Healthcare costs of patients with cancer stratified by Khorana score risk levels

Background: Reducing suffering at the end of life is important. Doing so requires a comprehensive understanding of the course of suffering for patients with cancer during their last year of life. This study describes trajectories of psychological, spiritual, physical, and functional suffering in the last year of life among patients with a solid metastatic cancer. Patients and Methods: We conducted a prospective cohort study of 600 patients with a solid metastatic cancer between July 2016 and December 2019 in Singapore. We assessed patients’ psychological, spiritual, physical, and functional suffering every 3 months until death. Data from the last year of life of 345 decedents were analyzed. We used group-based multitrajectory modeling to delineate trajectories of suffering during the last year of a patient’s life. Results: We identified 5 trajectories representing suffering: (1) persistently low (47% of the sample); (2) slowly increasing (14%); (3) predominantly spiritual (21%); (4) rapidly increasing (12%); and (5) persistently high (6%). Compared with patients with primary or less education, those with secondary (high school) (odds ratio [OR], 3.49; 95% CI, 1.05–11.59) education were more likely to have rapidly increasing versus persistently low suffering. In multivariable models adjusting for potential confounders, compared with patients with persistently low suffering, those with rapidly increasing suffering had more hospital admissions (β=0.24; 95% CI, 0.00–0.47) and hospital days (β=0.40; 95% CI, 0.04–0.75) during the last year of life. Those with persistently high suffering had more hospital days (β=0.70; 95% CI, 0.23–1.17). Conclusions: The course of suffering during the last year of life among patients with cancer is variable and related to patients’ hospitalizations. Understanding this variation can facilitate clinical decisions to minimize suffering and reduce healthcare costs at the end of life.

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Health care costs among adolescent young adults with cancer at a community-based hospital.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e18857 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e18857-e18857

Author(s):

Kekoa Taparra ◽

Alec Fitzsimmons ◽

Susan M. Frankki ◽

Andrea DeWall ◽

Fumiko Chino ◽

...

Keyword(s):

Young Adults ◽

Radiation Therapy ◽

Cancer Diagnosis ◽

Healthcare Costs ◽

Community Hospital ◽

Hospital Admissions ◽

Patients With Cancer ◽

Risk Of Death ◽

Increased Risk

e18857 Background: Adolescent Young Adults (AYAs) are likely to live for decades after a cancer diagnosis and thus have the potential to accumulate high healthcare costs. Prior research has shown high costs can be associated with increased risk of morbidity and mortality. However, there is limited understanding of how costs impact AYAs, especially in a community hospital. The purpose of this study is to 1) understand total community hospital healthcare costs for AYA patients with cancer, 2) identify risk factors for high costs, and 3) assess the impact of costs on survival. Methods: AYA patients (ages 15-29) treated at a community hospital were identified. Data collected included patient demographics, cancer characteristics, treatments (chemotherapy, radiation, surgery, immunotherapy, hormone therapy), support services (financial counseling, social work, survivorship), hospital admissions, miles from the hospital (great-circle distance), and all healthcare charges from one year prior to cancer diagnosis until last follow-up between 2000-2020. Multivariate logistic regression analyses were used to identify patients with costs greater than the median ($125K). Cox Proportional Hazard (CPH) regression models were used to identify factors associated with the risk of all-cause mortality. Results: A total of 388 AYA patients were identified with a median follow-up of 9 years and 97% survival. Most patients were age 30-39 years (62%), female (61%), white (95%), married (63%), non-smoker (59%), had insurance (78%), had early-stage cancer (85%), and were treated with surgery (83%). The most common cancers were melanoma (17%), breast cancer (14%), and thyroid cancer (14%). Median distance from treatment site was 23 miles. Median number of admissions was one. About a third of patients received chemotherapy (37%), radiation (28%), or hormone therapy (30%). Two-hundred thirty-three patients (60%) had complete healthcare cost data with a median total costs per patient of $123K (range, $73K-$215K). In adjusted analysis, patients with higher than median healthcare cost ( > $125K) had greater odds of hospital admission (odds ratio [OR] = 1.5, p < .001) and chemotherapy treatment (OR = 3.4, p = .005) as well as lower odds of living further from the hospital per one mile (OR = 0.3, p = .049) and being uninsured/unknown insurance (OR = 0.1, p = .047). In adjusted analysis, increased risk of death was associated with receiving radiation therapy (HR = 7.8, p = .02) and higher healthcare costs per $125K (HR = 3.8, p = .001). Conclusions: High costs of healthcare among AYA patients with cancer are related to chemotherapy, hospital admissions, and hospital proximity. High healthcare costs and radiation therapy may be associated with increased risk of death in the AYA population. This data may guide physician decision making for AYA patients ensuring mindfulness of high costs of care and how it relates to poor survival outcomes in community hospitals.

