National and State Estimates of the

Drug Abuse Treatment Gap:

2000 National Household Survey

on Drug Abuse

DEPARTMENT OF HEALTH AND HUMAN SERVICES

Substance Abuse and Mental Health Services Administration

Office of Applied Studies

Acknowledgments

This report was prepared by the Division of Population Surveys in the Office of Applied Studies 

(OAS), Substance Abuse and Mental Health Services Administration (SAMHSA), and by RTI, 

Research Triangle Park, North Carolina. Contributors at SAMHSA listed alphabetically are Joan 

Epstein, Joe Gfroerer, and Doug Wright. Work by RTI was performed under Contract No. 283-

98-9008. Contributors at RTI listed alphabetically are David Belton, Katie Bowman, David 

Chrest, Teresa Davis, Lea Drye, Ralph Folsom, Teresa Gurley, Shari Lambert, Bing Liu, Mary 

Ellen Marsden, Danny Occoquan, Dawn Odom, Lisa Packer, Brenda Porter, Pamela Couch 

Prevatt, Neerja Sathe, Richard Straw, Cheri Thomley, Michael Vorburger, and Akhil Vaish.

Public Domain Notice

All material appearing in this report is in the public domain and may be reproduced or copied 

without permission from SAMHSA. However, this publication may not be reproduced or 

distributed for a fee without the specific, written authorization of the Office of Communications, 

SAMHSA, U.S. Department of Health and Human Services. Citation of the source is 

appreciated. Suggested citation:

Office of Applied Studies. (2002). National and State Estimates of the Drug Abuse 

Treatment Gap: 2000 National Household Survey on Drug Abuse (NHSDA Series H-14,

DHHS Publication No. SMA 02-3640). Rockville, MD: Substance Abuse and Mental 

Health Services Administration. 

Chapter 1. Description of the NHSDA

1.1 Overview

This report presents information from the 2000 National Household Survey on Drug Abuse 

(NHSDA) on the number and percentage of the population in the Nation and in each State who 

need but did not receive treatment for an illicit drug use problem, referred to as the "treatment 

gap."

The NHSDA is an annual survey of the civilian, noninstitutionalized population of the United 

States who are 12 years old or older. It is the primary source of statistical information on the use 
of illegal drugs by the U.S. population. Conducted by the Federal Government since 1971, the 

survey collects data by administering questionnaires to a representative sample of the population 

through face-to-face interviews at their place of residence. The survey is sponsored by the 

Substance Abuse and Mental Health Services Administration (SAMHSA), and data collection is 

carried out by RTI of Research Triangle Park, North Carolina. The project is planned and 

managed by SAMHSA's Office of Applied Studies (OAS). This chapter contains a summary of 

the survey methodology.

1.2 NHSDA Methodology

The NHSDA collects information from residents of households, noninstitutional group quarters 

(e.g., shelters, rooming houses, dormitories), and civilians living on military bases. Persons 

excluded from the survey include homeless people who do not use shelters, active military 

personnel, and residents of institutional group quarters, such as jails and hospitals.

Prior to 1999, the NHSDA was conducted using a paper-and-pencil interviewing (PAPI) method, 

with an interview lasting about an hour. The NHSDA PAPI instrumentation consisted of a 

questionnaire booklet completed by an interviewer and a set of individual answer sheets 

completed by a respondent. All substance use questions and other sensitive questions appeared 

on the answer sheets so that the interviewer was not aware of the respondent's answers. Less 

sensitive questions, such as those on demographics, employment status, and household 

composition, were asked aloud by the interviewer and recorded in the questionnaire booklet.

Since 1999, the NHSDA interview has been carried out using a computer-assisted interviewing 

(CAI) method. The survey uses a combination of computer-assisted personal interviewing 

(CAPI) conducted by an interviewer and audio computer-assisted self-interviewing (ACASI). 

For the most part, questions previously administered by the interviewer are nowadministered by 

the interviewer using CAPI. Questions previously administered using answer sheets are now 

administered using ACASI, which is designed to provide the respondent with a highly private 

and confidential means of responding to questions and to increase the level of honest reporting of 

illicit drug use and other sensitive behaviors. Nevertheless, NHSDA estimates of treatment need 

and the treatment gap are based on self-reports, and their accuracy depends on respondents' 

truthfulness and memory. Because it is assumed that there is some level of underreporting by 

respondents, and because heavy drug users are believed to be underrepresented in the NHSDA 

sample because it is household-based, estimates of treatment need and the treatment gap based 

on the NHSDA are considered conservative.

Consistent with the 1999 NHSDA, the 2000 NHSDA sample employed a 50-State design with an 

independent, multistage area probability sample for each of the 50 States and the District of 

Columbia. The eight States with the largest population (which together account for 48 percent of 

the total U.S. population aged 12 or older) were designated as large sample States (California, 

Florida, Illinois, Michigan, New York, Ohio, Pennsylvania, and Texas). For these States, the 

design provided a sample large enough to support direct State estimates. 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) techniques. The design oversampled youths and 

young adults, so that each State's sample was approximately equally distributed among three age 
groups: 12 to 17 years, 18 to 25 years, and 26 years or older. To enhance the precision of trend 

measurement, half of the first-stage sampling units (area segments) in the 1999 sample were also 

in the 2000 sample. However, all of the households included in the 2000 sample were new.

Nationally, 169,769 addresses were screened for the 2000 survey and 71,764 persons were 

interviewed within the screened addresses. The survey was conducted from January through 

December 2000. Weighted response rates for household screening and for interviewing were 

92.8 and 73.9 percent, respectively.

1.3 Remainder of This Report

Chapter 2 presents national estimates of the need for treatment and the treatment gap. Overall 

treatment need and treatment gap estimates are discussed first, followed by discussion of 

treatment need estimates arranged by age, gender, race/ethnicity, geographic area, education, and 

employment. Chapter 3 focuses on State treatment gap estimates and includes a summary of the 

methodology used to calculate these estimates followed by the results and discussion. Two 

appendices also are included. Appendix A provides information on the measurement of 

dependence, abuse, treatment, and treatment need, and Appendix B provides technical details on 

the State estimation methodology.

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National and State Estimates of Drug Abuse Treatment Gap

Chapter 2. National Estimates of Treatment Need and the Treatment Gap

2.1 Overview

The 2000 National Household Survey on Drug Abuse (NHSDA) included a series of questions to 

assess dependence on and abuse of substances, as well as questions that asked whether 

respondents had received treatment for a problem related to substance use. The dependence and 

abuse questions were designed to measure dependence and abuse based on the diagnostic criteria 

specified in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSMIV) (American Psychiatric Association [APA], 1994). Questions on dependence asked about 

health, emotional problems, attempts to cut down on use, tolerance, withdrawal, and other 

symptoms associated with the substances used. Questions on abuse asked about problems at 

work, home and school; problems with family or friends; physical danger; and trouble with the 

law due to substances used. Dependence reflects a more severe substance use problem than 

abuse; persons were classified with abuse of a particular substance only if they were not 

dependent on that substance.

This report provides estimates of the prevalence and patterns of the need for and receipt of 

treatment specifically for problems associated with illicit drug use. It presents estimates of the 
"treatment gap," defined as persons who needed treatment in the past year but did not receive 

that treatment. An individual was defined as needing treatment if he or she was dependent on or 

had abused an illicit drug or received treatment for an illicit drug problem at a "specialty" 

substance abuse facility in the past 12 months (i.e., during the 12 months before being 

interviewed). "Specialty" facilities include drug and alcohol rehabilitation facilities (inpatient or 

outpatient), hospitals (inpatient only), and mental health centers. It should be noted that 

respondents who were not dependent on or abusing drugs but who had received specialty 

treatment were counted as needing treatment under this definition. This was appropriate because 

it was assumed that a diagnostic assessment determining treatment need was done prior to entry 

into treatment.

This chapter presents estimates of the treatment gap at the national level, including estimates of 

the need for and receipt of treatment for an illicit drug problem by demographic characteristics 

(see Tables 1 to 5 at the end of the chapter). Due to changes to the NHSDA questionnaire and to 

the definitions and estimation methods used for measuring treatment need, the estimates in this 

report are not comparable with prior estimates. Based on cognitive testing, questions to measure 

dependence in 1999 were revised to improve how well the questions were understood by 

respondents. These questions were also revised based on a review by experts in the field to 

determine how well the questions capture the meaning of the DSM-IV criteria. In addition, 

starting with the 2000 NHSDA, questions to measure abuse based on the DSM-IV were added to 

the NHSDA and a new method for estimating treatment need and the gap was employed. This 

new method uses a simpler and more widely accepted definition of treatment need (abuse and 

dependence) and does not employ a ratio adjustment to inflate the NHSDA numbers based on 

separate counts of the treatment and arrestee populations (Wright, Gfroerer, & Epstein, 1997). 

This adjustment did not produce estimates as accurate as those generated by the new approach. 

Therefore, the treatment gap estimate of 3.9 million in 2000 cannot be compared with earlier 

estimates from 1991 to 1998 that ranged from 2.5 million to 3.6 million individuals. 

Additionally, the previous ratio-adjusted treatment need and gap estimates were made only at the 

national level and were used by the Office of National Drug Control Policy (ONDCP) in its 

annual National Drug Control Strategy (see ONDCP, 2000). A more detailed description of the 

changes to the NHSDA methods for generating these estimates is given in Appendix A.

2.2 Overall Estimates of Treatment Need

In 2000, an estimated 4.7 million people aged 12 or older (2.1 percent of the total 

population) needed treatment for an illicit drug abuse problem. This includes 2.8 million 

classified by the survey with illicit drug dependence, 1.5 million classified with illicit 

drug abuse, and another 0.3 million who received specialty treatment but were not 

classified as dependent or abusing.

Of the 4.7 million people needing treatment, 0.8 million people (16.6 percent of the 

people who needed treatment) received treatment at a specialty facility.

The treatment gap was estimated to be 3.9 million people in 2000, or 1.7 percent of the 

total population.

Of the 3.9 million people who needed but did not receive treatment in 2000, an estimated 

381,000 reported that they felt they needed treatment for their drug problem. This 
includes an estimated 129,000 who reported that they had made an effort but were unable 

to get treatment and 252,000 who reported making no effort to get treatment.

Among the 3.9 million people who needed but did not receive treatment in 2000, 62.3 

percent were classified with drug dependence and 37.7 percent were classified with drug 

abuse. However, among the estimated 381,000 persons who felt they needed treatment 

for a drug problem, 88.5 percent were classified with drug dependence and 11.5 percent 

were classified with drug abuse.

2.3 Treatment Need, by Age

For the youngest age group (12 to 17), an estimated 1.1 million persons (4.6 percent of 

this population) needed treatment for an illicit drug abuse problem in 2000. Of this group, 

only 0.1 million people (11.4 percent of the people aged 12 to 17 years who needed 

treatment) received treatment, leaving an estimated treatment gap for youths of 1.0 

million (Figures 1 and 2).

The percent of the population in 2000 who needed treatment for an illicit drug use 

problem was highest among persons aged 18 to 25 years (5.7 percent) and lowest among 

persons aged 26 and older (1.1 percent). An estimated 1.6 million persons aged 18 to 25 

and 1.9 million persons aged 26 and older needed treatment for an illicit drug problem in 

2000.

Among the estimated 381,000 persons who did not receive treatment but reported that 

they felt they needed treatment for their drug problem in 2000, 74,000 were aged 12 to 

17, 103,000 were aged 18 to 25, and 204,000 were aged 26 and older. Thus, the 

percentage of the treatment gap that felt they needed treatment was 7.8 percent for those 

aged 12 to 17, 6.9 percent for those aged 18 to 25, and 14.3 percent for those aged 26 and 

older.

2.4 Treatment Need, by Gender

Among persons aged 12 or older in 2000, the percentage of males needing treatment for 

an illicit drug problem was higher than the percentage of females needing treatment (2.6 

vs. 1.6 percent). This translates to 2.7 million males and 1.9 million females needing 

treatment. On the other hand, the percentage receiving specialty treatment among those 

needing treatment was higher for females than males (19.0 vs. 15.0 percent). However, 

this was not a statistically significant difference.

Among youths aged 12 to 17 in 2000, the percentage of males needing treatment for an 

illicit drug problem was higher than the percentage of females needing treatment (5.0 vs. 

4.2 percent). The percentage receiving specialty treatment among youths needing 

treatment was higher for males than females (13.0 vs. 9.4 percent). This was not a 

statistically significant difference.

Figure 1 Percentages of Persons with Past Year Illicit Drug Abuse Treatment Need and 

Receipt, by Age: 2000
Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Figure 2 Numbers of Persons with Past Year Illicit Drug Abuse Treatment Need and 

Receipt, by Age: 2000
Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

2.5 Treatment Need, by Race/Ethnicity

In 2000, 2.0 percent of whites aged 12 or older needed treatment for an illicit drug 

problem. The percentage needing treatment was about the same among blacks and 

Hispanics (2.5 and 2.4 percent, respectively). The highest rates of those needing 

treatment were for persons reporting more than one race (5.5 percent) and for American 

Indians/Alaska Natives (4.3 percent); the lowest rate was for Asians (0.7 percent).

Among whites needing treatment, 17.8 percent received treatment at a specialty facility. 

Only 9.0 percent of all Hispanics needing treatment received treatment. The percentage 

receiving treatment among blacks who needed treatment was 18.6 percent, although the 

sampling error associated with this estimate is large and the estimate is not shown in the 

tables.

Whites accounted for most of the illicit drug treatment need in 2000. Of the 4.7 million 

persons needing treatment, 3.2 million (69 percent) were white. Whites accounted for 75 

percent of the persons who received specialty treatment for an illicit drug problem in 

2000.

2.6 Treatment Need, by Geographic Area
The percentage of persons needing treatment for an illicit drug problem was lowest in the 

East South Central division (1.6 percent) and highest in the New England division (3.2 

percent) in 2000.

The percentage of persons needing treatment for an illicit drug problem in 2000 was 

essentially the same for persons in large metropolitan counties, small metropolitan 

counties, and urbanized nonmetropolitan counties (2.3, 2.0, and 2.0 percent, 

respectively). The percentage needing treatment was lowest in completely rural counties 

(1.2 percent).

2.7 Treatment Need, by Education

Needing treatment is inversely related to educational status. Among adults aged 18 or 

older in 2000, those who had not completed high school had the highest percentage of 

persons needing treatment (2.9 percent), while college graduates had the lowest 

percentage of persons needing treatment for illicit drugs (1.1 percent). The percentage 

who received treatment among persons who needed treatment was 25.4 percent among 

those with less than a high school education.

2.8 Treatment Need, by Employment

Current employment status also correlated with treatment need in 2000. An estimated 7.7 

percent of unemployed adults aged 18 or older needed treatment for illicit drugs, while 

only 1.6 percent of full-time employed adults needed treatment for an illicit drug 

problem.

Most of the adult population needing treatment for an illicit drug problem in 2000 was 

employed. Of the estimated 3.6 million persons aged 18 or older who needed treatment, 

1.9 million were employed full time and 0.6 million were employed part time. Thus, an 

estimated 70 percent of adults needing treatment were employed. An estimated 359,000 

unemployed adults needed treatment.

Table 1. Estimated Numbers (in Thousands) of Persons Aged 12 or Older Who Needed and 

Received Treatment for an Illicit Drug Problem in the Past Year, by Demographic 

Characteristics: 2000

Demographic 

Characteristic

Needed Treatment for an Illicit Drug Problem in the Past

Year

Percentag

e Who 

Received 

Treatmen

t at a 

Specialty 

Facility 

Among 

Persons 

Total Who 

Received 

Treatment at a 

Specialty Facility

Did Not Receive 

Treatment at a 

Specialty Facility
Needed 

Treatmen t

Total 4,655 774 3,881 16.6

Age in Years

12-17 1,074 122 951 11.4

18-25 1,645 142 1,503 8.6

26 or older 1,937 510 1,427 26.3

Gender

Male 2,749 411 2,337 15.0

Female 1,907 363 1,544 19.0

Hispanic Origin and 

Race

Not Hispanic

White 

only 3,235 577 2,659 17.8

Black 

only 632 118 514*

Americ

an 

Indian 

or 

Alaska 

Native 

only 464 42*

Native 

Hawaii

an or 

other 

Pacific 

Islande r 10

37*

Asian 

only 541 54*

More 

than 

one 

race 103 21 82*

Hispanic 574 51 523 9.0
*Low precision; no estimate reported.

