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Enhancing the effectiveness of health carefor Ontarians through research

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 AssociateSunnybrook Health Sciences Centre

CIHR Institute for Health Services & Policy Research (IHSPR) Fellow & PhD CandidateDepartment of Health Policy, Management & Evaluation, University of Toronto

December 13, 2007

Rob Quinn MD FRCPC

Clinical AssociateSunnybrook Health Sciences Centre

CIHR Institute for Health Services & Policy Research (IHSPR) Fellow & PhD CandidateDepartment of Health Policy, Management & Evaluation, University of Toronto

December 13, 2007


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Outline of PresentationOutline 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

• 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


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How People with Kidney Failure PresentHow People with Kidney Failure Present

1. Slowly progressive chronic kidney disease Often followed in predialysis clinics Usually start as outpatients

2. Acute kidney injury requiring dialysis Develop kidney failure after acute illness Hospitalized; high resource utilization; high mortality rate; high

rate of recovery

3. 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

1. Slowly progressive chronic kidney disease Often followed in predialysis clinics Usually start as outpatients

2. Acute kidney injury requiring dialysis Develop kidney failure after acute illness Hospitalized; high resource utilization; high mortality rate; high

rate of recovery

3. 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


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Treatment ConsiderationsTreatment Considerations

• Transplantation Preferred therapy (cheapest; best outcomes) Limited supply of organs

• Hemodialysis (HD) Highest annual operating costs Performed in outpatient units (capital investment)

• Peritoneal Dialysis (PD) Lower annual operating costs vs. HD Capital costs minimal Home therapy

• Transplantation Preferred therapy (cheapest; best outcomes) Limited supply of organs

• Hemodialysis (HD) Highest annual operating costs Performed in outpatient units (capital investment)

• Peritoneal Dialysis (PD) Lower annual operating costs vs. HD Capital costs minimal Home therapy


*Canadian Institute for Health Information, CORR Annual Report, 2006 5

Treatment of Kidney Failure in CanadaTreatment 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)

• 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)


*United States Renal Data System, Annual Report, 2006 6

The Cost of Caring for PatientsThe 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

• 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


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Limitations of Existing LiteratureLimitations 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

• 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


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ObjectivesObjectives1. Identify all patients who received dialysis treatment in the

province between July 1, 1998 and December 31, 2005

2. Describe the disposition of this cohort 90 days following the initiation of therapy

3. Use time series analysis to model historical data and make projections about the need for dialysis services in the province

4. 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

1. Identify all patients who received dialysis treatment in the province between July 1, 1998 and December 31, 2005

2. Describe the disposition of this cohort 90 days following the initiation of therapy

3. Use time series analysis to model historical data and make projections about the need for dialysis services in the province

4. 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


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Data SourcesData 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)

• 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)


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• 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

• 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


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• 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

• 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


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TerminologyTerminology

“Incident Dialysis Patients”

All NEW dialysis patients during the time period of interest

“Incident Dialysis Patients”

All NEW dialysis patients during the time period of interest


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“Prevalent Patients”

ALL PATIENTS that you are providing dialysis therapy to at a given point in time

“Prevalent Patients”

ALL PATIENTS that you are providing dialysis therapy to at a given point in time


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“Prevalent Outpatients” Prevalent Outpatients – HD Prevalent Outpatients – PD

Patients that are the responsibility of the outpatient dialysis units

You need a “spot” for them

“Prevalent Outpatients” Prevalent Outpatients – HD Prevalent Outpatients – PD

Patients that are the responsibility of the outpatient dialysis units

You need a “spot” for them


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Statistical AnalysisStatistical 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

• 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


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• 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

• 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


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• 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

• 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


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Validity of ResultsValidity 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

• No gold standard to compare against

• Compared projections from previous report to observed data

• External validation Compared data to most recent CORR report

19Enhancing the effectiveness of health carefor Ontarians through research

RESULTSRESULTS

“The First 90 Days”“The First 90 Days”


