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Predicting the Growth in Dialysis Services in Ontario, Canada 2007-2011

Predicting the Growth in Dialysis Services in Ontario, Canada 2007-2011. Rob Quinn MD FRCPC Clinical Associate Sunnybrook Health Sciences Centre CIHR Institute for Health Services & Policy Research (IHSPR) Fellow & PhD Candidate

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Predicting the Growth in Dialysis Services in Ontario, Canada 2007-2011

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  1. Predicting the Growth in Dialysis Services in Ontario, Canada2007-2011 Rob Quinn MD FRCPC Clinical Associate Sunnybrook Health Sciences Centre CIHR Institute for Health Services & Policy Research (IHSPR) Fellow & PhD Candidate Department of Health Policy, Management & Evaluation, University of Toronto December 13, 2007 CONFIDENTIAL - NOT FOR DISTRIBUTION

  2. Outline of Presentation • Background (Renal 101) • Data Sources & Terminology • Brief Overview of Statistical Methods • “The First 90 Days” • Provincial Results • Making Sense of Data at the LHIN Level • Interpretive Cautions • Conclusions

  3. How People with Kidney Failure Present • Slowly progressive chronic kidney disease • Often followed in predialysis clinics • Usually start as outpatients • Acute kidney injury requiring dialysis • Develop kidney failure after acute illness • Hospitalized; high resource utilization; high mortality rate; high rate of recovery • Acute kidney injury in setting of chronic kidney disease • Precipitous decline in kidney function due to acute illness • Usually hospitalized; intermediate mortality & likelihood of recovery

  4. Treatment Considerations • Transplantation • Preferred therapy (cheapest; best outcomes) • Limited supply of organs • Hemodialysis (HD) • Highest annual operating costs • Performed in outpatient units (capital investment) • PeritonealDialysis (PD) • Lower annual operating costs vs. HD • Capital costs minimal • Home therapy

  5. Treatment of Kidney Failure in Canada • 31,000 patients required renal replacement therapy (RRT) in Canada by end of 2004* • 49% treated with Hemodialysis (HD) • 39% alive with a Functioning Transplant • 12% treated with Peritoneal Dialysis (PD) *Canadian Institute for Health Information, CORR Annual Report, 2006

  6. The Cost of Caring for Patients • Caring for patients with kidney failure is resource intensive* • 0.7% of Medicare population • Consume 5% of annual Medicare budget • Population of patients with kidney failure continues to grow • Projections of the need for dialysis services required to plan for need for equipment, facilities, personnel *United States Renal Data System, Annual Report, 2006

  7. Limitations of Existing Literature • Only included “chronic dialysis patients” • Based on registry data and do not capture all patients (“90-day rule”) • Often ignore patients with a history of transient dialysis treatment or prior transplant • Ignores the impact of patients with acute kidney injury requiring dialysis

  8. Objectives • Identify all patients who received dialysis treatment in the province between July 1, 1998 and December 31, 2005 • Describe the disposition of this cohort 90 days following the initiation of therapy • Use time series analysis to model historical data and make projections about the need for dialysis services in the province • To determine the proportion of dialysis activity that was attributable to hospitalized patients with acute renal failure to quantify the potential impact of this group on resource utilization

  9. Data Sources • Registered Persons Database (RPDB) • Ontario Health Insurance Plan (OHIP) physician billing claims • Ontario Diabetes Database (ODD) • Canadian Institute for Health Information (CIHI) – Discharge Abstracts Database (DAD)

  10. Data Sources • Administrative Health Data • Collected for purposes other than research • Already exists; no further expense to collect • Allows access to information for entire province; can highlight regional differences • Often use surrogate measures for variables of interest • Validation required • Time delays

  11. Data Sources • LHIN 10 (South East) • Alternative funding arrangement • Primary clinical data provided on prevalent outpatient HD and PD patients • Billing data looks reliable by July/Sept 2005 • Assumed rate of dialysis in Kingston had a constant relationship with the provincial rate • Generated expected values of variables for time period of interest in order to make forecasts

