Right cerebellar-left frontal (RC-LF) white matter integrity (WMI) has been associated with working memory. However, prior studies have employed measures of working memory that include processing speed and attention. We examined the relationships between the RC-LF WMI and processing speed, attention, and working memory to clarify the relationship of RC-LF WMI with a specific cognitive function. Right superior longitudinal fasciculus II (SLF II) WMI and visual attention were included as a negative control tract and task to demonstrate a double dissociation.

Adult survivors of childhood brain tumors [n = 29, age: M = 22 years (SD = 5), 45% female] and demographically matched controls were recruited (n = 29). Tests of auditory attention span, working memory, and visual attention served as cognitive measures. Participants completed a 3-T MRI diffusion-weighted imaging scan. Fractional anisotropy (FA) and radial diffusivity (RD) served as WMI measures. Partial correlations between WMI and cognitive scores included controlling for type of treatment.

A correlational double dissociation was found. RC-LF WMI was associated with auditory attention (FA: r = .42, p = .03; RD: r = −.50, p = .01) and was not associated with visual attention (FA: r = −.11, p = .59; RD: r = −.11, p = .57). SLF II FA WMI was associated with visual attention (FA: r = .44, p = .02; RD: r = −.17, p = .40) and was not associated with auditory attention (FA: r = .24, p = .22; RD: r = −.10, p = .62).

The results show that RC-LF WMI is associated with auditory attention span rather than working memory per se and provides evidence for a specificity based on the correlational double dissociation.

Prior studies indicate greater disease burden for obesity among rural compared with urban residents but no differences for mood disorder based on geographic location. Recent attention has focused on the need to examine regional rural–urban disparities in disease burden. We focused on mood disorders and obesity prevalence within three southeastern Minnesota counties served by the Mayo Clinic Center for Translational Science Award, in Rochester, Minnesota, as these were top priorities identified in community health needs assessments.

Cross-sectional study to assess the association of rural–urban locality on 5-year (2009–2014) prevalence of mood disorder and obesity obtained using the Rochester Epidemiological Project medical records linkage system, among subjects residing in three mixed rural–urban counties on April 1, 2014. Multivariable analyses adjusted for demographics, socioeconomic status using an individual housing-based measure, and counties.

The study cohort (percent rural location) included 91,202 (15%) for Olmsted, 10,197 (51%) in Dodge, and 10,184 (57%) in Wabasha counties. On multivariate analysis, 5-year prevalence of mood disorders and obesity was significantly greater for urban compared with rural residents, after adjusting for confounders; odds ratios (95% confidence intervals): 1.21 (1.17–1.26), P < 0.001, and 1.05 (1.01–1.10), P = 0.016, respectively. Observed effects were not modified in additional models adjusted for health care utilization (HCU; ≥1 general medical examination visit and flu vaccination).

Rural–urban health disparities for burden of mood disorders and obesity are independent of socioeconomic status and HCU in a Midwestern community. It is important to assess potential regional heterogeneity of rural–urban disparities on health outcomes.

Hunger relief agencies have a limited capacity to monitor the nutritional quality of their food. Validated measures of food environments, such as the Healthy Eating Index-2010 (HEI-2010), are challenging to use due to their time intensity and requirement for precise nutrient information. A previous study used out-of-sample predictions to demonstrate that an alternative measure correlated well with the HEI-2010. The present study revised the Food Assortment Scoring Tool (FAST) to facilitate implementation and tested the tool’s performance in a real-world food pantry setting.

We developed a FAST measure with thirteen scored categories and thirty-one sub-categories. FAST scores were generated by sorting and weighing foods in categories, multiplying each category’s weight share by a healthfulness parameter and summing the categories (range 0–100). FAST was implemented by recording all food products moved over five days. Researchers collected FAST and HEI-2010 scores for food availability and foods selected by clients, to calculate correlations.

Five food pantries in greater Minneapolis/St. Paul, Minnesota, USA.

Food carts of sixty food pantry clients.

The thirteen-category FAST correlated well with the HEI-2010 in prediction models (r = 0·68). FAST scores averaged 61·5 for food products moved, 63·8 for availability and 62·5 for client carts. As implemented in the real world, FAST demonstrated good correlation with the HEI-2010 (r = 0·66).

The FAST is a flexible, valid tool to monitor the nutritional quality of food in pantries. Future studies are needed to test its use in monitoring improvements in food pantry nutritional quality over time.

The impact of healthcare system integration on infection prevention programs is unknown. Using catheter-associated urinary tract infection (CAUTI) prevention as an example, we hypothesize that US Department of Veterans Affairs (VA) nursing homes have a more robust infection prevention infrastructure due to integration and centralization compared with non–VA nursing homes.

VA and non-VA nursing homes participating in the AHRQ Safety Program for Long-Term Care collaborative.

Nursing homes provided baseline information about their infection prevention programs to assess strengths and gaps related to CAUTI prevention via a needs assessment questionnaire.

A total of 353 of 494 nursing homes from 41 states (71%; 47 VA and 306 non-VA facilities) responded. VA nursing homes reported more hours per week devoted to infection prevention-related activities (31 vs 12 hours; P<.001) and were more likely to have committees that reviewed healthcare-associated infections. Compared with non-VA facilities, a higher percentage of VA nursing homes reported tracking CAUTI rates (94% vs 66%; P<.001), sharing CAUTI data with leadership (94% vs 70%; P=.014) and with nursing personnel (85% vs 56%, P=.003). However, fewer VA nursing homes reported having policies for appropriate catheter use (64% vs 81%; P=.004) and catheter insertion (83% vs 94%; P=.004).

Among nursing homes participating in an AHRQ-funded collaborative, VA and non-VA nursing homes differed in their approach to CAUTI prevention. Best practices from both settings should be applied universally to create an optimal infection prevention program within emerging integrated healthcare systems.

Infect Control Hosp Epidemiol 2017;38:287–293

To demonstrate the feasibility of applying the Healthy Eating Index-2010 (HEI-2010) to the hunger relief setting, specifically by assessing the nutritional quality of foods ordered by food shelves (front-line food provider) from food banks (warehouse of foods).

This Healthy FOOD (Feedback On Ordering Decisions) observational study used electronic invoices detailing orders made by 269 food shelves in 2013 and analysed in 2015 from two large Minnesota, USA food banks to generate HEI-2010 scores. Initial development and processing procedures are described.

The average total HEI-2010 score for the 269 food shelves was 62·7 out of 100 with a range from 28 to 82. Mean component scores for total protein foods, total vegetables, fatty acids, and seafood and plant proteins were the highest. Mean component score for whole grains was the lowest followed by dairy, total fruits, refined grains and sodium. Food shelves located in micropolitan areas and the largest food shelves had the highest HEI-2010 scores. Town/rural and smaller food shelves had the lowest scores. Monthly and seasonal differences in scores were detected. Limitations to this approach are identified.

Calculating HEI-2010 for food shelves using electronic invoice data is novel and feasible, albeit with limitations. HEI-2010 scores for 2013 identify room for improvement in nearly all food shelves, especially the smallest agencies. The utility of providing HEI-2010 scores to decision makers in the hunger relief setting is an issue requiring urgent study.