Go to the Table Of Contents

Appendix B: Statistical Methods and Measurement

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 living in the United States. 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. Also, persons living in institutional group quarters, such as prisons and residential drug use treatment centers, are not included in NSDUH, yet they 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; they are another population shown to have higher than average rates of illicit drug use. Appendix D describes other surveys that provide data for these populations.

B.2 Sampling Error and Statistical Significance

This report includes tables for national estimates (see Appendices F and G) that were drawn from a more comprehensive set of tables referred to as "detailed tables."8 The national estimates, along with the associated standard errors (SEs), were computed for all detailed tables, including those in this report, using a multiprocedure package, SUDAAN® Software for Statistical Analysis of Correlated Data. SUDAAN was designed for the statistical analysis of data collected using stratified, multistage cluster sampling designs, as well as other observational and experimental studies involving repeated measures or studies subject to cluster correlation effects (RTI International, 2008). The final, nonresponse-adjusted, and poststratified analysis weights were used in SUDAAN to compute unbiased design-based drug use estimates.

The sampling error (i.e., the standard error or SE) of an estimate is the error caused by the selection of a sample instead of conducting a census of the population. The sampling error may be reduced by selecting a large sample and/or 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 using SUDAAN for all estimates presented in this report using a Taylor series linearization approach that takes into account the effects of NSDUH's complex 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

Although the SEs of estimates of means and proportions can be calculated appropriately in SUDAAN using a Taylor series linearization approach, SEs of estimates of totals may be underestimated in situations where the domain size is poststratified to data from the U.S. Census Bureau. Because of this underestimation, alternatives for estimating SEs of totals were implemented.

Estimates of means or proportions, image representing p hatd, such as drug use prevalence estimates for a domain d, can be expressed as a ratio estimate:

Appendix B Equation,     D


where image representing Y hatd is a linear statistic estimating the number of substance users in the domain d and image representing N hatd is a linear statistic estimating the total number of persons in domain d (both users and nonusers). The SUDAAN software package is used to calculate direct estimates of image representing Y hatd and image representing N hatd (and, therefore, image representing p hatd) and also can be used to estimate their respective SEs. A Taylor series approximation method implemented in SUDAAN provides the estimate for the SE of image representing p hatd.

When the domain size, image representing N hatd, is free of sampling error, an appropriate estimate of the SE for the total number of substance users is

SE (image representing Y hatd) = image representing N hatdSE(image representing p hatd).


This approach is theoretically correct when the domain size estimates, image representing N hatd, are among those forced to match their respective U.S. Census Bureau population estimates through the weight calibration process (Chen et al., 2009) described in the 2007 NSDUH Methodological Resource Book (RTI International, 2009). In these cases, image representing N hatd is not subject to a sampling error induced by the NSDUH design. For a more detailed explanation of the weight calibration process, see Section A.3.3 in Appendix A.

For estimated domain totals, image representing Y hatd, where image representing N hatd is not fixed (i.e., where domain size estimates are not forced to match the U.S. Census Bureau population estimates), this formulation still may provide a good approximation if it can be assumed that the sampling variation in image representing N hatd is negligible relative to the sampling variation in image representing p hatd. This is a reasonable assumption for most cases in this study.

For various subsets of estimates, the above approach yielded an underestimate of the variance of a total because image representing N hatd was subject to considerable variation. Starting with the 2005 NSDUH report and continuing in the 2008 NSDUH report, a "mixed" method approach was implemented for all detailed tables to improve the accuracy of SEs and to better reflect the effects of weighting on the variance of total estimates. This approach assigns the method of SE calculation to domains (subgroups for which the estimates were calculated) within tables so that all estimates among a select set of domains with fixed image representing N hatd were calculated using the formula above, and all other estimates were calculated directly in SUDAAN, regardless of other estimates within the same table. The set of domains considered controlled (i.e., those with a fixed image representing N hatd) was restricted to main effects and two-way interactions in order to maintain continuity between years. Domains consisting of three-way interactions may be controlled in a single year but not necessarily in preceding or subsequent years. The use of such SEs did not affect the SE estimates for the corresponding proportions presented in the same sets of tables because all SEs for means and proportions are calculated directly in SUDAAN. As a result of the use of this mixed-method approach, the SEs for the total estimates within many detailed tables were calculated differently from those in NSDUH reports prior to the 2005 report.

Table B.1 at the end of this appendix contains a list of domains with a fixed image representing N hatd . This table includes both the main effects and two-way interactions and may be used to identify the method of SE calculation employed for estimates of totals in the various tables of this report. For example, Table G.13 in Appendix G of this report presents estimates of illicit drug use among persons aged 18 or older within the domains of gender, Hispanic origin and race, education, and current employment. Estimates among the total population (age main effect), males and females (age by gender interaction), and Hispanics and non-Hispanics (age by Hispanic origin interaction) were treated as controlled in this table, and the formula above was used to calculate the SEs. The SEs for all other estimates, including white and black or African American (age by Hispanic origin by race interaction) were calculated directly from SUDAAN. It is important to note that estimates presented in this report for racial groups are among non-Hispanics. For instance, the domain for whites is actually non-Hispanic whites and is therefore a two-way interaction.

B.2.2 Suppression Criteria for Unreliable Estimates

As has been done in past NSDUH reports, direct survey estimates produced for this study that are 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) (defined as the ratio of the SE over the estimate), nominal (actual) sample size, and effective sample size for each estimate.

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

RSE[-ln(image representing p hat)] > .175 when image representing p hat ≤ .5

or

RSE[-ln(1 - image representing p hat)] > .175 when image representing p hat > .5.

Using a first-order Taylor series approximation to estimate RSE[-ln(image representing p hat)] and RSE[-ln(1 - image representing p hat)], the following equation was derived and used for computational purposes when developing a suppression rule dependent on effective sample size:

Appendix B Equation > .175 when image representing p hat ≤ .5     D

or

Appendix B Equation > .175 when image representing p hat > .5.     D


The separate formulas for image representing p hat ≤ .5 and image representing p hat > .5 produce a symmetric suppression rule; that is, if image representing p hat is suppressed, 1-image representing p hat will be suppressed as well (see Figure B.1). When .05 < image representing p hat < .95, the symmetric properties of the rule produce local minimum of 50 at image representing p hat = .2 and at image representing p hat = .8. Using the minimum for the suppression rule would mean that estimates of image representing p hat between .05 and .95 would be suppressed if their corresponding effective sample sizes were less than 50. Within this same interval, a local maximum of 68 is found at image representing p hat = .5. To simplify requirements and maintain a conservative suppression rule, estimates of image representing p hat between .05 and .95 were suppressed if they had an effective sample size below 68.

Below is a graph. Click here for the text describing this graph.

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

Figure B.1

In addition, 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 image representing p hat < .00005 or if image representing p hat ≥ .99995).

Estimates of other totals (e.g., number of initiates) along with means and rates that are not bounded between 0 and 1 (e.g., mean age at first use and incidence rates) were suppressed if the RSEs of the estimates were larger than .5. Additionally, estimates of the mean age at first use were suppressed if the sample size was smaller than 10 respondents. Also, 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.2 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 is based on the p value of the test statistic and refers to the probability that a difference as large as that observed would occur due to random variability in the estimates if there were no difference in the prevalence estimates for the population groups being compared. The significance of observed differences in this report is reported at the .05 level. When comparing prevalence estimates, the null hypothesis (no difference between prevalence estimates) was tested against the alternative hypothesis (there is a difference in prevalence estimates) using the standard difference in proportions test expressed as

Appendix B Equation     D


where image representing p hat1 = first prevalence estimate, image representing p hat2 = second prevalence estimate, var (image representing p hat1) = variance of first prevalence estimate, var (image representing p hat2) = variance of second prevalence estimate, and cov (image representing p hat1, image representing p hat2) = covariance between image representing p hat1 and image representing p hat2. In cases where significance tests between years were performed, the prevalence estimate from the earlier year (e.g., 2002, 2003, 2004, 2005, 2006 or 2007) becomes the first prevalence estimate, and the prevalence estimate from the later year (e.g., 2003, 2004, 2005, 2006, 2007 or 2008) becomes the second prevalence estimate.

Under the null hypothesis, Z is asymptotically distributed as a normal random variable. Therefore, calculated values of Z can be referred to the unit normal distribution to determine the corresponding probability level (i.e., p value). Because the covariance term between the two estimates is not necessarily zero, SUDAAN was used to compute estimates of Z along with the associated p values 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; however, it should be noted that because it was necessary to calculate the SE outside of SUDAAN for domains forced by the weighting process to match their respective U.S. Census Bureau population estimates, the corresponding test statistics also were computed outside of SUDAAN.

When comparing population subgroups across three or more levels of a categorical variable, log-linear chi-square tests of independence of the subgroups and the prevalence variables were conducted using SUDAAN in order to first control the error level for multiple comparisons. If Shah's Wald F test (transformed from the standard Wald chi-square) indicated overall significant differences, the significance of each particular pairwise comparison of interest was tested using SUDAAN analytic procedures to properly account for the sample design (RTI International, 2008). Using the published estimates and SEs to perform independent t tests for the difference of proportions usually will 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.

As part of a comparative analysis discussed in Chapter 9, prevalence estimates from the Monitoring the Future (MTF) study, sponsored by the National Institute on Drug Abuse (NIDA), were presented for recency measures of selected substances (see Tables 9.1 and 9.2). The analyses focused on prevalence estimates for 8th and 10th graders and prevalence estimates for young adults aged 19 to 24 for 2002 through 2008. Estimates for the 8th and 10th grade students were calculated using MTF data as the simple average of the 8th and 10th grade estimates. Estimates for young adults aged 19 to 24 were calculated using MTF data as the simple average of three modal age groups: 19 and 20 years, 21 and 22 years, and 23 and 24 years. Published results were not available from NIDA for significant differences in prevalence estimates between years for these subgroups, so testing was performed using information that was available.

For the 8th and 10th grade average estimates, tests of differences were performed between 2008 and the 6 prior years. Estimates for persons in grade 8 and grade 10 were considered independent, simplifying the calculation of variances for the combined grades. Across years, the estimates for 2008 involved samples independent of those in 2002, 2003, 2004, 2005, and 2006, but from 2007 to 2008 the sample of schools overlapped 50 percent, creating a covariance in the estimates. Design effects published in Johnston et al. (2008b) for adjacent and nonadjacent year testing were used.

For the 19- to 24-year-old age group, tests of differences were done assuming independent samples between years an odd number of years apart because two distinct cohorts a year apart were monitored longitudinally at 2-year intervals. This is appropriate for comparisons of 2003, 2005, and 2007 with 2008. However, this results in conservative tests for comparisons of 2002, 2004, and 2006 data with 2008 data because it does not take into account covariances associated with repeated observations from the longitudinal samples. Estimates of covariances were not available.

As an example, the difference between the 2007 and 2008 averages of prevalence estimates for persons in grades 8 and 10 can be expressed as

image representing P bar2 - image representing P bar1,

where image representing P bar1 = (image representing p hat11 + image representing p hat12) / 2, image representing p hat11 and image representing p hat12 are the prevalence estimates for the 8th and 10th grades, respectively, for 2007; and image representing P bar2 is defined similarly for 2008. The variance of a prevalence estimate image representing p hat can be written as

Appendix B Equation,     D

where n is the sample size and D is the appropriate design effect obtained from the sampling design. In the MTF study, design effects were available for comparisons between adjacent-year (i.e., 2007 vs. 2008) estimates and nonadjacent-year (i.e., 2002 vs. 2008, 2003 vs. 2008, 2004 vs. 2008, 2005 vs. 2008, and 2006 vs. 2008) estimates; therefore, the variance of the difference between 2 years of estimates for a particular grade can be expressed as

Appendix B Equation,     D

where i = 1 indexes the 8th grade, i = 2 indexes the 10th grade, Di is the design effect appropriate for comparisons between estimates of the 2 years (with separate design effect parameters for adjacent and nonadjacent years), and the nji are the sample sizes corresponding to the indexed year and grade prevalence estimates, i, j = 1,2. Because the 8th and 10th grade samples were independently drawn, the variance of the difference between the 8th and 10th grade averages can be expressed as

Appendix B Equation.     D

The test statistic can therefore be written as

Appendix B Equation,     D


where Z is asymptotically distributed as a standard normal random variable.

B.3 Other Information on Data Accuracy

The accuracy of survey estimates can be affected by nonresponse, coding errors, computer processing errors, errors in the sampling frame, reporting errors, and other errors not due to sampling. They are sometimes referred to as "nonsampling errors." These types of errors and their impact are reduced through data editing, statistical adjustments for nonresponse, close monitoring and periodic retraining of interviewers, and improvement in various quality control procedures.

