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Results from the 2002 National Survey on Drug Use and Health (NSDUH)

Appendix B: Statistical Methods and Limitations of the Data

B.1 Target Population

An important limitation of estimates of drug use prevalence from the National Survey on Drug Use and Health (NSDUH) is that they are only designed to describe the target population of the survey—the civilian, noninstitutionalized population aged 12 or older. Although this population includes almost 98 percent of the total U.S. population aged 12 or older, it excludes some important and unique subpopulations who may have very different drug use patterns. For example, the survey excludes active military personnel, who have been shown to have significantly lower rates of illicit drug use. Persons living in institutional group quarters, such as prisons and residential drug treatment centers, are not included in NSDUH and have been shown in other surveys to have higher rates of illicit drug use. Also excluded are homeless persons not living in a shelter on the survey date, another population shown to have higher than average rates of illicit drug use. Appendix E describes other surveys that provide data for these populations.

B.2 Sampling Error and Statistical Significance

The national estimates, along with the associated variance components, were computed using a multiprocedure package, SUrvey DAta ANalysis (SUDAAN®) Software for Statistical Analysis of Correlated Data, which was designed for the statistical analysis of sample survey data from stratified, multistage cluster samples (RTI, 2001). The final, nonresponse-adjusted, and poststratified analysis weights were used to compute unbiased design-based drug use estimates.

The sampling error (i.e., the standard error [SE]) of an estimate is the error caused by the selection of a sample instead of conducting a census of the population. Sampling error is reduced by selecting a large sample and by using efficient sample design and estimation strategies, such as stratification, optimal allocation, and ratio estimation.

With the use of probability sampling methods in NSDUH, it is possible to develop estimates of sampling error from the survey data. These estimates have been calculated in SUDAAN for all estimates presented in this report using a Taylor series linearization approach that takes into account the effects of the complex NSDUH design features. The sampling errors are used to identify unreliable estimates and to test for the statistical significance of differences between estimates.

B.2.1 Variance Estimation for Totals

Estimates of proportions, p hat sub d, such as drug use prevalence rates, take the form of nonlinear statistics where the variances cannot be expressed in closed form. Variance estimation for nonlinear statistics in SUDAAN is performed using a first-order Taylor series approximation of the deviations of estimates from their expected values.

Corresponding to proportion estimates, p hat sub d, the number of drug users, cap y hat sub d, can be estimated as

First Equation     D

where cap n hat sub d is the estimated population total for domain d, and p hat sub d is the estimated proportion for domain d. The SE for the total estimate is obtained by multiplying the SE of the proportion by cap n hat sub d, that is,

Second Equation.     D

This approach is theoretically correct when the domain size estimates, cap n hat sub d, are among those forced to Census Bureau population projections through the weight calibration process. In these cases, cap n hat sub d is clearly not subject to sampling error. For a more detailed explanation of the weight calibration process, see Section A.3.2 in Appendix A.

For domain totals, cap y hat sub d, where cap n hat sub d is not fixed, this formulation may still provide a good approximation if it can be reasonably assumed that the sampling variation in cap n hat sub d is negligible relative to the sampling variation in p hat sub d. This is a reasonable assumption in most cases.

For a subset of the tables produced from the 2002 data, it was clear that the above approach yielded an underestimate of the variance of a total because cap n hat sub d was subject to considerable variation. In these cases, a different method was used to estimate variances. SUDAAN provides an option to directly estimate the variance of the linear statistic that estimates a population total. Using this option did not affect the SE estimates for the corresponding proportions presented in the same sets of tables.

B.2.2 Suppression Criteria for Unreliable Estimates

As has been done in past reports from the National Household Survey on Drug Abuse (NHSDA),1 direct survey estimates from the 2002 NSDUH considered to be unreliable due to unacceptably large sampling errors are not shown in this report and are noted by asterisks (*) in the tables containing such estimates. The criteria used for suppressing all direct survey estimates were based on the relative standard error (RSE), which is defined as the ratio of the standard error (SE) over the estimate, as well as on nominal sample size and on effective sample size. The criteria are summarized in Table B.1.

Proportion estimates (p hat) within the range [0 < p hat < 1], rates, and corresponding estimated number of users were suppressed if

Suppression Rule

or

Suppression Rule     D

Using a first-order Taylor series approximation to estimate the relative standard error of the negative of the natural log of p hat and the relative standard error of the negative of the natural log of 1 minus p hat, the following was obtained and used for computational purposes:

Computational Form

or

Computational Form     D

The separate formulas for p hat less than or equal to 0.5 and p hat > 0.5 produce a symmetric suppression rule (i.e., if p hat is suppressed, then 1 - p hat will be as well). This ad hoc rule requires an effective sample size in excess of 50. When 0.05 < p hat < 0.95, the symmetric property of the rule produces a local maximum effective sample size of 68 at p hat = 0.5. Thus, estimates with these values of p hat along with effective sample sizes falling below 68 are suppressed. See Figure B.1 for a graphical representation of the required minimum effective sample sizes as a function of the proportion estimated.

Figure B.1  Required Effective Sample as a Function of the Proportion Estimated

Figure B.1     D

A minimum nominal sample size suppression criterion (n = 100) that protects against unreliable estimates caused by small design effects and small nominal sample sizes was employed. Prevalence estimates also were suppressed if they were close to 0 or 100 percent (i.e., if p hat < .00005 or if p hat greater than or equal to .99995).

Estimates of other totals (e.g., number of initiates) along with means and rates (both not bounded between 0 and 1) were suppressed if RSE(p hat) > 0.5. Additionally, estimates of the mean age at first use were suppressed if the sample size was smaller than 10 respondents; moreover, the estimated incidence rate and number of initiates were suppressed if they rounded to 0.

The suppression criteria for various NSDUH estimates are summarized in Table B.1 at the end of this appendix.

B.2.3 Statistical Significance of Differences

This section describes the methods used to compare prevalence estimates in this report. Customarily, the observed difference between estimates is evaluated in terms of its statistical significance. "Statistical significance" refers to the probability that a difference as large as that observed would occur due to random error in the estimates if there were no difference in the prevalence rates for the population groups being compared. The significance of observed differences in this report is generally reported at the 0.05 and 0.01 levels. When comparing prevalence estimates, the null hypothesis (no difference between prevalence rates) can be tested against the alternative hypothesis (there is a difference in prevalence rates) using the standard difference in proportions test expressed as follows:

Fourth Equation     D

where p hat sub 1 = first prevalence estimate, p hat sub 2 = second prevalence estimate, var(p hat sub 1) = variance of first prevalence estimate, var(p hat sub 2) = variance of second prevalence estimate, and cov(p hat sub 1, p hat sub 2) = covariance between p hat sub 1 and p hat sub 2.

Under the null hypothesis, Z is asymptotically distributed as a normal random variable. Calculated values of Z can therefore be referred to as the unit normal distribution to determine the corresponding probability level (i.e., p value). The covariance term in the formula for Z will not always be 0. Estimates of Z, along with its p value, were calculated in SUDAAN, using the analysis weights and accounting for the sample design as described in Appendix A. A similar procedure and formula for Z were used for estimated totals.

When comparing prevalence measures between population subgroups, a χ2 test of independence of the subgroup and the prevalence variable was conducted first to control the error level for multiple comparisons. If the χ2 test indicated some significant differences, the significance of each particular subgroup comparison discussed in the report was tested using SUDAAN. Using the published estimates and standard errors to perform independent t tests for the difference of proportions will usually provide the same results as tests performed in SUDAAN. However, where the significance level is borderline, results may differ for two reasons: (1) the covariance term is included in SUDAAN tests whereas it is not included in independent t tests, and (2) the reduced number of significant digits shown in the published estimates may cause rounding errors in the independent t tests.

B.3 Nonsampling Error

Nonsampling errors can occur from nonresponse, coding errors, computer processing errors, errors in the sampling frame, reporting errors, and other errors not due to sampling. Nonsampling errors are reduced through data editing, statistical adjustments for nonresponse, close monitoring and periodic retraining of interviewers, and improvement in various quality control procedures.

Although nonsampling errors can often be much larger than sampling errors, measurement of most nonsampling errors is difficult or impossible. However, some indication of the effects of some types of nonsampling errors can be obtained through proxy measures, such as response rates and from other research studies.

