Temporal patterns of suicide and circulatory system disease-related mortality are inversely correlated in several countries

Abstract Background Nearly 800,000 suicides occur worldwide annually and suicide rates are increasing faster than population growth. Unfortunately, the pathophysiology of suicide remains poorly understood, which has hindered suicide prevention efforts. However, mechanistic clues may be found by studying effects of seasonality on suicide and other mortality causes. Suicides tend to peak in spring-summer periods and nadir in fall-winter periods while circulatory system disease-related mortality tends to exhibit the opposite temporal trends. This study aimed to determine for the first time whether monthly temporal cross-correlations exist between suicide and circulatory system disease-related mortality at the population level. If so and if common biological factors moderate risks for both mortality types, such factors may be discoverable and utilized to improve suicide prevention. Methods We conducted time series analyses of monthly mortality data from northern (England and Wales, South Korea, United States) and southern (Australia, Brazil) hemisphere countries during the period 2009–2018 (N = 41.8 million all-cause mortality cases). We used a Poisson regression variant of the standard cosinor model to determine peak months of mortality. We also estimated cross-correlations between monthly mortality counts from suicide and from circulatory system diseases. Results Suicide and circulatory disease-related mortality temporal patterns were negatively correlated in Australia (− 0.32), Brazil (− 0.57), South Korea (− 0.32), and in the United States (− 0.66), but no temporal correlation was discernable in England and Wales. Conclusions The negative temporal cross-correlations between these mortality types we found in 4 of 5 countries studied suggest that seasonal factors broadly and inversely moderate risks for circulatory disease-related mortality and suicide, but not in all regions, indicating that the effect is not uniform. Since the seasonal factors of temperature and light exert opposite effects on suicide and circulatory disease-related mortality in several countries, we propose that physiologically-adaptive circulatory system responses to heat and light may increase risk for suicide and should be studied to determine whether they affect suicide risk. For example, heat and light increase production and release of the bioactive gas nitric oxide and reduce circulatory system disease by relaxing blood vessel tone, while elevated nitric oxide levels are associated with suicidal behavior, inverse effects that parallel the inverse temporal mortality patterns we detected.

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Temporal patterns of suicide and circulatory system disease-related mortality are inversely correlated in several countries

10.21203/rs.3.rs-119139/v1 ◽

2020 ◽

Author(s):

Marc Kaufman ◽

Garrett Fitzmaurice

Keyword(s):

Nitric Oxide ◽

United States ◽

Suicide Prevention ◽

Circulatory System ◽

Circulatory Disease ◽

System Disease ◽

Seasonal Factors ◽

Cross Correlations ◽

Circulatory System Disease ◽

Related Mortality

Abstract Background Nearly 800,000 suicides occur worldwide annually and suicide rates are increasing faster than population growth. Unfortunately, the pathophysiology of suicide remains poorly understood, which has hindered suicide prevention efforts. However, mechanistic clues may be found by studying effects of seasonality on suicide and other mortality causes. Suicides tend to peak in spring-summer periods and nadir in fall-winter periods while circulatory system disease-related mortality tends to exhibit the opposite temporal trends. This study aimed to determine for the first time whether monthly temporal cross-correlations exist between suicide and circulatory system disease-related mortality at the population level. If so and if common biological factors moderate risks for both mortality types, such factors may be discoverable and utilized to improve suicide prevention.Methods We conducted time series analyses of monthly mortality data from northern (England and Wales, South Korea, United States) and southern (Australia, Brazil) hemisphere countries during the period 2009-2018 (N=41.8 million all-cause mortality cases). We used a Poisson regression variant of the standard cosinor model to determine cross-correlations between monthly mortality rates from suicide and from circulatory system diseases.Results Suicide and circulatory disease-related mortality temporal patterns were negatively correlated in Australia (-0.32), Brazil (-0.57), South Korea (-0.32), and in the United States (-0.66), but no temporal correlation was discernable in England and Wales.Conclusions The negative temporal cross-correlations between these mortality types we found in 4 of 5 countries studied suggest that seasonal factors broadly and inversely moderate risks for circulatory disease-related mortality and suicide. Since the seasonal factors of temperature and light exert opposite effects on suicide and circulatory disease-related mortality in several countries, we propose that physiologically-adaptive circulatory system responses to heat and light may increase risk for suicide and should be studied to determine whether they affect suicide risk. For example, heat and light increase production and release of the bioactive gas nitric oxide and reduce circulatory system disease by relaxing blood vessel tone, while elevated nitric oxide levels are associated with suicidal behavior, inverse effects that parallel the inverse temporal mortality patterns we detected.

