Influence of atmospheric parameters on human mortality data at different geographical levels

2021 ◽  
Vol 759 ◽  
pp. 144186
Author(s):  
Joseph Sánchez-Balseca ◽  
Agustí Pérez-Foguet
Keyword(s):  
Mortality Data ◽  
Atmospheric Parameters ◽  
Human Mortality

scholarly journals Adequate life-expectancy reconstruction for adult human mortality data

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0198485 ◽  
Author(s):  
László Németh ◽  
Trifon I. Missov
Keyword(s):  
Life Expectancy ◽  
Mortality Data ◽  
Human Mortality ◽  
Adult Human

scholarly journals What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database

2021 ◽  
Author(s):  
Ainhoa-Elena Léger ◽  
Stefano Mazzuco
Keyword(s):  
Principal Component ◽  
Premature Mortality ◽  
Developed Countries ◽  
Functional Principal Component Analysis ◽  
Mortality Data ◽  
Human Mortality ◽  
Functional Clustering ◽  
Scalar Data ◽  
Functional Principal Component ◽  
Eastern European Countries

AbstractThis study analyzed whether there are different patterns of mortality decline among low-mortality countries by identifying the role played by all the mortality components. We implemented a cluster analysis using a functional data analysis (FDA) approach, which allowed us to consider age-specific mortality rather than summary measures, as it analyses curves rather than scalar data. Combined with a functional principal component analysis, it can identify what part of the curves is responsible for assigning one country to a specific cluster. FDA clustering was applied to the data from 32 countries in the Human Mortality Database from 1960 to 2018 to provide a comprehensive understanding of their patterns of mortality. The results show that the evolution of developed countries followed the same pattern of stages (with different timings): (1) a reduction of infant mortality, (2) an increase of premature mortality and (3) a shift and compression of deaths. Some countries were following this scheme and recovering the gap with precursors; others did not show signs of recovery. Eastern European countries were still at Stage (2), and it was not clear if and when they will enter Stage 3. All the country differences related to the different timings with which countries underwent the stages, as identified by the clusters.

scholarly journals Late-life mortality plateaus through error, not evolution

2018 ◽  
Author(s):  
Saul Justin Newman
Keyword(s):  
Alternative Hypothesis ◽  
Low Frequency ◽  
Late Life ◽  
Specific Model ◽  
Mortality Data ◽  
Model Choice ◽  
Human Mortality ◽  
Age Range

AbstractThis study highlights how the mortality plateau in Barbi et al. [1] can be generated by low frequency, randomly distributed age misreporting errors. Furthermore, sensitivity of the late-life mortality plateau in Barbi et al. [1] to the particular age range selected for regression is illustrated. Collectively, the simulation of age misreporting errors in late-life human mortality data and a less specific model choice than that of Barbi et al. [1] highlight a clear alternative hypothesis to the cessation of ageing.

scholarly journals The Epidemiology of Human Mortality in West Azerbaijan Province, Iran, 2016

2020 ◽  
Vol 10 (4) ◽  
pp. 30049.1-30049.9
Author(s):  
Flora Farivarnia ◽  
◽  
Rasool Entezarmahdi ◽  
Mohammad Delirrad ◽  
◽  
...  
Keyword(s):  
Urban Areas ◽  
Causes Of Death ◽  
Vital Statistics ◽  
Mortality Data ◽  
Local Health ◽  
Death Registration ◽  
Registration System ◽  
Human Mortality ◽  
Significant Difference ◽  
West Azerbaijan

