Robert Neal Axon

Regional, geographic, and racial/ethnic variation in glycemic control in a national sample of veterans with diabetes.

OBJECTIVE We performed a retrospective analysis of a national cohort of veterans with diabetes to better understand regional, geographic, and racial/ethnic variation in diabetes control as measured by HbA1c. RESEARCH DESIGN AND METHODS A retrospective cohort study was conducted in a national cohort of 690,968 veterans with diabetes receiving prescriptions for insulin or oral hypoglycemic agents in 2002 that were followed over a 5-year period. The main outcome measures were HbA1c levels (as continuous and dichotomized at ≥8.0%). RESULTS Relative to non-Hispanic whites (NHWs), HbA1c levels remained 0.25% higher in non-Hispanic blacks (NHBs), 0.31% higher in Hispanics, and 0.14% higher in individuals with other/unknown/missing racial/ethnic group after controlling for demographics, type of medication used, medication adherence, and comorbidities. Small but statistically significant geographic differences were also noted with HbA1c being lowest in the South and highest in the Mid-Atlantic. Rural/urban location of residence was not associated with HbA1c levels. For the dichotomous outcome poor control, results were similar with race/ethnic group being strongly associated with poor control (i.e., odds ratios of 1.33 [95% CI 1.31–1.35] and 1.57 [1.54–1.61] for NHBs and Hispanics vs. NHWs, respectively), geographic region being weakly associated with poor control, and rural/urban residence being negligibly associated with poor control. CONCLUSIONS In a national longitudinal cohort of veterans with diabetes, we found racial/ethnic disparities in HbA1c levels and HbA1c control; however, these disparities were largely, but not completely, explained by adjustment for demographic characteristics, medication adherence, type of medication used to treat diabetes, and comorbidities.

Regional, Geographic, and Ethnic Differences in Medication Adherence among Adults with Type 2 Diabetes:

BACKGROUND: Medication adherence, a critical component of glycemic control for patients with type 2 diabetes, differs by race/ethnicity. However, few studies have examined regional and rural/urban differences in medication adherence and whether racial/ethnic differences persist after controlling for these differences. OBJECTIVE: To examine regional, rural/urban, and racial/ethnic differences in medication adherence in a national sample of veterans with type 2 diabetes. METHODS: We performed a cohort study of a national sample of veterans with diabetes (N = 690,968) receiving prescriptions for insulin or oral hypoglycemic agents in 2002. Patients were followed until death, loss to follow-up, or through December 2006. We calculated the annual medication possession ratio (MPR) for each veteran across 4 groups of medication users: individuals using (1) insulin only, (2) oral hypoglycemic agents only, (3) insulin combined with hypoglycemic agents, and (4) insulin or oral hypoglycemic agents (primary analysis). RESULTS: In longitudinal models for the primary analysis, adjusting for relevant covariates and time trends, MPR was significantly lower among non-Hispanic blacks (NHBs), Hispanics, and individuals with other/missing/unknown race/ethnicity (6.07%, 1.76%, and 2.83% lower, respectively) relative to non-Hispanic whites (NHWs). MPR was also 2.0% higher in rural versus urban veterans and 1.28% higher in the mid-Atlantic, 2.04% higher in the Midwest, and 0.76% lower in the West, relative to the South. There was a significant race/ethnicity and urban/rural interaction. In NHWs and NHBs, MPR was 1.91 % and 2.00% higher, respectively, in rural versus urban veterans; in contrast, in Hispanics, MPR was 1.0% lower in rural veterans relative to urban veterans. CONCLUSIONS: In a national longitudinal cohort of veterans with type 2 diabetes, we found significant regional, rural/urban, and racial/ethnic differences in MPR. Rural/urban residence modified the effect of race/ethnicity on MPR. Recognition of these differences can enable clinicians to better allocate resources and target quality improvement programs.

Attitudes and Practices of Resident Physicians Regarding Hypertension in the Inpatient Setting

J Clin Hypertens (Greenwich). 2010;12:698–705. ©2010 Wiley Periodicals, Inc.

A hospital discharge summary quality improvement program featuring individual and team-based feedback and academic detailing.

Background:Discharge summaries are an important component of hospital care transitions typically completed by interns in teaching hospitals. However, these documents are often not completed in a timely fashion or do not include pertinent details of hospitalization. This report outlines the development and impact of a curriculum intervention to improve the quality of discharge summaries by interns and residents in Internal Medicine. A previous study demonstrated that a discharge summary curriculum featuring individualized feedback was associated with improved summary quality, but few subsequent studies have described implementation of similar curricula. No information exists on the utility of other strategies such as team-based feedback or academic detailing. Methods:Study participants were 96 Internal Medicine intern and resident physicians at an academic medical center-based training program. A comprehensive evidence-based discharge summary quality improvement program was developed and implemented that featured a discharge summary template to facilitate summary preparation, individual feedback, team-based feedback, academic detailing and an objective discharge summary evaluation instrument. Results:The discharge summary evaluation instrument had moderate interrater reliability (&kgr; = 0.72). Discharge summary scores improved from mean score of 70% to 82% (P = 0.05). Interns and residents participating in this program also reported increased confidence in producing and critiquing summaries. Conclusions:A comprehensive discharge summary curriculum can be feasibly implemented within the context of a residency program. Team-based feedback and academic detailing may serve to reinforce individual feedback and extend program reach.

