OBJECTIVES/GOALS: To tackle population-level health disparities, quality dashboards can leverage individual socioeconomic status (SES) measures, which are not always readily accessible. This study aimed to assess the feasibility of a population health management strategy for colorectal cancer (CRC) screening rates using the HOUSES index and heatmap analysis. METHODS/STUDY POPULATION: We applied the 2019 Minnesota Community Measurement data for optimal CRC screening to eligible Mayo Clinic Midwest panel patients. SES was defined by HOUSES index, a validated SES measure based on publicly available property data for the U.S. population. We first assessed the association of suboptimal CRC screening rate with HOUSES index adjusting for age, sex, race/ethnicity, comorbidity, and Zip-code level deprivation by using a mixed effects logistic regression model. We then assessed changes in ranking for performance of individual clinics (i.e., % of patients with optimal CRC screening rate) before and after adjusting for HOUSES index. Geographical hotspots of high proportions of low SES AND high proportions of suboptimal CRC screening were superimposed to identify target population for outreach. RESULTS/ANTICIPATED RESULTS: A total of 58,382 adults from 41 clinics were eligible for CRC screening assessment in 2019 (53% Female). Patients with lower SES defined by HOUSES quartile 1-3 have significantly lower CRC screening compared to those with highest SES (HOUSES quartile 4) (adj. OR [95% CI]: 0.52 [0.50-0.56] for Q1, 0.66 [0.62-0.70] for Q2, and 0.81 [0.76-0.85]) for Q3). Ranking of 26 out of 41 (63%) clinics went down after adjusting for HOUSES index suggesting disproportionately higher proportion of underserved patients with suboptimal CRC screening. We were able to successfully identify hotspots of suboptimal CRC (area with greater than 130% of expected value) and overlay with higher proportion of underserved population (HOUSES Q1), which can be used for data-driven targeted interventions such as mobile health clinics. DISCUSSION/SIGNIFICANCE: HOUSES index and associated heatmap analysis can contribute to advancing health equity. This approach can aid health care organizations in meeting the newly established standards by The Joint Commission, which have elevated health equity to a national safety priority.

To investigate the symptoms of SARS-CoV-2 infection, their dynamics and their discriminatory power for the disease using longitudinally, prospectively collected information reported at the time of their occurrence. We have analysed data from a large phase 3 clinical UK COVID-19 vaccine trial. The alpha variant was the predominant strain. Participants were assessed for SARS-CoV-2 infection via nasal/throat PCR at recruitment, vaccination appointments, and when symptomatic. Statistical techniques were implemented to infer estimates representative of the UK population, accounting for multiple symptomatic episodes associated with one individual. An optimal diagnostic model for SARS-CoV-2 infection was derived. The 4-month prevalence of SARS-CoV-2 was 2.1%; increasing to 19.4% (16.0%–22.7%) in participants reporting loss of appetite and 31.9% (27.1%–36.8%) in those with anosmia/ageusia. The model identified anosmia and/or ageusia, fever, congestion, and cough to be significantly associated with SARS-CoV-2 infection. Symptoms’ dynamics were vastly different in the two groups; after a slow start peaking later and lasting longer in PCR+ participants, whilst exhibiting a consistent decline in PCR- participants, with, on average, fewer than 3 days of symptoms reported. Anosmia/ageusia peaked late in confirmed SARS-CoV-2 infection (day 12), indicating a low discrimination power for early disease diagnosis.

Energy deficit is common during prolonged periods of strenuous physical activity and limited sleep, but the extent to which appetite suppression contributes is unclear. The aim of this randomised crossover study was to determine the effects of energy balance on appetite and physiological mediators of appetite during a 72-h period of high physical activity energy expenditure (about 9·6 MJ/d (2300 kcal/d)) and limited sleep designed to simulate military operations (SUSOPS). Ten men consumed an energy-balanced diet while sedentary for 1 d (REST) followed by energy-balanced (BAL) and energy-deficient (DEF) controlled diets during SUSOPS. Appetite ratings, gastric emptying time (GET) and appetite-mediating hormone concentrations were measured. Energy balance was positive during BAL (18 (sd 20) %) and negative during DEF (–43 (sd 9) %). Relative to REST, hunger, desire to eat and prospective consumption ratings were all higher during DEF (26 (sd 40) %, 56 (sd 71) %, 28 (sd 34) %, respectively) and lower during BAL (–55 (sd 25) %, −52 (sd 27) %, −54 (sd 21) %, respectively; P condition < 0·05). Fullness ratings did not differ from REST during DEF, but were 65 (sd 61) % higher during BAL (P condition < 0·05). Regression analyses predicted hunger and prospective consumption would be reduced and fullness increased if energy balance was maintained during SUSOPS, and energy deficits of ≥25 % would be required to elicit increases in appetite. Between-condition differences in GET and appetite-mediating hormones identified slowed gastric emptying, increased anorexigenic hormone concentrations and decreased fasting acylated ghrelin concentrations as potential mechanisms of appetite suppression. Findings suggest that physiological responses that suppress appetite may deter energy balance from being achieved during prolonged periods of strenuous activity and limited sleep.

X-ray micro-computed tomography (μCT) is a technique which can obtain three-dimensional images of a sample, including its internal structure, without the need for destructive sectioning. Here, we review the capability of the technique and examine its potential to provide novel insights into the lifestyles of parasites embedded within host tissue. The current capabilities and limitations of the technology in producing contrast in soft tissues are discussed, as well as the potential solutions for parasitologists looking to apply this technique. We present example images of the mouse whipworm Trichuris muris and discuss the application of μCT to provide unique insights into parasite behaviour and pathology, which are inaccessible to other imaging modalities.

Variation in interference relationships have been shown for a number of crop-weed associations and may have an important effect on the implementation of decision support systems for weed management. Multiyear field experiments were conducted at eight locations to determine the stability of corn-foxtail interference relationships across years and locations. Two coefficients (I and A) of a rectangular hyperbola equation were estimated for each data set using nonlinear regression procedures. The I and A coefficients represent percent corn yield loss as foxtail density approaches zero and maximum percent corn yield loss, respectively. The coefficient I was stable across years at two locations and varied across years at four locations. Maximum yield loss (A) varied between years at one location. Both coefficients varied among locations. Although 3 to 4 foxtail plants m−-1 row was a conservative estimate of the single-year economic threshold (Tc ) of foxtail density, variation in I and A resulted in a large variation in Tc . Therefore, the utility of using common coefficient estimates to predict future crop yield loss from foxtail interference between years or among locations within a region is limited.

Variation in crop–weed interference relationships has been shown for a number of crop–weed mixtures and may have an important influence on weed management decision-making. Field experiments were conducted at seven locations over 2 yr to evaluate variation in common lambsquarters interference in field corn and whether a single set of model parameters could be used to estimate corn grain yield loss throughout the northcentral United States. Two coefficients (I and A) of a rectangular hyperbola were estimated for each data set using nonlinear regression analysis. The I coefficient represents corn yield loss as weed density approaches zero, and A represents maximum percent yield loss. Estimates of both coefficients varied between years at Wisconsin, and I varied between years at Michigan. When locations with similar sample variances were combined, estimates of both I and A varied. Common lambsquarters interference caused the greatest corn yield reduction in Michigan (100%) and had the least effect in Minnesota, Nebraska, and Indiana (0% yield loss). Variation in I and A parameters resulted in variation in estimates of a single-year economic threshold (0.32 to 4.17 plants m−1 of row). Results of this study fail to support the use of a common yield loss–weed density function for all locations.