Note: Respondents were classified as needing treatment for an illicit drug problem if they met at least one of three 

criteria during the past year: (1) dependent on any illicit drug; (2) abuse of any illicit drug; or (3) received treatment 

for an illicit drug problem at a specialty facility (i.e., drug and alcohol rehabilitation facilities [inpatient or 

outpatient], hospitals [inpatient only], and mental health centers). Illicit Drugs include marijuana/hashish, cocaine 

(including crack), inhalants, hallucinogens, heroin, or prescription-type psychotherapeutic (nonmedical use).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Table 2. Percentages of Persons Aged 12 or Older Who Needed and Received Treatment 

for an Illicit Drug Problem in the Past Year, by Demographic Characteristics: 2000

Demographic 

Characteristic

Needed Treatment for an Illicit Drug Problem in the Past

Year

Percentag

e Who 

Received 

Treatmen

t at a 

Specialty 

Facility 

Among 

Persons 

Who 

Needed 

Treatmen

Total t

Received 

Treatment at a 

Specialty Facility

Did Not Receive 

Treatment at a 

Specialty Facility

Total 2.1 0.3 1.7 16.6

Age in Years

12-17 4.6 0.5 4.1 11.4

18-25 5.7 0.5 5.2 8.6

26 or older 1.1 0.3 0.8 26.3

Gender

Male 2.6 0.4 2.2 15.0

Female 1.6 0.3 1.3 19.0

Hispanic Origin and 

Race

Not Hispanic

White 

only 2.0 0.4 1.6 17.8

Black 

only 2.5 0.5 2.0 *
Americ

an 

Indian 

or 

Alaska 

Native 

only 4.3 0.4 3.9 *

Native 

Hawaii

an or 

other 

Pacific 

Islande

r 1.8 0.5 1.4 *

Asian 

only 0.7 0.0 0.7 *

More 

than 

one 

race 5.5 1.1 4.4 *

Hispanic 2.4 0.2 2.2 9.0

*Low precision; no estimate reported.

NOTE: Respondents were classified as needing treatment for an illicit drug problem if they met at least one of three 

criteria during the past year: (1) dependent on any illicit drug; (2) abuse of any illicit drug; or (3) received treatment 

for an illicit drug problem at a specialty facility (i.e., drug and alcohol rehabilitation facilities [inpatient or 

outpatient], hospitals [inpatient only], and mental health centers). Illicit Drugs include marijuana/hashish, cocaine 

(including crack), inhalants, hallucinogens, heroin, or prescription-type psychotherapeutic (nonmedical use).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Table 3. Percentages of Persons Aged 12 or Older Who Needed and Received Treatment for 

an Illicit Drug Problem in the Past Year, by Geographic Characteristics: 2000

Geographic 

Characteristic

Needed Treatment for an Illicit Drug Problem in the Pas

t Year

Percenta

ge Who 

Received 

Treatmen

Total t at a 

Received 

Treatment at a 

Specialty Facility

Did Not Receive 

Treatment at a 

Specialty Facility
Specialty 

Facility 

Among 

Persons 

Who 

Needed 

Treatmen t

Total 2.1 0.3 1.7 16.6

Geographic Division

Northeast 2.4 0.4 2.0 17.2

New 

England 3.2 0.7 2.6*

Middle 

Atlantic 2.1 0.3 1.8 15.3

Midwest 1.8 0.3 1.5 17.8

East 

North 

Central 1.9 0.3 1.6 16.1

West 

North 

Central 1.7 0.4 1.4*

South 1.7 0.3 1.4 17.3

South 

Atlantic 1.7 0.3 1.4 20.0

East 

South 

Central 1.6 0.2 1.4 10.1

West 

South 

Central 1.8 0.3 1.5 16.6

West 2.7 0.4 2.3 14.7

Mountai n 2.8 0.4 2.4 13.9

Pacific 2.7 0.4 2.3 * 

County Type

Large metro 2.3 0.4 1.9 16.2

Small metro 2.0 0.3 1.7 15.5

250K - 1 

mil. pop. 2.0 0.3 1.7 15.1
<250K 

pop

. 2.0 0.3 1.7 16.7

Nonmetro 1.7 0.3 1.4 20.0

Urbaniz

ed 2.0 0.5 1.5 *

Less 

urbanize

d 1.7 0.3 1.4 *

Complet

ely rural 1.2 0.2 1.0 *

*Low precision; no estimate reported.

NOTE: Respondents were classified as needing treatment for an illicit drug problem if they met at least one of three 

criteria during the past year: (1) dependent on any illicit drug; (2) abuse of any illicit drug; or (3) received treatment 

for an illicit drug problem at a specialty facility (i.e., drug and alcohol rehabilitation facilities [inpatient or 

outpatient], hospitals [inpatient only], and mental health centers). Illicit Drugs include marijuana/hashish, cocaine 

(including crack), inhalants, hallucinogens, heroin, or prescription-type psychotherapeutic (nonmedical use).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Table 4. Percentages of Persons Aged 12 to 17 Who Needed and Received Treatment for an 

Illicit Drug Problem in the Past Year, by Demographic Characteristics: 2000

Demographic 

Characteristic

Needed Treatment for an Illicit Drug Problem in the Past

Year

Percentag

e Who 

Received 

Treatmen

t at a 

Specialty 

Facility 

Among 

Persons 

Who 

Needed 

Treatmen

Total t

Received 

Treatment at a 

Specialty Facility

Did Not Receive 

Treatment at a 

Specialty Facility

Total 4.6 0.5 4.1 11.4 

Gender
Male 5.0 0.6 4.3 13.0 

Female 4.2 0.4 3.8 9.4 

Hispanic Origin and 

Race

Not Hispanic

White only 4.8 0.6 4.2 12.6 

Black only 3.6 0.5 3.1 13.5 

American 

Indian or 

Alaska Native 

only 10.7 * 8.7 * 

Native 

Hawaiian or 

other Pacific 

Islander * * * * 

Asian only 2.8 * 2.8 * 

More than one 

race 2.5 * 2.5 * 

Hispanic 5.2 0.3 4.9 6.1 

Gender/Race/Hispan

ic Origin

Male - white 5.0 0.7 4.3 13.7 

Female -

white 4.5 0.5 4.0 11.4 

Male - black 4.7 0.8 3.9 * 

Female - black 2.4 0.2 2.3 * 

Male -

Hispanic 5.3 0.4 4.9 * 

Female -

Hispanic 5.0 0.2 4.8 * 

*Low precision; no estimate reported.

NOTE: Respondents were classified as needing treatment for an illicit drug problem if they met at least one of three 

criteria during the past year: (1) dependent on any illicit drug; (2) abuse of any illicit drug; or (3) received treatment 

for an illicit drug problem at a specialty facility (i.e., drug and alcohol rehabilitation facilities [inpatient or 

outpatient], hospitals [inpatient only], and mental health centers). Illicit Drugs include marijuana/hashish, cocaine 

(including crack), inhalants, hallucinogens, heroin, or prescription-type psychotherapeutic (nonmedical use).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.
Table 5. Percentages of Persons Aged 18 or Older Who Needed and Received Treatment for 

an Illicit Drug Problem in the Past Year, by Demographic Characteristics: 2000

Demographic 

Characteristic

Needed Treatment for an Illicit Drug Problem in the Past Y

ear

Percentag

e Who

Received 

Treatmen

t at

a 

Specialty 

Facility

Among 

Persons 

Who

Needed 

Treatmen

Total t

Received Treatment 

at a Specialty Facility

Did Not Receive 

Treatment at a 

Specialty Facility

Total 1.8 0.3 1.5 18.2

Gender

Male 2.3 0.4 1.9 15.5

Female 1.4 0.3 1.1 22.3

Hispanic Origin 

and Race

Not 

Hispanic

White only 1.7 0.3 1.4 19.3

Black only 2.3 0.5 1.9 *

American 

Indian or 

Alaska 

Native only 3.3 0.1 3.1 *

Native 

Hawaiian 

or other 

Pacific 

Islander 1.7 0.5 1.2 *

Asian only 0.5 0.0 0.4 *

More than 6.3 1.4 4.9 *
one race

Hispanic 2.0 0.2 1.8 10.2

Adult Education

< High 

school 2.9 0.7 2.2 25.4

High 

school 

graduate 1.7 0.3 1.4 18.9

Some 

college 1.9 0.3 1.6 15.8

College 

graduate 1.1 0.1 1.0 *

Current 

Employment

Full-time 1.6 0.2 1.4 14.1

Part-time 2.7 0.5 2.2 *

Unemploye

d 7.7 1.5 6.2 *

Other11.3 0.4 0.9 28.8

* Low precision; no estimate reported.

NOTE: Respondents were classified as needing treatment for an illicit drug problem if they met at least one of three 

criteria during the past year: (1) dependent on any illicit drug; (2) abuse of any illicit drug; or (3) received treatment 

for an illicit drug problem at a specialty facility (i.e., drug and alcohol rehabilitation facilities [inpatient or 

outpatient], hospitals [inpatient only], and mental health centers). Illicit Drugs include marijuana/hashish, cocaine 

(including crack), inhalants, hallucinogens, heroin, or prescription-type psychotherapeutic (nonmedical use).

1 Retired, disabled, homemaker, student, or "other."

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Chapter 3. Estimates of the Treatment Gap, by State

3.1 Summary of Methodology

This chapter presents State estimates of the percentages and numbers of persons needing but not 

receiving treatment for illicit drug use (see Tables 6 to 9 at the end of the chapter). The following 

discusses how the State estimates of the treatment gap are calculated. A more detailed discussion 

of this process is provided in Appendix B of this report.
For each respondent in the sample, one can determine whether a person needed but did not 

receive treatment for an illicit drug problem based on the following definition: An individual was 

counted in the treatment gap if he or she was dependent on or had abused an illicit drug but had 

not received treatment for his or her illicit drug problem at a "specialty" substance abuse facility 

in the past 12 months (i.e., in the 12 months before being interviewed). "Specialty" substance 

abuse facilities include drug and alcohol rehabilitation facilities (inpatient or outpatient), 

hospitals (inpatient only), and mental health centers.

The State estimates are based on a model that has essentially two components. One component is 

a national model using data from the 2000 National Household Survey on Drug Abuse 

(NHSDA). The national model includes demographic information (such as age and race), 

socioeconomic information on the local area (such as the percentage below the poverty level), 

and information specific to drug use (such as the marijuana possession arrest rate for the county). 

The information used in the national model is available at the Census block, Census tract, or 

county level.

The second component of the model is the information collected from the NHSDA respondents 

in each State. This direct sample component adjusts the results to reflect State- and local-level 

differences. These two components together produce the final estimate. In effect, for each State, 

two estimates of the treatment gap—one from a national model and one from just the sample 

data from the State—are combined to make the best estimate for the State. If a State is 

represented in the survey by a relatively small sample, and the direct sample estimate from the 

State is subject to significant sampling variation, more weight is given to the national 

component.

When the process is complete, the results are validated by comparing the estimates produced by 

the model with estimates based entirely on the sample data. This is done for areas having very 

large samples that can be assumed to produce "accurate" estimates without the need for models. 

The validation results showed that the model-based estimates for all persons aged 12 or older 

were quite accurate compared with the true (gold standard) State value—on average,within about 

4 percent of the true value. For example, if the true value in a State was 2 percent, the estimate 

would typically be within 0.08 of a percent of the true value.

The final set of State estimates also comes with a corresponding set of interval estimates within 

which the true State value will fall 95 percent of the time. For example, the estimate of the 

percentage treatment gap for the State of Idaho, for persons aged 12 or older, is 1.81 percent, 

with a prediction interval of (1.41, 2.28). Therefore, the probability is 95 percent that the true 

value for Idaho lies between 1.41 and 2.28 percent. The model-based estimates were also more 

precise than the corresponding survey-based estimates based on the sample only. The prediction 

intervals were on average 45 percent smaller (better) than the corresponding confidence intervals 

(CIs) around the strictly sample-based estimates. This superior precision is equivalent to an 

effective sample size of 3,300 as opposed to the true design-based sample size of 1,000. 

Comparisons for the specific age groups are provided in Appendix B.

A national map (Figure 3) illustrates the distribution of State estimates of the percentage 

treatment gap into "fifths" from lowest to highest. States with the highest treatment gap as a 
percentage of their population fall into the top quintile and are in red. States with the lowest 

treatment gaps are in the bottom quintile and are in white.1Typically, most States cluster around 

the national average, and some may only differ by a fraction of a percent. Therefore, it is 

important to consider the interval in determining the relative ranking of States.

3.2 Results

Nationally, 1.74 percent of persons aged 12 or older needed treatment but did not receive it in the 

past year.2

Of the 10 States with the highest percentage treatment gaps, 6 were Western States and 3 

were Northeastern States. Arizona had the highest percentage treatment gap of 2.29 

percent.

Figure 3 Percentages of Persons Aged 12 or Older Needing But Not Receiving Treatment 

for an Illicit Drug Problem in the Past Year, by State: 2000

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.
Figure 4 Numbers of Persons Aged 12 or Older Needing But Not Receiving Treatment for 

an Illicit Drug Problem in the Past Year, by State: 2000

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Although the 12 to 25 age group represented only about 23 percent of the total population 

aged 12 or older, nationally it constituted the majority of the treatment gap. About 62 

percent of all persons in the gap were in this age group. The 12 to 25 age group 

constituted anywhere from 51 percent (Washington) to 73 percent (Idaho) of the total 

number of persons in the treatment gap, depending on the State. Most of the States in the 

highest group for the treatment gap percentage had proportions of 12 to 25 year olds 

comprising their treatment gap that were lower than the national average.

In the lowest fifth, five States were Southern States, and four were Midwestern. The State 

with the lowest treatment gap for ages 12 or older was Iowa with an estimate of 1.37 

percent of the population.

Generally, as might be expected, States with larger populations had the largest estimated 

number of persons in the treatment gap. California had the largest number of persons in 

the gap, approximately 564,000, representing 14.1 percent of the national gap for persons 

12 or older. The other States with large treatment gap counts were Texas (288,000), New 

York (285,000), Florida (196,000), Illinois (164,000), Pennsylvania (160,000), Ohio 

(150,000), Michigan (138,000), New Jersey (110,000), and Georgia (110,000).

Because the range in State population sizes was larger than the range in the estimated 

State treatment gap percentages, the population size had a dominant impact on the 
treatment gap counts. Variations in the State percentage gap, however, resulted in 

significant differences among States of a similar size. For example, Arizona and Alabama 

had similar population sizes in 2000; however, for persons aged 12 or older, Arizona had 

a larger treatment gap (89,000) than Alabama (61,000) because of its higher treatment 

gap percentage (2.29 vs. 1.66 percent).

3.3 Discussion

These State-level estimates of the drug abuse treatment gap provide an important tool for 

treatment planners and policymakers at the Federal, State, and local levels. They represent the 

first available State estimates of the gap using a consistent methodology to allow valid 

comparisons across States. Although it is difficult to accurately measure characteristics affecting 

less than 2 percent of the population, the methodology used was able to identify significant 

variation in the treatment gap by State. The estimates ranged from as low as 1.37 percent up to 

2.29 percent of the States' populations aged 12 or older, indicating very real differences in the 

unmet treatment need across States. In an average-sized State, such as Maryland, this size 

difference represents tens of thousands of persons.

The treatment gap estimates give a single measure of the illicit drug problem in each State. More 

detailed assessments of problems at the State level, such as analysis of demographic differences 

and access to care, are not possible with these data. These issues can only be studied with the 

NHSDA at the national level. Interpretation of State-level patterns in the treatment gap can also 

be aided by using these estimates in conjunction with other measures produced at the State level 

from the NHSDA, such as rates of current use, initiation, and perceived risk of harm for illicit 

drugs and also for alcohol and tobacco.

