Mortality Differences Between Traditional Medicare and Medicare Advantage: A Risk-Adjusted Assessment Using Claims Data

Beveridge, Roy; Mendes, Sean M.; Caplan, Arial; Rogstad, Teresa L.; Olson, Vanessa; Williams, Meredith; McRae, Jacquelyn; Vargas, Stefan

doi:10.1177/0046958017709103

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Unlike previous research, which found a lower rate of mortality for people in a Medicare Advantage plan31, this study found a positive correlation between enrolment in an HMO for at least 6 of the 12 months prior to prediction and a statistically significant and higher risk of mortality. While not an indication of causation, one of several possible explanations for this correlation could be a higher likelihood of attention to resource utilisation and advanced directives in Medicare Advantage plans, and thus greater avoidance of aggressive end-of-life treatments.…”

Section: Discussioncontrasting

confidence: 99%

Development and validation of 15-month mortality prediction models: a retrospective observational comparison of machine-learning techniques in a national sample of Medicare recipients

Berg¹,

Gurley²

2019

BMJ Open

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ObjectiveThe objective is to develop and validate a predictive model for 15-month mortality using a random sample of community-dwelling Medicare beneficiaries.Data sourceThe Centres for Medicare & Medicaid Services’ Limited Data Set files containing the five per cent samples for 2014 and 2015.ParticipantsThe data analysed contains de-identified administrative claims information at the beneficiary level, including diagnoses, procedures and demographics for 2.7 million beneficiaries.SettingUS national sample of Medicare beneficiaries.Study designEleven different models were used to predict 15-month mortality risk: logistic regression (using both stepwise and least absolute shrinkage and selection operator (LASSO) selection of variables as well as models using an age gender baseline, Charlson scores, Charlson conditions, Elixhauser conditions and all variables), naïve Bayes, decision tree with adaptive boosting, neural network and support vector machines (SVMs) validated by simple cross validation. Updated Charlson score weights were generated from the predictive model using only Charlson conditions.Primary outcome measureC-statistic.ResultsThe c-statistics was 0.696 for the naïve Bayes model and 0.762 for the decision tree model. For models that used the Charlson score or the Charlson variables the c-statistic was 0.713 and 0.726, respectively, similar to the model using Elixhauser conditions of 0.734. The c-statistic for the SVM model was 0.788 while the four models that performed the best were the logistic regression using all variables, logistic regression after selection of variables by the LASSO method, the logistic regression using a stepwise selection of variables and the neural network with c-statistics of 0.798, 0.798, 0.797 and 0.795, respectively.ConclusionsImproved means for identifying individuals in the last 15 months of life is needed to improve the patient experience of care and reducing the per capita cost of healthcare. This study developed and validated a predictive model for 15-month mortality with higher generalisability than previous administrative claims-based studies.

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Section: Discussioncontrasting

confidence: 99%

Development and validation of 15-month mortality prediction models: a retrospective observational comparison of machine-learning techniques in a national sample of Medicare recipients

Berg¹,

Gurley²

2019

BMJ Open

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“…Without these data, we do not know the underlying health status and comorbidity profile of patients with AKI requiring dialysis and patients requiring incident dialysis covered by private insurance, who have no insurance, or who receive care under Medicare Advantage. Studies in the general Medicare population suggest beneficiaries with Medicare Advantage typically have lower utilization and better outcomes than Fee-for-Service beneficiaries (24, 25), and a recent study reported that ESKD beneficiaries enrolled in Medicare Advantage Special Needs Plans had lower mortality and hospital utilization compared with ESKD Fee-for-Service beneficiaries (26).…”

Section: Discussionmentioning

confidence: 99%

Acute Kidney Injury Requiring Dialysis and Incident Dialysis Patient Outcomes in US Outpatient Dialysis Facilities

