scholarly journals Spatiotemporal mapping and detection of mortality cluster due to cardiovascular disease with Bayesian hierarchical framework using integrated nested Laplace approximation: A discussion of suitable statistic applications in Kersa, Oromia, Ethiopia

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Melkamu Dedefo ◽  
Henry Mwambi ◽  
Sileshi Fanta ◽  
Nega Assefa
Keyword(s):  
Cause Of Death ◽  
Linear Models ◽  
Temporal Distribution ◽  
Mortality Data ◽  
Contributing Factors ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Non Parametric

Cardiovascular diseases (CVDs) are the leading cause of death globally and the number one cause of death globally. Over 75% of CVD deaths take place in low- and middle-income countries. Hence, comprehensive information about the spatio-temporal distribution of mortality due to cardio vascular disease is of interest. We fitted different spatio-temporal models within Bayesian hierarchical framework allowing different space-time interaction for mortality mapping with integrated nested Laplace approximations to analyze mortality data extracted from the health and demographic surveillance system in Kersa District in Hararege, Oromia Region, Ethiopia. The result indicates that non-parametric time trends models perform better than linear models. Among proposed models, one with non-parametric trend, type II interaction and second order random walk but without unstructured time effect was found to perform best according to our experience and. simulation study. An application based on real data revealed that, mortality due to CVD increased during the study period, while administrative regions in northern and south-eastern part of the study area showed a significantly elevated risk. The study highlighted distinct spatiotemporal clusters of mortality due to CVD within the study area. The study is a preliminary assessment step in prioritizing areas for further and more comprehensive research raising questions to be addressed by detailed investigation. Underlying contributing factors need to be identified and accurately quantified.

scholarly journals Assessing the Spatial and Spatio-Temporal Distribution of Forest Species via Bayesian Hierarchical Modeling

Forests ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 573 ◽  
Author(s):  
Óscar Rodríguez de Rivera ◽  
Antonio López-Quílez ◽  
Marta Blangiardo
Keyword(s):  
Hierarchical Modeling ◽  
Temporal Distribution ◽  
Short Term ◽  
Distribution Models ◽  
Address Climate Change ◽  
Bayesian Hierarchical ◽  
Temporal Models ◽  
Spatio Temporal ◽  
The Relationship

Climatic change is expected to affect forest development in the short term, as well as the spatial distribution of species in the long term. Species distribution models are potentially useful tools for guiding species choices in reforestation and forest management prescriptions to address climate change. The aim of this study is to build spatial and spatio-temporal models to predict the distribution of four different species present in the Spanish Forest Inventory. We have compared the different models and showed how accounting for dependencies in space and time affect the relationship between species and environmental variables.

Bayesian latent variable modelling of multivariate spatio-temporal variation in cancer mortality

2008 ◽  
Vol 17 (1) ◽  
pp. 97-118 ◽  
Author(s):  
Evangelia Tzala ◽  
Nicky Best
Keyword(s):  
Latent Variable ◽  
Joint Analysis ◽  
Mortality Data ◽  
Spatial And Temporal Patterns ◽  
Latent Factor ◽  
Bayesian Hierarchical ◽  
Cancer Data ◽  
Latent Variable Modelling ◽  
Latent Factor Models ◽  
Spatio Temporal

In this article, three alternative Bayesian hierarchical latent factor models are described for spatially and temporally correlated multivariate health data. The fundamentals of factor analysis with ideas of space— time disease mapping to provide a flexible framework for the joint analysis of multiple-related diseases in space and time with a view to estimating common and disease-specific trends in cancer risk are combined. The models are applied to area-level mortality data on six diet-related cancers for Greece over the 20-year period from 1980 to 1999. The aim of this study is to uncover the spatial and temporal patterns of any latent factor(s) underlying the cancer data that could be interpreted as reflecting some aspects of the habitual diet of the Greek population.

scholarly journals An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 384
Author(s):  
Francisca Corpas-Burgos ◽  
Miguel A. Martinez-Beneito
Keyword(s):  
Time Series ◽  
Disease Mapping ◽  
Mortality Data ◽  
Data Sets ◽  
Modeling Tools ◽  
Polynomial Fit ◽  
Proposed Model ◽  
Temporal Models ◽  
Temporal Model ◽  
Spatio Temporal

One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.

scholarly journals Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 117
Author(s):  
Zoe Gibbs ◽  
Chris Groendyke ◽  
Brian Hartman ◽  
Robert Richardson
Keyword(s):  
United States ◽  
Linear Models ◽  
Temporal Trends ◽  
The United States ◽  
Mortality Rates ◽  
Mortality Data ◽  
County Level ◽  
Spatio Temporal ◽  
Highly Correlated ◽  
Mortality Improvement

The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated.

