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Background: The relationship between treatment-related, cost-associated distress "financial toxicity" (FT) and quality-of life (QOL) in breast cancer patients remains poorly characterized. This study leverages validated patient-reported outcomes measures (PROMs) to analyze the association between FT and QOL and satisfaction among women undergoing ablative breast cancer surgery.Study Design: This is a single-institution cross-sectional survey of all female breast cancer patients (>18 years old) who underwent lumpectomy or mastectomy between January 2018 and June 2019. FT was measured via the 11-item COmprehensive Score for financial Toxicity (COST) instrument. The BREAST-Q and SF-12 were used to asses condition-specific and global QOL, respectively. Responses were linked with demographic and clinical data. Pearson correlation coefficient and multivariable regression were used to examine associations.Results: Our analytical sample consisted of 532 patients; mean age 58, mostly white (76.7%), employed (63.7%), married/committed (73.7%), with 64.3% undergoing reconstruction. Median household income was $80,000 to $120,000/year, and mean COST score was 28.0. After multivariable adjustment, a positive relationship for all outcomes was noted; lower COST (greater cost-associated distress) was associated with lower BREAST-Q and SF-12 scores. This relationship was strongest for BREAST-Q psychosocial well-being, for which we observed a 0.89 (95% CI 0.76-1.03) change per unit change in COST score.Conclusions: Financial toxicity captured in this study correlates with statistically significant and clinically important differences in BREAST-Q psychosocial well-being, patient satisfaction with reconstructed breasts, and SF-12 global mental and physical quality of life. Treatment costs should be included in the shared decision-making for breast cancer surgery. Future prospective outcomes research should integrate COST. [ABSTRACT FROM AUTHOR]

Background: All authorities recommend against prostate specific antigen (PSA) screening in men 75 years and older. However, some primary care physicians (PCPs) continue to have high rates of PSA, with large variation in testing. We assessed the tests, treatments, and payments for prostate cancer care in men aged 75 or older who have PCPs with high or low PSA testing rates.Methods: We performed a retrospective cohort study using the 2010 Medicare beneficiaries aged 75 or older in Texas, United States who had no prostate cancer in 2007-2009 and had an identifiable PCP. We first identified high vs. low PSA testing PCPs, and then grouped older men in the two PCP groups. We determined health care visits to any provider and to urologists in office and outpatient settings. We estimated the direct medical payments for prostate cancer care for diagnostics, treatments and visits to providers in 2010-2011 using the generalized gamma model with log link function.Results: In multilevel, multivariable analyses, 25.4% (n = 550) of PCPs had PSA testing rates in men aged 75 or older that were significantly higher than the mean rate of all 2,169 Texas PCPs; 29.4% (n = 638) had rates that were significantly lower. In all, 22,853 vs. 23,929 older men were cared for by PCPs with high vs. low testing rates. Older men cared for by high PSA rate PCPs were more likely to receive a PSA test (OR 3.64, 95% CI 3.48-3.80), a biopsy (OR 1.16, 95% CI 1.02-1.31), an ultrasound (OR 1.19, 95% CI 1.07-1.32) or any radiation treatment (OR 1.31, 95% CI 1.03-1.66) than men cared for by low PSA rate PCPs. Men with high PSA rate PCPs were 1.21 (95% CI 1.05-1.39) times more likely to have such outpatient visits. The average annual adjusted Medicare payments for prostate cancer care was $25.60 higher for patients cared for by PCPs with high PSA test rates.Conclusions: Older men seeing PCPs with high rates of PSA testing undergo more testing and treatments for prostate cancer, with higher Medicare insurance payments. Future studies are needed to delineate whether men seeing PCPs with low testing rates likely received PSA tests from other providers. [ABSTRACT FROM AUTHOR]

Objective. To determine whether racial/ethnic disparities in colonoscopy use vary by physician availability. Data Source. We used 100 percent Texas Medicare claims data for 2003-2007. Study Design. We identified beneficiaries aged 66-79 in 2007, examined racial/ethnic differences in colonoscopy use from 2003 to 2007, and estimated the percentage of white, black, and Hispanic beneficiaries who underwent colonoscopy by level of physician availability and area income. Principal Findings. For the 974,879 beneficiaries, colonoscopy use was higher in whites (40.7 percent) compared to blacks (35.0 percent) and Hispanics (28.7 percent, p < .001). For whites, increasing availability of colonoscopists and primary care physicians ( PCPs) was associated with higher colonoscopy use. For blacks and Hispanics, colonoscopy use was unchanged or decreased with increases in colonoscopist and PCP availability. In multilevel models, the odds of colonoscopy were 20 percent lower for blacks ( OR 0.80, 95 percent CI 0.79-0.82) and 32 percent lower for Hispanics ( OR 0.68, 95 percent CI 0.66-0.69) compared to whites; adjusting for availability of colonoscopists or PCPs did not attenuate racial/ethnic disparities. We found greater racial/ethnic disparities in areas with greater colonoscopist and PCP availability. Conclusions. Greater area availability of colonoscopists and PCPs is associated with increased use of colonoscopy in whites but decreased use in minorities, resulting in larger racial/ethnic disparities. [ABSTRACT FROM AUTHOR]

