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List of academic disciplines and sub-disciplines
An academic discipline or field of study is a branch of knowledge that is taught and researched as part of higher education.
A scholar's discipline is commonly defined and recognized by the university faculties and learned societies to which he or she belongs and the academic journals in which he or she publishes research. However, there exist no formal criteria for the status of an academic discipline. Disciplines vary between well-established ones that exist in almost all universities and have well-defined rosters of journals and conferences and nascent ones supported by only a few universities and publications.
A discipline usually has several sub-disciplines or branches, but the distinguishing lines between these are often both arbitrary and ambiguous.[1]
- 1 Overview
- 2 Humanities
- 3 Social sciences
- 4 Natural sciences
- 5 Formal sciences
- 6 Professions and applied sciences
- 6.1 Agriculture
- 6.2 Architecture and design
- 6.3 Business
- 6.4 Divinity
- 6.5 Education
- 6.6 Engineering
- 6.7 Environmental studies and forestry
- 6.8 Family and consumer science
- 6.9 Healthcare science
- 6.10 Human physical performance and recreation*
- 6.11 Journalism, media studies and communication
- 6.12 Law
- 6.13 Library and museum studies
- 6.14 Military sciences
- 6.15 Public administration
- 6.16 Social work
- 6.17 Transportation
- 7 See also
- 8 Notes
- 9 References
- 10 External links
The University of Paris in 1231 consisted of four faculties: Theology, Medicine, Canon Law and Arts.[2] Most academic disciplines have their roots in the mid-to-late-19th century secularization of universities, when the traditional curricula were supplemented with non-classical languages and literatures, social sciences such as political science, economics, sociology and public administration, and natural science and technology disciplines such as physics, chemistry, biology, and engineering.
In the early 20th century, new disciplines such as education and psychology were added. In the 1970s and 1980s, there was an explosion of new disciplines focusing on specific themes, such as media studies, women's studies, and black studies. Many disciplines designed as preparation for careers and professions, such as nursing, hospitality management, and corrections, also emerged in the universities. Finally, interdisciplinary scientific fields such as biochemistry and geophysics gained prominence as their contribution to knowledge became widely recognized.
There is no consensus on how some academic disciplines should be classified (e.g., whether anthropology and linguistics are social sciences disciplines or humanities disciplines). More generally, the proper criteria for organizing knowledge into disciplines are also open to debate.
An asterisk (*) denotes a field whose academic status has been debated among this article's editors.
Performing arts[edit]
Visual arts[edit]
Social sciences[edit]
Area studies[edit]
- African studies
- American studies
- Asian studies
- Indology (Indian studies)
- Iranian studies
- Japanology (Japanese studies)
- Korean studies
- Sinology (outline) (Chinese studies)
- Southeast Asian studies
- European studies
- Australian studies
- Middle East studies
Cultural and ethnic studies[edit]
Gender and sexuality studies[edit]
Political science[edit]
Natural sciences[edit]
Space sciences[edit]
Earth sciences[edit]
- See also Branches of earth sciences
Life sciences[edit]
- See also Branches of life sciences
- See also Branches of chemistry
- See also Branches of physics
Formal sciences[edit]
Computer sciences[edit]
- See also ACM Computing Classification System
- See also Branches of mathematics and AMS Mathematics Subject Classification
Systems science[edit]
Professions and applied sciences[edit]
Architecture and design[edit]
- See also Branches of engineering
Environmental studies and forestry[edit]
Family and consumer science[edit]
Healthcare science[edit]
Human physical performance and recreation*[edit]
Journalism, media studies and communication[edit]
Library and museum studies[edit]
Military sciences[edit]
Public administration[edit]
Social work[edit]
See also[edit]
- Academia
- Academic genealogy
- Curriculum
- Interdisciplinarity
- Transdisciplinary studies
- Classification of Instructional Programs
- Joint Academic Classification of Subjects
- Outline of education
- List of fields of doctoral studies
- ^ Andrew Abbott, Chaos of Disciplines University Of Chicago Press 2001 ISBN 0-226-00101-6
- ^ History of Education, Encyclopædia Britannica (1977, 15th edition), Macropaedia Volume 6, p. 337
- Andrew Abbott, Chaos of Disciplines University Of Chicago Press 2001 ISBN 0-226-00101-6
- Alexandra Oleson & John Voss (eds) The Organization of Knowledge in Modern America, 1860-1920 Johns Hopkins University Press 1979 ISBN 0-8018-2108-8
- US Department of Education Institute of Education Sciences. Classification of Instructional Programs (CIP). National Center for Education Statistics.
External links[edit]
- Classification of Instructional Programs (CIP 2000): Developed by the U.S. Department of Education's National Center for Education Statistics to provide a taxonomic scheme that will support the accurate tracking, assessment, and reporting of fields of study and program completions activity.
- Complete JACS (Joint Academic Classification of Subjects) from Higher Education Statistics Agency (HESA) in the United Kingdom
- Australian and New Zealand Standard Research Classification (ANZSRC 2008) (web-page) Chapter 3 and Appendix 1: Fields of research classification.


