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

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.

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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

Geospatial health ◽

10.4081/gh.2018.681 ◽

2018 ◽

Vol 13 (2) ◽

Cited By ~ 1

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.

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Bayesian hierarchical modeling of colorectal and breast cancer data in Missouri

10.32469/10355/66162 ◽

2018 ◽

Author(s):

◽

Jiang Du

Keyword(s):

Breast Cancer ◽

Colorectal Cancer ◽

Surveillance System ◽

The United States ◽

Cancer Surveillance ◽

Mortality Data ◽

Bayesian Hierarchical ◽

Cancer Data ◽

Related Data ◽

Incidence And Mortality

Data on cancer in the United States is collected through cancer registries. The Missouri Cancer Registry and Research Center (MCR-ARC) maintains a statewide cancer surveillance system and participate in research in support of the prevention of cancer and the reduction of the cancer burden among Missouri residents. We applied Bayesian hierarchical models to colorectal cancer (CRC) and breast cancer related data collected by the MCR-ARC. In the first project, CRC incidence and mortality rates in Missouri were studied with emphasis on different groups of people categorized by age, gender and county at diagnosis. The incidence and mortality data were aggregated into different spatial regions due to data confidential requirements, which was identified as a misaligned-region problem in multivariate disease mapping literature. The Marginally and Conditionally CAR models were built to address the problem. Later on, colorectal cancer screening (CRCS) prevalences were analyzed due to its importance to the early detection of CRC. We applied small area estimation techniques to produce county-level CRCS prevalences from the state-level Behavioral Risk Factor Surveillance System (BRFSS) data. The last two projects focused on breast cancer related data. One is about breast cancer survival analysis in Missouri with emphasis on detecting the spatial variation of survival time among counties in Missouri, after accounting for the differences in demographic and cancer stages. The other one is studying the disparities of breast cancer treatment delay with respect to patient's race, age, stage of cancer, county at diagnosis and year of diagnosis.

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Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-020-01808-x ◽

2020 ◽

Vol 34 (10) ◽

pp. 1421-1440 ◽

Cited By ~ 2

Author(s):

G. Vicente ◽

T. Goicoa ◽

M. D. Ugarte

Keyword(s):

Spatial Data ◽

Uttar Pradesh ◽

Joint Analysis ◽

Theoretical Point ◽

Gender Based Violence ◽

Multivariate Models ◽

Spatial And Temporal Patterns ◽

Monte Carlo Techniques ◽

Spatio Temporal ◽

Crimes Against Women

Abstract Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001–2014.

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Accumulated ambient air pollution and colon cancer incidence in Thailand

Scientific Reports ◽

10.1038/s41598-020-74669-7 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Kriangsak Jenwitheesuk ◽

Udomlack Peansukwech ◽

Kamonwan Jenwitheesuk

Keyword(s):

Colon Cancer ◽

Air Pollution ◽

Ambient Air ◽

Incidence Rates ◽

Ambient Air Pollution ◽

Health Security ◽

Bayesian Hierarchical ◽

Cancer Data ◽

Spatio Temporal ◽

Log Linear

Abstract This research examined the relationship between colon cancer risks and pollution in various areas of Thailand, using satellites to gather quantities of aerosols in the atmosphere. Bayesian hierarchical spatio-temporal model and the Poisson log-linear model were used to examine the incidence rates of colon cancer standardized by national references; from the database of the National Health Security Office, Ministry of Public Health of Thailand and NASA’s database from aerosol diagnostics model. Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) was used to explore disease-gender-specific spatio-temporal patterns of colon cancer incidences and accumulated air pollution-related cancers in Thailand between 2010 and 2016. A total of 59,605 patients were selected for the study. Due to concerns regarding statistical reliability between aerosol diagnostics model and colon cancer incidences, the posterior probabilities of risk appeared the most in dust PM2.5. It could be interpreted as relative risk in every increase of 10 μg/m3 in black carbon, organic carbon, and dust-PM2.5 levels were associated respectively with an increase of 4%, 4%, and 15% in the risks of colon cancer. A significant increase in the incidence of colon cancer with accumulated ambient air quality raised concerns regarding the prevention of air pollution. This study utilized data based on the incidences of colon cancer; the country’s database and linked cancer data to pollution. According to the database from NASA’s technology, this research has never been conducted in Thailand.

