The data from the present study are from the MACC Study, which began in 2000. Before recruiting participants, the state was divided into area-level units, referred to as geopolitical units (GPUs)The data from the present study are from the MACC Study, which began in 2000. Before recruiting participants, the state was divided into area-level units, referred to as geopolitical units (GPUs), on the basis of geographical and political boundaries thought to reflect local tobacco control environments. We constructed these units on the basis of the following criteria: 1) the boundaries of the GPUs fell within existing geographic and/or political limits, 2) the boundaries of the GPUs reflected patterns of local tobacco program activities, and 3) a sufficient number of teenagers resided in each GPU to meet the sample size requirements. Using these criteria, the state was divided into 129 GPUs. Next, a stratified random sample of 60 GPUs was selected, on the basis of the region of the state in which the GPU was located and distribution of race/ethnicity by GPU. Of the 60 GPUs, 24 were defined by county boundaries, 28 were defined by school districts, and 8 were subunits of cities (including local planning districts). Among the 60 GPUs, 28 were rural (47%), 21 were suburban (35%), 3 were cities, not within the metropolitan area (5%), and 8 were urban (13%).

Once the GPUs were defined and selected, an equal number of youth for each year of age between 12 and 16 were recruited to participate from each GPU. A combination of probability and quota sampling methods was used to ensure equal age distribution. Participants were recruited by telephone by Clearwater Research, Inc, using a modified random-digit–dial method. A total of 200,849 households were called to identify those with at least 1 teenager between the ages of 12 and 16 years within target GPUs. Among 6,213 eligible households, respondents were selected at random from age quota cells that were still open for each GPU. With parental consent, a total of 3,636 (58.5%) adolescents completed a 10- to 20-minute interview between October 2000 and March 2001. Study participants received $10 for participating. Approximately an equal number of boys (n = 1,789; 49%) and girls (n = 1,847; 51%) were enrolled in the study. The University of Minnesota institutional review board approved this study, and all participants provided informed consent to participate.

To describe the sociodemographic characteristics of the GPUs, we used Summary File 3 (15) from the US Census Bureau, which provides detailed information for a sample of the population at the census block group level. We selected the following sociodemographic characteristics thought to be related to smoking prevalence: percentage of population that was white, percentage with less than a high school education, percentage that was married, percentage living in an urban area, percentage aged 18 years or older that was English speaking, median housing value, percentage living in renter-occupied housing, median household income, percentage (aged 16 years and older) unemployed, and percentage living below the federal poverty level (income-to-poverty ratio <1.5). Since GPU boundaries could divide census block groups but contain intact census blocks, we first determined the proportion of the total population for each block group that was contained within the GPU by aggregating census blocks. We then used this value to adjust the numerator and denominator of those census characteristics expressed as proportions (eg, percentage of the population that was white) for each block group within the GPU to obtain the correct GPU-level proportion for a given census characteristic. For census characteristics expressed as a median (income, housing value), we took the average of the medians across all block groups within the GPU.

Two measures of tobacco use were derived from the youth telephone surveys. Lifetime smoking was defined as ever having smoked a cigarette, including taking a few puffs. Past 30-day smoking was defined as having smoked on 1 or more days during the past 30 days. GPU-level tobacco use prevalence was computed by calculating the percentage of youth in each GPU that reported lifetime and past 30-day smoking.

All analyses were conducted at the GPU level (n = 60). First, we calculated the coefficient of variation (CV) to assess the variability in GPU sociodemographic characteristics and prevalence of smoking (lifetime and past 30-day) across the 60 GPUs. The CV was calculated as the standard deviation divided by the mean, expressed as a percentage; a high CV represents greater variability across GPUs. Next, using a median split specific to each sociodemographic characteristic, we conducted t tests to assess differences in the mean prevalence of lifetime and mean past 30-day smoking for GPUs above and below the median for each GPU characteristic examined. Because previous studies have shown that different factors predict smoking for men and women at the individual level (16-18), we tested whether results for mean smoking prevalence (lifetime or past 30-day) should be stratified by sex. Finally, we used multiple linear regression to assess the variability in GPU-level lifetime and past 30-day smoking that was explained by GPU sociodemographic characteristics. All GPU sociodemographic characteristics found to be statistically significantly related to GPU-level smoking at the bivariate level (P < .05) were included in the regression models.