Table 6. Percentages of Persons Aged 12 or Older Needing But Not Receiving Treatment 

for an Illicit Drug Problem in the Past Year, by State, Ranked from Highest to Lowest: 

2000

State

Percentages

Estimate Prediction Interval

Arizona 2.29 (1.60 - 3.18)

California 2.19 (1.79 - 2.65)

Alaska 2.12 (1.56 - 2.80)

Massachusetts 2.11 (1.56 - 2.79)

Utah 2.11 (1.59 - 2.75)

Colorado 2.09 (1.55 - 2.75)

District of Columbia 2.08 (1.53 - 2.78)

New Hampshire 1.97 (1.52 - 2.51)

Washington 1.97 (1.39 - 2.71)
New York 1.93 (1.58

- 2.33)

Connecticut 1.92 (1.45- 2.49)

Oregon 1.92 (1.42- 2.54)

Vermont 1.92 (1.48- 2.44)

Minnesota 1.90 (1.43- 2.49)

Maryland 1.89 (1.40- 2.48)

Louisiana 1.83 (1.44- 2.31)

Idaho 1.81 (1.41- 2.28)

Nevada 1.81 (1.36- 2.35)

Maine 1.80 (1.38- 2.29)

Texas 1.79 (1.49- 2.14)

Delaware 1.76 (1.30- 2.31)

Michigan 1.74 (1.46- 2.05)

Georgia 1.73 (1.31- 2.23)

Hawaii 1.73 (1.15- 2.48)

New Mexico 1.73 (1.31- 2.24)

Wisconsin 1.71 (1.34- 2.15)

Rhode Island 1.70 (1.29- 2.19)

Tennessee 1.69 (1.29- 2.17)

Illinois 1.68 (1.38- 2.03)

Alabama 1.66 (1.26- 2.15)

Indiana 1.66 (1.28- 2.10)

New Jersey 1.64 (1.24- 2.13)

Kansas 1.63 (1.20- 2.16)

Kentucky 1.63 (1.26- 2.07)

Mississippi 1.63 (1.25- 2.09)

Ohio 1.62 (1.33- 1.94)

Nebraska 1.61 (1.23- 2.06)

Wyoming 1.61 (1.22- 2.10)

Montana 1.60 (1.22- 2.05)

Arkansas 1.58 (1.22- 2.00)

Oklahoma 1.58 (1.20- 2.06)

Pennsylvania 1.58 (1.32- 1.88)

Florida 1.55 (1.26- 1.87)

North Carolina 1.55 (1.17- 2.02)

Virginia 1.55 (1.16- 2.04)

South Carolina 1.54 (1.16- 2.01)
North Dakota 1.49 (1.14 - 1.92)

South Dakota 1.49 (1.13 - 1.92)

Missouri 1.48 (1.11 - 1.94)

West Virginia 1.47 (1.10 - 1.92)

Iowa 1.37 (1.02 - 1.82)

Note: Estimates are based on a survey-weighted hierarchical Bayes estimation approach, and the prediction 

(credible) intervals are generated by Markov Chain Monte Carlo techniques. Horizontal rules refer to quintile 

divisions shown in Figure 3.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Table 7. Estimated Numbers of Persons Aged 12 or Older Needing But Not Receiving 

Treatment for an Illicit Drug Problem in the Past Year, by State, Ranked from Highest to 

Lowest: 2000

State

Total

Estimate Prediction Interval 

California 563,676 (461,420 - 681,354)

Texas 287,765 (238,763 - 343,704)

New York 285,054 (233,386 - 344,527)

Florida 196,128 (160,449 - 237,262)

Illinois 164,309 (134,517 - 198,622)

Pennsylvania 160,117 (133,536 - 190,349)

Ohio 150,150 (123,896 - 180,234)

Michigan 137,607 (115,803 - 162,255)

New Jersey 110,186 (83,020 - 143,273)

Georgia 110,012 (83,253 - 141,784)

Massachusetts 108,669 (79,822 - 142,946)

North Carolina 98,671 (74,303 - 128,412)

Washington 94,245 (66,323 - 129,778)

Arizona 88,686 (61,861 - 122,928)

Virginia 87,768 (65,335 - 115,360)

Indiana 82,093 (63,426 - 104,093)

Maryland 80,734 (59,889 - 106,368)

Tennessee 78,992 (60,266 - 101,055)

Wisconsin 75,832 (58,708 - 94,080)

Minnesota 75,663 (56,404 - 98,355)
Colorado 71,131 (52,786 - 93,664)

Missouri 67,487 (50,268 - 88,011)

Louisiana 65,208 (51,141 - 82,168)

Alabama 60,846 (46,033 - 78,487)

Oregon 54,906 (40,135 - 71,855)

Kentucky 53,647 (41,501 - 67,985)

Connecticut 52,010 (39,045 - 67,330)

South Carolina 48,469 (36,463 - 62,805)

Oklahoma 43,449 (32,808 - 56,404)

Mississippi 37,181 (28,302 - 47,497)

Utah 36,474 (27,201 - 47,167)

Kansas 35,310 (25,915 - 46,538)

Arkansas 34,202 (26,365 - 43,209)

Iowa 32,845 (24,272 - 43,409)

Nevada 27,941 (21,071 - 36,296)

New Mexico 25,748 (19,531 - 33,350)

West Virginia 22,959 (17,142 - 29,879)

Nebraska 22,267 (16,953 - 28,303)

New Hampshire 19,883 (15,317 - 25,333)

Idaho 19,700 (15,320 - 24,692)

Maine 18,817 (14,469 - 24,008)

Hawaii 16,838 (11,247 - 24,197)

Rhode Island 13,983 (10,600 - 17,959)

Montana 12,396 (9,491 - 15,863)

Delaware 11,100 (8,216 - 14,555)

Alaska 10,381 (7,654 - 13,748)

Vermont 9,810 (7,568 - 12,500)

South Dakota 9,262 (7,010 - 11,863)

District of Columbia 8,820 (6,463 - 11,764)

North Dakota 8,019 (6,077 - 10,276)

Wyoming 6,872 (5,174 - 8,915)

Note: Estimates are based on a survey-weighted hierarchical Bayes estimation approach, and the prediction 

(credible) intervals are generated by Markov Chain Monte Carlo techniques. Horizontal rules refer to quintile 

divisions shown in Figure 4.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.
11220 

Table 8. Percentages of Persons Aged 12 or Older Needing But Not Receiving Treatment 

for an Illicit Drug Problem in the Past Year, by State: 2000

State

Total

Age Group (Years)

12-17 18-25 26 or Older

Estimat

e

Predictio

n

Interval

Estimat

e

Predictio

n

Interval

Estimat

e

Predictio

n

Interval

Estimat

e

Predictio

n

Interval

Total1

1.79 4.12 5.22 0.89 

Alabama 1.66 (1.2

6

- 2.15

)

3.52 (2.3

2

- 5.13

)

5.64 (3.9

4

- 7.79

)

0.75 (0.4

1

- 1.25

)

Alaska 2.12 (1.5

6

- 2.80

)

4.60 (2.9

1

- 6.90

)

4.83 (3.2

6

- 6.86

)

1.14 (0.5

9

- 1.98

)

Arizona 2.29 (1.6

0

- 3.18

)

4.50 (3.0

6

- 6.36

)

5.02 (3.4

9

- 6.96

)

1.48 (0.7

4

- 2.65

)

Arkansas 1.58 (1.2

2

- 2.00

)

4.23 (2.9

4

- 5.88

)

5.15 (3.6

5

- 7.04

)

0.62 (0.3

3

- 1.07

)

California 2.19 (1.7

9

- 2.65

)

5.16 (4.2

9

- 6.15

)

4.90 (3.9

2

- 6.04

)

1.26 (0.8

2

- 1.86

)

Colorado 2.09 (1.5

5

- 2.75

)

4.52 (3.0

8

- 6.36

)

5.36 (3.7

1

- 7.45

)

1.18 (0.6

4

- 2.00

)

Connecticut 1.92 (1.4

5

- 2.49

)

5.28 (3.5

4

- 7.54

)

6.54 (4.5

4

- 9.06

)

0.86 (0.4

7

- 1.45

)

Delaware 1.76 (1.3

0

- 2.31

)

4.24 (2.9

5

- 5.88

)

4.73 (3.2

3

- 6.66

)

0.95 (0.5

2

- 1.61

)

District of 

Columbia

2.08 (1.5

3

- 2.78

)

4.21 (2.6

9

- 6.27

)

4.84 (3.2

7

- 6.88

)

1.29 (0.7

1

- 2.15

)

Florida 1.55 (1.2

6

- 1.87

)

4.04 (3.1

4

- 5.11

)

5.21 (4.1

4

- 6.47

)

0.76 (0.4

8

- 1.15

)

Georgia 1.73 (1.3

1

- 2.23

)

4.01 (2.8

3

- 5.50

)

4.86 (3.4

0

- 6.72

)

0.85 (0.4

6

- 1.44

)

Hawaii 1.73 (1.1

5

- 2.48

)

5.29 (3.4

0

- 7.82

)

3.80 (2.4

5

- 5.60

)

0.97 (0.4

1

- 1.95

)

Idaho 1.81 (1.4

1

- 2.28

)

4.17 (2.8

2

- 5.93

)

5.48 (3.9

1

- 7.44

)

0.67 (0.3

8

- 1.09

)

Illinois 1.68 (1.3

8

- 2.03

)

3.51 (2.7

2

- 4.44

)

5.00 (3.9

9

- 6.18

)

0.86 (0.5

5

- 1.27

)

Indiana 1.66 (1.2

8

- 2.10

)

3.75 (2.5

6

- 5.30

)

5.40 (3.8

6

- 7.32

)

0.71 (0.4

1

- 1.17

)

Iowa 1.37 (1.0

2

- 1.82

)

3.20 (2.0

4

- 4.77

)

4.42 (2.9

4

- 6.36

)

0.59 (0.3

2

- 1.01

)

Kansas 1.63 (1.2

0

- 2.16

)

3.01 (1.8

8

- 4.57

)

4.58 (3.1

4

- 6.44

)

0.90 (0.5

0

- 1.51

)
Kentucky 1.63 (1.26

- 2.07 )4.00 (2.76- 5.59 )5.24 (3.68- 7.22 )0.70 (0.39- 1.15 )

Louisiana 1.83 (1.44- 2.31 )3.99 (2.73- 5.61 )5.57 (3.95- 7.61 )0.75 (0.41- 1.25 )

Maine 1.80 (1.38- 2.29 )5.31 (3.57- 7.58 )6.23 (4.32- 8.65 )0.70 (0.38- 1.18 )

Maryland 1.89 (1.40- 2.48 )4.71 (3.25- 6.59 )5.26 (3.65- 7.31 )1.02 (0.54- 1.75 )

Massachusett s2.11 (1.56- 2.79 )5.59 (3.87- 7.79 )6.00 (4.23- 8.23 )1.09 (0.58- 1.88 )

Michigan 1.74 (1.46- 2.05 )4.14 (3.27- 5.15 )6.00 (4.82- 7.36 )0.68 (0.43- 1.02 )

Minnesota 1.90 (1.43- 2.49 )4.29 (2.99- 5.94 )4.97 (3.46- 6.91 )1.02 (0.54- 1.74 )

Mississippi 1.63 (1.25- 2.09 )3.27 (2.16- 4.75 )5.12 (3.56- 7.10 )0.72 (0.41- 1.18 )

Missouri 1.48 (1.11- 1.94 )3.16 (2.03- 4.68 )4.61 (3.17- 6.47 )0.72 (0.38- 1.24 )

Montana 1.60 (1.22- 2.05 )4.68 (3.07- 6.79 )4.63 (3.21- 6.43 )0.65 (0.35- 1.09 )

Nebraska 1.61 (1.23- 2.06 )3.38 (2.20- 4.95 )5.15 (3.64- 7.05 )0.71 (0.38- 1.20 )

Nevada 1.81 (1.36- 2.35 )4.65 (3.09- 6.70 )5.27 (3.64- 7.35 )0.94 (0.54- 1.53 )

New 

Hampshire

1.97 (1.52- 2.51 )6.25 (4.19- 8.94 )5.82 (4.08- 8.01 )0.81 (0.45- 1.35 )

New Jersey 1.64 (1.24- 2.13 )3.48 (2.35- 4.94 )5.70 (3.99- 7.85 )0.82 (0.45- 1.39 )

New Mexico 1.73 (1.31- 2.24 )4.33 (2.86- 6.28 )4.20 (2.88- 5.89 )0.85 (0.47- 1.42 )

New York 1.93 (1.58- 2.33 )3.34 (2.52- 4.34 )6.89 (5.54- 8.44 )0.96 (0.61- 1.42 )

North 

Carolina

1.55 (1.17- 2.02 )3.05 (2.03- 4.40 )5.02 (3.58- 6.83 )0.81 (0.44- 1.36 )

North 

Dakota

1.49 (1.14- 1.92 )3.64 (2.39- 5.29 )4.09 (2.76- 5.83 )0.66 (0.35- 1.12 )

Ohio 1.62 (1.33- 1.94 )3.62 (2.80- 4.60 )5.11 (4.09- 6.28 )0.76 (0.48- 1.13 )

Oklahoma 1.58 (1.20- 2.06 )3.31 (2.15- 4.86 )4.81 (3.34- 6.69 )0.76 (0.42- 1.27 )

Oregon 1.92 (1.42- 2.54 )5.03 (3.46- 7.04 )5.52 (3.87- 7.61 )0.98 (0.50- 1.72 )

Pennsylvania 1.58 (1.32- 1.88 )3.05 (2.33- 3.93 )6.13 (4.94- 7.49 )0.72 (0.47- 1.07 )

Rhode Island 1.70 (1.29- 2.19 )4.08 (2.75- 5.81 )5.58 (3.88- 7.75 )0.82 (0.47- 1.34 )
South 

Carolina

1.54 (1.1

6

- 2.01

)

4.11 (2.7

3

- 5.93

)

4.48 (3.0

5

- 6.34

)

0.73 (0.4

0

- 1.23

)

South 

Dakota

1.49 (1.1

3

- 1.92

)

3.79 (2.4

0

- 5.68

)

4.27 (2.9

3

- 5.99

)

0.60 (0.3

2

- 1.02

)

Tennessee 1.69 (1.2

9

- 2.17

)

4.76 (3.2

9

- 6.64

)

5.10 (3.5

2

- 7.11

)

0.74 (0.4

0

- 1.24

)

Texas 1.79 (1.4

9

- 2.14

)

4.72 (3.7

8

- 5.82

)

4.50 (3.5

5

- 5.61

)

0.78 (0.4

7

- 1.23

)

Utah 2.11 (1.5

9

- 2.75

)

3.38 (2.2

2

- 4.92

)

4.91 (3.3

8

- 6.87

)

1.06 (0.5

7

- 1.81

)

Vermont 1.92 (1.4

8

- 2.44

)

4.56 (2.9

8

- 6.65

)

6.34 (4.6

2

- 8.46

)

0.84 (0.4

8

- 1.38

)

Virginia 1.55 (1.1

6

- 2.04

)

3.54 (2.4

7

- 4.91

)

4.37 (2.9

7

- 6.19

)

0.86 (0.4

7

- 1.44

)

Washington 1.97 (1.3

9

- 2.71

)

4.39 (2.9

8

- 6.21

)

4.37 (2.8

7

- 6.34

)

1.26 (0.6

2

- 2.26

)

West 

Virginia

1.47 (1.1

0

- 1.92

)

3.96 (2.7

0

- 5.60

)

4.58 (3.1

0

- 6.49

)

0.69 (0.3

8

- 1.16

)

Wisconsin 1.71 (1.3

4

- 2.15

)

4.44 (3.1

3

- 6.11

)

5.30 (3.7

5

- 7.25

)

0.71 (0.3

9

- 1.17

)

Wyoming 1.61 (1.2

2

- 2.10

)

3.12 (1.9

4

- 4.75

)

5.03 (3.4

8

- 7.00

)

0.72 (0.3

9

- 1.22

)

Note: Estimates are based on a survey-weighted hierarchical Bayes estimation approach, and the prediction 

(credible) intervals are generated by Markov Chain Monte Carlo techniques.