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< Return To Hearing
The Honorable Patrick LeahyFebruary 12, 2002 More than ever after the tragic events of September 11, we must maintain our commitment to refugees. Our refugee policies show our nation at its best, and we need to preserve them. I would like to thank Senator Kennedy for holding this hearing and emphasizing that point, and Senator Brownback for making this Committee's dedication to refugees truly bipartisan. I was pleased when the President announced last fall that the United States would accept 70,000 refugees in FY 2002, because it confirmed that our nation would not allow the terrorist attacks to interfere with our commitments to provide a home for people fleeing persecution and chaos throughout the world. I understood when refugee interviews slowed to a near halt after September 11 due to the removal of U.S. government personnel from various troubled regions of the world, and I agreed with the need to develop additional security mechanisms before admitting refugees, to ensure that no terrorist could abuse the admissions process. But I have been concerned by some of the conflicting signals being sent by different parts of the Administration, and I hope this hearing will assure the Committee that the President's directive to admit 70,000 refugees will be realized. Not long after the President announced his directive, others in the executive branch suggested that it was impossible to meet. The State Department made plans to admit only 50,000 refugees, and the Immigration and Naturalization Service did not have its normal complement of officers dedicated to conducting screening interviews. I joined with Senators Kennedy and Brownback in writing last month to INS Commissioner Ziglar and Secretary of State Powell to urge them to take the steps necessary. More recently, there have been encouraging signs from both the INS and the State Department. I was heartened by Commissioner Ziglar's address to the National Immigration Forum earlier this month, in which he said he would be detailing "a significant number of INS personnel to conduct refugee interviews worldwide with a goal of meeting 70,000 admissions this year." That is the right goal, and I thank Commissioner Ziglar for expressing it so publicly and for joining us today to discuss it. Similarly, the State Department has recently suggested that it too is committed to the task. Our refugee program shows our nation's commitment to the dispossessed and persecuted, and our continued dedication to it after the September 11 attacks shows that we will not sacrifice our ideals. Especially in these uncertain times, other nations may follow our lead if we scale back our commitments. I know there are now many logistical hurdles to overcome in implementing the program, but I am confident that our experts at the State Department and INS can get the job done. We must remember that there are thousands of desperate people in refugee camps around the world - including refugees from Afghanistan - waiting for the promise of a new life in America. There are also thousands of Americans, many in my State of Vermont, who stand ready to help these refugees adjust to life in the United States. This is a system that has worked in the past and will work in the future - preserving it is worth extraordinary effort, and I hope to hear today that the Administration intends to mount such an effort in the coming months. |

subset: commoncrawl


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View Edition 2012 | 2011 | 2009 |
Welcome to the 2010 edition of The Leaders, Reed Construction Data's annual ranking and survey of Canada's top general contractors.
Download the digital edition | |
Cover photo: Gary Campbell Photography |
INSIDE Looking Forward: A Message from Mark Casaletto, V.P. and General Manager, Reed Construction Data Graham Group Minding Their Business Bird Construction Co. CanaData Canada's Top 50 Leaders in Construction |
Download the PDF version (8.83MB) |


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I have a FTD subscription, so I get all FTD releases (besides the book) send to me at 21,50 euro a piece (postage included). I haven't ordered the DVD's yet. Still looking for the best price.
I'm also waiting for those new movie DVD's that are being released this summer.


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Man's best friend is a wingman, or wingdog, rather.
Two thirds of dog owners say that they have, on average, forged nearly five friendships thanks to their fury friends.
They say they spend about 8 minutes talking to people during stops. What do they talk about? Doggey habits like eating and grooming.
The poll surveyed 1,500 people and was conducted in conjunction with the Christmas DVD release of The Snowman and the Snowdog.
42 percent of the respondents in this study actually admit that they were suspicious of people who did not own a dog.


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"A good performance helps everyone. It gives them confidence, there's no doubt about that. It won't change him. He [De Gea] is a down-to-earth boy.
"As for Danny, his goal won't do him any harm. He's not a boy who lacks confidence. He wasn't fazed by the occasion. It's a great boost for the younger players coming up behind him, those in the Academy like Danny was. He's been with us since the age of nine.
"It's great testament to the development work we do with young players. We had young Nick Powell and Adnan Januzaj in the Reserves on Monday and you say to yourself: 'These are definite Manchester United first-team players.'
"We can see that. They have outstanding qualities. They can see that in trusting us to map out their careers and help them and develop them then they can get to Danny's level."
Powell impressed in a central attacking role, where he was often employed in his Crewe Alexandra days, against the Gunners' Under-21s while Belgian teenager Januzaj has started to shine at that level after being promoted from the Academy side.


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An important limitation of estimates of drug use prevalence from the National Survey on Drug Use and Health (NSDUH) is that they are only designed to describe the target population of the survey—the civilian, noninstitutionalized population aged 12 or older. Although this population includes almost 98 percent of the total U.S. population aged 12 or older, it excludes some important and unique subpopulations who may have very different drug use patterns. For example, the survey excludes active military personnel, who have been shown to have significantly lower rates of illicit drug use. 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.
The national estimates, along with the associated standard errors (SEs), were computed 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, 2004). 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) 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.
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, d, such as drug use prevalence estimates for a domain d, can be expressed as a ratio estimate:
where d is a linear statistic estimating the number of substance users in the domain d and d 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 d and d and also can be used to estimate their respective SEs. A Taylor series approximation method implemented in SUDAAN provides estimates for d and its SE.
When the domain size, d, is free of sampling error, an appropriate estimate of the SE for the total number of substance users is
This approach is theoretically correct when the domain size estimates, d, are among those forced to match their respective U.S. Census Bureau population estimates through the weight calibration process (Chen et al., 2006). In these cases, d 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.3.2 in Appendix A.
For estimated domain totals, d, where d 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 d is negligible relative to the sampling variation in d. 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 d was subject to considerable variation. In 2000, an approach was implemented to reflect more accurately the effects of the weighting process on the variance of total estimates. This approach consisted of calculating SEs of totals for all estimates in a particular detailed table using the formula above when a majority of estimates in a table were among domains in which d was fixed during weighting or if it could be assumed that the sampling variation in d was negligible. Detailed tables in which the majority of estimates were among domains where d was subject to considerable variability were calculated directly in SUDAAN. Starting with the 2005 NSDUH, a "mixed" method approach was implemented for all the 2005 detailed tables to improve on the accuracy of SEs. This method had been applied to selected tables in the 2004 NSDUH, but it was implemented across all tables for the 2005 NSDUH. This approach assigns the method of SE calculation to domains within tables so that all estimates among a select set of domains with fixed d 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 d) 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 1 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 prior NSDUH reports.
Table B.1 contains a list of domains with a fixed d. 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.
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 sample size, and effective sample size for each estimate.
Proportion estimates within the range [0 < < 1], rates, and the corresponding estimated number of users were suppressed if
RSE[-ln()] > .175 when ≤ .5
RSE[-ln(1 - > .175 when > .5.
Using a first-order Taylor series approximation to estimate RSE[-ln()] and RSE[-ln (1 - the following was obtained and used for computational purposes:
> .175 when ≤ .5 D
> .175 when > .5 D
The separate formulas for ≤ .5 and > .5 produce a symmetric suppression rule (i.e., if is suppressed, then 1 - will be as well). This ad hoc rule requires an effective sample size in excess of 50. When .05 < < .95, the symmetric property of the rule produces a local maximum effective sample size of 68 at = .5. Thus, estimates with these values of along with effective sample sizes falling below 68 are suppressed. See Figure B.1 for a graphical representation of the required minimum effective sample sizes as a function of the proportion estimated.
