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52 Background: Early palliative care (PC) is recommended for advanced cancers, but differential use has been documented by race and ethnicity. We examined whether racial and ethnic differences in early PC use were moderated by organization-level proportions of minority patients served. Methods: We identified patients aged ≥65.5 years diagnosed with advanced-stage breast, colorectal, non-small cell lung (NSCL), small cell lung (SCL), pancreas, and prostate cancers in 2010-2019 with ≥6 months survival and continuous fee-for-service coverage from the SEER-Medicare data. Early PC was identified by PC encounter diagnosis codes or hospice and palliative medicine (HPM) specialty codes within 90 days post-diagnosis or up to first hospice admission date. Treating physicians and organizations were assigned based on the plurality of visits within 180 days surrounding diagnosis. Using patient race and ethnicity information, organizations were classified based on the percent of racial and ethnic minorities patients (Low<10%, Medium 10-19%, and High Minority-Serving ≥20%). Multivariable logistic models assessed interacting effects of organizations' minority representation and individual race and ethnicity on early PC receipt, adjusting for patient characteristics. Results: Among 102,060 patients treated at 8,039 organizations, the percent receiving early PC increased from 1.44% in 2010 to 10.30% in 2019. High Minority-Serving organizations were larger size and more likely to employ a HPM specialist and to treat a higher proportion of patients in metropolitan but lower socioeconomic areas, and were dually Medicare-Medicaid eligible. After adjusting for patient characteristics, Hispanic patients were 1.6 percentage points (ppts, 95%CI=-3.1, -0.1) less likely to receive early PC than Non-Hispanic (NH) White patients in Low Minority-Serving organizations. In High Minority-Serving organizations, Hispanic and NH Black patients were 0.8 ppts (95%CI=0.1, 1.6) and 1.6 ppts (95%CI=1.0, 2.3) more likely to receive early PC than NH White patients. (Table). Conclusions: Racial and ethnic minorities were more likely to receive early PC than NH White at High Minority-Serving organizations but not Medium/Low Minority-Serving organizations. System-level factors may be modified to narrow racial and ethnic differences in early PC use. Adjusted difference in early palliative care by race and ethnicity. Organization Minority Representation Individual Adjusted Probability Difference (95% CI) Low NH White Ref Hispanic -1.6 (-3.1, -0.1) NH Black 0.01 (-1.7, 1.7) Other/Unknown 1.1 (-1.2, 3.4) Medium NH White Ref Hispanic -0.6 (-1.7, 0.5) NH Black -0.8 (-1.7, 0.2) Other/Unknown 0.4 (-0.9, 1.7) High Non-Hispanic White Ref Hispanic 0.8 (0.1, 1.6) NH Black 1.6 (1.0, 2.3) Other/Unknown -0.3 (-1.1, 0.4) [ABSTRACT FROM AUTHOR]

53 Background: The COVID-19 pandemic propelled a rapid shift to telemedicine use, including patients with advanced cancers needing palliative care (PC). Yet, little is known about disparities in telemedicine use for PC. We leveraged population-based data to examine the trend and factors associated with telemedicine use for PC among Medicare beneficiaries diagnosed with advanced cancers during the onset of COVID-19 pandemic. Methods: We identified patients aged ≥65.5 years diagnosed with advanced-stage breast, colorectal, non-small cell lung (NSCL), small cell lung (SCL), pancreas, and prostate cancers in 2015-2019 from the SEER-Medicare data. We included those with continuous fee-for-service coverage and had any PC claims from diagnosis to death, hospice admission, or by 12/31/2020 (whichever came first). Telemedicine was identified using procedure codes, modifiers, and place of service codes on PC claims. We described the percent of PC recipients using telemedicine each quarter in 2019-2020. Multivariable logistic models assessed patient and provider characteristics associated with telemedicine use among PC recipients in 2020. Results: Telemedicine use for PC was 0.24% to 0.48% across 4 quarters in 2019, which increased to 16.3% in Q1 and stabilized at about 30% in Q2-Q4 in 2020. Among 