Using Freshest Feasible Data for Medical Product Safety Surveillance - - PowerPoint PPT Presentation

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Using Freshest Feasible Data for Medical Product Safety Surveillance - - PowerPoint PPT Presentation

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Using Freshest Feasible Data for Medical Product Safety Surveillance in Mini- Sentinel: Potential and Challenges W. Katherine Yih, PhD, MPH Harvard Pilgrim Health Care Institute and Harvard Medical School January 31, 2013

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info@mini-sentinel.org 1

Using Freshest Feasible Data for Medical Product Safety Surveillance in Mini- Sentinel: Potential and Challenges

  • W. Katherine Yih, PhD, MPH

Harvard Pilgrim Health Care Institute and Harvard Medical School January 31, 2013

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Inpatient claims data lag, 3 data partners

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 >=52

Week after service date Proportion of data available Data ≥ 90% complete by 6 mo. after care date

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info@mini-sentinel.org 3

Mini-Sentinel data are relatively complete

 Data updated on quarterly basis  Typical example of timing:

In latest batch of data for M-S: Data available First care date Last care date

_↓_______↓____________________↓_____

Oct.

Dec. July

 The most recent data typically 6-9 months old

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Advantage of mature (less fresh) data

 PRO: data more complete and settled

In latest batch of data for M-S: Data available First care date Last care date

_↓_______↓____________________↓_____

Oct.

Dec. July

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Pros and cons of mature (less fresh) data

 PRO: data more complete and settled

In latest batch of data for M-S: Data available First care date Last care date

_↓_______↓____________________↓_____

Oct.

Dec. July

 CON: signal detection delayed

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Pros and cons of mature (less fresh) data

 PRO: data more complete and settled

In latest batch of data for M-S: Data available First care date Last care date

_↓_______↓____________________↓_____

Oct.

Dec. July

 CON: signal detection delayed

Especially problematic for influenza vaccine safety monitoring

Typical influenza vaccination timing

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Challenges of influenza vaccine safety monitoring

Influenza vaccination period relatively short, so data must be available soon after exposure to find safety problems in time to make a difference ________________________________________

Oct.

Dec. July Typical influenza vaccination timing

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Challenges of influenza vaccine safety monitoring

Influenza vaccination period relatively short, so data must be available soon after exposure to find safety problems in time to make a difference ________________________________________

Oct.

Dec. July Typical influenza vaccination timing

  • 1. Need fresher and frequently updated data
  • 2. Need to adjust for incomplete data
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info@mini-sentinel.org 9

  • 1. Getting fresher and frequent data

Freshest feasible data source is refreshed monthly

  • Available toward end of following calendar month (data

through Sept. available late Oct., etc.)

  • More timely than M-S Distributed Dataset

_______________________________________

Oct.

Dec. July

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  • 1. Getting fresher and frequent data

Freshest feasible data source is refreshed monthly

  • Available toward end of following calendar month (data

through Sept. available late Oct., etc.)

  • More timely than M-S Distributed Dataset

_______________________________________

Oct.

Dec. July

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info@mini-sentinel.org 11

  • 1. Getting fresher and frequent data

Freshest feasible data source is refreshed monthly

  • Available toward end of following calendar month (data

through Sept. available late Oct., etc.)

  • More timely than M-S Distributed Dataset

_______________________________________

Oct.

Dec. July

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  • 1. Getting fresher and frequent data

Freshest feasible data source is refreshed monthly

  • Available toward end of following calendar month (data

through Sept. available late Oct., etc.)

  • More timely than M-S Distributed Dataset

_______________________________________

Oct.

Dec. July

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Files to be created for influenza vaccine safety monitoring

SDFs

  • Sequential Data Files (SDFs)
  • Patient-level data, kept by data partners
  • Population = persons with medical claim on or after 9/1/2012

SCFs

  • Sequential Case Files (SCFs)
  • Patient-level data, kept by data partners
  • Population = persons per current SDFs with health outcome of

interest following influenza vaccination

SAFs

  • Sequential Analysis Files (SAFs)
  • Aggregate data, sent to M-S Operations Center for analysis
  • Vaccination population: vaccination per current SDFs
  • Cases population: cases per all SCF versions
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Expected timing of data refreshes and analyses

Week 1 2 3 4 5 6 7 8 9 DP1 SDF SAF SDF SAF SDF DP2 SDF SAF...  SDF SAF...  DP3 SDF SAF SDF SAF Analysis yes yes yes yes

  • Monthly but unsynchronized data refreshes by data

partners

  • Biweekly analyses by Operations Center (in weeks in red)
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  • 2. Adjusting for incomplete data

Two kinds of “incompleteness”

  • A. Lag in data availability →

B.