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 < .00005 or if ≥ .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.
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
where 1 = first prevalence estimate, 2 = second prevalence estimate, var(1) = variance of first prevalence estimate, var(2) = variance of second prevalence estimate, and cov(1,2) = covariance between 1 and 2. In cases where significance tests between years were performed, the prevalence estimate from the earlier year (e.g., 2002, 2003, or 2004) becomes the first prevalence estimate and the prevalence estimate from the later year (e.g., 2003, 2004, or 2005) 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 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 defined by three or more levels of a categorical variable, log-linear chi-square tests of independence of the subgroups and the prevalence variables were conducted first to control the error level for multiple comparisons. If the chi-square test 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. 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 2005. 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 2005 and the 3 prior years because estimates for persons in grade 8 and grade 10 in each of these 4 years were considered independent. Design effects published in Johnston et al. (2005c) for both adjacent and nonadjacent year testing were used to calculate variances so that significance testing could be done. For the 19- to 24-year-old age group, tests of differences were only performed between 2004 and 2005 and between 2002 and 2005. This is because the MTF is a longitudinal study for young adults where respondents are interviewed in alternating years. Therefore, the same young adults could have been interviewed in both 2005 and 2003, and independence between these years cannot be assumed. Weighted sample sizes published in Johnston et al. (2003c, 2004a, 2005a, 2006b) were used to estimate variances because design effects were not available for this subgroup.
As an example, the difference between the 2004 and 2005 averages of prevalence estimates for persons in grades 8 and 10 can be expressed as
where 1 = (11 + 12) / 2, 11 and 12 are the prevalence estimates for the 8th and 10th grades, respectively, for 2004; and 2 is defined similarly for 2005. The variance of a prevalence estimate can be written as
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., 2004 vs. 2005) estimates and nonadjacent year (i.e., 2002 vs. 2005 and 2003 vs. 2005) estimates; therefore, the variance of the difference between 2 years of estimates for a particular grade can be expressed as
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, 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
The test statistic can therefore be written as
where Z is asymptotically distributed as a standard normal random variable.
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.
In 2005, respondents continued to receive a $30 incentive in an effort to improve response rates over years prior to 2002. Of the 146,912 eligible households sampled for the 2005 NSDUH, 134,055 were screened successfully, for a weighted screening response rate of 91.3 percent (Table B.3). In these screened households, a total of 83,805 sample persons were selected, and completed interviews were obtained from 68,308 of these sample persons, for a weighted interview response rate of 76.2 percent (Table B.4). A total of 10,369 (16.0 percent) sample persons were classified as refusals or parental refusals, 3,088 (3.8 percent) were not available or never at home, and 2,040 (4.0 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 interview response rate was highest among 12 to 17 year olds (87.1 percent), females (77.8 percent), blacks (81.2 percent), in nonmetropolitan areas (79.2 percent), and among persons residing in the South (77.2 percent) (Table B.5).
The overall weighted response rate, defined as the product of the weighted screening response rate and weighted interview response rate, was 69.6 percent in 2005. 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). Thus, assuming the quantity (Pr - Pnr) is fixed over time, the improvement in response rates in 2002 through 2005 over prior years will result in estimates with lower nonresponse bias.
Among survey participants, item response rates were above 99 percent for most drug use items. However, inconsistent responses for some items were common. Estimates of substance use from NSDUH are based on responses to multiple questions by respondents, so that the maximum amount of information is used in determining whether a respondent is classified as a drug user. Inconsistencies in responses are resolved through a logical editing process that involves some judgment on the part of survey analysts. Additionally, missing or inconsistent responses are imputed using statistical methodology. Editing and imputation of missing responses are potential sources of error.
Most drug use prevalence estimates, including those produced for NSDUH, are based on self-reports of use. Although studies have generally supported the validity of self-report data, it is well documented that these data often are biased (underreported or overreported) by several factors, including the mode of administration, the population under investigation, and the type of drug (Bradburn & Sudman, 1983; Hser & Anglin, 1993). Higher levels of bias also are observed among younger respondents and those with higher levels of drug use (Biglan, Gilpin, Rorhbach, & Pierce, 2004). Methodological 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 community-based epidemiological studies (SRNT Subcommittee on Biochemical Verification, 2002). NSDUH utilizes widely accepted methodological practices for ensuring validity, such as encouraging privacy through audio computer-assisted self-interviewing (ACASI). Comparisons using these methods within NSDUH have been shown to reduce reporting bias (Aquilino, 1994; Turner, Lessler, & Gfroerer, 1992).
Several measurement issues are associated with the 2005 NSDUH that 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 psychological distress (SPD), and depression.
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], 2005b), a new measure related to incidence was introduced and has become the primary focus of Chapter 5 in this national results report. 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. Prior NSDUH reports have included long-term trends in incidence by calendar year based on these self-reports. Calendar year initiates refer to the number of new substance users reporting first use within a calendar year (between January 1 and December 31 of a specific prior year of a respondent's life) and are determined by the respondents' information on age and month at first use, interview date, and date of birth, as well as date of entry to the United States if respondents indicated they were not born in the United States (for more information on calendar year estimates, see Section B.4.1 in Appendix B of OAS, 2005b). Although calendar year estimates can provide useful indicators of long-term trends, they may be subject to substantial bias, as discussed later in this section. Calendar year estimates are not included in this report.
Beginning in 1999, the survey questionnaire allows for collection of year and month of first use for recent initiates. Month, day, and year of birth also are obtained directly or are imputed for item nonrespondents as part of the data postprocessing. Additionally, the questionnaire call record provides the date of the interview. By imputing a day of first use within the year and month of first use, 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:
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 could have occurred earlier.
Because estimates of incidence are based on retrospective reports of age at first drug use by survey respondents, they may be subject to memory-related biases, such as recall decay and telescoping. Recall decay occurs when respondents who initiated many years ago fail to report this use and will tend to result in a downward bias in estimates for earlier years (e.g., 1960s and 1970s). Telescoping refers to misreporting of an event in time. An event can be dated too remote (backward telescoping) or too recent (forward telescoping). Forward telescoping occurs, for example, when an 18-year-old respondent who first used at age 12 reports his or her age at first use as 14. Telescoping such as this will tend to result in an upward bias for estimates for more recent years.
There also is likely to be some underreporting bias due to the tendency for respondents to not report socially unacceptable behavior because of respondents' fear of disclosure. This bias is likely to have the greatest impact on recent estimates, which reflect more recent use and are based heavily on reporting by young respondents for some substances, particularly alcohol, cigarettes, and inhalants. Finally, for drug use that is frequently initiated at age 10 or younger, estimates based on retrospective reports 1 year later underestimate total incidence because 11-year-old (or younger) children are not sampled by NSDUH. Prior analyses showed that alcohol and cigarette (any use) incidence estimates could be affected significantly by this.
An evaluation of NSDUH retrospective estimates of incidence suggested that these types of bias are significant and differ by substance and length of recall (Gfroerer, Hughes, Chromy, Heller, & Packer, 2004). This study showed that, for very recent time periods, such as within the past year or in the prior 2 or 3 calendar years, bias in estimates of marijuana, cocaine, alcohol, and cigarettes appears to be small, but for all other substances there is significant downward bias. Bias for all substances was shown to increase the further back in time the estimates are made, suggesting an association with the length of recall.
Recent analysis on the recall period suggested the presence of both forward and backward telescoping effects when reporting first use of a substance. In particular, it appears that there was a tendency to report very recent events as if they had occurred further back in time, while more remote events may be reported to have occurred more recently. Because past year and calendar year initiation estimates are based on reports occurring in the first 12 months and the first 24 months prior to the interview date, respectively, both may be affected by these telescoping effects in different degrees. Because the past year reflects the most recent time period, past year incidence estimates may be most affected by forward telescoping. This may explain why past year estimates tend to show lower incidence than do calendar year estimates. On the other hand, calendar year estimates from 2 or 3 years prior to the survey may be more affected by backward telescoping, resulting in upward biased estimates for those years. In the same study, it was observed that for a given survey year and for several substances the most recent calendar year incidence estimate is usually lower than the two previous calendar year estimates, and that calendar year estimates tend to diminish as length of recall increases, probably as a result of recall bias.
Although it is clear that both the calendar year and the past year incidence estimates are affected by a variety of types of bias, both can provide useful epidemiological information for researchers and policymakers. Calendar year estimates, used with caution, can be analyzed to understand historical shifts in substance use as far back as the 1960s, when marijuana use began to become widespread in the United States. To track very recent shifts and patterns in incidence, however, past year incidence estimates have several important advantages and since 2004 have been the primary focus of the NSDUH national results report. The main advantages are as follows:
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 were computed. Starting with this 2005 NSDUH report, 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 when one can think of the estimates as reflecting all initial users regardless of current age. 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 2005 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 2004 survey had persons who were 11 years old on the date of the 2004 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.
To get an impression of the potential undercoverage in the current year, reports of substance use initiation reported in 2005 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 2005. A rough adjustment to recognize likely 2005 behaviors was based on a ratio of lifetime users aged 12 to 17 in 2005 to the same estimate for the prior applicable survey year. To illustrate the calculation, consider past year use of alcohol. In the 2005 survey, 4,274,000 persons were estimated to have initiated use of alcohol in the past year based on reports by persons 12 year old or older. In addition, an estimate of 110,552 persons 12 years old in 2005 also reported having initiated use of alcohol between 1 and 2 years earlier. These persons would have been past year initiates in the 2004 survey conducted on the same dates had the 2004 survey covered younger persons. The estimated number of lifetime users currently aged 12 to 17 was 10,305,889 for 2005 and 10,595,539 for 2004, indicating fewer overall initiates of alcohol use among persons aged 17 or younger in 2005. An adjusted estimate of initiation of alcohol use by persons who were 11 years old in 2005 is given by
Numerically, this yielded an adjusted estimate of 107,530 persons 11 years old on a 2005 survey date and initiating use of alcohol in the past year:
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 2003 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 268,249, or about 6 percent of the estimate based on past year initiation by persons 12 or older only (268,249/4,274,000 = 0.0628).
Based on similar analyses, the estimated undercoverage of past year initiates was about 6 percent for cigarettes, about 1.5 percent for marijuana, and about 26 percent for inhalants.
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 16.8 to 16.3 (or a decrease of about 3 percent) for alcohol, from 17.3 to 16.8 (or a decrease of about 3 percent) for cigarettes, from 20.6 to 20.5 (or a decrease of about 0.5 percent) for marijuana, and from 16.1 to 14.6 (or a decrease of about 10.5 percent) for inhalants.
The 2005 NSDUH computer-assisted interviewing (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) or 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 2005 NSDUH contained 19 NDSS questions that addressed five aspects of dependence:
Each of the five domains listed above can be assessed by a continuous 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 frequently missing data.
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 (Grau et al., 2005).
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 = Sometimes 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 = Sometimes 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 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.
The 2005 NSDUH CAI instrumentation included questions that were designed to measure dependence on and abuse of illicit drugs and alcohol. For these substances,7 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:
For alcohol, cocaine, heroin, pain relievers, sedatives, and stimulants, a respondent was defined as having dependence if he or she met three or more of seven dependence criteria, including the six standard criteria listed above plus a seventh withdrawal symptom criterion. 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:
Criteria used to determine whether a respondent was asked the dependence and abuse questions included responses from the core substance use questions and the frequency of substance use questions, as well as the noncore substance use questions. Unknown 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. Responses to the dependence and abuse questions that were inconsistent with the imputed substance use or frequency of substance use could have existed. Because different criteria and different combinations of criteria were used as skip logic for each substance, different types of inconsistencies may have occurred for certain substances between responses to the dependence and abuse questions and the imputed substance use and frequency of substance use as described below.
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?" 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.4.6), 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. 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.
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.
For this 2005 NSDUH report, serious psychological distress (SPD) was measured using the K6 screening instrument for nonspecific psychological distress (Kessler et al., 2003a). In NSDUH reports prior to 2004, the K6 scale was used to measure serious mental illness (SMI). For a discussion of the reasons that the K6 was used to measure SPD instead of SMI for the 2004 and later NSDUH reports, as well as details on a methodological study of the measurement of SMI, see Section B.4.4 of Appendix B in the 2004 NSDUH national results report (OAS, 2005b).
