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Appendix A: Description of the Surveys

A.1 Sample Design

The designs for the 2002 to 2006 National Surveys on Drug Use and Health (NSDUH)1 are part of coordinated sample designs providing estimates for all 50 States plus the District of Columbia. Survey years 2002 to 2004 come from a coordinated 5-year design for the 1999 through 2004 surveys, while the 2005 and 2006 designs are from a coordinated 5-year design for the 2005 through 2009 NSDUHs. The respondent universe is the civilian, noninstitutionalized population aged 12 years old or older residing within the 50 States and the District of Columbia. The survey includes persons living in noninstitutionalized group quarters (e.g., shelters, rooming/boarding houses, college dormitories, migratory workers' camps, halfway houses), and civilians living on military bases. Persons excluded from NSDUH include persons with no fixed household address (e.g., homeless and/or transient persons not in shelters), active-duty military personnel, and residents of institutional group quarters, such as correctional facilities, nursing homes, mental institutions, and long-term hospitals.

For the 50-State design, 8 States were designated as large sample States (California, Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas) with samples large enough to support direct State estimates. In 2006, sample sizes in these States ranged from 3,512 to 3,671. For the remaining 42 States and the District of Columbia, smaller, but adequate, samples were selected to support State estimates using small area estimation (SAE).2 Sample sizes in these States ranged from 862 to 1,000 in 2006.

In 2005 and 2006, States were first stratified into a total of 900 State sampling (SS) regions (48 regions in each large sample State and 12 regions in each small sample State). These regions were contiguous geographic areas designed to yield the same number of interviews on average.3 Unlike the 2002 through 2004 NSDUHs in which the first-stage sampling units were clusters of census blocks called "area segments," the first stage of selection for the 2005 through 2009 NSDUHs was census tracts.4 This stage was included to contain sample segments within a single census tract to the extent possible.5

For each SS region, 48 census tracts were selected with probability proportional to size. Within sampled census tracts, adjacent census blocks were combined to form the second-stage sampling units or area segments. One area segment was selected within each sampled census tract with probability proportional to population size to support the 5-year sample and any supplemental studies that the Substance Abuse and Mental Health Services Administration (SAMHSA) may choose to field.6 Of these segments, 24 were designated for the coordinated 5-year sample and 24 were designated as "reserve" segments. Eight sample segments per SS region were fielded during the 2006 survey year.

These sampled segments were allocated equally into four separate samples, one for each 3-month period (calendar quarter) during the year, so that the survey was essentially continuous in the field. In each of these area segments, a listing of all addresses was constructed and used as the frame for selecting a sample of addresses. Selected addresses were determined to be eligible sample units or to be ineligible. In the eligible sample units (which can be either households or units within group quarters), sample persons were randomly selected using an automated screening procedure programmed in a handheld computer carried by the interviewers. Persons aged 12 to 17 years and those aged 18 to 25 years were oversampled at this stage. Because of the large sample sizes in each year, there was no need to oversample racial/ethnic groups, as was done on surveys prior to 1999. Consistent with previous surveys in this series, the final respondent samples in each survey year were representative of the U.S. general population (since 1991, the civilian, noninstitutionalized population) aged 12 or older. In addition, State samples were representative of their respective State populations. More detailed information on the disposition of the national screening and interview samples can be found in Appendix B.

NSDUH covers residents of households (living in houses/townhouses, apartments, condominiums, etc.), persons in noninstitutional group quarters (e.g., shelters, rooming/boarding houses, college dormitories, migratory workers' camps, halfway houses), and civilians living on military bases. Although the survey covers residents of these types of units (they are given a nonzero probability of selection), the sample sizes of most specific groups are too small to provide separate estimates.

More information on the sample design can be found in the 2006 NSDUH sample design report by Morton et al. (2007) on the Office of Applied Studies (OAS) website (available at http://samhsa.gov/data/nsduh/methods.cfm).

A.2 Data Collection Methodology

The data collection method used in NSDUH involves in-person interviews with sample persons, incorporating procedures designed to maximize respondents' cooperation and willingness to report honestly about their illicit drug use behavior. Confidentiality is stressed in all written and oral communications with potential respondents. Respondents' names are not collected with the data, and computer-assisted interviewing (CAI) methods, including audio computer-assisted self-interviewing (ACASI), are used to provide a private and confidential setting to complete the interview.

