Data refining of sick listing data for statistics, analysis and forecasting

1. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Data refining of sick listing data for statistics, analysis and forecasting

2. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Part 1: What do we want to create?

3. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Administrative data in Sweden • Sweden has a history of extensive gathering of administrative individual data • Every person has an individual civic registration number which contains the birth date and four additional numbers • Due to the civic registration number it is possible to combine administrative information from various sources (on a individual level)

4. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Sick listing in Sweden • Sick listing in Sweden can be very lasting • Different degrees of partiality(25, 50 or 75 percent) • Sickness cash benefit and rehabilitation cash benefit • Employers pay for the first 14 days(has been 21 and 28) • Relation between sickness insurance and i.e. unemployment insurance and parental insurance • Seasonal variation

5. 100% 75% 50% 25% Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Waiting day Sick pay Sickness cash benefit Rehabilitation cash benefit Example: A sickness case Degree of partiality 1 3 4 5 6 7 8 9 10 11 2 Time Higher income entitling to sickness cash benefit Episodes with the same benefit, degree of partiality and daily compensation:

6. 100% 75% 50% 25% Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Waiting day Sick pay Sickness cash benefit Rehabilitation cash benefit Example: A sickness case Degree of partiality 1 3 4 5 6 7 8 9 10 11 2 Time Higher income entitling to sickness cash benefit Episodes with the same degree of partiality:

7. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Share of new sickness cases with part-time absence at the beginning of the case

8. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Share of new sickness cases with a history of a sickness case within preceding 90 days

9. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 • The common way for analysts when making their own project databases • Is it possible to make this process more effective? Special design project databases Separate processes to transform raw data Raw Data

10. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Yes, by setting up a common framework for the raw data we can be more time efficient and increase the general quality when we produce a designed project database Special design project databases Refined data transformed into the lowest common denominator Raw Data Refined database (MiDAS)

11. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Part 2:How do we create this?

12. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 The complexity in data Three ways of dealing with this: • Leave data untouched and put togetherthe information as it is • Make an effort to understand data andtake into account any defect in data • Exclude observations that don’t ”fit in”

13. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Data refining for analysis and forecasting • Quality assurance of individual data • Take care of as much of the information as possible in the administrative systems • Make data more accessible to optimize the use of data • Create multidimensional databases foranalysis on individual level • Detailed documentation

14. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Qualifications needed • Front line staff who know about activities and routines mirrored in the administrative systems • Analysts with some experience in programming who knows how to analyze data • In-house people to ensure that the knowledge remains within the organization => prefer employees to extern consultants

15. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Example: Sickness absence data

16. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Complexity in sick listing data • Correct corrections (by the book) • Incorrect corrections (not allowed but supported by the registration system) • A registration squeezed into an earlier registration • Incorrect registration of dates • ”False 1 January” • Late arrival of observations

17. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Correct correction of benefit and degree of partiality

18. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Incorrect correction of degree of partiality

19. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 A registration squeezed into an earlier registration 4 days rehabilitation cash benefit 7 days sickness cash benefit 19 days rehabilitation cash benefit

20. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 A registration squeezed into an earlier registration (continued)

21. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 ”False 1 January”

22. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Administrative data is complex • If you don’t handle the complexity,data can be hard to analyze on micro level • Even small errors in micro data willdecrease credibility in the data

23. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Part 3:The result

24. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Refined databases Episode data: • Sickness case • ”part sickness case” Panel data: • Month • Quarter • Year

25. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Example: Sick case data

26. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Example: Part sick case data

27. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Example: Year data

28. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 Number of people with occurrence of sick listing in Sweden

29. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 With this data structure you have… • … all sick listing data in one place: time, days, compensation • … always the same information but structured differently for different purposes

30. Data refining of sick listing data • Patric Tirmén and Niklas Österlund •2006-11-22 With this data structure you can… • … fast and easily create countless aggregated statistics • … analyze data on a micro level with high flexibility • … easily combine this data with other data • … create data sets suitable for forecasting