Clinical features, risk factors and a prediction model for in-hospital mortality among diabetic patients infected with COVID-19: data from a referral centre in Iran.

OBJECTIVES: The aim of this study was to identify risk factors of in-hospital mortality among diabetic patients infected with COVID-19. STUDY DESIGN: This is a retrospective cohort study. METHODS: Using logistic regression analysis, the independent association of potential prognostic factors and COVID-19 in-hospital mortality was investigated in three models. Model 1 included demographic data and patient history; model 2 consisted of model 1, plus vital signs and pulse oximetry measurements at hospital admission; and model 3 included model 2, plus laboratory test results at hospital admission. The odds ratios (ORs) and 95% confidence intervals (95% CIs) were reported for each predictor in the different models. Moreover, to examine the discriminatory powers of the models, a corrected area under the receiver-operating characteristic curve (AUC) was calculated. RESULTS: Among 560 patients with diabetes (men = 291) who were hospitalised for COVID-19, the mean age of the study population was 61.8 (standard deviation [SD] 13.4) years. During a median length of hospitalisation of 6 days, 165 deaths (men = 93) were recorded. In model 1, age and a history of cognitive impairment were associated with higher mortality; however, taking statins, oral antidiabetic drugs and beta-blockers was associated with a lower risk of mortality (AUC = 0.76). In model 2, adding the data for respiratory rate (OR 1.07 [95% CI 1.00-1.14]) and oxygen saturation (OR 0.95 [95% CI 0.92-0.98]) slightly increased the AUC to 0.80. In model 3, the data for platelet count (OR 0.99 [95% CI 0.99-1.00]), lactate dehydrogenase (OR 1.002 [95% CI 1.001-1.003]), potassium (OR 2.02 [95% CI 1.33-3.08]) and fasting plasma glucose (OR 1.04 [95% CI 1.02-1.07]) significantly improved the discriminatory power of the model to AUC 0.86 (95% CI 0.83-0.90). CONCLUSIONS: Among patients with type 2 diabetes, a combination of past medical and drug history and pulse oximetry data, with four non-expensive laboratory measures, was significantly associated with in-hospital COVID-19 mortality.

A description of spatial-temporal patterns of the novel COVID-19 outbreak in the neighbourhoods' scale in Tehran, Iran.

Background: Analyzing and monitoring the spatial-temporal patterns of the new coronavirus disease (COVID-19) pandemic can assist local authorities and researchers in detecting disease outbreaks in the early stages. Because of different socioeconomic profiles in Tehran's areas, we will provide a clear picture of the pandemic distribution in Tehran's neighbourhoods during the first months of its spread from February to July 2020, employing a spatial-temporal analysis applying the geographical information system (GIS). Disease rates were estimated by location during the 5 months, and hot spots and cold spots were highlighted. Methods: This study was performed using the COVID-19 incident cases and deaths recorded in the Medical Care Monitoring Centre from February 20, to July 20, 2020. The local Getis-Ord Gi* method was applied to identify the hotspots where the infectious disease distribution had significantly clustered spatially. A statistical analysis for incidence and mortality rates and hot spots was conducted using ArcGIS 10.7 software. Results: The addresses of 43,000 Tehrani patients (15,514 confirmed COVID-19 cases and 27,486 diagnosed as probable cases) were changed in its Geo-codes in the GIS. The highest incidence rate from February to July 2020 was 48 per 10,000 and the highest 5-month incidence rate belonged to central and eastern neighbourhoods. According to the Cumulative Population density of patients, the higher number is estimated by more than 2500 people in the area; however, the lower number is highlighted by about 500 people in the neighborhood. Also, the results from the local Getis-Ord Gi* method indicate that COVID-19 has formed a hotspot in the eastern, southeast, and central districts in Tehran since February. We also observed a death rate hot spot in eastern areas. Conclusion: Because of the spread of COVID-19 disease throughout Tehran's neighborhoods with different socioeconomic status, it seems essential to pay attention to health behaviors to prevent the next waves of the disease. The findings suggest that disease distribution has formed a hot spot in Tehran's eastern and central regions.

Safety and efficacy of Favipiravir in moderate to severe SARS-CoV-2 pneumonia.