Introductory letters are sent to sampled addresses, followed by an interviewer visit. A 5-minute screening procedure using a handheld computer involves listing all household members along with their basic demographic data. The computer uses the demographic data in a preprogrammed selection algorithm to select zero, one, or two sample person(s), depending on the composition of the household. This selection process is designed to provide the necessary sample sizes for the specified population age groupings and to select a subsample of pairs of individual respondents within the same households.

Interviewers immediately attempt to conduct the NSDUH interview with each selected person in the household. The interviewer requests the selected respondent to identify a private area in the home to conduct the interview away from other household members. The interview averages about an hour and includes a combination of CAPI (computer-assisted personal interviewing, in which the interviewer reads the questions) and ACASI (which is self-administered by the respondent).

The NSDUH interview consists of a core and supplemental sections. A core set of questions critical for basic trend measurement of prevalence estimates remains in the survey every year and comprises the first part of the interview. Supplemental questions, or modules, that can be revised, dropped, or added from year to year make up the remainder of the interview. The core consists of initial demographic items (which are interviewer-administered) and self-administered questions pertaining to the use of tobacco, alcohol, marijuana, cocaine, crack cocaine, heroin, hallucinogens, inhalants, pain relievers, tranquilizers, stimulants, and sedatives. Supplemental topics in the remaining self-administered sections include (but are not limited to) injection drug use, perceived risks of substance use, substance dependence or abuse, arrests, treatment for substance use problems, pregnancy and health care issues, and mental health issues. Supplemental demographic questions (which are interviewer-administered and follow the ACASI questions) address such topics as immigration, current school enrollment, employment and workplace issues, health insurance coverage, and income. It should be noted that some of the supplemental portions of the interview have remained in the survey, relatively unchanged, from year to year (e.g., current health insurance coverage, employment).

Thus, the interview begins in CAPI mode with the field interviewer (FI) reading the questions from the computer screen and entering the respondent's replies into the computer. The interview then transitions to the ACASI mode for the sensitive questions. In this mode, the respondent can read the questions silently on the computer screen and/or listen to the questions read through headphones and enter his or her responses directly into the computer. At the conclusion of the ACASI section, the interview returns to the CAPI mode with the interviewer completing the questionnaire. Each respondent who completes a full interview is given a $30.00 cash payment as a token of appreciation for his or her time.

No personal identifying information is captured in the CAI record for the respondent. Interviewers transmit the completed interview data to RTI in Research Triangle Park, North Carolina, via home telephone lines.

A.3 Data Processing

Computers at RTI direct the information to a raw data file that consists of one record for each completed interview. Even though editing and consistency checks are done by the CAI program during the interview, additional, more complex edits and consistency checks are completed at RTI. Cases are retained only if respondents provided data on lifetime use of cigarettes and at least nine other substances in the core section of the questionnaire. An important aspect of subsequent editing routines involves assignment of codes when respondents legitimately were skipped out of questions that definitely did not apply to them (e.g., if respondents never used a drug of interest). For key alcohol and other drug use measures, the editing procedures identify inconsistencies between related variables. Inconsistencies in variables pertaining to the most recent period that respondents used alcohol or other drugs are edited by assigning an "indefinite" period of use (e.g., use at some point in the lifetime, which could mean use in the past 30 days or past 12 months). Inconsistencies in other key substance use variables are edited by assigning missing data codes. These inconsistencies then are resolved through statistical imputation procedures, as discussed in Section A.3.1.

In addition, an important principle that was followed in editing NSDUH data was that data from core substance use modules (i.e., tobacco through sedatives, and including the core alcohol module) generally were not used to edit data in noncore modules, such as the consumption of alcohol module that was the source of estimates in some chapters of this report. In particular, noncore self-administered data (including the consumption of alcohol module in 2006) were never used to edit related variables in the core self-administered modules, such as alcohol. Consequently, variables in noncore sections of the interview could be inconsistent with variables in core sections. For example, respondents could report in the core alcohol module that they did not engage in binge alcohol use in the past 30 days (i.e., consumed five or more drinks in a single occasion on 0 days in the past 30 days), but they also could report in the noncore consumption of alcohol module that they had five or more drinks the last time they drank alcohol in the past 30 days. No further editing was done to make these core and noncore reports about binge alcohol use consistent with one another.

