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2001 State Estimates of Substance Use |
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An important limitation of the National Household Survey on Drug Abuse (NHSDA) estimates of drug use prevalence is that they are only designed to describe the target population of the survey-the civilian, noninstitutionalized population aged 12 or older. Although this population includes almost 98 percent of the total U.S. population age 12 or older, it excludes some important and unique subpopulations who may have very different drug-using patterns. For example, the survey excludes active military personnel, who have been shown to have significantly lower rates of illicit drug use. Persons living in institutional group quarters, such as prisons and residential drug treatment centers, are not included in the NHSDA and have been shown in other surveys to have higher rates of illicit drug use. Also excluded are homeless persons not living in a shelter on the survey date, another population shown to have higher than average rates of illicit drug use. Appendix H describes other surveys that provide data for these populations.
Nonsampling errors can occur from nonresponse, coding errors, computer processing errors, errors in the sampling frame, reporting errors, and other errors not due to sampling. Nonsampling errors are reduced through data editing, statistical adjustments for nonresponse, close monitoring and periodic retraining of interviewers, and improvement in various quality control procedures.
Although nonsampling errors can often be much larger than sampling errors, measurement of most nonsampling errors is difficult or impossible. However, some indication of the effects of some types of nonsampling errors can be obtained through proxy measures, such as response rates and from other research studies.
Response rates for the NHSDA were stable for the period from 1994 to 1998, with the screening response rate at about 93 percent and the interview response rate at about 78 percent (response rates discussed in this appendix are weighted). In 1999, the computer-assisted interviewing (CAI) screening response rate was 89.6 percent, and the interview response rate was 68.6 percent. A more stable and experienced field interviewer (FI) workforce improved these rates in 2000 and continued in 2001. Of the 171,519 eligible households sampled for the 2001 NHSDA main study, 157,471 were successfully screened for a weighted screening response rate of 91.9 percent (Table G.1). In these screened households, a total of 89,745 sample persons were selected, and completed interviews were obtained from 68,929 of these sample persons, for a weighted interview response rate of 73.3 percent (see Table G.5). A total of 13,478 (16.5 percent) sample persons were classified as refusals or parental refusals, 4,681 (5.3 percent) were not available or never at home, and 2,657 (4.9 percent) did not participate for various other reasons, such as physical or mental incompetence or language barrier (Table G.2). Table s G.3 and G.4 show the distribution of the selected sample by interview code and age group. The weighted interview response rate was highest among 12 to 17 year olds (82.2 percent), females (74.6 percent), blacks and Hispanics (75.0 and 78.8 percent, respectively), in nonmetropolitan areas (76.7 percent), and among persons residing in the South (74.4 percent) (Table G.5).
The overall weighted response rate, defined as the product of the weighted screening response rate and weighted interview response rate, was 61.5 percent in 1999, 68.6 percent in 2000, and 67.3 percent in 2001. Nonresponse bias can be expressed as the product of the nonresponse rate (1-R) and the difference between the characteristic of interest between respondents and nonrespondents in the population (Pr - Pnr). Thus, assuming the quantity (Pr - Pnr) is fixed over time, the improvement in response rates in 2000 and 2001 over 1999 will result in estimates with lower nonresponse bias.
Among survey participants, item response rates were above 97 percent for most questionnaire items. However, inconsistent responses for some items, including the drug use items, were common. Estimates of substance use from the NHSDA are based on the responses to multiple questions by respondents, so that the maximum amount of information is used in determining whether a respondent is classified as a drug user. Inconsistencies in responses are resolved through a logical editing process that involves some judgment on the part of survey analysts and is a potential source of nonsampling error. Because of the automatic routing through the CAI questionnaire (e.g., lifetime drug use questions that skip entire modules when answered "no"), there is less editing of this type than in the paper-and-pencil interviewing (PAPI) questionnaire used prior to the NHSDA redesign in 1999.
In addition, logical editing is used less often because with the CAI data, statistical imputation is relied upon more heavily to determine the final values of drug use variables in cases where there is the potential to use logical editing to make a determination. The combined amount of editing and imputation in the CAI data is still considerably less than the total amount used in prior PAPI surveys. For the 2001 CAI data, for example, 6.7 percent of the estimate of past month hallucinogen use was based on logically edited cases and 6.6 percent on imputed cases, for a combined amount of 13.3 percent. In the 1998 NHSDA (administered using PAPI), the amount of editing and imputation for past month hallucinogen use was 60.3 and 0.0 percent, respectively, for a total of 60.3 percent. The combined amount of editing and imputation for the estimate of past month heroin use was 5.7 percent for the 2001 CAI and 37.0 percent for the 1998 PAPI data.
