The Importance of Equity Value Judgments and Estimator-Estimand Alignment in Measuring Disparity and Identifying Targets to Reduce Disparity.

The choice of which covariates to adjust for (so-called allowability designation (AD)) in health disparity measurements reflects value judgments about inequitable versus equitable sources of health differences, which is paramount for making inferences about disparity. Yet, many off-the-shelf estimators used in health disparity research are not designed with equity considerations in mind, and they imply different ADs. We demonstrated the practical importance of incorporating equity concerns in disparity measurements through simulations, motivated by the example of reducing racial disparities in hypertension control via interventions on disparities in treatment intensification. Seven causal decomposition estimators, each with a particular AD (with respect to disparities in hypertension control and treatment intensification), were considered to estimate the observed outcome disparity and the reduced/residual disparity under the intervention. We explored the implications for bias of the mismatch between equity concerns and the AD in the estimator under various causal structures (through altering racial differences in covariates or the confounding mechanism). The estimator that correctly reflects equity concerns performed well under all scenarios considered, whereas the other estimators were shown to have the risk of yielding large biases in certain scenarios, depending on the interaction between their ADs and the specific causal structure.

Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

Propensity score methods for observational studies with clustered data: A review.

Propensity score methods are a popular approach to mitigating confounding bias when estimating causal effects in observational studies. When study units are clustered (eg, patients nested within health systems), additional challenges arise such as accounting for unmeasured confounding at multiple levels and dependence between units within the same cluster. While clustered observational data are widely used to draw causal inferences in many fields, including medicine and healthcare, extensions of propensity score methods to clustered settings are still a relatively new area of research. This article presents a framework for estimating causal effects using propensity scores when study units are nested within clusters and are nonrandomly assigned to treatment conditions. We emphasize the need for investigators to examine the nature of the clustering, among other properties, of the observational data at hand in order to guide their choice of causal estimands and the corresponding propensity score approach.

Flexible propensity score estimation strategies for clustered data in observational studies.

Existing studies have suggested superior performance of nonparametric machine learning over logistic regression for propensity score estimation. However, it is unclear whether the advantages of nonparametric propensity score modeling are carried to settings where there is clustering of individuals, especially when there is unmeasured cluster-level confounding. In this work we examined the performance of logistic regression (all main effects), Bayesian additive regression trees and generalized boosted modeling for propensity score weighting in clustered settings, with the clustering being accounted for by including either cluster indicators or random intercepts. We simulated data for three hypothetical observational studies of varying sample and cluster sizes. Confounders were generated at both levels, including a cluster-level confounder that is unobserved in the analyses. A binary treatment and a continuous outcome were generated based on seven scenarios with varying relationships between the treatment and confounders (linear and additive, nonlinear/nonadditive, nonadditive with the unobserved cluster-level confounder). Results suggest that when the sample and cluster sizes are large, nonparametric propensity score estimation may provide better covariate balance, bias reduction, and 95% confidence interval coverage, regardless of the degree of nonlinearity or nonadditivity in the true propensity score model. When the sample or cluster sizes are small, however, nonparametric approaches may become more vulnerable to unmeasured cluster-level confounding and thus may not be a better alternative to multilevel logistic regression. We applied the methods to the National Longitudinal Study of Adolescent to Adult Health data, estimating the effect of team sports participation during adolescence on adulthood depressive symptoms.

Factors associated with county-level mental health during the COVID-19 pandemic.

The objective of this study is to determine county-level factors associated with anxiety, depression, and isolation during the coronavirus disease 2019 (COVID-19) pandemic. This study used daily data from 23,592,355 respondents of a nationwide Facebook-based survey from April 2020 to July 2021, aggregated to the week-county level to yield 212,581 observations. Mental distress prevalences were modeled using weighted linear mixed-effects models with a county random effect. These models revealed that weekly percentages of mental distress were higher in counties with higher unemployment rates, populations, and education levels; higher percentages of females, young adults, individuals with a medical condition, and individuals very worried about their finances and COVID-19; and lower percentages of individuals who were working outside the home, living with children, without health insurance, and Black. Anxiety peaked in April 2020, depression in October 2020, and isolation in December 2020. Therefore, United States counties experienced the mental health effects of the pandemic differently dependent upon their characteristics, and mental distress prevalence varied across time.

