Rural-Urban Commuting Area Research Articles

Predictors of electronic health record (EHR) portal registration and frequency of portal use among patients with cancer prior to engagement in the IMPACT Consortium symptom management trials.

419 Background: The patient portal is part of an electronic health record (EHR) that allows patients to communicate with their healthcare team. The portal also provides a platform for patients to receive and complete symptom surveys that can be directly integrated into the EHR, allowing clinical care teams to monitor symptoms and provide cancer symptom management. The Improving Management of symPtoms during And following Cancer Treatment (IMPACT) consortium, supported by the National Cancer Institute’s Cancer MoonshotSM, aims to improve symptom control for cancer patients through assessment and symptom management interventions deployed via the EHR. This initiative presents an opportunity to examine portal enrollment and variation in use, factors critical to successful implementation, especially among groups that have high cancer symptom burden. To this end, we examine: 1) relationships between portal enrollment prior to the launch of IMPACT interventions and neighborhood broadband access, demographic, and social characteristics; and 2) frequency of pre-intervention portal use for any purpose among enrollees. Methods: Data are derived from two of three IMPACT research centers. Enrollment in and frequency of portal use, mode of accessing the portal (web vs. phone), social, demographic, and cultural factors were extracted from the EHR. Rural Urban Commuting Area (RUCA) codes were used to classify population density and degree of rurality. Broadband access was estimated using 2015-2019 American Community Survey estimates matched to zip codes from enrolled IMPACT patients and classified as a patient’s residence being in a community with high (≥85% of households) or low (< 85% of households) access. Bivariate comparisons and adjusted odds ratios were used to describe all associations. Results: Forty-seven percent of patients (22,596/48,034) were enrolled in the portal prior to the intervention. Patients in zip codes with low broadband access and those who were men, > 65 years old, not White, of Hispanic ethnicity, or disabled or not employed had significantly lower odds of being enrolled in the portal. If enrolled, 21% (n = 4825) used the portal at least once a week. Less variation was found in the average frequency of portal use, but patients younger than 40, and those who were Black, disabled, unemployed, or those who used a mobile device to access the portal had the lowest odds of accessing it at least once a week. Conclusions: Significant disparities in portal enrollment exist across demographic groups and among those with limited broadband access. Among those enrolled, most used the portal less than once a week. Fewer differences in frequency of use were observed by sociodemographic factors. Improving portal enrollment and frequency of use may be critical for symptom management interventions deployed via patient portals.

Why aren’t more patients with breast cancer enrolled in clinical trials?

84 Background: Only 2-8% of adult patients with cancer participate in clinical trials, likely due to strict exclusion criteria as well as financial and access issues. The primary objective of this study is to understand the population of patients with breast cancer who are and are not offered a clinical trial and the impact of exclusion criteria on enrollment. Methods: Inclusion and exclusion criteria from study protocols housed in OnCore and ClinicalTrials.gov were obtained for breast cancer-specific, therapeutic clinical trials open at the University of Alabama at Birmingham (UAB) from 2016 to 2020. Patients with breast cancer receiving oncology services at UAB from 2016 to 2020 were identified from electronic health records. Race and ethnicity and address were abstracted. Address was utilized to characterize patients as living in areas of higher vs. lower deprivation (Area Deprivation Index) and rural communities (Rural-Urban Commuting Area). Chart abstraction was conducted to assess if patients were offered a trial, eligible for a trial, reason for ineligibility, and enrollment in trial vs standard of care treatment. Results: 518 patients were included; 387 were offered a trial and 131 were not. The median age of patients offered a trial was 57 years old, whereas the median age of patients who were not offered a trial was 61. The majority of patients offered a trial were more often White (72% vs. 24% African American), resided in areas of lower disadvantage (70% vs 17% most disadvantaged), and urban residents (75% vs 13% rural). Of the 387 patients offered a trial, 319 (82%) enrolled, 34 (9%) declined enrollment and chose standard of care, and the remaining 34 (9%) were interested in enrollment but later found to be ineligible. Reasons for ineligibility of the 34 patients who were offered a trial included comorbidities (n = 9), tumor size (n = 7), metastases (n = 5), and previous cancer history (n = 4). Additionally, 9 patients were ineligible for miscellaneous reasons (abnormal labs, age, prescription, trial closed to accrual, tumor characteristics). Of the 131 patients that were not offered a clinical trial, 77 (59%) were ineligible for enrollment. Reasons for ineligibility included: stage 1 disease (n = 35), tumor size and characteristics (n = 24), and comorbidities and abnormal labs (n = 18). The remaining 54 patients would have been eligible, but their provider did not offer a clinical trial. Conclusions: Most patients who are offered a clinical trial are willing to participate; physicians not offering a trial to patients appears to be a driver for low enrollment. Strict exclusion criteria related to comorbidities limit trial participation. Further work is needed to understand the relative importance of these eligibility criteria in relation to validity. Efforts should be made to include patients in clinical trials that reflect the diverse patient population that will receive the drug in the future.

Neighborhood characteristics and dementia symptomology among community-dwelling older adults with Alzheimer's disease.

