Danielle Durham

The PEACE project: Identification of quality measures for hospice and palliative care

CONTEXT In 2008, the Centers for Medicare & Medicaid Services (CMS) required U.S. hospices to implement comprehensive quality improvement programs. CMS contracted with the Quality Improvement Organization in North and South Carolina to develop quality measures and instruments to assess hospice and palliative care quality. OBJECTIVES To develop a set of quality measures, with complete specifications, and data collection tools for use by hospice and palliative care providers in quality improvement. METHODS Quality measures were identified from: published literature, the National Quality Forum, CMS measures, the National Quality Measures Clearinghouse, and measures submitted by two national hospice organizations. Available data on the quality measures were gathered and pilot data were collected for measures with no available data. A Technical Expert Panel (TEP) rated quality measures on: importance, scientific soundness, feasibility and usability, using numeric scores for each dimension. Scores for quality measures were averaged across dimensions and across TEP members to identify measures for further development. RESULTS Of 174 measures identified, 88 were determined appropriate to the setting and were reviewed and rated by the TEP. Measures with overall scores ≥75th percentile (n = 23), measures with high importance scores (n = 7), measures for under-represented domains (n = 2), and process measures antecedent to TEP identified measures (n = 2), and were selected. CONCLUSIONS Specifications and data collection tools are available for 34 PEACE quality measures that were highly rated by experts in hospice and palliative care. Future research should assess the scientific soundness and responsiveness of these measures to quality improvement.

Determinants of Breast Cancer Treatment Delay Differ for African American and White Women

Background: Timeliness of care may contribute to racial disparities in breast cancer mortality. African American women experience greater treatment delay than White women in most, but not all studies. Understanding these disparities is challenging as many studies lack patient-reported data and use administrative data sources that collect limited types of information. We used interview and medical record data from the Carolina Breast Cancer Study (CBCS) to identify determinants of delay and assess whether disparities exist between White and African American women (n = 601). Methods: The CBCS is a population-based study of North Carolina women. We investigated the association of demographic and socioeconomic characteristics, healthcare access, clinical factors, and measures of emotional and functional well-being with treatment delay. The association of race and selected characteristics with delays of more than 30 days was assessed using logistic regression. Results: Household size, losing a job due to ones diagnosis, and immediate reconstruction were associated with delay in the overall population and among White women. Immediate reconstruction and treatment type were associated with delay among African American women. Racial disparities in treatment delay were not evident in the overall population. In the adjusted models, African American women experienced greater delay than White women for younger age groups: OR, 3.34; 95% confidence interval (CI), 1.07–10.38 for ages 20 to 39 years, and OR, 3.40; 95% CI, 1.76–6.54 for ages 40 to 49 years. Conclusions: Determinants of treatment delay vary by race. Racial disparities in treatment delay exist among women younger than 50 years. Impact: Specific populations need to be targeted when identifying and addressing determinants of treatment delay. Cancer Epidemiol Biomarkers Prev; 22(7); 1227–38. ©2013 AACR.

Effects of Distance to Care and Rural or Urban Residence on Receipt of Radiation Therapy Among North Carolina Medicare Enrollees With Breast Cancer

BACKGROUND Distance to oncology service providers and rurality may affect receipt of guideline-recommended radiation therapy (RT), but the extent to which these factors affect the care of Medicare-insured patients is unknown. METHODS Using cancer registry data linked to Medicare claims from the Integrated Cancer Information and Surveillance System (ICISS), we identified all women aged 65 years or older who were diagnosed with stage I, II, or III breast cancer from 2003 through 2005, who had Medicare claims through 2006, and who were clinically eligible for RT. We geocoded the address of each RT service provider’s practice location and calculated the travel distance from each patient’s residential address to the nearest RT provider. We used ZIP codes to classify each patient’s residence as rural or urban according to rural-urban commuting area codes. We used generalized estimating equations models with county-level clustering and interaction terms between distance categories and rural-urban status to estimate the effect of distance to care and rural-urban status on receipt of RT. RESULTS In urban areas, increasing distance to the nearest RT provider was associated with a lower likelihood of receiving RT (odds ratio [OR] = 0.54; 95% confidence interval [CI], 0.30-0.97) for those living more than 20 miles from the nearest RT provider compared with those living less than 10 miles away. In rural areas, those living within 10-20 miles of the nearest RT provider were more likely to receive RT than those living less than 10 miles away (OR = 1.73; 95% CI, 1.08-2.76). LIMITATIONS Results may not be generalizable to areas outside North Carolina or to non-Medicare populations. CONCLUSIONS Coordinated outreach programs targeted differently to rural and urban patients may be necessary to improve the quality of oncology care.

Quality Measures for Hospice and Palliative Care: Piloting the PEACE Measures

BACKGROUND The Carolinas Center for Medical Excellence launched the PEACE project in 2006, under contract with the Centers for Medicare & Medicaid Services (CMS), to identify, develop, and pilot test quality measures for hospice and palliative care programs. OBJECTIVES The project collected pilot data to test the usability and feasibility of potential quality measures and data collection processes for hospice and palliative care programs. Settings/subjects: Twenty-two hospices participating in a national Quality Improvement Collaborative (QIC) submitted data from 367 chart reviews for pain care and 45 chart reviews for nausea care. Fourteen additional hospices completed a one-time data submission of 126 chart reviews on 60 potential patient-level quality measures across eight domains of care and an organizational assessment evaluating structure and processes of care. DESIGN Usability was assessed by examining the range, variability and size of the populations targeted by each quality measure. Feasibility was assessed during the second pilot study by surveying data abstractors about the abstraction process and examining the rates of missing data. The impact of data collection processes was assessed by comparing results obtained using different processes. RESULTS Measures shown to be both usable and feasible included: screening for physical symptoms on admission and documentation of treatment preferences. Methods of data collection and measure construction appear to influence observed rates of quality of care. CONCLUSIONS We successfully identified quality measures with potential for use in hospices and palliative care programs. Future research is needed to understand whether these measures are sensitive to quality improvement interventions.

Use of Electronic Documentation for Quality Improvement in Hospice

Little evidence exists about the use of electronic documentation (ED) in hospice and its relationship to quality improvement (QI) practices. The purposes of this study were to (1) estimate the prevalence of ED use in hospice, (2) identify organizational characteristics associated with use of ED, and (3) determine whether quality measurement practices differed based on documentation format (electronic vs nonelectronic). Surveys concerning the use of ED for QI practices and the monitoring of quality-related care and outcomes were collected from 653 hospices. Users of ED were able to monitor a wider range of quality-related data than users of non-ED. Quality components such as advanced care planning, cultural needs, experience during care of the actively dying, and the number/types of care being delivered were more likely to be documented by users of ED. Use of ED may help hospices monitor quality and compliance.

Improving our understanding of the surgical oncology workforce.

Objective:This study characterizes the surgical oncology workforce as a baseline for future workforce projections. Background:Measuring the capacity of the surgical oncology workforce is difficult due to the wide variety of surgeons who contribute to surgical cancer care. We hypothesize that the bulk of surgical oncology care is provided by general surgeons. Methods:Using Medicare claims data linked to the North Carolina Central Cancer Registry, all patients 65 years or older who had a diagnosis of incident cancer of the bladder, breast, colon/rectum, esophagus, gallbladder, kidney, liver, lung, skin (melanoma-only), ovary, pancreas, prostate, small bowel, stomach, or uterus in 2005 and who underwent an extirpative procedure for cancer were identified. The proportion of procedures performed by different types of providers was examined. Results:A total of 7759 patients underwent 16,734 extirpative surgical procedures. Excluding procedures for gynecologic/urologic malignancies, the proportion of procedures performed by general surgeons and surgical oncologists was 48% and 12%, respectively. Patients treated by general surgeons were more likely to be older, female, minority, and from areas of high poverty. For each tumor type, travel distances were shorter for patients treated by general surgeons than those treated by specialists. Conclusions:Workforce projections must account for the significant overlap in the scope of services delivered by providers of different specialties and for the large contribution of general surgeons to cancer care. Efforts to improve the quality of cancer care need to move beyond centralization and focus on educating the surgeons who are providing the bulk of oncology care.

Quality improvement in hospice: adding a big job to an already big job?

Hospice organizations are adopting quality measurement and quality improvement (QI) practices to comply with the Medicare Conditions of Participation effective January 31, 2009. However, little is known about organizational best practices or specific needs during implementation. This study identified and described the barriers and facilitators to QI implementation in hospice. Using semistructured interviews with a national sample of key informants (n = 52) concerning facilitators and barriers to QI in hospice, 4 major themes emerged from the data regarding participants’ experiences and perceptions: (1) external factors constrain QI implementation; (2) internal factors limit capacity for QI; (3) research on best practices is limited; and (4) traditional QI may not be a good fit for hospice. Though challenging, participants provided recommendations that they believed would facilitate QI in hospice. Categorizing barriers and facilitators as within or outside an organization’s control may help organizations assess their capabilities and locate resources to address areas for improvement.

