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Modelling disparities in health services utilisation for older Blacks: a quantile regression framework

Published online by Cambridge University Press:  16 June 2014

ANDY SHARMA
ANDY SHARMA*
Affiliation:
University of Denver, Social Sciences Division, Policy Studies, Colorado, USA.
*
Address for correspondence: Andy Sharma, 2100 South University Blvd., University of Denver, Denver, CO 80208, USA. E-mail: [email protected]
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Abstract

With the on-going ageing of the United States population, resolving health disparities continues to be a prominent and worthwhile goal, particularly in the areas of promoting minority health and reducing racial/ethnic disparities. This analysis employs the 2004 and 2005 Household Component records from the Medical Expenditures Panel Survey, which correspond to data files H89 and H97, to examine utilisation by race across the entire distribution function; more specifically, applying the behavioural model of health services utilisation and employing a Quantile Regression (QR) framework. This is a noteworthy contribution because the conditional mean may not be the best approximation for a skewed-location distribution. In contrast, QR is robust to outliers and scale effects since the estimation minimises least absolute deviation. The sample consists of 2,525 older adults at least 65 years of age with 303 corresponding to Black and 2,222 corresponding to White. Results suggest older Blacks continue to utilise health services (i.e. office or clinic visits with a physician or medical provider) at lower levels and this is more pronounced at and below the median quantile (i.e. below the 50th cut-off). Usual source of care (USC) continues to play an important role. Beliefs surrounding the need for insurance and medical intervention are also significant and explain some of the racial disparities. Although utilisation disparities persist for older Blacks, collaborative and flexible models of care can reach this group.

Keywords

modelling health disparitiescount modelsregression and scale effects
Type
Articles
Information
Ageing & Society , Volume 35 , Issue 8 , September 2015 , pp. 1657 - 1683
Copyright
Copyright © Cambridge University Press 2014 

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