NHSDA estimates are based on self-reports of drug use, and their value depends on respondents' truthfulness and memory. Although many studies have generally established the validity of self-report data and the NHSDA procedures were designed to encourage honesty and recall, some degree of underreporting is assumed (Harrell, 1997; Harrison & Hughes, 1997; Rouse, Kozel, & Richards, 1985). No adjustment to NHSDA data is made to correct for this. The methodology used in the NHSDA has been shown to produce more valid results than other self-report methods (e.g., by telephone) (Aquilino, 1994; Turner, Lessler, & Gfroerer, 1992). However, comparisons of NHSDA data with data from surveys conducted in classrooms suggest that underreporting of drug use by youths in their homes may be substantial (Gfroerer, 1993; Gfroerer, Wright, & Kopstein, 1997).
The average annual numbers of marijuana initiates and rates by State were obtained using small area estimation (SAE) methods applied to the pooled 2000–2001 survey data and are, therefore, different from incidence estimates reported in the other reports. NHSDA State estimates of each substance use measure are produced by combining an estimate of the measure based on the State sample data with the estimate of the measure based on a national regression model applied to local-area county and Census block group/tract-level estimates from the State. The parameters of the regression model are estimated from the entire national sample. Because the 42 smaller (in terms of population) States and the District of Columbia have smaller samples than the eight large States, estimates for the smaller States rely more heavily on the national model. The model for each substance use measure typically utilizes from 50 to 100 independent variables in the estimation. These variables include basic demographic characteristics of respondents (e.g., age, race/ethnicity, and gender), demographic and socioeconomic characteristics of the Census tract or block group (e.g., average family income and percentage of single-mother households), and county-level substance abuse and other indicators (e.g., rate of substance abuse treatment, drug arrest rate, and drug- and alcohol-related mortality rate). Population counts by State and age group are applied to the estimated rates to obtain the estimated number of persons with the substance use characteristic. Corresponding to each SAE estimate is a 95 percent prediction interval (PI) that indicates the precision of the estimate. The PI accounts for variation due to sampling, as well as variation due to the model, and is derived from the process that generates the State SAE. There is a 95 percent probability that the true value lies within the interval.
The incidence estimates discussed in this report are based on the combination of two separate SAE measures, calculated from the pooled 2000–2001 data:
Average annual incidence rate ={(Number of marijuana initiates in past 24 months)/
[(Number of marijuana initiates in past 24 months * 0.5) +
Number of persons who never used marijuana]}/2
For diseases, the incidence rate for a population, IR, is defined as the number of new cases of the disease, N, divided by the person time, PT, of exposure (i.e., IR = N / PT). The person time of exposure can be measured for the full period of the study or for a shorter period. The person time of exposure ends at the time of diagnosis (e.g., Greenberg, Daniels, Flanders, Eley, & Boring, 1996, pp. 16–19). Similar conventions are applied for defining the incidence of first use of a substance.
Beginning in 1999, the NHSDA questionnaire allows for collection of year and month of first use for recent initiates. Month, day, and year of birth also are obtained directly or imputed in the process. In addition, the questionnaire call record provides the date of the interview. By imputing a day of first use within the year and month of first use reported or imputed, the key respondent inputs in terms of exact dates are known. Using these respondent inputs, one can determine whether a person's first use episode occurred in the 24 months prior to the interview.
With person time of exposure measured in terms of 2-year units of time, the correct multiplier for the number of initiates in the past 24 months in the denominator of the SAE-based Average annual incidence rate is the average fraction of the exposure interval experienced prior to the initiation. Direct survey estimates of this average fraction of exposure experience prior to the initiation could be formed for each State-by-age-group combination, but direct estimates would be too imprecise to include in the SAE incidence rate estimation. Instead, the average fraction of exposure among initiates was assumed to be ½ of the 2-year exposure period. This approximation follows from the assumption that initiation episodes are distributed uniformly over the 2-year exposure period. Note that the "never" users at interview were all exposed for the full 2-year initiation period. The 24-month SAE incidence rates were then transformed into average 12-month or average annual rates by the ½ multiplier. Alternatively, one can view the final multiplication by ½ as transforming the person time units of exposure in the denominator of the rate from the number of 2-year exposure units to the number of person years of exposure.
