Measuring the burden of multimorbidity among Medicare beneficiaries via condition counts and cumulative duration.
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ABSTRACT: Objective
The study's purpose was to describe the cumulative duration of 19 chronic conditions among Medicare fee-for-service (FFS) beneficiaries and examine variation in total expenditures explained by cumulative duration and condition counts.Design, setting, study design, and data extraction
In a retrospective cohort of FFS beneficiaries age ?68, 2015 Medicare enrollment and claims data (N = 20 124 230) were used to identify the presence or absence of 19 diagnosed chronic conditions, and to construct MCC categories (0-1, 2-3, 4-5, 6+) and cumulative duration of each of 19 conditions from the date of first possible occurrence in claims (1/1/1999) to the end of follow-up (date of death or 12/31/2015). Total Medicare expenditures were estimated using linear models adjusted for demographic characteristics.Principal findings
Multimorbidity was common (71.7 percent with 2+ conditions). The mean cumulative duration of all 19 conditions was 23.6 person-years, which varied greatly by age and number of conditions. Condition counts were more predictive of Medicare expenditures than cumulative duration (R-squared for continuous measures = 0.461 vs 0.272; R-squared for quartiles = 0.408 vs 0.266).Conclusions
The cumulative duration of chronic conditions varied widely for Medicare beneficiaries, especially for those with 6+ conditions, but was less predictive of total expenditures than condition counts.
SUBMITTER: Maciejewski ML
PROVIDER: S-EPMC6407342 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Publications
Measuring the burden of multimorbidity among Medicare beneficiaries via condition counts and cumulative duration.
Maciejewski Matthew L ML Hammill Bradley G BG
Health services research 20190220 2
<h4>Objective</h4>The study's purpose was to describe the cumulative duration of 19 chronic conditions among Medicare fee-for-service (FFS) beneficiaries and examine variation in total expenditures explained by cumulative duration and condition counts.<h4>Design, setting, study design, and data extraction</h4>In a retrospective cohort of FFS beneficiaries age ≥68, 2015 Medicare enrollment and claims data (N = 20 124 230) were used to identify the presence or absence of 19 diagnosed chronic condi ...[more]
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Project description:ObjectivesMost older adults have multimorbidity that impairs physical functioning, but it is difficult to quantify using claims data. We previously developed and validated a multimorbidity-weighted index (MWI) that embeds physical functioning through disease weightings. We mapped these conditions to International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and compared them with existing indices.DesignPopulation-based prospective cohort.SettingRespondents to the 2006-2016 waves of the Health and Retirement Study (HRS) with linked Medicare claims data and continuous enrollment in 2006.ParticipantsCommunity-dwelling Medicare-eligible HRS participants (N = 9923; mean age = 75.5 ± 8.5 y).MeasurementsIndividuals were followed for future physical functioning (2006-2014) and mortality (2007-2016). MWI conditions were mapped to ICD-9-CM codes to produce an ICD-coded MWI (MWI-ICD). We compared MWI-ICD, simple disease count, Charlson, Elixhauser, and the health-related quality of life comorbidity index (HRQOL-CI) through distributions, hazard ratios for mortality, and relationships with future physical functioning.ResultsMWI-ICD exhibited the broadest distribution and most unique values (5891). Left censoring was most pronounced for Charlson (34.3% score = 0) and Elixhauser (13.1% score = 0) vs MWI (5.0% score = 0). Hazard ratios and concordance (C)-statistics for mortality across extreme quartiles were similar for MWI-ICD, Elixhauser, and Charlson but lower for disease count and the HRQOL-CI. For physical functioning, MWI-ICD yielded the greatest contrast across extreme quartiles and overall coefficient of determination (R2 ).ConclusionMWI-ICD was significantly associated with mortality and future physical functioning and comparable with established metrics for mortality prediction although not weighted to mortality. MWI-ICD successfully captures diseases accumulation and functioning in claims data. J Am Geriatr Soc 68:999-1006, 2020.