Background: This article presents the most recent mortality data in West Azerbaijan Province (WAP) of Iran in 2016 based on selected characteristics, including age, sex, state of residence, and cause of death. Methods: After the local Ethics Committee approval, we obtained the data for this systematic database study from the local health registration office. The causes of death are processed following the International Classification of Diseases, tenth revision (ICD-10). The vital statistics were refined from the yearbooks of the National Organization for Civil Registration. Results: In 2016, a total of 14688 deaths were registered in the Health Death Registration System (HDRS) and 14622 in the Civil Death Registration System (CDRS) of WAP (~4% of Iran’s mortality). The Crude Mortality Rate (CMR) was 4.30 per 1000, showing an increase of 7.5% from the 2011 rate. Of the deceased, 56.9% were males, and 60% were attributed to urban areas. On average, men died 4 years earlier than women (61.2 vs 65.3 years, respectively). The major causes of death in 2016 remained the same as in 2011. Conclusion: The pattern of human mortality in WAP is comparatively consistent with the other regions of Iran except for the higher rate of neoplasms (19.6% compared to 10.7% of the country average) and the lower ratio of certain infectious and parasitic diseases (1% compared to 3.8% of the national average). There was a significant difference in registering the state of residence between HDRS and CDRS, which is highly recommended for further investigation in future studies.

scholarly journals Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data

2018 ◽  
Vol 38 ◽  
pp. 773-842 ◽  
Author(s):  
Joel E. Cohen ◽  
Christina Bohk ◽  
Roland Rau
Keyword(s):  
Mortality Data ◽  
Human Mortality ◽  
Taylor’S Law

scholarly journals Diferencias entre mujeres y hombres en la asociación entre la salud autopercibida y la mortalidad en las edades adultas en Europa

2018 ◽  
Author(s):  
Jordi Gumà ◽  
Amans Blanes
Keyword(s):  
Descriptive Analysis ◽  
Perceived Health ◽  
Future Research ◽  
Mortality Data ◽  
Gender Inequalities ◽  
The European Union ◽  
Human Mortality ◽  
The Difference ◽  
The Relationship ◽  
Health And Mortality

Objetivo: Explorar el patrón por edad de la relación entre el indicador de salud autopercibida y la mortalidad en las edades posteriores a la juventud (35-79) para hombres y mujeres en seis países europeos con trayectorias de morbilidad diferenciadas: Alemania, Francia, España, Italia, Polonia y Hungría. Métodos y datos: Descripción de los patrones tanto de la prevalencia de mala salud autopercibida como de la diferencia entre las transformaciones logarítmicas de las probabilidades de morir y de la prevalencia de mala salud en los años 2005 y 2009. Los datos proceden de la encuesta sobre Condiciones de Vida en la Unión Europea (EU-SILC) para la salud autopercibida, y de la Human Mortality Database (HMD) para la mortalidad. Resultados: Ambos indicadores muestran un patrón creciente por edad aunque el valor relativo de este incremento no es igual para la mortalidad y para la mala salud. La prevalencia de mala salud autopercibida aumenta con la edad con una intensidad menor que la mortalidad en ambos sexos en todos los países analizados. Este cambio en la relación entre ambos indicadores con la edad muestra valores similares entre mujeres y hombres, menos en el caso de Polonia y Hungría. Conclusiones: El cambio en la relación entre salud percibida y mortalidad con la edad se explicaría mediante la normalización por parte del individuo de la propia morbilidad. El diferente cambio en esta relación entre mujeres y hombres parece deberse a mayores niveles de desigualdad de género en aquellos países donde se observa esta diferencia, aunque estos resultados deberán comprobarse futuros trabajos.Goal: To explore the age pattern of the relationship between self-perceived health and mortality at ages beyond youth (35-79) for men and women in six European countries with different with different patterns of morbidity: Germany, France, Spain, Italy, Poland and Hungary. Methods and data sources: Descriptive analysis of the patterns of both the prevalence of poor self-perceived health and the difference between the logarithmic transformations of the mortality probabilities and the prevalence of poor health in 2005 and 2009. The data about self-perceived health come from the European Union statistics on income and living conditions (EU-SILC), whereas mortality data come from Human Mortality Database (HMD). Results: Both indicators show a growing pattern by age though the relative value of this increase is not equal in mortality and poor self-perceived health. Poor self-perceived health prevalence rises by age with a lower intensity than mortality for both sexes in all the analysed countries. This change by age in the relationship between both health outcomes shows similar values for women and men, with the exception of Poland and Hungary. Conclusions: The change in the relationship by age between self-perceived health and mortality would be explained by the process of standardization of individual’s morbidity. Different age changes between sexes seem to be related with higher levels of gender inequalities in countries where this difference is observed, though this must be confirmed in future research.

