Claims History Research Articles

Economic Burden of End Organ Damage Among Patients with Sickle Cell Disease in the US

Economic Burden of End Organ Damage Among Patients with Sickle Cell Disease in the US

PCN492 TREATMENT PATTERN OF HORMONE RECEPTOR-POSITIVE, HUMAN EPIDERMAL GROWTH FACTOR-2 NEGATIVE ADVANCED BREAST CANCER PATIENTS: ANALYSIS OF REAL-WORLD NATIONAL CLAIMS DATA

In hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC) patients, current treatment guidelines recommend three consecutive endocrine therapy (ET) regimens. This study was conducted to identify the real-world treatment patterns and to provide epidemiological data related to HR+, HER2- ABC in Korea. A retrospective observational study between 4/1/2013 and 8/31/2017 was conducted using Korean nationwide health insurance claims data. HR+, HER2- ABC patients were identified using ICD-10 codes (C50) and claims history (at least one ET, no HER2 targeted therapy, and a second and subsequent line of palliative therapy according to the Korean reimbursement guideline). Palliative treatments in the second and subsequent lines include Everolimus and Exemestane combination (EE), Gemcitabine and Albumin-bounded Paclitaxel, and Eribulin. Treatment patterns and its associated patient characteristics were assessed using descriptive statistics. Of those 2,208 HR+, HER2- ABC patients, 1,111 (50.3%), 685 (31.0%), and 412 (18.7%) received EE, chemotherapy (CT) including Gemcitabine and Albumin-bounded Paclitaxel, and Eribulin, respectively. Patients who received Eribulin had higher mean Charlson Comorbidity Index (CCI) score, and patients who received CT or Eribulin used more analgesics, and had higher mortality rate as compared to those who received EE. While 13.5% of patients treated with EE received CT in the previous line, 86.1% of them were followed by CT. Overall, patients treated with CT received higher proportion of prior and subsequent line of CT than ET. Although monotherapy was more frequently used than combination therapy in prior CT regimens across all treatment groups, considerable proportions used combination CT (29.9%, 36.1%, 33.5%). This study provides real-world evidence on the treatment pattern among HR+, HER2- ABC patients showing the variation between the real world practice and the treatment guidelines. New treatment options should be introduced to minimize current limitation with clinical management in Korea.

Bonus-Malus premiums under the dependent frequency-severity modeling

ABSTRACTA Bonus-Malus System (BMS) in insurance is a premium adjustment mechanism widely used in a posteriori ratemaking process to set the premium for the next contract period based on a policyholder's claim history. The current practice in BMS implementation relies on the assumption of independence between claim frequency and severity, despite the fact that a series of recent studies report evidence of a significant frequency-severity relationship, particularly in automobile insurance. To address this discrepancy, we propose a copula-based correlated random effects model to accommodate the dependence between claim frequency and severity, and further illustrate how to incorporate such dependence into the current BMS. We derive analytical solutions to the optimal relativities under the proposed framework and provide numerical experiments based on real data analysis to assess the effect of frequency-severity dependence in BMS ratemaking.

Association Between Spending and Survival of Chronic Heart Failure Across Veterans Affairs Medical Centers

