Final Report Part III ECHIM Pilot Data Collection Analyses and Dissemination

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months. A glance at the ICD-10 catalogue [6] reveals that only "depressive episodes" with variations of duration and severity are listed and codable with ICD-10 F32.-F34.-, and that the specification "chronic" does not appear at all but "recurrent" and "major" depressive episodes. Currently the questionnaire for the second EHIS wave (planned for 2014) is under revision, inter alia to improve the module on mental health. This might lead to a more consistent picture on depressions throughout Europe. Administratively deduced and register- based data on depression were delivered by the Czech Republic, Estonia, Finland, Hungary, Latvia, Lithuania, The Netherlands and the United Kingdom (the latter providing totals only). A comparison with survey data which is possible for the Czech Republic, Estonia, Hungary, and Latvia. Breakdown/ Country (year) Percentage CZ (2009) Percentage EE (2009) Percentage HU (2008) Percentage LV (2009) Total 0.91 1.0 2.31 0.18 Men 0.45 0.7 1.32 0.08 Women 1.29 1.2 3.21 0.27 Individuals aged 15-64 1.09 X 2.62 0.19 1.0 Individuals aged 65+ 1.61 X 3.11 0.27 X = individuals 15+ but no strata for elderly All of them provide much lower figures. The main reasons can be due to different coding procedures (see ICD-10 issue above), data sources and coverage of the total population. The exact reason is not known. Nevertheless, the pattern of men vs. women and labour force vs. elderly resembles the survey results. As a most drastic example, the United Kingdom (2009) reported a total depression prevalence percentage of 10.6% which exceeds all survey-based data of totals in other countries. The UK did not deliver any survey data and the reported estimates are based on the number of individuals aged 18+ from General Physicians' Practices as extracted from the Quality and Outcomes Framework (QOF) [7]. In the ECHIM pilot data collection, each EU Member State itself decided which is (are) the best data source(s) for calculating the register based estimates. Given the fact that not in all MS the health information system is well aligned with the health care system, there must be limitations to the intra- and inter-comparability of national 23
estimates. [1a+b] EHIS description http://circa.europa.eu/Public/irc/dsis/health/library?l=/methodologiessandsdatasc/healthsinterviewssurvey/ehis_wave_1/200 7-2008_methodology&vm=detailed&sb=Title and meta-data: http://epp.eurostat.ec.europa.eu/cache/ITY_SDDS/EN/hlth_ehis_esms.htm; last update 14 September 2011. [2] "The State of Men’s Health in Europe"; http://ec.europa.eu/health/population_groups/docs/men_health_extended_en.pdf, ISBN 978-92-79-20169-1; EU 2011 [3] Harrington J, Perry IJ, Lutomski J et al. ‘Living longer and feeling better: healthy lifestyle, self-rated health, obesity and depression in Ireland.’ European Journal of Public Health (2010) vol. 20: 91–95 [4] Luppino FS, de Wit LM, Bouvy PF et al. ‘Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies.’ Archives of Genetic Psychiatry (2010) vol. 67: 220–229 [5] MINDFUL - Mental Health Information and Determinants for the European Level http://info.stakes.fi/mindful/EN/frontpage.htm, Published 31.1.2006, Updated 29.4.2009 [6] International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) Version for 2010, Chapter V; Mental and behavioural disorders (F00-F99) http://apps.who.int/classifications/icd10/browse/2010/en#/F30-F39, [7] "The Quality and Outcomes Framework"; http://www.ic.nhs.uk/statistics-and-data-collections/audits-andperformance/the-quality-and-outcomes-framework, © 2012, The Health and Social Care Information Centre all source URLs lastly accessed on June 05 2012 24
Page 2 and 3: Joint Action for European Community
Page 4 and 5: 1. New ECHI data: ECHIM Pilot Data
Page 6 and 7: DG Health & Consumers and DG EMPL n
Page 8 and 9: In addition to survey-derived indic
Page 10 and 11: So the entire quest was depending o
Page 12 and 13: epresentativeness through the sampl
Page 14 and 15: 1.3.1. ECHI# 21 Diabetes ECHIM Indi
Page 16 and 17: Fig.1.3.1.2 Diabetes by educational
Page 18 and 19: Fig.1.3.1.3 Correlation between BMI
Page 20 and 21: This phenomenon might be explainabl
Page 22 and 23: B Table 1.3.2 Fig. 1.3.2.1 DATA PRE
Page 24 and 25: C DATA ANALYSIS Overall depression
Page 28 and 29: 1.3.3. ECHI# 24 Acute Myocardial In
Page 30 and 31: C DATA ANALYSIS A high percentage o
Page 32 and 33: Fig.1.3.3.2 Compatibility of submit
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Page 38 and 39: The submitted stroke attack rates i
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Page 52 and 53: C DATA ANALYSIS Overall COPD preval
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Page 56 and 57: 1.3.7. ECHI# 29 A-1 Injuries: home,
Page 58 and 59: Fig. 1.3.7.2 Injuries at home, leis
Page 60 and 61: Compared to the EHIS, in Switzerlan
Page 62 and 63: 1.3.8. ECHI# 29 A-2 Injuries: home,
Page 64 and 65: Fig. 1.3.8.2 Injuries at home, leis
