Medicaid Services Hierarchical Condition Category Research Articles

Incremental Health Care Costs of Self-Reported Functional Impairments and Phenotypic Frailty in Community-Dwelling Older Adults : A Prospective Cohort Study.

Health care systems need better strategies to identify older adults at risk for costly care to select target populations for interventions to reduce health care burden. To determine whether self-reported functional impairments and phenotypic frailty are associated with incremental health care costs after accounting for claims-based predictors. Prospective cohort study. Index examinations (2002 to 2011) of 4 prospective cohort studies linked with Medicare claims. 8165 community-dwelling fee-for-service beneficiaries (4318 women, 3847 men). Weighted (Centers for Medicare & Medicaid Services Hierarchical Condition Category index) and unweighted (count of conditions) multimorbidity and frailty indicators derived from claims. Self-reported functional impairments (difficulty performing 4 activities of daily living) and frailty phenotype (operationalized using 5 components) derived from cohort data. Health care costs ascertained for 36 months after index examinations. Average annualized costs (2020 U.S. dollars) were $13906 among women and $14598 among men. After accounting for claims-based indicators, average incremental costs of functional impairments versus no impairment in women (men) were $3328 ($2354) for 1 impairment increasing to $7330 ($11760) for 4 impairments; average incremental costs of phenotypic frailty versus robust in women (men) were $8532 ($6172). Mean predicted costs adjusted for claims-based indicators in women (men) varied by both functional impairments and the frailty phenotype ranging from $8124 ($11831) among robust persons without impairments to $18792 ($24713) among frail persons with 4 impairments. Compared with the model with claims-derived indicators alone, this model resulted in more accurate cost prediction for persons with multiple impairments or phenotypic frailty. Cost data limited to participants enrolled in the Medicare fee-for-service program. Self-reported functional impairments and phenotypic frailty are associated with higher subsequent health care expenditures in community-dwelling beneficiaries after accounting for several claims-based indicators of costs. National Institutes of Health.

Emergency Department and Hospital Utilization Among Older Adults Before and After Identification of Elder Mistreatment

Elder mistreatment is common and has serious health consequences. Little is known, however, about patterns of health care utilization among older adults experiencing elder mistreatment. To examine emergency department (ED) and hospital utilization of older adults experiencing elder mistreatment in the period surrounding initial mistreatment identification compared with other older adults. This retrospective case-control study used Medicare insurance claims to examine older adults experiencing elder mistreatment initially identified between January 1, 2003, and December 31, 2012, and control participants matched on age, sex, race and ethnicity, and zip code. Statistical analysis was performed in April 2022. We used multiple measures of ED and hospital utilization patterns (eg, new and return visits, frequency, urgency, and hospitalizations) in the 12 months before and after mistreatment identification. Data were adjusted using US Centers for Medicare and Medicaid Services Hierarchical Condition Categories risk scores. Chi-squared tests and conditional logistic regression models were used for data analyses. This study included 114 case patients and 410 control participants. Their median age was 72 years (IQR, 68-78 years), and 340 (64.9%) were women. Race and ethnicity were reported as racial or ethnic minority (114 [21.8%]), White (408 [77.9%]), or unknown (2 [0.4%]). During the 24 months surrounding identification of elder mistreatment, older adults experiencing mistreatment were more likely to have had an ED visit (77 [67.5%] vs 179 [43.7%]; adjusted odds ratio [AOR], 2.95 [95% CI, 1.78-4.91]; P < .001) and a hospitalization (44 [38.6%] vs 108 [26.3%]; AOR, 1.90 [95% CI, 1.13-3.21]; P = .02) compared with other older adults. In addition, multiple ED visits, at least 1 ED visit for injury, visits to multiple EDs, high-frequency ED use, return ED visits within 7 days, ED visits for low-urgency issues, multiple hospitalizations, at least 1 hospitalization for injury, hospitalization at multiple hospitals, and hospitalization for ambulatory care sensitive conditions were substantially more likely for individuals experiencing elder mistreatment. The rate of ED and hospital utilization for older adults experiencing elder mistreatment was much higher in the 12 months after identification than before, leading to more pronounced differences between case patients and control participants in postidentification utilization. During the 12 months after identification of elder mistreatment, older adults experiencing mistreatment were particularly more likely to have had high-frequency ED use (12 [10.5%] vs 8 [2.0%]; AOR, 8.23 [95% CI, 2.56-26.49]; P < .001) and to have visited the ED for low-urgency issues (12 [10.5%] vs 8 [2.0%]; AOR, 7.33 [95% CI, 2.54-21.18]; P < .001). In this case-control study of health care utilization, older adults experiencing mistreatment used EDs and hospitals more frequently and with different patterns during the period surrounding mistreatment identification than other older adults. Additional research is needed to better characterize these patterns, which may be helpful in informing early identification, intervention, and prevention of elder mistreatment.

