Lower health literacy predicts smoking relapse among racially/ethnically diverse smokers with low socioeconomic status

Background: Nearly half of U.S. adults have difficulties with health literacy (HL), which is defined as the ability to adequately obtain, process, and understand basic health information. Lower HL is associated with negative health behaviors and poor health outcomes. Racial/ethnic minorities and those with low socioeconomic status (SES) are disproportionately affected by poor HL. They also have higher smoking prevalence and more difficulty quitting smoking. Thus, lower HL may be uniquely associated with poorer cessation outcomes in this population. Methods: This study investigated the association between HL and smoking cessation outcomes among 200, low-SES, racially/ethnically diverse smokers enrolled in smoking cessation treatment. Logistic regression analyses adjusted for demographics (i.e., age, gender, race/ethnicity, relationship status), SES-related characteristics (i.e., education, income), and nicotine dependence were conducted to investigate associations between HL and smoking relapse at the end of treatment (3 weeks post quit day). Results: Results indicated that smokers with lower HL (score of <?64.5 on the Rapid Estimate of Adult Literacy in Medicine [REALM]) were significantly more likely than those with higher HL (score of ??64.5 on the REALM) to relapse by the end of treatment, even after controlling for established predictors of cessation including demographics, SES, and nicotine dependence (OR?=?3.26; 95% CI?=?1.14, 9.26). Conclusions: Findings suggest that lower HL may serve as an independent risk factor for smoking relapse among low-SES, racially/ethnically diverse smokers enrolled in treatment. Future research is needed to investigate longitudinal relations between HL and cessation outcomes and potential mechanisms of this relationship

Pathways between socioeconomic status and modifiable risk factors among African American smokers

Tema rada je iz dostupnih demografskih i ekonomskih pokazatelja zemalja te uspjeha na Olimpijskim igrama u Riju 2016. izraditi regresijski model koji će se kasnije moći koristiti u predikcijske svrhe. Kako bi se to postiglo prvo se definiraju pojmovi korelacije i regresije te se detaljnije obrađuju jednostavna i višestruka regresija. Pokazuje se koliko transformacija varijabli može povećati točnost predikcije te se definiraju kriteriji koji će biti važni prilikom odabira najboljeg regresijskog modela. Definiraju se odabrani ekonomski i demografski pokazatelji te način na koji se mjeri uspjeh pojedine države na Olimpijskim igrama 2016. godine. Nakon raznih testiranja odabran je najreprezentativniji regresijski model na temelju kojeg je napravljena prognoza rezultata za sljedeće Olimpijske igre koje će se održati 2020. godine u Tokiju