Exploring 2-1-1 service requests as potential markers for cancer control needs

Kassandra I Alcaraz, Lauren D Arnold, Katherine S Eddens, Choi Lai, Suchitra Rath, Regina Greer, Matthew W Kreuter, Kassandra I Alcaraz, Lauren D Arnold, Katherine S Eddens, Choi Lai, Suchitra Rath, Regina Greer, Matthew W Kreuter

Abstract

Background: Delivering health information and referrals through 2-1-1 is promising, but these systems need efficient ways of identifying callers at increased risk.

Purpose: This study explores the utility of using 2-1-1 service request data to predict callers' cancer control needs.

Methods: Using data from a large sample of callers (N=4101) to United Way 2-1-1 Missouri, logistic regression was used to examine the relationship between caller demographics and type of service request, and cancer control needs.

Results: Of six types of service requests examined, three were associated with one or more cancer control needs. Two of the service request types were associated also with health insurance status.

Conclusions: Findings suggest routinely collected 2-1-1 service request data may be useful in helping to efficiently identify callers with specific cancer prevention and control needs. However, to apply this approach in 2-1-1 systems across the country, further research and ongoing surveillance is necessary.

Copyright © 2012 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

References

    1. Kreuter MW, Eddens KS, Alcaraz KI, et al. Use of cancer control referrals by 2-1-1 callers: a randomized trial. Am J Prev Med. 2012;43(6S) Sxxx–Sxxx.
    1. Savas LS, Fernandez ME, et al. xxxFinal title will be added by editor at the proofing . Am J Prev Med. 2012;43(6S) Sxxx–Sxxx.
    1. Purnell JQ, Kreuter MW, Eddens KS, et al. Cancer control needs of 2-1-1 callers in Missouri, North Carolina, Texas and Washington. Journal of Health Care for the Poor and Underserved. 2012;23(2):752–767.
    1. Eddens K, Kreuter MW, Archer K. Proactive screening for health needs in United Way’s 2-1-1 information and referral service. Journal of Social Services Research. 2011;37(2):113–123.
    1. Taylor J, Raden N. Smart (enough) systems: How to deliver competitive advantage by automating hidden decisions. Prentice Hall; 2007.
    1. Hair Joe F., Jr. Knowledge creation in marketing: the role of predictive analytics. European Business Review. 2007;19(4):303–315.
    1. Paulus RA, Davis K, Steele GD. Continuous innovation in health care: Implications of the Geisinger experience. Health Affairs. 2008;27(5):1235–1245.
    1. Kamel Boulos MN, Sanfilippo AP, Corley CD, Wheeler S. Social web mining and exploitation for serious applications: Technosocial predictive analytics and related technologies for public health, environmental and national security surveillance. Computer Methods and Programs in Biomedicine. 2010;100(1):16–23.
    1. Rosengard C, Chambers D, Tulsky J, Long H, Chesney M. Value on health, health concerns and practices of women who are homeless. Women Health. 2001;34(2):29–44.
    1. Gelberg L, Browner CH, Lejano E, Arangua L. Access to women's health care: a qualitative study of barriers perceived by homeless women. Women Health. 2004;40(2):87–100.
    1. Schlossstein E, St Clair P, Connell F. Referral keeping in homeless women. J Community Health. 1991;16(6):279–285.
    1. CDC. Behavioral Risk Factor Surveillance System Survey Questionnaire. Atlanta, Georgia: DHHS, CDC; 2008.
    1. DeVoe JE, Fryer GE, Phillips R, Green L. Receipt of preventive care among adults: insurance status and usual source of care. American Journal of Public Health. 2003;93(5):786–791.

Source: PubMed

3
Tilaa