The burden of cardiovascular disease and death in chronic kidney disease (CKD) outpaces that of the other diseases and is not adequately described by traditional risk factors alone. Diminished activity of paraoxonase (PON)-1 is associated with increased oxidant stress, a common feature underlying the pathogenesis of CKD. We aimed to assess the prognostic value of circulating PON-1 protein and PON lactonase activity on adverse clinical outcomes across various stages and etiologies of CKD. Circulating PON-1 protein levels and PON lactonase activity were measured simultaneously in patients with CKD as well as a cohort of apparently healthy non-CKD subjects. Both circulating PON-1 protein levels and PON lactonase activity were significantly lower in CKD patients compared to the non-CKD subjects. Similarly, across all stages of CKD, circulating PON-1 protein and PON lactonase activity were significantly lower in patients with CKD compared to the non-CKD controls. Circulating PON lactonase activity, but not protein levels, predicted future adverse clinical outcomes, even after adjustment for traditional risk factors. The combination of lower circulating protein levels and higher activity within the CKD subjects were associated with the best survival outcomes. These findings demonstrate that diminished circulating PON lactonase activity, but not protein levels, predicts higher risk of future adverse clinical outcomes in patients with CKD.

Background. Little is known regarding long-term impacts of anti-tobacco media campaigns on youth smoking and related disparities in the United States. Methods. We examined longitudinal cohort data from Monitoring the Future (MTF) between 2000 and 2017 in modified Poisson regression models to understand the long-term impacts of televised Truth and state-sponsored ad campaign exposure at baseline (age 18) on first cigarette and daily smoking initiation 1 to 2 years later (at modal ages 19/20). We also used additive interactions to test for potential effect modification between campaign exposure and smoking outcomes by sex, race/ethnicity, and parental educational attainment. Results. We found no evidence for baseline media campaign exposure to be associated with first cigarette or daily smoking initiation at modal age 19/20. Further, results showed no evidence for effect modification between campaign exposure and first cigarette or daily smoking initiation. Conclusions. We found no evidence that baseline Truth and state-sponsored ad exposure was associated with first cigarette or daily smoking initiation at follow up, nor did we find any evidence for effect modification by sex, race/ethnicity, or parental education. We hypothesize that anti-tobacco media campaigns might have had a short-term impact on smoking behaviors, though these effects were not sustained long term.

Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719–30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and Nutrition Examination Survey (NHANES).

Background: Growing evidence suggests that severe acute COVID-19 illness increases the risk of long COVID (also known as post–COVID-19 condition ). However, few studies have examined associations between acute symptoms and long COVID onset. Objective: This study aimed to examine associations between acute COVID-19 symptom profiles and long COVID prevalence using a population-based sample. Methods: We used a dual mode (phone and web-based) population-based probability survey of adults with polymerase chain reaction–confirmed SARS-CoV-2 between June 2020 and May 2022 in the Michigan Disease Surveillance System to examine (1) how acute COVID-19 symptoms cluster together using latent class analysis, (2) sociodemographic and clinical predictors of symptom clusters using multinomial logistic regression accounting for classification uncertainties, and (3) associations between symptom clusters and long COVID prevalence using modified Poisson regression. Results: In our sample (n=4169), 15.9% (n=693) had long COVID, defined as new or worsening symptoms at least 90 days post SARS-CoV-2 infection. We identified 6 acute COVID-19 symptom clusters resulting from the latent class analysis, with flu-like symptoms (24.7%) and fever (23.6%) being the most prevalent in our sample, followed by nasal congestion (16.4%), multi-symptomatic (14.5%), predominance of fatigue (10.8%), and predominance of shortness of breath (10%) clusters. Long COVID prevalence was highest in the multi-symptomatic (39.7%) and predominance of shortness of breath (22.4%) clusters, followed by the flu-like symptom (15.8%), predominance of fatigue (14.5%), fever (6.4%), and nasal congestion (5.6%) clusters. After adjustment, females (vs males) had greater odds of membership in the multi-symptomatic, flu-like symptom, and predominance of fatigue clusters, while adults who were Hispanic or another race or ethnicity (vs non-Hispanic White) had greater odds of membership in the multi-symptomatic cluster. Compared with the nasal congestion cluster, the multi-symptomatic cluster had the highest prevalence of long COVID (adjusted prevalence ratio [aPR] 6.1, 95% CI 4.3‐8.7), followed by the predominance of shortness of breath (aPR 3.7, 95% CI 2.5‐5.5), flu-like symptom (aPR 2.8, 95% CI 1.9‐4.0), and predominance of fatigue (aPR 2.2, 95% CI 1.5‐3.3) clusters. Conclusions: Researchers and clinicians should consider acute COVID-19 symptom profiles when evaluating subsequent risk of long COVID, including potential mechanistic pathways in a research context, and proactively screen high-risk patients during the provision of clinical care.

Little research examines how tobacco quitlines affect disparities in smoking cessation in the United States. Our study utilized data from the Tobacco Use Supplement to the Current Population Survey (2010, 2011, 2012, 2015, 2018) (TUS-CPS) and state-level quitline data from the North American Quitline Consortium and National Quitline Data Warehouse. We ran multilevel logistic regression models assessing a state-run quitline’s budget, reach, number of counseling sessions offered per caller, and hours of operation on 90-day smoking cessation. Multiplicative interactions between all exposures and sex, race/ethnicity, income, and education were tested to understand potential effect modification. We found no evidence that budget, reach, number of counseling sessions, or hours available for counseling were associated with cessation in the main effects analyses. However, when looking at effect modification by sex, we found that higher budgets were associated with greater cessation in males relative to females. Further, higher budgets and offering more sessions had a stronger association with cessation among individuals with lower education, while available counseling hours were more strongly associated with cessation among those with higher education. No quitline characteristics examined were associated with smoking cessation. We found evidence for effect modification by sex and education. Despite proven efficacy at the individual-level, current resource allocation to quitlines may not be sufficient to improve rates of cessation.