Analysis at TK. Dr. Roland Linder. Plymouth. Hamburg, May 12 th, Techniker Krankenkasse Head Office, 10:00am - 11:00am,Venue K3

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1 Benefits of Data Collection and Data Analysis at TK Dr. Roland Linder Study Visit it Nuffield Trust, NHS, Manchester Business School and University it of Plymouth Hamburg, May 12 th, 2010 Techniker Krankenkasse Head Office, 10:00am - 11:00am,Venue K3

2 Content 1. Data analysis: what for? 2. Available Data and Data Warehousing 3. Data analysis: examples 4. Methodological aspects 5. Future topics of WINEG 6. Conclusion

3 1. Data analysis: what for? Data analysis of SHI routine data: what for? Reasons for disability: Prevalence of spinal and back disorders, d Benchmarking: Quality indicators as a tool for selective contracts stress disorders, etc. with care providers or pay for performance Usage of health care offers like vaccination or early diagnosis Public Health research: e.g., men's health: Increasing number of Utilisation: How many patients GP visits? suffering Increasing from diabetes demandsvisit on a medical practice DDD? specialised Implicationson for diabetes? the cost development with respect to an ageing Validation of medical studies that base population? on a small sample size Outcomes research: Predictive Modeling: Validation ofpredicting the benefit hospitalisation of innovationsor predicting regarding stents complications? or endoprothesis Possibility in to daily clinicalprevent routine deterioration (e.g., infections?) and unnecessary and the duration costsperiods e.g. by using everyday case management? life 3

4 1. Data analysis: what for? Health services research: questions Estimation of morbidity Demand for health services Specific patterns of health services Quality of health services Cost analysis Research on newly imposed laws and regulations Research on transfer of knowledge Outcomes research From: Schubert I, Köster I, Küpper-Nybelen J, Ihle P: Versorgungsforschung mit GKV-Routinedaten. Nutzungsmöglichkeiten versichertenbezogener Krankenkassendaten für Fragestellungen der Versorgungsforschung. Bundesgesundheitsbl - Gesundheitsforsch - Gesundheitsschutz 2008;51:

5 1. Data analysis: what for? Health services reseach at WINEG Health economics: Evaluation of economic efficiency Research on health services (Outcomes research): Usage in everyday life? NMDT Drugs DMP CM Prev. MVZ Patient information NMDT: DMP: CM: Prev.: MVZ: new methods for diagnosing and treatment Disease management programme Case management Prevention Ambulatory healthcare centre 5

6 Content 1. Data analysis: what for? 2. Available data and data warehousing 3. Data analysis: examples 4. Methodological aspects 5. Future topics of WINEG 6. Conclusion

7 2. Available data and data warehousing Data basis Data of 7.3 million insurants regarding 301 SGB V Hospitalisation 115b ( 301) Ambulant operations 295 Data from practitioners 300 Pharmaceuticals 44 Disability to work 301a Settlement costs of midwives 302 Settlement costs of other care providers, particularly of remedies and adjuvants 37, 37a, 37b, 38 Care, social therapy, palliative care and home care 137f Disease management programmes Master file data of insurants 7

8 2. Available data and data warehousing Data Warehouse 8

9 2. Available data and data warehousing A Definition of Data Warehousing A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. by Bill Inmon in

10 2. Available data and data warehousing A Definition of Data Warehousing A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. by Bill Inmon in

11 2. Available data and data warehousing A Definition of Data Warehousing A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. by Bill Inmon in

12 2. Available data and data warehousing A Definition of Data Warehousing A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. by Bill Inmon in

13 2. Available data and data warehousing A Definition of Data Warehousing A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. by Bill Inmon in

14 2. Available data and data warehousing A Definition of Data Warehousing A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. by Bill Inmon in

15 2. Available data and data warehousing Relational Database Management Systems A relational database serves for the electronic administration of data in computer systems. It relies on the relational model as introduced by E. F. Codd in To date, this relational model is the etablished standard. d The according database management system is called Relational Database Management System (RDBMS). A widely-used database language for queries and data manipulation is the Structured Query Language (SQL) that was also developed by Codd. 15

16 2. Available data and data warehousing Relational database model (roughly simplified) Hospitalisation Procedures ID_PK ID_FK ID_FK ID_FK Master file data of insurants Data from practitioners ID_PK ID_FK Diagnoses ID_FK ID_FK ID_PK Pharmaceuticals Charges ID_PK ID_FK ID_FK ID_FK

17 2. Available data and data warehousing Data Marts of TK 17

18 2. Available data and data warehousing Example: The Data Mart of drug prescriptions 18

19 2. Available data and data warehousing An introduction to SQL: example To what extent t did people consume drugs in 2008 that were insured by TK are at least 60 years old measured by Defined Daily Doses (DDD) with regard to gender and age? 19