BACKGROUND: We examined the safety and efficacy of a treatment protocol containing Favipiravir for the treatment of SARS-CoV-2. METHODS: We did a multicenter randomized open-labeled clinical trial on moderate to severe cases infections of SARS-CoV-2. Patients with typical ground glass appearance on chest computerized tomography scan (CT scan) and oxygen saturation (SpO2) of less than 93% were enrolled. They were randomly allocated into Favipiravir (1.6 gr loading, 1.8 gr daily) and Lopinavir/Ritonavir (800/200 mg daily) treatment regimens in addition to standard care. In-hospital mortality, ICU admission, intubation, time to clinical recovery, changes in daily SpO2 after 5 min discontinuation of supplemental oxygen, and length of hospital stay were quantified and compared in the two groups. RESULTS: 380 patients were randomly allocated into Favipiravir (193) and Lopinavir/Ritonavir (187) groups in 13 centers. The number of deaths, intubations, and ICU admissions were not significantly different (26, 27, 31 and 21, 17, 25 respectively). Mean hospital stay was also not different (7.9 days [SD = 6] in the Favipiravir and 8.1 [SD = 6.5] days in Lopinavir/Ritonavir groups) (p = 0.61). Time to clinical recovery in the Favipiravir group was similar to Lopinavir/Ritonavir group (HR = 0.94, 95% CI 0.75 - 1.17) and likewise the changes in the daily SpO2 after discontinuation of supplemental oxygen (p = 0.46) CONCLUSION: Adding Favipiravir to the treatment protocol did not reduce the number of ICU admissions or intubations or In-hospital mortality compared to Lopinavir/Ritonavir regimen. It also did not shorten time to clinical recovery and length of hospital stay.

Nationwide population-based surveys of Iranian COVID-19 Serological Surveillance (ICS) program: The surveys protocol.

Background: Serological surveillance of COVID-19 through conducting repetitive population-based surveys can be useful in estimating and monitoring changes in the prevalence of infection across the country. This paper presents the protocol of nationwide population-based surveys of the Iranian COVID-19 Serological Surveillance (ICS) program. Methods: The target population of the surveys is all individuals ≥6 years in Iran. Stratified random sampling will be used to select participants from those registered in the primary health care electronic record systems in Iran. The strata are the 31 provinces of the country, in which sampling will be done through simple random sampling. The sample size is estimated 858 individuals for each province (except for Tehran province, which is 2574) at the first survey. It will be recalculated for the next surveys based on the findings of the first survey. The participants will be invited by the community health workers to the safe blood sampling centers at the district level. After obtaining written informed consent, 10 mL of venous blood will be taken from the participants. The blood samples will be transferred to selected reference laboratories in order to test IgG and IgM antibodies against COVID-19 using an Iranian SARS-CoV-2 ELISA Kit (Pishtaz Teb). A serologically positive test is defined as a positive IgG, IgM, or both. After adjusting for the measurement error of the laboratory test, nonresponse bias, and sampling design, the prevalence of COVID-19 will be estimated at the provincial and national levels. Also, the approximate incidence rate of infection will be calculated based on the data of both consecutive surveys. Conclusion: The implementation of these surveys will provide a comprehensive and clear picture of the magnitude of COVID-19 infection and its trend over time for health policymakers at the national and subnational levels.

Prevalence of COVID-19 in Iran: results of the first survey of the Iranian COVID-19 Serological Surveillance programme.

OBJECTIVES: This study aims to estimate the prevalence of coronavirus disease 2019 (COVID-19) in the general population of Iran. METHODS: The target population was all Iranian people aged 6 years and older in the country. A stratified random sampling design was used to select 28 314 people from among the individuals registered in the electronic health record systems used in primary health care in Iran. Venous blood was taken from each participant and tested for the IgG antibody against COVID-19. The prevalence of COVID-19 was estimated at provincial and national levels after adjusting for the measurement error of the laboratory test, non-response bias and sampling design. RESULTS: Of the 28 314 Iranians selected, 11 256 (39.75%) participated in the study. Of these, 5406 (48.0%) were male and 6851 (60.9%) lived in urban areas. The mean (standard deviation) participant age was 35.89 (18.61) years. The adjusted prevalence of COVID-19 until 20 August 2020 was estimated as 14.2% (95% uncertainty interval 13.3%-15.2%), which was equal to 11 958 346 (95% CI 11 211 011-12 746 776) individuals. The adjusted prevalences of infection were 14.6%, 13.8%, 16.6%, 11.7% and 19.4% among men, women, urban population, rural population and individuals aged 60 years or more, respectively. Ardabil, Golestan and Khuzestan provinces had the highest prevalence and Alborz, Hormozgan and Kerman provinces had the lowest. CONCLUSIONS: Based on the study results, a large proportion of the Iranian population had not yet been infected by COVID-19. The observance of hygienic principles and social restrictions should therefore continue until the majority of the population has been vaccinated.

