Key Points Question What are the trends in nutritional quality of foods consumed from major US sources? Findings In this survey study of 20 905 children and 39 757 adults from 2003-2004 to 2017-2018, modest improvements were found in diet quality for foods from grocery stores and small improvements for foods from restaurants, each with disparities. Diet quality for foods from schools improved significantly, especially after 2010, and equitably across subgroups; by 2017-2018, food consumed at schools had the highest quality, followed by food from grocery stores, other sources, worksites, and restaurants. Meaning By 2017-2018, foods consumed at schools provided the best mean quality of major sources, without disparities, although further improvements are needed in all sources, especially restaurants, with a focus on reducing disparities.
Complex sample designs, involving stratified and/or multistage sampling with sample weighting, along with frequency matching, are used to select controls or cases for case-control studies. Examples that motivated this paper are the Kaposi sarcoma case-control study that was conducted in Sicily and the US kidney cancer case-control study. Survey design-based approaches can be inefficient for the analysis of case-control studies with frequency matching. We propose a weighting method that post-stratifies control sample weights to the estimated population distribution of the matching variables among cases. This weighting maintains the efficiency of frequency matching.The method proposed is evaluated by using simulation studies and is applied to the two case-control studies.
Many epidemiologic studies forgo probability sampling and turn to nonprobability volunteer‐based samples because of cost, response burden, and invasiveness of biological samples. However, finite population (FP) inference is difficult to make from the nonprobability sample due to the lack of population representativeness. Aiming for making inferences at the population level using nonprobability samples, various inverse propensity score weighting methods have been studied with the propensity defined by the participation rate of population units in the nonprobability sample. In this article, we propose an adjusted logistic propensity weighting (ALP) method to estimate the participation rates for nonprobability sample units. The proposed ALP method is easy to implement by ready‐to‐use software while producing approximately unbiased estimators for population quantities regardless of the nonprobability sample rate. The efficiency of the ALP estimator can be further improved by scaling the survey sample weights in propensity estimation. Taylor linearization variance estimators are proposed for ALP estimators of FP means that account for all sources of variability. The proposed ALP methods are evaluated numerically via simulation studies and empirically using the naïve unweighted National Health and Nutrition Examination Survey III sample, while taking the 1997 National Health Interview Survey as the reference, to estimate the 15‐year mortality rates.
For studies on population genetics, the use of representative random samples of the target population can avoid ascertainment bias. Genetic variation data from over a hundred genes were collected in a U.S. nationally representative sample in the Third National Health and Nutrition Examination Survey (NHANES III). Surveys such as the NHANES have complex stratified multistage cluster sample designs with sample weighting that can inflate variances and alter the expectations of test statistics. Thus, classical statistical tests of Hardy-Weinberg equilibrium (HWE) and homogeneity of HW disequilibrium (HHWD) for simple random samples are not suitable for data from complex samples. We propose using Wald tests for HWE and generalized score tests for HHWD that have been modified for complex samples. Monte Carlo simulation studies are used to investigate the finite sample properties of the proposed tests. Rao-Scott corrections applied to the tests were found to improve their type I error properties. Our methods are applied to the NHANES III genetic data for three loci involved in metabolizing lead in the body.
The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.