Is the Centers for Medicare and Medicaid Services Hierarchical Condition Category Risk Adjustment Model Satisfactory for Quantifying Risk After Spine Surgery?

The Centers for Medicare and Medicaid Services (CMS) hierarchical condition category (HCC) coding is a risk adjustment model that allows for the estimation of risk-and cost-associated with health care provision. Current models may not include key factors that fully delineate the risk associated with spine surgery. To augment CMS HCC risk adjustment methodology with socioeconomic data to improve its predictive capabilities for spine surgery. The National Inpatient Sample was queried for spinal fusion, and the data was merged with county-level coverage and socioeconomic status variables obtained from the Brookings Institute. We predicted outcomes (death, nonroutine discharge, length of stay [LOS], total charges, and perioperative complication) with pairs of hierarchical, mixed effects logistic regression models-one using CMS HCC score alone and another augmenting CMS HCC scores with demographic and socioeconomic status variables. Models were compared using receiver operating characteristic curves. Variable importance was assessed in conjunction with Wald testing for model optimization. We analyzed 653 815 patients. Expanded models outperformed models using CMS HCC score alone for mortality, nonroutine discharge, LOS, total charges, and complications. For expanded models, variable importance analyses demonstrated that CMS HCC score was of chief importance for models of mortality, LOS, total charges, and complications. For the model of nonroutine discharge, age was the most important variable. For the model of total charges, unemployment rate was nearly as important as CMS HCC score. The addition of key demographic and socioeconomic characteristics substantially improves the CMS HCC risk-adjustment models when modeling spinal fusion outcomes. This finding may have important implications for payers, hospitals, and policymakers.

Association between health system specialty pharmacy use and health care costs among national sample of Medicare Advantage beneficiaries.

