation below or equal to 93% is considered poor; Col% = Column percentage Table 5: Treatment and medication given to the COVID-19 positive patients during hospitalization disaggregated by their age and their blood oxygen saturation (SpO2) level measured during the initial examination Age Categories SpO2 Level Treatment and medication given (Reported Sample Size) 59 Years ≤ 93% > 93% All Patients (n = 16) (n = 47) (n = 114) (n = 120) (n = 65) (n = 58) (n = 110) (n = 194) N (Row%) N (Row%) N (Row%) N (Row%) N (Row%) N (Row%) P-values N (Col%) N (Col%) P-values N (Col%) Antibiotic (n = 414) No 4 (4.21) 10 (10.53) 41 (43.16) 22 (23.16) 9 (9.47) 9 (9.47) 0.006 7 (6.60) 55 (28.50) < 0.001 96 (23.19) Yes 12 (3.79) 36 (11.36) 73 (23.03) 97 (30.6) 53 (16.72) 46 (14.51) 99 (93.40) 138 (71.50) 318 (76.81) Hydroxychloroquine (n = 411) No 14 (4.83) 21 (7.24) 73 (25.17) 76 (26.21) 57 (19.66) 49 (16.90) < 0.001 90 (85.71) 140 (72.54) 0.01 290 (70.56) Yes 2 (1.68) 25 (21.01) 41 (34.45) 41 (34.45) 5 (4.20) 5 (4.20) 15 (14.29) 53 (27.46) 121 (29.44) Anticoagulant (n = 413) No 12 (6.67) 38 (21.11) 72 (40.00) 47 (26.11) 9 (5.00) 2 (1.11) < 0.001 14 (13.21) 86 (44.56) < 0.001 181 (43.83) Yes 4 (1.73) 8 (3.46) 42 (18.18) 71 (30.74) 53 (22.94) 53 (22.94) 92 (86.79) 107 (55.44) 232 (56.17) Antiviral (n = 417) No 16 (4.14) 46 (11.98) 111 (28.91) 109 (28.39) 54 (14.06) 48 (12.50) 0.002 92 (85.19) 182 (93.81) 0.013 386 (92.57) Yes 0 (0.00) 0 (0.00) 3 (9.68) 10 (32.26) 9 (29.03) 9 (29.03) 16 (14.81) 12 (6.19) 31 (7.43) Glucocorticoids (n = 412) No 15 (4.66) 40 (12.42) 105 (32.61) 91 (28.26) 43 (13.35) 28 (8.70) < 0.001 52 (49.52) 164 (84.97) < 0.001 324 (78.64) Yes 1 (1.14) 6 (6.82) 9 (10.23) 27 (30.68) 18 (20.45) 27 (30.68) 53 (50.48) 29 (15.03) 88 (21.36) Oxygen Supplement (n = 413) No 10 (6.62) 18 (11.92) 57 (37.75) 43 (28.48) 17 (11.26) 6 (3.97) < 0.001 1 (0.95) 79 (40.93) < 0.001 151 (36.56) Yes 6 (2.31) 27 (10.38) 57 (21.92) 76 (29.23) 45 (17.31) 49 (18.85) 104 (99.05) 114 (59.07) 262 (63.44) Ivermectin (n = 421) No 16 (4.08) 44 (11.22) 106 (27.04) 106 (27.04) 63 (16.07) 57 (14.54) 0.046 104 (94.55) 183 (94.33) 0.937 394 (93.59) Yes 0 (0.00) 3 (11.11) 8 (29.63) 14 (51.85) 1 (3.70) 1 (3.70) 6 (5.45) 11 (5.67) 27 (6.41) Others (n = 419) No 0 (0.00) 3 (11.11) 8 (29.63) 14 (51.85) 1 (3.70) 1 (3.70) 0.05 6 (5.50) 11 (5.70) 0.944 27 (6.44) Yes 16 (4.10) 44 (11.28) 106 (27.18) 106 (27.18) 62 (15.9) 56 (14.36) 103 (94.50) 182 (94.30) 392 (93.56) Note: Row% = Row percentage; Col% = Column percentage, SpO2 = blood oxygen saturation level Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2021 Read the published version in BMJ Open → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-157669","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original article","associatedPublications":[],"authors":[{"id":9133745,"identity":"eb73994d-2001-4a0d-a779-c259f70c25d4","order_by":0,"name":"Md Zabir Hasan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFACHoYDDCDEwMD4AMTlI0ULswGIy0aMFgaoFjYJMElIg3z72YOHKxjuyAMZxyq/5tjJsDEwP3x0A48Wxp68hINnGJ4ZbjiTl3Zbdlsy0GFsxsY5eLQwM+QYHGxgOMy4gSHH7LbkNmagFh42aXxa2PjfgLXYz+9/Y1Ysua2esBYeCYgtiQ03cswYP247TFiLhMS7hIMNBoeTN9x4YyzNuO04DxszAb/I9+ce/thQcdh2fn+O4cef26rt+dmbHz7GpwUCDCAUMziOmAkqRwKMP0hRPQpGwSgYBSMGAABLyEfzZHXtuAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8730-0054","institution":"University of British Columbia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Md","middleName":"Zabir","lastName":"Hasan","suffix":""},{"id":9133746,"identity":"0b90e476-153c-418b-969e-327c209328c2","order_by":1,"name":"Nirmol Kumar Biswas","email":"","orcid":"","institution":"Maternal \u0026amp; Child Health Training \u0026amp; Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nirmol","middleName":"Kumar","lastName":"Biswas","suffix":""},{"id":9133747,"identity":"3f9b478c-989e-403e-8406-dfe48cfaa3dc","order_by":2,"name":"Ahmad Monjurul Aziz","email":"","orcid":"","institution":"Maternal \u0026amp; Child Health Training \u0026amp; Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"Monjurul","lastName":"Aziz","suffix":""},{"id":9133748,"identity":"a595cb49-4d7c-4df6-a021-3904874a46e3","order_by":3,"name":"Juli Chowdhury","email":"","orcid":"","institution":"NICVD: National Institute of Cardiovascular Diseases","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juli","middleName":"","lastName":"Chowdhury","suffix":""},{"id":9133749,"identity":"2b6c54dc-8bfe-4e2e-8dc9-b8a90d9f6eb2","order_by":4,"name":"Shams Shabab Haider","email":"","orcid":"","institution":"BRAC University James P Grant School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shams","middleName":"Shabab","lastName":"Haider","suffix":""},{"id":9133750,"identity":"7be9268a-072d-4f98-91e1-a04517cc41e0","order_by":5,"name":"Malabika Sarker","email":"","orcid":"","institution":"BRAC University James P Grant School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Malabika","middleName":"","lastName":"Sarker","suffix":""}],"badges":[],"createdAt":"2021-01-27 15:18:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-157669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-157669/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1136/bmjopen-2021-055126","type":"published","date":"2021-11-01T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":5736469,"identity":"687837ad-d5e5-4fbb-9dc7-597c7859d562","added_by":"auto","created_at":"2021-02-08 15:34:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184672,"visible":true,"origin":"","legend":"The sample size of the different component of the study","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-157669/v1/04b02d378a95237d1197f117.png"},{"id":5736470,"identity":"8ba150cd-68f3-4ed1-a3f8-bad061abd8c9","added_by":"auto","created_at":"2021-02-08 15:34:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128554,"visible":true,"origin":"","legend":"Distribution of comorbidity among COVID-19 positive patients disaggregated by their gender","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-157669/v1/9da6d21df627d8eaabd4328a.png"},{"id":5738062,"identity":"a95868a3-af42-491c-88e5-5500446cd64a","added_by":"auto","created_at":"2021-02-08 15:37:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186146,"visible":true,"origin":"","legend":"Clinical symptoms presented COVID-19 positive patients during the triage","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-157669/v1/bacebd0ca68531cfadb8cbeb.png"},{"id":5738064,"identity":"a86889f4-8c5a-4eca-9d01-f92d65faab6d","added_by":"auto","created_at":"2021-02-08 15:37:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":214106,"visible":true,"origin":"","legend":"Variability of hospital stay across the age groups","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-157669/v1/69d3f4403ed58321d28790bb.png"},{"id":5738201,"identity":"ffa12a4b-23cf-49d6-bd58-86db94ce10ed","added_by":"auto","created_at":"2021-02-08 15:40:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1204476,"visible":true,"origin":"","legend":"Health complications reported during follow up tele-consultation","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-157669/v1/0db5d02da547f3f30555f75b.png"},{"id":16567268,"identity":"46a7b47e-f0aa-4286-a92f-39959e694825","added_by":"auto","created_at":"2021-12-17 21:42:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2110332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-157669/v1/cdae3d33-9863-4547-9e37-043ed6586b67.pdf"}],"financialInterests":"","formattedTitle":"Clinical profile and short-term outcomes of RT-PCR positive COVID-19 patients in a tertiary care hospital in Dhaka, Bangladesh","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, over the past year, the Coronavirus disease (COVID-19) pandemic has escalated and continues to threaten the health and wellbeing of the population. The virus has already transmitted to more than 75 million people from over 213 countries and territories, with nearly 1.6 million deaths as of 20 December 2020, indicating an overall death rate per number of diagnosed cases as 2.27% [1]. Early epidemiological studies on COVID-19 from Wuhan, China, reveals infection predominantly resulted in acute respiratory illness. However, the clinical spectrum ranged from asymptomatic or mild upper respiratory tract illness to severe viral Pneumonia with respiratory failure and even death [2,3]. Roughly 20% of cases lead to clinically complex and severe conditions. The most vulnerable group were adults older than 60 years of age with comorbid conditions, including diabetes, hypertension, and cardiovascular disease [2\u0026ndash;4]. Recent studies have also indicated that COVID-19's clinical spectrum may vary across diverse ethnic backgrounds and geographic locations worldwide [5].\u003c/p\u003e\n\u003cp\u003eIn the months following the emergence of the pandemic, health systems were overwhelmed due to the sheer number of the cases, partially attributed to the comparatively high \"basic reproductive number\" (R0) of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) \u0026ndash; ranged between 2.24 and 3.58 [6,7]. The high transmissibility of SARS-CoV-2 is particularly perilous for the densely populated countries [8\u0026ndash;10], specifically in Southeast Asia [11,12]. Therefore, the 162.6 million people of Bangladesh, one of the most densely populated nations, are especially vulnerable to this raging virus. The first confirmed case of SARS-CoV-2 infection was reported in Dhaka, the capital of Bangladesh, on 8 March 2020 [13], which was followed by a nationwide lockdown from 26 March 2020 to mitigate the transmission of the virus and allow the healthcare system to prepare itself from the onslaught of the COVID-19 cases [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, during the lockdown's initial days, there was a mass exodus of 11 million residents from Dhaka who took this opportunity to wait out the lockdown period in their home districts or villages, which likely only expedited the spread of the disease. On 25 April 2020, the lockdown was partially lifted to restart the economy by allowing workers to return to their station in ready-made garment factories, industries, and private offices. The migrating workforce with limited awareness and opportunity of social distancing and safe hygienic practices ultimately led to millions of additional viral transmissions [15]. After a series of extensions, the 'partial lockdown' was lifted, but cases had already been identified in all 64 districts nationwide [16]. By 11 January 2021, Bangladesh had reached 522,453 confirmed cases of COVID-19, with 566,801 recovered patients and 7,781 deaths. Though the country observed a decreasing number of weekly cases, the number of weekly deaths is gradually increasing [16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond the difficulties of enforcing the nationwide lockdown and promoting social distancing norms, the healthcare system of Bangladesh was also underprepared to handle such a large-scale pandemic [17]. At the start of the pandemic, only 1,169 Intensive Care Unit beds were available in the entire country, the majority (737 beds) in private hospitals. Besides, there was an insufficient supply of high-quality personal protective equipment, confirmative tests, medications, and logistics [18,19]. However, dealing with a healthcare emergency of this scale has left much room for research and examination of the system's effectiveness in treating patients.\u003c/p\u003e\n\u003cp\u003eAlthough previous studies detailed the clinical presentation of hospital-admitted Reverse transcription-polymerase chain reaction (RT-PCR) positive COVID-19 patients, very few of these originate from an LMIC, and fewer still from Bangladesh [20,21]. To our knowledge, no previous study from Bangladesh has been able to present a comprehensive clinical profile of CVOID-19 patients by examining the full suite of patients' characteristics, clinical presentation, diagnostic test results, treatment regiment, health outcomes, and any reported complications during the follow-up. This study describes the epidemiological and clinical features, as well as health outcomes of COVID‐19 patients admitted to a tertiary health facility in Dhaka, Bangladesh.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003e2.1\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Study setting and design\u003c/p\u003e\n\u003cp\u003eIt is a single-center, retrospective, observational case series study conducted at a Government tertiary health facility in Dhaka, Bangladesh. The healthcare facility was declared a COVID-19 dedicated hospital during the early stage of the pandemic and operated its COVID-19 inpatient service between 16 April to 5 September 2020. Information of all admitted confirmed COVID-19 patients in this facility were included in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Data source\u003c/p\u003e\n\u003cp\u003eDuring the study period \u0026ndash; between16 April to 5 September 2020 \u0026ndash; a total of 442 suspected COVID-19 patients were admitted to the study hospital. Among them, 422 patients had confirmed the SARS-CoV-2 infection by RT-PCR test, who were considered as the analytical sample for our study. We have retrospectively compiled the medical records of the hospitalized COVID-19 confirmed patients, which includes demographic information, presented signs and symptoms, self-reported comorbidity of the patients during the initial consultation, the result of diagnostic tests, and medications provided to the patients. As part of the facility's clinical care protocol, the discharged patients were reached out for follow-up via teleconsultation. We have also included information related to the persistent complications of COVID-19 among the discharged patients as part of the analysis. The sample size of the different components of the study is presented in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe information collected from the patients and their medical record were entered in a Microsoft EXCEL workbook by two researchers, followed by double-checking the record for any inconsistency. One senior researcher re-assessed the data quality by reviewing the raw data and made any necessary corrections. The clean data were imported into statistical software for analysis. We used Stata, version 15.1 [22], to perform the data management and statistical analysis, and to develop the data visualizations, we have used R software, version 4.0.1 [23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Variables and definitions\u003c/p\u003e\n\u003cp\u003eAll demographic covariates of the patients were recoded into categorical variables. Patients were stratified into age categories of \u0026ndash; less than 19 years, 19-24 years, 25-34 years, 35-49 years, 50-59 years, and more than 59 years, which corresponds to child and adolescents, younger adults, adults, older adults, seniors, and older seniors. While providing their demographic information, the patients self-reported their monthly income in four categories \u0026ndash; 5-10 thousand, 10-30 thousand, 30-50 thousand, and more than 50 thousand. Bangladeshi Taka (BDT).\u003c/p\u003e\n\u003cp\u003eDuring the initial consultation, the patients also provided information related to their smoking habits, existing comorbidities (such as Hypertension, Diabetes Mellitus. Asthma, chronic heart, and kidney diseases, etc.), history of contact with any confirmed COVID-19 patients, duration of symptoms, the outcome of the hospitalization. The symptoms include body temperature more than 38\u0026deg;C, cough, difficulty in breathing (dyspnea), sore throat, bodily discomfort (malaise), diarrhea, headache, weakness, runny nose, loss of taste, loss of sense of smell (anosmia), vomiting, vertigo, and abdominal pain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe have also compiled the clinical history of the patients, which includes medication provided to the patients in response to the illness resulted from COVID-19, and performed diagnostic tests. The medication history was clustered as antibiotics for secondary infections, Hydroxychloroquine, Anticoagulants, Glucocorticoid therapy, oxygen support, and other medications. However, we did not present the frequency or the doses of the medication.\u003c/p\u003e\n\u003cp\u003eWe have categorized the result of the diagnostic tests performed into \u0026ldquo;Good\u0026rdquo; or \u0026ldquo;Poor\u0026rdquo; prognostic values as follows: X-ray finding suggestive of pneumonia: as present or absent based on radiologist\u0026rsquo;s report; serum creatinine level: poor = \u0026gt;1.2mg/dL and good = \u0026le;1.2mg/dL, serum glutamic pyruvic transaminase (SGPT): poor = \u0026gt;40 U/L and good = \u0026le;40 U/L; serum c- reactive protein: poor = \u0026ge;6 mg/L and good = \u0026lt;6 mg/L; blood D-dimer: poor = \u0026gt;500 ng/mL and good = \u0026le;500 ng/mL; blood hemoglobin: poor = \u0026lt;10 gm/dL and good = \u0026ge;10 gm/dL; total count of White blood cell (WBC): poor = \u0026lt;4000/\u0026mu;L and good = \u0026ge; 4000/\u0026mu;L and \u0026lt;11000/\u0026mu;L; blood Neutrophil Lymphocyte ratio: poor = \u0026gt;3.5 and good = \u0026le;3.5; differential count of Monocytes: poor = \u0026gt;8% and good = 2-8%; differential count of Eosinophils: poor = \u0026gt;4% and good = 1-4%; and blood Platelet level: poor = \u0026lt;150000/\u0026mu;L and good = \u0026ge; 150000/\u0026mu;L.\u003c/p\u003e\n\u003cp\u003eDuring the follow-up via teleconsultation, the patients reported a wide range of persistent symptoms as self-reported complications. We have documented responses from the patients' medical records and aggregated them into broad categories of respiratory, cardiovascular, abdominal, otolaryngologic (ears, nose, and throat), musculoskeletal, febrile, post-COVID fatigue syndrome, other miscellaneous, death, and none as reported complications.\u003c/p\u003e\n\u003cp\u003e2.4\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Statistical Analysis\u003c/p\u003e\n\u003cp\u003eAs the case series of COVID-19 patients presented some selection bias\u0026ndash; the patients' sample was not selected by random sampling \u0026ndash; we have only focused on descriptive analysis of information derived from the COVID-19 patients. To describe the characteristics of the study sample, we have categorized their demographic information, health status, and signs and symptoms associated with COVID-19, which were summarized as counts and percentages. These attributes of the sample were further disaggregated across gender and age categories to assess their association using \u0026chi;2 test at the significance level of P \u0026lt; 0.05 (in case of small sample size, less than 5, in the bivariate cells Fisher's exact test was used).\u003c/p\u003e\n\u003cp\u003eNext, considering the lowest recorded Oxygen Saturation (SpO2) of the patient as a cardinal prognostic factor, we have explored the statistical association between a binary indicator of SpO2 (\u0026gt; 93% as good, and \u0026le; 93% as poor) and key demographic and health-related indicators of the patients. We have also investigated a similar association between the result of the diagnostic tests of the patients and their SpO2 to identify any significant association which may provide further insight on the hematological correlates of COVID-19.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo understand the patients' treatment regimen pattern, we have presented descriptive statistics of the medication provided to different age groups and genders. Lastly, we have attempted to visualize the persistent complications self-reported by the patients during the follow-up. As presented in Figure 1, the different components of the analysis had a varying number of samples due to the limited availability of the data. However, during the analysis, we did not impute any missing data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.5\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Compliance with ethical standards\u003c/p\u003e\n\u003cp\u003eThe result of this retrospective study was based on the data obtained during the clinical provision of care. During the compilation of the data, all medical records were anonymized to protect the confidentiality of the patients. The institutional review board of the James P. Grant School of Public Health, BRAC University, has provided the ethical approval of the study protocol (Ref. No: IRB-7 December'20-054).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Demographic and clinical characteristics of the patients\u003c/p\u003e\n\u003cp\u003eOf the 442 patients admitted to the hospital, 422 had SARS-CoV-2 infection confirmed by the RT-PCR test. Among the confirmed COVID-19 patients, 64% (n = 271) were male, and 36% (n = 150) were female, and among them four female patients were pregnant. The demographic and health characteristics of the patients are presented in Tables 1 and 2, disaggregated according to their gender and age.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the admitted COVID-19 patients, the majority of patients ( 28%, n = 120) were 35-49 years old. While most of the patients (38%, n = 159) were service holders, the study sample consisted of 17% (n = 71) healthcare workers. Around 41% (n = 168) of the patients could not recall any contact history with previously confirmed COVID-19 patients, and an additional 40% (n = 164) patients provided positive contact history. Before admitting to the hospital, almost two-third (66%, n = 277) of the patients had the symptoms of COVID-19 for one week. Half of the patients (52%, n = 154) reported having at least one underlying comorbidity.\u003c/p\u003e\n\u003cp\u003eMale patients reported a higher proportion of comorbidity than females (69% vs. 41%), though it was not statistically significant. However, looking into individual type of comorbidity, significantly higher proportion of male patients had Diabetes (P \u0026lt; 0.001), Hypertension (P \u0026lt; 0.001), Ischemic heart disease (P = 0.048) compared to the female patients (Figure 2), while 30% (n = 22) females presented with Asthma, compared to 14% (n = 16) men and this difference is statistically significant (P = 0.027). At triage in the hospital, out of 422 patients, 379 presented any clinical feature of COVID-19, and the most common symptom (80%) was fever (Figure 3). Consequently, the next three most frequent symptoms were associated with respiratory systems, which were cough (60%, n = 227), dyspnea (41%, n = 155), and sore throat (21%, n = 81).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe clinical record showed the blood SpO2 level of 304 patients (72% of the study sample). A lower SpO2 level is a critical factor indicating the severity of COVID-19, and Table 3 presents the SpO2 level (\u0026le; 93% vs.\u0026gt; 93%) according to the patients' characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe SpO2 level of the patients presented significant association with their age (P \u0026lt; 0.001), occupation (P = 0.002), and presence of underlying comorbidity (P \u0026lt; 0.001). We have observed that lower SpO2 levels were reported for older patients. Similarly, almost twice as many patients with lower SpO2 levels reported comorbidity (69% vs. 31%), indicating a strong association between the underlying health condition and the severity of the disease among confirmed COVID-19 patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Radiological and laboratory findings\u003c/p\u003e\n\u003cp\u003eOf the confirmed patients, 274 had their chest X-ray available, while computed tomography (C.T.) of the chest was not conducted due to resource constraints. Table 4 shows the radiological findings and the result of laboratory investigations during hospitalization. X-ray finding suggestive of Pneumonia was observed among 39 % (n = 107) of the patients, indicated by mixed inhomogeneous opacity in the posterior-anterior (P.A.) view of lung X-ray.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLaboratory findings suggests, only 3% (n = 2) and 10% (n = 7) patients presented leukopenia and thrombocytopenia accordingly. However, 29% (25 out of 85) patients had their Neutrophil Lymphocyte Ratio elevated more than 3.5 times. Among other findings, elevated level of SGPT (58.54%, n = 144), C-reactive Protein (37%, n = 90), serum creatinine (21%, n = 53), and D-dimer (22%, n \u0026ndash; 32) were observed. SpO2 level was significantly associated with radiological findings of Pneumonia (P \u0026lt; 0.001), serum Creatinine (P \u0026lt; 0.011), serum C-reactive Protein (P = 0.004), and Neutrophil Lymphocyte Ratio (P = 0.001). The patients with lower SpO2 level had the poor prognostic values of the radiological and laboratory findings.