38th Annual North American Meeting of the Society for Medical Decision Making

October 23 - 26, 2016

Methods: To determine BUSS-P validation and reliability and to ensure generalizability, multi-centre field testing was conducted at an academic (University Health Network, Toronto, ON) and a community (Trillium Health Partners, Mississauga, ON) hospital. Purposive sampling was used to accrue 112 BCa patients with varying disease severity and treatment history. Patients completed the BUSS-P and five other HRQOL and utility instruments (FACT-Bl, BCI, EQ-5D, SF-36, TTO). Whole and subscale Spearman’s rank correlations (r_s), as well as comparisons of BUSS-P scores across known-groups were used to assess construct validity. Reliability was assessed at two time-points, four weeks apart.

Results: The BUSS-P was found to have high whole-scale correlations with the FACT-Bl (r_s=0.82, p<0.0001), the EQ-5D (r_s=0.65, p<0.0001), and the SF-36v2 (PCS r_s=0.63, p<0.0001; MCS r_s=0.65, p<0.0001). Likewise, high subscale correlations were observed between the BUSS-P and the EQ-5D (emotional wellbeing: r_s=0.69, p<0.0001), the FACT-Bl (physical wellbeing: r_s=-0.70, p<0.0001), and the BCI (urinary issues: r_s=-0.62, p<0.0001). Median BUSS-P scores (M) were found to be significantly different (Kruskal-Wallis p=0.0008) across patients with differing disease severity: non-muscle invasive BC (M=85.0), cystectomy (M=80.0), and metastatic BC patients (M=67.50). Similarly, significant differences (p<0.0001) were noted between the median BUSS-P scores of patients with low comorbidity scores (Charlson Index <4, M=85.0) versus high comorbidity scores (Charlson Index ≥4, M=69.17); a finding that persists when cancer is excluded as a comorbid condition (Charlson Index <2 vs. ≥2, p=0.036). Lastly, excellent agreement was observed between BUSS-P scores at test and retest (ICC=0.79).

Conclusions: These results indicate that the BUSS-P is a valid and reliable instrument to measure HRQOL among all BCa patients (localized to metastatic disease). Future work collecting patient- and community member-generated utility weights to convert the questionnaire into a disease-specific utility instrument is under way.

This study aimed to evaluate the cost-effectiveness of haemodialysis (HD) and two forms of peritoneal dialysis (continuous ambulatory peritoneal dialysis [CAPD] and automated peritoneal dialysis [APD]) for patients with end-stage renal disease (ESRD) in Singapore. 

Method:

A Markov model was developed for patients who started dialysis with HD, CAPD or APD in a time horizon of 10 years. Event data (death, hospitalization, and transplantation) was taken from a hospital database and the national renal registry; health utility data came from published studies of Singaporean dialysis patients, and costs data was obtained from a local hospital and dialysis services providers. Outcome measures were 10-year costs (in 2015 Singaporean dollars [SG$]), quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs).

The base-case was a hypothetical cohort of 60-year-old non-diabetic ESRD patients who started dialysis with one of the three modalities and had no contradictions to any modality. We performed base-case cost-effectiveness analysis, one-way sensitivity analysis, and probabilistic sensitivity analysis with Monte Carlo simulation. A high-risk group of 60-year-old diabetic ESRD patients was also analyzed.

Result:

The base-case analysis showed that the QALYs were 3.27 with CAPD, 3.48 with APD and 4.69 with HD and the total costs were SG$169,872 for CAPD, 201,509 for APD and 306,827 for HD. The analysis of high risk group showed that the QALYs were 2.50 with CAPD, 2.54 with APD and 3.69 with HD. The total costs were SG$144,972 for CAPD, 169,282 for APD and 271,446 for HD.

For both base-case and high-risk groups, CAPD and HD had extended dominance over APD. The ICER of HD versus CAPD was SG$96,447 per QALY for base-case and 106,281 for high-risk group, respectively.

One-way sensitivity analyses showed that the ICER of HD versus CAPD was most sensitive to the utility for HD for base-case and high-risk groups. Probabilistic sensitivity analysis demonstrated that the probability of CAPD being the optimal choice was 36.2% for the base-case and 44.9% for the high-risk group at a willingness-to-pay threshold of SG$60,000 (US$43,000) per QALY. 