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Healthcare Resource Utilization and Costs Associated with Venous Thromboembolism Recurrence in Patients with Cancer

Blood ◽

10.1182/blood.v128.22.4734.4734 ◽

2016 ◽

Vol 128 (22) ◽

pp. 4734-4734

Author(s):

Alok A. Khorana ◽

Keith R. McCrae ◽

Dejan Milentijevic ◽

Jonathan Fortier ◽

François Laliberté ◽

...

Keyword(s):

Cancer Patients ◽

Resource Utilization ◽

Healthcare Costs ◽

Research Funding ◽

Healthcare Resource ◽

Healthcare Resource Utilization ◽

Patients With Cancer ◽

Equity Ownership ◽

Recurrent Vte

Abstract Introduction: Patients with cancer are not only at a high risk for developing primary but also recurrent venous thromboembolism (VTE). These events lead to increased burden of cancer management and healthcare costs. It was estimated that all-cause health care costs for cancer patients with VTE were $30,538/patient higher than in those without VTE (Khorana, 2013). To our knowledge, very little information exists on cost of VTE recurrence among cancer patients. The objective of this study was to analyze resource utilization and costs of patients with cancer experiencing a VTE recurrence using a large claims database. Methods: Medical and pharmacy claims from the Humana Database between 1/1/2013 and 05/31/2015 were analyzed. Newly diagnosed cancer patients with a first VTE diagnosis occurring after their first cancer diagnosis and with ≥1 dispensing of an anticoagulant agent within 7 days after their VTE diagnosis, were selected. Baseline characteristics were evaluated during the 6 month period prior to the index VTE. VTE recurrences were defined as hospitalizations with a primary diagnosis of VTE. Patients were classified into two groups: patients who experienced a VTE recurrence and patients who did not. Resource utilization and costs were evaluated for the entire follow up period, starting with the initiation of the anticoagulant therapy until whichever was earlier, end of eligibility or end of data. Healthcare resource utilization evaluated included number of hospitalizations, hospitalization days, emergency room (ER) visits, and outpatient visits. All-cause and VTE-related healthcare resource utilization was evaluated. Comparisons between patients with a VTE recurrence and patients without a VTE recurrence were performed using rate ratios (RR) and statistical differences between groups as well as 95% confidence intervals [95% CI] were calculated using Poisson regression models. All-cause and VTE-related healthcare costs were evaluated in per-patient-per-year (PPPY) and compared using mean cost difference. Results: A total of 2,428 newly diagnosed cancer patients who developed VTE and were treated with anticoagulants were identified. Of these, 413 (17.1%) experienced recurrent VTE during the follow up period. Patients who developed recurrent VTE and those who did not were similar in terms of age, gender, race, and region. No statistically significant differences between groups were observed in Charlson comorbidity index or in selected comorbidities during the 6 month baseline period. However, more patients with recurrent VTE recurrence had their index VTE documented during a hospitalization (61.3% vs. 55.4%, p=0.03). Patients with a VTE recurrence had significantly more ER and outpatient visits at baseline compared to those without recurrence, but no statistically significant difference was observed in baseline total healthcare costs ($29,352 vs. $27,955, p=0.44, respectively). The mean follow-up was similar between groups: 9.6 months for patients experiencing a VTE recurrence and 9.3 months for patients without a VTE recurrence (p=0.4059). Patients with a VTE recurrence had higher all-cause resource utilization rates (RRs; 95% CI) compared to patients without a VTE recurrence (hospitalization [2.37; 2.23 - 2.52], hospitalization days [2.64; 2.57 - 2.72], ER visits [1.62; 1.48 - 1.76], and outpatient visits [1.26; 1.24 - 1.28]). The rates of VTE-related hospitalization and VTE-related hospitalization days were close to $30,000 higher in patients with a VTE recurrence (Figure 1). The all-cause healthcare costs were $84,708 PPPY in patients with a VTE recurrence compared to $44,903 in patients without a VTE recurrence. The difference was mainly explained by lower VTE-related hospitalization costs (Figure 2). Conclusion: This real-world claims analysis showed that cancer patients with recurrent VTE consume significantly more healthcare resources. Total healthcare costs were nearly 2-fold higher in cohort with than in cohort without VTE recurrence. Close to 75% of the total cost difference was associated with VTE recurrence. VTE-related costs were ~4-fold higher in cohort with than in cohort without VTE recurrence. Reducing VTE recurrence in patients with cancer could lead to substantial healthcare cost savings. Figure 1 VTE-Related Healthcare Resource Utilization Figure 1. VTE-Related Healthcare Resource Utilization Figure 2 VTE-Related Healthcare Costs, PPPY Figure 2. VTE-Related Healthcare Costs, PPPY Disclosures Khorana: Pfizer: Consultancy, Honoraria; Bayer: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Halozyme: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Janssen Scientific Affairs, LLC: Consultancy, Honoraria, Research Funding; Leo: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria. McCrae:Janssen: Membership on an entity's Board of Directors or advisory committees. Milentijevic:Janssen Scientific Affairs: Employment, Equity Ownership. Fortier:Janssen Pharmaceuticals: Research Funding. Laliberté:Janssen Scientific Affairs: Research Funding. Crivera:Janssen Scientific Affairs, LLC, Raritan, New Jersey: Employment, Equity Ownership. Lefebvre:Janssen Scientific Affairs: Research Funding. Schein:Johnson & Johnson: Employment, Equity Ownership, Other: Own in excess of $10,000 of J&J stock.