scholarly journals Factors That Influence Climate Change-Related Mortality in the United States: An Integrative Review

2021 ◽  
Vol 18 (15) ◽  
pp. 8220
Author(s):  
Ruth McDermott-Levy ◽  
Madeline Scolio ◽  
Kabindra M. Shakya ◽  
Caroline H. Moore
Keyword(s):  
Climate Change ◽  
United States ◽  
Environmental Changes ◽  
Demographic Factors ◽  
The United States ◽  
Integrative Review ◽  
Health Determinants ◽  
Mortality Data ◽  
Human Mortality ◽  
Related Mortality

Global atmospheric warming leads to climate change that results in a cascade of events affecting human mortality directly and indirectly. The factors that influence climate change-related mortality within the peer-reviewed literature were examined using Whittemore and Knafl’s framework for an integrative review. Ninety-eight articles were included in the review from three databases—PubMed, Web of Science, and Scopus—with literature filtered by date, country, and keywords. Articles included in the review address human mortality related to climate change. The review yielded two broad themes in the literature that addressed the factors that influence climate change-related mortality. The broad themes are environmental changes, and social and demographic factors. The meteorological impacts of climate change yield a complex cascade of environmental and weather events that affect ambient temperatures, air quality, drought, wildfires, precipitation, and vector-, food-, and water-borne pathogens. The identified social and demographic factors were related to the social determinants of health. The environmental changes from climate change amplify the existing health determinants that influence mortality within the United States. Mortality data, national weather and natural disaster data, electronic medical records, and health care provider use of International Classification of Disease (ICD) 10 codes must be linked to identify climate change events to capture the full extent of climate change upon population health.

scholarly journals Social Capital and Human Mortality: Explaining the Rural Paradox with County-Level Mortality Data

2011 ◽  
Vol 76 (3) ◽  
pp. 347-374 ◽  
Author(s):  
Tse-Chuan Yang ◽  
Leif Jensen ◽  
Murali Haran
Keyword(s):  
Social Capital ◽  
Mortality Data ◽  
County Level ◽  
Human Mortality

Inequalities and Impact of Socioeconomic-Cultural Factors in Suicide Rates Across Italy

Crisis ◽  
2011 ◽  
Vol 32 (4) ◽  
pp. 178-185 ◽  
Author(s):  
Maurizio Pompili ◽  
Marco Innamorati ◽  
Monica Vichi ◽  
Maria Masocco ◽  
Nicola Vanacore ◽  
...  
Keyword(s):  
Suicide Rate ◽  
Age Groups ◽  
Premature Death ◽  
Mortality Rates ◽  
Mortality Data ◽  
Italian Population ◽  
Comprehensive Overview ◽  
Suicide Rates ◽  
Younger Age ◽  
Age And Sex

Background: Suicide is a major cause of premature death in Italy and occurs at different rates in the various regions. Aims: The aim of the present study was to provide a comprehensive overview of suicide in the Italian population aged 15 years and older for the years 1980–2006. Methods: Mortality data were extracted from the Italian Mortality Database. Results: Mortality rates for suicide in Italy reached a peak in 1985 and declined thereafter. The different patterns observed by age and sex indicated that the decrease in the suicide rate in Italy was initially the result of declining rates in those aged 45+ while, from 1997 on, the decrease was attributable principally to a reduction in suicide rates among the younger age groups. It was found that socioeconomic factors underlined major differences in the suicide rate across regions. Conclusions: The present study confirmed that suicide is a multifaceted phenomenon that may be determined by an array of factors. Suicide prevention should, therefore, be targeted to identifiable high-risk sociocultural groups in each country.

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