Direct and Indirect Cost of OA in POHEM

1. Direct and Indirect Cost of OA in POHEM Behnam Sharif Star team trainee Webinar APRIL 29-2011

2. Outline • Background: Cost of Osteoarthritis(OA) • Direct cost: • Results of the OA direct cost study • Implementing in (POHEM) • Indirect cost: • Components of indirect cost in OA • Issues of Implementing in (POHEM) • Future projects of indirect cost of OA

3. Background • Arthritis is a leading cause of chronic pain and mobility limitation . • Osteoarthritis (OA) accounts for approximately 50-65% of arthritic diseases (most common form of arthritis ) • Total cost of OA, including direct and indirect cost, accountsfor up to 0.2–0.8% of the gross domestic product of westernnations (1)

4. Background (cont’d) • Authors in (1) report that by the year 2026, over 6 million Canadians will suffer from arthritis (almost two fold increase compare to 2000). • Arthritis cost is substantial ( in comparison to other chronic diseases) and will be higher in future. • ADVANTAGES of using POHEM in cost projection studies: • Cost of illness (COI) studies project the future burden of OA by only considering the trend in population aging. • However, there is a large gap in projection studies of OA, which is due to ignoring the trend of obesity prevalence, an epidemic in western countries

5. Cost of OA • Direct cost: in-patient, out-patient, medication, out-of-pocket cost, and side effects of medications. Indirect cost categories are • Indirect cost : Absenteeism, Presenteeism, Informal caregiver’s productivity loss, work transition productivity loss.

6. Indirect cost • Work transitions (early retirement, changing jobs, reduced hours, etc.) • Work transitions have been discussed as being negligible in OA while early retirement discussed to be significant in OA patients • Absenteeism: OA-related productivity loss because of missed work days • Presenteeism: , OA-related at-work productivity loss • On average 56% (35%-77%) of total cost of OA including medical, pharmacy, absenteeism and presenteeism is attributed only to presenteeism (4) • Informal caregiver cost: Unlike RA, it constitute a significant portion of the indirect cost of OA (40% reported in (1).

7. Ratio of direct and indirect cost in OA • A cost study in Ontario in 2004 reported an average cost of 12,200$ per year for OA patients, with 80% accounted for indirect cost. (4) • Yelin and Callahan(7) estimates the costs of productivity losses to be $49.6 billion, 3.26 times greater than the total medical costs of 15.2 billion for OA patients in $US, year 2000 • Gabriel and colleagues(8), found average indirect cost to be $824 (1992 US dollars), i.e., 31% of the $2654 = direct medical charges for sample of patients with OA

8. Problems in COI studies • In a recent review, Lee et al.(4) mentioned the existence of huge variation among the cost estimates of OA in different studies. In (5): Observed an up to 40-fold variation in cost of Illness (COI) estimates for the same disorder. • Major problems in COI studies(5): Using cross-sectional data and lack of controls, one data source and ignoring comorbidities and heterogeneity of patients Advantages of using microsimulation for cost projection • Human capital vs. friction-based approach : Recent studies agree that : • indirect cost has been reported to be 4 times higher than direct cost in all types of arthritis • indirect cost of OA is 25-50% of its direct cost from a societal perspective

9. Cost in POHEM (Cont’d) • POHEM-cancer models (Lung, Breast and colorectal) have DIRECT COST only • For cost-effectiveness studies (and technology assessment models) both direct and indirect cost are needed.

10. Background-direct cost • Previous work has been conducted either on a macro level (e.g. cost of illness), comparison between types of arthritis (e.g. OA vs RA), or on cost of specific events (e.g. cost effectiveness of TJAs) • Little work has been conducted on OA specifically at a patient level, examining costs over time

11. Direct cost study of OA • Study done by Mushfiqur, Nick Bansback et al. in 2008. • Data source: BC Admin data, year 2003 • Methods: • Population Data BC: hospital admissions as well as office visits covered . • PharmaNet Data: A stratified random sample of 100,000 individuals --stratified according to different OA stages(and Non-OA) • St.Paul’s hospital cost model: hip and knee replacements • Out of Pocket cost: NPHS (Eric’s model) • Results:

12. Direct cost study of OA (Cont’d) • States: : With the exception of the hip and knee replacement analysis, patient records were categorized by age group (0-49, 50-59, 60-69, 70-79, 80-89, 90+), gender, stage (no OA, OA diagnosis, Surgeon visit, primary hip/knee replacement, revised hip/knee replacement) and time in stage (0-1.9 years, 2-4.9 years, 5 years+). • Average person years and weighted person years and cost were calculated and summed for each state. • Total cost divided by weighted person years was then used to calculate per person annual costs.

