Skip to main content
SpringerLink
Log in

Factors Associated with Collaboration Among Agencies Serving Children with Complex Chronic Conditions

  • Published:
Maternal and Child Health Journal Aims and scope Submit manuscript

Abstract

Our objective was to identify agency-level factors that increase collaborative relationships between agencies that serve children with complex chronic conditions (CCC). We hypothesized that an agency will collaborate with more partners in the network if the agency had a coordinator and participated in a community coalition. We surveyed representatives of 63 agencies that serve children with CCC in Forsyth County, North Carolina about their agencies’ collaborations with other agencies. We used social network analytical methods and exponential random graph analysis to identify factors associated with collaboration among agencies. The unit of analysis was the collaborative tie (n = 3,658) between agencies in the network. Agencies participating in a community coalition were 1.5 times more likely to report collaboration than agencies that did not participate in a coalition. Presence of a coordinator in an agency was not associated with the number of collaborative relationships. Agencies in existence for a longer duration (≥11 vs. ≤10 years; adjusted odds ratio (aOR): 2.1) and those with a higher proportion of CCC clientele (aOR: 2.1 and 1.6 for 11–30 % and ≥31 % compared to ≤10 %) had greater collaboration. Care coordination agencies and pediatric practices reported more collaborative relationships than subspecialty clinics, home-health agencies, durable medical equipment companies, educational programs and family-support services. Collaborative relationships between agencies that serve children with CCC are increased by coalition participation, longer existence and higher CCC clientele. Future studies should evaluate whether interventions to improve collaborations among agencies will improve clinical outcomes of children with CCC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. McPherson, M., Weissman, G., Strickland, B. B., et al. (2004). Implementing community-based systems of services for children and youths with special health care needs: How well are we doing? Pediatrics, 113(5 Suppl), 1538–1544.

    PubMed  Google Scholar 

  2. Child and Adolescent Health Measurement Initiative. (2005). National Survey of Children’s Health, Data Resource Center on Child and Adolescent Health website. www.nschdata.org. Accessed 05 July 2006.

  3. Kuo, D. Z., Cohen, E., Agrawal, R., et al. (2011). A national profile of caregiver challenges among more medically complex children with special health care needs. Archives of Pediatrics and Adolescent Medicine, 165(11), 1020–1026. doi:10.1001/archpediatrics.2011.172.

    Article  PubMed  Google Scholar 

  4. Feudtner, C., Christakis, D. A., & Connell, F. A. (2000). Pediatric deaths attributable to complex chronic conditions: A population-based study of Washington State, 1980–1997. Pediatrics, 106(1 Pt 2), 205–209.

    PubMed  CAS  Google Scholar 

  5. Srivastava, R., Stone, B. L., & Murphy, N. A. (2005). Hospitalist care of the medically complex child. Pediatric Clinics of North America, 52(4), 1165–1187. doi:10.1016/j.pcl.2005.03.007.

    Article  PubMed  Google Scholar 

  6. Cohen, E., Kuo, D. Z., Agrawal, R., et al. (2011). Children with medical complexity: An emerging population for clinical and research initiatives. Pediatrics, 127(3), 529–538. doi:10.1542/peds.2010-0910.

    Article  PubMed  Google Scholar 

  7. Buescher, P. A., Whitmire, J. T., Brunssen, S., et al. (2006). Children who are medically fragile in North Carolina: Using Medicaid data to estimate prevalence and medical care costs in 2004. Maternal and Child Health Journal, 10(5), 461–466. doi:10.1007/s10995-006-0081-3.

    Article  PubMed  Google Scholar 

  8. Berry, J. G., Hall, D. E., Kuo, D. Z., et al. (2011). Hospital utilization and characteristics of patients experiencing recurrent readmissions within children’s hospitals. Journal of the American Medical Association, 305(7), 682–690. doi:10.1001/jama.2011.122.

    Article  PubMed  CAS  Google Scholar 

  9. Simon, T. D., Berry, J., Feudtner, C., et al. (2010). Children with complex chronic conditions in inpatient hospital settings in the United States. Pediatrics, 126(4), 647–655. doi:10.1542/peds.2009-3266.

    Article  PubMed  Google Scholar 

  10. Neff, J. M., Sharp, V. L., Muldoon, J., et al. (2004). Profile of medical charges for children by health status group and severity level in a Washington State Health Plan. Health Series Research, 39(1), 73–89. doi:10.1111/j.1475-6773.2004.00216.x.