BackgroundNeuropsychiatric symptoms (NPSs) lead to myriad poor health outcomes among individuals with Alzheimer’s disease (AD). Prior studies have observed associations between the various aspects of the home environment and NPSs, but macro-level environmental stressors (e.g., neighborhood income) may also disrupt the neuronal microenvironment and exacerbate NPSs. Yet, to our knowledge, no studies have investigated the relationship between the neighborhood environment and NPSs.MethodsUsing 2010 data among older adults with AD collected from a sample of the South Carolina Alzheimer’s Disease Registry, we estimated cross-sectional associations between neighborhood characteristics and NPSs in the overall population and by race/ethnicity. Neighborhood measures (within a 1/2-mile radius of residence) came from the American Community Survey and Rural Urban Commuting Area Code. We categorized median household income into tertiles: < $30,500, $30,500–40,000, and > $40,000, and rurality as: rural, small urban, and large urban. Residential instability was defined as the percent of residents who moved within the past year. NPSs were defined using the Neuropsychiatric Inventory Questionnaire that included the composite measure of all 12 domains. Adjusting for age, sex/gender, race/ethnicity, and caregiver educational attainment, we used negative binomial regression to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for NPSs by neighborhood characteristics.ResultsAmong 212 eligible participants, mean age was 82 ± 8.7 years, 72% were women, and 55% non-Hispanic (NH)-Black. Individuals with AD living in < $30,500 vs. > $40,000 income neighborhoods had a 53% (PR = 1.53; 95% CI = 1.06–2.23) higher prevalence of NPSs while individuals living in rural vs. large urban neighborhoods had a 36% lower prevalence of NPSs (PR = 0.64; 95% CI = 0.45–0.90), after adjustment. We did not observe an association between residential instability and NPSs (PR = 0.92; 95% CI = 0.86–1.00); however, our estimates suggested differences by race/ethnicity where NH-White older adults living in residential instable areas had lower NPSs (PR = 0.89; 95% CI = 0.82–0.96) compared to NH-Black older adults (PR = 0.96; 95% CI = 0.86–1.07).DiscussionAcross racial/ethnic groups, individuals with AD had more symptomology when living in lower income areas. Pending replication, intervention efforts should consider resource allocation to high-need neighborhoods (e.g., lower income), and studies should investigate underlying mechanisms for this relationship.

Enriching the American Heart Association COVID-19 Cardiovascular Disease Registry Through Linkage With External Data Sources: Rationale and Design.

Background The AHA Registry (American Heart Association COVID-19 Cardiovascular Disease Registry) captures detailed information on hospitalized patients with COVID-19. The registry, however, does not capture information on social determinants of health or long-term outcomes. Here we describe the linkage of the AHA Registry with external data sources, including fee-for-service (FFS) Medicare claims, to fill these gaps and assess the representativeness of linked registry patients to the broader Medicare FFS population hospitalized with COVID-19. Methods and Results We linked AHA Registry records of adults ≥65 years from March 2020 to September 2021 with Medicare FFS claims using a deterministic linkage algorithm and with the American Hospital Association Annual Survey, Rural Urban Commuting Area codes, and the Social Vulnerability Index using hospital and geographic identifiers. We compared linked individuals with unlinked FFS beneficiaries hospitalized with COVID-19 to assess the representativeness of the AHA Registry. A total of 10 010 (47.0%) records in the AHA Registry were successfully linked to FFS Medicare claims. Linked and unlinked FFS beneficiaries were similar with respect to mean age (78.1 versus 77.9, absolute standardized difference [ASD] 0.03); female sex (48.3% versus 50.2%, ASD 0.04); Black race (15.1% versus 12.0%, ASD 0.09); dual-eligibility status (26.1% versus 23.2%, ASD 0.07); and comorbidity burden. Linked patients were more likely to live in the northeastern United States (35.7% versus 18.2%, ASD 0.40) and urban/metropolitan areas (83.9% versus 76.8%, ASD 0.18). There were also differences in hospital-level characteristics between cohorts. However, in-hospital outcomes were similar (mortality, 23.3% versus 20.1%, ASD 0.08; home discharge, 45.5% versus 50.7%, ASD 0.10; skilled nursing facility discharge, 24.4% versus 22.2%, ASD 0.05). Conclusions Linkage of the AHA Registry with external data sources such as Medicare FFS claims creates a unique and generalizable resource to evaluate long-term health outcomes after COVID-19 hospitalization.

Before and During Pandemic Telemedicine Use: An Analysis of Rural and Urban Safety-Net Clinics

IntroductionDifferences in face-to-face and telemedicine visits before and during the COVID-19 pandemic among rural and urban safety-net clinic patients were evaluated. In addition, this study investigated whether rural patients were as likely to utilize telemedicine for primary care during the pandemic as urban patients.MethodsUsing electronic health record data from safety-net clinics, patients aged ≥18 years with ≥1 visit before or during the COVID-19 pandemic, March 1, 2019–March 31, 2021, were identified, and trends in face-to-face and telemedicine (phone and video) visits for patients by rurality using Rural‒Urban Commuting Area codes were characterized. Multilevel mixed-effects regression models compared service delivery method during the pandemic by rurality.ResultsIncluded patients (N=1,015,722) were seen in 446 safety-net clinics: 83% urban, 10.3% large rural, 4.1% small rural, and 2.6% isolated rural. Before COVID-19, little difference in the percentage of encounters conducted face-to-face versus through telemedicine by rurality was found. Telemedicine visits significantly increased during the pandemic by 27.2 percentage points among patients in isolated rural areas to 52.3 percentage points among patients in urban areas. Rural patients overall had significantly lower odds of using telemedicine for a visit during the pandemic than urban patients.ConclusionsDespite the increased use of telemedicine in response to the pandemic, rural patients had significantly fewer telemedicine visits than those in more urban areas. Equitable access to telemedicine will depend on continued reimbursement for telemedicine services, but additional efforts are warranted to improve access to and use of health care among rural patients.

