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  • 1.
    Bergqvist, Kersti
    et al.
    Centre for Health Equity Studies, Stockholm University, Karolinska Institutet, Stockholm, Sweden .
    Åberg Yngwe, Monica
    Centre for Health Equity Studies, Stockholm University, Karolinska Institutet, Stockholm, Sweden .
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Centre for Health Equity Studies, Stockholm University, Karolinska Institutet, Stockholm, Sweden .
    Understanding the role of welfare state characteristics for health and inequalities - an analytical review2013In: BMC Public Health, ISSN 1471-2458, E-ISSN 1471-2458, Vol. 13, p. Art. no. 1234-Article, review/survey (Refereed)
    Abstract [en]

    Background: The past decade has witnessed a growing body of research on welfare state characteristics and health inequalities but the picture is, despite this, inconsistent. We aim to review this research by focusing on theoretical and methodological differences between studies that at least in part may lead to these mixed findings. Methods: Three reviews and relevant bibliographies were manually explored in order to find studies for the review. Related articles were searched for in PubMed, Web of Science and Google Scholar. Database searches were done in PubMed and Web of Science. The search period was restricted to 2005-01-01 to 2013-02-28. Fifty-four studies met the inclusion criteria. Results: Three main approaches to comparative welfare state research are identified; the Regime approach, the Institutional approach, and the Expenditure approach. The Regime approach is the most common and regardless of the empirical regime theory employed and the amendments made to these, results are diverse and contradictory. When stratifying studies according to other features, not much added clarity is achieved. The Institutional approach shows more consistent results; generous policies and benefits seem to be associated with health in a positive way for all people in a population, not only those who are directly affected or targeted. The Expenditure approach finds that social and health spending is associated with increased levels of health and smaller health inequalities in one way or another but the studies are few in numbers making it somewhat difficult to get coherent results. Conclusions: Based on earlier reviews and our results we suggest that future research should focus less on welfare regimes and health inequalities and more on a multitude of different types of studies, including larger analyses of social spending and social rights in various policy areas and how these are linked to health in different social strata. But, we also need more detailed evaluation of specific programmes or interventions, as well as more qualitative analyses of the experiences of different types of policies among the people and families that need to draw on the collective resources.

  • 2.
    Fors, Stefan
    et al.
    Aging Res Ctr, Karolinska Inst, SE-11330 Stockholm, Sweden .
    Lennartsson, Carin
    Aging Res Ctr, Karolinska Inst, SE-11330 Stockholm, Sweden .
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Live long and prosper? Childhood living conditions, marital status, social class in adulthood and mortality during mid-life: A cohort study2011In: Scandinavian Journal of Public Health, ISSN 1403-4948, E-ISSN 1651-1905, Vol. 39, no 2, p. 179-186Article in journal (Refereed)
    Abstract [en]

    Aims: The aim of the present study was to investigate the impact of childhood living conditions, marital status, and social class in adulthood on the risk of mortality during mid-life. Two questions were addressed: Is there an effect of childhood living conditions on mortality risk during mid-life and if so, is the effect mediated or modified by social class and/or marital status in adulthood? Methods: A nationally representative, Swedish, level of living survey from 1968 was used as baseline. The study included those aged 25-69 at baseline (n = 4082). Social conditions in childhood and adulthood were assessed using self-reports. These individuals were then followed for 39 years using registry data on mortality. Results: The results showed associations between childhood living conditions, marital status, social class in adulthood and mortality during mid life. Social class and familial conditions during childhood as well as marital status and social class in adulthood all contributed to the risk of mortality during mid-life. Individuals whose father's were manual workers, who grew up in broken homes, who were unmarried, and/or were manual workers in adulthood had an increased risk of mortality during mid life. The effects of childhood conditions were, in part, both mediated and modified by social class in adulthood. Conclusions: The findings of this study suggest that there are structural, social conditions experienced at different stages of the life course that affect the risk of mortality during mid-life.

