Matching Methods for Estimating Effects of Time-dependent Treatment on Survival Outcomes

In observational studies, treatment is often evaluated through its impact on survival time. However, when treatment initiation is time-dependent, existing methods are either inapplicable or yield treatment effect parameters with unsatisfactory interpretation. In this dissertation we propose methodology that evaluates the effect of time-dependent treatments in the context of survival functions. In Chapter II, we estimate the average treatment effect among the treated (ATT) in the setting where the covariates remain constant over time. Since the counterfactual absence-of-treatment experience is not observable for treated patients, we match (using prognostic scores) to similar yet-untreated subjects to mimic this counterfactual experience. Novel components of the work include the emphasis on big data sets; use of personalized nonparametric survival function estimators; and the fact that, through grouping, survival curves (as opposed to patients) are ultimately matched. In Chapter III, we propose alternative methods for the same general data structure as Chapter II. It is assumed that the data set is much smaller, implying different techniques to leverage the matching. Like Chapter II, methods proposed in Chapter III are applied to kidney transplant data. In Chapter IV, we extend our method to the setting where adjustment covariates are time-dependent. As a generalization of Chapter II, methods in Chapter IV use matching and in addition, they incorporate the partly conditional model for the pretreatment death hazard to adjust for the time-dependent variables. Patients were matched on their residual survival time. Methods were then applied to the deceased donor liver transplant data to quantify the transplant effect.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153369/1/dantzhu_1.pd

Multicenter Study of Staging and Therapeutic Predictors of Hepatocellular Carcinoma Recurrence Following Transplantation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146434/1/lt25194.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146434/2/lt25194_am.pd

Estimating the effect of a rare time‐dependent treatment on the recurrent event rate

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143592/1/sim7626_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143592/2/sim7626.pd

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Forecasting method of clean energy development potential considering sector coupling

Sector coupling includes not only the coupling within energy sectors such as electricity, heat and gas, but also the coupling between energy sector and transportation sector, construction sector and industrial sector. This article introduces a method for measuring the development potential of clean energy based on sector coupling. First, the analysis model of electric energy substitution potential is constructed, the analysis object is determined, and the analysis object is quantified. On the basis of the definition of clean energy development potential, an IPAT model for electric energy substitution is constructed to realize the comprehensive evaluation of clean energy. Secondly, based on Markov theory to realize the sub-path calculation of the development potential of clean energy. Finally, based on the IPAT model and the decoupling theoretical model, this paper sets up three different alternative scenarios to make a more comprehensive prediction and analysis of the medium and long-term clean energy development potential, and applies the introduced measurement methods to the calculation of China’s clean energy development potential. The results show the effectiveness of the algorithm in the calculation of the development potential of clean energy based on sector coupling

Effects of Varied Sulfamethazine Dosage and Exposure Durations on Offspring Mice

The development of antibiotics was a turning point in the history of medicine; however, their misuse and overuse have contributed to the current global epidemic of antibiotic resistance. According to epidemiological studies, early antibiotic exposure increases the risk of immunological and metabolic disorders. This study investigated the effects of exposure to different doses of sulfamethazine (SMZ) on offspring mice and compared the effects of exposure to SMZ on offspring mice in prenatal and early postnatal periods and continuous periods. Furthermore, the effects of SMZ exposure on the gut microbiota of offspring mice were analyzed using metagenome. According to the results, continuous exposure to high-dose SMZ caused weight gain in mice. IL-6, IL-17A, and IL-10 levels in the female offspring significantly increased after high-dose SMZ exposure. In addition, there was a significant gender difference in the impact of SMZ exposure on the gut microbiota of offspring: Continuous high-dose SMZ exposure significantly decreased the relative abundance of Ligilactobacillus murinus, Limosilactobacillus reuteri, Lactobacillus johnsonii, and Bifidobacterium pseudolongum (p < 0.05) in female offspring mice; however, these significant changes were not observed in male offspring mice

Analyzing the Impact of Traffic Congestion Mitigation: From an Explainable Neural Network Learning Framework to Marginal Effect Analyses

Computational graphs (CGs) have been widely utilized in numerical analysis and deep learning to represent directed forward networks of data flows between operations. This paper aims to develop an explainable learning framework that can fully integrate three major steps of decision support: Synthesis of diverse traffic data, multilayered traffic demand estimation, and marginal effect analyses for transport policies. Following the big data-driven transportation computational graph (BTCG) framework, which is an emerging framework for explainable neural networks, we map different external traffic measurements collected from household survey data, mobile phone data, floating car data, and sensor networks to multilayered demand variables in a CG. Furthermore, we extend the CG-based framework by mapping different congestion mitigation strategies to CG layers individually or in combination, allowing the marginal effects and potential migration magnitudes of the strategies to be reliably quantified. Using the TensorFlow architecture, we evaluate our framework on the Sioux Falls network and present a large-scale case study based on a subnetwork of Beijing using a data set from the metropolitan planning organization