For the 2001 NHSDA, mental health among adults was measured using a scale to ascertain serious mental illness (SMI). This scale consisted of six questions that ask respondents how frequently they experienced symptoms of psychological distress during the 1 month in the past year when they were at their worst emotionally. The use of this scale is based on a methodological study designed to evaluate several screening scales for measuring SMI in the NHSDA. These scales consisted of a truncated version of the World Health Organization (WHO) Composite International Diagnostic Interview Short Form (CIDI-SF) scale (Kessler, Andrews, Mroczek, Üstün, & Wittchen, 1998), the K10/K6 scale of nonspecific psychological distress (Furukawa, Kessler, Slade, & Andrews, 2003), and the WHO Disability Assessment Schedule (WHO-DAS) (Rehm et al., 1999).
The methodological study to evaluate the scales consisted of 155 respondents selected from a first-stage sample of 1,000 adults age 18 or older. First-stage respondents were selected from the Boston metropolitan area and screened on the telephone to determine whether they had any emotional problems. Respondents reporting emotional problems at the first stage were oversampled when selecting the 155 respondents at the second stage. The selected respondents were interviewed by trained clinicians in their home using both the NHSDA methodology and a structured clinical interview. The first interview included the three scales described above using audio computer-assisted self-interviewing (ACASI). Respondents completed the ACASI portion of the interview without discussing their answers with the clinician. After completing the ACASI interview, respondents were then interviewed using the 12-month nonpatient version of the Structured Clinical Interview for DSM-IV (SCID) (First, Spitzer, Gibbon, & Williams, 1997) and the Global Assessment of Functioning (GAF) (Endicott, Spitzer, Fleiss, & Cohen, 1976) to classify respondents as either having or not having SMI.
The data from the 155 respondents were analyzed using logistic regression analysis to predict SMI from the scores on the screening questions. Analysis of the model fit indicated that each of the scales alone and in combination were significant predictors of SMI and the best fitting models contained either the CIDI-SF or the K6/K10 alone. Receiver operating characteristic (ROC) curve analysis was used to evaluate the precision of the scales to discriminate between respondents with and without SMI. This analysis indicated that the K6 was the best predictor. The results of the methodological study are described in more detail in Kessler et al. (2002, 2003).
To score the items on the K6 scales, they were first coded from 0 to 4 and summed to yield a number between 0 and 24. This involved transforming response categories for the six questions (DSNERV1, DSHOPE, DSFIDG, DSNOCHR, DSEFFORT, and DSDOWN) given below so that "all of the time" is coded 4, "most of the time" is coded 3, "some of the time" 2, "a little of the time" 1, and "none of the time" 0, with "don't know" and "refuse" also coded 0. Summing across the transformed responses obtains a score with a range from 0 to 24. Respondents with a total score of 13 or greater were classified as having a past year SMI. This cutpoint was chosen to equalize false positives and false negatives.
The questions comprising the K6 scale are given below:
| DSNERV1 | Most people have periods when they are not at their best emotionally. Think of one month in the past 12 months when you were the most depressed, anxious, or emotionally stressed. If there was no month like this, think of a typical month. During that month, how often did you feel nervous? 1 All of the time |
Response categories are the same for the following questions:
| DSHOPE | During that same month when you were at your worst emotionally . . . how often did you feel hopeless? |
| DSFIDG | During that same month when you were at your worst emotionally . . . how often did you feel restless or fidgety? |
| DSNOCHR | During that same month when you were at your worst emotionally . . . how often did you feel so sad or depressed that nothing could cheer you up? |
| DSEFFORT | During that same month when you were at your worst emotionally . . . how often did you feel that everything was an effort? |
| DSDOWN | During that same month when you were at your worst emotionally . . . how often did you feel down on yourself, no good, or worthless? |
Aquilino, W. S. (1994). Interview mode effects in surveys of drug and alcohol use: A field experiment. Public Opinion Quarterly, 58, 210–240.
Endicott, J., Spitzer, R. L., Fleiss, J. L., & Cohen, J. (1976). The Global Assessment Scale: A procedure for measuring overall severity of psychiatric disturbance. Archives of General Psychiatry, 33, 766–771.
First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1997). Structured Clinical Interview for DSM-IV Axis I Disorders, Research Version, Non-patient Edition (SCID-I/NP). New York: New York State Psychiatric Institute, Biometrics Research.
Furukawa, T. A., Kessler, R. C., Slade, T., & Andrews, G. (2003). The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine, 33, 357–362.
Gfroerer, J. (1993). An overview of the National Household Survey on Drug Abuse and related methodological research. In Proceedings of the Survey Research Section of the American Statistical Association, Joint Statistical Meetings, Boston, Massachusetts, August 1992 (pp. 464–469). Alexandria, VA: American Statistical Association.
Gfroerer, J., Wright, D., & Kopstein, A. (1997). Prevalence of youth substance use: The impact of methodological differences between two national surveys. Drug and Alcohol Dependence, 47, 19–30.
Greenberg, R. S., Daniels, S. R., Flanders, W. D., Eley, J. W., & Boring, J. R. (1996). Medical epidemiology. Norwalk, CT: Appleton & Lange.
Harrell, A. V. (1997). The validity of self-reported drug use data: The accuracy of responses on confidential self-administered answer sheets. In L. Harrison & A. Hughes (Eds.), The validity of self-reported drug use: Improving the accuracy of survey estimates (NIH Publication No. 97–4147, NIDA Research Monograph 167, pp. 37–58). Rockville, MD: National Institute on Drug Abuse.
Harrison, L., & Hughes, A. (Eds.). (1997). The validity of self-reported drug use: Improving the accuracy of survey estimates (NIH Publication No. 97–4147, NIDA Research Monograph 167). Rockville, MD: National Institute on Drug Abuse.
Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S.-L., Walters, E. E., & Zaslavsky, A. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32, 959–976.
Kessler, R. C., Andrews, G., Mroczek, D., Üstün, T. B., & Wittchen, H.-U. (1998). The World Health Organization Composite International Diagnostic Interview Short Form (CIDI-SF). International Journal of Methods in Psychiatric Research, 7, 171–185.
Kessler, R. C., Barker, P. R., Colpe, L. J., Epstein, J. F., Gfroerer, J. C., Hiripi, E., Howes, M. J., Normand, S.-L. T., Manderscheid, R. W., Walters, E. E., & Zaslavsky, A. M. (2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60, 184–189.
Rehm, J., Üstün, T. B., Saxena, S., Nelson, C. B., Chatterji, S., Ivis, F., & Adlaf, E. (1999). On the development and psychometric testing of the WHO screening instrument to assess disablement in the general population. International Journal of Methods in Psychiatric Research, 8, 110–123.
Rouse, B. A., Kozel, N. J., & Richards, L. G. (Eds.). (1985). Self-report methods of estimating drug use: Meeting current challenges to validity (DHHS Publication No.ADM 85–1402, NIDA Research Monograph 57). Rockville, MD: National Institute on Drug Abuse.
Turner, C. F., Lessler, J. T., & Gfroerer, J. C. (Eds.). (1992). Survey measurement of drug use: Methodological studies (DHHS Publication No. ADM 92–1929). Rockville, MD: National Institute on Drug Abuse.
