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Diagnostics
Original research
Oregon’s approach to leveraging system-level data to guide a social determinants of health-informed approach to children’s healthcare
  1. Colleen P Reuland1,
  2. Jon Collins2,
  3. Lydia Chiang1,
  4. Valerie Stewart2,
  5. Aaron C Cochran3,
  6. Christopher W Coon2,
  7. Deepti Shinde2,
  8. Dana Hargunani2
  1. 1 Oregon Pediatric Improvement Partnership, Oregon Health & Science University Doernbecher Children's Hospital, Portland, Oregon, USA
  2. 2 Department of Human Services, Oregon Health Authority, Salem, Oregon, USA
  3. 3 Office of Reporting, Research, Analytics, and Implementation, Oregon Department of Human Services, Salem, Oregon, USA
  1. Correspondence to Colleen P Reuland, Oregon Pediatric Improvement Partnership, Oregon Health & Science University Doernbecher Children's Hospital, Portland, OR 97239, USA; reulandc{at}ohsu.edu

Abstract

Background Children’s health and healthcare use are impacted by both medical conditions and social factors, such as their home and community environment. As healthcare systems manage a pediatric population, information about these factors is crucial to providing quality care coordination.

Methods The authors developed a novel methodology combining medical complexity (using the Pediatric Medical Complexity Algorithm) and social complexity (using available family social factors known to impact a child’s health and healthcare use) to create a new health complexity model at both the population-level and individual-level. System-level data from Oregon’s Medicaid Management Information Systems and Integrated Client Services database was analysed, examining claims data and service utilization, to calculate the health complexity of children enrolled in Medicaid/Child Health Insurance Program (CHIP) across Oregon.

Results Of the 390 582 children ages 0 to 17 enrolled in Medicaid/CHIP in Oregon from July 2015 to June 2016, 83.4% (n=325 900) had some level of medical and/or social complexity and 22.1% (n=85 839) had health complexity (both medical and social complexity). Statistically significant (p<0.05) differences in health complexity were observed among attributed patients by Oregon’s 16 Coordinated Care Organizations, as well as by a child’s age, county of residence and race/ethnicity.

Conclusions Given the high proportion of children with health complexity, these findings demonstrate that a large number of Medicaid/CHIP-insured children could benefit from targeted care coordination and differential resource allocation. Reports have been shared with state, county and health system leaders to drive work across the state. This paper describes the collaborative process necessary for other states considering similar work.

  • health services research
  • pediatrics
  • child health
  • population
  • socioeconomic factors
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjinnov-2020-000452

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    Footnotes

    • Contributors CPR led the conceptualisation and development of this paper and was involved in all components of the article. She was the Principal Investigator on efforts specifically focussed on the development of the health complexity methods and supporting health system use of the metrics. JC oversaw the OHA Health Analytics team that conducted the medical complexity analysis and disseminated the reports, provided suggestions for edits to the article and approved the inclusion of the data findings and information about OHA uses of the data. LC co-led the development of the article structure and framework and led the writing of specific components of the article. LC was involved in all aspects of OPIP-led efforts. VS provided suggestions for edits to the document and oversaw the team within OHA Health Analytics who worked on the health complexity data. ACC provided suggestions for edits to the document and led the work within Office of Reporting, Research, Analytics and Implementation that related to the use of the ICS data. CWC provided suggestions for edits to the document and was a member of the OHA Health Analytics team. DS provided suggestions for edits to the document and was a member of the OHA Health Analytics team. DH provided suggestions for edits to the article, approved the inclusion of the data findings and information about OHA uses of the data and led several policy efforts and presentations to policymakers described in the article. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

    • Funding This study was funded by Lucile Packard Foundation for Children’s Health.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available in a public, open access repository. Data are publicly available on the Oregon Health Authority website.