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Background The provision of different types of mortality metrics (e.g., mortality rate ratios [MRRs] and life expectancy) allows the research community to access a more informative set of health metrics. The aim of this study was to provide a panel of mortality metrics associated with a comprehensive range of disorders and to design a web page to visualize all results. Methods and findings In a population-based cohort of all 7,378,598 persons living in Denmark at some point between 2000 and 2018, we identified individuals diagnosed at hospitals with 1,803 specific categories of disorders through the International Classification of Diseases-10th Revision (ICD-10) in the National Patient Register. Information on date and cause of death was obtained from the Registry of Causes of Death. For each of the disorders, a panel of epidemiological and mortality metrics was estimated, including incidence rates, age-of-onset distributions, MRRs, and differences in life expectancy (estimated as life years lost [LYLs]). Additionally, we examined models that adjusted for measures of air pollution to explore potential associations with MRRs. We focus on 39 general medical conditions to simplify the presentation of results, which cover 10 broad categories: circulatory, endocrine, pulmonary, gastrointestinal, urogenital, musculoskeletal, hematologic, mental, and neurologic conditions and cancer. A total of 3,676,694 males and 3,701,904 females were followed up for 101.7 million person-years. During the 19-year follow-up period, 1,034,273 persons (14.0%) died. For 37 of the 39 selected medical conditions, mortality rates were larger and life expectancy shorter compared to the Danish general population. For these 37 disorders, MRRs ranged from 1.09 (95% confidence interval [CI]: 1.09 to 1.10) for vision problems to 7.85 (7.77 to 7.93) for chronic liver disease, while LYLs ranged from 0.31 (0.14 to 0.47) years (approximately 16 weeks) for allergy to 17.05 (16.95 to 17.15) years for chronic liver disease. Adjustment for air pollution had very little impact on the estimates; however, a limitation of the study is the possibility that the association between the different disorders and mortality could be explained by other underlying factors associated with both the disorder and mortality. Conclusions In this study, we show estimates of incidence, age of onset, age of death, and mortality metrics (both MRRs and LYLs) for a comprehensive range of disorders. The interactive data visualization site (https://nbepi.com/atlas) allows more fine-grained analysis of the link between a range of disorders and key mortality estimates. In a population-based study, Oleguer Plana-Ripoll and colleagues report on and develop an online resource to study mortality metrics and life expectancy associated with different health conditions among individuals living in Denmark. Author summary Why was this study done? There have been many studies related to mortality linked to particular disorders, but these studies have not covered a comprehensive range of disorders. Previous studies have traditionally focused on relative measures of mortality (e.g., mortality rate ratios [MRRs]) or crude estimates of life expectancy that do not incorporate variation in the age of onset of the disorder. Here, the researchers address these issues in a comprehensive atlas of mortality estimates based on Danish registers. What did the researchers do and find? Based on 7,378,598 persons living in Denmark in 2000 to 2018, the researchers used national registers to identify individuals diagnosed with 1,803 specific categories of disorders. For each of these disorders, a panel of epidemiological and mortality metrics was estimated, including incidence rates, age-of-onset distributions, MRRs, and life years lost (LYLs). Within a set of 39 selected medical conditions, mortality rates were larger and life expectancy shorter for 37 conditions compared to the Danish general population. The researchers have prepared an interactive data visualization to optimize the interrogation of their findings (http://nbepi.com/atlas). What do these findings mean? This study allows a more fine-grained analysis of the associations between a comprehensive set of disorders and mortality-related estimates. These findings can guide health research and serve as a benchmark to evaluate future health interventions.

BackgroundAdvanced paternal age (APA) is associated with an increased risk of neurodevelopmental disorders such as autism and schizophrenia, as well as with dyslexia and reduced intelligence. The aim of this study was to examine the relationship between paternal age and performance on neurocognitive measures during infancy and childhood.Methods and findingsA sample of singleton children (n = 33,437) was drawn from the US Collaborative Perinatal Project. The outcome measures were assessed at 8 mo, 4 y, and 7 y (Bayley scales, Stanford Binet Intelligence Scale, Graham-Ernhart Block Sort Test, Wechsler Intelligence Scale for Children, Wide Range Achievement Test). The main analyses examined the relationship between neurocognitive measures and paternal or maternal age when adjusted for potential confounding factors. Advanced paternal age showed significant associations with poorer scores on all of the neurocognitive measures apart from the Bayley Motor score. The findings were broadly consistent in direction and effect size at all three ages. In contrast, advanced maternal age was generally associated with better scores on these same measures.ConclusionsThe offspring of older fathers show subtle impairments on tests of neurocognitive ability during infancy and childhood. In light of secular trends related to delayed fatherhood, the clinical implications and the mechanisms underlying these findings warrant closer scrutiny.

Background: Understanding the prevalence of schizophrenia has important implications for both health service planning and risk factor epidemiology. The aims of this review are to systematically identify and collate studies describing the prevalence of schizophrenia, to summarize the findings of these studies, and to explore selected factors that may influence prevalence estimates., Methods and Findings: Studies with original data related to the prevalence of schizophrenia (published 1965-2002) were identified via searching electronic databases, reviewing citations, and writing to authors. These studies were divided into "core" studies, "migrant" studies, and studies based on "other special groups." Between- and within-study filters were applied in order to identify discrete prevalence estimates. Cumulative plots of prevalence estimates were made and the distributions described when the underlying estimates were sorted according to prevalence type (point, period, lifetime, and lifetime morbid risk). Based on combined prevalence estimates, the influence of selected key variables was examined (sex, urbanicity, migrant status, country economic index, and study quality). A total of 1,721 prevalence estimates from 188 studies were identified. These estimates were drawn from 46 countries, and were based on an estimated 154,140 potentially overlapping prevalent cases. We identified 132 core studies, 15 migrant studies, and 41 studies based on other special groups. The median values per 1,000 persons (10%-90% quantiles) for the distributions for point, period, lifetime, and lifetime morbid risk were 4.6 (1.9-10.0), 3.3 (1.3-8.2), 4.0 (1.6-12.1), and 7.2 (3.1-27.1), respectively. Based on combined prevalence estimates, we found no significant difference (a) between males and females, or (b) between urban, rural, and mixed sites. The prevalence of schizophrenia in migrants was higher compared to native-born individuals: the migrant-to-native-born ratio median (10%-90% quantile) was 1.8 (0.9-6.4). When sites were grouped by economic status, prevalence estimates from "least developed" countries were significantly lower than those from both "emerging" and "developed" sites (p = 0.04). Studies that scored higher on a quality score had significantly higher prevalence estimates (p = 0.02)., Conclusions: There is a wealth of data about the prevalence of schizophrenia. These gradients, and the variability found in prevalence estimate distributions, can provide direction for future hypothesis-driven research.