Causal Effects of Stochastic PrEP Interventions on HIV Incidence Among Men Who Have Sex With Men.

Antiretroviral preexposure prophylaxis (PrEP) is highly effective in preventing human immunodeficiency virus (HIV) infection, but uptake has been limited and inequitable. Although interventions to increase PrEP uptake are being evaluated in clinical trials among men who have sex with men (MSM), those trials cannot evaluate effects on HIV incidence. Estimates from observational studies of the causal effects of PrEP-uptake interventions on HIV incidence can inform decisions about intervention scale-up. We used longitudinal electronic health record data from HIV-negative MSM accessing care at Fenway Health, a community health center in Boston, Massachusetts, from January 2012 through February 2018, with 2 years of follow-up. We considered stochastic interventions that increased the chance of initiating PrEP in several high-priority subgroups. We estimated the effects of these interventions on population-level HIV incidence using a novel inverse-probability weighted estimator of the generalized g-formula, adjusting for baseline and time-varying confounders. Our results suggest that even modest increases in PrEP initiation in high-priority subgroups of MSM could meaningfully reduce HIV incidence in the overall population of MSM. Interventions tailored to Black and Latino MSM should be prioritized to maximize equity and impact.

A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

Distributed Cox proportional hazards regression using summary-level information.

Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general distributed methodology to fit Cox proportional hazards models without sharing individual-level data in multi-site studies. We make inferences on the log hazard ratios based on an approximated partial likelihood score function that uses only summary-level statistics. This approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. In particular, the fitting of stratified Cox models can be carried out with only one file transfer of summary-level information. We derive the asymptotic properties of the proposed estimators and compare the proposed estimators with the maximum partial likelihood estimators using pooled individual-level data and meta-analysis methods through simulation studies. We apply the proposed method to a real-world data set to examine the effect of sleeve gastrectomy versus Roux-en-Y gastric bypass on the time to first postoperative readmission.

Trends in C-Reactive Protein Use in Early-onset Sepsis Evaluations and Associated Antibiotic Use.

OBJECTIVE: To determine the prevalence of C-reactive protein (CRP) use in early-onset sepsis (EOS) evaluations in neonatal intensive care units (NICUs) across the US over time and to determine the association between CRP use and antibiotic use. STUDY DESIGN: A retrospective cohort study of NICUs contributing data to Premier Healthcare Database from 2009 through 2021. EOS evaluation was defined as a blood culture charge ≤ 3 days after birth. CRP use for each NICU was calculated as the proportion of infants with a CRP test obtained ≤ 3 days after birth among those undergoing an EOS evaluation and categorized as, low (<25%); medium-low (25 to < 50%), medium-high (50 to < 75%), and high (≥75%). Outcomes included antibiotic use and mortality ≤ 7 days after birth. RESULTS: Among 572 NICUs, CRP use varied widely and was associated with time. The proportion of NICUs with high CRP use decreased from 2009 to 2021 (24.7% vs 17.4%, P < .001), and those with low CRP use increased (47.9% vs 64.8%, P < .001). Compared with low-use NICUs, high-use NICUs more frequently continued antibiotics > 3 days (10% vs 25%, P < .001). This association persisted in multivariable-adjusted regression analyses (adjusted risk ratio 1.95, 95%CI 1.54, 2.48). Risk of mortality was not different in high-use NICUs (adjusted risk difference -0.02%, 95%CI -0.04%, 0.0008%). CONCLUSIONS: CRP use in EOS evaluations varied widely across NICUs. High CRP use was associated with prolonged antibiotic therapy but not mortality ≤ 7 days after birth. Reducing routine CRP use in EOS evaluations may be a target for neonatal antibiotic stewardship efforts.

Factors Associated with Growth in a Cohort of Children with Complex Biventricular Congenital Heart Disease.