Insurance-Based Differences in Time to Diagnostic Follow-up after Positive Screening Mammography

Background: Insurance may lengthen or inhibit time to follow-up after positive screening mammography. We assessed the association between insurance status and time to initial diagnostic follow-up after a positive screening mammogram. Methods: Using 1995–2010 data from a North Carolina population-based registry of breast imaging and cancer outcomes, we identified women with a positive screening mammogram. We compared receipt of follow-up within 60 days of screening using logistic regression and evaluated time to follow-up initiation using Cox proportional hazards regression. Results: Among 43,026 women included in the study, 73% were <65 years and 27% were 65+ years. Median time until initial diagnostic follow-up was similar by age group and insurance status. In the adjusted model for women <65, uninsured women experienced a longer time to initiation of diagnostic follow-up [HR, 0.47; 95% confidence interval (CI), 0.25–0.89] versus women with private insurance. There were increased odds of these uninsured women not meeting the Centers for Disease Control and Prevention guideline for follow-up within 60 days (OR, 1.59; 95% CI, 1.31–1.94). Among women ages 65+, women with private insurance experienced a faster time to follow-up (adjusted HR, 2.09; 95% CI, 1.27–3.44) than women with Medicare and private insurance. Approximately 10% of women had no follow-up by 365 days. Conclusions: We found differences in time to initial diagnostic follow-up after a positive screening mammogram by insurance status and age group. Uninsured women younger than 65 years at a positive screening event had delayed follow-up. Impact: Replication of these findings and examination of their clinical significance warrant additional investigation. Cancer Epidemiol Biomarkers Prev; 25(11); 1474–82. ©2016 AACR.

Abstract PR07: Disparities in time to diagnostic follow up after screening mammography

Background: Screening mammography has been associated with as much as a 20% reduction in breast cancer mortality among women ages 40-74 years. An understudied dimension of screening that may impact mortality is time until follow-up after a positive mammogram. The primary objective of this study is to compare by race and insurance type the time to follow up after a positive screening mammogram. Methods: We use 1995-2010 data from the Carolina Mammography Registry (CMR), a population-based registry. The data are collected from women and the radiologist interpreting the mammogram and include patient demographics, patient risk factors (breast density, family history of breast cancer, menopausal status), the imaging examination performed, the reason for the visit, the radiologists9 assessment and recommendation for follow-up. The data are linked with cancer outcomes from the North Carolina Central Cancer Registry and abstracted pathology reports. In this study we included women ages 18 and older with a positive screening mammogram. A mammogram was positive if the Breast Imaging Reporting and Data System (BI-RADS) score was 0, 4, 5, or 3 with recommendation for biopsy evaluation. We defined time to diagnostic follow-up as number of days from the initial positive screening mammogram until the date of the first follow-up event (additional breast imaging or biopsy) before a cancer diagnosis or benign pathology result. We describe time to diagnostic follow-up for the cohort, by racial/ethnic status, and by insurance type (Medicare only, Medicaid, Medicare and Medicaid, Medicare and other insurer, all other insurers including private insurers, and No Insurance). We use Cox proportional hazards to estimate hazard ratios (HR) and 95% confidence intervals (CI) to evaluate the association between insurance type and diagnostic follow-up adjusting for race, patient residence, education, and imaging practice. Results: Of 97,304 positive screening mammograms included in the study, the majority of the sample was White, non-Hispanic (80%), Black, non-Hispanic (13%), Hispanic (1%), Multi-racial/other (1%), and Native American ( Discussion: In this breast cancer screening population women reporting Medicare alone or with supplemental insurance have increased hazard of diagnostic time until follow-up after a positive mammogram when compared to other women. The study may have a positive impact on maximizing the benefits of cancer screening and reducing disparities in breast cancer care. This abstract was also presented as Poster B72. Citation Format: Danielle Durham, Whitney Robinson, Sheila Lee, Stephanie Wheeler, James Bowling, Louise Henderson. Disparities in time to diagnostic follow up after screening mammography. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr PR07.