Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study.

Objective: To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making.

Design: Population-based cohort study (n = 35,997) included with a fall in 2013 and followed 1 year.

Methods: Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates.

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

Background: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables.

Assessing racial bias in type 2 diabetes risk prediction algorithms.

Risk prediction models for type 2 diabetes can be useful for the early detection of individuals at high risk. However, models may also bias clinical decision-making processes, for instance by differential risk miscalibration across racial groups. We investigated whether the Prediabetes Risk Test (PRT) issued by the National Diabetes Prevention Program, and two prognostic models, the Framingham Offspring Risk Score, and the ARIC Model, demonstrate racial bias between non-Hispanic Whites and non-Hispanic Blacks.

Sex differences in health care expenditures and mortality after spousal bereavement: A register-based Danish cohort study.

Background: Spousal bereavement is a life event that affects older people differently. We investigated the impact of spousal bereavement on medical expenditures and mortality in the general population, emphasizing on age and sex.

Methods: Data are from a population-based, retrospective cohort study following 924,958 Danish citizens over the age of 65 years, within 2011-2016.

Dynamical indicators in time series of healthcare expenditures predict mortality risk of older adults following spousal bereavement.

Background: The process of aging renders older people susceptible for adverse outcomes upon stress. Various indicators derived from complex systems theory have been proposed for quantifying resilience in living organisms, including humans. We investigated the ability of system-based indicators in capturing the dynamics of resilience in humans who suffer the adversity of spousal bereavement and tested their predictive power in mortality as a finite health transition.