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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

12.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Measurement of Lifespan in Drosophila melanogaster
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Longitudinal variability in mortality predicts COVID-19 deaths.

Jon O Lundberg1, Hugo Zeberg2

  • 1Department of Physiology and Pharmacology, Karolinska Institutet, SE-17177, Stockholm, Sweden. jon.lundberg@ki.se.

European Journal of Epidemiology
|July 4, 2021
PubMed
Summary

Pre-pandemic death rate variability in European countries predicted COVID-19 mortality. Countries with higher winter death rate fluctuations faced greater excess mortality during the pandemic, indicating intrinsic susceptibility to viral respiratory diseases.

Keywords:
All-cause mortalityCOVID-19CoronaInfluenzaSARS-CoV-2Virus

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Area of Science:

  • Epidemiology
  • Public Health
  • Viral Respiratory Diseases

Background:

  • COVID-19 pandemic exhibited significant variation in mortality rates across European nations.
  • Debate exists regarding the effectiveness of governmental measures in controlling disease spread and deaths.
  • Identifying factors influencing national pandemic susceptibility is crucial.

Purpose of the Study:

  • To investigate whether pre-pandemic factors could predict COVID-19 mortality.
  • To determine if longitudinal variability in death rates correlates with pandemic-related excess mortality.

Main Methods:

  • Analysis of longitudinal death rate variability during winter influenza seasons (2015-2019).
  • Correlation analysis (Spearman's rank correlation) between pre-pandemic death rate variability and 2020 excess mortality.
  • Comparison with demographic, economic, and healthcare system factors.

Main Results:

  • A significant positive correlation was found between pre-pandemic death rate variability and COVID-19 excess mortality (Spearman's ρ = 0.68, p < 0.001).
  • No significant correlation was observed with age, population density, latitude, GNP, health spending, ICU beds, urbanization, or vaccination rates.
  • Pre-pandemic death rate variability emerged as a potential predictor of pandemic mortality.

Conclusions:

  • Intrinsic national susceptibility to excess mortality from viral respiratory diseases, including COVID-19, may be linked to historical death rate variability.
  • This variability appears to be a more significant predictor than traditional demographic, economic, or healthcare infrastructure factors.