Quantitative assessment of participant knowledge and evaluation of participant satisfaction in the CARES training program

BACKGROUND: The purpose of the Community Alliance for Research Empowering Social change (CARES) training program was to (1) train community members on evidence-based public health, (2) increase their scientific literacy, and (3) develop the infrastructure for community-based participatory research (CBPR). OBJECTIVES: We assessed participant knowledge and evaluated participant satisfaction of the CARES training program to identify learning needs, obtain valuable feedback about the training, and ensure learning objectives were met through mutually beneficial CBPR approaches. METHODS: A baseline assessment was administered before the first training session and a follow-up assessment and evaluation was administered after the final training session. At each training session a pretest was administered before the session and a posttest and evaluation were administered at the end of the session. After training session six, a mid-training evaluation was administered. We analyze results from quantitative questions on the assessments, pre- and post-tests, and evaluations. RESULTS: CARES fellows knowledge increased at follow-up (75% of questions were answered correctly on average) compared with baseline (38% of questions were answered correctly on average) assessment; post-test scores were higher than pre-test scores in 9 out of 11 sessions. Fellows enjoyed the training and rated all sessions well on the evaluations. CONCLUSIONS: The CARES fellows training program was successful in participant satisfaction and increasing community knowledge of public health, CBPR, and research method ology. Engaging and training community members in evidence-based public health research can develop an infrastructure for community–academic research partnerships

Breast reconstruction after mastectomy at a comprehensive cancer center

BACKGROUND: Breast reconstruction after mastectomy is an integral part of breast cancer treatment that positively impacts quality of life in breast cancer survivors. Although breast reconstruction rates have increased over time, African American women remain less likely to receive breast reconstruction compared to Caucasian women. National Cancer Institute-designated Comprehensive Cancer Centers, specialized institutions with more standardized models of cancer treatment, report higher breast reconstruction rates than primary healthcare facilities. Whether breast reconstruction disparities are reduced for women treated at comprehensive cancer centers is unclear. The purpose of this study was to further investigate breast reconstruction rates and determinants at a comprehensive cancer center in St. Louis, Missouri. METHODS: Sociodemographic and clinical data were obtained for women who received mastectomy for definitive surgical treatment for breast cancer between 2000 and 2012. Logistic regression was used to identify factors associated with the receipt of breast reconstruction. RESULTS: We found a breast reconstruction rate of 54 % for the study sample. Women who were aged 55 and older, had public insurance, received unilateral mastectomy, and received adjuvant radiation therapy were significantly less likely to receive breast reconstruction. African American women were 30 % less likely to receive breast reconstruction than Caucasian women. CONCLUSION: These findings suggest that racial disparities in breast reconstruction persist in comprehensive cancer centers. Future research should further delineate the determinants of breast reconstruction disparities across various types of healthcare institutions. Only then can we develop interventions to ensure all eligible women have access to breast reconstruction and the improved quality of life it affords breast cancer survivors

Using Small-Area Analysis to Estimate County-Level Racial Disparities in Obesity Demonstrating the Necessity of Targeted Interventions

Data on the national and state levels is often used to inform policy decisions and strategies designed to reduce racial disparities in obesity. Obesity-related health outcomes are realized on the individual level, and policies based on state and national-level data may be inappropriate due to the variations in health outcomes within and between states. To examine county-level variation of obesity within states, we use a small-area analysis technique to fill the void for county-level obesity data by race. Five years of Behavioral Risk Factor Surveillance System data are used to estimate the prevalence of obesity by county, both overall and race-stratified. A modified weighting system is used based on demographics at the county level using 2010 census data. We fit a multilevel reweighted regression model to obtain county-level prevalence estimates by race. We compare the distribution of prevalence estimates of non-Hispanic Blacks to non-Hispanic Whites. For 25 of the 26 states included in our analysis there is a statistically significant difference between within-state county-level average obesity prevalence rates for non-Hispanic Whites and non-Hispanic Blacks. This study provides information needed to target disparities interventions and resources to the local areas with greatest need; it also identifies the necessity of doing so

Do subjective measures improve the ability to identify limited health literacy in a clinical setting?

BACKGROUND: Existing health literacy assessments developed for research purposes have constraints that limit their utility for clinical practice, including time requirements and administration protocols. The Brief Health Literacy Screen (BHLS) consists of 3 self-administered Single-Item Literacy Screener (SILS) questions and obviates these clinical barriers. We assessed whether the addition of SILS items or the BHLS to patient demographics readily available in ambulatory clinical settings reaching underserved patients improves the ability to identify limited health literacy. METHODS: We analyzed data from 2 cross-sectional convenience samples of patients from an urban academic emergency department (n = 425) and a primary care clinic (n = 486) in St. Louis, Missouri. Across samples, health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine-Revised (REALM-R), Newest Vital Sign (NVS), and the BHLS. Our analytic sample consisted of 911 adult patients, who were primarily female (62%), black (66%), and had at least a high school education (82%); 456 were randomly assigned to the estimation sample and 455 to the validation sample. RESULTS: The analysis showed that the best REALM-R estimation model contained age, sex, education, race, and 1 SILS item (difficulty understanding written information). In validation analysis this model had a sensitivity of 62%, specificity of 81%, a positive likelihood ratio (LR(+)) of 3.26, and a negative likelihood ratio (LR(−)) of 0.47; there was a 28% misclassification rate. The best NVS estimation model contained the BHLS, age, sex, education and race; this model had a sensitivity of 77%, specificity of 72%, LR(+) of 2.75, LR(−) of 0.32, and a misclassification rate of 25%. CONCLUSIONS: Findings suggest that the BHLS and SILS items improve the ability to identify patients with limited health literacy compared with demographic predictors alone. However, despite being easier to administer in clinical settings, subjective estimates of health literacy have misclassification rates >20% and do not replace objective measures; universal precautions should be used with all patients