BACKGROUND: Health care expenditures are growing rapidly. There is a growing body of literature showing that health system specialty pharmacy is associated with improvement in clinical outcomes; however, there is a lack of data on its effect on health care costs and utilization. OBJECTIVE: To perform exploratory research assessing the association between health system specialty pharmacy use and health care costs and utilization. METHODS: A retrospective cohort study was conducted examining medical and pharmacy claims from 2018 and 2019 of Medicare Advantage beneficiaries. Optum Advisory Service's proprietary deidentified Normative Health Information database was used, which includes claims, membership, and provider data for 12.6 million Medicare Advantage members. Members who filled a prescription at a health system specialty pharmacy and had a specialty provider participating in the health system specialty pharmacy care model in clinic were assigned to the intervention group. Members who did not use a health system specialty pharmacy but had the same provider (provider benchmark group) or different provider (network benchmark group) were considered as comparisons. The network benchmark group was further refined to match variation in health care cost due to geography. The primary outcome measure was total health care costs (across the medical and pharmacy benefit) on a per-patient per-month basis. Secondary outcomes were selected utilization drivers and cost subcomponents. Cost and utilization metrics were calculated on a risk-adjusted basis using Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC) risk score methodology. Differences were assessed for categorical variables with chi-square tests, and 2-tailed t-tests were used for continuous variables. RESULTS: Of the analytic sample of 9,780 members used in this study, 208 (2.1%) used health system specialty pharmacy services. During the 2018 baseline period, total health care costs and utilization were similar after CMS-HCC risk score adjustment ($9,520 among health system specialty pharmacy users; $8,691 among the provider benchmark group; $9,510 among the network benchmark group) but lower in 2019 ($7,060, $7,683, and $8,152, respectively). The differences in 2019 were primarily driven by savings in pharmacy and free-standing physician-related costs. CONCLUSIONS: Use of a health system specialty pharmacy is associated with a lower health care cost. Further study is required to analyze how drug and disease-specific interactions influence total health care costs and utilization for health system specialty pharmacy populations. DISCLOSURES: This study was funded by Shields Health Solutions and completed with Optum Advisory Services, which provided all analysis and was the sole source of data. University of Massachusetts Medical School investigators were independent context experts, who volunteered their time for this study. Hellems is employed by Optum Advisory Services; Fasching and Smith are employed by Shields Health Solutions; and Soni and McManus are employed by the University of Massachusetts Medical School. Soni received support from the National Institute of General Medical Science (T32GM107000), National Center for Advancing Translational Sciences (TL1-TR001454), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (1F30HD091975-03). McManus's time was supported by R01HL126911, R01HL137734, R01HL137794, R01HL135219, R01HL136660, U54HL143541 from the National Heart, Lung and Blood Institute. McManus has received research support from Bristol Myers Squibb, Care Evolution, Samsung, Apple Computer, Pfizer, Biotronik, Boehringer Ingelheim, Philips Research Institute, Flexcon, Fitbit; has consulted for Bristol Myers Squibb, Pfizer, Philips, Samsung Electronics, Rose Consulting, Boston Biomedical Associates, and FlexCon; and is also a member of the Operations Committee and Steering Committee for the GUARD-AF Study (NCT04126486), sponsored by Bristol Meyers Squibb and Pfizer. The other authors have nothing additional to disclose. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funders played no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Coding-Driven Changes In Measured Risk In Accountable Care Organizations.

Claims data, which form the foundation of risk adjustment in payment for health care services, may reflect efforts to capture more-or more severe-clinical conditions rather than true changes in health status. This can distort payments. We quantify this in the context of Medicare's accountable care organization (ACO) program by comparing risk scores derived from two different measurement approaches. One approach uses diagnoses coded on claims based on Centers for Medicare and Medicaid Services Hierarchical Condition Categories (HCC), and the other uses self-reported, survey-based health data from the Consumer Assessment of Healthcare Providers and Systems (CAHPS). During 2013-16 HCC-based risk scores grew faster than CAHPS-based risk scores (2.1percent versus 0.3percent annually), and the gap in HCC- and CAHPS-based risk score growth varied widely across ACOs. The average gap in risk score growth appears to be the result primarily of HCC coding practices rather than poor performance of the CAHPS model, suggesting that coding practices (not necessarily driven by ACO contracts) may account for most of the observed risk score growth for ACO beneficiaries.

Predicting avoidable hospital events in Maryland.

To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland. Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets. Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve. One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations. We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power. A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.

Utilization of Secure Messaging to Primary Care Departments

Secure messaging is a platform for email communication between patients and their physicians. Although patient-generated emails are associated with increased use of clinical services, greater member retention, and improved quality of care, secure messaging has a marked impact on primary care physicians' workload. To understand how the email topic and volume vary by demographics and clinical factors among members of a managed care organization. We analyzed all secure messages sent to primary care departments by adult members of Kaiser Permanente Southern California (KPSC) in 2017. Members with a higher volume of office visits and telephone appointment visits generated a higher volume of emails to primary care physician. Members with a Centers of Medicare and Medicaid Services Hierarchical Condition Category diagnosis history sent 3 times as many emails as those without such a diagnosis history. Women accounted for nearly two-thirds of emails despite making up only half of the KPSC member population. Less than one-fourth (21.4%) of members sent 2.3 million total emails to their physician. Medical advice was the most common reason for sending secure messages (24.7%) in a sample studied (n = 2397). These findings confirm the need for additional research to more accurately quantify the additional burden from secure message utilization on primary care physicians. Knowing the factors associated with secure messaging usage and message content could assist in building more efficient staffing models and creating more efficient routing that matches the message content with a physician's scope of practice.

