Corroboration of claims algorithm for second-line costs of metastatic colorectal cancer treatment with targeted agents

Abstract

Objective:

To refine a claims algorithm for identifying second-line systemic regimens for metastatic colorectal cancer (mCRC) based on clinical evidence and to compare costs during second-line treatment by targeted therapy administered.

Methods:

This retrospective analysis of a large US managed care database identified patients diagnosed with mCRC during 1 July 2007–30 June 2011. A claims-based algorithm was developed to identify patients with at least two lines of therapy (LOT) and the second LOT contained one targeted agent: bevacizumab or any anti-epidermal growth factor receptor (EGFR). Medical chart data from 92 patients were used to corroborate and refine the LOT algorithm. The positive predictive value (PPV) of the initial algorithm and refined algorithm for identification of second LOT are presented. The final algorithm was applied to claims data and two mutually exclusive second-line cohorts were examined: patients with bevacizumab- or cetuximab-containing regimens. Second-line healthcare costs were analyzed with generalized linear models adjusted for demographic and clinical characteristics.

Results:

The PPV increased from 50.0% (95% CI?=?39.4–60.6) for the initial algorithm to 72.1% (95% CI?=?59.2–82.9) for the final algorithm. Mean age in the cohorts (n?=?569) was 61 years; 58% were men. Days of therapy were similar for the bevacizumab (n?=?450) vs cetuximab (n?=?119) cohorts, respectively: 131 vs 148 in first LOT and 123 (both cohorts) in second LOT (p?≥?0.27). Total costs during second-line treatment in the bevacizumab cohort were lower by $12,318 (p?=?0.02) and medical costs were lower by $13,809 (p?=?0.01). Monthly total and medical costs were lower by $2728 (p?=?0.03) and $3133 (p?=?0.01), respectively. Results are based on commercially or Medicare-insured patients and may not be generalizable to Medicaid or uninsured patients.

Conclusions:

Corroboration of claim-based algorithms with medical chart data improved algorithm performance. Second-line total and medical costs were lower for mCRC patients treated with bevacizumab compared with cetuximab.     

Economic analysis of bevacizumab,cetuximab, and panitumumab with fluoropyrimidine-based chemotherapy in the first-line treatment of KRAS wild-type metastatic colorectal cancer (mCRC)

Abstract

Objective:

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in Canada (excluding non-melanoma skin cancers). Bevacizumab is a recombinant humanized monoclonal antibody that selectively binds to human vascular endothelial growth factor. A sub-study confirmed its effectiveness in KRAS wild-type patients. Recent evidence has shown clinical benefit from anti-epidermal growth factor treatments cetuximab and panitumumab in these patients. The cost-effectiveness, to the Canadian healthcare system, of fluoropyrimidine-based chemotherapy (FBC) in combination with bevacizumab, cetuximab, or panitumumab was assessed for first-line treatment of KRAS wild-type mCRC patients.

Methods:

A Markov model was developed and calibrated to progression-free/overall survival, using separately reported trial survival and adverse event results for each comparator. Health-state resource utilization was derived from published data and oncologist input. Utilities and unit prices were obtained from published literature and standard Canadian sources.

Results:

Results per patient are over a lifetime horizon, to a maximum of 10 years, with 5% annual discounting. Comparators are ordered by total cost and the incremental cost-effectiveness ratio (ICER) of each is determined against the previous non-dominated therapy. Compared to FBC alone, bevacizumab?+?FBC has an ICER of $131,600 per QALY gained. Compared to bevacizumab?+?FBC, panitumumab?+?FBC is dominated and cetuximab?+?FBC has an ICER of $3.8 million per QALY. In probabilistic sensitivity analysis, bevacizumab?+?FBC had ～100%, ～100%, and 98.9% probabilities of being more cost-effective than both of the other combination treatments at thresholds of $50,000/QALY, $100,000/QALY, and $200,000/QALY, respectively.