Association of Rurality, Race and Ethnicity, and Socioeconomic Status With the Surgical Management of Colon Cancer and Postoperative Outcomes Among Medicare Beneficiaries

Rural patients with colon cancer experience worse outcomes than urban patients, but the extent to which disparities are explained by social determinants is not known. To evaluate the association of rurality with surgical treatment and outcomes of colon cancer and to investigate the intersection of rurality with race and ethnicity and socioeconomic status. This cohort study included fee-for-service Medicare beneficiaries 65 years or older diagnosed with incident, nonmetastatic colon cancer between April 1, 2016, and September 30, 2018, with follow-up until December 31, 2018. Data were analyzed from August 3, 2020, to April 30, 2021. Rurality of patient's residence, categorized as metropolitan, micropolitan, or small town or rural, using Rural-Urban Commuting Area codes. Receipt of surgery, emergent surgery, or minimally invasive surgery (MIS); 90-day surgical complications; and 90-day mortality. Among 57 710 Medicare beneficiaries with incident, nonmetastatic colon cancer, 46.6% were men, 53.4% were women, and the mean (SD) age was 76.6 (7.2) years. In terms of race and ethnicity, 3.7% were Hispanic, 6.4% were non-Hispanic Black (hereinafter Black), 86.1% were non-Hispanic White (hereinafter White), and 3.8% were American Indian or Alaska Native, Asian or Pacific Islander, or unknown race or ethnicity. Patients residing in nonmetropolitan areas were more likely to undergo surgical resection than those residing in metropolitan areas (69.2% vs 63.9%; P < .001). Black race was independently associated with lower hazard of surgical resection (hazard ratio, 0.92 [95% CI, 0.88-0.95]). Race and ethnicity and measures of socioeconomic status did not modify the association of rurality with surgery. Beneficiaries from small town and rural areas had higher odds of undergoing emergent surgery (adjusted odds ratio [OR], 1.32 [95% CI, 1.20-1.44]) but lower odds of undergoing MIS (adjusted OR, 0.75 [95% CI, 0.70-0.80]), with similar findings for patients residing in micropolitan areas. Members of racial and ethnic minority groups who resided in small town and rural settings experienced higher odds of postoperative surgical complications (P = .001 for interaction) and mortality (P = .001 for interaction). Notably, White patients who resided in small town and rural areas experienced lower odds of postoperative mortality than their White metropolitan counterparts (adjusted OR, 0.81 [95% CI, 0.71-0.92]), but Black patients who resided in small town and rural areas had significantly higher odds of postoperative mortality (adjusted OR, 1.86 [95% CI, 1.16-2.97]) than their Black metropolitan counterparts. These findings suggest that Medicare beneficiaries from small town and rural areas were more likely to undergo surgery for nonmetastatic colon cancer than metropolitan beneficiaries but also more likely to undergo emergent surgery and less likely to have MIS. The experiences of rural patients varied by race; rurality was associated with higher postoperative mortality for Black patients but not for other racial and ethnic groups.

Distinct sociodemographic differences in incidence and survival rates for human papillomavirus (HPV)-like, non-HPV-like, and "other"-like oral cavity and pharynx cancers: An analysis of Surveillance, Epidemiology and End Results (SEER) Program data.

PurposeOral cavity and pharynx cancer (OCPC) cases are traditionally dichotomized into human papillomavirus (HPV) and non-HPV types. Using a proxy for HPV status, the objective was to evaluate differences in incidence and survival rates of OCPC anatomic sub-sites identified as: 1) HPV-like; 2) non-HPV-like (i.e., tobacco/alcohol-related); and 3) “other”-like (i.e., not predominantly HPV-like nor tobacco/alcohol-like) OCPCs.MethodsData from the Surveillance, Epidemiology and End Results Program were used to examine incidence and survival rates for OCPC categories over time and according to age, sex, race, ethnicity, stage at diagnosis, neighborhood socioeconomic status (i.e., nSES or Yost Index), and rurality/urbanity (i.e., Rural Urban Commuting Area (RUCA) codes). Although HPV status was unavailable in this dataset, OCPC anatomies and histologies were classified into three sub-categories, based on potential risk factors. Frequencies, average annual, age-adjusted incidence rates, five-year relative survival rates, and 95% confidence intervals were examined across and within OCPC categories.ResultsHPV-like OCPC incidence rates sharply increased from 1975 through 2015 while non-HPV-like and “other”-like OCPC rates decreased, all converging to similar rates from 2016 through 2018. Increasing over time for both categories, survival was highest for HPV-like and lowest for non-HPV-like OCPCs; survival for “other”-like OCPCs remained stable. Generally, across OCPC categories, incidence and survival rates were significantly higher among males vs. females, Whites vs. African Americans, and non-Hispanics vs. Hispanics. “Other”-like OCPC incidence decreased with increasing nSES tertiles, while no nSES differences were observed for HPV-like and non-HPV-like OCPCs. Incidence rates were significantly lower among urban (vs. rural) residents. For all OCPC categories, survival rates were significantly higher with increasing nSES and variable across RUCA categories.Conclusion/ImpactHPV-like and non-HPV-like OCPC cases had distinct sociodemographic differences; “other”-like OCPC cases were a sociodemographic blend of HPV-like and non-HPV-like OCPC cases, resembling more of the sociodemographic makeup of non-HPV-like OCPC cases. To prevent new OCPCs, additional studies are needed to epidemiologically and clinically differentiate between OCPC categories so that high-risk groups can be better targeted in future public health interventions.