2,945 PC recipients in 2020, 22.5% used telemedicine. Older patients (75-84 years and 85+ years) were 4.4 percentage points (ppts) and 8.6 ppts (p-values<0.05) less likely than patients ages 65-74 years to use telemedicine. Patients who were female (4.4 ppts), residing in higher socioeconomic status areas (Yost Quintile 5 vs 1: 7.7 ppts), treated by female providers (4.1 ppts), in hospital system (3.7 ppts vs. individual physician office) and organizations with hospice and palliative care specialists (8.9 ppts) were more likely to use telemedicine for PC (all p-values<0.05). (Table). Conclusions: Telemedicine use for PC surged during the onset of the COVID-19 pandemic, with considerable disparities by patient sociodemographic and provider characteristics. These disparities suggest potential barriers related to patients' technology literacy and internet access, provider awareness, and healthcare organization resources. Future mixed-method studies need to elucidate these barriers to design interventions at patient, provider, and system levels to ensure effective PC delivery. Selected factors associated with telemedicine use for palliative care. Absolute Probability Difference (95%CI) Age group (ref: 65-74) 75-84 -4.4 (-7.6, -1.2) 85+ -8.6 (-13.4, -3.7) Female (ref: Male) 4.4 (0.9, 7.9) Yost quintile (ref : Q1-Q2) Q3-Q4 2.2 (-1.7, 6.1) Q5 7.7 (3.3, 12.1) Hospital system (ref: Individual physician office) 3.7 (0.6, 6.7) Provider: Female (ref: Male) 4.1 (0.7, 7.5) Organization with hospice and palliative care specialists 8.9 (5.4, 12.4) [ABSTRACT FROM AUTHOR]

50 Background: Incarceration history is linked to limited access to care, including cancer screenings. Adverse consequences may also extend to their partners due to financial burdens and social stigma. This study examined associations of individual and partner incarceration history and receipt of cancer screenings in the United States. Methods: We identified individuals aged ≥ 50 years currently living with a partner who responded to the 2014-2020 Health and Retirement Study. Incarceration history was defined from responses to the question 'Have you ever been an inmate in a jail, prison, juvenile detention center, or other correctional facility?' People were categorized into 3 groups: 1) without individual or partner incarceration history, 2) with individual incarceration history only, and 3) with partner incarceration history only. People with both individual and partner incarceration history were excluded due to small sample size. We used multivariate logistic regression models to generate prevalence ratios (PRs) comparing receipt of any breast, cervical, and colorectal cancer screenings ≤2 years among screening-eligible people (defined by the US Preventive Services Taskforce guideline) with individual or partner incarceration history to people without incarceration history, stratifying by sex and controlling for age group, race and ethnicity, educational attainment, and comorbidity. Results: Of the sample, 0.9% and 11.8% of females and males reported individual incarceration history, respectively; 11.1% and 1.0% reported partner incarceration history, respectively. Compared to females without individual or partner incarceration history, women with partner incarceration history were less likely to receive breast cancer screening (PR:0.7, 95CI: 0.5-0.9); women with individual incarceration history were less likely to receive colorectal cancer screening in the past 2 years (PR: 0.7, 95% CI: 0.5-0.9). Compared to males without individual or partner incarceration history, men with partner incarceration history were less likely to receive colorectal cancer screening (PR: 0.5, 95% CI: 0.4-0.7). Conclusions: Individual or partner incarceration history was associated with lower prevalence of cancer screening. Programs to improve access to care and cancer screening among both people with incarcerations history and their partners are warranted. Adjusted association of individual and partner incarceration history and receipt of cancer screening. Female Male No Individual or Partner History With Individual History With Partner History No Individual or Partner History With Individual History With Partner History Cancer screening Breast (n=2556) Ref. 2.0 (0.5 - 8.3) 0.7 (0.5 - 0.9) -- Cervical (n=817) 1.4 (0.4 - 5.6) 0.9 (0.6 - 1.4) Colorectal (n=2682 for female, n=2207 for male) 0.7 (0.5 - 0.9) 0.9 (0.7 - 1.0) Ref. 1.0 (0.9 - 1.2) 0.5 (0.4 - 0.7) Ref: referent group. [ABSTRACT FROM AUTHOR]

