Structural Racism and Quantitative Causal Inference: A Life Course Mediation Framework for Decomposing Racial Health Disparities

Quantitative studies of racial health disparities often use static measures of self-reported race and conventional regression estimators, which critics argue is inconsistent with social-constructivist theories of race, racialization, and racism. We demonstrate an alternative counterfactual approach to explain how multiple racialized systems dynamically shape health over time, examining racial inequities in cardiometabolic risk in the National Longitudinal Study of Adolescent to Adult Health. This framework accounts for the dynamics of time-varying confounding and mediation that is required in operationalizing a “race” variable as part of a social process ( racism) rather than a separable, individual characteristic. We decompose the observed disparity into three types of effects: a controlled direct effect (“unobserved racism”), proportions attributable to interaction (“racial discrimination”), and pure indirect effects (“emergent discrimination”). We discuss the limitations of counterfactual approaches while highlighting how they can be combined with critical theories to quantify how interlocking systems produce racial health inequities.

Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy

Pasteurization is a crucial processing method in the food industry to ensure the safety of consumables. A major part of contemporary pasteurization processes involves using flash pasteurizer systems, where liquids are pumped through a pipe system to heat them for a predefined time. Accurately monitoring the amount of heat treatment applied to a product is challenging. This monitoring helps ensure that the correct heat impact (expressed in pasteurization units) is applied, which is commonly calculated as a product of time and temperature, taking achievability of the inactivation of the microorganisms into account. The state-of-the-art method involves a calculation of the applied pasteurization units using a one-point temperature measurement and the holding time for this temperature. Concerns about accuracy lead to high safety margins, reducing the quality of the pasteurized product. In this study, the applied pasteurization level was estimated using regression models trained with NIR spectroscopy data collected while pasteurizing fruit juices of different types and brands. Several conventional regression models were trained in combination with different preprocessing methods, including a novel prediction outlier detection method. Generalized juice models trained with the concatenated data of all types of juices demonstrated cross-validated scores of RMSECV ∼2.78 ± 0.09 and r2 0.96 ± 0.01, while separate juice models displayed averaged cross-validated scores of RMSECV ∼1.56 ± 0.04 and r2 0.98 ± 0.01. Thus, the model accuracy ±10–30 % is well within the standard safety margins.

Fuzzy bundling of corporate governance practices and performance of Indian firms

Purpose This paper aims to develop a framework that aids in achieving the desired state of financial performance for corporate enterprises based on distinct configurations of corporate governance (CG) practices. Design/methodology/approach This study uses a fuzzy-based system to arrive at a definitive configuration of CG practices that lead to a specific level of firm’s performance. Findings This analysis of the panel data of 92 National Stock Exchange–listed companies conducted for RONW on selected CG variables shows that eight fuzzy configurations lead to a particular state of RONW. The authors compare the results with the conventional regression-based scoring models. Originality/value Corporate enterprises can use the derived bundles of CG practices leading to a specific set of financial performance (RONW) to aid the decision-making process in defining and implementing their governance structures. The regulators can modify or customize the law-mandated CG practices to reduce redundancies and promote the national agenda of economic efficiency.

A comprehensive study on battery electric modeling approaches based on machine learning

Battery electric modeling is a central aspect to improve the battery development process as well as to monitor battery system behavior. Besides conventional physical models, machine learning methods show great potential to learn this task using in-vehicle data. However, the performance of data-driven approaches differs significantly depending on their application and utilized data set. Hence, a comparison among these methods is required beforehand to select the optimal candidate for a given task.In this work, we address this problem and evaluate the strengths and weaknesses of a wide range of possible machine learning approaches for battery electric modeling. In a comprehensive study, various conventional regression methods and neural networks are analyzed. Each method is trained and optimized based on a large and qualitative data set of automotive driving profiles. In order to account for the influence of time-dependent battery processes, both low pass filters and sliding window approaches are investigated.As a result, neural networks are found to be superior compared to conventional regression methods in terms of accuracy and model complexity. In particular, Feedforward and Convolutional Neural Networks provide the smallest average error deviations of around 0.16%, which corresponds to an RMSE of 5.57mV on battery cell level. With automotive time series data as focus, neural networks additionally benefit from their ability to learn continuously. This key capability keeps the battery models updated at low computational costs and accounts for changing electrical behavior as the battery ages during operation.