Differences in Hospital Readmission Risk across All Payer Groups in South Carolina

OBJECTIVE To evaluate differences in hospital readmission risk across all payers in South Carolina (SC). DATA SOURCES/STUDY SETTING South Carolina Revenue and Fiscal Affairs Office (SCRFA) statewide all payer claims database including 2,476,431 hospitalizations in SC acute care hospitals between 2008 and 2014. STUDY DESIGN We compared the odds of unplanned all-cause 30-day readmission for private insurance, Medicare, Medicaid, uninsured, and other payers and examined interaction effects between payer and index admission characteristics using generalized estimating equations. DATA COLLECTION SCRFA receives claims and administrative health care data from all SC health care facilities in accordance with SC state law. PRINCIPAL FINDINGS Odds of readmission were lower for females compared to males in private, Medicare, and Medicaid payers. African Americans had higher odds of readmission compared to whites across private insurance, Medicare, and Medicaid, but they had lower odds among the uninsured. Longer length of stay had the strongest association with readmission for private and other payers, whereas an increased number of comorbidities related to the highest readmission odds within Medicaid. CONCLUSIONS Associations between index admission characteristics and readmission likelihood varied significantly with payer. Findings should guide the development of payer-specific quality improvement programs.

Ethnic and geographic variations in multimorbidty: Evidence from three large cohorts

A common characteristic of patients seen at the Veterans Health Administration (VHA) is a high number of concurrent comorbidities (i.e. multimorbidity). This study (i) examines the magnitude and patterns of multimorbidity by race/ethnicity and geography; (ii) compares the level of variation explained by these factors in three multimorbidity measures across three large cohorts. We created three national cohorts for Veterans with chronic kidney disease (CKD:n = 2,190,564), traumatic brain injury (TBI:n = 167,954) and diabetes-mellitus (DM:n = 1,263,906). Multimorbidity was measured by Charlson-Deyo, Elixhauser and Walraven-Elixhauser scores. Multimorbidity differences by race/ethnicity and geography were compared using generalized linear models (GLM). Latent class analysis (LCA) was used to identify groups of conditions that are highly associated with race/ethnic groups. Differences in age (CKD,74.5, TBI,49.7, DM, 66.9 years), race (CKD,80.9%, TBI,76.4%, DM, 63.8% NHW) and geography (CKD,64.4%, TBI,70%, DM, 70.9% urban) were observed among the three cohorts. Accounting for these differences, GLM results showed that risk of multimorbidity in non-Hispanic blacks (NHB) with CKD were 1.16 times higher in urban areas and 1.10 times higher in rural areas compared to non-Hispanic whites (NHW) with CKD. DM and TBI showed similar results with risk for NHB, 1.05 higher in urban areas and 0.97 lower in rural areas for both diseases. Overall, our results show that (i) multimorbidity risk was higher for NHB in urban areas compared to rural areas in all three cohorts; (ii) multimorbidity risk was higher for Hispanics in urban areas compared to rural areas in the DM and CKD cohorts; and (iii) the highest overall multimorbidity risk of any race group or location exists for Hispanics in insular islands for all three disease cohorts. These findings are consistent among the three multimorbidity measures. In fact, our LCA also showed that a three class LC model based on Elixhauser or Charlson provides good discrimination by type and extent of multimorbidity.

Investigating the Potential for Bias When Using a Widely Accepted Medication Adherence Measure to Predict Mortality

Summary measures of medication adherence, such as the proportion of days covered (PDC), are often used to analyze the association between medication adherence and various health outcomes. We hypothesized that PDC and similar measures may lead to biased results in some situations when used to estimate the association between adherence and the outcome event (e.g., mortality). Thus, the objective was to determine the conditions under which PDC and similar measures might produce biased estimates of the association between adherence and mortality and to review methods to avoid such bias.

A Triangulated Qualitative Study of Veteran Decision-Making to Seek Care During Heart Failure Exacerbation: Implications of Dual Health System Use

Among Veterans, heart failure (HF) contributes to frequent emergency department visits and hospitalization. Dual health care system use (dual use) occurs when Veterans Health Administration (VA) enrollees also receive care from non-VA sources. Mounting evidence suggests that dual use decreases efficiency and patient safety. This qualitative study used constructivist grounded theory and content analysis to examine decision making among 25 Veterans with HF, for similarities and differences between all-VA users and dual users. In general, all-VA users praised specific VA providers, called services helpful, and expressed positive capacity for managing HF. In addition, several Veterans who described inadvertent one-time non-VA health care utilization in emergent situations more closely mirrored all-VA users. By contrast, committed dual users more often reported unmet needs, nonresponse to VA requests, and faster services in non-VA facilities. However, a primary trigger for dual use was VA telephone referral for escalating symptoms, instead of care coordination or primary/specialty care problem-solving.

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.