The Role of Individual-Level Factors in Rural Mortality Disparities

The role of individual risk factors in the rural‒urban mortality disparity is poorly understood. The purpose of this study was to explore the role of individual-level demographics and health behaviors on the association between rural residence and the risk of mortality. Cancer Prevention Study-II participants provided updated addresses throughout the study period. Rural‒Urban Commuting Area codes were assigned to participants' geocoded addresses as a time-varying exposure. Cox proportional hazards regression was used to estimate hazard ratios and 95% CIs for mortality associated with Rural‒Urban Commuting Area groups. After adjustment for age and sex, residents of rural areas/small towns had a small but statistically significant elevated risk of all-cause mortality compared with metropolitan residents (hazard ratio=1.04; 95% CI=1.01, 1.06). Adjustment for additional covariates attenuated the association entirely (hazard ratio=0.99; 95% CI=0.97, 1.01). Individually, adjustment for education (hazard ratio=0.99; 95% CI=0.97, 1.01), alcohol use (hazard ratio=1.01; 95% CI=0.99, 1.04), and moderate-to-vigorous intensity aerobic physical activity (hazard ratio=1.00; 95% CI=0.97, 1.02) eliminated the elevated risk. The elevated risk of death for rural compared with that for metropolitan residents appeared to be largely explained by individual-level demographics and health behaviors. If replicated in other subpopulations, these results suggest that modifiable factors may play an important role in reducing the rural mortality disparity.

Outdoor activities and sunburn among urban and rural families in a Western region of the US: Implications for skin cancer prevention

Prevention of skin cancer involves decreasing exposure to ultraviolet radiation (UVR) and avoiding sunburn, especially early in life. Individuals living in urban versus rural areas, as defined by the Rural Urban Commuting Area (RUCA) code classification, have different risks for skin cancer, likely due to differing patterns of outdoor activities and preventive behaviors employed when outdoors. However, few studies have examined differences in outdoor activities between rural and urban individuals and examined this among both adults and children. This study compared the outdoor activities, sun protection strategies, tanning behaviors, and sunburn occurrence of adults and children (n = 97 dyads) in rural versus urban settings in a Western region of the United States. The relationships between outdoor activities and sunburn occurrence were examined in both groups. Analyses employed Barnard’s Exact Test and logistic generalized estimating equations models. Individuals in rural and urban areas reported differences in sun protection strategies used, tanning behaviors, and outdoor activities. Individuals in urban areas more commonly reported use of certain forms of sun protection, such as sunscreen and shade, whereas long pants and skirt wearing were more common for children in rural areas. Individuals in rural areas were more likely than those in urban areas to engage in farm work, yard work, and youth sports. Gender differences in these outcomes were also identified. Skin cancer preventive interventions could be tailored for rural and urban families to target sun protection strategies to outdoor activities and to situations in which sunburns are likely to occur.

Rural Medicare beneficiaries are increasingly likely to be admitted to urban hospitals.

To determine whether rural Medicare FFS beneficiaries are more likely to be admitted to an urban hospital in 2018 than in 2010. We combined data from the 2010 to 2018 Hospital Service Area File (HSAF) and the 2010-2017 American Hospital Association (AHA) survey. We conducted a fixed-effects negative-binomial regression to determine whether urban hospital admissions from rural ZIP codes were increasing over time. We also conducted an exploratory geographically weighted regression. We transformed the HSAF data into a ZIP code-level file with all rural ZIP codes. We defined rural as having a Rural-Urban Commuting Area (RUCA) code ≥4. A hospital's system affiliation status was incorporated from the AHA survey. Controlling for distance to the nearest hospitals, an increase of 1 year was associated with a 2.0% increase (p < 0.001) in the number of admissions to urban hospitals from each rural ZIP code. New system affiliation of the nearest rural hospital was associated with an increase of 1.7% (p < 0.001). Even when controlling for distance to the nearest rural hospital (which reflects hospital closures), rural patients were increasingly likely to be admitted to an urban hospital.

Paper 62: Influence of Gender, Race/Ethnicity &amp; Socioeconomic Factors on Meniscus Treatment with Pediatric and Adolescent ACL Reconstruction

