Broadband Data Forensics: Spatiotemporal Variations of the Download/Upload Speed Metric Commonly Used to Evaluate Potential Telehealth Accessibility.

Introduction: The complicated task of evaluating potential telehealth access begins with the metrics and supporting datasets that seek toevaluate the presence and durability of broadband connections in a community. Broadband download/upload speeds are one of the popular metrics used to measure potential telehealth access, which is critical to health equity. An understanding of the limitations of these measures is important for drawing conclusions about the reality of the digital divide in telehealth access. The objective of this study was to assess spatiotemporal variations in broadband download/upload speeds. Method: We analyzed a sample of data from the Speedtest Intelligence Portal provided through the Ookla for Good initiative. Results: We found that variation is inherent across the states of Vermont, New Hampshire, Louisiana, and Utah. Conclusions: The variation suggests that when single measures of download/upload speeds are used to evaluate telehealth accessibility they may be masking the true magnitude of the digital divide.

Association between a network-based physician linchpin score and cancer patient mortality: a SEER-Medicare analysis.

BACKGROUND: Patients with cancer frequently require multidisciplinary teams for optimal cancer outcomes. Network analysis can capture relationships among cancer specialists, and we developed a novel physician linchpin score to characterize "linchpin" physicians whose peers have fewer ties to other physicians of the same oncologic specialty. Our study examined whether being treated by a linchpin physician was associated with worse survival. METHODS: In this cross-sectional study, we analyzed Surveillance, Epidemiology, and End Results-Medicare data for patients diagnosed with stage I to III non-small cell lung cancer or colorectal cancer (CRC) in 2016-2017. We assembled patient-sharing networks and calculated linchpin scores for medical oncologists, radiation oncologists, and surgeons. Physicians were considered linchpins if their linchpin score was within the top 15% for their specialty. We used Cox proportional hazards models to examine associations between being treated by a linchpin physician and survival, with a 2-year follow-up period. RESULTS: The study cohort included 10 081 patients with non-small cell lung cancer and 9036 patients with CRC. Patients with lung cancer treated by a linchpin radiation oncologist had a 17% (95% confidence interval = 1.04 to 1.32) greater hazard of mortality, and similar trends were observed for linchpin medical oncologists. Patients with CRC treated by a linchpin surgeon had a 22% (95% confidence interval = 1.03 to 1.43) greater hazard of mortality. CONCLUSIONS: In an analysis of Medicare beneficiaries with nonmetastatic lung cancer or CRC, those treated by linchpin physicians often experienced worse survival. Efforts to improve outcomes can use network analysis to identify areas with reduced access to multidisciplinary specialists.

Development of the Rural Perception Scale (RPS-18).

BACKGROUND: People living in rural areas often experience limited access to health resources, slow knowledge diffusion, and geographical isolation, and tend to be at higher risk for poor physical and mental health outcomes compared with nonrural populations. It is unclear, yet, how the concept of "rural" shapes observed differences from nonrural populations. We aim to develop a psychometrically sound scale to assess key dimensions that constitute individual-level perceived rurality. METHODS: We first conducted a broad literature review to identify a priori concepts related to rurality and adapted survey items measuring relevant constructs, such as loneliness, attitudes toward people living in rural areas, and perceived social membership. We used these conceptual constructs and measures to develop a survey questionnaire focused on rural perceptions. We recruit residents in 3 rural states: Kentucky, New Hampshire, and Vermont. Using the explorative factor analysis and second-order measurement model in the structural equation model framework, we developed a rural perception scale consisting of 18 items. RESULTS: We recruited 1,384 participants (n = 686 from KY; n = 698 from NH/VT) using Amazon Mechanical Turk (n = 897, 64.8%) and social media paid ads (n = 487, 35.2%). The average age of participants was 41 years old (SD = 15); 54.7% of respondents had less than college graduate education, and 94.2% reported their race as non-Hispanic White. Majority of the participants were from Rural Urban Commuting Area (RUCA)-designated urban areas (n = 798, 57.7%), followed by RUCA-designated large rural areas (257, 18.6%), RUCA-designated rural areas (n = 174, 12.6%) and RUCA-designated isolated areas (n = 133, 9.6%). Our final model revealed 4 latent constructs: "belonging" (Cronbach's α = 0.896), "attitudes toward rural life" (Cronbach's α = 0.807), "loneliness" (Cronbach's α = 0.898), and "community social ties to people in their community" (Cronbach's α = 0.846). CONCLUSIONS: We identified 4 subfactors of the umbrella concept of rurality that explain how people in rural regions may perceive being in rural environments and having rural lifestyles.

