Food insecurity is a constant struggle for many communities and food needs are often amplified during times of crisis. The objective of this article is to describe the progress in our community food policy council partnership by presenting the elements of success in facilitating policy change and programs that have helped our community in rural Pennsylvania respond to a variety of challenges, including the coronavirus disease 2019 (COVID-19) crisis. We also critically examine ongoing challenges and implications for our work. The elements that have contributed to sustaining our collective work include having a common agenda; collaboration; maintaining independent but mutually supporting member organizational goals; valuing those who are most impacted; and continuous communication. By applying these elements of partnership, the council remains focused on healthy food access, particularly during this COVID-19 crisis and ongoing food-related inequities.

ObjectiveThis study focused on understanding positive aspects of cancer among a large, national sample of survivors, 2, 5, and 10years' postcancer diagnosis, who responded to the American Cancer Society Study of Cancer Survivors - II (SCS-II) survey Please tell us about any positive aspects of having cancer. MethodsA sequential mixed methods approach examined (1) thematic categories of positive aspects from cancer survivors (n=5149) and (2) variation in themes by sociodemographics, cancer type, stage of disease, and length of survivorship. ResultsThemes comprised 21 positive aspects within Thornton's typology of benefits that cancer survivors attribute to their illness: life perspectives, self, and relationships. New themes pertaining to gratitude and medical support during diagnosis and treatment, health-related changes, follow-up/surveillance, and helping others emerged that are not otherwise included in widely used existing benefit finding cancer scales. Gratitude and appreciation for life were the most frequently endorsed themes. Sociodemographics and stage of disease were associated with positive aspect themes. Themes were not associated with survivor cohorts. ConclusionsNo differences in perceived positive aspects across survivor cohorts suggest that positive aspects of cancer may exist long after diagnosis for many survivors. However, variation across sociodemographics and clinical variables suggests cancer survivors differentially experience positive aspects from their cancer diagnosis. Implications for cancer survivorsThis analysis provides new information about cancer survivors' perceptions of positive aspects from their cancer and factors associated with benefit finding and personal growth. This information can be useful in further refining quality-of-life measures and interventions for cancer survivors.

Objective: Colonoscopy is a complex procedure that requires bowel preparation, sedation, and has the potential for substantial risk. Given this, we investigated colonoscopy patients' perceived and actual understanding of the procedure.Methods: Consecutive colonoscopy patients were enrolled and surveyed, with their caregivers, immediately prior to their procedure. Demographics, health literacy, socioeconomic status and perceived risks/benefits were assessed. Thematic analysis was conducted on open-ended responses and a 3-level outcome variable was created to categorize correctness of patients' and caregivers' understanding. Multinomial logistic regression was used to determine predictors of response level.Results: Patients (N = 1821) were 77% White, 60% female, and averaged 54 years old; caregivers were demographically similar. Among patients, bivariate analysis revealed that younger age, minority race, and low income, education, and health literacy were associated with incomplete understanding. Multinomial regression revealed that age, education, health literacy, first-time colonoscopy, and perceived risk-benefit difference discriminated among groups. Bivariate and multinomial results for caregivers were similar.Conclusion: Patients and caregivers varied on information, understanding and misconceptions about colonoscopy. Implications are discussed for inadequate: 1. informed consent, 2. bowel preparation, and 3. emotional preparation for cancer detection or adverse events.Practice Implications: Attention should be paid to patients' understanding of the purpose, anatomy, and logistics of colonoscopy, preferably prior to bowel preparation. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

BACKGROUND:Since the landmark study conducted by Huggins and Hodges in 1941, a failure to distinguish between the role of testosterone in prostate cancer development and progression has led to the prevailing opinion that high levels of testosterone increase the risk of prostate cancer. To date, this claim remains unproven.PRESENTATION OF THE HYPOTHESIS:We present a novel dynamic mode of the relationship between testosterone and prostate cancer by hypothesizing that the magnitude of age-related declines in testosterone, rather than a static level of testosterone measured at a single point, may trigger and promote the development of prostate cancer.TESTING THE HYPOTHESIS:Although not easily testable currently, prospective cohort studies with population-representative samples and repeated measurements of testosterone or retrospective cohorts with stored blood samples from different ages are warranted in future to test the hypothesis.IMPLICATIONS OF THE HYPOTHESIS:Our dynamic model can satisfactorily explain the observed age patterns of prostate cancer incidence, the apparent conflicts in epidemiological findings on testosterone and risk of prostate cancer, racial disparities in prostate cancer incidence, risk factors associated with prostate cancer, and the role of testosterone in prostate cancer progression. Our dynamic model may also have implications for testosterone replacement therapy.

