Danish Cause Of Death Registry Research Articles

Long-term residential road traffic noise and mortality in a Danish cohort

Transportation noise is a growing public health concern worldwide and epidemiological evidence has linked road traffic noise with mortality. However, incongruent effect estimates have been reported between incidence and mortality studies.Therefore, the present study aimed to investigate whether long-term exposure to residential road traffic noise at the most and least exposed façades was associated with all-cause, cardiovascular disease (CVD), ischemic heart disease (IHD), stroke, respiratory, or cancer mortality in a Danish cohort study.In a cohort of 52,758 individuals from Copenhagen and Aarhus, we estimated road traffic noise at the most and least exposed façades, as well as ambient air pollution, at all present and historical residential addresses from 1987 to 2016. Using the Danish cause of death register we identified cause-specific mortality. Analyses were conducted using Cox proportional hazards models.Ten-year time-weighted mean road traffic noise exposure at the most exposed façade was associated with an 8% higher risk for all-cause mortality per interquartile range (IQR; 10.4 dB) higher exposure level (95% CI: 1.05–1.11). Higher risks were also observed for CVD (HR = 1.13, 95% CI: 1.06–1.19) and stroke (HR = 1.11, 95% CI: 0.99–1.25) mortality. Road traffic noise at the least exposed façade (per IQR; 8.4 dB) was associated with CVD (HR = 1.09, 95% CI: 1.03–1.15), IHD (HR = 1.10, 95% CI: 1.01–1.21) and stroke (HR = 1.06, 95% CI: 0.95–1.19) mortality. Results were robust to adjustment for PM2.5 and NO2.In conclusion, this study adds to the body of evidence linking exposure to road traffic noise with higher risk of mortality.

Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark

Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may help advance the understanding of suicide risk. To examine sex-specific risk profiles for death from suicide using machine-learning methods and data from the population of Denmark. A case-cohort study nested within 8 national Danish health and social registries was conducted from January 1, 1995, through December 31, 2015. The source population was all persons born or residing in Denmark as of January 1, 1995. Data were analyzed from November 5, 2018, through May 13, 2019. Exposures included 1339 variables spanning domains of suicide risk factors. Death from suicide from the Danish cause of death registry. A total of 14 103 individuals died by suicide between 1995 and 2015 (10 152 men [72.0%]; mean [SD] age, 43.5 [18.8] years and 3951 women [28.0%]; age, 47.6 [18.8] years). The comparison subcohort was a 5% random sample (n = 265 183) of living individuals in Denmark on January 1, 1995 (130 591 men [49.2%]; age, 37.4 [21.8] years and 134 592 women [50.8%]; age, 39.9 [23.4] years). With use of classification trees and random forests, sex-specific differences were noted in risk for suicide, with physical health more important to men's suicide risk than women's suicide risk. Psychiatric disorders and possibly associated medications were important to suicide risk, with specific results that may increase clarity in the literature. Generally, diagnoses and medications measured 48 months before suicide were more important indicators of suicide risk than when measured 6 months earlier. Individuals in the top 5% of predicted suicide risk appeared to account for 32.0% of all suicide cases in men and 53.4% of all cases in women. Despite decades of research on suicide risk factors, understanding of suicide remains poor. In this study, the first to date to develop risk profiles for suicide based on data from a full population, apparent consistency with what is known about suicide risk was noted, as well as potentially important, understudied risk factors with evidence of unique suicide risk profiles among specific subpopulations.