    Article  Google Scholar 

  11. Taylor, E., Lake, T., Nysenbaum, J., Peterson, G., & Myers, D. (2011). Coordinated care in the medical neighborhood: Critical components and available mechanisms. White paper (prepared by mathematica policy research under contract no. HHSA290200900019I TO2). AHRQ Publication No. 11-0064. Rockville, MD: Agency for Healthcare Research and Quality.

  12. U.S. Department of Health and Human Services. Office of Disease Prevention and Health Promotion. Healthy People 2020. Washington, DC. Available at [Specific URL]. Accessed 26 April 2012.

  13. Scott, J., Tallia, A., Crosson, J. C., et al. (2005). Social network analysis as an analytic tool for interaction patterns in primary care practices. Annals of Family Medicine, 3(5), 443–448.

    Article  PubMed  Google Scholar 

  14. Kwait, J., Valente, T. W., & Celentano, D. D. (2001). Interorganizational relationships among HIV/AIDS service organizations in Baltimore: A network analysis. Journal of Urban Health, 78(3), 468–487.

    Article  PubMed  CAS  Google Scholar 

  15. Keating, N. L., Ayanian, J. Z., Cleary, P. D., et al. (2007). Factors affecting influential discussions among physicians: A social network analysis of a primary care practice. Journal of General Internal Medicine, 22(6), 794–798.

    Article  PubMed  Google Scholar 

  16. Hawe, P., Webster, C., & Shiell, A. (2004). A glossary of terms for navigating the field of social network analysis. Journal of Epidemiology and Community Health, 58(12), 971–975.

    Article  PubMed  Google Scholar 

  17. Farmer, J. E., Clark, M. J., Sherman, A., et al. (2005). Comprehensive primary care for children with special health care needs in rural areas. Pediatrics, 116(3), 649–656.

    Article  PubMed  Google Scholar 

  18. Palfrey, J. S., Sofis, L. A., Davidson, E. J., et al. (2004). The pediatric alliance for coordinated care: Evaluation of a medical home model. Pediatrics, 113(5 Suppl), 1507–1516.

    PubMed  Google Scholar 

  19. Kreuter, M. W., Lezin, N. A., & Young, L. A. (2000). Evaluating community-based collaborative mechanisms: Implications for practitioners. Health Promotion Practice, 1(1), 47–61.

    Article  Google Scholar 

  20. National Hospice and Palliative Care Organization. (2004). Caring for kids: How to Develop a Home-Based Support Program for Children and Adolescents with Life-Threatening Conditions. Alexandria, VA: National Hospice and Palliative Care Organization.

    Google Scholar 

  21. Nageswaran, S., Ip, E., Golden, & S. L., et al. (2012). Inter-agency collaboraiton in the care of children with complex chronic conditions. Academic Pediatrics.

  22. U.S. Census Bureau (2010). State and County QuickFacts. Data derived from Population Estimates, American Community Survey, Census of Population and Housing, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits, Consolidated Federal Funds Report. http://quickfacts.census.gov/qfd/states/00000.html. Accessed 26 April 2012.

  23. Friedman, S. R., Reynolds, J., Quan, M. A., et al. (2007). Measuring changes in interagency collaboration: An examination of the Bridgeport safe start initiative. Evaluation and Program Planning, 30(3), 294–306.

    Article  PubMed  Google Scholar 

  24. Network Genie. (2011). Tanglewood research incorporated. https://secure.networkgenie.com/.

  25. Luke, D. A., & Harris, J. K. (2007). Network analysis in public health: History, methods, and applications. Annual Review of Public Health, 28, 69–93.

    Article  PubMed  Google Scholar 

  26. Hanneman, R., & Riddle, M. (2005). Inroduction to social network methods. University of California, Riverside, Riverside, California. http://www.faculty.ucr.edu/~hanneman/nettext/. Accessed 25 August 2008.

  27. UCINET 6: Social Network Software. (2008). Analytic technologies. http://www.analytictech.com/ucinet/ucinet.htm. Accessed 25 August 2008.

  28. Goodreau, S. M., Handcock, M., Hunter, D. R., Butts, C. T., & Morris, M. (2008). A statnet tutorial. Journal of Statistical Software, 24(8), 1–26.

    PubMed  Google Scholar 

  29. Hunter, D. R., Handcock, M., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). ERGM: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software, 24(3), 1–29.

    PubMed  Google Scholar 

  30. Granner, M. L., & Sharpe, P. A. (2004). Evaluating community coalition characteristics and functioning: A summary of measurement tools. Health Education Research, 19(5), 514–532.