47 Background: Medicaid expansion under the Patient Protection and Affordable Care Act improved health insurance coverage among patients with cancer. Individuals with non-small cell lung cancer (NSCLC) may benefit from Medicaid expansion throughout the cancer control continuum. This study examined the associations of Medicaid expansion with stage at disease diagnosis, timely initiation and receipt of guideline-concordant treatment, and five-year overall survival among individuals with NSCLC. Methods: We identified individuals newly diagnosed at age 18-64 with stages I-IV first primary NSCLC between January 1, 2008 and December 31, 2019 in 50 states and Washington, D.C. from the National Cancer Database (n=332,368). We examined the associations of Medicaid expansion with (1) diagnosis stage, which was categorized into early (I&II) and late (III&IV); (2) timely initiation of guideline-concordant treatment within 30 days after cancer diagnosis (yes/no); (3) receipt of guideline-concordant treatment (yes/no) derived from the National Comprehensive Cancer Network cancer treatment guidelines; and (4) five-year overall survival. We applied a difference-in-differences (DD) approach to examine the changes in study outcomes associated with Medicaid expansion, controlling for key sociodemographic and clinical factors. Results: The percentage of early-stage diagnoses increased from 23.1% pre-expansion to 31.9% post-expansion in expansion states and from 22.3% to 29.5% in non-expansion states, translating to greater increase of 1.7 (95% confidence interval (CI): 0.3-3.1) percentage points (ppt) in expansion states after adjustment (Table). Medicaid expansion was also associated with greater increases in timely initiation of guideline-concordant care (DD: 1.6 (95% CI: 0.1-3.1) ppt) and five-year overall survival (DD: 1.3 (95% CI: 0.7-1.9) ppt). Conclusions: Medicaid expansion may improve access to NSCLC care and outcomes across the cancer control continuum, from early-stage diagnosis, treatment initiation, and overall survival. Association of Medicaid expansion and study outcomes among NSCLC patients. Expansion States Non-expansion States Pre-ME Post-ME Difference Pre-ME Post-ME Difference DD Adjusted DD Early-stage diagnosis 23.1 31.9 8.7 (7.4 to 10.1) 22.3 29.5 7.2 (6.2 to 10.1) 1.5 (-0.1 to 3.2) 1.7 (0.3 to 3.1) Timely initiation of guideline-concordant care 31.3 27.5 -3.9 (-5.3 to -2.4) 33.4 27.8 -5.6 (-6.24 to -2.4) 1.7 (0.1 to 3.3) 1.6 (0.1 to 3.1) Receipt of guideline-concordant care 65.2 68.2 2.9 (1.8 to 4.0) 62.9 66.4 3.5 (2.7 to 4.0) -0.6 (-2.0 to 0.8) -0.2 (-1.7 to 1.3) Five-year survival 21.3 34.8 13.5 (13.1 to 13.9) 18.6 30.2 11.6 (11.1 to 12.2) 1.9 (1.2 to 2.5) 1.3 (0.7 to 1.9) Abbreviation: ME, Medicaid expansion. [ABSTRACT FROM AUTHOR]

368 Background: Loneliness is associated with increased mortality risk in cancer survivors. However, effects of loneliness on healthcare utilization are understudied. This study examined associations of loneliness with having emergency room (ER) visit, usual source of care, and hospitalization in US adults with and without cancer history. Methods: We identified 1,844 cancer survivors and 9,517 adults without a cancer history from the 2021 Medical Expenditure Panel Survey. Loneliness was measured by 3 questions on feeling lack of companionship, left out, and isolated from others. Scores for each question were summed to create a total loneliness score, which was categorized into low/no loneliness (3-5) and high loneliness (6-9) based on previous literature. Separate logistic regression models were used to assess association of loneliness and whether having ER visit, usual source of care, and hospitalization. All models adjusted for age, region, sex, race, non-cancer health conditions, activity limitation, marital status, education, health insurance, and