Frailty Measure Improves Cost Prediction

The Centers for Medicare & Medicaid Services hierarchical condition category (CMS-HCC) is used to approximate healthcare costs and to determine payment

Relationship of a Claims-Based Frailty Index to Annualized Medicare Costs: A Cohort Study.

Medicare uses the Centers for Medicare & Medicaid Services Hierarchical Condition Category (CMS-HCC) model to predict patients' annualized Medicare costs in value-based payment programs. The CMS-HCC model does not include measures of frailty, and prior research shows that it systematically underpredicts costs for frail Medicare beneficiaries. To determine whether a claims-based frailty index can improve Medicare cost prediction. Retrospective cohort study. Medicare Current Beneficiary Survey linked to Medicare claims, 2006 to 2013. 16535 community-dwelling, fee-for-service beneficiaries representing 26705 patient-years. Patient frailty status was classified using a validated claims-based frailty index. The association between the frailty index and annualized Medicare costs was examined, and regression methods were used to compare observed Medicare costs versus predictions based on the standard CMS-HCC model with and without the frailty index. Mean costs were $5724 for the 8910 patients classified as robust (46.4% of patient-years), $12462 for the 8405 prefrail patients (41.6%), $26239 for the 2215 mildly frail patients (9.6%), and $44586 for the 593 patients classified as moderately to severely frail (2.5%). The frailty index addition to the CMS-HCC model predicted on average an additional $2712, $7915, and $16449 in costs for prefrail, mildly frail, and moderately to severely frail patients, respectively, beyond the CMS-HCC model alone. On average, the model with the frailty index addition resulted in more accurate predictions of costs for patients at all 4 levels of frailty. However, observed costs remained more widely distributed than predictions from the enhanced model at all levels of frailty. The claims-based index is a proxy for frailty and is likely less accurate than an in-person examination. The CMS-HCC model with the frailty index addition is an improvement over current Medicare cost prediction. None.

Association of Discharge Disposition with Outcomes.

Understanding the relationships between discharge disposition, readmissions, and cost of care is an important strategy for Accountable Care Organizations (ACOs) with aspirations to achieve shared savings. The purpose of this retrospective cohort study is to examine whether there is an association between the discharge dispositions of home with home health (HH) compared to skilled nursing facility (SNF) and the readmission rate and cost of care for Medicare ACO patients discharged from the hospital. The authors studied the variables associated with readmission rates and cost of care, including discharge disposition and risk score for 1151 patients attributed to an ACO. In multivariate logistic regression analysis, variables associated with increased risk for 30-day readmission were the Centers for Medicare & Medicaid Services Hierarchical Condition Category risk score and the discharge setting. Discharges to SNF were almost 5 times more likely to be readmitted to the hospital at 30 days compared to patients discharged to the HH setting. The cost of care is lower for the HH discharge disposition, with an $8678 per patient difference between the cost of care for patients discharged to HH and SNF levels of care. Findings from this study suggest that employing a transitional care planning approach that prioritizes discharging patients to the least restrictive next site of care, shifting patients from SNF disposition to HH as appropriate, is an effective strategy to improve readmission rates and cost of care.

Centers for Medicare & Medicaid Services Hierarchical Condition Category score as a predictor of readmission and reoperation following elective inpatient spine surgery.

