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ImportanceHistorical redlining was a discriminatory housing policy that placed financial services beyond the reach of residents in inner-city communities. The extent of the impact of this discriminatory policy on contemporary health outcomes remains to be elucidated.ObjectiveTo evaluate the associations among historical redlining, social determinants of health (SDOH), and contemporary community-level stroke prevalence in New York City.Design, Setting, and ParticipantsAn ecological, retrospective, cross-sectional study was conducted using New York City data from January 1, 2014, to December 31, 2018. Data from the population-based sample were aggregated on the census tract level. Quantile regression analysis and a quantile regression forests machine learning model were used to determine the significance and overall weight of redlining in relation to other SDOH on stroke prevalence. Data were analyzed from November 5, 2021, to January 31, 2022.ExposuresSocial determinants of health included race and ethnicity, median household income, poverty, low educational attainment, language barrier, uninsurance rate, social cohesion, and residence in an area with a shortage of health care professionals. Other covariates included median age and prevalence of diabetes, hypertension, smoking, and hyperlipidemia. Weighted scores for historical redlining (ie, the discriminatory housing policy in effect from 1934 to 1968) were computed using the mean proportion of original redlined territories overlapped on 2010 census tract boundaries in New York City.Main Outcomes and MeasuresStroke prevalence was collected from the Centers for Disease Control and Prevention 500 Cities Project for adults 18 years and older from 2014 to 2018.ResultsA total of 2117 census tracts were included in the analysis. After adjusting for SDOH and other relevant covariates, the historical redlining score was independently associated with a higher community-level stroke prevalence (odds ratio [OR], 1.02 [95% CI, 1.02-1.05]; P < .001). Social determinants of health that were positively associated with stroke prevalence included educational attainment (OR, 1.01 [95% CI, 1.01-1.01]; P < .001), poverty (OR, 1.01 [95% CI, 1.01-1.01]; P < .001), language barrier (OR, 1.00 [95% CI, 1.00-1.00]; P < .001), and health care professionals shortage (OR, 1.02 [95% CI, 1.00-1.04]; P = .03).Conclusions and RelevanceThis cross-sectional study found that historical redlining was associated with modern-day stroke prevalence in New York City independently of contemporary SDOH and community prevalence of some relevant cardiovascular risk factors.

HYW (Hong Yi Wu line) heavy oil emulsion in Xinjiang Oilfield (Karamay, China) is a kind of heavy oil with high viscosity and high emulsification. Its viscosity reaches 120,000 mPa·s at 40 °C. The emulsion has no demulsification. Even if the demulsification temperature reaches 90 degrees, the concentration of demulsifier reaches 260 mg/L. In this paper, a new process of thermochemical demulsification of heavy oil after blending is studied. First, SE low-viscosity oil with viscosity of 640 mPa·s and water cut of 90% was selected as blended oil. Study the viscosity of SE line and HYW line at different temperatures after fully blended. The results show that the heavy oil blended model conforms to Bingham model. When the temperature is 40 °C and the content of SE line is 30%, the viscosity is less than 10,000 mPa·s. With the increase of temperature, the viscosity continues to decline. When the temperature exceeds 80 °C, the viscosity is less than 1000 mPa·s. The final design SE line content is 30%, the demulsification temperature is 70 °C, and the demulsifier concentration is 160 mg/L as the best demulsification parameter. The field results show that the demulsification rate of heavy oil in this process reaches more than 90%. This experiment lays a foundation for demulsification of high emulsified crude oil developed by heavy oil in Xinjiang oilfield.

Multiple imputation is a widely used technique to handle missing data in large observational studies. For variable selection on multiply-imputed datasets, however, if we conduct selection on each imputed dataset separately, different sets of important variables may be obtained. MI-LASSO, one of the most popular solutions to this problem, regards the same variable across all separate imputed datasets as a group of variables and exploits Group-LASSO to yield a consistent variable selection across all the multiply-imputed datasets. In this paper, we extend the MI-LASSO model into Bayesian framework and utilize five different Bayesian MI-LASSO models to perform variable selection on multiplyimputed data. These five models consist of three shrinkage priors based and two discrete mixture prior based approaches. We conduct a simulation study investigating the practical characteristics of each model across various settings. We further demonstrate these methods via a case study using the multiply-imputed data from the University of Michigan Dioxin Exposure Study. The Python package BMIselect is hosted on Github under an Apache-2.0 license: https://github.com/zjg540066169/Bmiselect.

In the absence of a randomized experiment, a key assumption for drawing causal inference about treatment effects is the ignorable treatment assignment. Violations of the ignorability assumption may lead to biased treatment effect estimates. Sensitivity analysis helps gauge how causal conclusions will be altered in response to different magnitude of departure from the ignorability assumption. However, sensitivity analysis approaches for causal inference with multiple treatments and binary outcomes are scarce. We propose a flexible Monte Carlo sensitivity analysis approach for the complex multiple treatment settings with binary outcomes. We first derive the general bias form introduced by unmeasured confounding (UMC), with emphasis on theoretical properties uniquely relevant to multiple treatments. We then propose methods to encode the impact of UMC on potential outcomes and adjust the estimates of causal effects in which the presumed UMC is removed. Our proposed methods embed nested multiple imputation within the * To whom correspondence should be addressed.