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Treatment and medications\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients (n = 422) received symptomatic medication for their illness during hospitalization. Table 5 presents the treatment and medication given to patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMajority of the patients received antibiotics (77%, n = 318), and anticoagulants therapy (56%, n = 232). These proportions were even higher among the patients with lower SpO2 levels \u0026ndash; 93% and 87% for antibiotic and anticoagulants therapy accordingly. Overall, 63% (n = 262) patients received oxygen supplementation. Except for one patient, everyone presented with a SpO2 level \u0026le; 93% received oxygen supplement, and more than half of the patients (59%, n = 114) received oxygen supplement despite having their SpO2 level \u0026gt; 93%. Most of the older adults and senior patients received anticoagulant and glucocorticoid medications. Among all patients, 29% (n = 121) received Hydroxychloroquine and 6% (n = 27) received Ivermectin. More than 95% of these patients receiving these therapies were between 19 and 49 years. Moreover, a negligible number of patients with low SpO2 level received Hydroxychloroquine (14%, n = 15) and Ivermectin (5%, n = 6) therapies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe clinical record showed that 7.43% of the patients (n = 31) received antiviral medications. Only ten patients received injectable Remdesivir, and 21 patients received oral antiviral Favipiravir therapy. Among other drugs, four patients received Convalescent Plasma therapy, and three patients received injectable Tocilizumab (Interleukin-6 inhibitor).\u003c/p\u003e\n\u003cp\u003e3.4\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Clinical outcomes and persistent complications\u003c/p\u003e\n\u003cp\u003eThe median duration of hospital stay for the patients was 12 days; mean 12.36 days, standard deviation [SD] 6.51 days, and range 1 to 32 days. Across the age groups, significant variability of hospitalization duration (P = 0.012) (Figure 4). While the elderly patients (59+ years) had the most variability of hospital stay (S.D. = 7.30-day, range 1 to 31), their average hospitalization duration was 11 days. In contrast, younger patients (19-24 years) had, on average, the most prolonged hospital stay (mean 14.11 days, SD 5.92, and range 3 to 27 days).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore than 90% (n = 381) patients successfully weaned from SARS-CoV-2 infection (Table 1). During their hospital stay, 13 patients (3%) died due to COVID-19, 18 patients (4%) were referred out to other facilities, and eight patients (2%) were discharged voluntarily after signing risk bonds. After discharge, the hospital was able to conduct a teleconsultation to follow up on 399 patients. An additional eight deaths (2%) were reported during the follow-up teleconsultation (Figure 5). The follow-up was conducted on average, 66 days after the discharge of the patients (range 1 to 129 days). Out of 399 patients, 164 patients (41%) reported experiencing complications after hospital discharge. Among them, 84 patients (51%) reported one, 50 reported two (37%), 30 reported three compilations (22%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the follow-up period, additional eight deaths were reported. The majority of the patients' complications were associated with respiratory systems, consisting of around 62% (n = 52) of the first and 44% (n = 22) of the second complications. The type of respiratory complications consisted of cough and cold, chest heaviness, shortness of breath, and pain during breathing. Among other frequently reported complications were post-COVID fatigue syndrome, fever, and musculoskeletal pain.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis retrospective, observational case series study analyzed the clinical and epidemiological characteristics, and outcomes of RT-PCR positive COVID-19 patients admitted in a tertiary hospital in Dhaka, Bangladesh. Among the admitted patients, one-third were female, and one-third were service holders, 17% were healthcare providers and more than half of the patients presented with underlying comorbidity. Significantly, a higher number of male COVID-19 patients presented with diabetes, hypertension, and ischemic heart disease, whereas asthma was significantly prevalent among women patients. Several meta-analyses also indicated a higher disease burden of COVID-19 among male smokers and patients with comorbid conditions such as cardiovascular disease, hypertension, and diabetes [24\u0026ndash;26]. Around 9.35% of patients included in this study were asymptomatic during the initial assessment. Fever and respiratory symptoms were most common among the patients, reported in numerous other studies [21,27,28]. While several studies from China [28,29], Uzbekistan [30], and Brazil [31] reported weakness or fatigue as a common symptom, only 8% (n = 31 out of 377) of the patients reported that they experienced weakness during initial triage at the hospital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur result showed a SpO2 level of more than 36% (110 out of 304) hospitalized patients dropped below 94% \u0026ndash; which is an indicator of severe illness [32,33]. The lower SpO2 level or severe illness was significantly associated with older age and the presence of any comorbidity. Besides, the severe illness was significantly associated with radiological findings of Pneumonia [34], higher creatinine [27,35], c-reactive protein [20,35], and Neutrophil Lymphocyte ratio [36\u0026ndash;38]. A higher proportion of patients with low SpO2 levels frequently received antibiotics, anticoagulants, and glucocorticoid therapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the patient pool, a total of 21 deaths were reported \u0026ndash; 13 during the hospitalization period. Eight deaths were reported after discharge, which resulted in a total case-fatality ratio of 5.45% (21 out of 422), which is significantly higher than the national average case-fatality ratio of Bangladesh which is 1.47% [39]. We followed up with 399 patients to investigate their post-discharge complications, and around 41.10% reported at least one complication. Respiratory complications were reported most frequently as persistent symptoms after discharge [40]. Approximately, 31% of patients reported post-COVID fatigue syndrome as complications reported during the telemedicine consultation. Similar to other post-viral infections that cause Chronic Fatigue Syndrome (similar to Myalgic Encephalomyelitis), prolonged fatigue is commonly reported after COVID-19 infection [41,42].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing a comprehensive set of medical records is the core strength of the study. The clinical and follow-up data quality originated from the tertiary care hospital is exceptionally vigorous, making our result robust and reliable. However, we have to acknowledge a few limitations of this study. First, the result of this study cannot be generalized for the national context of Bangladesh. We have included confirmed COVID-19 cases admitted to the hospital, which can result in selection bias. It is also indicated by the high level of case-fatality ratio identified in the study [43]. Generally, more severe cases of COVID-19 were admitted to the hospital, which resulted from a non-randomized nature of sample recruitment in our study due to the sampling bias [44]. However, we are not exploring any inferential statistics \u0026ndash; just defining feature of COVID-19 cases \u0026ndash; the selection bias is inconsequential for our study. Secondly, the completeness of the data is a common complication while using medical records [45]. We decided not to impute any data point to account for the missingness. Instead, we wanted to be transparent [46] and explicitly report the data's missingness for each study component (Figure 1).\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the clinical and epidemiological characteristics and health outcomes of COVID-19 patients are significantly different across the patients' age groups and gender. Our study has also identified several significant associations with SpO2 level and several patient attributes, hematological correlations, and medication regimen. While we recommend multicenter studies with a larger patient cohort, this study has broken new ground in Bangladesh for clinical research on COVID-19. The result of this study will inform clinicians, public health researchers, and policymakers regarding the nature of COVID-19 in Dhaka, Bangladesh, which became an epicenter of the pandemic.\u0026nbsp;\u003c/p\u003e"},{"header":"6. Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the Director of the Maternal and Child Health Training Institute (MCHTI), Dr. Md. Shamsul Karim for his invaluable assistance. We also thank the health care providers of MCHTI for their dedication and relentless determination to deliver care to the patients infected with SARS-CoV-2. Lastly, we humbly acknowledge the patients admitted to the MCHTI hospital. Without their information, this paper could not be developed.\u003c/p\u003e\u003cp\u003e6.1\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Funding\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e6.2\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Conflicts of interest\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e6.3\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Availability of data and material\u003c/p\u003e\n\u003cp\u003eThe data supporting the finding of this study will be made available to any qualified researcher by the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e6.4\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Ethical Review\u003c/p\u003e\n\u003cp\u003eThe institutional review board of the James P. Grant School of Public Health, BRAC University, has provided the ethical approval of the study protocol (Ref. No: IRB-7 December'20-054).\u003c/p\u003e\n\u003cp\u003e6.5\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Authors' contributions\u003c/p\u003e\n\u003cp\u003eAll authors of this study contributed to the design and conceptualization. Establishing institutional collaboration and data acquisition was led by Nirmol Kumar Biswas, Juli Chowdhury, and Ahmad Monjurul Aziz. The analytical plan was developed collaboratively by Md Zabir Hasan and Malabika Sarker. The Analysis was performed by Md Zabir Hasan. The first draft of the manuscript was prepared by Md Zabir Hasan with the support of Shams Shabab Haider and with the supervision of Malabika Sarker and Nirmol Kumar Biswas. All authors reviewed the content of the manuscript and provided their critical comments. The final version of the manuscript was read approved by all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. COVID-19 Weekly Epidemiological Update [Internet]. World Health Organization; 2020 Dec. Available from: file:///C:/Users/admin/Downloads/20201222_Weekly_Epi_Update_19.pdf\u003c/li\u003e\n\u003cli\u003eChen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. Elsevier; 2020;395:507\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eWang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323:1061\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eHuang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. 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British Journal of Dermatology. 2017;177:1463\u0026ndash;5.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Characteristics of the COVID-19 positive patients disaggregated by their gender\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient's Characteristics (Reported Sample Size)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 271)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 150)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 421)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cem\u003eP-values\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Age Category in Years (n = 420)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eLess than 19 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e7 (2.