Patterns of Morbidity Across the Lifespan: A Population Segmentation Framework for Classifying Health Care Needs for All Ages.

BACKGROUND: Classification systems to segment such patients into subgroups for purposes of care management and population analytics should balance administrative simplicity with clinical meaning and measurement precision. OBJECTIVE: To describe and empirically apply a new clinically relevant population segmentation framework applicable to all payers and all ages across the lifespan. RESEARCH DESIGN AND SUBJECTS: Cross-sectional analyses using insurance claims database for 3.31 Million commercially insured and 1.05 Million Medicaid enrollees under 65 years old; and 5.27 Million Medicare fee-for-service beneficiaries aged 65 and older. MEASURES: The "Patient Need Groups" (PNGs) framework, we developed, classifies each person within the entire 0-100+ aged population into one of 11 mutually exclusive need-based categories. For each PNG segment, we documented a range of clinical and resource endpoints, including health care resource use, avoidable emergency department visits, hospitalizations, behavioral health conditions, and social need factors. RESULTS: The PNG categories included: (1) nonuser, (2) low-need child, (3) low-need adult, (4) low-complexity multimorbidity, (5) medium-complexity multimorbidity, (6) low-complexity pregnancy, (7) high-complexity pregnancy, (8) dominant psychiatric/behavioral condition, (9) dominant major chronic condition, (10) high-complexity multimorbidity, and (11) frailty. Each PNG evidenced a characteristic age-related trajectory across the full lifespan. In addition to offering clinically cogent groupings, large percentages (29%-62%) of patients in two pregnancy and high-complexity multimorbidity and frailty PNGs were in a high-risk subgroup (upper 10%) of potential future health care utilization. CONCLUSIONS: The PNG population segmentation approach represents a comprehensive measurement framework that captures and categorizes available electronic health care data to characterize individuals of all ages based on their needs.

In-Person and Telehealth Ambulatory Contacts and Costs in a Large US Insured Cohort Before and During the COVID-19 Pandemic.

Importance: This study assesses the role of telehealth in the delivery of care at the start of the COVID-19 pandemic. Objectives: To document patterns and costs of ambulatory care in the US before and during the initial stage of the pandemic and to assess how patient, practitioner, community, and COVID-19-related factors are associated with telehealth adoption. Design, Setting, and Participants: This is a cohort study of working-age persons continuously enrolled in private health plans from March 2019 through June 2020. The comparison periods were March to June in 2019 and 2020. Claims data files were provided by Blue Health Intelligence, an independent licensee of the Blue Cross and Blue Shield Association. Data analysis was performed from June to October 2020. Main Outcomes and Measures: Ambulatory encounters (in-person and telehealth) and allowed charges, stratified by characteristics derived from enrollment files, practitioner claims, and community characteristics linked to the enrollee's zip code. Results: A total of 36 568 010 individuals (mean [SD] age, 35.71 [18.77] years; 18 466 557 female individuals [50.5%]) were included in the analysis. In-person contacts decreased by 37% (from 1.63 to 1.02 contacts per enrollee) from 2019 to 2020. During 2020, telehealth visits (0.32 visit per person) accounted for 23.6% of all interactions compared with 0.3% of contacts in 2019. When these virtual contacts were added, the overall COVID-19 era patient and practitioner visit rate was 18% lower than that in 2019 (1.34 vs 1.64 visits per person). Behavioral health encounters were far more likely than medical contacts to take place virtually (46.1% vs 22.1%). COVID-19 prevalence in an area was associated with higher use of telehealth; patients from areas within the top quintile of COVID-19 prevalence during the week of their encounter were 1.34 times more likely to have a telehealth visit compared with those in the lowest quintile (the reference category). Persons living in areas with limited social resources were less likely to use telehealth (most vs least socially advantaged neighborhoods, 27.4% vs 19.9% usage rates). Per enrollee medical care costs decreased by 15% between 2019 and 2020 (from $358.32 to $306.04 per person per month). During 2020, those with 1 or more COVID-19-related service (1 470 721 members) had more than 3 times the medical costs ($1701 vs $544 per member per month) than those without COVID-19-related services. Persons with 1 or more telehealth visits in 2020 had considerably higher costs than persons having only in-person ambulatory contacts ($2214.10 vs $1337.78 for the COVID-19-related subgroup and $735.87 vs $456.41 for the non-COVID-19 subgroup). Conclusions and Relevance: This study of a large cohort of patients enrolled in US health plans documented patterns of care at the onset of COVID-19. The findings are relevant to policy makers, payers, and practitioners as they manage the use of telehealth during the pandemic and afterward.