  12. Terminology “Incident Dialysis Patients” All NEW dialysis patients during the time period of interest

  13. Terminology “Prevalent Patients” ALL PATIENTS that you are providing dialysis therapy to at a given point in time

  14. Terminology “Prevalent Outpatients” • Prevalent Outpatients – HD • Prevalent Outpatients – PD Patients that are the responsibility of the outpatient dialysis units You need a “spot” for them

  15. Statistical Analysis • All patients who received at least 1 dialysis treatment followed for 90 days • We then described: • Distribution of initial treatment modalities • Proportion of patients starting in hospital • Proportion of patients requiring outpatient treatment • Disposition 90 days after starting dialysis

  16. Statistical Analysis • The number of patients requiring treatment was determined at regularly spaced, 3-month intervals • Total number of incident dialysis patients • Total number of prevalent patients • Total number of prevalent outpatients • Total number of prevalent outpatient HD • Total number of prevalent outpatient PD

  17. Statistical Analysis • Time series techniques were used to model the historical incidence and prevalence counts • 4 different Time Series models constructed for each variable in SAS and examined for “fit” • Autoregressive Integrated Moving Average (ARIMA) • Stepwise Autoregressive • Exponential Smoothing • Winter’s Method (seasonality) • Models then used to forecast to 2011

  18. Validity of Results • No gold standard to compare against • Compared projections from previous report to observed data • External validation • Compared data to most recent CORR report

  19. RESULTS “The First 90 Days”

  20. Initial Form of Dialysis Treatment Hemodialysis 73% Peritoneal Dialysis 12% Continuous Dialysis (CRRT) 15% Based on data from 31,679 patients

  21. The First 90 Days of Dialysis • 62% of all new patients start dialysis in hospital • 27% die prior to discharge • 63% of all new patients will go on to require treatment in an outpatient dialysis facility

  22. Status at 90 Days Recovered 25% Dead 23% Alive on Dialysis 52%

  23. Impact of Acute Kidney Injury • Makes up ~60% of new patients • Capture 48% of new patients if wait until 90 days to start counting people • Only accounts for 3% of the prevalent population at any point in time • Disproportionate resource utilization • Divert resources from chronic population

  24. RESULTS Provincial Data: Incidence, Prevalence, and Modality Distribution

  25. The number of patients being treated with dialysis at any point in time is very predictable and is growing in a linear fashion • Projected to grow to 11,104 patients by 2011 • Confidence intervals (10,931-11,277) or +/- 1.6%

  26. Date Forecast Lower Limit Upper Limit 01/10/2005 8516 8405 8628 01/01/2006 8666 8544 8789 01/04/2006 8794 8668 8919 01/07/2006 8902 8774 9030 01/10/2006 9024 8895 9153 01/01/2007 9177 9046 9308 01/04/2007 9319 9187 9451 01/07/2007 9436 9302 9570 01/10/2007 9535 9399 9670 01/01/2008 9652 9510 9793 01/04/2008 9762 9615 9909 01/07/2008 9877 9726 10027 01/10/2008 10000 9848 10153 01/01/2009 10124 9969 10278 01/04/2009 10236 10079 10392 01/07/2009 10346 10188 10505 01/10/2009 10465 10305 10625 01/01/2010 10594 10432 10756 01/04/2010 10721 10557 10886 01/07/2010 10851 10683 11018 01/10/2010 10979 10809 11149 01/01/2011 11104 10931 11277 Projections of prevalent dialysis patients provided by quarter

  27. The number of prevalent outpatients being treated with dialysis at any point in time is also very predictable and is growing in a linear fashion • Projected to grow to 10,796 patients by 2011 • Confidence intervals (10,655 – 10,938) or +/- 1.3%