The US Department of Veterans Affairs (VA) provides health care to more than 2 000 000 veterans with chronic cardiovascular disease, yet little is known about how expenditures vary across VA Medical Centers (VAMCs), or whether VAMCs with greater health expenditures are associated with better health outcomes. To compare expenditures for patients with chronic heart failure (CHF) across the nation's VAMCs and examine the association between health care spending and survival. Retrospective cohort study using existing administrative data sets from the VA's Corporate Data Warehouse and each veteran's Medicare enrollment information and claims history for fee-for-service clinicians outside of the VA from 265 714 patients diagnosed with CHF between April 1, 2010, and December 31, 2013, who received care at any of 138 VAMCs or affiliated outpatient clinics nationwide. Patients were followed up through September 30, 2014. Data were analyzed from April 1, 2010, through September 30, 2014. Main outcomes were patient deaths per calendar quarter and aggregate VA costs per calendar quarter. Hierarchical generalized linear models with hospital-level random effects were estimated to calculate both risk-standardized annual health care expenditures and risk-standardized annual survival rates for veterans with CHF at each VAMC. The association between VAMC-level expenditures and survival was then modeled using local and linear regression. Of the 265 714 patients included, 261 132 (98.7%) were male; 224 353 (84.4%) were white; 41 110 (15.5%) were black, Asian, Pacific Islander, American Indian, or Alaskan Native; and 251 (0.1%) did not report race. Mean (SD) age of the patients included was 74 (10) years. Across 138 VAMCs, mean (95% CI) annual expenditures for veterans with CHF varied from $21 300 ($20 300-$22 400) to $52 800 ($49 400-$54 300) per patient, whereas annual survival varied between 81.4% to 88.9%. There was a modest V-shaped association between spending and survival such that adjusted survival was 1.7 percentage points higher at the minimum level of spending compared with the inflection point of $34 100 per year (P = .001) and 1.9 percentage points higher at the maximum level of spending compared with the inflection point (P = .006). Despite marked differences in mean annual expenditures per veteran, only a modest association was found between CHF spending and survival at the VAMC level, with slightly higher survival observed at the extremes of the spending range. Hospitals with high expenditures may be less efficient than their peer institutions in producing optimal health outcomes.

The Real-World Medicare Costs of Alzheimer Disease: Considerations for Policy and Care.

Headlines in popular media suggest that Alzheimer disease will bankrupt the Medicare program. Indeed, Alzheimer disease affects more than 5 million older Medicare beneficiaries. To compare total Medicare-covered (allowed) costs of patients with Alzheimer disease with the risk adjusted costs of beneficiaries without dementia over their last years of life, using claims data. Using the Medicare 5 Percent Limited Data Set claim files from 2006-2015, we conducted a cost impact analysis of costs for up to 8 years before the year of death. Risk adjustment was performed at a beneficiary level using Medicare's 2015 Hierarchical Condition Categories. Beneficiaries were classified into dementia categories based on their diagnoses during the last 3 years of life. Costs were trend adjusted to 2015. This study found that 40% of deceased beneficiaries have Alzheimer disease or unspecified dementia diagnoses in their claims history. In their last 9 years of life, Alzheimer disease added about 11% to the average $17,000 per year Medicare cost for same-risk beneficiaries without dementia. Like many diseases, Alzheimer disease and dementia are associated with aging, but unlike other diseases, families and Medicaid, rather than Medicare, bear most of the substantial cost burden. As research continues into Alzheimer treatments, it is not too early to consider how to better integrate Medicare and Medicaid to fund and improve patient outcomes, which will likely involve better diagnosis, treatment, and care coordination. Funding for this project was provided by the Alliance for Aging Research, which received funding from Biogen, Eli Lilly, and Janssen Pharmaceuticals. Peschin and Jenkins are employed by the Alliance for Aging Research. Scott was employed by the Alliance for Aging Research at the time of this study and also reports consulting fees from Piramal Imaging, General Electric, and Allergan, outside of this study. Scott is chair of the Board of Directors for the Alliance for Aging Research, which is a volunteer position, and is also president of Applied Policy, a health policy and reimbursement consultancy. Pyenson and Steffens are employed by Milliman, which was contracted to work on this study. Goss Sawhney and Rotter were employed by Milliman at the time this work was performed. Milliman is a consultant to thousands of organizations in the health care industry.

A National Examination Of Long-Term Care Setting, Outcomes, And Disparities Among Elderly Dual Eligibles.

