Dataset Information

Screening for impaired fasting glucose and diabetes using available health plan data.

ABSTRACT:

Aims

To develop and validate prediction equations to identify individuals at high risk for type 2 diabetes using existing health plan data.

Methods

Health plan data from 2005 to 2009 from 18,527 members of a Midwestern HMO without diabetes, 6% of whom had fasting plasma glucose (FPG) ?110mg/dL, and health plan data from 2005 to 2006 from 368,025 members of a West Coast-integrated delivery system without diabetes, 13% of whom had FPG ?110mg/dL were analyzed. Within each health plan, we used multiple logistic regression to develop equations to predict FPG ?110mg/dL for half of the population and validated the equations using the other half. We then externally validated the equations in the other health plan.

Results

Areas under the curve for the most parsimonious equations were 0.665 to 0.729 when validated internally. Positive predictive values were 14% to 32% when validated internally and 14% to 29% when validated externally.

Conclusion

Multivariate logistic regression equations can be applied to existing health plan data to efficiently identify persons at higher risk for dysglycemia who might benefit from definitive diagnostic testing and interventions to prevent or treat diabetes.

SUBMITTER: McEwen LN

PROVIDER: S-EPMC3714351 | biostudies-literature | 2013 Nov-Dec

REPOSITORIES: biostudies-literature

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Publications

Screening for impaired fasting glucose and diabetes using available health plan data.

McEwen Laura N LN Adams Sara R SR Schmittdiel Julie A JA Ferrara Assiamira A Selby Joseph V JV Herman William H WH

Journal of diabetes and its complications 20130412 6

<h4>Aims</h4>To develop and validate prediction equations to identify individuals at high risk for type 2 diabetes using existing health plan data.<h4>Methods</h4>Health plan data from 2005 to 2009 from 18,527 members of a Midwestern HMO without diabetes, 6% of whom had fasting plasma glucose (FPG) ≥110mg/dL, and health plan data from 2005 to 2006 from 368,025 members of a West Coast-integrated delivery system without diabetes, 13% of whom had FPG ≥110mg/dL were analyzed. Within each health plan ...[more]

PMID: 23587840

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Project description:BackgroundDiabetes is associated with higher fracture risk despite higher bone mineral density (BMD), with FRAX® underestimating risk. This study aimed to investigate FRAX score with and without BMD for women with normoglycaemia, impaired fasting glucose (IFG) and diabetes.MethodsAmong 566 women, aged 40-90?years, enrolled in the Geelong Osteoporosis Study, IFG was defined as fasting plasma glucose (FPG) ?5.5?mmol/L and diabetes as FPG???7.0?mmol/L, use of antihyperglycaemic medication and/or self-report. FRAX (Australia) 10-year probabilities of major osteoporotic (MOF) and hip fracture were calculated, with and without BMD, producing four FRAX scores per participant. Kruskal-Wallis test for non-parametric data was used to examine differences between the three glycaemia groups. Fractures over 10?years were ascertained using radiological reports. The number of fractures predicted by FRAX was compared with the number of fractures observed using Chi-square tests.ResultsFor MOF FRAX calculated without BMD, women with diabetes (n?=?67) tended to have a higher median score 7.1 (IQR 2.7-12.0) than normoglycaemia (n?=?252) (4.3 (IQR 1.9-9.9) and IFG (n?=?247) (5.1 (IQR 2.2-9.6)). For hip FRAX without BMD, diabetes tended to have a higher score (2.5 (IQR 06-4.3)) than normoglycaemia (1.2 (IQR 0.3-4.1)) and IFG (1.3 (IQR 0.3-4.1)). In the normoglycaemia and IFG groups, MOFs were underestimated; 15 predicted vs 28 observed, p?=?0.038; and 16 predicted vs 31 observed, p?=?0.021, respectively. Fractures were accurately estimated in all other groups.When including BMD, the association with diabetes was non-significant for both MOF FRAX (normoglycaemia 3.7 (IQR 1.9-8.0), IFG 4.3 (IQR 2.2-8.1) and diabetes 5.3 (IQR 2.7-9.4)) and hip FRAX scores (normoglycaemia 0.6 (IQR 0.2-2.5), IFG 0.8 (IQR 0.2-2.7) and diabetes 1.0 (IQR 0.3-3.0)). For normoglycaemia and IFG, MOFs were underestimated (normoglycaemia: 13 predicted vs 28 observed and IFG: 13 vs 31). For diabetes, both MOFs and hip fractures tended to be underestimated by FRAX with BMD (MOF: 4 predicted vs 11 observed, p?=?0.055, hip: 1 predicted vs 6 observed, p?=?0.052). Hip fractures were accurately estimated in the normoglycaemia and IFG groups.ConclusionsCompared with women who had normoglycaemia or IFG, women with diabetes tended to have a higher FRAX score for both MOF and hip fractures when BMD was not included. When BMD was included, there was no difference. Fractures in diabetes tended to be underestimated by FRAX with BMD. This suggests that FRAX calculations including BMD may not be accurate for estimating fractures in those with diabetes.

Dataset Information

Screening for impaired fasting glucose and diabetes using available health plan data.

Aims

Methods

Results

Conclusion

Publications

Screening for impaired fasting glucose and diabetes using available health plan data.

Similar Datasets

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