gms | German Medical Science

MAINZ//2011: 56. GMDS-Jahrestagung und 6. DGEpi-Jahrestagung

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V.
Deutsche Gesellschaft für Epidemiologie e. V.

26. - 29.09.2011 in Mainz

Estimating the association between Disability Adjusted Life Years and population health determinants – a cross-national ecologic study

Meeting Abstract

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  • Dietrich Plaß - Universität Bielefeld, Bielefeld
  • Alexander Krämer - Universität Bielefeld, Bielefeld
  • Paulo Pinheiro - Universität Bielefeld, Bielefeld

Mainz//2011. 56. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 6. Jahrestagung der Deutschen Gesellschaft für Epidemiologie (DGEpi). Mainz, 26.-29.09.2011. Düsseldorf: German Medical Science GMS Publishing House; 2011. Doc11gmds131

DOI: 10.3205/11gmds131, URN: urn:nbn:de:0183-11gmds1312

Veröffentlicht: 20. September 2011

© 2011 Plaß et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Background: Global Burden of Disease (GBD) estimates are increasingly used in Public Health to assess the impact of diseases on health-related quality of life of different populations and quantified by use of the Disability Adjusted Life Year (DALY) measure. In addition, data on various health determinants is largely available and easily accessible from different organisations on aggregated level. However, analyses on the association between such determinants and the burden of disease as measured by DALYs are sparse. The main objectives of this study were a) to investigate such associations by use of aggregated country-level data and b) to test feasibility and usefulness of such analyses.

Methods: DALY-rates per 100,000 population for 2004 from the GBD study were used as outcome variable. 26 publicly available structural and behavioural country-specific indicators served as explanatory variables (Sources: e.g. Global Health Observatory, World Bank). Data for 192 WHO member states was included in the analyses. A cross-national ecological study design, methods of correlation and bivariate/multivariate regression modelling were applied.

Results: Indicators showing strongest (bivariate) correlations (Spearman) with the total DALY burden were satisfaction index (-0.789), urban access to improved sanitation (-0.743), access to improved water source (-0.701), calorie supply (-0.697) and rural access to improved sanitation (-0.669). DALYs due to communicable, maternal, perinatal and nutritional conditions (Group I conditions of GBD study) were correlated with satisfaction index (-0.760), urban access to improved sanitation (-0.742), total access to improved sanitation (-0.700), access to improved water source (-0.667) and calorie supply (-0.656). DALYs due to non-communicable conditions (Group II conditions) showed correlations with corruption perception index (-0.729), satisfaction index (-0.676), passenger cars (-0.662), gross national income (-0.660), public health expenditure (0.617) and calorie supply (0.595). Multivariate analyses identified a model showing significant associations between the total burden and satisfaction index, access to improved water source, calorie supply and literacy rate (r²=0.72).

Conclusions: DALYs due to group I conditions which have a higher impact on population health in low and middle income countries were largely associated with indicators mainly informing about basic living conditions. DALYs due to group II conditions showed highest correlations with wealth indicators. Results from the regression analysis gave insights on the rates of DALY change. The analysis was limited due to the use of a selected set of aggregated indicators. The estimates do not allow for causal conclusions due to the cross-sectional study design. Upcoming investigation of multi-level effects will provide more sophisticated results.


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