The benefits of expanding funding for Medicaid long-term care home and community-based services (HCBS) relative to institutional care are often taken as self-evident. However, little is known about the outcomes of these services, especially for racial and ethnic minority groups, whose members tend to use the services more than whites do, and for people with dementia who may need high-intensity care. Using national Medicaid claims data on older adults enrolled in both Medicare and Medicaid, we found that overall hospitalization rates were similar for HCBS and nursing facility users, although nursing facility users were generally sicker as reflected in their claims history. Among HCBS users, blacks were more likely to be hospitalized than non-Hispanic whites were, and the gap widened among blacks and whites with dementia. Also, conditional on receiving HCBS, Medicaid HCBS spending was higher for whites than for nonwhites, and higher Medicare and Medicaid hospital spending for blacks and Hispanics did not offset this difference. Our findings suggest that home and community-based services need to be carefully targeted to avoid adverse outcomes and that the racial/ethnic disparities in access to high-quality institutional long-term care are also present in HCBS. Policy makers should consider the full costs and benefits of shifting care from nursing facilities to home and community settings and the potential implications for equity.

Hospital Readmissions in Medicare Home Healthcare: What Are the Leading Risk Indicators?

A large sample of all 2011 home healthcare users in traditional Medicare was analyzed to identify the risk indicators at start-of-care that were associated with the highest probability of readmission (N = 597,493). Thirty-five patient characteristics found in Outcome and Assessment Information Set, claims history, or other administrative data were associated with a 30-day readmission risk 30% to 100% above the average in the sample. Most of these characteristics were associated with a 30-day readmission probability of approximately 1 in 5, and several were associated with a readmission probability approaching 1 in 10 during the first 7 days. A majority of the high-risk characteristics were uncommon, and they tended not to occur together, suggesting they can be useful flags for clinicians in prioritizing cases to reduce readmissions. Readmission risk grows most quickly early in the episode of care; typically one-third of the readmissions in the first 30 days occurred by the end of 7 days. High-risk markers at 7 and 30 days were substantially the same, illustrating the importance of the early days at home in influencing the 30-day outcome. A variety of domains and characteristics are represented among the highest-risk markers, suggesting challenges to home healthcare clinicians in maintaining the knowledge and skills needed to address readmission prevention. We suggest possible responses to this problem as strategies to consider, and also discuss implications for assessment practices in home healthcare.

Medical data mining: The search for knowledge in workers' compensation claims.

Workers' compensation claims data are an underutilized source of research data. Data mining is a step in the process of knowledge discovery. This report is an inquiry into the question of whether data mining can be used to expand the research potential of workplace injury claims. A descriptive data-mining method is used to avoid the hazards of predictive data mining. A key decision in descriptive data mining is the selection of the descriptive variables to be included. A major purpose of claims processing is documentation of benefit decisions. This documentation is used in selecting the case records embedded in a claim history. This novel process is called beneficiation not only because of the obvious reference to benefit but also because it is a mining term that describes a process that further refines metallic ores. Claim beneficiation identified four data forms that contain descriptive information about workplace injury claim data. They are the first report of injury, the activity prescription form, a functional capacity evaluation, and a physical capability estimate. When the data variables from these forms are combined they become a workplace injury case registry history which is a new frontier for research in injury epidemiology.

Competition Damages Claims in Ireland: The Damages Directive Improves the Irish System Though Obstacles Remain

The Damages Directive and the implementing Irish legislation apply to infringements of EU (and Irish) competition law that occurred on or after 27 December 2016. Irish litigation rules were already well developed but the Directive will augment and add to the Irish rules on disclosure, the effect of national decisions on competition law, limitation periods, joint and several liability, quantification of harm, passing-on of overcharges and consensual dispute resolution. However, obstacles remain to bringing competition damages claims in Ireland. In particular, the absence of any class action system and litigation funding coupled with the lack of any real history of such claims will limit the ability to bring such actions in Ireland.

Evaluating the impact of telephonic nurse care management during high toxicity chemotherapy treatment.

