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National Institute of Public Health

National Institute of Public Health . Register-Based Research Opportunities and Challenges. Annette Kjær Ersbøll. Overview. Introduction History Danish registers Data quality Validity, Completeness and Coverage Register-based studies Opportunities and Challenges Future perspectives

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National Institute of Public Health

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  1. National Institute of Public Health Register-Based Research Opportunities and Challenges Annette Kjær Ersbøll

  2. Overview • Introduction • History • Danish registers • Data quality • Validity, Completeness and Coverage • Register-based studies • Opportunities and Challenges • Future perspectives • ECREPH – European Centre for register-based research

  3. Why register-based research Easy access to data – utilize existing data Easy to derive studies, e.g. cohort studies No limitations regarding sample size – total population Except for rare diseases Population-based studies feasible Follow-up easy No need to contact individuals in study No non-response bias (participation, reporting) Great statistical power

  4. Why register-based research Data independent of research question Valuable time has passed Latency issues easily handled Long term trends in disease incidence Administrative data of high quality

  5. Register-based research Opportunities Unique opportunities Fast and efficient answer Economy - Low cost Whole population • Challenges • Bias • Confounding • Validity • Coverage

  6. Register-based research Register – a definition Aims to have a complete list (systematic collection) of all individuals in the population (or in a specific group) Contains unique identification of each individual

  7. Register-based research Register-based Fast and efficient answer Economy - Low cost Whole population • Survey/Census • Time consuming • Expensive • Decreasing participation rate

  8. Registers – Nordic countries Nordic tradition for registers Denmark Sweden Finland Based on unique personal identification number

  9. History Long tradition for registers of the Danish population Births and deaths have been registered in church records since 1645 First Danish census in 1769 (27 censuses in Denmark) Danish population register in 1924 (each town and parish)

  10. History Introduction of the Danish CRP-number – unique personal identification number in 1968 Denmark performed the first register-based census in 1981 (Finland in 1990)

  11. Size of Danish population 5.543.819 797.584

  12. Privacy issues – public attitude Many administrative registers – discussions about privacy Statistical use of administrative data often involves linkage of different registers Register administrator – ”Big Brother Syndrome”

  13. Privacy issues – public attitude People know that administrative authorities collect the same data that the register administrator uses for statistical purposes General public – important that they appreciate and understand the benefit of using registers for statistical purposes Up-to-date register legislation Procedures and handling of registers - open and transparent

  14. Privacy issues – public attitude In Denmark People have a strong faith in register administration People seem to believe that statistical use of registers is rational

  15. Danish registers Three base registers (unique identification number) Population register Business register (enterprises) Property registers (real estate, buildings, dwellings)

  16. Danish registers Population Education Employment Income Taxation Patient register Cancer register Cause of death Prescription medicine … Value added tax Monthly wage sums Income Foreign trade Patent … Population Business GIS Buildings Dwellings Real Estate Price … Property

  17. Overview Hist

  18. Organisation of registers in Denmark Data owners, e.g. Statistics Denmark Danish National Board of Health Danish Medicines Agency Clinical Databases (e.g. PathoBank) Data availability Statistics Denmark Danish National Board of Health Free of charge for researchers

  19. Data quality and coverage Validity Completeness Coverage Missing data

  20. Data quality and coverage Predetermined data collection Collection not controlled by researcher Research topic needs to be in suit with the register/database Lack of information on exactly how data are collected/generated Difficult to validate

  21. Data quality and coverage Predetermined data collection Advantage Data are independent of specific research question Disadvantage Data recorded for different purpose Data recorded in an optimal way? Method of data collection predetermined – by others than the researchers (e.g. administrators)

  22. Completeness Proportion of individuals in target population correctly classified in the register Degree of completeness is related to sensitivity

  23. Completeness – methods to evaluate Compare data sources (with / without golden standard) Comprehensive records review Aggregated methods Capture-recapture

  24. Completeness – methods to evaluate Danish National Register on Regular Dialysis and Transplantation (NRDT) Hommel et al. (2010), Nephrol Dial Transplant All Danish patients treated for end-stage renal disease All patients in NRDT or National Patient Register (NPR) Incident patients 2001-04 identified in NPR NRDT was compared with NPR (as golden standard)

