Stressors and resources mediate the association of socioeconomic position with health behaviours

Peer reviewedPublisher PD

Equity-specific effects of 26 Dutch obesity-related lifestyle interventions

Reducing health inequalities is a policy priority in many developed countries. Little is known about effective strategies to reduce inequalities in obesity and its underlying behaviors. The goal of the study was to investigate differential effectiveness of interventions aimed at obesity prevention, the promotion of physical activity or a healthy diet by SES. Evidence acquisition: Subgroup analyses in 2010 and 2011 of 26 Dutch studies funded by The Netherlands Organization for Health Research and Development after 1990 (n=17) or identified by expert contact (n=9). Methodologic quality and differential effects were synthesized in harvest plots, subdivided by setting, age group, intensity, and time to follow-up. Evidence synthesis: Seven lifestyle interventions were rated more effective and four less effective in groups with high SES; for 15 studies no differential effects could be demonstrated. One study in the healthcare setting showed comparable effects in both socioeconomic groups. The only mass media campaign provided modest evidence for higher effectiveness among those with high SES. Individually tailored and workplace interventions were either more effective in higher-SES groups (n=4) or no differential effects were demonstrated (n=9). School-based studies (n=7) showed mixed results. Two of six community studies provided evidence for better effectiveness in lower-SES groups; none were more effective in higher-SES groups. One high-intensity community-based study provided best evidence for higher effectiveness in low-SES groups. Conclusions: Although for the majority of interventions aimed at obesity prevention, the promotion of physical activity, or a healthy diet, no differential effectiveness could be demonstrated, interventions may widen as well as reduce socioeconomic inequalities in these outcomes. Equityspecific subgroup analyses contribute to needed knowledge about what may work to reduce socioeconomic inequalities in obesity and underlying health behaviors

Prevention for elderly people: Demand-oriented or problem-oriented?

Objective:\ud To examine the association between self-expressed information needs and corresponding observed health and lifestyle issues in elderly people.\ud \ud Methods:\ud Data were used from the 2006 community health survey in Utrecht, a medium-sized city in the Netherlands. Data of 1634 people aged 55 years and older were available for analyses (response 64%). Multivariate logistic regression analyses were used to examine possible associations between expressed information needs and variables on demographics, lifestyle risk behaviours, physical and psychosocial health.\ud \ud Results:\ud Several significant associations were found between information needs and corresponding health and lifestyle problems. However, the explanatory power of the observed problems was generally low, explaining only 7% of the informational needs on lifestyle, and 17% and 28% of the informational needs on physical and psychosocial health, respectively.\ud \ud Conclusions:\ud The findings suggest that prevention amongst the elderly should not be solely based on information needs, but also on observed lifestyle and health. Implications for the use of different approaches of prevention and behavioural models underlying interventions are discussed

Prevention for elderly people: Demand-oriented or problem-oriented?

Objective To examine the association between self-expressed information needs and corresponding observed health and lifestyle issues in elderly people.Methods Data were used from the 2006 community health survey in Utrecht, a medium-sized city in the Netherlands. Data of 1634 people aged 55 years and older were available for analyses (response 64%). Multivariate logistic regression analyses were used to examine possible associations between expressed information needs and variables on demographics, lifestyle risk behaviours, physical and psychosocial health.Results Several significant associations were found between information needs and corresponding health and lifestyle problems. However, the explanatory power of the observed problems was generally low, explaining only 7% of the informational needs on lifestyle, and 17% and 28% of the informational needs on physical and psychosocial health, respectively.Conclusions The findings suggest that prevention amongst the elderly should not be solely based on information needs, but also on observed lifestyle and health. Implications for the use of different approaches of prevention and behavioural models underlying interventions are discussed.Elderly Prevention Information needs Demand-oriented Problem-oriented

Social Exclusion Index-for Health Surveys (SEI-HS): a prospective nationwide study to extend and validate a multidimensional social exclusion questionnaire

Abstract Background Social exclusion (SE) refers to the inability of certain groups or individuals to fully participate in society. SE is associated with socioeconomic inequalities in health, and its measurement in routine public health monitoring is considered key to designing effective health policies. In an earlier retrospective analysis we demonstrated that in all four major Dutch cities, SE could largely be measured with existing local public health monitoring data. The current prospective study is aimed at constructing and validating an extended national measure for SE that optimally employs available items. Methods In 2012, a stratified general population sample of 258,928 Dutch adults completed a version of the Netherlands Public Health Monitor (PHM) questionnaire in which 9 items were added covering aspects of SE that were found to be missing in our previous research. Items were derived from the SCP social exclusion index, a well-constructed 15-item instrument developed by the Netherlands Institute for Social Research (SCP). The dataset was randomly divided into a development sample (N =129,464) and a validation sample (N = 129,464). Canonical correlation analysis was conducted in the development sample. The psychometric properties were studied and compared with those of the original SCP index. All analyses were then replicated in the validation sample. Results The analysis yielded a four dimensional index, the Social Exclusion Index for Health Surveys (SEI-HS), containing 8 SCP items and 9 PHM items. The four dimensions: “lack of social participation”, “material deprivation”, “lack of normative integration” and “inadequate access to basic social rights”, were each measured with 3 to 6 items. The SEI-HS showed adequate internal consistency for both the general index and for two of four dimension scales. The internal structure and construct validity of the SEI-HS were satisfactory and similar to the original SCP index. Replication of the SEI-HS in the validation sample confirmed its generalisability. Conclusion This study demonstrates that the SEI-HS offers epidemiologists and public health researchers a uniform, reliable, valid and efficient means of assessing social exclusion and its underlying dimensions. The study also provides valuable insights in how to develop embedded measures for public health surveillance

The association between social exclusion or inclusion and health in EU and OECD countries: a systematic review

BACKGROUND: Social exclusion (SE), or the inability to participate fully in society, is considered one of the driving forces of health inequalities. Systematic evidence on this subject is pertinent but scarce. This review aims to systematically summarise peer reviewed studies examining the association between the multidimensional concepts of SE and social inclusion (SI) and health among adults in EU and OECD countries. METHODS: The protocol was registered on Prospero (CRD42017052718). Three major medical databases were searched to identify studies published before January 2018, supplemented by reference and citation tracking. Articles were included if they investigated SE or SI as a multidimensional concept with at least two out of the four dimensions of SE/SI, i.e. economic, social, political and cultural. A qualitative synthesis was conducted. RESULTS: Twenty-two observational studies were included. In the general population, high SE/low SI was associated with adverse mental and general health. For physical health, the evidence was inconclusive. In groups at high risk of SE, support was found for the association between high SE/low SI and adverse mental health but no conclusions could be drawn for physical and general health. CONCLUSIONS: This review found evidence for the association between high SE/low SI and adverse health outcomes, particularly mental health outcomes. The evidence is mainly based on cross-sectional studies using simple and often ad hoc indicators of SE/SI. The development and use of validated measures of SE/SI and more longitudinal research is needed to further substantiate the evidence base and gain better understanding of the causal pathways

Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer

Background: Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy. Material and methods: Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed. Results: In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61–0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47–0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (p =.027) and borderline significant in the validation dataset (p =.053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54–0.71) and 0.62 (95%CI 0.49–0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor. Conclusions: A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables

Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer

<p><b>Background:</b> Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy.</p> <p><b>Material and methods:</b> Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed.</p> <p><b>Results:</b> In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61–0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47–0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (<i>p</i> = .027) and borderline significant in the validation dataset (<i>p</i> = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54–0.71) and 0.62 (95%CI 0.49–0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor.</p> <p><b>Conclusions:</b> A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.</p