The Impact of Various Risk Assessment Time Frames on the Performance of Opioid Overdose Forecasting Models.

BACKGROUND: An individual's risk for future opioid overdoses is usually assessed using a 12-month "lookback" period. Given the potential urgency of acting rapidly, we compared the performance of alternative predictive models with risk information from the past 3, 6, 9, and 12 months. METHODS: We included 1,014,033 Maryland residents aged 18-80 with at least 1 opioid prescription and no recorded death in 2015. We used 2015 Maryland prescription drug monitoring data to identify risk factors for nonfatal opioid overdoses from hospital discharge records and investigated fatal opioid overdose from medical examiner data in 2016. Prescription drug monitoring program-derived predictors included demographics, payment sources for opioid prescriptions, count of unique opioid prescribers and pharmacies, and quantity and types of opioids and benzodiazepines filled. We estimated a series of logistic regression models that included 3, 6, 9, and 12 months of prescription drug monitoring program data and compared model performance, using bootstrapped C-statistics and associated 95% confidence intervals. RESULTS: For hospital-treated nonfatal overdose, the C-statistic increased from 0.73 for a model including only the fourth quarter to 0.77 for a model with 4 quarters of data. For fatal overdose, the area under the curve increased from 0.80 to 0.83 over the same models. The strongest predictors of overdose were prescription fills for buprenorphine and Medicaid and Medicare as sources of payment. CONCLUSIONS: Models predicting opioid overdose using 1 quarter of data were nearly as accurate as models using all 4 quarters. Models with a single quarter may be more timely and easier to identify persons at risk of an opioid overdose.

Predictive Modeling of Opioid Overdose Using Linked Statewide Medical and Criminal Justice Data.

Importance: Responding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems. Objective: To develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data. Design, Setting, and Participants: A cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019. Exposures: Controlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters. Main Outcomes and Measures: Fatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016. Results: There were 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI, 2.10-7.11]), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI, 2.20-3.86]). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85. Conclusions and Relevance: In this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.

A revised classification algorithm for assessing emergency department visit severity of populations.

OBJECTIVES: Analyses of emergency department (ED) use require visit classification algorithms based on administrative data. Our objectives were to present an expanded and revised version of an existing algorithm and to use this tool to characterize patterns of ED use across US hospitals and within a large sample of health plan enrollees. STUDY DESIGN: Observational study using National Hospital Ambulatory Medical Care Survey ED public use files and hospital billing data for a health plan cohort. METHODS: Our Johns Hopkins University (JHU) team classified many uncategorized diagnosis codes into existing New York University Emergency Department Algorithm (NYU-EDA) categories and added 3 severity levels to the injury category. We termed this new algorithm the NYU/JHU-EDA. We then compared visit distributions across these 2 algorithms and 2 other previous revised versions of the NYU-EDA using our 2 data sources. RESULTS: Applying the newly developed NYU/JHU-EDA, we classified 99% of visits. Based on our analyses, it is evident that an even greater number of US ED visits than categorized by the NYU-EDA are nonemergent. For the first time, we provide a more complete picture of the level of severity among patients treated for injuries within US hospital EDs, with about 86% of such visits being nonsevere. Also, both the original and updated classification tools suggest that, of the 38% of ED visits that are clinically emergent, the majority either do not require ED resources or could have been avoided with better primary care. CONCLUSIONS: The updated NYU/JHU-EDA taxonomy appears to offer cogent retrospective inferences about population-level ED utilization.

Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data.

INTRODUCTION: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids. METHODS: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination. RESULTS: Predictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). CONCLUSIONS: A model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.

Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation with the Johns Hopkins Adjusted Clinical Groups® System.

Electronic health records (EHR) are valuable to define phenotype selection algorithms used to identify cohorts ofpatients for sequencing or genome wide association studies (GWAS). To date, the electronic medical records and genomics (eMERGE) network institutions have developed and applied such algorithms to identify cohorts with associated DNA samples used to discover new genetic associations. For complex diseases, there are benefits to stratifying cohorts using comorbidities in order to identify their genetic determinants. The objective of this study was to: (a) characterize comorbidities in a range of phenotype-selected cohorts using the Johns Hopkins Adjusted Clinical Groups® (ACG®) System, (b) assess the frequency of important comorbidities in three commonly studied GWAS phenotypes, and (c) compare the comorbidity characterization of cases and controls. Our analysis demonstrates a framework to characterize comorbidities using the ACG system and identified differences in mean chronic condition count among GWAS cases and controls. Thus, we believe there is great potential to use the ACG system to characterize comorbidities among genetic cohorts selected based on EHR phenotypes.

Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults.

BACKGROUND: Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Penalized linear regression represents a practical and incremental step forward that provides transparency and interpretability within the familiar regression framework. This study conducted an in-depth comparison of prediction performance of standard and penalized linear regression in predicting future health care costs in older adults. METHODS AND FINDINGS: This retrospective cohort study included 81,106 Medicare Advantage patients with 5 years of continuous medical and pharmacy insurance from 2009 to 2013. Total health care costs in 2013 were predicted with comorbidity indicators from 2009 to 2012. Using 2012 predictors only, OLS performed poorly (e.g., R2 = 16.3%) compared to penalized linear regression models (R2 ranging from 16.8 to 16.9%); using 2009-2012 predictors, the gap in prediction performance increased (R2:15.0% versus 18.0-18.2%). OLS with a reduced set of predictors selected by lasso showed improved performance (R2 = 16.6% with 2012 predictors, 17.4% with 2009-2012 predictors) relative to OLS without variable selection but still lagged behind the prediction performance of penalized regression. Lasso regression consistently generated prediction ratios closer to 1 across different levels of predicted risk compared to other models. CONCLUSIONS: This study demonstrated the advantages of using transparent and easy-to-interpret penalized linear regression for predicting future health care costs in older adults relative to standard linear regression. Penalized regression showed better performance than OLS in predicting health care costs. Applying penalized regression to longitudinal data increased prediction accuracy. Lasso regression in particular showed superior prediction ratios across low and high levels of predicted risk. Health care insurers, providers and policy makers may benefit from adopting penalized regression such as lasso regression for cost prediction to improve risk adjustment and population health management and thus better address the underlying needs and risk of the populations they serve.

Assessing markers from ambulatory laboratory tests for predicting high-risk patients.

OBJECTIVES: This exploratory study used outpatient laboratory test results from electronic health records (EHRs) for patient risk assessment and evaluated whether risk markers based on laboratory results improve the performance of diagnosis- and pharmacy-based predictive models for healthcare outcomes. STUDY DESIGN: Observational study of a patient cohort over 2 years. METHODS: We used administrative claims and EHR data over a 2-year period for a population of continuously insured patients in an integrated health system who had at least 1 ambulatory visit during the first year. We performed regression tree analyses to develop risk markers from frequently ordered outpatient laboratory tests. We added these risk markers to demographic and Charlson Comorbidity Index models and 3 models from the Johns Hopkins Adjusted Clinical Groups system to predict individual cost, inpatient admission, and high-cost patients. We evaluated the predictive and discriminatory performance of 5 lab-enhanced models. RESULTS: Our study population included 120,844 patients. Adding laboratory markers to base models improved R2 predictions of costs by 0.1% to 3.7%, identification of high-cost patients by 3.4% to 121%, and identification of patients with inpatient admissions by 1.0% to 188% for the demographic model. The addition of laboratory risk markers to comprehensive risk models, compared with simpler models, resulted in smaller improvements in predictive power. CONCLUSIONS: The addition of laboratory risk markers can significantly improve the identification of high-risk patients using models that include age, gender, and a limited number of morbidities; however, models that use comprehensive risk measures may be only marginally improved.

