A spatiotemporal quantile regression model for emergency department expenditures

Neelon, Brian; Li, Fan; Burgette, Lane F.; Neelon, Sara E. Benjamin

doi:10.1002/sim.6480

Cited by 17 publications

(14 citation statements)

References 38 publications

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“…Spatial quantile regression has been studied by other papers, both from Bayesian and frequentist perspectives, generally with continuous responses such as house prices, house rentals, or medical expenditures (e.g. [11], [12]). Reich et al [13] adopt a spatial Bayesian approach to a continuous (positive) response, namely maximum ozone readings.…”

Section: Methods Overview and Literaturementioning

confidence: 99%

Quantile Regression for Area Disease Counts: Bayesian Estimation using Generalized Poisson Regression

Congdon

2017

ijsmr

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Generalized linear models based on Poisson regression are commonly applied to count data for area morbidity outcomes, focused on modelling the conditional mean of the response as a function of a set of risk factors. Mean regression models may be sensitive to outliers and provide no information on other distributional features of the response. We consider instead a Poisson lognormal hierarchical approach to quantile regression of spatially configured count data, allowing for observed risk factors and spatially correlated unobserved risk factors. This technique has the advantage that a profile of the relative outcome risk across quantiles can be obtained, including estimates of uncertainty (e.g. the uncertainty attaching to 2.5% or 5% relative risk quantiles). An application involves counts of emergency hospitalisations for self-harm for 6791 small areas in England. Known risk factors are area deprivation, a measure of social fragmentation and a measure of rural status. It is shown that impact of these predictors varies between quantiles, and that hierarchical quantile regression generally produces narrower risk intervals, except for outlier areas, and leads to a higher number of areas being classed as high risk.Keywords: Hierarchical quantile regression. Relative risk. Risk intervals. Elevated risk. Self-harm.By contrast, quantile regression estimates the relationship between the ! th quantile Q Y (! | X) of the

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Section: Methods Overview and Literaturementioning

confidence: 99%

Quantile Regression for Area Disease Counts: Bayesian Estimation using Generalized Poisson Regression

Congdon

2017

ijsmr

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“…The effect of spatial dependence on disease transmission is commonly measured using spatial models [28][29][30][31][32] including the conditional autoregressive, geographically weighted regression, hierarchical Bayesian, and Moran's I models. These models measure the effect of spatial dependence on disease transmission using the distance between the origin and the destination.…”

Section: Introductionmentioning

confidence: 99%

Modelling input-output flows of severe acute respiratory syndrome in mainland China

Wang

et al. 2016

BMC Public Health

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Background: Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear. Methods: We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows. Results: The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destinationbased spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.

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“…Under the Bayesian framework, results equivalent to those found in Equation 2.2 can be obtained by placing an asymmetric Laplace distribution (ALD) on the error terms and flat, improper priors on each of the elements of boldβτ (Lee and Neocleous, 2010; Neelon et al, 2015), though diffuse normal priors, which will be used here, are commonly implemented as well. The ALD was first proposed in this context by Yu and Moyeed (2001), and its appropriateness as an error distribution for QR has been extensively addressed since, even for cases where the true errors do not follow the ALD (Sriram et al, 2013).…”

Section: Quantile Regression Methodsmentioning

confidence: 99%

A Bayesian two-part quantile regression model for count data with excess zeros

King

Song

2018

Statistical Modelling

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Quantile regression (QR) allows one to model the effect of covariates across the entire response distribution, rather than only at the mean, but QR methods have been almost exclusively applied to continuous response variables produced by a single data-generating process. Of the few studies that have performed QR on count data, none have accounted for excess zeros from a Bayesian perspective, as does the hurdle model that we propose. In this article, we propose a Bayesian two-part QR model for count data with excess zeros. The proposed model is compared to a frequentist approach via simulation, and its usefulness is displayed on two real datasets. In each application, multiple covariates are found to have differing effects across the response distribution, with special attention given to the nature of those effects in the outermost response distribution quantiles.

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A spatiotemporal quantile regression model for emergency department expenditures

Cited by 17 publications

References 38 publications

Quantile Regression for Area Disease Counts: Bayesian Estimation using Generalized Poisson Regression

Quantile Regression for Area Disease Counts: Bayesian Estimation using Generalized Poisson Regression

Modelling input-output flows of severe acute respiratory syndrome in mainland China

A Bayesian two-part quantile regression model for count data with excess zeros

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