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THE NEW RADIOCARBON DATASET FOR TRILITH MONUMENTS OF SOUTHEASTERN ARABIA

Radiocarbon ◽

10.1017/rdc.2020.123 ◽

2020 ◽

pp. 1-11

Author(s):

R Garba ◽

P Demján ◽

I Svetlik ◽

D Dreslerová

Keyword(s):

Iron Age ◽

Temporal Patterns ◽

Temporal Analysis ◽

Coastal Plains ◽

Spatial And Temporal Patterns ◽

Two Phase ◽

Stone Monuments ◽

South Central ◽

Geographical Expansion ◽

Spatio Temporal

ABSTRACT Triliths are megalithic monuments scattered across the coastal plains of southern and southeastern Arabia. They consist of aligned standing stones with a parallel row of large hearths and form a space, the meaning of which is undoubtedly significant but nonetheless still unknown. This paper presents a new radiocarbon (14C) dataset acquired during the two field seasons 2018–2019 of the TSMO (Trilith Stone Monuments of Oman) project which investigated the spatial and temporal patterns of the triliths. The excavation and sampling of trilith hearths across Oman yielded a dataset of 30 new 14C dates, extending the use of trilith monuments to as early as the Iron Age III period (600–300 BC). The earlier dates are linked to two-phase trilith sites in south-central Oman. The three 14C pairs collected from the two-phase trilith sites indicated gaps between the trilith construction phases from 35 to 475 years (2 σ). The preliminary spatio-temporal analysis shows the geographical expansion of populations using trilith monuments during the 5th to 1st century BC and a later pull back in the 1st and 2nd century AD. The new 14C dataset for trilith sites will help towards a better understanding of Iron Age communities in southeastern Arabia.

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Latent Variable Modelling of the Relationship Between Flow and Exercise-induced Feelings: An Intuitive Appraisal Perspective

European Physical Education Review ◽

10.1177/1356336x000063002 ◽

2000 ◽

Vol 6 (3) ◽

pp. 230-248 ◽

Cited By ~ 12

Author(s):

Costas I. Karageorghis ◽

Symeon P. Vlachopoulos ◽

Peter C. Terry

Keyword(s):

Latent Variable ◽

Latent Variable Modelling ◽

Exercise Induced ◽

The Relationship

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Effect of environmental factors on the spatio-temporal patterns of influenza spread

Epidemiology and Infection ◽

10.1017/s0950268809002283 ◽

2009 ◽

Vol 137 (10) ◽

pp. 1377-1387 ◽

Cited By ~ 27

Author(s):

K. M. L. CHARLAND ◽

D. L. BUCKERIDGE ◽

J. L. STURTEVANT ◽

F. MELTON ◽

B. Y. REIS ◽

...

Keyword(s):

Environmental Factors ◽

Solar Radiation ◽

Temporal Patterns ◽

Dew Point ◽

Bayesian Hierarchical ◽

Influenza Activity ◽

The Usa ◽

Geographical Position ◽

Spatio Temporal ◽

Epidemic Timing

SUMMARYAlthough spatio-temporal patterns of influenza spread often suggest that environmental factors play a role, their effect on the geographical variation in the timing of annual epidemics has not been assessed. We examined the effect of solar radiation, dew point, temperature and geographical position on the city-specific timing of epidemics in the USA. Using paediatric in-patient data from hospitals in 35 cities for each influenza season in the study period 2000–2005, we determined ‘epidemic timing’ by identifying the week of peak influenza activity. For each city we calculated averages of daily climate measurements for 1 October to 31 December. Bayesian hierarchical models were used to assess the strength of association between each variable and epidemic timing. Of the climate variables only solar radiation was significantly related to epidemic timing (95% CI −0·027 to −0·0032). Future studies may elucidate biological mechanisms intrinsically linked to solar radiation that contribute to epidemic timing in temperate regions.

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