1 This estimate is the sum of the hierarchical Bayes estimates across all States and the District of Columbia and 

typically is not equal to the direct sample-weighted estimate for the Nation.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

11220

Table 9. Estimated Numbers of Persons Aged 12 or Older Needing But Not Receiving 

Treatment for an Illicit Drug Problem in the Past Year, by State: 2000

State

Total

Age Group (Years)

12-17 18-25 26 or Older

Estim

ate

Prediction

Interval

Estim

ate

Prediction

Interval

Estim

ate

Prediction

Interval

Estim

ate

Prediction

Interval

Total1

3,994,3

21 963,682 

1,511,8

23 

1,518,8

16 

Alabama 60,846 (46,03 - 78,48 13,085 (8,602 - 19,04 26,845 (18,76 - 37,07 20,916 (11,53 - 34,96
3 7) 0) 7 4) 3 4)

Alaska 10,381 (7,654 - 13,74

8)

2,879 (1,819 - 4,316) 3,451 (2,332 - 4,904) 4,051 (2,101 - 7,079)

Arizona 88,686 (61,86

1

- 122,9

28)

19,499 (13,25

9

- 27,57

1)

25,902 (18,03

5

- 35,90

9)

43,284 (21,68

5

- 77,12

4)

Arkansas 34,202 (26,36

5

- 43,20

9)

9,509 (6,605 - 13,21

7)

14,384 (10,20

1

- 19,64

0)

10,309 (5,505 - 17,67

5)

California 563,676 (461,4

20

- 681,3

54)

147,129 (122,1

73

- 175,4

48)

172,043 (137,5

55

- 212,1

95)

244,504 (158,0

79

- 361,1

94)

Colorado 71,131 (52,78

6

- 93,66

4)

16,164 (11,03

4

- 22,77

9)

24,240 (16,80

1

- 33,71

4)

30,727 (16,59

0

- 52,05

2)

Connectic

ut

52,010 (39,04

5

- 67,33

0)

13,550 (9,089 - 19,34

8)

20,130 (13,98

9

- 27,91

1)

18,329 (9,975 - 30,89

0)

Delaware 11,100 (8,216 - 14,55

5)

2,743 (1,910 - 3,807) 3,719 (2,539 - 5,241) 4,637 (2,513 - 7,823)

District of 

Columbia

8,820 (6,463 - 11,76

4)

1,852 (1,181 - 2,757) 2,820 (1,903 - 4,009) 4,148 (2,288 - 6,926)

Florida 196,128 (160,4

49

- 237,2

62)

47,578 (36,95

5

- 60,20

9)

71,294 (56,57

2

- 88,50

5)

77,256 (48,88

3

- 116,2

33)

Georgia 110,012 (83,25

3

- 141,7

84)

27,273 (19,26

0

- 37,40

5)

41,947 (29,32

4

- 57,98

6)

40,792 (22,06

9

- 69,16

0)

Hawaii 16,838 (11,24

7

- 24,19

7)

5,034 (3,235 - 7,439) 4,375 (2,823 - 6,453) 7,429 (3,144 - 14,89

5)

Idaho 19,700 (15,32

0

- 24,69

2)

5,408 (3,654 - 7,689) 9,029 (6,443 - 12,26

4)

5,263 (2,998 - 8,588)

Illinois 164,309 (134,5

17

- 198,6

22)

34,985 (27,16

4

- 44,29

6)

65,356 (52,14

0

- 80,76

6)

63,967 (41,22

9

- 94,76

2)

Indiana 82,093 (63,42

6

- 104,0

93)

19,227 (13,10

4

- 27,15

7)

35,911 (25,68

5

- 48,69

3)

26,955 (15,34

4

- 44,01

2)

Iowa 32,845 (24,27

2

- 43,40

9)

7,980 (5,092 - 11,89

2)

14,102 (9,375 - 20,31

2)

10,764 (5,795 - 18,33

9)

Kansas 35,310 (25,91

5

- 46,53

8)

7,244 (4,526 - 10,97

7)

13,406 (9,189 - 18,83

3)

14,661 (8,086 - 24,44

3)

Kentucky 53,647 (41,50

1

- 67,98

5)

13,165 (9,088 - 18,39

5)

22,798 (16,01

0

- 31,37

8)

17,684 (9,940 - 29,13

5)

Louisiana 65,208 (51,14

1

- 82,16

8)

16,667 (11,41

8

- 23,43

5)

28,934 (20,51

5

- 39,50

0)

19,607 (10,77

9

- 32,89

2)

Maine 18,817 (14,46

9

- 24,00

8)

5,463 (3,668 - 7,796) 7,565 (5,244 - 10,51

6)

5,789 (3,164 - 9,736)

Maryland 80,734 (59,88

9

- 106,3

68)

19,869 (13,68

6

- 27,79

1)

26,850 (18,61

7

- 37,33

3)

34,014 (17,94

7

- 58,74

0)

Massachu

setts

108,669 (79,82

2

- 142,9

46)

28,215 (19,51

3

- 39,30

8)

36,641 (25,82

3

- 50,25

5)

43,812 (23,11

3

- 75,33

5)

Michigan 137,607 (115,8

03

- 162,2

55)

34,424 (27,20

1

- 42,91

1)

61,890 (49,77

3

- 75,90

9)

41,293 (26,20

5

- 62,04

1)

Minnesota 75,663 (56,40 - 98,35 18,474 (12,87 - 25,61 26,808 (18,63 - 37,23 30,382 (16,19 - 51,99
4 5) 4 0) 5 6) 7 0)

Mississip

pi

37,181 (28,30

2

- 47,49

7)

8,488 (5,598 - 12,32

0)

16,533 (11,49

5

- 22,94

8)

12,160 (6,858 - 19,97

3)

Missouri 67,487 (50,26

8

- 88,01

1)

15,037 (9,666 - 22,28

7)

27,465 (18,86

2

- 38,53

2)

24,985 (13,27

0

- 42,96

7)

Montana 12,396 (9,491 - 15,86

3)

3,955 (2,599 - 5,745) 4,616 (3,207 - 6,415) 3,825 (2,080 - 6,462)

Nebraska 22,267 (16,95

3

- 28,30

3)

5,205 (3,390 - 7,627) 9,747 (6,885 - 13,35

2)

7,315 (3,948 - 12,42

1)

Nevada 27,941 (21,07

1

- 36,29

6)

6,816 (4,533 - 9,808) 9,672 (6,679 - 13,49

3)

11,453 (6,581 - 18,54

0)

New 

Hampshir

e

19,883 (15,31

7

- 25,33

3)

6,566 (4,397 - 9,381) 7,006 (4,918 - 9,640) 6,310 (3,487 - 10,53

2)

New 

Jersey

110,186 (83,02

0

- 143,2

73)

21,851 (14,78

3

- 31,06

1)

44,599 (31,24

8

- 61,47

8)

43,737 (23,83

1

- 73,74

8)

New 

Mexico

25,748 (19,53

1

- 33,35

0)

7,533 (4,968 - 10,91

4)

8,854 (6,078 - 12,42

6)

9,362 (5,161 - 15,64

9)

New York 285,054 (233,3

86

- 344,5

27)

49,307 (37,17

9

- 64,03

2)

125,708 (101,1

75

- 154,0

35)

110,039 (70,58

4

- 163,5

80)

North 

Carolina

98,671 (74,30

3

- 128,4

12)

19,877 (13,21

7

- 28,66

8)

39,033 (27,84

5

- 53,05

1)

39,762 (21,74

5

- 66,93

7)

North 

Dakota

8,019 (6,077 - 10,27

6)

2,259 (1,484 - 3,287) 3,162 (2,132 - 4,506) 2,598 (1,399 - 4,431)

Ohio 150,150 (123,8

96

- 180,2

34)

34,443 (26,62

2

- 43,77

6)

61,867 (49,58

8

- 76,13

3)

53,840 (34,32

5

- 80,49

4)

Oklahoma 43,449 (32,80

8

- 56,40

4)

10,098 (6,561 - 14,83

6)

17,632 (12,22

8

- 24,53

0)

15,719 (8,643 - 26,32

0)

Oregon 54,906 (40,13

5

- 71,85

5)

13,900 (9,562 - 19,45

5)

19,589 (13,73

0

- 26,99

6)

21,417 (10,89

0

- 37,85

6)

Pennsylva

nia

160,117 (133,5

36

- 190,3

49)

30,162 (22,96

5

- 38,85

1)

72,657 (58,58

9

- 88,90

1)

57,298 (37,09

3

- 84,63

5)

Rhode 

Island

13,983 (10,60

0

- 17,95

9)

3,417 (2,304 - 4,866) 5,282 (3,671 - 7,329) 5,284 (3,007 - 8,625)

South 

Carolina

48,469 (36,46

3

- 62,80

5)

13,398 (8,892 - 19,32

4)

17,298 (11,75

3

- 24,47

9)

17,773 (9,780 - 29,81

2)

South 

Dakota

9,262 (7,010 - 11,86

3)

2,784 (1,762 - 4,171) 3,739 (2,567 - 5,248) 2,739 (1,477 - 4,668)

Tennessee 78,992 (60,26

6

- 101,0

55)

22,063 (15,24

8

- 30,77

9)

30,487 (21,07

7

- 42,51

5)

26,442 (14,39

7

- 44,65

3)

Texas 287,765 (238,7

63

- 343,7

04)

88,677 (70,97

5

- 109,2

78)

106,489 (84,04

8

- 132,8

55)

92,599 (55,61

3

- 145,3

27)

Utah 36,474 (27,20

1

- 47,16

7)

8,360 (5,490 - 12,17

7)

15,995 (11,01

7

- 22,37

9)

12,120 (6,473 - 20,70

1)

Vermont 9,810 (7,568 - 12,50

0)

2,511 (1,642 - 3,663) 3,980 (2,901 - 5,308) 3,320 (1,873 - 5,454)
Virginia 87,768 (65,33

5

- 115,3

60)

19,913 (13,88

4

- 27,61

0)

30,225 (20,55

0

- 42,76

1)

37,630 (20,61

3

- 63,30

4)

Washingt

on

94,245 (66,32

3

- 129,7

78)

21,368 (14,51

7

- 30,24

0)

26,444 (17,40

3

- 38,41

7)

46,433 (23,04

8

- 83,56

9)

West 

Virginia

22,959 (17,14

2

- 29,87

9)

5,606 (3,816 - 7,922) 8,916 (6,041 - 12,64

7)

8,437 (4,621 - 14,13

5)

Wisconsin 75,832 (58,70

8

- 94,08

0)

21,142 (14,87

6

- 29,07

7)

31,298 (22,16

7

- 42,77

8)

23,392 (13,04

6

- 38,86

5)

Wyoming 6,872 (5,174 - 8,915) 1,531 (951 - 2,331) 3,089 (2,139 - 4,303) 2,252 (1,216 - 3,830)

Note: Estimates are based on a survey-weighted hierarchical Bayes estimation approach, and the prediction 

(credible) intervals are generated by Markov Chain Monte Carlo techniques.

1 This estimate is the sum of the hierarchical Bayes estimates across all States and the District of Columbia and 

typically is not equal to the direct sample-weighted estimate for the Nation.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Chapter 3: End Notes

1

States were ranked from lowest to highest based on the percentage gap to two decimals. Nine States were included 

in the third (middle) "fifth." Also see Figure 4 for the comparable numbers of persons.

2 This is the national sample weighted estimate. Also, shown in Tables 8 and 9 are the corresponding "Totals" that 

represent the weighted average across States of the model-based estimates. The "Totals" are similar, but not 

identical, to the corresponding sample weighted national estimates.

References

American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders

(3rd rev. ed.). Washington, DC: American Psychiatric Association.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders

(4th ed.). Washington, DC: American Psychiatric Association.

Folsom, R. E., & Judkins, D. R. (1997). Substance abuse in states and metropolitan areas: 

Model based estimates from the 1991-1993 National Household Surveys on Drug Abuse: 

Methodology report (DHHS Publication No. SMA 97-3140, Methodology Series M-1; available 

at /methods.htm#methods). Rockville, MD: Substance Abuse and Mental Health Services 

Administration, Office of Applied Studies.

Folsom, R. E., Shah, B., & Vaish, A. (1999). Substance abuse in states: A methodological report 

on model based estimates from the 1994-1996 National Household Surveys on Drug Abuse. In 
Proceedings of the Section on Survey Research Methods of the American Statistical Association

(pp. 371-375). Washington, DC: American Statistical Association.

Office of Applied Studies. (1996). Substance abuse in states and metropolitan areas: Model 

based estimates from the 1991-1993 National Household Surveys on Drug Abuse-Summary 

report (available in WordPerfect 6.1 file format at /analytic.htm). Rockville, MD: Substance 

Abuse and Mental Health Services Administration.

Office of Applied Studies. (1998). Analyses of substance abuse and treatment need issues

(DHHS Publication No. SMA 98-3227, Analytic Series A-7; available at /analytic.htm). 

Rockville, MD: Substance Abuse and Mental Health Services Administration.

Office of Applied Studies. (2000). Summary of findings from the 1999 National Household 

Survey on Drug Abuse (DHHS Publication No. SMA 00-3466, NHSDA Series H-12; available at 

/p0000016.htm#special). Rockville, MD: Substance Abuse and Mental Health Services 

Administration.

Office of Applied Studies. (2001). Summary of findings from the 2000 National Household 

Survey on Drug Abuse (DHHS Publication No. SMA 01-3549, NHSDA Series H-13; available at 

/p0000016.htm#special). Rockville, MD: Substance Abuse and Mental Health Services 

Administration, Office of Applied Studies.

Office of National Drug Control Policy. (2000). The national drug control strategy: 2000 annual 

report (NCJ-180082; available at 

http://www.ncjrs.org/ondcppubs/publications/policy/ndcs00/index.html). Washington, DC: The 

White House.

Wright, D., Gfroerer, J., & Epstein, J. (1997). Ratio estimation of hardcore drug use. Journal of 

Official Statistics, 13, 401-416.

Appendices

Appendix A: Measurement of Dependence, Abuse, Treatment, and Treatment 

Need

The National Household Survey on Drug Abuse (NHSDA) has been used for many years to 

measure problematic substance use, including the need for and receipt of treatment. Different 

methods and definitions have been used, based on the particular focus of each analysis 

undertaken. This report presents estimates of the treatment gap and the need for and receipt of 

treatment for an illicit drug problem. Prior NHSDA reports and special analyses have included 

estimates of these measures. However, due to significant changes to the NHSDA questionnaire 

and the definitions and estimation methods used, the estimates of these measures from the 2000 

NHSDA are not comparable with prior estimates. This appendix describes the changes and their 

impact on estimates.

A.1 Dependence
Since 1991, the NHSDA has included questions to estimate dependence. The questions have 

been based on criteria established by the American Psychiatric Association (APA) in its 

Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R, DSM-IV; APA, 1987, 

1994). Questions in the 1991 to 1993 NHSDAs were based on the DSM-III-R definition of 

dependence, and questions in the 1994 to 2000 NHSDAs were based on the DSM-IV definition 

of dependence. DSM-IV defines a person as dependent if he or she met three out of seven 

dependence criteria (for substances with a withdrawal criterion) or three out of six criteria (for 

substances without a withdrawal criterion).

As part of an ongoing process to evaluate and improve the questions in the NHSDA, questions 

that were used in 1999 were cognitively tested to determine how well they were understood by 

respondents and to determine whether any particular phrases or words were problematic. The 

questions were also reviewed by an expert in the field to determine how well the questions 

captured the meaning of the DSM-IV criteria. Based on these assessments, the questions were 

revised for the 2000 NHSDA. Some individual questions were divided into several less complex 

questions, and revisions were made to improve question wording. Table A.1 at the end of this 

appendix indicates the questions used in 1999 and the revised questions used in 2000 to measure 

each DSM-IV criterion for each substance.

The revised dependence questions are generally more restrictive and less global than the ones 

used in 1999. Prevalence estimates for each criterion by substance are given in Table A.2 for the 

1999 and 2000 NHSDAs. For most criteria, the 2000 estimate is smaller than the 1999estimate. 

This is probably due to the more restrictive nature of the questions covering the criteria in 2000. 

For example, criterion 3 was previously covered by a question asking whether the drug was used 

much more often or in larger amounts than intended. In 2000, criterion 3 was covered by two 

questions, one asking whether limits were set on the use of the substance and another asking if 

the limits were kept. To meet the criterion, a person must have a positive response to the first 

question and a negative response to the second question. For marijuana, the estimate for criterion 

3 was 0.4 percent in 2000 and 1.4 percent in 1999. 

The generally lower prevalences in 2000 for individual criteria resulted in a lower estimated 

prevalence for dependence. Estimates of dependence for 1999 and 2000 for any illicit drug and 

by specific substances (illicit drugs and alcohol) are given in Table A.3. Estimates of dependence 

for 1999 and 2000 for any illicit drug and for alcohol by demographic subgroups are given in 

Table A.4. The estimated percentage of persons aged 12 or older dependent on any illicit drug 

was 23 percent smaller in 2000 than in 1999 (1.6 percent in 1999 vs. 1.2 percent in 2000). The 

estimated percentage of persons dependent on alcohol was 38 percent smaller in 2000 than in 

1999 (3.7 percent in 1999 vs. 2.3 percent in 2000). Estimates of dependence for any illicit drug 

were generally smaller in 2000 than in 1999 by most demographic subgroups. Although 

estimates of dependence were smaller in 2000 than in 1999, they followed similar patterns by 

demographic subgroups.

Estimates of dependence by State for 1999 are based on the 1999 dependence questions and not 

the revised dependence questions used in 2000. Thus, these dependence estimates are not 

comparable with estimates of dependence by State in 2000.
A.2 Abuse

In 2000, questions to measure abuse based on the DSM-IV were introduced into the NHSDA for 

the first time. These questions were designed to cover the four abuse criteria defined in the 

DSM-IV. Table A.1 indicates the abuse questions in the 2000 NHSDA used to cover each abuse 

criterion. According to the DSM-IV, a person is defined with abuse if he or she meets one or 

more of the abuse criteria and does not meet the definition for dependence. The questions on 

abuse were cognitively tested and reviewed by experts in the field. Estimates of abuse are given 

in Table A.3 for individual substances and in Table A.4 by demographic characteristics. 

Estimates of abuse were smaller than estimates of dependence for any illicit drug and for specific 

illicit drugs. However, for alcohol the estimated percentage with abuse (3.1 percent) was larger 

than the estimated percentage with dependence (2.3 percent) in 2000.

A.3 Illicit Drug Treatment Need and Gap

In recent years, the Substance Abuse and Mental Health Services Administration (SAMHSA) 

produced estimates of illicit drug treatment need and the treatment gap (i.e., persons who needed 

but did not receive treatment) using two basic methods: adjusted and unadjusted. The adjusted 

estimates incorporated a ratio adjustment technique that inflated the NHSDA numbers based on 

separate counts of treatment and arrestee populations (Wright, Gfroerer, & Epstein, 1997). These 

ratio-adjusted treatment need and gap estimates were made only at the national level and were 

used by the Office of National Drug Control Policy (ONDCP) in its annual national drug control 

strategy. Unadjusted treatment need and gap estimates were used in most analyses of NHSDA 

data, including several SAMHSA publications. Although both the adjusted and unadjusted 

estimates employed the same definitions of need, the ratio adjustment produces estimates that are 

20 to 30 percent higher than the unadjusted estimates. Starting with the 2000 NHSDA, a single 

new method for estimating treatment need and the gap has been employed. This new method was 

developed by an interagency work group chaired by ONDCP. The method uses a simpler and 

more widely accepted definition of treatment need than had been used previously, and it does not 

employ the ratio adjustment. There are two reasons that the ratio adjustment is no longer used: 

(a) to provide more accurate trend measurement, and (b) to allow the possibility of subgroup 

analysis. The ratio adjustment methodology is problematic because it depends on external data 

that are not available with the consistency over time or by geographic and demographic 

subpopulations.

