Faculty. W4: Identifying Patients with Rare Disorders Using Administrative Data. O Dan Malone, RPh, PhD University of Arizona

Transcription

1 W4: Identifying Patients with Rare Disorders Using Administrative Data Faculty O Dan Malone, RPh, PhD University of Arizona O Danny Yeh, PhD Biogen O Ed Armstrong Strategic Therapeutics, LLC O Eric Bell, MBA One Tall Tree Consulting 1

2 Agenda O Overview of rare disorders O Drug development needs for rare disorders O Case examples for identifying patients O Data visualization approaches to patient identification O Group Discussion Common Terms O Rare disease O Orphan drug O Orphan disease O Rare disorder O Rare condition 2

3 What is a Rare Disorder? O US Definition O No more than 200,000 individuals (Approximately 65/100,000 O Europe O 50 /100,000 O Asia O Korea 5/100,000 O China 76/100,000 O Australia 9/100,000 Expect N for various size health plans for Ivacaftor for Cystic Fibrosis O Cystic Fibrosis Prevalence 0.07 / 100,000 O G551D mutation in US 4.4% O Plan A 500,000: 2 patients O Plan B 2,000,000: 7 patients O Plan C 400,000: 13 patients 3

4 Diagnosis Issues O ICD-9 codes O Sensitivity and specificity are often poor O Lack of diagnostic codes for rare diseases (esp with ICD-9-CM) Rector et al. Health Service Research 2004:39: Medication Issues O Therapies not always disorder specific O J code billing vs. pharmacy claims Rector et al. Health Service Research 2004:39:

5 Importance and Challenges of Evidence Generation in Rare Disease Danny Yeh PhD HEOR, Global Market Access, Biogen Orphan Drugs - Designations and Approvals by FDA Total Designations Approved Source: FDA Orphan Drug Product designation database - Accessed on May 1 st

6 Assessing the Value of Orphan Drugs O Multiple criteria decision analysis (MCDA) An example from NICE s highly specialized technology (HST) interim methods and process guide O Nature of the condition O Impact of the new technology O Cost to the NHS and Personal Social Services O Value for money O Impact of the technology beyond direct health benefits O Impact of the technology on the delivery of the specialized service Challenges of Assessing the Value of Orphan Drugs O Limited information in literature O Uncertainty in epidemiology information O Uncertainty in natural history O Heterogeneous patient population O Limited information from clinical trials O Small sample size O Short duration of follow up O Heterogeneous response 6

7 Spinal Muscular Atrophy (SMA) SMA is classified into subtypes under the International SMA Consortium (ISMAC) system, based on age of onset and maximum function achieved. Type Other Names Age at Onset Life Span Highest Function Achieved SMA I Acute spinal muscular atrophy; Werdnig Hoffman disease 0-6 months < 2 years Never sit independently SMA II Chronic spinal muscular atrophy; Dubowitz disease 7-18 months Varies from 2 years to the third decade of life Sit independently, never stand SMA III Juvenile spinal muscular atrophy; Kugelberg-Welander disease >18 months Normal Stand and walk alone Though classified into different types, the disorder demonstrates a continuous range of severity and overlap. Sources: Mitchell LR et al, Lancet 2008, ; If we want to build a global value dossier (GVD) for SMA today Disease Chapters Challenges Epidemiology - Uncertainty in prevalence - Lack of country-specific data Natural history - Uncertainty in natural history, especially for childhood onset SMA - Lack of data that bridge surrogate endpoints and long-term outcomes Unmet needs - Limited PRO/HRQoL burden information - Lack of care giver burden data - No data to show economic burden 7

8 What will be needed for an economic burden study using claims data? O A data system with sufficient length of observation O A big database to identify tiny rare disease cases that allows meaningful analysis O An reliable algorithm for sample selection Challenges and Solutions to Studying Rare Diseases Edward P Armstrong, Pharm.D. Strategic Therapeutics, LLC and University of Arizona College of Pharmacy 8

