Dataset Information

Examining the association between utilization management and downstream cardiovascular imaging.

ABSTRACT: OBJECTIVES: To examine the association of echocardiography utilization management (EUM) program with downstream cardiac imaging utilization. DATA SOURCES/STUDY SETTING: Administrative claims data from commercial health plans in Indiana, Ohio, Kentucky, Wisconsin, and Georgia. STUDY DESIGN: Patients undergoing index cardiovascular imaging with no imaging in the preceding year were identified (N = 112,308). Claims-derived cardiac risk scores were used for one-to-one propensity score matching of patients subject to EUM to patients without EUM (n = 96,906). Downstream cardiac imaging utilization for 12-24 months postindex imaging was analyzed using generalized linear models and Cox proportional hazards model. PRINCIPAL FINDINGS: Downstream cardiac imaging tests were performed for 10,630 (21.9 percent) and 12,012 (24.8 percent) patients in the EUM and non-EUM groups, respectively. At 12-month follow-up, adjusted utilization was 15.2 (95 percent CI, 7.6-22.5) tests per 1,000 initially tested patients lower in the EUM group (p < .001). The likelihood of obtaining downstream cardiac imaging in the EUM group was 7.0 percent lower than the non-EUM group (hazard ratio: 0.930; 95 percent CI, 0.897-0.964, p < .001). CONCLUSIONS: Downstream cardiac imaging is relatively common among commercially insured patients. Every 10 initial diagnostic tests yielded two downstream imaging tests in first 24 months. EUM program was associated with lower volumes of downstream imaging.

SUBMITTER: Agiro A

PROVIDER: S-EPMC4213052 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Examining the association between utilization management and downstream cardiovascular imaging.

Agiro Abiy A Sylwestrzak Gosia G Shah Christiane C Power Thomas T DeVries Andrea A

Health services research 20140409 5

<h4>Objectives</h4>To examine the association of echocardiography utilization management (EUM) program with downstream cardiac imaging utilization.<h4>Data sources/study setting</h4>Administrative claims data from commercial health plans in Indiana, Ohio, Kentucky, Wisconsin, and Georgia.<h4>Study design</h4>Patients undergoing index cardiovascular imaging with no imaging in the preceding year were identified (N = 112,308). Claims-derived cardiac risk scores were used for one-to-one propensity s ...[more]

PMID: 24712294

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Project description:ObjectiveWhile prior research shows that mental illness is associated with lower utilization of screening imaging, little is known about how mental illness impacts use of diagnostic imaging, other than for screening. This study explores the association between a history of anxiety or depression in the prior year and utilization of diagnostic imaging.MethodsCommercial and Medicare Advantage health plan claims from 2017 and 2018 from patients with plans from one national organization were extracted. Exclusions were made for patients without continuous plan enrollment. History of anxiety or depression was determined using 2017 claims, and downstream diagnostic imaging was determined using 2018 claims. Univariate associations were assessed with Chi-square tests. A matched sample was created using Coarsened Exact Matching, with history of mental illness serving as the treatment variable. Logistic regressions were used to calculate adjusted odds ratios, before and after matching, controlling for age, sex, urbanicity, local income, comorbidities, claims history, region, and health plan characteristics. Associations between mental illness and chest imaging, neuroimaging, and emergency department imaging were also evaluated.ResultsThe sample included 2,381,851 patients before matching. Imaging was significantly more likely for patients with a history of anxiety (71.1% vs. 55.7%, P < .001) and depression (73.2% vs. 55.3%, P < .001). The adjusted odds of any imaging were 1.24 (95% confidence interval [CI]: 1.22-1.26) for patients with a history of anxiety, and 1.43 (CI: 1.41-1.45) for patients with a history of depression before matching, and 1.18 (CI: 1.16-1.20) for a history of anxiety and 1.33 (CI: 1.32-1.35) for a history of depression after matching. Adjusted analyses found significant, positive associations between mental illness and chest imaging, neuroimaging, and emergency department imaging both before and after matching.DiscussionIn contrast to prior findings on screening, anxiety and depression were associated with greater likelihood of diagnostic imaging within the population studied.

Project description:There is tremendous interest in understanding how neighborhoods impact health by linking extant social and environmental drivers of health (SDOH) data with electronic health record (EHR) data. Studies quantifying such associations often use static neighborhood measures. Little research examines the impact of gentrification-a measure of neighborhood change-on the health of long-term neighborhood residents using EHR data, which may have a more generalizable population than traditional approaches. We quantified associations between gentrification and health and healthcare utilization by linking longitudinal socioeconomic data from the American Community Survey with EHR data across two health systems accessed by long-term residents of Durham County, NC, from 2007 to 2017. Census block group-level neighborhoods were eligible to be gentrified if they had low socioeconomic status relative to the county average. Gentrification was defined using socioeconomic data from 2006 to 2010 and 2011-2015, with the Steinmetz-Wood definition. Multivariable logistic and Poisson regression models estimated associations between gentrification and development of health indicators (cardiovascular disease, hypertension, diabetes, obesity, asthma, depression) or healthcare encounters (emergency department [ED], inpatient, or outpatient). Sensitivity analyses examined two alternative gentrification measures. Of the 99 block groups within the city of Durham, 28 were eligible (N = 10,807; median age = 42; 83% Black; 55% female) and 5 gentrified. Individuals in gentrifying neighborhoods had lower odds of obesity (odds ratio [OR] = 0.89; 95% confidence interval [CI]: 0.81-0.99), higher odds of an ED encounter (OR = 1.10; 95% CI: 1.01-1.20), and lower risk for outpatient encounters (incidence rate ratio = 0.93; 95% CI: 0.87-1.00) compared with non-gentrifying neighborhoods. The association between gentrification and health and healthcare utilization was sensitive to gentrification definition.

Dataset Information

Examining the association between utilization management and downstream cardiovascular imaging.

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Examining the association between utilization management and downstream cardiovascular imaging.

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