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Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a long history, starting in 1932 with R.A. Fisher and including more recent endorsements by the U.S. Food and Drug Administration and the European Medicines Agency. Here, we address an important practical consideration: how to select the adjustment approach-which variables and in which form-to maximize precision, while maintaining Type-I error control. Balzer et al. previously proposed Adaptive Pre-specification within TMLE to flexibly and automatically select, from a prespecified set, the approach that maximizes empirical efficiency in small trials (N < 40). To avoid overfitting with few randomized units, selection was previously limited to working generalized linear models, adjusting for a single covariate. Now, we tailor Adaptive Pre-specification to trials with many randomized units. Using V-fold cross-validation and the estimated influence curve-squared as the loss function, we select from an expanded set of candidates, including modern machine learning methods adjusting for multiple covariates. As assessed in simulations exploring a variety of data-generating processes, our approach maintains Type-I error control (under the null) and offers substantial gains in precision-equivalent to 20%-43% reductions in sample size for the same statistical power. When applied to real data from ACTG Study 175, we also see meaningful efficiency improvements overall and within subgroups.

BACKGROUND: Black-White racial disparities in cancer mortality are well-documented in the US. Given the estimated shortage of oncologists over the next decade, understanding how access to oncology care might influence cancer disparities is of considerable importance. We aim to examine the association between oncology provider density in a county and Black-White cancer mortality disparities. METHODS: An ecological study of 1048 US counties was performed. Oncology provider density was estimated using the 2013 National Plan and Provider Enumeration System data. Black:White cancer mortality ratio was calculated using 2014-2018 age-standardized cancer mortality rates from State Cancer Profiles. Linear regression with covariate adjustment was constructed to assess the association of provider density with (1) Black:White cancer mortality ratio, and (2) cancer mortality rates overall, and separately among Black and White persons. RESULTS: The mean Black:White cancer mortality ratio was 1.12, indicating that cancer mortality rate among Black persons was on average 12% higher than that among White persons. Oncology provider density was significantly associated with greater cancer mortality disparities: every 5 additional oncology providers per 100 000 in a county was associated with a .02 increase in the Black:White cancer mortality ratio (95% CI: .007 to .03); however, the unexpected finding may be explained by further analysis showing that the relationship between oncology provider density and cancer mortality was different by race group. Every 5 additional oncologists per 100 000 was associated with a 1.6 decrease per 100 000 in cancer mortality rates among White persons (95% CI: -3.0 to -.2), whereas oncology provider density was not associated with cancer mortality among Black persons. CONCLUSION: Greater oncology provider density was associated with significantly lower cancer mortality among White persons, but not among Black persons. Higher oncology provider density alone may not resolve cancer mortality disparities, thus attention to ensuring equitable care is critical.

IntroductionMeta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.ObjectiveThe objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.MethodsWe used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.ResultsOnly 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P  100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.

ObjectiveTo assess the magnitude of racial-ethnic disparities in pandemic-related social stressors and examine frontline work's moderating relationship on these stressors.Data sourcesEmployed Californians' responses to the Institute for Governmental Studies (IGS) poll from April 16-20, 2020, were analyzed. The Pandemic Stressor Scale (PSS) assessed the extent to which respondents experienced or anticipated problems resulting from the inability to pay for basic necessities, job instability, lacking paid sick leave, unavailability of childcare, and reduced wages or work hours due to COVID-19.Study designMixed-effects generalized linear models estimated (1) racial-ethnic disparities in pandemic stressors among workers during the first COVID-19 surge, adjusting for covariates, and (2) tested the interaction between race-ethnicity and frontline worker status, which includes a subset of essential workers who must perform their job on-site, to assess differential associations of frontline work by race-ethnicity.Data collectionThe IGS poll data from employed workers (n = 4795) were linked to the 2018 Centers for Disease Control and Prevention Social Vulnerability Index at the zip code level (N = 1068).Principal findingsThe average PSS score was 37.34 (SD = 30.49). Whites had the lowest PSS score (29.88, SD = 26.52), and Latinxs had the highest (50.74, SD = 32.61). In adjusted analyses, Black frontline workers reported more pandemic-related stressors than White frontline workers (PSS = 47.73 vs. 36.96, p

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners.

Linear models are becoming increasingly popular to investigate brain activity in response to continuous and naturalistic stimuli. In the context of auditory perception, these predictive models can be 'encoding', when stimulus features are used to reconstruct brain activity, or 'decoding' when neural features are used to reconstruct the audio stimuli. These linear models are a central component of some brain–computer interfaces that can be integrated into hearing assistive devices (e.g., hearing aids). Such advanced neurotechnologies have been widely investigated when listening to speech stimuli but rarely when listening to music. Recent attempts at neural tracking of music show that the reconstruction performances are reduced compared with speech decoding. The present study investigates the performance of stimuli reconstruction and electroencephalogram prediction (decoding and encoding models) based on the cortical entrainment of temporal variations of the audio stimuli for both music and speech listening. Three hypotheses that may explain differences between speech and music stimuli reconstruction were tested to assess the importance of the speech‐specific acoustic and linguistic factors. While the results obtained with encoding models suggest different underlying cortical processing between speech and music listening, no differences were found in terms of reconstruction of the stimuli or the cortical data. The results suggest that envelope‐based linear modelling can be used to study both speech and music listening, despite the differences in the underlying cortical mechanisms. [ABSTRACT FROM AUTHOR]

