Smoothing Regional Maps Using Empirical Bayes Predictors

Cressie, Noel

doi:10.1111/j.1538-4632.1992.tb00253.x

Cited by 92 publications

(37 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test for spatial autocorrelation among observed county hospitalization rates, a special Moran's I statistic for empirical Bayes smoothed rates of hospitalizations was used (see Assunção and Reis, 1999). Since counties with small populations and low (positive) hospitalization counts can have relatively high hospitalization rates, empirical Bayes smoothing was used to deflate the weight of counties with relatively small populations to avoid misleading information due to extremely high rates from the instability of variance in hospitalization rates (Cressie, 1992;Bailey and Gatrell, 1995;Anselin et al, 2004).…”

Section: Discussionmentioning

confidence: 99%

Ecological factors associated with pandemic influenza A (H1N1) hospitalization rates in California, USA: a geospatial analysis

Maliszewski

Wei

2011

Geospat Health

View full text Add to dashboard Cite

Abstract. The 2009 H1N1 influenza A virus subtype (H1N1) pandemic had a large impact in the United States of America (USA), causing an estimated 192,000 to 398,000 hospitalizations and 8,720 to 18,050 deaths between April 2009 and midMarch 2010. Recent research on the 2009 H1N1 pandemic has largely focused on individual, non-spatial demographic characterizations (e.g. age and race/ethnicity) associated with H1N1 hospitalizations. Broader ecological factors such as transportation use, land use and other socioeconomic factors are important aspects of influenza studies that have not been empirically examined. This research explores and identifies ecological factors associated with 2009 H1N1 pandemic hospitalization rates. We conducted a spatial regression analysis of county level hospitalization rates from 3 April to 15 September, 2009 obtained via the California Department of Public Health. Hospitalization rates were found to be spatially dependent. Public transportation usage rates and agricultural land use proportions were significant environmental factors positively related to hospitalization rates. Consistent with public health official's assumptions and existing evidence, county percentages of persons less than 18 years of age were positively associated with hospitalization. These findings help to clarify the limited consensus and dubious evidence on the role of broader ecological factors associated with pandemic influenza. A better understanding of the ecological risk factors associated with hospitalizations should also benefit public health officials with respect to their work aiming at improving emergency supply allocation and non-pharmaceutical intervention strategies in the context of an influenza pandemic.

show abstract

Section: Discussionmentioning

confidence: 99%

Ecological factors associated with pandemic influenza A (H1N1) hospitalization rates in California, USA: a geospatial analysis

Maliszewski

Wei

2011

Geospat Health

View full text Add to dashboard Cite

show abstract

“…Much of this work has been inspired by the search for valid tests for the clustering of cases of rare diseases; see, for example Stone (1988), Cuzik and Edwards (1990), and Cressie (1992). Besag and Newell (1991) draw a useful distinction between general and focused tests.…”

Section: Introductionmentioning

confidence: 99%

Local Spatial Autocorrelation Statistics: Distributional Issues and an Application

Ord¹,

Getis

1995

Geographical Analysis

2,792

1,426

View full text Add to dashboard Cite

show abstract

“…The w sj denotes for spatial contiguity matrix arranged based on Queen Method, which is defined as w sj = 1 if sandjare neighbor and w sj = 0 for others [6,15,16].It is assumed that β 0 has a flat distribution, β p has a normal distribution with zero mean, τ β is the parameter precision One of techniques to obtain conjugate prior v s (CAR) of (1) is to standardize into adjency matrix [3,4,13,27]. Then the CAR model in(1)…”

Section: The Convolution Model and Its Extensionmentioning

confidence: 99%

“…Initially, researchers used standardized mortality ratio (SMR) to analyze RR epidemic without consideration to environmental heterogeneity effects, for example in [6,7]. However, [14,18]treated the RR epidemic analysis as a complex problem by including many uncertainty factors.…”

Section: Introductionmentioning

confidence: 99%

Extended convolution model to bayesian spatio-temporal for diagnosing the DHF endemic locations

Mukhsar

Abapihi

Sani

et al. 2016

Journal of Interdisciplinary Mathematics

View full text Add to dashboard Cite

We develop a Bayesian spatio-temporal model based on a spatial Poisson-Lognormal convolution model. The convolution model consists of two component, i.e.; heterogeneity (fixed effects) and uncertainty (local and global random effects). We introduce an additional component in the development model so-called temporal trend. The developed model is adjusted based on dengue hemorrhagic fever (DHF) characteristics data. The DHF cases are considered as two-level hierarchical data since the cases are measured at smallest locations (as level 1) and the locations are nested to larger area (as level 2). Because the data are discrete with non-normal distribution, the development model is expressed as a generalized linear model. The parameters of the model are estimated using its full conditional distribution, respectively, by applying Bayesian Markov Chain Monte Carlo (MCMC) computational *technique. We apply the model to analyze the endemic locations of DHF cases in the city of Kendari, Southeast Sulawesi, Indonesia. The developed model can be well identifying the DHF prone locations in the city more than the convolution model. In this model we use two predictors, i.e. rainfall and population density, and both of them are statistically significant. Applying the model to the real data, the results show that Kadia and Puwatu districts are endemic locations. The best time to intervene the DHF risk in the both locations to prevent the spread to other locations was in the month of January

show abstract

Smoothing Regional Maps Using Empirical Bayes Predictors

Cited by 92 publications

References 25 publications

Ecological factors associated with pandemic influenza A (H1N1) hospitalization rates in California, USA: a geospatial analysis

Ecological factors associated with pandemic influenza A (H1N1) hospitalization rates in California, USA: a geospatial analysis

Local Spatial Autocorrelation Statistics: Distributional Issues and an Application

Extended convolution model to bayesian spatio-temporal for diagnosing the DHF endemic locations

Contact Info

Product

Resources

About