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Systematic reviews have consistently shown that individuals with mental disorders have an increased risk of premature mortality. Traditionally, this evidence has been based on relative risks or crude estimates of reduced life expectancy. The aim of this study was to compile a comprehensive analysis of mortality-related health metrics associated with mental disorders, including sex-specific and age-specific mortality rate ratios (MRRs) and life-years lost (LYLs), a measure that takes into account age of onset of the disorder. In this population-based cohort study, we included all people younger than 95 years of age who lived in Denmark at some point between Jan 1, 1995, and Dec 31, 2015. Information on mental disorders was obtained from the Danish Psychiatric Central Research Register and the date and cause of death was obtained from the Danish Register of Causes of Death. We classified mental disorders into ten groups and causes of death into 11 groups, which were further categorised into natural causes (deaths from diseases and medical conditions) and external causes (suicide, homicide, and accidents). For each specific mental disorder, we estimated MRRs using Poisson regression models, adjusting for sex, age, and calendar time, and excess LYLs (ie, difference in LYLs between people with a mental disorder and the general population) for all-cause mortality and for each specific cause of death. 7 369 926 people were included in our analysis. We found that mortality rates were higher for people with a diagnosis of a mental disorder than for the general Danish population (28·70 deaths [95% CI 28·57–28·82] vs 12·95 deaths [12·93–12·98] per 1000 person-years). Additionally, all types of disorders were associated with higher mortality rates, with MRRs ranging from 1·92 (95% CI 1·91–1·94) for mood disorders to 3·91 (3·87–3·94) for substance use disorders. All types of mental disorders were associated with shorter life expectancies, with excess LYLs ranging from 5·42 years (95% CI 5·36–5·48) for organic disorders in females to 14·84 years (14·70–14·99) for substance use disorders in males. When we examined specific causes of death, we found that males with any type of mental disorder lost fewer years due to neoplasm-related deaths compared with the general population, although their cancer mortality rates were higher. Mental disorders are associated with premature mortality. We provide a comprehensive analysis of mortality by different types of disorders, presenting both MRRs and premature mortality based on LYLs, displayed by age, sex, and cause of death. By providing accurate estimates of premature mortality, we reveal previously underappreciated features related to competing risks and specific causes of death. Danish National Research Foundation.

This software note announces a new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log-log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently-published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open-source developments, we invite others to freely use and contribute to the software.

A number of methods have been developed to infer differential rates of species diversification through time and among clades using time-calibrated phylogenetic trees. However, we lack a general framework that can delineate and quantify heterogeneous mixtures of dynamic processes within single phylogenies. I developed a method that can identify arbitrary numbers of time-varying diversification processes on phylogenies without specifying their locations in advance. The method uses reversible-jump Markov Chain Monte Carlo to move between model subspaces that vary in the number of distinct diversification regimes. The model assumes that changes in evolutionary regimes occur across the branches of phylogenetic trees under a compound Poisson process and explicitly accounts for rate variation through time and among lineages. Using simulated datasets, I demonstrate that the method can be used to quantify complex mixtures of time-dependent, diversity-dependent, and constant-rate diversification processes. I compared the performance of the method to the MEDUSA model of rate variation among lineages. As an empirical example, I analyzed the history of speciation and extinction during the radiation of modern whales. The method described here will greatly facilitate the exploration of macroevolutionary dynamics across large phylogenetic trees, which may have been shaped by heterogeneous mixtures of distinct evolutionary processes.

Longitudinal count data is a common type of discrete data in fields like medicine and social sciences. The Poisson regression model is an important method for analysing count data, and the standard Poisson regression model requires that the mean and variance of discrete count data are equal. However, longitudinal count data are often over-dispersed in practice, i.e., the variance of the data is greater than the mean. Besides, the degree of dispersion between the mean and variance of longitudinal count data varies over time. A generalized Poisson regression model based on joint modeling of the mean and dispersion parameters of over-dispersed longitudinal count data is proposed, which simultaneously describes the trajectories of longitudinal mean and dispersion parameters over time and explains how they are dependent on other covariates. The simulation results and real data analysis illustrate the accuracy and effectiveness of the proposed method.

Cholera remains a persistent health problem in sub-Saharan Africa and worldwide. Cholera can be controlled through appropriate water and sanitation, or by oral cholera vaccination, which provides transient (∼3 years) protection, although vaccine supplies remain scarce. We aimed to map cholera burden in sub-Saharan Africa and assess how geographical targeting could lead to more efficient interventions. We combined information on cholera incidence in sub-Saharan Africa (excluding Djibouti and Eritrea) from 2010 to 2016 from datasets from WHO, Médecins Sans Frontières, ProMED, ReliefWeb, ministries of health, and the scientific literature. We divided the study region into 20 km × 20 km grid cells and modelled annual cholera incidence in each grid cell assuming a Poisson process adjusted for covariates and spatially correlated random effects. We combined these findings with data on population distribution to estimate the number of people living in areas of high cholera incidence (>1 case per 1000 people per year). We further estimated the reduction in cholera incidence that could be achieved by targeting cholera prevention and control interventions at areas of high cholera incidence. We included 279 datasets covering 2283 locations in our analyses. In sub-Saharan Africa (excluding Djibouti and Eritrea), a mean of 141 918 cholera cases (95% credible interval [CrI] 141 538–146 505) were reported per year. 4·0% (95% CrI 1·7–16·8) of districts, home to 87·2 million people (95% CrI 60·3 million to 118·9 million), have high cholera incidence. By focusing on the highest incidence districts first, effective targeted interventions could eliminate 50% of the region's cholera by covering 35·3 million people (95% CrI 26·3 million to 62·0 million), which is less than 4% of the total population. Although cholera occurs throughout sub-Saharan Africa, its highest incidence is concentrated in a small proportion of the continent. Prioritising high-risk areas could substantially increase the efficiency of cholera control programmes. The Bill & Melinda Gates Foundation.

