A spatial analysis of mental retardation of unknown cause and maternal residence during pregnancy

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Huiling Zhen
Andrew B. Lawson *
Suzanne McDermott
Archana Pande Lamichhane
Marjorie Aelion
(*) Corresponding Author:
Andrew B. Lawson | alawson@gwm.ec.edu


Spatial analysis is useful for the identification of areas of elevated risk of adverse health outcomes and generation of hypotheses. Identification of clusters based on maternal residence during pregnancy provides an important tool to investigate risk exposures. However, even though mental retardation (MR) is a substantial public health problem, there are no previous analyses of spatial clustering of childhood MR using individual case data. In this paper, we examine the use of the Bayesian hierarchical modeling approach in the analysis of MR clustering. We used data from South Carolina Medicaid and birth certificates, in which address codes for each month of pregnancy are available. MR cases with unknown cause were identified in the study population. A Bayesian local likelihood cluster modeling technique was applied to compute the relative risk of MR and its corresponding P-value for each geo-coded location, and the P-value surface was contoured as a heat image to identify the MR clusters. The characteristics of the study population were analyzed using chi-square tests and the results confirm that clustering does occur for MR. The shapes of the identified MR clusters were found to be irregular and the observed MR rate in the identified MR cluster area was found to be double the rate for the larger South Carolina region. The descriptive analysis of study population characteristics showed that the children with MR were more likely to be male and had mothers who were older than 34 years at the time of birth as well as being African American, preterm and of low birth weight compared to children without MR.

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