20 2. Available data and data warehousing Simple SQL-Statements SELECT ID_Insurant, Insurant Sex, Age FROM INSURANTS SELECT ID_Insurant, Insurant DDD FROM APO2008 ID_Insurant Sex Age ID_Insurant DDD M M F F M

21 2. Available data and data warehousing A more complex SQL-Statement SELECT Sex, Age, SUM(DDD) / COUNT(ID_Insurant) AS DDD_AVG FROM INSURANTS AS i LEFT JOIN APO2008 AS a ON i.id_insurant = a.id_insurant WHERE Age >= 60 GROUP BY Sex, Age ORDER BY Sex, Age Sex Age DDD_AVG M M M F F

22 2. Available data and data warehousing Demography: demand for pharmaceuticals 22

23 2. Available data and data warehousing Increasing demand for DDD by middle-aged men 23

24 2. Available data and data warehousing Pros & Cons using SHI routine data + Transsectional and interdisciplinary data + Huge sample size + Long-term and continuous documentation + No interview bias + Up-to-date information -? Diagnoses may be influenced by financial issues - Missing of clinical data like laboratory data, blood pressure, BMI, and soft data like nicotine abuse, severity of disease, life quality, lifestyle, social environment, etc. 24

25 Content 1. Data analysis: what for? 2. Available Data and Data Warehousing 3. Data analysis: examples 4. Methodological aspects 5. Future topics of WINEG 6. Conclusion

26 3. Data analysis: examples Public interest in endoprotheses 26

27 3. Data analysis: examples Question How long is the real life time of endoprotheses? or: How many percent of the endoprotheses have to be replaced or removed within the first 2 years after primary operation? 27

28 3. Data analysis: examples Early reoperations 28

29 3. Data analysis: examples Matrix implantation vs. complication (hip joint %) x 821.j 821.g 821.f 821.e 821.b 821.a ,2 0,9 0,4 0,2 3,2 25,2 26,7 4,5 26,7 1,9 1,5 7,9 0,4 0,4 n = n = n = n = n = 79 2,0 2,0 2,0 2,0 6,1 14,3 26,5 4,1 14,3 4,1 18,4 2,0 2,0 22,2 22,2 22,2 11,1 11,1 11,1 18,8 31,3 3,1 9,4 3,1 31,3 2,5 1,3 1,3 7,6 58,2 13,9 7,6 1,3 3,8 1,3 1, ,3 28,6 14,3 28,6 14,3 n = n = n = 5 60,0 40,0 8,3 66,7 8,3 8,3 29

30 3. Data analysis: examples Matrix implantation vs. complication (hip joint %) x 821.j 821.g 821.f 821.e 821.b 821.a ,2 0,9 0,4 0,2 3,2 25,2 26,7 4,5 26,7 1,9 1,5 7,9 0,4 0,4 n = n = n = n = n = 79 2,0 2,0 2,0 2,0 6,1 14,3 26,5 4,1 14,3 4,1 18,4 2,0 2,0 22,2 22,2 22,2 11,1 11,1 11,1 18,8 31,3 3,1 9,4 3,1 31,3 2,5 1,3 1,3 7,6 58,2 13,9 7,6 1,3 3,8 1,3 1, ,3 28,6 14,3 28,6 14,3 n = n = n = 5 60,0 40,0 8,3 66,7 8,3 8,3 30

31 3. Data analysis: examples Evaluation for DMP Diabetes mellitus 2 Research question: The impact of DMP on outcome and costs First study in Germany with complex statistical control group-method Database TK-insurants between January 1 st 2006 to December 31 st 2008 SHI data Matching criteria Age Sex Level of care Socioeconomic parameters Drug expenses Hospital expenses DDD Significant comorbidities Group D: DMP-participants p Group NM: control group 31

32 3. Data analysis: examples Amputation of a diabetic foot or toe of DMP participants vs. non-participants 32

33 3. Data analysis: examples Comparison of incidences of stroke of DMP participants vs. non-participants 33

34 3. Data analysis: examples Comparison of incidences of myocardial infarction of DMP participants vs. non-participants 34

35 3. Data analysis: examples Comparison of incidences of significant comorbidities weighted by DALY: DMP participants vs. non-participants Comorbidities Weight (DALY) Myocardial infarction 0,439 Stroke 0,920 Peripheral artery occlusive 0,000 disease Blindness 0,552 End-stage renal disease 0,098 Neuropathy 0,072 Amputation 0,102 Dyslipidemia 0,000 Ischemic heart disease 0,124 If the incidences of comorbidity are weighted by DALY, DMP seems to have no advantage or disadvantage with respect to the significant comorbidities compared to regular care. 35