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e9 (6.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" width=\"8%\"\u003e\n\u003cp\u003e0.478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e16 (3.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e19-24 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e30 (11.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e17 (11.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e47 (11.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e25-34 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e71 (26.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e43 (28.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e114 (27.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e35-49 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e80 (29.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e40 (26.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e120 (28.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e50-59 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e41 (15.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e24 (16.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e65 (15.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eMore than 59 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e41 (15.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e17 (11.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e58 (13.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Occupation (n = 415)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eWage Earner\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e12 (4.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e4 (2.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"7\" width=\"8%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e16 (3.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eBusiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e51 (19.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e1 (0.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e52 (12.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eService\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e131 (49.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e28 (18.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e159 (38.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eHealthcare Worker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e31 (11.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e40 (26.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e71 (17.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eHousewife\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e62 (41.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e62 (14.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eStudent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e18 (6.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e12 (8.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e30 (7.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eUnemployed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e22 (8.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e3 (2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e25 (6.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e\u003cstrong\u003eMonthly Income in BDT (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e5,000-10,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e138 (52.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e70 (46.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"8%\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e208 (50.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e10,000-30,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e76 (29.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e45 (30.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e121 (29.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e30,000-50,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e12 (4.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e12 (8.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e24 (5.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eMore than 50,0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e35 (13.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e23 (15.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e58 (14.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking Status of Patient (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e206 (78.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e148 (99.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"8%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e354 (86.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e55 (21.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e1 (0.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e56 (13.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePresence of Any Comorbidity (n = 412)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e149 (57.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e77 (51.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"8%\"\u003e\n\u003cp\u003e0.259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e226 (54.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e112 (42.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e73 (48.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e185 (45.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"57%\"\u003e\n\u003cp\u003e\u003cstrong\u003eHistory of Contact with COVID-19 Case (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e63 (24.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e16 (10.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"8%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e79 (19.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e85 (32.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e79 (52.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e164 (39.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e113 (43.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e55 (36.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e168 (40.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"57%\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration of Symptoms during Initial Assessment (n = 418)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eAsymptomatic during initial assessment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e25 (9.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e14 (9.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" width=\"8%\"\u003e\n\u003cp\u003e0.827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e39 (9.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e1-7 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e173 (64.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e104 (69.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e277 (66.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e8-14 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e56 (20.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e26 (17.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e82 (19.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e15-21 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e9 (3.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e5 (3.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e14 (3.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eMore than 21 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e4 (1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e1 (0.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e5 (1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome of Hospitalization (n = 421)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eDeath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e10 (3.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e3 (2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"8%\"\u003e\n\u003cp\u003e0.681\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e13 (3.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eRecovery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e242 (89.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e139 (92.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e381 (90.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eReferred to other facilities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e13 (4.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e5 (3.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e18 (4.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eDischarge on risk bond\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e5 (1.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e3 (2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e8 (1.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"57%\"\u003e\n\u003cp\u003e\u003cstrong\u003eReported any Complication during Follow-up (n = 399)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e154 (61.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e81 (55.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"8%\"\u003e\n\u003cp\u003e0.239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e235 (58.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"42%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e98 (38.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"17%\"\u003e\n\u003cp\u003e66 (44.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e164 (41.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"100%\"\u003e\n\u003cp\u003eNote:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Col% = Column Percentage\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Characteristics of the COVID-19 positive patients disaggregated by their age category\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"26%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient's Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Reported Sample Size)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt; 19 Years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e19-24 Years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e25-34 Years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e35-49 Years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e50-59 Years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gt; 59 Years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll Patients\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 16)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 47)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 114)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 120)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 65)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 58)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 420)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eP-Values\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Gender (n = 420)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e7 (43.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e30 (63.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e71 (62.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e80 (66.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e41 (63.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e41 (70.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0.478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e270 (64.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e9 (56.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e17 (36.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e43 (37.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e40 (33.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e24 (36.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e17 (29.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e150 (35.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Occupation (n = 415)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eWage Earner\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (2.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e4 (3.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e9 (7.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e2 (3.