Deriving practice-level estimates from physician-level surveys

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Deriving practice-level estimates from
physician-level surveys
Catharine W. Burt , EdD and Esther Hing,
MPH. Chief, Ambulatory Care Statistics
Branch Session 32
June 20, 2007 ICES III, Montreal, Canada
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Topics

• Introduction
• Multiplicity theory
• Re-weighting methods
• Application to NAMCS
• Assumptions
• Analytical example
• Limitations

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Multiplicity theory

• Multiplicity occurs when the same observation
  unit can be counted multiple times among the
  selection units
• eg., same patient is counted in multiple records
  of visits/discharges or same medical practice is
  counted in records of multiple physicians
• Using principles of network sampling, you can
  adjust weights to estimate the observations of
  interest rather than the selection units

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Desired observation units
V V V V
v v
V
Survey selection units
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Greek for the Geeks
the selection probability of physician i (i
1, , N) and
if physician i is affiliated with practice j, and
if physician i is not affiliated with practice j.
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Weight adjustment to estimate X?? ??

• Observation weight
• selection weight /
  M
• where M is the multiplicity information for
  the selection unit

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Re-weighting methodology

• Assumptions and definitions
• Use multiplicity information from the physician
  data to adjust physician-level estimates into
  practice-level estimates
• Dividing the physician sampling weight by number
  of physicians in the practice provides a measure
  of practices

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Physicians ? practices example

• Samples of physician records in medical
  practices
• Physician data have the same practice included in
  multiple observations.
• If we knew how many physicians were in the same
  practice as the sampled physicians, then we can
  adjust the estimator to account for the
  multiplicity.

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Application to NAMCS

• National Ambulatory Medical Care Survey
• Annual survey of 3,000 nationally representative
  office-based physicians in patient care
• Excludes radiologists, anesthesiologists, and
  pathologists and federally-employed physicians
• Face-to-face induction interview asks physicians
  questions about his/her office practice
• Records are weighted by the inverse of the
  probability of selection, adjusted for
  nonresponse (60 RR), with a calibration ratio
  to annual totals

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Induction interview content

• Number of locations
• Number of other physicians
• Ownership
• Type of office
• Private, clinic, HMO, faculty practice plan, etc
• EMR adoption
• Revenue sources

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Assumptions

• Used the first location reported
• Assumes practice information provided by sample
  physician is a constant for the practice
• Does not account for multiplicity of practices
  within a physician
• i.e., Ignores the fact that some physicians are
  affiliated with multiple practices (about 1 of
  physicians)

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3 medical practices with a total of 7 physicians
V V V V
v v
V
Solo practice
Partner practice
Group practice
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Probability of selecting a practice
V V V V
v v
V
2/7
1/7
4/7
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Multiplicity factor
V V V V
V
v v
1/7
4/7
2/7
1
.5
.25
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Multiplicity information

• How many other physicians practice with you at
  this location?
• M 1 of other physicians
• Practice weight physician weight / M

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Re-weighting example
Practice size Physician weight Multiplicity adjustment Practice weight
solo 10 1 10
partner 20 .5 10
3 40 .333 13.3
4 40 .25 10
Sum 110 physicians 43 practices
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• Practice weight
• physician weight / practice size
• physician weight ? 311,200 physicians
• 8,000
• practice weight ? 161,200 practices
• 5,300

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Percent distribution of office-based medical
physicians and practices by size
Physicians
Practices
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Computerized administrative and clinical support
systems
80
74.2
Practices
Physicians
69.2
70
60
50
40
30
19.0
15.0
20
9.2
6.5
10
0
Uses electronic billing
Uses EMR
Uses CPOE
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Limitations of NAMCS data

• Good
• National estimates of practices
• Characteristics that are common among physicians

• Bad
• Characterizing practices
• Underestimates larger practices
• Be careful how you define size
• First-listed location
• Location with most visits
• Location of the visit