OBJECTIVES:We aimed to highlight sociodemographic differences in how patients access colonoscopy. METHODS:We invited all eligible patients (n = 2500) from 2 academy-affiliated colonoscopy centers in Alachua County, Florida (1 free standing, 1 hospital based), to participate in a precolonoscopy survey (September 2011-October 2013); patients agreeing to participate (n = 1841, response rate = 73.6%) received a $5.00 gift card. RESULTS:We found sociodemographic differences in referral pathway, costs, and reasons associated with obtaining the procedure. Patients with the ideal pathway (referred by their regular doctor for age-appropriate screening) were more likely to be Black (compared with other minorities), male, high income, employed, and older. Having the colonoscopy because of symptoms was associated with being female, younger, and having lower income. We found significant differences for 1 previously underestimated barrier, having a spouse to accompany the patient to the procedure. CONCLUSIONS:Patients' facilitators and barriers to colonoscopy differed by sociodemographics in our study, which implies that interventions based on a single facilitator will not be effective for all subgroups of a population.

The objectives of this study are to better understand the lived experience of food insecurity in our community and to examine the impact of a community-based program developed to increase access to local, healthy foods. Participants were given monthly vouchers to spend at local farmers' markets and invited to engage in a variety of community activities. Using a community-based participatory research framework, mixed methods were employed. Survey results suggest that most respondents were satisfied with the program and many increased their fruit and vegetable consumption. However, over 40% of respondents reported a higher level of stress over having enough money to buy nutritious meals at the end of the program. Photovoice results suggest that the program fostered cross-cultural exchanges, and offered opportunities for social networking. Building on the many positive outcomes of the program, community partners are committed to using this research to further develop policy-level solutions to food insecurity.

Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. The goal of the present article is to review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and 2 epidemiologic data analyses. The first analysis addresses determinants of repeat mammography screening use using data from the 2005 National Health Interview Survey. The second analysis addresses disparities in preventive oral health care using data from the 2008 Florida Behavioral Risk Factor Surveillance System Survey.

Purpose Noninfectious comorbidities such as cardiovascular diseases have become increasingly prevalent and occur earlier in life in persons with HIV infection. Despite the emerging body of literature linking environmental exposures to chronic disease outcomes in the general population, the impacts of environmental exposures have received little attention in HIV-infected population. The aim of this study is to investigate whether individuals living with HIV have elevated prevalence of heavy metals compared to non-HIV infected individuals in United States. Methods We used the National Health and Nutrition Examination Survey (NHANES) 2003-2010 to compare exposures to heavy metals including cadmium, lead, and total mercury in HIV infected and non-HIV infected subjects. Results In this cross-sectional study, we found that HIV-infected individuals had higher concentrations of all heavy metals than the non-HIV infected group. In a multivariate linear regression model, HIV status was significantly associated with increased blood cadmium (p=0.03) after adjusting for age, sex, race, education, poverty income ratio, and smoking. However, HIV status was not statistically associated with lead or mercury levels after adjusting for the same covariates. Conclusions Our findings suggest that HIV-infected patients might be significantly more exposed to cadmium compared to non-HIV infected individuals which could contribute to higher prevalence of chronic diseases among HIV-infected subjects. Further research is warranted to identify sources of exposure and to understand more about specific health outcomes.

Academic libraries provide value to their institutions on many levels, one of which is information literacy (IL) instruction. Librarians have the opportunity to guide students through the research process, teach students how to think critically, evaluate resources, and use resources ethically. It is beneficial for librarians to assess student learning after these sessions to demonstrate how libraries support the academic mission of their institutions. This article will address ways to assess the effectiveness of integrating information literacy into college courses by taking a close look at a partnership developed between a professor and two librarians at a small, private four-year institution.