cancer diagnosis. We used a cancer history by loneliness interaction term to examine the differential associations of loneliness and healthcare utilization by cancer history. Results: 2,367 (23%) adults without a cancer history and 450 (22%) cancer survivors reported loneliness. All adults averagely aged over 50, with more female (non-cancer: 57.1 vs 49.9%, cancer: 68.0 vs 55.9%) and those with non-cancer health conditions ≥3 (non-cancer: 20.6 vs 15.4%, cancer: 54.0 vs 44.4%) in the loneliness group. Cancer history modified the association between loneliness and ER visit (interaction term p-value 0.04), but didn't modify the association with having usual source of care (p=0.38) or hospitalization (p=0.87). Loneliness was associated with having ER visit (OR 1.49, 95% CI 1.11-2.00) in cancer survivors while not in non-cancer patients (OR 1.08, 95% CI 0.92-1.27). No significant association was observed between loneliness and having usual source of care or hospitalization. Conclusions: Loneliness is associated with ER visit in US cancer survivors. Findings warrant further study of impact of loneliness on clinical outcomes in cancer survivors, with the aim of enhancing strategies to mitigate loneliness and optimize healthcare utilization. Association of loneliness and healthcare utilization. No Cancer History (9,517, 85%) Cancer History (1,844, 15%) Loneliness No (7,150, 77%) Yes (2,367, 23%) No (1,394, 78%) Yes (450, 22%) Odds ratio Having ER visit Ref 1.08 (0.92-1.27) Ref 1.49 (1.11-2.00) Having usual source of care 1.08 (0.89-1.30) 0.90 (0.62-1.30) Having hospitalization 0.93 (0.79-1.09) 0.96 (0.62-1.48) [ABSTRACT FROM AUTHOR]

350 Background: Cancer survivors and their families often experience financial hardship due to high out-of-pocket medical costs and lost income from time away from work during treatment for patients and informal family caregivers. Little is known about detailed income, wealth, and debt among families with a history of cancer. Methods: The 2021 Panel Study of Income Dynamics was used to identify families with a cancer history. Family characteristics included survey response person's age, sex, marital status, and race and ethnicity, number of dependent children living in the family, and time since cancer diagnosis. For families with multiple cancer diagnoses, time since cancer diagnosis was calculated according to the most recent cancer diagnosis. Family income was assessed by total income in tax year 2020, which consisted of combined taxable and transfer income of the response person, spouse/partner, and other family members. Wealth with and without equity (i.e., value of home minus mortgage) were assessed by sum of values of different types of assets (e.g. savings, checking, bonds, retirement accounts) net of debts (e.g., credit card debt, student loans, and medical bills). Results: A total of 701 families were identified with a cancer history. Within families, half of cancer survivors were 18-64 years and the other half were >65 years at the survey. About two thirds were married or living with partners, three fourths of response persons were male, one fifth had dependent children living in the same family, two thirds were non-Hispanic white, and two thirds of cancer survivors were diagnosed > 6 years prior to the survey. The median family income was $67,498 (inter-quartile range: $33,480 to $121,200), and the median wealth with and without equity were $188,000 and $5,5000, respectively. Moreover, 29.9% of families had credit debt (median: $4,500; inter-quartile range [IQR]: $1800-$14,000); 11.3% of families carried student loans (median: $4,500; IQR: $15,000-$60,000); and 10.7% of families had outstanding medical bills (median: $4,000; IQR: $2,000-$9,000). Conclusions: Families with a history of cancer have multiple types of debts, including medical debt, even years after diagnosis. Efforts to identify changes in wealth and income before and after cancer diagnosis are warranted. Income, wealth, and debt among families with a history of cancer. Family income Wealth without equity Wealth with equity Mean (Std) Median (IQR) Mean (Std) Median (IQR) Mean (Std) Median (IQR) $94,776 (123,870) $67,498 (33,480, 121,200) $459,956 (1,142,647) $5,5000 (4,000, 391,364) $649,912 (1,307,959) $188,000 (30,400, 645,000) Credit card debt (n=693) Student loans (n=698) Medical bills (n=627) n (%) Mean (Std) Median (IQR) n (%) Mean (Std) Median (IQR) n (%) Mean (Std) Median (IQR) 207 (29.9%) $8,832 (10,300) $4,500 (1800, 14,000) 79 (11.3%) $55,948 (75,256) $35,000 (15,000, 60,000) 67 (10.7%) $13,309 (32,525) $4000 (2,000, 9,000) [ABSTRACT FROM AUTHOR]

