Tobacco Farmer Interest and Success in Income Diversification

As farm income from tobacco production has declined in recent years, there has been increasing interest in identifying alternative sources of income for tobacco farmers in the southern United States The recent termination of the tobacco quota program has accelerated the exit of tobacco farmers and has heightened concern regarding the availability of substitutes for tobacco production. In this study, we examine factors influencing tobacco farmers’ attempts to identify profitable alternatives to tobacco, their off-farm employment behavior, and changes in acres of tobacco cultivated using survey data collected from a panel of North Carolina tobacco farmers combined with market datadiversification, farm programs, farmer survey, quota buyout, tobacco, Crop Production/Industries, Farm Management, Financial Economics, C33, Q12, Q18,

Statistical issues related to dietary intake as the response variable in intervention trials.

The focus of this paper is dietary intervention trials. We explore the statistical issues involved when the response variable, intake of a food or nutrient, is based on self-report data that are subject to inherent measurement error. There has been little work on handling error in this context. A particular feature of self-reported dietary intake data is that the error may be differential by intervention group. Measurement error methods require information on the nature of the errors in the self-report data. We assume that there is a calibration sub-study in which unbiased biomarker data are available. We outline methods for handling measurement error in this setting and use theory and simulations to investigate how self-report and biomarker data may be combined to estimate the intervention effect. Methods are illustrated using data from the Trial of Nonpharmacologic Intervention in the Elderly, in which the intervention was a sodium-lowering diet and the response was sodium intake. Simulations are used to investigate the methods under differential error, differing reliability of self-reports relative to biomarkers and different proportions of individuals in the calibration sub-study. When the reliability of self-report measurements is comparable with that of the biomarker, it is advantageous to use the self-report data in addition to the biomarker to estimate the intervention effect. If, however, the reliability of the self-report data is low compared with that in the biomarker, then, there is little to be gained by using the self-report data. Our findings have important implications for the design of dietary intervention trials. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd

A Web-Based, Positive Emotion Skills Intervention for Enhancing Posttreatment Psychological Well-Being in Young Adult Cancer Survivors (EMPOWER): Protocol for a Single-Arm Feasibility Trial

BACKGROUND: Adolescent and young adult cancer survivors (AYAs) experience clinically significant distress and have limited access to supportive care services. Interventions to enhance psychological well-being have improved positive affect and reduced depression in clinical and healthy populations but have not been routinely tested in AYAs. OBJECTIVE: The aim of this protocol is to (1) test the feasibility and acceptability of a Web-based positive emotion skills intervention for posttreatment AYAs called Enhancing Management of Psychological Outcomes With Emotion Regulation (EMPOWER) and (2) examine proof of concept for reducing psychological distress and enhancing psychological well-being. METHODS: The intervention development and testing are taking place in 3 phases. In phase 1, we adapted the content of an existing, Web-based positive emotion intervention so that it would be suitable for AYAs. EMPOWER targets 8 skills (noticing positive events, capitalizing, gratitude, mindfulness, positive reappraisal, goal setting, personal strengths, and acts of kindness) and is delivered remotely as a 5-week, Web-based intervention. Phase 2 consisted of a pilot test of EMPOWER in a single-arm trial to evaluate feasibility, acceptability, retention, and adherence and to collect data on psychosocial outcomes for proof of concept. In phase 3, we are refining study procedures and conducting a second pilot test. RESULTS: The project was part of a career development award. Pilot work began in June 2015, and data collection was completed in March 2019. The analysis is ongoing, and results will be submitted for publication by May 2020. CONCLUSIONS: If this intervention proves feasible and acceptable, EMPOWER will be primed for a subsequent large, multisite randomized controlled trial. As a scalable intervention, it will be ideally suited for AYA survivors who would otherwise not have access to supportive care interventions to help manage posttreatment distress and enhance well-being. TRIAL REGISTRATION: ClinicalTrials.gov NCT02832154, https://clinicaltrials.gov/ct2/show/NCT02832154. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/1707

A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment

In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS446 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Breakfast Consumption Is Positively Associated with Usual Nutrient Intakes among Food Pantry Clients Living in Rural Communities

Background: Breakfast consumption has declined over the past 40 y and is inversely associated with obesity-related diet and health outcomes. The breakfast pattern of food pantry clients and its association with diet is unknown. Objective: The objective is to investigate the association of breakfast consumption with diet quality and usual nutrient intakes among food pantry clients (n = 472) living in rural communities. Methods: This was an observational study using cross-sectional analyses. English-speaking participants ≥18 y (or ≥19 y in Nebraska) were recruited from 24 food pantries in rural high-poverty counties in Indiana, Michigan, Missouri, Nebraska, Ohio, and South Dakota. Participants were surveyed at the pantry regarding characteristics and diet using 24-h recall. A second recall was self-completed or completed via assisted phone call within 2 wk of the pantry visit. Participants were classified as breakfast skippers when neither recall reported breakfast ≥230 kcal consumed between 04:00 and 10:00; breakfast consumers were all other participants. The Healthy Eating Index-2010 was modeled with breakfast pattern using multiple linear regression. Mean usual intake of 16 nutrients was estimated using the National Cancer Institute Method and compared across breakfast pattern groups. Usual nutrient intake was compared with the Estimated Average Requirement (EAR) or Adequate Intake (AI) to estimate the proportion of population not meeting the EAR or exceeding the AI. Results: A total of 56% of participants consumed breakfast. Compared with breakfast skippers, breakfast consumers had 10–59% significantly higher usual mean intakes of all nutrients (P ≤ 0.05), and had 12–21% lower prevalence of at-risk nutrient intakes except for vitamin D, vitamin E, and magnesium. Conclusions: Adult food pantry clients living in rural communities experienced hardships in meeting dietary recommendations. Breakfast consumption was positively associated with usual nutrient intakes in this population. This trial was registered at clinicaltrials.gov as NCT03566095