Identifying Consistent High-cost Users in a Health Plan: Comparison of Alternative Prediction Models.

BACKGROUND: High-cost users in a period may not incur high-cost utilization in the next period. Consistent high-cost users (CHUs) may be better targets for cost-saving interventions. OBJECTIVES: To compare the characteristics of CHUs (patients with plan-specific top 20% medical costs in all 4 half-year periods across 2008 and 2009) and point high-cost users (PHUs) (top users in 2008 alone), and to build claims-based models to identify CHUs. RESEARCH DESIGN: This is a retrospective cohort study. Logistic regression was used to predict being CHUs. Independent variables were derived from 2007 claims; 5 models with different sets of independent variables (prior costs, medications, diagnoses, medications and diagnoses, medications and diagnoses and prior costs) were constructed. SUBJECTS: Three-year continuous enrollees aged from 18 to 62 years old from a large administrative database with $100 or more yearly costs (N=1,721,992). MEASURES: Correlation, overlap, and characteristics of top risk scorers derived from 5 CHUs models were presented. C-statistics, sensitivity, and positive predictive value were calculated. RESULTS: CHUs were characterized by having increasing total and pharmacy costs over 2007-2009, and more baseline chronic and psychosocial conditions than PHUs. Individuals' risk scores derived from CHUs models were moderately correlated (∼0.6). The medication-only model performed better than the diagnosis-only model and the prior-cost model. CONCLUSIONS: Five models identified different individuals as potential CHUs. The recurrent medication utilization and a high prevalence of chronic and psychosocial conditions are important in differentiating CHUs from PHUs. For cost-saving interventions with long-term impacts or focusing on medication, CHUs may be better targets.

Health care costs, utilization and patterns of care following Lyme disease.

BACKGROUND: Lyme disease is the most frequently reported vector borne infection in the United States. The Centers for Disease Control have estimated that approximately 10% to 20% of individuals may experience Post-Treatment Lyme Disease Syndrome - a set of symptoms including fatigue, musculoskeletal pain, and neurocognitive complaints that persist after initial antibiotic treatment of Lyme disease. Little is known about the impact of Lyme disease or post-treatment Lyme disease symptoms (PTLDS) on health care costs and utilization in the United States. OBJECTIVES: 1) to examine the impact of Lyme disease on health care costs and utilization, 2) to understand the relationship between Lyme disease and the probability of developing PTLDS, 3) to understand how PTLDS may impact health care costs and utilization. METHODS: This study utilizes retrospective data on medical claims and member enrollment for persons aged 0-64 years who were enrolled in commercial health insurance plans in the United States between 2006-2010. 52,795 individuals treated for Lyme disease were compared to 263,975 matched controls with no evidence of Lyme disease exposure. RESULTS: Lyme disease is associated with $2,968 higher total health care costs (95% CI: 2,807-3,128, p<.001) and 87% more outpatient visits (95% CI: 86%-89%, p<.001) over a 12-month period, and is associated with 4.77 times greater odds of having any PTLDS-related diagnosis, as compared to controls (95% CI: 4.67-4.87, p<.001). Among those with Lyme disease, having one or more PTLDS-related diagnosis is associated with $3,798 higher total health care costs (95% CI: 3,542-4,055, p<.001) and 66% more outpatient visits (95% CI: 64%-69%, p<.001) over a 12-month period, relative to those with no PTLDS-related diagnoses. CONCLUSIONS: Lyme disease is associated with increased costs above what would be expected for an easy to treat infection. The presence of PTLDS-related diagnoses after treatment is associated with significant health care costs and utilization.

Patient sharing and quality of care: measuring outcomes of care coordination using claims data.