The following discussion explains the change in the methodology for estimating treatment need 

and the gap and how the change affects the resulting estimates. 

A.3.1 Definitions of Treatment Need

For the 1991 to 1999 NHSDAs, a respondent was classified as needing treatment (total treatment 

need) if he or she met at least one of four criteria during the past year: (1) dependent on any 

illicit drug; (2) used marijuana daily, or used some other illicit drug on at least 52 days; (3) was 

an injection drug user or used heroin at least once; and/or (4) received any treatment for drug 

abuse. Respondents needing treatment were further classified into "level 1" and "level 2" 

treatment need. Respondents needing treatment for a more severe drug problem were defined 
with level 2 treatment need. Respondents classified with needing treatment who did not meet the 

definition for level 2 treatment need were defined with level 1 treatment need. Respondents 

classified with level 2 drug abuse treatment need met at least one of the following five criteria in 

the past year: (1) dependent on any illicit drug other than marijuana; (2) used marijuana daily and 

were dependent on marijuana; (3) used cocaine on 52 or more days, or used some other 

illicitdrug daily; (4) were injection drug users or used heroin at least once; and/or (5) received 

treatment for drug abuse at a specialty facility (hospital [as an inpatient], mental health center, or 

drug treatment facility). Level 2 treatment need and specialty treatment are the measures used in 

calculating the "treatment gap" for the 1991 through 1999 NHSDAs (Office of Applied Studies 

[OAS], 1998).

The new definition of treatment need for 2000 classifies a respondent as needing treatment if he 

or she meets the criteria for dependence or abuse (DSM-IV) or received treatment at a specialty 

facility. Plans are to continue using this revised definition of treatment need in future years.

A.3.2 Comparison of Old Ratio-Adjusted Estimates with New Estimates

It is difficult to compare estimates of treatment need and the "treatment gap" used by ONDCP 

prior to 1999 with the new estimates for 2000 because several important changes to the NHSDA 

in 1999 and 2000 affected the estimates. Between 1998 and 1999, the NHSDA switched from a 

paper-and-pencil-interviewing (PAPI) mode to a computer-assisted interviewing (CAI) mode. 

All questions on drug use, dependence, and treatment need were administered using audio

computer-assisted self-interviewing (ACASI) in 2000. There were also major changes to the 

sample design. The revision of the dependence questions in 2000 also affected the treatment 

need estimate. Besides these changes to the NHSDA, the change in the definition of treatment 

need in 2000 and the ratio adjustment used previously have had a significant impact on the 

estimates. As discussed below, estimates using the old definition (level 2) of treatment need on 

the 2000 file were 45 percent lower than estimates using the 2000 definition on the 2000 file. 

The ratio adjustment increased the old estimates by 20 to 30 percent, but it is not included in the 

new estimates of treatment need in 2000.

Table A.5 compares the old ratio-adjusted estimates of illicit drug treatment need and gap from 

1991 to 1998 with the new estimates for 2000. The estimated numbers who needed treatment 

ranged from 4.6 million to 5.7 million from 1991 to 1998. The estimate for 2000 using the new 

methodology was 4.7 million. Although the new estimate is similar in size to the old estimates, 

this does not necessarily reflect stability in the numbers of persons in the population who need 

treatment. The methods upon which these estimates are based are quite different, as explained 

earlier. Furthermore, the estimated numbers of persons who received treatment were very 

different with the new and old methods. These estimates ranged from 1.6 million to 2.1 million 

from 1991 to 1998 and 0.8 million in 2000 using the new method. This probably reflects the fact 

that the 1991 to 1998 estimates incorporated the ratio adjustment, which essentially replaced the 

estimated numbers receiving treatment from the NHSDA (based on respondents' self-report) with 

an independent count of the numbers receiving treatment. The independent count used in this 

ratio adjustment was derived from a variety of sources, primarily the Uniform Facility Data Set 

(UFDS). The estimated treatment gap, which ranged from 2.5 million to 3.6 million for the 1991 

to 1998 estimates, was somewhat higher with the new estimate for 2000 (3.9 million). Again, 
due to the major changes in estimation methodology, the data do not indicate any real increase in 

the treatment gap. The higher gap number is simply the result of the change in methodology. 

Furthermore, the difference in gap estimates is likely due more to the change in the definition of 

treatment need than it is to the change in the way treatment is estimated. This is because the ratio 

adjustment previously used inflates the treatment need estimate at least as much as it inflates the 

treatment estimate because the treatment need estimates (both old and new) include treated 

persons by definition.

To gain insight into the effects of the various changes, it is helpful to analyze estimates from the 

old and new definitions without the ratio adjustment. Some analyses are discussed below.

A.3.3 Comparison of Old Unadjusted Estimates with New Estimates

Estimates of total treatment need and level 2 treatment need that do not include the ratio 

adjustment for 1999 are compared with estimates of treatment need for 2000 in Table A.6 by 

demographic subgroups. The 2000 estimates of treatment need were generally larger than the 

1999 estimates of level 2 treatment need and smaller than the 1999 estimates of total treatment 

need by demographic subgroups. The estimated percentage of the population needing treatment 

in 2000 (2.1 percent) was smaller than the estimated percentage of the population with total 

treatment need in 1999 (3.5 percent) and larger than the estimated percentage of the population 

with level 2 treatment need in 1999 without a ratio adjustment (1.5 percent). It is impossible to 

determine how much of the difference between the 1999 and 2000 estimates is due to a real 

change in the population needing treatment and how much is due to the change in the definitions.

To analyze the impact of the change in the treatment need definition, estimates were run based 

on both definitions using the same data file. Specifically, using the 2000 NHSDA file, estimates 

of treatment need were calculated using the 1999 definition (level 2) (and 2000 dependence 

questions) and compared with estimates of treatment need calculated using the 2000 definition 

on the same file. With the 1999 definition, 1.2 percent of the population needed treatment (2.7 

million persons), while with the 2000 definition, 2.1 percent of the population needed treatment 

(4.7 million persons). Estimates by demographic subgroups are given in Table A.7. Estimates of 

treatment need with the 2000 definition were larger than estimates of treatment need with the 

1999 (level 2) definition overall and for each demographic subgroup, indicating that the change 

in definition between 1999 and 2000 resulted in an increase in the estimates of treatment need.

The estimate of level 2 treatment need using the 1999 definition on the 2000 NHSDA file (1.2 

percent) was slightly smaller than the estimate of treatment need using the same definition on the 

1999 NHSDA file (1.5 percent), indicating that there was probably little change in the numbers 

of people needing treatment between 1999 and 2000. The slight decrease in the estimate on the 

2000 file could be due to the fact that the dependence questions in 2000 were more restrictive, 

resulting in smaller estimates of dependence.

To compare the new and old definitions of treatment need in their coverage of various 

populations of drug users, several populations with various patterns and symptoms indicative of 

a drug use problem were identified. The proportions in each population who would be classified 

as needing treatment were compared under the old and new definitions. Table A.8 indicates for 
each of these populations the percentage of the population classified with level 2 treatment need 

using the 1999 definition, the percentage classified with treatment need using the 2000 

definition, the percentage classified with dependence, and the percentage classified with abuse. 

Some of these populations were completely covered by level 2 treatment need because they were 

part of the definition of level 2 treatment need. This was true for heroin users, injection drug 

users, and weekly cocaine users. For these populations, the interest was in how well they would 

be covered by the new definition of treatment need and what proportion would be covered by 

abuse and dependence. More than 80 percent of the heroin users and the injection drug users 

were covered by the new definition of treatment need. Approximately 59 percent of weekly 

cocaine users were covered by the new definition of treatment need.

For some populations, the proportions covered by the old and new definitions of treatment need 

were not substantially different from each other. This was the case for weekly illicit drug users 

and daily marijuana users. The percentage classified with treatment need under the old definition 

among persons using an illicit drug weekly or more often was 41.2 percent, and the percentage 

classified as needing treatment under the new definition was 39.1 percent. Among daily 

marijuana users, 34.6 percent were classified as needing treatment under the old definition, and 

35.6 percent were classified as needing treatment under the new definition.

Also compared were the percentages of persons meeting the old and new definitions of treatment 

need among each other. Among those who met the new definition of treatment need, 46.4 

percent also met the old (level 2) definition of treatment need. Among those who met the old 

(level 2) definition of treatment need, 80.7 percent also met the new definition of treatmentneed. 

The estimated percentage of persons in the population meeting both the old and new definitions 

of treatment need was 0.9 percent.

A.3.4 Specialty Treatment

Persons who received specialty treatment in the past year but did not meet the criteria for 

dependence or abuse were included in the definition of treatment need because it was assumed 

that if a person received treatment, he or she probably needed it at some point in the past year. 

Keeping these people in the definition of treatment need does not affect the estimate of the 

number of people in the "treatment gap" because these people are included in the estimate of 

treatment need and the estimate of receiving treatment. Among the persons who received 

specialty treatment in the past year but did not meet the criteria for dependence or abuse, 53.2 

percent were still in some kind of treatment at the time of interview, 27.9 percent had 

successfully completed treatment, 28.1 were arrested and booked in the past year, and 41.1 

percent were on probation, parole, or other conditional release at some time in the past year.

In both 1999 and 2000, persons were defined as receiving specialty treatment if they received 

treatment in the past year at a hospital (as an inpatient), a mental health center, or a drug 

treatment facility. However, in the 2000 NHSDA, there were some changes from 1999 in the 

manner in which people were asked about treatment at specific locations. These changes resulted 

in a difference in the way specialty treatment was tabulated. In 1999, when a person was asked 

about treatment at a specific location, he or she was not asked whether the treatment was for 

alcohol or drugs. Thus, if a person reported receiving treatment for alcohol and drugs in the past 
year, it was assumed that he or she received treatment for alcohol and drugs at each location that 

he or she reported receiving treatment.

Because at a specific location a person may only receive treatment for alcohol or only receive 

treatment for drugs, a question was added to determine whether the treatment received at a 

specific location was for alcohol only, drugs only, or both. In 2000, if a person reported receiving 

treatment for alcohol and drugs in the past year, and reported specific locations where he or she 

received treatment, the person was further asked for each location reported whether the treatment 

at that location was for alcohol, drugs, or both. As a result, some people who might have been 

counted as receiving specialty treatment for illicit drugs in 1999 would not be counted in 2000. 

An estimated 0.8 million persons (0.3 percent of the population) received specialty treatment for 

illicit drugs in 2000. If the estimate for 2000 had been tabulated in the same manner as in 1999, 

the estimate of the numbers of persons receiving specialty treatment for illicit drugs would be 0.9 

million persons (0.4 percent of the population).

Table A.1 Questions in the 1999 NHSDA and Corresponding Questions in the 2000 

NHSDA for Each DSM-IV Criterion for Dependence and Abuse

DSM 

Criterion

Questions in the 1999 NHSDA Used to 

Cover the DSM-IV Criteria

Questions in the 2000 NHSDA Used to 

Cover the DSM-IV Criteria

Dependence

1.

During the past 12 months, have you 

built up a tolerance for the drug so that 

the same amount of the drug had less 

effect than before?

During the past 12 months, did you need to 

use more of the drug than you used to in 

order to get the effect you wanted?

During the past 12 months, did you notice 

that using the same amount of the drug had 

less effect on you than it used to?

2.

For cigarettes, alcohol, heroin, 

analgesics, sedatives, stimulants:

(For cocaine or crack only:

During the past 12 months, have you felt 

kind of blue or down when the effect of 

the drug you were using was wearing 

off?)

During the past 12 months, have you 

had any of these symptoms as the effect 

of the drug was wearing off? Symptoms 

vary by drug (see next page)

Only for cigarettes, alcohol, cocaine, heroin, 

analgesics, sedatives, stimulants:

During the past 12 months did you cut down 

or stop using the drug at least one time?

(For cocaine or crack only:

During the past 12 months, have you felt 

kind of blue or down when you cut down or 

stopped using the drug?

During the past 12 months, did you have __ 

or more of these symptoms after you cut 

back or stopped using the drug? The 
During the past 12 months, did you use 

more of that drug to get over or avoid 

the bad aftereffects of using that drug?

symptoms and number needed to meet this 

criteria varies by drug. (See next page)

3.

During the past 12 months, have you 

used that kind of drug much more often 

or in larger amounts than you intended 

to?

During the past 12 months, did you try to 

set limits on how often or how much of the 

drug you would use?

If above was answered yes:

Were you able to keep to the limits you set 

or did you often use more than you intended 

to?

4.

During the past 12 months, did you want 

to try to stop or cut down on your use of 

that drug but found you couldn't?

During the past 12 months, did you want to 

or try to cut down or stop using the drug?

During the past 12 months, were you able to 

cut down or stop using the drug every time 

you wanted to or tried to?

5.

During the past 12 months, did you have 

a period of a month or more when you 

spent a great deal of time getting the 

drug, using the drug, or getting over its 

effects?

During the past 12 months, was there a 

month or more when you spent a lot of your 

time getting or using the drug?

During the past 12 months, was there a 

month or more when you spent a lot of your 

time getting over the effects of the drug?

6.

During the past 12 months, has your use 

of that drug often kept you from 

working, going to school, taking care of 

children, or engaging in recreational 

activities?

This question is about important activities 

such as working, going to school, taking 

care of children, doing fun things such as 

hobbies and sports, and spending time with 

friends and family.

During the past 12 months, did using the 

drug cause you to give up or spend less time 

doing these types of important activities?

7.

a. During the past 12 months, has your 

use of the drug caused you any health 

problems?

b. During the past 12 months, has your 

use of the drug caused you to have any 

emotional or psychological problems 

such as feeling uninterested in things, 

feeling depressed, feeling suspicious of 

people, feeling paranoid, or having 

During the past 12 months, did you have 

any problems with your emotions, nerves or 

mental health that were probably caused or 

made worse by your use of the drug?

Did you continue to use the drug even 

though you thought it was causing you to 

have problems with your emotions, nerves 

or mental health?
strange ideas? During the past 12 months, did you have 

any physical health problems that were 

probably caused or made worse by your use 

of the drug?

Did you continue to use the drug even 

though you thought it was causing you to 

have physical problems?

Abuse

1. Not asked in 1999 NHSDA.

Sometimes people who use this drug have 

serious problems at home, work or school—

such as:

-neglecting their children

-missing work or school

-doing a poor job at work or school

-losing a job or dropping out of school

During the past 12 months, did using this 

drug cause you to have serious problems 

like this either at work, school or home?

2. Not asked in 1999 NHSDA.

During the past 12 months did you regularly 

use the drug and then do something where 

using the drug might have put you in 

physical harm?

3. Not asked in 1999 NHSDA.

During the past 12 months, did using the 

drug cause you to do things that repeatedly 

got you in trouble with the law?

4. Not asked in 1999 NHSDA.

During the past 12 months, did you have 

any problems with family or friends that 

were probably caused by your use of the 

drug ?

Did you continue to use the drug even 

though you thought it caused problems with 

family or friends?

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999 and 2000.

Table A.2 Percentages Reporting Having Met DSM-IV Criteria, by Specific Substances: 

1999 and 2000

Substance DSM-IV Criteria
Criterio

n 1

Criterio

n 2

Criterio

n 3

Criterio

n 4

Criterio

n 5

Criterio

n 6

Criterio

n 7

199

9

200

0

199

9

200

0

199

9

200

0

199

9

200

0

199

9

200

0

199

9

200

0

199

9

200

0

Marijuana 0.8 1.5 -- -- 1.4 0.4 1.6 0.4 1.5 2.2 0.5 0.6 1.1 0.6

Cocaine 0.3 0.3 0.4 0.1 0.4 0.1 0.3 0.1 0.4 0.3 0.2 0.2 0.4 0.2

Heroin 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1

Hallucinoge

ns

0.1 0.2 -- -- 0.1 0.0 0.1 0.0 0.2 0.2 0.1 0.1 0.2 0.1

Inhalants 0.1 0.1 -- -- 0.1 0.0 0.1 0.0 0.1 0.1 0.0 0.0 0.1 0.0

Pain 

Relievers 0.1 0.4 0.2 0.2 0.3 0.1 0.2 0.1 0.3 0.4 0.2 0.2 0.2 0.1

Tranquilizer

s

0.1 0.2 -- -- 0.1 0.0 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1

Stimulants 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Sedatives 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0

Alcohol 3.0 6.5 3.4 1.1 7.2 1.6 6.2 1.6 4.4 6.4 1.5 1.9 2.9 1.9

-- Not available.