B.3.1 Screening and Interview Response Rate Patterns

In 2002, response rates were improved over prior years by providing respondents with a $30 incentive. Of the 150,162 eligible households sampled for the 2002 NSDUH main study, 136,349 were successfully screened for a weighted screening response rate of 90.7 percent (Table B.2). In these screened households, a total of 80,581 sample persons were selected, and completed interviews were obtained from 68,126 of these sample persons, for a weighted interview response rate of 78.6 percent (Table B.3). A total of 7,583 (13.3 percent) sample persons were classified as refusals or parental refusals, 3,252 (4.5 percent) were not available or never at home, and 1,620 (3.7 percent) did not participate for various other reasons, such as physical or mental incompetence or language barrier (see Table B.3, which also shows the distribution of the selected sample by interview code and age group). The weighted interview response rate was highest among 12 to 17 year olds (90.0 percent), females (80.0 percent), blacks and Hispanics (82.2 and 80.9 percent, respectively), in nonmetropolitan areas (81.4 percent), and among persons residing in the Midwest (80.0 percent) (Table B.4).

The overall weighted response rate, defined as the product of the weighted screening response rate and weighted interview response rate, was 71.3 percent in 2002. Nonresponse bias can be expressed as the product of the nonresponse rate (1–R) and the difference between the characteristic of interest between respondents and nonrespondents in the population (cap p sub r minus cap p sub n sub r). Thus, assuming the quantity (cap p sub r minus cap p sub n sub r) is fixed over time, the improvement in response rates in 2002 over prior years will result in estimates with lower nonresponse bias.

B.3.2 Inconsistent Responses and Item Nonresponse

Among survey participants, item response rates were above 99 percent for most questionnaire items. However, inconsistent responses for some items, including the drug use items, were common. Estimates of substance use from NSDUH are based on responses to multiple questions by respondents, so that the maximum amount of information is used in determining whether a respondent is classified as a drug user. Inconsistencies in responses are resolved through a logical editing process that involves some judgment on the part of survey analysts and is a potential source of nonsampling error.

B.3.3 Validity of Self-Reported Use

NSDUH estimates are based on self-reports of drug use, and their value depends on respondents' truthfulness and memory. Although many studies have generally established the validity of self-report data and the NSDUH procedures were designed to encourage honesty and recall, some degree of underreporting is assumed (Harrell, 1997; Harrison & Hughes, 1997; Rouse, Kozel, & Richards, 1985). No adjustment to NSDUH data is made to correct for this. The methodology used in NSDUH has been shown to produce more valid results than other self-report methods (e.g., by telephone) (Aquilino, 1994; Turner, Lessler, & Gfroerer, 1992). However, comparisons of NSDUH data with data from surveys conducted in classrooms suggest that underreporting of drug use by youths in their homes may be substantial (Gfroerer, 1993; Gfroerer, Wright, & Kopstein, 1997).

B.4 Incidence Estimates

For diseases, the incidence rate for a population is defined as the number of new cases of the disease, N, divided by the person time, PT, of exposure or

IR is the quotient of N over PT

The person time of exposure can be measured for the full period of the study or for a shorter period. The person time of exposure ends at the time of diagnosis (e.g., Greenberg, Daniels, Flanders, Eley, & Boring, 1996, pp. 16–19). Similar conventions are applied for defining the incidence of first use of a substance.

Beginning in 1999, the survey questionnaire allows for collection of year and month of first use for recent initiates. Month, day, and year of birth also are obtained directly or imputed in the process. In addition, the questionnaire call record provides the date of the interview. By imputing a day of first use within the year and month of first use reported or imputed, the key respondent inputs in terms of exact dates are known. Exposure time can be determined in terms of days and converted to an annual basis.

Having exact dates of birth and first use also allows the person time of exposure during the targeted period, t, to be determined. Let the target time period for measuring incidence be specified in terms of dates; for example, the period 1998 would be specified as

T equals open square bracket t sub 1 comma t sub 2 close parenthesis equals open square bracket 1 Jan 1988 comma 1 Jan 1999 close parenthesis

a period that includes 1 January 1998 and all days up to but not including 1 January 1999. The target age group also can be defined by a half-open interval as A equals open square bracket a sub 1 comma a sub 2 close parenthesis. For example, the age group 12 to 17 would be defined by A equals open square bracket 12 comma 18 close parenthesis for persons at least age 12, but not yet age 18. If person i was in age group a during period t, the time and age interval, cap l sub t comma sub a comma sub i can then be determined by the intersection:

Fifth Equation     D

assuming the time of birth can be written in terms of day (cap d cap o cap b sub i), month (cap m cap o cap b sub i), and year (cap y cap o cap b sub i). Either this intersection will be empty (Cap l sub t comma sub a comma sub i equals the null set) or it will be designated by the half-open interval, [l sub t comma sub a comma sub i equals open square bracket m sub 1 comma sub i, comma m sub 2 comma sub i, close parenthesis, where

M sub 1 comma sub i equals the later of either t sub 1 or the date when the respondent turned a sub one years old

and

M sub 2 comma sub i equals the earlier of either t sub 2 or the date when the respondent turned a sub two years old

The date of first use, t sub f sub u comma sub d comma sub i, also is expressed as an exact date. An incident of first drug d use by person i in age group a occurs in time T sub f sub u comma sub d comma sub i is in the half open time interval indicated by open square bracket m sub 1 comma sub i, comma m sub 2 comma sub i, close parenthesis. The indicator function cap i sub i, a function of d, a, and t used to count incidents of first use is set to 1 when T sub f sub u comma sub d comma sub i is in the half open time interval indicated by open square bracket m sub 1 comma sub i, comma m sub 2 comma sub i, close parenthesis and to 0 otherwise. The person-time exposure measured in years and denoted by e sub i, a function of d, a, and t for a person i of age group a depends on the date of first use. If the date of first use precedes the target period (T sub f sub u comma sub d comma sub i is less than m sub 1 comma sub i), then person-time exposure equals 0. If the date of first use occurs after the target period or if person i has never used drug d, then

Person-time exposure is equal to the ratio of two quantites. The numerator is the difference between m sub 2 comma sub i and m sub 1 comma sub i and the denominator is 365

If the date for first use occurs during the target period cap l sub t comma sub a comma sub i, then

Person time exposure equals the ratio of two quantities. The numerator is the difference between t sub f sub u comma sub d sub i comma sub i and m sub 1 comma sub i, and the denominator is 365

Note that both cap i sub i, a function of d, a, and t and person-time exposure are set to 0 if the target period cap l sub t comma sub a comma sub i is empty (i.e., person i is not in age group a during any part of time t). The incidence rate is then estimated as a weighted ratio estimate:

Tenth Equation     D

where the w sub i are the analytic weights.

Starting in 2002, estimates were reported separately for males and females, as well as overall. These estimates only use data from the 2002 survey because 2002 estimates provide a new baseline for measuring change. Therefore, even though the methodology between 1999–2001 and 2002 is the same, the estimates are not comparable. For a more detailed explanation of the incidence methodology, see Packer, Odom, Chromy, Davis, and Gfroerer (2002). The estimates in this report are based on retrospective reports of age at first drug use by survey respondents interviewed during 2002. Because they are based on retrospective reports as was the case for earlier estimates, they may be subject to some of the same kinds of biases.

Bias due to differential mortality occurs because some persons who were alive and exposed to the risk of first drug use in the historical periods shown in the tables died before the 2002 NSDUH was conducted. This bias is probably very small for estimates shown in this report. Incidence estimates also are affected by memory errors, including recall decay (tendency to forget events occurring long ago) and forward telescoping (tendency to report that an event occurred more recently than it actually did). These memory errors would both tend to result in estimates for earlier years (i.e., 1960s and 1970s) that are downwardly biased (because of recall decay) and estimates for later years that are upwardly biased (because of telescoping). There also is likely to be some underreporting bias due to social acceptability of drug use behaviors and respondents' fear of disclosure. This is likely to have the greatest impact on recent estimates, which reflect more recent use and reporting by younger respondents. Finally, for drug use that is frequently initiated at age 10 or younger, estimates based on retrospective reports 1 year later underestimate total incidence because 11–year–old (and younger) children are not sampled by NSDUH. Prior analyses showed that alcohol and cigarette (any use) incidence estimates could be significantly affected by this. Therefore, for these drugs, only 2001 age-specific rates and the number of initiates 18 or older were reported.