BACKGROUND: With the goal of improving clinical efficiency and effectiveness, programs to enhance care coordination are a major focus of health care reform. OBJECTIVE: To examine whether "care density"--a claims-based measure of patient sharing by office-based physicians--is associated with measures of quality. Care density is a proxy measure that may reflect how frequently a patient's doctors collaborate. RESEARCH DESIGN: Cohort study using administrative databases from 3 large commercial insurance plans. SUBJECTS: A total of 1.7 million adult patients; 31,675 with congestive heart failure, 78,530 with chronic obstructive pulmonary disease, and 240,378 with diabetes. MEASURES: Care density was assessed in 2008. Prevention Quality Indicators (PQIs), 30-day readmissions, and Healthcare Effectiveness Data and Information Set quality indicators were measured in the following year. RESULTS: Among all patients, we found that patients with the highest care density density--indicating high levels of patient sharing among their office-based physicians--had significantly lower rates of adverse events measured as PQIs compared with patients with low-care density (odds ratio=0.88; 95% confidence interval, 0.85-0.92). A significant association between care density and PQIs was also observed for patients with diabetes mellitus but not congestive heart failure or chronic obstructive pulmonary disease. Diabetic patients with higher care density scores had significantly lower odds of 30-day readmissions (odds ratio=0.68, 95% confidence interval, 0.48-0.97). Significant associations were observed between care density and Healthcare Effectiveness Data and Information Set measures although not always in the expected direction. CONCLUSION: In some settings, patients whose doctors share more patients had lower odds of adverse events and 30-day readmissions.

Comorbid condition care quality in cancer survivors: role of primary care and specialty providers and care coordination.

PURPOSE: The purpose of this study is to investigate provider specialty, care coordination, and cancer survivors' comorbid condition care. METHODS: This retrospective cross-sectional Surveillance, Epidemiology, and End Results (SEER)-Medicare study included cancer survivors diagnosed in 2004, 2-3 years post-cancer diagnosis, in fee-for-service Medicare. We examined (1) provider specialties (primary care providers (PCPs), oncology specialists, other specialists) visited post-hospitalization, (2) role of provider specialties in chronic and acute condition management, and (3) an ambulatory care coordination measure. Outcome measures covered (1) visits post-hospitalization for nine conditions, (2) chronic disease management (lipid profile, diabetic eye exam, diabetic monitoring), and (3) acute condition management (electrocardiogram (EKG) for congestive heart failure (CHF), imaging for CHF, EKG for transient ischemic attack, cholecystectomy, hip fracture repair). RESULTS: Among 8661 cancer survivors, patients were more likely to visit PCPs than oncologists or other specialists following hospitalizations for 8/9 conditions. Patients visiting a PCP (vs. not) were more likely to receive recommended care for 3/3 chronic and 1/5 acute condition indicators. Patients visiting a nother specialist (vs. not) were more likely to receive recommended care for 3/3 chronic and 2/5 acute condition indicators. Patients visiting an oncology specialist (vs. not) were more likely to receive recommended care on 2/3 chronic indicators and less likely to receive recommended care on 1/5 acute indicators. Patients at greatest risk for poor coordination were more likely to receive appropriate care on 4/6 indicators. CONCLUSIONS: PCPs are central to cancer survivors' non-cancer comorbid condition care quality. Implications for Cancer Survivors PCP involvement in cancer survivors' care should be promoted.

Patient sharing among physicians and costs of care: a network analytic approach to care coordination using claims data.

