Our study was carried out to characterize respiratory tract microbiota in patients with “COVID-like pneumonia” in Kazakhstan and analyze differences between COVID-19 positive and negative groups. Sputum samples were collected from hospitalized patients, ≥18 years old, in the three cities in Kazakhstan with the highest COVID-19 burden in July 2020. Isolates were identified by MALDI-TOF MS. Susceptibility testing was performed by disk diffusion. We used SPSS 26 and MedCalc 19 for statistical analysis. Among 209 patients with pneumonia, the median age was 62 years and 55% were male. RT-PCR-confirmed SARS-CoV-2 cases were found in 40% of patients, and 46% had a bacterial co-infection. Co-infection was not associated with SARS-CoV-2 RT-PCR test results, but antibiotic use was. The most frequent bacteria were Klebsiella pneumoniae (23%), Escherichia coli (12%), and Acinetobacter baumannii (11%). Notably, 68% of Klebsiella pneumoniae had phenotypic evidence of extended-spectrum beta-lactamases in disk diffusion assays, 87% of Acinetobacter baumannii exhibited resistance to beta-lactams, and >50% of E. coli strains had evidence of ESBL production and 64% were resistant to fluoroquinolones. Patients with a bacterial co-infection had a higher proportion of severe disease than those without a co-infection. The results reinforce the importance of using appropriate targeted antibiotics and effective infection control practices to prevent the spread of resistant nosocomial infections.

It has been established that the presence of depression is accompanied by an increased risk of morbidity and mortality in cerebrovascular and cardiovascular diseases and diabetes. The aim of this research was to estimate depressive symptom prevalence among the population in Central Kazakhstan and to define the relationship with social-demographic and behavioral factors. 1820 respondents of the population of Central Kazakhstan, aged 25 to 65, were performed. Participants included 777 urban and 1043 rural residents. Depressive symptoms assessed with the Patient Health Questionnaire (PHQ-9). The results showed that some degree of depressive symptoms was detected in 75.7% of the respondents. A minimal degree of depressive symptoms was observed in 28.51%, mild in 27.7%, moderate in 13.7%, and severe and very severe degree of depressive symptoms in 4.6% and 1.2%, respectively; the absence of depression symptoms was reported in 24.3% of the respondents. The study found a relationship between the prevalence of depressive symptoms and factors such as gender, education, income, presence of chronic diseases, and physical activity. We have not found a correlation between the frequencies of depressive symptoms with age, employment, character of labor, and marital status.

(1) Background: Endothelial dysfunction is a key mechanism in the pathogenesis of COVID-19. High endothelin-1 during COVID-19 is associated with severe complications and increased mortality rates during hospitalization. This study is aimed to investigate the association of endothelin-1 levels with the risk of 30-day and 12-month all-cause mortality in patients with prior COVID-19. (2) Methods: A prospective study was conducted involving patients with COVID-19 in Karaganda, Kazakhstan. The level of endothelin-1 in the blood serum was evaluated by ELISA. Univariate and multivariate Cox regression was used to determine factors and significance of endothelin-1 associated with the risk of mortality within 30 and 365 days from hospitalization. (3) Results: The median endothelin-1 was higher in the group of patients who passed away within 30 days. The group showed statistically significant differences when compared to healthy volunteers from the control group (p = 0.0001), surviving patients (p = 0.001), and those who passed away within a year (p = 0.002). (4) Conclusions: Endothelin-1 levels are associated with increased mortality risk during the acute period of COVID-19, while plasma endothelin-1 level association with COVID-19 survivor mortality risk does not persist after 12 months.

The aim of this study was to determine the prevalence of A. baumannii antibiotic-resistant strains in Kazakhstan and to characterize genotypes related to epidemic “high-risk” clones. Two hundred and twenty four A. baumannii isolates from four cities of Kazakhstan in 2011–2019 were studied. Antibiotic susceptibility testing was performed by using broth microdilutions method according to EUCAST (v 11.0) recommendations. The presence of bla_OXA-23-like, bla_{OXA-24/40-like,}bla_OXA-58-like,bla_VIM,bla_IMP, and bla_NDM genes was determined by PCR. Genotyping was performed using high-throughput real-time PCR detection of 21 SNPs at 10 chromosomal loci used in existing MLST schemes. Resistance rates to imipenem, meropenem, amikacin, gentamicin, and ciprofloxacin were 81.3%, 78.6%, 79.9%, 65.2%, and 89.3%, respectively. No colistin resistant isolates were detected. The values of the MIC 50% and the MIC 90% of tigecycline were 0.125 mg/L, only four isolates (1.8%) had the ECOFF value >0.5 mg/L. The presence of acquired carbapenemase genes was found in 82.2% strains, including bla_OXA-23-like (78.6%) or bla_OXA-58-like (3.6%) genes. The spreading of carbapenem resistant A. baumannii strains in Kazakhstan was associated with epidemic “high-risk” clonal groups, predominantly, CG208(92)^OXF/CG2^PAS (80.8%) and less often CG231(109)^OXF/CG1^PAS (1.8%).

Infant mortality rate (IMR) is a crucial indicator of healthcare performance and a reflection of a country’s socioeconomic development. We analyzed the trends of IMR in Central Asia (CA) countries and its determinants in Kazakhstan, which is a middle-income country. Linear regression was used for IMR trend analysis in CA countries from 2000 to 2020 and for exploring associations between IMR and socioeconomic factors, health service-related factors, and population health indicators-related factors. A gamma generalized linear model was applied to define associations with various determinants. Our analysis revealed that IMR has decreased in all CA countries, with Kazakhstan having the lowest rate in 2000 and 2020. Our results suggest that socioeconomic indicators, such as total unemployment, Gini index, current health expenditure, gross domestic product (GDP), proportion of people living in poverty, and births by 15–19-year-old mothers, were associated with increased infant mortality rates. Improving socioeconomic conditions, investing in healthcare systems, reducing poverty and income inequality, and improving access to education, are all potential issues for further development. Addressing these factors may be critical for improving maternal and child health outcomes in the region.

Maternal mortality ratio is one of the sensitive indicators that can characterize the performance of healthcare systems. In our study we aimed to compare the maternal mortality ratio in the Central Asia region from 2000 to 2020, determine its trends and evaluate the association between the maternal mortality ratio and Central Asia countries’ total health expenditures. We also compared the maternal mortality causes before and during the pandemic in Kazakhstan. The data were derived from the public statistical collections of each Central Asian country. During the pre-pandemic period, Central Asian nations had a downward trend of maternal mortality. Maternal mortality ratio in Central Asian countries decreased by 38% from 47.3 per 100,000 live births in 2000 to 29.5 per 100,000 live births in 2020. Except for Uzbekistan, where this indicator decreased, all Central Asian countries experienced a sharp increase in maternal mortality ratio in 2020. The proportion of indirect causes of maternal deaths in Kazakhstan reached 76.3% in 2020. There is an association between the maternal mortality ratio in Central Asian countries and their total health expenditures expressed in national currency units (r max = −0.89 and min = −0.66, p < 0.01). The study revealed an issue in the health data availability and accessibility for research in the region. The findings suggest that there must be additional efforts from the local authorities to enhance the preparedness of Central Asian healthcare systems for the new public health challenges and to improve health data accessibility.