  25. Completeness – methods to evaluate Hommel et al. Nephrol Dial Transplant 2010;25:947-51

  26. Completeness – methods to evaluate Capture – Recapture Two data sources / two methods Two samples (e.g. case finding method and census sample) Assumptions Closed population Equal chance of being included in each sample Independence - methods Estimate sensitivity of case-finding (%) - how well the methods perform at finding cases

  27. Validity Information bias (misclassification) Risk of substantial errors due to many people entering data Coding practice Variation in coding (between persons, departments, hospitals, …) Changes in coding and classifications over time Disease diagnoses (ICD-8 until 1993, ICD-10 from 1994) Classifying length of educations (7-10 grade, basic, short-medium, long) DRG taxation (changes in fees for diagnoses and treatments) Data breaks

  28. Coverage Missing data – undercoverage Compulsory versus optional fields Economic benefit Legislation – some diseases should be reported by law

  29. Missing data - undercoverage Pattern of missingness What does missing information mean Missing value in register Event not happened / or not reported Missing value – recoded (e.g. missing  lowest income) Under-coverage Highest education among immigrants or persons with educations from abroad

  30. Bias No/limited problem Selection bias No/little problem Total population – reduce risk of selection bias Long follow-up Recall bias Non-response bias

  31. Bias Problems Information (misclassification) bias E.g. diagnoses Differential (e.g. recall, interviewer), Non-differential Confounding Could be a substantial problem Lack of confounding variables Only few unspecific confounders are available E.g. life style and behaviour

  32. Methods to adjust for confounding Schneeweis. Pharmacoepidemiology and drug safety 2006; 15: 291–303

  33. Methods to adjust for confounding External adjustment External adjustment of the exposure-outcome association given additional information on confounders from e.g. a survey External adjustment – joint distribution of confounders –using propensity score Sensitivity analysis Identify the strength of residual confounding that would be necessary to explain an observed exposure-outcome association

  34. Methods to adjust for confounding Use of Comorbidity score for control of confounding Health status often important Comorbidity adjustment Comorbidity score Age – simple but unspecific Charlson index

  35. Statistical issues Analysis of total population p-values Confidence intervals Significance testing relevant Relevant size of efficacy measure – clinical relevance

  36. ECREPH European Centre for Register-based Health-related Population Research – public health, major diseases and welfare www.ecreph.org

  37. ECREPH European Centre for Register-based Research Who we are Multidisciplinary centre Cover topics within: public health, epidemiology, applied statistics and spatial modelling Professor, senior researcher, assist prof, post doc, PhD students and Master students

  38. ECREPH European Centre for Register-based Research Aims Increase knowledge to and use of Danish population-based health related registers Facilitate research collaboration based on health related registers Offer training courses and research education within register-based research

  39. ECREPH European Centre for Register-based Research Research themes Economic crises and public health consequences GIS and health geographics Validation of registers Methodological developments in register-based research

  40. ECREPH European Centre for Register-based Research Research themes Economic crises and public health consequences Evaluate consequences of increased unemployment on risk of health (e.g. mental health and disorder) Are certain groups more prone to negative health consequences than others Inequalities and geographical differences

  41. ECREPH European Centre for Register-based Research Research themes GIS and health geographics Association between certain types of cancer and environmental risk factors Spatial surveillance Spatial risk factors Risk mapping

  42. ECREPH European Centre for Register-based Research Research themes GIS and health geographics

  43. GIS and health geographics

  44. ECREPH European Centre for Register-based Research Research themes Validation of registers Methods for validation of register data

  45. ECREPH European Centre for Register-based Research Research themes Methodological development in register-based research RR and SMR estimation - shinkage

  46. ECREPH European Centre for Register-based Research PhD Courses Epidemiology in register-based research Advanced methods in register-based epidemiology Spatial epidemiology

  47. ECREPH European Centre for Register-based Research www.ecreph.org Database (overview) of existing Danish registers Special issue – Scand J Pub Health (2011) Presentation of current registers Review of research based on current registers

  48. Future perspectives Methods for validation of registers In absence of golden standard Handling missing data / under-coverage Nordic registers Differences in health care systems – and health consequences Improving surveys using registers Imputation of missing data in survey using registers Health geographics Inequalities, distance to hospitals, … Evaluating effect of preventive measures

  49. Thank you for your attention

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