Summary We propose a new class of priors for Bayesian hypothesis testing, which we name ‘cake priors’. These priors circumvent the Jeffreys–Lindley paradox (also called Bartlett's paradox) a problem associated with the use of diffuse priors leading to nonsensical statistical inferences. Cake priors allow the use of diffuse priors (having one's cake) while achieving theoretically justified inferences (eating it too). We demonstrate this methodology for Bayesian hypotheses tests for various common scenarios. The resulting Bayesian test statistic takes the form of a penalised likelihood ratio test statistic. Under typical regularity conditions, we show that Bayesian hypothesis tests based on cake priors are Chernoff consistent, that is, achieve zero type I and II error probabilities asymptotically. We also discuss Lindley's paradox and argue that the paradox occurs with small and vanishing probability as sample size increases. [ABSTRACT FROM AUTHOR]

Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.

Modeling and drawing inference on the joint associations between single-nucleotide polymorphisms and a disease has sparked interest in genome-wide associations studies. In the motivating Boston Lung Cancer Survival Cohort (BLCSC) data, the presence of a large number of single nucleotide polymorphisms of interest, though smaller than the sample size, challenges inference on their joint associations with the disease outcome. In similar settings, we find that neither the debiased lasso approach (van de Geer et al., 2014), which assumes sparsity on the inverse information matrix, nor the standard maximum likelihood method can yield confidence intervals with satisfactory coverage probabilities for generalized linear models. Under this large n, diverging p scenario, we propose an alternative debiased lasso approach by directly inverting the Hessian matrix without imposing the matrix sparsity assumption, which further reduces bias compared to the original debiased lasso and ensures valid confidence intervals with nominal coverage probabilities. We establish the asymptotic distributions of any linear combinations of the parameter estimates, which lays the theoretical ground for drawing inference. Simulations show that the proposed refined debiased estimating method performs well in removing bias and yields honest confidence interval coverage. We use the proposed method to analyze the aforementioned BLCSC data, a large-scale hospital-based epidemiology cohort study investigating the joint effects of genetic variants on lung cancer risks.

Background and Aims: No studies have evaluated the relationship between maternal arterial partial pressure of carbon dioxide (mPaCO2) and umbilical cord venous partial pressure of carbon dioxide (PCO2) in critically ill pregnant women at delivery. Based on the studies in healthy pregnant women, an mPaCO2 target of ≤50 mmHg is a suggested threshold during mechanical ventilation in critically ill parturients. We evaluated the relationship between mPaCO2 and neonatal cord gases in critically ill parturients at delivery as the primary objective. The relationship between mPaCO2 and APGAR scores at delivery was also analysed as a secondary objective. Methods: Maternal and neonatal cord gas data at delivery and APGAR scores were obtained by a retrospective chart review of 25 consecutive parturients with severe respiratory compromise who were delivered during mechanical ventilation. Linear regression was used to assess the relationship between mPaCO2 and umbilical artery and vein PCO2 and between mPaCO2 and APGAR scores at 1 and 5 min. Results: There was a positive correlation between mPaCO2 and neonatal cord venous PCO2 (P = 0.013). Foetal venous PCO2 exceeded predelivery mPaCO2 by 17.5 (7.5) mmHg. There was an inverse relationship between mPaCO2 and neonatal APGAR scores at 1 and 5 min (P = 0.006 and P = 0.007, respectively). Conclusion: Foetal cord venous PCO2 can be predicted if mPaCO2 values are known. Unlike in healthy pregnant women, there was an inverse relationship between rising mPaCO2 levels and neonatal APGAR scores in critically ill pregnant women who had several associated compounding factors.

PURPOSE: Capture-recapture methods estimate the size of hidden populations by leveraging the proportion of overlap of the population on independent lists. Log-linear modeling relaxes the assumption of list independence, but best model selection criteria remain uncertain. Incorrect model selection can deliver incorrect and even implausible size estimates. METHODS: We used simulations to model when capture-recapture methods deliver biased or unbiased estimates and compare model selection criteria. Simulations included five scenarios for list dependence among three incomplete lists of population of interest. We compared metrics of log-linear model selection, accuracy, and precision. We also compared log-linear model performance to the decomposable graph approach (a Bayesian model average), the sparse multiple systems estimation (SparseMSE) approach that accounts for zero or low cell counts, and the Sample Coverage approach. RESULTS: Log-linear models selected by Akaikes information criterion (AIC) calculated accurate population size estimates in all scenarios except for those with sparse or zero cell counts. In these scenarios, the decomposable graph and the Sample Coverage models produced more accurate size estimates. CONCLUSIONS: Conventional capture-recapture model selection fails with sparse cell counts. This naïve approach to model selection should be replaced with the implementation of multiple different models in order triangulate the truth in real-world applications.