Background: Studying team-based primary care using 100% national outpatient Medicare data is not feasible, due to limitations in the availability of this dataset to researchers., Methods: We assessed whether analyses using different sets of Medicare data can produce results similar to those from analyses using 100% data from an entire state, in identifying primary care teams through social network analysis. First, we used data from 100% Medicare beneficiaries, restricted to those within a primary care services area (PCSA), to identify primary care teams. Second, we used data from a 20% sample of Medicare beneficiaries and defined shared care by 2 providers using 2 different cutoffs for the minimum required number of shared patients, to identify primary care teams., Results: The team practices identified with social network analysis using the 20% sample and a cutoff of 6 patients shared between 2 primary care providers had good agreement with team practices identified using statewide data (F measure: 90.9%). Use of 100% data within a small area geographic boundary, such as PCSAs, had an F measure of 83.4%. The percent of practices identified from these datasets that coincided with practices identified from statewide data were 86% versus 100%, respectively., Conclusions: Depending on specific study purposes, researchers could use either 100% data from Medicare beneficiaries in randomly selected PCSAs, or data from a 20% national sample of Medicare beneficiaries to study team-based primary care in the United States., Competing Interests: The authors declare no conflict of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: It is unclear whether Medicare data can be used to identify type and degree of collaboration between primary care providers (PCPs) [medical doctors (MDs), nurse practitioners, and physician assistants] in a team care model., Methods: We surveyed 63 primary care practices in Texas and linked the survey results to 2015 100% Medicare data. We identified PCP dyads of 2 providers in Medicare data and compared the results to those from our survey. Sensitivity, specificity, and positive predictive value (PPV) of dyads in Medicare data at different threshold numbers of shared patients were reported. We also identified PCPs who work in the same practice by Social Network Analysis (SNA) of Medicare data and compared the results to the surveys., Results: With a cutoff of sharing at least 30 patients, the sensitivity of identifying dyads was 27.8%, specificity was 91.7%, and PPV 72.2%. The PPV was higher for MD-nurse practitioner/physician assistant pairs (84.4%) than for MD-MD pairs (61.5%). At the same cutoff, 90% of PCPs identified in a practice from the survey were also identified by SNA in the corresponding practice. In 5 of 8 surveyed practices with at least 3 PCPs, about ≤20% PCPs identified in the practices by SNA of Medicare data were not identified in the survey., Conclusions: Medicare data can be used to identify shared care with low sensitivity and high PPV. Community discovery from Medicare data provided good agreement in identifying members of practices. Adapting network analyses in different contexts needs more validation studies.