Refining 2SVCA Method for Measuring Telehealth Accessibility of Primary Care Physicians in Baton Rouge, Louisiana.

Equity in health care delivery is a longstanding concern of public health policy. Telehealth is considered an important way to level the playing field by broadening health services access and improving quality of care and health outcomes. This study refines the recently developed "2-Step Virtual Catchment Area (2SVCA) method" to assess the telehealth accessibility of primary care in the Baton Rouge Metropolitan Statistical Area, Louisiana. The result is compared to that of spatial accessibility via physical visits to care providers based on the popular 2-Step Floating Catchment Area (2SFCA) method. The study shows that both spatial and telehealth accessibilities decline from urban to low-density and then rural areas. Moreover, disproportionally higher percentages of African Americans are in areas with higher spatial accessibility scores; but such an advantage is not realized in telehealth accessibility. In the study area, absence of broadband availability is mainly a rural problem and leads to a lower average telehealth accessibility than physical accessibility in rural areas. On the other side, lack of broadband affordability is a challenge across the rural-urban continuum and is disproportionally associated with high concentrations of disadvantaged population groups such as households under the poverty level and Blacks.

Geographic Access to Pediatric Cancer Care in the US.

Importance: Although access to pediatric cancer care has implications for use of such care and patient outcomes, little is known about the geographic accessibility of pediatric cancer care and how it may vary by population characteristics across the continental US. Objective: To estimate the travel time to pediatric cancer care settings in the continental US, identify potential disparities among subgroups of children and adolescents and young adults (AYAs), and identify areas needing improved access to pediatric cancer care. Design, Setting, and Participants: This cross-sectional study collected data from August 1 to December 1, 2021. Pediatric oncologists' service locations in 2021 served as the pediatric cancer care settings, data for which were scraped from 2 websites containing information about health professionals. Demographic characteristics for younger children and AYAs aged 0 to 21 years were obtained from the 2015 to 2019 American Community Survey 5-year estimates. Data were analyzed from January 1 to April 31, 2022. Main Outcomes and Measures: The main outcome was the travel time from geographic centroids of zip code tabulation areas to the nearest pediatric oncologist. The median (IQR) travel times for each demographic subgroup were estimated. Per capita pediatric oncologist supply was calculated by dividing the total number of pediatric oncologists for each state or US Census division by its population. Results: Of the 90 498 890 children and AYAs included in the study, 63.6% were estimated to travel less than 30 minutes and 19.7% to travel between 30 and 60 minutes (for a total of 83.3%) to the nearest pediatric oncologist. Median (IQR) travel times were longest for the American Indian or Alaska Native pediatric population (46 [16-104] minutes) and residents of rural areas (95 [68-135] minutes), areas with high deprivation levels (36 [13-72] minutes), and the South (24 [13-47] minutes) and Midwest (22 [11-51] minutes) compared with the general population of children and AYAs. The pediatric oncologist supply was lowest in Wyoming (0 oncologists per 100 000 pediatric population) and highest in Washington, DC (53.3 oncologists per 100 000 pediatric population). Pediatric oncologist supply across Census divisions was lowest in the Mountain division (3.3 oncologists per 100 000 pediatric population) and highest in the New England division (8.1 oncologists per 100 000 pediatric population). Conclusions and Relevance: Results of this study showed that most children and AYAs in the continental US had adequate access to pediatric cancer care, although disparities existed among racial and ethnic groups and residents in rural areas, areas with high deprivation levels, and some Southern and Midwestern states. Reducing these disparities may require innovative approaches, such as expanding the capabilities of local facilities and creating partnerships with adult oncology centers and primary care physicians.

The interaction of rurality and rare cancers for travel time to cancer care.