ObjectivesTo make projections of cigarette consumption that incorporate state-specific trends in smoking behaviors, assess the potential for states to reach an ideal target, and identify State-specific targets for cigarette consumption.MethodsWe used 70 years (1950-2020) of annual state-specific estimates of per capita cigarette consumption (expressed as packs per capita or "ppc") from the Tax Burden on Tobacco reports (N = 3550). We summarized trends within each state by linear regression models and the variation in rates across states by the Gini coefficient. Autoregressive Integrated Moving Average (ARIMA) models were used to make state-specific forecasts of ppc from 2021 through 2035.ResultsSince 1980, the average rate of decline in US per capita cigarette consumption was 3.3% per year, but rates of decline varied considerably across US states (SD = 1.1% per year). The Gini coefficient showed growing inequity in cigarette consumption across US states. After reaching its lowest level in 1984 (Gini = 0.09), the Gini coefficient began increasing by 2.8% (95% CI: 2.5%, 3.1%) per year from 1985 to 2020 and is projected to continue to increase by 48.1% (95% PI = 35.3%, 64.2%) from 2020 to 2035 (Gini = 0.35; 95% PI: 0.32, 0.39). Forecasts from ARIMA models suggested that only 12 states have a realistic chance (≥50%) of reaching very low levels of per capita cigarette consumption (≤13 ppc) by 2035, but that all US states have opportunity to make some progress.ConclusionWhile ideal targets may be out of reach for most US states within the next decade, every US state has the potential to lower its per capita cigarette consumption, and our identification of more realistic targets may provide a helpful incentive.

Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different feature spaces, a joint model is fit on multiple feature spaces simultaneously. To adapt regularization strength to each feature space, ridge regression is extended to banded ridge regression, which optimizes a different regularization hyperparameter per feature space. The present paper proposes a method to decompose over feature spaces the variance explained by a banded ridge regression model. It also describes how banded ridge regression performs a feature-space selection, effectively ignoring non-predictive and redundant feature spaces. This feature-space selection leads to better prediction accuracy and to better interpretability. Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms. Finally, several methods are proposed to address the computational challenge of fitting banded ridge regressions on large numbers of voxels and feature spaces. All implementations are released in an open-source Python package called Himalaya.

Background: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. Methods: We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. Results: First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. Conclusions: We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Recently a nonparametric method called LS‐imputation has been proposed for large‐scale trait imputation based on a GWAS summary dataset and a large set of genotyped individuals. The imputed trait values, along with the genotypes, can be treated as an individual‐level dataset for downstream genetic analyses, including those that cannot be done with GWAS summary data. However, since the covariance matrix of the imputed trait values is often too large to calculate, the current method imposes a working assumption that the imputed trait values are identically and independently distributed, which is incorrect in truth. Here we propose a "divide and conquer/combine" strategy to estimate and account for the covariance matrix of the imputed trait values via batches, thus relaxing the incorrect working assumption. Applications of the methods to the UK Biobank data for marginal association analysis showed some improvement by the new method in some cases, but overall the original method performed well, which was explained by nearly constant variances of and mostly weak correlations among imputed trait values. [ABSTRACT FROM AUTHOR]

We study the random weighting method for M-test in a linear model with dependent errors. The asymptotic distribution of this test statistic is derived under some conditions. This article shows that the random weighting method can be used for deciding the critical value for the M-test regardless of whether the null hypothesis is true or not. Finally, the performance of these results is evaluated by a simulation study. [ABSTRACT FROM AUTHOR]

We consider the problem of bias arising from conditioning on a post-outcome collider. We illustrate this with reference to Elwert and Winship (2014) but we go beyond their study to investigate the extent to which inverse probability weighting might offer solutions. We use linear models to derive expressions for the bias arising in different kinds of post-outcome confounding, and we show the specific situations in which inverse probability weighting will allow us to obtain estimates that are consistent or, if not consistent, less biased than those obtained via ordinary least squares regression. [ABSTRACT FROM AUTHOR]

Conventional propensity score methods encounter challenges when unmeasured confounding is present, as it becomes impossible to accurately estimate the gold-standard propensity score when data on certain confounders are unavailable. Propensity score calibration (PSC) addresses this issue by constructing a surrogate for the gold-standard propensity score under the surrogacy assumption. This assumption posits that the error-prone propensity score, based on observed confounders, is independent of the outcome when conditioned on the gold-standard propensity score and the exposure. However, this assumption implies that confounders cannot directly impact the outcome and that their effects on the outcome are solely mediated through the propensity score. This raises concerns regarding the applicability of PSC in practical settings where confounders can directly affect the outcome. While PSC aims to target a conditional treatment effect by conditioning on a subject's unobservable propensity score, the causal interest in the latter case lies in a conditional treatment effect conditioned on a subject's baseline characteristics. Our analysis reveals that PSC is generally biased unless the effects of confounders on the outcome and treatment are proportional to each other. Furthermore, we identify 2 sources of bias: 1) the noncollapsibility of effect measures, such as the odds ratio or hazard ratio and 2) residual confounding, as the calibrated propensity score may not possess the properties of a valid propensity score. [ABSTRACT FROM AUTHOR]

