IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i5p1778-d1343264.html
   My bibliography  Save this article

A Lesson for Sustainable Health Policy from the Past with Implications for the Future

Author

Listed:
  • Göran Svensson

    (Marketing Department, Kristiania University College, 0107 Oslo, Norway)

  • Rocio Rodriguez

    (Marketing Department, Kristiania University College, 0107 Oslo, Norway
    Marketing Department, University of Murcia, 30100 Murcia, Spain)

  • Carmen Padin

    (Applied Economics Department, Universidade de Vigo, 36310 Vigo, Spain)

Abstract

Evidently, there are lessons to be learned on sustainable health policies from the SARS-CoV-2 pandemic. The past is a source of knowledge and experiences for the implementation and application of sustainable health policies in the future. This study has revealed doubts about the use of 7- and 14-days incidences, which have been applied as assessment approaches to the sustainable health policies used to control and monitor the SARS-CoV-2 pandemic across societies. Seven- and fourteen-day incidences have been used to determine measures and counter-measures against SARS-CoV-2 rather than infection rates. The research objective of this study was to assess the predictive abilities of infection rates versus 7- and 14-day incidences on SARS-CoV-2-related mortality and morbidity. The objective was also to assess the structural properties of a set of SARS-CoV-2-related variables. This study addressed the question of whether there is a lesson learned in terms of sustainable health policies on the use of 7- and 14-day incidences versus infection rates to predict SARS-CoV-2-related mortality and morbidity in a given context. We contend that there is at least one lesson to be learned on sustainable health policies from the SARS-CoV-2 pandemic. The infection rate was categorized as the independent manifest variable, as it is the one which is hypothesized to cause an effect on the outcome of the others in society regarding mortality and morbidity. Consequently, hospitalized patients, ICU patients and the deceased were categorized as dependent manifest variables. We tested the research model using Covariance-Based Structural Equation Modeling (CB-SEM) based on the first year of pandemic data before vaccines were used. This study indicates that the infection rates provided an enhanced predictability for SARS-CoV-2-related mortality and morbidity compared to 7- and 14-day incidences. The findings reported based on CB-SEM suggested that this has been a suitable way to assess the direct, indirect and mediating effects between a selection of SARS-CoV-2-related variables. We propose that our assessment approach to SARS-CoV-2 can be used as a complementary tool in decision-making on pandemic countermeasures to assess the health, social and economic costs of mortality and morbidity in a given context. We consider the finding that infection rates, rather than 7- and 14-day incidences, better predict SARS-CoV-2-related mortality and morbidity is a crucial lesson learned on sustainable health policies from the past, to be a crucial lesson for the future.

Suggested Citation

  • Göran Svensson & Rocio Rodriguez & Carmen Padin, 2024. "A Lesson for Sustainable Health Policy from the Past with Implications for the Future," Sustainability, MDPI, vol. 16(5), pages 1-12, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1778-:d:1343264
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/5/1778/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/5/1778/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roberto Morcillo-Jimenez & Karel Gutiérrez-Batista & Juan Gómez-Romero, 2023. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data," Energies, MDPI, vol. 16(4), pages 1-29, February.
    2. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H., 2020. "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    5. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    6. Jose M. Martin-Moreno & Antoni Alegre-Martinez & Victor Martin-Gorgojo & Jose Luis Alfonso-Sanchez & Ferran Torres & Vicente Pallares-Carratala, 2022. "Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(9), pages 1-16, May.
    7. Lazebnik, Teddy, 2023. "Computational applications of extended SIR models: A review focused on airborne pandemics," Ecological Modelling, Elsevier, vol. 483(C).
    8. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    9. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    10. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    11. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    12. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    13. Behnood, Ali & Mohammadi Golafshani, Emadaldin & Hosseini, Seyedeh Mohaddeseh, 2020. "Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    14. Muhammad Nauman Zahid & Simone Perna, 2021. "Continent-Wide Analysis of COVID 19: Total Cases, Deaths, Tests, Socio-Economic, and Morbidity Factors Associated to the Mortality Rate, and Forecasting Analysis in 2020–2021," IJERPH, MDPI, vol. 18(10), pages 1-10, May.
    15. Antoni Wiliński & Łukasz Kupracz & Aneta Senejko & Grzegorz Chrząstek, 2022. "COVID-19: average time from infection to death in Poland, USA, India and Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4729-4746, December.
    16. ArunKumar, K.E. & Kalaga, Dinesh V. & Kumar, Ch. Mohan Sai & Kawaji, Masahiro & Brenza, Timothy M, 2021. "Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    17. Oladunni Abimbola Amos & Yusuff Adebayo Adebisi & Adeola Bamisaiye & Alaka Hassan Olayemi & Esther Bosede Ilesanmi & Alumuku Iordepuun Micheal & Aniekan Ekpenyong & Don Eliseo Lucero‐Prisno, 2021. "COVID‐19 and progress towards achieving universal health coverage in Africa: A case of Nigeria," International Journal of Health Planning and Management, Wiley Blackwell, vol. 36(5), pages 1417-1422, September.
    18. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1778-:d:1343264. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.