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Background The COVID-19 pandemic is a significant public health crisis that is hitting hard on people's health, well-being, and freedom of movement, and affecting the global economy. Scientists worldwide are competing to develop therapeutics and vaccines; currently, three drugs and two vaccine candidates have been given emergency authorization use. However, there are still questions of efficacy with regard to specific subgroups of patients and the vaccine's scalability to the general public. Under such circumstances, understanding COVID-19 symptoms is vital in initial triage; it is crucial to distinguish the severity of cases for effective management and treatment. This study aimed to discover symptom patterns and overall symptom rules, including rules disaggregated by age, sex, chronic condition, and mortality status, among COVID-19 patients. Methods This study was a retrospective analysis of COVID-19 patient data made available online by the Wolfram Data Repository through May 27, 2020. We applied a widely used rule-based machine learning technique called association rule mining to identify frequent symptoms and define patterns in the rules discovered. Result In total, 1,560 patients with COVID-19 were included in the study, with a median age of 52 years. The most frequently occurring symptom was fever (67%), followed by cough (37%), malaise/body soreness (11%), pneumonia (11%), and sore throat (8%). Myocardial infarction, heart failure, and renal disease were present in less than 1% of patients. The top ten significant symptom rules (out of 71 generated) showed cough, septic shock, and respiratory distress syndrome as frequent consequents. If a patient had a breathing problem and sputum production, then, there was higher confidence of that patient having a cough; if cardiac disease, renal disease, or pneumonia was present, then there was a higher confidence of septic shock or respiratory distress syndrome. Symptom rules differed between younger and older patients and between male and female patients. Patients who had chronic conditions or died of COVID-19 had more severe symptom rules than those patients who did not have chronic conditions or survived of COVID-19. Concerning chronic condition rules among 147 patients, if a patient had diabetes, prerenal azotemia, and coronary bypass surgery, there was a certainty of hypertension. Conclusion The most frequently reported symptoms in patients with COVID-19 were fever, cough, pneumonia, and sore throat; while 1% had severe symptoms, such as septic shock, respiratory distress syndrome, and respiratory failure. Symptom rules differed by age and sex. Patients with chronic disease and patients who died of COVID-19 had severe symptom rules more specifically, cardiovascular-related symptoms accompanied by pneumonia, fever, and cough as consequents.

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Novel coronavirus disease 2019 (COVID-19) is a growing public health crisis. Despite initial focus on the elderly population with comorbidities, it seems that large studies from the worst affected countries follow a sex-disaggregation pattern. Analysis of available data showed marked variations in reported cases between males and females among different countries with higher mortality in males.  At this early stage of the pandemic, medical datasets at the individual level are not available; therefore, it is challenging to conclude how different factors have impacted COVID-19 susceptibility. Thus, in the absence of patients’ level data, we attempted to provide a theoretical description of how other determinants have affected COVID-19 susceptibility in males compared to females.  In this article, we have identified and discussed possible biological and behavioral factors that could be responsible for the increased male susceptibility. Biological factors include - an absence of X-chromosomes (a powerhouse for immune-related genes), a high level of testosterone that inhibits antibody production, and the presence of Angiotensin-converting enzyme 2 (ACE2) receptors that facilitate viral replication. Similarly, behavioral factors constitute - higher smoking and alcohol consumptions, low level of handwashing practices, and high-risk behavior like non-adherence to health services and reluctance to follow public health measures in males. Keywords: COVID-19; gender; males; sex disaggregation; susceptibility

Objective:Identify changes in the prevalence and antimicrobial resistance patterns of potentially pathogenic bacteria in urine cultures during a 2-year antimicrobial stewardship intervention program in nursing homes (NHs).Design:Before-and-after intervention study.Setting:The study included 27 NHs in North Carolina.Methods:We audited all urine cultures ordered before and during an antimicrobial stewardship intervention. Analyses compared culture rates, culture positive rates, and pathogen antimicrobial resistance patterns.Results:Of 6,718 total urine cultures collected, 68% were positive for potentially pathogenic bacteria. During the intervention, significant reductions in the urine culture and positive culture rates were observed (P= .014). Most of the identified potentially uropathogenic isolates wereEscherichia coli(38%),Proteusspp (13%), andKlebsiella pneumoniae(12%). A significant decrease was observed during the intervention period in nitrofurantoin resistance amongE. coli(P≤ .001) and ciprofloxacin resistance amongProteusspp (P≤ .001); however carbapenem resistance increased forProteusspp (P≤ .001). Multidrug resistance also increased forProteusspp compared to the baseline. The high baseline resistance ofE. colito the commonly prescribed antimicrobials ciprofloxacin and trimethoprim-sulfamethoxazole (TMP/SMX) did not change during the intervention.Conclusions:The antimicrobial stewardship intervention program significantly reduced urine culture and culture-positive rates. Overall, very high proportions of antimicrobial resistance were observed among common pathogens; however, antimicrobial resistance trended downward but reductions were too small and scattered to conclude that the intervention significantly changed antimicrobial resistance. Longer intervention periods may be needed to effect change in resistance patterns.