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1.
Evaluation of DAA
(direct-acting antiviral) access across the US: the interplay between Hepatitis C (HCV) patients and insurance type Jason Katz1 , Aiyaz Mohammed2 , James Deemer1 , Elizabeth Baynton2 1 Ipsos Healthcare, New York, NY, United States, 2 Ipsos Healthcare, London, United Kingdom Access to highly effective new Direct Acting Antiviral (DAA) regimens containing sofosbuvir (and/or ledipasvir) or ombitasvir/paritaprevir/ ritonavir and dasabuvir come at a high cost, making health insurance companies reluctant to cover all patients - mitigating treatment access across the US. This has led to several lawsuits surrounding denial of coverage of drugs against health insurance companies who have set tight restrictions on treatment access1 . Factors such as gender, age, severity of liver damage, and the extent of viral load have been shown to significantly impact one’s chances of gaining access to treatment 2 . The aim of this study was to analyze the effect of previously associated factors on treatment outcome using 2015 Ipsos HCV Therapy Monitor data and Chi-squared Automatic Interaction Detection (CHAID). Here we leverage our chart study on Hepatitis C patients and a CHAID analysis to determine how viral load, comorbidities, substance abuse, insurance, and fibrosis score influence treatment decisions. 1. Abram, S. (2015, June 1). Woman sues Anthem Blue Cross for denying hepatitis C drug Harvoni. Retrieved October 27, 2015. 2. Do A, Mittal Y, Liapakis A, Cohen E, Chau H, Bertuccio C, et al. (2015) Drug Authorization for Sofosbuvir/Ledipasvir (Harvoni) for Chronic HCV Infection in a Real-World Cohort: A New Barrier in the HCV Care Cascade. PLoS ONE 10(8): e0135645. doi:10.1371/journal.pone.0135645 Ipsos Healthcare’s HCV Therapy Monitor, running since 2005 in the USA, reports on 150 physicians per quarter across the USA. Physicians provide patient demographic, disease and treatment data on treated and untreated HCV patients seen within each study period. In the abstract we used January to March 2015 data, but in this analysis we used HCV Therapy Monitor data from January to August 2015, the months in which all sofosbuvir, sofosbuvir+ledipasvir, and ombitasvir/paritaprevir/ritonavir and dasabuvir regimens were available. We also added a Chi-squared Automatic Interaction Detection (CHAID) analysis using R programming, with treatment status as the dependent variable and fibrosis, viral load, insurance, and comorbidities as the independent variables. All variables are categorized in order to provide a high level view of these factors in the analysis. The CHAID analysis was performed without a predetermined formula but was pruned to keep all patient observations to 30 patients and above. The analysis was replicated independently. The results section will be limited to specific branches of the CHAID decision tree. Premise Methods Results • Of the variables included, the CHAID analysis identified fibrosis as the most influential to DAA access χ2(1, N=4755)=501.462 p.01 • For F2 patients, access to new DAA treatment was significantly attributed to whether the patient was a ‘substance abuser’ (patients who are IV drug users and/or alcohol abusers) χ2(1, N=913)=79.856 p.01, where 88.5% of substance abusers are not treated versus 46.4% of the ’non abusers’. Within the ‘non abuser’ branch, the next most significant variable is patients’ viral load χ2(2, N=782)=20.057 p.01. For the patients with a low viral load (6M IU/ml of viral RNA), new DAA access reduces slightly with 48.5% lacking treatment. Amongst the patients with lower viral load, access to treatment is subsequently dependent on the presence of additional comorbidities χ2(1, N=596)=9.518 p.01. Patients without additional comorbidities experienced improved treatment access with 38% of patients lacking treatment (n=158); the presence of comorbidities left the patients slightly less likely to receive treatment at 52.3% (n=438). Moreover, in patients with a high viral load (6M IU/ml of viral RNA) access to treatment is higher with 32.2% lacking treatment. This segment is significantly influenced by insurance-type χ2(1, N=146)=28.763 p.01. Over half of the publically-insured patients did not receive treatment at 59.3% (n=54), whilst the privately insured patients were more likely to access new DAAs; only 16.3% of these patients lacked treatment (n=92). Conclusion Future Work • In F4 patients, determining whether the patient was a substance abuser primarily determined the likelihood of obtaining DAA treatment χ2(1, N=834)=53.220 p.01, with 61% of all patients not receiving treatment. Amongst the ‘substance abusers’, the not treated fraction increased to 88.1% (n=691). In contrast, patients who were ‘non-abusers’ were more inclined to receive treatment with 55.4% lacking a new DAA (n=143). Overall, the CHAID analysis has 70% predictive accuracy when differentiating non-treated vs treated status with a DAA. CHAID analysis in conjunction with the depth of information captured in the Ipsos HCV Therapy Monitor denoted the most influential factors most frequently associated in determining access to new DAA treatment in HCV patients. This effect was most evident in patients with moderate liver damage, with no substance abuse and higher viral loads, where there was heavy reliance on private insurance in order to obtain access to treatment. This same segmentation is not required on the cirrhotic branch, where we see substance abuse status holding the most prominent influence on treatment status. One of the most obvious trends here is that as fibrosis levels increase insurance becomes less of an issue, whilst compliance, and therefore substance abuse, must always be accounted for, even with the high risk pool (cirrhotic) patients. Here we see that these factors are not independent of one another and must be properly weighted by the CHAID analysis in order to reveal the most insightful findings. Results may be further optimized with careful additions and edits. Future work involves performing expanded CHAID analysis including age, gender, MELD score, and a comorbidity score; insurance categories can also be expanded to list to actual insurance type. Additionally, an arbitrary stratification of the currently treated patient segment into patients who have achieved RVR and under treatment for more than 4 weeks vs those who have not can be applied, with a view to excluding the latter segment; this definition can be used to refine the treated segment to patients that have definitely commenced their treatment, excluding those that have been prescribed a new DAA but may not necessarily have received the script/commenced taking the treatment yet. We believe that these amendments will add an enhanced level of detail to our analysis, increasing confidence intervals of the findings and help further pinpoint the underserved markets. 35.6% 46.4% 83.3% 68.4% 88.5% Yes 67.5% 6M IU/ml 32.2% 52.3% 38.0% 52.5% 16.3% 88.1% 61.0% 55.4% 158 48.5% 64.1% 4755 Fibrosis Score IPSOS HCV TM Q1-Q3’15 pa ents F2 F3 913 834 Is the pa ent an ‘Abuser’ Is the pa ent an ‘Abuser’ No 782 HCV viral load 596 No Yes 691 143 6M IU/ml 146 Insurance Does the pa ent have comorbidi es? 438 92 Private Yes No F1 F4 131 40 NS 59.3%54 Government Not Tested 1368 Sample size % untreated Top driver of treatment decision Second er drivers Third er drivers Fourth er drivers All data collected online; © Ipsos 2015, all rights reserved.
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