NOTE: Criterion 1: Needed to use substance more than before to get desired effects or noticed that the same amount 

of substance use had less effect than before. Criterion 2: Reported experiencing two or more additional substance 

withdrawal symptoms at the same time that lasted longer than a day after substance use was cut back or stopped. 

Also, for cocaine and stimulants, respondent must have reported feeling blue or down when trying to stop or cut 

down using substance (not a necessary criterion for dependence of marijuana, hallucinogens, inhalants, or 

tranquilizers). Criterion 3: Used substance more often than intended and was unable to keep set limits on substance 

use. Criterion 4: Inability to cut down or stop using substance every time tried or wanted to. Criterion 5: Spent a 

great deal of time over a period of a month getting, using, or getting over the effects of substance. Criterion 6: 

Substance use reduced or eliminated involvement or participation in important activities. Criterion 7: Continued to 

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

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999 and 2000.

Table A.3 Total Population and Percentages of Persons Aged 12 or Older Classified with 

Dependence on Specific Substances in 1999 and 2000 and Classified with Abuse of Specific 

Substances in 2000
Dependence 1999 Dependence 2000 Abuse 2000

Substance

Number

(in 

Thousands) Percent

Number

(in 

Thousands) Percent

Number

(in 

Thousands) Percent

Any Illicit Drug13,554 1.6 2,771 1.2 1,538 0.7

Marijuana 2,319 1.0 1,676 0.8 1,164 0.5

Cocaine 770 0.3 557 0.2 190 0.1

Heroin 141 0.1 164 0.1 20 0.0

Hallucinogens 256 0.1 151 0.1 251 0.1

Inhalants 103 0.0 101 0.0 77 0.0

Any 

Psychotherapeutic 718 0.3 698 0.3 386 0.2

Pain Reliever 447 0.2 443 0.2 279 0.1

Tranquilizers 148 0.1 149 0.1 115 0.1

Stimulants 278 0.1 238 0.1 83 0.0

Sedatives 86 0.0 81 0.0 39 0.0

Alcohol 8,201 3.7 5,089 2.3 7,021 3.1

NOTE: Dependence and abuse are based on the definition found in the 4th edition of the Diagnostic and Statistical 

Manual of Mental Disorders (DSM-IV) (APA, 1994).

1Any Illicit Drug includes marijuana/hashish, cocaine (including crack), heroin, hallucinogens (including LSD and 

PCP), inhalants, or any prescription-type psychotherapeutic used nonmedically.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999 and 2000.

Table A.4 Percentages of Persons Aged 12 or Older Reporting Past Year Illicit Drug or 

Alcohol Dependence, by Demographic Characteristics: 1999 and 2000

Demographic 

Characteristic

Type of Past Year Dependence Type of Past Year Abuse

Any Illicit 

Drug Alcohol

Any 

Illicit 

Drug Alcohol

1999 2000 1999 2000 2000 2000

Total 1.6 1.2 3.7 2.3 0.7 3.1

Age in Years

12-17 3.3 2.4 3.6 1.8 2.0 3.3

18-25 4.7 3.5 9.2 4.6 2.0 8.1

26 or older 0.9 0.7 2.8 2.0 0.3 2.3

Gender
Male 2.0 1.5 4.9 3.1 0.9 4.5

Female 1.3 1.0 2.6 1.5 0.5 1.9

Hispanic Origin and Race

Not Hispanic

White only 1.5 1.2 3.8 2.2 0.6 3.3

Black only 2.3 1.6 3.1 2.4 0.7 2.2

American Indian or 

Alaska Native only 4.7 1.6 5.1 3.4 2.5 4.4

Native Hawaiian or 

other Pacific Islander * 1.4 * 1.3 0.1 1.5

Asian only 0.8 0.5 2.2 2.0 0.3 1.3

More than one race 2.6 2.5 7.7 2.8 * 3.6

Hispanic 1.9 1.2 3.9 2.4 1.1 3.5

*Low precision; no estimate reported.

NOTE: Dependence is based on the definition found in the 4th ed. of the Diagnostic and Statistical Manual of 

Mental Disorders (DSM-IV) (APA, 1994).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999 and 2000.

Table A.5 Treatment Gap for Persons Aged 12 or Older: 1991 to 1998 and 2000

Old Method

New Metho

d

1

1991 1992 1993 1994 1995 1996 1997 1998 2000

Needs Treatment (in Thousands) 5,14

8

4,71

6

4,74

1

4,61

0

4,64

6

5,30

3

5,72

6

5,03

1

4,655

Received Treatment (in Thousan

ds)

1,64

9

1,81

4

1,84

8

1,98

4

2,12

1

1,97

3

2,13

7

2,13

7

774

% Treated232% 38% 39% 43% 46% 37% 37% 43% 16.6%

% Not Treated268% 62% 61% 57% 54% 63% 63% 57% 83.4%

Treatment Gap (in Thousands) 3,49

9

2,90

4

2,89

3

2,62

6

2,52

5

3,33

0

3,58

9

2,89

4

3,881

NOTE: Because of changes in the NHSDA and in the methodology, no treatment gap numbers are included for 

1999. The "treatment gap" consists of those persons who needed treatment for an illicit drug problem but did not 

receive treatment. "Needs treatment" refers to level 2 treatment need. "Received treatment" refers to treatment 

received for drug abuse at a specialty facility (hospital [as an inpatient], mental health center, or drug treatment 

facility).
1 Corresponds to the 2000 definition of "treatment need," not the definition of level 2 treatment need.

2

"% treated" and the "% not treated" are among those persons who need treatment in the past year.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1991-1998 and 2000.

Table A.6 Comparison of Treatment Need Estimates from the 1999 and 2000 NHSDAs for 

Percentages of Persons Aged 12 or Older

1999 NHSDA (Without Ratio Adjustm

ent)

2000 

NHSDA

Demographic Characteristic

Total Treatment 

Need1

Level 2 Treatment 

Need1

Treatme

nt Need2

Total 3.5 1.5 2.1

Age in Years

12-17 6.6 2.1 4.6

18-25 8.5 3.5 5.7

26 or older 2.2 1.1 1.1

Gender

Male 4.4 2.0 2.6

Female 2.6 1.1 1.6

Hispanic Origin and Race

Not Hispanic

White only 3.1 1.4 2.0

Black only 4.9 2.1 2.5

American Indian or Alaska Native

only 9.2 3.9 4.3

Native Hawaiian or other Pacific 

Islander 0.8 * 1.8
Asian only 2.2 0.5 0.7

More than one race 6.3 3.0 5.5

Hispanic 4.2 1.8 2.4

*Low precision; no estimate reported.

1 Respondents were classified as needing treatment for illicit drug abuse if they met at least one of four criteria 

during the past year: (1) dependent on any illicit drug; (2) used marijuana daily, or used some other illicit drug on at 

least 52 days; (3) were injection drug users or used heroin; or (4) received any treatment for drug abuse. 

Respondents were classified with level 2 drug abuse treatment need if they met at least one of the following five 

criteria in the past year: (1) used marijuana daily and were dependent on marijuana; (2) were dependent on an illicit 

drug other than marijuana; (3) used cocaine on 52 or more days, or used inhalants, hallucinogens, pain relievers, 

tranquilizers, sedatives, or stimulants daily; (4) were injection drug users or used heroin; or (5) received treatment 

for drug abuse at a specialty facility (i.e., a hospital as an inpatient, a mental health center, or a drug abuse facility).

2

For the 2000 definition of "needing treatment," respondents were classified as needing treatment for illicit drug 

abuse if they met at least one of three criteria during the past year: (1) dependent on any illicit drug; (2) abuse of any 

illicit drug; or (3) received treatment for drug abuse at a specialty facility (i.e., drug and alcohol rehabilitation 

facilities [inpatient or outpatient], hospitals [inpatient only], and mental health centers).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 1999 and 2000.

Table A.7 Comparison of Treatment Need Estimates Using the 1999 Definition of Level 2 

Treatment Need and the 2000 Definition of Treatment Need on the 2000 NHSDA's 

Estimated Numbers of Persons Aged 12 or Older

1999 Definition of Treatment

Need (Level 2)1

2000 Definition of Treatment

Need2

Demographic 

Characteristic

Number

(in

Thousands) Percent

Number

(in

Thousands) Percent

Total 2,677 1.2 4,655 2.1

Age in Years

12-17 424 1.8 1,074 4.6

18-25 769 2.7 1,645 5.7

26-34 504 1.5 730 2.2

35 or older 981 0.7 1,207 0.9

Gender

Male 1,554 1.4 2,749 2.6

Female 1,123 1.0 1,907 1.6

Hispanic Origin and Race

Not Hispanic
White only 1,907 1.2 3,235 2.0

Black only 353 1.4 632 2.5

American Indian or 

Alaska Native only 37 3.4 46 4.3

Native Hawaiian or 

other Pacific Islander 10 1.8 10 1.8

Asian only 28 0.4 54 0.7

More than one race 43 2.3 103 5.5

Hispanic 299 1.3 574 2.4

1 Respondents were classified with level 2 drug abuse treatment need if they met at least one of the following five 

criteria in the past year: (1) used marijuana daily and were dependent on marijuana; (2) were dependent on an illicit 

drug other than marijuana; (3) used cocaine on 52 or more days, or used inhalants, hallucinogens, pain relievers, 

tranquilizers, sedatives, or stimulants daily; (4) were injection drug users or used heroin; or (5) received treatment 

for drug abuse at a specialty facility (i.e., a hospital as an inpatient, a mental health center, or a drug abuse facility).

2

For the 2000 Definition of "needing treatment," respondents were classified as needing treatment for illicit drug 

abuse if they met at least one of three criteria during the past year: (1) dependent on any illicit drug; (2) abuse of any 

illicit drug; or (3) received treatment for drug abuse at a specialty facility (i.e., drug and alcohol rehabilitation 

facilities [inpatient or outpatient], hospitals [inpatient only], and mental health centers).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Table A.8 Percentages Reporting Needing Treatment (1999 Definition - Level 2), Needing 

Treatment (2000 Definition), Abuse Only, and Dependence Only for Any Illicit Drug in the 

Past Year among Persons Aged 12 or Older: 2000

Treatment Need

Drug Use-Related Activities

Size of 

Population

(in 

Thousands

)

1999 

Definitio

n (Level 

2)

2000 

Definitio

n

Abus

e 

Only 

(2000

)

Dependenc

e Only

Used an illicit drug weekly or more ofte

n 3,644 41.2 39.1 9.6 28.3

Used heroin at least once 308 100.0 80.4 6.9 68.6

Used a needle to inject heroin, cocaine 

or stimulants 322 100.0 81.4 3.2 77.6

Used marijuana daily 998 34.6 35.6 9.2 26.0

Used cocaine weekly or more often 792 100.0 58.5 8.9 48.8

Met two or more DSM-IV dependence 

criteria 6,903 28.1 52.4 12.5 39.0
Used cocaine weekly with two or more 

dependence criteria 525 100.0 84.0 10.1 73.6

Had treatment for a drug problem at a 

hospital, treatment center, or mental 

health center 774 100.0 100.0 9.5 45.7

Had any type of treatment 1,268 67.2 73.0 9.5 36.2

Used inhalants weekly 354 36.7 50.5 12.3 37.7

Used psychotherapeutic weekly 2,642 31.9 37.0 10.2 25.6

With dependence or abuse 4,308 42.1 100.0 35.7 64.3

1999 definition (level 2) 2,677 100.0 80.7 5.9 61.8

NOTE: Dependence is based on the definition found in the 4th ed. of the Diagnostic and Statistical Manual of 

Mental Disorders (DSM-IV) (APA, 1994).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Appendix B: State Estimation Methodology

B.1 Background

In response to the need for State-level information on substance abuse problems, the Substance 

Abuse and Mental Health Services Administration (SAMHSA) began developing and testing 

small area estimation (SAE) methods for the National Household Survey on Drug Abuse 

(NHSDA) in 1994 under a contract with RTI of Research Triangle Park, North Carolina. That 

developmental work used logistic regression models with data from the combined 1991 to 1993 

NHSDAs and local area indicators, such as drug-related arrests, alcohol-related death rates, and 

block group/tract-level characteristics from the 1990 Census that were found to be associated 

with substance abuse. In 1996, the results were published for 25 States for which there were 

sufficient sample data (Office of Applied Studies [OAS], 1996). A subsequent report described 

the methodology in detail and noted areas in which improvements were needed (Folsom & 

Judkins, 1997).

The increasing need for State-level estimates of substance use led to the decision to expand the 

NHSDA to provide estimates for all 50 States and the District of Columbia on an annual basis 

beginning in 1999. It was determined that, with the use of modeling similar to that used with the 

1991 to 1993 NHSDA data in conjunction with a sample designed for State-level estimation, a 

sample of about 67,500 persons would be sufficient to make reasonably precise estimates.

The State-based NHSDA sample design implemented in 1999 had the following characteristics:

States are stratified into field interviewer (FI) regions that covered the geography of each 

State. The FI regions are comprised of contiguous Census tracts and counties and 

designed to yield about 75 interviews per region. In the 42 smaller States (by population) 
and the District of Columbia, there are 12 FI regions; in the eight largest States, there are 

48 FI regions.

Within each region, eight segments are randomly selected and two are allocated to each 

calendar quarter of data collection.

Within each segment, households are screened, and a sample of one to two persons per 

household is selected. An average of nine responding persons per segment is sought.

The samples are selected so that approximately 900 responding persons, 300 in each age 

group (12 to 17, 18 to 25, and 26 or older), are drawn in each of the 42 States and the 

District of Columbia. In the eight large States, the person samples are allocated equally to 

the three age groups with overall respondent sample sizes ranging from 2,669 to 4,681.

Tables B.1, B.2, and B.3 present, respectively, the achieved response rates, the survey 

population sizes (by State and age group), and the associated samples sizes.

In preparation for the modeling of the 1999 data, RTI used the data from the combined 1994-96 

NHSDAs to develop an improved methodology that utilized more local area data and produced 

better estimates of the accuracy of the State estimates (Folsom, Shah, & Vaish, 1999). That effort 

involved the development of procedures that would validate the results for geographic areas with 

large samples. This work was reviewed by a panel with expertise in small area estimation.3They 

approved of the methodology, but suggested further improvements for the modeling to be used to 

produce the 1999 State estimates. Those improvements have been incorporated into the 

methodology finally used for the State estimates included in this report. The methodology, called 

Survey-Weighted Hierarchical Bayes Estimation (HB), is described below.

B.2 Goals of Modeling 

There were several goals underlying the estimation process. The first was to model substance 

use-related rates at the lowest possible level and aggregate over the levels to form the State 

estimates. The chosen level of aggregation was the 32 age group (12 to 17, 18 to 25, 26 to 34, 

35+) by race/ethnicity (white-not Hispanic, black-not Hispanic, Hispanic, other) by gender cells 

at the block group level. Estimated population counts are obtained from a private vendor for each 

block group for each of the 32 cells. This level of aggregation was desired because the NHSDA 

first stage of sample selection was at the block group level, so that there would be data at this 

level to fit a model. In addition, there was a great deal of information from the Census at the 

block group level that could be used as predictors in the models. If substance use-related rates 

could be estimated for each of the 32 cells at the block group level, it would only be necessary to 

multiply by the estimated population counts and aggregate to the State level. 

Another goal of the estimation process was to include the sampling weight in the model in such a 

way that the small area estimates would converge to the design-based (sample-weighted) 

estimate when they are aggregated to a sufficient sample size. There was a desire for the 

estimates to have this characteristic so that there would be consistency with the survey-weighted 

national estimates based on the entire sample.

A third goal was to include as much local source data as possible, especially data related to each 

substance use measure. This would help provide a better fit beyond the strictly sociodemographic 
information. The desire was to use national sources of these data so that there would be 

consistency of collection and estimation methodology across States.

Recognizing that estimates based solely on these "fixed" effects would not reflect differences 

across States due to differences in laws, enforcement activities, advertising campaigns, outreach 

activities, and other such unique State contributions, a fourth goal was to include "random" 

effects to compensate for these differences. The types of random effects that could be supported 

by NHSDA data were a function of the size of sample and the model fit to the sample data. For 

the 1999 survey, random effects were included at the State level and for substate regions 

comprised of three (typically neighboring) FI regions. Although this grouping of the three FI 

regions was principally motivated by the need to accumulate enough sample to support good 

model fitting for the low prevalence NHSDA outcomes, it was also reasoned that it would be 

possible to produce substate HB estimates for areas comprised of these FI region groups, once 2 

or 3 years of NHSDA data were available, because that would yield substate region samples of at 

least 400 respondents. For substate areas like counties and large municipalities that do not 

conform to the substate region boundaries, HB estimates could be derived from their elemental 

block group-level contributions, but the direct survey data employed in the estimation of the 

associated substate region effects would not be restricted to the county or city of interest. This 

mismatch of FI region and county/large municipality boundaries weakens the theoretical appeal 

of the associated HB estimate. For this reason, substate HB estimates probably should be 

restricted to areas that can be matched reasonably well to FI region groups.