13. Direct cost study of OA (results)

14. Outline • Background: Cost of Osteoarthritis(OA) • Direct cost: • Results of the OA direct cost study • Implementing in (POHEM) • Indirect cost: • Components of indirect cost in OA • Issues of Implementing in (POHEM) • Future projects of indirect cost of OA

15. POHEM (Cost modules included)

16. 2001 …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. Slide form Bill’s presentation (October-2009)-Modified for inclusion of cost (by Bsh) Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ VARIABLE age sex province health region immigration status education level income quartile body mass index smoking status diabetic status HUI SROA Survey sample weight VALUE 44 male Ontario York non-immigrant post-secondary Q4 (richest) 32.2 kg/m2 (obese) smoker non-diabetic 0.96 Yes 100.32 • OA Prevalence in 2001? • Apply OA prevalence rates (conditional on SROA, HUI, BMI, age, sex) •  yes • Assign OA status • OS1 • Assign direct cost (conditional on sex, age group, OA-status, OA-cycle) • OADirect_cost • Assign survival time to next OA event(s) •  3.7 years to OS2 •  5.1 years to surgery • Every year on birthday • update risk factor profile • evaluate hazard of dying •  +48.9 years

17. 2002 age sex province health region immigration status education level income quartile body mass index smoking status diabetic status HUI OA status (OS1) CostOA 2001 …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. POHEM example Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ • Every year on birthday • update risk factor profile • evaluate hazard of developing disease • evaluate hazard of dying

18. 2002 … 2001 …….. …….. …….. …….. Death …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. POHEM Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ 2003 …….. …….. • >100,000 records on CCHS representing ~30 million Canadians • 3 hours on a PC- 12 GHz RAM- Cpu=i7 Intel-980

19. 2001 …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. …….. Indirect cost? Starting Population: Canadian Community Health Survey 2001 (CCHS) cross-sectional representation of the Canadian population aged 18+ VALUE 44 male Ontario York non-immigrant post-secondary Q4 (richest) 20,000$-30,000$ 32.2 kg/m2 (obese) smoker non-diabetic 0.96 Yes, 100.32 Employed Sales • OA Prevalence in 2001? • Apply OA prevalence rates (conditional on SROA, HUI, BMI, age, sex) •  yes • Assign OA status • OS1 • Assign direct cost (conditional on sex, age group, OA-status, OA-cycle) • OADirect_cost • Assign survival time to next OA event(s) •  3.7 years to OS2 •  5.1 years to surgery • Every year on birthday • update risk factor profile • evaluate hazard of developing disease • no new diseases in 2001 • evaluate hazard of dying •  8.9 years VARIABLE age sex province health region immigration status education level income quartile Income categories body mass index smoking status diabetic status HUI SROA, Survey sample weight Job Status Job Categories New variables form CCHS

20. Implementing direct cost in POHEM Step 1. Defining new parameter  OADirect-cost • Four dimensions -based on Direct cost final results : • (Age categories, Sex, OA cycle, OA state) • OAcost[sex][OA_age][OA_stage][OA_cycle] • Code: put the parameter in “base(OA).dat” file; Declare it in “OA.mpp” Step 2. Define a function to update the direct Cost variable (the same way as done for other risk factors in the patient(ACTOR) profile) Step 3. Define a Table for output in “TAB.OA.mpp” to output the: (1) Total direct cost of OA ( sex-specific), (2) Average direct cost per OA patient (sex-specific), etc.