OBJECTIVES: To evaluate patterns and determinants of longitudinal growth among children requiring complex biventricular repair for congenital heart disease, as well as to assess for associations of growth with early feeding modality, comorbidities, postoperative complications, and socioeconomic characteristics. STUDY DESIGN: A single-institution retrospective cohort study was performed in children born February 1999 to March 2009 with complex congenital heart disease who underwent biventricular repair before age 4 years, defined by Risk Adjustment in Congenital Heart Surgery-1 category 3-5. Clinical characteristics, height, weight, and body mass index (BMI) from ages 2-12 years were collected by chart review. Neighborhood-level socioeconomic data were identified using a geographic information system approach. The adjusted association of covariates with growth outcomes was estimated using multivariable linear regression models using generalized estimating equations. RESULTS: Compared with population growth curves, the cohort (n = 150) trended toward early decrease in age-adjusted weight and height. Early tube feeding was significantly associated with decreased BMI before adolescence (-0.539; 95% CI -1.02, -0.054; P = .029). In addition, other clinical and perioperative characteristics had significant associations with growth, including low birth weight, preoperative tube feeds, need for multiple bypass runs, and diagnosis of feeding disorder. CONCLUSIONS: Early childhood growth in children with complex biventricular repair may be impaired. Early tube feeding was associated with decreased BMI over the course of early childhood, which may indicate a need for continued close nutrition follow-up and support even beyond the duration of tube feeds.

Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data.

A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely in practice. However, the DeLong method as implemented in popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify the DeLong algorithm using ranks and extend it to accommodate missing data by using a mixed model approach for multivariate data. Simulation results demonstrate the validity and efficiency of our procedure for data missing at random. We illustrate our proposed procedure in SAS, Stata, and R using the original DeLong data.

Circulating microRNA-33b levels are associated with the presence and severity of coronary heart disease.

MicroRNA-33b (miR-33b) affected various biological pathways in regulating cholesterol homeostasis which may link to the pathogenesis of atherosclerotic lesions. However, whether this marker is associated with the presence and severity of coronary heart disease (CHD) is undetermined. We aim to explore the diagnostic value of circulating miR-33b level in the presence and severity of CHD. Altogether 320 patients were enrolled, including 240 patients diagnosed with CHD while 80 were classified as controls after CAG examination. Circulating miR-33b level was analyzed in all subjects, the Gensini score was calculated to assess the severity of stenotic lesions. The association between miR-33b and the presence and severity of CHD was analyzed, and the diagnostic potential of miR-33b of CHD was performed by the receiver operating characteristic (ROC) analysis. The CHD group had higher miR-33b levels (p < 0.001), and the miR-33b content significantly elevated following an increasing Gensini score (p for trend < 0.001). After adjustments for potential risk factors, such as several blood lipid markers, miR-33b remained a significant determinant for CHD (p < 0.001). ROC analysis disclosed that the AUC was 0.931. The optimal cutoff value of miR-33b was with a sensitivity of 81.3% and a specificity of 98.7% in differentiating CHD. It can prognosticate that the higher level of miR-33b was linked to increased severity of disease in CHD patients. Thus, the application of this marker might assist in the diagnosis and classification of CHD patients. Nevertheless, additional studies with larger sample sizes will be required to verify these results.

Gabapentin as a novel adjunct for postoperative irritability after superior cavopulmonary connection operation in children.