Although these types of errors often can be much larger than sampling errors, measurement of most of these errors is difficult. However, some indication of the effects of some types of these 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 2008, respondents continued to receive a $30 incentive in an effort to maximize response rates. The weighted screening response rate (SRR) is defined as the weighted number of successfully screened households9 divided by the weighted number of eligible households (as defined in Table B.3), or

Appendix B Equation     D


where whh is the inverse of the unconditional probability of selection for the household and excludes all adjustments for nonresponse and poststratification defined in Section A.3.3 of Appendix A. Of the 160,133 eligible households sampled for the 2008 NSDUH, 142,938 were screened successfully, for a weighted screening response rate of 89.0 percent (Table B.3). At the person level, the weighted interview response rate (IRR) is defined as the weighted number of respondents divided by the weighted number of selected persons (see Table B.4), or

Appendix B Equation     D


where wi is the inverse of the probability of selection for the person and includes household-level nonresponse and poststratification adjustments (adjustments 1, 2, and 3 in Section A.3.3 of Appendix A). To be considered a completed interview, a respondent must provide enough data to pass the usable case rule.10 In the 142,938 screened households, a total of 86,435 sample persons were selected, and completed interviews were obtained from 68,736 of these sample persons, for a weighted IRR of 74.4 percent (Table B.4). A total of 12,075 (17.8 percent) sample persons were classified as refusals or parental refusals, 3,306 (3.7 percent) were not available or never at home, and 2,318 (4.1 percent) did not participate for various other reasons, such as physical or mental incompetence or language barrier (see Table B.4, which also shows the distribution of the selected sample by interview code and age group). Among demographic subgroups, the weighted IRR was higher among 12 to 17 year olds (84.7 percent), females (76.4 percent), blacks (78.8 percent), persons in the South (76.6 percent), and residents of nonmetropolitan areas (77.2 percent) than among other related groups (Table B.5).

The overall weighted response rate, defined as the product of the weighted screening response rate and weighted interview response rate or

ORR = SRR × IRR

was 66.3 percent in 2008. 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 (Pr - Pnr). By maximizing NSDUH response rates, it is hoped that the bias due to the difference between the estimates from respondents and nonrespondents is minimized. Drug use surveys are particularly vulnerable to nonresponse due to the difficult nature of accessing heavy drug users. In a study that matched 1990 census data to 1990 NHSDA nonrespondents,11 it was found that populations with low response rates did not always have high drug use rates. For example, although some populations were found to have low response rates and high drug use rates (e.g., residents of large metropolitan areas and males), other populations had low response rates and low drug use rates (e.g., older adults and high-income populations). Therefore many of the potential sources of bias tend to cancel each other in estimates of overall prevalence (Gfroerer, Lessler, & Parsley, 1997a).

B.3.2 Inconsistent Responses and Item Nonresponse

Among survey participants, item response rates were above 99 percent for most drug use items. However, respondents could give inconclusive or inconsistent information about whether they ever used a given drug (i.e., "yes" or "no") and, if they had used a drug, when they last used it; the latter information is needed to identify those lifetime users of a drug who used it in the past year or past month. In addition, respondents could give inconsistent responses to items such as when they first used a drug compared with their most recent use of a drug. These missing or inconsistent responses first are resolved where possible through a logical editing process. Additionally, missing or inconsistent responses are imputed using statistical methodology (Ault et al., 2009). These imputation procedures in NSDUH are based on responses to multiple questions, so that the maximum amount of information is used in determining whether a respondent is classified as a user or nonuser, and if the respondent is classified as a user, whether the respondent is classified as having used in the past year or the past month. For example, ambiguous data on the most recent use of cocaine are statistically imputed based on a respondent's data for use (or most recent use) of tobacco products, alcohol, inhalants, marijuana, hallucinogens, and nonmedical use of prescription psychotherapeutic drugs. Nevertheless, editing and imputation of missing responses are potential sources of measurement error. For more information on editing and statistical imputation, see Sections A.3.1 and A.3.2 of Appendix A. Additional information on editing and statistical imputation procedures can be found online at http://oas.samhsa.gov/nsduh/methods.cfm.

B.3.3 Data Reliability

A reliability study was conducted as part of the 2006 NSDUH to assess the reliability of responses to the NSDUH questionnaire. An interview/reinterview method was employed in which 3,136 individuals were interviewed on two occasions during 2006 generally 5 to 15 days apart; the initial interviews in the reliability study were a subset of the main study interviews. The reliability of the responses was assessed by comparing the responses of the first interview with the responses from the reinterview.

Results for the reliability of selected substance use and mental health variables are presented in Table B.6. Reliability is expressed in the table by estimates of Cohen's kappa (κ) (Cohen, 1960), which can be interpreted according to benchmarks proposed by Landis and Koch (1977, p. 165):

None of the values for the substance use variables presented in Table B.6 fell below 0.82, indicating substantial to nearly perfect response consistency on these measures. Reliability statistics obtained for the substance dependence or abuse and major depressive episode (MDE) measures were moderate to substantial, while substance abuse treatment and mental health treatment variables showed almost perfect consistency. For further information on the reliability of a wide range of measures contained in NSDUH, see the complete methodology report (Chromy et al., 2009).

B.3.4 Validity of Self-Reported Substance Use

Most substance use prevalence estimates, including those produced for NSDUH, are based on self-reports of use. Although studies generally have supported the validity of self-report data, it is well documented that these data often are biased (underreported or overreported). The bias varies by several factors, including the mode of administration, the setting, the population under investigation, and the type of drug (Aquilino, 1994; Brener et al., 2006; Harrison & Hughes, 1997; Tourangeau & Smith, 1996; Turner, Lessler, & Gfroerer, 1992). NSDUH utilizes widely accepted methodological practices for increasing the accuracy of self-reports, such as encouraging privacy through audio computer-assisted self-interviewing (ACASI) and providing assurances that individual responses will remain confidential. Comparisons using these methods within NSDUH have shown that they reduce reporting bias (Gfroerer, Eyerman, & Chromy, 2002). Various procedures, such as biological specimens (e.g., urine, hair, saliva), proxy reports (e.g., family member, peer), and repeated measures (e.g., recanting), have been used to validate self-report data (Fendrich, Johnson, Sudman, Wislar, & Spiehler, 1999). However, these procedures often are impractical or too costly for general population epidemiological studies (SRNT Subcommittee on Biochemical Verification, 2002).

A study cosponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA) and the National Institute on Drug Abuse (NIDA) examined the validity of NSDUH self-report data on drug use among persons aged 12 to 25. The study found that it is possible to collect urine and hair specimens with a relatively high response rate in a general population survey, and that most youths and young adults reported their recent drug use accurately in self-reports (Harrison, Martin, Enev, & Harrington, 2007). However, there were some reporting differences in either direction, with some respondents not reporting use but testing positive, and some reporting use but testing negative. Technical and statistical problems related to the hair tests precluded presenting comparisons of self-reports and hair test results, while small sample sizes for self-reports and positive urine test results for opiates and stimulants precluded drawing conclusions about the validity of self-reports of these drugs. Further, inexactness in the window of detection for drugs in biological specimens and biological factors affecting the window of detection could account for some inconsistency between self-reports and urine test results.

B.4 Measurement Issues

Several measurement issues associated with the 2008 NSDUH may be of interest and are discussed in this section. Specifically, these issues include the methods for measuring incidence; nicotine (cigarette) dependence; substance dependence and abuse; serious mental illness (SMI); serious psychological distress (SPD); major depressive episode (MDE); and methamphetamine use.

B.4.1 Incidence

In epidemiological studies, incidence is defined as the number of new cases of a disease occurring within a specific period of time. Similarly, in substance use studies, incidence refers to the first use of a particular substance.

In the 2004 NSDUH national results report (Office of Applied Studies [OAS], 2005), a new measure related to incidence was introduced and since then has become the primary focus of Chapter 5 in this national results report series. The incidence measure is termed "past year initiation" and refers to respondents whose date of first use of a substance was within the 12 months prior to their interview date. This measure is determined by self-reported past year use, age at first use, year and month of recent new use, and the interview date.

Since 1999, the survey questionnaire has allowed for collection of year and month of first use for recent initiates (i.e., persons who used a particular substance for the first time in a given survey year). Month, day, and year of birth also are obtained directly or are imputed for item nonrespondents as part of the data postprocessing. Additionally, the computer-assisted interviewing (CAI) instrument records and provides the date of the interview. By imputing a day of first use within the year and month of first use, a specific date of first use, tfu,d,i, can be used for estimation purposes.

Past year initiation among persons using a substance in the past year can be viewed as an indicator variable defined as follows:

Appendix B Equation.     D


where DOIi, MOIi, and YOIi denote the day, month, and year of the interview, respectively, and tfu,d,i denotes the date of first use.

The calculation of this estimate does not take into account whether a respondent initiated substance use while a resident of the United States. This method of calculation has little effect on past year estimates and allows for direct comparability with other standard measures of substance use because the populations of interest for the measures will be the same (i.e., both measures examine all possible respondents and are not restricted to those initiating substance use only in the United States).

One important note for incidence estimates is the relationship between main categories and subcategories of substances (e.g., illicit drugs would be a main category, and inhalants and marijuana would be subcategories in relation to illicit drugs). For most measures of substance use, any member of a subcategory is by necessity a member of the main category (e.g., if a respondent is a past month user of a particular drug, then he or she is also a past month user of illicit drugs in general). However, this is not the case with regard to incidence statistics. Because an individual can only be an initiate of a particular substance category (main or sub) a single time, a respondent with lifetime use of multiple substances may not, by necessity, be included as a past year initiate of a main category, even if he or she were a past year initiate for a particular subcategory because his or her first initiation of other substances within the main category could have occurred earlier.

In addition to estimates of the number of persons initiating use of a substance in the past year, estimates of the mean age of past year first-time users of these substances are computed. Unless specified otherwise, estimates of the mean age at initiation in the past 12 months have been restricted to persons aged 12 to 49 so that the mean age estimates reported are not influenced by those few respondents who were past year initiates at age 50 or older. As a measure of central tendency, means are influenced heavily by the presence of extreme values in the data, and this constraint should increase the utility of these results to health researchers and analysts by providing a better picture of the substance use initiation behaviors among the civilian, noninstitutionalized population in the United States. This constraint was applied only to estimates of mean age at first use and does not affect estimates of incidence.

Because NSDUH is a survey of persons aged 12 years old or older at the time of the interview, younger individuals in the sample dwelling units are not eligible for selection into the NSDUH sample. Some of these younger persons may have initiated substance use during the past year. As a result, past year initiate estimates suffer from undercoverage if a user assumes that these estimates reflect all initial users instead of only for those above the age of 11. For earlier years, data can be obtained retrospectively based on the age at and date of first use. As an example, persons who were 12 years old on the date of their interview in the 2008 survey may report having initiated use of cigarettes between 1 and 2 years ago; these persons would have been past year initiates reported in the 2007 survey had persons who were 11 years old on the date of the 2007 interview been allowed to participate in the survey. Similarly, estimates of past year use by younger persons (age 10 or younger) can be derived from the current survey, but they apply to initiation in prior years and not the survey year.

To get an impression of the potential undercoverage in the current year, reports of substance use initiation reported by persons aged 12 or older were estimated for the years in which these persons would have been 1 to 11 years younger. These estimates do not necessarily reflect behavior by persons 1 to 11 years younger in the current survey. Instead, the data for the 11 year olds reflect initiation in the year prior to the current survey; the data for the 10 year olds reflect behavior between the 12th and 23rd months prior to this year's survey, and so on. A very rough way to adjust for the difference in the years that the estimate pertains to without considering changes in the population is to apply an adjustment factor to each age-based estimate of past year initiates. This adjustment factor can be based on a ratio of lifetime users aged 12 to 17 in the current survey year to the same estimate for the prior applicable survey year. To illustrate the calculation, consider past year use of alcohol. In the 2008 survey, 134,098 persons 12 years old in 2008 were estimated to have initiated use of alcohol between 1 and 2 years earlier. These persons would have been past year initiates in the 2007 survey conducted on the same dates had the 2007 survey covered younger persons. The estimated number of lifetime users currently aged 12 to 17 was 9,540,037 for 2008 and 9,949,469 for 2007, indicating fewer overall initiates of alcohol use among persons aged 17 or younger in 2008. Thus, an adjusted estimate of initiation of alcohol use by persons who were 11 years old in 2008 is given by

Appendix B Equation     D


This yielded an adjusted estimate of 128,580 persons 11 years old on a 2008 survey date and initiating use of alcohol in the past year:

Appendix B Equation     D


A similar procedure was used to adjust the estimated number of past year initiates among persons who would have been 10 years old on the date of the interview in 2006 and for younger persons in earlier years. The overall adjusted estimate for past year initiates of alcohol use by persons 11 years of age or younger on the date of the interview was 252,980, or about 5.7 percent of the estimate based on past year initiation by persons 12 or older only (252,980 ÷ 4,466,102 = 0.0566).