Post-vaccination follow-up interval not fully elapsed To avoid bias, both must be taken into account.

0% 20% 40% 60% 80% 100% 4 8 12 16 20 24 28 32 36 40 44 48 >=52

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200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

  • Nov. 18
  • Nov. 25
  • Dec. 2
  • Dec. 9
  • Dec. 16
  • Dec. 23
  • Dec. 30
  • Jan. 6
  • Jan. 13
  • Jan. 20
  • Jan. 27
  • Feb. 3
  • Feb. 10
  • Feb. 17
  • Feb. 24
  • Mar. 3
  • Mar. 10
  • Mar. 17
  • Mar. 24
  • Mar. 31
  • Apr. 14

Cumulative inactivated H1N1 vaccine doses

Week of Analysis Cumulative vaccine doses

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1 2 3 4 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

  • Nov. 18
  • Nov. 25
  • Dec. 2
  • Dec. 9
  • Dec. 16
  • Dec. 23
  • Dec. 30
  • Jan. 6
  • Jan. 13
  • Jan. 20
  • Jan. 27
  • Feb. 3
  • Feb. 10
  • Feb. 17
  • Feb. 24
  • Mar. 3
  • Mar. 10
  • Mar. 17
  • Mar. 24
  • Mar. 31
  • Apr. 14

Log-likelihood ratio Cumulative inactivated H1N1 vaccine doses

Week of Analysis Critical Value of Log-Likelihood Ratio Cumulative vaccine doses No adjustment

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1 2 3 4 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

  • Nov. 18
  • Nov. 25
  • Dec. 2
  • Dec. 9
  • Dec. 16
  • Dec. 23
  • Dec. 30
  • Jan. 6
  • Jan. 13
  • Jan. 20
  • Jan. 27
  • Feb. 3
  • Feb. 10
  • Feb. 17
  • Feb. 24
  • Mar. 3
  • Mar. 10
  • Mar. 17
  • Mar. 24
  • Mar. 31
  • Apr. 14

Log-likelihood ratio Cumulative inactivated H1N1 vaccine doses

Week of Analysis Critical Value of Log-Likelihood Ratio Cumulative vaccine doses Data lag adjustment only

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1 2 3 4 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

  • Nov. 18
  • Nov. 25
  • Dec. 2
  • Dec. 9
  • Dec. 16
  • Dec. 23
  • Dec. 30
  • Jan. 6
  • Jan. 13
  • Jan. 20
  • Jan. 27
  • Feb. 3
  • Feb. 10
  • Feb. 17
  • Feb. 24
  • Mar. 3
  • Mar. 10
  • Mar. 17
  • Mar. 24
  • Mar. 31
  • Apr. 14

Log-likelihood ratio Cumulative inactivated H1N1 vaccine doses

Week of Analysis Critical Value of Log-Likelihood Ratio Cumulative vaccine doses Partial interval adjustment only

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1 2 3 4 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

  • Nov. 18
  • Nov. 25
  • Dec. 2
  • Dec. 9
  • Dec. 16
  • Dec. 23
  • Dec. 30
  • Jan. 6
  • Jan. 13
  • Jan. 20
  • Jan. 27
  • Feb. 3
  • Feb. 10
  • Feb. 17
  • Feb. 24
  • Mar. 3
  • Mar. 10
  • Mar. 17
  • Mar. 24
  • Mar. 31
  • Apr. 14

Log-likelihood ratio Cumulative inactivated H1N1 vaccine doses

Week of Analysis Critical Value of Log-Likelihood Ratio Cumulative vaccine doses Partial interval and data lag adjustments

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Conclusion

 PROS of using fresher data

  • Gain in timeliness ~5-8 mo.
  • Necessary for influenza vaccine safety monitoring

 CONS of using fresher data

  • Some loss of accuracy despite adjustments for data

incompleteness and flux

  • Takes extra effort to produce these data—more frequent

refreshes, different source files, special file structures

  • Each product needs a separate extract

 We can use fresher data, but probably not

worthwhile to do so on routine basis

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What constitutes a comprehensive safety surveillance system?

  • Semi-automated routine

surveillance, applying general tools with minor adaptations to address the specific product But also…

  • Ability to bring specialized

expertise to bear on specific issue(s) that may arise in product lifecycle