6623 Background: Care management is aimed at improving quality and cost of cancer care. To measure this we evaluated inpatient (IP) and emergency room (ER) admissions and costs of chemotherapy patients receiving versus not receiving telephonic nurse care management. Methods: 1,609 patients were studied over a 35 month period, of which 802 enrolled in a voluntary holistic telephonic nurse care management program. Patients were identified based on having been prescribed or initiating treatment with a high toxicity chemotherapy agent within the previous 60 days, following which notification was received from an oncology pathways intermediary. Nurse care managers engaged patients one or more times per month based on patient need, educating on side effects, pain management, where to seek care, and general well-being. IP and ER were measured during the 14 days following chemotherapy administration (Risk-Period) and compared to a baseline of all remaining patient claims history, excluding 28 days following chemotherapy administration (14 day Risk Period and 14 day washout period). Results: IP rates per 1,000 Risk Period Days for managed patients decreased during the 14 days following chemotherapy by 44% as compared to their baseline (3.02 v 5.39, p≤0.001), while non-managed patients increased by 29% (2.33 v 1.81, p=0.003). ER rates per 1,000 Risk Period Days were not significantly different for managed patients (2.72 v 2.35, p=0.188) and significantly increased by 117% for non-managed patients (2.34 v 1.08, p≤0.001). Baseline utilization for managed patients was more than twice the rate of non-managed patients, suggesting that a greater need for support may influence voluntary participation in care management. Conclusions: Telephonic nurse care management meaningfully reduced IP and ER admissions and their associated costs. [Table: see text]

Flexible (panel) Regression Models for Bivariate Count–Continuous Data with an Insurance Application

SummaryWe propose a flexible regression model that is suitable for mixed count–continuous panel data. The model is based on a compound Poisson representation of the continuous variable, with bivariate random effect following a polynomial-expansion-based joint density. Besides the distributional flexibility that it offers, the model allows for closed form forecast updating formulae. This property is especially important for insurance applications, in which the future individual insurance premium should be regularly updated according to one’s own past claim history. An application to vehicle insurance claims is provided.

A machine learning approach for individual claims reserving in insurance

AbstractAccurate loss reserves are an important item in the financial statement of an insurance company and are mostly evaluated by macrolevel models with aggregate data in run‐off triangles. In recent years, a new set of literature has considered individual claims data and proposed parametric reserving models based on claim history profiles. In this paper, we present a nonparametric and flexible approach for estimating outstanding liabilities using all the covariates associated to the policy, its policyholder, and all the information received by the insurance company on the individual claims since its reporting date. We develop a machine learning–based method and explain how to build specific subsets of data for the machine learning algorithms to be trained and assessed on. The choice for a nonparametric model leads to new issues since the target variables (claim occurrence and claim severity) are right‐censored most of the time. The performance of our approach is evaluated by comparing the predictive values of the reserve estimates with their true values on simulated data. We compare our individual approach with the most used aggregate data method, namely, chain ladder, with respect to the bias and the variance of the estimates. We also provide a short real case study based on a Dutch loan insurance portfolio.

Decades in Death’s Twilight

The average amount of time that death row inmates spend on death row has ballooned over the past decade, and for death row inmates in the state of Texas, the entire duration of that increased time will be spent in solitary confinement. This raises the following question: Is solitary confinement now considered to be part of the punishment, one that may be worse than the death penalty itself? This article discusses the history of solitary confinement in U.S. prisons and cites scientific literature which posits that long-term solitary confinement can cause serious psychological damage. It examines “death row syndrome,” a term that refers to the psychological illness or disorder exhibited by an inmate who has spent a prolonged period of time in harsh conditions on death row. The article reviews the Polunsky Unit, which currently houses Texas’s death row and has been described as one of the worst prisons in the United States. The article also discusses the long history of Lackey claims, which allege that prolonged confinements under a death sentence breach the Eighth Amendment’s prohibition against cruel and unusual punishment. The unresolved dissent within the Supreme Court regarding this subject is presented and discussed. This article contends that the prolonged solitary confinement of a Texas inmate on death row is a violation of the Eighth Amendment, and concludes that the only solution is to end the practice of automatic and permanent solitary confinement.