BACKGROUND: Improving care coordination is a national priority and a key focus of health care reforms. However, its measurement and ultimate achievement is challenging. OBJECTIVE: To test whether patients whose providers frequently share patients with one another-what we term 'care density'-tend to have lower costs of care and likelihood of hospitalization. DESIGN: Cohort study PARTICIPANTS: 9,596 patients with congestive heart failure (CHF) and 52,688 with diabetes who received care during 2009. Patients were enrolled in five large, private insurance plans across the US covering employer-sponsored and Medicare Advantage enrollees MAIN MEASURES: Costs of care, rates of hospitalizations KEY RESULTS: The average total annual health care cost for patients with CHF was $29,456, and $14,921 for those with diabetes. In risk adjusted analyses, patients with the highest tertile of care density, indicating the highest level of overlap among a patient's providers, had lower total costs compared to patients in the lowest tertile ($3,310 lower for CHF and $1,502 lower for diabetes, p < 0.001). Lower inpatient costs and rates of hospitalization were found for patients with CHF and diabetes with the highest care density. Additionally, lower outpatient costs and higher pharmacy costs were found for patients with diabetes with the highest care density. CONCLUSION: Patients treated by sets of physicians who share high numbers of patients tend to have lower costs. Future work is necessary to validate care density as a tool to evaluate care coordination and track the performance of health care systems.

Risk adjustment in health insurance exchanges for individuals with mental illness.

OBJECTIVE: In 2014, an estimated 15 million individuals who currently do not have health insurance, including many with chronic mental illness, are expected to obtain coverage through state insurance exchanges. The authors examined how two mechanisms in the Affordable Care Act (ACA), namely, risk adjustment and reinsurance, might perform to ensure the financial solvency of health plans that have a disproportionate share of enrollees with mental health conditions. Risk adjustment is an ACA provision requiring that a federal or state exchange move funds from insurance plans with healthier enrollees to plans with sicker enrollees. Reinsurance is a provision in which all plans in the state contribute to an overall pool of money that is used to reimburse costs to individual market plans for expenditures of any individual enrollee that exceed a high predetermined level. METHOD: Using 2006--2007 claims data from a sample of private and public health plans, the authors compared expected health plan compensation under diagnosis-based risk adjustment with actual health care expenditures, under different assumptions for chronic mental health and medical conditions. Analyses were conducted with and without the addition of $100,000 reinsurance. RESULTS: Risk adjustment performed well for most plans. For some plans with a high share of enrollees with mental health conditions, underpayment was substantial enough to raise concern. Reinsurance appeared to be helpful in addressing the most serious underpayment problems remaining after risk adjustment. Risk adjustment performed similarly for health plan cohorts that had a disproportionate share of enrollees with chronic mental health and medical conditions. CONCLUSIONS: Cost models indicate that the regulatory provisions in the ACA requiring risk adjustment and reinsurance can help protect health plans covering treatment for mentally ill individuals against risk selection. This model analysis may be useful for advocates for individuals with mental illness in considering their own state's insurance exchange.

Adjusting for risk selection in state health insurance exchanges will be critically important and feasible, but not easy.

The Affordable Care Act calls for the establishment of state-level health insurance exchanges. The viability and success of these exchanges will require effective risk-adjustment strategies to compensate for differences in enrollees' health status across health plans. This article describes why the Affordable Care Act could lead to favorable or adverse risk selection across plans. It reviews provisions in the act and recent proposed regulations intended to mitigate the problem of risk selection. We performed a simulation that showed that under the premium rating restrictions in the law, large incentives for insurers to attract healthier enrollees will be likely to persist-resulting in substantial overpayment to plans with very healthy enrollees and underpayment to plans with very sick members. We conclude that risk adjustment based on patients' diagnoses, such as will be in place from 2014 on, will yield payments to insurers that will be more accurate than what will come solely from the age-adjusted and other rating allowed by the act. We also describe additional challenges of implementing risk adjustment.

Development and validation of a model for predicting inpatient hospitalization.

