THE EFFECTIVENESS OF MEDICAL AND SOCIAL SECURITY OF THE POPULATION OF UKRAINE UNDER THE INFLUENCE OF THE PANDEMIC
Abstract
There has yet to be a consensus among scientists regarding the set of determinants that influenced the course of the pandemic and resilience to it. The main goal of the study is to determine the optimal parameters for building a system of medical and social welfare for the population in an effective and resilient COVID-19 format. The systematization of literary sources and approaches to solving the problem shows that the task of identifying the readiness of the medical and social security of a country for possible epidemiological threats is relevant among scientists. Still, the obtained results differ from one region to another. The relevance of this scientific topic of problem-solving lies in the fact that each country has its specifics and mentality. That is, it is only possible to develop universal recommendations for some countries simultaneously. That is why the study of determining the optimal parameters for building a system of medical and social security for the population in an effective and resilient COVID-19 format was conducted for Ukraine, for each region separately. The research is carried out in the following logical sequence: collection and processing of statistical information on medical and social security of the regions of Ukraine; elimination of the effect of multicollinearity of indicators; distribution of determinants into destructors and stabilizers; in particular, the number of those who have recovered is a stabilizer, the rest are destroyers; data normalization; linguistic evaluation of variables; introduction of logical rules for applying fuzzy logic toolkit. The object of the study is the regional medical and social sphere of Ukraine. The article presents the results of an empirical search for a set of optimal determinants for countering the pandemic, which showed that the overall level of effectiveness of countering the pandemic in Ukraine with the available indicators is 73.5%. The study empirically confirms and theoretically proves that important indicators for effective countermeasures against possible epidemiological challenges are, first of all, the availability of hospitals - there is a large number of doctors, hospital beds, inpatients; as well as a responsible attitude of the population to their health through preventive examinations, visits to doctors and refusal of self-medication.
References
Атлас громадського здоров'я України. URL: https://npsi.phc.org.ua/PH_Atlas?indicator=164&tab=infographics
Гриценко К. Метод діагностування рівня капіталізації страхових компаній з використанням теорії нечіткої логіки. Вісник Університету банківської справи Національного банку України. 2010. Т. 2, № 8. С. 241–244.
Матвійчук А. Моделювання фінансової стійкості підприємств із застосуванням теорій нечіткої логіки, нейронних мереж і дискримінантного аналізу. Вісн. НАН україни. 2010. № 9. С. 24–46.
Центр медстатистики - за 2021 рік. URL: http://medstat.gov.ua/ukr/MMXXI.html (дата звернення: 30.09.2023).
Alin A. Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics. 2010. Vol. 2. No. 3. P. 370–374. DOI: https://doi.org/10.1002/wics.84
Bielova I., Pakhnenko O., Bukhtiarova A. Measuring the health of ukraine’s economy using fuzzy logic. Market infrastructure. 2021. No. 57. DOI: https://doi.org/10.32843/infrastruct57-2
Chornyi O.V. State policy of healthcare transformation as a factor of social and economic stability during the pandemic covid-19 period in ukraine. "Scientific notes of taurida V.I. vernadsky university", series "public administration". 2020. No. 4. P. 108–112. DOI: https://doi.org/10.32838/tnu-2663-6468/2020.4/17
Stojkoski V. et al. Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: Evidence from Bayesian model averaging. Scientific reports. 2022. Vol. 12. No. 1. DOI: https://doi.org/10.1038/s41598-022-10894-6
Mishra N. P. et al. Global impacts of pre- and post-COVID-19 pandemic: focus on socio-economic consequences. Sensors international. 2020. Vol. 1. DOI: https://doi.org/10.1016/j.sintl.2020.100042
Khalatbari-Soltani S. et al.Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. Journal of epidemiology and community health. 2020. Vol. 74. Is. 8. DOI: https://doi.org/10.1136/jech-2020-214297
Silva-García J.T. et al. Integral Index of Water Quality: A New Methodological Proposal for Surface Waters. Water. 2023. Vol. 15. No. 7. DOI: https://doi.org/10.3390/w15071414
Momot V. The COVID-19 pandemic as a socio-economic phenomenon. Socio-Economic research bulletin. 2022. Vol. 3-4. No. 82-83. P. 56–80. DOI: https://doi.org/10.33987/vsed.3-4(82-83).2022.65-80
Uscher-Pines L. et al. Planning for an influenza pandemic: social justice and disadvantaged groups. Hastings center report. 2007. Vol. 37. No. 4. P. 32–39. DOI: https://doi.org/10.1353/hcr.2007.0064
Nano Prawoto, Eko Priyo Purnomo, Abitassha Az Zahra. The impacts of covid-19 pandemic on socio-economic mobility in indonesia. International journal of economics and business administration. 2020. Vol. VIII, Issue 3. P. 57–71. DOI: https://doi.org/10.35808/ijeba/486
Nan X. et al. Public health messaging during the COVID-19 pandemic and beyond: lessons from communication science. Health communication. 2021. Vol. 37. No. 1. P. 1–19. DOI: https://doi.org/10.1080/10410236.2021.1994910
Park K.Y. et al. Respiratory virus surveillance in Canada during the COVID-19 pandemic: an epidemiological analysis of the effectiveness of pandemic-related public health measures in reducing seasonal respiratory viruses test positivity. Plos one. 2021. Vol. 16. No. 6. DOI: https://doi.org/10.1371/journal.pone.0253451
Sá F. Do vaccinations reduce inequality in Covid-19 mortality? Evidence from England. Social science & medicine. 2022. Vol. 305. DOI: https://doi.org/10.1016/j.socscimed.2022.115072
Serrano-Guerrero J., Romero F.P. & Olivas J.A. Fuzzy logic applied to opinion mining: A review. Knowledge-Based Systems. 2021. Vol. 222. DOI: https://doi.org/10.1016/j.knosys.2021.107018
Varkey R.S. et al. Socioeconomic determinants of COVID‐19 in Asian countries: an empirical analysis. Journal of public affairs. 2020. Vol. 21. Is. 4. DOI: https://doi.org/10.1002/pa.2532
Statement on the fifteenth meeting of the IHR (2005) Emergency Committee on the COVID-19 pandemic. URL: https://www.who.int/news/item/05-05-2023-statement-on-the-fifteenth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic
Vraga E.K., Jacobsen K.H. Strategies for effective health communication during the coronavirus pandemic and future emerging infectious disease events. World medical & health policy. 2020. Vol. 12. No. 3. P. 233–241. DOI: https://doi.org/10.1002/wmh3.359
Atlas of public health in Ukraine. Available at: https://npsi.phc.org.ua/PH_Atlas?indicator=164&tab=infographics
Hrytsenko K. (2010) Method of diagnosing the level of capitalisation of insurance companies using the theory of fuzzy logic. Bulletin of the University of Banking of the National Bank of Ukraine, vol. 2, no. 8, pp. 241–244.