Dahlerus

Segal

et al. 2021

CJASN

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Background and Objectives: About 30% of patients with acute kidney injury (AKI) may require ongoing dialysis in the outpatient setting after hospital discharge. A 2017 CMS policy change allows Medicare beneficiaries with AKI requiring dialysis to receive outpatient treatment in dialysis facilities. Outcomes for these patients have not been reported. We compare patient characteristics and mortality among AKI dialysis patients and non-AKI incident dialysis patients. Design, setting, participants, and measurements: Retrospective cohort design, using 2017 Medicare Claims to follow outpatient AKI dialysis patients and non-AKI incident dialysis patients up to 365 days. Outcomes are unadjusted and adjusted mortality using Kaplan-Meier estimation for unadjusted survival probability, Poisson regression for monthly mortality, and Cox proportional hazards modeling for adjusted mortality. Results: 10,821 of 401,973 (3%) Medicare dialysis patients had at least one AKI claim, and 52,626 patients were Medicare non-AKI incident dialysis patients. AKI dialysis patients were more likely to be White (76% vs 70%), non-Hispanic (92% vs 87%), and age 60 or greater (82% vs 72%) compared to non-AKI incident dialysis patients. Unadjusted mortality was markedly higher for AKI dialysis patients compared to non-AKI incident dialysis patients. Adjusted mortality differences between both cohorts persisted through month 4 of the follow-up period (all P<0.01) then declined and were no longer statistically significant. Adjusted monthly mortality stratified by Black and other race between AKI dialysis patients vs non-AKI incident dialysis patients was lower throughout month 4 (1.5 v .60, 1.20 v 0.84, 1.00 v 0.80, 0.95 v 0.74, all P<0.001 which persisted through month 7. Overall adjusted mortality risk was 22% higher for AKI dialysis patients (1.22, CI 1.17, 1.27). Conclusions: In fully adjusted analyses AKI dialysis patients had higher early mortality compared to non-AKI incident dialysis patients, but these differences declined after several months. Differences were also observed by age, race and ethnicity within both patient cohorts.

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“…The current literature does not sufficiently focus on assessing the appropriate number of additional procedures or diagnoses within a patient visit. Instead, the focus has been on developing risk-adjustment models concentrating on health outcomes [ 6 , 7 , 8 , 17 , 18 ]. These models do not address coding intensity.…”

Section: Introductionmentioning

confidence: 99%

Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics

et al. 2022

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Variations in procedure coding intensity, defined as excess coding of procedures versus industry (instead of clinical) standards, can result in differentials in quality of care for patients and have additional implications for facilities and payors. The literature regarding coding intensity of procedures is limited, with a need for risk-adjusted methods that help identify over- and under-coding using commonly available data, such as administrative claims. Risk-adjusted metrics are needed for quality control and enhancement. We propose a two-step approach to risk adjustment, using a zero-inflated Poisson model, applied to a hip-knee arthroplasty cohort discharged during 2019 (n = 313,477) for patient-level risk adjustment, and a potential additional layer for adjustment based on facility-level characteristics, when desired. A 21.41% reduction in root-mean-square error was achieved upon risk adjustment for patient-level factors alone. Furthermore, we identified facilities that over- and under-code versus industry coding expectations, adjusting for both patient-level and facility-level factors. Excess coding intensity was found to vary across multiple levels: (1) geographically across U.S. Census regional divisions; (2) temporally with marked seasonal components; (3) by facility, with some facilities largely departing from industry standards, even after adjusting for both patient- and facility-level characteristics. Our proposed method is simple to implement, generalizable, it can be used across cohorts with different sets of information available, and it is not limited by the accessibility and sparsity of electronic health records. By identifying potential over- and under-coding of procedures, quality control personnel can explore and assess internal needs for enhancements in their health delivery services and monitor subsequent quality improvements.

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Mortality Differences Between Traditional Medicare and Medicare Advantage: A Risk-Adjusted Assessment Using Claims Data

Cited by 6 publications

References 15 publications

Development and validation of 15-month mortality prediction models: a retrospective observational comparison of machine-learning techniques in a national sample of Medicare recipients

Development and validation of 15-month mortality prediction models: a retrospective observational comparison of machine-learning techniques in a national sample of Medicare recipients

Acute Kidney Injury Requiring Dialysis and Incident Dialysis Patient Outcomes in US Outpatient Dialysis Facilities

Modeling Coding Intensity of Procedures in a U.S. Population-Based Hip/Knee Arthroplasty Inpatient Cohort Adjusting for Patient- and Facility-Level Characteristics

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