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EURO GBD SE DATA COLLECTION Gwenn Menvielle Inserm, France Consortium meeting Rotterdam, March 25-26 2010.

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Presentation on theme: "EURO GBD SE DATA COLLECTION Gwenn Menvielle Inserm, France Consortium meeting Rotterdam, March 25-26 2010."— Presentation transcript:

1 EURO GBD SE DATA COLLECTION Gwenn Menvielle Inserm, France Consortium meeting Rotterdam, March

2 Plan  Mortality datasets  Cross-sectionnal unlinked (CSU) datasets  Longitudinal datasets  Risk factors datasets  National datasets (NHS: National Health Survey)  International datasets  Milestones for the coming months

3 General objective of the project  Data on socioeconomic inequalities in mortality (total and by cause of death), self- reported morbidity (self-assessed health, functional impairment), selected diseases (incidence of ischemic heart disease, cancers) and risk factors (smoking, alcohol, physical inactivity, overweight/obesity, high blood pressure, cholesterol, high blood glucose) in will be collected from national and international data sources.  Data sources include national and regional longitudinal census-linked mortality studies, national and international health interview and health examination surveys, and regional and national disease incidence registries.  An effort will be made to cover as many countries of the European Union (including neighboring countries like Norway, Switzerland, Russia, …) as possible. Based on the experiences of a previous project (Eurothine, which covered the 1990s) we expect to be able to include more than 20 countries in the database.

4 Mortality data  Data on socioeconomic inequalities in mortality (total and by cause of death), self- reported morbidity (self-assessed health, functional impairment), selected diseases (incidence of ischemic heart disease, cancers) and risk factors (smoking, alcohol, physical inactivity, overweight/obesity, high blood pressure, cholesterol, high blood glucose) in will be collected from national and international data sources.  Data sources include national and regional longitudinal census-linked mortality studies, national and international health interview and health examination surveys, and regional and national disease incidence registries.  An effort will be made to cover as many countries of the European Union (including neighboring countries like Norway, Switzerland, Russia, …) as possible. Based on the experiences of a previous project (Eurothine, which covered the 1990s) we expect to be able to include more than 20 countries in the database.

5 Mortality data  Objective  To build a database on socioeconomic inequalities in mortality in Europe in  To cover Europe, to include more than 20 countries  Format of the dataset  Census around 2000 Demographic characteristics  Mortality during the early 2000s Information by causes of death

6 Countries included Green: Cross-sectional unliked datasets Red: Longitudinal datasets  22 countries or populations  New countries/populations  « Holes » remain

7 Cross-sectional unlinked datasets  Already collected for Eurothine CountryCensusMortality Hungary Czech Republik Poland Estonia (Lithuania)

8 Longitudinal linked datasets RegionCountryCensusMortality Northern EuropeNorway2001Nov 2001-Nov 2006 Sweden2001? Finland2000 Denmark Baltic countriesLithuania UKEngland&Wales2001PROBLEM Scotland2001PROBLEM Western EuropeNetherlands2000 Belgium (Brussels) Austria2001May 2001-May 2002 Switzerland2000Jan 2001-Dec 2005 France1999Mar 1999-Dec 2007

9 Longitudinal datasets RegionCountryCensusMortality Southern Europe ItalyTurin2001Nov 2001-Oct 2006 Tuscany (Florence, Leghorn, Prato) 2001Oct 2001-Dec 2005 SpainMadrid2001Nov 2001-June 2003 Barcelona Basque country2001 Central EuropeSlovenia2002PROBLEM Bulgaria

10 Data collection update Green: received Yellow: coming Violet: no access to the data Red: Problem  New countries  More than 20 populations  More complex analysis process (not all will be run in Rotterdam)

11 Data collection update CountryEuro-GBD-SE Follow-upNb deathsNb person years Norway ,70213,286,050 Brussels ,3471,845,940 Austria ,2095,183,680 Switzerland ,65318,557,583 Turin ,2782,824,679 Madrid ,9165,593,910 Barcelona ,0788,077,659

12 Data collection update CountryEuro-GBD-SEEurothine (age 30-74) Follow-upNb deathsNb person years Follow-upNb deaths Nb person years Norway ,70213,286, ,02219,956,767 Brussels ,3471,845, ,34924,861,015 Austria ,2095,183, Switzerland ,65318,557, ,25127,910,587 Turin ,2782,824, ,6214,873,109 Madrid ,9165,593, ,5853,663,333 Barcelona ,0788,077, ,1018,151,810