Short-term effects of ambient particulate matter (PM) on cardiopulmonary morbidity and mortality have been consistently documented. However, no study has investigated its long-term effects on breast cancer survival. We selected all female breast cancer cases (n = 255,128) available in the California Surveillance Epidemiology and End Results cancer data. These cases were linked to 1999–2009 California county-level PM daily monitoring data. We examined the effect of PM on breast cancer survival. Results from Kaplan–Meier survival analysis show that female breast cancer cases living in areas with higher levels of PM10 and PM2.5 had a significant shorter survival than those living in areas with lower exposures (p < 0.0001). The results from marginal cox proportional hazards models suggest that exposure to higher PM10 (HR 1.13, 95 % CI 1.02–1.25, per 10 μg/m3) or PM2.5 (HR 1.86, 95 % CI 1.12–3.10, per 5 μg/m3) was significantly associated with early mortality among female breast cancer cases after adjusting for individual-level covariates such as demographic factors, cancer stage and year diagnosed, and county-level covariates such as socioeconomic status and accessibility to medical resources. Interactions between cancer stage and PM were also observed; the effect of PM on survival was more pronounced among individuals diagnosed with early stage cancers. This study suggests that exposure to high levels of PM may have deleterious effects on the length of survival from breast cancer, particularly among women diagnosed with early stage cancers. The findings from this study warrant further investigation.

In order to adjust individual‐level covariate effects for confounding due to unmeasured neighborhood characteristics, we have recently developed conditional pseudolikelihood methods to estimate the parameters of a proportional odds model for clustered ordinal outcomes with complex survey data. The methods require sampling design joint probabilities for each within‐neighborhood pair. In the present article, we develop a similar methodology for a baseline category logit model for clustered multinomial outcomes and for a loglinear model for clustered count outcomes. All of the estimators and asymptotic sampling distributions we present can be conveniently computed using standard logistic regression software for complex survey data, such as sas proc surveylogistic. We demonstrate validity of the methods theoretically and also empirically by using simulations. We apply the new method for clustered multinomial outcomes to data from the 2008 Florida Behavioral Risk Factor Surveillance System survey in order to investigate disparities in frequency of dental cleaning both unadjusted and adjusted for confounding by neighborhood. Copyright © 2012 John Wiley & Sons, Ltd.

In social epidemiology, an individual's neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding by unmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. With a simple random sample and a binary outcome, a conditional logistic regression analysis that treats individuals within a neighborhood as a matched set is a natural method to use. The authors present a generalization of this method for ordinal outcomes and complex sampling designs. The method is based on a proportional odds model and is very simple to program using standard software such as SAS PROC SURVEYLOGISTIC (SAS Institute Inc., Cary, North Carolina). The authors applied the method to analyze racial/ethnic differences in dental preventative care, using 2008 Florida Behavioral Risk Factor Surveillance System survey data. The ordinal outcome represented time since last dental cleaning, and the authors adjusted for individual-level confounding by gender, age, education, and health insurance coverage. The authors compared results with and without additional adjustment for confounding by neighborhood, operationalized as zip code. The authors found that adjustment for confounding by neighborhood greatly affected the results in this example.

When investigating health disparities, it can be of interest to explore whether adjustment for socioeconomic factors at the neighborhood level can account for, or even reverse, an unadjusted difference. Recently, we proposed new methods to adjust the effect of an individual‐level covariate for confounding by unmeasured neighborhood‐level covariates using complex survey data and a generalization of conditional likelihood methods. Generalized linear mixed models (GLMMs) are a popular alternative to conditional likelihood methods in many circumstances. Therefore, in the present article, we propose and investigate a new adaptation of GLMMs for complex survey data that achieves the same goal of adjusting for confounding by unmeasured neighborhood‐level covariates. With the new GLMM approach, one must correctly model the expectation of the unmeasured neighborhood‐level effect as a function of the individual‐level covariates. We demonstrate using simulations that even if that model is correct, census data on the individual‐level covariates are sometimes required for consistent estimation of the effect of the individual‐level covariate. We apply the new methods to investigate disparities in recency of dental cleaning, treated as an ordinal outcome, using data from the 2008 Florida Behavioral Risk Factor Surveillance System (BRFSS) survey. We operationalize neighborhood as zip code and merge the BRFSS data with census data on ZIP Code Tabulated Areas to incorporate census data on the individual‐level covariates. We compare the new results to our previous analysis, which used conditional likelihood methods. We find that the results are qualitatively similar. Copyright © 2012 John Wiley & Sons, Ltd.