Conclusion:

For first-line treatment of KRAS-WT mCRC, bevacizumab?+?FBC is associated with substantially lower costs as compared to panitumumab?+?FBC or cetuximab?+?FBC. Key limitations were that survival curves and adverse event rates were taken from separate clinical trials and that an indirect comparison was not included. Given these findings, bevacizumab is likely to offer the best value for money for this patient population.     

Major bleeding risk and healthcare economic outcomes of non-valvular atrial fibrillation patients newly-initiated with oral anticoagulant therapy in the real-world setting

Aims: This study compared the risk for major bleeding (MB) and healthcare economic outcomes of patients with non-valvular atrial fibrillation (NVAF) after initiating treatment with apixaban vs rivaroxaban, dabigatran, or warfarin.

Methods: NVAF patients who initiated apixaban, rivaroxaban, dabigatran, or warfarin were identified from the IMS Pharmetrics Plus database (January 1, 2013–September 30, 2015). Propensity score matching (PSM) was used to balance differences in patient characteristics between study cohorts: patients treated with apixaban vs rivaroxaban, apixaban vs dabigatran, and apixaban vs warfarin. Risk of hospitalization and healthcare costs (all-cause and MB-related) were compared between matched cohorts during the follow-up.

Results: During the follow-up, risks for all-cause (hazard ratio [HR]?=?1.44, 95% confidence interval [CI]?=?1.2–1.7) and MB-related (HR?=?1.57, 95% CI?=?1.0–2.4) hospitalizations were significantly greater for patients treated with rivaroxaban vs apixaban. Adjusted total all-cause healthcare costs were significantly lower for patients treated with apixaban vs rivaroxaban ($3,950 vs $4,333 per patient per month [PPPM], p?=?.002) and MB-related medical costs were not statistically significantly different ($100 vs $233 PPPM, p?=?.096). Risk for all-cause hospitalization (HR?=?1.98, 95% CI?=?1.6–2.4) was significantly greater for patients treated with dabigatran vs apixaban, although total all-cause healthcare costs were not statistically different. Risks for all-cause (HR?=?2.22, 95% CI?=?1.9–2.5) and MB-related (HR?=?2.05, 95% CI?=?1.4–3.0) hospitalizations were significantly greater for patients treated with warfarin vs apixaban. Total all-cause healthcare costs ($3,919 vs $4,177 PPPM, p?=?.025) and MB-related medical costs ($96 vs $212 PPPM, p?=?.026) were significantly lower for patients treated with apixaban vs warfarin.

Limitations: This retrospective database analysis does not establish causation.

Conclusions: In the real-world setting, compared with rivaroxaban and warfarin, apixaban is associated with reduced risk of hospitalization and lower healthcare costs. Compared with dabigatran, apixaban is associated with lower risk of hospitalizations.     

Impact of completing chronic hepatitis C (CHC) treatment on post-therapy healthcare cost

Background:

Chronic hepatitis C (CHC) is associated with significant economic burden. This study evaluated the healthcare cost alleviation associated with treatment of CHC.

Methods:

Health insurance claims from 60 self-insured US companies were analyzed (01/2001–03/2012). Adult patients with ≥1 CHC diagnosis (ICD-9-CM: 070.44, 070.54), initiating interferon, and with ≥2 dispensings and with ≥48 weeks of follow-up were selected. Patients diagnosed with HIV or who completed only 24 weeks of interferon therapy (a surrogate for CHC genotypes 2 and 3) were excluded from the study. Interferon users were categorized into complete and discontinued therapy cohorts. During the post–48-week treatment period, cohorts were compared for healthcare resource utilization using rate ratios (RRs), as well as healthcare costs using per-patient per-year (PPPY) cost differences.

Results:

A total of 1017 patients who completed and 953 patients who discontinued interferon therapy were identified. Relative to the discontinued therapy cohort, the completed therapy cohort had significantly fewer hospitalizations (RR [95% CI]?=?0.74 [0.68, 0.81], p?p?p?=?0.039), which translated into significantly lower total healthcare costs PPPY (cost difference [95% CI]?=?$4540 [1570, 7680], p?=?0.004) and hospitalization costs (cost difference [95% CI]?=?$3039 [1140, 5248], p?=?0.002). Non–CHC-related costs accounted for 55% and CHC-related costs for 45% of the all-cause cost difference between cohorts.

Limitations:

Claims data may have contained inaccuracies, and genotypes of patients with CHC could not be confirmed. The study consisted of privately insured individuals and may not be generalizable to the entire CHC population.

Conclusion:

Compared to discontinued therapy patients, CHC patients who completed interferon therapy and presumably had a higher rate of achieving SVR were found to have lower levels of healthcare resource utilization and costs post-therapy. The reduction was primarily in costs associated with non–HCV-related comorbidities.     

Comparison of medical costs and healthcare resource utilization of post-menopausal women with HR+/HER2− metastatic breast cancer receiving everolimus-based therapy or chemotherapy: a retrospective claims database analysis

Objective:

To analyze medical costs and healthcare resource utilization (HRU) associated with everolimus-based therapy or chemotherapy among post-menopausal women with hormone-receptor-positive, human-epidermal-growth-factor-receptor-2-negative (HR+/HER2?) metastatic breast cancer (mBC).

Methods:

Patients with HR+/HER2? mBC who discontinued a non-steroidal aromatase inhibitor and began a new line of treatment with everolimus-based therapy or chemotherapy (index therapy/index date) between July 20, 2012 and April 30, 2014 were identified from two large claims databases. All-cause, BC-related, and adverse event (AE)-related medical costs (in 2014 USD) and all-cause HRU per patient per month (PPPM) were analyzed for both treatment groups across patients’ first four lines of therapies for mBC. Adjusted differences in costs and HRU between the everolimus and chemotherapy treatment group were estimated pooling all lines and using multivariable generalized linear models, accounting for difference in patient characteristics.

Results:

A total of 3298 patients were included: 902 everolimus-treated patients and 2636 chemotherapy-treated patients. Compared to chemotherapy, everolimus was associated with significantly lower all-cause (adjusted mean difference?=?$3455, p?<?0.01) and BC-related ($2510, p?<?0.01) total medical costs, with inpatient ($1344, p?<?0.01) and outpatient costs ($1048, p?<?0.01) as the main drivers for cost differences. Everolimus was also associated with significantly lower AE-related medical costs ($1730, p?<?0.01), as well as significantly lower HRU (emergency room incidence rate ratio [IRR]?=?0.83; inpatient IRR?=?0.74; inpatient days IRR?=?0.65; outpatient IRR?=?0.71; BC-related outpatient IRR?=?0.57; all p?<?0.01).

Conclusions:

This retrospective claims database analysis of commercially-insured patients with HR+/HER2? mBC in the US showed that everolimus was associated with substantial all-cause, BC-related, and AE-related medical cost savings and less utilization of healthcare resources relative to chemotherapy.     

Comparative net cost impact of the utilization of panitumumab versus cetuximab for the treatment of patients with metastatic colorectal cancer in Canada

Objective:

Clinical practice guidelines support the use of the epidermal growth factor receptor (EGFR) inhibitors panitumumab and cetuximab for the treatment of metastatic colorectal cancer (mCRC) after failure of other chemotherapy regimens, based on significant clinical benefits in patients with wild-type KRAS. The purpose of the analysis was to compare provincial hospital costs when using panitumumab vs cetuximab with or without irinotecan in this patient population using a Net Impact Analysis (NIA) approach.

Methods:

The NIA determined the total per patient cost of the reimbursed regimens of panitumumab vs cetuximab in British Columbia, Alberta, Manitoba, Ontario, and Québec. Utilization of healthcare resources related to EGFR inhibitor infusions, follow-up monitoring, and treatment of adverse events (AEs) were also included. Healthcare resource use including drugs, medical supplies, laboratory testing, oncology infusion time, and healthcare professionals’ time was obtained through expert consultation and the use was then multiplied by the province-specific cost of each resource. Numerous sensitivity analyses were conducted.

