Effects of Buffer Size and Shape on the Association of Neighborhood SES and Adult Fruit and Vegetable Consumption.

Introduction: Neighborhood environment factors are relevant for dietary behaviors, but associations between home neighborhood context and disease prevention behaviors vary depending on the definition of neighborhood. The present study uses a publicly available dataset to examine whether associations between neighborhood socioeconomic status (NSES) and fruit/vegetable (FV) consumption vary when NSES is defined by different neighborhood sizes and shapes. Methods: We analyzed data from 1,736 adults with data in GeoFLASHE, a geospatial extension of the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating Study (FLASHE). We examined correlations of NSES values across neighborhood buffer shapes (circular or street network) and sizes (ranging from 400 to 1,200 m) and ran weighted simple and multivariable regressions modeling frequency of FV consumption by NSES for each neighborhood definition. Regressions were also stratified by gender. Results: NSES measures were highly correlated across various neighborhood buffer definitions. In models adjusted for socio-demographics, circular buffers of all sizes and street buffers 750 m and larger were significantly associated with FV consumption frequency for women only. Conclusion: NSES may be particularly relevant for women's FV consumption, and further research can examine whether these associations are explained by access to food stores, food shopping behavior, and/or psychosocial variables. Although different NSES buffers are highly correlated, researchers should conceptually determine spatial areas a priori.

Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures.

Health disparities are commonplace and of broad interest to policy makers, but are also challenging to measure and communicate. The Health Disparity Calculator software (HD*Calc, v1.2.4) offers Monte Carlo simulation (MCS)-based confidence interval (CI) estimation of eleven disparity measures. The MCS approach provides accurate CI estimation, except when data are scarce (e.g., rare cancers). To address sparse data challenges to CI estimation, we propose two solutions: 1) employing the gamma distribution in the MCS and 2) utilizing a zero-inflated Poisson estimate for Poisson sampling in simulation experiments. We evaluate each solution through simulation studies using female breast, female brain, lung, and cervical cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. We compare the coverage probabilities (CPs) of eleven health disparity measures based on simulated datasets. The truncated normal distribution implemented in the MCS with the standard Poisson samples (the default setting of HD*Calc) leads to less-than-optimal coverage probabilities (<95%). When both the gamma distribution and the estimated mean from the zero-inflated Poisson are used for the MCS, the coverage probabilities are close to the nominal level of 95%. Simulation studies also demonstrate that collapsing age categories for better CI estimation is not a pragmatic solution.

Statistical inferences of extended concentration indices for directly standardized rates.

The relative concentration index (RCI) and the absolute concentration index (ACI) have been widely used for monitoring health disparities with ranked health determinants. The RCI has been extended to allow value judgments about inequality aversion by Pereira in 1998 and by Wagstaff in 2002. Previous studies of the extended RCI have focused on survey sample data. This paper adapts the extended RCI for use with directly standardized rates (DSRs) calculated from population-based surveillance data. A Taylor series linearization (TL)-based variance estimator is developed and evaluated using simulations. A simulation-based Monte Carlo (MC) variance estimator is also evaluated as a comparison. Following Wagstaff's approach in 1991, we extend the ACI for use with DSRs. In all simulations, both the TL and MC methods produce valid variance estimates. The TL variance estimator has a simple, closed form that is attractive to users without sophisticated programming skills. The TL and MC estimators have been incorporated into a beta version of the National Cancer Institute's Health Disparities Calculator, a free statistical software tool that enables the estimation of 11 commonly used summary measures of health disparities for DSRs.

Variance Estimation and Confidence Intervals for 11 Commonly Used Health Disparity Measures.

There is increased interest in eliminating health disparities in the United States and worldwide. Broadly defined, health disparities refer to preventable inequalities in health status, such as cancer to ethnicity, socioeconomic status, gender, education, environment, and geographic locations. To make informed health policy decisions, it is essential to precisely measure the magnitude of disparities and assess trends over time. The Health Disparities Calculator (HD*Calc) is free statistical software that calculates 11 commonly used measures of health disparities and provides corresponding 95% CIs for the 11 measures using either an analytic method or a Monte Carlo simulation-based method; however, the derivation of SEs and coverage properties of the CIs have not been formally evaluated. We used simulation studies to assess the coverage properties of these CIs. We have also conducted bias analyses for measures implemented in HD*Calc using age-adjusted cancer incidence rates from national, state, and county level SEER data. The results of these analyses indicate that HD*Calc should be used with caution to construct CIs for some health disparity measures when the proportion of zero event counts is greater than 25%.

Urban/Rural Differences in Breast and Cervical Cancer Incidence: The Mediating Roles of Socioeconomic Status and Provider Density.