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Initial Form of Dialysis Treatment

Based on data from 31,679 patients

Hemodialysis

73%Peritoneal Dialysis

12%

Continuous Dialysis (CRRT)

15%


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The First 90 Days of DialysisThe 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

• 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


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Status at 90 Days

Dead

23%

Recovered

25%

Alive on Dialysis

52%


Impact of Acute Kidney InjuryImpact 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

• 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

24Enhancing the effectiveness of health carefor Ontarians through research

RESULTSRESULTS

Provincial Data: Incidence, Prevalence, and Modality

Distribution

Provincial Data: Incidence, Prevalence, and Modality

Distribution


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•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%


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Projections of prevalent dialysis patients provided by quarter

Date Forecast Lower Limit Upper Limit01/10/2005 8516 8405 862801/01/2006 8666 8544 878901/04/2006 8794 8668 891901/07/2006 8902 8774 903001/10/2006 9024 8895 915301/01/2007 9177 9046 930801/04/2007 9319 9187 945101/07/2007 9436 9302 957001/10/2007 9535 9399 967001/01/2008 9652 9510 979301/04/2008 9762 9615 990901/07/2008 9877 9726 1002701/10/2008 10000 9848 1015301/01/2009 10124 9969 1027801/04/2009 10236 10079 1039201/07/2009 10346 10188 1050501/10/2009 10465 10305 1062501/01/2010 10594 10432 1075601/04/2010 10721 10557 1088601/07/2010 10851 10683 1101801/10/2010 10979 10809 1114901/01/2011 11104 10931 11277


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•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%


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•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)



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•PD growth not as predictable, but still allows confident forecasts

•Projected to grow to 1,629 patients by 2011 (15% of prevalent outpatients)



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•Incidence counts are more variable, but demonstrate clear trend

•Projected to grow to 1,605 patients per quarter by 2011



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79% 79% 80% 82% 83% 83% 82%

21% 21% 20% 18% 17% 17% 18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1999 2000 2001 2002 2003 2004 2005

Year

%

HD PD

79% 79% 80% 82% 83% 83% 82%

21% 21% 20% 18% 17% 17% 18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1999 2000 2001 2002 2003 2004 2005

Year

%

HD PD

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


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Provincial Data – SummaryProvincial 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

• 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


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Accuracy of ProjectionsAccuracy 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

• 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


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Consistency of ProjectionsConsistency 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

• 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


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LHIN ForecastsLHIN 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?”


• 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?”


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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?”

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?”


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• Need to know modality mix (PD vs. HD) in prevalent outpatients in order to plan new HD units (total prevalent outpatient HD, PD)

“How many outpatient HD spots do I need?”


• Need to know modality mix (PD vs. HD) in prevalent outpatients in order to plan new HD units (total prevalent outpatient HD, PD)

“How many outpatient HD spots do I need?”


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LHIN Data – SummaryLHIN 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

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


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Interpretive CautionsInterpretive 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

• 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


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Interpretive CautionsInterpretive Cautions

• HD patients include: Short, Daily HD Nocturnal HD Home HD

• Prevalence of home-based HD therapies varies from region-to-region

• HD patients include: Short, Daily HD Nocturnal HD Home HD

• Prevalence of home-based HD therapies varies from region-to-region


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Other ConsiderationsOther 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

• 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


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ConclusionsConclusions

• 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

• 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


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ConclusionsConclusions

• 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

• 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


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AcknowledgementsAcknowledgements

• Rahim Moineddin (Statistician)

• Laurence Chong & Cindy Huo (Analysts)

• Michael Paterson

• Jan Hux

• Matthew Oliver

• Ginette Daigle

• Kingston Regional Dialysis Program

• Rahim Moineddin (Statistician)

• Laurence Chong & Cindy Huo (Analysts)

• Michael Paterson

• Jan Hux

• Matthew Oliver

• Ginette Daigle

• Kingston Regional Dialysis Program

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