Disparities in treatment patterns and mortality in prostate cancer: Interaction between Black race and end-stage kidney disease.

BACKGROUND: Black men and men with end-stage kidney disease have lower rates of treatment and higher mortality for prostate cancer. We studied the interaction of end-stage kidney disease (ESKD) with Black race for treatment rates and mortality for men with prostate cancer. METHODS AND RESULTS: We included 516 Black and 551 White men with ESKD before prostate cancer 22,299 Black men, and 141,821 White men without ESKD who were 40 years or older from the Surveillance, Epidemiology, and End-Results-Medicare data (2004-2016). All Black men with or without ESKD and White men with ESKD had higher prostate-specific antigen levels at diagnosis than White men without ESKD. Black men with ESKD had the lowest rates for treatment in both local and advanced stages of prostate cancer (age-adjusted risk ratio: 0.76, 95% Confidence Interval (CI): 0.71-0.82 for local stage and age-adjusted risk ratio: 0.82, 95% CI: 0.76-0.9 for advanced stages) compared to White men without ESKD. Compared to White men without ESKD, prostate cancer-specific mortality was higher in White men with ESKD for both local and advanced stages (age-adjusted hazard ratio: 1.8, 95% CI: 1.2-2.8 and HR: 1.6, 95% CI: 1.2-2.2) and it was higher for ESKD Black men only in advanced stage prostate cancer (age-adjusted hazard ratio: 2.4, 95% CI: 1.5-3.6). CONCLUSION: Our findings suggest that having a comorbidity such as ESKD makes Black men more vulnerable to racial disparities in prostate cancer treatment and mortality.

County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study.

We used machine learning methods to explore sociodemographic and environmental determinants of health (SEDH) associated with county-level stroke mortality in the USA. We conducted a cross-sectional analysis of individuals aged ≥15 years who died from all stroke subtypes between 2016 and 2020. We analyzed 54 county-level SEDH possibly associated with age-adjusted stroke mortality rates/100,000 people. Classification and Regression Tree (CART) was used to identify specific county-level clusters associated with stroke mortality. Variable importance was assessed using Random Forest analysis. A total of 501,391 decedents from 2397 counties were included. CART identified 10 clusters, with 77.5% relative increase in stroke mortality rates across the spectrum (28.5 vs 50.7 per 100,000 persons). CART identified 8 SEDH to guide the classification of the county clusters. Including, annual Median Household Income ($), live births with Low Birthweight (%), current adult Smokers (%), adults reporting Severe Housing Problems (%), adequate Access to Exercise (%), adults reporting Physical Inactivity (%), adults with diagnosed Diabetes (%), and adults reporting Excessive Drinking (%). In conclusion, SEDH exposures have a complex relationship with stroke. Machine learning approaches can help deconstruct this relationship and demonstrate associations that allow improved understanding of the socio-environmental drivers of stroke and development of targeted interventions.

Treatment Patterns and Survival Outcomes in Patients With Breast Cancer on Medicaid, Pre- and Post-Expansion.