  • 3.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Små välfärdsresurser ger sämre hälsa2011In: Tvärsnitt, ISSN 0348-7997, no 3-4, p. 52-55Article in journal (Other (popular science, discussion, etc.))
  • 4.
    Mackenbach, J. P.
    et al.
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Kulhánová, I.
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Bopp, M.
    Univ Zurich, Inst Social & Prevent Med, CH-8006 Zurich, Switzerland.
    Deboosere, P.
    Vrije Univ Brussel, Dept Sociol, Brussels, Belgium.
    Eikemo, T. A.
    NTNU, Dept Sociol & Polit Sci, Trondheim, Norway.
    Hoffmann, R.
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Kulik, M. C.
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Leinsalu, M.
    Natl Inst Hlth Dev, Dept Epidemiol & Biostat, Tallinn, Estonia.
    Martikainen, P.
    Univ Helsinki, Dept Sociol, FIN-00014 Helsinki, Finland.
    Menvielle, G.
    Pierre Louis Inst Epidemiol & Publ Hlth, Dept Social Epidemiol, INSERM, UMR S 1136, Paris, France.
    Regidor, E.
    Univ Complutense Madrid, Dept Prevent Med & Publ Hlth, E-28040 Madrid, Spain.
    Wojtyniak, B.
    Natl Inst Publ Hlth, Natl Inst Hyg, Dept Monitoring & Anal Populat Hlth, Warsaw, Poland.
    Östergren, O.
    Ctr Hlth Equ Studies, Stockholm, Sweden.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Ctr Hlth Equ Studies, Stockholm, Sweden.
    Biggeri, A.
    Florence, Italy.
    Borrell, C.
    Barcelona, Spain.
    Brown, L.
    Newport, United Kingdom .
    Costa, G.
    Turin, Italy.
    Esnaola, S.
    Vitoria Gasteiz, Spain .
    Klotz, J.
    Vienna, Austria.
    Kovacs, K.
    Budapest, Hungary .
    Lange, A.
    Copenhagen, Denmark .
    Rodriguez-Sanz, M.
    Barcelona, Spain .
    Strand, B. H.
    Oslo, Norway .
    White, C.
    Newport, United Kingdom .
    Variations in the relation between education and cause-specific mortality in 19 European populations: A test of the "fundamental causes" theory of social inequalities in health2015In: Social Science and Medicine, ISSN 0277-9536, E-ISSN 1873-5347, Vol. 127, p. 51-62Article in journal (Refereed)
    Abstract [en]

    Link and Phelan have proposed to explain the persistence of health inequalities from the fact that socioeconomic status is a "fundamental cause" which embodies an array of resources that can be used to avoid disease risks no matter what mechanisms are relevant at any given time. To test this theory we compared the magnitude of inequalities in mortality between more and less preventable causes of death in 19 European populations, and assessed whether inequalities in mortality from preventable causes are larger in countries with larger resource inequalities.We collected and harmonized mortality data by educational level on 19 national and regional populations from 16 European countries in the first decade of the 21st century. We calculated age-adjusted Relative Risks of mortality among men and women aged 30-79 for 24 causes of death, which were classified into four groups: amenable to behavior change, amenable to medical intervention, amenable to injury prevention, and non-preventable.Although an overwhelming majority of Relative Risks indicate higher mortality risks among the lower educated, the strength of the education-mortality relation is highly variable between causes of death and populations. Inequalities in mortality are generally larger for causes amenable to behavior change, medical intervention and injury prevention than for non-preventable causes. The contrast between preventable and non-preventable causes is large for causes amenable to behavior change, but absent for causes amenable to injury prevention among women. The contrast between preventable and non-preventable causes is larger in Central & Eastern Europe, where resource inequalities are substantial, than in the Nordic countries and continental Europe, where resource inequalities are relatively small, but they are absent or small in Southern Europe, where resource inequalities are also large.In conclusion, our results provide some further support for the theory of "fundamental causes". However, the absence of larger inequalities for preventable causes in Southern Europe and for injury mortality among women indicate that further empirical and theoretical analysis is necessary to understand when and why the additional resources that a higher socioeconomic status provides, do and do not protect against prevailing health risks.

