The Healthy Communities Study is designed to assess relationships between characteristics of community programs and policies targeting childhood obesity and children’s BMI, diet, and physical activity. The study involved a complex data collection protocol implemented over a 2-year period (2013–2015) across a diverse sample of up to 125 communities, defined as public high school catchment areas. The protocol involved baseline assessment within each community that included in-person or telephone interviews regarding community programs and policies and in-home collection of BMI, nutritional, and physical activity outcomes from a sample of up to 81 children enrolled in kindergarten through eighth grade in public schools. The protocol also involved medical record reviews to establish a longitudinal trajectory of BMI for an estimated 70% of participating children. Staged sampling was used to collect less detailed measures of physical activity and nutrition across the entire sample of children, with a subset assessed using more costly, burdensome, and detailed measures. Data from the Healthy Community Study will be analyzed using both cross-sectional and longitudinal models that account for the complex design and correct for measurement error and bias using a likelihood-based Markov chain Monte Carlo methodology. This methods paper provides insights into the complex design features of the Healthy Communities Study and may serve as an example for future large-scale studies that assess the relationship between community-based programs and policies and health outcomes of community residents.

Although wildlife intrusion and untreated manure have been associated with microbial contamination of produce, relatively few studies have examined the survival of Escherichia coli on produce under field conditions following contamination (e.g., via splash from wildlife feces). This experimental study was performed to estimate the die-off rate of E. coli on preharvest lettuce following contamination with a fecal slurry. During August 2015, field-grown lettuce was inoculated via pipette with a fecal slurry that was spiked with a three-strain cocktail of rifampin-resistant nonpathogenic E. coli. Ten lettuce heads were harvested at each of 13 time points following inoculation (0, 2.5, 5, and 24 h after inoculation and every 24 h thereafter until day 10). The most probable number (MPN) of E. coli on each lettuce head was determined, and die-off rates were estimated. The relationship between sample time and the log MPN of E. coli per head was modeled using a segmented linear model. This model had a breakpoint at 106 h (95% confidence interval = 69, 142 h) after inoculation, with a daily decrease of 0.70 and 0.19 log MPN for 0 to 106 h and 106 to 240 h following inoculation, respectively. These findings are consistent with die-off rates obtained in similar studies that assessed E. coli survival on produce following irrigation. Overall, these findings provide die-off rates for E. coli on lettuce that can be used in future quantitative risk assessments.

BackgroundThe odds ratio (OR) is used as an important metric of comparison of two or more groups in many biomedical applications when the data measure the presence or absence of an event or represent the frequency of its occurrence. In the latter case, researchers often dichotomize the count data into binary form and apply the well-known logistic regression technique to estimate the OR. In the process of dichotomizing the data, however, information is lost about the underlying counts which can reduce the precision of inferences on the OR.MethodsWe propose analyzing the count data directly using regression models with the log odds link function. With this approach, the parameter estimates in the model have the exact same interpretation as in a logistic regression of the dichotomized data, yielding comparable estimates of the OR. We prove analytically, using the Fisher information matrix, that our approach produces more precise estimates of the OR than logistic regression of the dichotomized data. We also show the gains in precision using simulation studies and real-world datasets. We focus on three related distributions for count data: geometric, Poisson, and negative binomial.ResultsIn simulation studies, confidence intervals for the OR were 56–65% as wide (geometric model), 75–79% as wide (Poisson model), and 61–69% as wide (negative binomial model) as the corresponding interval from a logistic regression produced by dichotomizing the data. When we analyzed existing datasets using our approach, we found that confidence intervals for the OR could be up to 64% shorter (36% as wide) compared to if the data had been dichotomized and analyzed using logistic regression.ConclusionsMore precise estimates of the OR can be obtained directly from the count data by using the log odds link function. This analytic approach is easy to implement in software packages that are capable of fitting generalized linear models or of maximizing user-defined likelihood functions.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0568-9) contains supplementary material, which is available to authorized users.

The Healthy Communities Study is one of the largest studies to assess the relationship between characteristics of community programs and policies to prevent childhood obesity and obesity-related outcomes. The purpose of this paper is to describe the protocol that was developed for collecting the anthropometric data for the study and the procedures for analyzing the data. Data were collected from 2013 to 2015 and analyses will be completed by mid-2016. During in-home visits, Healthy Communities Study staff collected height, weight, and waist circumference measurements from child participants and height and weight measurements from adult participants. The protocol for obtaining these measurements was adapted from the protocol used by the National Health and Nutrition Examination Survey, with modifications to accommodate assessments conducted in homes rather than in a Mobile Examination Center. In addition to anthropometric data from in-home visits, the Healthy Communities Study collected retrospective height and weight measurements from the medical records of child participants. These data were used to calculate trajectories of BMI and BMI z-scores. The study implemented procedures for ensuring the accuracy of the in-home measurements and abstracted medical data. These procedures included automatically checking the ranges on entered data, reviewing data for end-digit patterns, and abstracting selected medical records using two independent abstractors to assess agreement. The collection of longitudinal height and weight measures will allow researchers to address several pressing questions related to how characteristics of community programs and policies are associated with obesity-related outcomes among children.

Citation: Boggie, M. A., S. A. Carleton, D. P. Collins, J. Vradenburg, and C. J. Sroka. 2018. Using stable isotopes to estimate reliance on agricultural food subsidies and migration timing for a migratory bird. Ecosphere 9(2):e02083.10. 1002/ecs2.2083 Abstract. Anthropogenic activities have adversely transformed terrestrial ecosystems consequently limiting many species to more fragmented areas and increasing human-wildlife conflicts. Under some circumstances, this creates a need for management programs to support wildlife populations by subsidizing food resources. Evaluation and improvement of supplementary feeding practices should be implemented to determine dietary importance of supplementary food and identify when to make food resources available, an important consideration for migratory species using seasonal habitats. Large aggregations of greater sandhill cranes (Antigone canadensis tabida) wintering in the Middle Rio Grande Valley of central New Mexico have come into conflict with agricultural practices. Resulting crop depredation on private lands has consequently required a mitigation program that subsidizes cranes with cultivated corn to manage their foraging behavior and provide nutritive support. To assess dependency of cranes on corn subsidies and estimate arrival dates of migratory sandhill cranes, we measured stable isotope ratios of liver and muscle tissues of sandhill cranes and their food items during winter. Over 60% of sandhill crane diet in the winter came from corn subsidies. Rates of carbon isotope incorporation in liver and muscle tissues were 0.03 d À1 AE 0.02 (mean AE SE) and 0.02 d À1 AE 0.01, respectively, and differed predictably by metabolic activity of different tissues. Estimated arrival date on wintering grounds derived from rates of carbon isotope incorporation was November 6 AE 3 d (mean AE SE) and was within 17 d of the estimated arrival date on the wintering grounds of sandhill cranes equipped with satellite transmitters (November 23 AE 2 d). Our approach demonstrates a field-based application of intrinsic biomarkers to inform supplementary feeding practices for wildlife populations by identifying dietary response to supplementary food. Additionally, estimating arrival on wintering grounds supports management and conservation decisions by synchronizing availability of supplementary food resources with arrival times.