The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025
A comparison of classifications for geographic location and their associations with tobacco use among US adults.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ozga Jenny E, Milstred Andrea, Blank Melissa D, Rayens Mary Kay, Keller-Hamilton Brittney, Roberts Megan E, Himelhoch Seth, Stanton Cassandra A
Аннотация
PURPOSE: This study compared two classifications of rurality and their associations with cigarette, e-cigarette, and smokeless tobacco (SLT) use among a nationally representative sample of 31,196 US adults.
METHODS: Data from Wave 1 of the Population Assessment of Tobacco and Health Study. Weighted descriptive statistics and multivariable logistic regressions assessed whether two classifications of rurality were differentially associated with past 30-day (P30D) cigarette, e-cigarette, or SLT use in separate models. Classifications were (1) the US Census Bureau's classification as urban/non-urban; and (2) the National Center for Education Statistic (NCES)'s classification as urban/suburban/town/rural. This study is reported in accordance with STROBE guidelines.
FINDINGS: With the Census Bureau classification, 79.3% were in urban areas. With the NCES classification, 34.3% were in urban, 35.1% in suburban, 9.4% in town, and 21.1% in rural areas. With the Census Bureau classification, non-urban (vs. urban) residence was associated with reduced odds of e-cigarette use (AOR = 0.79; 95% CI = 0.70-0.88) and increased odds of SLT use (AOR = 2.32; 95% CI = 1.97-2.72). With the NCES classification with urban as reference, rural residence was associated with reduced odds of e-cigarette use (AOR = 0.77; 95% CI = 0.75-0.98); both town (AOR = 2.16; 95% CI = 1.69-2.78) and rural (AOR = 2.75; 95% CI = 2.16, 3.48) were associated with increased odds of SLT use. Location was not associated with cigarette use for either classification.
CONCLUSIONS: Location was similarly associated with P30D e-cigarette and SLT use across both classifications in adjusted models. The use of classifications with more categories may be beneficial to understand nuanced location differences in tobacco use.
METHODS: Data from Wave 1 of the Population Assessment of Tobacco and Health Study. Weighted descriptive statistics and multivariable logistic regressions assessed whether two classifications of rurality were differentially associated with past 30-day (P30D) cigarette, e-cigarette, or SLT use in separate models. Classifications were (1) the US Census Bureau's classification as urban/non-urban; and (2) the National Center for Education Statistic (NCES)'s classification as urban/suburban/town/rural. This study is reported in accordance with STROBE guidelines.
FINDINGS: With the Census Bureau classification, 79.3% were in urban areas. With the NCES classification, 34.3% were in urban, 35.1% in suburban, 9.4% in town, and 21.1% in rural areas. With the Census Bureau classification, non-urban (vs. urban) residence was associated with reduced odds of e-cigarette use (AOR = 0.79; 95% CI = 0.70-0.88) and increased odds of SLT use (AOR = 2.32; 95% CI = 1.97-2.72). With the NCES classification with urban as reference, rural residence was associated with reduced odds of e-cigarette use (AOR = 0.77; 95% CI = 0.75-0.98); both town (AOR = 2.16; 95% CI = 1.69-2.78) and rural (AOR = 2.75; 95% CI = 2.16, 3.48) were associated with increased odds of SLT use. Location was not associated with cigarette use for either classification.
CONCLUSIONS: Location was similarly associated with P30D e-cigarette and SLT use across both classifications in adjusted models. The use of classifications with more categories may be beneficial to understand nuanced location differences in tobacco use.