As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge computing resources are exploited to process more of the data locally. This regime of analytics is coined as Federated Data Analytics (FDA). Despite the recent success stories of FDA, most literature focuses exclusively on deep neural networks. In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression. Our treatment is built upon hierarchical modeling that allows borrowing strength across multiple groups. To this end, we propose two federated hierarchical model structures that provide a shared representation across devices to facilitate information sharing. Notably, our proposed frameworks are capable of providing uncertainty quantification, variable selection, hypothesis testing, and fast adaptation to new unseen data. We validate our methods on a range of real-life applications, including condition monitoring for aircraft engines. The results show that our FDA treatment for linear models can serve as a competing benchmark model for the future development of federated algorithms. [ABSTRACT FROM AUTHOR]

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics. [ABSTRACT FROM AUTHOR]

Potato genetic improvement begins with crossing cultivars or breeding clones which often have complementary characteristics for producing heritable variation in segregating offspring, in which phenotypic selection is used thereafter across various vegetative generations (Ti). The aim of this research was to determine whether tetrasomic genomic best linear unbiased predictors (GBLUPs) may facilitate selecting for tuber yield across early Ti within and across breeding sites in inbred (S1) and hybrid (F1) tetraploid potato offspring. This research used 858 breeding clones for a T1 trial at Umeå (Norrland, 63°49′30″ N 20°15′50″ E) in 2021, as well as 829 and 671 clones from the breeding population for T2 trials during 2022 at Umeå and Helgegården (Skåne, 56°01′46″ N 14°09′24″ E), respectively, along with their parents (S0) and check cultivars. The S1 and F1 were derived from selfing and crossing four S0. The experimental layout was an augmented design of four-plant plots across testing sites, where breeding clones were non-replicated, and the parents and cultivars were placed in all blocks between the former. The genomic prediction abilities (r) for tuber weight per plant were 0.5944 and 0.6776 in T2 at Helgegården and Umeå, respectively, when T1 at Umeå was used as the training population. On average, r was larger in inbred than in hybrid offspring at both breeding sites. The r was also estimated using multi-environment data (involving at least one S1 and one F1) for T2 performance at both breeding sites. The r was strongly influenced by the genotype in both S1 and F1 offspring irrespective of the breeding site. [ABSTRACT FROM AUTHOR]

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners. [ABSTRACT FROM AUTHOR]

BACKGROUND: Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods. PURPOSE: We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices. METHODS: A fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52 kg/m2 . Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model. RESULTS: We achieved coefficients of determination (R2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat), and all root-mean-squared errors were below 3.0 kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x. CONCLUSIONS: Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.

While the incidence and prevalence of type 2 diabetes is higher among Latino/as, Latino men are disproportionately affected and have poorer outcomes. We aimed to determine whether gender impacted any outcomes in a culturally tailored type 2 diabetes (T2D) intervention and to evaluate the effects of gender and intervention participation intensity on outcomes at 6-month follow-up. Nested path and regression models were compared with the likelihood ratio test and information criteria in a sample of Latino/a adults with T2D (n = 222) participating in a T2D community health worker (CHW)-led intervention. Path analysis showed that the effect of the intervention did not vary by gender. The intervention was associated with significant improvements in knowledge of T2D management 0.24 (0.10); p = 0.014, diabetes distress, -0.26 (0.12); p = 0.023, and self-efficacy, 0.61 (0.21); p = 0.005. At 6-month follow-up, improved self-management was associated with greater self-efficacy and Hemoglobin A1c (HbA1c) was lower by -0.18 (0.08); p = 0.021 for each unit of self-management behavior. Linear regressions showed that class attendance and home visits contributed to positive intervention results, while gender was non-significant. Pathways of change in a CHW-led culturally tailored T2D intervention can have a significant effect on participant behaviors and health status outcomes, regardless of gender.

This paper addresses patient heterogeneity associated with prediction problems in biomedical applications. We propose a systematic hypothesis testing approach to determine the existence of patient subgroup structure and the number of subgroups in patient population if subgroups exist. A mixture of generalized linear models is considered to model the relationship between the disease outcome and patient characteristics and clinical factors, including targeted biomarker profiles. We construct a test statistic based on expectation maximization (EM) algorithm and derive its asymptotic distribution under the null hypothesis. An important computational advantage of the test is that the involved parameter estimates under the complex alternative hypothesis can be obtained through a small number of EM iterations, rather than optimizing the objective function. We demonstrate the finite sample performance of the proposed test in terms of type-I error rate and power, using extensive simulation studies. The applicability of the proposed method is illustrated through an application to a multicenter prostate cancer study.

