The effect of underlying diabetes disease on clinical outcome and survival in patients with Covid-19: a propensity score matching study.

PURPOSE: Coronavirus increases mortality rate in people with underlying disease. The purpose of the present research was to compare the clinical outcomes in Covid-19 patients with and without underlying diabetes disease using propensity score matching. METHODS: A matched case-control study was conducted on 459 diabetic patients with Covid-19 (case group) and 459 non-diabetic patients with Covid-19 (control group). Matching in two groups was performed using propensity score matching method. The effect of covariates on the clinical outcome of the patients (recovery-death) was assessed using logistic regression and the associations of factors with the patients' survival were determined using Cox proportional hazards regression model. Data were analyzed using R software. RESULTS: The mean (standard deviation) age of patients in the case and control groups were 65.77 (12.2) and 65.8 (12.24), respectively. 196 patients (43%) in the case group, and 249 patients (54%) in the control group were male (with P-value < 0.05). The logistic regression model showed that the variables of age, level of blood oxygen (SpO2), ICU admission, length of hospitalization, cancer and diabetes affected patients' death. Furthermore, the resuts of the Cox regression showed that the variables of age, level of blood oxygen (SpO2), ICU admission,cancer and diabetes were related to survival of the patients. It was found that diabetes was significantly associated with mortality from COVID-19 with odds ratio of 2.88 (95% CI: 1.80-4.69; P < 0.01) and hazard ratio of 1.45 (95% CI: 1.01-2.03; P = 0.05). CONCLUSION: The underlying diabetes significantly increases the mortality among patients with Covid-19, so special care should be taken for this high risk group if they develop Covid-19.

The effect of underlying diabetes disease on clinical outcome and survival in patients with Covid-19: a propensity score matching study

Purpose Coronavirus increases mortality rate in people with underlying disease. The purpose of the present research was to compare the clinical outcomes in Covid-19 patients with and without underlying diabetes disease using propensity score matching. Methods A matched case–control study was conducted on 459 diabetic patients with Covid-19 (case group) and 459 non-diabetic patients with Covid-19 (control group). Matching in two groups was performed using propensity score matching method. The effect of covariates on the clinical outcome of the patients (recovery-death) was assessed using logistic regression and the associations of factors with the patients' survival were determined using Cox proportional hazards regression model. Data were analyzed using R software. Results The mean (standard deviation) age of patients in the case and control groups were 65.77 (12.2) and 65.8 (12.24), respectively. 196 patients (43%) in the case group, and 249 patients (54%) in the control group were male (with P-value < 0.05). The logistic regression model showed that the variables of age, level of blood oxygen (SpO2), ICU admission, length of hospitalization, cancer and diabetes affected patients' death. Furthermore, the resuts of the Cox regression showed that the variables of age, level of blood oxygen (SpO2), ICU admission,cancer and diabetes were related to survival of the patients. It was found that diabetes was significantly associated with mortality from COVID-19 with odds ratio of 2.88 (95% CI: 1.80–4.69;P < 0.01) and hazard ratio of 1.45 (95% CI: 1.01–2.03;P = 0.05). Conclusion The underlying diabetes significantly increases the mortality among patients with Covid-19, so special care should be taken for this high risk group if they develop Covid-19.

Identifying the Risk Factors for Mortality in Patients with Cancer and COVID-19 in Hamadan, the West of Iran.

PURPOSE: Since cancer patients are at higher risk of COVID-19, the present study was conducted to investigate the epidemiology of these patients and identify the affecting risk factors on their mortality. METHODS: The present retrospective cohort study was conducted on 66 hospitalized patients with cancer and COVID-19 in Hamadan in 2020. In the present study, demographic, clinical, and laboratory information and patients' outcome were collected through a checklist and its impact on death was assessed. Data were analyzed in SPSS-24 software and the significance level of the tests was considered at 5%. RESULTS: The mean (standard deviation (SD)) age of patients was 61.6 (13.5) years. Forty patients (60.6%) were male. Twenty and five patients (37.9%) died at the end of study. The results of logistic regression model revealed that the nausea, mechanical ventilation, admission to ICU, and length of hospital stay in the ward had a significant impact on the odds of death among cancer patients with COVID-19 (p < 0.05). CONCLUSION: Owing to high mortality rate in cancer patients with COVID-19 and due to underlying diseases and more severe clinical symptoms than other patients with coronavirus, these patients need intensive care and specific treatments. However, screening these patients and early identifying and vaccinating of them can reduce the mortality rate in these patients.

The Impact of Control Measures and Holiday Seasons on Incidence and Mortality Rate of COVID-19 in Iran.

BACKGROUND: Preventive measures on the COVID-19 pandemic is an effective way to control its spread. We aimed to investigate the effect of control measures and holiday seasons on the incidence and mortality rate of COVID-19 in Iran. STUDY DESIGN: An observational study. METHODS: The daily data of confirmed new cases and deaths in Iran were taken from the Johns Hopkins University COVID-19 database. We calculated weekly data from 19 Feb to 6 Oct 2020. To estimate the impact of control measures and holiday seasons on the incidence rate of new cases and deaths, an autoregressive hidden Markov model (ARHMM) with two hidden states fitted the data. The hidden states of the fitted model can distinguish the peak period from the non-peak period. RESULTS: The control measures with a delay of one-week and two-week had a decreasing effect on the new cases in the peak and non-peak periods, respectively (P=0.005). The holiday season with a two-week delay increased the total number of new cases in the peak periods (P=0.031). The peak period for the occurrence of COVID-19 was estimated at 3 weeks. In the peak period of mortality, the control measures with a three-week delay decreased the COVID-19 mortality (P=0.010). The expected duration of staying in the peak period of mortality was around 6 weeks. CONCLUSION: When an increasing trend was seen in the country, the control measures could decline the incidence and mortality related to COVID-19. Implementation of official restrictions on holiday seasons could prevent an upward trend of incidence for COVID-19 during the peak period.