Empirical Bayes Estimates of Age-Standardized Relative Risks for Use in Disease Mapping

Biometrics ◽  
1987 ◽  
Vol 43 (3) ◽  
pp. 671 ◽  
Author(s):  
David Clayton ◽  
John Kaldor
Keyword(s):  
Empirical Bayes ◽  
Disease Mapping ◽  
Bayes Estimates ◽  
Relative Risks

scholarly journals On estimation the relative risk of small area and visualization spatio-temporal map

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoxiao Song ◽  
Yan Li ◽  
Le Cai ◽  
Wei Liu ◽  
Wenlong Cui
Keyword(s):  
Relative Risk ◽  
Small Area ◽  
Empirical Bayes ◽  
Disease Risk ◽  
Risk Estimation ◽  
Risk Index ◽  
Disease Mapping ◽  
R Software ◽  
Relative Risks ◽  
Spatio Temporal

ObjectiveThe purpose is to propose a serial of approach for estimation for disease risk for ILI in "small area" and present the risk values by spatio-temporal disease mapping or an interactive visualization with HTML format.IntroductionDisease mapping is a method used to descript the geographical variation in risk (heterogeneity of risk) and to provide the potential reason (factors or confounders) to explain the distribution. Possibly the most famous uses of disease mapping in epidemiology were the studies by John Snow of the cholera epidemics in London. Accurate estimation relative risk of small areas such as mortality and morbidity, by different age, ethnic group, interval and regions, is important for government agencies to identify hazards and mitigate disease burden. Recently, as the innovative algorithms and the available software, more and more disease risk index has been pouring out. This abstract will provide several estimation risk index, from raw incidence to model-based relative risks, and use visual approach to display them.MethodsAll the data are from a syndromic surveillance and real-time early warning system in the Yunnan province in the China. For brief introduction aim, we are using the ILI (Influenza-like illness) data in December 2017 in one county. The relative risks of disease in small area are including: raw incidence, a standardized morbidity ratio (SMR), Empirical Bayes smoothing estimation relative risk (EB-RR) and the Besag-York-Mollio model (BYM). The incidence in each small area is common used for descriptive the risk but fail to comparable directly since the different population at risk in each area. SMR is a good way to deal with this incomparability. But SMR can give rise to imprecisely estimate in areas with small populations. Empirical Bayes estimation approach has been used for smoothing purpose and can be seen as a compromise between relative risks and P-values. However, all above approaches are inept to have spatial or spatio-temporal structure in mind. BYM based the Bayesian inference can handle both the area-specific spatial structured component (such as intrinsic conditional autoregressive component) and the exchangeable random effect (unstructured component). All the analyses are implemented in the R software with INLA package (http://www.r-inla.org). The outcome of relative risk estimation with visual way and interactive maps showing are using ggplot2 and leaflet packages.Results1, the spatio-temporal raw cases of ILI from 2017/12/01 to 2017/12/31 is Fig.12. the SMR and EB-RR estimation RR of ILI are in Fig.2 and Fig.33. the most excited is the interactive visualization with HTML format for all the risk indexes is visited http://rpubs.com/ynsxx/424814 in detail. And the screenshot is Fig.4ConclusionsSmall area disease risk estimation is important for disease prevention and control. The faster function of computer with power R software can lead to advance in disease mapping, allowing for complex spatio-temporal models and communicate the results with visualization way. 

scholarly journals Empirical Bayes Estimates of Development Reliability for One Shot Devices

2009 ◽  
Vol 29 (4) ◽  
pp. 35-46 ◽  
Author(s):  
John Quigley ◽  
Tim Bedford ◽  
Lesley Walls
Keyword(s):  
Empirical Bayes ◽  
Bayes Estimates

scholarly journals Online relative risks/rates estimation in spatial and spatio-temporal disease mapping

2019 ◽  
Vol 172 ◽  
pp. 103-116 ◽  
Author(s):  
Aritz Adin ◽  
Tomás Goicoa ◽  
María Dolores Ugarte
Keyword(s):  
Disease Mapping ◽  
Relative Risks ◽  
Spatio Temporal

Empirical Bayes Estimation of Relative Risks in Disease Mapping

2002 ◽  
Vol 53 (3-4) ◽  
pp. 213-224 ◽  
Author(s):  
P. Lahiri ◽  
Tapabrata Maiti
Keyword(s):  
Empirical Bayes ◽  
Bayes Estimation ◽  
Standardized Mortality Ratio ◽  
Bayes Method ◽  
Relative Risks ◽  
Incidence And Mortality ◽  
Alternative Index ◽  
Mortality Ratio ◽  
Large Geographical Region ◽  
Measure Of Uncertainty

Standardized mortality ratio (SMR) is a popular index of incidence and mortality from diseases such as cancer. Although SMR is quite reliable to measure relative risk for a large geographical region (e.g., a country), it is unreliable for a small-area (e.g. , a district) . In this paper, we introduce an empirical Bayes method to produce an alternative index as well as its measure of uncertainty. An example is provided to demonstrate the method.

scholarly journals Empirical Bayes estimates for a two-way cross-classified model

2018 ◽  
Vol 46 (4) ◽  
pp. 1693-1720 ◽  
Author(s):  
Lawrence D. Brown ◽  
Gourab Mukherjee ◽  
Asaf Weinstein
Keyword(s):  
Empirical Bayes ◽  
Bayes Estimates

The descriptive epidemiology of suicide in Tuscany, 1988-2002

2006 ◽  
Vol 15 (3) ◽  
pp. 202-210 ◽  
Author(s):  
Michele Arcangelo Martiello ◽  
Francesco Cipriani ◽  
Fabio Voller ◽  
Eva Buiatti ◽  
Mariano Giacchi
Keyword(s):  
Spatial Analysis ◽  
Rural Communities ◽  
Mortality Rate ◽  
Empirical Bayes ◽  
Mining District ◽  
Mine Closure ◽  
Descriptive Epidemiology ◽  
Bayes Estimates ◽  
Crude Mortality Rate ◽  
Crude Mortality

SUMMARYAims – To describe the epidemiology of Suicide in Tuscany according to the triad of time, place and person. Methods – The 4, 764 cases of suicide, defined according to categories E950-E959 of ICD-9 in Tuscany over the period 1988–2002, were obtained from the Tuscan Mortality Register. Mortality indicators were calculated and analyzed. The spatial analysis was carried out by deriving Empirical Bayes Estimates for the 287 municipalities. Results – The crude mortality rate in the 2000–2002 is 7.8 per 100000 population (male: 12.4; female: 3.5). The age-standardized rate in the 2000–2002 is 5.8 per 100, 000 population (male: 9.6; female: 2.6). The highest risk for suicide, especially in the case of males, are concentrated in the southern hinterland Tuscany, in a cluster of rural municipalities that represent the old mining district of Tuscany. The SMRs according to residential municipality (population per square kilometre), confirm a greater risk of suicide for males residing in rural communities. Conclusions – The cluster of excessive mortality from suicide in Southern Tuscany could be the consequence of social determinants, related to the urban and social crisis following agriculture decline and mine closure.Declaration of Interest: none.

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