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COVID-19: Original Article
Anticipating the Need for Healthcare Resources Following the Escalation of the COVID-19 Outbreak in the Republic of Kazakhstan
Yuliya Semenova, Lyudmila Pivina, Zaituna Khismetova, Ardak Auyezova, Ardak Nurbakyt, Almagul Kauysheva, Dinara Ospanova, Gulmira Kuziyeva, Altynshash Kushkarova, Alexandr Ivankov, Natalya Glushkova
J Prev Med Public Health. 2020;53(6):387-396.   Published online October 5, 2020
DOI: https://doi.org/10.3961/jpmph.20.395
  • 6,020 View
  • 264 Download
  • 16 Crossref
AbstractAbstract PDF
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.
Summary

Citations

Citations to this article as recorded by  
  • Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study
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    PLOS ONE.2023; 18(1): e0280067.     CrossRef
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    Case Reports in Medicine.2023; 2023: 1.     CrossRef
  • Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan
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    Healthcare.2023; 11(5): 752.     CrossRef
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    Nazym Iskakova, Zaituna Khismetova, Dana Suleymenova, Zhanat Kozhekenova, Zaituna Khamidullina, Umutzhan Samarova, Natalya Glushkova, Yuliya Semenova
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    Epidemiology and Infection.2023;[Epub]     CrossRef
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    Frontiers in Public Health.2023;[Epub]     CrossRef
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    Healthcare.2023; 11(22): 2968.     CrossRef
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    Yuliya Semenova, Zhanna Kalmatayeva, Ainash Oshibayeva, Saltanat Mamyrbekova, Aynura Kudirbekova, Ardak Nurbakyt, Ardak Baizhaxynova, Paolo Colet, Natalya Glushkova, Alexandr Ivankov, Antonio Sarria-Santamera
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    Yuliya Semenova, Varvara Trenina, Lyudmila Pivina, Natalya Glushkova, Yersin Zhunussov, Erlan Ospanov, Geir Bjørklund
    International Journal of Healthcare Management.2022; 15(4): 314.     CrossRef
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    Heliyon.2021; 7(3): e06561.     CrossRef
  • Comparative Analysis Of Triage Systems At Emergency Departments Of Different Countries: Implementation In Kazakhstan
    Lyudmila Pivina, Assylzhan M. Messova, Yersin T. Zhunussov, Zhanar Urazalina, Zhanna Muzdubayeva, Diana Ygiyeva, Murat Muratoglu, Gulnara Batenova, Sharbanu Uisenbayeva, Yulia Semenova
    Russian Open Medical Journal.2021;[Epub]     CrossRef
Original Article
Determinants of Health Care Expenditures and the Contribution of Associated Factors: 16 Cities and Provinces in Korea, 2003-2010
Kimyoung Han, Minho Cho, Kihong Chun
J Prev Med Public Health. 2013;46(6):300-308.   Published online November 28, 2013
DOI: https://doi.org/10.3961/jpmph.2013.46.6.300
  • 10,740 View
  • 118 Download
  • 18 Crossref
AbstractAbstract PDF
Objectives

The purpose of this study was to classify determinants of cost increases into two categories, negotiable factors and non-negotiable factors, in order to identify the determinants of health care expenditure increases and to clarify the contribution of associated factors selected based on a literature review.

Methods

The data in this analysis was from the statistical yearbooks of National Health Insurance Service, the Economic Index from Statistics Korea and regional statistical yearbooks. The unit of analysis was the annual growth rate of variables of 16 cities and provinces from 2003 to 2010. First, multiple regression was used to identify the determinants of health care expenditures. We then used hierarchical multiple regression to calculate the contribution of associated factors. The changes of coefficients (R2) of predictors, which were entered into this analysis step by step based on the empirical evidence of the investigator could explain the contribution of predictors to increased medical cost.

Results

Health spending was mainly associated with the proportion of the elderly population, but the Medicare Economic Index (MEI) showed an inverse association. The contribution of predictors was as follows: the proportion of elderly in the population (22.4%), gross domestic product (GDP) per capita (4.5%), MEI (-12%), and other predictors (less than 1%).

Conclusions

As Baby Boomers enter retirement, an increasing proportion of the population aged 65 and over and the GDP will continue to increase, thus accelerating the inflation of health care expenditures and precipitating a crisis in the health insurance system. Policy makers should consider providing comprehensive health services by an accountable care organization to achieve cost savings while ensuring high-quality care.

Summary

Citations

Citations to this article as recorded by  
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    Banu Tantan, Sevkiye Sence Turk
    Journal of Urban Planning and Development.2024;[Epub]     CrossRef
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JPMPH : Journal of Preventive Medicine and Public Health