Objectives: The objectives of this study were to determine the association between patients' functional status at discharge from skilled nursing facility (SNF) care and 30-day potentially preventable hospital readmissions, and to examine common reasons for potentially preventable readmissions., Design: Retrospective cohort study., Setting: SNFs and acute care hospitals submitting claims to Medicare., Participants: National cohort of Medicare fee-for-service beneficiaries discharged from SNF care between July 15, 2013, and July 15, 2014 (n = 693,808). Average age was 81.4 (SD 8.1) years, 67.1% were women, and 86.3% were non-Hispanic white., Measurements: Functional items from the Minimum Data Set 3.0 were categorized into self-care, mobility, and cognition domains. We used specifications for the SNF potentially preventable 30-day postdischarge readmission quality metric to identify potentially preventable readmissions., Results: The overall observed rate of 30-day potentially preventable readmissions following SNF discharge was 5.7% (n = 39,318). All 3 functional domains were independently associated with potentially preventable readmissions in the multivariable models. Odds ratios for the most dependent category versus the least dependent category from multilevel models adjusted for patients' sociodemographic and clinical characteristics were as follows: mobility, 1.54 (95% confidence interval [CI] 1.49-1.59); self-care, 1.50 (95% CI 1.44-1.55); and cognition, 1.12 (95% CI 1.04-1.20). The 5 most common conditions were congestive heart failure (n = 7654, 19.5%), septicemia (n = 7412, 18.9%), urinary tract infection/kidney infection (n = 4297, 10.9%), bacterial pneumonia (n = 3663, 9.3%), and renal failure (n = 3587, 9.1%). Across all 3 functional domains, septicemia was the most common condition among the most dependent patients and congestive heart failure among the least dependent., Conclusions: Patients with functional limitations at SNF discharge are at increased risk of hospital readmissions considered potentially preventable. Future research is needed to determine whether improving functional status reduces risk of potentially preventable readmissions among this vulnerable population., (Copyright © 2017 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Background: The characteristics of primary care providers (PCPs) who use hospitalists are unknown., Methods: Retrospective study using 100% Texas Medicare claims from 2001 through 2009. Descriptive statistics characterized proportion of PCPs using hospitalists over time. Trajectory analysis and multilevel models of 1172 PCPs with ≥20 inpatients in every study year characterized how PCPs adopted the hospitalist model and PCP factors associated with this transition., Results: Hospitalist use increased between 2001 and 2009. PCPs who adopted the hospitalist model transitioned rapidly. In multilevel models, hospitalist use was associated with US training (odds ratio [OR] 1.46, 95% confidence interval [CI]: 1.23-1.73 in 2007-2009), family medicine specialty (OR: 1.46, 95% CI: 1.25-1.70 in 2007-2009), and having high outpatient volumes (OR: 1.32, 95% CI: 1.20-1.44 in 2007-2009). Over time, relative hospitalist use decreased among female PCPs (OR: 1.91, 95% CI: 1.46-2.50 in 2001-2003; OR: 1.50, 95% CI: 1.15-1.95 in 2007-2009), those in urban locations (OR: 3.34, 95% CI: 2.72-4.09 in 2001-2003; OR: 2.22, 95% CI: 1.82-2.71 in 2007-2009), and those with higher inpatient volumes (OR: 1.05, 95% CI: 0.95-1.18 in 2001-2003; OR: 0.55, 95% CI: 0.51-0.60 in 2007-2009). Longest-practicing PCPs were more likely to transition in the early 2000s, but this effect disappeared by the end of the study period (OR: 1.35, 95% CI: 1.06-1.72 in 2001-2003; OR: 0.92, 95% CI: 0.73-1.17 in 2007-2009). PCPs with practice panels dominated by patients who were white, male, or had comorbidities are more likely to use hospitalists., Conclusions: PCP characteristics are associated with hospitalist use. The association between PCP characteristics and hospitalist use has evolved over time., (© 2015 Society of Hospital Medicine.)

Importance: Certain antimicrobial drugs interact with sulfonylureas to increase the risk of hypoglycemia., Objective: To determine the risk of hypoglycemia and associated costs in older patients prescribed glipizide or glyburide who fill a prescription for an antimicrobial drug., Design, Setting, and Participants: This was a retrospective cohort study of Texas Medicare claims from 2006 to 2009 for patients 66 years or older who were prescribed glipizide or glyburide and who also filled a prescription for 1 of the 16 antimicrobials most commonly prescribed for this population., Methods: We assessed hypoglycemia events and associated Medicare costs in patients prescribed 1 of 7 antimicrobial agents thought to interact with sulfonylureas, using noninteracting antimicrobials as a comparison. We used a repeated measure logistic regression, controlling for age, sex, ethnicity, Medicaid eligibility, comorbidity, prior emergency department visits for hypoglycemia, prior hospitalizations for any cause, nursing home residence, and indication for the antimicrobial. We estimated odds of hypoglycemia, number needed to harm, deaths during hospitalization for hypoglycemia, and Medicare costs for hypoglycemia treatment., Main Outcomes and Measures: Any hospitalization or emergency department visit owing to hypoglycemia within 14 days of antimicrobial exposure., Results: In multivariable analyses controlling for patient characteristics and indication for antimicrobial drug use, clarithromycin (odds ratio [OR], 3.96 [95% CI, 2.42-6.49]), levofloxacin (OR, 2.60 [95% CI, 2.18-3.10]), sulfamethoxazole-trimethoprim (OR, 2.56 [95% CI, 2.12-3.10]), metronidazole (OR, 2.11 [95% CI, 1.28-3.47]), and ciprofloxacin (OR, 1.62 [95% CI, 1.33-1.97]) were associated with higher rates of hypoglycemia compared with a panel of noninteracting antimicrobials. The number needed to harm ranged from 71 for clarithromycin to 334 for ciprofloxacin. Patient factors associated with hypoglycemia included older age, female sex, black or Hispanic race/ethnicity, higher comorbidity, and prior hypoglycemic episode. In 2009, 28.3% of patients prescribed a sulfonylurea filled a prescription for 1 of these 5 antimicrobials, which were associated with 13.2% of all hypoglycemia events in patients taking sulfonylureas. The treatment of subsequent hypoglycemia adds $30.54 in additional Medicare costs to each prescription of 1 of those 5 antimicrobials given to patients taking sulfonylureas., Conclusions and Relevance: Prescription of interacting antimicrobial drugs to patients on sulfonylureas is very common, and is associated with substantial morbidity and increased costs.