BACKGROUND: Breast and cervical cancer incidence vary by urbanicity, and several ecological factors could contribute to these patterns. In particular, cancer screening or other sociodemographic and health care system variables could explain geographic disparities in cancer incidence. METHODS: Governmental and research sources provided data on 612 counties in the Surveillance, Epidemiology, and End Results program for rural-urban continuum code, socioeconomic status (SES) quintile, percent non-Hispanic White residents, density of primary care physicians, cancer screening, and breast and cervical cancer incidence rates (2009-2013). Ecological mediation analyses used weighted least squares regression to examine whether candidate mediators explained the relationship between urbanicity and cancer incidence. RESULTS: As urbanicity increased, so did breast cancer incidence (߈ = 0.23; p < .001). SES quintile and density of primary care physicians mediated this relationship, whereas percent non-Hispanic White suppressed it (all p < .05); county-level mammography levels did not contribute to the relationship. After controlling for these variables, urbanicity and breast cancer incidence were no longer associated (߈ = 0.11; p > .05). In contrast, as urbanicity increased, cervical cancer incidence decreased (߈ = -0.33; p < .001). SES quintile and density of primary care physicians mediated this relationship (both p < .05); percent non-Hispanic White and Pap screening levels did not contribute to the relationship. After controlling for these variables, the relationship between urbanicity and cervical cancer incidence remained significant (߈ = -0.13; p < .05). CONCLUSIONS: County-level SES and density of primary care physicians explained the relationships between urbanicity and breast and cervical cancer incidence. Improving these factors in more rural counties could ameliorate geographic disparities in breast and cervical cancer incidence.

Availability of TNM Staging Data Elements in the Medical Record and Training Needs Assessment: Results from the 2014 SEER Training Needs Assessment for TNM Study.

BACKGROUND: In 2016, the cancer registry community will directly assign T, N and M components of stage. The Surveillance, Epidemiology, and End Results program implemented a field study to determine how often T, N and M were not available in the medical record, requiring the registrar to directly assign clinical or pathologic TNM stage components. The field study also identified specific training needs. METHODS: T, N and M status were collected from multiple sources within medical records for a total of 280 cases, 56 each from breast, prostate, colon, lung, and ovarian cancer. TNM data elements were also directly assigned by a series of reviewers and by study participants using the medical records with TNM information redacted. Availability of physician-assigned TNM was estimated from the medical record. Also, participant responses were compared to preferred answers. RESULTS: Pathologic T, N and M were available more often in the medical records than were clinical values and varied by site. Pathologic T and N were available for about two-thirds of the cases, but the clinical elements were available for only about 20% of cases. The agreement between participant responses and review panel assignments varied by data element and cancer site. Agreement was modest for most data elements and cancer sites, ranging from 54% for clinical T to 92% for clinical M for all cancer sites combined. CONCLUSIONS: The data elements for TNM staging and stage group were often missing from the medical records, so registrars in the field will need to assign TNM frequently. Furthermore, the results of this study strongly suggest that more training is required, even among those who currently assign TNM.

Knowledge of energy balance guidelines and associated clinical care practices: the U.S. National Survey of Energy Balance Related Care among Primary Care Physicians.

OBJECTIVE: To assess primary care physicians' (PCPs) knowledge of energy balance related guidelines and the association with sociodemographic characteristics and clinical care practices. METHOD: As part of the 2008 U.S. nationally representative National Survey of Energy Balance Related Care among Primary Care Physicians (EB-PCP), 1776 PCPs from four specialties who treated adults (n=1060) or children and adolescents (n=716) completed surveys on sociodemographic information, knowledge of energy balance guidelines, and clinical care practices. RESULTS: EB-PCP response rate was 64.5%. For PCPs treating children, knowledge of guidelines for healthy BMI percentile, physical activity, and fruit and vegetables intake was 36.5%, 27.0%, and 62.9%, respectively. For PCPs treating adults, knowledge of guidelines for overweight, obesity, physical activity, and fruit and vegetables intake was 81.4%, 81.3%, 70.9%, and 63.5%, respectively. Generally, younger, female physicians were more likely to exhibit correct knowledge. Knowledge of weight-related guidelines was associated with assessment of body mass index (BMI) and use of BMI-for-age growth charts. CONCLUSION: Knowledge of energy balance guidelines among PCPs treating children is low, among PCPs treating adults it appeared high for overweight and obesity-related clinical guidelines and moderate for physical activity and diet, and was mostly unrelated to clinical practices among all PCPs.

MEASURING EVERYDAY RACIAL/ETHNIC DISCRIMINATION IN HEALTH SURVEYS: How Best to Ask the Questions, in One or Two Stages, Across Multiple Racial/Ethnic Groups?

While it is clear that self-reported racial/ethnic discrimination is related to illness, there are challenges in measuring self-reported discrimination or unfair treatment. In the present study, we evaluate the psychometric properties of a self-reported instrument across racial/ ethnic groups in a population-based sample, and we test and interpret findings from applying two different widely-used approaches to asking about discrimination and unfair treatment. Even though we found that the subset of items we tested tap into a single underlying concept, we also found that different groups are more likely to report on different aspects of discrimination. Whether race is mentioned in the survey question affects both frequency and mean scores of reports of racial/ethnic discrimination. Our findings suggest caution to researchers when comparing studies that have used different approaches to measure racial/ethnic discrimination and allow us to suggest practical empirical guidelines for measuring and analyzing racial/ethnic discrimination. No less important, we have developed a self-reported measure of recent racial/ethnic discrimination that functions well in a range of different racial/ethnic groups and makes it possible to compare how racial/ethnic discrimination is associated with health disparities among multiple racial/ethnic groups.