Spatio-temporal patterns of the COVID-19 pandemic, and place-based influential factors at the neighborhood scale in Tehran.

Since its emergence in late 2019, the COVID-19 pandemic has attracted the attention of researchers in various fields, including urban planning and design. However, the spreading patterns of the disease in cities are still not clear. Historically, preventing and controlling pandemics in cities has always been challenging due to various factors such as higher population density, higher mobility of people, and higher contact frequency. To shed more light on the spread patterns of the pandemic, in this study we analyze 43,000 confirmed COVID-19 cases at the neighborhood level in Tehran, the capital of Iran. To examine spatio-temporal patterns and place-based factors contributing to the spread of the pandemic, we used exploratory spatial data analysis and spatial regression. We developed a geo-referenced database composed of 12 quantitative place-based variables related to physical attributes, land use and public transportation facilities, and demographic status. We also used the geographically weighted regression model for the local examination of spatial non-stationarity. According to the results, population density (R2 = 0.88) and distribution of neighborhood centers (R2 = 0.59), drugstores (R2 = 0.64), and chain stores (R2 = 0.59) are the main factors contributing to the spread of the disease. Additionally, density of public transportation facilities showed a varying degree of contribution. Overall, our findings suggest that demographic composition and major neighborhood-level physical attributes are important factors explaining high rates of infection and mortality. Results contribute to gaining a better understanding of the role of place-based attributes that may contribute to the spread of the pandemic and can inform actions aimed at achieving Sustainable Development Goals, particularly Goals 3 and 11.

Estimation of the basic reproduction number (𝑠0) of the COVID-19 epidemic in Iran.

Background: Estimation of the basic reproduction number of an infectious disease is an important issue for controlling the infection. Here, we aimed to estimate the basic reproduction number (𝑅0) of COVID-19 in Iran. Methods: To estimate 𝑅0 in Iran and Tehran, the capital, we used 3 different methods: exponential growth rate, maximum likelihood, and Bayesian time-dependent. Daily number of confirmed cases and serial intervals with a mean of 4.27 days and a standard deviation of 3.44 days with gamma distribution were used. Sensitivity analysis was performed to show the importance of generation time in estimating 𝑅0. Results: The epidemic was in its exponential growth 11 days after the beginning of the epidemic (Feb 19, 2020) with doubling time of 1.74 (CI: 1.58-1.93) days in Iran and 1.83 (CI: 1.39-2.71) in Tehran. Nationwide, the value of 𝑅0 from February 19 to 29 using exponential growth method, maximum likelihood, and Bayesian time-dependent methods was 4.70 (95% CI: 4.23-5.23), 3.90 (95% CI: 3.47- 4.36), and 3.23 (95% CI: 2.94-3.51), respectively. In addition, in Tehran, 𝑅0 was 5.14 (95% CI: 4.15-6.37), 4.20 (95% CI: 3.38-5.14), and 3.94 (95% CI: 3.45-4.40) for exponential growth, maximum likelihood, and Bayesian time-dependent methods, respectively. Bayesian time dependent methods usually provide less biased estimates. The results of sensitivity analyses demonstrated that changes in the mean generation time affect estimates of 𝑅0. Conclusion: The estimate of 𝑅0 for the COVID-19 ranged from 3.94 to 5.14 in Tehran and from 3.23 to 4.70 in nationwide using different methods, which were significantly larger than 1, indicating the potential of COVID-19 to cause an outbreak.

Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios

BACKGROUND: Iran is one of the first few countries that was hit hard with the coronavirus disease 2019 (COVID-19) pandemic. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios. METHODS: We developed a susceptible-exposed-infected/infectious-recovered/removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UIs). RESULTS: Under scenario A, we estimated 5 196 000 (UI 1 753 000-10 220 000) infections to happen till mid-June with 966 000 (UI 467 800-1 702 000) hospitalizations and 111 000 (UI 53 400-200 000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (ie, 550 000) and change the epidemic peak from 66 000 on June 9, to 9400 on March 1, 2020. Scenario E also reduces the hospitalizations by 92% (ie, 74 500), and deaths by 93% (ie, 7800). CONCLUSION: With no approved vaccination or therapy available, we found physical distancing and isolation that include public awareness and case-finding and isolation of 40% of infected people could reduce the burden of COVID-19 in Iran by 90% by mid-June.