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Managing a diabetes database: using a data dictionary and processing health records
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EUropean Best Information through Regional Outcomes in Diabetes
Managing a diabetes database: using a data dictionary and processing health records
Scott CunninghamUniversity of Dundee, Scotland
The BIRO Academy2nd Residential Course
Brussels, 23rd-25th January 2011
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The BIRO Academy 2nd Residential Course Brussels, 23rd-25th January 2011
Introduction
• Background• Defining a European common diabetes
dataset• Defining a European diabetes data
dictionary• Processing health records• Conclusions
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Background
• BIRO WP2: Core diabetes indicators– clinical review of current evidence
• Candidate indicators• Core indicators• Basis for dataset
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Background
• Accurate comparisons of diabetes indicators • Standardised data currency– Consistent data storage– Consistent aggregation
• Data standards– Quality– Relevance– Consistency– Avoid duplication
• Reduction in costs of data development
• EUBIROD Common Diabetes Dataset
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Common Diabetes Dataset
• “The WP will ensure the definition of a minimum dataset that can be used as a common data reference for the extraction of compatible entities at the international level.”
• “A coding/decoding mechanism will be identified to translate regional data in entities that can contribute to the definition of a shared information system.”
• Existing data standards…
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Common Diabetes Dataset
• Methodology– Clinical review (BIRO WP2)– Analysis of BIRO partner datasets (7 partners)
• Scottish Diabetes Dataset• DiabCare• Umbria AMD data file• EUCID project used for validation
– Review carried out for EUBIROD• 20 partners
• Common dataset for diabetes– Clinical definitions– International System of units (SI)
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Common Diabetes Dataset
• DiabCare data items do not have clear definitions
• Forum for Quality System in Diabetes (FQSD) and Scottish definitions available
• EUBIROD definitions based on understanding of common methods of recording
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Common Diabetes Dataset
• Each BIRO Dataset item recorded as a “Parameter”
• Parameters have a unique reference– Are clearly defined– Have an associated data type– May have a unit of measurement (e.g.kg/m2)– May have an upper or lower boundary– May have a locally defined guideline value– Based on evidence and best practice
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Common Diabetes Dataset
• Data items assigned “validity” rating after comparison across datasets– “High” Validity: High Validity items consistent
across ≥90% of analysed datasets– “Medium” Validity: consistent across ≥60% and
<90% of analysed datasets– “Low” Validity: those which are consistent across
<60% of analysed datasets
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Common Diabetes Dataset
• 25 High Validity Data Items Identified• Basic Patient Information
– e.g. Type of Diabetes, Date of Birth, Year of Diagnosis
• Risk Factors– e.g. Smoking Status
• Clinical Measurements– e.g. Weight, Height, SBP, DBP, HbA1c, Creatinine
• Examinations– e.g. Eye Examinations
• Outcomes– e.g. End Stage Renal Failure
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Common Diabetes Dataset
• Clinical profile data– Country– Data Source Type
• e.g. GP, Hospital Clinic (Diabetes), Regional Register
– Name of Clinic– Clinic Denominator (Population)– Geographical Area– Website– Contact details
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Common Diabetes Dataset
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Common Diabetes Dataset
• Already used for data repository design– Cyprus
• Available and browseable online• EUBIROD Dataset:
http://www.eubirod.eu/webPortal/?q=en/node/8
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Common Diabetes Dataset
• Standardised European data currency• Mandatory items– Unique patient identifier– Diabetes diagnosis– Date of diagnosis– Gender– Outcome dates (approximated or default if
necessary)
• Basis of data dictionary…
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Diabetes Data Dictionary
• “The WP will create an electronic directory inclusive of concept and data dictionaries for diabetes care and prevention, thereby allowing to dynamically link the clinical knowledge-base to the systematic evaluation of health systems outcomes.”