PURPOSE: Geographic access to cancer care is known to significantly impact utilization and outcomes. Longer travel times have negative impacts for patients requiring highly specialized care, such as for rare cancers, and for those in rural areas. Scant population-based research informs geographic access to care for rare cancers and whether rurality impacts that access. METHODS: Using Medicare data (2014-2015), we identified prevalent cancers and cancer-directed surgeries, chemotherapy, and radiation. We classified cancers as rare (incidence <6/100,000/year) or common (incidence ≥6/100,000/year) using previously published thresholds and categorized rurality from ZIP code of beneficiary residence. We estimated travel time between beneficiaries and providers for each service based on ZIP code. Descriptive statistics summarized travel time by rare versus common cancers, service type, and rurality. FINDINGS: We included 1,169,761 Medicare beneficiaries (21.9% in nonmetropolitan areas), 87,399; 7.5% had rare cancers, with 9,133,003 cancer-directed services. Travel times for cancer services ranged from approximately 29 minutes (25th percentile) to 68 minutes (75th percentile). Travel times were similar for rare and common cancers overall (median: 45 vs 43 minutes) but differed by service type; 13.4% of surgeries were >2 hours away for rare cancers, compared to 8.3% for common cancers. Increasing rurality disproportionately increased travel time to surgical care for rare compared to common cancers. CONCLUSIONS: Travel times to cancer services are longest for surgery, especially among rural residents, yet not markedly longer overall between rare versus common cancers. Understanding geographic access to cancer care for patients with rare cancers is important to delivering specialized care.

Multiscale analysis of cancer service areas in the United States.

The purpose of delineating Cancer Service Areas (CSAs) is to define a reliable unit of analysis, more meaningful than geopolitical units such as states and counties, for examining geographic variations of the cancer care markets using geographic information systems (GIS). This study aims to provide a multiscale analysis of the U.S. cancer care markets based on the 2014-2015 Medicare claims of cancer-directed surgery, chemotherapy, and radiation. The CSAs are delineated by a scale-flexible network community detection algorithm automated in GIS so that the patient flows are maximized within CSAs and minimized between them. The multiscale CSAs include those comparable in size to those 4 census regions, 9 divisions, 50 states, and also 39 global optimal CSAs that generates the highest modularity value. The CSAs are more effective in capturing the U.S. cancer care markets because of its higher localization index, lower cross-border utilizations, and shorter travel time. The first two comparisons reveal that only a few regions or divisions are representative of the underlying cancer care markets. The last two comparisons find that among the 39 CSAs, 54% CSAs comprise multiple states anchored by cities near inner state borders, 28% are single-state CSAs, and 18% are sub-state CSAs. Their (in)consistencies across state borders or within each state shed new light on where the intervention of cancer care delivery or the adjustment of cancer care costs are needed to meet the challenges in the U.S. cancer care system. The findings could guide stakeholders to target public health policies for more effective coordination of cancer care in improving outcomes and reducing unnecessary costs.

Use of a Novel Network-Based Linchpin Score to Characterize Accessibility to the Oncology Physician Workforce in the United States.

Importance: Physician headcounts provide useful information about the cancer care delivery workforce; however, efforts to track the oncology workforce would benefit from new measures that capture how essential a physician is for meeting the multidisciplinary cancer care needs of the region. Physicians are considered linchpins when fewer of their peers are connected to other physicians of the same specialty as the focal physician. Because they are locally unique for their specialty, these physicians' networks may be particularly vulnerable to their removal from the network (eg, through relocation or retirement). Objective: To examine a novel network-based physician linchpin score within nationwide cancer patient-sharing networks and explore variation in network vulnerability across hospital referral regions (HRRs). Design, Setting, and Participants: This cross-sectional study analyzed fee-for-service Medicare claims and included Medicare beneficiaries with an incident diagnosis of breast, colorectal, or lung cancer from 2016 to 2018 and their treating physicians. Data were analyzed from March 2022 to October 2022. Exposures: Physician characteristics assessed were specialty, rurality, and Census region. HRR variables assessed include sociodemographic and socioeconomic characteristics and use of cancer services. Main Outcomes and Measures: Oncologist linchpin score, which examined the extent to which a physician's peers were connected to other physicians of the same specialty as the focal physician. Network vulnerability, which distinguished HRRs with more linchpin oncologists than expected based on oncologist density. χ2 and Fisher exact tests were used to examine relationships between oncologist characteristics and linchpin score. Spearman rank correlation coefficient (ρ) was used to measure the strength and direction of relationships between HRR network vulnerability, oncologist density, population sociodemographic and socioeconomic characteristics, and cancer service use. Results: The study cohort comprised 308 714 patients with breast, colorectal, or lung cancer. The study cohort of 308 714 patients included 161 206 (52.2%) patients with breast cancer, 76 604 (24.8%) patients with colorectal cancer, and 70 904 (23.0%) patients with lung cancer. In our sample, 272 425 patients (88%) were White, and 238 603 patients (77%) lived in metropolitan areas. The cancer patient-sharing network included 7221 medical oncologists and 3573 radiation oncologists. HRRs with more vulnerable networks for medical oncology had a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.19; 95% CI, 0.08 to 0.29). HRRs with more vulnerable networks for radiation oncology had a higher percentage of beneficiaries living in poverty (ρ, 0.17; 95% CI, 0.06 to 0.27), and a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.21; 95% CI, 0.09 to 0.31), and lower rates of cohort patients receiving radiation therapy (ρ, -0.18; 95% CI, -0.28 to -0.06; P = .003). The was no association between network vulnerability for medical oncology and percent of cohort patients receiving chemotherapy (ρ, -0.03; 95% CI, -0.15 to 0.08). Conclusions and Relevance: This study found that patient-sharing network vulnerability was associated with poverty and lower rates of radiation therapy. Health policy strategies for addressing network vulnerability may improve access to interdisciplinary care and reduce treatment disparities.

