The dietary antioxidant quality score (DAQS) is a valid and reliable tool for assessing the overall antioxidant potency of a diet by considering the synergetic effects of dietary antioxidants. Non-alcoholic fatty liver disease (NAFLD) is linked to the imbalance of the body’s oxidant and antioxidant defense system. The objective of the present research was to investigate the possible associations between DAQS and odds of NAFLD in a large population of the Azar cohort study. The present propensity score–matched case–control study was applied to a population of 14 655 individuals. Demographic, anthropometric and dietary data were gathered, and biochemical markers were measured. The DAQS was evaluated based on the daily dietary intake of vitamin E, vitamin A, vitamin C, Se and Zn, compared with the daily recommended intake. The multivariable logistic regression analysis was employed to determine the association between DAQS with NAFLD-related outcomes. After propensity score matching based on age, gender and body mass index (BMI), participants were allocated into NAFLD (n 660) and non-NAFLD (n 1234) groups. Findings indicated significant differences in age, weight, BMI, waist:hip ratio, TAG, HDL-cholesterol and LDL-cholesterol and dietary intake between percentiles of DAQS in NAFLD patients. Nonetheless, no significant associations were observed between DAQS and NAFLD before and after propensity score matching. Comparing the results to prior research underlines the need for a comprehensive approach for exploring the association between dietary antioxidants, serum antioxidant level and biochemical indices in NAFLD, which is essential for the efficient clarification of the underlying mechanisms.

Propensity score methods are an important tool to help reduce confounding in non-experimental studies and produce more accurate causal effect estimates. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error. Recent work has shown that ignoring such error could lead to bias in treatment effect estimates. In this paper, we consider an additional complication: that of differential measurement error across treatment groups, such as can occur if a covariate is measured differently in the treatment and control groups. We propose two flexible Bayesian approaches for handling differential measurement error when estimating average causal effects using propensity score methods. We consider three scenarios: systematic (i.e., a location shift), heteroscedastic (i.e., different variances), and mixed (both systematic and heteroscedastic) measurement errors. We also explore various prior choices (i.e., weakly informative or point mass) on the sensitivity parameters related to the differential measurement error. We present results from simulation studies evaluating the performance of the proposed methods and apply these approaches to an example estimating the effect of neighborhood disadvantage on adolescent drug use disorders.

Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates that determine the assignment, can provide reasonable estimates under the condition of correct specification of the regression function. However, it is usually difficult to specify the correct regression function because the dimensions of the dependent variables and covariates are typically large. Therefore, the propensity score adjustment methods have been proposed, since they do not require the specification of the regression function and have been applied to several applied studies. However, these methods produce biased estimates if the assignment mechanism is incorrectly specified. In order to make a more robust inference, it would be more useful to develop an estimation method that integrates the regression approach with the propensity score methodology. In this study we propose a doubly robust-type estimation method for marginal multiple group structural equation modeling. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. A simulation study indicates that the proposed estimation method is more robust than the existing methods.

The risk factors specific to the elderly population for severe coronavirus disease 2019 (COVID-19) caused by the Omicron variant of concern (VOC) are not yet clear. We performed an exploratory analysis using logistic regression to identify risk factors for severe COVID-19 illness among 4,868 older adults with a positive severe acute respiratory coronavirus 2 (SARS-CoV-2) test result who were admitted to a healthcare facility between 1 January 2022 and 16 May 2022. We then conducted one-to-one propensity score (PS) matching for three factors – dementia, admission from a long-term care facility and poor physical activity status – and used Fisher's exact test to compare the proportion of severe COVID-19 cases in the matched data. We also estimated the average treatment effect on treated (ATT) in each PS matching analysis. Of the 4,868 cases analysed, 1,380 were severe. Logistic regression analysis showed that age, male sex, cardiovascular disease, cerebrovascular disease, chronic lung disease, renal failure and/or dialysis, physician-diagnosed obesity, admission from a long-term care facility and poor physical activity status were risk factors for severe disease. Vaccination and dementia were identified as factors associated with non-severe illness. The ATT for dementia, admission from a long-term care facility and poor physical activity status was −0.04 (95% confidence interval −0.07 to −0.01), 0.09 (0.06 to 0.12) and 0.17 (0.14 to 0.19), respectively. Our results suggest that poor physical activity status and living in a long-term care facility have a substantial association with the risk of severe COVID-19 caused by the Omicron VOC, while dementia may be associated with non-severe illness.

This paper estimates the effects of teenage childbearing on education, working, physical and mental health, and physical activity of young girls in Mexico using two waves of the nationally representative Mexican Family Life Survey. We employ a propensity score matching model that accounts for a rich set of baseline covariates that predict teenage childbearing to attempt to reduce the bias due to confounding variables associated with teenage childbearing. The results demonstrate that teenage childbearing is associated with an increase in the probability of being overweight, and reductions in physical activity and the probability of high school completion. Moreover, the results are consistent when we employ sibling fixed effects to account for unobservable family background.

We implemented six confounding adjustment methods: (1) covariate-adjusted regression, (2) propensity score (PS) regression, (3) PS stratification, (4) PS matching with two calipers, (5) inverse probability weighting and (6) doubly robust estimation to examine the associations between the body mass index (BMI) z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding v. formula only (n=437) and cesarean section v. vaginal delivery (n=1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were −0.33 (95% CI −0.53, −0.13) and −0.24 (−0.46, −0.02), respectively. The other approaches resulted in smaller n (204–276) because of poor overlap of covariates, but CIs were of similar width except for inverse probability weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from −0.01 to −0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method.

L'innovation est devenue un facteur clé de la croissance économique. La question des incitations à l'innovation au sein des entreprises est donc primordiale. Dans ce papier, nous nous intéressons au type d'incitations monétaires reçues par les inventeurs au sein des entreprises avec une attention particulière à la mobilité inter-entreprise de ces derniers. Les résultats montrent un rendement salarial positif pour les inventeurs, celui-ci est plus important pour les inventeurs ayant connu une mobilité inter-entreprise, ce qui pourrait suggérer que les entreprises sont prêtes à payer les connaissances acquises par les inventeurs au sein des autres entreprises. Par contre, l'utilisation de stock-options comme incitation pour les inventeurs semble moins répandue dans les entreprises françaises que dans les entreprises étrangères.