Page 66 and 67: accidents in separate categories an
Page 70 and 71: C DATA ANALYSIS The 16-country aver
Page 72 and 73: 1.3.10. ECHI# 30 A-2 Injuries: road
Page 74 and 75: Fig. 1.3.10.2 Road traffic injuries
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The EHIS questions on “road traff
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B Table 1.3.11 Fig. 1.3.11.1 DATA P
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The non-fatal injury statistic of t
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C DATA ANALYSIS In the adult popula
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1.3.13. ECHI# 43 Blood pressure ECH
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Fig. 1.3.13.2 Prevalence of high bl
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D REMARKS AND FURTHER INFORMATION A
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C DATA ANALYSIS The percentage of r
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population. Information on effectiv
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C DATA ANALYSIS In the adult popula
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1.3.16. ECHI# 50 Consumption of veg
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Fig. 1.3.16.2 Daily consumption of
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The Irish data were extracted from
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C DATA ANALYSIS Overall influenza u
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for all people at high risk defined
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The lower figures reported from eas
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General remark on the preferred dat
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1.3.20. ECHI Indicator Data Sheet #
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Fig. 1.3.20.2 Colon cancer screenin
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[3] Eurostat database, see (http://
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C DATA ANALYSIS In a cross-country
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Further data were delivered by Icel
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1.3.22. ECHI# 72-1 Selected outpati
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Fig. 1.3.22.2 Mean number of self-r
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NB: The data from Finland originate
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Fig. 1.3.22.3 Administratively dedu
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Fig. 1.3.23.2 Mean number of self-r
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NB: The German data are the most re
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Fig. 1.3.24.2 Proportion of populat
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[1a+b] EHIS description http://circ
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Possible solutions and appropriate
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Table 2.1 Data sources for the ECHI
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Table 2.1 (continued) Data sources
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Table 2.1. (continued) Data sources
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2.1 Presentation of the ECHI shortl
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2.2 Content of the HEIDI data tool
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3. Lessons learned, Conclusions and
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4. Acknowledgement This work was en
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ECHI# 29 (A) 29 (B) 30 (A) 30 (B) 4
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Annex II EHIS data collection: Surv
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175
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177
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Annex III Data tables of indicators
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1.3.3 Data Table ECHI#24 Acute myoc
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1.3.5 Data Table ECHI#26 Asthma "Pr
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1.3.7 Data Table ECHI#29 (A) -1 Inj
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1.3.9 Data Table ECHI#30 (A)-1 Inju
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1.3.11 Data Table ECHI#30 (B) Injur
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1.3.13 Data Table ECHI#43 Blood Pre
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1.3.15 Data Table ECHI#49 Consumpti
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1.3.17 Data Table ECHI#57 Influenza
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1.3.19 Data Table ECHI#59 Cervical
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1.3.21 Data Table ECHI#71 General p
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1.3.23 Data Table ECHI#72-2 Selecte
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Annex IV List of Abbreviations Abbr
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