BACKGROUND: The objective of this study was to evaluate the impact of Medicaid expansion on breast cancer treatment and survival among Medicaid-insured women in Ohio, accounting for the timing of enrollment in Medicaid relative to their cancer diagnosis and post-expansion heterogeneous Medicaid eligibility criteria, thus addressing important limitations in previous studies. METHODS: Using 2011-2017 Ohio Cancer Incidence Surveillance System data linked with Medicaid claims data, we identified women aged 18 to 64 years diagnosed with local-stage or regional-stage breast cancer (n=876 and n=1,957 pre-expansion and post-expansion, respectively). We accounted for women's timing of enrollment in Medicaid relative to their cancer diagnosis, and flagged women post-expansion as Affordable Care Act (ACA) versus non-ACA, based on their income eligibility threshold. Study outcomes included standard treatment based on cancer stage and receipt of lumpectomy, mastectomy, chemotherapy, radiation, hormonal treatment, and/or treatment for HER2-positive tumors; time to treatment initiation (TTI); and overall survival. We conducted multivariable robust Poisson and Cox proportional hazards regression analysis to evaluate the independent associations between Medicaid expansion and our outcomes of interest, adjusting for patient-level and area-level characteristics. RESULTS: Receipt of standard treatment increased from 52.6% pre-expansion to 61.0% post-expansion (63.0% and 59.9% post-expansion in the ACA and non-ACA groups, respectively). Adjusting for potential confounders, including timing of enrollment in Medicaid, being diagnosed in the post-expansion period was associated with a higher probability of receiving standard treatment (adjusted risk ratio, 1.14 [95% CI, 1.06-1.22]) and shorter TTI (adjusted hazard ratio, 1.14 [95% CI, 1.04-1.24]), but not with survival benefits (adjusted hazard ratio, 1.00 [0.80-1.26]). CONCLUSIONS: Medicaid expansion in Ohio was associated with improvements in receipt of standard treatment of breast cancer and shorter TTI but not with improved survival outcomes. Future studies should elucidate the mechanisms at play.

County-level socio-environmental factors and obesity prevalence in the United States.

AIMS: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques. MATERIALS AND METHODS: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest. RESULTS: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%). CONCLUSION: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

Individual-level home values and cancer mortality in a statewide registry.

BACKGROUND: Prior work assessing disparities in cancer outcomes has relied on regional socioeconomic metrics. These metrics average data across many individuals, resulting in a loss of granularity and confounding with other regional factors. METHODS: Using patients' addresses at the time of diagnosis from the Ohio Cancer Incidence Surveillance System, we retrieved individual home price estimates from an online real estate marketplace. This individual-level estimate was compared with the Area Deprivation Index (ADI) at the census block group level. Multivariable Cox proportional hazards models were used to determine the relationship between home price estimates and all-cause and cancer-specific mortality. RESULTS: A total of 667 277 patients in Ohio Cancer Incidence Surveillance System were linked to individual home prices across 16 cancers. Increasing home prices, adjusted for age, stage at diagnosis, and ADI, were associated with a decrease in the hazard of all-cause and cancer-specific mortality (hazard ratio [HR] = 0.92, 95% confidence interval [CI] = 0.92 to 0.93, and HR = 0.95, 95% CI = 0.94 to 0.95, respectively). Following a cancer diagnosis, individuals with home prices 2 standard deviations above the mean had an estimated 10-year survival probability (7.8%, 95% CI = 7.2% to 8.3%) higher than those with home prices 2 standard deviations below the mean. The association between home price and mortality was substantially more prominent for patients living in less deprived census block groups (Pinteraction < .001) than for those living in more deprived census block groups. CONCLUSION: Higher individual home prices were associated with improved all-cause and cancer-specific mortality, even after accounting for regional measures of deprivation.

Accounting for Medicaid expansion and regional policy and programs to advance equity in cancer prevention in the United States.