Based on similar analyses, the estimated undercoverage of past year initiates was 5.6 percent for cigarettes, 0.7 percent for marijuana, and 22.8 percent for inhalants. These 2008 results are comparable with undercoverage estimates presented in prior reports using data from the 2005 through 2007 surveys.

The undercoverage of past year initiates aged 11 or younger also affects the mean age at first use estimate. An adjusted estimate of the mean age at first use was calculated using a weighted estimate of the mean age at first use based on the current survey and the numbers of persons aged 11 or younger in the past year obtained in the aforementioned analysis for estimating undercoverage of past year initiates. Analysis results showed that the mean age at first use was changed from 17.0 to 16.6 (or a decrease of 2.6 percent) for alcohol, from 17.4 to 16.9 (or a decrease of 2.8 percent) for cigarettes, from 17.8 to 17.7 (or a decrease of 0.4 percent) for marijuana, and from 15.9 to 14.6 (or a decrease of 8.0 percent) for inhalants. The percentage decreases reported above are comparable with results generated in prior survey years.

B.4.2 Nicotine (Cigarette) Dependence

The 2008 NSDUH's CAI instrumentation included questions designed to measure nicotine dependence among current cigarette smokers. Nicotine dependence is based on criteria derived from the Nicotine Dependence Syndrome Scale (NDSS) (Shiffman, Hickcox, Gnys, Paty, & Kassel, 1995; Shiffman, Waters, & Hickcox, 2004) and the Fagerstrom Test of Nicotine Dependence (FTND) (Fagerstrom, 1978; Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991). The above-mentioned criteria were first used to measure nicotine dependence in NSDUH in 2003.

The conceptual roots of the NDSS (Edwards & Gross, 1976) are similar to those behind the American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV), concept of dependence (APA, 1994). The 2008 NSDUH contained 19 NDSS questions that addressed five aspects of dependence:

  1. Smoking drive (compulsion to smoke driven by nicotine craving and withdrawal)
    1. After not smoking for a while, you need to smoke in order to feel less restless and irritable.

    2. When you don't smoke for a few hours, you start to crave cigarettes.

    3. You sometimes have strong cravings for a cigarette where it feels like you're in the grip of a force you can't control.

    4. You feel a sense of control over your smoking - that is, you can "take it or leave it" at any time.

    5. You sometimes worry that you will run out of cigarettes.

  2. Nicotine tolerance
    1. Since you started smoking, the amount you smoke has increased.

    2. Compared to when you first started smoking, you need to smoke a lot more now in order to be satisfied.

    3. Compared to when you first started smoking, you can smoke much, much more now before you start to feel anything.

  3. Continuous smoking
    1. You smoke cigarettes fairly regularly throughout the day.

    2. You smoke about the same amount on weekends as on weekdays.

    3. You smoke just about the same number of cigarettes from day to day.

    4. It's hard to say how many cigarettes you smoke per day because the number often changes.

    5. It's normal for you to smoke several cigarettes in an hour, then not have another one until hours later.

  4. Behavioral priority (preferring smoking over other reinforcing activities)
    1. You tend to avoid places that don't allow smoking, even if you would otherwise enjoy them.

    2. There are times when you choose not to be around your friends who don't smoke because they won't like it if you smoke.

    3. Even if you're traveling a long distance, you'd rather not travel by airplane because you wouldn't be allowed to smoke.

  5. Stereotypy (fixed patterns of smoking)
    1. Do you have any friends who do not smoke cigarettes?

    2. The number of cigarettes you smoke per day is often influenced by other things - how you're feeling, or what you're doing, for example.

    3. Your smoking is not affected much by other things. For example, you smoke about the same amount whether you're relaxing or working, happy or sad, alone or with others.

Each of the five domains listed above can be assessed by a separate measure, but an average score across all domains also can be obtained for overall nicotine dependence (Shiffman et al., 2004). The NDSS algorithm for calculating this average score was based on the respondent's answers to 17 of the 19 questions listed above. The two items regarding nonsmoking friends (4b and 5a) were excluded due to higher item nonresponse rates.

To optimize the number of respondents who could be classified for nicotine dependence, imputation was utilized for all respondents who answered all but 1 of the 17 nicotine dependence questions that were used in the NDSS algorithm. The imputation was based on weighted least square regressions using the other 16 NDSS items as covariates in the model (Ault et al., 2009).

Responses to items 1a-c, 1e, 2a-c, 3a-c, 4a, 4c, and 5c were coded from 1 to 5 where

1 = Not at all true of me
2 = Somewhat true of me
3 = Moderately true of me
4 = Very true of me
5 = Extremely true of me

Responses to items 1d, 3d, 3e, and 5b were reverse coded from 5 to 1 where

5 = Not at all true of me
4 = Somewhat true of me
3 = Moderately true of me
2 = Very true of me
1 = Extremely true of me

The NDSS score was calculated as the sum of the responses to the previous questions divided by 17. The NDSS score was only calculated for current cigarette smokers who had complete data (based on actual reporting and imputation) for all 17 questions.

A current cigarette smoker was defined as nicotine dependent if his or her NDSS score was greater than or equal to 2.75. If the NDSS score for a current cigarette smoker was less than 2.75 or the NDSS score was not defined, then the respondent was determined to be nondependent based on the NDSS. The threshold of 2.75 was derived by examining the distribution of scores in other samples of smokers administered the NDSS, including a contrast of scores obtained for nondependent smokers (chippers) versus heavy smokers (Shiffman, Paty, Kassel, Gnys, & Zettler-Segal, 1994).

The FTND is a multi-item measure of dependence, but much of its ability to discriminate dependent smokers derives from a single item that assesses how soon after waking that smokers have their first cigarette (Heatherton, Kozlowski, Frecker, Rickert, & Robinson, 1989). Because most nicotine is cleared from the bloodstream overnight, smokers typically wake in nicotine deprivation, and rapid movement to smoke is considered a sign of dependence. A current cigarette smoker was defined as nicotine dependent based on the FTND if the first cigarette smoked was within 30 minutes of waking up on the days that he or she smoked.

Using both the NDSS and the FTND measures described above, a current cigarette smoker was defined as having nicotine dependence in the past month if he or she met either the NDSS or FTND criteria for dependence.

B.4.3 Illicit Drug and Alcohol Dependence and Abuse

The 2008 NSDUH CAI instrumentation included questions that were designed to measure dependence on and abuse of illicit drugs and alcohol. For these substances,12 dependence and abuse questions were based on the criteria in the DSM-IV (APA, 1994).

Specifically, for marijuana, hallucinogens, inhalants, and tranquilizers, a respondent was defined as having dependence if he or she met three or more of the following six dependence criteria:

  1. Spent a great deal of time over a period of a month getting, using, or getting over the effects of the substance.

  2. Used the substance more often than intended or was unable to keep set limits on the substance use.

  3. Needed to use the substance more than before to get desired effects or noticed that the same amount of substance use had less effect than before.

  4. Inability to cut down or stop using the substance every time tried or wanted to.

  5. Continued to use the substance even though it was causing problems with emotions, nerves, mental health, or physical problems.

  6. The substance use reduced or eliminated involvement or participation in important activities.

For alcohol, cocaine, heroin, pain relievers, sedatives, and stimulants, a seventh withdrawal criterion was added. A respondent was defined as having dependence if he or she met three or more of seven dependence criteria. The seventh withdrawal criterion is defined by a respondent reporting having experienced a certain number of withdrawal symptoms that vary by substance (e.g., having trouble sleeping, cramps, hands tremble).

For each illicit drug and alcohol, a respondent was defined as having abused that substance if he or she met one or more of the following four abuse criteria and was determined not to be dependent on the respective substance in the past year:

  1. Serious problems at home, work, or school caused by the substance, such as neglecting your children, missing work or school, doing a poor job at work or school, or losing a job or dropping out of school.

  2. Used the substance regularly and then did something that might have put you in physical danger.

  3. Use of the substance caused you to do things that repeatedly got you in trouble with the law.

  4. Had problems with family or friends that were probably caused by using the substance and continued to use the substance even though you thought the substance use caused these problems.

Criteria used to determine whether a respondent was asked the dependence and abuse questions during the interview included responses from the core substance use questions and the frequency of substance use questions, as well as the noncore substance use questions. Missing or incomplete responses in the core substance use and frequency of substance use questions were imputed. However, the imputation process did not take into account reported data in the noncore (i.e., substance dependence and abuse) CAI modules. This may have resulted in responses to the dependence and abuse questions that were inconsistent with the imputed substance use or frequency of substance use.

For alcohol and marijuana, respondents were asked the dependence and abuse questions if they reported substance use on more than 5 days in the past year, or if they reported any substance use in the past year but did not report their frequency of past year use. Therefore, inconsistencies could have occurred where the imputed frequency of use response indicated less frequent use than required for respondents to be asked the dependence and abuse questions originally.

For cocaine, heroin, and stimulants, respondents were asked the dependence and abuse questions if they reported past year use in a core drug module or past year use in the noncore special drugs module. Thus, inconsistencies could have occurred when the response to a core substance use question indicated no use in the past year, but responses to dependence and abuse questions indicated substance dependence or abuse for the respective substance.

In 2005, two new questions were added to the noncore special drugs module about past year methamphetamine use: "Have you ever, even once, used methamphetamine?" and "Have you ever, even once, used a needle to inject methamphetamine?" In 2006, an additional follow-up question was added to the noncore special drugs module confirming prior responses about methamphetamine use: "Earlier, the computer recorded that you have never used methamphetamine. Which answer is correct?" The responses to these new questions were used in the skip logic for the stimulant dependence and abuse questions. Based on the decisions made during the methamphetamine analysis (see Section B.4.8), respondents who indicated past year methamphetamine use solely from these new special drug use questions (i.e., did not indicate methamphetamine use from the core drug module or other questions in the special drugs module) were categorized as NOT having past year stimulant dependence or abuse regardless of how they answered the dependence and abuse questions. Furthermore, if these same respondents were categorized as not having past year dependence on or abuse of any other substance (e.g., pain relievers, tranquilizers, or sedatives for the psychotherapeutic drug grouping), then they were categorized as NOT having past year dependence on or abuse of psychotherapeutics, illicit drugs, illicit drugs or alcohol, and illicit drugs and alcohol.

In 2008, questionnaire logic for determining hallucinogen, stimulant, and sedative dependence or abuse was modified. The revised skip logic used information collected in the noncore special drugs module in addition to that collected in questions from the core drug modules. Respondents were asked about hallucinogen dependence and abuse if they additionally reported in the special drugs module using Ketamine, DMT, AMT, Foxy, or Salvia divinorum; stimulant dependence and abuse if they reported additionally using Adderall®; and sedative dependence and abuse if they reported additionally using Ambien®. Complying with the previous decision to exclude respondents whose methamphetamine use was based solely on responses in a noncore module from being classified as having stimulant dependence or abuse, respondents who indicated past year hallucinogen, stimulant, or sedative use based solely on these special drug questions were categorized as NOT having past year dependence on or abuse of the relevant substance regardless of how they answered the dependence and abuse questions.

Respondents might have provided ambiguous information about past year use of any individual substance, in which case these respondents were not asked the dependence and abuse questions for that substance. Subsequently, these respondents could have been imputed to be past year users of the respective substance. In this situation, the dependence and abuse data were unknown; thus, these respondents were classified as not dependent on or abusing the respective substance. However, such a respondent never actually was asked the dependence and abuse questions.

B.4.4 Effects of Questionnaire Changes on Mental Health Measures

Several important changes were made to the adult mental health module in the 2008 NSDUH questionnaire. These changes not only provide valuable new data on mental health, but also affect some of the measures that have been collected in NSDUH since 2004. This section summarizes the questionnaire changes and their impact on NSDUH estimates.

Description of Questionnaire Changes. From 2004 to 2007, the mental health module for adults consisted of two primary components. First, a 12-month K6 distress scale was administered, then questions about lifetime and 12-month major depressive episode (MDE) were asked. In 2008, the K6 questions were modified to collect data on distress in the past 30 days and in the past 12 months (see Section B.4.5 for details). In addition, adult respondents were administered one of two impairment scales—an abbreviated World Health Organization Disability Assessment Schedule (WHODAS) (see Section B.4.6 for details) or the Sheehan Disability Scale (SDS)—and suicidal ideation questions. These new impairment and suicide questions were placed after the K6 questions, but before the MDE questions. A random split-sample design was implemented for adults where respondents in sample A were administered the WHODAS scale and respondents in sample B were administered the SDS. All adult respondents were administered the suicidal ideation questions after the impairment items, but before the MDE items.