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"7\" width=\"9%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e16 (3.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eBusiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e8 (7.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e22 (18.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e12 (18.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e10 (17.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e52 (12.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eService\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e20 (44.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e53 (46.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e50 (42.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e24 (36.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e12 (20.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e159 (38.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eHealthcare Worker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e13 (28.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e32 (28.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e17 (14.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e8 (12.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e71 (17.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eHousewife\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (2.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e8 (7.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e20 (16.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e19 (29.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e14 (24.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e62 (14.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eStudent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e14 (87.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e9 (20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e7 (6.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e30 (7.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eUnemployed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e2 (12.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (2.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (0.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e21 (36.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e25 (6.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u003cstrong\u003eMonthly Income in BDT (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e5,000-10,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e5 (31.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e32 (71.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e65 (58.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e64 (54.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e21 (32.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e21 (36.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"4\" width=\"9%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e208 (50.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e10,000-30,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e6 (37.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e3 (6.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e39 (35.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e32 (27.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e21 (32.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e20 (35.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e121 (29.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e30,000-50,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e2 (12.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e3 (6.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e4 (3.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e6 (5.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e5 (7.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e4 (7.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e24 (5.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eMore than 50,0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e3 (18.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e7 (15.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e3 (2.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e15 (12.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e18 (27.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e12 (21.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e58 (14.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking Status of Patient (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e16 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e38 (84.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e95 (85.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e103 (88.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e57 (87.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e45 (80.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0.440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e354 (86.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e7 (15.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e16 (14.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e14 (11.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e8 (12.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e11 (19.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e56 (13.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"35%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePresence of Any Comorbidity (n = 412)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e15 (93.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e39 (86.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e89 (80.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e65 (55.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e10 (15.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e8 (14.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e226 (54.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e1 (6.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e6 (13.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e22 (19.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e52 (44.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e55 (84.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e49 (85.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e185 (45.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003eHistory of Contact with COVID-19 Case (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e3 (18.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e7 (15.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e16 (14.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e28 (23.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e13 (20.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e12 (21.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0.109\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e79 (19.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e7 (43.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e18 (40.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e56 (50.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e42 (35.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e28 (43.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e13 (22.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e164 (39.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e6 (37.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e20 (44.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e39 (35.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e47 (40.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e24 (36.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e32 (56.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e168 (40.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" width=\"52%\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration of Symptoms during Initial Assessment (n = 418)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eAsymptomatic during initial assessment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e2 (12.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e11 (23.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e14 (12.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e10 (8.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"5\" width=\"9%\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e39 (9.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e1-7 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e11 (68.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e29 (61.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e75 (66.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e76 (64.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e45 (69.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e40 (70.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e276 (66.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e8-14 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e2 (12.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e2 (4.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e20 (17.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e29 (24.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e18 (27.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e11 (19.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e82 (19.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e15-21 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e1 (6.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e4 (8.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e3 (2.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e2 (1.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e4 (7.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e14 (3.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eMore than 21 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (2.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (0.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (0.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e5 (1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome of Hospitalization (n = 421)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eDeath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e1 (6.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e3 (2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e5 (7.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e4 (6.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"4\" width=\"9%\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e13 (3.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eRecovery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e15 (93.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e46 (97.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e106 (93.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e112 (93.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e56 (86.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e45 (77.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e380 (90.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eReferred to other facilities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e5 (4.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e3 (2.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e2 (3.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e8 (13.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e18 (4.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eDischarge on risk bond\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (2.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e2 (1.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e2 (1.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e2 (3.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e1 (1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e8 (1.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003eReported any Complication during Follow-up (n = 399)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e10 (66.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e35 (79.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e73 (65.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e59 (51.