Covid-19 Sentry

Contents

From Preprints

  1. We identified three key reasons for this fall in patient demand. First, COVID-19-related hygiene measures and behavioral changes significantly reduced non-COVID-19 infectious diseases. Second, consultations relating to chronic diseases fell sharply. Third, certain medical investigations and interventions were postponed or cancelled. Despite the drop in hospital attendances and admissions, COVID-19 is said to have brought the Japanese health care system to the brink of collapse. In this context, we explore longstanding systematic issues, finding that Japan9s abundant supply of beds and current payment system may have introduced a perverse incentive to overprovide services, creating a mismatch between patient needs and the supply of health care resources. Poor coordination among health care providers and the highly decentralized governance of the health care system have also contributed to the crisis. In order to ensure the long-term sustainability of the Japanese health care system beyond COVID-19, it is essential to promote specialization and differentiation of medical functions among hospitals, to strengthen governance, and to introduce appropriate payment reform.

    🖺 Full Text HTML: The paradox of the COVID-19 pandemic: the impact on patient demand in Japanese hospitals
  1. compliance with hygiene measures (two items), and (d) trust in COVID-19 vaccination (seven items). Cronbach coefficients alpha for the four factors that emerged from the exploratory factor analysis were greater than 0.82. Pearson correlation coefficients for the 16 items and the four factors were greater than 0.67 (p-value<0.001 in all cases). Conclusions: We developed a reliable and valid questionnaire to measure attitudes toward COVID-19 vaccination and pandemic. Further studies should be conducted to expand our knowledge and infer more valid results.

    🖺 Full Text HTML: Development and validation of a questionnaire to measure attitudes toward COVID-19 vaccination and pandemic
  1. deaths from COVID-19 related atypical severe blood clots (cerebral venous sinus thrombosis & portal vein thrombosis). For a million people aged >70 years where 70% received first dose and 35% received two doses, our model estimated <1 death from TTS, 25 deaths prevented under low transmission, and >3000 deaths prevented under high transmission. Risks versus benefits varied significantly between age groups and transmission levels. Under high transmission, deaths prevented by AZ vaccine far exceed deaths from TTS (by 8 to >4500 times depending on age). Probability of dying from COVID-related atypical severe blood clots was 58-126 times higher (depending on age and sex) than dying from TTS. To our knowledge, this is the first example of the use of Bayesian networks for risk-benefit analysis for a COVID-19 vaccine. The model can be rapidly updated to incorporate new data, adapted for other countries, extended to other outcomes (e.g., severe disease), or used for other vaccines.

    🖺 Full Text HTML: Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework

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