Two changes in the mental health module for the 2008 NSDUH seemed to provide the potential for changes in estimates related to the reporting of past 12-month K6—and consequently, serious psychological distress (SPD) and major depressive episode (MDE) (see Section B.4.7 for details on MDE and Section B.4.5 for details on the K6 scale and SPD). In 2007, a single set of six K6 items asked adult respondents to report how often they experienced certain emotions or feelings during the one month in the past 12 months that they were the most depressed, anxious, or stressed. In 2008, adult respondents first were asked about these feelings in the past 30 days. If there was a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days, they then were asked the same K6 items about this month as well. Thus, the past year K6 score in 2008 was now created for each person based on responses to items regarding either the past 30 days (if they said they did not have any other month that was worse) or the worst month in the past 12 months. This change in questionnaire structure may have affected K6 scores and SPD prevalences for the worst month in the past year that was created from the K6 items.

The second major area of questionnaire changes involved the insertion of the two impairment scales through the split sample and suicidal ideation questions ahead of questions measuring adult depression. This introduced the potential for context effects on the suicide and MDE items. Context effects are those changes in the responses to a "target" question because of its placement after one or more context questions. In short, a context effect may be said to take place when the response to a question is affected by information that is not part of the question itself. For example, the content of a preceding question may affect the interpretation of a subsequent question. Or a respondent may answer a subsequent question in a manner that is consistent with responses to a preceding question if the two questions are closely related to each other. It was hypothesized that placement of the impairment and suicide questions, as well as the change in the K6 questions, ahead of the depression items may affect responses to the depression items. In addition, the split-sample testing of two different impairment scales raised the possibility that these two sets of impairment items may differentially affect responses in the suicide items and the depression section. It also was considered possible that the earlier items in the adult depression section could be differentially affected compared with the later items that were used to create variables related to MDE.

Even though the entire adult sample was split into two subsamples, and each adult respondent was eligible to be administered the WHODAS or SDS, some respondents were not administered either of the impairment scales. Respondents with a K6 score of zero (i.e., they answered "none of the time" for all six of the past 30-day K6 items or "none of the time" for all six of the past 30-day and all six of the worst 12-month K6 items) were not administered either of the impairment scales. However, all adults were administered the suicidal ideation items, which followed the impairment scales, prior to answering the depression items. For analytic purposes, data from the two subsamples are compared, regardless of whether the impairment scale was actually administered, to get a sense of the overall effect of the administration of the two impairment scales on the MDE estimates.

Examining Changes in 2008 SPD Estimates. For adults aged 18 or older, estimates of past year K6 scores and the percentage with SPD based on the entire 2008 sample, as well as the WHODAS and SDS subsamples, were compared with estimates based on 2007 data. There were significant differences in the mean past year K6 scores between 2008 and 2007 (4.9 vs. 5.2 percent, respectively), thus suggesting a lack of comparability between the 2 years (Table B.7). Across each of the six items forming the past year K6 score, a higher percentage of respondents selected "none of the time" as a response (e.g., "how often felt restless in worst month") (Table B.8).

No significant difference was detected in the overall percentage of adults with past year SPD between 2007 and 2008 (10.9 vs. 10.3 percent) (Table B.7). Unexpectedly, however, a significantly lower percentage of adults with past year SPD was estimated from the WHODAS subsample compared with the SDS subsample (9.7 vs. 10.8 percent) (Table B.7). These results are unexpected because the SDS and WHODAS impairment items follow the K6 items; therefore, differences in SPD scores were not expected between the two split samples.

Examining Context Effects in 2008 MDE Estimates. For adults aged 18 or older, both past year and lifetime MDE estimates based on the entire sample were significantly lower than comparable estimates from 2007 (6.8 vs. 7.5 percent and 13.3 vs. 14.2 percent, respectively), suggesting the possibility of a presence of context effects (Table B.7). The three questions (ASC21 on "ever felt sad/empty," ASC22 on "ever felt discouraged," and ASC23 on "ever lost interest") that determine whether a respondent will be asked the depression items also showed a lower percentage of individuals answering with a "yes" for 2008 compared with 2007. These results also were compared with comparable youth items to see whether there was possibly a general downward trend in the overall population. Although the youth estimates in 2008 were lower than those in 2007, there were no significant differences in any of the five youth depression-related estimates between 2007 and 2008.

In addition, there appeared to be differences in the MDE estimates depending upon which impairment scale was used. Except for the first gate question (ASC21 on "ever felt sad/empty"), no statistically significant differences were detected between estimates based on the 2008 SDS subsample and the 2007 estimates. However, both past year and lifetime MDE estimates based on the WHODAS subsample were significantly lower than comparable estimates from 2007 (6.4 vs. 7.5 percent and 12.8 vs. 14.2 percent, respectively), suggesting the possibility of a presence of differential context effects based on the impairment scale used (Table B.7).

Differences in past year and lifetime MDE between 2008 and 2007 and between the WHODAS and SDS subsamples appear to be related to demographic variables, such as gender, age, race/ethnicity, education, marital status, and annual family income. MDE estimates based on the WHODAS subsample appear to have more differences in estimates based on demographic characteristics compared with 2007 relative to estimates based on the SDS subsample (Table B.9).

It also should be pointed out that the lower MDE estimate in 2008 could in part be due to a real drop in MDE prevalence. However, the rates were relatively stable from 2005 to 2007, and a significant decline in overall lifetime MDE is illogical if no reporting anomalies occur.

Examining Differences in Suicidal Ideation Estimates. Comparisons of suicidal ideation reporting between samples A and B revealed no significant differences. As shown in Table B.7, of respondents in the WHODAS sample, 3.6 percent reported seriously thinking about suicide in the past 12 months. In comparison, 3.8 percent of respondents in the SDS sample seriously considered suicide.

Conclusions. Further analysis is needed to better understand the nature of the changes in the reporting of K6 and MDE associated with questionnaire differences (i.e., between 2007 and 2008 and between sample A and sample B in 2008). These analyses may lead to the development of statistical adjustments to provide comparable estimation and more complete trend measurement. The WHODAS versus SDS comparisons are important because the 2009 and subsequent NSDUHs will include only the WHODAS scale, so comparability between 2008 and later years may require dropping the SDS half sample. Based on the analysis completed so far, a conservative approach was taken for this report. Comparisons are not made between samples where context effects or questionnaire differences are suspected to have significantly affected responses. Thus, no trends are presented for SPD or adult MDE, and only the WHODAS half sample is used for 2008 adult MDE estimation to facilitate future trend analysis. The full sample has been used in the report for suicidal ideation estimation.

B.4.5 Serious Psychological Distress

For this 2008 NSDUH report, serious psychological distress (SPD) was measured using the K6 screening instrument for nonspecific psychological distress (Kessler et al., 2003a). However, the 2008 NSDUH employed a different module of K6 questions, which captured distress levels in the past month as well as during the worst month of the past 12 months.

In 2008, the K6 consisted of two sets of six questions that asked respondents how frequently they experienced symptoms of psychological distress during two different time periods: (1) during the past 30 days, and (2) the one month in the past year when they were at their worst emotionally. Respondents were asked only about the second time period if they indicated that there was a month in the past 12 months when they felt more depressed, anxious, or emotionally stressed than they felt during the past 30 days.

The six past month questions comprising the K6 scale are given as follows:

NERVE30
During the past 30 days, 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:
HOPE30
During the past 30 days, how often did you feel hopeless?
FIDG30
During the past 30 days, how often did you feel restless or fidgety?
NOCHR30
During the past 30 days, how often did you feel so sad or depressed that nothing could cheer you up?
EFFORT30
During the past 30 days, how often did you feel that everything was an effort?
DOWN30
During the past 30 days, how often did you feel down on yourself, no good, or worthless?

To create a score, the six items (NERV30, HOPE30, FIDG30, NOCHR30, EFFORT30, and DOWN30) on the K6 scale were coded from 0 to 4 so that "all of the time" was coded 4, "most of the time" 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 resulted in a score with a range from 0 to 24. Respondents with a total score of 13 or greater were classified as having past month SPD as that cut point has been shown to be highly correlated with serious mental illness (SMI) (Colpe, Epstein, Barker, & Gfroerer, 2009).

The 12-month data are used in the models for SMI (see Section B.4.6 for details), while the 30-day data are used to estimate SPD for this report. See Section B.4.4 for a discussion of how the past year estimates from the new K6 questions compare with past year estimates based on the K6 questions in 2007 and earlier NSDUHs.

Note that the 2008 detailed tables (OAS, 2009) report past month SPD estimates; therefore, these estimates cannot be compared with past year SPD estimates reported in prior years.

B.4.6 Serious Mental Illness

In NSDUH reports prior to 2004, the K6 distress scale was used to measure serious mental illness (SMI). However, SAMHSA discontinued producing SMI estimates with the release of the 2004 data because of concerns about the validity of using only the K6 distress scale without an impairment scale; see Section B.4.4 of Appendix B in the 2004 NSDUH national findings report (OAS, 2005) for a discussion. The new SMI estimates presented in this 2008 report are not comparable with the SMI estimates previously produced from NSDUH.

On May 20, 1993, SAMHSA's Center for Mental Health Services (CMHS) published its definition of SMI in the Federal Register:

Pursuant to Section 1912(c) of the Public Health Services Act, as amended by Public Law 102-321, "adults with serious mental illness" are defined as the following:

In December 2006, a technical advisory group (TAG) meeting of expert consultants was convened by CMHS to solicit recommendations for mental health surveillance data collection strategies among the U.S. population. The panel recommended that NSDUH should be used to make estimates of SMI among adults and that SAMHSA should conduct methodological studies to calibrate NSDUH's mental health items with a "gold standard" clinical psychiatric interview. In response, SAMHSA's OAS initiated a Mental Health Surveillance Study (MHSS) under its NSDUH contract to develop and implement the methods for SMI estimation. At the time, NSDUH contained a six-item scale (K6) with five response options in each item that captured information on psychological distress (Kessler et al., 2003a). However, the K6 scale does not capture information on functional impairment, which is needed to determine whether a respondent can be categorized as having SMI under SAMHSA's definition. In consultation with the TAG, two candidate impairment scales were selected by SAMHSA to be added to the 2008 NSDUH. They are an abridged version of the World Health Organization Disability Assessment Scale (WHODAS; Rehm et al., 1999) and the Sheehan Disability Scale (SDS; Leon, Olfson, Portera, Farber, & Sheehan, 1997). The MHSS study had two primary objectives:

An initial step of the MHSS was to modify these scales for use in a general population survey, including minor changes to question wording and reducing the length. In particular, an analysis was conducted to examine the psychometric properties of the WHODAS scale items contained in the 2002-2004 NSDUH using item response theory (IRT) methods (Novak, 2007). First, an exploratory factor analysis of the original 16-item scale was conducted to determine whether or not the WHODAS was unidimensional, a required feature for IRT analysis. The exploratory factor analysis confirmed that the WHODAS tapped a dominant factor or construct of "impairment." Next, a series of two-parameter and polytomous IRT models were estimated to examine the discrimination and difficulty properties of each item. This provided insight into how well the items discriminated between adults with high and low levels of impairment and measured the underlying severity of impairment. Eight candidates emerged as the most informative and least redundant items. The reduced 8-item scale had a high correlation (r = .97, p < .001) with the full 16-item WHODAS scale. Differential item functioning analyses across demographic categories (race/ethnicity, gender, and education) were conducted to test for item bias. No significantly large test item bias was observed across major demographic groupings. Thus, these findings document that the WHODAS scale is unidimensional and that the subset of 8 items capture the information represented in the full 16-item scale with no significant loss of information.

In 2008, the new impairment questions were added to the mental health module. To compare the efficacy of two alternative impairment scales, a random split-sample design was used in the mental health module, where all respondents received the K6, but each half of the sample received one of the two impairment scales. The respondents who were administered the WHODAS were designated as sample A, and the respondents who were administered the SDS were designated as sample B.

In addition, a subsample of approximately 1,500 adult NSDUH participants was recruited for a follow-up clinical interview to provide data for calibration of the NSDUH full-sample interview data on mental health status. The randomization of the impairment scales was maintained within this clinical interview subsample, which is referred to as the MHSS sample, so that about half of the MHSS sample participants (approximately 750) were administered the WHODAS and half were administered the SDS. A diagram illustrating the structure of the MHSS sampling design is given in Figure B.2.

