ORAL ABSTRACTS: HEALTH SERVICES, OUTCOMES & POLICY RESEARCH
Salvage radiotherapy (SRT) can be offered to men with prostate cancer who evidence rising PSA levels after radical prostatectomy (RP). Although SRT may achieve biochemical responses, there is no level 1 evidence that shows a survival benefit. The purpose of this study is to describe the impact of SRT on health-related quality of life (HRQoL), and to investigate whether SRT timing (time between RP and SRT) is associated with HRQoL outcomes.
All SRT patients (n=241) and all RP-only patients (n=1005) were selected from a prospective database (2004-2015) of the Antoni van Leeuwenhoek hospital in Amsterdam, the Netherlands. The database contains HRQoL and prostate problem assessments up to two years after last treatment (Figure 1). Mixed effects growth modelling adjusting for significant differences in patient characteristics and baseline HRQoL was used to analyse the association between: (1) ‘treatment' (RP-only vs SRT) and (2) ‘timing of SRT' with changes in HRQoL.
(1) SRT patients showed significantly (p<0.05) poorer recovery from urinary, bowel, and erectile function after their last treatment than RP-only patients (clinically meaningful differences for urinary and erectile function). (2) Patients with a longer interval (≥7months) between RP and SRT reported significantly better sexual satisfaction after SRT (p=0.02), and a better recovery of urinary function (p=0.03).
Up to two years after treatment, SRT patients reported poorer HRQoL in several HRQoL domains as compared to RP-only patients, but not in overall HRQoL. Delaying the start of SRT after RP may limit the incidence and duration of urinary and sexual problems. Nevertheless, decisions regarding SRT timing should also be based on the potential benefits in terms of disease recurrence.
Method(s): Prospective study in ten Dutch hospitals recruiting patients scheduled for prostate biopsy (N=388), from which 126 patients were later diagnosed with Pca and 262 patients served as control group without Pca. Questionnaires were filled out at biopsy (T0, N=377, response rate 97.2%) and after diagnosis, but before treatment was started (T1, N=80, response rate 63.5%). Measures included personality traits (big five, optimism, self-efficacy) and HRQoL (EORTC QLQ-C30 and prostate specific PR25-module). Analyses were performed using T-tests, ANOVA and Pearson correlations.
Result(s): At biopsy no difference was observed in HRQoL between patients later diagnosed with Pca and the control group without Pca. In Pca patients HRQoL then declined following diagnosis. Optimism was positively related to the Global health subscale at T0 (r(338)=.307, p<.001), but not anymore at T1. At T1 only self-efficacy was correlated with Global health (r(67)=.256, p=.03). The largest decline in HRQoL following Pca diagnosis was found on the role functioning subscale.
Conclusion(s): We found evidence that the decline of HRQoL in Pca patients already starts after diagnosis rather than after treatment. In an uncertain situation like prostate biopsy, optimism seems to affect HRQoL. However, after diagnosis we found patients’ self-efficacy to be positively correlated with HRQoL. This may indicate that if a patient believes he is able to do what is needed given his situation, HRQoL is higher. Assumed no major changes have occured in patients’ physical condition in the period around Pca diagnosis, the observed decline in HRQoL may have a psycho-social cause. Interventions to improve self-efficacy in Pca patients following diagnosis could be useful.
Method(s): We analyzed cohort data from the population-based California Healthcare Cost and Utilization Project (HCUP) database from 2007-2010 (n=131,634) for primary TKA discharges in adults ages 50+. We evaluated predictors of hospital LOS, the difference in days between date of admission and date of discharge, using negative binomial regression to model effect(s) of covariates of interest on LOS; we also fit a logistic regression model to predict a binary outcome of “long stay” (top 5% of stays). Finally, we used logistic regression to predict odds of 90-day readmission following TKA. We included demographics (age, sex, race/ethnicity, Medicaid insurance as proxy for low income), comorbidities (including depression), and admission year.
Result(s): Median LOS was 3 days, with a mean of 3.4 days (sd=1.7 days). Overall 90-day readmission rate was almost 17%. A depression diagnosis was associated with a significantly longer LOS (1.05 times longer: 95% CI: 1.04-1.06) and odds of 90-day readmission (OR: 1.21 95%CI:1.13-1.29). LOS and odds of readmission increased with age, as high as 1.15 (95% CI: 1.14-1.17) times longer for those over age 80 than the reference category of 50-54 year olds. There was a significantly longer LOS and odds of readmission associated with being Black compared to White (OR: 1.11; 95% CI: 1.10-1.12 for LOS; OR:1.36, 95%CI: 1.10-1.66 for readmission) and being insured with Medicaid compared to other insurance types (OR: 1.22; 95% CI: 1.20-1.24 for LOS; OR:1.39; 95%CI:1.12-1.73 for readmission).
Conclusion(s): Even after controlling for other chronic conditions and non-modifiable covariates, we found significant associations of depression with longer LOS and readmission rates. Promoting care coordination across disciplines for the management of patients’ non-orthopedic comorbidities prior to surgery, particularly in higher risk patients with depression, could have a positive influence on orthopedic surgery outcomes, patient overall well-being, and ultimately healthcare resource utilization.