Background: The risk of readmission varies among hospitals. This variation has led the Centers of Medicare and Medicaid services to reduce payments to hospitals with excess readmissions. The contribution of patient characteristics, hospital characteristics and provider type to the variation in risk of readmission among hospitals has not been determined., Objective: To describe the variation in risk of readmission among hospitals and partition it by patient characteristics, hospital characteristics and provider type., Design: Retrospective research design of 100% Texas Medicare data using multilevel, multivariable models., Subjects: A total of 514,064 admissions of Medicare beneficiaries to 272 hospitals in Texas for medical diagnoses during the years 2008 and 2009., Main Measures: Using hierarchical generalized linear models, we describe the hospital-specific variation in risk of readmission that is attributable to patients characteristics, hospital characteristics and provider type by measuring the variance and intraclass correlation coefficients., Key Results: Of the total variation in risk of readmission, only a small amount (0.84%) is attributed to hospitals. In further analyses modeling the components of this variation among hospitals, differences in patient characteristics in the hospitals explained 56.2% of the variation. Hospital characteristics and the type of provider explained 9.3% of the variation among hospitals and 0.08% of the total variation in risk of readmission., Conclusions: Patient characteristics are the largest contributor to variation in risk of readmission among hospitals. Measurable hospital characteristics and the type of inpatient provider contribute little to variation in risk of readmission among hospitals.

Background: There have been no prior population-based studies of variation in performance of hospitalists., Objective: To measure the variation in performance of hospitalists., Design: Retrospective research design of 100 % Texas Medicare data using multilevel, multivariable models., Subjects: 131,710 hospitalized patients cared for by 1,099 hospitalists in 268 hospitals from 2006-2009., Main Measures: We calculated, for each hospitalist, adjusted for patient and disease factors (case mix), their patients' average length of stay, rate of discharge home or to skilled nursing facility (SNF) and rate of 30-day mortality, readmissions and emergency room (ER) visits., Key Results: In two-level models (admission and hospitalist), there was significant variation in average length of stay and discharge location among hospitalists, but very little variation in 30-day mortality, readmission or emergency room visit rates. There was stability over time (2008-2009 vs. 2006-2007) in hospitalist performance. In three-level models including admissions, hospitalists and hospitals, the variation among hospitalists was substantially reduced. For example, hospitals, hospitalists and case mix contributed 1.02 %, 0.75 % and 42.15 % of the total variance in 30-day mortality rates, respectively., Conclusions: There is significant variation among hospitalists in length of stay and discharge destination of their patients, but much of the variation is attributable to the hospitals where they practice. The very low variation among hospitalists in 30-day readmission rates suggests that hospitalists are not important contributors to variations in those rates among hospitals.

Objective: To examine whether having a primary care physician (PCP) is associated with reduced ethnic disparities for colorectal cancer (CRC) screening and whether clustering of minorities within PCPs contributes to the disparities., Data Sources/study Setting: Retrospective cohort study of Medicare beneficiaries age 66-75 in 2009 in Texas., Study Design: The percentage of beneficiaries up to date in CRC screening in 2009 was stratified by race/ethnicity. Multilevel models were used to study the effect of having a PCP and PCP characteristics on the racial and ethnic disparities on CRC screening., Data Collection/extraction Methods: Medicare data from 2000 to 2009 were used to assess prior CRC screening., Principal Findings: Odds of undergoing CRC screening were more than twice as high in patients with a PCP (OR = 2.05, 95 percent CI 2.03-2.07). After accounting for clustering and PCP characteristics, the black-white disparity in CRC screening rates almost disappears and the Hispanic-white disparity decreases substantially., Conclusions: Ethnic disparities in CRC screening in the elderly are mostly explained by decreased access to PCPs and by clustering of minorities within PCPs less likely to screen any of their patients., (© Health Research and Educational Trust.)