Effect of Maillard reaction products on oxidation products in ground chicken breast.

Three amino acid-sugar solutions were adjusted to pH 8.0, heated and lyophilized prior to addition to ground chicken breast (GCB). GCB with no additives, GCB with 0.01% BHT, GCB with 0.1 or 0.2mg/g glucose heated with arginine, valine, or histidine were prepared. Thiobarbituric acid reactive substances (TBARS), volatiles determined by gas chromatography, and Hunter L*, a* and b* values were monitored over nine days. Multiple linear regression models were used to determine the effects of the studied factors on the corresponding outcome variables. a* values of GCB ranged from 1.60 to 4.90 over nine days of storage. While Maillard reaction products (MRP) lowered oxidation compared to control, no significant difference in TBARS between MRP solutions heated for 8 or 24h was found. Further, 0.1mg/g heated glucose-valine mixture decreased aldehydes up to 72.87%. Therefore, shelf-life of GCB could be extended using 0.1 or 0.2mg/g MRP.

Risk adjustment for measuring health outcomes: an application in VA long-term care.

An empirically derived risk adjustment model is useful in distinguishing among facilities in their quality of care. We used Veterans Affairs (VA) administrative databases to develop and validate a risk adjustment model to predict decline in functional status, an important outcome measure in long-term care, among patients residing in VA long-term care facilities. This model was used to compare facilities on adjusted and unadjusted rates of decline. Predictors of decline included age, time between assessments, baseline functional status, terminal illness, pressure ulcers, pulmonary disease, cancer, arthritis, congestive heart failure, substance-related disorders, and various neurologic disorders. The model performed well in the development and validation databases (c statistics, 0.70 and 0.68, respectively). Risk-adjusted rates and rankings of facilities differed from unadjusted ratings. We conclude that judgments of facility performance depend on whether risk-adjusted or unadjusted decline rates are used. Valid risk adjustment models are therefore necessary when comparing facilities on outcomes.

Evaluating diagnosis-based case-mix measures: how well do they apply to the VA population?

BACKGROUND: Diagnosis-based case-mix measures are increasingly used for provider profiling, resource allocation, and capitation rate setting. Measures developed in one setting may not adequately capture the disease burden in other settings. OBJECTIVES: To examine the feasibility of adapting two such measures, Adjusted Clinical Groups (ACGs) and Diagnostic Cost Groups (DCGs), to the Department of Veterans Affairs (VA) population. RESEARCH DESIGN: A 60% random sample of veterans who used health care services during FY 1997 was obtained from VA inpatient and outpatient administrative databases. A split-sample technique was used to obtain a 40% sample (n = 1,046,803) for development and a 20% sample (n = 524,461) for validation. METHODS: Concurrent ACG and DCG risk adjustment models, using 1997 diagnoses and demographics to predict FY 1997 utilization (ambulatory provider encounters, and service days-the sum of a patient's inpatient and outpatient visit days), were fitted and cross-validated. RESULTS: Patients were classified into groupings that indicated a population with multiple psychiatric and medical diseases. Model R-squares explained between 6% and 32% of the variation in service utilization. Although reparameterized models did better in predicting utilization than models with external weights, none of the models was adequate in characterizing the entire population. For predicting service days, DCGs were superior to ACGs in most categories, whereas ACGs did better at discriminating among veterans who had the lowest utilization. CONCLUSIONS: Although "off-the-shelf" case-mix measures perform moderately well when applied to another setting, modifications may be required to accurately characterize a population's disease burden with respect to the resource needs of all patients.