    Article  PubMed  CAS  Google Scholar 

  31. Care coordination in the medical home. (2005). Integrating health and related systems of care for children with special health care needs. Pediatrics, 116(5), 1238–1244.

    Article  Google Scholar 

  32. Cott, C. (1997). “We decide, you carry it out”: A social network analysis of multidisciplinary long-term care teams. Social Science and Medicine, 45(9), 1411–1421.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

Funding for the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript was provided by NICHD R21HD061793; PI: Nageswaran.

Author information

Authors and Affiliations

  1. Department of Pediatrics, Wake Forest School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC, 27157, USA

    Savithri Nageswaran, Shannon L. Golden & T. Michael O’Shea

  2. Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Savithri Nageswaran, Douglas Easterling, T. Michael O’Shea & Edward H. Ip

  3. Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Edward H. Ip

  4. Tanglewood Research Inc., Greensboro, NC, USA

    William B. Hansen

Authors
  1. Savithri Nageswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shannon L. Golden
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Douglas Easterling
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Michael O’Shea
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. William B. Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Edward H. Ip
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Savithri Nageswaran.

Appendix: Survey Questions

Appendix: Survey Questions

 

  1. 1.

    In the past year, with which of the following agencies has your agency collaborated in providing services for children with complex chronic conditions? By collaboration, we mean any relationship that involves exchanging information, sharing resources, and/or coordinating services for the benefit of children with complex chronic conditions. [Select all that apply. Please do not select your own agency.] [Main Question—List of all actors in the network]

  2. 2.

    Do the following agencies serve as a resource for your agency in your role as a service provider to children with complex chronic conditions? “Serving as a resource” might include: providing care-related information, offering advice or consultation on how to serve a client or family etc. [Matrix of agencies identified in Q1 with “Yes” and “No” options]

  3. 3.

    Does your agency serve as a resource to the following agencies as they provide services to children with complex-chronic conditions? [Matrix of agencies identified in Q1 with “Yes” and “No” options]

  4. 4.

    Has your agency referred children with complex chronic conditions to the following agencies in the past year? [Matrix of agencies identified in Q1 with “Yes” and “No” options]

  5. 5.

    Has your agency received referral about children with complex chronic conditions from the following agencies in the past year? [Matrix of agencies identified in Q1 with “Yes” and “No” options]

  6. 6.

    With which of these agencies would you like to collaborate more in order to effectively provide services for children with complex chronic conditions? [List of all actors in the network]

  7. 7.

    What is your title in your agency? [Text Box]

  8. 8.

    Please select the item that best describes your agency: [Drop down list: “private for-profit”; “private non-profit”; “government”; “other”]

  9. 9.

    How long has your agency been serving children? [Drop down list: less than 5 years, 6–10 years, 11–20 years, more than 20 years]

  10. 10.

    On average, how many children does your agency serve each week? [Drop down list: <10, 11–20, 21–50, 51–100, >100]

  11. 11.

    How many staff members are directly involved in providing services to children? [Drop down menu: <5, 6–10, 11–20, 21–30, more than 30]

  12. 12.

    Is there a designated person in your agency whose job it is to collaborate with other agencies on behalf of clients in your agency? [Drop down list: “yes” “no” “unsure”]

  13. 13.

    About what percentage of clients in your agency are children with complex chronic conditions? We define children with complex chronic conditions as children who are medically fragile, dependent on technology, or have a life-limiting condition. [Drop down list: <1 %; 1–10 %; 11–20 %; 21–30 %; and >31 %]

  14. 14.

    In which of the following community coalitions do representatives from your agency participate? Select all that apply.

    1. (a)

      Pediatric Community Alliance (PCA)

    2. (b)

      Local Collaborative Team

    3. (c)

      Local Interagency Coordinating Council (LICC)

    4. (d)

      Forsyth Adolescent Health Coalition

    5. (e)

      Forsyth County Healthy Carolinians Coalition

    6. (f)

      Infant Mortality Reduction Coalition

    7. (g)

      Lead Poisoning Prevention Coalition

    8. (h)

      School Health Alliance

    9. (i)

      Forsyth Futures

    10. (j)

      Safe Kids Coalition

    11. (k)

      Other coalitions not listed here

    12. (l)

      None

    13. (m)

      Don’t know

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nageswaran, S., Golden, S.L., Easterling, D. et al. Factors Associated with Collaboration Among Agencies Serving Children with Complex Chronic Conditions. Matern Child Health J 17, 1533–1540 (2013). https://doi.org/10.1007/s10995-012-1032-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10995-012-1032-9

Keywords

Search

Navigation