Relative validity and reliability of a food frequency questionnaire in youth with type 1 diabetes

To evaluate the relative validity and reliability of the SEARCH food frequency questionnaire (FFQ) that was modified from the Block Kids Questionnaire

Sugar-sweetened beverage intake and cardiovascular risk factor profile in youth with type 1 diabetes: application of measurement error methodology in the SEARCH Nutrition Ancillary Study

The SEARCH Nutrition Ancillary Study aims to investigate the role of dietary intake on the development of long-term complications of type 1 diabetes in youth, and capitalise on measurement error (ME) adjustment methodology. Using the National Cancer Institute (NCI) method for episodically consumed foods, we evaluated the relationship between sugar-sweetened beverage (SSB) intake and cardiovascular risk factor profile, with the application of ME adjustment methodology. The calibration sample included 166 youth with two FFQ and three 24 h dietary recall data within 1 month. The full sample included 2286 youth with type 1 diabetes. SSB intake was significantly associated with higher TAG, total and LDL-cholesterol concentrations, after adjusting for energy, age, diabetes duration, race/ethnicity, sex and education. The estimated effect size was larger (model coefficients increased approximately 3-fold) after the application of the NCI method than without adjustment for ME. Compared with individuals consuming one serving of SSB every 2 weeks, those who consumed one serving of SSB every 2 d had 3·7 mg/dl (0·04 mmol/l) higher TAG concentrations and 4·0 mg/dl (0·10 mmol/l) higher total cholesterol and LDL-cholesterol concentrations, after adjusting for ME and covariates. SSB intake was not associated with measures of adiposity and blood pressure. Our findings suggest that SSB intake is significantly related to increased lipid levels in youth with type 1 diabetes, and that estimates of the effect size of SSB on lipid levels are severely attenuated in the presence of ME. Future studies in youth with diabetes should consider a design that will allow for the adjustment for ME when studying the influence of diet on health status

Comparative validation of standard, picture-sort and meal-based food-frequency questionnaires adapted for an elderly population of low socio-economic status

OBJECTIVE: To compare the validity of a modified Block food-frequency questionnaire (FFQ), a picture-sort administration of the FFQ (PSFFQ) and a meal pattern-based questionnaire (MPQ) in a multi-ethnic population of low socio-economic status (SES). DESIGN: Participants completed six 24-hour dietary recalls (24HR) over six months; the FFQ, PSFFQ and MPQ were completed in random order in the subsequent month. Instruments were interviewer-administered. The PSFFQ and MPQ were developed in formative research concerning difficulties for older adults in responding to standard food-frequency instruments. SETTING: Rural North Carolina, USA. Subjects One hundred and twenty-two African American, Native American and white adults aged > or = 65 years, with approximately one-third in each ethnic group. Inclusion criteria included education < or = 12 years and income < or = 150% of national poverty level or Medicaid recipient. RESULTS: Comparing median intakes from the average of the 24HR with the three diet assessment instruments, the MPQ tended to overestimate intakes compared with the FFQ and PSFFQ. Correlations among nutrients obtained by the 24HR and the other three instruments were generally statistically significant and positive. Across nutrients, the PSFFQ was most highly correlated with the 24HR for women, while the FFQ was most highly correlated with the 24HR for men. CONCLUSIONS: Dietary assessments using 24HR and FFQ were similar to results reported elsewhere, although correlations between 24HR and FFQ were somewhat lower. Interviewer-administered dietary assessments should be used with caution to evaluate dietary intake among older adults with low SES. Gender differences and the lower correlations should be investigated more thoroughly to assist in choosing dietary assessment instruments for this population

Epidemiologic analyses with error-prone exposures: review of current practice and recommendations.

PURPOSE: Variables in observational studies are commonly subject to measurement error, but the impact of such errors is frequently ignored. As part of the STRengthening Analytical Thinking for Observational Studies Initiative, a task group on measurement error and misclassification seeks to describe the current practice for acknowledging and addressing measurement error. METHODS: Task group on measurement error and misclassification conducted a literature survey of four types of research studies that are typically impacted by exposure measurement error: (1) dietary intake cohort studies, (2) dietary intake population surveys, (3) physical activity cohort studies, and (4) air pollution cohort studies. RESULTS: The survey revealed that while researchers were generally aware that measurement error affected their studies, very few adjusted their analysis for the error. Most articles provided incomplete discussion of the potential effects of measurement error on their results. Regression calibration was the most widely used method of adjustment. CONCLUSIONS: Methods to correct for measurement error are available but require additional data regarding the error structure. There is a great need to incorporate such data collection within study designs and improve the analytical approach. Increased efforts by investigators, editors, and reviewers are needed to improve presentation of research when data are subject to error