Method(s): We conducted interviews among a random sample of 2,103 adult enrollees in individual market health insurance plans offered in California in 2014. We used self-reported household income and size to assess subsidy eligibility. Those eligible for CSR+PTC subsidies had incomes ≤250% of the federal poverty level (FPL), those only eligible for PTC subsidies were between 251-400% FPL. We examined whether these enrollees chose subsidy-eligible plans: to receive the PTC, enrollees had to select a plan through the exchange; to receive the CSR, enrollees had to select an on-exchange Silver-tier plan. We also examined the types of assistance enrollees received when choosing their plan and the perceived affordability of their premiums and out-of-pocket costs during the year. We used multivariate models to adjust for sociodemographic characteristics.
Result(s): In California’s 2014 individual insurance market, 51% of enrollees were at or below 250% FPL, 22% were between 251-400% FPL, and 27% were >400% FPL. Among the subsidy-eligible, 17% purchased plans off-exchange (e.g., directly through insurers), thus forgoing subsidies (14% of PTC+CSR-eligible and 25% of PTC-eligible). Among CSR-eligible enrollees who chose on-exchange plans, 77% enrolled in Silver plans vs. 17% Bronze and 7% Gold/Platinum plans. Subsidy-eligible enrollees who purchased off- vs. on-exchange plans were less likely to receive help from Covered California counselors (9% vs. 32%) and more likely to use insurance agent/brokers (31% vs. 24%). Among subsidy-eligible enrollees, those who purchased off- vs. on-exchange were more likely to have a lot of difficulty paying premiums (e.g., 27% vs. 12%, OR=2.7, 95%CI: 1.5-4.9 among those 251-400% FPL); among CSR+PTC-eligible enrollees (≤250% FPL) those who purchased on-exchange Bronze vs. Silver plans more frequently had difficulty paying out-of-pocket costs (16% vs. 8%, OR=2.3, 95%CI: 1.3-4.3).
Conclusion(s): Many lower-income enrollees appeared to forfeit available premium and/or cost-sharing assistance by purchasing plans that were ineligible for subsidies. Enrollees who received subsidies perceived their insurance and medical care to be more affordable than those who did not.
Method(s): We studied five antibiotics that have gone through a health technology assessment process in at least two European countries since 2010: fidaxomicin, aztreonam, ceftaroline fosamil, tigecycline, and colistimethate sodium. We selected the drugs to include a mix of new technologies and reformulations of older products. For each antibiotic, we identified every report from a health technology assessment body publicly available in English, Spanish, German, or Dutch. We systematically reviewed the reports to identify the evidence, sources of value, and other factors the agency considered in the health technology assessment. We supplemented this review by interviewing the pharmaceutical companies that developed the products to collect additional information about the health technology assessment process and its data and modelling requirements.
Result(s): We found health technology assessments from at least two agencies for each product. Fidaxomicin was the most widely studied product with reports. The health technology assessments are based on clinical trial data and simple economic models that focus primarily on the direct treatment benefit of the drugs to patients. The threat of antibiotic resistance was mentioned irregularly. The assessments did not consider the value of antibiotics in enabling surgeries and other procedures, the insurance value of having an approved antibiotic ready when a new resistant outbreak emerges, or the diversity value of having multiple drugs with different modes of action available for a given infection.
Conclusion(s): Current health technology assessment practices do not encapsulate the full value of antibiotics. Ignoring the types of value unique to antibiotics may result in their being undervalued, which could make it less attractive for pharmaceutical companies to invest in research and development of new antibiotic. There is a need for simple modelling frameworks that can better capture the true economic value of antibiotics.
Method(s): Barriers and facilitators to Telehomecare were explored with over thirty hours of ethnographic observation and 89 semi-structured interviews (39 patients, 16 nurses, 7 physicians, 12 administrators, 13 decision-makers and 2 technicians), which were conducted across three Local Health Integration Networks (LHINs) in Ontario, with each LHIN representing a case study. Combination of purposeful and snowball sampling was used to recruit study participants. Phone or in-person interviews were conducted and ranged from 20 minutes to 2 hours in duration. Interviews were audio-taped, transcribed, and coded inductively using a descriptive content analytic approach to identify common themes and patterns (constant comparison) within and across the LHINs and across five levels of a multi-level framework (technology, patients, providers, organizations, and structures).
Result(s): Key findings include common themes of high caseload and unrealistic enrollment targets found across the LHINs. High patient caseload such (60 or higher) was identified as a strong barrier in providing quality patient care. Common critical facilitators found were patient motivation, confidence and willingness. Organizational culture also emerged as a predominant theme across all LHIN. More specifically, when the organizational culture is open and respectful, all levels of staff were able to connect with each other and feel their beliefs and insights were valued. Similarly, the role of an ‘Engagement Lead’ was found as a critical facilitator for program implementation contributing to increased awareness and referrals to the program.
Conclusion(s): Despite the potential of Telehomecare to strengthen models of health care provision, challenges remain. Key barriers and facilitators impacting the implementation and adoption of Telehomecare across the province were identified. Some were common across all LHINs, while others were context driven and LHIN specific. By strengthening program facilitators and successfully addressing the barriers, the implementation and adoption of Telehomecare can be significantly improved. Further implementation of Telehomecare must involve continuous assessments and dialogue (both local and broad-based) of what is working and not working with multiple stakeholders. This can inform decision-making that better reflects the needs of all program stakeholders.