The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study.

BACKGROUND: The COVID-19 pandemic exacerbated existing racial/ethnic health disparities in the United States. Monitoring nationwide Twitter conversations about COVID-19 and race/ethnicity could shed light on the impact of the pandemic on racial/ethnic minorities and help address health disparities. OBJECTIVE: This paper aims to examine the association between COVID-19 tweet volume and COVID-19 cases and deaths, stratified by race/ethnicity, in the early onset of the pandemic. METHODS: This cross-sectional study used geotagged COVID-19 tweets from within the United States posted in April 2020 on Twitter to examine the association between tweet volume, COVID-19 surveillance data (total cases and deaths in April), and population size. The studied time frame was limited to April 2020 because April was the earliest month when COVID-19 surveillance data on racial/ethnic groups were collected. Racially/ethnically stratified tweets were extracted using racial/ethnic group-related keywords (Asian, Black, Latino, and White) from COVID-19 tweets. Racially/ethnically stratified tweets, COVID-19 cases, and COVID-19 deaths were mapped to reveal their spatial distribution patterns. An ordinary least squares (OLS) regression model was applied to each stratified dataset. RESULTS: The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, an increase of 1 Asian tweet was correlated with 288 Asian cases (P<.001) and 93.4 Asian deaths (P<.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (P<.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (P<.001); and an increase of 1 White tweet was linked to 60.2 White deaths (P<.001). CONCLUSIONS: Using racially/ethnically stratified Twitter data as a surveillance indicator could inform epidemiologic trends to help estimate future surges of COVID-19 cases and potential future outbreaks of a pandemic among racial/ethnic groups.

Rural-Urban Differences in Breast Cancer Surgical Delays in Medicare Beneficiaries.

BACKGROUND: Delays between breast cancer diagnosis and surgery are associated with worsened survival. Delays are more common in urban-residing patients, although factors specific to surgical delays among rural and urban patients are not well understood. METHODS: We used a 100% sample of fee-for-service Medicare claims during 2007-2014 to identify 238,491 women diagnosed with early-stage breast cancer undergoing initial surgery and assessed whether they experienced biopsy-to-surgery intervals > 90 days. We employed multilevel regression to identify associations between delays and patient, regional, and surgeon characteristics, both in combined analyses and stratified by rurality of patient residence. RESULTS: Delays were more prevalent among urban patients (2.5%) than rural patients (1.9%). Rural patients with medium- or high-volume surgeons had lower odds of delay than patients with low-volume surgeons (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.58-0.88; OR = 0.74, 95% CI = 0.61-0.90). Rural patients whose surgeon operated at ≥ 3 hospitals were more likely to experience delays (OR = 1.29, 95% CI = 1.01-1.64, Ref: 1 hospital). Patient driving times ≥ 1 h were associated with delays among urban patients only. Age, black race, Hispanic ethnicity, multimorbidity, and academic/specialty hospital status were associated with delays. CONCLUSIONS: Sociodemographic, geographic, surgeon, and facility factors have distinct associations with > 90-day delays to initial breast cancer surgery. Interventions to improve timeliness of breast cancer surgery may have disparate impacts on vulnerable populations by rural-urban status.