B.5 Retrospective Lifetime Prevalence Measures

Retrospective measures of lifetime substance use prevalence were obtained for prior years based on the 2002 sample. Lifetime prevalence measures are defined as of a specified date as the ratio

Cap pr equals the ratio of cap n sub users over cap n sub all

where the numerator,cap n sub users, represents all persons who report lifetime use as of that date and the numerator,cap n sub all, represents both lifetime users and nonusers. For NSDUH current year estimates, the specified date is the date of interview for each respondent.

As was described in Section B.4, complete data on a respondent's exact date of first substance use is known or imputed during the processing of the current year's data. In addition, the date of interview and date of birth are on the current year's data file. These data make it possible to retrospectively estimate lifetime prevalence measures for prior years based on the current year respondents.

Because comparisons of prevalence rates across years from this analysis are based on a common sample, the precision of trend estimates is improved. On the negative side, the retrospective measures do not properly reflect the impacts of migration and mortality.2 To control for the possible effects of mortality, the retrospective estimates are limited to the younger age groups: 12 to 17 and 18 to 25. In addition, retrospective prevalence estimates may be biased due to memory errors. As noted in the discussion of incidence estimates (Section B.4), recall decay leads to a general downward bias. Forward telescoping (the tendency to report initial substance use more recently than it actually occurred) will create downward bias in early years, but have little impact on recent estimates. It also should be noted that due to the sampling strategy that selects older persons with lower probabilities of selection, the estimates for early years (reported by persons who are now 26 or older) are based on much smaller sample sizes and subject to more sampling error.

A key assumption for computing retrospective lifetime prevalence estimates is that the month and day of the respondent use and age status in prior years is based on the same month and day as the date of interview in the current survey year. Retrospective estimates, PR(d,a,t), of lifetime substance d use were prepared for 1965 to 2002 as a simple ratio estimate for year t and age group a as:

Eleventh Equation     D

where w sub i is the respondent's analytic weight for 2002. The values of x sub i, a function of a and t and y sub i, a function of d, a, and t are determined from

For the current survey year, x sub i, a function of a and t has a value of 1 if the current age of respondent i is in the interval a, and a value of 0 otherwise. If the age interval is 12 to 17, then the respondent must be at least 12, but not yet 18. For the current survey year, y sub i, a function of d, a, and t has a value of 1 if x sub i, a function of a and t has a value of 1 and respondent i is a lifetime user of substance d. For current lifetime users, this means that their reported date of first use is on or before the date of interview (i.e., if t sub f sub u comma sub d comma sub i occurred no later than the date of the interview). Otherwise, y sub i, a function of d, a, and t has a value of 0.

For prior years, it is first necessary to compute the difference in the years as delta t equals the difference between the year of the interview of respondent i and t. Then, x sub i, a function of a and t has a value of 1 if respondent i retrospectively adjusted age, a sub i minus delta t is in the interval a, and a value of 0 otherwise. Also, y sub i, a function of d, a, and t has a value of 1 if x sub i, a function of a and t has a value of 1, respondent i is a lifetime user of substance d, and the reported date of first use is on or before an adjusted date of interview (i.e., if t sub f sub u comma sub d comma sub i occurred no later than delta t years before the date of the interview of respondent i). Otherwise, y sub i, a function of d, a, and t has a value of 0.

B.6 Serious Mental Illness Estimates

For the 2002 survey, mental health among adults was measured using a scale to ascertain serious mental illness (SMI). This scale consisted of six questions that ask respondents how frequently they experienced symptoms of psychological distress during the 1 month in the past year when they were at their worst emotionally. The use of this scale is based on a methodological study designed to evaluate several screening scales for measuring SMI in NSDUH. These scales consisted of a truncated version of the World Health Organization (WHO) Composite International Diagnostic Interview Short Form (CIDI-SF) scale (Kessler, Andrews, Mroczek, Üstün, & Wittchen, 1998), the K10/K6 scale of nonspecific psychological distress (Furukawa, Kessler, Slade, & Andrews, 2003), and the WHO Disability Assessment Schedule (WHO-DAS) (Rehm et al., 1999).

