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Matern Child Health J. 2012 Apr;16 Suppl 1:S170-7. doi: 10.1007/s10995-012-1006-y.

Measuring the impact of Hurricane Katrina on access to a personal healthcare provider: the use of the National Survey of Children's Health for an external comparison group.

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1
National Center for Disaster Preparedness, Mailman School of Public Health, Columbia University, 215 W. 125th Street, Suite 303, New York, NY 10027, USA. nas51@columbia.edu

Abstract

This paper examined the effect of Hurricane Katrina on children's access to personal healthcare providers and evaluated the use of propensity score methods to compare a nationally representative sample of children, as a proxy for an unexposed group, with a smaller exposed sample. 2007 data from the Gulf Coast Child and Family Health (G-CAFH) Study, a longitudinal cohort of households displaced or greatly impacted by Hurricane Katrina, were matched with 2007 National Survey of Children's Health (NSCH) data using propensity score techniques. Propensity scores were created using poverty level, household educational attainment, and race/ethnicity, with and without the addition of child age and gender. The outcome was defined as having a personal healthcare provider. Additional confounders (household structure, neighborhood safety, health and insurance status) were also examined. All covariates except gender differed significantly between the exposed (G-CAFH) and unexposed (NSCH) samples. Fewer G-CAFH children had a personal healthcare provider (65 %) compared to those from NSCH (90 %). Adjusting for all covariates, the propensity score analysis showed exposed children were 20 % less likely to have a personal healthcare provider compared to unexposed children in the US (OR = 0.80, 95 % CI 0.76, 0.84), whereas the logistic regression analysis estimated a stronger effect (OR = 0.28, 95 % CI 0.21, 0.39). Two years after Hurricane Katrina, children exposed to the storm had significantly lower odds of having a personal health care provider compared to unexposed children. Propensity score matching techniques may be useful for combining separate data samples when no clear unexposed group exists.

PMID:
22456985
DOI:
10.1007/s10995-012-1006-y
[Indexed for MEDLINE]
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