Objectives:The rate of anterior cruciate ligament reconstruction (ACLR) in pediatric/adolescent populations has increased over time. However, there is less evidence to support the relative change in concomitant meniscus procedures over time. Demographics and socioeconomic (SES) factors may be implicated in the rate and usage of meniscectomy vs. meniscal repair as treatment. The purpose of this study is to assess if the rate of concomitant meniscus procedure with ACLR is increasing and what factors may be contributing to this rise. We hypothesize that age, sex, race/ethnicity, income, or insurance type may affect the rate of concomitant meniscus procedures.Methods:The Pediatric Health Information System (PHIS) Database was queried for all patients 18 years or younger who underwent ACLR with or without concomitant meniscus procedures based on CPT code from 2015-2019. Data from 39 out of 52 centers were included based on completeness of their data. Basic demographic information including age, sex, self-identified race/ethnicity, and insurance status was collected. Urban and rural designation was identified based on rural-urban commuting area (RUCA) codes. Based on the 2015 Department Health & Human Services Federal Poverty Guidelines (FPL), patients were stratified into lower (<199% of FPL) and higher (≥200% of FPL) income brackets. Linear regression was used to model trends and multiple logistic regression modeling was used to test for associations.Results:14,398 patients underwent ACLR during the study period with 8,337 patients (58%) having concomitant meniscal procedures with a 1.24-fold increase over five years (Figure 1). Of the 8,337 patients with concomitant meniscus treatment, 46% had a meniscectomy and 54% had a meniscus repair. There was a 0.86-fold change in meniscectomy and a 1.70-fold increase in meniscus repair during the study period (Figure 2). Males, older patients, Black race, living in an urban area, and those with non-private insurance had increased odds of undergoing a concomitant meniscus procedure (all p<0.05). Patients of Black or other race and those with non-private insurance had increased odds of having a meniscectomy vs. meniscus repair (all p£0.01). There were no associations detected between income bracket and our outcomes.Conclusions:This study shows that in pediatric and adolescent patients undergoing ACLR, there is a rise in concomitant meniscus procedures from 2015-2019. In addition, patients of non-white race and those with non-private insurance have increased odds of undergoing meniscectomy vs. meniscus repair. This study identifies access and inequality issues in ACLR with concomitant meniscus procedures in pediatric and adolescent populations.Figure 1.ACL Reconstruction vs ACL Reconstruction with Concomitant Meniscus Procedures from 2015-2019Figure 2.ACL Reconstruction with Concomitant Meniscus Procedures: Meniscectomy vs. Meniscus Repair

Geographic distribution of colonoscopy providers in the United States: An analysis of medicare claims data.

Screening colonoscopy is one of the few procedures that can prevent cancer. While the majority of colonoscopies in the USA are performed by gastroenterologists, general surgeons play a key role in at-risk, rural populations. The aim of this study was to examine geographic practice patterns in colonoscopy using a nationwide Medicare claims database. The 2017 Medicare Provider Utilization and Payment database was used to identify physicians performing colonoscopy. Providers were classified as gastroenterologists, surgeons, ambulatory surgical centers (ASCs), or other. Rural-Urban Commuting Area classification at the zip code level was used to determine whether the practice location for an individual provider was in a rural area/small town (&lt; 10,000 people), micropolitan area (10-50,000 people), or metropolitan area (&gt; 50,000 people). Claims data from 3,861,187 colonoscopy procedures on Medicare patients were included. The majority of procedures were performed by gastroenterologists (57.2%) and ASCs (32.1%). Surgeons performed 6.8% of cases overall. When examined at a zip code level, surgeons performed 51.6% of procedures in small towns/rural areas and 21.7% of procedures in micropolitan areas. Individual surgeons performed fewer annual procedures as compared to gastroenterologists (median 51 vs. 187, p &lt; 0.001). Surgeons perform the majority of colonoscopies in rural zip codes on Medicare patients. High-quality, surgical training in endoscopy is essential to ensure access to colonoscopy for patients outside of major metropolitan areas.

Real-world practice patterns in the diagnosis of recurrent metastatic breast cancer in Washington state.

e13640 Background: Evidence-based, national guidelines for the diagnosis of recurrent metastatic breast cancer (MBC) recommend confirmation of recurrence with biopsy and reassessment of biomarker status. Real world practice patterns may demonstrate disparities in adherence to guidelines with implications for patients and health systems. Methods: We utilized the Hutchinson Institute for Cancer Outcomes Research (HICOR) data repository that links Washington State cancer registry data to enrollment and claims from the major insurance payers in the state. We identified women &gt; 18 years old diagnosed with recurrent MBC between 2008 and 2017 with evidence of enrollment in a commercial plan (Premera or Regence), Medicare, or Medicaid. Recurrence in Stage I-III patients was detected through identification of ICD 9/10 codes for metastatic disease or resumption of breast cancer systemic therapy (after minimum of 4 months from completion of early breast cancer therapy). Using claims, we identified receipt of and factors associated with biopsy, biomarker re-assessment, and treatment administered at recurrence. Results: We identified 715 patients with recurrent MBC (any ER or HER2 status) with median age of 62 (range 52-73). The majority of the cohort were Caucasian (89%) with the rest comprising of Asian, Black, American Indian, and Hispanic patients. Based on the Rural Urban Commuting Area (RUCA) classification, patients predominantly lived in metropolitan neighborhoods (97%). Approximately 13% of patients lived in areas of high deprivation (area of deprivation index, ADI, 8-10). Patients had either Commercial (53.1%), Medicaid (4.2%), Medicare (29.7%) or multiple (13.0%) insurance. Patients were primarily treated at high volume centers (70.9%), though 23% of patients were seen at low volume centers (&lt;25 breast cancer patients/year). Of the patients with recurrent MBC, 49.5% received a biopsy to confirm metastatic diagnosis. Similarly, 48.7% of recurrent MBC patients underwent biomarker reassessment. Patients with highest co-morbidity index (&gt;2) were more likely to undergo biopsy confirmation (20.3% vs 13.0%, p = 0.02). Biopsy was more often performed in patients receiving care at a high-volume center compared to low-volume center (74.3% vs 18.6%, p = 0.03). First line treatment selection was directly associated with receipt of biopsy and biomarker testing. Hormone therapy only was more common in patients who did not undergo biopsy (62.3% vs 37.7%, p &lt;0.001) or biomarker reassessment (62.7% vs 37.3%, p &lt;0.001). Conclusions: Our study shows there is variation across Washington state in biopsy and biomarker assessment in the diagnosis of recurrent metastatic breast cancer. Nearly half of cases had metastatic biopsy omitted. Our findings demonstrate downstream implications for treatment selection and support the need for quality initiatives to improve adherence to guidelines.