16 Background: The cost of cancer care rose dramatically over the past decades, increasing the financial burden on patients. However, current estimates of healthcare spending and out-of-pocket burden rely heavily on data from older populations that may not reflect contemporary treatment patterns in working-aged adults or variation in type of insurance coverage. Methods: We used the 2012-2021 Colorado Central Cancer Registry linked to the All-Payer Claims Database to identify adults aged 22-63 years newly diagnosed with invasive cancer who were consecutively enrolled on the same insurance type or the month of diagnosis and 6 months after (or until death). We used quantile regression to adjust spending by age and sex and report median insurer reimbursements for medical care and pharmacy costs and out-of-pocket spending (copays, deductibles, coinsurance) for the most common insurance and cancer types. Results: Among 31,179 individuals newly diagnosed with cancer, the median total spending six months after diagnosis across all cancers ranged from 59,022 for those insured by private non-health maintenance organization (HMO) plans to 26,887 for those enrolled on Medicaid and 19,152 for those on Medicare Advantage (MA) plans (Table). The majority of spending was for medical care. Among individuals insured by private non-HMO plans, those with lymphoma had the highest total median spending at 130,408, followed by lung (94,207), breast ($87,830), and leukemia ($82,445). Although total spending was markedly lower among those insured by Medicaid, among this group, individuals with leukemia ($50,399) and lymphoma (48,747) experienced the highest median spending. Out-of-pocket spending was substantially higher for those insured by private non-HMO plans (3,582 or approximately 511/month) across all cancer types, and highest for lymphomas (3,966) and lung ($3,455) cancers. Conclusions: Total healthcare spending in the 6 months after a cancer diagnosis is considerable in the working-age population, with a high out-of-pocket burden for patients with private non-HMO coverage. Median spending in 6 months after cancer diagnosis (in 2021 dollars), adjusted for age and sex. Medical Care Plan Payments* Pharmacy Plan Payments* Out of Pocket Spending* Total Spending* Overall (n=31,793) Dual Medicaid/Medicare $24,081 $1,793 $19 $28,806 Medicaid $23,415 $641 $13 $26,887 Medicare TM $20,698 $797 $3,275 $27,028 Medicare MA $14,227 $570 $2,069 $19,152 Private non-HMO $51,196 $274 $3,582 $59,022 Private HMO $36,414 $219 $2,622 $42,916 Median costs significantly different across all payers, p<0.05. [ABSTRACT FROM AUTHOR]

156 Background: Individuals diagnosed with cancer often bear significant health care costs and productivity loss, with over half incurring cancer-related medical debt. Medical debt is increasingly recognized as a social determinant of health care and health outcomes. Using newly available nationwide county-level medical debt information from 2011 to 2019 linked with national cancer registry data, we examined the association of county-level medical debt with survival among individuals newly diagnosed with cancer in the US. Methods: Individuals aged ≥18 years with newly diagnosed first primary cancer in 2011-2019 were identified from the National Cancer Database, which was linked with credit bureau data on county-level percentage of adults with medical debt in collections based on patients' year of diagnosis. Patients were followed from diagnosis through December 31, 2019. Hazard Ratios (HR) and were derived from Cox proportional hazard models to estimate the associations of county-level medical debt quartiles with overall survival, adjusting for year of diagnosis, age, sex, race and