Addi P L van Bergen1,2, Annelies van Loon3, Stella J M Hoff4, Judith R L M Wolf5, Albert M van Hemert6. 1. GGD Hollands Midden, Parmentierweg 49, 2316ZV, Leiden, The Netherlands. avanbergen@ggdhm.nl. 2. Department of Psychiatry, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. avanbergen@ggdhm.nl. 3. GGD Amsterdam, Nieuwe Achtergracht 100, 1018 WT, Amsterdam, The Netherlands. 4. The Netherlands Institute of Social Research|SCP, Bezuidenhoutseweg 30, 2594 AV, Den Haag, The Netherlands. 5. Impuls, The Netherlands Center for Social Care Research, Radboud University Medical Center, Geert Grooteplein Zuid 21, 6525 GA, Nijmegen, The Netherlands. 6. Department of Psychiatry, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Abstract
BACKGROUND: Population segmentation and risk stratification are important strategies for allocating resources in public health, health care and social care. Social exclusion, which is defined as the cumulation of disadvantages in social, economic, cultural and political domains, is associated with an increased risk of health problems, low agency, and as a consequence, a higher need for health and social care. The aim of this study is to test social exclusion against traditional social stratifiers to identify high-risk/high-need population segments. METHODS: We used data from 33,285 adults from the 2016 Public Health Monitor of four major cities in the Netherlands. To identify at-risk populations for cardiovascular risk, cancer, low self-rated health, anxiety and depression symptoms, and low personal control, we compared relative risks (RR) and population attributable fractions (PAF) for social exclusion, which was measured with the Social Exclusion Index for Health Surveys (SEI-HS), and four traditional social stratifiers, namely, education, income, labour market position and migration background. RESULTS: The analyses showed significant associations of social exclusion with all the health indicators and personal control. Particular strong RRs were found for anxiety and depression symptoms (7.95) and low personal control (6.36), with corresponding PAFs of 42 and 35%, respectively. Social exclusion was significantly better at identifying population segments with high anxiety and depression symptoms and low personal control than were the four traditional stratifiers, while the two approaches were similar at identifying other health problems. The combination of social exclusion with a low labour market position (19.5% of the adult population) captured 67% of the prevalence of anxiety and depression symptoms and 60% of the prevalence of low personal control, as well as substantial proportions of the other health indicators. CONCLUSIONS: This study shows that the SEI-HS is a powerful tool for identifying high-risk/high-need population segments in which not only ill health is concentrated, as is the case with traditional social stratifiers, but also a high prevalence of anxiety and depression symptoms and low personal control are present, in addition to an accumulation of social problems. These findings have implications for health care practice, public health and social interventions in large cities.
BACKGROUND: Population segmentation and risk stratification are important strategies for allocating resources in public health, health care and social care. Social exclusion, which is defined as the cumulation of disadvantages in social, economic, cultural and political domains, is associated with an increased risk of health problems, low agency, and as a consequence, a higher need for health and social care. The aim of this study is to test social exclusion against traditional social stratifiers to identify high-risk/high-need population segments. METHODS: We used data from 33,285 adults from the 2016 Public Health Monitor of four major cities in the Netherlands. To identify at-risk populations for cardiovascular risk, cancer, low self-rated health, anxiety and depression symptoms, and low personal control, we compared relative risks (RR) and population attributable fractions (PAF) for social exclusion, which was measured with the Social Exclusion Index for Health Surveys (SEI-HS), and four traditional social stratifiers, namely, education, income, labour market position and migration background. RESULTS: The analyses showed significant associations of social exclusion with all the health indicators and personal control. Particular strong RRs were found for anxiety and depression symptoms (7.95) and low personal control (6.36), with corresponding PAFs of 42 and 35%, respectively. Social exclusion was significantly better at identifying population segments with high anxiety and depression symptoms and low personal control than were the four traditional stratifiers, while the two approaches were similar at identifying other health problems. The combination of social exclusion with a low labour market position (19.5% of the adult population) captured 67% of the prevalence of anxiety and depression symptoms and 60% of the prevalence of low personal control, as well as substantial proportions of the other health indicators. CONCLUSIONS: This study shows that the SEI-HS is a powerful tool for identifying high-risk/high-need population segments in which not only ill health is concentrated, as is the case with traditional social stratifiers, but also a high prevalence of anxiety and depression symptoms and low personal control are present, in addition to an accumulation of social problems. These findings have implications for health care practice, public health and social interventions in large cities.
Keywords:
Anxiety and depression; Kessler-10; Pearlin mastery scale; Personal control; Population health; Public health monitoring; Social determinants of health; Social exclusion
Authors: Hamish M E Foster; Carlos A Celis-Morales; Barbara I Nicholl; Fanny Petermann-Rocha; Jill P Pell; Jason M R Gill; Catherine A O'Donnell; Frances S Mair Journal: Lancet Public Health Date: 2018-11-20
Authors: Addi P L van Bergen; Stella J M Hoff; Hanneke Schreurs; Annelies van Loon; Albert M van Hemert Journal: BMC Public Health Date: 2017-03-14 Impact factor: 3.295