  • 5.
    Mackenbach, Johan P.
    et al.
    Erasmus MC, Dept Publ Hlth, POB 2040, NL-3000 CA Rotterdam, Netherlands.
    Kulhanova, Ivana
    Erasmus MC, Dept Publ Hlth, POB 2040, NL-3000 CA Rotterdam, Netherlands.
    Artnik, Barbara
    Fac Med, Dept Publ Hlth, Ljubljana, Slovenia.
    Bopp, Matthias
    Univ Zurich, Epidemiol Biostat & Prevent Inst, CH-8006 Zurich, Switzerland.
    Borrell, Carme
    Agencia Salut Publ Barcelona, Barcelona, Spain.
    Clemens, Tom
    Univ Edinburgh, Sch Geosci, Edinburgh, Midlothian, Scotland.
    Costa, Giuseppe
    Univ Turin, Dept Clin Med & Biol, I-10124 Turin, Italy.
    Dibben, Chris
    Univ Edinburgh, Sch Geosci, Edinburgh, Midlothian, Scotland.
    Kalediene, Ramune
    Lithuanian Univ Hlth Sci, Kaunas, Lithuania.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Ctr Hlth Equ Studies, Stockholm, Sweden.
    Martikainen, Pekka
    Univ Helsinki, Dept Sociol, FIN-00014 Helsinki, Finland.
    Menvielle, Gwenn
    Univ Paris 04, INSERM, IPLESP, UMRS 1136, F-75230 Paris 05, France.
    Ostergren, Olof
    Ctr Hlth Equ Studies, Stockholm, Sweden.
    Prochorskas, Remigijus
    Rodriguez-Sanz, Maica
    Agencia Salut Publ Barcelona, Barcelona, Spain.
    Strand, Bjorn Heine
    Norwegian Inst Publ Hlth, Div Epidemiol, Oslo, Norway.
    Looman, Caspar W. N.
    Erasmus MC, Dept Publ Hlth, POB 2040, NL-3000 CA Rotterdam, Netherlands.
    de Gelder, Rianne
    Erasmus MC, Dept Publ Hlth, POB 2040, NL-3000 CA Rotterdam, Netherlands.
    Changes in mortality inequalities over two decades: register based study of European countries2016In: BMJ. British Medical Journal, E-ISSN 1756-1833, Vol. 353, article id i1732Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE To determine whether government efforts in reducing inequalities in health in European countries have actually made a difference to mortality inequalities by socioeconomic group. DESIGN Register based study. DATA SOURCE Mortality data by level of education and occupational class in the period 1990-2010, usually collected in a census linked longitudinal study design. We compared changes in mortality between the lowest and highest socioeconomic groups, and calculated their effect on absolute and relative inequalities in mortality (measured as rate differences and rate ratios, respectively). SETTING All European countries for which data on socioeconomic inequalities in mortality were available for the approximate period between years 1990 and 2010. These included Finland, Norway, Sweden, Scotland, England and Wales (data applied to both together), France, Switzerland, Spain (Barcelona), Italy (Turin), Slovenia, and Lithuania. RESULTS Substantial mortality declines occurred in lower socioeconomic groups in most European countries covered by this study. Relative inequalities in mortality widened almost universally, because percentage declines were usually smaller in lower socioeconomic groups. However, as absolute declines were often smaller in higher socioeconomic groups, absolute inequalities narrowed by up to 35%, particularly among men. Narrowing was partly driven by ischaemic heart disease, smoking related causes, and causes amenable to medical intervention. Progress in reducing absolute inequalities was greatest in Spain (Barcelona), Scotland, England and Wales, and Italy (Turin), and absent in Finland and Norway. More detailed studies preferably using individual level data are necessary to identify the causes of these variations. CONCLUSIONS Over the past two decades, trends in inequalities in mortality have been more favourable in most European countries than is commonly assumed. Absolute inequalities have decreased in several countries, probably more as a side effect of population wide behavioural changes and improvements in prevention and treatment, than as an effect of policies explicitly aimed at reducing health inequalities.