The K6 consists of six questions that ask respondents how frequently they experienced symptoms of psychological distress during the 1 month in the past year when they were at their worst emotionally. The use of this scale for SPD (or SMI prior to 2004) was based on a methodological study designed to evaluate several screening scales for measuring SMI in NSDUH. These scales consisted of a truncated version of the World Health Organization (WHO) Composite International Diagnostic Interview Short Form (CIDI-SF) scale (Kessler, Andrews, Mroczek, Üstün, & Wittchen, 1998), the K10/K6 scale of nonspecific psychological distress (Kessler et al., 2003a), and a truncated version of the WHO Disability Assessment Schedule (WHO-DAS) (Rehm et al., 1999).
The six questions comprising the K6 scale are given as follows:
To create a score, the six items (DSNERV1, DSHOPE, DSFIDG, DSNOCHR, DSEFFORT, and DSDOWN) on the K6 scales 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 year SPD (or SMI prior to 2004). This cut point was chosen to equalize false positives and false negatives.
In the 2003 NSDUH, the mental health module (i.e., the serious mental illness module) contained a truncated version of the CIDI-SF scale, the K10/K6 scale, and a truncated version of the WHO-DAS scale to mirror the questions used by Kessler et al. (2003a). Thus, the module contained a broad array of questions about mental health (i.e., panic attacks, depression, mania, phobias, generalized anxiety, posttraumatic stress disorder, and use of mental health services) that preceded the K6 items, and the four extra questions in the K10 scale were interspersed among the items in the K6 scale. In the 2004 NSDUH, the sample of respondents 18 or older was split evenly between the "long form" module, which included all items in the mental health module used in the 2003 NSDUH (sample A), and a "short form" module consisting only of the K6 items (sample B). The "short form" version was introduced to reduce interview time, removing questions that were not needed for estimation of SPD, and to provide space for a new module on depression. Inclusion of the "long form" version in half of the sample was to measure the impact on the K6 responses of changing the context of the K6.
Results from the 2004 NSDUH showed large differences between the two samples in both the K6 total score and the proportion of respondents with a K6 total score of 13 or greater. These differences were most pronounced in the 18 to 25 age group. These differences suggest that the K6 scale is not context-independent; that is, respondents appear to respond to the K6 items differently depending on whether the scale is preceded by a broad array of other mental health questions.
Given the difference in K6 reporting between the A (long form) and B (short form) samples, the 2004 SPD estimates presented in the 2004 detailed tables and 2004 NSDUH national results report are based only on the A sample, which used a mental health module identical to that used in 2002 and 2003. In the 2005 NSDUH, only the "short form" SPD module was used; therefore, the 2004 SPD estimates presented in the 2005 detailed tables and in this 2005 NSDUH national results report are based on the B sample, so that 2004 and 2005 estimates are comparable. Note that the 2004 SPD estimates reported in the 2004 detailed tables (OAS, 2005a) are different from the 2004 SPD estimates reported in the 2005 detailed tables, and SPD estimates reported in the 2005 detailed tables are not comparable with estimates reported in previous years.
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 of the lifetime and past year prevalence of MDE, treatment for MDE, and role impairment resulting from MDE. Separate modules were administered to adults (aged 18 or older) and adolescents (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 adolescent questions 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 adolescents, there are minor wording differences in a few questions between the adult and adolescent 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.
In 2004, a split-sample design was implemented where adults in sample B received the depression module while adult respondents in sample A did not. All adolescents were administered the adolescent depression module. In 2005, all adult and adolescent respondents were administered their respective depression modules.
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. In addition to lifetime MDE, NSDUH measures past year MDE. 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 past year depression then are asked questions from the Sheehan Disability Scale (SDS) to measure the severity of the past year depression.
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 adolescent module were modified slightly to use wording more appropriate for youths. It should be noted that no exclusions were made for MDE caused by medical illness, bereavement, or substance use disorders.
The 2005 NSDUH also collects data on role impairment using the SDS, which is a measure of the impact of depression on a person's daily activities based on four domains in a person's life. Each question uses an 11-point scale, where 0 corresponds to no interference, 1-3 corresponds to mild interference, 4-5 corresponds to moderate interference, 7-9 corresponds to severe interference, and 10 corresponds to very severe interference. The overall role impairment is defined as the highest level of severity of role impairment across all four SDS role domains. Respondents also were asked to report the number of days in the past year in which they were "totally unable to work or carry out normal activities" because of depression. Estimates for role impairment are calculated separately for youths and adults because the four domains are slightly different for the two groups. The questions are listed below.
One challenge in measuring nonmedical use of prescription psychotherapeutic drugs is that drugs that have been manufactured by legitimate pharmaceutical companies under government regulation can become popular as drugs of abuse and may instead be produced illegally. In particular, most methamphetamine that is currently used nonmedically in the United States is produced by clandestine laboratories within the United States or abroad rather than by the legitimate pharmaceutical industry. Given that questions on methamphetamine use are first asked in the core prescription stimulants module, one concern in measuring methamphetamine use is that some methamphetamine users could fail to recognize the drug when it is presented in this context, which could lead to underreporting.
To address this, new questions were added to the noncore special drugs module in the 2005 NSDUH 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. These new noncore questions differ from the methamphetamine use questions asked in the core stimulants module by asking about methamphetamine use removed from the context of prescription drug use, and including more descriptive information relevant to this drug. Respondents who did not indicate in the stimulants module that they had used methamphetamine received the following item:
Methamphetamine, also known as crank, ice, crystal meth, speed, glass, and many other names, is a stimulant that usually comes in crystal or powder forms. It can be smoked, "snorted," swallowed or injected. Have you ever, even once, used methamphetamine?
Respondents who answered "Yes" to this question then were asked a question to classify them as past month, past year, or lifetime users.
To assess the impact of the new methamphetamine questions, weighted estimates from 2005 were generated and compared for two different scenarios: (1) only core stimulant data from 2005; and (2) core stimulant data, previous noncore data from the special drugs module, and new methamphetamine variables that were added to the special drugs module in 2005. Comparisons were made for the following lifetime, past year, and past month measures: nonmedical use of methamphetamine, nonmedical use of stimulants, nonmedical use of psychotherapeutics, and illicit drug use (including the nonmedical use measures described previously).