Operations research methods focus on formulating decision models, models that approximate the real work environment to mathematical models, and their optimal solution helps the decision maker to make optimal decisions that guarantee the greatest profit or the lowest cost. These models were built based on the data collected on the practical issue under study, and these data are classic values, specific values that are correct and accurate during the period in which these data are collected, which means that they remain ideal if the surrounding conditions remain the same as those in which they were collected. Data, and since the aim of this study is to make ideal decisions and to develop future plans through which we achieve the greatest profit or the least cost, It was necessary to search for a study that fits all the conditions in which the work environment passes in the present and the future. If we take data, neural values, indeterminate values - uncertain - leave nothing to chance, and take into account all conditions from worst to best. In this paper, we will present a complementary study to what we have done in previous research, the purpose of which was to reformulate some operational research methods using neutrosophic data. We will reformulate the assignment problem and one of the methods for its solution, which is the Hungarian method, using neutrosophic concepts, and we will explain the difference between using classical and neutrosophic data through examples.

Exposure to per- and polyfluoroalkyl substances (PFAS) during pregnancy and lactation is of increasing public health concern, but little is known about longitudinal changes in maternal PFAS concentrations from pregnancy to a few years postpartum. We quantified 11 PFAS in 251 serum samples prospectively collected from 42 Northern California mothers during the first, second, and third trimesters of pregnancy and at 3, 6, and 24 months after delivery over 2009-2017. We fit separate linear mixed models during pregnancy, early postpartum, and late postpartum to estimate percent changes of PFAS for each subperiod. Among five PFAS detected in more than 99% of samples, linear and branched perfluorooctanesulfonate (n- and Sm-PFOS), linear perfluorooctanoate (n-PFOA), and perfluorononanoate (PFNA) concentrations changed -4% to -3% per month during pregnancy. During early postpartum, perfluorohexanesulfonate (PFHxS) and n-PFOA concentrations changed -6% and -5%, respectively, per month, and Sm-PFOS and PFNA concentrations changed -1% per month. During late postpartum, n-PFOS, Sm-PFOS, and PFNA concentrations changed -1% per month. Breastfeeding duration was the primary determinant of n-PFOA and PFNA concentrations during late postpartum, showing negative associations. Our findings might be useful for reconstructing reliable prenatal or early life PFAS exposures for offspring.

This study examined how resting-state functional magnetic resonance imaging (rs-fMRI) data quality and availability relate to clinical and sociodemographic variables within the Adolescent Brain Cognitive Development Study. A sample of participants with an adequate sample of quality baseline rs-fMRI data containing low average motion (framewise displacement ≤ 0.15; low-noise; n = 4,356) was compared to a sample of participants without an adequate sample of quality data and/or containing high average motion (higher-noise; n = 7,437) using Chi-squared analyses and t-tests. A linear mixed model examined relationships between clinical and sociodemographic characteristics and average head motion in the sample with low-noise data. Relative to the sample with higher-noise data, the low-noise sample included more females, youth identified by parents as non-Hispanic white, and youth with married parents, higher parent education, and greater household incomes (ORs = 1.32-1.42). Youth in the low-noise sample were also older and had higher neurocognitive skills, lower BMIs, and fewer externalizing and neurodevelopmental problems (ds = 0.12-0.30). Within the low-noise sample, several clinical and demographic characteristics related to motion. Thus, participants with low-noise rs-fMRI data may be less representative of the general population and motion may remain a confound in this sample. Future rs-fMRI studies of youth should consider these limitations in the design and analysis stages in order to optimize the representativeness and clinical relevance of analyses and results.

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models-especially N-mixtures with beta-binomial detection submodels-were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models.

AbstractA previous study demonstrated that the Pediatric Quality of Life Inventory, a health-related quality-of-life instrument consisting of physical and psychosocial domain scores, reliably differentiates between children with varying severities of traumatic brain injuries (N = 729) 3, 12, and 24 mos after injury. However, the Pediatric Quality of Life Inventory physical domain score alone may simplify evaluation outcomes in physical rehabilitation and clinical research when comparing different trauma interventions. Therefore, we performed a secondary analysis to evaluate and compare the discriminative capacity of traumatic brain injury severity for changes in the overall Pediatric Quality of Life Inventory or the Pediatric Quality of Life Inventory physical domain score alone. We used linear mixed models to assess the change of outcome scores from baseline compared with arm-injury controls. Somers' D was calculated to compare discriminatory capacity with injury severity as a predictor of change in Pediatric Quality of Life Inventory outcome scores. We found that traumatic brain injury severity in children can be differentiated by the Pediatric Quality of Life Inventory physical domain score alone. However, at all follow-up time points, traumatic brain injury severity had higher discriminatory capacity for changes in the overall Pediatric Quality of Life Inventory. Our results suggest that the overall Pediatric Quality of Life Inventory should be used preferentially in children with traumatic brain injuries, although further investigation of the physical domain is warranted in conditions where physical injuries may predominate.

