Socioeconomic position and participation in baseline and follow-up visits: the Inter99 study

Bender, Anne Mette; Jørgensen, Torben; Helbech, Bodil; Linneberg, Allan; Pisinger, Charlotta

doi:10.1177/2047487312472076

Cited by 72 publications

(59 citation statements)

References 23 publications

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“…1). Low social status, as measured by low educational level, low income and unemployment has earlier been associated with a lower participation rate (Bender et al, 2012, Artac et al, 2013, Hoebel et al, 2013, Jensen et al, 2012, Cochrane et al, 2013, Dalsgaard et al, 2009). The underlying mechanisms are not fully understood, but educational attainment might influence a person's health literacy and thus the ability to understand the invitation material and the importance of early disease detection (Bo et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…For that purpose, the ‘active non-attenders’ were excluded from the analysis. Analyses were adjusted for confounders according to the literature (Bender et al, 2012, Jensen et al, 2013). The model on age was adjusted for sex, educational attainment, occupational status and income.…”

Section: Methodsmentioning

confidence: 99%

“…However, it is inconclusive if regular health examinations conducted by general medical practices reduces mortality and morbidity rates (Krogsbøll et al, 2012). A well-known challenge of screening and systematic health checks is low uptake (Krogsbøll et al, 2012) which seems to be influenced by a number of factors (Dryden et al, 2012, Bender et al, 2012). A higher participation rate has, in many studies, been found to be associated with higher age (Jones et al, 1993, Waller et al, 2013, Artac et al, 2013), being a woman (Waller et al, 2013, Culica et al, 2002, Thomas et al, 1993), and with socio-demographic factors such as having a job; higher educational level; higher income; and living with a partner (Bender et al, 2012, Boshuizen et al, 2006, Hoebel et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…A well-known challenge of screening and systematic health checks is low uptake (Krogsbøll et al, 2012) which seems to be influenced by a number of factors (Dryden et al, 2012, Bender et al, 2012). A higher participation rate has, in many studies, been found to be associated with higher age (Jones et al, 1993, Waller et al, 2013, Artac et al, 2013), being a woman (Waller et al, 2013, Culica et al, 2002, Thomas et al, 1993), and with socio-demographic factors such as having a job; higher educational level; higher income; and living with a partner (Bender et al, 2012, Boshuizen et al, 2006, Hoebel et al, 2014). Only a few studies have investigated the association of people's morbidity with non-attendance in general health examinations (Dryden et al, 2012, Boshuizen et al, 2006, Thorogood et al, 1993), since it is difficult to obtain records of disease for those who do not want to participate.…”

Section: Introductionmentioning

confidence: 99%

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Patterns of attendance to health checks in a municipality setting: the Danish ‘Check Your Health Preventive Program’

Bjerregaard

Maindal

Bruun

et al. 2017

Preventive Medicine Reports

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We aimed to investigate the determinants of attendance to a preventive health check program and to explore the homogeneity of the attenders.4853 eligible persons living in the municipality of Randers, Denmark, from 2012 to 2013, aged 30–49 years, received an invitation to attend the ‘Check Your Health Preventive Program’. Data was obtained from the Danish National Registers. Socio-demographic factors, use of preventive services, morbidity were examined as determinants of attendance by Poisson regression analyses. A chi-squared automatic interaction detection decision tree analysis was used to identify mutually exclusive groups.In total, 55% of the invited population attended (49% men). Attenders were more likely to be: of higher age; immigrants; cohabiting; have: higher socio-economic status; higher use of preventive services and lower morbidity. Decision tree analysis revealed six groups, with the most important variable being income: 1) low income, low education (A = attendance rate: 38%; P = population size: 11%); 2) low income, education higher than 10 years, living alone (A: 41%; P: 5%); 3) low income, education higher than 10 years, cohabiting (A: 56%; P: 16%); 4) middle income (A: 60%; P: 34%); 5) high income, living alone (A: 56%; P: 4%); 6) high income, cohabiting (A: 69%; P: 30%).More than half of a general population voluntarily attended a general health check, despite a resource intensive offer. People with low resources had lower attendance rates. This study adds a detailed description of mutually exclusive groups of attenders, for use in future planning and implementation of preventive actions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Patterns of attendance to health checks in a municipality setting: the Danish ‘Check Your Health Preventive Program’

Bjerregaard

Maindal

Bruun

et al. 2017

Preventive Medicine Reports

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“…Non-responder in the Inter99 had lower socioeconomic status [41]. Many studies on birth weight and lung function were limited by a high percentage of non-responders [11,13,14].…”

Section: Weaknessesmentioning

confidence: 99%

The impact of birth weight on the level of lung function and lung function decline in the general adult population. The Inter99 study

Baumann

Godtfredsen

Lange

et al. 2015

Respiratory Medicine

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Proactive multipurpose health risk screening in health care settings: Methods, design, and reach

Guertler

Moehring

Krause

et al. 2019

Int J Methods Psych Res

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Objectives Real world implementation of proactive screening and brief intervention in health care is threatened by high cost. Using e‐health interventions and screening for multiple health risk factors may provide more efficiency. We describe methodological details of a proactive multipurpose health risk screening in health care settings and report on participation rates, participants' characteristics, and participation factors. Methods Patients between 18 and 64 years from ambulatory practices and hospitals were proactively approached by study assistants at three sites for a computerized screening on harmful alcohol and tobacco consumption, depressive symptoms, insufficient fruit/vegetable consumption, physical inactivity and overweight. On the basis of their health risk pattern, a computerized algorithm allocated patients to one of five studies each of them addressing a psychiatric research question. Results Among all eligible patients, 13,763 (86.5%) were screened. Younger age and being female predicted screening participation. Of those with complete data (n = 12,828), 82.9% reported at least two health risks and 34.0% were eligible for a study. Study participation ranged between 35.2% and 50.8%, and was associated with socio‐demographics and problem severity. Conclusions This study supports the use of systematic proactive screening for multiple health risks in health care settings as it is more resource‐saving than single focused screening.

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Socioeconomic position and participation in baseline and follow-up visits: the Inter99 study

Cited by 72 publications

References 23 publications

Patterns of attendance to health checks in a municipality setting: the Danish ‘Check Your Health Preventive Program’

Patterns of attendance to health checks in a municipality setting: the Danish ‘Check Your Health Preventive Program’

The impact of birth weight on the level of lung function and lung function decline in the general adult population. The Inter99 study

Proactive multipurpose health risk screening in health care settings: Methods, design, and reach

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