A universal, objective predictor of postoperative resource utilization following inpatient spine surgery has not been clearly established. The Centers for Medicare & Medicaid Services (CMS) Hierarchical Condition Category (HCC) risk adjustment model, based on a formula using patient demographics and coded diagnoses, is currently used to prospectively estimate financial risk in Medicare Advantage patients; however, the value of this score as a clinical tool is currently unknown. The authors present an analysis evaluating the utility of the CMS HCC score as a universal predictive tool for patients undergoing inpatient spine surgery. A total of 1966 consecutive patients (551 with lumbar laminectomy [LL] alone, 592 with lumbar laminectomy and fusion [LF], and 823 with anterior cervical discectomy and fusion [ACDF]) undergoing inpatient spine surgery at a single institution from January 2014 to May 2018 were included in this retrospective outcomes study. Perioperative outcome measures included procedure time, 30-day readmission, reoperation, hospital length of stay (LOS), opioid utilization measured by morphine milligram equivalents (MMEs), and cost of inpatient hospitalization (in US dollars). Published CMS algorithms were incorporated into the electronic health records and used to calculate HCC scores for all patients. Patients were stratified into HCC score quartiles. Linear regression was performed on LOS, procedure time, inpatient opioid consumption, discharge opioid prescriptions, and cost to identify predictors of HCC quartiles when controlling for procedure type. One-way ANOVA and Pearson's chi-square analysis were used to compare perioperative outcomes stratified by HCC score. Across all procedures, the HCC score demonstrated significant association with 30-day readmission (OR 1.45, 95% CI 1.11-1.91, p = 0.007). The average BMI, median American Society of Anesthesiologists score, and 30-day readmission rate were similar across procedures (LL: 30.6 kg/m2, 2, 3.6%; LF: 30.6 kg/m2, 2, 4.6%; ACDF: 30.2 kg/m2, 2, 3.9%; p = 0.265, 0.061, and 0.713, respectively). LOS (p < 0.0001), duration of procedure (p < 0.0001), discharge MME (p = 0.031), total cost (p < 0.001), daily MME (p < 0.001), reoperation (p < 0.001), and 30-day readmission rate (p < 0.001) were significantly different between HCC quartiles. The HCC score may hold value as an objective, automated predictor of postoperative resource utilization and outcomes, including readmission and reoperation. This may have value as a universal, reproducible tool to target clinical interventions for higher-risk patients.

Predicting Future High-Cost Schizophrenia Patients Using High-Dimensional Administrative Data.

The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions. The study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods. This was a retrospective study using a payer administrative database. The study cohort consisted of 97,862 patients diagnosed with schizophrenia (ICD9 code 295.*) from January 2009 to June 2014. Training (n = 34,510) and study evaluation (n = 30,077) cohorts were derived based on 12-month observation and prediction windows (PWs). The target was average total cost/patient/month in the PW. Three models (baseline, intermediate, final) were developed to assess the value of different variable categories for cost prediction (demographics, coverage, cost, health-care utilization, antipsychotic medication usage, and clinical conditions). Scalable orthogonal regression, significant attribute selection in high dimensions method, and random forests regression were used to develop the models. The trained models were assessed in the evaluation cohort using the regression R2, patient classification accuracy (PCA), and cost accuracy (CA). The model performance was compared to the Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC) model. At top 10% cost cutoff, the final model achieved 0.23 R2, 43% PCA, and 63% CA; in contrast, the CMS-HCC model achieved 0.09 R2, 27% PCA with 45% CA. The final model and the CMS-HCC model identified 33 and 22%, respectively, of total cost at the top 10% cost cutoff. Using advanced feature selection leveraging detailed health care, medication utilization features, and supervised machine learning methods improved the ability to predict and identify future high-cost patients with schizophrenia when compared with the CMS-HCC model.

Association of provider opioid prescribing practices and the Centers for Medicare and Medicaid Services hierarchical condition category score: A retrospective examination of correlation between the volume of provider-prescribed opioid medications and provider panel complexity.