Event-related potentials (ERPs) are advantageous for investigating cognitive development. However, their application in infants/children is challenging given children's difficulty in sitting through the multiple trials required in an ERP task. Thus, a large problem in developmental ERP research is high subject exclusion due to too few analyzable trials. Common analytic approaches (that involve averaging trials within subjects and excluding subjects with too few trials, as in ANOVA and linear regression) work around this problem, but do not mitigate it. Moreover, these practices can lead to inaccuracies in measuring neural signals. The greater the subject exclusion, the more problematic inaccuracies can be. We review recent developmental ERP studies to illustrate the prevalence of these issues. Critically, we demonstrate an alternative approach to ERP analysis-linear mixed effects (LME) modeling-which offers unique utility in developmental ERP research. We demonstrate with simulated and real ERP data from preschool children that commonly employed ANOVAs yield biased results that become more biased as subject exclusion increases. In contrast, LME models yield accurate, unbiased results even when subjects have low trial-counts, and are better able to detect real condition differences. We include tutorials and example code to facilitate LME analyses in future ERP research.

ObjectiveTo evaluate the real-world safety of hysteroscopic compared with laparoscopic surgical sterilization.MethodsWe conducted a retrospective cohort study of Medicaid claims for hysteroscopic or laparoscopic sterilization procedures performed in California, 2008-2014, among women aged 18-50 years. After excluding postpartum procedures, we applied log-linear (Poisson) event-history regression models for clustered person-period data, weighted for propensity to receive either sterilization procedures, and adjusted for sociodemographic and clinical characteristics that may affect outcomes of interest to patients and physicians. We assessed the following outcomes: procedural complications, additional surgical procedures (eg, hysterectomy), repeat sterilization procedures, pelvic pain, pelvic inflammatory disease (PID), abdominal pain, nonabdominal pain, and abnormal uterine bleeding.ResultsWe identified 5,906 women who had undergone hysteroscopic and 23,965 who had undergone laparoscopic sterilization. After adjusting for sociodemographic and health history, women who had hysteroscopic sterilization were less likely to have claims for procedural complications (eg, transfusion, P

ABSTRACT Chrysanthemum (Dendranthema grandiflora) is the second most produced and commercialized ornamental plant in the world. Measuring leaf area through non-destructive methods is fundamental for studies on its growth and production. The estimation of leaf area by linear dimensions of the leaves can be a strategy for this purpose. The objective of this study was to find allometric equations to estimate the leaf area of chrysanthemum. The linear, linear without intercept, quadratic, cubic, power, and exponential regression models were used for the analysis. The choice of equations was based on the highest coefficients of determination. The non-destructive method using allometric models has accuracy for estimating the leaf area (LA) of chrysanthemum from the product between leaf length (L) and leaf width (W). The LA of chrysanthemum can be estimated using the equation ŷ = 0.6611*LW0.9490 (L - leaf length; W - leaf width). This equation will allow researchers and producers to determine leaf area non-destructively.

Background: Modeling of wastewater treatment plants is necessary to predict their later works. In this research, three methods were compared to predict some parameters at the outlet of wastewater treatment plant in Hama city in Syria. Methods: In this paper, three methods (linear regression, power regression, and regression trees) to model wastewater treatment plant in Hama city were compared to predict the parameters at the outlet of the plant (cBOD5out, CODout, TSSout) in terms of the parameters at the inlet of the plant (Qin, cBOD5in, CODin, TSSin). Results: When predicting cBOD5out, the values of RMSE of the test data set were 4.4105, 4.3875, and 3.8418; when predicting CODout, the values of RMSE of the test data set were 6.9325, 6.8003, and 5.3232; and when predicting TSSout, the values of root mean squared error (RMSE) of the test data set were 3.7781, 3.6936, and 3.2391 using linear regression, power regression, and regression trees (RTs), respectively. Conclusion: According to the results, the RTs outperforms in predicting cBOD5out, CODout, and TSSout because this method achieved the least RMSE of the test data set.