Leveraging Machine Learning to Estimate Effect Modification

Sociologists are often interested in estimating and testing whether some causal effect varies by a modifier of interest. The conventional regression estimator for effect modification is inflexible in functional form and prone to misspecification bias. Machine Learning (ML) algorithms can aid the estimation of effect modification in observational studies by controlling for confounders in a highly flexible, automated, yet principled way. Therefore, leveraging ML for effect modification helps reduce misspecification bias and enhance the credibility of causal identification. We introduce a novel estimator that estimates effect modification in a familiar regression framework after using ML algorithms to fit nuisance components of the model. We show that this estimator is more flexible than the conventional regression model while more efficient and suitable for theory-driven sociological research than other ML-based methods. We use the new estimator to study the modification in the effect of a college degree on adult family income by gender and family income in adolescence in the United States. Along these two dimensions, the benefits of a college degree are rather equally distributed.

Domain Adaptive Ship Detection in Optical Remote Sensing Images

With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between different domains. Specifically, the proposed method minimizes the domain discrepancies via both image-level adaption and instance-level adaption. In image-level adaption, we use multiple receptive field integration and channel domain attention to enhance the feature’s resistance to scale and environmental changes, respectively. Moreover, a novel boundary regression module is proposed in instance-level adaption to correct the localization deviation of the ship proposals caused by the domain shift. Compared with conventional regression approaches, the proposed boundary regression module is able to make more accurate predictions via the effective extreme point features. The two adaption components are implemented by learning the corresponding domain classifiers respectively in an adversarial training way, thereby obtaining a robust model suitable for both of the two domains. Experiments on both supervised and unsupervised domain adaption scenarios are conducted to verify the effectiveness of the proposed method.

POS0612 EXPLORING EQUIVALENCE BETWEEN BIOSIMILAR SB4 AND REFERENCE ETANERCEPT BY ASSESSING EFFECTIVENESS IN RHEUMATOID ARTHRITIS PATIENTS TREATED IN ORDINARY CLINICAL PRACTICE