OBJECTIVES: Describing our institution's off-label use of gabapentin to treat irritability after superior cavopulmonary connection surgery and its impact on subsequent opiate and benzodiazepine requirements. METHODS: This is a single-center retrospective cohort study including infants who underwent superior cavopulmonary connection operation between 2011 and 2019. RESULTS: Gabapentin was administered in 74 subjects (74/323, 22.9%) during the observation period, with a median (IQR) starting dose of 5.7 (3.3, 15.0) mg/kg/day and a maximum dose of 10.7 (5.5, 23.4) mg/kg/day. Infants who underwent surgery in 2015-19 were more likely to receive gabapentin compared with those who underwent surgery in 2011-14 (p < 0.0001). Infants prescribed gabapentin were younger at surgery (137 versus 146 days, p = 0.007) and had longer chest tube durations (1.8 versus 0.9 days, p < 0.001), as well as longer postoperative intensive care (5.8 versus 3.1 days, p < 0.0001) and hospital (11.5 versus 7.0 days, p < 0.0001) lengths of stays. The year of surgery was the only predisposing factor associated with gabapentin administration in multivariate analysis. In adjusted linear regression, infants prescribed gabapentin on postoperative day 0-4 (n = 64) had reduced benzodiazepine exposure in the following 3 days (-0.29 mg/kg, 95% CI -0.52 - -0.06, p = 0.01) compared with those not prescribed gabapentin, while no difference was seen in opioid exposure (p = 0.59). CONCLUSIONS: Gabapentin was used with increasing frequency during the study period. There was a modest reduction in benzodiazepine requirements associated with gabapentin administration and no reduction in opioid requirements. A randomised controlled trial could better assess gabapentin's benefits postoperatively in children with congenital heart disease.

Robust causal inference of drug-drug interactions.

There is growing interest in developing causal inference methods for multi-valued treatments with a focus on pairwise average treatment effects. Here we focus on a clinically important, yet less-studied estimand: causal drug-drug interactions (DDIs), which quantifies the degree to which the causal effect of drug A is altered by the presence versus the absence of drug B. Confounding adjustment when studying the effects of DDIs can be accomplished via inverse probability of treatment weighting (IPTW), a standard approach originally developed for binary treatments and later generalized to multi-valued treatments. However, this approach generally results in biased results when the propensity score model is misspecified. Motivated by the need for more robust techniques, we propose two empirical likelihood-based weighting approaches that allow for specifying a set of propensity score models, with the second method balancing user-specified covariates directly, by incorporating additional, nonparametric constraints. The resulting estimators from both methods are consistent when the postulated set of propensity score models contains a correct one; this property has been termed multiple robustness. In this paper, we derive two multiply-robust estimators of the causal DDI, and develop inference procedures. We then evaluate their finite sample performance through simulation. The results demonstrate that the proposed estimators outperform the standard IPTW method in terms of both robustness and efficiency. Finally, we apply the proposed methods to evaluate the impact of renin-angiotensin system inhibitors (RAS-I) on the comparative nephrotoxicity of nonsteroidal anti-inflammatory drugs (NSAID) and opioids, using data derived from electronic medical records from a large multi-hospital health system.

Meta-analysis with sample-standardization in multi-site studies.

PURPOSE: To conceptualize a particular target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to analytically examine sample-standardization as a meta-analytic method compared with inverse-variance weighted meta-analyses. METHODS: The target population of interest is all and only all individuals from the data-contributing sites. Standardization, a general conditioning technique frequently employed for confounding control, was adopted to estimate the network-wide causal treatment effect. Specifically, the proposed sample-standardization yields a meta-analysis estimator, that is, a weighted summation of site-specific results, where the weight for a site is the proportion of its size in the entire network. This sample-standardization estimator was evaluated analytically in comparison to estimators from inverse-variance weighted fixed-effect and random-effects meta-analyses in terms of statistical consistency. RESULTS: A proof is reported to justify the consistency of the sample-standardization estimator with and without treatment effect heterogeneity by site. Both inverse-variance weighted fixed-effect and random-effects meta-analyses were found to generally result in inconsistent estimators in the presence of treatment effect heterogeneity by site for this particular target population and estimand. CONCLUSIONS: Sample-standardization is a valid approach to generate causal inference in multi-site studies when the target population comprises all and only all individuals within the network, even in the presence of heterogeneity of treatment effect by site. Multi-site studies should clearly specify the target population and estimand to help select the most appropriate meta-analytic methods.