Background The minimum clinical importance difference (MCID) of the quality of life scale is an important parameter to explain and define the score changes of the scale, which can promote the clinical application of the quality of life scale as a tool to evaluate the effectiveness of treatment and intervention. MCID may be influenced by a variety of factors and there are few current studies on MCID of the Functional Assessment of Cancer Therapy-Generic Scale (FACT-G) . Objective To develop the MCID of FACT-G scale of patients with cervical precancerous lesions by a variety of methods and analyze the influence of patients' social demographic characteristics on the MCID of the FACT-G scale, so as to provide quantitative basis for clinicians to judge the clinical effectiveness of interventions. Methods A total of 66 patients with cervical precancerous lesions who received operation related to cervical precancerous lesions from October 2020 to November 2021 were included as study subjects in the First Affiliated Hospital of Shihezi University. The social demographic characteristics and disease-related information of the patients were collected, and the FACT-G scale was used to evaluate the quality of life of the patients before and 1 month after operation. The anchoring methods and distribution method were used to develop MCID for FACT-G scales. Multiple linear regression was used to analyze the influencing factors of MCID of the FACT-G scale. Results The four anchors including Global Rating of Change Questionnaire (GRCQ) , entry 10 of Global Rating of Change Questionnaire (SF-36 scale) , utility values (UI) of the Five-level EuroQol Five-dimensional Questionnaire (EQ-5D-5L scale) and EQ Visual Analogue Scale (EQ-VAS) were selected. The MCID of the total score of the FACT-G scale developed by the anchoring method ranged from 7.048 to 12.932, the MCID of the total scores of the FACT-G scale developed by the distribution method ranged from 3.696 to 10.243. The MCID developed by the anchoring method and distribution method were inconsistent. The MCID developed by the anchor-based GRCQ was considered as an example, there were 34 patients with slight improvement after operation were screened. The results of multiple linear regression analysis showed that that single (β=15.264, P89.5 (β=-7.600, P=0.001) were the influencing factors of MCID of the FACT-G total score in patients with cervical precancer lesions. Conclusions The MCIDs of FACT-G scale developed by different calculation methods are inconsistent. For patients with different characteristics of cervical precancerous lesions, MCIDs of the FACT-G scale should be selected to determine the effectiveness of clinical decisions and the clinical relevance of changes in quality of life after treatment.

Potato genetic improvement begins with crossing cultivars or breeding clones which often have complementary characteristics for producing heritable variation in segregating offspring, in which phenotypic selection is used thereafter across various vegetative generations (Ti). The aim of this research was to determine whether tetrasomic genomic best linear unbiased predictors (GBLUPs) may facilitate selecting for tuber yield across early Ti within and across breeding sites in inbred (S1) and hybrid (F1) tetraploid potato offspring. This research used 858 breeding clones for a T1 trial at Umeå (Norrland, 63°49′30″ N 20°15′50″ E) in 2021, as well as 829 and 671 clones from the breeding population for T2 trials during 2022 at Umeå and Helgegården (Skåne, 56°01′46″ N 14°09′24″ E), respectively, along with their parents (S0) and check cultivars. The S1 and F1 were derived from selfing and crossing four S0. The experimental layout was an augmented design of four-plant plots across testing sites, where breeding clones were non-replicated, and the parents and cultivars were placed in all blocks between the former. The genomic prediction abilities (r) for tuber weight per plant were 0.5944 and 0.6776 in T2 at Helgegården and Umeå, respectively, when T1 at Umeå was used as the training population. On average, r was larger in inbred than in hybrid offspring at both breeding sites. The r was also estimated using multi-environment data (involving at least one S1 and one F1) for T2 performance at both breeding sites. The r was strongly influenced by the genotype in both S1 and F1 offspring irrespective of the breeding site.

The density of a fluid or gas, which depends on the temperature, pressure and concentration of dissolved substances or suspended particles, changes under the influence of a large number of physical factors. We assume that an undisturbed liquid is heterogeneous. The propagation of periodic flows in viscous, uniformly stratified fluids is considered. The analysis is based on a system of fundamental equations for the transfer of energy, momentum and matter in periodic flows. Taking into account the compatibility condition, dispersion relations are constructed for two-dimensional internal, acoustic and surface linear periodic flows with a positive definite frequency and complex wave number in a compressible viscous fluid exponentially stratified by density. The temperature conductivity and diffusion effects are neglected. The obtained regularly perturbed solutions of the dispersion equations describe the conventional weakly damped waves. The families of singular solutions, specific for every kind of periodic flow, characterize the before unknown thin ligaments that accompany each type of wave. In limited cases, the constructed regular solutions transform into well-known expressions for a viscous homogeneous and an ideal fluid. Singular solutions are degenerated in a viscous homogeneous fluid or disappear in an ideal fluid. The developing method of the fundamental equation system analysis is directed to describe the dynamics and spatial structure of periodic flows in heterogeneous fluids in linear and non-linear approximations. [ABSTRACT FROM AUTHOR]

It is noteworthy that in the last decade, there has been an increase in the number of studies predicting normal diameter, total height, and stem volume based on stump dimensions. Therefore, the objectives were: a) to determine the mathematical model that best estimates normal diameter, total height, stem volume, and captured carbon as a function of stump diameter for Pinus arizonica Engelm. in northern Mexico; and b) to generate mathematical models through data processing in the Microsoft Excel program. Using a targeted sampling design, we selected 264 Pinus arizonica Engelm. trees to generate the database. The development of prediction models for normal diameter, total height, total tree volume, and captured carbon as a function of stump diameter was carried out using the Microsoft Excel database management package. The fit's adequacy was analyzed based on residuals and statistics such as the root mean square error, the adjusted coefficient of determination, and the coefficient of variation. Model fits indicate a linear trend for the normal diameter variable, while for total height, the model turned out to be logarithmic. As for total tree volume and captured carbon, the relationship is exponential in relation to stump diameter. The R2 adj fits were highly reliable for estimating normal diameter, total tree volume, and captured carbon, with values exceeding 95%. The development of prediction models using Microsoft Excel is viable according to the results presented here. The tested techniques can be replicated by forestry technicians, environmental inspectors, and forest landowners who do not have specialized knowledge in the generation prediction models. [ABSTRACT FROM AUTHOR]