  • 6.
    Mackenbach, Johan P.
    et al.
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Kulhanova, Ivana
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Menvielle, Gwenn
    INSERM, Ctr Res Epidemiol & Populat Hlth CESP, U1018, Villejuif, France.
    Bopp, Matthias
    Univ Zurich, Inst Social & Prevent Med, CH-8006 Zurich, Switzerland.
    Borrell, Carme
    Agencia Salut Publ Barcelona, Barcelona, Spain.
    Costa, Giuseppe
    Univ Turin, Dept Clin Med & Biol, Turin, Italy.
    Deboosere, Patrick
    Vrije Univ Brussel, Dept Sociol, Brussels, Belgium.
    Esnaola, Santiago
    Basque Govt, Dept Publ Hlth, Vitoria, Spain.
    Kalediene, Ramune
    Lithuanian Univ Hlth Sci, Kaunas, Lithuania.
    Kovacs, Katalin
    Cent Stat Off, Demog Res Inst, Budapest, Hungary.
    Leinsalu, Mall
    Natl Inst Hlth Dev, Dept Epidemiol & Biostat, Tallinn, Estonia.
    Martikainen, Pekka
    Univ Helsinki, Dept Sociol, Helsinki, Finland.
    Regidor, Enrique
    Univ Complutense Madrid, Dept Prevent Med & Publ Hlth, Madrid, Spain.
    Rodriguez-Sanz, Maica
    Agencia Salut Publ Barcelona, Barcelona, Spain.
    Strand, Bjorn Heine
    Norwegian Inst Publ Hlth, Div Epidemiol, Oslo, Norway.
    Hoffmann, Rasmus
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Eikemo, Terje A.
    Erasmus MC, Dept Publ Hlth, NL-3000 CA Rotterdam, Netherlands.
    Ostergren, Olof
    Ctr Hlth Equ Studies, Stockholm, Sweden.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Ctr Hlth Equ Studies, Stockholm, Sweden.
    Trends in inequalities in premature mortality: a study of 3.2 million deaths in 13 European countries2015In: Journal of Epidemiology and Community Health, ISSN 0143-005X, E-ISSN 1470-2738, Vol. 69, no 3, p. 207-217Article in journal (Refereed)
    Abstract [en]

    Background Over the last decades of the 20th century, a widening of the gap in death rates between upper and lower socioeconomic groups has been reported for many European countries. For most countries, it is unknown whether this widening has continued into the first decade of the 21st century. Methods We collected and harmonised data on mortality by educational level among men and women aged 30-74 years in all countries with available data: Finland, Sweden, Norway, Denmark, England and Wales, Belgium, France, Switzerland, Spain, Italy, Hungary, Lithuania and Estonia. Results Relative inequalities in premature mortality increased in most populations in the North, West and East of Europe, but not in the South. This was mostly due to smaller proportional reductions in mortality among the lower than the higher educated, but in the case of Lithuania and Estonia, mortality rose among the lower and declined among the higher educated. Mortality among the lower educated rose in many countries for conditions linked to smoking (lung cancer, women only) and excessive alcohol consumption (liver cirrhosis and external causes). In absolute terms, however, reductions in premature mortality were larger among the lower educated in many countries, mainly due to larger absolute reductions in mortality from cardiovascular disease and cancer (men only). Despite rising levels of education, population-attributable fractions of lower education for mortality rose in many countries. Conclusions Relative inequalities in premature mortality have continued to rise in most European countries, and since the 1990s, the contrast between the South (with smaller inequalities) and the East (with larger inequalities) has become stronger. While the population impact of these inequalities has further increased, there are also some encouraging signs of larger absolute reductions in mortality among the lower educated in many countries. Reducing inequalities in mortality critically depends upon speeding up mortality declines among the lower educated, and countering mortality increases from conditions linked to smoking and excessive alcohol consumption such as lung cancer, liver cirrhosis and external causes.

  • 7.
    Mäki, Netta E.
    et al.
    Univ Helsinki, Dept Social Res, FIN-00014 Helsinki, Finland.
    Martikainen, Pekka T.
    Univ Helsinki, Dept Social Res, FIN-00014 Helsinki, Finland.
    Eikemo, Terje
    Univ Med Ctr Rotterdam, Erasmus MC, Dept Publ Hlth, Rotterdam, Netherlands.
    Menvielle, Gwenn
    Pierre Louis Inst Epidemiol & Publ Hlth, Dept Social Epidemiol, INSERM, UMR S 1136, Paris, France.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Nursing Sciences. Ctr Hlth Equ Studies, CHESS, Stockholm, Sweden.
    Östergren, Olof
    CHESS, Centre for Health Equity Studies, Sweden .
    Mackenbach, Johan P.
    Univ Med Ctr Rotterdam, Erasmus MC, Dept Publ Hlth, Rotterdam, Netherlands.
    The potential for reducing differences in life expectancy between educational groups in five European countries: the effects of obesity, physical inactivity and smoking2014In: Journal of Epidemiology and Community Health, ISSN 0143-005X, E-ISSN 1470-2738, Vol. 68, no 7, p. 635-640Article in journal (Refereed)
    Abstract [en]

    Introduction This study assesses the effects of obesity, physical inactivity and smoking on life expectancy (LE) differences between educational groups in five European countries in the early 2000s. Methods We estimate the contribution of risk factors on LE differences between educational groups using the observed risk factor distributions and under a hypothetically more optimal risk factor distribution. Data on risk factor prevalence were obtained from the Survey of Health, Ageing and Retirement in Europe study, and data on mortality from census-linked data sets for the age between 50 and 79 according to sex and education. Results Substantial differences in LE of up to 2.8 years emerged between men with a low and a high level of education in Denmark, Austria and France, and smaller differences among men in Italy and Spain. The educational differences in LE were not as large among women. The largest potential for reducing educational differences was in Denmark (25% among men and 41% among women) and Italy (14% among men). Conclusions The magnitude of the effect of unhealthy behaviours on educational differences in LE varied between countries. LE among those with a low or medium level of education could increase in some European countries if the behavioural risk factor distributions were similar to those observed among the highly educated.

