Introduction <p>ASEAN, comprising 10 nations and the world’s seventh-largest economy, effectively mitigated COVID-19 through trade, healthcare, and cultural integration despite political disparities. Nevertheless, rapid policy shifts in COVID-19 control, combined with varying vaccination rates, notably influenced COVID-19 mortality waves (CMW). The distinct outbreak patterns among countries underscore the crucial need to accurately identify factors related to daily new COVID-19 deaths (DNCD).</p> Methods <p>The retrospective study examined Our World in Data database to assess factors affecting DNCD in ASEAN from vaccination start to May 1st, 2024. Joinpoint regression analysis, performed using Joinpoint Trend Analysis Software 5.1.0, identified bell-shaped patterns in CMW, while a Weighted Least Squares (WLS) model and Bayesian Model Averaging, executed in R 4.3.1, determined the primary predictors influencing DNCD.</p> Results <p>Joinpoint regression identified that 5 out of 10 countries experienced their CMW, lasting 767 days and resulting in 236,098 deaths. The WLS model, with a strong model fit (R<sup>2</sup> = 92.09%), reveals significant associations. Each 1% increase in COVID-19 vaccination (– 0.96; 95% CI – 1.19; – 0.72), each $1000 increase in GDP per capita (– 10.35; 95% CI – 11.09; – 9.61), each 1% increase in stringency index (– 3.89; 95%CI – 4.28;-3.51), and each additional hospital bed per thousand people (– 53.83; 95% CI – 60.50; – 47.16) are associated with lower DNCD. Conversely, increases in active cases (2.33; 95% CI 2.26;2.40) and cardiovascular death rate (0.90; 95% CI 0.85; 0.96) correlate with higher DNCD.</p> Conclusion <p>The study highlights vaccination, economic stability, healthcare infrastructure, and stringency measures in reducing DNCD. Policymakers should prioritize vaccination and healthcare improvements while monitoring active cases and cardiovascular health for future preparedness.</p>

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COVID-19 mortality waves in ASEAN driven by demographic, policy, and cardiovascular factors

  • Ngoc Huy Nguyen,
  • Son Dinh Thanh Le,
  • Ha Thi Thu Bui,
  • Viet Quoc Hoang,
  • Cuong Cao Do,
  • Vu Thi Thu Trang

摘要

Introduction

ASEAN, comprising 10 nations and the world’s seventh-largest economy, effectively mitigated COVID-19 through trade, healthcare, and cultural integration despite political disparities. Nevertheless, rapid policy shifts in COVID-19 control, combined with varying vaccination rates, notably influenced COVID-19 mortality waves (CMW). The distinct outbreak patterns among countries underscore the crucial need to accurately identify factors related to daily new COVID-19 deaths (DNCD).

Methods

The retrospective study examined Our World in Data database to assess factors affecting DNCD in ASEAN from vaccination start to May 1st, 2024. Joinpoint regression analysis, performed using Joinpoint Trend Analysis Software 5.1.0, identified bell-shaped patterns in CMW, while a Weighted Least Squares (WLS) model and Bayesian Model Averaging, executed in R 4.3.1, determined the primary predictors influencing DNCD.

Results

Joinpoint regression identified that 5 out of 10 countries experienced their CMW, lasting 767 days and resulting in 236,098 deaths. The WLS model, with a strong model fit (R2 = 92.09%), reveals significant associations. Each 1% increase in COVID-19 vaccination (– 0.96; 95% CI – 1.19; – 0.72), each $1000 increase in GDP per capita (– 10.35; 95% CI – 11.09; – 9.61), each 1% increase in stringency index (– 3.89; 95%CI – 4.28;-3.51), and each additional hospital bed per thousand people (– 53.83; 95% CI – 60.50; – 47.16) are associated with lower DNCD. Conversely, increases in active cases (2.33; 95% CI 2.26;2.40) and cardiovascular death rate (0.90; 95% CI 0.85; 0.96) correlate with higher DNCD.

Conclusion

The study highlights vaccination, economic stability, healthcare infrastructure, and stringency measures in reducing DNCD. Policymakers should prioritize vaccination and healthcare improvements while monitoring active cases and cardiovascular health for future preparedness.