Insurance Continuity and Receipt of Diabetes Preventive Care in Oregon’s Community Health Centers

Research Team Jen DeVoe, Oregon Health & Science University Dept of Family Medicine Rachel Gold, Kaiser Permanente Center for Health Research Amit Shah, Multnomah County Health Department Susan Chauvie, Our Community Health Information Network

NATIONAL CONTEXT Community Health Centers (CHCs) provide crucial services to the uninsured and underinsured. To help CHCs improve services to vulnerable populations, in partnership with state and local governments, novel evaluation techniques and research methods are needed.

OREGON CONTEXT Our Community Health Information Network (OCHIN), a non-profit collaboration of CHCs, manages collective “real time” electronic health data. Each patient has a unique OCHIN identifier; records are linked across clinics and patients can be tracked.

BROAD RESEARCH OBJECTIVES To develop and refine methods for studying OCHIN data. To demonstrate how the study of this data could be used to inform state policy discussions.

SPECIFIC RESEARCH OBJECTIVE To assess the associations between continuity of insurance coverage and the likelihood of receiving diabetes preventive care services among adult patients in the OCHIN system.

METHODS – Data Source Our Community Health Information Network (OCHIN) 100 agencies, 300,000 clients, >800,000 annual visits. Complete set of EPICSystems Practice Management data available from all sites, starting in Direct link with state public insurance enrollment!!!!

METHODS – Study Population Adults (aged >18 years) visiting OCHIN clinics with diabetes. Two-year period ( ) Two visits associated with common ICD-9 code for diabetes mellitus 6,127 adults

METHODS – Study Variables Outcome Variables – four recommended diabetic preventive services, received at least once in 2005 (orders & billing data) Glycated hemoglobin (HbA1c) Lipid screening (LDL) Influenza vaccination (flu shot) Nephropathy screening (urine microalbumin)

METHODS – Study Variables Primary Independent Variable – insurance continuity (assessed quarterly) Uninsured all year Insured all year Partially insured Covariates – age, gender, race/ethnicity, household income.

METHODS – Analysis Descriptives Bivariate and Multivariate Logistic Regression Models SAS version 9.0

RESULTS—Descriptives Insurance Coverage, 2005 OCHIN Population N = 116,859 OCHIN Diabetics N=6,127 Continuously Insured35.4%53.5% Partially Insured18.3%16.2% Uninsured All Year46.3%30.3%

RESULTS—Descriptives Insurance Coverage, 2005 Total N Micro- albumin Flu shotLDLHbA1c Continuously Insured 3, %34.9%37.5% 53.4% Partially Insured %31.8%34.0%50.8% Uninsured All Year 1, %26.6%34.5%56.5% Total 6, %31.9%36.0%53.9%

RESULTS—Multivariate Analyses Insurance Coverage, 2005 Micro- albumin Adj OR (95% CI)* Flu Shot Adj OR (95% CI)* LDL Adj OR (95% CI)* HbA1c Adj OR (95% CI)* Continuously Insured 1.47 (1.25, 1.74) 1.60 (1.39, 1.85) 1.24 (1.08, 1.43) 1.02 (0.88, 1.18) Partially Insured 1.24 (1.01, 1.53) 1.26 (1.05, 1.51) 0.95 (0.80, 1.13) 0.77 (0.65,0.92) Uninsured All Year 1.00 *Adjusted Odds Ratio (95% Confidence Interval); adjusted for age, gender, race/ethnicity, household income.

RESULTS—Multivariate Analyses Partial Insurance Coverage in 2005 Micro- albumin Adj OR (95% CI)* Flu Shot Adj OR (95% CI)* LDL Adj OR (95% CI)* HbA1c Adj OR (95% CI)* 7-9 months coverage 0.85 ( ) 0.89 ( ) 0.75 ( ) 1.10 ( ) 4-6 months coverage 0.85 ( ) 0.85 ( ) 0.74 ( ) 1.05 ( ) 1-3 months coverage 1.00 *Adjusted Odds Ratio (95% Confidence Interval); adjusted for age, gender, race/ethnicity, household income.

SUMMARY CHCs care for a large percentage of uninsured and underinsured patients. Even with crucial access to CHCs, uninsured and partially insured diabetic patients had lower rates of preventive services, compared to those with continuous coverage.

POLICY IMPLICATIONS Safety net clinics mitigate some but not all of the disadvantage from being uninsured. Discontinuous insurance coverage may contribute to disrupted care.

POLICY IMPLICATIONS Policy efforts must continue to strengthen the healthcare safety net delivery system while simultaneously creating sustainable solutions for healthcare financing.

POLICY IMPLICATIONS Networks such as OCHIN can be developed by states to build collective datasets to facilitate policy-relevant research.  Some unique OCHIN features: One patient record across several clinics Link to state public insurance data Compatibility with SAS software

ACKNOWLEDGEMENTS Dr. Gold received support from the Oregon Clinical and Translational Research Institute (OCTRI), grant number UL1 RR from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Dr. DeVoe is currently funded by a K08 Mentored Clinical Scientist Award, grant number K08 HS from the Agency for Healthcare Research and Quality (AHRQ).