A Framework for Equitable Partnerships to Promote Cancer Prevention and Control in Rural Settings.

Rural populations continue to experience persistent cancer disparities compared with urban populations particularly in cancers that can be prevented or detected early through screening and vaccination. Although the National Cancer Institute and the larger cancer research community have identified rural community partnerships as the foundation for reducing the disparities, we have identified limited application of community-based participatory research in cancer prevention and control research. Guided by the Community-Based Participatory Research Conceptual Model and our collective experience, we provide a framework for a community-cancer center partnership that focuses on promoting health equity. In this commentary, we articulate that the partnership process must foster capacity for communities and cancer centers, strive for rural representation in clinical trials and biobanking, build a pipeline for dissemination and implementation research, and create a bidirectional flow of knowledge between communities and academic institutions. Authentic partnerships with rural communities should be the ultimate goal of cancer centers, and the process described in this commentary can serve as an initial platform to build capacity and continue to strive toward that goal.

A national evaluation of geographic accessibility and provider availability of obesity medicine diplomates in the United States between 2011 and 2019.

BACKGROUND/OBJECTIVES: Obesity is a pressing health concern within the United States (US). Obesity medicine "diplomates" receive specialized training, yet it is unclear if their accessibility and availability adequately serves the need. The purpose of this research was to understand how accessibility has evolved over time and assess the practicality of serving an estimated patient population with the current distribution and quantity of diplomates. METHODS: Population-weighted Census tracts in US counties were mapped to the nearest facility on a road network with at least one diplomate who specialized in adult (including geriatric) care between 2011 and 2019. The median travel time for all Census tracts within a county represented the primary geographic access measure. Availability was assessed by estimating the number of diplomates per 100 000 patients with obesity and the number of facilities able to serve assigned patients under three clinical guidelines. RESULTS: Of the 3371 diplomates certified since 2019, 3036 were included. The median travel time (weighted for county population) fell from 28.5 min [IQR: 13.7, 68.1] in 2011 to 9.95 min [IQR: 7.49, 18.1] in 2019. There were distinct intra- and inter-year travel time variations by race, ethnicity, education, median household income, rurality, and Census region (all P < 0.001). The median number of diplomates per 100 000 with obesity grew from 1 [IQR: 0.39, 1.59] in 2011 to 5 [IQR: 2.74, 11.4] in 2019. In 2019, an estimated 1.7% of facilities could meet the recommended number of visits for all mapped patients with obesity, up from 0% in 2011. CONCLUSIONS: Diplomate geographic access and availability have improved over time, yet there is still not a high enough supply to serve the potential patient demand. Future studies should quantify patient-level associations between travel time and health outcomes, including whether the number of available diplomates impacts utilization.

Delineation of Cancer Service Areas Anchored by Major Cancer Centers in the United States.

Defining a reliable geographic unit pertaining to cancer care is essential in its assessment, planning, and management. This study aims to delineate and characterize the cancer service areas (CSA) accounting for the presence of major cancer centers in the United States. We used the Medicare enrollment and claims from January 1, 2014 to September 30, 2015 to build a spatial network from patients with cancer to cancer care facilities that provided inpatient and outpatient care of cancer-directed surgery, chemotherapy, and radiation. After excluding those without clinical care or outside of the United States, we identified 94 NCI-designated and other academic cancer centers from the members of the Association of American Cancer Institutes. By explicitly incorporating existing specialized cancer referral centers, we refined the spatially constrained Leiden method that accounted for spatial adjacency and other constraints to delineate coherent CSAs within which the service volumes were maximal but minimal between them. The derived 110 CSAs had a high mean localization index (LI; 0.83) with a narrow variability (SD = 0.10). The variation of LI across the CSAs was positively associated with population, median household income, and area size, and negatively with travel time. Averagely, patients traveled less and were more likely to receive cancer care within the CSAs anchored by cancer centers than their counterparts without cancer centers. We concluded that CSAs are effective in capturing the local cancer care markets in the United States. They can be used as reliable units for studying cancer care and informing more evidence-based policy. Significance: Using the most refined network community detection method, we can delineate CSAs in a more robust, systematic, and empirical manner that incorporates existing specialized cancer referral centers. The CSAs can be used as a reliable unit for studying cancer care and informing more evidence-based policy in the United States. The cross-walk tabulation of ZIP code areas, CSAs, and related programs for CSAs delineation are disseminated for public access.