Ulinastatin inhibits the metastasis of nasopharyngeal carcinoma by involving uPA/uPAR signaling.

Distant metastasis is the primary reason for treatment failure in patients with nasopharyngeal carcinoma (NPC). In this study, we investigated the effect of ulinastatin (UTI) on NPC metastasis and its underlying mechanism. Highly-metastatic NPC cell lines S18 and 58F were treated with UTI and the effect on cell proliferation, migration, and invasion were determined by MTS and Transwell assays. S18 cells with luciferase-expressing (S18-1C3) were injected into the left hind footpad of nude mice to establish a model of spontaneous metastasis from the footpad to popliteal lymph node (LN). The luciferase messenger RNA (mRNA) was measured by quantitative polymerase chain reaction (qPCR), and the metastasis inhibition rate was calculated. Key molecular members of the UTI-related uPA, uPAR, and JAT/STAT3 signaling pathways were detected by qPCR and immunoblotting. UTI suppressed the migration and infiltration of S18 and 5-8F cells and suppressed the metastasis of S18 cells in vivo without affecting cell proliferation. uPAR expression decreased from 24 to 48 h after UTI treatment. The antimetastatic effect of UTI is partly due to the suppression of uPA and uPAR. UTI partially suppresses NPC metastasis by downregulating the expression of uPA and uPAR.

Variance estimation in inverse probability weighted Cox models.

Inverse probability weighted Cox models can be used to estimate marginal hazard ratios under different point treatments in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations. However, this estimator does not incorporate the uncertainty in estimating the weights and tends to overestimate the variance, leading to inefficient inference. Here we propose a new variance estimator that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of terms that are not independently and identically distributed in a Cox partial likelihood score equation. We prove analytically that the robust sandwich variance estimator is conservative and establish the asymptotic equivalence between the proposed variance estimator and one obtained through linearization by Hajage et al. in 2018. In addition, we extend our proposed variance estimator to accommodate clustered data. We compare the finite sample performance of the proposed method with alternative methods through simulation studies. We illustrate these different variance methods in both independent and clustered data settings, using a bariatric surgery dataset and a multiple readmission dataset, respectively. To facilitate implementation of the proposed method, we have developed an R package ipwCoxCSV.

Estimating the marginal hazard ratio by simultaneously using a set of propensity score models: A multiply robust approach.

The inverse probability weighted Cox model is frequently used to estimate the marginal hazard ratio. Its validity requires a crucial condition that the propensity score model be correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in the empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to compare the risk of postoperative hospitalization between sleeve gastrectomy and Roux-en-Y gastric bypass using data from a large medical claims and billing database. We further extend the development to multisite studies to enable each site to postulate multiple site-specific propensity score models.

Decomposing the decoupling of plastics consumption and economic growth in G7 and China: Evidence from 2001 to 2020 based on China's import ban.

Plastic recycling is critical for dematerializing of plastics. It has a profound implication on decoupling economic growth from environmental pressure and advancing waste plastic governance domestically and internationally while identifying drivers that might improve decoupling. In this study, plastic consumption and recycling patterns are presented, and the factors influencing the acceleration of dematerialization subsequent to the ban were investigated in the G7 countries and China. The results show that plastic consumption increases from 7.60 million metric tons (mt) to 12.60 mt between 2017 and 2019, and subsequently rapidly decreases to 6.84 mt in 2020. The plastic recycling rate drastically decreased by 21.3% in 2017, and decreased slightly from 2017 to 2020, at an annual rate of 2.9% on average. China's ban shocked the decoupling trends, which showed resilience and motivated the development of robust plastic recycling, and the global recycling transformation pattern accelerated the dematerialization of plastics. Decoupling performances of the G7 and China gradually stabilized in 2019, and all the countries were strongly decoupled in 2020, although decoupling index (DI) fluctuates from 2017 to 2020. Among the recycling-trading drivers, the improvement of waste plastic quality in recycling contributes more to decoupling, the recycling rate shows a more negative decoupling effect on China before the ban, and the population effect is weak relative to other influencing factors. The factors revealed the mechanism of decoupling of plastic consumption in the recycling-trading process, and the recyclability improvement in terms of plastic quality is important for dematerialization.

