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Zhao, Yang; Ash, Arlene S.; Ellis, Randall P.; Slaughter, James

doi:10.1023/a:1019711617120

Cited by 25 publications

(6 citation statements)

References 7 publications

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“…As a sensitivity analysis (results shown in Appendix), we also used random effect analyses (STATA xtreg, re option), clustered at the patient level, adjusting for the patient’s age, gender, race/ethnicity, neighborhood socio-economic status (SES), baseline DxCG risk score 20 (with interaction by Medicare insurance status), medical center, seasonality (calendar month), and medical center specific secular trend. We also repeated all the analyses only among the subset of continuously-enrolled patients (results shown in appendix).…”

Section: Methodsmentioning

confidence: 99%

Implementation of an Outpatient Electronic Health Record and Emergency Department Visits, Hospitalizations, and Office Visits Among Patients With Diabetes

Reed

Huang

Brand

et al. 2013

JAMA

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Section: Methodsmentioning

confidence: 99%

Implementation of an Outpatient Electronic Health Record and Emergency Department Visits, Hospitalizations, and Office Visits Among Patients With Diabetes

Reed

Huang

Brand

et al. 2013

JAMA

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“…The DxCG and the closely-related Center for Medicare and Medicaid Services (CMS)-HCC models have performed well in predicting costs in a variety of populations, 18-24 with DxCG-HCC model being commonly used by private insurers and the CMS-HCC model being used to risk-adjust Medicare capitated payments since 2004. 1,6,25 …”

Section: Methodsmentioning

confidence: 99%

Risk Adjustment for Health Care Financing in Chronic Disease

et al. 2013

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BACKGROUND Adjustment for differing risks among patients is usually incorporated into newer payment approaches, and current risk models rely on age, gender, and diagnosis codes. It is unknown the extent to which controlling additionally for disease severity improves cost prediction. Failure to adjust for within-disease variation may create incentives to avoid sicker patients. We address this issue among patients with chronic obstructive pulmonary disease (COPD). METHODS Cost and clinical data were collected prospectively from 1,202 COPD patients at Kaiser Permanente. Baseline analysis included age, gender, and diagnosis codes (using the Diagnostic Cost Group Relative Risk Score [RRS]) in a general linear model predicting total medical costs in the following year. We determined whether adding COPD severity measures—FEV1, 6 minute walk test, dyspnea score, body-mass index, and BODE Index (composite of the other four measures)—improved predictions. Separately, we examined household income as a cost predictor. RESULTS Mean costs were $12,334/year. Controlling for RRS, each ½ standard deviation worsening in COPD severity factor was associated with $629 to $1,135 in increased annual costs (all p<0.01). The lowest stratum of FEV1 (<30% normal) predicted $4,098 (95%CI $576–$8,773) additional costs. Household income predicted excess costs when added to the baseline model (p=0.038), but this became non-significant when also incorporating BODE Index. CONCLUSIONS Disease severity measures explain significant cost variations beyond current risk models, and adding them to such models appears important to fairly compensate organizations that accept responsibility for sicker COPD patients. Appropriately controlling for disease severity also accounts for costs otherwise associated with lower socioeconomic status.

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“…As described in our previous publication,35,36 individuals who initiated (a 90-day medication gap) duloxetine or pregabalin in 2006 were first identified. All selected patients were required to: have at least one fibromyalgia diagnosis (ICD-9-CM 729.1) during the 12-month pre-index period, be aged between 18 and 64 years as of the index date, have 12-month continuous enrollment in the pre-index and post-index periods, and have 31+ total duloxetine or pregabalin supply days over the entire 12-month post-index period.…”

Section: Methodsmentioning

confidence: 99%

“…As in the previous study,35,36 propensity score stratification was applied to construct duloxetine and pregabalin cohorts with similar demographics, health plan types, geographic regions, comorbid medical conditions, prior health care costs, and prior select medication history (ie, antidepressants, anticonvulsants, opioids, nonsteroidal anti-inflammatory drugs, sleep and antianxiety medications, skeletal muscle relaxants, dopamine agonists, topical agents, and 5-HT 3 antagonists). A logistic regression predicting the probability of initiating duloxetine versus pregabalin was estimated first.…”

Section: Methodsmentioning

confidence: 99%

“…This RiskSmart software applies a comprehensive disease classification system that automatically organizes comprehensive ICD-9-CM codes (more than 15,000) recorded in administrative claims database into 781 homogeneous clinical categories (DxGroups) 36. Using the same software, all claims from inpatient admissions, outpatient hospital services, physician office services, and emergency room were processed separately.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparing common reasons for inpatient and outpatient visits between commercially-insured duloxetine or pregabalin initiators with fibromyalgia

Zhao

Sun²,

Bernauer³

2012

JPR

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BackgroundThe purpose of this study was to examine the main reasons for inpatient or outpatient visits after initiating duloxetine or pregabalin.MethodsCommercially insured patients with fibromyalgia and aged 18–64 years who initiated duloxetine or pregabalin in 2006 with 12-month continuous enrollment before and after initiation were identified. Duloxetine and pregabalin cohorts with similar demographics, pre-index clinical and economic characteristics, and pre-index treatment patterns were constructed via propensity scoring stratification. Reasons for inpatient admissions, physician office visits, outpatient hospital visits, emergency room visits, and primary or specialty care visits over the 12 months post-index period were examined and compared. Logistic regression was used to assess the contribution of duloxetine versus pregabalin initiation to the most common reasons for visits, controlling for cross-cohort differences.ResultsPer the study design, the duloxetine (n = 3711) and pregabalin (n = 4111) cohorts had similar demographics (mean age 51 years, 83% female) and health care costs over the 12-month pre-index period. Total health care costs during the 12-month post-index period were significantly lower for duloxetine patients than for pregabalin patients ($19,378 versus $27,045, P < 0.05). Eight of the 10 most common reasons for inpatient admissions and outpatient hospital (physician office, emergency room, primary or specialty care) visits were the same for both groups. Controlling for cross-cohort differences, duloxetine patients were less likely to be hospitalized due to an intervertebral disc disorder or major depressive disorder, to have a physician office visit due to nonspecific backache/other back/neck pain (NB/OB/NP) disorder, or to go to specialty care due to a soft tissue, NB/OP/NP, or intervertebral disc disorder. However, duloxetine patients were more likely to have a primary care visit due to a soft tissue disorder, essential hypertension, or other general symptoms.ConclusionAmong similar commercially insured patients with fibromyalgia who initiated duloxetine or pregabalin, duloxetine patients had significantly lower health care costs over the 12-month post-index period. The leading reasons for inpatient or outpatient visits were also somewhat different.

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Untitled

Cited by 25 publications

References 7 publications

Implementation of an Outpatient Electronic Health Record and Emergency Department Visits, Hospitalizations, and Office Visits Among Patients With Diabetes

Implementation of an Outpatient Electronic Health Record and Emergency Department Visits, Hospitalizations, and Office Visits Among Patients With Diabetes

Risk Adjustment for Health Care Financing in Chronic Disease

Comparing common reasons for inpatient and outpatient visits between commercially-insured duloxetine or pregabalin initiators with fibromyalgia

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