Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-17T15:20:39.830Z Has data issue: false hasContentIssue false
  • English
  • Français

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]
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Bannay, H., Jarus, T., Jongbloed, L., Yazigi, M. and Dean, E. 2013. Culture as a variable in health research: perspectives and caveats. Health Promotion International, 29, 1, 1–9.Google ScholarPubMed
Amy, N. K., Aalborg, A., Lyons, P. and Keranen, L. 2006. Barriers to routine gynecological cancer screening for White and African-American obese women. International Journal of Obesity, 30, 1, 147–55.CrossRefGoogle ScholarPubMed
Andersen, R. 1968. A Behavioral Model of Families' Use of Health Services. Research Series Number 25, Center for Health Administration Studies, University of Chicago, Chicago.Google Scholar
Ayalon, L. and King-Kallimanis, L. 2010. Trading years for perfect health: results from the Health and Retirement Study. Journal of Aging and Health, 22, 8, 1184–97.CrossRefGoogle ScholarPubMed
Bailey, E. J. 1987. Sociocultural factors and health care-seeking behavior among Black Americans. Journal of the National Medical Association, 79, 4, 389–392.Google ScholarPubMed
Bernstein, A. 2004. Health Care in America: Trends in Utilization. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, Maryland.Google Scholar
Bernstein, R. and Edwards, T. 2008. An older and more diverse nation by midcentury. US Census Bureau News, 14.Google Scholar
Bhandari, A. and Wagner, T. 2006. Self-reported utilization of health care services: improving measurement and accuracy. Medical Care Research and Review, 63, 2, 217–35.CrossRefGoogle ScholarPubMed
Bibb, S. C., Padden, D. L. and Ferguson, W. 2012. Moderators of access and utilization of clinical preventive services in older adults. Journal of Advanced Nursing, 68, 2, 335–48.CrossRefGoogle Scholar
Blanchard, J. and Lurie, N. 2005. Preventive care in the United States: are blacks finally catching up? Ethnicity & Disease, 15, 3, 498–504.Google ScholarPubMed
Brown, I., Thompson, J., Tod, A. and Jones, G. 2006. Primary care support for tackling obesity: a qualitative study of the perceptions of obese patients. British Journal of General Practice: The Journal of the Royal College of General Practitioners, 56, 530, 666–72.Google ScholarPubMed
Cabana, M. D. and Jee, S. H. 2004. Does continuity of care improve patient outcomes? Journal of Family Practice, 53, 12, 974–80.Google ScholarPubMed
Cernin, P. A., Cresci, K., Jankowski, T. B. and Lichtenberg, P. A. 2010. Reliability and validity testing of the short-form health survey in a sample of community-dwelling African American older adults. Journal of Nursing Measurement, 18, 1, 49–59.CrossRefGoogle Scholar
Cheak-Zamora, N. C., Wyrwich, K. W. and McBride, T. D. 2009. Reliability and validity of the SF-12v2 in the medical expenditure panel survey. Quality of Life Research, 18, 6, 727–35.CrossRefGoogle ScholarPubMed
Chen, J. Y., Diamant, A., Pourat, N. and Kagawa-Singer, M. 2005. Racial/ethnic disparities in the use of preventive services among the elderly. American Journal of Preventive Medicine, 29, 5, 388–95.CrossRefGoogle ScholarPubMed
Cho, Y. I., Arozullah, A. M., Crittenden, K. S. and Lee, S.-Y. D. 2008. Effects of health literacy on health status and health service utilization amongst the elderly. Social Science and Medicine, 66, 8, 1809–16.CrossRefGoogle ScholarPubMed
Cohen, B. 2004. Introduction. In Anderson, N. B., Bulatao, R. B. and Cohen, B. (eds), Critical Perspectives on Racial and Ethnic Differences in Health in Late Life. National Academies Press, Washington DC, 1–24.Google Scholar
Cohen, S. B. and Wun, L.-M. 2005. A comparison of household and medical provider reported health care utilization and an estimation strategy to correct for response error. Journal of Economic and Social Measurement, 30, 2, 115–26.CrossRefGoogle Scholar
Corbie-Smith, G., Flagg, E. W., Doyle, J. P. and O'Brien, M. A. 2002. Influence of usual source of care on differences by race/ethnicity in receipt of preventive services. Journal of General Internal Medicine, 17, 6, 458–64.CrossRefGoogle Scholar
DeNavas-Walt, C., Proctor, B. D., Smith, J. C. and US Census Bureau 2011. Income, Poverty, and Health Insurance Coverage in the United States: 2010. US Census Bureau, Washington DC.Google Scholar