Matviichuk A. (2010) Modelling the financial stability of enterprises using the theories of fuzzy logic, neural networks and discriminant analysis. Bulletin of the National Academy of Sciences of Ukraine, no. 9, pp. 24–46.
Centre for Medical Statistics - 2021. Available at: http://medstat.gov.ua/ukr/MMXXI.html (accessed September 30, 2023).
Alin A. (2010) Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics, no. 2(3), pp. 370–374. DOI: https://doi.org/10.1002/wics.84
Bielova I., Pakhnenko O., Bukhtiarova A. (2021) Measuring the health of ukraine’s economy using fuzzy logic. Market infrastructure, no. 57. DOI: https://doi.org/10.32843/infrastruct57-2
Chornyi O. V. S(2020) tate policy of healthcare transformation as a factor of social and economic stability during the pandemic covid-19 period in ukraine. "Scientific notes of taurida V.I. vernadsky university", series "public administration", no. 4, pp. 108–112. DOI: https://doi.org/10.32838/tnu-2663-6468/2020.4/17
Stojkoski V. et al. (2022) Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: Evidence from Bayesian model averaging. Scientific reports, vol. 12, no. 1. DOI: https://doi.org/10.1038/s41598-022-10894-6
Mishra N. P. et al. Global impacts of pre- and post-COVID-19 pandemic: focus on socio-economic consequences. Sensors international. 2020. Vol. 1. DOI: https://doi.org/10.1016/j.sintl.2020.100042
Khalatbari-Soltani S. et al. (2020) Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. Journal of epidemiology and community health, vol. 74, is. 8. DOI: https://doi.org/10.1136/jech-2020-214297
Silva-García J. T. et al. (2023) Integral Index of Water Quality: A New Methodological Proposal for Surface Waters. Water, vol. 15, no. 7. DOI: https://doi.org/10.3390/w15071414
Momot V. (2022) The COVID-19 pandemic as a socio-economic phenomenon. Socio-Economic research bulletin, vol. 3-4, no. 82-83, pp. 56–80. DOI: https://doi.org/10.33987/vsed.3-4(82-83).2022.65-80
Uscher-Pines L. et al. (2007) Planning for an influenza pandemic: social justice and disadvantaged groups. Hastings center report, vol. 37, no. 4, pp. 32–39. DOI: https://doi.org/10.1353/hcr.2007.0064
Nano Prawoto, Eko Priyo Purnomo, Abitassha Az Zahra (2020) The impacts of covid-19 pandemic on socio-economic mobility in indonesia. International journal of economics and business administration, vol. VIII, issue 3, pp. 57–71. DOI: https://doi.org/10.35808/ijeba/486
Nan X. et al. (2021) Public health messaging during the COVID-19 pandemic and beyond: lessons from communication science. Health communication, vol. 37, no. 1, pp. 1–19. DOI: https://doi.org/10.1080/10410236.2021.1994910
Park K. Y. et al. (2021) Respiratory virus surveillance in Canada during the COVID-19 pandemic: an epidemiological analysis of the effectiveness of pandemic-related public health measures in reducing seasonal respiratory viruses test positivity. Plos one, vol. 16, no. 6. DOI: https://doi.org/10.1371/journal.pone.0253451
Sá F. (2022) Do vaccinations reduce inequality in Covid-19 mortality? Evidence from England. Social science & medicine, vol. 305. DOI: https://doi.org/10.1016/j.socscimed.2022.115072
Serrano-Guerrero J., Romero F. P. & Olivas J. A. (2021) Fuzzy logic applied to opinion mining: A review. Knowledge-Based Systems, vol. 222. DOI: https://doi.org/10.1016/j.knosys.2021.107018
Varkey R. S. et al. (2020) Socioeconomic determinants of COVID‐19 in Asian countries: an empirical analysis. Journal of public affairs, vol. 21, is. 4. DOI: https://doi.org/10.1002/pa.2532
Statement on the fifteenth meeting of the IHR (2005) Emergency Committee on the COVID-19 pandemic. Available at: https://www.who.int/news/item/05-05-2023-statement-on-the-fifteenth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic
Vraga E. K., Jacobsen K. H. (2020) Strategies for effective health communication during the coronavirus pandemic and future emerging infectious disease events. World medical & health policy, vol. 12, no. 3, pp. 233–241. DOI: https://doi.org/10.1002/wmh3.359