Prevalence estimates for scenario 2 were greater than those using only the core stimulant data. For example, the lifetime prevalence of nonmedical methamphetamine use among persons aged 12 or older increased from 4.3 percent for core data only to 6.4 percent for core plus noncore data. See Table B.6 for a comparison of estimates for 2005 based on these two scenarios. It should be noted that the estimates presented in Table B.6 are based on different data (i.e., core vs. core plus noncore data), but from the same respondents. Given the high correlation resulting from pairs of responses on the same individuals, very small differences are detectable. As can be seen in the table, the difference between the estimate based only on the core data and that with the added noncore data for lifetime use of methamphetamine is significant—both statistically and in terms of its magnitude. However, as methamphetamine use becomes an increasingly smaller part of the broader drug groups (i.e., from stimulants to psychotherapeutics to any illicit drug use), the differences between the core only and core plus noncore estimates become very small in terms of magnitude, but they are still statistically detectable.
On the one hand, then, these findings suggest that estimates of nonmedical use of methamphetamine (and by extension, nonmedical use of stimulants) based only on core data could be underestimating the true population prevalence. However, larger estimates of nonmedical use of methamphetamine based on both core and noncore answers could be an artifact of asking a second set of questions ONLY from persons who answered NO the first time. Repeating questions for any drug only to those who denied use the first time could artificially increase the positive responses, and doing so only for methamphetamine could result in a disproportionate reporting of that drug relative to the others in the survey. In addition, because the respondents reporting methamphetamine use in the new questions essentially have contradicted their prior responses, some may have made a mistake on the new question. Thus, some follow-up items to clarify this inconsistency were added beginning with the 2006 NSDUH. The items sought to identify respondents who had failed to report methamphetamine use in response to the earlier question because they did not consider methamphetamine to be a prescription drug. These are the only "additional" methamphetamine users picked up by the new questions that should be included in prevalence estimates. The new items added in 2006 are as follows:
Earlier, the computer recorded that you have never used Methamphetamine, Desoxyn or Methedrine. Which answer is correct?
[IF 'YES' TO ABOVE ITEM] Why did you report earlier that you had never used Methamphetamine?
An early review of the unweighted 2006 quarter 1 raw data indicates that approximately half (48.5 percent) of the respondents who indicated methamphetamine use in the special drugs module previously did not report methamphetamine use in the core stimulants module because they did not think of methamphetamine as a prescription drug (see column two in Table B.7). The other half who indicated methamphetamine use on follow-up in the special drugs module either reported in the new consistency checks that they never used methamphetamine (i.e., their earlier answer in the core stimulants module was correct) or that they previously had not reported methamphetamine use for some reason other than not recognizing it in the context of prescription drugs (see columns one and three in Table B.7).
These preliminary analyses of data from the 2006 NSDUH show that it will be important to use data from these new consistency check questions in further investigations of how best to estimate the prevalence of methamphetamine use in NSDUH. In particular, the new 2005 methamphetamine data alone do not provide sufficient information to provide an adjusted estimate of the prevalence of nonmedical methamphetamine use in 2005. For this reason, the methamphetamine use estimates presented in this report and in the detailed tables for 2005 continue to use data based only on the original core stimulant items. Thus, for the purpose of examining trends in nonmedical methamphetamine use, the 2005 estimates remain comparable with estimates generated in prior years.
In the 2005 NSDUH, responses to the new methamphetamine questions added to the noncore special drugs module also were used in the skip logic for questions that were presented in other modules for stimulant dependence and abuse in the past year, driving under the influence of illicit drugs in the past year, and the source of the methamphetamine that persons last used. However, these additional reports of methamphetamine use from these new questions in the special drugs module were not used in 2005 for estimating the prevalence of stimulant dependence or abuse in the past year and driving under the influence of illicit drugs in the past year. Thus, estimates in 2005 for stimulant dependence or abuse and driving under the influence of illicit drugs should be comparable with estimates generated in prior years. In addition, reports of methamphetamine use from these new special drugs questions were not used in analyzing new data in 2005 on how respondents obtained the last methamphetamine that they used.
Hurricanes Katrina and Rita hit the Gulf Coast in the fall of 2005. At the end of August, Hurricane Katrina caused large-scale damage and destruction in the coastal regions of Louisiana, Mississippi, and Alabama. In September, Hurricane Rita devastated portions of Texas and Louisiana. The impact of the hurricanes on the NSDUH sample was evaluated, and a plan of action was developed.
First, the areas that were most likely to be affected according to the paths of the hurricanes were identified. Field management then assessed the level of damage in the affected quarters 3 and 4 area segments in the NSDUH sample. Because quarter 3 data collection was nearing completion and the quarter 4 sample was already in place, it was too late to draw a new sample as a supplement. However, a 20 percent reserve sample was drawn at the time the sample was selected, and the decision was made to release this additional sample in the States of Louisiana, Mississippi, and Alabama in quarter 4.