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End-of-life healthcare costs higher in patients with cancer

PharmacoEconomics & Outcomes News ◽

10.1007/s40274-016-3006-4 ◽

2016 ◽

Vol 752 (1) ◽

pp. 12-12

Keyword(s):

End Of Life ◽

Healthcare Costs ◽

Patients With Cancer

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ATORVASTATIN IN PRIMARY PREVENTION AMONG MEN WITH HIGH CORONARY RISK LEVELS

CARDIOVASCULAR THERAPY AND PREVENTION ◽

10.15829/1728-8800-2013-1-54-59 ◽

2013 ◽

Vol 12 (1) ◽

pp. 54-59

Author(s):

I. V. Osipova ◽

N. V. Pyrikova ◽

O. N. Antropova ◽

A. G. Zaltsman ◽

A. I. Miroshnichenko ◽

...

Keyword(s):

Primary Prevention ◽

High Risk ◽

Ldl Cholesterol ◽

Preventive Intervention ◽

Risk Group ◽

High Risk Group ◽

Prevention Programme ◽

Coronary Risk ◽

Risk Levels ◽

Score Risk

Aim.To assess the effectiveness of a complex programme of primary cardiovascular prevention, including statin therapy (Liptonorm), among men from an occupational sample who have high coronary risk levels.Material and methods.The occupational sample included male train drivers and train driver assistants, aged 40–55 yeas. The primary prevention programme included the assessment of the risk factors (RFs) and SCORE risk levels; the development of an individual prevention plan; the Workplace Health School, with Self-Control Diary distribution; and the 6-month administration of Liptonorm (mean dose 14,7±5,1 mg/d) in the high-risk group.Results.In 2010–2011, 224 men participated in the primary prevention programme. The high-risk group, as assessed by the SCORE scale, comprised 14,3%. The results of preventive measures, including the 6-month Liptonorm therapy, are presented for the high-risk group. In particular, 29,4% of the men stopped smoking. The daily number of cigarettes smoked at workplace decreased by 5,1. Consumption of >2 drinks per day, overweight, and abdominal obesity prevalence decreased by 12,5%. The prevalence of insufficient rest time and night sleep <7 hours decreased by 28,1%. Fifty per cent of men increased their physical activity levels, while 34,4% increased their consumption of vegetables. Mean levels of blood pressure reduced by 5 mm Hg. Liptonorm therapy was associated with the achievement of target levels of low-density lipoprotein (LDL) cholesterol (in 84,4%) and the reduction in the levels of triglycerides (by 0,2 mmol/l), total cholesterol (by 1,3 mmol/l), and LDL cholesterol (by 0,7 mmol/l). As a result, the levels of total cardiovascular risk decreased by 1,7%. Therefore, at baseline, out of 224 men, 14,3% had high SCORE levels; after the preventive intervention, this proportion was only 3,6%, as 10,7% moved into the category of moderate SCORE risk.Conclusion.The workplace administration of the complex preventive programme, including statin administration, facilitates modification of behavioural RFs, achievement of target blood lipid levels, and total coronary risk reduction.

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Healthcare Costs in Patients with Cancer Increase with Increasing Risk of Venous Thromboembolism

Blood ◽

10.1182/blood-2018-99-117121 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3799-3799 ◽

Cited By ~ 1

Author(s):

Michael B. Streiff ◽

Keith R. McCrae ◽

Nicole M. Kuderer ◽

Dejan Milentijevic ◽

Guillaume Germain ◽

...

Keyword(s):

Healthcare Costs ◽

Research Funding ◽

Index Date ◽

Outpatient Visit ◽

Baseline Period ◽

Newly Diagnosed ◽

Patients With Cancer ◽

Hospitalization Costs ◽

The Mean ◽

Cost Differences

Abstract Introduction: Venous thromboembolism (VTE) is a cause of significant morbidity and mortality in cancer patients and its prevalence in this population is increasing. The risk of VTE varies with different factors and the Khorana risk model, a clinical VTE risk assessment algorithm, has been developed to predict the risk of VTE in patients with cancer. Information regarding the economic burden associated with the risk of VTE is limited. The current study evaluates healthcare costs associated with different risks of VTE based on Khorana risk scores (KRS) among patients newly diagnosed with cancer. Methods: The Optum©'s Clinformatics® Data Mart database (01/2012 - 09/2017) was used to select patients ≥18 years with ≥1 hospitalizations or 2 outpatient medical claims with a cancer diagnosis (index date) who initiated chemotherapy or radiation therapy within 45 days of the index date. Patients were also required to have ≥6 months of eligibility prior to the index date (i.e., baseline period), no evidence of a VTE during the baseline period, no anticoagulant therapy used during the baseline period or up until a VTE event, and no evidence of major surgery following the index date. Patient also had to have ≥1 laboratory result for hemoglobin, leukocyte, and platelet counts within 28 days before initiating their cancer treatment. The KRS (calculated using the index cancer site, body mass index, and laboratory results prior to treatment [i.e., platelet, leukocyte, and hemoglobin counts]) was used to classify patients in the following cohorts based on KRS: 0, 1, 2, and ≥3. Patients were observed from the index date up to 12 months post index, end of data availability, death, or end of insurance coverage, whichever occurred first. All-cause and VTE-related healthcare costs (i.e., total healthcare, hospitalization, emergency room visit, and outpatient visit costs) were assessed and reported per-patient-per-month (PPPM) in 2018 USD. VTE-related costs were defined based on claims with a primary or secondary diagnosis of VTE and also included anticoagulant therapy costs. Unadjusted and adjusted cost differences (adjusting for age, sex, year and month of index date, insurance type, Quan-Charlson comorbidity index [CCI] score, Elixhauser comorbidities with a proportion ≥5%, type of cancer at index, and healthcare utilization and costs) were calculated. Results: A total of 6,194 patients (KRS=0: 2,488; KS=1: 2,125; KRS=2: 1,074; KRS≥3: 507) were included in this study. The mean age was 68 years, 48% to 52% of patients were female, and the mean CCI ranged from 1.1 to 1.4. The mean follow-up period ranged from 6.9 months for the KRS≥3 cohort to 9.6 months for the KRS=0 cohort. All-cause total healthcare costs PPPM were $8,826 (KRS=0), $11,598 (KRS=1), $14,028 (KRS=2), and $16,211 (KRS≥3) (see Table 1). All-cause hospitalization costs and outpatient visit costs PPPM also increased with VTE risk. Mean unadjusted all-cause total healthcare cost differences were $2,771 for the KRS= 1 vs 0 group; $5,201 for the KRS=2 vs 0 group; and $7,384 for the KRS≥3 vs 0 group. All-cause hospitalization cost differences were $901 for the KRS=1 vs 0 group; $2,416 for the KRS=2 vs 0 group; and $3,698 for the KRS≥3 vs 0 group. Likewise, all-cause outpatient visit cost differences PPPM were $1,005 KRS=1 vs 0 group; $1,525 for the KRS=2 vs 0 group; $1,816 for the KRS≥3 vs 0 group. Similar patterns were observed for VTE-related healthcare cost differences between cohorts. Adjusted analyses yielded similar cost differences between cohorts. Conclusions: Patients newly diagnosed with cancer, who are at a higher risk of a VTE, experienced significantly higher all-cause and VTE-related healthcare costs compared to patients with a lower risk of VTE. VTE-related costs represented 2% to 6.2% of the total costs for KRS=0 to KRS≥3, and were mainly driven by VTE-related hospitalization costs. Part of these costs and consequences of VTE could potentially be reduced in the higher risk subgroups with outpatient prophylaxis Disclosures Kuderer: Myriad Genetics: Consultancy; Pfizer: Consultancy; Mylan: Consultancy, Other: Travel, Accommodations, Expenses; Celldex: Consultancy; Halozyme: Consultancy; Coherus Biosciences: Consultancy, Other: Travel, Accommodations, Expenses; Janssen Scientific Affairs, LLC: Consultancy, Other: Travel, Accommodations, Expenses. Milentijevic:Janssen Scientific Affairs, LLC: Employment, Equity Ownership. Germain:Janssen Scientific Affairs, LLC: Research Funding. Laliberté:Janssen Scientific Affairs, LLC: Research Funding. Le:Janssen Scientific Affairs, LLC: Research Funding. Lefebvre:Janssen Scientific Affairs, LLC: Research Funding. Lyman:Generex Biotechnology: Membership on an entity's Board of Directors or advisory committees; Halozyme; G1 Therapeutics; Coherus Biosciences: Consultancy; Amgen: Other: Research support. Khorana:Bayer: Consultancy; Pfizer: Consultancy; Sanofi: Consultancy; Janssen: Consultancy.