ethnicity, insurance status, comorbidity score, metropolitan status, county-level social deprivation index and medically underserved area designation, and state of residency. Stratified analyses were conducted by cancer type and sociodemographic and clinical factors. Results: A total of 7,558,658 individuals newly diagnosed with cancer were identified, with a median county-level medical debt of 18%, ranging from 0% to 56%. County-level medical debt decreased since 2015. The highest percentage of county-level medical debts was found among patients racialized as non-Hispanic Black, uninsured, and living in medically underserved areas. In adjusted analyses, patients living in counties with the highest quartile of medical debt had 17% increased risks of death compared with those living in counties with the lowest quartile of medical debt (HR=1.17, 95%CI=1.16-1.18), with a statistically significant dose-response relationship for the higher quartiles (P-trend<.0001). The pattern was consistently seen across cancer types as well as socioeconomic and clinical groups. Conclusions: County-level medical debt was strongly associated with worse survival among individuals newly diagnosed with cancer, consistent across socioeconomics and clinical subgroups. Policies preventing and relieving medical debt maybe effective ways to improve cancer outcomes at population level. Hazard ratios for risk of death associated with county-level medical debt by cancer site and stage at diagnosis. County-Level Share of Population with Medical Debt Q1 Q2 Q3 Q4 All cancers 1 1.08 1.13 1.17 Female breast 1 1.10 1.13 1.16 Prostate 1 1.06 1.11 1.13 Lung 1 1.06 1.10 1.12 Colon and rectum 1 1.07 1.11 1.14 Stage I-III 1 1.09 1.14 1.19 Stage IV 1 1.07 1.11 1.13 All P for trend <.0001. [ABSTRACT FROM AUTHOR]

136 Background: Medical financial hardship (MFH) in cancer survivors is well-documented. Little is known about how disability impacts healthcare needs and costs in cancer survivorship. We examined associations of disability and MFH among survivors in the US. Methods: We identified cancer survivors ≥18 years with (n=2,795) and without (n=12,263) a disability from the National Health Interview Survey 2019 to 2022. The Washington Group Short Set, a validated six-item measure intended to describe the functional status of adults (e.g. disability in seeing, hearing, or walking) was used to identify those with disabilities. MFH was categorized into three domains: 1) material (e.g. problems paying medical bills), 2) psychological (e.g. worrying about medical bills), and 3) behavioral (e.g. delaying/forgoing care due to cost). Adjusted logistic regressions were used to examine associations of disability and MFH among cancer survivors. Results: Cancer survivors with a disability were more likely to experience any MFH (57.2% vs. 47.0%, p<0.05) or any individual domain (Material: 26.4% vs. 13.3%, p<0.05; Psychological: 50.5% vs. 43.0%, p<0.05; Behavioral: 23.2% vs. 12.9%, p<0.05) compared with those without a disability. In addition, survivors with a disability were more likely to report experiencing multiple domains of MFH (29.1% vs. 15.9%; p<0.05). Conclusions: Cancer survivors with a disability face higher risks of MFH. Our findings can inform interventions alleviating MFH in this especially vulnerable population. Association of disability with medical financial hardship among cancer survivors, National Health Interview Survey 2019-2022. Cancer Survivors with a Disability n = 2,795 Cancer Survivors without a Disability n = 12,263 AME (95% CI) AME (95% CI) Any material financial hardship 26.4 (24.7, 28.0) 13.3 (12.5, 14.2) Any psychological financial hardship 50.5 (49.1, 52.0) 43.0 (42.0, 44.0) Any behavioral financial hardship 23.2 (21.7, 24.7) 12.9 (12.0, 13.7) Any MFH 57.2 (55.8, 58.6) 47.0 (46.0, 48.0) 1 domain of MFH 57.2 (55.8, 58.6) 47.0 (46.0, 48.0) 2+ domains of MFH 29.1 (27.5, 30.6) 15.0 (14.1, 15.8) Note: In adjusted analyses, covariates included age, sex, race/ethnicity, marital status, education, family income as % of Federal Poverty Level, insurance, year, and survey design; AME=Average Marginal Effect; CI = Confidence Interval. [ABSTRACT FROM AUTHOR]