The methodological study to evaluate the scales consisted of 155 respondents selected from a first-stage sample of 1,000 adults aged 18 or older. First-stage respondents were selected from the Boston metropolitan area and screened on the telephone to determine whether they had any emotional problems. Respondents reporting emotional problems at the first stage were oversampled when selecting the 155 respondents at the second stage. The selected respondents were interviewed by trained clinicians in respondents' homes using both the NSDUH methodology and a structured clinical interview. The first interview included the three scales described above using audio computer-assisted self-interviewing (ACASI). Respondents completed the ACASI portion of the interview without discussing their answers with the clinician. After completing the ACASI interview, respondents then were interviewed using the 12–month nonpatient version of the Structured Clinical Interview for DSM-IV (SCID) (First, Spitzer, Gibbon, & Williams, 1997) and the Global Assessment of Functioning (GAF) (Endicott, Spitzer, Fleiss, & Cohen, 1976) to classify respondents as either having or not having SMI.

The data from the 155 respondents were analyzed using logistic regression analysis to predict SMI from the scores on the screening questions. Analysis of the model fit indicated that each of the scales alone and in combination were significant predictors of SMI and the best fitting models contained either the CIDI-SF or the K10/K6 alone. Receiver operating characteristic (ROC) curve analysis was used to evaluate the precision of the scales to discriminate between respondents with and without SMI. This analysis indicated that the K6 was the best predictor. The results of the methodological study are described in more detail in a paper describing the K10/K6 scale of nonspecific psychological distress (Kessler et al., 2003).

To score the items on the K6 scales, they were first coded from 0 to 4 and summed to yield a number between 0 and 24. This involved transforming response categories for the six questions (DSNERV1, DSHOPE, DSFIDG, DSNOCHR, DSEFFORT, and DSDOWN) given below so that "all of the time" is coded 4, "most of the time" is coded 3, "some of the time" 2, "a little of the time" 1, and "none of the time" 0, with "don't know" and "refuse" also coded 0. Summing across the transformed responses results in a score with a range from 0 to 24. Respondents with a total score of 13 or greater were classified as having a past year SMI. This cutpoint was chosen to equalize false positives and false negatives.

The questions comprising the K6 scale are given as follows:

DSNERV1
Most people have periods when they are not at their best emotionally. Think of one month in the past 12 months when you were the most depressed, anxious, or emotionally stressed. If there was no month like this, think of a typical month.

During that month, how often did you feel nervous?

1    All of the time
2    Most of the time
3    Some of the time
4    A little of the time
5    None of the time
DK/REF

Response categories are the same for the following questions:

DSHOPE
During that same month when you were at your worst emotionally . . . how often did you feel hopeless?
DSFIDG
During that same month when you were at your worst emotionally . . . how often did you feel restless or fidgety?
DSNOCHR
During that same month when you were at your worst emotionally . . . how often did you feel so sad or depressed that nothing could cheer you up?
DSEFFORT
During that same month when you were at your worst emotionally . . . how often did you feel that everything was an effort?
DSDOWN
During that same month when you were at your worst emotionally . . . how often did you feel down on yourself, no good, or worthless?

 

Table B.1  Summary of 2002 NSDUH Suppression Rules

Estimate Suppress if:
Prevalence rate,p hat, with nominal sample size, n, and design effect, deff The estimated prevalence rate, p hat, is < 0.00005 or greater than or equal to 0.99995, or

The ratio of two quantities is greater than 0.175. The numerator of the ratio is the standard error of p hat divided by p hat. The denominator is the negative of the natural logarithm of p hat when p hat less than or equal to 0.5, or

The ratio of two quantities is greater than 0.175. The numerator of the ratio is the standard error of p hat divided by 1 minus p hat. The denominator is the negative of the natural logarithm of 1 minus p hat when p hat greater than 0.5, or

Effective n<68, or

n<100,

where Effective n is the quotient of n over the design effect

Note: The rounding portion of this suppression rule for prevalence rates will produce some estimates that round at one decimal place to 0.0 or 100.0 percent but are not suppressed from the tables.

Estimated number
(numerator of p hat)
The estimated prevalence rate, p hat, is suppressed.

Note: In some instances when p hat is not suppressed, the estimated number may appear as a 0 in the tables; this means that the estimate is > 0 but < 500 (estimated numbers are shown in thousands).