Objective:Opioids are being prescribed at increasing rates in primary care practices, and among individual providers there is significant variability in opioid prescribing. Primary care practices also vary significantly in complexity of their patients, ranging from healthy patients to those with multiple comorbidities. Our objective was to examine individual primary care providers for an association between their opioid prescribing and the complexity/risk of their panel of patients (a panel of patients is a group of patients whose medical care is the responsibility of a specific healthcare provider or care team).Methods:We retrospectively examined 12 months of opioid prescription data from a primary care practice. We obtained counts of opioids prescribed by providers in the Mayo Clinic, Rochester, Minnesota primary care practice. For patients paneled (assigned) to family medicine and internal medicine, we used the Centers for Medicare and Medicaid Services hierarchical condition category patient risk score as a measure of patient complexity. After adjusting the opioid counts for panel patient count (to get opioid counts per patient), we used linear regression analysis to determine the correlation between the hierarchical condition category risk and the amount of opioid prescribed by individual providers.Results:Among our combined 103 primary care providers, opioid unit counts prescribed per patient were highly correlated with the providers’ hierarchical condition category panel risk score (r2 = 0.54). After excluding three outliers, r2 was 0.74. With and without the outliers, the correlation was very significant (p < 0.0001). Subgroup analysis of panels with hierarchical condition category ⩽ 0.45 showed no correlation of opioid prescribing volume with hierarchical condition category (r2 < 0.02; p = 0.32). Provider panels with hierarchical condition category > 0.45 showed significant correlation with hierarchical condition category (r2 = 0.26; p = 0.001).Conclusion:When examining differences in primary care providers’ opioid prescribing practices, the Centers for Medicare and Medicaid Services endorsed risk score (the hierarchical condition category score) can help adjust for population differences of a provider’s patients.

Comparison of Comorbidity Scores in Predicting Surgical Outcomes.

The optimal methodology for assessing comorbidity to predict various surgical outcomes such as mortality, readmissions, complications, and failure to rescue (FTR) using claims data has not been established. Compare diagnosis-based and prescription-based comorbidity scores for predicting surgical outcomes. We used 100% Texas Medicare data (2006-2011) and included patients undergoing coronary artery bypass grafting, pulmonary lobectomy, endovascular repair of abdominal aortic aneurysm, open repair of abdominal aortic aneurysm, colectomy, and hip replacement (N=39,616). The ability of diagnosis-based [Charlson comorbidity score, Elixhauser comorbidity score, Combined Comorbidity Score, Centers for Medicare and Medicaid Services-Hierarchical Condition Categories (CMS-HCC)] versus prescription-based Chronic disease score in predicting 30-day mortality, 1-year mortality, 30-day readmission, complications, and FTR were compared using c-statistics (c) and integrated discrimination improvement (IDI). The overall 30-day mortality was 5.8%, 1-year mortality was 17.7%, 30-day readmission was 14.1%, complication rate was 39.7%, and FTR was 14.5%. CMS-HCC performed the best in predicting surgical outcomes (30-d mortality, c=0.797, IDI=4.59%; 1-y mortality, c=0.798, IDI=9.60%; 30-d readmission, c=0.630, IDI=1.27%; complications, c=0.766, IDI=9.37%; FTR, c=0.811, IDI=5.24%) followed by Elixhauser comorbidity index/disease categories (30-d mortality, c=0.750, IDI=2.37%; 1-y mortality, c=0.755, IDI=5.82%; 30-d readmission, c=0.629, IDI=1.43%; complications, c=0.730, IDI=3.99%; FTR, c=0.749, IDI=2.17%). Addition of prescription-based scores to diagnosis-based scores did not improve performance. The CMS-HCC had superior performance in predicting surgical outcomes. Prescription-based scores, alone or in addition to diagnosis-based scores, were not better than any diagnosis-based scoring system.

Semen quality, infertility and mortality in the USA.

