Bayesian Hierarchical Factor Analysis for Efficient Estimation across Race/Ethnicity.

Patient reported outcomes are gaining more attention in patient-centered health outcomes research and quality of life studies as important indicators of clinical outcomes, especially for patients with chronic diseases. Factor analysis is ideal for measuring patient reported outcomes. If there is heterogeneity in the patient population and when sample size is small, differential item functioning and convergence issues are challenges for applying factor models. Bayesian hierarchical factor analysis can assess health disparity by assessing for differential item functioning, while avoiding convergence problems. We conducted a simulation study and used an empirical example with American Indian minorities to show that fitting a Bayesian hierarchical factor model is an optimal solution regardless of heterogeneity of population and sample size.

Prevalence of smoking, quit attempts and access to cessation treatment among adults with mental illness in Brazil: a cross-sectional analysis of a National Health Survey.

OBJECTIVES: Determine the national prevalence of smoking and factors related to smoking among adults with mental illness (PLWMI; people living with mental illness) in Brazil. DESIGN: Cross-sectional study. SETTING: We used data from the nationally representative general health survey Pesquisa Nacional de Saude of 2013, which included the Global Adult Tobacco Survey (GATS) module. PARTICIPANTS: The survey used a complex probabilistic sample to collect data from 60 202 Brazilians 18 years or older. Primary and secondary outcomes: smoking prevalence and access to smoking cessation treatment. We also assessed past tobacco use, quit attempts and quit ratio among people with and without mental illness. Analyses were conducted in R and were weighted to account for the survey design and generate national estimates. RESULTS: In Brazil, the 2013 smoking prevalence among PLWMI was 28.4% and among people with no mental illness was 12.8%. Both groups had high rates of past-year quit attempts (51.6% vs 55.3%) but the lifetime quit ratio among PLWMI was much lower than those with no mental illness (37% vs 54%). Adjusted odds showed PLWMI were more likely to be current smokers (OR (95% CI)=2.60 (2.40 to 2.82), less likely to be former smokers (OR (95% CI)=0.62 (0.55 to 0.70)) and as likely to have tried to quit in the past year (OR (95% CI)=0.90 (0.78 to 1.02)). Very few (3.7%) PLWMI and fewer with no mental illness (2.6%) received cessation treatment. CONCLUSION: Smoking rates among PLWMI are roughly double the rate in the general population. Compared with Brazilian smokers without mental illness, those with mental illness were significantly less likely to quit even though as many tried to. Few Brazilians appear to be using publicly available cessation services. Expanding utilisation of treatment might be a good place to start for Brazil to further decrease the prevalence of smoking among PLWMI.