  • 8.
    Mäki, Netta
    et al.
    Univ Helsinki, Dept Social Res, FIN-00014 Helsinki, Finland.
    Martikainen, Pekka
    Univ Helsinki, Dept Social Res, FIN-00014 Helsinki, Finland.
    Eikemo, Terje
    Univ Med Ctr Rotterdam, Erasmus MC, Dept Publ Hlth, Rotterdam, Netherlands.
    Menvielle, Gwenn
    INSERM, Ctr Res Epidemiol & Populat Hlth, Villejuif, France.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Stockholm Univ, Karolinska Inst, Ctr Hlth Equity Studies, CHESS, Stockholm, Sweden.
    Östergren, Olof
    Stockholm Univ, Karolinska Inst, Ctr Hlth Equity Studies, CHESS, Stockholm, Sweden.
    Jasilionis, Domantas
    Max Planck Institute for Demographic Research, Germany.
    Mackenbach, Johan P.
    Univ Med Ctr Rotterdam, Erasmus MC, Dept Publ Hlth, Rotterdam, Netherlands.
    Educational differences in disability-free life expectancy: a comparative study of long-standing activity limitation in eight European countries2013In: Social Science and Medicine, ISSN 0277-9536, E-ISSN 1873-5347, Vol. 94, p. 1-8Article in journal (Refereed)
    Abstract [en]

    Healthy life expectancy is a composite measure of length and quality of life and an important indicator of health in aging populations. There are few cross-country comparisons of socioeconomic differences in healthy life expectancy. Most of the existing comparisons focus on Western Europe and the United States, often relying on older data. To address these deficiencies, we estimated educational differences in disability-free life expectancy for eight countries from all parts of Europe in the early 2000s. Long-standing severe disability was measured as a Global Activity Limitation Indicator (GALI) derived from the European Union Statistics on Income and Living Conditions (EU-SILC) survey. Census-linked mortality data were collected by a recent project comparing health inequalities between European countries (the EURO-GBD-SE project). We calculated sex-specific educational differences in disability-free life expectancy between the ages of 30 and 79 years using the Sullivan method. The lowest disability-free life expectancy was found among Lithuanian men and women (33.1 and 39.1 years, respectively) and the highest among Italian men and women (42.8 and 44.4 years, respectively). Life expectancy and disability-free life expectancy were directly related to the level of education, but the educational differences were much greater in the latter in all countries. The difference in the disability-free life expectancy between those with a primary or lower secondary education and those with a tertiary education was over 10 years for males in Lithuania and approximately 7 years for males in Austria, Finland and France, as well as for females in Lithuania. The difference was smallest in Italy (4 and 2 years among men and women, respectively). Highly educated Europeans can expect to live longer and spend more years in better health than those with lower education. The size of the educational difference in disability-free life expectancy varies significantly between countries. The smallest and largest differences appear to be in Southern Europe and in Eastern and Northern Europe, respectively. (C) 2013 Elsevier Ltd. All rights reserved.