Background: Families in Adams County with an income between 160% and 250% of the Federal Poverty Income Guidelines and ineligible for federal food assistance programs were determined to be in the “food gap.” In collaboration with Adams County Farm Fresh Markets and the Center for Public Service at Gettysburg College, the Adams County Food Policy Council developed the Fair Share Program to provide monthly food vouchers and educational sessions to a group of families in the food gap to use at farmers markets in Gettysburg, PA. The goals of the program were to provide families not eligible for federal food assistance with an increased ability to purchase healthy foods, increase fruit and vegetable consumption, support local farms the local economy, and provide nutrition education and support. Purpose: We sought to identify the effectiveness of the pilot Fair Share Program in reaching its goals and to determine ways to improve the program in the future. Methods: 25 families who participated in the Fair Share Program during the summer of 2011 were given surveys at the start of the program, and interviews were conducted with participants at the end of the program. Surveys were given to the participating vendors at the farmers markets at the conclusion of the program. A bivariate analysis of the participant survey was done comparing results from Hispanic and non-Hispanic participants using SPSS Statistics 17.0, while the vendor surveys and interviews were evaluated qualitatively. Results: There were several noteworthy differences between the habits and perceptions of the Hispanic and non-Hispanic participants, including fruit and vegetable consumption patterns, reasons for not shopping at the farmer’s markets, and where food is typically obtained from. Interviews indicated that both participants and vendors had overall positive experiences with the program even though challenges including price and language differences were experienced. Conclusion: The Fair Share Project reached its goals and had a positive impact on the community. Improvements should be made if the program is to be continued in the future to address the challenges participants faced while participating, and there is strong support for continuation and extension of the program.

Jennifer L. Nguyen, MPH1; Jessica R. Schumacher, PhD2; Amy B. Dailey, PhD3; Tracey E. Barnett PhD1; Thomas J. George Jr. MD3; Evelyn King-Marshall, MPH1; Felix Lorenzo, BS1; Emmett Martin, MPH1; Jane McGinley, BS3; Regina Reed1; Gregory Riherd, BS1; Elisa Rodriguez, PhD1; Shahnaz Sultan, MD3; Britany Telford, BS1; & Barbara A. Curbow, PhD1 University of Florida, Department of Behavioral Science & Community Health1 University of Wisconsin at Madison, Health Innovation Program2, Gettysburg College, Department of Health Sciences, Gettysburg, Pennsylvania3 University of Florida, College of Medicine4

Background: Despite a considerable number of studies describing the relationship between area-level socioeconomic conditions and mammography screening, definitive conclusions have yet to be drawn. The aim of this study was to examine the relationship between area-level socioeconomic position (SEP) and repeat mammography screening, using nationwide U.S. census SEP data linked to a nationally representative sample of women who participated in the 2005 National Health Interview Survey (NHIS). Methods: An area-level SEP index using 2000 U.S. census tract data was constructed and categorized into quartiles, including information on unemployment, poverty, housing values, annual family income, education, and occupation. Repeat mammography utilization (dichotomous variable) was defined as having three mammograms over the course of 6 years (24-month interval), which must have included a recent mammogram (in past 2 years). Results were obtained by ordinary multivariable logistic regression for survey data. Women ages 46 to 79 years (n = 7,352) were included in the analysis. Results: In a model adjusted for sociodemographics, health care factors, and known correlates of mammography screening, women living in more disadvantaged areas had lower odds of engaging in repeat mammography than women living in the most advantaged areas [OR comparing quartile 4 (most disadvantaged) to quartile 1 (most advantaged) = 0.63; 95% confidence interval, 0.50–0.80]. Conclusion: The results of this nationwide study support the hypothesis that area-level SEP is independently associated with mammography utilization. Impact: These findings underscore the importance of addressing area-level social inequalities, if uptake of mammography screening guidelines is to be realized across all social strata. Cancer Epidemiol Biomarkers Prev; 20(11); 2331–44. ©2011 AACR.

We show how to use generalized linear mixed models to adjust for confounding by cluster of the effect of a within-cluster covariate. We derive estimators for both a cluster-specific causal effect and a population-averaged causal effect.