| Screening Result | 1999 NHSDA | 2000 NHSDA | 2001 NHSDA | |||
|---|---|---|---|---|---|---|
| Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
|
| Total Sample | 223,868 | 100.00 | 215,860 | 100.00 | 203,544 | 100.00 |
| Ineligible cases | 36,026 | 15.78 | 33,284 | 15.09 | 32,025 | 15.40 |
| Eligible cases | 187,842 | 84.22 | 182,576 | 84.91 | 171,519 | 84.60 |
| Ineligibles | 36,026 | 100.00 | 33,284 | 100.00 | 32,025 | 100.00 |
| Vacant | 18,034 | 49.71 | 16,796 | 50.76 | 16,489 | 51.71 |
| Not a primary residence | 4,516 | 12.90 | 4,506 | 13.26 | 4,706 | 14.69 |
| Not a dwelling unit | 4,626 | 12.70 | 3,173 | 9.33 | 2,913 | 8.66 |
| All military personnel | 482 | 1.22 | 414 | 1.21 | 327 | 0.93 |
| Other, ineligible | 8,368 | 23.46 | 8,395 | 25.43 | 7,590 | 24.00 |
| Eligible Cases | 187,842 | 100.00 | 182,576 | 100.00 | 171,519 | 100.00 |
| Screening complete | 169,166 | 89.63 | 169,769 | 92.84 | 157,471 | 91.86 |
| No one selected | 101,537 | 54.19 | 99,999 | 55.36 | 90,530 | 52.11 |
| One selected | 44,436 | 23.63 | 46,981 | 25.46 | 43,601 | 25.94 |
| Two selected | 23,193 | 11.82 | 22,789 | 12.03 | 23,340 | 13.82 |
| Screening not complete | 18,676 | 10.37 | 12,807 | 7.16 | 14,048 | 8.14 |
| No one home | 4,291 | 2.38 | 3,238 | 1.82 | 3,383 | 1.90 |
| Respondent unavailable |
651 | 0.36 | 415 | 0.24 | 392 | 0.24 |
| Physically or mentally incompetent |
419 | 0.24 | 310 | 0.16 | 357 | 0.20 |
| Language barrier— Hispanic |
102 | 0.06 | 83 | 0.05 | 130 | 0.09 |
| Language barrier— other |
486 | 0.28 | 434 | 0.27 | 590 | 0.39 |
| Refusal | 11,097 | 5.92 | 7,535 | 4.14 | 8,525 | 4.93 |
| Other, access denied | 1,536 | 1.08 | 748 | 0.45 | 613 | 0.35 |
| Other, eligible | 38 | 0.02 | 7 | 0.00 | 9 | 0.00 |
| Other, problem case | 56 | 0.03 | 37 | 0.02 | 49 | 0.03 |
| Final Interview Code | 1999 NHSDA | 2000 NHSDA | 2001 NHSDA | |||
|---|---|---|---|---|---|---|
| Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
|
| Total Selected Persons | 89,883 | 100.00 | 91,961 | 100.00 | 89,745 | 100.00 |
| Interview complete | 66,706 | 68.55 | 71,764 | 73.93 | 68,929 | 73.31 |
| No one at dwelling unit | 1,795 | 2.13 | 1,776 | 2.02 | 1,728 | 2.00 |
| Respondent unavailable | 3,897 | 4.53 | 3,058 | 3.52 | 2,953 | 3.30 |
| Breakoff | 50 | 0.07 | 72 | 0.09 | 79 | 0.12 |
| Physically/mentally incompetent | 1,017 | 2.62 | 1,053 | 2.57 | 1,020 | 2.43 |
| Language barrier—Spanish | 168 | 0.12 | 109 | 0.08 | 190 | 0.17 |
| Language barrier—Other | 480 | 1.46 | 441 | 1.06 | 470 | 1.30 |
| Refusal | 11,276 | 17.98 | 10,109 | 14.99 | 10,961 | 15.60 |
| Parental refusal | 2,888 | 1.01 | 2,655 | 0.88 | 2,517 | 0.92 |
| Other | 1,606 | 1.53 | 924 | 0.86 | 898 | 0.86 |
| Final Interview Code | 1999 NHSDA | 2000 NHSDA | 2001 NHSDA | |||
|---|---|---|---|---|---|---|
| Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
|
| Total Selected Persons | 32,011 | 100.00 | 31,242 | 100.00 | 28,188 | 100.00 |
| Interview complete | 25,384 | 78.07 | 25,756 | 82.58 | 23,178 | 82.18 |
| No one at dwelling unit | 322 | 1.09 | 278 | 0.86 | 254 | 0.92 |
| Respondent unavailable | 872 | 3.04 | 617 | 2.05 | 551 | 2.13 |
| Breakoff | 13 | 0.03 | 18 | 0.05 | 17 | 0.05 |
| Physically/mentally incompetent | 244 | 0.76 | 234 | 0.76 | 219 | 0.79 |