On the spatio-temporal distribution of M 7.0+ worldwide seismicity with a non-parametric statistics

2008 ◽  
Vol 449 (1-4) ◽  
pp. 97-104 ◽  
Author(s):  
Licia Faenza ◽  
Warner Marzocchi ◽  
Paola Serretti ◽  
Enzo Boschi
Keyword(s):  
Temporal Distribution ◽  
Parametric Statistics ◽  
Spatio Temporal ◽  
Non Parametric

scholarly journals The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

2020 ◽  
Vol 44 (5) ◽  
pp. 591-604 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Ángel Serrano-Aroca
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Transmission Risk ◽  
Temporal Models ◽  
Wide Range ◽  
The World ◽  
Modelling Techniques ◽  
Spatio Temporal ◽  
Hot Weather

The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19’s global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission.

scholarly journals Spatio-Temporal Distribution of Carabids Influenced by Small-Scale Admixture of Oak Trees in Pine Stands

Diversity ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 398
Author(s):  
Alexandra Wehnert ◽  
Sven Wagner ◽  
Franka Huth
Keyword(s):  
Linear Models ◽  
Interspecific Interactions ◽  
Temporal Distribution ◽  
Quercus Petraea ◽  
Small Scale ◽  
Spatial Effects ◽  
Oak Trees ◽  
Geostatistical Models ◽  
Temporal And Spatial ◽  
Spatio Temporal

In a region with poor soil fertility, low annual precipitation and large areas of homogenous Pinus sylvestris L. forests, conservation of old sessile oak (Quercus petraea (Matt.) Liebl.) trees is one option to enrich structure and species richness. We studied the affinities of Carabus coriaceus, C. violaceus, C. hortensis and C. arvensis for specific tree species and the resultant intra- and interspecific interactions. We focused on their temporal and spatial distributions. Pitfall traps were used as a surface-related capture method on a grid over an area of three hectares. Generalised linear models and generalised linear geostatistical models were used to analyse carabid activity densities related to distance-dependent spatial effects corresponding to tree zones (oak, oak–pine, pine). The results demonstrated significant spatial affinities among these carabids, especially for females and during the period of highest activity. Individuals of C. coriaceus showed a tendency to the oak zone and C. hortensis exhibited a significant affinity to the oak–pine mixture. Imagines of C. arvensis and C. violaceus were more closely related to pine. The observed temporal and spatial coexistence of the different Carabus species reveals that single admixed old oak trees can support greater diversity within pine-dominated forests.

scholarly journals Spatio-temporal distribution patterns of Chironomidae communities in the wadis of Northern Tunisia

2022 ◽  
Vol 82 ◽  
Author(s):  
S. Boulaaba ◽  
S. Zrelli ◽  
A. Hedfi ◽  
M. Ben Ali ◽  
M. Boumaiza ◽  
...  
Keyword(s):  
Linear Models ◽  
Environmental Gradients ◽  
Abiotic Factors ◽  
Temporal Distribution ◽  
Thermal Conditions ◽  
Sodium Concentration ◽  
Dominant Group ◽  
Water Conductivity ◽  
Northern Tunisia ◽  
Spatio Temporal

Abstract In Northern Tunisia, seasonal streams, called wadi, are characterized by extreme hydrological and thermal conditions. These freshwater systems have very particular features as a result of their strong irregularity of flow due to limited precipitation runoff regime, leading to strong seasonal hydrologic fluctuations. The current study focused on the spatio-temporal distribution of chironomids in 28 sampling sites spread across the Northern Tunisia. By emplying PERMANOVA, the results indicated a significant spatio-temporal variation along various environmental gradients. The main abiotic factors responsible for noted differences in the spatial distribution of chironomids in wadi were the conductivity and temperature, closely followed by altitude, pH, salinity, talweg slope and dissolved oxygen, identified as such by employing distance-based linear models’ procedure. The Distance-based redundancy analysis ordination showed two main groups: the first clustered the Bizerte sites, which were characterized by high water conductivity, sodium concentration and salinity. The second main group comprised sites from the Tell zone and was characterized by low temperatures, neutral pH, low conductivity and nutrients content. The subfamily TANYPODIINAE (e.g., Prochladius sp., Prochladius choerus (Meigen, 1804) and Macropelopia sp.) was the dominant group at Tell zone, whereas species such as Diamesa starmachi (Kownacki et Kownacha, 1970) and Potthastia gaedii (Meigen, 1838) were found only in Tell Wadis. In contrast, chironomid species such as Diamesa starmachi (Kownacki et Kownacha, 1970), Potthastia gaedii (Meigen, 1838), Procladius choreus (Meigen, 1804) were specific for Tell Mountain. Cap Bon wadis region was dominated by genus Cladotanytarsus sp. The results of this survey liked the taxonomic composition of chironomid assemblages to the variation of hydromorphological and physic-chemical gradients across the northern Tunisia wadis.

