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Background: Individuals who perceive medical discrimination often face adverse consequences. How individuals perceive their own aging experiences may influence perceived medical discrimination due to age by generating expectations that they will receive poor treatment from clinicians, which may be amplified for individuals who also perceive discrimination because of race. This study explored the relationship between self‐perceptions of aging (SPA) and perceived medical discrimination due to age, race, and other reasons. Methods: We used three waves (2008, 2012, 2016) from the nationally representative Health and Retirement Study (HRS) data. Our sample included 10,188 community‐residing individuals aged 51 and over. SPA were measured by two domains: positive SPA and negative SPA. Multinomial logistic regression that adjusted for demographic, health characteristics, and year‐fixed effects was conducted to estimate the relationship between SPA and categories of perceived medical discrimination (age, race, age and race, and other). Results: Approximately 11% of the population perceived discrimination because of age or race in the medical setting from 2008 to 2016. Older adults who had a positive SPA were 15% [Adjusted relative risk ratio (ARR): 0.85, 95% CI: 0.79–0.91], 13% [ARRR: 0.87, 95% CI: 0.76–1.00], and 20% [ARRR: 0.80, 95% CI: 0.74–0.88] less likely to experience perceived medical discrimination due to age, race, and the intersection of age and race, respectively, than those who did not, holding other factors constant. Those who held a negative SPA were 38% [ARRR: 1.38, 95% CI: 1.28–1.48] more likely to report perceived medical discrimination due to age and 12% [ARRR: 1.12, 95% CI: 1.03–1.21] more likely to report perceived medical discrimination due to other reasons. Conclusions: Holding a positive perception of aging may help reduce perceived medical discrimination because of age and race, which may in turn improve communication and lead to timely and appropriate treatment. See related Editorial by Timothy W. Farrell. in this issue. [ABSTRACT FROM AUTHOR]

The existence of water markets establishes water prices, promoting trading of water from low- to high-valued uses. However, market participants can face uncertainty when asking and offering prices because water rights are heterogeneous, resulting in inefficiency of the market. This paper proposes three random forest regression models (RFR) to predict water price in the western United States: a full variable set model and two reduced ones with optimal numbers of variables using a backward variable elimination (BVE) approach. Transactions of 12 semiarid states, from 1987 to 2009, and a dataset containing various predictors, were assembled. Multiple replications of k-fold cross-validation were applied to assess the model performance and their generalizability was tested on unused data. The importance of price influencing factors was then analyzed based on two plausible variable importance rankings. Results show that the RFR models have good predictive power for water price. They outperform a baseline model without leading to overfitting. Also, the higher degree of accuracy of the reduced models is insignificant, reflecting the robustness of RFR to including lower informative variables. This study suggests that, due to its ability to automatically learn from and make predictions on data, RFR-based models can aid water market participants in making more efficient decisions. [ABSTRACT FROM AUTHOR]