PURPOSE Electronic Health Record (EHR) databases in community health centers (CHCs) present new opportunities for quality improvement, comparative effectiveness, and health policy research. We aimed (1) to create individual-level linkages between EHR data from a network of CHCs and Medicaid claims from 2005 through 2007; (2) to examine congruence between these data sources; and (3) to identify sociodemographic characteristics associated with documentation of services in one data set vs the other. METHODSWe studied receipt of preventive services among established diabetic patients in 50 Oregon CHCs who had ever been enrolled in Medicaid (N = 2,103). We determined which services were documented in EHR data vs in Medicaid claims data, and we described the sociodemographic characteristics associated with these documentation patterns. RESULTSIn 2007, the following services were documented in Medicaid claims but not the EHR: 11.6% of total cholesterol screenings received, 7.0% of total infl uenza vaccinations, 10.5% of nephropathy screenings, and 8.8% of tests for glycated hemoglobin (HbA 1c ). In contrast, the following services were documented in the EHR but not in Medicaid claims: 49.3% of cholesterol screenings, 50.4% of infl uenza vaccinations, 50.1% of nephropathy screenings, and 48.4% of HbA 1c tests. Patients who were older, male, Spanish-speaking, above the federal poverty level, or who had discontinuous insurance were more likely to have services documented in the EHR but not in the Medicaid claims data.CONCLUSIONS Networked EHRs provide new opportunities for obtaining more comprehensive data regarding health services received, especially among populations who are discontinuously insured. Relying solely on Medicaid claims data is likely to substantially underestimate the quality of care.

Background Patients receive care in safety net clinics regardless of insurance status; however, diabetes preventive care receipt might vary in patients with differing levels of insurance continuity. Methods In a retrospective cohort study, using electronic health record data from adults with diabetes receiving care in 50 safety net clinics in Oregon in 2005–2007, we conducted adjusted logistic regressions to model the associations between amount of time with insurance and rates of receipt of lipid screening, influenza vaccination, nephropathy screening (urine microalbumin), and DM control (glycosylated hemoglobin) screening. Results Of 3,384 adults with diabetes, 711 were ‘partially’ insured (covered 1–99% of the 3-year study period), 909 had no coverage, and 1,764 were continuously insured. In adjusted models, persons with partial or no coverage during the 3-year study period were less likely to receive most preventive services, compared to those with continuous coverage. We found no evidence of a dose-response relationship with increasing duration of coverage, nor of a threshold amount of partial coverage, associated with better receipt of care. Conclusions Safety net clinic patients need both access to primary care and continuous insurance. All patients with partial coverage, regardless of the extent of time with insurance, had lower odds of receiving preventive care.

The Children's Health Insurance Program Reauthorization Act of 2009 (CHIPRA) includes provisions for identifying standardized pediatric care quality measures. These 24 "CHIPRA measures" were designed to be evaluated by using claims data from health insurance plan populations. Such data have limited ability to evaluate population health, especially among uninsured people. The rapid expansion of data from electronic health records (EHRs) may help address this limitation by augmenting claims data in care quality assessments. We outline how to operationalize many of the CHIPRA measures for application in EHR data through a case study of a network of >40 outpatient community health centers in 2009-2010 with a single EHR. We assess the differences seen when applying the original claims-based versus adapted EHR-based specifications, using 2 CHIPRA measures (Chlamydia screening among sexually active female patients; BMI percentile documentation) as examples. Sixteen of the original CHIPRA measures could feasibly be evaluated in this dataset. Three main adaptations were necessary (specifying a visit-based population denominator, calculating some pregnancy-related factors by using EHR data, substituting for medication dispense data). Although it is feasible to adapt many of the CHIPRA measures for use in outpatient EHR data, information is gained and lost depending on how numerators and denominators are specified. We suggest first steps toward application of the CHIPRA measures in uninsured populations, and in EHR data. The results highlight the importance of considering the limitations of the original CHIPRA measures in care quality evaluations.

Methods: A mailed survey collected demographic and lifestyle characteristics. Descriptive analyses and multivariable logistic regression, adjusting for demographics, were performed to evaluate the survey data. Results: The CMHS cohort is comprised of WNH (62%, 51,909/84,170), 14% (11,407/84,170) Hispanic, 8% (6,298/84,170) African-American, 11% (8,705/84,170) Asian/Pacific Islander, and 5% (6,733/84,170) other/mixed men. Of the 8,705 Asian/Pacific Islanders, 602 identified themselves as Asian-Indian. Although most Asian-Indian men were first generation immigrants (94%, 568/602), over three-fourths had resided in the US for 16+ years. Age distribution did not differ between Asian-Indian and WNH men. Asian-Indians were more likely than WNHs to live in a low income household (22%, 134/602 vs. 15%, 7,963/51,901), yet had considerably higher educational attainment (77% v 53%, with college degree). AsianIndian men more often reported a healthy BMI (18.5-24.9) [Adjusted Odds Ratio (AOR) = 1.83 (95% CI 1.54-2.18)] and more often consumed <30% calories from fat [AOR = 2.57 (95% CI 2.13-3.11)]. There were no differences for fruit and vegetable consumption; however, Asian-Indian men were more likely to have never smoked and to abstain from alcohol. While Asian-Indian men were less likely to report moderate/vigorous physical activity > 3.5 hours/week [AOR = 0.54 (95% CI 0.46-0.64)], there was little difference in sedentary activity time spent outside of work. Conclusion: Despite a higher prevalence of CVD among Asian Indian men, in the CMHS we found Asian-Indian men had fewer CVD-related lifestyle risk factors. These results suggest risk factors other than lifestyle behaviors may be major contributors to CVD in the Asian Indian population.