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

Author

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  • 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
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    References listed on IDEAS

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    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).
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