  28. Majority of prevalent patients are on HD so curve resembles that of “all prevalent patients” and “prevalent outpatients” • Projected to grow to 9,157 patients by 2011 (85% prevalent outpatients) • Confidence intervals (8,999 – 9,316) or +/- 1.7%

  29. PD growth not as predictable, but still allows confident forecasts • Projected to grow to 1,629 patients by 2011 (15% of prevalent outpatients) • Confidence intervals (1,532 – 1,726) or +/- 5.6%

  30. Incidence counts are more variable, but demonstrate clear trend • Projected to grow to 1,605 patients per quarter by 2011 • Confidence intervals (1,415 – 1,795) or +/- 11.8%

  31. Proportion of Prevalent Outpatients Treated with PD vs. HD by Year

  32. Provincial Data – Summary • Historical, average annual growth rates • Incidence 4.9% • Prevalence 7.2% • 4,000 new patients will require outpatient dialysis each year by 2010 • Nearly 11,000 prevalent outpatients will require treatment by 2011 (85% HD) • Able to make confident forecasts at a provincial level for all variables

  33. Accuracy of Projections • Forecast of total prevalent dialysis patients in the province on Jan 1, 2005: • Forecasted value (Previous Report): 8,100 • Actual value: 8,063 • Difference = 37 patients (0.5% absolute error) • Short-term projections very accurate

  34. Consistency of Projections • Change in forecast of total prevalent dialysis patients in province Jan 1, 2010: • Forecasted value (Last report): 10,605 • Forecasted value (2007 report): 10,594 • Difference = 11 patients (0.1%) • Additional year of data has not changed forecasts out to 2010 to any significant degree

  35. LHIN Forecasts From a planning perspective, • Need to know total number of prevalent patients to understand resources required to treat them (total prevalent patients) “At any given time, how many dialysis patients am I treating?”

  36. LHIN Forecasts From a planning perspective, • Need to know total number of prevalent patients who are “property” of the outpatient units (total prevalent outpatients) “How many patients are my outpatient units responsible for, and how many spots do I need to have?”

  37. LHIN Forecasts From a planning perspective, • Need to know modality mix (PD vs. HD) in prevalent outpatients in order to plan new HD units (totalprevalent outpatient HD, PD) “How many outpatient HD spots do I need?”

  38. LHIN Data – Summary Confident in Predictions • Total prevalent patients, prevalent outpatients • Total prevalent outpatient HD Less Confident in Predictions • “SMALL NUMBERS IN SMALL LHINS” • Total prevalent PD patients • Incident patients

  39. Interpretive Cautions • Identify individuals based on where they live, not necessarily where they seek dialysis treatment • A number of patients treated at a given program may live outside current LHIN boundaries • LHIN-specific numbers may not reflect practice of particular regional program

  40. Interpretive Cautions • HD patients include: • Short, Daily HD • Nocturnal HD • Home HD • Prevalence of home-based HD therapies varies from region-to-region

  41. Other Considerations • Impact of a regional dialysis program on resource consumption beyond the provision of dialysis services • Personnel • Hospitalization • Invasive procedures (access-related complications) • Interventional radiology & surgical support • Provision of home care services • Rehabilitation, long-term care, palliative services • Support for other services • Oncology • Cardiac Surgery • Critical Care

  42. Conclusions • Growth in dialysis patients is considerable, but fairly predictable at a provincial level • All incident patients (including acute renal failure) should be accounted for in the analysis • Feasible to develop a plan for expansion that is long-term, transparent, & consistent

  43. Conclusions • Must consider need for personnel & other services to support regional programs • Need to confirm accuracy of administrative data how it can be used most effectively in planning for provision of renal services • Influence of alternative funding arrangements and the potential need for alternative sources of information

  44. Acknowledgements • Rahim Moineddin (Statistician) • Laurence Chong & Cindy Huo (Analysts) • Michael Paterson • Jan Hux • Matthew Oliver • Ginette Daigle • Kingston Regional Dialysis Program

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