Rhodobacter amnigenus sp. nov. and Rhodobacter ruber sp. nov., isolated from freshwater habitats.

Two bacterial strains, designated HSP-20T and CCP-1T, isolated from freshwater habitats in Taiwan, were characterized by polyphasic taxonomy. Both strains were Gram-stain-negative, aerobic, non-motile and rod-shaped. Cells of strains HSP-20T and CCP-1T formed pink and dark red coloured colonies, respectively. Both strains contained bacteriochlorophyll a, and showed optimum growth under anaerobic conditions by photoheterotrophy, but no growth by photoautotrophy. Phylogenetic analyses based on 16S rRNA gene and whole-genome sequences indicated that both strains belonged to the genus Rhodobacter. Analysis of 16S rRNA gene sequences showed that strains HSP-20T and CCP-1T shared 98.3 % sequence similarity and were closely related to Rhodobacter tardus CYK-10T (96.0 %) and Rhodobacter flagellatus SYSU G03088T (96.0 %), respectively. Both strains shared common chemotaxonomic characteristics including Q-10 as the major isoprenoid quinone, C18 : 1 ω7c as the predominant fatty acid, and phosphatidylethanolamine, phosphatidylglycerol and phosphatidylcholine as the main polar lipids. The DNA G+C content of both strains was 66.2 mol%. The average nucleotide identity, average amino acid identity and digital DNA-DNA hybridization values between these two novel isolates and their closest relatives were below the cut-off values of 95-96, 90 and 70 %, respectively, used for species demarcation. On the basis of phenotypic and genotypic properties and phylogenetic inference, both strains should be classified as novel species within the genus Rhodobacter, for which the names Rhodobacter amnigenus sp. nov. (=BCRC 81193T=LMG 31334T) and Rhodobacter ruber sp. nov. (=BCRC 81189T=LMG 31335T) are proposed.

Global trends and predictors of face mask usage during the COVID-19 pandemic.

BACKGROUND: Guidelines and recommendations from public health authorities related to face masks have been essential in containing the COVID-19 pandemic. We assessed the prevalence and correlates of mask usage during the pandemic. METHODS: We examined a total of 13,723,810 responses to a daily cross-sectional online survey in 38 countries of people who completed from April 23, 2020 to October 31, 2020 and reported having been in public at least once during the last 7 days. The outcome was individual face mask usage in public settings, and the predictors were country fixed effects, country-level mask policy stringency, calendar time, individual sociodemographic factors, and health prevention behaviors. Associations were modeled using survey-weighted multivariable logistic regression. RESULTS: Mask-wearing varied over time and across the 38 countries. While some countries consistently showed high prevalence throughout, in other countries mask usage increased gradually, and a few other countries remained at low prevalence. Controlling for time and country fixed effects, sociodemographic factors (older age, female gender, education, urbanicity) and stricter mask-related policies were significantly associated with higher mask usage in public settings. Crucially, social behaviors considered risky in the context of the pandemic (going out to large events, restaurants, shopping centers, and socializing outside of the household) were associated with lower mask use. CONCLUSION: The decision to wear a face mask in public settings is significantly associated with sociodemographic factors, risky social behaviors, and mask policies. This has important implications for health prevention policies and messaging, including the potential need for more targeted policy and messaging design.

Zoanthamine Alkaloid Derivatives from the Zoantharian Zoanthus vietnamensis with Antimetastatic Activity.