Results:

Based on the dosing regimens in place in each province, the total annual per patient cost of panitumumab ranged from $22,203–$32,600, while the total annual per patient cost of cetuximab treatment varied from $30,321–$40,908. Treatment with panitumumab resulted in lower costs in all cost categories including drug acquisition, infusion preparation/administration, patient monitoring, and AE management. Per patient savings with panitumumab ranged from a low of $3815 in British Columbia to a high of $10,603 in Ontario. In sensitivity analyses, panitumumab remained cost saving in all scenarios where the savings ranged from $150–$16,006 per patient.

Conclusions:

Treating chemorefractory mCRC patients with panitumumab rather than cetuximab reduced healthcare resource costs. Provincial healthcare savings achieved with the use of panitumumab could potentially be re-allocated to other cancer treatments, although further study would be needed to validate this assumption.     

The economic burden of switching targeted disease-modifying anti-rheumatic drugs among rheumatoid arthritis patients

Aims: To estimate real world healthcare costs and resource utilization of rheumatoid arthritis (RA) patients associated with targeted disease modifying anti-rheumatic drugs (tDMARD) switching in general and switching to abatacept specifically.

Materials and methods: RA patients initiating a tDMARD were identified in IMS PharMetrics Plus health insurance claims data (2010–2016), and outcomes measured included monthly healthcare costs per patient (all-cause, RA-related) and resource utilization (inpatient stays, outpatient visits, emergency department [ED] visits). Generalized linear models were used to assess (i) average monthly costs per patient associated with tDMARD switching, and (ii) among switchers only, costs of switching to abatacept vs tumor necrosis factor inhibitors (TNFi) or other non-TNFi. Negative binomial regressions were used to determine incident rate ratios of resource utilization associated with switching to abatacept.

Results: Among 11,856 RA patients who initiated a tDMARD, 2,708 switched tDMARDs once and 814 switched twice (to a third tDMARD). Adjusted average monthly costs were higher among patients who switched to a second tDMARD vs non-switchers (all-cause: $4,785 vs $3,491, p?p?p?p?=?.021), and numerically lower all-cause costs ($4,444 vs $4,741, p?=?0.188). Switchers to TNFi relative to abatacept had more frequent inpatient stays after switch (incidence rate ratio (IRR) = 1.85, p?=?.031), and numerically higher ED visits (IRR = 1.32, p?=?.093). Outpatient visits were less frequent for TNFi switchers (IRR = 0.83, p?Limitations and conclusions: Switching to another tDMARD was associated with higher healthcare costs. Switching to abatacept, however, was associated with lower RA-related costs, fewer inpatient stays, but more frequent outpatient visits compared to switching to a TNFi.     

Switching of biologic disease modifying anti-rheumatic drugs in patients with rheumatoid arthritis in a real world setting

Objectives:

This study examined total healthcare costs and rates of patients with rheumatoid arthritis (RA) who switch biologic disease-modifying anti-rheumatic drug (bDMARD) therapy in a real world setting.

Methods:

A retrospective longitudinal analysis was conducted in patients with RA using IMS PharMetrics Plus database from 1/1/2004 to 3/31/2010. The first-line cohort included patients newly initiated on abatacept or the tumor necrosis factor-alpha inhibitors (anti-TNFs) adalimumab, etanercept, or infliximab, with 12 months of continuous follow-up. The second-line cohort included patients initiating a bDMARD with evidence of a different bDMARD within the previous 2 years and with 12 months of continuous follow-up. Switching was defined as a different bDMARD claim within a 200% gap in days supply from the previous bDMARD claim. Non-switchers stayed on their bDMARD in the follow-up period. Monthly total healthcare costs for switchers and non-switchers and rates of bDMARD switching were examined. Switch rates for each bDMARD were also compared.

Results:

First-line switchers had significantly higher monthly total healthcare costs after the switch than non-switchers ($3759 vs $2343; p?p?Limitations:

There are no clinical data available in this database and, therefore, this study did not examine the clinical drivers of healthcare costs and switch rates.

Conclusions:

Monthly total healthcare costs were higher for bDMARD switchers following the switch compared to non-switchers. Patients on abatacept switched less frequently than patients on anti-TNFs. This study highlights the need to identify patients who are likely to switch in order to ensure they receive the appropriate therapy which may improve outcomes and decrease healthcare costs.     

