Pilot study protocol to inform a future longitudinal study of ageing using linked administrative data: Healthy AGeing in Scotland (HAGIS)

Douglas, Elaine; Rutherford, Alasdair; Bell, David

doi:10.1136/bmjopen-2017-018802

Cited by 16 publications

(18 citation statements)

References 8 publications

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“…Healthy Ageing in Scotland (HAGIS) is a proposed longitudinal study of older individuals living in Scotland, currently consisting of a pilot sample of 1000. Participants were identified by household; the household was contacted by letter and everyone living there was invited who met one of the following two conditions: (1) adults who were at least 50 years of age at the time of data collection, (2) partners of adults aged 50 years or older who were themselves 45 years or older (Douglas, Rutherford, & Bell, 2018 ). Despite including individuals older than 70, to ensure that there was no age overlap between the younger and older samples after variables were selected and processed, individuals older than 69 years were removed from our analytic HAGIS sample, leaving 612 participants.…”

Section: Methodsmentioning

confidence: 99%

“…General cognitive function was assessed using several widely used tests in each cohort. The particular tests that were available differed in each cohort (Douglas et al, 2018 ; Shankar et al, 2013 ; Taylor et al, 2018 ). In each cohort, we conducted a principal component analysis and took the first component of all test scores and used that as our measure of cognitive function in subsequent analyses.…”

Section: Methodsmentioning

confidence: 99%

“…(1) adults who were at least 50 years of age at the time of data collection, (2) partners of adults aged 50 years or older who were themselves 45 years or older (Douglas, Rutherford, & Bell, 2018). Despite including individuals older than 70, to ensure that there was no age overlap between the younger and older samples after variables were selected and processed, individuals older than 69 years were removed from our analytic HAGIS sample, leaving 612 participants.…”

Section: Younger Samplesmentioning

confidence: 99%

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Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study

Iveson

Deary

2020

Psychol. Med.

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Background Loneliness is a growing public health issue in the developed world. Among older adults, loneliness is a particular challenge, as the older segment of the population is growing and loneliness is comorbid with many mental as well as physical health issues. Comorbidity and common cause factors make identifying the antecedents of loneliness difficult, however, contemporary machine learning techniques are positioned to tackle this problem. Methods This study analyzed four cohorts of older individuals, split into two age groups – 45–69 and 70–79 – to examine which common psychological and sociodemographic are associated with loneliness at different ages. Gradient boosted modeling, a machine learning technique, and regression models were used to identify and replicate associations with loneliness. Results In all cohorts, higher emotional stability was associated with lower loneliness. In the older group, social circumstances such as living alone were also associated with higher loneliness. In the younger group, extraversion's association with lower loneliness was the only other confirmed relationship. Conclusions Different individual and social factors might underlie loneliness differences in distinct age groups. Machine learning methods have the potential to unveil novel associations between psychological and social variables, particularly interactions, and mental health outcomes.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Younger Samplesmentioning

confidence: 99%

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Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study

Iveson

Deary

2020

Psychol. Med.

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“…Further research is required to better understand which of these factors best explain sample differences in the NCDS, and moreover, whether our results hold in other exposure-outcome relationships in different cohort studies. Pioneering techniques, such as using administrative data to create longitudinal weights (Douglas et al, 2018) for cohort data could be explored to help generate estimates of maximum relevance to public health policy.…”

Section: Discussionmentioning

confidence: 99%

Are ‘healthy cohorts’ real-world relevant? Comparing the National Child Development Study (NCDS) with the ONS Longitudinal Study (LS)

Archer

Xun

Stuchbury

et al. 2020

Longitudinal and Life Course Studies

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Comparisons between cohort studies and nationally representative ‘real-world’ samples are limited. The NCDS (1958 British birth cohort) follows those born in Britain in a single week in March 1958 (n=18,558); and the ONS Longitudinal Study (LS) contains linked census data and life events for a 1% sample of the population of England and Wales (> 1 million records; allowing for sub-samples by age, ethnicity, or other socio-demographic factors). Common country-and age-matched socio-demographic variables were extracted from the closest corresponding time-points, NCDS 55-year survey in 2013 (n=8107) and LS respondents aged 55 in 2011 (n=7052). Longitudinal associations between socio-demographic exposures (from the NCDS 46-survey in 2004 and LS respondents aged 45 in 2001) and long-term limiting illness (from NCDS 2013 and LS respondents 2011, aged 55) were assessed using logistic regression. The NCDS 55-year sample had similar characteristics to LS respondents aged 55 for sex and marital status, but the NCDS sample had lower levels of long-term limiting illness (19.7% vs 22.8%), non-white ethnicity (2.1% vs 11.7%) and living in South England (46.9% vs 50.1%), and higher levels of full-time employment (61.2% vs 55.2%), working in professional/higher managerial occupations (35.7% vs 29.2%), and living with a spouse (69.1% vs 64.9%), all p<0.001. Nevertheless, longitudinal associations between socio-demographic exposures and long-term limiting illness were similar in the NCDS and LS samples (all tests of between-study heterogeneity in mutually adjusted models p>0.09) suggesting these NCDS findings are largely generalisable to the population of England and Wales.

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“…The difficulty of establishing impact with longitudinal studies was one of the themes I touched on in my keynote address to last year's SLLS conference. It continues to interest me, given that my colleagues and I have now completed a pilot study for a new longitudinal study of ageing in Scotland (Douglas, Rutherford & Bell, 2018).…”

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confidence: 99%

The impact imperative

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2018

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Pilot study protocol to inform a future longitudinal study of ageing using linked administrative data: Healthy AGeing in Scotland (HAGIS)

Cited by 16 publications

References 8 publications

Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study

Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study

Are ‘healthy cohorts’ real-world relevant? Comparing the National Child Development Study (NCDS) with the ONS Longitudinal Study (LS)

The impact imperative

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