BACKGROUND: Many studies compare state-level outcomes to estimate changes attributable to Medicaid expansion. However, it is imperative to conduct more granular, demographic-level analyses to inform current efforts on cancer prevention among low-income adults. Therefore, the authors compared the volume of patients with cancer and disease stage at diagnosis in Ohio, which expanded its Medicaid coverage in 2014, with those in Georgia, a nonexpansion state, by cancer site and health insurance status. METHODS: The authors used state cancer registries from 2010 to 2017 to identify adults younger than 64 years who had incident female breast cancer, cervical cancer, or colorectal cancer. Multivariable Poisson regression was conducted by cancer type, health insurance, and state to examine the risk of late-stage disease, adjusting for individual-level and area-level covariates. A difference-in-differences framework was then used to estimate the differences in risks of late-stage diagnosis in Ohio versus Georgia. RESULTS: In Ohio, the largest increase in all three cancer types was observed in the Medicaid group after Medicaid expansion. In addition, significantly reduced risks of late-stage disease were observed among patients with breast cancer on Medicaid in Ohio by approximately 7% and among patients with colorectal cancer on Medicaid in Ohio and Georgia after expansion by approximately 6%. Notably, the authors observed significantly reduced risks of late-stage diagnosis among all patients with colorectal cancer in Georgia after expansion. CONCLUSIONS: More early stage cancers in the Medicaid-insured and/or uninsured groups after expansion suggest that the reduced cancer burden in these vulnerable population subgroups may be attributed to Medicaid expansion. Heterogeneous risks of late-stage disease by cancer type highlight the need for comprehensive evaluation frameworks, including local cancer prevention efforts and federal health policy reforms. PLAIN LANGUAGE SUMMARY: This study looked at how Medicaid expansion affected cancer diagnosis and treatment in two states, Ohio and Georgia. The researchers found that, after Ohio expanded their Medicaid program, there were more patients with cancer among low-income adults on Medicaid. The study also found that, among people on Medicaid, there were lower rates of advanced cancer at the time of diagnosis for breast cancer and colon cancer in Ohio and for colon cancer in Georgia. These findings suggest that Medicaid expansion may be effective in reducing the cancer burden among low-income adults.

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors.

BACKGROUND: Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. METHODS: We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties. RESULTS: Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). CONCLUSION: ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

Geographical Patterns and Risk Factor Association of Cardio-Oncology Mortality in the United States.

Cardio-oncology mortality (COM) is a complex issue that is compounded by multiple factors that transcend a depth of socioeconomic, demographic, and environmental exposures. Although metrics and indexes of vulnerability have been associated with COM, advanced methods are required to account for the intricate intertwining of associations. This cross-sectional study utilized a novel approach that combined machine learning and epidemiology to identify high-risk sociodemographic and environmental factors linked to COM in United States counties. The study consisted of 987,009 decedents from 2,717 counties, and the Classification and Regression Trees model identified 9 county socio-environmental clusters that were closely associated with COM, with a 64.1% relative increase across the spectrum. The most important variables that emerged from this study were teen birth, pre-1960 housing (lead paint indicator), area deprivation index, median household income, number of hospitals, and exposure to particulate matter air pollution. In conclusion, this study provides novel insights into the socio-environmental drivers of COM and highlights the importance of utilizing machine learning approaches to identify high-risk populations and inform targeted interventions for reducing disparities in COM.

Ibrutinib Is Associated With Increased Cardiovascular Events and Major Bleeding in Older CLL Patients.

Background: Early ibrutinib trials showed an association between ibrutinib use and risk of bleeding and atrial fibrillation (AF) in younger chronic lymphocytic leukemia (CLL) patients. Little is known about these adverse events in older CLL patients and whether increased AF rates are associated with increased stroke risk. Objectives: To compare the incidence of stroke, AF, myocardial infarction, and bleeding in CLL patients treated with ibrutinib with those who were treated without ibrutinib in a linked SEER-Medicare database. Methods: The incidence rate of each adverse event for treated and untreated patients was calculated. Among those treated, inverse probability weighted Cox proportional hazards regression models were used to calculate HRs and 95% CIs for the association between ibrutinib treatment and each adverse event. Results: Among 4,958 CLL patients, 50% were treated without ibrutinib and 6% received ibrutinib. The median age at first treatment was 77 (IQR: 73-83) years. Compared with those treated without ibrutinib, those treated with ibrutinib had a 1.91-fold increased risk of stroke (95% CI: 1.06-3.45), 3.65-fold increased risk of AF (95% CI: 2.42-5.49), a 4.92-fold increased risk of bleeding (95% CI: 3.46-7.01) and a 7.49-fold increased risk of major bleeding (95% CI: 4.32-12.99). Conclusions: In patients a decade older than those in the initial clinical trials, treatment with ibrutinib was associated with an increased risk of stroke, AF, and bleeding. The risk of major bleeding is higher than previously reported and underscores the importance of surveillance registries to identify new safety signals.