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Diabetes Data Dictionary
• Explain ‘data about the data’• Formally document dataset compliance• Issues with local data– Local knowledge essential to explain anomalies
• Local recording methods• Data quality criteria• XML documentation
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Diabetes Data Dictionary
• Web application to allow entry of local info• Data can be reviewed and updated as local
datasets change• Explain local deviations from definitions• Gain local knowledge• Consistent output produced across all
partners• Validated XML created for each data source
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Diabetes Data Dictionary
• Type of diabetes
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Diabetes Data Dictionary
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Diabetes Data Dictionary
• Data standards evolve:– HbA1c: IFCC measurement (mmol/mol)– Waist circumference– Estimated glomerular filtration rate (eGFR)– Laterality for foot/eye data?– Previous foot ulcer– Additional defined diabetes types
• LADA, MODY, Pancreatic Pathology, Secondary Diabetes, Drug Induced, etc
– New drug therapies
• Boundary values included to omit anomalies
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Diabetes Data Dictionary
Parameter Lower Boundary Upper Boundary
Date of Birth ≥01/01/1900 Current Date
Date of Diagnosis ≥DOB Current Date
Episode Date ≥DOB Current Date
Cigarettes per day 0 100
Alcohol Intake 0 60
Weight 5 300
Height 0.3 3
Body Mass Index 0.01 100
Systolic Blood Pressure 10 400
Diastolic Blood Pressure 10 300
HbA1c 2.15 25.02
Creatinine 3 1999
Total Cholesterol 0.01 50
HDL 0.01 5
Triglycerides 0.01 100
Average Injections 0 20
LDL 0.01 15
Commencement of patient activity ≥DOB Current Date
Completion of patient activity ≥DOB Current Date
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Processing Health Records
• Mappings defined from local datasets to EUBIROD standard– Local controlled vocabularies– Non-standard units of measurement
• Dataset and data dictionary at core of EUBIROD developments– More later!
• Case study – Scotland!
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Processing Health Records
• Scottish Care Information – Diabetes Collaboration (SCI-DC)
• National system for NHS Scotland• Began as a research project (DARTS)– Started small and developed
• First minimum dataset – June 1998• National system status 2002• Full coverage by 2006– All GP sites and hospital clinics– Specialist services and web data entry
• Shared electronic record for diabetes
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Processing Health Records
• Extract in standard format• Consolidate into common structures• Record linkage using unique reference
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Clinical Guidelines
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Processing Health Records
• System designed for clinical care• Supporting single data entry across systems• Secondary data use– Clinical audit– Research and enhancing evidence base– Direct patient access
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Tayside – Annual Report 2010
Duration in years as a percentage of diabetes type in Tayside
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Scottish Diabetes Survey 2009
Diabetes prevalence in each NHS board
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Monitoring for Quality Improvement
• Scotland’s diabetes prevalence– 2002: 103,835 (2%)– 2009: 228,004 (4.4%)
• Type 1 diabetes: 13%• Type 2 diabetes: 87%
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Monitoring for Quality Improvement
• 2005 – 2007 estimates: diabetes-related admissions account for ~12% of Scotland’s inpatient costs of £2.4bn– £26 million for type 1 diabetes– £275 million for type 2 diabetes
• One in ten people in hospital have diabetes• 60% of people with diabetes admitted as inpatients
have been admitted as emergencies• 2008: Diabetes UK estimated that diabetes
accounted for 10% off all NHS expenditure in UK– £9 billion per year, or £1 million every hour– Double a 2002 Department of Health estimate
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Monitoring for Quality Improvement
• 2009 Scottish Diabetes Survey– 9735 (4.3%) reported as having ever had a foot
ulcer– 1132 (0.5%) had a lower limb amputation– Lower limb amputation rates are 15 times higher
in people with diabetes than those without
– 21471 (9.5%) have a record of a previous MII– 11575 (5.1%) recorded as having had a CVA– 80% of people with diabetes will die from
cardiovascular complications
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Monitoring for Quality Improvement
• 2009 Scottish Diabetes Survey– 20.6% had retinopathy present in one or both eyes– 1,824 (0.8%) people with diabetes recorded as blind– Diabetic retinopathy is the leading cause off blindness in
people of working age in the UK
– 254 (0.9%) off those with type 1 and 872 (0.4%) of those with type 2 diabetes have been recorded as having end stage renal failure
– 32.7% overweight (BMI 25-29.9kg/m2)– 51.0% obese (BMI 30kg/m2 or over)– 19.5% are current smokers
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EUCID
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EUBIROD
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Conclusions
• Data standards improve:– Quality– Relevance– Consistency– Avoid duplication
• If you measure it, you can improve it!• All levels of infrastructure can be reported– Local– National– International
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Conclusions
• We have covered:– Defining a European common diabetes dataset– Defining a European diabetes data dictionary– Processing health records
• Case study – Scotland
• Principles can be applied internationally
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References
• EUBIROD Data Dictionary:http://www.eubirod.eu/webPortal/?q=en/node/8
• The International System of Units:http://www.bipm.org/en/si/
• NHS Scotland Data Dictionary:http://www.datadictionary.scot.nhs.uk/
• EUBIROD Data Source Questionnaire:http://questionnaire.eubirod.eu/
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Questions?
Thank you for your attention!