  • 9.
    Ostlin, Piroska
    et al.
    World Health Organization Regional Office for Europe, Copenhagen, Denmark.
    Schrecker, Ted
    Department of Epidemiology and Community Medicine, Institute of Population Health, University of Ottawa, Ottawa, Canada.
    Sadana, Ritu
    World Health Organization, Geneva, Switzerland.
    Bonnefoy, Josiane
    School of Public Health, University of Chile, Santiago, Chile.
    Gilson, Lucy
    University of Cape Town, Cape Town, South Africa.
    Hertzman, Clyde
    Human Early Learning Partnership (HELP), University of British Columbia, Vancouver, Canada.
    Kelly, Michael P
    Centre for Public Health Excellence, National Institute for Health and Clinical Excellence, London, United Kingdom.
    Kjellstrom, Tord
    National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
    Labonté, Ronald
    Department of Epidemiology and Community Medicine, Institute of Population Health, University of Ottawa, Ottawa, Canada.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Muntaner, Carles
    Social Equity and Health Section, Centre for Addiction and Mental Health and Bloomberg, Faculty of Nursing and Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
    Popay, Jennie
    Division of Health Research, Lancaster University, Lancaster, United Kingdom.
    Sen, Gita
    Indian Institute of Management, Centre for Public Policy, Bangalore, India.
    Vaghri, Ziba
    Human Early Learning Partnership (HELP), University of British Columbia, Vancouver, Canada.
    Priorities for research on equity and health: towards an equity-focused health research agenda.2011In: PLoS Medicine, ISSN 1549-1277, E-ISSN 1549-1676, Vol. 8, no 11, p. e1001115-Article in journal (Refereed)
    Abstract [en]

    Piroska Östlin and colleagues argue that a paradigm shift is needed to keep the focus on health equity within the social determinants of health research agenda.

  • 10.
    Pega, Frank
    et al.
    Health Inequalities Research Program, University of Otago, Wellington, New Zealand .
    Carter, Kristie
    Health Inequalities Research Program, University of Otago, Wellington, New Zealand .
    Kawachi, Ichiro
    Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA, United States .
    Davis, Peter
    Centre of Methods and Policy Application in the Social Sciences, Department of Sociology, University of Auckland, Auckland, New Zealand.
    Gunasekara, Fiona Imlach
    Health Inequalities Research Program, University of Otago, Wellington, New Zealand .
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Centre for Health Equity Studies, Stockholm University Karolinska Institutet, Stockholm, Sweden .
    Blakely, Tony
    Health Inequalities Research Program, University of Otago, Wellington, New Zealand .
    The impact of in-work tax credit for families on self-rated health in adults: a cohort study of 6900 New Zealanders2013In: Journal of Epidemiology and Community Health, ISSN 0143-005X, E-ISSN 1470-2738, Vol. 67, no 8, p. 682-688Article in journal (Refereed)
    Abstract [en]

    Background In-work tax credit (IWTC) for families, a welfare-to-work policy intervention, may impact health status by improving income and employment. Most studies estimate that IWTCs in the USA and the UK have no effect on self-rated health (SRH) and several other health outcomes, but these estimates may be biased by confounding. The current study estimates the impact of one such IWTC intervention (called In-Work Tax Credit) on SRH in adults in New Zealand, controlling more fully for confounding. Methods We used data from seven waves (2002-2009) of the Survey of Family, Income and Employment, restricted to a balanced panel of adults in families. The exposures, eligibility for IWTC and the amount of IWTC a family was eligible for, were derived for each wave by applying government eligibility and entitlement criteria. The outcome, SRH, was collected annually. We used fixed effects regression analyses to eliminate time-invariant confounding and adjusted for measured time-varying confounders. Results Becoming eligible for IWTC was associated with no detectable change in SRH over the past year (=0.001, 95% CI -0.022 to 0.023). A $1000 increase in the IWTC amount a family was eligible for increased SRH by 0.003 units (95% CI -0.005 to 0.011). Conclusions This study found that becoming eligible for IWTC or a substantial increase in the IWTC amount was not associated with any detectable difference in SRH over the short term. Future research should investigate the impact of IWTC on health over the longer term.