scholarly journals How have changes in death by cause and age group contributed to the recent stalling of life expectancy gains in Scotland? Comparative decomposition analysis of mortality data, 2000-02 to 2015-17

2019 ◽  
Author(s):  
Julie Ramsay ◽  
Jonathan Minton ◽  
Colin Fischbacher ◽  
Lynda Fenton ◽  
Maria Kaye-Bardgett ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Cause Of Death ◽  
Age Groups ◽  
Decomposition Analysis ◽  
Mortality Data ◽  
Age Group ◽  
Contributing Factors ◽  
Mortality Trends ◽  
Life Expectancy At Birth ◽  
The Impact

BackgroundAnnual gains in life expectancy in Scotland were slower in recent years than in the previous two decades. This analysis investigates how deaths in different age groups and from different causes have contributed to annual average change in life expectancy across two time periods: 2000-02 to 2012-14 and 2012-14 to 2015-17. MethodsLife expectancy at birth was calculated from death and population counts, disaggregated by five-year age-group and by underlying cause of death. Arriaga’s method of life expectancy decomposition was applied to produce estimates of the contribution of different age-groups and underlying causes to changes in life expectancy at birth for the two periods.FindingsAverage annual life expectancy gains between 2012-14 to 2015-17 were markedly smaller than in the earlier period. Almost all age-groups saw worsening mortality trends, which deteriorated for most cause of death groups between 2012-14 and 2015-17. In particular, the previously observed substantial life expectancy gains due to reductions in mortality from circulatory causes, which most benefited those aged 55-84 years, more than halved. Mortality rates for those aged 30-54 years and 90+ years worsened, due in large part to increases in drug-related deaths, and dementia and Alzheimer’s disease respectively. InterpretationFuture research should seek to explain the changes in mortality trends for all age-groups and causes. More investigation is required to establish to what extent shortcomings in the social security system and public services may be contributing to the adverse trends and preventing mitigation of the impact of other contributing factors, such as influenza outbreaks.

Child and Adolescent Suicide Deaths in New Mexico, 1990-1994

Crisis ◽  
2000 ◽  
Vol 21 (1) ◽  
pp. 36-44 ◽  
Author(s):  
DD Werenko ◽  
LM Olson ◽  
L Fullerton-Gleason ◽  
AW Lynch ◽  
RE Zumwalt ◽  
...  
Keyword(s):  
New Mexico ◽  
Cause Of Death ◽  
American Indians ◽  
Adolescent Suicide ◽  
Suicide Attempts ◽  
Medical Examiner ◽  
Contributing Factors ◽  
Targeted Interventions ◽  
And Gender ◽  
Youth Activity

The suicide death rate in New Mexico is consistently higher than the national rate. Among adolescents, suicide is the third leading cause of death nationally, but in New Mexico it is the second leading cause of death. This study describes the pattern of adolescent suicide deaths in New Mexico. We conducted a retrospective review of all medical examiner autopsies for adolescent suicides (ages 20 years and younger) in New Mexico from 1990-1994. Records were reviewed for demographics and possible contributing factors such as depression, previous attempts, and alcohol and drug use. We identified 184 suicide deaths among children and adolescents ages 9-20 years for an overall rate of 12.9 per 100,000. Our rates for ages 5-9 years (0.2), 10-14 years (3.8), and 15-19 years (22.3) are over twice the U.S. rates. Suicide deaths resulted primarily from firearms (67%), hanging (16%), poisoning (6%), inhalation (4%), and other methods (7%). Method varied by ethnicity (p = .01) and gender (p = .03); males and non-Hispanic Whites were overrepresented among firearm deaths. Firearm ownership was known in 60 (48%) of the firearm deaths. Of these, 53% of the firearms belonged to a family member, 25% to the decedent, and 22% to a friend. Over one-third of decedents (41%) experienced mental disorders, primarily depressed mood and clinical depression. Previous suicide attempts were noted for 15% of the decedents. Some 50% of the decedents had alcohol or drugs present at the time of death; among American Indians/Alaska Natives, 74% had drugs or alcohol present (p = .003). Targeted interventions are needed to reduce adolescent suicide in New Mexico. We suggest raising awareness about acute and chronic contributing factors to suicide; training physicians to look for behavioral manifestations of depression; and involving physicians, teachers, and youth activity leaders in efforts to limit firearm accessibility, such as advising parents to remove firearms from their households.

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