2000 State Estimates of Substance Use & Mental Health
There are major benefits to combining data from 2 or more years of the National Household Survey on Drug Abuse (NHSDA) in the model used to produce State estimates of substance use. The resulting estimates are far more precise than estimates based on data collected in a single year. Even more important is the fact that 2 years of data mean that State-specific information will have much greater weight in the equations used in the estimation. In this model as the size of the State-specific sample increases, the importance of information derived from other sources decreases. If the State sample were large enough, only data derived from State respondents would determine the estimates. By adding 2 years of data together to produce estimates, the substance behavior of the State population has a far greater impact on the results. This strategy of combining 2 or more years of data to produce estimates works well as long as the variable being studied does not change dramatically during the period involved.
State estimates of the prevalence of substance use can provide, among other things, information on the geographic clustering of these problems. Many factors can influence the nature of State and local prevalence rates including local culture and social norms, State and local policies, and the sources, supply, and marketing of drugs. The findings in this report reveal varying degrees of clustering of illicit drug problems depending on the substance. States with the highest prevalence of illicit drug use include six Western, three Northeastern, and one Southern State (Figure 2.1; Table A.2). By contrast, there was greater State clustering associated with alcohol and tobacco use. The highest rates of both "binge" alcohol use and general alcohol use were found in Northern States. The highest rates of past month cigarette and tobacco use were in the South.
It has long been assumed that there is an inverse relationship between the perception of risk in using substances and the prevalence rate. The lower the perception that use involves risk, the higher the prevalence. "Binge" alcohol use provides an example of this relationship at the State level. Seven States where respondents had the lowest perception that "binge" drinking involved risks had the highest levels of "binge" alcohol use (Figures 3.5 and 3.9; Table s A.16 and A.18). However, many of the States that ranked highest in past month "binge" alcohol use were not the same as those that ranked highest in past month alcohol use (Figures 3.1 and 3.5; Table s A.14 and A.16).
The same relationship with respect to the perception of risk and prevalence of use was found with cigarettes. States with high rates of cigarette use had a relatively low perception that heavy use of cigarettes can be risky. Although most tobacco use is in the form of cigarettes, a few States had significantly higher rates of past month tobacco use than past month cigarette use. For example, 39 percent of those 12 years or older in West Virginia reported using tobacco in the month prior to the interview, but only 31 percent reported using cigarettes. This implies that 8 percent used a tobacco product other than cigarettes (Figures 4.1 and 4.5; Table s A.20 and A.22).
Because marijuana is the most commonly used illicit drug, most of the States with the highest illicit drug use were also the States with the highest past month marijuana use (Figures 2.1 and 2.5; Table s A.2 and A.4). States where the rate of first-time use of marijuana was high also tended to be States with the highest rates of past month marijuana use although the correlation was somewhat less than one might expect (Figures 2.5 and 2.13; Table s A.4 and A.8). Of the States in the top fifth with respect to past month use of an illicit drug, six were also in the top fifth for past month use of an illicit drug other than marijuana (Figures 2.1 and 2.16; Table s A.2 and A.10). Seven of the States with the highest levels of past month use of illicit drugs other than marijuana also had the highest level of past year use of cocaine (Figures 2.16 and 2.20; Table s A.10 and A.12). In general, a State that had a high level of use of one substance also tended to have high levels of related substances.
States that ranked high for substance use by all persons 12 years or older also ranked high in use of substances by the population aged 26 or older. This relationship derives from the fact that the latter group represents 77 percent of the total population 12 years or older. Although the 26 or older population often drove the prevalence rates in the 12 or older population in a State, rates among the 12 to 17 and 18 to 25 age groups may not have followed suit. For example, California displayed a high rate for past month illicit drug use among all persons aged 12 or but the rates in the 12 to 17 and 18 to 25 age groups were similar to the national average (Figures 2.1 to 2.3; Table A.2). On the other hand, Colorado, Massachusetts, Rhode Island, and Delaware had high rates of use of any illicit drug among all three age groups.
Another pattern that can be inferred by comparing the States that displayed the highest rates of substance use among youths 12 to 17 years of age with the States having high rates in the 26 or older age group is that the former group was much more variable. Part of the reason for this is that there is more potential for change among the 12 to 17 (and 18 to 25) age groups because of their smaller population size and because the younger age groups represent ages of initiation and experimentation and groups that are probably more susceptible to influence and change.
With respect to the measures of abuse and dependence, some of the patterns seemed inconsistent. For example, South Dakota had the highest rate of dependence on or abuse of alcohol (7.5 percent), but the State was not among the States with the highest rates of past month alcohol use. On the other hand, South Dakota was one of the States with the highest rates of "binge" alcohol use (25.7 percent) (Figure 3.5; Table A.16). Generally, States with high prevalence rates for alcohol dependence or abuse were not the same States that had high prevalence rates for illicit drug dependence or abuse. Only three of the States in the top fifth with the highest rates of alcohol dependence or abuse (Alaska, Colorado, and Massachusetts) were also in the group of States with the highest levels of illicit drug dependence or abuse (Figures 5.1 and 5.9; Table s A.26 and A.30).
There was some degree of relationship between high rates of past year illicit drug dependence or abuse and high rates of past year cocaine use at the State level. Six States were ranked among the highest for both measures: Arizona, Alaska, the District of Columbia, Massachusetts, Colorado, and Delaware (Figures 2.20 and 5.9; Table s A.12 and A.30). Eight out of ten of the States with the highest rates of cocaine use among youths were in the West. Three States (New Mexico, Colorado, and Nevada) were in the top fifth for all three age groups (12 to 17, 18 to 25, and 26 or older) (Figures 2.21 to 2.23; Table A.12). Comparing these results with the 1999 Treatment Episode Data Set (TEDS) figures of the unadjusted number of admissions to treatment per 100,000 population by State (Office of Applied Studies [OAS], 2001c), only four States with high rates of admission also had high estimated rates of past year use of cocaine: Colorado, Alaska, the District of Columbia, and Massachusetts.
Not only did geographic clustering of States occur among those with high prevalence rates, but similar clustering was evident among the States with the lowest rates as well. For example, only one Southern State (Delaware) and no State in the Midwest was included in the set of States with the highest level of past month illicit drug use (population 12 years or older). By contrast, four Southern States and five Midwestern States appeared in the set of States with the lowest rates for current illicit drug use (Figure 2.1; Table A.2). Also, a number of Southern States fell into the highest fifth for past month use of cigarettes; but none of these States was in the highest fifth for either past month illicit drug use or past month marijuana use (Figures 2.1, 2.5, and 4.5; Table s A.2, A.4, and A.22).
It is difficult to find other data to validate the estimates discussed in this report and presented in the tables. In the past, national estimates from the NHSDA have been compared with estimates from the Behavioral Risk Factor Surveillance System (BRFSS) and the Youth Risk Behavior Survey (YRBS) sponsored by the Centers for Disease Control and Prevention (CDC) (Centers for Disease Control and Prevention [CDC], 2001a, 2001b). However, these CDC surveys did not focus extensively on substance use, employed different data collection methods, did not cover all of the States on an annual basis, and had varying degrees in potential response and nonresponse bias. It is, therefore, difficult to know how much confidence should be placed in comparisons of results.
Although external validation of NHSDA findings are problematic, internal validation of the States can be useful (for details, see Volume II, Section B.4.2 in Appendix B). To validate the modeling process, data from 1999 and 2000 were combined for each of the eight largest States, resulting in sample sizes of about 7,500 per State. Given the large sample sizes and the precision of estimates based on samples of this size, the sample estimates for each of the eight States were considered to be the true values. Replicating exactly the sample design and model-estimation procedures used in the 42 States and the District of Columbia (based on samples of about 900 persons aged 12 or older) in each of the eight large States and repeating this exercise many times, estimates were made for four substance measures and three age groups for each of the eight "pseudo" States. Comparing the results with the true values in each of the eight States, the State model estimates (for all persons aged 12 or older) were very close to the true values (i.e., the bias as a percentage of the estimated prevalence rate was very small) (Table s B.22 to B.25):
However, the model may not be able to adequately adjust for differential nonresponse and bias effects at the State level. There were significant differences in the response rates between States with the lowest and highest rates. In 1999, for example, Massachusetts had the lowest response rate at 49.8 percent and Mississippi had the highest rate at 78.2. In 2000, the range of response rates was somewhat smaller with the Illinois rate at 58.2 percent and the Kentucky rate at 80.6 percent. If there were bias resulting from nonresponse that varied in relation to the rates, it would raise questions about comparisons among States. (See Volume II, Table s B.27 and B.28 in Appendix B, for interview response rates by State in 1999 and 2000.)
There was, in fact, some suggestion that the State nonresponse rates and the prevalence levels of substance use were somehow related. Averaging State response rates for 1999 and 2000 NHSDA and comparing the result with the rate of past month marijuana use by persons 12 years or older revealed a -0.42 correlation, suggesting that lower State response rates may be associated with higher State marijuana prevalence rates. This result is not sufficient to conclude there was in fact nonresponse bias. For such bias to exist after nonresponse adjustments have been made requires that the true probabilities for persons to respond to the survey still depend to some degree on whether they have used a substance or not.
Research has shown that the more socially unacceptable the substance, the greater the tendency to not report its use (Harrison, 1997). Therefore, one might anticipate very little underreporting if the question asked whether the respondent had ever used marijuana during his or her lifetime, but more extensive underreporting if asked about past month use of heroin. Some of the uncertainty about the extent and nature of the underreporting is being addressed by a validity study using hair and urine samples provided by respondents in the NHSDA.
This page was last updated on December 30, 2008.