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis

ObjectiveTo develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.MethodsThis retrospective study used claims data from Truven databases from January 2006 to September 2015 (Segment 1) and October 2015 to February 2018 (Segment 2). Machine learning identified features differentiating patients with AS from matched controls; selected features were used as inputs in developing Model A/B to identify patients likely to have AS. Model A/B was trained and developed in Segment 1, and patients predicted to have AS in Segment 1 were followed up in Segment 2 to evaluate the predictive capability of Model A/B.ResultsOf 228,471 patients in Segment 1 without any history of AS, Model A/B predicted 1923 patients to have AS. Ultimately, 1242 patients received an AS diagnosis in Segment 2; 120 of these were correctly predicted by Model A/B, yielding a positive predictive value (PPV) of 6.24%. The diagnostic accuracy of Model A/B compared favorably with that of a clinical model (PPV, 1.29%) that predicted AS based on spondyloarthritis features described in the Assessment of SpondyloArthritis international Society classification criteria. A simplified linear regression model created to test the operability of Model A/B yielded a lower PPV (2.55%).ConclusionsModel A/B performed better than a clinically based model in predicting a diagnosis of AS among patients in a large claims database; its use may contribute to early recognition of AS and a timely diagnosis.

A dependent frequency–severity approach to modeling longitudinal insurance claims

In nonlife insurance, frequency and severity are two essential building blocks in the actuarial modeling of insurance claims. In this paper, we propose a dependent modeling framework to jointly examine the two components in a longitudinal context where the quantity of interest is the predictive distribution. The proposed model accommodates the temporal correlation in both the frequency and the severity, as well as the association between the frequency and severity using a novel copula regression. The resulting predictive claims distribution allows to incorporate the claim history on both the frequency and severity into ratemaking and other prediction applications. In this application, we examine the insurance claim frequencies and severities for specific peril types from a government property insurance portfolio, namely lightning and vehicle claims, which tend to be frequent in terms of their count. We discover that the frequencies and severities of these frequent peril types tend to have a high serial correlation over time. Using dependence modeling in a longitudinal setting, we demonstrate how the prediction of these frequent claims can be improved.

Predictive Modeling of the Hospital Readmission Risk from Patients\u2019 Claims Data Using Machine Learning: A Case Study on COPD

Chronic Obstructive Pulmonary Disease (COPD) is a prevalent chronic pulmonary condition that affects hundreds of millions of people all over the world. Many COPD patients got readmitted to hospital within 30 days after discharge due to various reasons. Such readmission can usually be avoided if additional attention is paid to patients with high readmission risk and appropriate actions are taken. This makes early prediction of the hospital readmission risk an important problem. The goal of this paper is to conduct a systematic study on developing different types of machine learning models, including both deep and non-deep ones, for predicting the readmission risk of COPD patients. We evaluate those different approaches on a real world database containing the medical claims of 111,992 patients from the Geisinger Health System from January 2004 to September 2015. The patient features we build the machine learning models upon include both knowledge-driven ones, which are the features extracted according to clinical knowledge potentially related to COPD readmission, and data-driven features, which are extracted from the patient data themselves. Our analysis showed that the prediction performance in terms of Area Under the receiver operating characteristic (ROC) Curve (AUC) can be improved from around 0.60 using knowledge-driven features, to 0.653 by combining both knowledge-driven and data-driven features, based on the one-year claims history before discharge. Moreover, we also demonstrate that the complex deep learning models in this case cannot really improve the prediction performance, with the best AUC around 0.65.