Mammography adherence in relation to function-related indicators in older women.

Prior studies of screening mammography patterns by functional status in older women show inconsistent results. We used Breast Cancer Surveillance Consortium-Medicare linked data (1999-2014) to investigate the association of functional limitations with adherence to screening mammography in 145,478 women aged 66-74 years. Functional limitation was represented by a claims-based function-related indicator (FRI) score which incorporated 16 items reflecting functional status. Baseline adherence was defined as mammography utilization 9-30 months after the index screening mammography. Longitudinal adherence was examined among women adherent at baseline and defined as time from the index mammography to end of the first 30-month gap in mammography. Multivariable logistic regression and Cox proportional hazards models were used to investigate baseline and longitudinal adherence, respectively. Subgroup analyses were conducted by age (66-70 vs. 71-74 years). Overall, 69.6% of participants had no substantial functional limitation (FRI score 0), 23.5% had some substantial limitations (FRI score 1), and 6.8% had serious limitations (FRI score ≥ 2). Mean age at baseline was 68.5 years (SD = 2.6), 85.3% of participants were white, and 77.1% were adherent to screening mammography at baseline. Women with a higher FRI score were more likely to be non-adherent at baseline (FRI ≥ 2 vs. 0: aOR = 1.13, 95% CI = 1.06, 1.20, p-trend < 0.01). Similarly, a higher FRI score was associated with longitudinal non-adherence (FRI ≥ 2 vs. 0: aHR = 1.16, 95% CI = 1.11, 1.22, p-trend < 0.01). Effect measures of FRI did not differ substantially by age categories. Older women with a higher burden of functional limitations are less likely to be adherent to screening mammography recommendations.

Cost-Effectiveness of Screening Mammography Beyond Age 75 Years : A Cost-Effectiveness Analysis.

BACKGROUND: The cost-effectiveness of screening mammography beyond age 75 years remains unclear. OBJECTIVE: To estimate benefits, harms, and cost-effectiveness of extending mammography to age 80, 85, or 90 years according to comorbidity burden. DESIGN: Markov microsimulation model. DATA SOURCES: SEER (Surveillance, Epidemiology, and End Results) program and Breast Cancer Surveillance Consortium. TARGET POPULATION: U.S. women aged 65 to 90 years in groups defined by Charlson comorbidity score (CCS). TIME HORIZON: Lifetime. PERSPECTIVE: National health payer. INTERVENTION: Screening mammography to age 75, 80, 85, or 90 years. OUTCOME MEASURES: Breast cancer death, survival, and costs. RESULTS OF BASE-CASE ANALYSIS: Extending biennial mammography from age 75 to 80 years averted 1.7, 1.4, and 1.0 breast cancer deaths and increased days of life gained by 5.8, 4.2, and 2.7 days per 1000 women for comorbidity scores of 0, 1, and 2, respectively. Annual mammography beyond age 75 years was not cost-effective, but extending biennial mammography to age 80 years was ($54 000, $65 000, and $85 000 per quality-adjusted life-year [QALY] gained for women with CCSs of 0, 1, and ≥2, respectively). Overdiagnosis cases were double the number of deaths averted from breast cancer. RESULTS OF SENSITIVITY ANALYSIS: Costs per QALY gained were sensitive to changes in invasive cancer incidence and shift of breast cancer stage with screening mammography. LIMITATION: No randomized controlled trials of screening mammography beyond age 75 years are available to provide model parameter inputs. CONCLUSION: Although annual mammography is not cost-effective, biennial screening mammography to age 80 years is; however, the absolute number of deaths averted is small, especially for women with comorbidities. Women considering screening beyond age 75 years should weigh the potential harms of overdiagnosis versus the potential benefit of averting death from breast cancer. PRIMARY FUNDING SOURCE: National Cancer Institute and National Institutes of Health.

Spatial Behavior of Cancer Care Utilization in Distance Decay in the Northeast Region of the U.S.

