Epidemiology, clinical characteristics, and virologic features of COVID-19 patients in Kazakhstan: A nation-wide retrospective cohort study

Sergey Yegorov, Maiya Goremykina, Raifa Ivanova, Sara V Good, Dmitriy Babenko, Alexandr Shevtsov, Kelly S MacDonald, Yersin Zhunussov, COVID-19 Genomics Research Groupon behalf of the Semey COVID-19 Epidemiology Research Group, Sergey Yegorov, Maiya Goremykina, Raifa Ivanova, Sara V Good, Dmitriy Babenko, Alexandr Shevtsov, Kelly S MacDonald, Yersin Zhunussov, COVID-19 Genomics Research Groupon behalf of the Semey COVID-19 Epidemiology Research Group

Abstract

Background: The earliest coronavirus disease-2019 (COVID-19) cases in Central Asia were announced in March 2020 by Kazakhstan. Despite the implementation of aggressive measures to curb infection spread, gaps remain in the understanding of the clinical and epidemiologic features of the regional pandemic.

Methods: We did a retrospective, observational cohort study of patients with laboratory-confirmed COVID-19 hospitalized in Kazakhstan between February and April 2020. We compared demographic, clinical, laboratory and radiological data of patients with different COVID-19 severities on admission. Logistic regression was used to assess factors associated with disease severity and in-hospital death. Whole-genome SARS-CoV-2 analysis was performed in 53 patients.

Findings: Of the 1072 patients with laboratory-confirmed COVID-19 in March-April 2020, the median age was 36 years (IQR 24-50) and 484 (45%) were male. On admission, 683 (64%) participants had asymptomatic/mild, 341 (32%) moderate, and 47 (4%) severe-to-critical COVID-19 manifestation; 20 in-hospital deaths (1•87%) were reported by 5 May 2020. Multivariable regression indicated increasing odds of severe disease associated with older age (odds ratio 1•05, 95% CI 1•03-1•07, per year increase; p<0•001), the presence of comorbidities (2•34, 95% CI 1•18-4•85; p=0•017) and elevated white blood cell count (WBC, 1•13, 95% CI 1•00-1•27; p=0•044) on admission, while older age (1•09, 95% CI 1•06-1•13, per year increase; p<0•001) and male sex (5•63, 95% CI 2•06-17•57; p=0•001) were associated with increased odds of in-hospital death. The SARS-CoV-2 isolates grouped into seven phylogenetic lineages, O/B.4.1, S/A.2, S/B.1.1, G/B.1, GH/B.1.255, GH/B.1.3 and GR/B.1.1.10; 87% of the isolates were O and S sub-types descending from early Asian lineages, while the G, GH and GR isolates were related to lineages from Europe and the Americas.

Interpretation: Older age, comorbidities, increased WBC count, and male sex were risk factors for COVID-19 disease severity and mortality in Kazakhstan. The broad SARS-CoV-2 diversity suggests multiple importations and community-level amplification predating travel restriction.

Funding: Ministry of Education and Science of the Republic of Kazakhstan.

Keywords: COVID-19; Central Asia; Clinical characteristics; Disease risk factors; Disease severity; Kazakhstan; Molecular epidemiology; SARS-CoV-2; SARS-CoV-2 genomics.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2021 The Author(s).

Figures

Figure 1
Figure 1
A. The retrospective cohort study profile, B. The SARS-CoV-2 genomic study profile.
Figure 2
Figure 2
Epidemic curve of the confirmed COVID-19 cases in the current study compared to the official statistics on confirmed COVID-19 cases in Kazakhstan in March-April 2020 according to the Republican Centre for Health Development and World Health Organization (WHO). Official statistics were obtained from the WHO website .
Figure 3
Figure 3
Distribution of patients with laboratory-confirmed COVID-19 across Kazakhstan. Based on data for 573 patients, for whom the site of initial diagnosis was known in the current study.
Figure 4
Figure 4
Regions of travel and transportation used by laboratory-confirmed COVID-19 patients with a recent history of international travel.
Figure 5
Figure 5
A. Phylogenetic tree depicting the Kazakhstan (“Kaz”) virus isolates in the context of globally circulating SARS-CoV-2 lineages. Each clade is denoted by a corresponding Global Initiative on Sharing All Influenza Data (GISAID) clade name; the Pangolin lineage names are given in brackets. Branch lengths measured in units of substitutions per site. Tree is coloured based on the GISAID nomenclature (see legend). B. Phylodynamic analysis of the Kazakhstan SARS-CoV-2 sequences in the international context. Maximum likelihood tree of Kazakhstan viral sequences and a subset of international sequences (see Methods), coloured by region of origin.