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e27 (44.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e31 (57.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" rowspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e235 (58.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e5 (33.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e9 (20.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e38 (34.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e55 (48.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e34 (55.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"9%\"\u003e\n\u003cp\u003e23 (42.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e164 (41.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"23\" width=\"100%\"\u003e\n\u003cp\u003eNote:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Col% = Column percentage\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Presentation of blood oxygen saturation level (SpO2: \u0026le; 93% vs. \u0026gt; 93%) according to their characteristics of the COVID-19 positive patients\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient's Characteristics \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Reported Sample Size)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpO2 Level \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026le; 93%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpO2 Level \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gt; 93%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients with reported SpO2 \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 110)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 194)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 304)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cem\u003eP-Values\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Age Category in Years (n = 420)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eLess than 19 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e2 (1.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e6 (3.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" width=\"13%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e8 (2.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e19-24 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e6 (5.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e16 (8.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e22 (7.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e25-34 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e11 (10.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e70 (36.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e81 (26.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e35-49 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e27 (24.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e58 (30.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e85 (28.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e50-59 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e29 (26.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e26 (13.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e55 (18.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eMore than 59 Years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e34 (31.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e17 (8.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e51 (16.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Gender (n = 421)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e70 (64.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e118 (60.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"13%\"\u003e\n\u003cp\u003e0.559\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e188 (62.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e39 (35.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e76 (39.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e115 (37.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Occupation (n = 415)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eWage Earner\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e5 (4.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e8 (4.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"7\" width=\"13%\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e13 (4.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eBusiness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e20 (18.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e18 (9.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e38 (12.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eService\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e33 (30.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e79 (41.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e112 (37.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eHealthcare Worker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e12 (11.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e35 (18.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e47 (15.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eHousewife\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e25 (22.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e26 (13.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e51 (17.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eStudent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e2 (1.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e16 (8.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e18 (6.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eUnemployed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e12 (11.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e9 (4.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e21 (7.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003eMonthly Income in BDT (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e5,000-10,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e50 (45.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e91 (48.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"13%\"\u003e\n\u003cp\u003e0.886\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e141 (47.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e10,000-30,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e35 (32.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e53 (28.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e88 (29.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e30,000-50,000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e7 (6.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e11 (5.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e18 (6.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eMore than 50,0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e17 (15.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e33 (17.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e50 (16.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking Status of Patient (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eNon-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e97 (88.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e165 (87.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"13%\"\u003e\n\u003cp\u003e0.752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e262 (88.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eSmoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e12 (11.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e23 (12.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e35 (11.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003ePresence of Any Comorbidity (n = 412)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e34 (30.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e110 (58.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"13%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e144 (48.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e76 (69.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e78 (41.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e154 (51.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"58%\"\u003e\n\u003cp\u003e\u003cstrong\u003eHistory of Contact with COVID-19 Case (n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e21 (19.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e36 (19.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"13%\"\u003e\n\u003cp\u003e0.225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e57 (19.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e35 (32.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e78 (41.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e113 (38.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eUnknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e53 (48.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e74 (39.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e127 (42.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"58%\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration of Symptoms during Initial Assessment (n = 418)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eAsymptomatic during initial assessment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e2 (1.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e12 (6.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" width=\"13%\"\u003e\n\u003cp\u003e0.146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e14 (4.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e1-7 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e71 (64.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e132 (68.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e203 (67.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e8-14 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e30 (27.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e41 (21.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e71 (23.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e15-21 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e4 (3.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e6 (3.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e10 (3.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eMore than 21 Days\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e3 (2.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e1 (0.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e4 (1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome of Hospitalization (n = 421)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eDeath\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e9 (8.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e1 (0.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"13%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e10 (3.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eRecovery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e88 (80.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e185 (95.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e273 (90.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eReferred to other facilities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e11 (10.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e3 (1.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e14 (4.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eDischarge on risk bond\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e2 (1.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e4 (2.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e6 (1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"58%\"\u003e\n\u003cp\u003e\u003cstrong\u003eReported any Complication during Follow-up (n = 399)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e46 (46.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e108 (57.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"13%\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e154 (53.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"44%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e54 (54.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e79 (42.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e133 (46.