A novel compound zoanone A (1), together with eight new alkaloids, 3ß,14α-dihydroxy-28-deoxyzoanthenamine (2), 7α-hydroxy-28-deoxyzoanthenamine (3), 3α-hydroxyzoanthenamine (4), 7ß-hydroxyzoanthenamine (5), 28α-methoxyzoanthenamine (6), 28α-methoxykuroshine A (7), 30-hydroxykuroshine A (8), and 3ß-hydroxy-11-deketo-kuroshine B (9), was isolated from the zoantharian Zoanthus vietnamensis. Their structures were elucidated by the comprehensive analyses of IR, mass spectrometry, NMR, and UV spectroscopic data. The absolute configuration of 1 was established by single-crystal X-ray crystallographic analysis using Cu Kα radiation. A plausible biosynthetic pathway of 1 was proposed. Antiangiogenic and antilymphangiogenic activities of the isolated metabolites were tested and evaluated. The results showed that all isolated compounds had weak antimetastatic activity.

Pipoxolan suppresses the inflammatory factors of NF-κB, AP-1, and STATs, but activates the antioxidative factor Nrf2 in LPS-stimulated RAW 264.7 murine macrophage cells.

Although pipoxolan (PIPO) is a smooth muscle relaxant, its anti-inflammatory capability has not been studied. Therefore, we investigated the anti-inflammatory molecular mechanisms of PIPO in lipopolysaccharide (LPS)-induced RAW 264.7 macrophages. In this study, we used the MTT assay to evaluate the cytotoxicity, applied the enzyme-linked immunosorbent assay to determine the inflammatory cytokines, and performed Western blotting to assess protein expression. The results showed that PIPO significantly inhibited cytokine production, including nitric oxide, prostaglandin E2 , tumor necrosis factor-α, and interleukin-6. PIPO also suppressed the pro-inflammatory mediator expression with inducible nitric oxide synthase and cyclooxygenase-2. Moreover, PIPO prohibited the multiple inflammatory transcription factor pathways, including inhibitor kappa B/nuclear factor of the κ light chain enhancer of B cells (NF-κB), mitogen-activated protein kinase/activator protein-1 (AP-1), Janus kinase/signal transducer and activator of transcription (STAT), and toll-like receptor 4 (TLR4)/serine/threonine kinase (AKT). Besides, PIPO effectively activated the nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 antioxidative pathway. Collectively, PIPO may attenuate the inflammatory effects via influencing the LPS/TLR4 receptor binding; suppress the expression of anti-inflammatory transcription factors NF-κB, AP-1, and STAT; and activating the antioxidative transcription factor Nrf2 in LPS-stimulated mouse RAW 264.7 cells.

Incentive spirometry is an effective strategy to improve the quality of postoperative care in patients.

Postoperative pulmonary complications (PPCs) most commonly occur after thoracic surgery. Not only prolonged hospital stay and increased financial expenses but also morbidity and even mortality may be troublesome for those with PPCs. Herein, we aimed to conduct a comprehensive systematic review and meta-analysis of available data to examine the effectiveness of incentive spirometry (IS) to reduce PPCs and shorten hospital stay. This systematic review and meta-analysis included 5 randomized controlled trials (RCT) and 3 retrospective cohort study (10,322 patients in total) in PubMed, Embase and Cochrane Library until September 31, 2021. We assessed the clinical efficacy of IS using length of hospital stay, PPCs, postoperative pneumonia, and postoperative atelectasis with meta-analysis, meta-regression and trial sequential analysis (TSA). With this meta-analysis, the length of hospital stay in patients undergoing IS was significantly shorter (1.8 days) than that in patients not receiving IS (MD = -1.80, 95% CI = -2.95 to -0.65). Patients undergoing IS also had reduced risk of PPCs (32%) and postoperative pneumonia (17.9%) with statistical significance than patients not undergoing IS (PPC: OR = 0.68, 95% CI = 0.51-0.90) (Pneumonia: OR = 0.821, 95% CI = 0.677-0.995).In meta-regression, the benefits of undergoing IS in patients with preoperative predicted FEV1 of <80% in a linear fashion with decreasing PPCs. IS is an effective modality to improve the quality of postoperative care for patients after pulmonary resection, compared with the control group without using IS; and applying IS has favorable outcomes of shorter length of hospital stay (1.8 days) and lower occurrence of PPCs (32% of risk reduction), which are conclusive and robust based on our validation via TSA. Moreover, the IS device is more beneficial for patients with preoperative predicted FEV1 of <80% than that in others.