PURPOSE: Spatial behavior of patients in utilizing health care reflects their travel burden or mobility, accessibility for medical service, and subsequently outcomes from treatment. This paper derives the best-fitting distance decay function to capture the spatial behaviors of cancer patients in the Northeast region of the U.S., and examines and explains the spatial variability of such behaviors across sub-regions. PRINCIPAL RESULTS: (1) 46.8%, 85.5%, and 99.6% of cancer care received was within a driving time of 30, 60 and 180 minutes, respectively. (2) The exponential distance decay function is the best in capturing the travel behavior of cancer patients in the region and across most sub-regions. (3) The friction coefficient in the distance decay function is negatively correlated with the mean travel time. (4) The best-fitting function forms are associated with network structures. (5) The variation of the friction coefficient across sub-regions is related to factors such as urbanicity, economic development level, and market competition intensity. MAJOR CONCLUSIONS: The distance decay function offers an analytic metric to capture a full spectrum of travel behavior, and thus a more comprehensive measure than average travel time. Examining the geographic variation of travel behavior needs a reliable analysis unit such as organically defined "cancer service areas", which capture relevant health care market structure and thus are more meaningful than commonly-used geopolitical or census area units.

Association of Second-Opinion Strategies in the Histopathologic Diagnosis of Cutaneous Melanocytic Lesions With Diagnostic Accuracy and Population-Level Costs.

IMPORTANCE: Diagnostic variation among pathologists interpreting cutaneous melanocytic lesions could lead to suboptimal care. OBJECTIVE: To estimate the potential association of second-opinion strategies in the histopathologic diagnosis of cutaneous melanocytic lesions with diagnostic accuracy and 1-year population-level costs in the US. DESIGN, SETTING, AND PARTICIPANTS: Decision analysis with 1-year time horizon including melanocytic lesion diagnoses available from US pathologists participating in the Melanoma Pathology Study (M-Path) and from the study panel of reference pathologists who classified cases using the MPATH-Dx classification tool. M-Path data collection occurred from July 2013 through March 2015; analyses for the present study were performed between April 2015 and January 2021. EXPOSURES: Various second-opinion strategies for interpretation of melanocytic cutaneous lesions. MAIN OUTCOMES AND MEASURES: Estimated accuracy of pathologists' diagnoses, defined as concordance with the reference panel diagnoses, and 1-year postbiopsy medical costs under various second-opinion strategies. Expected percentage of concordant diagnoses, including percentages of overinterpretation and underinterpretation, and 1-year costs of medical care per 100 000 in the US population. RESULTS: Decision-analytic model parameters were based on diagnostic interpretations for 240 cases by 187 pathologists compared with reference panel diagnoses. Without second opinions, 83.2% of diagnoses in the US were estimated to be accurate-ie, concordant with the reference diagnosis; with overinterpretation (8.0%) or underinterpretation (8.8%), and 16 850 misclassified diagnoses per 100 000 biopsies. Accuracy increased under all second-opinion strategies. Accuracy (87.4% concordance with 3.6% overinterpretation and 9.1% underinterpretation) and cost (an increase of more than $10 million per 100 000 biopsies per year) were highest when second opinions were universal (eg, performed on all biopsies), relative to no second opinions. A selective second-opinion strategy based on pathologists' desire or institutional requirements for a second opinion was most accurate (86.5% concordance; 4.4% overinterpretation; 9.1% underinterpretation) and would reduce costs by more than $1.9 million per 100 000 skin biopsies relative to no second opinions. Improvements in diagnostic accuracy with all second-opinion strategies were associated with reductions in overinterpretation but not underinterpretation. CONCLUSIONS AND RELEVANCE: In this decision-analytic model, selective second-opinion strategies for interpretation of melanocytic skin lesions showed the potential to improve diagnostic accuracy and decrease costs relative to no second opinions or universal second opinions.

Assessment of a Risk-Based Approach for Triaging Mammography Examinations During Periods of Reduced Capacity.

