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Martin Kenneally Brenda Lynch m.kenneally@ucc.ie brendalynch@ucc.ie

Integrating Administrative Data in Health Studies: A Case Study . Martin Kenneally Brenda Lynch m.kenneally@ucc.ie brendalynch@ucc.ie. Objectives. Profile the Health Status of Irish Regions (2010) Link Regional Health Profiles to Regional Prescribing.

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Martin Kenneally Brenda Lynch m.kenneally@ucc.ie brendalynch@ucc.ie

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  1. Integrating Administrative Data in Health Studies: A Case Study Martin Kenneally Brenda Lynch m.kenneally@ucc.iebrendalynch@ucc.ie

  2. Objectives • Profile the Health Status of Irish Regions (2010) • Link Regional Health Profiles to Regional Prescribing. • Incorporate Regional Demographics, Community Drug Scheme Coverage rates and Prescribing Norms • Simulate effects of Health Status, GMS coverage & Demographics on Prescribing Rates & Cost outcomes. • Identify correlates of health status

  3. Profile Regional Health Status • Construct a Composite Health Index to; • (a) Calibratethe health status of 8 Irish regions below • (b) Use Ireland as Standard with base value of 100.

  4. Profiling Steps 1. Select Health Indicators: (mainly morbidity rates) for each type of major health condition 2. Standardized Rates = national/regional morbidity rate *100 3. Aggregate Rates: using Prescription Weights. 3(a) Aggregate separatelyfor GMS, DP and LTI drug schemes in each region. 3(b) Then Aggregate across schemes in a region (using coverage weights) to obtain that region’s Regional Health Index • (i) Use Index to BenchmarkRegional Health Status • (ii) Use Index to Benchmark Regional Health Gaps • (iii) Simulate selected policy outcomes • (iv) Identify Correlates of Health Status & Health Gaps

  5. Official/Administrative Datasets Available • Official: CSO e.g. QNHS – Health Module (Used) • Admin: non-CSO, respondent centred, dedicated focus e.g. • IPH: General. Morbidity Prevalence Rates by County (Used) • PCRS: Drug Scheme Coverage & Prescribing data (Used) • Health Atlas: Focus on Public Patients • Tilda: Focus on Over 50s (Ageing) • SLAN: Focus on Lifestyle, Attitudes & Nutrition. Periodic. • CME: General, 2008/9only [BNF not ICD codes used]. Technical Challenges • Missing Definitions, sources and methods • Disjoint Definitions/Concepts e.g. CSO vs PCRS ‘regions’

  6. Health Data Gaps QNHS Health Module covers 19 Health Conditions: (i) ‘Adults only’ (excludes under 18s) (ii) Rates refer to “at any time in a respondent’s past” (iii) Excludes GERD & pregnancy/immunization services and (iv) Does not cross-tabulate conditions by region/medical cover. PCRS: Publishes prescribing by scheme, region, age & gender butdoes not publish allied morbidity rates on the same basis. Upshot, we don’t know; How much morbidity rates of ‘public’ & ‘private’ patients differ How much GP visit rates reflect ill-health v’s type of health cover How much prescribing rates reflect ill-health v’s health cover

  7. Community Drug Schemes Incorporated Incorporated • 1. GMS (General Medical Services) Means tested. Income adjusted for mortgage/housing, childcare, travel costs, savings etc. Discretionary Medical Card also granted to avoid “unduehardship”. • 2. DP (Drug Payment Scheme) – Not means tested. Person/family pays first €144/month; HSE pays any excess • 3. LTI (Long Term Illness) Not means tested. Schedule includes - Cerebral Palsy, Spina Bifida, Epilepsy Acute Leukaemia, Multiple Sclerosis, Diabetes & *************************************************************************************** Not incorporated • HTD (High Tech Drug Scheme) – mainlyhospital originated anti-rejection drugs for transplants and chemotherapy

  8. Regional Population % Covered by Each Scheme in 2010

  9. Methodology • Select 28 health indicators (18 prevalence rates/10 others) • Assign to 6 ATC Health Categories/Dimensions: Alimentary, Cardio, CNS, Respiratory, Various & Other • Break down 6 ATC categories into 24 Therapeutic Drug Groups • Construct(prescription weighted) Composite Health Indicesfor the 6 ATC categories under each scheme in each region. • Aggregate scheme-specific Indices into Regional Indices (using scheme coverage weights). Base Value is Ireland = 100 .

  10. KL Composite Health Index

  11. Largest Health Gaps by ATC Category

  12. Health Gaps & Weights in East & Midlands Regions

  13. Simulated Prescribing & Cost Outcomes • We constructed & validated a simulation model. • Simulation Model incorporatesregional health status, scheme coverage & prescribing norms; • Simulates number & type of drug prescribed in each region in 2010 with high (97%) accuracy • Prescribing semi-elasticity w.r.t. GMS coverage is twice semi-elasticity w.r.t. health-status • Pattern and causes of regional unit drug cost variations still under investigation.

  14. Income, Demographics, Coverage & Health Status

  15. Unanswered Questions & Policy Issues • Macro-causality pattern of regional health status remains “Smudged” • “Ground-clearing”: lacking the with precision of, say, Kabiret al. 2013 on CHD • North-West v’s North East (for example): • Why is GMS cover in NW so much higher? (Equity) • Is poorer NW health status due to poor demographics? • How much do other factors contribute to health status?

  16. Recommendations to Increase Usability • Working Party of Official & Admin Groups to Agree; • Common and individual domains • Common base observation unit (e.g. DEDs for SAPS & Census) • Common publication unit (NUTS3 or NUTS4) • Harmonised methodologies. • ‘Definitions, Sources and Methods’ manual (IPH/PCRS) • Linked prescribing and morbidity data (for policy analysis) • Published accessible anonymised (Statbankstyle) Archive Tables • Increased professional statistical input • The End

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