Rural-urban disparities in Geriatric Assessment (GA) impairments and mortality among older adults with cancer: Results from the Cancer and Aging Resilience Evaluation (CARE) Registry.

12012 Background: Rural-urban disparities persist in cancer incidence and mortality, despite improvement in cancer screening and treatment. Older adults are at increased risk for cancer, represent the majority of cancer cases, and are more likely to reside in rural areas than younger adults are. GAs are recommended in clinical management of older adults with cancer, in part to identify aging-related impairments predictive of adverse outcomes; few studies have explored rural-urban disparities in GA impairments and mortality among older patients with cancer. Methods: We included 937 older adults (≥60y) from the CARE registry recently diagnosed with cancer who underwent GA at their first pre-chemotherapy visit to the UAB oncology clinic. Rural-urban status using Rural-Urban Commuting Area (RUCA) codes, classified the cohort by residence in metropolitan, micropolitan, and rural/small town areas. We included self-rated performance status (PS) (≥2 on Eastern Cooperative Oncology Group PS), instrumental activities of daily living (IADL) [≥1 impairment], physical and mental health-related quality of life (HRQoL) [t-score &lt; 40 on PROMIS 10-item Global Health], and overall survival as outcomes. Logistic regression evaluated the association between rural-urban status and each outcome (except overall survival, where we used Cox regression analyses). Micropolitan residence was chosen as the reference category due to similar high risk in both rural and urban areas. Results: Median age at study participation was 69.0y (Interquartile range: 64.0-74.0); 12.4% resided in rural, 14.8% in micropolitan and 72.8% in urban areas; 22.5% were diagnosed with colorectal cancer, 19.0% with pancreatic, and 12.4% with hepatobiliary; 74.7% were Stage III/IV. Participants in rural areas were more likely to be white and less educated. After adjustment for age, sex, race, education and cancer type/stage, rural residence was associated with increased odds of impaired PS (OR = 1.93, 95% CI: 1.10-3.40), limitations in IADLs (OR = 1.79, 95% CI: 1.03-3.10), and impaired physical HRQoL (OR = 1.84, 95% CI: 1.05-3.22) compared to micropolitan residence. Urban residence was not significantly associated with any GA outcomes compared to micropolitan residence. Rural residence was associated with higher hazard of death compared to micropolitan residence (HR = 1.88, 95% CI: 1.09-3.24) as well as urban residence (HR = 1.67, 95% CI: 1.13-2.45). Conclusions: Among older adults with newly diagnosed cancer, rural residence was associated with impaired PS, limitations in IADLs, impaired physical HRQoL, and reduced overall survival. Implementation of routine GA among older adults in rural areas may aid in early identification and intervention on GA impairments to improve cancer outcomes.

Population-based impact of rurality and neighborhood-level socioeconomic disadvantage on pediatric cancer mortality in Washington State.

6537 Background: Emerging evidence suggests mortality among children with cancer differs based on socioeconomic factors. The effects of residential location in relation to these factors are not well-characterized. We examined associations of rurality and neighborhood-level socioeconomic (SE) disadvantage with mortality in pediatric patients with cancer. Methods: We conducted a retrospective cohort study using Washington State (WA) Cancer Registry data (1992-2013) linked to state birth (1974-2013) and death records (1992-2013) to identify all children born in WA diagnosed with cancer &lt; 20 years. We defined rural residence as patient addresses within 2010 census tract level rural-urban commuting area (RUCA) codes of ≥4.0 at diagnosis. Neighborhood-level socioeconomic disadvantage was determined using 2010 census block-group level Area Deprivation Index (ADI) quintiles normalized to WA. Patient addresses within the highest ADI quintile were categorized as having SE disadvantage. children in four mutually exclusive groups was compared using Kaplan-Meier analysis, pairwise log rank testing, and Cox proportional hazard ratios (HR): non-rural with SE disadvantage, rural without SE disadvantage, rural with SE disadvantage, and non-rural without SE disadvantage (). Models were adjusted for sex, race and ethnicity, age at diagnosis, birth year, and cancer type. Results: We identified 4,417 children for analysis. Median length of follow up among survivors was 5.0 years (inter-quartile range: 1.0-11.5). SE disadvantaged and 13% were rural. Pairwise log rank tests showed mortality differences among children living in rural, SE disadvantaged, or rural and SE disadvantaged neighborhoods when compared with children without either factor (individual p-values all &lt; 0.005); no other differences were noted. Relative to children in non-rural areas without SE disadvantage, the mortality HR for those in non-rural areas with SE disadvantage was 1.68 (95% confidence intervals [CI] 1.37-2.07). The HR for children in rural areas without SE disadvantage was 1.59 (95% CI 1.19-2.12). The HR for children in rural areas with SE disadvantage was 1.56 (95% CI 1.20-2.03). In sub-analyses, associations for rurality and SE disadvantage remained significant for leukemia mortality, but CNS and solid tumor mortality was only associated with SE disadvantage (but not rural) status. Acute lymphoblastic leukemia mortality was associated with rural (but not SE disadvantage) status. Conclusions: Children with cancer living in socioeconomically disadvantaged and/or rural neighborhoods at diagnosis had higher mortality relative to those in non-rural areas with lower neighborhood deprivation. Associations varied by disease type. The individual effects of SE disadvantage and rurality suggest that interventions should be designed to target both factors.