References

    1. Hu B, Guo H, Zhou P, Shi Z-L. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol 2020; published online Oct 6. DOI:10.1038/s41579-020-00459-7.
    1. Hopman J, Allegranzi B, Mehtar S. Managing COVID-19 in low- and middle-income countries. JAMA. 2020;323:1549–1550.
    1. Balakrishnan VS. COVID-19 response in central Asia. The Lancet Microbe. 2020;1:e281.
    1. Semenova Y, Glushkova N, Pivina L. Epidemiological Characteristics and Forecast of COVID-19 outbreak in the Republic of Kazakhstan. J Korean Med Sci. 2020;35 doi: 10.3346/jkms.2020.35.e227.
    1. Bayesheva D, Boranbayeva R, Turdalina B, et al. COVID-19 in the paediatric population of Kazakhstan. Paediatr Int Child Health 2020; 1–7.
    1. Liu D, Cui P, Zeng S. Risk factors for developing into critical COVID-19 patients in Wuhan, China: a multicenter, retrospective, cohort study. EClinicalMedicine. 2020;25 doi: 10.1016/j.eclinm.2020.100471.
    1. Williamson EJ, Walker AJ, Bhaskaran K. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–436.
    1. Wu JT, Leung K, Bushman M. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med. 2020;26:506–510.
    1. Zhou F, Yu T, Du R. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–1062.
    1. Hadfield J, Megill C, Bell SM. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018;34:4121–4123.
    1. Bogner P, Capua I, Lipman DJ, Cox NJ. A global initiative on sharing avian flu data. Nature. 2006;442:981.
    1. Clinical management of COVID-19. (accessed Dec 14, 2020).
    1. Guan W-J, Ni Z-Y, Hu Y. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708–1720.
    1. Tabata S, Imai K, Kawano S. Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the diamond princess cruise ship: a retrospective analysis. Lancet Infect Dis. 2020;20:1043–1050.
    1. Richardson S, Hirsch JS, Narasimhan M. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA. 2020;323:2052–2059.
    1. Sze S, Pan D, Nevill CR, et al. Ethnicity and clinical outcomes in COVID-19: a systematic review and meta-analysis. EClinicalMedicine 2020;: 100630.
    1. Kazakhstan: WHO Coronavirus Disease (COVID-19) Dashboard. (accessed Dec 14, 2020).
    1. Seemann T, Lane CR, Sherry NL. Tracking the COVID-19 pandemic in Australia using genomics. Nat Commun. 2020;11:4376.
    1. Lu R, Zhao X, Li J. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020;395:565–574.
    1. Munblit D, Nekliudov NA, Bugaeva P, et al. StopCOVID cohort: An observational study of 3,480 patients admitted to the Sechenov University hospital network in Moscow city for suspected COVID-19 infection. Clin Infect Dis 2020; published online Oct 9. DOI:10.1093/cid/ciaa1535.
    1. Demkina AE, Morozov SP, Vladzymyrskyy AV, et al. Risk factors for outcomes of COVID-19 patients: an observational study of 795 572 patients in Russia. medRxiv 2020;: 2020.11.02.20224253.
    1. Moiseev S, Avdeev S, Brovko M, Bulanov N, Tao E, Fomin V. Outcomes of intensive care unit patients with COVID-19: a nationwide analysis in Russia. Anaesthesia 2020; published online Oct 5. DOI:10.1111/anae.15265.
    1. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020;323:1239–1242.
    1. Popkin BM, Du S, Green WD. Individuals with obesity and COVID-19: a global perspective on the epidemiology and biological relationships. Obes Rev. 2020;21:e13128.
    1. RECOVERY Collaborative Group, Horby P, Lim WS, et al. Dexamethasone in hospitalized patients with Covid-19 - preliminary report. N Engl J Med 2020; published online July 17. DOI:10.1056/NEJMoa2021436.
    1. Carotti M, Salaffi F, Sarzi-Puttini P, et al. Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists. Radiol Med 2020;: 1–11.
    1. Korber B, Fischer WM, Gnanakaran S. Tracking changes in SARS-CoV-2 spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell. 2020;182:812–827. e19.
    1. Lemey P, Hong SL, Hill V. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2. Nat Commun. 2020;11:5110.
    1. Leung K, Wu JT, Liu D, Leung GM. First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet. 2020;395:1382–1393.

Source: PubMed

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