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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Actuarial Approach01:20

Actuarial Approach

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,...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

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Related Experiment Video

Updated: Jul 6, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Mortality after long-term sickness absence: prospective cohort study.

Sturla Gjesdal1, Peder R Ringdal, Kjell Haug

  • 1Department of Public Health and Primary Health Care, and Health Economics, University of Bergen, Norway. sturla.gjesdal@isf.uib.no

European Journal of Public Health
|March 12, 2008
PubMed
Summary

Individuals with long-term sickness absence (LTSA) face higher mortality rates, particularly men and women with cancer diagnoses. Musculoskeletal conditions did not show elevated mortality risks in this study.

Related Experiment Videos

Last Updated: Jul 6, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Occupational Health
  • Public Health
  • Epidemiology

Background:

  • Long-term sickness absence (LTSA) is a significant public health concern.
  • Estimating excess mortality associated with LTSA is crucial for understanding its impact.
  • Identifying risk factors for mortality among individuals with LTSA is essential for targeted interventions.

Purpose of the Study:

  • To estimate excess mortality following long-term sickness absence (LTSA).
  • To identify socio-demographic and diagnostic factors contributing to mortality risk after LTSA.

Main Methods:

  • A prospective cohort study was conducted in a Norwegian county from 1994-2003.
  • A sample of 3386 individuals with LTSA (>8 weeks) was compared to the general population for all-cause mortality.
  • Comparative Mortality Figures (CMF) and Standardized Mortality Ratios (SMR) were calculated for diagnostic groups.

Main Results:

  • Excess mortality was observed in both male (CMF 2.0) and female (CMF 1.5) samples with LTSA.
  • Cancer diagnoses were strongly associated with increased mortality, especially in women (SMR 16.1).
  • Among men, mental and other diagnoses (respiratory, neurological, digestive) also showed elevated mortality risks (SMRs 1.7-1.8).

Conclusions:

  • The study confirms excess mortality after LTSA, consistent with findings from Finland and the UK.
  • Cancer cases accounted for all excess mortality among women with LTSA.
  • For men, diagnoses beyond musculoskeletal conditions, including mental and other categories, contributed to excess mortality.