Quality of Diabetes Care Among Cancer Survivors With Diabetes

Background: Among patients with chronic medical conditions, unrelated conditions are often undertreated. Objective: To compare the quality of diabetes care delivered to diabetic patients with and without cancer in a large regional integrated delivery system. Design: Observational cohort study using propensity score methods to control for baseline differences between diabetic patients with and without a history of cancer. Subjects: A total of 5773 Kaiser Northern California members with diabetes and previous cancer and 23,092 members with diabetes and no previous cancer. Measures: Nine measures of diabetes technical quality and clinical outcomes in 2003. Results: Relative to diabetic patients without cancer, those with cancer had higher adjusted rates of HbA1c testing (66.3% vs. 64.4%; P = 0.02), HbA1c control (73.4% vs. 70.9%; P < 0.001), and urine microalbumin testing (59.1% vs. 55.2%; P < 0.001) but lower rates of low-density lipoprotein (LDL) cholesterol control (40.7% vs. 42.2%; P = 0.02) and statin use if LDL >100 mg/dL (76.7% vs. 80.6%; P < 0.001). The groups had similar rates of LDL cholesterol testing, dilated retinal examinations, blood pressure control, and angiotensin converting enzyme (ACE) inhibitor use for hypertension (all P ≥ 0.20). Conclusions: Despite the potential for cancer-related services to compete with delivery of diabetes care, diabetic patients with cancer received care of generally similar quality relative to diabetic patients without cancer in this integrated delivery system. Nevertheless, the quality of diabetes care delivered to all patients could be improved, particularly the control of LDL cholesterol and blood pressure. Combining data from electronic disease registries has the potential for monitoring quality of care delivered to patients with more than 1 major medical illness.

[1]  Jian-Min Yuan,et al.  Diabetes mellitus and risk of colorectal cancer in the Singapore Chinese Health Study. , 2006, Journal of the National Cancer Institute.

[2]  B. Zinman,et al.  Diabetes mellitus and breast cancer: a retrospective population-based cohort study , 2006, Breast Cancer Research and Treatment.

[3]  N. Keating,et al.  Physical and Mental Health Status of Older Long‐Term Cancer Survivors , 2005, Journal of the American Geriatrics Society.

[4]  S. Larsson,et al.  Diabetes mellitus and risk of colorectal cancer: a meta-analysis. , 2005, Journal of the National Cancer Institute.

[5]  R. D'Agostino.  Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .

[6]  J. Samet,et al.  Fasting serum glucose level and cancer risk in Korean men and women. , 2005, JAMA.

[7]  C. Earle,et al.  Under use of necessary care among cancer survivors , 2004, Cancer.

[8]  C. Carney,et al.  Receipt of Diabetes Services by Insured Adults With and Without Claims for Mental Disorders , 2004, Medical care.

[9]  Ellen Meara,et al.  Impact of Medicare coverage on basic clinical services for previously uninsured adults. , 2003, JAMA.

[10]  E. Winer,et al.  Quality of non-breast cancer health maintenance among elderly breast cancer survivors. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  J. Meyerhardt,et al.  Impact of diabetes mellitus on outcomes in patients with colon cancer. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  G. Imbens,et al.  Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization , 2001, Health Services and Outcomes Research Methodology.

[13]  J Oh,et al.  Diabetes Quality Improvement Project. , 1999, Pennsylvania medicine.

[14]  D. Redelmeier,et al.  The treatment of unrelated disorders in patients with chronic medical diseases. , 1998, The New England journal of medicine.

[15]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[16]  J. Selby,et al.  Excess Costs of Medical Care for Patients With Diabetes in a Managed Care Population , 1997, Diabetes Care.

[17]  Toni Martin,et al.  Physician and Patient Prevention Practices in NIDDM in a Large Urban Managed-Care Organization , 1995, Diabetes Care.

[18]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[19]  P. Rosenbaum.  Model-Based Direct Adjustment , 1987 .

[20]  D. Rubin,et al.  Reducing Bias in Observational Studies Using Subclassification on the Propensity Score , 1984 .

[21]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[22]  Gwyn McClelland.  Survivors , 1891, The Hospital.

[23]  B. McDowell,et al.  National Committee for Quality Assurance. , 2004, Social work.

[24]  John Van Hoewyk,et al.  A multivariate technique for multiply imputing missing values using a sequence of regression models , 2001 .

[25]  J. Selby,et al.  First 20 months' experience with use of metformin for type 2 diabetes in a large health maintenance organization. , 1999, Diabetes care.

[26]  B. Vladeċk.  From the Health Care Financing Administration. , 1994, JAMA.

[27]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.