scholarly journals Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 282
Author(s):  
Mabel Morales-Otero ◽  
Vicente Núñez-Antón
Keyword(s):  
Infant Mortality ◽  
Count Data ◽  
Spatial Dependence ◽  
Poisson Model ◽  
Mortality Rates ◽  
Estimation Methods ◽  
Neighborhood Structure ◽  
Specific Context ◽  
Count Data Models ◽  
Conditional Mean

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.

scholarly journals Spatio-temporal modelling of tick life-stage count data with spatially varying coefficients

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Thabo Lephoto ◽  
Henry Mwambi ◽  
Oliver Bodhlyera ◽  
Holly Gaff
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Life Stage ◽  
Poisson Model ◽  
Information Criteria ◽  
Count Data Models ◽  
Varying Coefficients ◽  
Time Interaction ◽  
York County ◽  
Spatio Temporal

There is a vast amount of geo-referenced data in many fields of study including ecological studies. Geo-referencing is usually by point referencing; that is, latitudes and longitudes or by areal referencing, which includes districts, counties, states, provinces and other administrative units. The availability of large geo-referenced datasets for modelling has necessitated the development and application of spatial statistical methods. However, spatial varying coefficients models exploring the abundance of tick counts remain limited. In this study we used data that was collected and prepared by researchers in the Department of Biological Sciences from the Old Dominion University, Virginia, USA. We modelled tick life-stage counts and abundance variability from 12 sampling locations, with 5 different habitats (numbered 1-5), three habitat types; namely: woods, edges and grass; collected monthly from May 2009 through December 2018. Spatio-temporal Poisson and spatio-temporal negative binomial (NB) count data models were fitted to the data and compared using the deviance information criteria (DIC). The NB model outperformed the Poisson models with all its DIC values being smaller than those of the Poisson model. Results showed that the covariates varied spatially across counties. There was a decreasing time (in years) effect over the study period. However, even though the time effect was decreasing over the study period, space-time interaction effects were seen to be increasing over time in York County.

scholarly journals Can Generalized Poisson model replace any other count data models? An evaluation

2021 ◽  
pp. 100774
Author(s):  
Bijesh Yadav ◽  
Lakshmanan Jeyaseelan ◽  
Visalakshi Jeyaseelan ◽  
Jothilakshmi Durairaj ◽  
Sebastian George ◽  
...  
Keyword(s):  
Count Data ◽  
Poisson Model ◽  
Data Models ◽  
Count Data Models ◽  
Generalized Poisson ◽  
Generalized Poisson Model

Can Indirect Estimation Methods and the Medical Officer of Health Reports 'Correct' Distorted Infant Mortality Rates Reported by the Registrar General? The Case of London, 1896–1911

2021 ◽  
pp. 57-81
Author(s):  
Sarah L Rafferty
Keyword(s):  
Infant Mortality ◽  
Medical Officer ◽  
Mortality Rates ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Place Of Residence ◽  
Indirect Estimation ◽  
Registrar General ◽  
Estimation Techniques ◽  
The Rich

The Registrar General's Returns are an integral source for historical demographers. Concerns have been raised, however, over the geographical accuracy of their pre-1911 mortality figures when institutional deaths were not redistributed to place of residence. This paper determines the extent of the distortions caused by institutional mortality in the context of aggregate infant mortality rates for London's registration sub-districts. The potential of two alternative methods to 'correct' these distortions is then assessed. The first method uses indirect estimation techniques based on data from the 1911 Fertility Census, and the second exploits the rich detail available from the Medical Officer of Health reports. Through narrowing the focus to seven London registration sub-districts over the years 1896–1911, it is shown that both suggested alternative methods remove the institutional mortality biases found in the Registrar General's figures, yet they come with their own limitations.

Conditional overdispersed models: Application to count area data

2017 ◽  
Vol 27 (10) ◽  
pp. 2964-2988
Author(s):  
Edilberto Cepeda-Cuervo ◽  
Michel Córdoba ◽  
Vicente Núñez-Antón
Keyword(s):  
Data Structure ◽  
Infant Mortality ◽  
Spatial Structure ◽  
Spatial Correlation ◽  
Health Survey ◽  
Count Data ◽  
Mortality Rates ◽  
Neighborhood Structures ◽  
The One ◽  
Area Data

This paper proposes alternative models for the analysis of count data featuring a given spatial structure, which corresponds to geographical areas. We assume that the overdispersion data structure partially results from the existing and well justified spatial correlation between geographical adjacent regions, so an extension of existing overdispersion models that include spatial neighborhood structures within a Bayesian framework is proposed. These models allow practitioners to quantify the association explained by the considered neighborhood structures and the one modelled by additional factors. Finally, using the information provided by the Colombian National Demographic and Health Survey, the usefulness of the proposed models is illustrated by fitting them to infant mortality rates and to data including the proportion of mothers who, after giving birth to their last child, underwent a postnatal screening period in Colombia.

