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BMC Public Health. 2016 Aug 5;16:734. doi: 10.1186/s12889-016-3299-z.

Potential health gains and health losses in eleven EU countries attainable through feasible prevalences of the life-style related risk factors alcohol, BMI, and smoking: a quantitative health impact assessment.

Author information

1
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. S.Lhachimi@Erasmusmc.nl.
2
Research Group for Evidence Based Public Health, Institute for Public Health and Nursing, University Bremen & Leibniz Institute for Epidemiology and Prevention Research, Bremen, Germany. S.Lhachimi@Erasmusmc.nl.
3
Department of Statistics and Mathematical Modeling, Expertise Centre for Methodology and Information Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. S.Lhachimi@Erasmusmc.nl.
4
Department of Public Health, Heinrich Heine University, Duesseldorf, Germany. S.Lhachimi@Erasmusmc.nl.
5
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
6
Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
7
Center for Prevention and Health Services Research (PZO), National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
8
Descriptive Studies and Health Planning Unit, Fondazione IRCCS "Istituto Nazionale Tumori", Milan, Italy.
9
Department of Pharmacology & Therapeutics, Trinity Centre for health sciences, St James's Hospital, Dublin, Ireland.
10
Tobacco Control Unit, Institut Català d'Oncologia-IDIBELL, L'Hospitalet de Llobregat Barcelona, Barcelona, Spain.
11
Department of Clinical Sciences, School of Medicine, Campus of Bellvitge, Universitat de Barcelona, Barcelona, Spain.
12
IASO -the International Association for the Study of Obesity, IOTF -the International Obesity TaskForce, London, UK.
13
European Centre on Health of Societies in Transition, London School of Hygiene and Tropical Medicine, London, UK.
14
Department of Statistics and Mathematical Modeling, Expertise Centre for Methodology and Information Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
15
Division of Human Nutrition, Wageningen University, Wageningen, Netherlands.

Abstract

BACKGROUND:

Influencing the life-style risk-factors alcohol, body mass index (BMI), and smoking is an European Union (EU) wide objective of public health policy. The population-level health effects of these risk-factors depend on population specific characteristics and are difficult to quantify without dynamic population health models.

METHODS:

For eleven countries-approx. 80 % of the EU-27 population-we used evidence from the publicly available DYNAMO-HIA data-set. For each country the age- and sex-specific risk-factor prevalence and the incidence, prevalence, and excess mortality of nine chronic diseases are utilized; including the corresponding relative risks linking risk-factor exposure causally to disease incidence and all-cause mortality. Applying the DYNAMO-HIA tool, we dynamically project the country-wise potential health gains and losses using feasible, i.e. observed elsewhere, risk-factor prevalence rates as benchmarks. The effects of the "worst practice", "best practice", and the currently observed risk-factor prevalence on population health are quantified and expected changes in life expectancy, morbidity-free life years, disease cases, and cumulative mortality are reported.

RESULTS:

Applying the best practice smoking prevalence yields the largest gains in life expectancy with 0.4 years for males and 0.3 year for females (approx. 332,950 and 274,200 deaths postponed, respectively) while the worst practice smoking prevalence also leads to the largest losses with 0.7 years for males and 0.9 year for females (approx. 609,400 and 710,550 lives lost, respectively). Comparing morbidity-free life years, the best practice smoking prevalence shows the highest gains for males with 0.4 years (342,800 less disease cases), whereas for females the best practice BMI prevalence yields the largest gains with 0.7 years (1,075,200 less disease cases).

CONCLUSION:

Smoking is still the risk-factor with the largest potential health gains. BMI, however, has comparatively large effects on morbidity. Future research should aim to improve knowledge of how policies can influence and shape individual and aggregated life-style-related risk-factor behavior.

KEYWORDS:

Alcohol; BMI; Health impact assessment; Life-style related risk-factors; Modeling; Smoking

PMID:
27495151
PMCID:
PMC4975898
DOI:
10.1186/s12889-016-3299-z
[Indexed for MEDLINE]
Free PMC Article

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