One of the difficulties of typical SAE has been obtaining good estimates of the accuracy of the 

estimates with prediction intervals that give a good representation of the true probability of 

coverage of the intervals. Therefore, the final major goal was to provide accurate prediction 

intervals—ones that would approach the usual sample-based intervals as the sample size 

increases.

B.3 Predictors Used in Logistic Regression Models

Local area data used as potential predictor variables in the logistic regression models were 

obtained from several sources, including Claritas, the Census Bureau, the FBI (Uniform Crime 

Reports), Health Resources and Services Administration (Area Resource File), SAMHSA 

(Uniform Facility Data Set), and the National Center for Health Statistics (mortality data). The 

list of sources and the actual variables that were selected as independent predictors for each age 

group for the estimation of the treatment gap are provided below.

B.3.1 Sources of Data

Claritas: Demographic data package called Building Block Basic, Age by Race from 

Claritas for 1999 with projections to 2004; the estimates for 2000 population counts were 

used. 

Census Bureau: 1990 Census, demographic and socioeconomic variables; July 1997 

Food Stamp participation rates. 
Federal Bureau of Investigation: Uniform Crime Report (UCR), UCR arrest totals from 

http://fisher.lib.Virginia.EDU/crime/ (the most current data are for 1997 for most 

counties, and previous years' data were used in a few cases). 

Health Resources and Services Administration: Area Resource File (ARF), some 

variables relating to income and employment from the ARF February 2000 release from 

the Bureau of Health Professions, Office of Research and Planning. 

National Center for Health Statistics: Mortality data using International Classification of 

Diseases, 9th revision (ICD-9), 1993 to 1998; ICD-9 death rate data from the Centers for 

Disease Control and Prevention at the National Center for Health Statistics. 

SAMHSA, Office of Applied Studies: Uniform Facility Data Set (UFDS), 1997 to 1998 

UFDS data on drug and alcohol treatment rates from Synectics for Management 

Decisions, Inc. 

B.3.2 Predictor Variables in Final Model, by Age Group

Age Group 1 (Ages 12 to 17)

2001-2003 State cost of services index, 

gender, 

Hispanic, 

non-Hispanic black, 

non-Hispanic other (non-white, non-black), 

linear effect for males of the county opium/cocaine possession arrest rate, 

linear effect for non-Hispanic others of the percentage of the housing units in tract built 

in 1939 or earlier, 

linear effect for the Northeast region of the percentage of the tract population with some 

college and no degree, 

linear effect for the Southern region of the percentage of the block group population aged 

55 to 64, 

linear effect for non-Hispanic blacks of the percentage of the block group population 

aged 19 to 24, 

linear effect for non-Hispanic others of the percentage of the county population aged 25 

to 34, 

linear effect for Hispanics of the percentage of the county population who are black, 

linear effect for non-Hispanic others of the percentage of the county population who are 

black, and 

linear effect for the Northeast region of the percentage of the county population who are 

black. 

Age Group 2 (Ages 18 to 25)

quadratic effect of the percentage of the county population aged 19 to 24, 

quadratic effect of the percentage of the housing units that are rented in the tract, 

gender, 

Hispanic, 

non-Hispanic black, 
non-Hispanic other (non-white, non-black), 

linear effect for the Southern region of the percentage of families in the county below 

poverty level, 

linear effect for males of the percentage of the females in the tract older than 16 years old 

in the labor force, 

linear effect for males of the percentage of the tract population with an associate's degree, 

linear effect for the Northeast region of the percentage of the population in the block 

group aged 65 or older, 

linear effect for the Northeast region of the percentage of the population in the tract that 

is female, and 

resident in a county where the per capita income is in the 4th or 5th decile and the food 

stamp participation rate is in one of the lowest three deciles. 

Age Group 3 (Ages 26 to 34)

quadratic effect of the percentage of the persons in the tract aged 16 to 64 with a work 

disability, 

gender, 

Hispanic, 

non-Hispanic black, and 

non-Hispanic other. 

Age Group 4 (Ages 35 or Older)

linear effect of the percentage of the population in the block group aged 35 to 44, 

gender, 

Hispanic, 

non-Hispanic black, 

non-Hispanic other, and 

residence in a tract where the percentage of the population who are white is in one of the 

six lowest deciles, and the percentage of the population who are American Indian, 

Eskimo, or Aleut is in the 7th decile or higher and the county is a metropolitan statistical 

area (MSA) with 1 million or more people. 

B.4 Method of Selecting Independent Variables for the Models

For the 1999 SAE exercise, independent variables for modeling each of the substance use 

measures were first identified by a CHAID (Chi-squared Automatic Interaction Detector) 

algorithm. CHAID does not use sample weights. Prior to this process, all the continuous 

variables were categorized using deciles and were treated as ordinal in CHAID. Region was 

treated as a nominal categorical variables in CHAID. Significant independent variables from 

each model that were final nodes in the tree-growing process were identified as indicator 

variables destined for inclusion at a later step.

Independently, a SAS stepwise logistic regression model was fit for each dependent variable by 

age group. The SAS stepwise was used because it was able to quickly run all of the variables for 
all of the models, although it was recognized that the software would not take into account the 

complex sample design and the weights. The independent variables included all the first-order or 

linear polynomial trend contrasts across the 10 levels of the categorized variables, as well as the 

gender, region, and race variables. Significant variables (at the 3 percent level) were identified 

from this process. Based on this list, another list of variables was created that included the 

second- and third-order polynomials and the interaction of the first-order polynomials with the 

gender, race, and region variables.

Next, the variables from the CHAID process and the SAS process were entered into a SAS 

stepwise logistic model at the 1 percent significance level. Because of past concerns about 

overfitting of the data in earlier estimation using the 1991 to 1993 NHSDA data, the significance 

levels were made quite stringent. These variables were then entered into a SUDAAN logistic 

regression model because the SUDAAN software would adjust for the effects of the weights and 

other aspects of the complex sample design. All variables that were still significant at the 1 

percent significance level were entered into the survey-weighted HB process.

Independently, a factor-analytic approach was used to determine the important variables to 

include in the model. This approach would allow the data to self-identify the important 

dimensions. The concern here was to use an alternative method that would have a certain face 

validity. That method was utilized to identify an independent set of variables that were then 

processed through the HB estimation. The results, however, in terms of model-fit and prediction 

intervals were generally not as good as with the CHAID/SAS/SUDAAN screening process for 

candidate independent variables. Also, the factor-analytic approach involved an inherently 

subjective step to attribute names to the various factor loadings, and the interest was more in the 

predictive ability of variables than in a substantive description of the dimensions. Nevertheless, it 

was encouraging to see that the results of the two approaches gave reasonably similar results. For 

these reasons, the estimates in this report were those based on the latter method that started with 

the CHAID process.

To select variables for the 2000 treatment gap model, an alternative to the 1999 approach was 

also implemented. This alternative, designed to further reduce the risk of overfitting, involved 

splitting the 2000 sample into two halves with the 7,200 sample area segments (block clusters) 

used as sampling units for the splitting. One of those half-samples was designated the training 

sample, and its complement was assigned the role of validation sample. The 1999 variable 

selection strategy was then applied to the training sample with a less stringent 10 percent 

significance level for retaining variables. Note that with a sample size one-half as large, the 

training sample would yield standard errors for the logistic regression coefficients that were 

expected to be inflated by a factor of 1.4. Therefore, a training sample significance level of 7 

percent would be expected to yield a significance level of 1 percent in the full sample. The 10 

percent level was chosen for the training sample after trying several alternatives. Once the 

variables were chosen using the training sample, the model was refit on the validation sample 

and variables that were not significant at the 10 percent level were dropped. The two alternative 

models resulting from the 1999 variable selection method and the new 2000 alternative were 

both subjected to the internal benchmarking validation exercise described later in this appendix 

(Section B.7). The new method produced small area estimates that were noticeably less biased 
for the 26 or older age groups and the 12 or older age groups. Based on this result, the alternative 

set of predictor variables was chosen.

B.5 General Model Description

The model can be characterized as a complex mixed model (including both fixed and random 

effects) of the form:

=X + ZU

Each of the symbols represents a matrix or vector. The leading term X is the usual (fixed) 

regression contribution, and ZU represents random effects for the States and FI region groups 

that the data will support and for which estimates are desired. Not obvious from the notation is 

that the form of the model is a logistic model used to estimate dichotomous data. The vector 

has elements ln[ ijk /(1- ijk)], where the ijk is the propensity for the kth person in the jth FI 

composite region in the ith State to engage in the behavior of interest (e.g., to use marijuana in the 

past month). Also not obvious from the notation is that the model fitting utilizes the final 

"sample" weights as discussed above. The "sample" weights have been adjusted for nonresponse 

and poststratified to known Census counts.

The estimate for each State behaves like a "weighted" average of the direct survey estimate in 

that State and the predicted value based on the national regression model. The "weights" in this 

case are functions of the relative precision of the sample-based estimate for the State and the 

predicted estimate based on the national model. The eight large States have large samples, and 

thus more "weight" is given to the sample estimate relative to the model-based regression 

estimate. The 42 small States and the District of Columbia put relatively more "weight" on the 

regression estimate because of their smaller samples. The national regression estimate actually 

uses national parameters that are based on the full sample of approximately 72,000 persons; 

however, the regression estimate for a specific State is based on applying the national regression 

parameters to that State's "local" county, block group, and tract-level predictor variables and 

summing to the State level. Therefore, even the national regression component of the estimate 

for a State includes "local" State data. 

The goal then was to come up with the best estimates of and U. This would lead to the best 

estimates of , which would in turn lead to the best estimate of . Once the best estimate of for 

each block group and each age/race/gender cell within a block group has been estimated, the 

results could be weighted by the projected Census population counts at that level to make 

estimates for any geographic area larger than a block group.

B.6 Implementation of Modeling

The solution to the equation for in the above section is not straightforward but involves a series 

of iterative steps to generate values of the desired fixed and random effects from the underlying 

joint distribution. The details of the technique will be described in more detail in a 

methodological report currently in progress. In the interim, the basic process can be described as 

follows.
Let denote the matrix of fixed effects, be the matrix of State random effects i = 1-51, and v

denote the matrix of FI composite region effects j within State i. Because the goal is to estimate 

separate models for four age groups, it is assumed that the random effect vectors are four variate 

Normal with null mean vectors and 4X4 covariance matrices D and Dv, respectively. To 

estimate the individual effects, a Bayesian approach is used to represent the joint density 

function given the data by f( , , v, Dv, D | y). According to the Bayes process, this can be 

estimated once the conditional distributions are known: 

f1( | , v, Dv, D , y), f2(Dv, D | , v, y), and f3( , v | , Dv, D , y).

To generate random draws from these distributions, Markov Chain Monte Carlo (MCMC) 

processes need to be used. These are a body of methods for generating pseudo-random draws 

from probability distributions via Markov chains. A Markov chain is fully specified by its 

starting distribution P(X0) and the transition kernel P(Xt |Xt-1).

Each MCMC step that involves the vector of binary outcome variables y in the conditioning set 

needs first to be modified by defining a pseudo-likelihood using survey weights. In defining 

pseudo-likelihood, weights are introduced after scaling them to the effective sample size based 

on a suitable design effect. Note that with the pseudo-likelihood, the covariance matrix of the 

pseudo-score functions is no longer equal to the pseudo-information matrix; therefore, a 

sandwich-type of covariance matrix was used to compute the design effect. In this process, 

weights are largely assumed to be noninformative (i.e., unrelated to the outcome variable y). The 

assumption of noninformative weights is useful in finding tractable expressions for the 

appropriate information matrix of the pseudo-score functions. The pseudo-log-likelihood remains 

an unbiased estimate of the finite-population log-likelihood regardless of this assumption.

Step I [ a | , v, y] (note that this does not depend oin D , Dv)

With flat prior for a, the conditional posterior is proportional to the pseudo-likelihood function. 

For large samples, this posterior can be approximated by the multivariate Normal distribution 

with mean vector equal to the pseudo-maximum likelihood estimate and with asymptotic 

covariance matrix having the associated sandwich form. Assuming that the survey weights are 

noninformative makes the age group specific a vectors conditionally independent of each other. 

Therefore, the a can be updated separately at each MCMC cycle. 

Step II [ i| , vi, D , y] (this does not depend on Dv)

Here, the conditional posterior is proportional to the product of the prior g( i|.), the pseudolikelihood function f(y|.) as well as the prior p( ,D ); this last prior can be omitted as it does not 

involve i. Calculating the denominator (or the normalization constant) of the posterior 

distribution for i requires multidimensional integration and is numerically intractable. To get 

around this problem, the Metropolis-Hastings (M-H) algorithm is used that requires a dominating 

density convenient for Monte Carlo sampling. For this purpose, the mode and curvature of the 

conditional posterior distribution are used; these can be simply obtained from its numerator. 

Then a Gaussian distribution is used with matching mode and curvature to define the dominating 

density for M-H. As with the age group specific a parameters, the State-specific random effect 
vectors i are conditionally independent of each other and can be updated separately at each 

MCMC cycle.

Step III [vij | , i, Dv, y] (this does not depend on D )

Similar to step II.

Step IV [D | ] , [Dv | v] (here, and v include all the information from y)

Here, the pseudo-likelihood involving design weights comes in implicitly through the 

conditioning parameters and v evaluated at the current cycle. An exact conditional posterior 

distribution is obtained because the inverse Wishart priors for D and Dv are conjugate.

Remarks

In the NHSDA application, three FI regions were combined to form a minimum of four 

substate regions with corresponding random effects. This was done to ensure adequate 

sample sizes for estimation purposes. 

There is self-calibration built in to the modeling. This is achieved via design effect 

scaling of survey weights incorporated in the conditional posterior density so that small 

area estimates for large States become asymptotically equivalent to the direct estimates. 

Similarly, survey-weighted estimates of the fixed parameters (in particular, the intercept) 

give calibration of the aggregate of small area estimates to the national direct estimate. 

For posterior variance estimation purposes, the survey weights were largely assumed to 

be noninformative. The survey design effects on the posterior variance are therefore 

restricted to unequal weighting effects. It was assumed that all the design-related 

clustering effects are represented by between-State and between-substate (within State) 

variability of random effects. This does not take care of variability at lower levels of 

clustering. However, sample size is not sufficient at lower levels to support stable 

estimates of random effects for area segments. 

If the logistic mixed model fits well, the variance estimates should be reasonable. The 

self-calibration property provides some protection against model breakdown. Research is 

currently under way to develop a new MCMC algorithm that fully accounts for survey 

design effects on the small area estimate posterior prediction intervals. 

B.7 Validation and Other Results

The following validation methodology was implemented at the time of the estimation of the 2000 

percentage treatment gap and is specific to this measure. Validation was also conducted earlier at 

the time of the first release of the 1999 NHSDA data (OAS, 2000) and was based on the seven 

variables discussed in that report. Subsequently, an error in the imputation program was 

discovered and corrected, and the corrected file was used for the validation of the treatment gap 

estimation. Further information about the impact of the error on the previously released data 

from the 1999 NHSDA is provided in the 2000 Summary of Findings (OAS, 2001).
To validate the fit of the SAE models, the eight large sample States were used as internal 

benchmarks. For this purpose, 6 pseudo FI regions within each large sample State were created 

by pooling the 48 initial regions into 6 groups of 8. Each of these 6 pseudo-FI regions were then 

expected to have 16 area segments per calendar quarter. For each of these pseudo FI region-byquarter sets of 16 area segments, any segments devoid of interviews were first randomly replaced 

by a selection from the non-empty segments in the set. The completed set of 16 segments from 

each pseudo-FI region-by-quarter combination was then randomly partitioned into 8 replicates of 

2 segments each. When combined, each pair of large sample States had 12 pseudo-FI regions. By 

pooling one segment pair from each of the 48 pseudo-FI region-by-quarter combinations, 8 

substate replicates were formed. Each of these 8 substate replicates mimicked the size and design 

structure of a small sample State.

Having created 8 pseudo-small State samples and associated universe-level files for each of the 4 

paired States, SAEs were then produced for the 32 pseudo-States. Table B.4 shows these 32 

substate SAEs and their direct survey-weighted analogs for the percentage treatment gap.4

Relative absolute biases of the substate estimates are shown where the full State sample direct 

estimate is used as the benchmark value.