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Anxiety, Type a Behavior, and Cardiovascular Disease

The International Journal of Psychiatry in Medicine ◽

10.2190/updt-4qcv-fhr3-lud6 ◽

1987 ◽

Vol 16 (2) ◽

pp. 123-129 ◽

Cited By ~ 6

Author(s):

Myung Ae Lee ◽

Oliver G. Cameron

Keyword(s):

Circulatory System ◽

Type A ◽

Type A Behavior ◽

Symptom Checklist ◽

Circulatory Disease ◽

Jenkins Activity Survey ◽

Male And Female ◽

System Disease ◽

Activity Survey ◽

Circulatory System Disease

Males with anxiety disorders appear to have increased mortality due to circulatory system disease, and Type A behavior is a risk factor for coronary heart disease (CHD). Thus, we determined Type A behavior in anxious patients. Fifty-seven DSM-III defined anxiety disorder patients completed the Jenkins Activity Survey (JAS) and Symptom Checklist-90-Revised (SCL-90-R). Significantly more male (92%) than female (52%) anxious patients had Type A behavior. Correlations between the JAS scales and SCL-90-R subscales were also different between male and female patients; in males, significant correlations were observed for SCL-90-R anxiety with both JAS Type A and JAS Hard-Driving and Competitive, and for SCL-90-R hostility with JAS Hard-Driving and Competitive. However, there were no consistent correlations between the JAS and the SCL-90-R subscales in females. A trend for fathers of anxious patients to have an increased prevalence of CHD was also observed. The increased incidence of Type A behavior in male, but not in female, anxious patients suggest a mechanism for increased mortality due to circulatory disease in male anxiety patients.

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Modelling and Simulation in Biomedical Research

Gaming and Simulations ◽

10.4018/978-1-60960-195-9.ch313 ◽

2011 ◽

pp. 794-806

Author(s):

Dolores A. Steinman ◽

David A. Steinman

Keyword(s):

Fluid Dynamics ◽

Visual Representation ◽

Circulatory System ◽

Disease Development ◽

Step Process ◽

Non Invasive ◽

System Disease ◽

Computational Fluid Dynamics Cfd ◽

Circulatory System Disease

In the following chapter, the authors will discuss the development of medical imaging and, through specific case studies, its application in elucidating the role of fluid mechanical forces in cardiovascular disease development and therapy (namely the connection between flow patterns and circulatory system disease - atherosclerosis and aneurysms) by means of computational fluid dynamics (CFD). The research carried in the Biomedical Simulation Laboratory can be described as a multi-step process through which, from the reality of the human body through the generation of a mathematical model that is then translated into a visual representation, a refined visual representation easily understandable and used in the clinic is generated. Thus, the authors’ daily research generates virtual representations of blood flow that can serve two purposes: a) that of a model for a phenomenon or disease or b) that of a model for an experiment (non-invasive way of determining the best treatment option).

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Regional Variability in the Access to Cardiac Rehabilitation in Poland

Healthcare ◽

10.3390/healthcare8040468 ◽

2020 ◽

Vol 8 (4) ◽

pp. 468

Author(s):

Maciej Jankowiak ◽

Justyna Rój

Keyword(s):

Cardiovascular Diseases ◽

Cardiac Rehabilitation ◽

Circulatory System ◽

Rehabilitation Services ◽

Regional Variability ◽

System Disease ◽

Geographical Accessibility ◽

Care Costs ◽

Circulatory System Disease ◽

Good Access

Equitable access to cardiological rehabilitation services is one of the important elements in the effectiveness of the treatment of cardiovascular diseases as cardiological rehabilitation is an important part of circulatory system disease prevention and treatment. However, in many countries among others, Poland suffers from the underutilization of cardiac rehabilitation services. Cardiovascular diseases are the worldwide number one cause of mortality, morbidity, and disability and are responsible for the substantial increase in health care costs. Thus, the aim of the research was the analysis of geographical accessibility to cardiac rehabilitation services in Poland. Perkal’s method was employed in this research. The conducted research allowed to recognize the regional variation, but also made it possible to classify Polish voivodeships in terms of the level of availability achieved. This enables the identification of voivodeships that provide a good, or even very good, access to cardiology rehabilitation services and those characterized by low, or very low access. It was found that there was a slight regional variability in the access to cardiological rehabilitation services. However, the sufficient development of a rehabilitation infrastructure has been also recognized.

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Application of Data Science Technology on Research of Circulatory System Disease Prediction Based on a Prospective Cohort

Algorithms ◽

10.3390/a11100162 ◽

2018 ◽

Vol 11 (10) ◽

pp. 162

Author(s):

Haijing Tang ◽

Guo Chen ◽

Yu Kang ◽

Xu Yang

Keyword(s):

Prediction Model ◽

Chronic Diseases ◽

Prediction Models ◽

Proportional Hazards Model ◽

Circulatory System ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Disease Prediction ◽

System Disease ◽

Circulatory System Disease

Chronic diseases represented by circulatory diseases have gradually become the main types of diseases affecting the health of our population. Establishing a circulatory system disease prediction model to predict the occurrence of diseases and controlling them is of great significance to the health of our population. This article is based on the prospective population cohort data of chronic diseases in China, based on the existing medical cohort studies, the Kaplan–Meier method was used for feature selection, and the traditional medical analysis model represented by the Cox proportional hazards model was used and introduced. Support vector machine research methods in machine learning establish circulatory system disease prediction models. This paper also attempts to introduce the proportion of the explanation variation (PEV) and the shrinkage factor to improve the Cox proportional hazards model; and the use of Particle Swarm Optimization (PSO) algorithm to optimize the parameters of SVM model. Finally, the experimental verification of the above prediction models is carried out. This paper uses the model training time, Accuracy rate(ACC), the area under curve (AUC)of the Receiver Operator Characteristic curve (ROC) and other forecasting indicators. The experimental results show that the PSO-SVM-CSDPC disease prediction model and the S-Cox-CSDPC circulation system disease prediction model have the advantages of fast model solving speed, accurate prediction results and strong generalization ability, which are helpful for the intervention and control of chronic diseases.

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