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"100%\"\u003e\n\u003cp\u003eNote:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Oxygen saturation above 93% is considered a good prognostic indicator, and a saturation below or equal to 93% is \u0026nbsp;\u0026nbsp; considered poor; Col% = Column percentage\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Radiological and laboratory findings of the COVID-19 positive patients during hospitalization and the association with their blood oxygen saturation level measured during the initial examination\u003c/p\u003e\n\u003ctable border=\"1\" width=\"720\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 59px;\"\u003e\n\u003ctd style=\"height: 94px;\" rowspan=\"2\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatient's Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Reported Sample Size)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"103\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpO2 Level \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026le; 93%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpO2 Level \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gt; 93%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll Patients\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 110)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 194)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e\u003cem\u003eP-Values\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eX-ray Finding\u003c/strong\u003e\u0026nbsp;\u003cstrong\u003eSuggestive of Pneumonia (n = 274)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 48px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eAbsent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e24 (28.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e96 (72.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e167 (60.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePresent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e61 (71.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e36 (27.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e107 (39.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eCreatinine Level (n = 258)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026gt; 1.2mg/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e23 (26.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e16 (12.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e53 (20.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: \u0026le; 1.2mg/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e64 (73.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e110 (87.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e205 (79.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eSGPT Level (n = 246)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026gt; 40 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e49 (59.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e67 (55.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e144 (58.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: \u0026le; 40 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e33 (40.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e54 (44.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e102 (41.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eC-reactive Protein Test (n = 244)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026ge; 6 mg/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e43 (52.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e38 (31.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e90 (36.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: \u0026lt; 6 mg/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e39 (47.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e81 (68.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e154 (63.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eD-dimer Level (n = 143)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026gt; 500 ng/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e20 (28.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e9 (15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e32 (22.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: \u0026le; 500 ng/mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e49 (71.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e51 (85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e111 (77.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eBlood Hemoglobin Level (n = 87)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026lt; 10 gm/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e11 (40.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e11 (28.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e28 (32.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: \u0026ge; 10 gm/dl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e16 (59.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e28 (71.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e59 (67.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eWBC Total Count (n = 79) \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026lt; 4000/\u0026mu;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e1 (2.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e2 (2.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: \u0026ge; 4000/\u0026mu;L and \u0026lt;11000/\u0026mu;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e24 (100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e34 (97.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e77 (97.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeutrophil Lymphocyte Ratio (n = 86)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026gt; 3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e15 (55.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e6 (15.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e25 (29.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: \u0026le; 3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e12 (44.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e33 (84.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e61 (70.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eMonocytes Differential Count (n = 86)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026gt; 8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e21 (31.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e21 (31.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e22 (25.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: 2-8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e45 (68.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e45 (68.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e64 (74.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003eEosinophils Differential Count (n = 86)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026gt; 4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e3 (11.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e4 (10.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e8 (9.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood: 1-4%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e24 (88.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e35 (89.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e78 (90.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003e\u003cstrong\u003ePlatelet Level (n = 71)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35.25px;\"\u003e\n\u003ctd style=\"height: 35.25px;\" width=\"306\"\u003e\n\u003cp\u003ePoor: \u0026lt; 150000/\u0026mu;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.25px;\" width=\"103\"\u003e\n\u003cp\u003e2 (9.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.25px;\" width=\"104\"\u003e\n\u003cp\u003e5 (14.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 70.25px;\" rowspan=\"2\" width=\"103\"\u003e\n\u003cp\u003e0.535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.25px;\" width=\"104\"\u003e\n\u003cp\u003e7 (9.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"306\"\u003e\n\u003cp\u003eGood:\u0026nbsp; \u0026ge; 150000/\u0026mu;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"103\"\u003e\n\u003cp\u003e20 (90.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e29 (85.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"104\"\u003e\n\u003cp\u003e64 (90.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 48px;\"\u003e\n\u003ctd style=\"height: 48px;\" colspan=\"5\" width=\"720\"\u003e\n\u003cp\u003eNote:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Oxygen saturation above 93% is considered a good prognostic indicator, and a saturation below or equal to 93% is \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; considered poor; Col% = Column percentage\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e Treatment and medication given to the COVID-19 positive patients during hospitalization disaggregated by their age and their blood oxygen saturation (SpO2) level measured during the initial examination\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"46%\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge Categories\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"17%\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpO2 Level\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"11%\"\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment and \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emedication given\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Reported Sample Size) \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt; 19 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYears\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e19-24 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYears\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e25-34 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYears\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e35-49 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYears\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e50-59 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYears\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gt; 59 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYears\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026le; 93%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gt; 93%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll Patients\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 16)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 47)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 114)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 120)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 65)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 58)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 110)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 194)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Row%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Row%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Row%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Row%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Row%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Row%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003eN (Col%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003eN (Col%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003eP-values\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u003cem\u003eN (Col%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"19%\"\u003e\n\u003cp\u003e\u003cstrong\u003eAntibiotic (n = 414)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e4 (4.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e10 (10.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e41 (43.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e22 (23.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e9 (9.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e9 (9.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e7 (6.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e55 (28.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e96 (23.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e12 (3.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e36 (11.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e73 (23.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e97 (30.