Understanding Factors Leading to Surgical Attrition for "Resectable" Gastric Cancer.

OBJECTIVES: We used a novel combined analysis to evaluate various factors associated with failure to surgical resection in non-metastatic gastric cancer. METHODS: We identified factors associated with the receipt of surgery in publicly available clinical trial data for gastric cancer and in the National Cancer Database (NCDB) for patients with stages I-III gastric adenocarcinoma. Next, we evaluated variable importance in predicting the receipt of surgery in the NCDB. RESULTS: In published clinical trial data, 10% of patients in surgery-first arms did not undergo surgery, mostly due to disease progression and 15% of patients in neoadjuvant therapy arms failed to reach surgery. Effects related to neoadjuvant administration explained the increased attrition (5%). In the NCDB, 61.7% of patients underwent definitive surgery. In a subset of NCDB patients resembling those enrolled in clinical trials (younger, healthier, and privately insured patients treated at high-volume and academic centers) the rate of surgery was 79.2%. Decreased likelihood of surgery was associated with advanced age (OR 0.97, p < 0.01), Charlson-Deyo score of 2+ (OR 0.90, p < 0.01), T4 tumors (OR 0.39, p < 0.01), N+ disease (OR 0.84, p < 0.01), low socioeconomic status (OR 0.86, p = 0.01), uninsured or on Medicaid (OR 0.58 and 0.69, respectively, p < 0.01), low facility volume (OR 0.64, p < 0.01), and non-academic cancer programs (OR 0.79, p < 0.01). CONCLUSION: Review of clinical trials shows attrition due to unavoidable tumor and treatment factors (~ 15%). The NCDB indicates non-medical patient and provider characteristics (i.e., age, insurance status, facility volume) associated with attrition. This combined analysis highlights specific opportunities for improving potentially curative surgery rates.

Patterns of Diagnostic Evaluation and Determinants of Treatment in Older Patients With Non-transfusion Dependent Myelodysplastic Syndromes.

BACKGROUND: Older patients with myelodysplastic syndromes (MDS), particularly those with no or one cytopenia and no transfusion dependence, typically have an indolent course. Approximately, half of these receive the recommended diagnostic evaluation (DE) for MDS. We explored factors determining DE in these patients and its impact on subsequent treatment and outcomes. PATIENTS AND METHODS: We used 2011-2014 Medicare data to identify patients ≥66 years of age diagnosed with MDS. We used Classification and Regression Tree (CART) analysis to identify combinations of factors associated with DE and its impact on subsequent treatment. Variables examined included demographics, comorbidities, nursing home status, and investigative procedures performed. We conducted a logistic regression analysis to identify correlates associated with receipt of DE and treatment. RESULTS: Of 16 851 patients with MDS, 51% underwent DE. patients with MDS with no cytopenia (n = 3908) had the lowest uptake of DE (34.7%). Compared to patients with no cytopenia, those with any cytopenia had nearly 3 times higher odds of receiving DE [adjusted odds ratio (AOR), 2.81: 95% CI, 2.60-3.04] and the odds were higher for men than for women [AOR, 1.39: 95%CI, 1.30-1.48] and for Non-Hispanic Whites [vs. everyone else (AOR, 1.17: 95% CI, 1.06-1.29)]. The CART showed DE as the principal discriminating node, followed by the presence of any cytopenia for receiving MDS treatment. The lowest percentage of treatment was observed in patients without DE, at 14.6%. CONCLUSION: In this select older patients with MDS, we identified disparities in accurate diagnosis by demographic and clinical factors. Receipt of DE influenced subsequent treatment but not survival.

Geographic Variation and Risk Factor Association of Early Versus Late Onset Colorectal Cancer.