Background:Biosimilar drugs follow a tailored approval pathway that usually includes a Phase III comparative efficacy randomized controlled trial with a high internal but low external validity. Therefore, observational studies with high external validity are important to reassure patients and physicians that there are no clinically meaningful differences in effectiveness between a biosimilar and its reference drug. A EULAR Task Force systematic review and others have noted that recent comparative effectiveness studies often do not disclose applied analytical methods in sufficient detail, with many studies not adjusting for confounders nor accounting for attrition or missing data. 1,2Objectives:To apply the EULAR Points to Consider for Comparative Effectiveness Research (CER) in an analysis of reference etanercept (ETN) and SB4 biosimilar ETN in patients with rheumatoid arthritis (RA) treated in ordinary clinical practice in Norway.Methods:ETN-naÏve patients with RA starting ETN treatment between January 2010 and July 2018 at five centres in Norway were followed for at least 1 year; the 2 cohorts remained on either ETN or SB4 throughout. The primary outcome was DAS28 at Week 52. This CER has been designed to formally assess equivalence for DAS28, based on the accepted equivalence margin of 0.6.3 Conventional regression and propensity score (PS) models have been applied for the primary outcome evaluation of DAS28 at Week 52. Based on clinical knowledge, the confounders adjusted for in the step-wise PS model were age, gender, DAS28, order of biologics, and concomitant conventional synthetic disease-modifying anti-rheumatic drugs. A standardized difference (d) of <0.1 indicates a good match.Results:In the unmatched sample, there were 575 patients treated with reference ETN and 299 treated with SB4. Before PS matching, baseline mean (SD) DAS28 was different between the ETN and SB4 groups, 4.3 (1.2) vs 4.0 (1.3), (d) = 0.25. After PS matching, there were 176 RA patients in each group; baseline mean (SD) DAS28 was 4.1 (1.2) vs 4.1 (1.3), (d) = 0.05. At Week 52, the difference (mean [95% confidence interval (CI)]) between reference ETN and SB4 for primary outcome DAS28 at Week 52 was -0.02 (-0.33 to 0.29) in the unmatched analysis. Since the entire 95% CI is within the pre-defined equivalence margin of 0.6, equivalence at Week 52 has formally been shown. The analysis of the PS matched groups to Week 52 is ongoing and results will be presented in the poster.Conclusion:Our results show the importance of adopting proper analytical techniques when comparing a biosimilar with its reference product. A conventional regression model may not fully account for differences in key clinical measures (in this instance, disease activity) between the two groups at baseline, and therefore the Week 52 results might be biased. The propensity score matched model ensures comparability of the groups at baseline and therefore the validity of the Week 52 results should be more robust.References:[1]Cantini F and Benucci M. Mandatory, cost-driven switching from originator etanercept to its biosimilar SB4: possible fallout on non-medical switching. Ann Rheum Dis 2020; 79: e13.[2]Lauper K KJ, De Wit M, Fautrel B, et al. A Systematic Review to Inform the EULRA Points to Consider When Analysing and Reporting Comparative Effectiveness Research With Observational Data in Rheumatology. Annals of the Rheumatic Diseases 2020; 79:[3]Fransen J and van Riel PL. The Disease Activity Score and the EULAR response criteria. Clin Exp Rheumatol 2005; 23: S93-99.Acknowledgements:The authors wish to acknowledge Janet Addison and Ulrich Freudensprung of Biogen for their intellectual contributions to this abstract and Bjørg Tilde Fevang for providing data from Haukeland University Hospital in Bergen. Editorial support for the preparation of this abstract was provided by Excel Scientific Solutions (Fairfield, CT, USA); funding was provided by Biogen International GmbH.Disclosure of Interests:Glenn Haugeberg Grant/research support from: Biogen, Gunnstein Bakland: None declared, Erik Rødevand: None declared, Inger Johanne Widding Hansen: None declared, Andreas Diamantopoulos: None declared, Are Hugo Pripp: None declared

Intragenerational social mobility and self-rated oral health in the british cohort study

Background Most studies on social mobility and oral health have focused on movement between generations (intergenerational mobility) rather than movement within an individual’s own lifetime (intragenerational mobility). The aim of this study was to investigate the association between intragenerational social mobility from early to middle adulthood and self-rated oral health. Methods This study used data from 6524 participants of the 1970 British Birth Cohort Study, an ongoing population-based birth cohort of individuals born in England, Scotland and Wales. Participants’ socioeconomic position was indicated by occupational social class at age 26 and 46 years (the first and latest adult waves, respectively). Self-rated oral health was measured at age 46 years. The association between social mobility and adult oral health was assessed using conventional regression models and diagonal reference models, adjusting for gender, ethnicity, country of residence and residence area. Results Over a fifth of participants (22.2%) reported poor self-rated oral health at age 46 years. In conventional regression analysis, the odds ratios for social mobility varied depending on whether they were adjusted for social class of origin or destination. In addition, all social trajectories had greater odds of reporting poor oral health than non-mobile adults in class I/II. In diagonal reference models, both upward (Odds Ratio 0.79; 95% CI 0.63–0.99) and downward mobility (0.90; 95% CI 0.71–1.13) were inversely associated with poor self-rated oral health. The origin weight was 0.48 (95% CI 0.33–0.63), suggesting that social class of origin was as important as social class of destination. Conclusion This longitudinal analysis showed that intragenerational social mobility from young to middle adulthood was associated with self-rated oral health, independent of previous and current social class.