BACKGROUND: Hospitalizations are costly for health insurers and society. OBJECTIVES: To develop and validate a predictive model for acute care hospitalization from administrative claims for a population including all age groups. RESEARCH DESIGN: We constructed a retrospective cohort study using a US health plan claims database, including annual person-level files with demographic markers, and morbidity and utilization measures. We developed and validated the model using separate data. PARTICIPANTS: The validation sample included 4.7 million persons enrolled for at least 6 months in 2006 and 1 or more months in 2007. MEASURES: Risk factors and outcome variables were obtained from administrative claims data using the Adjusted Clinical Group (ACG) system. Utilization variables were added, and models were fitted with multivariate logistic regression. RESULTS: A 3.2% of patients had a hospitalization during a 1-year period, and 20% of patients who had been hospitalized during the previous year were rehospitalized. Effect sizes of risk factors were modest with odds ratios <1.5. Odds ratios were greater than 1.5 for age ≥80 years, 3+ prior hospitalizations, 3+ emergency room visits, 20 ACG morbidity categories, and 40 diseases including high impact neoplasms, bipolar disorder, cerebral palsy, chromosomal anomalies, cystic fibrosis, and hemolytic anemia. Model performance of ACG hospitalization models was good (AUC=0.80) and superior to a prior hospitalization model (AUC=0.75) and a Charlson comorbidity hospitalization model (AUC=0.78). CONCLUSIONS: A validated population-based predictive model for hospital risk estimates individual risk for future hospitalization. The model could be useful to health plans and care managers.

Ambulatory specialist use by nonhospitalized patients in us health plans: correlates and consequences.

Approximately 7 of 10 (and 95% of the elderly) people in US health plans see one or more specialists in a year. Controlling for extent of morbidity, discontinuity of primary care physician visits is associated with seeing more different specialists. Having a general internist as the primary care physician is associated with more different specialists seen. Controlling for differences in the degree of morbidity, receiving care from multiple specialists is associated with higher costs, more procedures, and more medications, independent of the number of visits and age of the patient.

Health of Medicare Advantage plan enrollees at 1 year after Hurricane Katrina.

OBJECTIVE: To assess the effects of Hurricane Katrina on mortality, morbidity, disease prevalence, and service utilization during 1 year in a cohort of 20,612 older adults who were living in New Orleans, Louisiana, before the disaster and who were enrolled in a managed care organization (MCO). STUDY DESIGN: Observational study comparing mortality, morbidity, and service use for 1 year before and after Hurricane Katrina, augmented by a stratified random sample of 303 enrollees who participated in a telephone survey after Hurricane Katrina. METHODS: Sources of data for health and service use were MCO claims. Mortality was based on reports to the MCO from the Centers for Medicare & Medicaid Services; morbidity was measured using adjusted clinical groups case-mix methods derived from diagnoses in ambulatory and hospital claims data. RESULTS: Mortality in the year following Hurricane Katrina was not significantly elevated (4.3% before vs 4.9% after the hurricane). However, overall morbidity increased by 12.6% (P <.001) compared with a 3.4% increase among a national sample of Medicare managed care enrollees. Nonwhite subjects from Orleans Parish experienced a morbidity increase of 15.9% (P <.001). The prevalence of numerous treated medical conditions increased, and emergency department visits and hospitalizations remained significantly elevated during the year. CONCLUSIONS: The enormous health burden experienced by older individuals and the disruptions in service utilization reveal the long-term effects of Hurricane Katrina on this vulnerable population. Although quick rebuilding of the provider network may have attenuated more severe health outcomes for this managed care population, new policies must be introduced to deal with the health consequences of a major disaster.

Medication, diagnostic, and cost information as predictors of high-risk patients in need of care management.

OBJECTIVE: To contrast the advantages and limitations of using medication, diagnostic, and cost data to prospectively identify candidates for care management programs. METHODS: Risk scores from prior-cost information and a set of clinically based predictive models (PMs) derived from diagnostic and medication data sources, as well as from a combination of all 3 data sources, were assigned to a national sample of commercially insured, non-elderly adults (n = 2,259,584). Clinical relevance of risk groups and statistical performance using future costs as the outcome were contrasted across the PMs. RESULTS: Compared with prior cost, diagnostic and medication-based PMs identified high-risk groups with a higher burden of clinically actionable characteristics. Statistical performance was similar and in some cases better for the clinical PMs compared with prior cost. The best classification accuracy was obtained with a comprehensive model that united diagnostic, medication, and prior-cost risk factors. CONCLUSIONS: Clinically based PMs are a better choice than prior cost alone for programs that seek to identify high-risk groups of patients who are amenable to care management services.