In an exception to the principle that items from noncore modules not be used to edit core variables, new methamphetamine items added to the special drugs noncore module in 2005 and 2006 were considered in the estimates of methamphetamine use in the past year among past year alcohol users in the top section of Table 4.5 in Appendix C. These methamphetamine items were added to better account for how methamphetamine is supplied and obtained. Unlike other stimulants that are available by prescription, most methamphetamine in the United States is supplied through illicit manufacturing and trafficking rather than through the conventional prescription drug distribution process. Therefore, one concern is that methamphetamine use may have been underestimated in NSDUH due to its inclusion within a set of questions about prescription-type drugs. Specifically, survey respondents who used methamphetamine might not have reported its use when questions about it were asked in the context of other questions about prescription pharmaceuticals. Section B.4.6 in Appendix B of the 2006 NSDUH national findings report (Office of Applied Studies [OAS], 2007a) provides a discussion of the new items and the process used to generate the prevalence estimates based on them.

A.3.1 Statistical Imputation

For some key variables that still had missing or ambiguous values after editing, statistical imputation was used to replace these values with appropriate response codes. For example, the response is ambiguous if the editing procedures assigned a respondent's most recent use of alcohol or other drugs to "use at some point in the lifetime," with no definite period within the lifetime. In this case, the imputation procedures assign a definite value for when the respondent last used the substance (e.g., in the past 30 days, more than 30 days ago but within the past 12 months, more than 12 months ago). Similarly, if a response is completely missing, the imputation procedures replace missing values with nonmissing ones.

In most cases, missing or ambiguous values are imputed in NSDUH using a methodology called predictive mean neighborhoods (PMN), which was developed specifically for the 1999 survey and used in all subsequent survey years. The PMN method offers a rigorous and flexible method that was implemented to improve the quality of estimates and allow more variables to be imputed. Some of the key reasons for implementing this method include the following: (1) the ability to use covariates to determine donors is far greater than that offered in the hot deck, (2) the relative importance of covariates can be determined by standard estimating equation techniques, (3) the correlations across response variables can be accounted for by making the imputation multivariate, and (4) sampling weights can be easily incorporated in the models. The PMN method has some similarity with the predictive mean matching method of Rubin (1986) except that, for the donor records, Rubin used the observed variable value (not the predictive mean) to compute the distance function. Also, the well-known method of nearest neighbor imputation is similar to PMN, except that the distance function is in terms of the original predictor variables and often requires somewhat arbitrary scaling of discrete variables. PMN is a combination of a model-assisted imputation methodology and a random nearest neighbor hot-deck procedure. The hot-deck procedure is set up in such a way that imputed values are made consistent with preexisting nonmissing values for other variables. Whenever feasible, the imputation of variables using PMN is multivariate, in which imputation is accomplished on several response variables at once. Variables requiring imputation using PMN are the core demographic variables, core alcohol and other drug use variables (recency of use, frequency of use, and age at first use), income, health insurance, and noncore demographic variables for work status, immigrant status, and the household roster.

In the modeling stage of PMN, the model chosen depends on the nature of the response variable Y. In the 2006 NSDUH, the models included binomial logistic regression, multinomial logistic regression, Poisson regression, and ordinary linear regression, where the models incorporated the sampling design weights.

In general, hot-deck imputation replaces an item nonresponse (missing or ambiguous value) with a recorded response that is donated from a "similar" respondent who has nonmissing data. For random nearest neighbor hot-deck imputation, the missing or ambiguous value is replaced by a responding value from a donor randomly selected from a set of potential donors. Potential donors are those defined to be "close" to the unit with the missing or ambiguous value according to a predefined function called a distance metric. In the hot-deck stage of PMN, the set of candidate donors (the "neighborhood") consists of respondents with complete data who have a predicted mean close to that of the item nonrespondent. The predicted means are computed both for respondents with and without missing data, which differs from Rubin's method where predicted means are not computed for the donor respondent (Rubin, 1986). In particular, the neighborhood consists of either the set of the closest 30 respondents or the set of respondents with a predicted mean (or means) within 5 percent of the predicted mean(s) of the item nonrespondent, whichever set is smaller. If no respondents are available who have a predicted mean (or means) within 5 percent of the item nonrespondent, the respondent with the predicted mean(s) closest to that of the item nonrespondent is selected as the donor.

