There is no report on the serial interval (SI) of coronavirus disease 2019 (COVID-19) in Iran, the present report aims to estimate the SI and time-varying R of COVID-19 in western Iran. In this study, there were 1477 confirmed, probable and suspected cases of severe acute respiratory syndrome coronavirus 2 for Kermanshah from 22 February to 9 April. The close contacts of the confirmed cases were identified using telephone follow up of patients and their contacts. The SI distribution was used as an alternative. We fitted different models using the clinical onset dates of patients with their close contact (infector–infectee). Also, we applied a ‘serial interval from sample’ approach as a Bayesian methodology for estimating reproduction number. From 22 February to 29 March, 247 COVID-19 cases were confirmed by RT-PCR. Close contact between 21 patients (21 infector–infectee pairs), including 12 primary cases and 21 secondary cases, was confirmed. The mean and standard deviation of the SI were estimated as 5.71 and 3.89 days. The R varied from 0.79 to 1.88 for a 7-day time-lapse and ranged from 0.92 to 1.64 for a 14-day time-lapse on raw data. Also, the R varied from 0.83 to 1.84 for 7-day time-lapse and from 0.95 to 1.54 for a 14-day time-lapse using moving average data, respectively. It can be concluded that the low reproduction number for COVID-19 in Kermanshah province is an indication of the effectiveness of preventive and interventive programmes such as quarantine and isolation. Consequently, continuing these preventive measures is highly recommended.

Background: Obesity and dyslipidemia are important risk factors for hypertension (HTN). When these two conditions coexist, they may interact in a synergistic manner and increase the risk of developing HTN and its associated complications. The aim of this study was to investigate the synergistic effect of general and central obesity with dyslipidemia on the risk of HTN. Method: Data from 40,387 individuals aged 25 to 64 years were obtained from a repeated cross-sectional study examining risk factors for non-communicable diseases (STEPS) in 2007, 2011 and 2016. Body mass index (BMI) was calculated as a measure of general obesity and waist circumference (WC) as a measure of central obesity. Dyslipidemia was defined as the presence of at least one of the lipid abnormalities. Hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg or current use of antihypertensive medication. To analyze the synergistic effect between obesity and dyslipidemia and HTN, the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) were calculated. A weighted logistic regression model was performed to estimate the odds ratios (ORs) for the risk of HTN. Results: The results showed an association between obesity, dyslipidemia and hypertension. The interaction between obesity and dyslipidemia significantly influences the risk of hypertension. In hypertensive patients, the presence of general obesity increased from 14.55% without dyslipidemia to 64.36% with dyslipidemia, while central obesity increased from 13.27 to 58.88%. This interaction is quantified by RERI and AP values of 0.15 and 0.06 for general obesity and 0.24 and 0.09 for central obesity, respectively. The corresponding SI of 1.11 and 1.16 indicate a synergistic effect. The OR also show that the risk of hypertension is increased in the presence of obesity and dyslipidemia. Conclusion: Obesity and dyslipidemia are risk factors for HTN. In addition, dyslipidemia with central obesity increases the risk of HTN and has a synergistic interaction effect on HTN. Therefore, the coexistence of obesity and lipid abnormalities has many clinical implications and should be appropriately monitored and evaluated in the management of HTN.

Overweight and obesity not only are major risk factors for number of chronic diseases, but also a risk factor for pregnancy complications in women. The present study aims to investigate the association between pre-pregnancy BMI and the persistence and duration of BF. The electronic databases including Medline (PubMed), Scopus, Embase, Web of Science and Google Scholar were searched for papers with titles and/or abstracts including one of our keywords and published up to 15 April 2019. For dose-response relationship, the two-stage random-effects meta-analysis was performed using the “dosresmeta” function in R software. Thirty-two studies with the effect of pre-pregnancy BMI on BF initiation, intention and duration were included in the present study. Based on crude and adjusted OR models, the risk of BF cessation increased by 4% (OR = 1.04; 95% CI: 1.02–1.05) with an increase in a unit of BMI. In addition, based on crude and adjusted RR models, the risk of BF cessation increases by 2% and 1% (crude RR = 1.02; 95% CI: 1.01–1.03 and adjusted RR = 1.01; 95% CI: 0.99–1.02) with an increase in one unit of BMI. Based on the result, the health care professionals and other key stakeholders should be aware of the impact excess weight, and that women who are overweight or obese should be encouraged with continued access to guidance, counseling and support, starting from conception, to maximize BF outcomes.

Introduction: This study aimed to perform a meta-analysis on the prevalence of metabolic syndrome (MetS) among patients with asthma and to measure the association asthma has with MetS.Methods:The Web of Science, Medline, Scopus, Embase and Google Scholar were searched using the "Asthma", "Metabolic Syndrome", "Dysmetabolic Syndrome", "Cardiovascular Syndrome", "Insulin Resistance Syndrome", "Prevalence", "Odds Ratio", "Cross-Sectional Studies", and "Case-Control Studies" keywords. All observational studies reporting the prevalence of MetS among people with and without asthma were included in the study. In the presence of heterogeneity, random-effects models were used to pool the prevalence and odds ratios (OR), as measures of association in cross-sectional and case-control/ cohort studies, respectively.Results: The prevalence of MetS among patients with asthma (8 studies) and the OR comparing the prevalence of MetS among patients with and without asthma (5 studies) were pooled separately. The pooled prevalence of MetS among patients with asthma was found to be 25% (95% confidence interval (CI): 13%–38%). In contrast, the overall pooled OR for MetS in patients with asthma, compared to healthy controls, was 1.34 (95% CI: 0.91–1.76), which was not statistically significant.Conclusion: The prevalence of MetS was relatively high in patients with asthma. Furthermore, the odds of MetS was higher in patients with asthma, compared to healthy controls, although this difference was not statistically significant. More original studies among different populations are needed in order to more accurately examine the association between asthma and MetS, as well as the relationship asthma has with the individual components of MetS.