  • 11.
    Pega, Frank
    et al.
    Univ Otago, Hlth Inequal Res Programme, Wellington, New Zealand.
    Kawachi, Ichiro
    Harvard Univ, Sch Publ Hlth, Dept Social & Behav Sci, Boston, MA 02115 USA.
    Rasanathan, Kumanan
    United Nations Childrens Fund UNICEF, Hlth Sect, New York, NY 10022 USA.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Stockholm Univ, Karolinska Inst, Ctr Hlth Equ Studies, SE-10691 Stockholm, Sweden.
    Politics, policies and population health: A commentary on Mackenbach, Hu and Looman (2013)2013In: Social Science and Medicine, ISSN 0277-9536, E-ISSN 1873-5347, Vol. 93, no S1, p. 176-179Article in journal (Other academic)
  • 12.
    Stickley, Andrew
    et al.
    London Sch Hyg & Trop Med, European Ctr Hlth Soc Transit, London WC1, England .
    Leinsalu, Mall
    Natl Inst Hlth Dev, Dept Epidemiol & Biostat, Tallinn, Estonia .
    Kunst, Anton E.
    Univ Amsterdam, Acad Med Ctr, Dept Publ Hlth, NL-1105 AZ Amsterdam, Netherlands .
    Bopp, Matthias
    Univ Zurich, Inst Social & Prevent Med, CH-8006 Zurich, Switzerland .
    Strand, Bjorn Heine
    Norwegian Inst Publ Hlth, Div Epidemiol, Oslo, Norway .
    Martikainen, Pekka
    Univ Helsinki, Dept Sociol, Helsinki, Finland .
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Kovacs, Katalin
    HCSO, Demog Res Inst, Budapest, Hungary .
    Artnik, Barbara
    Univ Ljubljana, Fac Med, Dept Publ Hlth, Ljubljana, Slovenia .
    Kalediene, Ramune
    Lithuanian Univ Hlth Sci, Dept Hlth Management, Kaunas, Lithuania .
    Rychtarikova, Jitka
    Charles Univ Prague, Fac Sci, Dept Demog & Geodemog, Prague, Czech Republic .
    Wojtyniak, Bogdan
    Natl Inst Hyg, Natl Inst Publ Hlth, Dept Ctr Populat Hlth Monitoring & Anal, PL-00791 Warsaw, Poland .
    Mackenbach, Johan P.
    Erasmus MC, Dept Publ Hlth, Rotterdam, Netherlands.
    Socioeconomic inequalities in homicide mortality: a population-based comparative study of 12 European countries2012In: European Journal of Epidemiology, ISSN 0393-2990, E-ISSN 1573-7284, Vol. 27, no 11, p. 877-884Article in journal (Refereed)
    Abstract [en]

    Recent research has suggested that violent mortality may be socially patterned and a potentially important source of health inequalities within and between countries. Against this background the current study assessed socioeconomic inequalities in homicide mortality across Europe. To do this, longitudinal and cross-sectional data were obtained from mortality registers and population censuses in 12 European countries. Educational level was used to indicate socioeconomic position. Age-standardized mortality rates were calculated for post, upper and lower secondary or less educational groups. The magnitude of inequalities was assessed using the relative and slope index of inequality. The analysis focused on the 35-64 age group. Educational inequalities in homicide mortality were present in all countries. Absolute inequalities in homicide mortality were larger in the eastern part of Europe and in Finland, consistent with their higher overall homicide rates. They contributed 2.5 % at most (in Estonia) to the inequalities in total mortality. Relative inequalities were high in the northern and eastern part of Europe, but were low in Belgium, Switzerland and Slovenia. Patterns were less consistent among women. Socioeconomic inequalities in homicide are thus a universal phenomenon in Europe. Wide-ranging social and inter-sectoral health policies are now needed to address the risk of violent victimization that target both potential offenders and victims.

  • 13.
    Van Raalte, A.A.
    et al.
    Max Planck Institute for Demographic Research, Rostock, Germany.
    Kunst, A
    Department of Public Health, Academic MC, University of Amsterdam, Amsterdam, Netherlands.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Leinsalu, M
    Department of Epidemiology and Biostatistics, The National Institute for Health Development, Tallinn, Estonia.
    Martikainen, P
    Department of Sociology, University of Helsinki, Helsinki, Finland.
    Artnik, B
    Department of Public Health, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
    Deboosere, P
    Department of Social Research, Vrije Universiteit Brussel, Brussels, Belgium.
    Stirbu, I
    Department of Public Health, Erasmus Medical Centre, Rotterdam, Netherlands.
    Wojtyniak, B
    Department of Monitoring and Analyses of Population Health, National Institute of Public Health-National Institute of Hygiene, Warsaw, Poland.
    Mackenbach, J
    Department of Public Health, Erasmus Medical Centre, Rotterdam, Netherlands.
    The contribution of educational inequalities to lifespan variation2012In: Population Health Metrics, ISSN 1478-7954, E-ISSN 1478-7954, Vol. 10, p. Art. no. 3-Article in journal (Refereed)
    Abstract [en]

