Article type
Year
Abstract
Background:
For evidence-based public health (EBPH) not only is establishing policy evidence challenging, but also the transferability of findings is not as straightforward as for clinical evidence. Certain findings may only be valid under certain country- or population-specific circumstances. Hence, the need for tools to quantify the effects of changes in risk-factors on population health is increasingly recognised, but the data demands for population health modeling are challenging. We introduce the DYNAMO-HIA database (DH-DB) - an internally consistent data collection for assessing the effect of life-style related risk-factors on causally related disease incidence, prevalence, and mortality data - for public use. The DH-DB contains country-specific data on selected population and health measures for 27 European Union (EU) member states, with varying completeness, mostly derived from already existing data sources.
Description:
The DH-DB contains population-level data on three life-style related risk-factors (body mass index, smoking, and alcohol consumption), incidence, prevalence and mortality from nine diseases (ischemic heart disease, chronic obstructive pulmonary disease, stroke, diabetes, and lung-, esophageal-, colorectal-, breast-, and oral-cancer), and on population size, projected births, and total mortality. It also includes relative risks of cause-specific and overall mortality associated with exposure to the risk factors. These data are uniquely suited for (dynamic) population health modelling, such as in health impact assessment, as the data are trend-free, internally consistent, sex-specific, and smoothed to generate 1-year age intervals.
Conclusion:
The DYNAMO-HIA database provides unique data for modeling the effects on population health of changes in life-style related risk-factors, covering approximately 80% of the EU-27 population, enabling comparative analysis of policy interventions across the EU.
For evidence-based public health (EBPH) not only is establishing policy evidence challenging, but also the transferability of findings is not as straightforward as for clinical evidence. Certain findings may only be valid under certain country- or population-specific circumstances. Hence, the need for tools to quantify the effects of changes in risk-factors on population health is increasingly recognised, but the data demands for population health modeling are challenging. We introduce the DYNAMO-HIA database (DH-DB) - an internally consistent data collection for assessing the effect of life-style related risk-factors on causally related disease incidence, prevalence, and mortality data - for public use. The DH-DB contains country-specific data on selected population and health measures for 27 European Union (EU) member states, with varying completeness, mostly derived from already existing data sources.
Description:
The DH-DB contains population-level data on three life-style related risk-factors (body mass index, smoking, and alcohol consumption), incidence, prevalence and mortality from nine diseases (ischemic heart disease, chronic obstructive pulmonary disease, stroke, diabetes, and lung-, esophageal-, colorectal-, breast-, and oral-cancer), and on population size, projected births, and total mortality. It also includes relative risks of cause-specific and overall mortality associated with exposure to the risk factors. These data are uniquely suited for (dynamic) population health modelling, such as in health impact assessment, as the data are trend-free, internally consistent, sex-specific, and smoothed to generate 1-year age intervals.
Conclusion:
The DYNAMO-HIA database provides unique data for modeling the effects on population health of changes in life-style related risk-factors, covering approximately 80% of the EU-27 population, enabling comparative analysis of policy interventions across the EU.