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Project description:ImportanceA growing proportion of the population is enrolling in Medicare Advantage (MA), which typically offers additional benefits compared with traditional Medicare (TM).ObjectiveTo determine whether frailty and frailty trajectories differ between MA enrollees and TM enrollees.Design, setting, and participantsThis retrospective cohort study used data from the National Health and Aging Trends Study (2015-2016). Analyses were conducted from August 2023 to March 2024. Participants were community-dwelling Medicare beneficiaries aged 65 years and older.ExposureEnrollment in MA vs TM.Main outcomes and measuresFrailty was calculated by a frailty index (FI) (range, 0-1, with higher values indicating greater frailty) and the Fried Frailty Phenotype (FFP) score (range, 0-5, with higher values indicating greater frailty). Physical performance, including Short Physical Performance Battery (SPPB) score (range, 0-12, with higher values indicating better performance), and gait speed (meters per second) were measured. The primary outcome was the difference in FI and FFP scores from the 2015 baseline assessment to the 2016 follow-up assessment. Secondary outcomes include the 1-year changes in SPPB and gait speed.ResultsThe final cohort consisted of 7063 participants (2775 [23.1%] aged >80 years; 4040 [54.7%] female), representing a sample of the 38.8 million beneficiaries. There were 2583 (35.0%) MA enrollees (13.6 million) and 4480 (65.0%) TM enrollees (25.2 million). At baseline, the FI score was similar between MA and TM enrollees (mean [SD], 0.22 [0.15] vs 0.21 [0.14]), although MA enrollees had worse phenotypic frailty (496 participants [15.2%] vs 811 participants [13.7%] considered frail by FFP score), SPPB scores (mean [SD], 6.91 [3.34] vs 7.21 [3.27]), and gait speed (0.79 [0.24] m/s vs 0.82 [0.23] m/s) than TM enrollees. One year later, there were no differences between MA and TM enrollees in the 1-year change in FI score (mean [SD], 0.016 [0.071] vs 0.014 [0.066]; adjusted mean difference, 0.001 [95% CI, -0.004 to 0.005]), FFP score (mean [SD], 0.017 [1.004] vs 0.007 [0.958]; adjusted mean difference, -0.009 [95% CI, -0.067 to 0.049]), SPPB score (mean [SD], -0.144 [2.064] vs -0.211 [1.968]; adjusted mean difference, 0.068 [95% CI, -0.076 to 0.212]), and gait speed (mean [SD], -0.0160 [0.148] m/s vs -0.007 [0.148] m/s; adjusted mean difference, -0.010 m/s [95% CI, -0.067 to 0.049 m/s]).Conclusions and relevanceIn this cohort study of Medicare beneficiaries from 2015, MA enrollees experienced similar declines in frailty over 1 year compared with TM enrollees. Future work should examine whether the specific types of services covered by health insurance can impact frailty and health trajectories for older adults.
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Project description:BackgroundRobustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.MethodsWe conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.FindingsOur Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations.InterpretationIncorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.FundingNational Institute for Health and Care Research.
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Project description:ImportanceAmerican Indian and Alaska Native persons face significant health disparities; however, data regarding the burden of cardiovascular disease in the current era is limited.ObjectiveTo determine the incidence and prevalence of cardiovascular disease, the burden of comorbid conditions, including cardiovascular disease risk factors, and associated mortality among American Indian and Alaska Native patients with Medicare insurance.Design, setting, and participantsThis was a population-based cohort study conducted from January 2015 to December 2019 using Medicare administrative data. Participants included American Indian and Alaska Native Medicare beneficiaries 65 years and older enrolled in both Medicare part A and B fee-for-service Medicare. Statistical analyses were performed from November 2022 to April 2023.Main outcomes and measuresThe annual incidence, prevalence, and mortality associated with coronary artery disease (CAD), heart failure (HF), atrial fibrillation/flutter (AF), and cerebrovascular disease (stroke or transient ischemic attack [TIA]).ResultsAmong 220 598 American Indian and Alaska Native Medicare beneficiaries, the median (IQR) age was 72.5 (68.5-79.0) years, 127 402 were female (57.8%), 78 438 (38.8%) came from communities in the most economically distressed quintile in the Distressed Communities Index. In the cohort, 44.8% of patients (98 833) were diagnosed with diabetes, 61.3% (135 124) were diagnosed with hyperlipidemia, and 72.2% (159 365) were diagnosed with hypertension during the study period. The prevalence of CAD was 38.6% (61 125 patients) in 2015 and 36.7% (68 130 patients) in 2019 (P < .001). The incidence of acute myocardial infarction increased from 6.9 per 1000 person-years in 2015 to 7.7 per 1000 patient-years in 2019 (percentage change, 4.79%; P < .001). The prevalence of HF was 22.9% (36 288 patients) in 2015 and 21.4% (39 857 patients) in 2019 (P < .001). The incidence of HF increased from 26.1 per 1000 person-years in 2015 to 27.0 per 1000 person-years in 2019 (percentage change, 4.08%; P < .001). AF had a stable prevalence of 9% during the study period (2015: 9.4% [14 899 patients] vs 2019: 9.3% [25 175 patients]). The incidence of stroke or TIA decreased slightly throughout the study period (12.7 per 1000 person-years in 2015 and 12.1 per 1000 person-years in 2019; percentage change, 5.08; P = .004). Fifty percent of patients (110 244) had at least 1 severe cardiovascular condition (CAD, HF, AF, or cerebrovascular disease), and the overall mortality rate for the cohort was 19.8% (43 589 patients).Conclusions and relevanceIn this large cohort study of American Indian and Alaska Native patients with Medicare insurance in the US, results suggest a significant burden of cardiovascular disease and cardiometabolic risk factors. These results highlight the critical need for future efforts to prioritize the cardiovascular health of this population.
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Project description:Background/objectivesNo data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims.DesignIn this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard.SettingMass-General-Brigham Healthcare (MGB), Massachusetts, USA.ParticipantsFrom an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates.InterventionNone.MeasurementsWe evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV).ResultsBeneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97).ConclusionThis study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.
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Project description:BackgroundEach year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes.MethodsUsing a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV).ResultsBeneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)].ConclusionsA delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.
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