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e53 (16.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e46 (14.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e99 (93.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e138 (71.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e318 (76.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"19%\"\u003e\n\u003cp\u003e\u003cstrong\u003eHydroxychloroquine \u003c/strong\u003e\u003cstrong\u003e(n = 411)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e14 (4.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e21 (7.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e73 (25.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e76 (26.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e57 (19.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e49 (16.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e90 (85.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e140 (72.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e290 (70.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e2 (1.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e25 (21.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e41 (34.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e41 (34.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e5 (4.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e5 (4.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e15 (14.29)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e53 (27.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e121 (29.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"19%\"\u003e\n\u003cp\u003e\u003cstrong\u003eAnticoagulant (n = 413)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e12 (6.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e38 (21.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e72 (40.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e47 (26.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e9 (5.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e2 (1.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e14 (13.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e86 (44.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e181 (43.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e4 (1.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e8 (3.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e42 (18.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e71 (30.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e53 (22.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e53 (22.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e92 (86.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e107 (55.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e232 (56.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"19%\"\u003e\n\u003cp\u003e\u003cstrong\u003eAntiviral (n = 417)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e16 (4.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e46 (11.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e111 (28.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e109 (28.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e54 (14.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e48 (12.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e92 (85.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e182 (93.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e386 (92.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e3 (9.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e10 (32.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e9 (29.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e9 (29.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e16 (14.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e12 (6.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e31 (7.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"19%\"\u003e\n\u003cp\u003e\u003cstrong\u003eGlucocorticoids (n = 412)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e15 (4.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e40 (12.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e105 (32.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e91 (28.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e43 (13.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e28 (8.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e52 (49.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e164 (84.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e324 (78.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1 (1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e6 (6.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e9 (10.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e27 (30.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e18 (20.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e27 (30.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e53 (50.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e29 (15.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e88 (21.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"19%\"\u003e\n\u003cp\u003e\u003cstrong\u003eOxygen Supplement (n = 413)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e10 (6.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e18 (11.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e57 (37.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e43 (28.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e17 (11.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e6 (3.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e1 (0.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e79 (40.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e151 (36.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e6 (2.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e27 (10.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e57 (21.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e76 (29.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e45 (17.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e49 (18.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e104 (99.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e114 (59.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e262 (63.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"19%\"\u003e\n\u003cp\u003e\u003cstrong\u003eIvermectin (n = 421)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e16 (4.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e44 (11.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e106 (27.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e106 (27.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e63 (16.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e57 (14.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e104 (94.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e183 (94.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.937\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e394 (93.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e3 (11.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e8 (29.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e14 (51.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1 (3.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1 (3.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e6 (5.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e11 (5.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e27 (6.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u003cstrong\u003eOthers (n = 419)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e0 (0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e3 (11.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e8 (29.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e14 (51.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1 (3.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e1 (3.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e6 (5.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e11 (5.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003e0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e27 (6.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e16 (4.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e44 (11.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e106 (27.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e106 (27.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e62 (15.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"7%\"\u003e\n\u003cp\u003e56 (14.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e103 (94.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003e182 (94.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e392 (93.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" width=\"89%\"\u003e\n\u003cp\u003eNote:\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Row% = Row percentage; Col% = Column percentage, SpO2 = blood oxygen saturation level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Clinical Characteristics, Post-COVID-19 Complications, Post-COVID-19 Fatigue Syndrome, Oxygen Saturation, Bangladesh ","lastPublishedDoi":"10.21203/rs.3.rs-157669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-157669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAfter one year since emerging from Wuhan, China, the Coronavirus disease 2019 (COVID-19) pandemic is still raging worldwide. Still, there is a dearth of original research exploring the attributes of COVID-19 patients from lower-and middle-income countries, such as Bangladesh. Based on a case series from a tertiary healthcare center, this observational study has explored the epidemiological and clinical profile of COVID-19 patients in Dhaka, Bangladesh. A total of 422 COVID-19 confirmed patients (via Reverse transcription-polymerase chain reaction test) were enrolled in this study. We have compiled patients' medical records and reported their demographic, socioeconomic, and clinical features, treatment history, health outcome, and post-discharge complications descriptively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003ePatients were predominantly male (64%), between 35 to 49 years (28%), with at least one comorbidity (52%), and had COVID-19 symptoms for one week before hospitalization (66%). A significantly higher proportion (P\u0026lt;0.05) of male patients had diabetes, hypertension, and ischemic heart disease, while females had a significantly higher proportion (P\u0026lt;0.05) of asthma. The most common symptoms were fever (80%), cough (60%), dyspnea (41%), and sore throat (21%). Most patients received antibiotics (77%) and anticoagulant therapy (56%) and stayed in the hospital for an average of 12 days. Over 90% of patients were successfully weaned, while 3% died from COVID-19, and 41% reported complications after discharge.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe diversity of clinical and epidemiological characteristics and health outcomes of COVID-19 patients across age groups and gender is noteworthy. Our result will inform the clinicians and epidemiologists of Bangladesh of their COVID-19 mitigation effort.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Clinical profile and short-term outcomes of RT-PCR positive COVID-19 patients in a tertiary care hospital in Dhaka, Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-08 15:34:33","doi":"10.21203/rs.3.rs-157669/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e35ae50-e16b-4f6e-8069-398757a6603f","owner":[],"postedDate":"February 8th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":2291101,"name":"Virology"},{"id":2291102,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2021-12-17T21:42:32+00:00","versionOfRecord":{"articleIdentity":"rs-157669","link":"https://doi.org/10.1136/bmjopen-2021-055126","journal":{"identity":"bmj-open","isVorOnly":true,"title":"BMJ Open"},"publishedOn":"2021-11-01 00:00:00","publishedOnDateReadable":"November 1st, 2021"},"versionCreatedAt":"2021-02-08 15:34:33","video":"","vorDoi":"10.1136/bmjopen-2021-055126","vorDoiUrl":"https://doi.org/10.1136/bmjopen-2021-055126","workflowStages":[]},"version":"v1","identity":"rs-157669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-157669","identity":"rs-157669","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}