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logistic regression to compare the EOL radiotherapy use between elderly (≥ 65) and non-elderly cancer patients while controlling for cancer types, gender, region, socioeconomic status, comorbidity, survival duration, and year of death. We calculated the proportion of radiotherapy-related costs in total medical costs in EOL and quantified patients whose proportion was at the top 10% percentile as those with overly aggressive EOL radiotherapy and examine the associated factors with logistic regression. Results: Our study cohort consisted of 17,441 patients. EOL radiotherapy use was reported in10.1% patients, with a higher percentage observed among non-elderly patients (13.77% vs. 8.38%, P< .0001). Non-elderly patients were more likely to receive EOL radiotherapy after controlling for confounders [adjusted odds ratio (AOR): 1.828, 95% CI 1.639 – 2.039]. Among patients with EOL radiotherapy, overly aggressive care was found to be more common among nonelderly patients in unadjusted (11.62% vs. 8.74%, P= 0.0461) and adjusted analyses (AOR: 1.526; 95% CI: 1.09 – 2.136). Conclusions: In thoracic cancers, we found that younger patients are more susceptible to overly aggressive EOL care. PCN181 Acute Myeloid Leukemia: A Retrospective Claims Analysis of Resource Utilization and Expenditures for De Novo Patients from First-Line Induction to Remission and Relapse Irish W1, Ryan MP2, Gache LM2, Gunnarsson C2, Bell T3, Shapiro M3 1CTI Clinical Trial and Consulting Services, Raleigh, NC, USA, 2CTI Clinical Trial and Consulting Services, Cincinnati, OH, USA, 3Pfizer, Inc., New York, NY, USA
Objectives: The objective of this study was to estimate resource utilization and expenditures for AML patients in a real world claims database. Methods: All AML patients with ICD-9 diagnosis code of 205.00 and a record of hospitalization within 14 days after their initial diagnosis between 1/1/2009 - 1/31/2015 were identified in the MarketScan claims databases. Patients had a minimum of two AML diagnosis codes and ≥ 6 months of enrollment prior to first diagnosis. Patients with a record of first line induction to a record of remission were followed. A subset had a record of a second treatment period defined as time from relapse to remission. Patient demographics, AML risk factors, and comorbidities, were recorded. Descriptive analysis of utilization and expenditures (in 2014 dollars) were reported for each cohort. Results: 1,660 patients met the inclusion criteria. The mean (SD) age was 58.3 (16.8), with 51.2% male patients. Over 90% of patients had at least one risk factor for AML. Mean healthcare expenditures for the resulting subset of patients selected with a record of first line induction to remission (n= 700) were $209,305 (SD $152,888). Of these patients, 510 were lost to follow up, 28 had a record of inpatient death and 162 had a record of relapse. Of the 162 that had a record of relapse, 84 had a record of a second remission. Expenditures for these patients (n= 84) from relapse to remission were $143,530 (SD $203,666); 59.5% were admitted to a hospital for an average 18.4 hospital days; and 19.1% had at least one emergency room visit. Conclusions: In this descriptive analysis of patients we found that treating AML patients poses a significant healthcare burden, both during de novo and relapse. With people living longer, the number of cases of AML are expected to increase in the future. PCN182 Economic Burden of the Patients with Lung Cancer in Xinjiang Province, China: A Real-World Research MAO W, Chen W Fudan University, Shanghai, China
Objectives: To compare the economic burden of lung cancer patients with different treatments in real world. Methods: Patients were identified by discharge diagnosis (ICD-10) of hospital admissions from the urban employees’ health insurance claims database during 2012-2015 and their records of hospital admissions and outpatient visits were extracted. Treatments were classified into chemotherapy and tyrosine kinase inhibitor (TKI) according to clinical guideline. Results: A cohort of 394 patients has been averagely followed up for 13.88 months, among which 72.84% were male, the average age was 66.08 and 66.50% were patients over 60 years old. During the observation, patients had 4.38 hospital admissions and 20.75 outpatient visits on average with expense of USD3,430 per admission and USD224 per visit accordingly. The total medical expense was USD21,438 with 23.94% by out-of-pocket (OOP). The monthly total expense was USD2,398 and hospital admission accounted for over 74%. 157 patients treated by chemotherapy were averagely followed up for 13.14 months, during which 6.69 hospital admissions and 17.06 outpatient visits have been observed. The monthly expense was USD3,223 with 21.82% by OOP and chemotherapy expense took up for 14.91% of total expense. 115 patients treated with TKI were observed for 13.57 months with 5.37 hospital admissions and 25.91 outpatient visits. The monthly expense was USD3,437 with 26.57% by OOP and TKI expense accounted for 44.15% of total expense. Conclusions: Financial protection from health insurance is crucial to patients with cancer, who are suffering from heavy economic burden. Chemotherapy and TKI treatments have similar total medical expense but chemotherapy requires more supportive treatments while TKI group has significant lower hospital admission rate. 1USD= 6.5CNY PCN183 Explaining the Variation in Oncology Medication Spending Across Selected Oecd Countries Manning R, Selck F Bates White Economic Consulting, Washington, DC, USA
Objectives: Spending patterns on oncology medications vary considerably across Organization for Economic Cooperation and Development (OECD) countries. For example, France’s percent of oncology medication spending is nearly four percentage points higher than the United States and nearly three percentage points higher than Japan. Further examination of country-specific medication spend reveals a significant amount of variation in generic oncology medication adoption and utilization of biologic drugs. This paper examines whether country-level demographic and institutional differences can explain this variation. Methods: Annual