The effects of exposure to fine particulate matter ([Formula: see text]) during wildland fires are not well understood in comparison with [Formula: see text] exposures from other sources. We examined the cardiopulmonary effects of short-term exposure to [Formula: see text] on smoke days in the United States to evaluate whether health effects are consistent with those during non-smoke days. We examined cardiopulmonary hospitalizations among adults [Formula: see text] y of age, in U.S. counties ([Formula: see text]) within [Formula: see text] of 123 large wildfires during 2008-2010. We evaluated associations during smoke and non-smoke days and examined variability with respect to modeled and observed exposure metrics. Poisson regression was used to estimate county-specific effects at lag days 0-6 (L0-6), adjusted for day of week, temperature, humidity, and seasonal trend. We used meta-analyses to combine county-specific effects and estimate overall percentage differences in hospitalizations expressed per [Formula: see text] increase in [Formula: see text]. Exposure to [Formula: see text], on all days and locations, was associated with increased hospitalizations on smoke and non-smoke days using modeled exposure metrics. The estimated effects persisted across multiple lags, with a percentage increase of 1.08% [95% confidence interval (CI): 0.28, 1.89] on smoke days and 0.67% (95% CI: [Formula: see text], 1.44) on non-smoke days for respiratory and 0.61% (95% CI: 0.09, 1.14) on smoke days and 0.69% (95% CI: 0.19, 1.2) on non-smoke days for cardiovascular outcomes on L1. For asthma-related hospitalizations, the percentage increase was greater on smoke days [6.9% (95% CI: 3.71, 10.11)] than non-smoke days [1.34% (95% CI: [Formula: see text], 3.77)] on L1. The increased risk of [Formula: see text]-related cardiopulmonary hospitalizations was similar on smoke and non-smoke days across multiple lags and exposure metrics, whereas risk for asthma-related hospitalizations was higher during smoke days. https://doi.org/10.1289/EHP3860.

N-mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p. They are popular because they allow inference about N while controlling for factors that influence p without the need for marking animals. Using a capture-recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the \"constant p\" assumption) scientifically plausible alternative models in which N (or its expectation) is non-identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N-mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p.

This paper provides a statistical analysis of three common methods of regression for Poisson data in the presence of Poisson background, namely the joint fit with two parametric models for the source and the background, the use of a nonparametric model for the background known as the wstat method, and the regression with a fixed background. The nonparametric background method, which is a popular method for spectral data, is found to be significantly biased, especially in the low-count and background-dominated regimes. Similar conclusions apply to the fixed-background regression. The joint-fit method, on the other hand, simultaneously affords reliable hypothesis testing by means of the usual Cash statistic and unbiased reconstruction of source parameters. We also investigate the effect of nonparametric regression on the number of effective degrees of freedom by means of the Efron degree of freedom function. We find that the wstat method adds a significantly larger number of degrees of freedom, compared to the number of free parameters in the source model. The other two methods have a number of degrees of freedom consistent with the number of adjustable parameters, at least for the simple models investigated in this paper.

Amazon's Mechanical Turk (MTurk) is frequently used to administer health-related surveys and experiments at a low cost, but little is known about its representativeness with regards to health status and behaviors. A cross-sectional survey comprised of questions from the nationally-representative 2014 Behavioral Risk Factor Surveillance System (BRFSS) and 2014 National Health and Nutrition Examination Survey (NHANES) was administered to 591 MTurk workers and 393 masters in 2016. Health status (asthma, depression, BMI, and general health), health behaviors (influenza vaccination, health insurance, smoking, and physical activity), and demographic characteristics of the two MTurk populations (workers and masters) were compared to each other and, using Poisson regression, to a nationally-representative BRFSS and NHANES samples. Workers and master demographics were similar. MTurk users were more likely to be aged under 50 years compared to the national sample (86% vs. 55%) and more likely to complete a college degree than the national sample (50% vs. 26%). Adjusting for covariates, MTurk users were less likely to be vaccinated for influenza, to smoke, to have asthma, to self-report being in excellent or very good health, to exercise, and have health insurance but over twice as likely to screen positive for depression relative to a national sample. Results were fairly consistent among different age groups. MTurk workers are not a generalizable population with regards to health status and behaviors; deviations did not follow a trend. Appropriate health-related uses for MTurk and ways to improve upon the generalizability of MTurk health studies are proposed.

PurposeRates of hospital-treated self-harm are highest among young people. The current study examined trends in rates of self-harm among young people in Ireland over a 10-year period, as well as trends in self-harm methods.MethodsData from the National Self-Harm Registry Ireland on presentations to hospital emergency departments (EDs) following self-harm by those aged 10–24 years during the period 2007–2016 were included. We calculated annual self-harm rates per 100,000 by age, gender and method of self-harm. Poisson regression models were used to examine trends in rates of self-harm.ResultsThe average person-based rate of self-harm among 10–24-year-olds was 318 per 100,000. Peak rates were observed among 15–19-year-old females (564 per 100,000) and 20–24-year-old males (448 per 100,000). Between 2007 and 2016, rates of self-harm increased by 22%, with increases most pronounced for females and those aged 10–14 years. There were marked increases in specific methods of self-harm, including those associated with high lethality.ConclusionsThe findings indicate that the age of onset of self-harm is decreasing. Increasing rates of self-harm, along with increases in highly lethal methods, indicate that targeted interventions in key transition stages for young people are warranted.