The economic burden of common adverse events associated with metastatic colorectal cancer treatment in the United States

Aims: Adverse events (AEs) associated with treatments for metastatic colorectal cancer (mCRC) may compromise the course of treatment, impact quality-of-life, and increase healthcare resource utilization. This study assessed the direct healthcare costs of common AEs among mCRC patients in the US.

Methods: Adult mCRC patients treated with chemotherapy or targeted therapies were identified from administrative claims databases (2009–2014). Up to the first three mCRC treatment episodes per patient were considered and categorized as with or without the AE system/organ category during the episode. Total healthcare costs (2014 USD) were measured by treatment episode and reported on a monthly basis. Treatment episodes with the AE category were matched by treatment type and line of treatment to those without the AE category. Adjusted total cost differences were estimated by comparing costs during treatment episodes with vs without the AE category using multivariate regression models; p-values were estimated with bootstrap.

Results: A total of 4158 patients with ≥1 mCRC treatment episode were included (mean age?=?59 years; 58% male; 60% with liver and 14% with lung metastases; 2,261 [54%] with a second and 1,115 [27%] with a third episode). On average, two treatment episodes were observed per patient with an average length of 166 days per episode. Adjusted monthly total cost difference by AE category included hematologic ($1,480), respiratory ($1,253), endocrine/metabolic ($1,213), central nervous system (CNS; $1,136), and cardiovascular ($1,036; all p?Limitations: Claims do not include information on the cause of AEs, and potentially less severe AEs may not have been reported by the physician when billing the medical service. This study aimed to assess the association between costs and AEs and not the causation of AEs by treatment.

Conclusions: The most costly AEs among mCRC patients were hematologic, followed by respiratory, endocrine/metabolic, CNS, and cardiovascular.     

Real-world treatment patterns and healthcare costs among biologic-naive patients initiating apremilast or biologics for the treatment of psoriasis

Objective: This study compared real-world treatment patterns and healthcare costs among biologic-naive psoriasis patients initiating apremilast or biologics.

Methods: A retrospective cohort study was conducted using the Optum Clinformatics? claims database. Patients with psoriasis were selected if they had initiated apremilast or biologics between January 1, 2014, and December 31, 2015; had 12?months of pre-index and post-index continuous enrollment in the database; and were biologic-naive. The index date was defined as the date of the first claim for apremilast or biologic, and occurred between January 1, 2014, and December 31, 2015. Treatment persistence was defined as continuous treatment without a?>?60-day gap in therapy (discontinuation) or a switch to a different psoriasis treatment during the 12-month post-index period. Adherence was defined as a medication possession ratio (MPR) of ≥ 80% while persistent on the index treatment. Persistence-based MPR was defined as the number of days with the medication on hand measured during the patients’ period of treatment persistence divided by the duration of the period of treatment persistence. Because patients were not randomized, apremilast patients were propensity score matched up to 1:2 to biologic patients to adjust for possible selection bias. Treatment persistence/adherence and all-cause healthcare costs were evaluated. Cost differences were determined using Wilcoxon rank-sum tests.

Results: In all, 343 biologic-naive patients initiating apremilast were matched to 680 biologic-naive patients initiating biologics. After matching, patient characteristics were similar between cohorts. Twelve-month treatment persistence was similar for biologic-naive patients initiating apremilast vs biologics (32.1% vs 33.2%; p?=?0.7079). While persistent on therapy up to 12?months, per-patient per-month (PPPM) total healthcare costs were significantly lower among biologic-naive cohorts initiating apremilast vs biologics ($2,214 vs $5,184; p?p?p?p?Limitations: Data were limited to individuals with United Healthcare commercial and Medicare Advantage insurance plans, and may not be generalizable to psoriasis patients with other insurance or without health insurance coverage.

Conclusion: Biologic-naive patients with similar patient characteristics receiving apremilast vs biologics had significantly lower PPPM costs, even when they switched to biologics during the 12-month post-index period. These results may be useful to payers and providers seeking to optimize psoriasis care while reducing healthcare costs.