The impact of financial toxicity in gastrointestinal cancer patients.

BACKGROUND: Biopsychosocial distress screening is a critical component of comprehensive cancer care. Financial issues are a common source of distress in this patient population. This study uses a biopsychosocial distress screening tool to determine the factors associated with financial toxicity and the impact of these stressors on gastrointestinal cancer patients. METHODS: A 48-question, proprietary distress screening tool was administered to patients with gastrointestinal malignancies from 2009 to 2015. This validated, electronically-administered tool is given to all new patients. Responses were recorded on a 5-point Likert scale from 1 (not a problem) to 5 (very severe problem), with responses rated at ≥3 indicative of distress. Univariate and multivariate logistic regressions were used to analyze the data. RESULTS: Most of the 1,027 patients had colorectal (50%) or hepatobiliary (31%) malignancies. Additionally, 34% of all patients expressed a high level of financial toxicity. Age greater than 65 (odds ratio: 0.63, 95% confidence interval: 0.47-0.86, P < .01), college education (odds ratio: 0.53, 95% confidence interval: 0.38-0.73, P < .0001), being partnered (odds ratio: 0.61, 95% confidence interval: 0.44-0.84, P < .01), and annual income greater than $40,000 (odds ratio: 0.27, 95% confidence interval: 0.19-0.38, P < .0001) were all protective against financial toxicity on univariate analysis. Also, heavy tobacco use was associated significantly with increased distress on univariate analysis (odds ratio: 2.79, 95% confidence interval: 1.38-5.78, P < .01). With the exception of partnered status (odds ratio: 1.18, 95% confidence interval: 0.76-1.85, P = .46), all these variables retained their significant association with financial toxicity in the multivariate model. CONCLUSION: Financial toxicity impacts a large number of cancer patients. Further study of at-risk populations may identify patients who would benefit from pre-emptive education and counseling interventions as part of their routine cancer care.

Socioeconomic Status and the Prediction of Health Promoting Dietary Behaviours: A Systematic Review and Meta-Analysis Based on the Theory of Planned Behaviour.

BACKGROUND: Dietary behaviours are strongly patterned by socioeconomic status (SES). However, the role of SES in the self-regulation of health promoting dietary behaviours is not fully understood. This systematic review with meta-analysis investigated whether four individual-level measures of SES (income, occupation, education, and ethnicity) moderate the relationships between Theory of Planned Behaviour (TPB) variables and different health promoting dietary behaviours in adults. METHODS: A systematic literature search identified 106 studies from 77 articles providing information on TPB variables, SES, and health promoting dietary behaviours-choosing health promoting foods and restricting health compromising foods. Random-effects meta-analyses were conducted to generate pooled correlations corrected for sampling and measurement error, and meta-regression was used to test moderating effects of study-level SES. RESULTS: All TPB variables were significantly and positively associated with both health promoting dietary behaviours, with intention having the strongest correlation with behaviour. However, none of the relationships between TPB variables and health promoting dietary behaviours were significantly moderated by study-level SES. CONCLUSIONS: Results suggest robust associations between TPB predictors and healthy dietary behaviour that are not patterned by individual-level SES measures.

Part 3. Modeling of Multipollutant Profiles and Spatially Varying Health Effects with Applications to Indicators of Adverse Birth Outcomes.

The highly intercorrelated nature of air pollutants makes it difficult to examine their combined effects on health. As such, epidemiological studies have traditionally focused on single-pollutant models that use regression-based techniques to examine the marginal association between a pollutant and a health outcome. These relatively simple, additive models are useful for discerning the effect of a single pollutant on a health outcome with all other pollutants held to fixed values. However, pollutants occur in complex mixtures consisting of highly correlated combinations of individual exposures. For example, evidence for synergy among pollutants in causing health effects has been recently reviewed by Mauderly and Samet (2009). Also, studies cited in the Ozone Criteria Document (U.S. Environmental Protection Agency [U.S. EPA*] 2006) confirmed that synergisms between ozone and other pollutants have been demonstrated in laboratory studies involving humans and animals. Thus, the highly correlated nature of air pollution exposures makes marginal, single-pollutant models inadequate. This issue was raised in a report by the National Research Council (NRC 2004), which called for a multipollutant approach to air quality management. Here we present and apply a series of statistical approaches that treat patterns of covariates as a whole unit, stochastically grouping pollutant patterns into clusters and then using these cluster assignments as random effects in a regression model. Using this approach, the effect of a multipollutant pattern, or profile, is determined in a manner that takes into account the uncertainty in the clustering process. The models are set in a Bayesian framework, and in general, Markov chain Monte Carlo (MCMC) techniques (Gilks et al. 1998). For interpretation purposes, a best clustering is derived, and the uncertainty related to this best clustering is determined by utilizing model averaging techniques, in a manner such that consistent clustering obtained by the estimation process generally yields smaller standard errors while inconsistent clustering is generally associated with larger errors. These multivariate methods are applied to a range of different problems related to air pollution exposures, namely an association of multipollutant profiles with indicators of poverty and to an assessment of the association between measures of various air pollutants, patterns of socioeconomic status (SES), and birth outcomes. All of these studies involve an examination of regional-level exposures, at the census tract (CT) and census block group (CBG) levels, and individual-level outcomes throughout Los Angeles (LA) County. Results indicate that effects of pollutants vary spatially and vary in a complex interconnected manner that cannot be discerned using standard additive line ar models. Results obtaine d from these studies can be used to efficiently use limited resources to inform policies in targeting are as where air pollution reductions result in maximum health benefits.

Ethnic differences in the use of regular mammography: the multiethnic cohort.

Women's regular use of mammography over a 6 year interval was examined among women aged 45-75 in the Hawaii and Los Angeles Multiethnic Cohort (MEC). The analyses included 81,722 African American, Japanese, Latina, Native Hawaiian, and White females using self-reported mammography history from 1993 to 1998. Ninety-one percent of MEC women reported ever having a mammogram, however only 36% reported regular annual and 48% reported regular biennial mammography over the interval. Mammography was lowest among women who were obese, had a high school education or less, or who were aged 70 and over. Regular mammography use during follow-up was low compared to prior studies reporting on recent mammography. African American, Latina, and Native Hawaiian women had significantly lower annual and biennial mammography use compared to White women even after controlling for age, education, family history, body mass index and hormone therapy indicating that gaps exist in mammography that remain unexplained by known predictors of screening behavior.