The influence of active coping and perceived stress on health disparities in a multi-ethnic low income sample

<p>Abstract</p> <p>Background</p> <p>Extensive research has shown that ethnic health disparities are prevalent and many psychological and social factors influence health disparities. Understanding what factors influence health disparities and how to eliminate health disparities has become a major research objective. The purpose of this study was to examine the impact of coping style, stress, socioeconomic status (SES), and discrimination on health disparities in a large urban multi-ethnic sample.</p> <p>Methods</p> <p>Data from 894 participants were collected via telephone interviews. Independent variables included: coping style, SES, sex, perceived stress, and perceived discrimination. Dependent variables included self-rated general and oral health status. Data analysis included multiple linear regression modeling.</p> <p>Results</p> <p>Coping style was related to oral health for Blacks (B = .23, p < .05) and for Whites there was a significant interaction (B = -.59, p < .05) between coping style and SES for oral health. For Blacks, active coping was associated with better self-reported health. For Whites, low active coping coupled with low SES was significantly associated with worse oral health. Coping style was not significantly related to general health. Higher perceived stress was a significant correlate of poorer general health for all ethnoracial groups and poorer oral health for Hispanics and Blacks. SES was directly related to general health for Hispanics (.B = .27, p < .05) and Whites (B = .23, p < .05) but this relationship was mediated by perceived stress.</p> <p>Conclusion</p> <p>Our results indicate that perceived stress is a critical component in understanding health outcomes for all ethnoracial groups. While SES related significantly to general health for Whites and Hispanics, this relationship was mediated by perceived stress. Active coping was associated only with oral health.</p

Patterns of dual use of cigarettes and smokeless tobacco among US males: findings from national surveys

Background: In the USA, consumption of moist snuff continues to increase and cigarette manufacturers now control nearly its entire market. Manufacturers have developed new products that represent cigarette brand extension and in test marketing are promoting dual use of cigarettes and snuff. This study examined patterns of concurrent use of smokeless tobacco (ST) and cigarettes among young people and adults in the USA just before cigarette companies' control of the nation's ST market. Methods: Data were drawn from four US nationally representative surveys. Stratified analyses applied sampling weights and accounted for the complex sample designs. Results: Cigarette smoking was substantially more prevalent among young males who used ST than among those who did not. Among adult males, those who smoked daily were less likely than others to have used snuff every day. Men who used moist snuff daily had the lowest prevalence of daily smoking, but the prevalence of daily smoking was relatively high among men who used moist snuff less than daily. Unsuccessful past-year attempts by daily smokers to quit smoking were more prevalent among non-daily snuff users (41.2%) than among those who had never used snuff (29.6%). Conclusions: Although dual daily use of ST and cigarettes is relatively uncommon in the USA, concurrent ST use is more common among adolescent and young adult male smokers than among more mature tobacco users. Among adult males, daily smoking predominates and non-daily ST use is very strongly associated with current smoking. Adult male smokers who also use ST daily tend to have relatively high levels of serum cotinine and high prevalence of a major indicator for tobacco dependence

Selecting a comparison group for 5-year oral and pharyngeal cancer survivors: Two methods

BACKGROUND: To assess potential long-term consequences of cancer treatment, studies that include comparison groups are needed. These comparison groups should be selected in a way that allows the subtle long-range effects of cancer therapy to be detected and distinguishes them from the effects of aging and other risk factors. The purpose of this investigation was to test two methods of recruiting a comparison group for 5-year oral and pharyngeal cancer survivors (peer-nominated and listed sample) with emphasis on feasibility and the quality of the match. METHODS: Participants were drawn from a pool of 5-year survivors treated at a large Southeastern hospital. A peer-nominated sample was solicited from the survivors. A listed sample matched on sex, age, and zip code was purchased. Telephone interviews were conducted by a professional call center. RESULTS: The following represent our key findings: The quality of matching between survivors and listed sample was better than that between survivors and peer-nominated group in age and sex. The quality of matching between the two methods on other key variables did not differ except for education, with the peer method providing a better match for the survivors than the listed sample. The yield for the listed sample method was greater than for the peer-nominated method. The cost per completed interview was greater for the peer-nominated method than the listed sample. CONCLUSION: This study not only documents the methodological challenges in selecting a comparison group for studies examining the late effects of cancer treatment among older individuals but also documents challenges in matching groups that potentially have disproportionate levels of comorbidities and at-risk health behaviors

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages