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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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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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Autism Spectrum Disorder01:19

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
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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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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.
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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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.
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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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Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
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Mortality in Autism: A Longitudinal Register-Based Study.

Maria Marinopoulou1,2,3, Caroline Mårland4,5, Christopher Gillberg4,5

  • 1Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. maria.marinopoulou@gnc.gu.se.

Journal of Autism and Developmental Disorders
|April 16, 2026
PubMed
Summary
This summary is machine-generated.

Adults with autism spectrum disorder (ASD) and intellectual disability (ID) face a higher mortality risk compared to the general population. This highlights the need for targeted health monitoring and support for this vulnerable group.

Keywords:
AdultsAutism spectrum disorderIntellectual disabilityLongitudinal studyMortality

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Area of Science:

  • Public Health
  • Autism Spectrum Disorder Research
  • Mortality Studies

Background:

  • Autism Spectrum Disorder (ASD) is a neurodevelopmental condition affecting social interaction and communication.
  • Co-occurring intellectual disability (ID) is common in individuals with ASD.
  • Limited research exists on long-term mortality risks in adults with ASD, particularly those with ID.

Purpose of the Study:

  • To investigate mortality rates and causes of death in a population-based cohort of adults with ASD.
  • To compare mortality findings in adults with ASD against a matched general population sample.
  • To identify specific risk factors for mortality within the ASD cohort.

Main Methods:

  • A Swedish population-based cohort of 113 adults diagnosed with ASD (born 1962-1984) was followed from 2000-2023.
  • The ASD cohort was compared to a matched general population sample (N=1130).
  • Data on mortality and causes of death were obtained from the National Cause of Death Register; survival analysis and Cox regression were employed.

Main Results:

  • The ASD group exhibited a significantly higher mortality rate (9.7%) compared to the general population group (2.7%) (p < .001).
  • Individuals with ASD had a 3.77 times higher risk of mortality (Hazard Ratio = 3.77, p < .001).
  • A higher mortality rate was observed in individuals with severe ID within the ASD cohort (p < .05).

Conclusions:

  • Adults with ASD, especially those with co-occurring ID, face an elevated risk of premature death.
  • Findings underscore the critical need for enhanced health surveillance and proactive health promotion strategies for individuals with ASD.
  • Special attention should be directed towards individuals with ASD and co-occurring ID to mitigate mortality risks.