Polyamide Thin-Film Composite Janus Membranes Avoiding Direct Contact between Feed Liquid and Hydrophobic Pores for Excellent Wetting Resistance in Membrane Distillation

Hydrophobic membranes are very susceptible to pore wetting when they contact the feed water containing surfactants or low-surface-tension liquids in membrane distillation (MD). Avoiding direct contact between feed water and hydrophobic membrane pores is a potential strategy to control membrane pore wetting. In this study, we successfully fabricated a polyamide thin-film composite (TFC) Janus membrane through interfacial polymerization, with a hydrophobic microporous membrane as the substrate. The fabricated TFC Janus membrane showed a super antiwetting ability when treating the hypersaline water containing surfactants (>0.4 mM sodium dodecyl sulfate) or ethanol (>40% v/v). The optical coherence tomography (OCT) observation revealed that no liquid water was present at the distillate-facing side of the polyamide layer. Therefore, we ascribed the super antiwetting ability to the fact that the polyamide layer could prevent the feed liquid from directly contacting hydrophobic pores. The TFC Janus membrane could also avoid the wetting induced by gypsum scaling because the polyamide layer could act as a barrier to hinder the intrusion of gypsum crystals into hydrophobic pores. In addition to the antiwetting ability, the TFC Janus membrane showed 10–20% increases in vapor flux, despite the existence of a dense polyamide layer. Because interfacial polymerization is the most commonly used method for the fabrication of commercial TFC membranes, this study provides a facile and scalable method to fabricate membranes with robust antiwetting ability

Regular exercise attenuates alcoholic myopathy in zebrafish by modulating mitochondrial homeostasis.

Alcoholic myopathy is caused by chronic consumption of alcohol (ethanol) and is characterized by weakness and atrophy of skeletal muscle. Regular exercise is one of the important ways to prevent or alleviate skeletal muscle myopathy. However, the beneficial effects and the exact mechanisms underlying regular exercise on alcohol myopathy remain unclear. In this study, a model of alcoholic myopathy was established using zebrafish soaked in 0.5% ethanol. Additionally, these zebrafish were intervened to swim for 8 weeks at an exercise intensity of 30% of the absolute critical swimming speed (Ucrit), aiming to explore the beneficial effects and underlying mechanisms of regular exercise on alcoholic myopathy. This study found that regular exercise inhibited protein degradation, improved locomotion ability, and increased muscle fiber cross-sectional area (CSA) in ethanol-treated zebrafish. In addition, regular exercise increases the functional activity of mitochondrial respiratory chain (MRC) complexes and upregulates the expression levels of MRC complexes. Regular exercise can also improve oxidative stress and mitochondrial dynamics in zebrafish skeletal muscle induced by ethanol. Additionally, regular exercise can activate mitochondrial biogenesis and inhibit mitochondrial unfolded protein response (UPRmt). Together, our results suggest regular exercise is an effective intervention strategy to improve mitochondrial homeostasis to attenuate alcoholic myopathy

16S rDNA analysis of periodontal plaque in chronic obstructive pulmonary disease and periodontitis patients

This study investigated if chronic obstructive pulmonary disease (COPD) is correlated with periodontitis via periodontal microbiota and if certain bacteria affect periodontitis as well as COPD. Moreover, the study investigated whether suffering from COPD is associated with a decrease in the richness and diversity of periodontal microbiota. Subgingival plaque was obtained from 105 patients. Bacterial DNA was isolated from 55 COPD and 50 non-COPD participants (either with or without periodontitis). 16S rRNA gene metagenomic sequencing was used to characterize the microbiota and to determine taxonomic classification. In the non-periodontitis patients, suffering from COPD resulted in a decrease in bacteria richness and diversity in the periodontal microenvironment. An increase in the genera Dysgonomonas, Desulfobulbus, and Catonella and in four species (Porphyromonas endodontalis, Dysgonomonas wimpennyi, Catonella morbi, and Prevotella intermedia) in both COPD and periodontitis patients suggests that an increase in these periodontitis-associated microbiota may be related to COPD. Three genera (Johnsonella, Campylobacter, and Oribacterium) were associated with COPD but not with periodontitis. The decrease in the genera Arcanobacterium, Oribacterium, and Streptomyces in COPD patients implies that these genera may be health-associated genera, and the decrease in these genera may be related to disease. These data support the hypothesis that COPD is correlated with periodontitis via these significantly changed specific bacteria