DeVoe, J. E., Fryer, G. E., Phillips, R. and Green, L. 2003. Receipt of preventive care among adults: insurance status and usual source of care. American Journal of Public Health, 93, 5, 786–91.CrossRefGoogle ScholarPubMed
Drury, C. A. and Louis, M. 2002. Exploring the association between body weight, stigma of obesity, and health care avoidance. Journal of the American Academy of Nurse Practitioners, 14, 12, 554–61.CrossRefGoogle ScholarPubMed
Dunlop, D. D., Manheim, L. M., Song, J. and Chang, R. W. 2002. Gender and ethnic/racial disparities in health care utilization among older adults. Journals of Gerontology, 57, 4, 221–33.CrossRefGoogle ScholarPubMed
Escarce, J. J., Epstein, K. R., Colby, D. C. and Schwartz, J. S. 1993. Racial differences in the elderly's use of medical procedures and diagnostic tests. American Journal of Public Health, 83, 7, 948–54.CrossRefGoogle ScholarPubMed
Escarce, J. J. and Mcguire, T. G. 2004. Changes in racial differences in use of medical procedures and diagnostic tests among elderly persons: 1986–1997. American Journal of Public Health, 94, 10, 1795–1799.CrossRefGoogle ScholarPubMed
Fan, J. X., Wen, M., Jin, L. and Wang, G. 2013. Disparities in healthcare utilization in China: do gender and migration status matter? Journal of Family and Economic Issues, 34, 1, 52–63.CrossRefGoogle Scholar
Flower, L. and Nonnemaker, L. 2010. Improvements to Medicare's Preventive Services Under Health Reform. AARP Public Policy Institute, Washington DC.Google Scholar
Franks, P., Lubetkin, E. I., Gold, M. R. and Tancredi, D. J. 2003. Mapping the SF-12 to preference-based instruments: convergent validity in a low-income, minority population. Medical Care, 41, 11, 1277–83.CrossRefGoogle Scholar
Greene, W. 2008. Functional forms for the negative binomial model for count data. Economics Letters, 99, 3, 585–90.CrossRefGoogle Scholar
Hanmer, J., Lawrence, W. F., Anderson, J. P., Kaplan, R. M. and Fryback, D. G. 2006. Report of nationally representative values for the noninstitutionalized US adult population for 7 health-related quality-of-life scores. Medical Decision Making, 26, 4, 391–400.CrossRefGoogle ScholarPubMed
Hao, L. and Naiman, D. Q. 2007. Quantile Regression. Sage, Thousand Oaks, California.CrossRefGoogle Scholar
Hartley, D. 2004. Rural health disparities, population health, and rural culture. American Journal of Public Health, 94, 10, 1675–1678.CrossRefGoogle ScholarPubMed
Jakobsson, U. 2007. Using the 12-item Short Form health survey (SF-12) to measure quality of life among older people. Aging Clinical and Experimental Research, 19, 6, 457–64.CrossRefGoogle ScholarPubMed
Koenker, R. 2004. Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91, 1, 74–89.CrossRefGoogle Scholar
Koenker, R. 2005. Quantile Regression. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Koenker, R. and Bassett, G. Jr. 1978. Regression quantiles. Econometrica, 46, 1, 33–50.CrossRefGoogle Scholar
Koenker, R. and Geling, O. 2001. Reappraising medfly longevity: a quantile regression survival analysis. Journal of the American Statistical Association, 96, 454, 458–68.CrossRefGoogle Scholar
Lambrew, J. M., DeFreise, G. H., Carey, T. S., Ricketts, T. C. and Biddle, A. K. 1996. The effects of having a regular doctor on access to primary care. Medical Care, 34, 2, 138–51.CrossRefGoogle ScholarPubMed
Larson, C. O., Schlundt, D., Patel, K., Hargreaves, M. and Beard, K. 2008. Validity of the SF-12 for use in a low-income African American community-based research initiative (REACH 2010). Preventing Chronic Disease, 5, 2, A44.Google Scholar
Machado, J. and Santos Silva, J. 2005. Quantiles for counts. Journal of the American Statistical Association, 100, 472, 1226–37.CrossRefGoogle Scholar
McLaughlin, L. A. and Braun, K. L. 1998. Asian and Pacific Islander cultural values: considerations for health care decision making. Health & Social Work, 23, 2, 116–26.CrossRefGoogle ScholarPubMed
Mills, A., Ataguba, J. E., Akazili, J., Borghi, J., Garshong, B., Makawia, S., Mtei, G., Harris, B., Macha, J., Meheus, F. and McIntyre, D. 2012. Equity in financing and use of health care in Ghana, South Africa, and Tanzania: implications for paths to universal coverage. The Lancet, 380, 9837, 126–33.CrossRefGoogle ScholarPubMed
Miranda, A. 2006. Qcount: Stata Program to Fit Quantile Regression Models for Count Data. Statistical Software Components, Boston College Software, Boston.Google Scholar