What is the relationship between semen parameters and mortality in men evaluated for infertility? Among men undergoing an infertility evaluation, those with abnormal semen parameters have a higher risk of death, suggesting a possible common etiology between infertility and mortality. Conflicting data exist that suggest either an inverse relationship or no relationship between semen quality and mortality. A study cohort was identified from two centers, each specializing in infertility care. In California, we identified men with data from 1994 to 2011 in the Stanford Reproductive Endocrinology and Infertility semen database. In Texas, we identified men with data from 1989 to 2009 contained in the andrology database at the Baylor College of Medicine Special Procedures Laboratory who were evaluated for infertility. Mortality was determined by data linkage to the National Death Index or Social Security Death Index. Comorbidity was estimated based on calculation of the Charlson Comorbidity Index or Centers for Medicare & Medicaid Services-Hierarchical Condition Categories Model. In all, 11,935 men were evaluated for infertility from 1989 to 2011. During 92 104 person years of follow-up, 69 of 11,935 men died (0.58%). The mean age at infertility evaluation was 36.6 years with a mean follow-up of 7.7 years. Compared with the general population, men evaluated for infertility had a lower risk of death with 69 deaths observed compared with 176.7 expected (Standardized mortality rate 0.39, 95% CI 0.30-0.49). When stratified by semen parameters, however, men with impaired semen parameters (i.e. male factor infertility) had significantly higher mortality rates compared with men with normal parameters (i.e. no male factor infertility). Low semen volume, sperm concentration, sperm motility, total sperm count and total motile sperm count were all associated with higher risk of death. In contrast, abnormal sperm morphology was not associated with mortality. While adjusting for current health status attenuated the association between semen parameters and mortality, men with two or more abnormal semen parameters still had a 2.3-fold higher risk of death compared with men with normal semen (95% CI 1.12-4.65). Our cohort represents infertile men, which may limit generalizability. As comorbidity relied on administrative data, granular information on each man regarding infertility diagnosis and lifestyle factors was unavailable. Men with impaired semen parameters have an increased mortality rate in the years following an infertility evaluation suggesting semen quality may provide a marker of health. This study is supported in part by P01HD36289 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development, National Institutes of Health (to D.J.L. and L.I.L.). The project was also partially supported by an NIH CTSA award number UL1 RR025744. None of the authors has any conflict of interest to declare.

A population health approach to reducing observational intensity bias in health risk adjustment: cross sectional analysis of insurance claims

Objective To compare the performance of two new approaches to risk adjustment that are free of the influence of observational intensity with methods that depend on diagnoses listed in administrative...

Medicare Home Health Care Patient Case-Mix Before and After the Balanced Budget Act of 1997: Effect on Dual Eligible Beneficiaries

The Balanced Budget Act (BBA) of 1997 changed the payment system for Medicare home health care (HHC) from cost-based to prospective reimbursement. We used Medical Expenditure Panel Survey data to assess the impact of the BBA on Medicare HHC patient case-mix measured by the Centers for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCC) model. There was a significant increase in Medicare HHC patient case-mix between the pre-BBA and Prospective Payment System (PPS) periods. The increase in the standardized-predicted risk score from the Interim Payment System period to PPS was nearly 4 times greater for the dual eligibles (Medicare-Medicaid) than for the Medicare-only population. This significantly greater rise in the HHC resources required by dual eligibles as compared to nonduals could be due to a shift in HHC payers from Medicare only to Medicaid rather than be an actual increase in case-mix per se.

Abstract 5921: Medicare Beneficiaries with Mild to Severe Heart Failure See 15–23 Different Providers Annually

