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Prev Med. 2014 Apr;61:42-7. doi: 10.1016/j.ypmed.2014.01.008. Epub 2014 Jan 15.

Elder abuse and socioeconomic inequalities: a multilevel study in 7 European countries.

Author information

1
Institute of Public Health, University of Porto, Porto, Portugal; Department of Clinical Epidemiology, Predictive Medicine and Public Health, University of Porto Medical School, Porto, Portugal. Electronic address: silfraga@med.up.pt.
2
Department of Public Health Science, Protestant University of Applied Sciences, Ludwigsburg, Germany.
3
Institute of Public Health, University of Porto, Porto, Portugal; Department of Clinical Epidemiology, Predictive Medicine and Public Health, University of Porto Medical School, Porto, Portugal.
4
Centro de Investigación Biomedica en Red de Salud Mental (CIBERSAM), University of Granada, Granada, Spain.
5
Department of Sociology, National School of Public Health, Athens, Greece.
6
Centre of Socio-Economic Research on Ageing, Italian National Institute of Health and Science on Aging, I.N.R.C.A., Ancona, Italy.
7
Department of Health Management, Lithuanian University of Health Sciences, Kaunas, Lithuania.
8
Department of Health Sciences, Section of Public Health Sciences, Mid Sweden University, Sundsvall, Sweden.

Abstract

OBJECTIVES:

To compare the prevalence of elder abuse using a multilevel approach that takes into account the characteristics of participants as well as socioeconomic indicators at city and country level.

METHODS:

In 2009, the project on abuse of elderly in Europe (ABUEL) was conducted in seven cities (Stuttgart, Germany; Ancona, Italy; Kaunas, Lithuania, Stockholm, Sweden; Porto, Portugal; Granada, Spain; Athens, Greece) comprising 4467 individuals aged 60-84 years. We used a 3-level hierarchical structure of data: 1) characteristics of participants; 2) mean of tertiary education of each city; and 3) country inequality indicator (Gini coefficient). Multilevel logistic regression was used and proportional changes in Intraclass Correlation Coefficient (ICC) were inspected to assert explained variance between models.

RESULTS:

The prevalence of elder abuse showed large variations across sites. Adding tertiary education to the regression model reduced the country level variance for psychological abuse (ICC=3.4%), with no significant decrease in the explained variance for the other types of abuse. When the Gini coefficient was considered, the highest drop in ICC was observed for financial abuse (from 9.5% to 4.3%).

CONCLUSION:

There is a societal and community level dimension that adds information to individual variability in explaining country differences in elder abuse, highlighting underlying socioeconomic inequalities leading to such behavior.

KEYWORDS:

Elder abuse; Inequalities; Multinational study; Violence

PMID:
24440160
DOI:
10.1016/j.ypmed.2014.01.008
[Indexed for MEDLINE]
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