The State-specific relative absolute bias (RB) quantities in Table B.4 equal the absolute 

differences of the averaged eight substate small area estimates and the State full sample designbased benchmark (e.g., California and Texas) divided by the benchmark. The average relative 

absolute bias (ARB) is the simple average across the four combined-State pairs of the RBs. The 

average relative bias across the 32 pseudo-States was only about 4 percent. This implies, on 

average, for a pseudo-State (similar in design and sample size to the 42 small States and the 

District of Columbia) with an estimated 2 percent treatment gap that the true value in the 

population is within 0.08 percent.

To compare the overall precision of the small area estimates with the direct survey estimates, 

ratios of the corresponding 95 percent Bayes prediction intervals, which fully account for the 

posterior variance of the fixed and random effect parameters, were compared with the 

corresponding direct survey 95 percent confidence intervals. These results are displayed in Table 

B.5.

The SAE and direct intervals are summarized by showing average ratios of the relative interval 

widths (the interval width for a State divided by the corresponding estimate for that State) by 

State and overall averages of the ratios across States. The average relative width across the 32 

pseudo-States is about 1.80. This indicates generally that the confidence intervals for direct 

design-based estimates based on the same sample size would be 1.8 times larger than the 

prediction intervals resulting from the HB approach. The HB estimates are equivalent in 

precision to a direct estimate based on a sample that is 3.3 times larger. The tables also present 

the average relative root mean square (RMSQ), a measure that takes into consideration both the 

(small) bias and the variance in the HB estimation.

B.8 Caveats
Table B.1 shows the screening, interview, and overall response rate for each State and the 

District of Columbia. As mentioned in the text, these variable response rates can be associated 

with variable levels of nonresponse bias. In addition, there may be varying levels of response

bias as a result of underreporting (and sometimes overreporting) use of illicit substances. For 

1999 and 2000, the assumption being made is that the biases from these two sources are constant 

across States so that comparisons among States still hold. 

Another possible contributor to bias in the State estimates, and the estimates in general, was the 

effect of editing and imputation. In developing the editing and imputation process, the desire was 

to minimize the amount of editing, typically somewhat subjective, and instead let the random 

imputation process supply any partially missing information. Overall, the percentage of imputed 

information was quite small for most substances. For example, respondents may have indicated 

that they used the drug in their lifetime or in the past year, but left blank the question about use 

in the past month. The method is based on a multivariate imputation in which some demographic 

and other substance use information from the respondent is used to determine a donor who is 

similar in those characteristics but has supplied data for the drug in question. Often, information 

was also available from the partial respondent on the recency of drug use. For many of the 

records, this auxiliary information was available. For a small portion, no auxiliary information 

was available, in which case a random donor with similar drug use patterns and demographic 

characteristics was used.

Table B.1 2000 NHSDA Weighted CAI Screening and Interview Response Rates, by State

State

Screenin

g 

Response 

Rate

Intervie

w 

Respons

e Rate

Overall 

Respons

e Rate State

Screenin

g 

Response 

Rate

Intervie

w 

Respons

e Rate

Overall 

Respons

e Rate

Total 92.84 73.93 68.64 Missouri 92.25 70.80 65.31

Alabama 95.50 77.98 74.47 Montana 94.91 80.21 76.13

Alaska 95.43 80.24 76.58 Nebraska 93.13 74.58 69.46

Arizona 92.99 73.78 68.61 Nevada 92.08 74.44 68.54

Arkansas 97.19 81.00 78.73 New 

Hampshire

92.41 75.12 69.42

California 90.99 69.50 63.24 New Jersey 91.96 66.56 61.21

Colorado 94.84 75.26 71.37 New 

Mexico

97.43 80.80 78.72

Connecticut 89.83 71.36 64.10 New York 88.78 73.73 65.46

Delaware 92.91 68.25 63.42 North 

Carolina

94.51 73.19 69.17

District of Columbi

a

93.50 85.56 80.00 North 

Dakota

94.43 79.46 75.03
Florida 94.64 75.73 71.67 Ohio 94.89 75.79 71.92

Georgia 92.95 69.76 64.84 Oklahoma 93.06 74.85 69.66

Hawaii 91.95 78.45 72.14 Oregon 91.87 73.91 67.90

Idaho 93.94 74.45 69.94 Pennsylvani

a

94.37 73.50 69.36

Illinois 88.71 65.59 58.19 Rhode 

Island

91.26 74.11 67.63

Indiana 92.62 73.87 68.42 South 

Carolina

94.69 77.84 73.71

Iowa 94.78 80.00 75.83 South 

Dakota

95.15 76.67 72.95

Kansas 92.28 73.45 67.79 Tennessee 90.25 72.45 65.39

Kentucky 95.79 84.14 80.59 Texas 94.72 78.12 74.00

Louisiana 95.04 80.81 76.80 Utah 95.11 83.44 79.36

Maine 92.39 78.46 72.49 Vermont 92.62 80.80 74.83

Maryland 94.88 76.88 72.94 Virginia 91.44 75.18 68.75

Massachusetts 89.77 66.45 59.65 Washington 93.59 75.45 70.61

Michigan 93.19 73.18 68.20 West 

Virginia

95.19 78.17 74.41

Minnesota 94.66 80.62 76.32 Wisconsin 94.33 75.06 70.81

Mississippi 93.60 79.14 74.07 Wyoming 95.41 76.61 73.09

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Table B.2 Estimated Numbers (in Thousands) of Persons Aged 12 or Older, by Age Group 

and State: 2000

State Total

Age Group (Years)

12-17 18-25 26 or Older

Total 223,280 23,368 28,984 170,927

Alabama 3,654 371 476 2,807

Alaska 491 63 71 357

Arizona 3,866 434 516 2,916

Arkansas 2,159 225 279 1,655

California 25,736 2,851 3,513 19,371

Colorado 3,411 358 452 2,601

Connecticut 2,701 257 308 2,136
Delaware 630 65 79 487

District of Columbia 424 44 58 321

Florida 12,693 1,178 1,368 10,147

Georgia 6,354 680 863 4,811

Hawaii 975 95 115 764

Idaho 1,083 130 165 789

Illinois 9,768 998 1,306 7,465

Indiana 4,949 512 665 3,772

Iowa 2,390 249 319 1,822

Kansas 2,155 240 293 1,622

Kentucky 3,287 329 435 2,524

Louisiana 3,561 418 519 2,624

Maine 1,047 103 122 822

Maryland 4,281 421 510 3,349

Massachusetts 5,119 504 611 4,004

Michigan 7,918 832 1,032 6,053

Minnesota 3,954 431 539 2,985

Mississippi 2,270 259 323 1,688

Missouri 4,534 476 596 3,462

Montana 776 85 100 591

Nebraska 1,376 154 189 1,032

Nevada 1,544 146 184 1,214

New Hampshire 1,007 105 120 782

New Jersey 6,717 629 783 5,305

New Mexico 1,490 174 211 1,105

New York 14,782 1,476 1,825 11,480

North Carolina 6,365 651 777 4,936

North Dakota 535 62 77 396

Ohio 9,292 951 1,212 7,129

Oklahoma 2,744 306 367 2,072

Oregon 2,827 276 355 2,197

Pennsylvania 10,117 988 1,186 7,943

Rhode Island 821 84 95 642

South Carolina 3,130 326 386 2,418

South Dakota 619 73 88 458
Tennessee 4,657 464 598 3,595

Texas 16,057 1,877 2,368 11,813

Utah 1,715 248 326 1,142

Vermont 512 55 63 394

Virginia 5,648 563 691 4,395

Washington 4,784 487 606 3,691

West Virginia 1,553 141 195 1,216

Wisconsin 4,376 476 590 3,310

Wyoming 425 49 61 315

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Table B.3 Survey Sample Size for Persons Aged 12 or Older, by Age Group and State: 2000

State Total

Age Group (Years)

12-17 18-25 26 or Older

Total 71,764 25,717 22,613 23,434

Alabama 936 294 337 305

Alaska 833 294 257 282

Arizona 927 292 303 332

Arkansas 960 310 364 286

California 5,022 2,365 1,354 1,303

Colorado 911 278 298 335

Connecticut 891 299 262 330

Delaware 928 321 297 310

District of Columbia 918 259 340 319

Florida 3,478 1,194 1,140 1,144

Georgia 1,145 520 330 295

Hawaii 945 309 307 329

Idaho 894 311 283 300

Illinois 3,660 1,262 1,128 1,270

Indiana 1,061 405 353 303

Iowa 921 284 324 313

Kansas 897 291 323 283

Kentucky 1,018 341 345 332
Louisiana 939 356 278 305

Maine 901 321 234 346

Maryland 967 332 317 318

Massachusetts 1,002 378 298 326

Michigan 3,576 1,234 1,090 1,252

Minnesota 893 297 306 290

Mississippi 917 309 320 288

Missouri 893 314 302 277

Montana 914 276 334 304

Nebraska 906 311 291 304

Nevada 925 305 284 336

New Hampshire 883 280 246 357

New Jersey 1,200 553 289 358

New Mexico 874 315 267 292

New York 3,589 1,160 1,142 1,287

North Carolina 1,043 418 326 299

North Dakota 896 288 320 288

Ohio 3,678 1,227 1,215 1,236

Oklahoma 973 303 374 296

Oregon 864 288 275 301

Pennsylvania 3,997 1,474 1,195 1,328

Rhode Island 950 293 324 333

South Carolina 855 275 269 311

South Dakota 855 289 272 294

Tennessee 947 367 285 295

Texas 4,020 1,498 1,307 1,215

Utah 1,031 362 372 297

Vermont 981 344 320 317

Virginia 1,047 437 274 336

Washington 1,006 408 289 309

West Virginia 950 322 286 342

Wisconsin 1,119 453 312 354

Wyoming 828 301 255 272

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.
Table B.4 Simulated Substate Prevalence Rates, Relative Absolute Bias, and Root Mean 

Square for Persons Needing But Not Receiving Treatment for an Illicit Drug Problem in 

the Past Year: 2000

Needing But Not Receiving Treatment for an Illicit Drug Problem

Total 12-17 18-25 26 or Older

California and Texas 

SAE

2.18 5.30 4.79 1.21

California and Texas 

DBE

2.01 5.34 4.95 0.95

CA_TX1 2.25 4.69 4.20 1.51

CA_TX2 2.27 6.24 5.17 1.12

CA_TX3 2.34 5.97 5.09 1.27

CA_TX4 2.59 5.37 5.71 1.59

CA_TX5 2.13 5.11 4.93 1.16

CA_TX6 2.12 4.98 4.69 1.21

CA_TX7 1.96 4.36 4.43 1.13

CA_TX8 2.09 5.28 4.20 1.21

RMSQ 13.75 11.03 10.36 38.26

REL ABS BIAS 10.46 1.66 3.06 34.08

New York and 

Florida SAE

1.84 3.90 6.78 0.85

New York and 

Florida DBE

1.82 3.48 7.04 0.85

NY_FL1 1.70 3.98 6.23 0.76

NY_FL2 1.88 4.04 6.94 0.87

NY_FL3 1.93 4.51 7.36 0.81

NY_FL4 1.88 3.93 6.69 0.92

NY_FL5 1.82 3.66 6.30 0.93

NY_FL6 1.69 4.13 5.80 0.78

NY_FL7 1.59 3.68 5.37 0.77

NY_FL8 2.02 3.78 7.52 0.99

RMSQ 7.37 15.84 12.32 9.65

REL ABS BIAS 0.37 13.97 7.33 0.94

Ohio and Michigan 

SAE

1.64 3.84 5.44 0.70
Ohio and Michigan 

DBE

1.66 4.00 5.59 0.67

OH_MI1 1.52 3.21 5.34 0.64

OH_MI2 1.72 3.91 5.27 0.82

OH_MI3 1.75 3.98 5.49 0.81

OH_MI4 1.59 4.35 5.00 0.63

OH_MI5 1.60 4.46 5.16 0.61

OH_MI6 1.62 3.49 5.73 0.66

OH_MI7 1.80 3.21 6.01 0.89

OH_MI8 1.63 3.91 5.25 0.70

RMSQ 5.31 12.04 6.37 16.34

REL ABS BIAS 0.37 4.68 3.25 7.16

Pennsylvania and 

Illinois SAE

1.74 3.40 5.92 0.86

Pennsylvania and 

Illinois DBE

1.70 3.17 5.84 0.85

PA_IL1 1.83 3.02 5.67 1.05

PA_IL2 1.69 4.03 5.05 0.84

PA_IL3 1.69 3.22 6.47 0.72

PA_IL4 1.75 3.85 5.74 0.83

PA_IL5 2.03 4.10 6.98 0.96

PA_IL6 1.65 3.06 5.47 0.86

PA_IL7 1.77 3.23 6.85 0.77

PA_IL8 1.78 3.39 5.23 1.01

RMSQ 7.65 16.41 11.93 13.63

REL ABS BIAS 4.11 10.08 1.54 4.09

AVERAGE RMSQ 8.52 13.83 10.24 19.47

AVERAGE REL 

ABS BIAS

3.83 7.60 3.79 11.57

Note: Relative Absolute Bias = |(Combined State Design-Based Estimate (DBE) - Mean of Eight Substate Small 

Area Estimates (SAE)|/Combined State Design-Based Estimate.

Note: Root Mean Square (RMSQ) = Sqrt(Mean Squared Differences of Substate Small Area Estimates with Respect 

to Combined State Design-Based Estimates)/Combined State Design-Based Estimate.

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.
Table B.5 Ratio of Relative Width of Design-Based Confidence Intervals to Small Area 

Estimation Prediction Intervals for Persons Needing But Not Receiving Treatment for an 

Illicit Drug Problem in the Past Year: 2000

Ratio of Relative Width

Total 12-17 18-25 26 or Older

CA_TX1 1.35 1.60 1.95 1.03

CA_TX2 1.37 1.13 1.09 5.19

CA_TX3 1.42 1.38 1.58 1.89

CA_TX4 1.69 1.61 1.81 1.87

CA_TX5 1.19 1.15 1.57 2.37

CA_TX6 1.63 1.13 1.77 2.69

CA_TX7 1.54 1.43 2.04 2.73

CA_TX8 1.78 1.41 1.94 3.17

California and Texas 1.37 1.09 1.18 1.83

AVERAGE OVER 8 SUBSTATES 1.50 1.35 1.72 2.62

NY_FL1 1.39 1.91 1.30 5.42

NY_FL2 2.57 1.77 1.26 2.99

NY_FL3 1.42 1.91 1.65 2.57

NY_FL4 2.96 2.17 1.65 2.89

NY_FL5 2.42 2.41 1.55 2.74

NY_FL6 2.00 1.61 1.36 3.55

NY_FL7 1.54 1.85 1.84 2.62

NY_FL8 1.73 2.18 1.40 1.88

New York and Florida 1.61 1.27 1.16 1.75

AVERAGE OVER 8 SUBSTATES 2.01 1.97 1.50 3.08

OH_MI1 2.34 1.74 2.24 5.12

OH_MI2 2.16 1.90 1.24 2.18

OH_MI3 2.15 1.30 1.78 2.73

OH_MI4 2.03 1.70 1.65 5.23

OH_MI5 1.55 1.17 1.99 *

OH_MI6 1.59 1.49 1.42 5.48

OH_MI7 1.84 2.20 1.17 1.73

OH_MI8 1.55 1.49 1.80 1.17

Ohio and Michigan 1.37 1.22 1.01 1.42

AVERAGE OVER 8 SUBSTATES 1.90 1.62 1.66 3.38
PA_IL1 1.63 2.36 1.79 1.23

PA_IL2 2.52 1.86 2.24 3.75

PA_IL3 1.74 2.00 1.46 *

PA_IL4 1.59 1.34 2.12 2.31

PA_IL5 1.66 1.49 1.29 2.11

PA_IL6 1.79 2.12 1.32 1.94

PA_IL7 1.90 2.51 1.37 5.44

PA_IL8 2.12 1.94 1.49 1.76

Pennsylvania and Illinois 1.48 1.38 1.30 1.36

AVERAGE OVER 8 SUBSTATES 1.87 1.95 1.64 2.65

* Relative width not computed due to design-based estimate of zero.

Note: Relative Width Ratio = (Length of Design-Based Confidence Interval/Design-Based Estimate)/(Length of 

Small Area Estimate Prediction Interval/Small Area Estimate).

Source: SAMHSA, Office of Applied Studies, National Household Survey on Drug Abuse, 2000.

Appendix B: End Notes

3 The panel included William Bell of the U.S. Bureau of the Census; Partha Lahiri of the University of Nebraska; 

Balgobin Nandram of Worcester Polytechnic Institute and the National Center for Health Statistics; Wesley 

Schaible, formerly Associate Commissioner for Research and Evaluation at the Bureau of Labor Statistics; and Alan 

Zaslavsky of Harvard University. Other attendees involved in the development or discussion were Ralph Folsom, 

Judith Lessler, Avinash Singh, and Akhil Vaish of RTI and Doug Wright of SAMHSA. 

4 The validation results were based on a preliminary model; therefore, the combined State estimates shown in Table 

B.4 generally will not agree with estimates made by combining the corresponding State estimates from Table 6 or 7

in Chapter 3.