oncology medication spending in the United States, Japan, Germany, United Kingdom, France, Italy, and Spain are compiled from IMS Health data from 2001 to 2013 and linked to a variety of country-level indicators that, based on prior literature, could explain observed differences in (1) overall oncology spending as a percentage of the country’s total prescription medication budget, (2) share of generic medication utilization when a generic is available, and (3) the utilization share of different types of oncology therapies such as hormonal, cytotoxic, and targeted biologic treatments. Country-level factors used to explain this variation include cancer prevalence, per capita income, health insurance system classification, and access to physicians. A variety of econometric panel data methods are used to evaluate the associations between selected country-level indicators and the outcomes listed above. Results: Preliminary results suggest a moderate association between health system classification, physician access, and oncology medication spend. Specifically, we find that countries with social insurance systems with greater access to physicians are more likely to use targeted biologic medications in place of cytotoxics. The tendency to use biologics also explained most of the variation observed in generic oncology medication utilization. Conclusions: Differences in country-level oncology medication spending are primarily driven by a higher demand for targeted biologic medications. This demand can be partially explained by increased physician access and health insurance system type. PCN184 Health Care Utilization and Costs Associated with Breast and Female Genital System Cancers: An Analysis using the 2013 MEPS Database Oak B, Nambiar S, Seoane-Vazquez E, Eguale T MCPHS University, Boston, MA, USA
Objectives: In 2015, 231,840 new cases for breast cancer and 98,000 new cases for female genital system cancers were estimated to be diagnosed in the US. Breast cancer is the most prevalent type of cancer and ranks second as cause of cancer death, after lung cancer, in women in the US. This study evaluated the utilization of health care services, costs and sources of payment associated with breast and female genital system cancers. Methods: A retrospective analysis was conducted using data from the Household Component of the Medical Expenditure Panel Survey (MEPS) database for the year 2013. We assessed the Medical Conditions and Event data files to identify female patients with breast and genital system cancers, in Outpatient, Inpatient, Emergency Room, Office-based Medical Provider, Home Health and Prescribed Medicines events and linked them to person-level variables from the full year consolidated files. Descriptive statistical analyses were performed using SAS® 9.2 and Microsoft Excel 2013 for health care utilization and costs for each type of cancer and event. Results: We identified patients who had a diagnosis code for breast, uterine, cervical and other female genital system cancers. We identified 236 unique patients who accounted for 1822 unique cancer-associated medical events. Breast cancer accounted for 77.08% of the patients. Office-based medical provider visits accounted for 72.7% of all the events and 44% of the total payments. Prescription drugs were linked to 20% of the office-based events. Private insurance accounted for 50.09% of all health care costs. Medicare and Medicaid accounted for 27.9% and 11.4%, respectively. Out-of-pocket payments represented 5% of the total payments. Breast cancer was associated with 70.2% of the total costs. Conclusions: Breast and female genital system cancers result in substantial health care costs. Most of the cost associated with these cancers is paid by private health insurance. PCN185 Are Oncology Drug Prices in the United States too High? A Targeted Review of the Literature Solon C1, Bustamante MM2, Yong C2 1CBPartners, San Francisco, CA, USA, 2CBPartners, New York, NY, USA
Objectives: This study aimed to identify and extract the key arguments defending and criticizing the cost of cancer drugs in the USA that have been published since 2012. Methods: A targeted review of articles published since 2012 was undertaken in May 2015 to identify relevant articles on the cost of cancer drugs in the USA. Varying combinations of key words were used: price OR cost AND drug OR treatment AND cancer OR oncology. Several high impact publications were searched: The Wall Street Journal, JAMA, The New York Times, Cancer Research, Blood, Journal of Clinical Oncology, Health Affairs, The New England Journal of Medicine, and The National Bureau of Economic Research. Data extraction was then conducted to identify the key products cited, outcomes assessed, and arguments made for and against high cost therapies. Results: 52 articles were identified – of these, 7 did not directly discuss drug pricing and were excluded. Of the 45 articles deemed relevant, the majority (73%; n= 33) provided arguments critiquing high cost cancer therapies, while just 24% (n= 11) provided arguments defending them. The most common arguments against high cost products referred to the disproportionate cost increases over time (N= 12) and barrier to patient access (N= 13). In contrast, the most common arguments for high cost products noted the advancements to medical research and improvements to patient outcomes (N= 12), as well as the significant financial investments associated with drug pricing (N= 9). Most articles were published in 2014/2015 (n= 32). Conclusions: Recently, there has been increasing interest in the cost of cancer drugs in the USA, with conflicting perspectives from manufacturers and payers / providers. However, current USA drug prices may be unsustainable and collective efforts will be needed to achieve a balance of incentives for manufacturers to invest in innovation, while ensuring optimal patient access to the best care available. PCN186 The Health Service Utilization of Liver Cancer Inpatients with Different types of Health Insurance in China Li Y1, Ma Y2, Fang Y2 1Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 2Beijing University of Chinese Medicine, Beijing, China