Importance: Breast cancer screening, surveillance, and diagnostic imaging services were profoundly limited during the initial phase of the coronavirus disease 2019 (COVID-19) pandemic. Objective: To develop a risk-based strategy for triaging mammograms during periods of decreased capacity. Design, Setting, and Participants: This population-based cohort study used data collected prospectively from mammography examinations performed in 2014 to 2019 at 92 radiology facilities in the Breast Cancer Surveillance Consortium. Participants included individuals undergoing mammography. Data were analyzed from August 10 to November 3, 2020. Exposures: Clinical indication for screening, breast symptoms, personal history of breast cancer, age, time since last mammogram/screening interval, family history of breast cancer, breast density, and history of high-risk breast lesion. Main Outcomes and Measures: Combinations of clinical indication, clinical history, and breast cancer risk factors that subdivided mammograms into risk groups according to their cancer detection rate were identified using classification and regression trees. Results: The cohort included 898 415 individuals contributing 1 878 924 mammograms (mean [SD] age at mammogram, 58.6 [11.2] years) interpreted by 448 radiologists, with 1 722 820 mammograms in individuals without a personal history of breast cancer and 156 104 mammograms in individuals with a history of breast cancer. Most individuals were aged 50 to 69 years at imaging (1 113 174 mammograms [59.2%]), and 204 305 (11.2%) were Black, 206 087 (11.3%) were Asian or Pacific Islander, 126 677 (7.0%) were Hispanic or Latina, and 40 021 (2.2%) were another race/ethnicity or mixed race/ethnicity. Cancer detection rates varied widely based on clinical indication, breast symptoms, personal history of breast cancer, and age. The 12% of mammograms with very high (89.6 [95% CI, 82.3-97.5] to 122.3 [95% CI, 108.1-138.0] cancers detected per 1000 mammograms) or high (36.1 [95% CI, 33.1-39.3] to 47.5 [95% CI, 42.4-53.3] cancers detected per 1000 mammograms) cancer detection rates accounted for 55% of all detected cancers and included mammograms to evaluate an abnormal mammogram or breast lump in individuals of all ages regardless of breast cancer history, to evaluate breast symptoms other than lump in individuals with a breast cancer history or without a history but aged 60 years or older, and for short-interval follow-up in individuals aged 60 years or older without a breast cancer history. The 44.2% of mammograms with very low cancer detection rates accounted for 13.1% of detected cancers and included annual screening mammograms in individuals aged 50 to 69 years (3.8 [95% CI, 3.5-4.1] cancers detected per 1000 mammograms) and all screening mammograms in individuals younger than 50 years regardless of screening interval (2.8 [95% CI, 2.6-3.1] cancers detected per 1000 mammograms). Conclusions and Relevance: In this population-based cohort study, clinical indication and individual risk factors were associated with cancer detection and may be useful for prioritizing mammography in times and settings of decreased capacity.

Surgeon and medical oncologist peer network effects on the uptake of the 21-gene breast cancer recurrence score assay.

BACKGROUND: Drivers behind the adoption of gene expression profiling in breast cancer oncology have been shown to include exposure to physician colleagues' use of a given genomic test. We examined adoption of the Oncotype DX 21-gene breast cancer recurrence score assay (ODX) in the United States after its incorporation into clinical guidelines. The influence of patient-sharing ties and co-location with prior adopters and the role of these potential exposures across medical specialties on peers' adoption of the test were examined. METHODS: We conducted a retrospective cohort study of women with incident breast cancer using a 100% sample of fee-for-service Medicare enrollee claims over 2008-2011. Peer networks connecting medical oncologists and surgeons treating these patients were constructed using patient-sharing and geographic co-location. The impact of peer connections on the adoption of ODX by physicians and testing of patients was modeled with multivariable hierarchical regression. RESULTS: Altogether, 156,229 women identified with incident breast cancer met criteria for cohort inclusion. A total of 7689 ODX prescribing physicians were identified. Co-location with medical oncologists who adopted the test in the early period (2008-2009) was associated with a 1.38-fold increase in the odds of a medical oncologist adopting ODX in 2010-2011 (95% CI = 1.04-1.83), as was co-location with early-adopting surgeons (odds ratio [OR] = 1.25, 95% CI = 1.00-1.58). Patients whose primary medical oncologist was linked to an early-adopting surgeon through co-location (OR = 1.17, 95% CI = 1.04-1.32) or both patient-sharing and co-location (OR = 1.17, 95% CI = 1.03-1.34) were more likely to receive ODX. CONCLUSIONS: Exposure to surgeon early adopters through peer networks and co-location was predictive of ODX uptake by medical oncologists and testing of patients. Interventions focused on the role of surgeons in molecular testing may improve the implementation of best practices in breast cancer care.