Below is a graph. Click here for the text describing this graph.

Figure B.2 Structure of Mental Health Surveillance Study Sampling Design

Figure B.2

The MHSS sample was stratified, based on respondents' K6 scores in 2008, to optimize the MHSS sample allocation for calibration modeling. Strata were constructed according to seven scoring bands described in Table B.10. Assumed SMI rates were estimated using K6 score distribution data from the 2006 NSDUH and raw K6 score and clinical case data from the National Comorbidity Survey Replication (NCS R) clinical calibration study.13 Sampling rates for the 2008 study are substantially lower for K6 scores 0 to 7 under the assumption that fewer clinical positives would occur in that scoring range. Table B.10 shows the expected sample distribution for the 1,500 clinical follow-up interviews and the expected number of those with positive SMI status. The design effect for a prevalence estimate of SMI due to this two-phase sample stratified by K6 scores is 0.2121 (i.e., the variance is reduced almost fivefold in comparison with a simple random sample). Because the usual design effect for adults in the main survey is approximately 3.0 (e.g., for the prevalence of SPD), the overall design effect for the MHSS sample is estimated to be 0.6363. Thus, the effective sample size is approximately 2,357, and the projected standard error and relative standard error of an estimate of SMI are 0.59 percent and 6.57 percent, respectively. The overall expected proportion of positive SMI counts is 0.305.

The probability sample of 1,500 clinical follow-up interviews was distributed across four calendar quarters, with a slightly larger sample in the first quarter (425 follow-up interviews) and the remaining sample equally divided among the remaining quarters (approximately 358 interviews in each of quarters 2 through 4 for a combined sample of 1,075 follow-up interviews). The intention of the larger sample in quarter 1 was to provide some cushion in case the clinical interview response rates were lower than anticipated and to generate an adequate sample size for a preliminary analysis using data collected from the first 6 months of 2008. It was expected that 85 percent of those subsampled for the MHSS would agree to participate in the clinical follow-up interview, and the actual participation rate among those who agreed to complete the interview was projected to be 90 percent. As shown in Table B.11, the overall actual weighted agreement and completion rates were 76.5 and 76.3 percent, respectively (unweighted rates were 86.3 and 76.0 percent, respectively), and rates are provided for each of the seven K6 score categories.

The unweighted and weighted response rates for each of the seven K6 score categories are given in Table B.12. The unweighted response rates were fairly similar between the two half samples, but there appeared to be some differences in the weighted response rates across the K6 score categories, particularly in the "4 to 5" and "6 to 7" categories. A total of 1,506 respondents completed the clinical interview, but 4 cases were discarded because of unusual weights or because all mental health item scores were missing. The final usable MHSS dataset consisted of 1,502 records, with 761 belonging to the WHODAS half sample, and 741 belonging to the SDS half sample.

Within 2 to 4 weeks of the NSDUH main interview, each participant in the MHSS was administered standard clinical interview measures by mental health clinicians via paper-and-pencil interviewing (PAPI) over the telephone. The standard clinical interview administered to this subsample was the Structured Clinical Interview for DSM-IV-TR Axis I Disorder Non-Patient Edition (SCID) adapted for this study by Michael First, M.D. (First, Spitzer, Gibbon, & Williams, 1997). Functional impairment ratings were assigned by clinical interviewers using the Global Assessment of Function (GAF) scale. A respondent was coded positive for SMI if he or she was determined to have any of the mental disorders assessed in the adapted SCID and a GAF score of 50 or below, and this was assumed to be the "gold standard" assessment of SMI, corresponding with SAMHSA's official definition.

Using the combined clinical interview and standard NSDUH computer-assisted interview (CAI) data for the 1,500 MHSS respondents, statistical models were developed between the clinical "gold standard" assessment of SMI and survey-based scores derived from the K6 and WHODAS/SDS scales (depending on which half sample the respondent belonged to). For estimating SMI in the past year, the "past year K6 total score," defined as the higher of the past 30-day K6 total score and the worst month in past 12-month K6 total score, was used (see Section B.4.5 for details on the K6). A variety of models were evaluated to identify the single best model (one for each half sample) to use for the production of SMI estimates. Each model allowed the predicted probability of having SMI for each respondent to be calculated, and an optimal cut point was identified that equalized the weighted number of false positives and false negatives by comparing the "gold standard" SMI estimates with those based on the model and cut point (i.e., predicted probabilities at or above the cut point were coded as SMI positive).

Descriptive analyses examined the distribution of respondent characteristics in the clinical interview sample to check for imbalances between the two half samples. Model-based analyses were conducted to develop algorithms based on the K6 scale and each of the impairment scales in turn, and receiver operating characteristic (ROC) analyses were conducted on the algorithms to select the optimal cut point for determining SMI status. Models to determine SMI were evaluated based on three criteria: (1) model robustness (e.g., preference given to parsimonious models that could be generalized to data beyond that used in the modeling process); (2) minimization of misclassification errors in SMI prediction (i.e., exhibiting reasonable ROC statistics, such as sensitivity and AUC, defined as the area under the ROC curve based on an optimal cut point [(sensitivity + specificity)/2]); and (3) reasonable SMI estimates based on the full 12-month dataset (i.e., balanced across several demographic subgroups and across the WHODAS and the SDS half samples). Initial modeling analysis, done with the first 6 months of data collected under the MHSS, showed that the WHODAS provides more accurate prediction of SMI in NSDUH. Consequently, this impairment scale was chosen for administration in the 2009 and subsequent surveys. Final models chosen for SMI estimation with the 2008 dataset are described below. More details can be obtained from Aldworth et al. (2009).

The process of selecting models began by developing separate weighted logistic regression prediction models for the K6 and each of the two impairment scales, respectively. With SMI status based on having a SCID diagnosis plus a GAF less than or equal to 50, the response variable Y was defined so that Y = 1 when an SMI diagnosis is positive; otherwise, Y = 0. If X is a vector of explanatory variables, then the response probability π = Pr(Y = 1|X) can be estimated using the following logistic regression models for the WHODAS and SDS half samples, respectively:

logit(πw) ≡ log[πw / (1 - πw)] = -4.7500 + 0.2098Xk + 0.3839Xw     (1)

logit(πs) = -4.4924 + 0.2960Xk + 0.2242Xs     (2)

where the Xk, Xw, and Xs terms refer to K6, WHODAS, and SDS terms, respectively, and are defined as follows:

The reason behind the alternative past year K6 score was that SMI prevalence was typically extremely low for respondents with past year K6 scores less than 8, and the prevalence rates only started increasing once scores were 8 or greater. Alternative versions of the WHODAS and SDS scores were driven by the idea that a dichotomous measure dividing severely impaired from less severely impaired respondents might fit better than a linear continuous measure.

The modeling and ROC statistics of these models are given in Tables B.13, B.14, and B.15. ROC statistics are provided for subgroups of four demographic variables. Table B.16 shows the levels of WHODAS, SDS, and K6 that are necessary to classify a respondent as having SMI.

B.4.7 Major Depressive Episode (Depression)

Beginning in 2004, modules related to major depressive episode (MDE) derived from DSM-IV (APA, 1994) criteria for major depression, were included in the questionnaire. These questions permit estimates to be calculated for prevalence of MDE and treatment for MDE. Separate modules were administered to adults aged 18 or older and youths aged 12 to 17. The adult questions were adapted from the depression section of the National Comorbidity Survey-Replication (NCS-R; Harvard School of Medicine, 2005), and the questions for youths were adapted from the depression section of the National Comorbidity Survey-Adolescent (NCS-A; Harvard School of Medicine, 2005). To make the modules developmentally appropriate for youths, there are minor wording differences in a few questions between the adult and youth modules. Revisions to the questions in both modules were made primarily to reduce its length and to modify the NCS questions, which are interviewer-administered, to the ACASI format used in NSDUH. In addition, some revisions, based on cognitive testing, were made to improve comprehension. Furthermore, even though titles similar to those used in the NCS were used for the NSDUH modules, the results of these items may not be directly comparable. This is mainly due to differing modes of administration in each survey (ACASI in NSDUH vs. computer-assisted personal interviewing [CAPI] in NCS), revisions to wording necessary to maintain the logical processes of the ACASI environment, and possible context effects resulting from deleting questions not explicitly pertinent to severe depression.

Since 2004, the NSDUH questions that determine MDE have remained unchanged. However, because of the changes in other mental health items that precede the MDE questions (K6, suicide, and impairment) in the 2008 questionnaire, the reporting on MDE questions among adults appears to have been affected. Thus, MDE estimates for 2008 were not compared with prior NSDUH estimates for trend purposes in this report. See Section B.4.4 for a discussion.

According to DSM-IV, a person is defined as having had MDE in his or her lifetime if he or she has had at least five or more of the following nine symptoms nearly every day in the same 2-week period, where at least one of the symptoms is a depressed mood or loss of interest or pleasure in daily activities (APA, 1994): (1) depressed mood most of the day; (2) markedly diminished interest or pleasure in all or almost all activities most of the day; (3) significant weight loss when not sick or dieting, or weight gain when not pregnant or growing, or decrease or increase in appetite; (4) insomnia or hypersomnia; (5) psychomotor agitation or retardation; (6) fatigue or loss of energy; (7) feelings of worthlessness; (8) diminished ability to think or concentrate or indecisiveness; and (9) recurrent thoughts of death or suicidal ideation. Respondents who have had MDE in their lifetime are asked if, during the past 12 months, they had a period of depression lasting 2 weeks or longer while also having some of the other symptoms mentioned. Those reporting that they have are defined as having had MDE in the past year and then are asked questions from the SDS to measure the level of functional impairment in major life activities reported to be caused by the MDE in the past 12 months (Leon et al., 1997).

NSDUH measures the nine attributes associated with MDE as defined in DSM-IV with the following questions. Note that the questions shown are taken from the adult depression module. A few of the questions in the youth module were modified slightly to use wording more appropriate for youths aged 12 to 17. It should be noted that no exclusions were made for MDE caused by medical illness, bereavement, or substance use disorders.

1. Depressed mood most of the day

The following questions refer to the worst or most recent period of time when the respondent experienced any or all of the following: sadness, discouragement, or lack of interest in most things.

During that [worst/most recent] period of time…

  1. … did you feel sad, empty, or depressed most of the day nearly every day?
  2. … did you feel discouraged about how things were going in your life most of the day nearly every day?

2. Markedly diminished interest or pleasure in all or almost all activities most of the day

  1. … did you lose interest in almost all things like work and hobbies and things you like to do for fun?
  2. … did you lose the ability to take pleasure in having good things happen to you, like winning something or being praised or complimented?

3. Weight

In answering the next questions, think about the [worse/most recent] period of time.

  1. Did you have a much smaller appetite than usual nearly every day during that time?
  2. Did you have a much larger appetite than usual nearly every day?
  3. Did you gain weight without trying to during that [worst/most recent] period of time?
    1. … because you were growing?
    2. … because you were pregnant?
    3. How many pounds did you gain?
  4. Did you lose weight without trying to?
    1. … because you were sick or on a diet?
    2. How many pounds did you lose?

4. Insomnia or hypersomnia

  1. Did you have a lot more trouble than usual falling asleep, staying asleep, or waking too early nearly every night during that [worst/most recent] period of time?
  2. During that [worst/most recent] period of time, did you sleep a lot more than usual nearly every night?

5. Psychomotor agitation or retardation

  1. Did you talk or move more slowly than is normal for you nearly every day?
  2. Were you so restless or jittery nearly every day that you paced up and down or couldn't sit still?

6. Fatigue or loss of energy

  1. During that [worst/most recent] period of time, did you feel tired or low in energy nearly every day even when you had not been working very hard?

7. Feelings of worthlessness

  1. Did you feel that you were not as good as other people nearly every day?
  2. Did you feel totally worthless nearly every day?

8. Diminished ability to think or concentrate or indecisiveness

  1. During that [worst/most recent] time period, did your thoughts come much more slowly than usual or seem confused nearly every day?
  2. Did you have a lot more trouble concentrating than usual nearly every day?
  3. Were you unable to make decisions about things you ordinarily have no trouble deciding about?

9. Recurrent thoughts of death or recurrent suicidal ideation

  1. Did you often think about death, either your own, someone else's, or death in general?
  2. During that period, did you ever think it would be better if you were dead?
  3. Did you think about committing suicide?