Conclusion:

CAPD may be a cost-effective therapy compared with HD and APD in ESRD patients in Singapore. These findings are potentially useful to all stakeholders of the dialysis services in Singapore.

Methods: We used 2007-14 claims from Truven Health Analytics MarketScan Databases to obtain average monthly per-patient expenditures for orally administered anticancer drugs approved by the FDA between 2000 and 2013.  We exploited exogenous variation in the age of diagnosis for different cancer types - and therefore the proportion of individuals diagnosed with different cancers that became eligible for Medicare’s prescription drug coverage – to isolate the impact of Part D on the average monthly per-patient expenditures for oral chemotherapy.  We defined the proportion of each cancer type that was Medicare-eligible as the proportion of patients who were age 65 or older at the time of initial cancer diagnosis, which we obtained for 15 unique cancer types from the Surveillance, Epidemiology and End Results program database.  We used fixed-effects regression models with robust standard errors to evaluate changes over time in monthly costs of oral chemotherapy within each cancer site.  All costs were inflated to 2014 USD.

Results: Across all cancer types, the average monthly cost of oral anticancer drugs increased 8.7% per year over inflation between 2007 and 2014.  Monthly costs increased significantly faster over time for cancer sites with a larger exposure to the Medicare market.  The average monthly cost of using oral chemotherapy rose 1.9% (95% CI: 0.3, 3.5) faster per year for every 1% increase in the share of the cancer site eligible for Medicare.  These cancer-specific trends were driven primarily by greater shifts in utilization towards newly FDA-approved and more expensive oral anticancer therapies rather than larger increases in the costs of existing drugs.

Conclusions: We observed substantial increases in the monthly costs of oral anticancer drugs that may be partly attributable to the reimbursement incentives produced by Medicare Part D.  The impact of Medicare Part D’s mandate to include all anticancer drugs on plans’ formularies on pharmaceutical innovation in the market for oral chemotherapy warrants further exploration.

To compare Thais' estimated and ideal distributions of governmental spending on healthcare services for the rich and poor.

Method:

The survey was conducted by face-to-face interviews in Thailand from February to April 2015. A nationally representative, probability-based, random sample of Thais (N = 3,500) was asked to estimate the distribution of governmental spending on healthcare services for Thais in each of the five income quintiles. Respondents then reported their ideal distributions: how they thought the government should allocate healthcare spending to rich and poor Thais.

We compared estimated and ideal distributions to each other, and to the actual distributions of healthcare spending for the rich and poor. We focused our analyses on government healthcare spending on the richest (top 20%) and poorest (bottom 20%) groups.

Results were analyzed in aggregate and stratified by participants' gender, age, education, and income. Statistical significance was determined at p < 0.05.

Result:

Three thousand and five hundred participants completed the interviews. The response rate was 72.4% (3,500 out of 4,833). Mean age was 41.1 years and half of participants (50.8%) were female.

Respondents underestimated how much of the healthcare budget (i.e., proportion of total healthcare budget) was spent on the richest group (21.9% versus 54.8%) and preferred that a smaller proportion be used for the richest group than their estimate (12.0% versus 21.9%), p < 0.001 for both comparisons. Respondents overestimated how much of the healthcare budget was spent on the poorest group (21.1% versus 5.7%) and preferred that a larger proportion be used for the poorest group compared to their estimate (33.1% versus 21.1%), p < 0.001 for both comparisons. See Figure 1 for full results.

Importantly, respondents preferred that the government spend a significantly higher proportion of healthcare budget on the poorest group compared to the richest group (33.1% versus 12.0%), p < 0.001 – the exact opposite pattern of current spending on the poorest (5.7%) and richest (54.8%).

We found similar results in all subgroup analyses regardless of respondents' gender, age, education and income.

Conclusion:

Thais underestimate the disparities in governmental healthcare spending on the rich and poor. They prefer that the government allocate a higher proportion of healthcare budget to the poor and that the proportion for the poor is larger than the proportion for the rich.