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Impact of smoking on health system costs among cancer patients in a retrospective cohort study in Ontario, Canada

BMJ Open ◽

10.1136/bmjopen-2018-026022 ◽

2019 ◽

Vol 9 (6) ◽

pp. e026022

Author(s):

Wanrudee Isaranuwatchai ◽

Claire de Oliveira ◽

Nicole Mittmann ◽

William K (Bill) Evans ◽

Alice Peter ◽

...

Keyword(s):

Cohort Study ◽

Health System ◽

Healthcare Costs ◽

Smoking Status ◽

Administrative Databases ◽

Cancer Site ◽

Cancer Stage ◽

System Perspective ◽

Patients With Cancer ◽

The Impact

ObjectiveSmoking is the main modifiable cancer risk factor. The objective of this study was to examine the impact of smoking on health system costs among newly diagnosed adult patients with cancer. Specifically, costs of patients with cancer who were current smokers were compared with those of non-smokers from a publicly funded health system perspective.MethodsThis population-based cohort study of patients with cancer used administrative databases to identify smokers and non-smokers (1 April 2014–31 March 2016) and their healthcare costs in the 12–24 months following a cancer diagnosis. The health services included were hospitalisations, emergency room visits, drugs, home care services and physician services (from the time of diagnosis onwards). The difference in cost (ie, incremental cost) between patients with cancer who were smokers and those who were non-smokers was estimated using a generalised linear model (with log link and gamma distribution), and adjusted for age, sex, neighbourhood income, rurality, cancer site, cancer stage, geographical region and comorbidities.ResultsThis study identified 3606 smokers and 14 911 non-smokers. Smokers were significantly younger (61 vs 65 years), more likely to be male (53%), lived in poorer neighbourhoods, had more advanced cancer stage,and were more likely to die within 1 year of diagnosis, compared with non-smokers. The regression model revealed that, on average, smokers had significantly higher monthly healthcare costs ($5091) than non-smokers ($4847), p<0.05.ConclusionsSmoking status has a significant impact on healthcare costs among patients with cancer. On average, smokers incurred higher healthcare costs than non-smokers. These findings provide a further rationale for efforts to introduce evidence-based smoking cessation programmes as a standard of care for patients with cancer as they have the potential not only to improve patients’ outcomes but also to reduce the economic burden of smoking on the healthcare system.

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