Abstract P4-10-02: Impact of raloxifene adherence in breast cancer risk

Abstract BACKGROUND: Raloxifene is a selective estrogen receptor modulator that has demonstrated to reduce breast cancer risk and reduce the incidence of vertebral fractures. Based on these effects, raloxifene is used as a risk reduction agent and to prevent osteoporosis in postmenopausal women. Little is known about the raloxifene adherence rates or the relationship between adherence and breast cancer incidence. METHODS: Women 60 years or older without a breast cancer history were identified in the MarketScan database (2008-2015). We identified women who received raloxifene by searching for prescription claims. Proportion of days covered (PDC) were calculated, adherence was defined as a PDC >80% in the first year after initial prescription claim. We identified factors associated with adherence. ICD-9 codes were used to identify incident cases of invasive breast cancer and cumulative incidence rates were calculated. A multivariable Cox model with propensity score method (matching variables included year of claim, age, comorbidities and family history of breast cancer) was used to evaluate the association between raloxifene adherence and breast cancer risk. All statistical tests were two-sided. RESULTS: A total of 16,179 women were included in the analysis. We identified that during the first year of treatment 6,716 (40.2%) women had a PDC >80% and thus were considered to be adherent. Factors associated with increased adherence included the use of generic drug, mail order of 90 days supply and family history of breast cancer (all p<0.001). Using propensity score matching, the 5 year-cumulative incidence of invasive breast cancer was 1.5% among those not adherent and 0.9% among those adherent to raloxifene (p=0.01). Similarly, the 9 year-cumulative rates were 3.6% and 1.9% respectively (p=0.01). After adjusting for potential confounders, patients that were adherent to raloxifene during the first year of treatment had a lower risk of invasive breast cancer compared to those that were non-adherent (HR=0.64; 95%CI 0.04-0.9). CONCLUSIONS: Among women 60 years of age or older receiving raloxifene, adherence to therapy was associated with lower risk of invasive breast cancer. Efforts to ensure adherence and compliance are crucial so patients can receive full benefit from this therapy. Citation Format: Chavez-MacGregor M, Lei X, Zhao H, Bevers TB, Brewster A, Giordano SH. Impact of raloxifene adherence in breast cancer risk [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-10-02.

An approach to merit rating by means of autoregressive sequences

A posteriori ratemaking is widely applied in the premium calculation of property and casualty products, particularly in third-party automobile insurance, which usually uses a bonus–malus system for premium adjustment. The present paper suggests an alternative to common frameworks that are designed as random walks on graphs of mostly finite states representing premium levels. The proposed premium calculation model is governed by the policyholder’s claim history through a recursive equation. This new autoregressive scheme is structurally different from the ones in use. Relevant metrics that measure the system’s optimality are evaluated, partially in analytical form. Through a comparison with existing models and parameterisation from real-life data, the new model is put into context and its practical relevance is investigated.

From Caravans to the Courts: A Practical Guide to Matter of A-B-’s Implication for Transgender Women of the Northern Triangle

On June 11, 2018, former Attorney General Jeff Sessions overruled the 2014 precedent decision, Matter of A-R-C-G-, which had opened the door to asylum claims based on domestic violence as a product of deeply entrenched patriarchal norms. In Matter of A-B-, Session's held that “[g]enerally, claims by aliens pertaining to domestic violence or gang violence perpetrated by non-governmental actors will not qualify for asylum.” This decision may affect transgender cases in a less obvious manner than women fleeing gender-based violence, such that advocates may not directly defend against A-B- as thoroughly. This paper is a practical guide for attorneys representing transgender asylum seekers from the Northern Triangle after Matter of A-B-. In order to understand and address the new obstacles facing transgender women seeking safety in the U.S., I first examine the history of transgender asylum claims in the U.S. and the systemic violence facing transgender women in Honduras, Guatemala, and El Salvador. In the second section of this paper, I qualify the legal hurdles that this decision introduced while presenting the alternative arguments available for overcoming A-B-‘s heightened standard.

Missiology as a theological and academic discipline

Missiology is a relatively young discipline in the faculty of theology and has often found its place in combination with other disciplines. The first part of the article offers an overview on missiology. Historically mission studies have been identified as pragmatical. But missiology has a history of academic research and claims an important role in theology. Today missiology offers different methodological approaches and a hermeneutical lens. But it also uses methods from other disciplines, especially as part of interdisciplinary studies. The second part of the article shows exemplary how missiology, on the one hand, applies methods, for example, from practical theology. But as one main ingredient in missiology, the contextual lens expands research in missiology beyond practical theology, as presented in the praxis cycle of Kritzinger. Missiology offers, in combination with other theological disciplines, similar possibilities and provides new perspectives in theological research.