| Language barrier—Spanish | 15 | 0.03 | 10 | 0.03 | 18 | 0.08 |
| Language barrier—Other | 58 | 0.18 | 50 | 0.20 | 34 | 0.11 |
| Refusal | 1,808 | 5.97 | 1,455 | 4.52 | 1,247 | 4.14 |
| Parental refusal | 2,885 | 9.50 | 2,641 | 8.35 | 2,517 | 8.95 |
| Other | 410 | 1.33 | 183 | 0.59 | 153 | 0.64 |
| Final Interview Code | 1999 NHSDA | 2000 NHSDA | 2001 NHSDA | |||
|---|---|---|---|---|---|---|
| Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
Sample Size |
Weighted Percentage |
|
| Total Selected Persons | 57,872 | 100.00 | 60,719 | 100.00 | 61,557 | 100.00 |
| Interview complete | 41,322 | 67.41 | 46,008 | 72.92 | 45,751 | 72.29 |
| No one at dwelling unit | 1,473 | 2.25 | 1,498 | 2.16 | 1,474 | 2.12 |
| Respondent unavailable | 3,025 | 4.71 | 2,441 | 3.69 | 2,402 | 3.43 |
| Breakoff | 37 | 0.07 | 54 | 0.09 | 62 | 0.13 |
| Physically/mentally incompetent | 773 | 2.85 | 819 | 2.78 | 801 | 2.62 |
| Language barrier—Spanish | 153 | 0.13 | 99 | 0.09 | 172 | 0.18 |
| Language barrier—Other | 422 | 1.62 | 391 | 1.16 | 436 | 1.43 |
| Refusal | 9,468 | 19.41 | 8,654 | 16.22 | 9,714 | 16.92 |
| Parental refusal | 3 | 0.00 | 14 | 0.01 | 0 | 0.00 |
| Other | 1,196 | 1.55 | 741 | 0.89 | 745 | 0.88 |
| 1999 NHSDA | 2000 NHSDA | 2001 NHSDA | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Selected Persons |
Completed Interviews |
Weighted Response Rate |
Selected Persons |
Completed Interviews |
Weighted Response Rate |
Selected Persons |
Completed Interviews |
Weighted Response Rate |
|
| Total | 89,883 | 66,706 | 68.55% | 91,961 | 71,764 | 73.93% | 89,745 | 68,929 | 73.31% |
| Age in Years | |||||||||
| 12–17 | 32,011 | 25,384 | 78.07% | 31,242 | 25,756 | 82.58% | 28,188 | 23,178 | 82.18% |
| 18–25 | 30,439 | 22,151 | 71.21% | 29,424 | 22,849 | 77.34% | 30,304 | 22,931 | 75.51% |
| 26 or older | 27,433 | 19,171 | 66.76% | 31,295 | 23,159 | 72.17% | 31,253 | 22,820 | 71.75% |
| Gender | |||||||||
| Male | 43,883 | 31,987 | 67.12% | 44,899 | 34,375 | 72.68% | 43,949 | 33,109 | 71.92% |
| Female | 46,000 | 34,719 | 69.81% | 47,062 | 37,389 | 75.09% | 45,796 | 35,820 | 74.58% |
| Race/Ethnicity | |||||||||
| Hispanic | 11,203 | 8,755 | 74.59% | 11,454 | 9,396 | 77.95% | 10,885 | 8,777 | 78.78% |
| White | 63,211 | 46,272 | 67.98% | 64,517 | 49,631 | 73.39% | 63,228 | 48,016 | 72.65% |
| Black | 10,552 | 8,044 | 70.39% | 10,740 | 8,638 | 76.19% | 10,584 | 8,295 | 74.98% |
| All other races | 4,917 | 3,635 | 59.28% | 5,250 | 4,099 | 67.31% | 5,048 | 3,841 | 66.65% |
| Region | |||||||||
| Northeast | 16,794 | 11,830 | 64.03% | 18,959 | 14,394 | 71.68% | 19,180 | 14,444 | 71.02% |
| Midwest | 24,885 | 18,103 | 69.63% | 25,428 | 19,355 | 73.23% | 25,560 | 19,212 | 73.25% |
| South | 27,390 | 21,018 | 70.93% | 27,217 | 22,041 | 76.38% | 26,278 | 20,609 | 74.44% |
| West | 20,814 | 15,755 | 67.47% | 20,357 | 15,974 | 72.68% | 18,727 | 14,664 | 73.51% |
| County Type | |||||||||
| Large metropolitan | 36,101 | 25,901 | 65.15% | 37,754 | 28,744 | 71.77% | 35,395 | 26,403 | 71.00% |
| Small metropolitan | 30,642 | 22,612 | 69.98% | 31,400 | 24,579 | 74.96% | 31,740 | 24,575 | 74.66% |
| Nonmetropolitan | 23,140 | 18,193 | 74.97% | 22,807 | 18,441 | 77.58% | 22,610 | 17,951 | 76.72% |
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