13 Variables collected  Age  Sex  Place of residence (rural/urban)  Marital status  Housing tenure (tenant/owner)  Education (as detailed as possible)  Occupational class  Causes of death

14 GBD methodology  Objective  To adapt metrics like the Population Attributable Fraction (PAF) and other methods developed in the Global Burden of Disease study to the needs of EURO-GBD-SE.  Format of the data  Cross-sectional  Causes of death causally associated with risk factors  This implies  A new structure for the file  A new list of causes of death

15 New structure of the file  Solution to get cross-sectional data  Ask information (nb person years, nb subjects and nb of deaths) year by year  CSU datasets already in the good format  With this new structure  Study socioeconomic inequalities as in Eurothine add up for person years and nb of deaths  Apply the GBD methodolog take data for one year, possibly +-1 year in order to get more deaths and person-years

16 New list of causes of death  New list that accounts for the GBD needs  We used the classification proposed by the GBD group  Some very specific causes of death Will be studied with other causes of death  Example: High BMI  IHD, ischemic stroke, hypertensive disease, diabetes mellitus, cancer of corpus uteri, colon cancer, kidney cancer, postmenopausal breast cancer  Small inconsistency with CSU datasets  Colorectal cancer, kidney and bladder cancer

17 Risk factors  Data on socioeconomic inequalities in mortality (total and by cause of death), self- reported morbidity (self-assessed health, functional impairment), selected diseases (incidence of ischemic heart disease, cancers) and risk factors (smoking, alcohol, physical inactivity, overweight/obesity, high blood pressure, cholesterol, high blood glucose) in will be collected from national and international data sources.  Data sources include national and regional longitudinal census-linked mortality studies, national and international health interview and health examination surveys, and regional and national disease incidence registries.  An effort will be made to cover as many countries of the European Union (including neighboring countries like Norway, Switzerland, Russia, …) as possible. Based on the experiences of a previous project (Eurothine, which covered the 1990s) we expect to be able to include more than 20 countries in the database.

18 Risk factors  National health surveys (NHS) collected for Eurothine  Self-reported risk factors  Self-reported morbidity  Completed with  International surveys  National surveys with measured risk factors

19 NHS

20  Risk factors collected  Socio and demographic variable (age, sex, education)  Smoking  Alcohol (poor comparability)  Diet  Leisure time physical activity  BMI (self-reported)

21 International surveys  Characteristics of the survey  Representative of the general population  Information on risk factors, collected with comparable questionnaires  Collected around the 2000s  Information for all European countries (West&East)  Extensive review of what is available  Not many surveys with these criteria

22 International surveys  European Working Conditions Survey  Available for Western and Eastern European countries  2000 & 2001  Work related factors Physical work factors, working time, work organisation, information and consultation, psychosocial factors…  Eurobarometer  Data for Eastern European countries in 2005  Norway and Switzerland are missing  Smoking, alcohol, height, weight, physical activity

23 Measure of SES in these surveys  Variable available  Age when finishing full time education  Problem  Lower vs upper secondary education  Upper secondary education (vocational) vs university  The European Quality of Life Survey characterises education with the two following variables  Tertiary/secondary/primary education  Age when finishing full time education

24 Surveys with measured risk factors  The GBD methodology requires measured and not self- declared risk factors (BMI, blood measurements, blood pressure)  Relevant studies should be defined in relation with mortality data  No use to get detailed measured risk factors if we do not have the mortality data in the correct format  Example : Health Survey for England 2003  Interviewer visit: height and weight measurement  Nurse visit: blood pressure, blood sample (total&HDL cholesterol, C- reactive protein), eating habits (salt, fat)

25 Milestones for the coming months Mortality data  Collection of last mortality datasets  7 countries/populations missing, including France and NL  Solving problems for E&W, Scotland and Slovenia  Harmonisation of variables  Education level  Occupational class  Preparation of datasets for the « classical » socioeconomic inequalities analyses  Inequalities in mortality and morbidity, and in summary measures (WP7 & WP8)  Specification file for mortality datasets

26 Milestones for the coming months GBD analyses  In collaboration with WP4, WP6 & WP9  Preparation of the mortality datasets  National and international health surveys  Collection of surveys (national)  Overview of variables available  Harmonisation of variables when relevant  Definition of education level

27 Milestones for the coming months GBD analyses  Computation of  Mortality rates all cause/cause specific  By age group and SES  RR for the different risk factors by age and SES  This will be done also after M12 when the analyses will be conduted


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