Rural cancer care access in the United States: A worsening problem?

e18521 Background: Rural counties have a decreased incidence of cancer relatively to non-rural counties; however, they have higher death rates from cancer. This disparity in death rates is worsening. Patients in rural areas face significant barriers to accessing oncologic care – limited access to oncologic providers and significant travel distances to treatment chief among them. We aimed to assess trends in geographic distribution of medical and radiation oncologists to ascertain trends in access to oncologic specialists in rural areas. Methods: We used the Medicare Physician &amp; Other Practitioners database published by the Centers for Medicare &amp; Medicaid Services from 2013 and 2019 (the earliest and latest available) to identify radiation oncologists and medical oncologists (including hematology/oncology) based on the Provider Type field. We used the Rural Urban Commuting Area Codes as defined by the US Department of Agriculture; rural was defined as non-metropolitan areas with a &lt; 30% commuting flow to a large urban center consistent with the definition from the WWAMI Rural Health Research Center. The US Health Resources &amp; Services Administration, using a similar definition, defines 19.7% of the US population as rural. Results: In 2013, we identified 10,440 medical oncologists and 4,292 radiation oncologists in the United States. 7.6% of medical oncologists and 8.9% of radiation oncologists practiced in rural areas. In 2019, 7.4% of medical oncologists and 8.5% of radiation oncologists practiced in rural areas (Table). There were 1,430 more medical oncologists practicing in 2019 as compared to 2013; only 83 (5.8%) practice in a rural area. There were 436 additional radiation oncologists with only 21 (4.8%) practicing in a rural area. Growth in both medical oncologists (χ2 = 8.47, p = 0.02) and radiation oncologists (χ2 = 8.47, p = 0.004) was more likely to be in urban than rural areas. Conclusions: While disparities in oncologist distribution along the rural-urban continuum are well known, we establish that these disparities are becoming more pronounced despite increasing awareness. This raises concern that disparities in cancer outcomes in rural patients will continue to worsen. Alternative strategies to increases access to cancer care in rural areas are critical.[Table: see text]

The role of rurality in cancer treatment disruptions among patients with cancer diagnosed with SARS-CoV-2: An analysis of the ASCO Survey on COVID-19 in Oncology Registry.

6554 Background: U.S. rural cancer patients experience multifactorial barriers to cancer treatment; however, little is known about the impact of the pandemic on cancer treatment delays or discontinuations (TDD) in the rural context. Our objective was to evaluate the role of rurality at both the patient and clinic level on cancer TDD among patients living with cancer with SARS-CoV-2 infection. Methods: We used data from the ASCO Survey on COVID-19 in Oncology Registry (March 2020-July 2021), which includes cancer patients diagnosed with SARS-CoV-2 (n = 3193). Data included patient demographics, SARS-CoV-2 treatment, cancer characteristics, and modifications to cancer treatment plans. Cancer-related TDD was defined as any treatment postponed &gt; two weeks from the original scheduled date. Rurality was defined using the USDA Rural-Urban Commuting Area schema. We compared cancer characteristics, COVID-19 outcomes, and TDD by rurality of cancer patients, and TDD by rurality of oncology practices. We computed adjusted odds ratios (aOR) using multivariable logistic regression to evaluate rurality with TDD adjusting for age, race/ethnicity, sex, comorbidities, ECOG score, cancer extent, pandemic time period based on case peaks ( &lt; 06/2020, 06-12/2020, 01-07/2021), and COVID-19 severity. Results: Rural cancer patients (n = 499, 16%) with SARS-CoV-2 were mostly over 50 years (87%), female (57%), and NH-White (81%) with solid tumors (76%). Most rural patients received oncology treatment in urban areas (65%, p &lt; 0.001). Rural patients were less likely to receive care through telemedicine (18%) compared to urban patients (26%) (p &lt; 0.001). At SARS-CoV-2 diagnosis, rural patients were scheduled to receive drug-based therapy (72%), radiation therapy (8%), surgery (4%), or transplant (1%). Rural versus urban cancer patients with SARS-CoV-2 were less likely to experience TDD (41% vs. 51%) (p &lt; 0.001). Among patients treated at rural oncology clinics, urban cancer patients were more likely to experience TDD (65%) compared with rural patients (47%) (p &lt; 0.001). Similarly, among patients treated at urban oncology clinics, urban cancer patients were also more likely to experience TDD (51%) compared with rural patients (38%) (p &lt; 0.001). In multivariable analyses, rural cancer patients were 28% less likely to experience TDD (aOR:0.72, 95% CI: 0.55-0.94) than urban cancer patients. Oncology practice rurality was not associated with TDD (aOR: 1.19, 95% CI: 0.81-1.76). Conclusions: Rural cancer patients were less likely to experience TDD than urban patients supporting the urban-rural paradox i.e., geographic distance to cancer care facilities is not consistently associated with treatment delivery in expected ways. Future work should focus on area-level factors of the rural cancer patient experience to disentangle potential reasons for TDD during the pandemic.

Urban-rural disparities in childhood obesogenic environments in the United States: Application of differing rural definitions.

