Antimicrobial resistance pattern in clinical Escherichia coli and Pseudomonas aeruginosa isolates obtained from a secondary-care hospital prior to and during the COVID-19 pandemic in Kuwait

Introduction The study objective was to compare the prevalence of antimicrobial resistance (AMR) in clinical Escherichia coli and Pseudomonas aeruginosa isolates obtained from a secondary-care hospital prior to and during the COVID-19 pandemic in Kuwait. Methods A retrospective descriptive study was conducted based on AMR profiles of clinical Escherichia coli and Pseudomonas aeruginosa isolates. The AMR data represented isolates from five specimen types (body fluids;blood;respiratory;wound, bone, or other tissues;and urine) of patients admitted to four wards (surgical, medical, pediatric, and maternal-postnatal). Tested isolates between January 2019 and February 2020 represented the pre-COVID-19 pandemic period in Kuwait, whereas those from February 2020 until April 2021 represented the 'during COVID -19' period. Results A total of 1,303 isolates (57.2% E. coli and 42.8% P. aeruginosa) were analyzed. For ceftazidime, ertapenem and meropenem, the prevalence of AMR in E. coli was significantly (p<0.05) lower in pre-COVID-19 wards compared to that during COVID-19, whereas for other antibiotics (i.e., cefepime, gentamicin, and trimethoprim/sulfamethoxazole), the prevalence of AMR in pre-COVID-19 was significantly higher than that during COVID-19. The prevalence of AMR to gentamicin in P. aeruginosa isolates from non-COVID-19 wards (52.8%) was significantly higher (p<0.001) than that from COVID-19 wards (35.0%) and from the pre-COVID-19 period (32.9%). The multidrug-resistance (MDR) prevalence was 37.4% for E. coli and 32.1% for P. aeruginosa isolates. The odds of MDR in E. coli isolates from the COVID-19 medical wards were significantly lower (OR=0.27, [95%CI: 0.09-0.80], p=0.018) compared to the pre-COVID-19 wards. The odds of MDR E. coli and P. aeruginosa isolates by COVID-19 status stratified by specimen type were not different (p>0.05). Conclusions No major differences in AMR in E. coli and P. aeruginosa prevalence by specimen type and wards prior to and during the COVID-19 pandemic was observed at this hospital. The high reported MDR prevalence calls for better infection control and prevention.

Early estimation of the epidemiological parameters of novel coronavirus disease (COVID-2019) outbreak in Iran: 19 Feb-15 March, 2020.

AIM: To estimate the epidemiological parameters related to the Covid-19 outbreak in Iran. BACKGROUND: Estimating the epidemiological parameters of new public health threat (COVID-19) is essential to support and inform public health decision-making in different communities including Iran. METHODS: We established a mathematical model to estimate the epidemiological parameters from 19 Feb to 15 March based on daily COVID-19 confirmed cases in Iran. Then, we estimated the effect of early traffic restriction on our estimation. RESULTS: We estimated the R0 at 2.11 (95% CI, 1.87-2.50) and the infected number at 92,260 (95% CI: 59,263 -152,212) by 15 March. Our estimate for the ascertainment rate was about 1.2% (95% CI: 1.1-1.4). The latent period estimation was 4.24 (95% CI: 2.84-6.65). We observed a decline in our estimate after considering the traffic restriction. CONCLUSION: Our results suggest that health authorities in Iran must take impactful strategies to control the COVID-19 outbreak to reach R0<1. Therefore, the establishment of complementary, multilateral, and cost-effective measures for the treatment of symptomatic and early diagnosis and isolation of asymptomatic cases/contacts are strongly recommended because of low ascertainment rate and large number of infected cases. We additionally recommend that traffic restriction be combined with other controlling measures.

Excess all-cause mortality and COVID-19 reported fatality in Iran (April 2013-September 2021): age and sex disaggregated time series analysis.

OBJECTIVE: The actual impact of the pandemic on COVID-19 specific mortality is still unclear due to the variability in access to diagnostic tools. This study aimed to estimate the excess all-cause mortality in Iran until September 2021 based on the national death statistics. RESULTS: The autoregressive integrated moving average was used to predict seasonal all-cause death in Iran (R-squared = 0.45). We observed a 38.8% (95% confidence interval (CI) 29.7%-40.1%) rise in the all-cause mortality from 22 June 2020 to 21 June 2021. The excess all-cause mortality per 100,000 population were 178.86 (95% CI 137.2-220.5, M:F ratio = 1.3) with 49.1% of these excess deaths due to COVID-19. Comparison of spring 2019 and spring 2021 revealed that the highest percent increase in mortality was among men aged 65-69 years old (77%) and women aged 60-64 years old (86.8%). Moreover, the excess mortality among 31 provinces of Iran ranged from 109.7 (Hormozgan) to 273.2 (East-Azerbaijan) per 100,000 population. In conclusion, there was a significant rise in all-cause mortality during the pandemic. Since COVID-19 fatality explains about half of this rise, the increase in other causes of death and underestimation in reported data should be concerned by further studies.

Effectiveness of BNT162b2 and ChAdOx1 Vaccines against Symptomatic COVID-19 among Healthcare Workers in Kuwait: A Retrospective Cohort Study.

BACKGROUND: Estimating vaccine effectiveness (VE) against severe, acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among healthcare workers (HCWs) is necessary to demonstrate protection from the disease. Between 24 December 2020 and 15 June 2021, we determined the factors associated with vaccine coverage and estimated VE against SARS-CoV-2 infection in HCWs at a secondary hospital in Kuwait. METHODS: We extracted sociodemographic, occupational, SARS-CoV-2 infection, and vaccination data for eligible HCWs from the hospital records. Vaccine coverage percentages were cross-tabulated with the HCW factors. Cox regression was used to estimate hazard ratios in vaccinated versus unvaccinated. RESULTS: 3246 HCWs were included in the analysis, of which 82.1% received at least one vaccine dose (50.4% only one dose of ChAdOx1, 3.3% only one dose of BNT162b2, and 28.3% two doses of BNT162b2). However, 17.9% of HCWs were unvaccinated. A significantly lower vaccination coverage was reported amongst female HCWs, younger age group (20-30 years), and administrative/executive staff. The adjusted VE of fully vaccinated HCWs was 94.5% (95% CI = 89.4-97.2%), while it was 75.4% (95% CI = 67.2-81.6%) and 91.4% (95% CI = 65.1-97.9%) in partially vaccinated for ChAdOx1 and BNT162b2, respectively. CONCLUSIONS: BNT162b2 and ChAdOx1 vaccines prevented most symptomatic infections in HCWs across age groups, nationalities, and occupations.

Analysis of intervention effectiveness using early outbreak transmission dynamics to guide future pandemic management and decision-making in Kuwait.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities. Kuwait reported its first imported cases of COVID-19 on February 24, 2020. Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management. The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave. Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed (SEAIR) transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait. We fit case data from the first 96 days in the model to estimate the effective reproduction number and used Google mobility data to refine community contact matrices. The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness. The model can help inform future pandemic wave management, not only in Kuwait but for other countries as well.

Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts.

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the [Formula: see text] formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible [Formula: see text] for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a "tunneling" effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.