The Role of α7nAChR-Mediated Cholinergic Anti-Inflammatory Pathway in Vagal Nerve Regulated Atrial Fibrillation.

The aim was to investigate the role of the α7nAChR-mediated cholinergic anti-inflammatory pathway in vagal nerve regulated atrial fibrillation (AF).18 beagles (standard dogs for testing) were used in this study, and the effective refractory period (ERP) of atrium and pulmonary veins and AF inducibility were measured hourly during rapid atrial pacing at 800 beats/minute for 6 hours in all beagles. After cessation of 3 hours of RAP, the low-level vagal nerve stimulation (LL-VNS) group (n = 6) was given LL-VNS and injection of salinne (0.5 mL/GP) into four GPs, the methyllycaconitine (MLA, the antagonist of α7nAChR) group (n = 6) was given LL-VNS and injection of MLA into four GPs, and the Control group (n = 6) was given saline into four GPs and the right cervical vagal nerve was exposed without stimulation. Then, the levels of the tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), acetylcholine (ACh), STAT3, and NF-κB proteins were measured. During the first 3 hours of RAP, the ERPs gradually decreased while the dispersion of ERPs (dERPs) and AF inducibility gradually increased in all three groups. During the last 3 hours of 6 hours' RAP in this study, the ERPs in the LL-VNS group were higher, while the dERPs and AF inducibility were significantly lower when compared with the Control and MLA groups at the same time points. The levels of ACh in the serum and atrium in the LL-VNS and MLA groups were higher than in the Control group, and the levels of TNF-α and IL-6 were higher in the Control and MLA groups than in the LL-VNS group. The concentrations of STAT3 in RA and LA tissues were higher in the LL-VNS group while those of NF-κB were lower.In conclusion, the cholinergic anti-inflammatory pathway mediated by α7nACh plays an important role in low-level vagal nerve-regulated AF.

Experimental studies of hydrothermal liquefaction of kitchen waste with H+, OH- and Fe3+ additives for bio-oil upgrading.

Kitchen waste (KW) has gradually become a prominent problem in municipal solid waste treatment. Hydrothermal liquefaction (HTL) is a promising method used to make fuel oil from food and KW. However, the upgrading of bio-oil is particularly important for the sake of industrial reuse. In this study, the KW from university restaurants was subjected to HTL experiments in order to study theoretical feasibility. With the change of conversion temperature and residence time, the optimal conversion working conditions in this study were determined according to the quality and yield of the bio-oil. Moreover, the bio-oil upgrading effects of different additives (hydrogen chloride, sodium hydroxide, and iron(III) chloride) on the HTL of KW were studied. Alkaline additives have an inhibitory effect on the bio-oil yield and positive effect on coke yield. Acidic additives and iron (Fe)-containing additives can promote bio-oil yield. As an important aspect of upgrading, the effect on the nitrogen content of bio-oil with additives was revealed. The alkaline and Fe-containing additives have little effect on reducing the viscosity of the bio-oil while with the appropriate ratio (2.5 mol•kg-1) of acidic additives to the raw material, the static and dynamic fluidity of the oil phase products are reduced to about 0.1 Pa•s.

[Mechanism of Gynostemma pentaphyllum in treatment of metabolism associated fatty liver disease based on network pharmacology and molecular docking].