In addition to releasing the maximum sample, several other actions were taken. First, conference calls were held with field staff to discuss special procedures. Field staff were reminded to apply the residency rule for eligibility8 and to pick up displaced persons wherever they currently were residing. Field staff also were instructed to assign a pending status code to housing units that were vacant or damaged and to return midway through the quarter to see whether the dwelling unit had become reoccupied.9 Finally, temporary housing units were picked up by applying the half-open interval rule.10
Age Group | |
12-17 | |
18-25 | |
26-34 | Age Group x Gender (e.g., Males Aged 12 to 17) |
35-49 | |
50-64 | |
65 or Older | |
All Combinations of Groups Listed Above1 | Age Group x Hispanic Origin (e.g., Hispanics or Latinos Aged 18 to 25) |
Gender | |
Male | |
Female | |
Hispanic Origin | Age Group x Race (e.g., White Aged 26 or Older) |
Hispanic or Latino | |
Not Hispanic or Latino | |
Race | |
White | Age Group x Geographic Region (e.g., Persons Aged 12 to 25 in the Northeast) |
Black or African American | |
Geographic Region | |
Northeast | |
Midwest | Age Group x Geographic Division (e.g., Persons Aged 65 or Older in New England) |
South | |
West | |
Geographic Division | |
New England | Gender x Hispanic Origin (e.g., Not Hispanic or Latino Males) |
Middle Atlantic | |
East North Central | |
West North Central | |
South Atlantic | Hispanic Origin x Race (e.g., Not Hispanic or Latino Whites) |
East South Central | |
West South Central | |
Mountain | |
Pacific | |
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, 2005. |
Estimate | Suppress if: |
Prevalence Rate, , with Nominal Sample Size, n, and Design Effect, deff |
(1) The estimated prevalence rate, , is < 0.00005 or ≥ 0.99995, or (2) when ≤ 0.5, or D when > 0.5, or D (3) Effective n < 68, where Effective 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 ) |
The estimated prevalence rate, , is suppressed. Note: In some instances when 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, , with Nominal Sample Size, n |
(1) RSE , or (2) n < 10. |
Number of Initiates, | (1) The number of initiates, rounds to < 1,000 initiates, or (2) RSE() > 0.5 . |
SE = standard error; RSE = relative standard error; deff = design effect. Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2005. |
2004 | 2005 | 2004 | 2005 | |
Ineligible Cases | 26,902 | 29,046 | 15.76 | 16.59 |
Cases | 142,612 | 146,912 | 84.24 | 83.41 |
Vacant | 15,204 | 16,377 | 56.24 | 55.56 |
Not a Primary Residence | 4,122 | 5,310 | 15.54 | 18.89 |
Not a Dwelling Unit | 2,062 | 1,979 | 7.51 | 6.57 |
All Military Personnel | 282 | 251 | 1.07 | 0.85 |
Other, Ineligible | 5,232 | 5,129 | 19.65 | 18.12 |
Screening Complete | 130,130 | 134,055 | 90.92 | 91.33 |
No One Selected | 73,732 | 76,670 | 50.86 | 51.39 |
One Selected | 30,499 | 30,633 | 21.53 | 21.13 |
Two Selected | 25,899 | 26,752 | 18.53 | 18.82 |
Screening Not Complete | 12,482 | 12,587 | 9.08 | 8.67 |
No One Home | 2,207 | 1,992 | 1.55 | 1.27 |
Respondent Unavailable | 259 | 247 | 0.18 | 0.16 |
Physically or Mentally Incompetent | 265 | 324 | 0.17 | 0.20 |
Language Barrier—Hispanic | 51 | 43 | 0.04 | 0.04 |
Language Barrier—Other | 391 | 317 | 0.32 | 0.23 |
Refusal | 8,588 | 9,197 | 6.10 | 6.30 |
Other, Access Denied | 660 | 699 | 0.67 | 0.45 |
Other, Eligible | 10 | 7 | 0.01 | 0.00 |
Segment Not Accessible | 0 | 0 | 0.00 | 0.00 |
Screener Not Returned | 15 | 17 | 0.01 | 0.01 |
Fraudulent Case | 14 | 10 | 0.02 | 0.00 |
Electronic Screening Problem | 22 | 4 | 0.02 | 0.00 |
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2004 and 2005. |
Sample Size | Weighted Percentage | Sample Size | Weighted Percentage | Sample Size | Weighted Percentage | |||||||
2004 | 2005 | 2004 | 2005 | 2004 | 2005 | 2004 | 2005 | 2004 | 2005 | 2004 | 2005 | |
Interview Complete | 67,760 | 68,308 | 77.00 | 76.19 | 22,309 | 22,565 | 88.56 | 87.10 | 45,451 | 45,743 | 75.66 | 74.91 |
No One at Dwelling Unit | 1,156 | 1,306 | 1.50 | 1.65 | 147 | 206 | 0.54 | 0.76 | 1,009 | 1,100 | 1.61 | 1.75 |
Respondent Unavailable | 1,762 | 1,782 | 2.40 | 2.10 | 302 | 332 | 1.28 | 1.31 | 1,460 | 1,450 | 2.53 | 2.20 |
Break-Off | 46 | 38 | 0.10 | 0.06 | 7 | 9 | 0.03 | 0.04 | 39 | 29 | 0.10 | 0.07 |
Physically/Mentally Incompetent | 699 | 827 | 1.71 | 1.97 | 143 | 165 | 0.57 | 0.63 | 556 | 662 | 1.84 | 2.12 |
Language Barrier - Hispanic | 131 | 144 | 0.14 | 0.15 | 12 | 10 | 0.04 | 0.03 | 119 | 134 | 0.15 | 0.17 |
Language Barrier - Other | 398 | 383 | 1.23 | 1.14 | 27 | 26 | 0.09 | 0.15 | 371 | 357 | 1.37 | 1.26 |
Refusal | 7,871 | 8,632 | 14.52 | 15.30 | 583 | 700 | 2.17 | 2.75 | 7,288 | 7,932 | 15.96 | 16.76 |
Parental Refusal | 1,491 | 1,737 | 0.65 | 0.71 | 1,491 | 1,737 | 6.24 | 6.80 | 0 | 0 | 0.00 | 0.00 |
Other | 659 | 648 | 0.74 | 0.72 | 120 | 90 | 0.49 | 0.44 | 539 | 558 | 0.77 | 0.76 |
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2004 and 2005. |
2004 | 2005 | 2004 | 2005 | 2004 | 2005 | |
81,973 | 83,805 | 67,760 | 68,308 | 77.00% | 76.19% | |
12-17 | 25,141 | 25,840 | 22,309 | 22,565 | 88.56% | 87.10% |
18-25 | 27,408 | 27,337 | 23,075 | 22,764 | 83.87% | 83.06% |
26 or Older | 29,424 | 30,628 | 22,376 | 22,979 | 74.22% | 73.50% |
Male | 40,194 | 41,054 | 32,697 | 32,787 | 75.44% | 74.45% |
Female | 41,779 | 42,751 | 35,063 | 35,521 | 78.46% | 77.80% |
Hispanic | 11,020 | 11,582 | 9,218 | 9,535 | 79.06% | 77.80% |
White | 55,544 | 56,838 | 45,557 | 45,962 | 76.71% | 75.64% |
Black | 9,562 | 9,453 | 8,268 | 8,093 | 81.85% | 81.21% |
All Other Races | 5,847 | 5,932 | 4,717 | 4,718 | 67.21% | 69.70% |
Northeast | 16,674 | 16,994 | 13,523 | 13,711 | 75.14% | 73.66% |
Midwest | 22,920 | 23,542 | 18,889 | 19,154 | 77.63% | 76.42% |
South | 24,820 | 25,411 | 20,807 | 20,818 | 78.65% | 77.16% |
West | 17,559 | 17,858 | 14,541 | 14,625 | 75.38% | 76.42% |
Large Metropolitan | 37,103 | 37,712 | 30,077 | 29,960 | 75.72% | 74.42% |
