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Estimating Drug Use Prevalence Using Latent Class Models with Item Count Response as One Indicator

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Title: Estimating Drug Use Prevalence Using Latent Class Models with Item Count Response as One Indicator


1
Estimating Drug Use Prevalence Using Latent Class
Models with Item Count Response as One Indicator
  • Paul Biemer
  • RTI International and
  • University of North Carolina

2
Presentation Outline
  • Describe the item count (IC) method
  • Present standard IC estimates of cocaine use and
    compare them with direct estimates
  • Describe a method for adjusting the standard
    estimates for measurement bias
  • Present the bias corrected estimates
  • Implications for future applications of IC

3
What is the item count method?
  • Used for estimating the prevalence of sensitive
    behaviors
  • Sensitive behavior is one of a small number of
    behaviors in a list
  • Respondents indicate only how many behaviors in
    the list apply, not which ones
  • If the average number of other behaviors is
    known, prevalence of the sensitive behavior can
    be estimated

4
Illustration One Pair of Lists
ICQ (short list)
ICQ (long list)
Long list short list sensitive item
5
Illustration One Pair of Lists
ICQ (short list)
ICQ (long list)
Long list short list sensitive item
6
Prevalence Estimate for Single Pair Design
Prevalence avg count for long list avg count
for short list
7
Example of Youth ICQ ICQ1 Short
  • Next is a list of things that you may or may not
    have done in the past 12 months. How many of the
    things on this list did you do in the past 12
    months, that is since DATE 12 MONTHS AGO.
  • Rode with a drunk driver
  • Walked alone after dark through a dangerous
    neighborhood
  • Rode a bicycle without a helmet
  • Went swimming or played outdoor sports when it
    was lightning

8
Example of Youth ICQ ICQ1 Long
  • Next is a list of things that you may or may not
    have done in the past 12 months. How many of the
    things on this list did you do in the past 12
    months, that is since DATE 12 MONTHS AGO.
  • Rode with a drunk driver
  • Walked alone after dark through a dangerous
    neighborhood
  • Rode a bicycle without a helmet
  • Went swimming or played outdoor sports when it
    was lightning
  • Used cocaine, in any form, one or more times

9
Results Using the Standard IC Estimator
10
Item Count Estimates by Age and Gender
11
Pseudo IC Variable
  • Recall each of the 4 IC short-list item was asked
    separately
  • Form a pseudo- IC variable corresponding to the
    IC short-list response where
  • Pseudo-IC number of positive responses to the
  • 4 IC short-list questions asked separately

12
Item Count Response by Pseudo-Item Count Response
for Both Short IC Questions
13
Objective of the Modeling Approach
  • Combine all data on cocaine use including
  • Direct question
  • Item count pair of questions
  • Pseudo-item count data
  • Apply latent class models to predict cocaine use
  • Why latent class models?
  • Accounts for measurement error in all the
    observations
  • Model assumptions are plausible for the current
    application

14
Central Idea for the Modeling Approach
Let A short form response D long form
response A is an indicator of X (latent
variable) D is an indicator of Z (latent
variable)
Standard IC estimator is
Use LCA to estimate Z and X and form
Repeat this for each of the two IC pairs
15
Path Model for One IC Pair of Questions
Grouping variable
Short IC Question
Pseudo Short IC Question
Cocaine
Long IC Question
16
Path Model for One IC Pair of Questions
Grouping variable
Short IC Question
Pseudo Short IC Question
Cocaine
Long IC Question
17
Path Model for One IC Pair of Questions
Grouping variable
Short IC Question
Pseudo Short IC Question
Cocaine
Long IC Question
18
Data Likelihood
Random split half-sample
MAR
Subsample I
Subsample II
19
where
denotes summation over x, y and z xy.
20
Estimation of Cocaine Use Prevalence
Parameters
Estimators
from LCA
Cocaine prevalence
21
Corrected Estimator of Cocaine Prevalence
Corrected cocaine use prevalence
NSDUH Estimate
Correction estimated from LCM
22
Results Using the LCM-based Estimator
23
Estimates of Classification Accuracy from LCM
24
NSDUH and Model-based IC Estimates of Past Year
Cocaine Use Prevalence by Gender and Age
25
Summary
  • Despite careful design and large sample size, the
    standard item count method failed
  • Estimates of cocaine use prevalence were less
    than direct estimates from NSDUH
  • Major cause appeared to be measurement error
  • Difficult response task
  • IC masking may be ineffective for eliciting
    truthful counts
  • IC direct questions may be interpreted
    differently
  • Latent class model corrections were successful at
    reducing downward bias
  • NSDUH estimates were increased by 40 on average
  • Standard errors were much larger

26
Further reading -
  • Biemer, P. and Brown, G. (2005). Model-based
    Estimation of Drug Use Prevalence with Item Count
    Data, Journal of Official Statistics, Vol. 21,
    No. 3.
  • Biemer, P., B.K. Jordan, M. Hubbard, and D.
    Wright (2005). A Test of the Item Count
    Methodology for Estimating Cocaine Use
    Prevalence. In Kennet, J., and J. Gfroerer
    (Eds.), Evaluating and Improving Methods Used in
    the National Survey on Drug Use and Health.
    Rockville, MD SAMHSA
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