21. Outline • Background: Cost of Osteoarthritis(OA) • Direct cost: • Results of the OA direct cost study • Implementing in (POHEM) • Indirect cost: • Components of indirect cost in OA • Issues of Implementing in (POHEM) • Future projects of indirect cost of OA

22. Indirect cost- future projects Objective of Indirect cost projects: • Estimating the Indirect Costs of Osteoarthritis using POHEM • This will include: Early retirement, Absenteeism and presenteeism due to OA. • Although numerous studies estimated cost of OA, this the first study ( to our knowledge) that uses an individual-level simulation model (POHEM-OA) to estimate and project the cost burden of OA.

23. Indirect cost– future projects (Cont’d) • Job status (Employed or not) are needed in all of the indirect cost projects. • Goal is to provide same type of table as in Direct cost for absenteeism and presenteeism. • For Early retirement, we will use an event-based approach (Using Relative Risks of leaving the work force based on sex, age and OA status) suing NPHS and PALS • Absenteeism and presenteeism using MoH data • Informal caregiver cost  Literature

24. Early retirement project Methods • Retrospective cohort • NPHS : The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons will be interviewed every two years. • We Used 7 available cycle of the National Population Health Survey Data (NPHS) from (1994/1995) to (2006/2007). • In 2000, detailed question on Arthritis including: Surgery, type of arthritis, medication, etc. • We used questions on arthritis type (Osteoarthritis, Rheumatoid arthritis and other types), time of physician diagnosis, surgery status for definition of OA(cycle, state) as our main explanatory variable. • Sample both non-OA and OA matching on Age and sex. • Performing a conditional logistic model to estimate the Relative risks

25. Absenteeism • MoH data on 2250 OA patients, • Questions on :Being absent last year or not? How many days? • Two stage model: • Stage1- Estimating the differential probability of absenteeism for OA cases. Explanatory variable : OA stages, cycle, sex, age categories, job type, etc. • Stage2- Estimating number of days for those who had reported of being absent last year due to OA. • Result of same table as in direct cost . One for probability for missing work, one for number of days. • Implemented in POHEM, based on the income calculated daily.

26. MoH Data- Absenteeism model • Outcome: probability fo being absent (Stage 1), Number of days absent (stage 2) • Population: Only OA patients. • Covariates: age categories, sex, time since diagnosis of OA (<5 years, 5-9, 10-19 and >20), HUI and education level • Job categories: 16 groups of job-types (Accounting, construction, management, etc) • Comorbidities: hypertension, hyperlipidemia, anxiety disorders, diabetes, or asthma

27. Discussion • Individual-level cost estimates using microsimulation model are different from Cost of illness studies. Since, the final goal is to implement (unit) cost estimates into microsimulation model (POHEM-OA) • Two types of model for individual-level cost estimation: • State-based: Providing a table of different stages of OA and cost estimates for each cell of the table. This is done in direct cost , absenteeism and presenteeism projects. • Event-based (Such as Early retirement model): we are able to include the individual-level probability of early retirement into the simulation model as an event based on an individual state (age, sex, type of job and other covariates).

28. References • (1) S. Gupta, G. A. Hawker4, A. Laporte, R. Croxford and P. C. Coyte. The economic burden of disabling hip and knee osteoarthritis (OA) from the perspective of individuals living with this condition. Rheumatology 2005; 44:1531–1537. • (2)Systematic review of the long-term effects and economic consequences of treatments for obesity and implications for health improvement, Health Technology Assessment 2004; Vol. 8: No. 21 A Avenell, J Broom, TJ Brown, A Poobalan, L Aucott, SC Stearns, WCS Smith, RT Jung, MK Campbell and AM Grant • (3). NHS Centre for Reviews and Dissemination. A systematic review of the interventions for the prevention and treatment of obesity, and the maintenance of weight loss. NHS CRD Report No. 10. York: University of York; 1997. • (4). CMAJ • April 10, 2007 • 176(8), Synopsis of the 2006 Canadian clinical practice guidelines, on the management and prevention of obesity in adults and children, David C.W. Lau, for the Obesity Canada Clinical Practice Guidelines Steering Committee and Expert Panel

29. Questions ?