    Background: Studies of socioeconomic inequalities in mortality consistently point to higher death rates in lower socioeconomic groups. Yet how these between-group differences relate to the total variation in mortality risk between individuals is unknown.Methods: We used data assembled and harmonized as part of the Eurothine project, which includes census-based mortality data from 11 European countries. We matched this to national data from the Human Mortality Database and constructed life tables by gender and educational level. We measured variation in age at death using Theil’s entropy index, and decomposed this measure into its between- and within-group components.Results: The least-educated groups lived between three and 15 years fewer than the highest-educated groups, the latter having a more similar age at death in all countries. Differences between educational groups contributed between 0.6% and 2.7% to total variation in age at death between individuals in Western European countries and between 1.2% and 10.9% in Central and Eastern European countries. Variation in age at death is larger and differs more between countries among the least-educated groups.Conclusions: At the individual level, many known and unknown factors are causing enormous variation in age at death, socioeconomic position being only one of them. Reducing variations in age at death among less-educated people by providing protection to the vulnerable may help to reduce inequalities in mortality between socioeconomic groups. 

  • 14.
    van Raalte, Alyson
    et al.
    Max Planck Institute for Demographic Research, Life Course Dynamics and Demographic Change research group, Konrad-Zuse Straße 1, 18057 Rostock, Germany.
    Kunst, Anton
    Department of Public Health, Academic MC, University of Amsterdam, Amsterdam, The Netherlands.
    Deboosere, Patrick
    Department of Social Research, Vrije Universiteit Brussel, Brussels, Belgium.
    Leinsalu, Mall
    Department of Epidemiology and Biostatistics, The National Institute for Health Development, Tallinn, Estonia.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Martikainen, Pekka
    Department of Sociology, University of Helsinki, Helsinki, Finland.
    Strand, Björn Heine
    Division of Epidemiology, Norwegian Institute of Public Health, Nydalen, Oslo, Norway.
    Artnik, Barbara
    Department of Public Health, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
    Wojtyniak, Bogdan
    Department of Monitoring and Analyses of Population Health, National Institute of Public Health-National Institute of Hygiene, Warsaw, Poland.
    Mackenbach, Johan
    Department of Public Health, Erasmus Medical Centre, Rotterdam, The Netherlands.
    More variation in lifespan in lower educated groups: evidence from 10 European countries2011In: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685, Vol. 40, p. 1703-1714Article in journal (Refereed)
    Abstract [en]

    Background Whereas it is well established that people with a lower socio-economic position have a shorter average lifespan, it is less clear what the variability surrounding these averages is. We set out to examine whether lower educated groups face greater variation in lifespans in addition to having a shorter life expectancy, in order to identify entry points for policies to reduce the impact of socio-economic position on mortality.

    Methods We used harmonized, census-based mortality data from 10 European countries to construct life tables by sex and educational level (low, medium, high). Variation in lifespan was measured by the standard deviation conditional upon survival to age 35 years. We also decomposed differences between educational groups in lifespan variation by age and cause of death.

    Results Lifespan variation was higher among the lower educated in every country, but more so among men and in Eastern Europe. Although there was an inverse relationship between average life expectancy and its standard deviation, the first did not completely predict the latter. Greater lifespan variation in lower educated groups was largely driven by conditions causing death at younger ages, such as injuries and neoplasms.

    Conclusions Lower educated individuals not only have shorter life expectancies, but also face greater uncertainty about the age at which they will die. More priority should be given to efforts to reduce the risk of an early death among the lower educated, e.g. by strengthening protective policies within and outside the health-care system.

  • 15.
    Östergren, Olof
    et al.
    Centre for Health Equity Studies, Stockholm University / Karolinska Institutet, Stockholm, Sweden.
    Menvielle, Gwenn
    Inserm CESP Centre for Research in Epidemiology and Population Health, Villejuif, France.
    Lundberg, Olle
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Adjustment method to ensure comparability between populations reporting mortality data in different formats in the EURO-GBD-SE project: Working document2011Report (Other academic)
    Abstract [en]

    Introduction: In some of the longitudinal data sets within the EURO-GBD-SE project, information on age isonly available at baseline, all person years and deaths are attributed to the baseline age; thismeans that information about age at death is unavailable. This will cause a bias whencomparing mortality between data sets in which age at death is reported and data sets in whichage at baseline is reported. Mortality estimates in populations that have age at baseline will behigher than the estimations obtained where age at death is known; since people are notallowed to move into the next age category as they grow older, the population will, in reality,be older in the former case. The data sets that are formatted with age at baseline only areBrussels, Denmark, Finland, Norway, Sweden and Switzerland.To make results comparable, we developed an adjustment procedure that corrects for this bias.This procedure should be applied to the datasets formatted with the age at baseline and is represented in this document.

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