Five consecutive epidemiological waves of COVID-19: a population-based cross-sectional study on characteristics, policies, and health outcome

Author:

Amin Rozhin,Sohrabi Mohammad-Reza,Zali Ali-Reza,Hannani Khatereh

Abstract

Abstract Background This study was conducted with the intension of providing a more detailed view about the dynamics of COVID-19 pandemic. To this aim, characteristics, implemented public health measures, and health outcome of COVID-19 patients during five consecutive waves of the disease were assessed. Methods This study was a population-based cross-sectional analysis of data on adult patients who were diagnosed with COVID-19 during five waves of the disease in Iran. Chi-squared test, One-way ANOVA, and Logistic Regression analysis were applied. A detailed literature review on implemented public health policies was performed by studying published documents and official websites responsible for conveying information about COVID-19. Results Data on 328,410 adult patients was analyzed. Main findings indicated that the probability of dying with COVID-19 has increased as the pandemic wore on, showing its highest odd during the third wave (odds ratio: 1.34, CI: 1.283–1.395) and has gradually decreased during the next two waves. The same pattern was observed in the proportion of patients requiring ICU admission (P < 0.001). First wave presented mainly with respiratory symptoms, gastrointestinal complaints were added during the second wave, neurological manifestations with peripheral involvement replaced the gastrointestinal complaints during the third wave, and central nervous system manifestations were added during the fourth and fifth waves. A significant difference in mean age of patients was revealed between the five waves (P < 0.001). Moreover, results showed a significant difference between men and women infected with COVID-19, with men having higher rates of the disease at the beginning. However, as the pandemic progressed the proportion of women gradually increased, and ultimately more women were diagnosed with COVID-19 during the fifth wave. Our observations pointed to the probability that complete lockdowns were the key measures that helped to mitigate the virus spread during the first twenty months of the pandemic in the country. Conclusion A changing pattern in demographic characteristics, clinical manifestations, and severity of the disease has been revealed as the pandemic unfolded. Reviewing COVID-19-related public health interventions highlighted the importance of immunization and early implementation of restrictive measures as effective strategies for reducing the acute burden of the disease.

Funder

Research Deputy of the School of Medicine at Shahid Beheshti University of Medical Sciences in Tehran, Iran

Publisher

Springer Science and Business Media LLC

Subject

Infectious Diseases

Reference38 articles.

1. World Health Organization. Naming the coronavirus disease (COVID-19) and the virus that causes it 2021. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it.

2. World Health Organization. Timeline: WHO's COVID-19 response 2021. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline.

3. World Health Organization. Coronavirus disease (COVID-19) pandemic 2021. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.

4. Takian A, Raoofi A, Kazempour-Ardebili S. COVID-19 battle during the toughest sanctions against Iran. Lancet. 2020;395(10229):1035–6.

5. World Health Organization. Iran (Islamic Republic of) 2021. Available from: https://www.who.int/countries/irn/.

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