NSDUH also collects data on impairment using the SDS, which is a measure of mental health–related impairment in four major life activities or role domains. These four domains are defined separately for adults aged 18 or older and youths aged 12 to 17 to reflect the different roles associated with the two age groups. Each module consists of four questions, and each item uses an 11-point scale line, where 0 corresponds to no interference, 1 to 3 correspond to mild interference, 4 and 5 correspond to moderate interference, 7 to 9 correspond to severe interference, and 10 corresponds to very severe interference. Impairment score is defined as the single highest severity level of role impairment across the four SDS role domains. Ratings greater than or equal to 7 on the scale were considered severe impairment. In addition to past year MDE, NSDUH shows estimates for past year MDE with severe impairment. Estimates for severe impairment are calculated separately for youths and adults because the four domains are slightly different for the two groups. The questions pertaining to the four domains are listed below for both groups.

Adult Depression Module: Functional Impairment

ASDSHOME
Think about the time in the past 12 months when these problems with your mood were most severe.

Using the 0 to 10 scale shown below, where 0 means no interference and 10 means very severe interference, select the number that describes how much these problems interfered with your ability to do each of the following activities during that period. You can use any number between 0 and 10 to answer.

Appendix B Equation.     D

 
How much did your [depression symptoms] interfere with your ability to do home management tasks, like cleaning, shopping, and working around the house, apartment, or yard?
ASDSWORK
During the time in the past 12 months when your [depression symptoms] were most severe, how much did this interfere with your ability to work?
ASDSREL
How much did your [depression symptoms] interfere with your ability to form and maintain close relationships with other people during that period of time?
ASDSSOC
How much did [depression symptoms] interfere with your ability to have a social life during that period of time?

Youth Depression Module: Functional Impairment

YSDSHOME
Think about the time in the past 12 months when these problems with your mood were the worst.

Using the 0 to 10 scale shown below, where 0 means no problems and 10 means very severe problems, select the number that describes how much your [depression symptoms] caused problems with your ability to do each of the following activities during that time. You can use any number between 0 and 10 to answer.

Appendix B Equation.     D

 
How much did your [depression symptoms] cause problems with your chores at home?
YSDSWORK
During the time in the past 12 months when your [depression symptoms] were worst, how much did this cause problems with your ability to do well at school or work?
YSDSREL
How much did your [depression symptoms] cause problems with your ability to get along with your family during that time?
YSDSSOC
How much did your [depression symptoms] cause problems with your ability to have a social life during that time?

B.4.8 Revised Estimates of Methamphetamine Use

A challenge in measuring nonmedical use of prescription drugs comes when those drugs are produced illegally. Drugs that have been manufactured by legitimate pharmaceutical companies under government regulation may become popular drugs of abuse, stimulating illegal production. In particular, most methamphetamine that currently is used nonmedically in the United States is produced by clandestine laboratories within the United States or abroad rather than by the legitimate pharmaceutical industry. Questions on methamphetamine use in NSDUH are first asked in the stimulants module in the core section of the questionnaire in the context of questions about nonmedical use of prescription stimulants. Therefore, one concern in measuring methamphetamine use in NSDUH is that some methamphetamine users may fail to report use if they do not recognize the drug when it is presented in the prescription drug context.

To address this concern, questions were added to the special drugs module in the noncore section of the NSDUH questionnaire beginning in 2005 to capture information from respondents who may have used methamphetamine but did not recognize it as a prescription drug and therefore did not report use in the core stimulants module. Results of analyses for these added methamphetamine items are presented in Section B.4.6 in Appendix B of the 2007 national results report (OAS, 2008). Beginning in 2006, estimates of methamphetamine use, nonmedical use of stimulants, and nonmedical use of psychotherapeutic drugs were based on responses to the methamphetamine items in the core stimulants module as well as the methamphetamine items in the noncore special drugs module. The analyses showed that measures of use of methamphetamine, prescription psychotherapeutics, and stimulants were higher when data from the new methamphetamine use items were taken into account.

Section B.4.6 in the 2007 national results report also discusses the adjustment procedures that were used to create estimates of the use of methamphetamine, nonmedical use of stimulants, and nonmedical use of prescription drugs for 2002 through 200514 for comparability with estimates from 2006 onward. The estimates for the nonmedical use of stimulants and psychotherapeutic drugs in this report are not comparable with corresponding estimates in NSDUH reports prior to the 2007 data year, and the methamphetamine use estimates in this report also are not comparable with those in NSDUH reports for survey years prior to 2006.

Table B.1 – Demographic and Geographic Domains Forced to Match Their Respective U.S. Census Bureau Population Estimates through the Weight Calibration Process, 2008
Main Effects Two-Way Interactions
1 Combinations of the age groups (including but not limited to 12 or older, 18 or older, 26 or older, 35 or older, and 50 or older) also were forced to match their respective U.S. Census Bureau population estimates through the weight calibration process.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
Age Group  
12-17  
18-25  
26-34  
35-49  
50-64  
65 or Older  
All Combinations of Groups Listed Above1  
  Age Group × Gender
Gender (e.g., Males Aged 12 to 17)
Male  
Female  
  Age Group × Hispanic Origin
Hispanic Origin (e.g., Hispanics or Latinos Aged 18 to 25)
Hispanic or Latino  
Not Hispanic or Latino  
  Age Group × Race
Race (e.g., Whites Aged 26 or Older)
White  
Black or African American  
  Age Group × Geographic Region
Geographic Region (e.g., Persons Aged 12 to 25 in the Northeast)
Northeast  
Midwest  
South Age Group × Geographic Division
West (e.g., Persons Aged 65 or Older in New England)
   
Geographic Division  
New England Gender × Hispanic Origin
Middle Atlantic (e.g., Not Hispanic or Latino Males)
East North Central  
West North Central  
South Atlantic Hispanic Origin × Race
East South Central (e.g., Not Hispanic or Latino Whites)
West South Central  
Mountain  
Pacific  
Table B.2 – Summary of 2008 NSDUH Suppression Rules
Estimate Suppress if:
deff = design effect; RSE = relative standard error; SE = standard error.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
Prevalence Rate, image representing p hat, with Nominal Sample Size, n, and Design Effect, deff (1) The estimated prevalence rate, image representing p hat, is < .00005 or ≥ .99995, or
(2) Appendix B Equation > .175 when image representing p hat ≤ .5, or     D

      Appendix B Equation > .175 when image representing p hat > .5, or     D

(3) Effective n < 68, where Effective n = Appendix B Equation or     D

(4) n < 100.

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 image representing p hat)
The estimated prevalence rate, image representing p hat, is suppressed.

Note: In some instances when image representing p hat is not suppressed, the estimated number may appear as a 0 in the tables. This means that the estimate is greater than 0 but less than 500 (estimated numbers are shown in thousands).
Mean Age at First Use, image representing x bar, with Nominal Sample Size, n (1) RSE (image representing x bar) > .5, or