Small Metropolitan | 27,404 | 28,263 | 22,972 | 23,418 | 78.12% | 77.69% |
Nonmetropolitan | 17,466 | 17,830 | 14,711 | 14,930 | 79.23% | 79.19% |
Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2004 and 2005. |
Time Period/Age Group | NONMEDICAL USE OF METHAMPHETAMINE | NONMEDICAL USE OF STIMULANTS | NONMEDICAL USE OF PSYCHOTHERAPEUTICS1 | ILLICIT DRUGS2 | ||||
Core Data3 | Core & Noncore Data4 |
Core Data3 | Core & Noncore Data4 |
Core Data3 | Core & Noncore Data4 |
Core Data3 | Core & Noncore Data4 |
LIFETIME | 4.258b | 6.392 | 7.845b | 9.607 | 20.027b | 20.823 | 46.084b | 46.114 |
12-17 | 1.168b | 1.981 | 3.374b | 3.988 | 11.887b | 12.197 | 27.693a | 27.724 |
18-25 | 5.177b | 8.385 | 11.071b | 13.601 | 30.345b | 31.276 | 59.171 | 59.211 |
26 or Older | 4.520b | 6.646 | 7.891b | 9.676 | 19.332b | 20.171 | 46.306a | 46.333 |
PAST YEAR | 0.533b | 0.820 | 1.139b | 1.413 | 6.238b | 6.396 | 14.407b | 14.442 |
12-17 | 0.670b | 1.059 | 1.983b | 2.255 | 8.302b | 8.414 | 19.883 | 19.880 |
18-25 | 1.484b | 2.267 | 3.563b | 4.266 | 15.044b | 15.334 | 34.220a | 34.273 |
26 or Older | 0.348b | 0.533 | 0.599b | 0.798 | 4.413b | 4.553 | 10.186a | 10.223 |
PAST MONTH | 0.210b | 0.313 | 0.439b | 0.537 | 2.633b | 2.711 | 8.108b | 8.123 |
12-17 | 0.261b | 0.398 | 0.675a | 0.761 | 3.308a | 3.370 | 9.903 | 9.909 |
18-25 | 0.596b | 0.827 | 1.338b | 1.556 | 6.263b | 6.374 | 20.141a | 20.179 |
26 or Older | 0.136b | 0.212 | 0.249b | 0.328 | 1.905b | 1.979 | 5.754 | 5.766 |
*Low precision; no estimate reported. NOTE: These estimates are based on different methods (i.e., core vs. core & noncore data) of assessing the same measures from the same respondents from the 2005 NSDUH. Due to the high within-subject correlation between these estimates and the large sample size, even a small difference between estimates may be statistically significant. a Difference between core estimate and core & noncore estimate is statistically significant at the 0.05 level. b Difference between core estimate and core & noncore estimate is statistically significant at the 0.01 level. 1 Nonmedical Use of Psychotherapeutics includes the nonmedical use of pain relievers, tranquilizers, stimulants, or sedatives and does not include over-the-counter drugs. 2 Illicit Drugs include marijuana/hashish, cocaine (including crack), heroin, hallucinogens, inhalants, or prescription-type psychotherapeutics used nonmedically. 3 Estimates were created by using only core data and are thus directly comparable with the 2004 estimates. 4 Estimates were created by incorporating core data, preexisting data, and new noncore data from the Special Drugs module. Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 2005. |
Time Period/Age Group | DID NOT USE METHAMPHETAMINE | USED METHAMPHETAMINE | |
Did Not Report Use in Core Because Did Not Think Methamphetamine Was a Prescription Drug | Did Not Report Use in Core for Some Other Reason 1 | ||
12-17 | 26.9 | 50.0 | 23.1 |
18-25 | 28.7 | 44.5 | 26.8 |
26 or Older | 17.9 | 52.9 | 29.3 |
12-17 | 28.6 | 42.9 | 28.6 |
18-25 | 30.2 | 44.2 | 25.6 |
26 or Older | 11.1 | 55.6 | 33.3 |
12-17 | 50.0 | 25.0 | 25.0 |
18-25 | 30.0 | 50.0 | 20.0 |
26 or Older | 0.0 | 100.0 | 0.0 |
NOTE: Respondents were asked the new consistency question added in 2006 only if they reported having used methamphetamine in the noncore special drugs questions (first added in 2005) after having initially reported no methamphetamine use in the core stimulants module. The population in this table reflects the population as shown in the difference between the core & noncore and core methamphetamine estimates found in Table B.6. For example, based on raw data from quarter 1 of 2006, 330 respondents reported lifetime use of methamphetamine in the noncore questions (SD17a) after having reported no use in the core. The new consistency question (SD17a1) captured 77 respondents who confirmed that they had never used methamphetamine (SD17a1), 251 respondents who reported lifetime methamphetamine use, and 2 respondents who did not provide further information (don't know/refused). 1 Other reasons include responses of "made a mistake when answered the earlier question about ever using methamphetamine" and "some other reason." Source: SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, Quarter 1 of 2006. |
7 Substances include alcohol, marijuana, cocaine, heroin, hallucinogens, inhalants, pain relievers, tranquilizers, stimulants, and sedatives.
8 The residency rule for eligibility requires that a person reside at a selected dwelling unit at least half of the quarter in order to be eligible for the survey.
9 Standard procedure is to assign a final status code on the initial visit and not return to a vacant unit.
10 For more details on the 2005 NSDUH sample, see the sample design report in the 2005 NSDUH Methodological Resource Book (Morton et al., 2006).
This page was last updated on June 03, 2008. |


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text:
With this 1500 Watt Heater Fireplace, you'll be able to stay cozy all winter long without the hassle of buying or chopping wood. Even when it's off, this faux fireplace will look great hanging on any wall in the living room, bedroom or bathroom.
This 1500 Watt Heater Fireplace by Warm House is sleek and contemporary, with rich Mahogany trim. The 6.25" depth of this wall mount fireplace helps it look almost flush with the wall, for enhanced modern style. This electric fireplace has adjustable flame brightness for ambiance, two heat settings for warmth and a wireless remote control for convenience! Safety thermal cut-off is built-in to prevent overheating.
750 and 1500 Watt Heat Settings
Heating Type: Coiled Wire Resistive Heat
Fan Motor with Aluminum Blows Heat from the Top
Remote control requires two AAA batteries (not included)
Mounting hardware included
Pebbles included
Instruction manual
Fire Window Dimensions: 20 x 11.75 inches
Two prong power cord is 70 inches long