In fiscal year 2003, Medicare beneficiaries with heart failure (HF) accounted for 37% of all Medicare spending and nearly 50% of all hospital inpatient costs. On average, each beneficiary had 10.3 outpatient and 2 inpatient visits specifically for HF. Despite significant improvements in medical care for HF, mortality and hospital admissions remain high. No data exist regarding the number of providers ordering and providing care for this population. An analysis of fiscal year 2005 Medicare claims was conducted, using a 5% sample standard analytic and denominator file, limited data set version to extrapolate the 34,150,200 Medicare beneficiaries. Three cohorts were defined according to mild, moderate, severe HF employing the Centers for Medicare and Medicaid Services Hierarchical Condition Categories Model and Chronic Care Improvement Program definitions. HMO enrollees, persons without Part A and Part B coverage, and those outside the United States were excluded. We identified physicians by using the unique physician identification number of performing physicians. Based on inclusion criteria, 173,863 beneficiaries were identified. The average number of providers providing care in all sites were 15.9, 18.6, 23.1 for beneficiaries with mild, moderate, and severe HF, respectively; and 10.1, 11.5, and 12.1 in the outpatient setting, respectively. The average number of providers ordering care in all sites consisted of 8.3, 9.6, and 11.2 for beneficiaries with mild, moderate, and severe HF, respectively; and 6.5,7.3, and 7.8 in the outpatient setting, respectively. For beneficiaries with mild disease, only 10% of all office visits were specifically for HF, while those with moderate or severe disease, only 20% were specifically for HF. Medicare beneficiaries with HF, even those with mild disease, have a large number of providers ordering and providing care. These data highlight the importance for developing systems and processes of coordinated care for this population.

Abstract 2707: The Magnitude of Health Care Resource Utilization by Medicare Beneficiaries with Heart Failure

Introduction: By 2010, the number of individuals ≥ 65 years with a heart failure (HF) diagnosis should increase by an additional 700,000. As the financial burden of HF is expected to substantially increase, we examined health care expenditures of Medicare beneficiaries with HF to estimate the current healthcare costs and resource allocation. Methods: An analysis of 2005 Medicare claims was conducted, using a 5% sample standard analytic and denominator file, limited data set version to extrapolate the 34,150,200 Medicare beneficiaries. The cohort was defined by the Centers for Medicare and Medicaid Services Hierarchical Condition Categories Model which requires one HF diagnosis from a physician or hospital outpatient department/inpatient bill. HMO enrollees, persons without Part A and Part B coverage, and those outside the United States were excluded. Results: Based on inclusion criteria, 260,076 beneficiaries were identified. Beneficiaries with HF accounted for 13% of the total beneficiary population and 37% of all Medicare spending. Reimbursement for hospital inpatient admissions, physician visits, and hospital outpatient visits accounted for $12,556; $5,875; and $2,753 per-capita, respectively. In one year, 22% of all beneficiaries required hospitalization compared to 59% of beneficiaries with HF. Thirty-one percent of beneficiaries with HF had ≥ 2 inpatient admissions. Twenty-four percent of all hospital discharges were for HF, either as a principal diagnosis or co-morbidity, accounting for $30.4 billion. On average, 8.3 different outpatient and inpatient providers ordered services for a single beneficiary. Beneficiaries with at least two prior HF hospitalizations within the index period had on average 3.04 physician visits every three months. Only 26% of these visits were conducted by a cardiologist. Conclusion : Medicare beneficiaries with HF impose a tremendous burden on Medicare, consisting of over one-third of Medicare spending. It will be important to determine how much of this burden is due to HF and how much to comorbid conditions. Development of specialized Medicare HF Management Programs, also providing comprehensive care for co-morbidities, could curtail these admissions and potentially reduce costs.

Cost of Caring for Medicare Beneficiaries with Parkinson's Disease: Impact of the CMS-HCC Risk-Adjustment Model

Previous studies have demonstrated that Medicare risk-adjusted capitation models do not adequately compensate programs serving primarily disabled or frail populations. Using the Medicare Current Beneficiary Survey, we demonstrate that the Centers for Medicare and Medicaid Services-Hierarchical Condition Categories (CMS-HCC) model calculates Medicare capitation payments for Parkinson's patients more accurately than for the general population. The discrepancies between the predicted and actual expenditures estimated at various disability levels were smaller for Parkinson's patients than for other beneficiaries. If the CMS-HCC payment model were to apply to programs that draw a significant percentage of their participants from the Parkinson's disease community, these programs likely would be compensated fairly.