(2) n < 10.
Table B.3 – Weighted Percentages and Sample Sizes for 2007 and 2008 NSDUHs, by Final Screening Result Code
Final Screening Result Code Sample Size
2007
Sample Size
2008
Weighted
Percentage
2007
Weighted
Percentage
2008
1 Examples of "Other, Ineligible" cases are those in which all residents lived in the dwelling unit for less than half of the calendar quarter and dwelling units that were listed in error.
2 "Other, Access Denied" includes all dwelling units to which the field interviewer was denied access, including locked or guarded buildings, gated communities, and other controlled access situations.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2007 and 2008.
TOTAL SAMPLE 192,092 194,815 100.00 100.00
Ineligible Cases 33,681 34,682 17.00 17.50
Eligible Cases 158,411 160,133 83.00 82.50
INELIGIBLES 33,681 34,682 17.00 17.50
10 - Vacant 18,585 19,308 55.98 56.04
13 - Not a Primary Residence 6,280 7,189 18.28 20.63
18 - Not a Dwelling Unit 2,595 2,582 7.55 7.32
22 - All Military Personnel 291 340 0.81 1.01
Other, Ineligible1 5,930 5,263 17.38 14.99
ELIGIBLE CASES 158,411 160,133 83.00 82.50
Screening Complete 141,487 142,938 89.45 89.04
30 - No One Selected 82,420 83,422 51.33 51.22
31 - One Selected 31,949 32,213 20.46 20.30
32 - Two Selected 27,118 27,303 17.66 17.52
Screening Not Complete 16,924 17,195 10.55 10.96
11 - No One Home 3,213 3,111 1.88 1.82
12 - Respondent Unavailable 434 401 0.25 0.26
14 - Physically or Mentally Incompetent 319 358 0.19 0.23
15 - Language Barrier—Hispanic 84 91 0.05 0.05
16 - Language Barrier—Other 439 468 0.28 0.33
17 - Refusal 11,164 11,611 7.00 7.47
21 - Other, Access Denied2 1,235 1,113 0.87 0.77
24 - Other, Eligible 9 14 0.00 0.01
27 - Segment Not Accessible 0 0 0.00 0.00
33 - Screener Not Returned 16 15 0.01 0.01
39 - Fraudulent Case 11 13 0.01 0.01
44 - Electronic Screening Problem 0 0 0.00 0.00
Table B.4 – Weighted Percentages and Sample Sizes for 2007 and 2008 NSDUHs, by Final Interview Code
Final Interview Code 12+
Sample
Size
2007
12+
Sample
Size
2008
12+
Weighted
Percentage
2007
12+
Weighted
Percentage
2008
12-17
Sample
Size
2007
12-17
Sample
Size
2008
12-17
Weighted
Percentage
2007
12-17
Weighted
Percentage
2008
18+
Sample
Size
2007
18+
Sample
Size
2008
18+
Weighted
Percentage
2007
18+
Weighted
Percentage
2008
1 "Other" includes eligible person moved, data not received from field, too dangerous to interview, access to building denied, computer problem, and interviewed wrong household member.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2007 and 2008.
TOTAL 85,774 86,435 100.00 100.00 26,191 26,501 100.00 100.00 59,583 59,934 100.00 100.00
70 - Interview Complete 67,870 68,736 73.94 74.45 22,475 22,559 85.35 84.73 45,395 46,177 72.65 73.29
71 - No One at Dwelling Unit 1,565 1,366 1.79 1.46 242 230 0.93 0.78 1,323 1,136 1.89 1.54
72 - Respondent Unavailable 2,111 1,940 2.35 2.23 403 363 1.50 1.38 1,708 1,577 2.45 2.33
73 - Break-Off 103 68 0.16 0.11 14 10 0.05 0.04 89 58 0.17 0.12
74 - Physically/ Mentally Incompetent 839 876 1.93 1.88 178 205 0.66 0.77 661 671 2.08 2.01
75 - Language Barrier – Hispanic 185 199 0.21 0.23 9 7 0.07 0.03 176 192 0.22 0.25
76 -Language Barrier – Other 437 383 1.16 1.00 27 39 0.14 0.18 410 344 1.27 1.10
77 - Refusal 9,896 9,883 16.76 16.87 739 765 2.73 2.77 9,157 9,118 18.35 18.46
78 - Parental Refusal 1,985 2,192 0.82 0.88 1,985 2,192 8.06 8.71 0 0 0.00 0.00
91 – Fraudulent Case 27 10 0.03 0.01 5 0 0.02 0 22 10 0.03 0.01
Other1 756 782 0.84 0.86 114 131 0.50 0.61 642 651 0.88 0.89
Table B.5 – Response Rates and Sample Sizes for 2007 and 2008 NSDUHs, by Demographic Characteristics
Demographic Characteristic Selected Persons
2007
Selected Persons
2008
Completed
Interviews
2007
Completed
Interviews
2008
Weighted
Response Rate
2007
Weighted
Response Rate
2008
Note: Estimates are based on demographic information obtained from screener data and are not consistent with estimates on demographic characteristics presented in the 2007 and 2008 sets of detailed tables.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2007 and 2008.
TOTAL 85,774 86,435 67,870 68,736 73.94% 74.45%
AGE IN YEARS            
12-17 26,191 26,501 22,475 22,559 85.35% 84.73%
18-25 28,085 29,091 22,409 23,468 79.76% 80.67%
26 or Older 31,498 30,843 22,986 22,709 71.42% 72.00%
GENDER            
Male 42,280 42,460 32,802 33,120 72.06% 72.39%
Female 43,494 43,975 35,068 35,616 75.69% 76.37%
RACE/ETHNICITY            
Hispanic 12,501 13,079 10,011 10,395 76.11% 74.61%
White 57,200 56,842 44,870 45,003 73.29% 74.43%
Black 9,660 9,947 8,087 8,327 79.97% 78.75%
All Other Races 6,413 6,567 4,902 5,011 65.50% 66.66%
REGION            
Northeast 17,486 17,336 13,642 13,594 71.65% 72.48%
Midwest 24,150 24,383 19,110 19,314 74.34% 74.93%
South 25,737 25,641 20,683 20,877 75.75% 76.59%
West 18,401 19,075 14,435 14,951 72.52% 72.24%
COUNTY TYPE            
Large Metropolitan 38,758 38,682 29,837 30,133 72.04% 72.46%
Small Metropolitan 28,633 29,254 23,074 23,478 75.42% 76.40%
Nonmetropolitan 18,383 18,499 14,959 15,125 77.41% 77.19%
Table B.6 – Kappa Statistics for Selected Substance Use, Substance Use Treatment, and Mental Health Variables: 2006 NSDUH Reliability Study
Variable Lifetime Past Year
-- Not available.
1 Substance dependence or abuse is dependence on or abuse of illicit drugs or alcohol and is based on definitions in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Dependence or abuse estimates presented in the Reliability Study are among past year users only, which differ from estimates in the NSDUH detailed tables for the total population. Also, unlike the standard definition of abuse used in the NSDUH detailed tables, abuse was defined independently from dependence in the Reliability Study, meaning that a respondent could be classified as having dependence and as having abused. 
2 Received Substance Use Treatment refers to treatment received in order to reduce or stop illicit drug or alcohol use, or for medical problems associated with illicit drug or alcohol use. It includes treatment received at any location, such as a hospital, rehabilitation facility (inpatient or outpatient), mental health center, emergency room, private doctor's office, self-help group, or prison/jail. Substance Use Treatment questions were asked only of respondents who previously indicated ever using alcohol or drugs and having ever received treatment for alcohol or drug use.
3 MDE is defined as a period of at least 2 weeks when a person experienced a depressed mood or loss of interest or pleasure in daily activities and had a majority of the symptoms for depression as described in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Lifetime MDE is based on multiple questions comprising nine MDE criteria as well as multiple gatekeeper questions. Past year MDE was asked only of respondents who had lifetime MDE or met the suicidal ideation criterion.
4 Outpatient mental health treatment/counseling is defined as having received treatment at any of the following locations for problems with emotions, nerves, or mental health: outpatient mental health clinic or center or office of a private therapist, psychologist, psychiatrist, social worker, or counselor that was not part of a clinic.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2006 Reliability Study (n = 3,136).
AGED 12 OR OLDER    
Cigarette Use 0.92 0.93
Alcohol Use 0.83 0.90
Marijuana Use 0.93 0.82
Substance Dependence or Abuse1 -- 0.67
Substance Use Treatment2 0.89 0.87
AGED 18 OR OLDER    
Major Depressive Episode (MDE)3 0.67 0.52
Outpatient Mental Health Treatment4 -- 0.85
Prescription Medication Mental Health Treatment -- 0.85
Table B.7 – Selected Mental Health Measures among Persons Aged 18 or Older, by Survey Year and Scale: Percentages
Adult Mental Health Measure 2005 2006 2007 2008 2008
WHODAS3
2008 SDS3
-- Not available.
Difference between this estimate and the 2007 estimate is statistically significant at the .05 level. ^ Difference between the 2008 Sheehan Disability Scale (SDS) estimate and the 2008 World Health Organization Disability Assessment Schedule (WHODAS) estimate is statistically significant at the .05 level.
1 Major depressive episode (MDE) is defined as in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), which specifies a period of at least 2 weeks when a person experienced a depressed mood or loss of interest or pleasure in daily activities and had a majority of specified depression symptoms. Respondents with unknown MDE data have been excluded.
2 Serious psychological distress (SPD) is defined as having a score of 13 or higher on the K6 scale. See Section B.4.5 in Appendix B of the Results from the 2008 National Survey on Drug Use and Health: National Findings.
3 Half of the adult sample received the WHODAS, while the other half of the adult sample received the SDS.
4 The K6 scale consists of six questions that gather information on how frequently a respondent experienced symptoms of psychological distress during the 1 month in the past year when he or she was at his or her worst emotionally. Responses to these six questions are combined to produce a score ranging from 0 to 24.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2005, 2006, 2007, and 2008.
MDE in Lifetime 1 14.23 13.87 14.16 13.27 12.76 13.74
MDE in Past Year1 7.29 7.21 7.46 6.80 6.40 7.19^
Felt Sad/Empty 36.96 35.92 35.96 31.46 31.04 31.87
Felt Discouraged 9.36 9.29 8.91 8.65 8.36 8.95
Lost Interest 3.06 3.26 2.83 2.62 2.61 2.60
SPD in Past Year2 11.30 11.29 10.92 10.31 9.73 10.84^
Mean K6 Score in Past Year4 5.27 5.24 5.18 4.89 4.82 4.95
SPD in Past 30 Days2 -- -- -- 4.52 4.28 4.73
Mean K6 Score in Past 30 Days4 -- -- -- 3.75 3.73 3.78
Seriously Thought about Suicide -- -- -- 3.69 3.64 3.75
Made Suicide Plans -- -- -- 1.02 .98 1.06
Attempted Suicide -- -- -- .49 .47 .49
Received Medical Attention for Suicide Attempt -- -- -- .30 .29 .31
Stayed Overnight in Hospital for Suicide Attempt -- -- -- .22 .22 .21
Table B.8 – Past Year K6 Item Response Distributions, by Survey Year and Scale: Percentages
K6 Item Response in Past Year 2005 2006 2007 2008 2008
(WHODAS)
2008 (SDS)
Difference between this estimate and the 2007 estimate is statistically significant at the .05 level. ^ Difference between the 2008 Sheehan Disability Scale (SDS) estimate and the 2008 World Health Organization Disability Assessment Schedule (WHODAS) estimate is statistically significant at the .05 level.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2005, 2006, 2007, and 2008.
How Often Felt Nervous            
DK/RF (RECODED) 0.65 0.82 0.81 0.37 0.40 0.35
All of the Time 2.73 2.77 2.77 3.37 3.22 3.50
Most of the Time 7.58 7.68 7.35 8.26 8.34 8.17
Some of the Time 21.55 21.09 21.05 19.42 19.68 19.23
A Little of the Time 35.79 34.99 34.73 32.20 32.11 32.35
None of the Time 31.71 32.65 33.29 36.38 36.25 36.40
How Often Felt Hopeless            
DK/RF (RECODED) 0.60 0.75 0.74 0.34 0.38 0.32
All of the Time 2.31 2.58 2.75 2.62 2.49 2.74
Most of the Time 5.38 5.30 4.94 5.06 4.66 5.42^
Some of the Time 12.57 12.22 12.18 10.45 10.17 10.71
A Little of the Time 19.96 19.49 19.57 16.74 17.17 16.33
None of the Time 59.17 59.66 59.82 64.80 65.13 64.48
How Often Felt Restless            
DK/RF (RECODED) 0.67 0.76 0.85 0.42 0.42 0.44
All of the Time 2.63 2.77 2.89 2.52 2.48 2.53
Most of the Time 6.90 6.65 6.67 5.78 5.62 5.95
Some of the Time 18.74 18.45 18.13 16.61 16.48 16.72
A Little of the Time 29.94 29.58 29.16 29.33 28.87 29.86
None of the Time 41.13 41.79 42.29 45.35 46.14 44.50^
How Often Could Not Be Cheered Up            
DK/RF (RECODED) 0.57 0.66 0.77 0.35 0.41 0.31
All of the Time 2.34 2.43 2.57 2.45 2.30 2.62
Most of the Time 5.80 5.48 5.22 5.06 4.87 5.27
Some of the Time 12.16 12.20 12.12 9.81 9.57 10.07
A Little of the Time 20.56 20.40 20.57 17.22 17.46 16.91
None of the Time 58.57 58.83 58.74 65.10 65.38 64.82
How Often Felt Everything Was an Effort            
DK/RF (RECODED) 0.83 0.86 0.93 0.69 0.66 0.72
All of the Time 4.22 4.58 4.43 4.50 4.32 4.68
Most of the Time 7.91 7.61 7.52 7.53 7.57 7.53
Some of the Time 16.38 16.74 16.03 14.69 14.50 14.80
A Little of the Time 27.30 26.42 26.23 25.76 25.55 25.97
None of the Time 43.36 43.79 44.87 46.83 47.40 46.30
How Often Felt No Good            
DK/RF (RECODED) 0.66 0.64 0.81 0.37 0.35 0.39
All of the Time 2.72 3.02 2.95 2.29 2.22 2.39
Most of the Time 5.70 5.58 5.53 4.36 4.29 4.43
Some of the Time 11.92 11.84 12.04 9.29 9.34 9.27
A little of the Time 21.10 20.71 20.09 16.99 16.98 16.97
None of the Time 57.91 58.21 58.59 66.70 66.82 66.54
Table B.9 – Major Depressive Episode Status among Persons Aged 18 or Older, by Key Demographics and Survey Year and Scale: Percentages
Demographic Characteristic Had Past
Year MDE1:
2007
Had Past
Year MDE1:
2008
Had Past
Year MDE1:
2008
(WHODAS)
Had Past
Year MDE1:
2008
(SDS)
Had Lifetime
MDE1:
2007
Had Lifetime
MDE1:
2008
Had Lifetime
MDE1: 2008
(WHODAS)
Had Lifetime
MDE1: 2008
(SDS)
* Low precision; no estimate reported. Difference between the 2008 estimate and the 2007 estimate is statistically significant at the .05 level. ^ Difference between the 2008 Sheehan Disability Scale (SDS) estimate and the 2008 World Health Organization Disability Assessment Schedule (WHODAS) estimate is statistically significant at the .05 level.