Objective: Analysis of textural features of pulmonary nodules in chest CT, also known as radiomics, has several potential clinical applications, such as diagnosis, prognostication, and treatment response monitoring. For clinical use, it is essential that these features provide robust measurements. Studies with phantoms and simulated lower dose levels have demonstrated that radiomic features can vary with different radiation dose levels. This study presents an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. Methods: Nineteen patients with a total of thirty-five pulmonary nodules underwent four chest CT scans at different radiation dose levels (60, 33, 24, and 15 mAs) in a single session. The nodules were manually delineated. To assess the robustness of features, we calculated the intra-class correlation coefficient (ICC). To visualize the effect of milliampere-second variation on groups of features, a linear model was fitted to each feature. We calculated bias and calculated the R2 value as a measure of goodness of fit. Results: A small minority of 15/100 (15%) radiomic features were considered stable (ICC > 0.9). Bias increased and R2 decreased at lower dose, but shape features seemed to be more robust to milliampere-second variations than other feature classes. Conclusion: A large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations. For a subset of features, it was possible to correct this variability by a simple linear model. However, the correction became increasingly less accurate at lower radiation dose levels. Clinical relevance statement: Radiomic features provide a quantitative description of a tumor based on medical imaging such as computed tomography (CT). These features are potentially useful in several clinical tasks such as diagnosis, prognosis prediction, treatment effect monitoring, and treatment effect estimation. Key Points: • The vast majority of commonly used radiomic features are strongly influenced by variations in radiation dose level. • A small minority of radiomic features, notably the shape feature class, are robust against dose-level variations according to ICC calculations. • A large subset of radiomic features can be corrected by a linear model taking into account only the radiation dose level. [ABSTRACT FROM AUTHOR]

Operations research methods focus on formulating decision models, models that approximate the real work environment to mathematical models, and their optimal solution helps the decision maker to make optimal decisions that guarantee the greatest profit or the lowest cost. These models were built based on the data collected on the practical issue under study, and these data are classic values, specific values that are correct and accurate during the period in which these data are collected, which means that they remain ideal if the surrounding conditions remain the same as those in which they were collected. Data, and since the aim of this study is to make ideal decisions and to develop future plans through which we achieve the greatest profit or the least cost, It was necessary to search for a study that fits all the conditions in which the work environment passes in the present and the future. If we take data, neural values, indeterminate values - uncertain - leave nothing to chance, and take into account all conditions from worst to best. In this paper, we will present a complementary study to what we have done in previous research, the purpose of which was to reformulate some operational research methods using neutrosophic data. We will reformulate the assignment problem and one of the methods for its solution, which is the Hungarian method, using neutrosophic concepts, and we will explain the difference between using classical and neutrosophic data through examples. [ABSTRACT FROM AUTHOR]

ObjectivesWe examined the association between multimorbidity and social participation and whether purpose in life and life satisfaction moderate this relationship.MethodsParticipants were 12,825 Health and Retirement Study adults. We used multiple linear regression to examine the association between a cumulative-updated multimorbidity-weighted index (MWI) and social participation.ResultsAmong adults with average purpose in life or life satisfaction, MWI was associated with lower social participation. For those with above average purpose in life, each 1-point increase in MWI was associated with a 0.11-point (95% confidence interval [CI]: [0.07, 0.14]) better social participation score. Participants with above average life satisfaction experienced a 0.04-point (95% CI: [0.02, 0.07]) better social participation score with each 1-point increase in MWI.DiscussionMultimorbidity was associated with worse social participation, but this was reversed by above average purpose in life and life satisfaction. Interventions that improve well-being should be assessed to enhance social participation among older adults with any degree of multimorbidity.

Life expectancy (LE) is a core measure of population health. Studies have confirmed the predictive importance of modifiable determinants on LE, but less is known about their association with LE change over time at the US county level. In addition, we explore the predictive association of LE change with COVID-19 mortality. We used a linear regression model to calculate county-level annual LE change from 2011 to 2016, and categorized LE change (≤-0.1 years change per year as decreasing, ≥0.1 years as increasing, otherwise no change). A multinomial regression model was used to determine the association between modifiable determinants of health indicators from the County Health Rankings and LE change. A Poisson regression model was used to evaluate the relationship between change in life expectancy and COVID-19 mortality through September 2021. Among 2943 counties, several modifiable determinants of health were significantly associated with odds of being in increasing LE or decreasing LE counties, including adult smoking, obesity, unemployment, and proportion of children in poverty. The presence of an increasing LE in 2011-2016, as compared to no change, was significantly associated with a 5% decrease in COVID-19 mortality between 2019 and 2021 (β = 0.953, 95% CI: 0.943, 0.963). We demonstrated that change in LE at the county level is a useful metric for tracking public health progress, measuring the impact of public health initiatives, and gauging preparedness and vulnerability for future public health emergencies.