Biomedical subjects

Minge Xie

Publications and source records attributed to Minge Xie.

5 recordsLinked to original sources

PubMed2005

Seasonal patterns in monthly hemoglobin A1c values.

The purpose of this study was to investigate seasonal variations in population monthly hemoglobin A(1c) (A1c) values over 2 years (from October 1998 to September 2000) among US diabetic veterans. The study cohort included 285,705 veterans with 856,181 A1c tests. The authors calculated the monthly average A1c values for the overall population and for subpopulations defined by age, sex, race, insulin use, and climate regions. A1c values were higher in winter and lower in summer with a difference of 0.22. The proportion of A1c values greater than 9.0% followed a similar seasonal pattern that varied from 17.3% to 25.3%. Seasonal autoregressive models including trigonometric function terms were fit to the monthly average A1c values. There were significant seasonal effects; the seasonal variation was consistent across different subpopulations. Regions with colder winter temperatures had larger winter-summer contrasts than did those with warmer winter temperatures. The seasonal patterns followed trends similar to those of many physiologic markers, cardiovascular and other diabetes outcomes, and mortality. These findings have implications for health-care service research in quality-of-care assessment, epidemiologic studies investigating population trends and risk factors, and clinical trials or program evaluations of treatments or interventions.

Age Distribution↗

PubMed2005

Assessing quality of diabetes care by measuring longitudinal changes in hemoglobin A1c in the Veterans Health Administration.

CONTEXT: A1c levels are widely used to assess quality of diabetes care provided by health care systems. Currently, cross-sectional measures are commonly used for such assessments. OBJECTIVE: To study within-patient longitudinal changes in A1c levels at Veterans Health Administration (VHA) facilities as an alternative to cross-sectional measures of quality of diabetes care. DESIGN: Longitudinal study using institutional data on individual patient A1c level over time (October 1, 1998-September 30, 2000) with time variant and invariant covariates. SETTING: One hundred and twenty-five VHA facilities nationwide, October 1, 1998-September 30, 2000. PATIENTS: Diabetic veteran users with A1c measurement performed using National Glycosylated Hemoglobin Standardization Project certified A1c lab assay methods. EXPOSURES: Characteristics unlikely to reflect quality of care, but known to influence A1c levels, demographics, and baseline illness severity. MAIN OUTCOME MEASURE: Monthly change in A1c for average patient cared for at each facility. RESULTS: The preponderance of facilities showed monthly declines in within-patient A1c over the study period (mean change of -0.0148 A1c units per month, range -0.074 to 0.042). Individual facilities varied in their monthly change, with 105 facilities showing monthly declines (70 significant at .05 level) and 20 showing monthly increases (5 significant at .05 level). Case-mix adjustment resulted in modest changes (mean change of -0.0131 case-mix adjusted A1c units per month, range -0.079 to 0.043). Facilities were ranked from worst to best, with attached 90 percent confidence intervals. Among the bottom 10 ranked facilities, four remained within the bottom decile with 90 percent confidence. CONCLUSIONS: There is substantial variation in facility-level longitudinal changes in A1c levels. We propose that evaluation of change in A1c levels over time can be used as a new measure to reflect quality of care provided to populations of individuals with chronic disease.

Aged↗

PubMed2005

Use of administrative data to risk adjust amputation rates in a national cohort of medicare-enrolled veterans with diabetes.

BACKGROUND: A reduction in diabetes-related lower extremity amputations is a national health care priority. OBJECTIVE: To develop a risk adjustment model for total amputation rates, using claims data. RESEARCH DESIGN: A retrospective longitudinal cohort analysis of veteran clinical users of the Veterans Health Administration (VHA)--Veterans with diabetes who were Medicare nonhealth maintenance organization enrolled in 1997 or 1998. Baseline risks ascertained in 1997 to 1998 were used to adjust Veterans Integrated Service Networks (VISN) amputation rates in 1999. MEASURES: Individual-level amputation outcome in VHA and private hospitals in 1999; VISN-level amputation rates adjusted for age, gender, race, foot risk factors, and macro- and microvascular complications; and rankings of 22 VISNs on amputation rates. RESULTS: A total of 218,528 patients incurred 3077 (14.1 per 1000) amputations in 1999, with 10.6 to 18.0 amputations per 1000 across 22 VISNs. Age, gender, race, prior amputation, infections, ulcers, peripheral vascular disease, and vascular complications were significant independent predictors of amputation (R = 0.20); demographic variables accounted for < 1% of the variance. The C statistic of the final model was 0.83. VISN rankings using age-, gender-, and race-adjusted rates were not substantially altered compared with rankings using the full risk-adjusted model (Spearman rank correlation, 0.85). CONCLUSION: Addition of foot risk and comorbidity variables increased the discrimination of a predictive model for total amputations in an elderly, largely male population of veterans with diabetes compared with use of demographic data alone. The authors suggest that this model be validated in other settings with availability of individual-level claims data.

Age Distribution↗

PubMed2005

Diabetes healthcare quality report cards: how accurate are the grades?

OBJECTIVE: To evaluate the accuracy and precision of random sampling in identifying healthcare system outliers in diabetes performance measures. STUDY DESIGN: Cross-sectional analysis of 79 Veterans Health Administration facilities serving 250 317 patients with diabetes mellitus between October 1, 1999, and September 30, 2000. METHODS: Primary outcome measures were poor glycosylated hemoglobin (A1C) control and good low-density lipoprotein cholesterol (LDL-C) and blood pressure (BP) control. Facility performance for each measure was calculated using 150 separate random samples and was compared with results using the bootstrap method as the criterion standard for determining outlier status (defined as a >/=5% difference from the mean, within the 10th or 90th percentile, or >/=2 SDs from the mean). RESULTS: The study population was largely male (97.4%), with 54.0% of subjects being 65 years or older. The facility-level mean performances were 22.8% for poor A1C control, 53.1% for good LDL-C control, and 55.3% for good BP control. Comparing the random sampling method with the bootstrap method, the sensitivity ranged between 0.64 and 0.83 for the 3 outcome measures, positive predictive values ranged between 0.55 and 0.88, and specificity and negative predictive values ranged between 0.88 and 0.99. CONCLUSIONS: The specificity and negative predictive value of the random sampling method in identifying nonoutliers in performance were generally high, while its sensitivity and positive predictive value were moderate. The use of random sampling to determine performance for individual outcome measures may be most appropriate for internal quality improvement rather than for public reporting.

Aged↗

PubMed2003

Transformations of covariates for longitudinal data.

This paper develops a general approach for dealing with parametric transformations of covariates for longitudinal data, where the responses are modeled marginally and generalized estimating equations (GEEs) are used for estimation of regression parameters. We propose an iterative algorithm for obtaining regression and transformation parameters from estimating equations, utilizing existing software for GEE problems. The algorithmic technique is closely related to that used in the Box-Tidwell transformation in classical linear regression, but we develop it under the GEE setting and for more general transformation functions. We provide supporting theorems for consistency and asymptotic Normality of the estimates. Inference between two nested models is also considered. This methodology is applied to two data sets. One consists of pill dissolution data, the other is taken from the Pittsburgh Youth Study (PYS). The PYS is a prospective longitudinal study of the development of delinquency, substance use, and mental health in male youth. We use the model-based parametric approach to examine the association between alcohol use at an early stage of adolescent development and delinquency over the course of adolescence.

Adolescent↗