| Titre : | Using Hospitalization for Ambulatory Care Sensitive Conditions to Measure Access to Primary Health Care: an Application of Spatial Structural Equation Modeling. |
| Titre original: | Utilisation de l'hospitalisation pour des conditions propices aux soins ambulatoires comme indicateur de l'accès aux soins de santé primaire: une application de la modélisation par équations structurelles du territoire. |
| Auteurs : | M.M. HOSSAIN ; J.N. LADIKTA |
| Type de document : | Article |
| Dans : | INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS (8, 01/01/2009) |
| Article en page(s) : | 1-8, tabl., graph., fig., carte |
| Note générale : | Référence : Réf. bib. |
| Langues: | Anglais |
| Catégories : |
[BDSP5] Etudes méthodes et statistiques [NI] > Méthodologie [BDSP5] Etudes méthodes et statistiques [NI] > Méthodologie > Evaluation > Indicateur [BDSP5] Etudes méthodes et statistiques [NI] > Méthodologie > Modèle [BDSP5] Géographie > Géographie humaine > Disparité régionale [BDSP5] Géographie > Géographie humaine > Distance [BDSP5] Géographie politique > Monde > Amérique > Amérique du Nord > Etats Unis [BDSP5] Information sanitaire > Mesure santé > Indicateur santé [BDSP5] Politique santé > Planification sanitaire [BDSP5] Système soins > Accès soins [BDSP5] Système soins > Filière soins > Soins hospitaliers > Hospitalisation [BDSP5] Système soins > Santé communautaire > Soins santé primaire [BDSP5] Thérapeutique > Thérapeutique chirurgicale > Intervention chirurgicale |
| Résumé : | In 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. For 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. Locating 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. |
| En ligne : | http://www.ij-healthgeographics.com/content/8/1/51 |