Suchergebnisse

Purpose: To identify trajectories of smoking behaviors of a cohort of youth followed through young adulthood from 2000 to 2013. Design: The Minnesota Adolescent Community Cohort study, a population-based cohort study. Setting: Nationwide, originating in the Midwestern United States. Participants: Cohort of youth surveyed for 14 years beginning at ages 12 to 16 (N = 4241 at baseline; 59% recruitment rate). Measures: Main variable of interest was the number of days smoked in the past 30 days. Also included time-varying and time-invariant covariates. Analysis: We utilized growth mixture modeling to group individuals into trajectories over time. Results: We identified 5 distinct trajectories: nonsmokers (59.5%), early-onset regular smokers (14.2%), occasional smokers (11.5%), late-onset regular smokers (9.4%), and quitters (5.3%). Adjusted models showed that early- and late-onset regular smokers (compared to nonsmokers) had lower odds of attending or graduating from a 4-year college ( P < .05). Participants in all smoking classes compared to nonsmokers had greater odds of having more close friends who smoked ( P < .05). Conclusion: Our results show that individuals in their teens through young adulthood can be classified into 5 smoking trajectories. More people in this age range remained abstainers than found in most previous studies; however, a sizable group was identified as regular smokers by the time they reached young adulthood. Interventions targeted at teens, including those that address social and environmental influences, are clearly still needed to prevent escalation of smoking as they move toward young adulthood.

Recent advances in handheld computer hardware and software may provide alternatives to paper-based data collection methods. The authors compared data collected with paper forms to data collected with handheld computer-based forms in a field observation study of alcohol purchase attempts at 47 community festivals in a large metropolitan area. Agreement between data collected with paper forms and data collected with handheld computers was greater than 95%. Computer-based forms handled branching patterns better and yielded data that were immediately available for analyses. Paper forms handled written comments better. Handheld computers are a feasible alternative to paper forms for field data collection.

OBJECTIVE: Our objectives were to explore attitudes regarding food retail policy and government regulation among managers of small food stores and examine whether manager views changed due to the 2014 Minneapolis Staple Foods Ordinance, a city policy requiring retailers to stock specific healthy products. DESIGN: Manager interviewer-administered surveys were used to assess views on food retail policy four times from 2014 to 2017. We examined baseline views across manager and store and neighbourhood characteristics using cross-sectional regression analyses and examined changes over time using mixed regression models. In 2017, open-ended survey questions asked about manager insights on the Minneapolis Staple Foods Ordinance. SETTING: Minneapolis, MN, where the ordinance was enacted, and St. Paul, MN, a control community, USA. PARTICIPANTS: Managers from 147 small food retail stores. RESULTS: At baseline, 48 % of managers were likely to support a policy requiring stores to stock healthy foods/beverages, 67·5 % of managers were likely to support voluntary programmes to help retailers stock healthy foods and 23·7 % agreed government regulation of business is good/necessary. There was a significant increase in overall support for food retail policies and voluntary programmes from 2014 to 2017 (P < 0·01); however, neither increase differed by city, suggesting no differential impact from the ordinance. Minneapolis store managers reported some challenges with ordinance compliance and offered suggestions for how local government could provide support. CONCLUSIONS: Findings suggest that managers of small food retail stores are becoming increasingly amenable to healthy food policies; yet, challenges need to be addressed to ensure healthy food is available to all customers.

The social environment in which lesbian, gay, bisexual, transgender, and queer (LGBTQ) youth live influences health and wellbeing. We describe the development of the LGBTQ Supportive Environments Inventory (LGBTQ SEI), designed to quantify the LGBTQ-inclusiveness of social environments in the US and Canada. We quantify aspects of the social environment: 1) Presence/quality of LGBTQ youth-serving organizations; 2) LGBTQ-inclusive Community Resources; 3) Socioeconomic and Political environment. Using GIS tools, we aggregated data to buffers around 397 schools in 3 regions. The LGBTQ SEI can be used to assess the role of the social environment in reducing health disparities for LGBTQ youth.

Purpose. To identify and describe homogenous classes of male college students based on their weight-related behaviors (e.g., eating habits, physical activity, and unhealthy weight control) and to examine differences by sexual orientation. Design. Study design was a cross-sectional sample of 2- and 4-year college students. Setting. Study setting was forty-six 2- and 4-year colleges in Minnesota. Subjects. Study subjects comprised 10,406 college males. Measures. Measures were five categories of sexual orientation derived from self-reported sexual identity and behavior (heterosexual, discordant heterosexual [identifies as heterosexual and engages in same-sex sexual behavior], gay, bisexual, and unsure) and nine weight-related behaviors (including measures for eating habits, physical activity, and unhealthy weight control). Analysis. Latent class models were fit for each of the five sexual orientation groups, using the nine weight-related behaviors. Results. Overall, four classes were identified: "healthier eating habits" (prevalence range, 39.4%–77.3%), "moderate eating habits" (12.0%–30.2%), "unhealthy weight control" (2.6%–30.4%), and "healthier eating habits, more physically active" (35.8%). Heterosexual males exhibited all four patterns, gay and unsure males exhibited four patterns that included variations on the overall classes identified, discordant heterosexual males exhibited two patterns ("healthier eating habits" and "unhealthy weight control"), and bisexual males exhibited three patterns ("healthier eating habits," "moderate eating habits," and "unhealthy weight control"). Conclusion. Findings highlight the need for multibehavioral interventions for discordant heterosexual, gay, bisexual, and unsure college males, particularly around encouraging physical activity and reducing unhealthy weight control behaviors.

We describe a multi‐step method of coding the strength of 18 alcohol policies included in the Alcohol Policy Information System for each of the 50 states. After thoroughly reviewing each policy area, we chose components that were most important in categorizing the strength or restrictiveness of the policy using the following criteria: overall reach, enforceability, and implementation. We determined a unique coding scheme for each policy area. The total number of categories per policy area ranged from two to six, with categories numbered in an ordered sequence from least to most restrictive. We provide three examples of our coding schemes: Keg Registration, Underage Possession, and Sunday Sales. We also rank the states on their alcohol policy sum score. This study demonstrates how alcohol policies can be measured quantitatively, an important step for assessing the effects of alcohol policies on various outcomes.

This study investigates socioeconomic differences in patterns and trends of tobacco consumption over time among youth in India. Additionally, the distribution of tobacco use risk factors across social class was examined. The data were derived from a longitudinal study of adolescents, Project MYTRI. Students in eight private [high socioeconomic status (SES)] (n=2881) and eight government (lower SES) (n=5476) schools in two large cities in India (Delhi and Chennai) were surveyed annually about their tobacco use and related psychosocial risk factors from 2004 to 2006. Results suggest the relationship between SES and tobacco use over time was not consistent. At baseline (in 2004), lower SES was associated with higher prevalence of tobacco use but the relation between SES and tobacco use reversed two years later (2006). These findings were mirrored in the distribution of related psychosocial risk factors by SES at baseline (in 2004), and thereafter in 2006. Implications for prevention scientists and future intervention programs are considered.