Patterns of multispecialty care for low- and intermediate-risk prostate cancer in the use of active surveillance.

BACKGROUND: Multidisciplinary models of care have been advocated for prostate cancer (PC) to promote shared decision-making and facilitate quality care. Yet, how this model applies to low-risk disease where the preferred management is expectant remains unclear. Accordingly, we examined recent practice patterns in specialty visits for low/intermediate-risk PC and resultant use of active surveillance (AS). METHODS: Using SEER-Medicare, we ascertained whether patients saw urology and radiation oncology (i.e., multispecialty care) versus urology alone, based on self-designated specialty codes, for newly diagnosed PC from 2010 to 2017. We also examined the association with AS, defined as the absence of treatment within 12 months of diagnosis. Time trends were analyzed using Cochran-Armitage test. Chi-squared and logistic regression analyses were applied to compare sociodemographic and clinicopathologic characteristics between these models of care. RESULTS: The proportion of patients seeing both specialists was 35.5% and 46.5% for low- and intermediate-risk patients respectively. Trend analysis showed a decline in multispecialty care in low-risk patients (44.1% to 25.3% years 2010-2017; P < 0.001). Between 2010 and 2017, the use of AS increased 40.9% to 68.6% (P < 0.001) and 13.1% to 24.6% (P < 0.001) for patients seeing urology and those seeing both specialists respectively. Age, urban residence, higher education, SEER region, co-morbidities, frailty, Gleason score, predicted receipt of multispecialty care (all P < 0.02). CONCLUSIONS: Uptake of AS among men with low-risk PC has occurred primarily under the purview of urologists. While selection is certainly at play, these data suggest that multispecialty care may not be required to promote the utilization of AS for men with low-risk PC.

Use of expectant management based on prostate cancer risk and health status: How far are we from a risk-adapted approach?

OBJECTIVES: While active surveillance, a form of expectant management (EM), is preferred for patients with low-risk prostate cancer (PCa), some favor a more risk-adapted approach that recognizes patient preferences and condition-specific factors. However, previous research has shown non-patient-related factors often drive PCa treatment. In this context, we characterized trends in AS with respect to disease risk and health status. METHODS AND MATERIALS: Using SEER-Medicare data, we identified men 66 years and older diagnosed with localized low- and intermediate-risk PCa from 2008 to 2017 and examined receipt of EM, defined as the absence of treatment (i.e., surgery, cryotherapy, radiation, chemotherapy, and androgen deprivation therapies) within 1 year of diagnosis. We performed bivariable analysis to compare trends in use for EM vs. treatment, stratified by disease risk (i.e., Gleason 3+3, 3+4, 4+3; PSA<10, 10-20) and health status (i.e., NCI Comorbidity Index (NCI), frailty, life expectancy). We then ran a multivariable logistic regression model to examine determinants of EM. RESULTS: Within this cohort, 26,364 (38%) were categorized as low-risk (i.e., Gleason 3+3 and PSA<10) and 43,520 (62%) as intermediate-risk (i.e., all others). Over the study period, use of EM significantly increased across all risk groups, except for Gleason 4+3 (P = 0.662), as well across all health status groups. However, linear trends did not differ significantly between frail vs. nonfrail patients for both those categorized as low-risk (P = 0.446) and intermediate-risk (P = 0.208). Trends also did not differ between NCI 0 vs. 1 vs. >1 for low-risk PCa (P = 0.395). In the multivariable models, EM was associated with increasing age and being frail for men with both low- and intermediate risk disease. Conversely, EM selection was negatively associated with higher comorbidity score. CONCLUSIONS: EM increased significantly over time for patients with low- and favorable intermediate-risk disease, with the most notable differences based on age and Gleason score. In contrast, trends in uptake of EM did not differ substantively by health status, suggesting that physicians may not be effectively incorporating patient health into PCa treatment decisions. Additional work is needed to develop interventions that recognize health status as an essential component of a risk-adapted approach.

Mapping the Memorial Anxiety Scale for Prostate Cancer to the SF-6D.

PURPOSE: To create a crosswalk that predicts Short Form 6D (SF-6D) utilities from Memorial Anxiety Scale for Prostate Cancer (MAX-PC) scores. METHODS: The data come from prostate cancer patients enrolled in the North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study (NC ProCESS, N = 1016). Cross-sectional data from 12- to 24-month follow-up were used as estimation and validation datasets, respectively. Participants' SF-12 scores were used to generate SF-6D utilities in both datasets. Beta regression mixture models were used to evaluate SF-6D utilities as a function of MAX-PC scores, race, education, marital status, income, employment status, having health insurance, year of cancer diagnosis and clinically significant prostate cancer-related anxiety (PCRA) status in the estimation dataset. Models' predictive accuracies (using mean absolute error [MAE], root mean squared error [RMSE], Akaike information criterion [AIC] and Bayesian information criterion [BIC]) were examined in both datasets. The model with the highest prediction accuracy and the lowest prediction errors was selected as the crosswalk. RESULTS: The crosswalk had modest prediction accuracy (MAE = 0.092, RMSE = 0.114, AIC = - 2708 and BIC = - 2595.6), which are comparable to prediction accuracies of other SF-6D crosswalks in the literature. About 24% and 52% of predictions fell within ± 5% and ± 10% of observed SF-6D, respectively. The observed mean disutility associated with acquiring clinically significant PCRA is 0.168 (standard deviation = 0.179). CONCLUSION: This study provides a crosswalk that converts MAX-PC scores to SF-6D utilities for economic evaluation of clinically significant PCRA treatment options for prostate cancer survivors.

Medication adherence among recipients with chronic diseases enrolled in a state Medicaid program.

Limited information is currently available about medication adherence for common chronic conditions among the Medicaid population. The primary objective of this study was to assess medication adherence among Medicaid recipients with depression, diabetes, epilepsy, hypercholesterolemia, and hypertension. Factors influencing adherence were determined. The authors also assessed whether adherence influences the utilization of acute care services. The target population included nonelderly adult recipients (ages 21-64 years) who were continuously enrolled in the Mississippi (MS) Medicaid fee-for-service program from January 1, 2006 to December 31, 2007. Recipients were identified who had a medical services claim with a diagnosis of depression, diabetes, epilepsy, hypercholesterolemia, or hypertension in calendar year 2006. Within each chronic disease sample, medication adherence was determined using calendar year 2007 data for recipients who met inclusion and exclusion criteria. Recipients with adherence ≥80% were classified as adherent. Logistic regression analyses were used to determine the factors that predict medication adherence and the effect of adherence on concurrent all-cause acute care service (ie, hospitalization, emergency room visit) utilization. Approximately 24% of recipients with depression, 35.9% with diabetes, 53.6% with epilepsy, 32% with hypercholesterolemia, and 42.2% with hypertension were adherent. Within each chronic disease sample, males and whites had higher adherence than females and blacks. After controlling for demographic and disease-related covariates, recipients who were adherent had lower concurrent acute care service utilization than nonadherent recipients. Given the inverse relationship between adherence and acute care service utilization, policy makers should consider implementing educational interventions aimed at improving adherence in this underprivileged population.