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

Suicide After Partner's Death in the Elderly Population: Results From a Nationwide Cohort Study in Italy

OBJECTIVE

This study aims to investigate the effect of partner loss on suicide mortality for surviving partners in the elderly Italian population and to explore differences according to sex and time elapsed since the loss.

DESIGN

This was a historical cohort study.

SETTING

All Italian residents registered by the 15th Italian Population Census (9 October 2011) were linked to emigration records and death certificates for 2012-2017 to track migration, vital status, and cause of death.

PARTICIPANTS

5,068,414 individuals living as a couple, as registered in the census, and aged ≥69 years on January 1, 2012.

MEASUREMENTS

Mortality rate ratios (MRR) estimated through Poisson regression models were used to compare suicide mortality at age ≥75 years between subjects who experienced partner loss and those who did not.

RESULTS

Among people who experienced partner loss, there were 383 suicide deaths in men and 90 in women. Suicide mortality was higher in older men and women who experienced the loss compared to those who did not, and the impact of the spouse loss on mortality was stronger in men (age-adjusted MRR=2.83) than in women (1.41). Among men the excess risk was particularly high during the first year following the loss; in women, no substantial differences in the excess risk were observed over the follow-up period.

CONCLUSION

Study findings provide evidence of the sex differences in the impact of spouse loss based on data from a large national cohort and reinforce the need for monitoring suicide risk in recently widowed older people.

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.

METHODS

This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013-2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis).

RESULTS

The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models.

CONCLUSION

Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals.