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BACKGROUND: More frequent and intense exposure to extreme heat conditions poses a serious threat to public health. However, evidence on the association between heat and specific diagnoses of morbidity is still limited. We aimed to comprehensively assess the short-term association between cause-specific hospital admissions and high temperature, including the added effect of temperature variability and heat waves and the effect modification by humidity and air pollution. METHODS: We used data on cause-specific hospital admissions, weather (i.e., temperature and relative humidity), and air pollution [i.e., fine particulate matter with aerodynamic diameter [less than or equal to]2.5 [micro]m ([PM.sub.2.5]), fine particulate matter with aerodynamic diameter [less than or equal to]10 [micro]m ([PM.sub.10]), N[O.sub.2], and ozone ([O.sub.3])] for 48 provinces in mainland Spain and the Balearic Islands between 1 January 2006 and 31 December 2019. The statistical analysis was performed for the summer season (June-September) and consisted of two steps. We first applied quasi-Poisson generalized linear regression models in combination with distributed lag nonlinear models (DLNM) to estimate province-specific temperature--morbidity associations, which were then pooled through multilevel univariate/multivariate random-effect meta-analysis. RESULTS: High temperature had a generalized impact on cause-specific hospitalizations, while the added effect of temperature variability [i.e., diurnal temperature range (DTR)] and heat waves was limited to a reduced number of diagnoses. The strongest impact of heat was observed for metabolic disorders and obesity [relative risk (RR) = 1.978; 95% empirical confidence interval (eCI): 1.772, 2.208], followed by renal failure (1.777; 95% eCI: 1.629, 1.939), urinary tract infection (1.746; 95% eCI: 1.578, 1.933), sepsis (1.543; 95% eCI: 1.387, 1.718), urolithiasis (1.490; 95% eCI: 1.338, 1.658), and poisoning by drugs and nonmedicinal substances (1.470; 95% eCI: 1.298, 1.665). We also found differences by sex (depending on the diagnosis of hospitalization) and age (very young children and the elderly were more at risk). Humidity played a role in the association of heat with hospitalizations from acute bronchitis and bronchiolitis and diseases of the muscular system and connective tissue, which were higher in dry days. Moreover, heat-related effects were exacerbated on high pollution days for metabolic disorders and obesity ([PM.sub.2.5]) and diabetes ([PM.sub.10], [O.sub.3]). DISCUSSION: Short-term exposure to heat was found to be associated with new diagnoses (e.g., metabolic diseases and obesity, blood diseases, acute bronchitis and bronchiolitis, muscular and connective tissue diseases, poisoning by drugs and nonmedicinal substances, complications of surgical and medical care, and symptoms, signs, and ill-defined conditions) and previously identified diagnoses of hospital admissions. The characterization of the vulnerability to heat can help improve clinical and public health practices to reduce the health risks posed by a warming planet. https://doi.org/10.1289/EHP13254, Introduction Climate change is increasing the frequency and intensity of exposure to extreme heat conditions, which poses a serious threat to public health globally. (1) In some high-income regions such [...]