Weight perceptions and perceived risk for diabetes and heart disease among overweight and obese women, Suffolk County, New York, 2008

INTRODUCTION: Many Americans fail to accurately identify themselves as overweight and underestimate their risk for obesity-related diseases. The purpose of this study was to investigate associations between weight perceptions and perceived risk for diabetes and heart disease among overweight or obese women. METHODS: We examined survey responses from 397 overweight or obese female health center patients on disease risk perceptions and weight perceptions. We derived odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression analyses to examine predictors of perceived risk for diabetes and heart disease. We further stratified results by health literacy. RESULTS: Perceiving oneself as overweight (OR, 2.78; 95% CI, 1.16-6.66), believing that being overweight is a personal health problem (OR, 2.46; 95% CI, 1.26-4.80), and family history of diabetes (OR, 3.22; 95% CI, 1.53-6.78) were associated with greater perceived risk for diabetes. Perceiving oneself as overweight (OR, 4.33; 95% CI, 1.26-14.86) and family history of heart disease (OR, 2.25; 95% CI, 1.08-4.69) were associated with greater perceived risk for heart disease. Among respondents with higher health literacy, believing that being overweight was a personal health problem was associated with greater perceived risk for diabetes (OR, 4.91; 95% CI, 1.68-14.35). Among respondents with lower health literacy, perceiving oneself as overweight was associated with greater perceived risk for heart disease (OR, 4.69; 95% CI, 1.02-21.62). CONCLUSION: Our findings indicate an association between accurate weight perceptions and perceived risk for diabetes and heart disease in overweight or obese women. This study adds to research on disease risk perceptions in at-risk populations

Detecting Multiple Change Points in Piecewise Constant Hazard Functions

The National Cancer Institute (NCI) suggests a sudden reduction in prostate cancer mortality rates, likely due to highly successful treatments and screening methods for early diagnosis. We are interested in understanding the impact of medical breakthroughs, treatments, or interventions, on the survival experience for a population. For this purpose, estimating the underlying hazard function, with possible time change points, would be of substantial interest, as it will provide a general picture of the survival trend and when this trend is disrupted. Increasing attention has been given to testing the assumption of a constant failure rate against a failure rate that changes at a single point in time. We expand the set of alternatives to allow for the consideration of multiple change-points, and propose a model selection algorithm using sequential testing for the piecewise constant hazard model. These methods are data driven and allow us to estimate not only the number of change points in the hazard function but where those changes occur. Such an analysis allows for better understanding of how changing medical practice affects the survival experience for a patient population. We test for change points in prostate cancer mortality rates using the NCI Surveillance, Epidemiology,and End Results dataset

Racial and ethnic heterogeneity in self-reported diabetes prevalence trends across Hispanic subgroups, National Health Interview Survey, 1997–2012

INTRODUCTION: We examined racial/ethnic heterogeneity in self-reported diabetes prevalence over 15 years. METHODS: We used National Health Interview Survey data for 1997 through 2012 on 452,845 adults aged 18 years or older. Annual self-reported diabetes prevalence was estimated by race/ethnicity and education. We tested for trends over time by education and race/ethnicity. We also analyzed racial/ethnic and education trends in average annual prevalence. RESULTS: During the 15 years studied, diabetes prevalence differed significantly by race/ethnicity (P < .001) and by Hispanic subgroup (P < .001). Among participants with less than a high school education, the 5-year trend in diabetes prevalence was highest among Cubans and Cuban Americans (β(5YR) = 4.8, P = .002), Puerto Ricans (β(5YR) = 2.2, P = .06), non-Hispanic blacks (β(5YR) = 2.2, P < .001), and non-Hispanic whites (β(5YR) = 2.1, P < .001). Among participants with more than a high school education, non-Hispanic blacks had the highest average annual prevalence (5.5%) and Puerto Ricans had the highest 5-year trend in annual diabetes prevalence (β(5YR) = 2.6, P = .001). CONCLUSIONS: In this representative sample of US adults, results show ethnic variations in diabetes prevalence. The prevalence of diabetes is higher among Hispanics than among non-Hispanic whites, unevenly distributed across Hispanic subgroups, and more pronounced over time and by education. Findings support disaggregation of data for racial/ethnic populations in the United States to monitor trends in diabetes disparities and the use of targeted, culturally appropriate interventions to prevent diabetes