Association of Pre-Pregnancy Obesity and COVID-19 with Poor Pregnancy Outcome.

BACKGROUND AND OBJECTIVES: During the COVID-19 pandemic, a possible overlap of obesity and COVID-19 infection has raised concerns among patients and healthcare professionals about protecting pregnant women from developing a severe infection and unwanted pregnancy outcomes. The aim of this study was to evaluate the associations of body mass index with clinical, laboratory, and radiology diagnostic parameters as well as pregnancy complications and maternal outcomes in pregnant patients with COVID-19. MATERIALS AND METHODS: Clinical status, laboratory, and radiology diagnostic parameters and pregnancy outcomes were analyzed for pregnant women hospitalized between March 2020 and November 2021 in one tertiary-level university clinic in Belgrade, Serbia, due to infection with SARS-CoV-2. Pregnant women were divided into the three sub-groups according to their pre-pregnancy body mass index. For testing the differences between groups, a two-sided p-value <0.05 (the Kruskal-Wallis and ANOVA tests) was considered statistically significant. RESULTS: Out of 192 hospitalized pregnant women, obese pregnant women had extended hospitalizations, including ICU duration, and they were more likely to develop multi-organ failure, pulmonary embolism, and drug-resistant nosocomial infection. Higher maternal mortality rates, as well as poor pregnancy outcomes, were also more likely to occur in the obese group of pregnant women. Overweight and obese pregnant women were more likely to develop gestational hypertension, and they had a higher grade of placental maturity. CONCLUSIONS: Obese pregnant women hospitalized due to COVID-19 infection were more likely to develop severe complications.

Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture-Recapture Approaches.

Background and Objectives: Coronavirus disease 19 (COVID-19) has emerged as the most devastating syndemic of the 21st century, with worrisome and sustained consequences for the entire society. Despite the relative success of vaccination programs, the global threat of the novel coronavirus SARS-CoV-2 is still present and further efforts are needed for its containment and control. Essential for its control and containment is getting closer to understanding the actual extent of SARS-CoV-2 infections. Material and Methods: We present a model based on the mortality data of Kazakhstan for the estimation of the underlying epidemic dynamic-with both the lag time from infection to death and the infection fatality rate. For the estimation of the actual number of infected individuals in Kazakhstan, we used both back-casting and capture-recapture methods. Results: Our results suggest that despite the increased testing capabilities in Kazakhstan, official case reporting undercounts the number of infections by at least 60%. Even though our count of deaths may be either over or underestimated, our methodology could be a more accurate approach for the following: the estimation of the actual magnitude of the pandemic; aiding the identification of different epidemiological values; and reducing data bias. Conclusions: For optimal epidemiological surveillance and control efforts, our study may lead to an increased awareness of the effect of COVID-19 in this region and globally, and aid in the implementation of more effective screening and diagnostic measures.

Seropositivity of SARS-CoV-2 in the Population of Kazakhstan: A Nationwide Laboratory-Based Surveillance.

The data on seroprevalence of anti-SARS-CoV-2 antibodies in Kazakhstani population are non-existent, but are needed for planning of public health interventions targeted to COVID-19 containment. The aim of the study was to estimate the seropositivity of SARS-CoV-2 infection in the Kazakhstani population from 2020 to 2021. We relied on the data obtained from the results from "IN VITRO" laboratories of enzyme-linked immunosorbent assays for class G immunoglobulins (IgG) and class M (IgM) to SARS-CoV-2. The association of COVID-19 seropositivity was assessed in relation to age, gender, and region of residence. Additionally, we related the monitoring of longitudinal seropositivity with COVID-19 statistics obtained from Our World in Data. The total numbers of tests were 68,732 for SARS-CoV-2 IgM and 85,346 for IgG, of which 22% and 63% were positive, respectively. The highest rates of positive anti-SARS-CoV-2 IgM results were seen during July/August 2020. The rate of IgM seropositivity was the lowest on 25 October 2020 (2%). The lowest daily rate of anti-SARS-CoV-2 IgG was 17% (13 December 2020), while the peak of IgG seropositivity was seen on 6 June 2021 (84%). A longitudinal serological study should be envisaged to facilitate understanding of the dynamics of the epidemiological situation and to forecast future scenarios.

Clinical characteristics and risk factors for disease severity and mortality of COVID-19 patients with diabetes mellitus in Kazakhstan: A nationwide study.

BACKGROUND: Diabetes mellitus (DM) is associated with higher risk of developing infectious disease and COVID-19 is not the exception. There is a need to generate more data on clinical characteristics and risks of COVID19 patients presenting with DM. In this retrospective study we aimed to report on demographic features, clinical data, and outcomes of COVID-19 patients with DM in comparison with age- and sex-matched patients without DM. METHODS: This was a retrospective study that relied on the nationwide data on all COVID-19 patients who were diagnosed from 14 March to 18 April, 2020. Overall, there were 31 cases with DM for which we randomly matched 4 patients without DM by age and sex. RESULTS: COVID-19 patients with associated DM had less beneficial outcomes and more severe disease course both at hospital admission and final diagnosis, as compared with the age and sex-matched non-DM patients. Diabetics were more predisposed to impaired breathing (29.0 % versus 4.9 % in controls), nausea/vomiting (6.5 % versus 0 % in controls) and weakness/lethargy (45.2 % versus 26.0 % in controls). Finally, 48.4 % of diabetics showed the signs of pneumonia on CT scans versus 20.3 % of non-diabetics (p = 0.001), and 32.3 % of DM patients were admitted to intensive care units as compared with just 5.7 % of non-DM patients (p<0.001). CONCLUSION: There is a need to envisage early status monitoring and supportive care in this vulnerable category of patients to enable better prognosis.

Anticipating the Need for Healthcare Resources Following the Escalation of the COVID-19 Outbreak in the Republic of Kazakhstan.

OBJECTIVES: The lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available. METHODS: We constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization's COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks. RESULTS: Over the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively. CONCLUSIONS: Republic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.

Epidemiological Characteristics and Forecast of COVID-19 Outbreak in the Republic of Kazakhstan.

BACKGROUND: Coronavirus disease 2019 (COVID-19) pandemic entered Kazakhstan on 13 March 2020 and quickly spread over its territory. This study aimed at reporting on the rates of COVID-19 in the country and at making prognoses on cases, deaths, and recoveries through predictive modeling. Also, we attempted to forecast the needs in professional workforce depending on implementation of quarantine measures. METHODS: We calculated both national and local incidence, mortality and case-fatality rates, and made forecast modeling via classic susceptible-exposed-infected-removed (SEIR) model. The Health Workforce Estimator tool was utilized for forecast modeling of health care workers capacity. RESULTS: The vast majority of symptomatic patients had mild disease manifestations and the proportion of moderate disease was around 10%. According to the SEIR model, there will be 156 thousand hospitalized patients due to severe illness and 15.47 thousand deaths at the peak of an outbreak if no measures are implemented. Besides, this will substantially increase the need in professional medical workforce. Still, 50% compliance with quarantine may possibly reduce the deaths up to 3.75 thousand cases and the number of hospitalized up to 9.31 thousand cases at the peak. CONCLUSION: The outcomes of our study could be of interest for policymakers as they help to forecast the trends of COVID-19 outbreak, the demands for professional workforce, and to estimate the consequences of quarantine measures.