The present study explored the mechanism of action of Gynostemma pentaphyllum in the treatment of metabolism associa-ted fatty liver disease(MAFLD) by network pharmacology and molecular docking. The main active components and action targets of G. pentaphyllum were collected from TCMSP. Disease-related targets were obtained from GeneCards, OMIM and TTD, and the common targets of the three databases were screened out, which were converted to the genes with standard names by UniProt. The drug-disease common target genes were obtained through Venn tool and uploaded to STRING for the construction of the protein-protein interaction(PPI) network. Cytoscape was used to construct and analyze the drug-active component-common target-disease network. The gene ontology(GO) analysis and Kyoto encyclopedia of genes and genomes(KEGG) pathway enrichment analysis were performed on the common targets by DAVID. Pymol was adopted to perform molecular docking of active components and the common targets and predict their binding ability. Twenty-four active components(such as gypenosides, quercetin and sitosterol) of G. pentaphyllum were screened out. Ninety-two targets were obtained and 54 common targets were identified. Key targets included TNF, IL6, PTGS2, TP53, CCL2 and VEGFA. GO analysis on biological processes, molecular functions and cellular components and KEGG pathway analysis were performed, and the results indicated that NF-κB, PI3 K-Akt, TNF and HIF-1 signaling pathways were mainly involved. Molecular docking results showed that gypenosides and quercetin had a strong binding ability to TNF, IL6 and PTGS2. The findings of this study revealed that the therapeutic efficacy of G. pentaphyllum on MAFLD might be achieved by resisting inflammation and oxidative stress and improving insulin resistance, providing ideas and a theoretical basis for the development and application of G. pentaphyllum in the treatment of MAFLD.

Antioxidants suppress radiation-induced apoptosis via inhibiting MAPK pathway in nasopharyngeal carcinoma cells.

Nasopharyngeal carcinoma (NPC) is relatively sensitive to ionizing radiation, and radiotherapy is the main treatment modality for non-metastatic NPC. Radiation therapy generates overproduction of reactive oxygen species (ROS), which can cause DNA damage and induce apoptosis in tumors, thereby killing the malignant cells. Although dietary antioxidant supplementation reduces oxidative stress and promotes tumor progression, the effects of antioxidants on the NPC cells upon radiation have not been reported. In the present study, we showed that antioxidants (ß-Carotene, NAC, GSH) played an anti-apoptotic role in response to radiation via decreasing ROS production and inhibiting MAPK pathway in NPC cells. Based on that, we conclude that the use of supplemental antioxidants during radiotherapy should be avoided because of the possibility of tumor protection and reduced treatment efficacy.

Causal inference with noisy data: Bias analysis and estimation approaches to simultaneously addressing missingness and misclassification in binary outcomes.

Causal inference has been widely conducted in various fields and many methods have been proposed for different settings. However, for noisy data with both mismeasurements and missing observations, those methods often break down. In this paper, we consider a problem that binary outcomes are subject to both missingness and misclassification, when the interest is in estimation of the average treatment effects (ATE). We examine the asymptotic biases caused by ignoring missingness and/or misclassification and establish the intrinsic connections between missingness effects and misclassification effects on the estimation of ATE. We develop valid weighted estimation methods to simultaneously correct for missingness and misclassification effects. To provide protection against model misspecification, we further propose a doubly robust correction method which yields consistent estimators when either the treatment model or the outcome model is misspecified. Simulation studies are conducted to assess the performance of the proposed methods. An application to smoking cessation data is reported to illustrate the use of the proposed methods.

Weighted causal inference methods with mismeasured covariates and misclassified outcomes.

Inverse probability weighting (IPW) estimation has been widely used in causal inference. Its validity relies on the important condition that the variables are precisely measured. This condition, however, is often violated, which distorts the IPW method and thus yields biased results. In this paper, we study the IPW estimation of average treatment effects for settings with mismeasured covariates and misclassified outcomes. We develop estimation methods to correct for measurement error and misclassification effects simultaneously. Our discussion covers a broad scope of treatment models, including typically assumed logistic regression models and general treatment assignment mechanisms. Satisfactory performance of the proposed methods is demonstrated by extensive numerical studies.