Ogden, L. L., Richards, C. L. and Shenson, D. 2012. Clinical preventive services for older adults: the interface between personal health care and public health services. American Journal of Public Health, 102, 3, 419–25.CrossRefGoogle ScholarPubMed
Pearson, E. S. 1950. On questions raised by the combination of tests based on discontinuous distributions. Biometrika, 37, 3/4, 383–98.CrossRefGoogle ScholarPubMed
Pleis, J. R., Ward, B. W., Lucas, J. W., National Health Interview Survey US and National Center for Health Statistics US 2010. Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2009: Data from the National Health Interview Survey. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, Maryland.Google ScholarPubMed
Puhl, R. M. and Heuer, C. A. 2009. The stigma of obesity: a review and update. Obesity, 17, 5, 941–64.CrossRefGoogle ScholarPubMed
Saultz, J. W. and Lochner, J. 2005. Interpersonal continuity of care and care outcomes: a critical review. Annals of Family Medicine, 32, 2, 159–66.CrossRefGoogle Scholar
Schoen, C., Osborn, R., Doty, M. M., Bishop, M., Peugh, J. and Murukutla, N. 2007. Toward higher-performance health systems: adults’ health care experiences in seven countries, 2007. Health Affairs, 26, 6, w717–34.CrossRefGoogle ScholarPubMed
Sharma, A. 2009 a. Using count models to examine later-life health utilization. Conference presentation at the 74th annual meeting of the Population Association of America, Detroit, Michigan. April 30–May 2, 2009.Google Scholar
Sharma, A. 2009 b. Spatial Distribution of Disability across the US. Conference presentation at the CDC Prevention Research Centers–Healthy Aging Research Network (PRC–HAN) 2009 annual conference at the University of North Carolina, Chapel Hill, North Carolina. September 15–16, 2009.Google Scholar
Shenson, D. and Adams, M. 2008. The Vote and Vax program: public health at polling places. Journal of Public Health Management and Practice, 14, 5, 476–80.CrossRefGoogle ScholarPubMed
Shenson, D., Anderson, L., Slonim, A. and Benson, W. 2012. Vaccinations and preventive screening services for older adults: opportunities and challenges in the USA. Perspectives in Public Health, 132, 4, 165–70.CrossRefGoogle ScholarPubMed
Sox, C. M., Swartz, K., Burstin, H. R. and Brennan, T. A. 1998. Insurance or a regular physician: which is the most powerful predictor of health care? American Journal of Public Health, 88, 3, 364–70.CrossRefGoogle ScholarPubMed
Stevens, W. L. 1950. Fiducial limits of the parameter of a discontinuous distribution. Biometrika, 37, 1/2, 117–29.CrossRefGoogle ScholarPubMed
Trivedi, P. and Munkin, M. 2010. Recent developments in cross section and panel count models. In Handbook of Empirical Economics and Finance, 87–131.Google Scholar
United States (US) Census Bureau 2010. Profile America: Facts for Feature. Black African-American History Month: February 2011. CB11-FF.01, US Census Bureau, Washington DC.Google Scholar
Walker, K. O., Steers, N., Liang, L. J., Morales, L. S., Forge, N., Jones, L. and Brown, A. F. 2010. The vulnerability of middle-aged and older adults in a multiethnic, low-income area: contributions of age, ethnicity, and health insurance. Journal of the American Geriatrics Society, 58, 12, 2416–22.CrossRefGoogle Scholar
Ware, J. E. Jr, Kosinski, M. and Keller, S. D. 1996. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Medical Care, 34, 3, 220–33.CrossRefGoogle ScholarPubMed
White, K. M. 2002. Longevity advances in high-income countries, 1955–96. Population and Development Review, 28, 1, 59–76.CrossRefGoogle Scholar
Winkelmann, R. 2004. Health care reform and the number of doctor visits – an econometric analysis. Journal of Applied Econometrics, 19, 4, 455–72.CrossRefGoogle Scholar
Winkelmann, R. 2006. Reforming health care: evidence from quantile regressions for counts. Journal of Health Economics, 25, 1, 131–45.CrossRefGoogle ScholarPubMed
Winkelmann, R. 2008. Econometric Analysis of Count Data. Springer, Berlin.Google Scholar
Wolinsky, F. D., Mosely, R. R. 2nd and Coe, R. M. 1986. A cohort analysis of the use of health services by elderly Americans. Journal of Health and Social Behavior, 27, 3, 209–19.CrossRefGoogle ScholarPubMed
Xu, K. T. 2002. Usual source of care in preventive service use: a regular doctor versus a regular site. Health Services Research, 37, 6, 1509–29.CrossRefGoogle ScholarPubMed