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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

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 with...

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Autores principales: Lhachimi, Stefan K., Nusselder, Wilma J., Smit, Henriette A., Baili, Paolo, Bennett, Kathleen, Fernández, Esteve, Kulik, Margarete C., Lobstein, Tim, Pomerleau, Joceline, Boshuizen, Hendriek C., Mackenbach, Johan P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4975898/
https://www.ncbi.nlm.nih.gov/pubmed/27495151
http://dx.doi.org/10.1186/s12889-016-3299-z
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author Lhachimi, Stefan K.
Nusselder, Wilma J.
Smit, Henriette A.
Baili, Paolo
Bennett, Kathleen
Fernández, Esteve
Kulik, Margarete C.
Lobstein, Tim
Pomerleau, Joceline
Boshuizen, Hendriek C.
Mackenbach, Johan P.
author_facet Lhachimi, Stefan K.
Nusselder, Wilma J.
Smit, Henriette A.
Baili, Paolo
Bennett, Kathleen
Fernández, Esteve
Kulik, Margarete C.
Lobstein, Tim
Pomerleau, Joceline
Boshuizen, Hendriek C.
Mackenbach, Johan P.
author_sort Lhachimi, Stefan K.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12889-016-3299-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-49758982016-08-07 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 Lhachimi, Stefan K. Nusselder, Wilma J. Smit, Henriette A. Baili, Paolo Bennett, Kathleen Fernández, Esteve Kulik, Margarete C. Lobstein, Tim Pomerleau, Joceline Boshuizen, Hendriek C. Mackenbach, Johan P. BMC Public Health Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12889-016-3299-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-05 /pmc/articles/PMC4975898/ /pubmed/27495151 http://dx.doi.org/10.1186/s12889-016-3299-z Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lhachimi, Stefan K.
Nusselder, Wilma J.
Smit, Henriette A.
Baili, Paolo
Bennett, Kathleen
Fernández, Esteve
Kulik, Margarete C.
Lobstein, Tim
Pomerleau, Joceline
Boshuizen, Hendriek C.
Mackenbach, Johan P.
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4975898/
https://www.ncbi.nlm.nih.gov/pubmed/27495151
http://dx.doi.org/10.1186/s12889-016-3299-z
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