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      Construction of a Multisite DataLink Using Electronic Health Records for the Identification, Surveillance, Prevention, and Management of Diabetes Mellitus: The SUPREME-DM Project

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          Abstract

          Introduction

          Electronic health record (EHR) data enhance opportunities for conducting surveillance of diabetes. The objective of this study was to identify the number of people with diabetes from a diabetes DataLink developed as part of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) project, a consortium of 11 integrated health systems that use comprehensive EHR data for research.

          Methods

          We identified all members of 11 health care systems who had any enrollment from January 2005 through December 2009. For these members, we searched inpatient and outpatient diagnosis codes, laboratory test results, and pharmaceutical dispensings from January 2000 through December 2009 to create indicator variables that could potentially identify a person with diabetes. Using this information, we estimated the number of people with diabetes and among them, the number of incident cases, defined as indication of diabetes after at least 2 years of continuous health system enrollment.

          Results

          The 11 health systems contributed 15,765,529 unique members, of whom 1,085,947 (6.9%) met 1 or more study criteria for diabetes. The nonstandardized proportion meeting study criteria for diabetes ranged from 4.2% to 12.4% across sites. Most members with diabetes (88%) met multiple criteria. Of the members with diabetes, 428,349 (39.4%) were incident cases.

          Conclusion

          The SUPREME-DM DataLink is a unique resource that provides an opportunity to conduct comparative effectiveness research, epidemiologic surveillance including longitudinal analyses, and population-based care management studies of people with diabetes. It also provides a useful data source for pragmatic clinical trials of prevention or treatment interventions.

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          Most cited references18

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          Full Accounting of Diabetes and Pre-Diabetes in the U.S. Population in 1988–1994 and 2005–2006

          OBJECTIVE—We examined the prevalences of diagnosed diabetes, and undiagnosed diabetes and pre-diabetes using fasting and 2-h oral glucose tolerance test values, in the U.S. during 2005–2006. We then compared the prevalences of these conditions with those in 1988–1994. RESEARCH DESIGN AND METHODS—In 2005–2006, the National Health and Nutrition Examination Survey included a probability sample of 7,267 people aged ≥12 years. Participants were classified according to glycemic status by interview for diagnosed diabetes and by fasting and 2-h glucoses measured in subsamples. RESULTS—In 2005–2006, the crude prevalence of total diabetes in people aged ≥20 years was 12.9%, of which ∼40% was undiagnosed. In people aged ≥20 years, the crude prevalence of impaired fasting glucose was 25.7% and of impaired glucose tolerance was 13.8%, with almost 30% having either. Over 40% of individuals had diabetes or pre-diabetes. Almost one-third of the elderly had diabetes, and three-quarters had diabetes or pre-diabetes. Compared with non-Hispanic whites, age- and sex-standardized prevalence of diagnosed diabetes was approximately twice as high in non-Hispanic blacks (P < 0.0001) and Mexican Americans (P = 0.0001), whereas undiagnosed diabetes was not higher. Crude prevalence of diagnosed diabetes in people aged ≥20 years rose from 5.1% in 1988–1994 to 7.7% in 2005–2006 (P = 0.0001); this was significant after accounting for differences in age and sex, particularly in non-Hispanic blacks. Prevalences of undiagnosed diabetes and pre-diabetes were generally stable, although the proportion of total diabetes that was undiagnosed decreased in Mexican Americans. CONCLUSIONS—Over 40% of people aged ≥20 years have hyperglycemic conditions, and prevalence is higher in minorities. Diagnosed diabetes has increased over time, but other conditions have been relatively stable.
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            Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data.