36 3. Data analysis: examples Comparison of emergency hospitalization of DMP participants vs. non-participants The emergency hospitalisation rate of the DMP-participants is only marginally better than for non-participants. 36

37 3. Data analysis: examples Comparison of demands for Defined Daily Dose (DDD) of DMP participants vs. non-participants The demand for DDD of the DMP-participants is above the demand of the control group throughout the observed quarters. 37

38 3. Data analysis: examples Student's work at WINEG Almost 40% of patients with diabetes or hypertension do not receive adequate pharmaceutical treatment. WHY THAT??? Nicole Jenett, doctoral thesis: Pharmacotherapeutic care of patients suffering from diabetes mellitus type 2, hypertension and dyslipidemia Stefan Tesch, doctoral thesis: Analysis and explanation of non-adherence regarding treatment of patients t suffering from diabetes 38

39 Content 1. Data analysis: what for? 2. Available Data and Data Warehousing 3. Data analysis: examples 4. Methodological aspects 5. Future topics of WINEG 6. Conclusion

40 4. Methodological aspects Effect of propensity score matching Parameter Group D Group N p (D vs. N) Group NM p (D vs. N) Myocardial infarction 0.99 % 1.08 % % Stroke 1.70 % 1.52 % % Occlusive vascular disease 6.75 % 4.45 % < % Blindness 051% % % Renal Insufficiency 0.34 % 0.45 % < % Polyneuropathy 6.80 % 1.95 % % Amputation of foot ot toe 0.19 % 0.10 % < % Level of care % 2.17 % < % Level of care % 1.48 % < % Level of care % 0.46 % < % Additionally, the following variables were considered: gender, education, job, age, demands for DDD, pharmaceutical costs and costs for inpatient care 40

41 4. Methodological aspects How to calculate the Propensity Score (PS) Var A Var B Var C DMP-participation yes(1) or no(0) Pat.-ID Var A Var B Var C DMP-participation Propensity Score (PS)

42 4. Methodological aspects Propensity Score Interval Matching DMP-participants no DMP-participants Pat.-ID PS < PS Pat.-ID < PS < PS PS PS

43 4. Methodological aspects Seeking the optimal matching procedure Propensity Score Matching algorithms Logistic Artificial Neural Adaptive Regression Network Regression Pairwise matching Interval matching One matching partner Several matching partners No maximum distance With maximum distance No maximum distance With maximum distance k-nearest Neighbor algorithm (k=1) Caliper algorithm Kernel algorithm Radiusalgorithm k-nearest Neighbor algorithm (k>1) Caliper algorithm 43

44 Content 1. Data analysis: what for? 2. Available Data and Data Warehousing 3. Data analysis: examples 4. Methodological aspects 5. Future topics of WINEG 6. Conclusion

45 5. Future topics of WINEG Student's work at WINEG Philipp Wewering, diploma thesis: Propensity Score Matching: Generating control groups as an instrument to evaluate innovations in the health care system based on SHI routine data Ines Zutz, bachelor's thesis: Developing an SQL generator for exploring health care of insurants suffering from epilepsy 45

46 5. Future topics of WINEG Prototypic software 46

47 5. Future topics of WINEG Top ten comorbidities of epilepsy The comorbidities of epileptic exceed the comorbidities of non-epileptic by a factor up to

48 5. Future topics of WINEG For decades, the following claim persists: >> A cost-effective and efficient health care system must first of all serve the patients and the insured. The medical 'Data Warehouse' is the crucial instrument to develop a data- and outcome-oriented oriented public health service. << Bothner U, Meissner FW: Data Mining und Data Warehouse: Wissen aus medizinischen Datenbanken nutzen. [Using Knowledge from Medical Databases] Dt Ärztebl 1998; 95: A

49 5. Future topics of WINEG As a consequence, further doctoral theses are planned: How useful are SHI routine data for generating medical working hypothesis? Who is egg and who is hen? To what extent can SHI routine data support the identification of causal chains? About the potential of transsectional and interdisciplinary SHI routine data to support medical practicing To what extent can SHI routine data serve as quality indicators for purpose of quality assurance? 49

50 6. Conclusion The analysis of SHI routine data enables an important contribution to the scientific discussion. Time has come to use the increasing data sources to the insurants' advantage (with adequate elaborateness*). * AGENS & Arbeitsgruppe Epidemiologische Methoden Arbeitsgruppe Erhebung und Nutzung von Sekundärdaten d der DGSMP, Arbeitsgruppe Epidemiologische i i Methoden DGEpi, GMDS, DGSMP (2008) GPS Gute Praxis Sekundärdatenanalyse: Revision nach grundlegender Überarbeitung. Version 2. (letzter Zugriff: ), see: Swart E: Good Practice of secondary data analysis. J Public Health 13 (2005)

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