Mean age at first use, x bar, with nominal sample size, n relative standard error of x bar is greater than 0.5, or

n>10.

Incidence rate, r hat Rounds to < 0.1 per 1,000 person-years of exposure, or

relative standard error of r hat is greater than 0.5.

Number of initiates, t hat Rounds to < 1,000 initiates, or

relative standard error of t hat is greater than 0.5.

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2002.

 

Table B.2  Weighted Percentages and Sample Sizes for 2002 NSDUH, by Screening Result Code

Screening Result 2002 NSDUH
Sample
Size
Weighted
Percentage
Total Sample 178,013 100.00
     Ineligible cases 27,851 15.27
     Eligible cases 150,162 84.73
Ineligibles 27,851 15.27
     Vacant 14,417 51.55
     Not a primary residence 4,580 17.36
     Not a dwelling unit 2,403 8.16
     Resident < 1/2 of quarter 0 0.00
     All military personnel 289 1.08
     Other, ineligible 6,162 21.86
Eligible Cases 150,162 84.73
     Screening complete 136,349 90.72
          No one selected 80,557 53.14
          One selected 30,738 20.58
          Two selected 25,054 17.00
     Screening not complete 13,813 9.28
          No one home 3,031 2.02
          Respondent unavailable 411 0.26
          Physically or mentally incompetent 307 0.20
          Language barrier—Hispanic 66 0.05
          Language barrier—other 461 0.35
          Refusal 8,556 5.86
          Other, access denied 471 0.30
          Other, eligible 12 0.01
          Segment not accessible 0 0.00
          Screener not returned 15 0.01
          Fraudulent case 479 0.21
          Electronic screening problem 4 0.00

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2002.

 

Table B.3  Weighted Percentages and Sample Sizes for 2002 NSDUH, by Final Interview Code

Final Interview Code Persons Aged
12 or Older
Persons Aged
12–17
Persons Aged
18 or Older
Sample
Size
Weighted
Percentage
Sample
Size
Weighted
Percentage
Sample
Size
Weighted
Percentage
Total 80,581 100.00 26,230 100.00 54,351 100.00
Interview Complete 68,126 78.56 23,659 89.99 44,467 77.20
No One at Dwelling Unit 1,359 1.81 182 0.70 1,177 1.94
Respondent Unavailable 1,893 2.71 329 1.20 1,564 2.89
Break-Off 48 0.10 9 0.04 39 0.11
Physically/Mentally Incompetent 692 1.75 161 0.57 531 1.89
Language barrier—Spanish 138 0.19 9 0.04 129 0.21
Language barrier—Other 327 1.09 24 0.13 303 1.21
Refusal 6,276 12.73 464 1.81 5,812 14.03
Parental Refusal 1,307 0.55 1,307 5.15 0 0.00
Other 415 0.52 86 0.38 329 0.53

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2002.

 

Table B.4  Response Rates and Sample Sizes for 2002 NSDUH, by Demographic Characteristics

  2002 NSDUH
Selected
Persons
Completed
Interviews
Weighted
Response Rate
Total 80,581 68,126 78.56%
Age in Years      
     12–17 26,230 23,659 89.99%
     18–25 27,216 23,271 85.16%
     26 or older 27,135 21,196 75.81%
Gender      
     Male 39,453 32,766 77.06%
     Female 41,128 35,360 79.99%
Race/Ethnicity      
     Hispanic 10,250 8,692 80.93%
     White 55,594 46,834 78.23%
     Black 9,385 8,143 82.24%
     All other races 5,352 4,457 70.50%
Region      
     Northeast 16,490 13,706 75.57%
     Midwest 22,588 19,180 80.01%
     South 24,530 20,900 79.99%
     West 16,973 14,340 77.33%
County Type      
     Large metropolitan 32,294 26,792 76.85%
     Small metropolitan 28,121 23,944 79.50%
     Nonmetropolitan 20,166 17,390 81.38%

Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2002.


1 Beginning with the 2002 survey year, the survey name was changed from the National Household Survey on Drug Abuse (NHSDA) to the National Survey on Drug Use and Health (NSDUH).

2 The same limitations apply to the estimates of incidence rates for prior years based on the current sample responses.

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This page was last updated on June 03, 2008.