Objective To investigate a new approach to calculating cause-related standardized mortality rates that involves assigning weights to each cause of death reported on death certificates. Methods We derived cause-related standardized mortality rates from death certificate data for France in 2010 using: (i) the classic method, which considered only the underlying cause of death; and (ii) three novel multiple-cause-of-death weighting methods, which assigned weights to multiple causes of death mentioned on death certificates: the first two multiple-cause-of-death methods assigned non-zero weights to all causes mentioned and the third assigned non-zero weights to only the underlying cause and other contributing causes that were not part of the main morbid process. As the sum of the weights for each death certificate was 1, each death had an equal influence on mortality estimates and the total number of deaths was unchanged. Mortality rates derived using the different methods were compared. Findings On average, 3.4 causes per death were listed on each certificate. The standardized mortality rate calculated using the third multiple-cause-of-death weighting method was more than 20% higher than that calculated using the classic method for five disease categories: skin diseases, mental disorders, endocrine and nutritional diseases, blood diseases and genitourinary diseases. Moreover, this method highlighted the mortality burden associated with certain diseases in specific age groups. Conclusion A multiple-cause-of-death weighting approach to calculating cause-related standardized mortality rates from death certificate data identified conditions that contributed more to mortality than indicated by the classic method. This new approach holds promise for identifying underrecognized contributors to mortality. Objectif Etudier une nouvelle approche permettant de calculer des taux comparatifs de mortalite due a differentes causes en ponderant chaque cause de deces declaree sur les certificats de deces. Methodes Nous avons calcule des taux comparatifs de mortalite due a differentes causes a partir des donnees de certificats de deces emis en France en 2010 suivant: (i) la methode classique, oli nous avons uniquement tenu compte de la cause sous-jacente de deces; et (ii) trois nouvelles methodes de ponderation de causes multiples de deces, qui conslstaient a appliquer une ponderation aux differentes causes de deces mentionnees sur les certificats de deces: les deux premieres methodes tenant compte de plusleurs causes de deces consistalent a appliquer une ponderation autre que zero a toutes les causes mentionnees et la troisieme consistait a appliquer une ponderation autre que zero uniquement a la cause sous-jacente et a d'autres causes aggravantes, exterieures au processus pathologlque principal. La somme des ponderations pour chaque certlficat de deces etait de 1. Ainsi, chaque deces avalt la meme influence sur les estimations de la mortalite, sans changer le nombre total de deces. Les taux de mortalite obtenus suivant ces differentes methodes ont ensulte ete compares. Resultats En moyenne, 3,4 causes etaient mentionnees sur chaque certificat de deces. Le taux comparatlf de mortalite calcule selon la troisieme methode de ponderation de causes multiples de deces etait plus de 20% superieur a celui calcule selon la methode classique pour cinq categories de maladies: maladies de la peau, troubles mentaux, maladies endocriniennes et nutritionnelles, maladies du sang et maladies uro-genitales. En outre, cette methode a mis en relief la charge de mortalite associee a certaines maladies dans des groupes d'age specifiques. Conclusion L'approche consistant a ponderer des causes multiples de deces afin de calculer des taux comparatifs de mortalite due a differentes causes a partir des donnees figurant sur des certificats de deces a permis de reperer les pathologies qui contribuaient plus a la mortalite que ce qu'indlqualt la methode classique. Cette nouvelle approche devrait permettre d'identifier les facteurs peu reconnus qui contribuem pourtant a la mortalite. Objetivo Investigar un nuevo enfoque para calcular las tasas estandarizadas de mortalidad relacionada con las causas que implique la asignacion de variables de cada causa de muerte Indicada en los certificados de defuncion. Metodos Se derivaron las tasas estandarizadas de mortalidad relacionada con las causas de certificados de defuncion en Francia en 2010 utilizando: (i) el metodo clasico, que consideraba unicamente la causa subyacente de la muerte; y (ii) tres nuevos metodos de evaluacion de multiples causas de muerte, que asignaban variables a varias causas de muerte mencionadas en los certificados de defuncion: los primeros dos metodos de multiples causas de muerte asignaron variables no nulas en todas las causas mencionadas y el tercero asigno las mismas variables solo a la causa subyacente y otras causas contribuyentes que no formaban parte del proceso morbido principal. Dado que la suma de las variables de cada certificado era 1, cada defuncion tenia la misma influencia en las estimaciones de mortalidad y el numero total de muertes permanecio Intacto. Se compararon las tasas de mortalidad derivadas utilizando los distintos metodos. Resultados De media, cada certificado enumeraba 3,4 causas por muerte. La tasa de mortalidad estandarizada calculada utilizando el tercer metodo de evaluacion de multiples causas de muerte fue mas de un 20% superior a la calculada utilizando el metodo clasico para cinco categorias de enfermedades: enfermedades cutaneas, trastornos mentales, enfermedades endocrinas y nutricionales, enfermedades sanguineas y enfermedades genitourinarias. Asimismo, este metodo destaco el umbral de mortalidad relacionado con determinadas enfermedades de grupos de edades en particular. Conclusion Un enfoque de evaluacion de multiples causas de muerte para calcular las tasas estandarizadas de mortalidad relacionada con las causas de datos recopilados de certificados de defuncion identifico las condiciones que contribuyeron mas a la mortalidad que las indicadas en el metodo clasico. Este nuevo enfoque promete identificar contribuyentes no reconocidos a la mortalidad., Introduction Good understanding of mortality data is essential for developing and evaluating health policies. The causes of any death are usually reported on parts I and II of a death [...]

BackgroundCoding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d’épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. ObjectiveThis article investigates the application of deep neural network methods to coding underlying causes of death. MethodsThe investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject’s age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject’s underlying cause of death was then formulated as a predictive modelling problem. A deep neural network−based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach’s superiority was assessed via bootstrap. ResultsThe proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. ConclusionsThis article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.

Objective: Obtaining better knowledge on the outcomes of patients who attempt suicide is crucial for suicide prevention. The aim of our study was to determine the causes of death 1 year after a suicide attempt (SA) in the VigilanS program, mortality rates, and risk factors associated with any cause of death and suicide., Methods: A prospective cohort of 7,406 people who had attempted suicide between January 1, 2017, and December 31, 2018, was included in the study. The vital status of each participant was sought, and the cause of death was established through a phone call to their general practitioner or psychiatrist. Second, the relationship between sociodemographic and clinical factors and death by suicide within 1 year of an SA was assessed using a multivariable Cox model., Results: At 1 year, 125 (1.7%) participants had died, 77 of whom died by suicide. Half of the deaths occurred within the first 4 months after an SA. Hanging (20.3%; 24/125) and self-poisoning (19.5%; 23/125) were the methods the most often used for suicide. We demonstrated that male sex (HR = 1.79 [1.13-2.82], P  = .01) and being 45 years of age or older (between 45 and 64 years old, HR = 2.08 [1.21-3.56], P  < .01; 65 years or older, HR = 5.36 [2.72-10.54], P  < .01) were associated with a higher risk of death by suicide 1 year after an SA and that being younger than 25 years was associated with a lower risk (HR = 0.22 [0.07-0.76], P  = .02)., Conclusions: One out of 100 people who attempted suicide died by suicide within 1 year after an SA. Greater vigilance is required in the first months following an SA, especially for males older than 45 years., Trial Registration: ClinicalTrials.gov identifier: NCT03134885., (© Copyright 2023 Physicians Postgraduate Press, Inc.)