In the univariate case (where only one variable is imputed using PMN), the neighborhood of potential donors is determined by calculating the relative distance between the predicted mean for an item nonrespondent and the predicted mean for each potential donor, then choosing those means defined by the distance metric. The pool of donors is restricted further to satisfy logical constraints whenever necessary (e.g., age at first crack use must not be less than age at first cocaine use).

Whenever possible, missing or ambiguous values for more than one response variable are considered at a time. In this (multivariate) case, the distance metric is a Mahalanobis distance (Manly, 1986) rather than a relative Euclidean distance. Whether the imputation is univariate or multivariate, only missing or ambiguous values are replaced, and donors are restricted to be logically consistent with the response variables that are not missing. Furthermore, donors are restricted to satisfy "likeness constraints" whenever possible. That is, donors are required to have the same values for variables highly correlated with the response. If no donors are available who meet these conditions, these likeness constraints can be loosened. For example, donors for the age at first use variable are required to be of the same age as recipients, if at all possible. Further details on the PMN methodology are provided in RTI International (2008) and by Singh, Grau, and Folsom (2001, 2002).

Although statistical imputation could not proceed separately within each State due to insufficient pools of donors, information about each respondent's State of residence was incorporated in the modeling and hot-deck steps. For most drugs, respondents were separated into three "State usage" categories as follows: respondents from States with high usage of a given drug were placed in one category, respondents from States with medium usage into another, and the remainder into a third category. This categorical "State rank" variable was used as one set of covariates in the imputation models. In addition, eligible donors for each item nonrespondent were restricted to be of the same State usage category (i.e., the same "State rank") as the nonrespondent.

A.3.2 Development of Analysis Weights

The general approach to developing and calibrating analysis weights involved developing design-based weights, dk, as the product of the inverse of the selection probabilities at each selection stage. The 2005 and 2006 NSDUHs used a four-stage sample selection scheme in which an extra selection stage of census tracts was added before the selection of a segment. Thus, the design-based weights, dk, for the 2005 and 2006 NSDUHs incorporated the extra layer of sampling selection to reflect the change in sample design relative to the 2002 to 2004 NSDUHs. Adjustment factors, ak(λ), then were applied to the design-based weights to adjust for nonresponse, to poststratify to known population control totals, and to control for extreme weights when necessary. In view of the importance of State-level estimates with the 50-State design, it was necessary to control for a much larger number of known population totals. Several other modifications to the general weight adjustment strategy that had been used in past surveys also were implemented for the first time beginning with the 1999 CAI sample.

Weight adjustments were based on a generalization of Deville and Särndal's (1992) logit model. This generalized exponential model (GEM) (Folsom & Singh, 2000) incorporates unit-specific bounds (image representing script lk, uk), kimage representing elements, for the adjustment factor ak(λ) as follows:

Appendix A Equation     D

where ck are prespecified centering constants, such that image representing script lk < ck < uk and Ak = (uk - image representing script lk) / (uk - ck)(ck - image representing script lk). The variables image representing script lk, ck, and uk are user-specified bounds, and λ is the column vector of p model parameters corresponding to the p covariates x. The λ-parameters are estimated by solving

Appendix A Equation     D

where capital T tilde sub x denotes control totals that could be either nonrandom, as is generally the case with poststratification, or random, as is generally the case for nonresponse adjustment.

The final weights wk = dkak(λ) minimize the distance function Δ(w,d) defined as

Appendix A Equation     D

This general approach was used at several stages of the weight adjustment process, including (1) adjustment of household weights for nonresponse at the screener level, (2) poststratification of household weights to meet population controls for various demographic groups by State, (3) adjustment of household weights for extremes, (4) poststratification of selected person weights, (5) adjustment of responding person weights for nonresponse at the questionnaire level, (6) poststratification of responding person weights, and (7) adjustment of responding person weights for extremes.

Every effort was made to include as many relevant State-specific covariates (typically defined by demographic domains within States) as possible in the multivariate models used to calibrate the weights (nonresponse adjustment and poststratification steps). Because further subdivision of State samples by demographic covariates often produced small cell sample sizes, it was not possible to retain all State-specific covariates (even after meaningful collapsing of covariate categories) and still estimate the necessary model parameters with reasonable precision. Therefore, a hierarchical structure was used in grouping States with covariates defined at the national level, at the census division level within the Nation, at the State group within the census division, and, whenever possible, at the State level. In every case, the controls for the total population within a State and the five age groups (12 to 17, 18 to 25, 26 to 34, 35 to 49, 50 or older) within a State were maintained except that, in the last step of poststratification of person weights, six age groups (12 to 17, 18 to 25, 26 to 34, 35 to 49, 50 to 64, 65 or older) were used. Census control totals by age, race, gender, and Hispanicity were required for the civilian, noninstitutionalized population of each State. Beginning with the 2002 NSDUH, the Population Estimates Branch of the U.S. Census Bureau has produced the necessary population estimates in response to a special request based on the 2000 census.

Consistent with the surveys from 1999 onward, control of extreme weights through separate bounds for adjustment factors was incorporated into the GEM calibration processes for both nonresponse and poststratification. This is unlike the traditional method of winsorization in which extreme weights are truncated at prespecified levels and the trimmed portions of weights are distributed to the nontruncated cases. In GEM, it is possible to set bounds around the prespecified levels for extreme weights, and then the calibration process provides an objective way of deciding the extent of adjustment (or truncation) within the specified bounds. A step was added to poststratify the household-level weights to obtain census-consistent estimates based on the household rosters from all screened households; these household roster-based estimates then provided the control totals needed to calibrate the respondent pair weights for subsequent planned analyses. An additional step poststratified the selected person sample to conform to the adjusted roster estimates. This additional step takes advantage of the inherent two-phase nature of the NSDUH design. The final step poststratified the respondent person sample to external census data (defined within the State whenever possible, as discussed above). For more detailed information, see the 2006 NSDUH Methodological Resource Book (RTI International, 2008).

In addition to the person-level analysis weights discussed above, person pair–level analysis weights also were calculated. In each year of NSDUH, the person pair–level analysis weights and person-level analysis weights shared the same weight components at the screening dwelling unit (SDU) level. In addition to these common weight components, the person pair–level analysis weights had several specific weight components: (1) inverse of person-pair selection probability, (2) poststratification of selected person-pair weights, (3) adjustment of responding person-pair weights for nonresponse, (4) poststratification of responding person-pair weights, and (5) adjustment of responding person-pair weights for extremes. The person-pair analysis weights were the product of all of the weight components. For more detailed information, see the 2006 NSDUH Methodological Resource Book (RTI International, 2008).

For many populations of interest in this report, 5 years of NSDUH data were combined to obtain annual averages. The person-level weights and the person-pair weight for estimates based on the annual averages were obtained by dividing the person-level analysis weights and the person-pair analysis weight for the 5 specific years by a factor of 5.


End Notes

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

2 SAE is a hierarchical Bayes modeling technique used to make State-level estimates for approximately 20 substance-use-related measures. For more details, see the State Estimates of Substance Use from the 2004-2005 National Surveys on Drug Use and Health (Wright, Sathe, & Spagnola, 2007).

3 Areas were defined using 2000 census geography. Dwelling units (DUs) and population counts were obtained from the 2000 census data supplemented with revised population counts from Claritas (http://cluster1.claritas.com/claritas/Default.jsp).

4 Census tracts are relatively permanent statistical subdivisions of counties and provide a stable set of geographic units across decennial census periods.

5 Some census tracts had to be aggregated in order to meet the minimum DU requirement of 150 DUs in urban areas and 100 DUs in rural areas.

6 For more details on the 5-year sample, see the 2006 sample design report in the 2006 NSDUH Methodological Resource Book (Morton, Chromy, Hunter, & Martin, 2007).

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