A spatial structural equation modelling framework for health count responses

Congdon, Peter; Almog, Michael; Curtis, Sarah; Ellerman, Raymond

doi:10.1002/sim.2921

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…Liu et al [16] and Wang and Wall [17] generalize this application to the exponential family of distributions. Congdon et al [18] extended the generalized spatial structure equation models to incorporate spatially-structured and unstructured random effects at the measurement level.…”

Section: Introductionmentioning

confidence: 99%

Using hospitalization for ambulatory care sensitive conditions to measure access to primary health care: an application of spatial structural equation modeling

Hossain

Laditka

2009

Int J Health Geogr

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BackgroundIn data commonly used for health services research, a number of relevant variables are unobservable. These include population lifestyle and socio-economic status, physician practice behaviors, population tendency to use health care resources, and disease prevalence. These variables may be considered latent constructs of many observed variables. Using health care data from South Carolina, we show an application of spatial structural equation modeling to identify how these latent constructs are associated with access to primary health care, as measured by hospitalizations for ambulatory care sensitive conditions. We applied the confirmatory factor analysis approach, using the Bayesian paradigm, to identify the spatial distribution of these latent factors. We then applied cluster detection tools to identify counties that have a higher probability of hospitalization for each of the twelve adult ambulatory care sensitive conditions, using a multivariate approach that incorporated the correlation structure among the ambulatory care sensitive conditions into the model.ResultsFor the South Carolina population ages 18 and over, we found that counties with high rates of emergency department visits also had less access to primary health care. We also observed that in those counties there are no community health centers.ConclusionLocating such clusters will be useful to health services researchers and health policy makers; doing so enables targeted policy interventions to efficiently improve access to primary care.

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Section: Introductionmentioning

confidence: 99%

Using hospitalization for ambulatory care sensitive conditions to measure access to primary health care: an application of spatial structural equation modeling

Hossain

Laditka

2009

Int J Health Geogr

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“…In cases where clear spatial causal relationships cannot be justified at all scales, it may be preferable to optimize the SE-SEM for a single lag distance as described in Description of the method: Fit and evaluate SEM models for each lag distance bin. The SE-SEM methodology outlined in this paper differs substantially from the alternative spatial SEM approaches available in the literature (Wang and Wall 2003, Liu et al 2005, Congdon et al 2007, Oud and Folmer 2008, Congdon 2010. Those methods have not achieved widespread usage in the natural sciences.…”

Section: Discussionmentioning

confidence: 99%

“…One common approach is to incorporate a distance measure as an observed or latent variable in a standard SEM model (Bailey and Krzanowski 2012). Other methods have largely been developed for health and sociometric data aggregated by administrative districts such as counties or city wards such as the modeling of spatially structured residuals accounting for relationships between adjacent districts (Congdon et al 2007, Oud and Folmer 2008, Congdon 2010. Also proposed are an extension of the common factor model to include neighborhood information (Wang and Wall 2003) and a hierarchical extension for simultaneous modeling of relationships between latent variables while accounting for spatial relationships (Liu et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Spatially explicit structural equation modeling

Lamb

Mengersen

Stewart

et al. 2014

Ecology

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Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE‐SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE‐SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.

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“…[6][7][8] Por esta razón se ha planteado en la literatura una serie de alternativas, incluidos el uso de la regresión binomial negativa, 9 los modelos de clase latente 5 o los modelos de ecuaciones estructurales. 10 En este último sentido, el objetivo de este trabajo es describir algunas de las alternativas estadísticas disponibles para el análisis de las variables de conteo y comparar los distintos modelos expuestos para evidenciar sus ventajas y desventajas, todo ello en el contexto general del uso de los servicios de salud.…”

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“…No obstante, hay tres que en particular vale la pena mencionar. Primero, y sin dejar de reconocer la complejidad vinculada con el uso de servicios de salud, se ha hecho uso de los modelos de ecuaciones estructurales,10,22,23 los cuales han mostrado cierta flexibilidad para modelar las relaciones de causalidad entre las variables relacionadas con el uso de servicios de salud. En segundo lugar aparecen los modelos de clase latente o mezclas finitas, los cuales se han aplicado también en el estudio del uso de servicios de salud 24,25.…”

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