FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation

Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes like age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. To address these, we introduce Fairness through the Equitable Rate of Improvement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our experiments show that FERI maintains high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrates an ability to improve fairness without sacrificing accuracy. Specifically, for gender, FERI reduces the demographic parity disparity by 71.74%, and for the age group, it decreases the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advances fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems

Kriged and modeled ambient air levels of benzene in an urban environment: an exposure assessment study

<p>Abstract</p> <p>Background</p> <p>There is increasing concern regarding the potential adverse health effects of air pollution, particularly hazardous air pollutants (HAPs). However, quantifying exposure to these pollutants is problematic.</p> <p>Objective</p> <p>Our goal was to explore the utility of kriging, a spatial interpolation method, for exposure assessment in epidemiologic studies of HAPs. We used benzene as an example and compared census tract-level kriged predictions to estimates obtained from the 1999 U.S. EPA National Air Toxics Assessment (NATA), Assessment System for Population Exposure Nationwide (ASPEN) model.</p> <p>Methods</p> <p>Kriged predictions were generated for 649 census tracts in Harris County, Texas using estimates of annual benzene air concentrations from 17 monitoring sites operating in Harris and surrounding counties from 1998 to 2000. Year 1999 ASPEN modeled estimates were also obtained for each census tract. Spearman rank correlation analyses were performed on the modeled and kriged benzene levels. Weighted kappa statistics were computed to assess agreement between discretized kriged and modeled estimates of ambient air levels of benzene.</p> <p>Results</p> <p>There was modest correlation between the predicted and modeled values across census tracts. Overall, 56.2%, 40.7%, 31.5% and 28.2% of census tracts were classified as having 'low', 'medium-low', 'medium-high' and 'high' ambient air levels of benzene, respectively, comparing predicted and modeled benzene levels. The weighted kappa statistic was 0.26 (95% confidence interval (CI) = 0.20, 0.31), indicating poor agreement between the two methods.</p> <p>Conclusions</p> <p>There was a lack of concordance between predicted and modeled ambient air levels of benzene. Applying methods of spatial interpolation for assessing exposure to ambient air pollutants in health effect studies is hindered by the placement and number of existing stationary monitors collecting HAP data. Routine monitoring needs to be expanded if we are to use these data to better assess environmental health risks in the future.</p

Influence of safety warnings on ESA prescribing among dialysis patients using an interrupted time series

BACKGROUND: In March, 2007, a black box warning was issued by the Food and Drug Administration (FDA) to use the lowest possible erythropoiesis-stimulating agents (ESA) doses for treatment of anemia associated with renal disease. The goal is to determine if a change in ESA use was observed following the warning among US dialysis patients. METHODS: ESA therapy was examined from September 2004 through August 2009 (thirty months before and after the FDA black box warning) among adult Medicare hemodialysis patients. An interrupted time series model assessed the impact of the warnings. RESULTS: The FDA black box warning did not appear to influence ESA prescribing among the overall dialysis population. However, significant declines in ESA therapy after the FDA warnings were observed for selected populations. Patients with a hematocrit ≥36% had a declining month-to-month trend before (−164 units/week, p = <0.0001) and after the warnings (−80 units/week, p = .001), and a large drop in ESA level immediately after the black box (−4,744 units/week, p = <.0001). Not-for-profit facilities had a declining month-to-month trend before the warnings (−90 units/week, p = .009) and a large drop in ESA dose immediately afterwards (−2,487 units/week, p = 0.015). In contrast, for-profit facilities did not have a significant change in ESA prescribing. CONCLUSIONS: ESA therapy had been both profitable for providers and controversial regarding benefits for nearly two decades. The extent to which a FDA black box warning highlighting important safety concerns influenced use of ESA therapy among nephrologists and dialysis providers was unknown. Our study found no evidence of changes in ESA prescribing for the overall dialysis population resulting from a FDA black box warning

Surveillance of the Colorectal Cancer Disparities Among Demographic Subgroups - A Spatial Analysis

To understand the extent of the demographic and geographic disparities, the present study examined colorectal cancer mortality in 15 demographic groups in Texas counties between 1990 and 2001. The study confirmed the excess mortality in some Texas counties found in the literature, identified 13 additional excess mortality regions, and found 4 health regions with persistent excess mortality involving several population subgroups. The study suggested that Health disparities of colorectal cancer mortality continue to exist in Texas demographic subpopulations. Health education and intervention programs should be directed to the at-risk sub-populations in the identified regions

Validation of the Gravity Model in Predicting the Global Spread of Influenza

The gravity model is often used in predicting the spread of influenza. We use the data of influenza A (H1N1) to check the model’s performance and validation, in order to determine the scope of its application. In this article, we proposed to model the pattern of global spread of the virus via a few important socio-economic indicators. We applied the epidemic gravity model for modelling the virus spread globally through the estimation of parameters of a generalized linear model. We compiled the daily confirmed cases of influenza A (H1N1) in each country as reported to the WHO and each state in the USA, and established the model to describe the relationship between the confirmed cases and socio-economic factors such as population size, per capita gross domestic production (GDP), and the distance between the countries/states and the country where the first confirmed case was reported (i.e., Mexico). The covariates we selected for the model were all statistically significantly associated with the global spread of influenza A (H1N1). However, within the USA, the distance and GDP were not significantly associated with the number of confirmed cases. The combination of the gravity model and generalized linear model provided a quick assessment of pandemic spread globally. The gravity model is valid if the spread period is long enough for estimating the model parameters. Meanwhile, the distance between donor and recipient communities has a good gradient. Besides, the spread should be at the early stage if a single source is taking into account

Alendronate as an Effective Countermeasure to Disuse Induced Bone loss

Microgravity, similar to diuse immobilization on earth, causes rapid bone loss. This loss is believed to be an adaptive response to the reduced musculoskelatal forces in space and occurs gradually enough that changes occurring during short duration space flight are not a concern. Bone loss, however, will be a major impediment for long duration missions if effective countermeasures are not developed and implemented. Bed rest is used to simulate the reduced mechanical forces in humans and was used to test the hypothesis that oral alendronate would reduce the effects of long duration (17 weeks) inactivity on bone. Eight male subjects were given daily oral doses of alendronate during 17 weeks of horizontal bed rest and compared with 13 male control subjects not given the drug. Efficacy was evaluated based on measurements of bone markers, calcium balance and bone density performed before, during and after the bed rest. The results show that oral alendronate attenuates most of the characteristic changes associated with long duration bed rest and presumably space flight

Statistical Modeling of Extracellular Vesicle Cargo to Predict Clinical Trial Outcomes For Hypoplastic Left Heart Syndrome

Cardiac-derived c-kit+ progenitor cells (CPCs) are under investigation in the CHILD phase I clinical trial (NCT03406884) for the treatment of hypoplastic left heart syndrome (HLHS). The therapeutic efficacy of CPCs can be attributed to the release of extracellular vesicles (EVs). to understand sources of cell therapy variability we took a machine learning approach: combining bulk CPC-derived EV (CPC-EV) RNA sequencing and cardiac-relevan