scholarly journals Commentary on Bishop & Almquist : Beyond a descriptive life‐course epidemiology

Addiction ◽  
2021 ◽  
Vol 116 (3) ◽  
pp. 641-642
Author(s):  
Anne Hammarström
Keyword(s):  
Life Course ◽  
Life Course Epidemiology

scholarly journals Commentary: The value of life course epidemiology in low- and middle-income countries: an ageing perspective

2016 ◽  
Vol 45 (4) ◽  
pp. 997-999 ◽  
Author(s):  
Stephen M Tollman ◽  
Shane A Norris ◽  
Lisa F Berkman
Keyword(s):  
Life Course ◽  
Value Of Life ◽  
Middle Income ◽  
Life Course Epidemiology ◽  
Middle Income Countries ◽  
Low And Middle Income

scholarly journals Sources of confounding in life course epidemiology

2018 ◽  
Vol 10 (3) ◽  
pp. 299-305 ◽  
Author(s):  
S. Santos ◽  
D. Zugna ◽  
C. Pizzi ◽  
L. Richiardi
Keyword(s):  
Life Course ◽  
Meta Analysis ◽  
Internal Validity ◽  
Observational Research ◽  
Individual Participant Data ◽  
Confounding By Indication ◽  
Life Course Epidemiology ◽  
Combining Data ◽  
Individual Participant ◽  
Different Populations

AbstractIn epidemiologic analytical studies, the primary goal is to obtain a valid and precise estimate of the effect of the exposure of interest on a given outcome in the population under study. A crucial source of violation of the internal validity of a study involves bias arising from confounding, which is always a challenge in observational research, including life course epidemiology. The increasingly popular approach of meta-analyzing individual participant data from several observational studies also brings up to discussion the problem of confounding when combining data from different populations. In this study, we review and discuss the most common sources of confounding in life course epidemiology: (i) confounding by indication, (ii) impact of baseline selection on confounding, (iii) time-varying confounding and (iv) mediator–outcome confounding. We also discuss the issue of addressing confounding in the context of an individual participant data meta-analysis.

Life Course Epidemiology

2015 ◽  
pp. 67-75 ◽  
Author(s):  
Gita D. Mishra ◽  
Diana Kuh ◽  
Yoav Ben-Shlomo
Keyword(s):  
Life Course ◽  
Life Course Epidemiology

How to use sequence analysis for life course epidemiology? An example on HIV-positive Sub-Saharan migrants in France

2018 ◽  
Vol 72 (6) ◽  
pp. 507-512
Author(s):  
Anne Gosselin ◽  
Annabel Desgrées du Loû ◽  
Eva Lelièvre
Keyword(s):  
Sequence Analysis ◽  
Life Course ◽  
Social Epidemiology ◽  
Cox Model ◽  
Hiv Care ◽  
Event History ◽  
Hiv Diagnosis ◽  
Life Course Epidemiology ◽  
Paris Region ◽  
Sub Saharan

BackgroundLife course epidemiology is now an established field in social epidemiology; in sociodemography, the quantitative analysis of biographies recently experienced significant trend from event history analysis to sequence analysis. The purpose of this article is to introduce and adapt this methodology to a social epidemiology question, taking the example of the impact of HIV diagnosis on Sub-Saharan migrants’ residential trajectories in the Paris region.MethodsThe sample consists of 640 migrants born in Sub-Saharan Africa receiving HIV care. They were interviewed in healthcare facilities in the Paris region within the PARCOURS project, conducted from 2012 to 2013, using life event history calendars, which recorded year by year their health, family and residential histories. We introduce a two-step methodological approach consisting of (1) sequence analysis by optimal matching to build a typology of migrants’ residential pathways before and after diagnosis, and (2) a Cox model of the probability to experience changes in the residential situation.ResultsThe seven-clusters typology shows that for a majority, the HIV diagnosis did not entail changes in residential situation. However 30% of the migrants experienced a change in their residential situation at time of diagnosis. The Cox model analysis reveals that this residential change was in fact moving in with one’s partner (HR 2.99, P<0.000) rather than network rejection.ConclusionThis original combination of sequence analysis and Cox models is a powerful process that could be applied to other themes and constitutes a new approach in the life course epidemiology toolbox.Trial registration numberNCT02566148.

Life course epidemiology of anxiety disorders

2013 ◽  
pp. 97-110
Author(s):  
Renee D. Goodwin ◽  
Katja Beesdo-Baum ◽  
Susanne Knappe ◽  
Dan J. Stein
Keyword(s):  
Anxiety Disorders ◽  
Life Course ◽  
Life Course Epidemiology

Life course epidemiology and analysis

2015 ◽  
pp. 679-692 ◽  
Author(s):  
Diana Kuh ◽  
Yoav Ben-Shlomo ◽  
Kate Tilling ◽  
Rebecca Hardy
Keyword(s):  
Life Course ◽  
Life Course Epidemiology

Life Course Epidemiology

2014 ◽  
pp. 1521-1549 ◽  
Author(s):  
Yoav Ben-Shlomo ◽  
Gita Mishra ◽  
Diana Kuh
Keyword(s):  
Life Course ◽  
Life Course Epidemiology

scholarly journals Commentary: Incorporating concepts and methods from causal inference into life course epidemiology

2017 ◽  
Vol 46 (2) ◽  
pp. 771-771 ◽  
Author(s):  
Bianca L De Stavola ◽  
Rhian M Daniel
Keyword(s):  
Life Course ◽  
Causal Inference ◽  
Life Course Epidemiology
Sign in / Sign up

Export Citation Format

Share Document