Association of Non-Pharmaceutical Interventions to Reduce the Spread of SARS-CoV-2 With Anxiety and Depressive Symptoms: A Multi-National Study of 43 Countries.

Objectives: To examine the association of non-pharmaceutical interventions (NPIs) with anxiety and depressive symptoms among adults and determine if these associations varied by gender and age. Methods: We combined survey data from 16,177,184 adults from 43 countries who participated in the daily COVID-19 Trends and Impact Survey via Facebook with time-varying NPI data from the Oxford COVID-19 Government Response Tracker between 24 April 2020 and 20 December 2020. Using logistic regression models, we examined the association of [1] overall NPI stringency and [2] seven individual NPIs (school closures, workplace closures, cancellation of public events, restrictions on the size of gatherings, stay-at-home requirements, restrictions on internal movement, and international travel controls) with anxiety and depressive symptoms. Results: More stringent implementation of NPIs was associated with a higher odds of anxiety and depressive symptoms, albeit with very small effect sizes. Individual NPIs had heterogeneous associations with anxiety and depressive symptoms by gender and age. Conclusion: Governments worldwide should be prepared to address the possible mental health consequences of stringent NPI implementation with both universal and targeted interventions for vulnerable groups.

Sustainable Development and Performance Evaluation of Marble-Waste-Based Geopolymer Concrete.

The key objective of this study was to develop marble-based geopolymer concrete and examine the viability of its application as a sustainable structural material for the construction industry. The results of the research demonstrated that marble-based geopolymer concrete can be developed, and its physical/mechanical properties were shown to have a very good performance. According to various experimental tests and a large-scale ready-mixed plant test, it was found that the marble-based geopolymer concrete displayed a good workability and was not easily influenced by temperature changes. The results showed that marble-based geopolymer concrete has an excellent potential for further engineering development in the future.

Cholinergic and glutamatergic transmission at synapses between pedunculopotine tegmental nucleus axonal terminals and A7 catecholamine cell group noradrenergic neurons in the rat.

We characterized transmission from the pedunculopotine tegmental nucleus (PPTg), which contains cholinergic and glutamatergic neurons, at synapses with noradrenergic (NAergic) A7 neurons. Injection of an anterograde neuronal tracer, biotinylated-dextran amine, into the PPTg resulted in labeling of axonal terminals making synaptic connection with NAergic A7 neurons. Consistent with this, extracellular stimulation using a train of 10 pulses at 100 Hz evoked both fast and slow excitatory synaptic currents (EPSCs) that were blocked, respectively, by DNQX, a non-N-methyl-d-aspartate receptor blocker, or atropine, a cholinergic muscarinic receptor (mAChR) blocker. Interestingly, many spontaneous-like, but stimulation-dependent, EPSCs, were seen for up to one second after the end of stimulation and were blocked by DNQX and decreased by EGTA-AM, a membrane permeable form of EGTA, showing they are glutamatergic EPSCs causing by asynchronous release of vesicular quanta. Moreover, application of atropine or carbachol, an mAChR agonist, caused, respectively, an increase in the number of asynchronous EPSCs or a decrease in the frequency of miniature EPSCs, showing that mAChRs mediated presynaptic inhibition of glutamatergic transmission of the PPTg onto NAergic A7 neurons. In conclusion, our data show direct synaptic transmission of PPTg afferents onto pontine NAergic neurons that involves cooperation of cholinergic and glutamatergic transmission. This dual-transmitter transmission drives the firing rate of NAergic neurons, which may correlate with axonal and somatic/dendritic release of NA.