            To optimize methods for identifying patients with diabetes based on computerized records and to obtain best estimates of diabetes prevalence in Department of Veterans Affairs (VA) patients. The VA Diabetes Epidemiology Cohort (DEpiC) is a linked national database of all VA patients since 1998 with data from VA medical visits, Medicare claims, pharmacy and laboratory records, and patient surveys. Using DEpiC, we examined concordance of diabetes indicators, including ICD-9-CM codes (250.xx), prescription drug treatment, HbA(1c) tests, and patient self-report. We determined the optimal criterion for identifying diabetes and used it in estimating diabetes prevalence in the VA. The best criterion was a prescription for a diabetes medication in the current year and/or 2+ diabetes codes from inpatient and/or outpatient visits (VA and Medicare) over a 24-month period. This definition had high sensitivity (93%) and specificity (98%) against patient self-report, and reasonable rates of HbA(1c) testing (75%). HbA(1c) testing alone added few additional cases, and a single diagnostic code added many patients, but without confirmation (reduced specificity). However, including codes from Medicare was critical. Applying this definition for 1998-2000, we identified an average of 500,000 VA patients with diabetes per year. We also estimated high and increasing diabetes prevalence rates of 16.7% in FY1998, 18.6% in FY1999, and 19.6% in FY2000 and an incidence estimated to be approximately 2% per year. Development and evaluation of methodology for analyzing computerized patient data can improve the identification of patients with diabetes. The increasing high prevalence of diabetes in VA patients will present challenges for clinicians and health system management.
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              Identifying persons with diabetes using Medicare claims data.

              The objective of this study was to develop and validate a method for identifying Medicare beneficiaries with diabetes by using Medicare claims data. We used self-reports of diabetes status from participants in the Medicare Current Beneficiary Survey to determine disease status, and then we examined these participants' Medicare claims. Using self-reported diabetes status as the "gold standard," we determined the sensitivity, specificity, and reliability of claims data in identifying beneficiaries with diabetes. We found that to construct a method that is adequately sensitive (> or = 70%), highly specific (> or = 97.5%), and reliable (kappa > or = 0.80), researchers must combine information from different types of Medicare claims files, use 2 years of data to identify cases, and require at least 2 diagnoses of diabetes among claims involving ambulatory care. Since these criteria are met by more than one method, the choice of method should be governed by the goals of the research as well as more practical concerns.
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                Author and article information

                Journal
                Prev Chronic Dis
                Prev Chronic Dis
                PCD
                Preventing Chronic Disease
                Centers for Disease Control and Prevention
                1545-1151
                2012
                07 June 2012
                : 9
                : E110
                Affiliations
                [1]Jay Desai, Patrick J. O’Connor, HealthPartners Research Foundation, Minneapolis, Minnesota; Jennifer Elston Lafata, Henry Ford Health System, Detroit, Michigan; Jean M. Lawrence, Kaiser Permanente Southern California, Pasadena, California; Ram D. Pathak, Marshfield Clinic, Marshfield, Wisconsin; Marsha A. Raebel, John F. Steiner, Kaiser Permanente Colorado, Denver, Colorado; Robert J. Reid, Group Health Research Institute, Seattle, Washington; Joseph V. Selby, Patient Centered Outcomes Research Institute, Washington, DC; Barbara G. Silverman, Maccabi Healthcare Services, Tel Aviv, Israel; W.F. Stewart, Geisinger Health System, Danville, Pennsylvania; Suma Vupputuri, Kaiser Permanente Georgia, Atlanta, Georgia; Beth Waitzfelder, Kaiser Permanente Hawaii, Honolulu, Hawaii. Dr Elston Lafata is also affiliated with Virginia Commonwealth University, Richmond, Virginia.
                Author notes
                Gregory A. Nichols, PhD, Kaiser Permanente Center for Health Research, 3800 N Interstate Ave, Portland, OR 97227. Telephone: 503-335-6733. E-mail: Greg.nichols@ 123456kpchr.org .
                Article
                11_0311
                10.5888/pcd9.110311
                3457753
                22677160
                6289828e-ce2f-452a-8d45-b86f6c1e96bb
                History
                Categories
                Original Research

                Health & Social care
                Health & Social care

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