The CV for the GPU sociodemographic characteristics ranged from 5.7% (percentage aged ≥18 y that was English speaking) to 55.4% (percentage with an income-to-poverty ratio <1.5) (Table 1). The coefficient of variation was 30% or above for most (7 of 10) of the GPU sociodemographic characteristics. Less variation was found for the percentage that was white, married, and English speaking (of those ≥18 years).

The percentage of adolescents that ever smoked varied from 13.3% to 53.3% across the 60 GPUs, with a mean of 33.1% (CV = 26%). The Figure shows the smoking prevalence rates in Minnesota by GPU. Mean prevalence of lifetime smoking was similar for boys (33.6%) and girls (32.5%) (P = .67) (data not shown). The prevalence of past 30-day smoking ranged from 3.2% to 18.6% across the 60 GPUs, with a mean of 9.5% (CV = 41.1%) (Table 1). The mean prevalence of past 30-day smoking was higher for girls (10.4%) than boys (8.6%) (P = .04) across the GPUs (data not shown).

Mean lifetime smoking prevalence above and below the median for each GPU characteristic was similar for boys and girls, so bivariate results are reported for the entire sample (Table 2). Mean lifetime smoking prevalence was significantly higher in GPUs with a higher percentage of residents with less than a high school education, a lower percentage of residents living in an urban area, lower median housing value and a lower median household income, a higher percentage of residents aged 16 years or older who are unemployed, and a higher percentage of residents with an income-to-poverty ratio less than 1.5. The mean prevalence of lifetime smoking in GPUs with a higher percentage of English-speaking residents aged 18 years or older approached significance.

Patterns observed for past 30-day smoking differed for boys and girls, so results are reported separately. Among girls, we found differences in the mean prevalence of past 30-day smoking for above compared with below the median for all 10 GPU sociodemographic characteristics; for 6 of the 10, these differences were significant. Areas with a higher smoking prevalence among girls were GPUs with a higher percentage of residents with less than a high school degree, a lower percentage of residents living in an urban area, a lower median housing value, a lower median household income, a higher percentage of residents aged 16 years or older who were unemployed, and more residents with an income-to-poverty ratio less than 1.5. Among boys, none of the comparisons in mean smoking prevalence above and below the median value for any GPU sociodemographic characteristic was significant.

Multivariate models were conducted to assess how much variability in area-level smoking was accounted for by all the area-level characteristics combined. Multivariate models predicting overall lifetime smoking (boys and girls combined)Multivariate models were conducted to assess how much variability in area-level smoking was accounted for by all the area-level characteristics combined. Multivariate models predicting overall lifetime smoking (boys and girls combined) and past 30-day smoking among girls each included 6 independent variables: 1) percentage of the population with less than a high school education, 2) percentage of residents living in an urban area, 3) median housing value, 4) median household income, 5) percentage of residents who were unemployed, and 6) percentage of residents with an income-to-poverty ratio less than 1.5. The model predicting mean lifetime prevalence of smoking was significant (F [6,53] = 4.18, P = .002), with the GPU sociodemographic characteristics accounting for 32% of the variability in mean lifetime smoking prevalence. The overall model predicting mean prevalence of past 30-day smoking among girls was also statistically significant (F [6,53] = 3.29, P = .008), with the GPU sociodemographic characteristics accounting for 27% of the variance. A multivariate model was not calculated for boys because none of the GPU sociodemographic characteristics was significantly associated with mean prevalence of past 30-day smoking in bivariate analyses (Table 2). In the multivariate models, none of the individual GPU-level sociodemographic characteristics was independently associated with mean smoking prevalence, after controlling for all other GPU sociodemographic characteristics. This is probably because of the high correlation among some of the predictor variables, which ranged from ±.05 to ±.93. The high correlations observed, however, do not affect the interpretation of the r ² value (the amount of variability explained by the area-level sociodemographic characteristics).