NOTE: Major depressive episode (MDE) is defined as in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), which specifies a period of at least 2 weeks when a person experienced a depressed mood or loss of interest or pleasure in daily activities and had a majority of specified depression symptoms.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2007 and 2008.
Total 7.46 6.80 6.40 7.19^ 14.16 13.27 12.76 13.74
Gender                
Male 5.26 4.78 4.59 4.96 10.30 9.79 9.15 10.38
Female 9.52 8.69 8.09 9.27^ 17.77 16.51 16.12 16.88
Age in Years                
18-25 8.92 8.62 8.74 8.59 14.65 13.87 13.86 14.07
26-49 8.48 7.81 7.36 8.23 16.16 14.86 14.30 15.43
50 or Older 5.80 5.06 4.52 5.56 11.74 11.33 10.69 11.80
Race/Ethnicity                
Not Hispanic or Latino 7.64 7.14 6.59 7.66^ 14.70 13.89 13.27 14.46^
White 8.08 7.46 6.96 7.92^ 15.97 15.01 14.28 15.66^
Black or African American 6.06 6.31 4.93 7.55^ 9.90 9.65 8.90 10.32
American Indian or Alaska Native 9.19 5.66 4.94 6.55 14.42 9.98 10.08 9.56
Native Hawaiian or Other Pacific Islander * * * * * * * *
Asian 2.92 3.12 3.58 2.66 5.12 6.95 7.77 6.03
Two or More Races 12.11 11.11 12.75 11.59 20.27 17.56 19.49 17.99
Hispanic or Latino 6.32 4.66 5.19 4.15 10.66 9.29 9.48 9.15
Education                
<High School 8.61 6.11 5.54 6.76 13.11 9.09 8.46 9.75
High School Graduate 7.07 6.78 6.81 6.76 11.99 12.26 12.02 12.50
Some College 8.22 8.11 7.40 8.89 16.65 15.59 14.57 16.67
College Graduate 6.50 6.01 5.50 6.37 14.86 14.59 14.26 14.71
Marital Status                
Married 5.26 5.00 4.51 5.49^ 11.67 10.99 10.35 11.65
Widowed 7.91 4.88 3.87 5.73 13.80 10.32 9.56 10.71
Divorced/Separated 13.09 11.73 11.43 12.07 21.62 21.80 21.44 21.99
Never Married 9.23 8.65 8.56 8.69 15.80 14.56 14.36 14.75
Family Income                
<$20,000 10.15 10.53 10.07 11.18 15.97 15.47 15.11 15.88
$20,000-$49,999 7.77 7.03 6.99 6.95 14.16 13.00 12.90 13.00
$50,000-$74,999 7.45 6.59 6.18 7.01 14.44 13.72 12.80 14.67
$75,000 or More 5.53 4.75 4.01 5.43^ 12.92 12.13 11.35 12.84
Table B.10 – Mental Health Surveillance Study Sample Allocation (n = 1,500)
K6 Score Percent of
Population1
Assumed
SMI Rate
(Percent)
Sampling
Rate
(Percent)
Expected
Sample Size
Expected
SMI Count
K6 = Six-item psychological distress scale, SMI = serious mental illness.
1 Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2006.
0 to 3 48.04 0.03 0.0084 96 0
4 to 5 13.98 0.30 0.0228 88 0
6 to 7 11.16 0.30 0.0345 110 0
8 to 9 6.95 10.00 0.1026 200 20
10 to 11 5.53 13.00 0.1190 214 28
12 to 15 8.00 40.00 0.1689 450 180
16 or Higher 6.34 67.00 0.1349 343 230
TOTAL 100.00 8.95   1,501 458
Table B.11 – Mental Health Surveillance Study Agreement and Completion Response Rates, by K6 Score (Unweighted and Weighted)
K6 Score Number Selected Number Agreed to
Participate
Number
Completed
Agreed to
Participate
(Percent)
URR
Agreed to
Participate
(Percent)
WRR
Completed
(Percent)
URR
Completed
(Percent)
WRR
URR = unweighted response rate, WRR = weighted response rate.
NOTE: This table excludes four cases from the Mental Health Surveillance Study (MHSS) sample because of unusual weights or because all mental health item scores were missing.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
0 to 3 163 121 97 74.2 70.9 80.2 72.0
4 to 5 146 125 101 85.6 85.1 80.8 83.0
6 to 7 158 131 108 82.9 80.9 82.4 84.3
8 to 9 324 272 211 84.0 70.1 77.6 82.2
10 to 11 296 257 208 86.8 80.7 80.9 87.3
12 to 15 672 583 443 86.8 86.1 76.0 72.2
16 or Higher 532 488 334 91.7 87.1 68.4 66.5
TOTAL 2,291 1,977 1,502 86.3 76.5 76.0 76.3
Table B.12 – Response Rates (Unweighted and Weighted), by K6 Score Category
K6 Score Sample A
(WHODAS)
Number
Selected
Sample A
(WHODAS)
Number
Completed
Sample A
(WHODAS)
URR
(Percent)
Sample A
(WHODAS)
WRR
(Percent)
Sample B
(SDS)
Number
Selected
Sample B
(SDS)
Number
Completed
Sample B
(SDS) URR
(Percent)
Sample B
(SDS) WRR
(Percent)
K6 = Six-item psychological distress scale, SDS = Sheehan Disability Scale, URR = unweighted response rate, WHODAS = Eight-item World Health Organization Disability Assessment Schedule, WRR = weighted response rate.
NOTE: This table excludes four cases from the Mental Health Surveillance Study (MHSS) sample because of unusual weights or because all mental health item scores were missing.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
0 to 3 83 51 61.5 55.2 80 46 57.5 46.9
4 to 5 77 54 70.1 62.0 69 47 68.1 78.0
6 to 7 77 49 63.6 59.3 81 59 72.8 77.4
8 to 9 161 103 64.0 61.8 163 108 66.3 53.8
10 to 11 156 106 68.0 67.1 140 102 72.9 76.3
12 to 15 331 225 68.0 64.2 341 218 63.9 60.3
16 or Higher 289 173 59.9 58.0 243 161 66.3 58.0
TOTAL 1,174 761 64.8 58.5 1,117 741 66.3 58.3
Table B.13 – Final WHODAS and SDS Models
WHODAS Model Beta Beta SE T Statistic P Value DF Wald P Value
Alt = alternative, DF = degrees of freedom, K6 = Six-item psychological distress scale, PY = past year, SDS = Four-item Sheehan Disability Scale, SE = standard error, WHODAS = Eight-item World Health Organization Disability Assessment Schedule.
NOTE: Alternative past year K6 score: past year K6 score < 8 recoded as 0; past year K6 score 8-24 recoded as 1-17.
NOTE: Alternative WHODAS score: WHODAS item scores < 2 recoded as 0; WHODAS item scores 2-3 recoded as 1, then summed for a score ranging from 0 to 8.
NOTE: Alternative SDS Score: SDS item scores < 7 recoded as 0; SDS item scores 7-10 recoded as 1, then summed for a score ranging from 0 to 4.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
Intercept -4.7500 0.3517 -13.5072 0.0000    
Alt PY K6 0.2098 0.0755 2.7769 0.0060 1 0.0060
Alt WHODAS 0.3839 0.1248 3.0750 0.0024 1 0.0024
SDS Model            
Intercept -4.4924 0.5223 -8.6011 0.0000    
Alt PY K6 0.2960 0.0956 3.0957 0.0023 1 0.0023
Alt SDS 0.2242 0.3918 0.5721 0.5679 1 0.5679
Table B.14 – Final ROC Statistics of Final WHODAS Model: Weighted Numbers in Thousands
Demographic Subset for
Final WHODAS Model:
Alternative Past Year K6
Score + Alternative
WHODAS Score
Cut Point P N Pred_P Pred_N TP TN FP FN Sens Spec AUC PPV NPV
AUC = area under receiver operating characteristic (ROC) curve based on optimal cut point [(sensitivity + specificity)/2], FN = number of false negatives based on prediction, FP = number of false positives based on prediction, N = number of negative SMI cases, NPV = negative predictive value (TN/Pred_N), P = number of positive SMI cases, PPV = positive predictive value (TP/Pred_P), Pred_N = number of predicted negative cases, Pred_P = number of predicted positive cases, Sens = sensitivity (TP/P), Spec = specificity (TN/N), TN = number of true negatives based on prediction, TP = number of true positives based on prediction, WHODAS = Eight-item World Health Organization Disability Assessment Schedule.
NOTE: Alternative past year K6 score: past year K6 score < 8 recoded as 0; past year K6 score 8-24 recoded as 1-17.
NOTE: Alternative WHODAS score: WHODAS item scores < 2 recoded as 0; WHODAS item scores 2-3 recoded as 1, then summed for a score ranging from 0 to 8.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
TOTAL 0.26972 4,977 108,453 5,116 108,314 2,516 105,853 2,600 2,461 0.506 0.976 0.741 0.492 0.977
GENDER                            
Male 0.26972 1,724 56,524 1,759 56,490 814 55,579 945 911 0.472 0.983 0.728 0.463 0.984
Female 0.26972 3,253 51,928 3,358 51,824 1,703 50,273 1,655 1,551 0.523 0.968 0.746 0.507 0.970
AGE                            
18-25 0.26972 881 15,652 1,466 15,068 496 14,682 970 386 0.562 0.938 0.750 0.338 0.974
26-49 0.26972 2,375 44,385 2,459 44,301 1,162 43,088 1,298 1,213 0.489 0.971 0.730 0.472 0.973
50+ 0.26972 1,721 48,415 1,191 48,945 859 48,082 333 863 0.499 0.993 0.746 0.721 0.982
RACE/ETHNICITY                            
White, Not Hispanic 0.26972 4,538 68,714 4,384 68,868 2,228 66,558 2,156 2,310 0.491 0.969 0.730 0.508 0.966
Black, Not Hispanic 0.26972 286 13,860 483 13,663 230 13,606 253 56 0.804 0.982 0.893 0.476 0.996
Other, Not Hispanic 0.26972 33 11,163 153 11,043 23 11,032 130 10 0.686 0.988 0.837 0.148 0.999
Hispanic 0.26972 120 14,716 96 14,740 35 14,655 60 85 0.293 0.996 0.644 0.368 0.994
EDUCATION                            
< High School 0.26972 693 8,876 737 8,833 455 8,594 282 239 0.656 0.968 0.812 0.618 0.973
High School Graduate 0.26972 2,028 32,772 1,506 33,294 812 32,079 694 1,216 0.401 0.979 0.690 0.539 0.963
Some College 0.26972 1,251 33,258 1,772 32,737 651 32,137 1,121 600 0.520 0.966 0.743 0.367 0.982
College Graduate 0.26972 1,005 33,546 1,102 33,450 598 33,043 504 407 0.595 0.985 0.790 0.543 0.988
Table B.15 – Final ROC Statistics of Final SDS Model: Weighted Numbers in Thousands
Demographic Subset for
Final SDS Model:
Alternative Past Year K6
Score + Alternative SDS
Score
Cut Point P N Pred_P Pred_N TP TN FP FN Sens Spec AUC PPV NPV
AUC = area under receiver operating characteristic (ROC) curve based on optimal cutpoint [(sensitivity + specificity)/2], FN = number of false negatives based on prediction, FP = number of false positives based on prediction, N = number of negative SMI cases, NPV = negative predictive value (TN/Pred_N), P = number of positive SMI cases, PPV = positive predictive value (TP/Pred_P), Pred_N = number of predicted negative cases, Pred_P = number of predicted positive cases, Sens = sensitivity (TP/P), Spec = specificity (TN/N), TN = number of true negatives based on prediction, TP = number of true positives based on prediction, SDS = Four-item Sheehan Disability Scale.
NOTE: Alternative past year K6 score: past year K6 score < 8 recoded as 0; past year K6 score 8-24 recoded as 1-17.
NOTE: Alternative SDS Score: SDS item scores < 7 recoded as 0; SDS item scores 7-10 recoded as 1, then summed for a score ranging from 0 to 4.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
TOTAL 0.26657 4,744 106,748 4,837 106,655 1,782 103,693 3,055 2,963 0.376 0.971 0.673 0.368 0.972
GENDER                            
Male 0.26657 2,636 47,669 1,801 48,504 895 46,763 906 1,741 0.340 0.981 0.660 0.497 0.964
Female 0.26657 2,109 59,079 3,036 58,152 887 56,930 2,150 1,222 0.421 0.964 0.692 0.292 0.979
AGE                            
18-25 0.26657 787 15,618 1,331 15,074 596 14,883 735 191 0.758 0.953 0.855 0.448 0.987
26-49 0.26657 1,737 51,335 2,507 50,565 879 49,707 1,628 858 0.506 0.968 0.737 0.351 0.983
50+ 0.26657 2,220 39,795 999 41,017 306 39,102 693 1,914 0.138 0.983 0.560 0.307 0.953
RACE/ETHNICITY                            
White, Not Hispanic 0.26657 2,740 78,741 2,925 78,556 1,325 77,141 1,600 1,415 0.484 0.980 0.732 0.453 0.982
Black, Not Hispanic 0.26657 1,373 9,847 531 10,688 33 9,349 498 1,339 0.024 0.949 0.487 0.063 0.875
Other, Not Hispanic 0.26657 539 2,753 1,211 2,081 394 1,935 818 145 0.731 0.703 0.717 0.325 0.930
Hispanic 0.26657 92 15,408 170 15,330 30 15,268 140 63 0.323 0.991 0.657 0.176 0.996
EDUCATION                            
< High School 0.26657 1,690 9,137 424 10,403 197 8,909 227 1,493 0.116 0.975 0.546 0.464 0.856
High School Graduate 0.26657 627 39,117 1,147 38,597 430 38,400 717 197 0.686 0.982 0.834 0.375 0.995
Some College 0.26657 1,454 27,081 1,803 26,731 527 25,804 1,276 927 0.363 0.953 0.658 0.292 0.965
College Graduate 0.26657 973 31,414 1,463 30,924 628 30,579 835 345 0.645 0.973 0.809 0.429 0.989
Table B.16 – K6 Cut Points for Each WHODAS and SDS Total Score
Alternative WHODAS Total Score Alternative Worst K6 SMI Cut Point Worst K6 SMI Cut Point
K6 = Six-item psychological distress scale, SDS = four-item Sheehan Disability Scale, SMI = serious mental illness, WHODAS = eight-item World Health Organization Disability Assessment Schedule.
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2008.
0 17 24
1 17 24
2 15 22
3 13 20
4 11 18
5 9 16
6 7 14
7 6 13
8 4 11
Alternative SDS Total Score Alternative Worst K6 SMI Cut Point Worst K6 SMI Cut Point
0 12 19
1 11 18
2 11 18
3 10 17
4 9 16


End Notes

8 This comprehensive set of tables is available at http://oas.samhsa.gov/WebOnly.htm#NSDUHtabs.

9 A successfully screened household is one in which all screening questionnaire items were answered by an adult resident of the household and either zero, one, or two household members were selected for the NSDUH interview.

10 The usable case rule requires that a respondent answer "yes" or "no" to the question on lifetime use of cigarettes and "yes" or "no" to at least nine additional lifetime use questions.

11 Prior to 2002, NSDUH was known as the National Household Survey on Drug Abuse (NHSDA).

12 Substances include alcohol, marijuana, cocaine, heroin, hallucinogens, inhalants, pain relievers, tranquilizers, stimulants, and sedatives.

13 R. C. Kessler, "Scidsmi-table-073107 (2) (2).doc," personal communication via e-mail to L. J. Colpe, August 1, 2007.

14 Although additional methamphetamine use items were included in the special drugs module in 2005, the 2005 survey did not include additional follow-up questions that were added in 2006. Hence, data from 2005 needed to be included in the adjustment procedures.

Go to Top of PageGo to the Table of Contents