Research is needed that identifies environmental resourcedisparities and applies multiple rural definitions. Therefore, this study aims to examine urban-rural differences in food and physical activity(PA) environment resource availability by applying several commonly used rural definitions. We also examine differences in resourceavailability within urban-rural categories that are typically aggregated. Six food environment variables (access to grocery/superstores, farmers' markets, fast food, full-service restaurants, convenience stores, and breastfeeding-friendly facilities) and 4 PA environment variables (access to exercise opportunities and schools, walkability, and violent crimes) were included in the childhood obesogenic environment index (COEI). Total COEI, PA environment, and food environment index scores were generated by calculating the average percentile for related variables. US Department of Agriculture Urban Influence Codes, Office of Management and Budget codes, Rural-Urban Continuum Codes, Census Bureau Population Estimates for percent rural, and Rural Urban Commuting Area Codes were used. One-way ANOVA was used to detect urban-rural differences. The greatest urban-rural disparities in COEI (F=310.2, P<.0001) and PA environment (F=562.5, P<.0001) were seen using RUCC codes. For food environments, the greatest urban-rural disparities were seen using Census Bureau percent rural categories (food: F=24.9, P<.0001). Comparing remote rural categories, differences were seen for food environments (F=3.1, P=.0270) and PA environments (F=10.2, P<.0001). Comparing metro-adjacent rural categories, differences were seen for PA environment (F=4.7, P=.0090). Findings inform future research on urban and rural environments by outlining major differences between urban-rural classifications in identifying disparities in access to health-promotingresources.

Disparities in Access to Radiation Therapy by Race and Ethnicity in the United States With Focus on American Indian/Alaska Native People

Striking disparities in access to radiation therapy (RT) exist, especially among racial and ethnic-minority patients. We analyzed census block group data to evaluate differences in travel distance to RT as a function of race and ethnicity, socioeconomic status, and rurality. The Directory of Radiotherapy Centers provided the addresses of facilities containing linear accelerators for RT. We classified block groups as majority (≥ 50%) American Indian/Alaska Native (AI/AN), black, white, Asian, no single racial majority, or Hispanic regardless of race. We used the Area Deprivation Index to classify deprivation and Rural-Urban Commuting Area codes to classify rurality. Generalized linear mixed models tested associations between these factors and distance to nearest RT facility. Median distance to nearest RT facility was 72 miles in AI/AN-majority block groups, but 4 to 7 miles in block groups with non-AI/AN majorities. Multivariable models estimated that travel distances in AI/AN-majority block groups were 39 to 41 miles longer than in areas with non-AI/AN majorities. Travel distance was 1.3 miles longer in the more deprived areas versus less deprived areas and 16 to 32 miles longer in micropolitan, small town, and rural areas versus metropolitan areas. Cancer patients in block groups with AI/AN-majority populations, nonmetropolitan location, and low socioeconomic status experience substantial travel disparities in access to RT. Future research with more granular community- and individual-level data should explore the many other known barriers to access to cancer care and their relationship to the barriers posed by distance to RT care.

Influence of Gender, Race/Ethnicity &amp; Socioeconomic Factors on Meniscus Treatment with Pediatric and Adolescent Acl Reconstruction

Background:The rate of anterior cruciate ligament reconstruction (ACLR) in pediatric/adolescent populations has increased over time. However, there is less evidence to support the relative change in concomitant meniscus procedures over time. Demographics and socioeconomic (SES) factors may be implicated in the rate and usage of meniscectomy vs. meniscal repair as treatment.Hypothesis/Purpose:The purpose of this study is to assess if the rate of concomitant meniscus procedure with ACLR is increasing and what factors may be contributing to this rise. We hypothesize that age, sex, race/ethnicity, income, or insurance type may affect the rate of concomitant meniscus procedures.Methods:The Pediatric Health Information System (PHIS) Database was queried for all patients 18 years or younger who underwent ACLR with or without concomitant meniscus procedures based on CPT code from 2015-2019. Data from 39 out of 52 centers were included based on completeness of their data. Basic demographic information including age, sex, self-identified race/ethnicity, and insurance status was collected. Urban and rural designation was identified based on rural-urban commuting area (RUCA) codes. Based on the 2015 Department Health & Human Services Federal Poverty Guidelines (FPL), patients were stratified into lower (≤199% of FPL) and higher (≥200% of FPL) income brackets. Linear regression was used to model trends and multiple logistic regression modeling was used to test for associations.Results:14,398 patients underwent ACLR during the study period with 8,337 patients (58%) having concomitant meniscal procedures with a 1.24-fold increase over five years (Figure 1). Of the 8,337 patients with concomitant meniscus treatment, 46% had a meniscectomy and 54% had a meniscus repair. There was a 0.86-fold change in meniscectomy and a 1.70-fold increase in meniscus repair during the study period (Figure 2). Males, older patients, Black race, living in an urban area, and those with non-private insurance had increased odds of undergoing a concomitant meniscus procedure (all p<0.05). Patients of Black or other race and those with non-private insurance had increased odds of having a meniscectomy vs. meniscus repair (all p≤0.01). There were no associations detected between income bracket and our outcomes.Conclusions:This study shows that in pediatric and adolescent patients undergoing ACLR, there is a rise in concomitant meniscus procedures from 2015-2019. In addition, patients of non-white race and those with non-private insurance have increased odds of undergoing meniscectomy vs. meniscus repair. This study identifies access and inequality issues in ACLR with concomitant meniscus procedures in pediatric and adolescent populations.Figure 1.ACL Reconstruction vs ACL Reconstruction with Concomitant Meniscus Procedures from 2015-2019Figure 2.ACL Reconstruction with Concomitant Meniscus Procedures: Meniscectomy vs. Meniscus Repair