9 O O O O Two Rare Diseases Hemophilia O Genetic disorder that can cause bleeding complications O Hemophilia A affects 1 per 5,000 males O O Hemophilia B affects 1 in 30,000 males Differences in severity, factor levels, and clinical bleeding complications Spinal Muscular Atrophy O Genetic disorder that impacts control of muscle movement O Affects approximately 1 in 10,000 babies Difficulty locating a database that provided an adequate sample size for these two diseases O Desire to monitor for 5 years for complications such as bleeding into joints with hemophilia and mechanical ventilation with SMA Small sample size available in a number of databases Diagnostic Coding Concerns O Hemophilia A and B are distinct diseases with different ICD-9 codes O ICD-9 code for hemophilia A (factor VIII deficiency) for hemophilia B (factor IX deficiency) O However, it was common to observe individual patients receiving codes for both diseases over time O Coding for Factor VIII and Factor IX treatments had less overlap 9

10 Spinal Muscular Atrophy (SMA) SMA is classified into subtypes under the International SMA Consortium (ISMAC) system, based on age of onset and maximum function achieved. Type Other Names Age at Onset Life Span Highest Function Achieved SMA I Acute spinal muscular atrophy; Werdnig Hoffman disease 0-6 months < 2 years Never sit independently SMA II Chronic spinal muscular atrophy; Dubowitz disease 7-18 months Varies from 2 years to the third decade of life Sit independently, never stand SMA III Juvenile spinal muscular atrophy; Kugelberg-Welander disease >18 months Normal Stand and walk alone Though classified into different types, the disorder demonstrates a continuous range of severity and overlap. Sources: Mitchell LR et al, Lancet 2008, ; Spinal Muscular Atrophy O Spinal Muscular Atrophy has different ICD-9 codes for disease types related to disease severity and clinical signs and symptoms O ICD-9 code is Type I; is Types II or III O Individual patients commonly received multiple different codes O Unable to distinguish SMA type from data contained in database O Caution with adults having an ICD-9 code, but question if they have the disease? Miscoding? 10

11 Department of Defense Database O Patient data on direct care (within DOD health care facilities) and purchased care (contracted care to outside civilian health care facilities) O Hemophilia O There were 207 persons that met eligibility criteria and for whom medical record data were available to classify the severity of disease. O Median age was 7.0 years, range <1 to 61 years of age O Spinal Muscular Atrophy O 239 cases, age at first SMA diagnosis in database was mean 7.5 ± 6.4 years O 45 cases first SMA diagnosis before 1 year of age in database (0.42 ± 0.27 years) O Caregiver data available Proportion of time periods with adherence 60% in patients with severe hemophilia Hematology 2015;20:

12 Annualized Average Per Patient Healthcare Costs by Category and Time of SMA Diagnosis Poster PND25, ISPOR 2015, Philadelphia, PA. 3 Visualizations Used During SMA Project Eric Julian Bell, MBA One Tall Tree Consulting 12

13 Rare Disease = Small Samples = Be Careful! O Include and exclude patients carefully O Don t want to exclude a patient who has the disease O Don t want to include a patient who doesn t have the disease O Need to be flexible O Inclusion / exclusion criteria O Enrollment period criteria O Study period definition Study-Design versus Reporting O Visualizations for reporting are used to display aggregate results O Visualizations for planning need to show individual patient histories O But possibly also allow for a high-level aggregated view O Understanding data availability O Understanding coding issues O Understanding event sequencing 13

14 Data Reality How much data do we have? Is it coded the way we expected? Enrollment period problems? Do we have data back to birth? Are events sequenced the way we expect? Data Prep Understanding the Data Reality Running Analyses Reporting Results Specific Issues O Spinal Muscular Atrophy O Symptoms start between birth and adolescence O Wanted to focus on before and after first diagnosis O ICD9 codes exist for Type 1 versus Type 2/3 O Based on age of onset O Database O Department of Defense includes data from two health care systems O Military hospitals ( direct care ) O Civilian hospitals ( purchased care ) 14

15 Data Coverage Heat Map O It s a good place to start looking at the data O SMA is a severe disease. We expect to see a lot of claims Patient Timeline: ICD9 Coding O We had anticipated being able to analyze Type I and Type 2/3 patients separately 15

16 Patient Timeline: ICD9 Coding O We had anticipated being able to analyze Type I and Type 2/3 patients separately Patient Timeline: Study Period O Gaps in enrollment periods would normally exclude patients O Need to understand event sequencing O Birth date O Date of death O Enrollment periods O Annual expenditures 16

17 Birth date Death date Date of first observed SMA ---- Enrollment periods 17

18 Workshop O What issues / concerns have you encountered with administrative data and rare conditions? O Other solutions to case finding? O Natural language processing of EHR data? O Registry to administrative data? O National/international patient advocacy groups to administrative data? 18