Informing America's Policy on Illegal Drugs What We Don't Know Keeps Hurting Us (2001) / Chapter Skim
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Appendix D: How Do Response Problems Affect Survey Measurement of Trends in Drug Use?
Appendix D: How Do Response Problems Affect Survey Measurement of Trends in Drug Use?
Pages 321-348
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From page 321... ...
Monitoring the Future (MTF) surveys high school students, and the National Household Survey of Drug Abuse (NHSDA)
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. than to determine the absolute level." To illustrate the inferential problems that arise from nonresponse and inaccurate response, consider using the MTF and the NHSDA surveys to draw inferences on the annual prevalence of use rates for adolescents.
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— ~9~\ ~9~\ ~9~\ to ~9~ ~9~' ~96, Year 9Oo9 99~ 993 99~ 9~; FIGURE D.1 Annual prevalence rate of use of an illegal drug for people ages 1217, 1975-1999, National Household Survey of Drug Abuse (NHSDA) and Monitoring the Future (MTF)
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Available: www.isr.umich.edu/src/mtf; accessed 11/ 16/99. Office of Applied Studies National Household Survey on Drug Abuse: Main Findings 1997, Department of Health and Human Services, Substance Abuse and Mental Health Services Administration.
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In the drug use context, however, analysis imposing no assumptions at all seems too conservative because there is good reason to believe that observed rates are lower bounds.4 I take this as a starting point. That is, I assume that in 1991 no less than 29 percent of 12th graders and 13 percent of adolescents ages 12-17 consumed illegal drugs.
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, and the usage rates among respondents, PA = 1 I zig = 1~. The data cannot reveal the prevalence rates among those who did not respond, PA = 1 I zig = 0~.
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P[yt=1 1 Zt=O] Data Missing at Random The most common assumption used to identify the fraction of users is to assume that, conditional on certain covariates, the prevalence rate for nonrespondents equals the rate for respondents.7 That is, nonresponse is random conditional on these covariates.
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= 0 ] .s Notice that the lower bound is the fraction of respondents who use illegal drugs in the past year, while the upper bound increases with the fraction of nonrespondents.
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Data from the NHSDA imply that the annual prevalence rate of use 9Using the law of total probability to weight the fraction of respondents and nonrespondents, the upper bound prevalence rates are found by assuming that all nonrespondents use drugs. In 1991, for example, the upper bound rate of 40 = 29*
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330 ., 5o au V)
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More generally, the data could imply a positive trend even if one allowed for the possibility that fewer nonrespondents used illegal drugs, as long as the reduction in the prevalence rate for nonrespondents was not too large. If one is unwilling to rule out the possibility of extreme variation in the behavior of nonrespondents, for example, that all nonrespondents used drugs in 1991 but only 19 percent used drugs in 1997, then the data are less likely to reveal the direction of the trend.
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Likewise, as the observed prevalence rate of use increases over time, the switching threshold for nonrespondents must also increase. Table D.2 displays the estimated switching threshold along with the annual trends in usage rates from the MTF.
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Thus, uncertainty about the direction of the trend exists only if one cannot rule out the possibility that the trend for nonrespondents is three times the negative of the observed trend. Since the observed prevalence rate increased by 5.7 points from 1991 to 1997, the trend is positive if the usage rates for nonrespondents did not fall by more than 17.1 points from 1991 to 1997.
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Alternatively, it might be that all negative reports are invalid, in which case the entire population may have consumed illegal drugs. Thus, to draw inferences about the fraction of users in the United States, one must impose assumptions about self-reporting errors.
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, or who falsely claim to have abstained, P [w ~ = 0, y ~ = 1~. The data identify prevalence rates if the fraction of false negatives is exactly offset by the fraction of false positive reports, that is, if PI we = 0, z ~ = 0]
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< P JR-2: The fraction of false negative reports exceeds the fraction of false positive reports.
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The upper bound depends on both the self-reported prevalence rates as well as the largest possible fraction of false negative reports, P The upper bound P on the fraction of false reports is not revealed by the data but instead must be known or assumed by the researcher.
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-{P[wt=O,yt=11-P[Wt=1,yt=0] ~ The growth in prevalence rates equals the trend in reported use plus the trend in net misreporting, where net misreporting is defined as the difference between the fraction of false negative and false positive reports.
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340 Cot .
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Suppose there is a positive relationship between false negative reporting and the stigmatization of illegal drugs. In particular, assume that JR-3: the fraction of false positive reports, P ~ w' = 1, Y t = 0]
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Intuitively, if both the observed trends in stigma and in the fraction of drug users are positive, the data reveal that the prevalence rates increased. In particular, the trend in illegal drug consumption will be no less than the observed trend and no greater than the reported trend plus P
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may also be used to examine whether the prevalence rate increased from 1991 to 1997. Over this period, the observed rate increased while stigma appeared to be decreasing.
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CONCLUSIONS The Monitoring the Future survey and the National Household Survey of Drug Abuse provide important data for tracking the numbers and characteristics of illegal drug users in the United States. Response problems, however, continue to hinder credible inference.
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. Thus, the upper bound on the trend in prevalence rates equals the observed trend plus P
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If the observed trend is negative, the lower bound is found by setting K = KmaX. Thus, under assumptions IR 1-4 (A5)
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Witt 1996 Estimation of measurement bias in self-reports of drug use with applications to the National Household Survey on Drug Abuse. Journal of Official Statistics 12~3~:275-300.
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Office of Applied Studies 1997 National Household Survey on Drug Abuse: Main Findings 1997.
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