Infant mortality in England, 1538–2000: the parish register period, 1538–1837

2019 ◽  
pp. 103-204
Author(s):  
Chris Galley
Keyword(s):  
Infant Mortality ◽  
Mortality Rates ◽  
Future Research ◽  
Principal Source ◽  
Parish Registers

This paper, the second of four, examines patterns and trends in infant mortality during the period 1538–1837 when the principal source available to examine these issues is parish registers. It explains how to calculate infant mortality rates from parish registers, identifies trends and discusses possible explanations for the patterns of change identified. The paper also shows how new estimates of infant mortality can be readily undertaken and ends with suggestions for future research.

The Joy of Statistics

2019 ◽  
Author(s):  
Steve Selvin
Keyword(s):  
Coronary Heart Disease ◽  
Short Stories ◽  
Heart Disease ◽  
Infant Mortality ◽  
Statistical Methods ◽  
Ethnic Differences ◽  
Coronary Heart Disease Risk ◽  
Disease Risk ◽  
Mortality Rates ◽  
Heart Disease Risk

The Joy of Statistics consists of a series of 42 “short stories,” each illustrating how elementary statistical methods are applied to data to produce insight and solutions to the questions data are collected to answer. The text contains brief histories of the evolution of statistical methods and a number of brief biographies of the most famous statisticians of the 20th century. Also throughout are a few statistical jokes, puzzles, and traditional stories. The level of the Joy of Statistics is elementary and explores a variety of statistical applications using graphs and plots, along with detailed and intuitive descriptions and occasionally using a bit of 10th grade mathematics. Examples of a few of the topics are gambling games such as roulette, blackjack, and lotteries as well as more serious subjects such as comparison of black/white infant mortality rates, coronary heart disease risk, and ethnic differences in Hodgkin’s disease. The statistical description of these methods and topics are accompanied by easy to understand explanations labeled “how it works.”

scholarly journals Health capabilities and the determinants of infant mortality in Brazil, 2004–2015: an innovative methodological framework

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alexandre Bugelli ◽  
Roxane Borgès Da Silva ◽  
Ladislau Dowbor ◽  
Claude Sicotte
Keyword(s):  
Infant Mortality ◽  
Educational Attainment ◽  
Health Professionals ◽  
Capita Income ◽  
Mortality Rates ◽  
Fertility Rate ◽  
Employment Rate ◽  
Infant Deaths ◽  
Bolsa Familia ◽  
Per Capita

Abstract Background Despite the implementation of a set of social and health policies, Brazil has experienced a slowdown in the decline of infant mortality, regional disparities and persistent high death levels, raising questions about the determinants of infant mortality after the implementation of these policies. The objective of this article is to propose a methodological approach aiming at identifying the determinants of infant mortality in Brazil after the implementation of those policies. Method A series of multilevel panel data with fixed effect nested within-clusters were conducted supported by the concept of health capabilities based on data from 26 Brazilian states between 2004 and 2015. The dependent variables were the neonatal, the infant and the under-five mortality rates. The independent variables were the employment rate, per capita income, Bolsa Família Program coverage, the fertility rate, educational attainment, the number of live births by prenatal visits, the number of health professionals per thousand inhabitants, and the access to water supply and sewage services. We also used different time lags of employment rate to identify the impact of employment on the infant mortality rates over time, and household income stratified by minimum wages to analyze their effects on these rates. Results The results showed that in addition to variables associated with infant mortality in previous studies, such as Bolsa Família Program, per capita income and fertility rate, other factors affect child mortality. Educational attainment, quality of prenatal care and access to health professionals are also elements impacting infant deaths. The results also identified an association between employment rate and different infant mortality rates, with employment impacting neonatal mortality up to 3 years and that a family income below 2 minimum wages increases the odds of infant deaths. Conclusion The results proved that the methodology proposed allowed the use of variables based on aggregated data that could hardly be used by other methodologies.

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