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


RESULTS
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.


CONCLUSIONS
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.
Hossain
Md Monir
MM
Biostatistics, Epidemiology and Research Design (BERD) Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, UT Professional Building, Room 1100.25, 6410 Fannin Street, Houston, TX 77030, USA. monir.hossain@uth.tmc.edu
Laditka
James N
JN
D000328:Adult; D000553:Ambulatory Care / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D004632:Emergency Medical Services / Q000706:statistics & numerical data; D040362:Geographic Information Systems; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data; D011320:Primary Health Care / Q000706:statistics & numerical data*; D013022:South Carolina / Q000453:epidemiology