Global model analysis by landscaping

Daniel J. Navarro, In Jae Myung, Mark A. Pitt and Woojae Ki

Shared Genetic Background Between Cerebrospinal Fluid Biomarkers and Risk for Alzheimer’s Disease: A Two-Sample Mendelian Randomization Study

Background: Whether the epidemiological association of amyloid beta (Aβ) and tau pathology with Alzheimer’s disease (AD) is causal remains unclear. Recent failures to demonstrate the efficacy of several Aβ-modifying drugs may indicate a possibility that the observed association is not causal, which led to efforts to develop tau-directed treatments whose efficacy remains tentative. Methods: Herein, we conducted a two-sample Mendelian randomisation analysis to investigate shared genetic background between cerebrospinal fluid (CSF) biomarkers for amyloid and tau pathology and risk for AD, and to find genetic evidence for causal association between these CSF biomarkers and risk for AD. We used summary statistics of genome-wide association study (GWAS) for CSF biomarkers (Aβ 1-42 , phosphorylated tau 181 [p-tau], and total tau [t-tau]) in 3,146 individuals and for late-onset AD (LOAD) in 21,982 LOAD cases and 41,944 cognitively-normal controls. We tested association between changes in the genetically-predicted CSF biomarkers and LOAD risk. Results: We found a decrease in the LOAD risk per one-standard deviation (SD) increase in the genetically-predicted CSF Aβ (odds ratio [OR], 2.87×10 -3 for AD; 95% confidence interval [CI], 1.54×10 -4 –0.05; p = 8.91×10 -5 ). Conversely, we observed an increase in the LOAD risk per one-SD increase in the genetically-predicted CSF p-tau (OR, 19.46; 95% CI, 1.50–2.52×10 2 ; p = 0.02) and t-tau (OR, 33.80; 95% CI, 1.57–7.29×10 2 ; p = 0.02). Conclusions: Our findings suggest a shared genetic background between the CSF biomarkers and LOAD risk. Although it requires validation by future studies including more genetic variants identified in large-scale GWASs for CSF biomarkers, our results suggest a causal association between CSF biomarkers and risk for LOAD

Celebrity Suicides and Their Differential Influence on Suicides in the General Population: A National Population-Based Study in Korea

Objective: Although evidence suggests that there is an increase in suicide rates in the general population following celebrity suicide, the rates are heterogeneous across celebrities and countries. It is unclear which is the more vulnerable population according to the effect sizes of celebrity suicides to general population. Methods: All suicide victims in the general population verified by the Korea National Statistical Office and suicides of celebrity in South Korea were included for 7 years from 2005 to 2011. Effect sizes were estimated by comparing rates of suicide in the population one month before and after each celebrity suicide. The associations between suicide victims and celebrities were examined. Results: Among 94,845 suicide victims, 17,209 completed suicide within one month after 13 celebrity suicides. Multivariate logistic regression analyses revealed that suicide victims who died after celebrity suicide were significantly likely to be of age 20-39, female, and to die by hanging. These qualities were more strongly associated among those who followed celebrity suicide with intermediate and high effect sizes than lower. Younger suicide victims were significantly associated with higher effect size, female gender, white collar employment, unmarried status, higher education, death by hanging, and night-time death. Characteristics of celebrities were significantly associated with those of general population in hanging method and gender. Conclusion: Individuals who commit suicide after a celebrity suicide are likely to be younger, female, and prefer hanging as method of suicide, which are more strongly associated in higher effect sizes of celebrity suicide

Additive interaction of mid- to late-life depression and cerebrovascular disease on the risk of dementia: a nationwide population-based cohort study

Background Dementia is a progressive neurocognitive disease with a substantial social burden. No apparent breakthroughs in treatment options have emerged so far; thus, disease prevention is essential for at-risk populations. Depression and cerebrovascular disease (CVD) are independent risk factors for dementia, but no studies have examined their interaction effect on dementia risk. This study aimed to identify the association of depression and CVD with the risk of dementia and evaluate whether dementia risk among patients with comorbid depression and CVD is higher than the sum of the individual risk due to each condition. Methods A population-based cohort study was conducted to analyze the Korean National Health Insurance Service-National Sample Cohort data of all individuals over 50 years of age. Individuals who had not been diagnosed with dementia at baseline were included and followed up from January 1, 2005, to December 31, 2013. A time-varying Cox proportional hazard regression model adjusted for potential confounding factors was used for the analysis. The interaction between depression and CVD was estimated based on the attributable proportion (AP), relative excess risk due to interaction (RERI), synergy index (SI), and multiplicative-scale interaction. Results A total of 242,237 participants were included in the analytical sample, of which 12,735 (5.3%) developed dementia. Compared to that for participants without depression or CVD, the adjusted hazard ratio for the incidence of dementia for those with depression alone was 2.35 (95% confidence interval [CI] 2.21–2.49), CVD alone was 3.25 (95% CI 3.11–3.39), and comorbid depression and CVD was 5.02 (95% CI 4.66–5.42). The additive interaction between depression and CVD was statistically significant (AP—0.08, 95% CI 0.01–0.16; RERI—0.42, 95% CI 0.03–0.82; SI—1.12, 95% CI 1.01–1.24). The multiplicative interaction was significant too, but the effect was negative (0.66, 95% CI 0.60–0.73). Conclusions In this population-based nationwide cohort with long-term follow-up, depression and CVD were associated with an increased risk of dementia, and their coexistence additively increased dementia risk more than the sum of the individual risks.This study was supported by grants from Sungkyunkwan University (Sungkyun Research Fund 2017), Eisai Inc. and the National Research Foundation (NRF) funded by the Korean government (MSIT, 2020R1A2C2101276 to DKK), Republic of Korea

Global model analysis by parameter space partitioning

To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale performance) often mismatches their testable precision in experiments, where qualitative, ordinal predictions are the norm. Parameter space partitioning is a solution that evaluates model performance at a qualitative level. There exists a partition on the model’s parameter space that divides it into regions that correspond to each data pattern. Three application examples demonstrate its potential and versatility for studying the global behavior of psychological models.Mark A. Pitt, Woojae Kim, Daniel J. Navarro, and Jay I. Myun

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Association between diurnal temperature range and emergency department visits for multiple sclerosis: A time-stratified case-crossover study

Although multiple sclerosis (MS) has been the leading cause of neurologically-induced disability in young adults, risk factors for the relapse and acute aggravation of MS remain unclear. A few studies have suggested a possible role of temperature changes on the relapse and acute aggravation of MS. We investigated the association between short-term exposure to wide diurnal temperature ranges (DTRs) and acute exacerbation of MS requiring an emergency department (ED) visit. A total of 1265 patients visited EDs for acute aggravation of MS as the primary disease in Seoul between 2008 and 2014 from the national emergency database. We conducted a conditional logistic regression analysis of the time-stratified case-crossover design to compare DTRs on the ED visit days for MS and those on control days matched according to the day of the week, month, and year. We examined possible associations with other temperature-related variables (ambient temperature, between-day temperature change, and sunlight hours). Short-term exposure to wide DTRs immediately increased the risk of ED visits for MS. Especially, 2-day average (lag0-1) DTR levels on the day of and one day prior to ED visits exhibited the strongest association (an 8.81% [95% CI: 3.46%-14.44%] change in the odds ratio per 1 degrees C increase in the DTR). Other temperature-related variables were not associated with MS aggravation. Our results suggest that exposure to wider DTR may increase the risk of acute exacerbation of MS. Given the increasing societal burden of MS and the increasing temperature variability due to climate change, further studies are required. (C) 2020 Elsevier B.V. All rights reserved.N