1997 National Household Survey on Drug Abuse:  Preliminary Results

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V.Adjustment of 1979-1993 NHSDA Estimates to Account for the New Survey Methodology Used in the 1994 and Later NHSDAS

The NHSDA is an important source of data for policy makers, not only because it provides measures of substance abuse for a single year, but also because the series of surveys over the last several years provides a measure of change in substance abuse in the population over time. Beginning in 1994, the NHSDA began using an improved questionnaire and estimation procedure based on a series of studies and consultations with drug survey experts and data users. Because this new methodology produces estimates that are not directly comparable to previous estimates, the 1979-1993 NHSDA estimates presented in this report were adjusted to account for the new methodology that was begun in 1994.

Nearly all of the 1979-1993 substance use prevalence estimates presented in this report were adjusted using a simple ratio correction factor that was estimated at the total population level using data from the pooled 1993 and 1994 NHSDAs. The remaining substance use prevalence estimates were adjusted by formally modeling the effect of the new methodology, relative to the old methodology, using data from the 1994 NHSDA. The modeling procedure was used for the more prevalent substance use measures that changed significantly between the old- and new-version NHSDA questionnaires. The modeling procedure was particularly desirable for the more prevalent measures because the procedure was able to use a greater number of potentially significant explanatory variables in the adjustment compared to the simple ratio correction factor. Each of the procedures are discussed below.

Ratio Adjustment

Most of the 1979-1993 NHSDA estimates were adjusted using a ratio correction factor that measured the effect of the new methodology, relative to the old methodology, using data from the 1993 and 1994 NHSDAs. As explained in the Introduction in this report, the 1994 NHSDA was designed to generate two sets of estimates. The first set of estimates, which in previous reports was referred to as the 1994-A set of estimates, was based on the same questionnaire and editing method that was used in 1993 (and earlier). The second set of estimates, referred to as the 1994-B set, was based on the new NHSDA survey methodology. Since the 1994-A estimates were generated from a sample that was roughly one-fourth the size of the 1994-B, to increase the precision of the ratio correction factor, the 1994-A sample was pooled with the 1993 sample.

The 1979-1993 NHSDA estimates that were adjusted using the ratio correction factor included estimates of lifetime, past year and past month use of cocaine, crack, inhalants, hallucinogens (including PCP and LSD), heroin, any psychotherapeutic, stimulants, sedatives, tranquilizers, analgesics, any illicit drug other than marijuana and smokeless tobacco as well as estimates of past year frequency of use of marijuana, cocaine and alcohol. This adjustment was computed at the total sample level and was equally applied to all corresponding estimates computed among subgroups of the total population. Consequently, for example, the same ratio adjustment was used to correct all estimates of past year cocaine use, regardless of the demographic subgroup under consideration. Mathematically, this ratio adjustment can be expressed as follows:

Suppose i denotes the sampled respondent, y i denotes a 0/1 variable to indicate nonuse or use of some particular substance, and w_i denotes the sample weight. Then the ratio adjustment was computed as:

R ~=~ {sum from {i in S_{1994-B}} w_i~ y_i} over {sum from {i in S_{1993~ union ~1994-A}} w_i ~y_i} ~=~

{y bar_{1994-B}} over {y bar_{1993~ union ~1994-A}}

The latter equality is true because the sample weights in the pooled 1993 and 1994-A sample were adjusted slightly so that they would sum to the same demographic control totals as the 1994-B sample across the variables typically used in the NHSDA post stratification procedure.

Model-Based Adjustment

A model based method of computing adjustments that would account for the changes in the NHSDA methodology was used for estimates of the use of the more prevalent drugs including lifetime, past year and past month use of alcohol, marijuana, cigarettes, any illicit drug as well as past month binge drinking and past month heavy drinking. It was also used for measures of perceived risk of harm, but only for items which had wording changes in 1994.

The model that was used is based on a constrained exponential model originally proposed by Deville & Särndal (1992). Similar to the ratio adjustment, this method of adjusting previous estimates models the combined effect of all measurement error differences between the new and old methodologies. This model offers the primary advantages of allowing (1) a greater numberof potentially significant explanatory variables in the adjustment and (2) bounding the resulting adjustment between predetermined thresholds. This a priori bounding eliminates extreme adjustments that might otherwise occur, particularly for small subpopulations. Additionally, the model fitting procedure used to compute the adjustment forces the adjusted 1994-A estimates to equal the 1994-B estimates within the subpopulations represented by the dummy variables in the vector of model predictors. Mathematically, this model can be expressed as follows:

R_i~=~{L(U-1) ~+~ U(1-L) e^{-AX_i beta} } over

{(U-1) ~+~ (1-L) e^{-AX_i beta} } (1)

Where the ratio adjustment R i can be interpreted as:

R_i~={ FUNC {Probability~Of~Reporting~Use~With~The~New~Survey~Methodology}} over

{ FUNC {Probability~Of~Reporting~Use~With~The~Old~Survey~Methodology}}

In equation (1) the constant A is simply a scale factor set equal toleft[ U-L right]~ DIV ~left[ (1-L)(U-1) right], beta are the model coefficients, and X_i

is a vector of explanatory variables. The explanatory variables considered in the models consisted of the categorical indicator variables for age group and race/ethnicity. The parameters L and U are the predetermined constants that force the estimated R_i

to be

L ~<=~ R_{i}~ <=~ U ~~~~~~~~~ func {for ~\all~ i ~\and~\for~\any~\value~\of~X_i beta}


Notice that if the constant L is set equal to zero and U approaches _ , then the constant A approaches 1, and equation (1) reduces to the familiar, unconstrained exponential model:

R_i~=~{e^{-X_i beta} }.

The model parameter vector beta

in (1) was estimated by solving the generalized raking equations:

Sum from {i in S_{1994-A}} w_i ~R_i~X_i^T~y_i ~~=~~

Sum from {i in S_{1994-B}} w_i ~X_i^T~y_i

subject to the constraints.

Notice from the above raking equations that the estimated adjustment R_i

forces the 1994-A estimate to equal the 1994-B estimate for any subpopulation represented by an indicator variable in X_i

. Therefore, for example, if an appropriate indicator for the age group=12-17 year-olds was included in X_i

, then the model-based estimate of the R_i‘s would produce an adjusted prevalence estimate using the 1994-A sample that exactly equaled the prevalence estimate generated from the 1994-B sample for the 12-17 year-old age group.

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This page was last updated on February 05, 2009.