The proportion of patients diagnosed with colorectal cancer (CRC) at age < 50 (early-onset CRC, or EOCRC) has steadily increased over the past three decades relative to the proportion of patients diagnosed at age ≥ 50 (late-onset CRC, or LOCRC), despite the reduction in CRC incidence overall. An important gap in the literature is whether EOCRC shares the same community-level risk factors as LOCRC. Thus, we sought to (1) identify disparities in the incidence rates of EOCRC and LOCRC using geospatial analysis and (2) compare the importance of community-level risk factors (racial/ethnic, health status, behavioral, clinical care, physical environmental, and socioeconomic status risk factors) in the prediction of EOCRC and LOCRC incidence rates using a random forest machine learning approach. The incidence data came from the Surveillance, Epidemiology, and End Results program (years 2000-2019). The geospatial analysis revealed large geographic variations in EOCRC and LOCRC incidence rates. For example, some regions had relatively low LOCRC and high EOCRC rates (e.g., Georgia and eastern Texas) while others had relatively high LOCRC and low EOCRC rates (e.g., Iowa and New Jersey). The random forest analysis revealed that the importance of community-level risk factors most predictive of EOCRC versus LOCRC incidence rates differed meaningfully. For example, diabetes prevalence was the most important risk factor in predicting EOCRC incidence rate, but it was a less important risk factor of LOCRC incidence rate; physical inactivity was the most important risk factor in predicting LOCRC incidence rate, but it was the fourth most important predictor for EOCRC incidence rate. Thus, our community-level analysis demonstrates the geographic variation in EOCRC burden and the distinctive set of risk factors most predictive of EOCRC.

Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach.

Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the "phenotypes", or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas.

Understanding Disparities in Receipt of Complex Gastrointestinal Cancer Surgery at a Small Geographic Scale.

OBJECTIVE: To define neighborhood-level disparities in the receipt of complex cancer surgery. BACKGROUND: Little is known about the geographic variation of receipt of surgery among patients with complex gastrointestinal (GI) cancers, especially at a small geographic scale. METHODS: This study included individuals diagnosed with 5 invasive, nonmetastatic, complex GI cancers (esophagus, stomach, pancreas, bile ducts, liver) from the Ohio Cancer Incidence Surveillance System during 2009 and 2018. To preserve patient privacy, we combined US census tracts into the smallest geographic areas that included a minimum number of surgery cases (n=11) using the Max-p-regions method and called these new areas "MaxTracts." Age-adjusted surgery rates were calculated for MaxTracts, and the Hot Spot analysis identified clusters of high and low surgery rates. US Census and CDC PLACES were used to compare neighborhood characteristics between the high- and low-surgery clusters. RESULTS: This study included 33,091 individuals with complex GI cancers located in 1006 MaxTracts throughout Ohio. The proportion in each MaxTract receiving surgery ranged from 20.7% to 92.3% with a median (interquartile range) of 48.9% (42.4-56.3). Low-surgery clusters were mostly in urban cores and the Appalachian region, whereas high-surgery clusters were mostly in suburbs. Low-surgery clusters differed from high-surgery clusters in several ways, including higher rates of poverty (23% vs. 12%), fewer married households (40% vs. 50%), and more tobacco use (25% vs. 19%; all P <0.01). CONCLUSIONS: This improved understanding of neighborhood-level variation in receipt of potentially curative surgery will guide future outreach and community-based interventions to reduce treatment disparities. Similar methods can be used to target other treatment phases and other cancers.

Treatment patterns and survival of low and intermediate-risk prostate cancer in end-stage kidney disease: A retrospective population cohort study.

BACKGROUND: In accordance with guidelines, observation with or without active surveillance for low-risk prostate cancer increased in recent years in the general population. We compared treatment patterns and mortality for low- and intermediate-risk prostate cancer and mortality rates among end-stage kidney disease (ESKD) and non-ESKD patients. METHODS: This is a retrospective population-based observational cohort study of Surveillance, Epidemiology, and End Results-Medicare data of men aged 66 years and older with localized prostate cancer (2004-2015). ESKD status was determined using Medicare billing codes. Multivariable logistic regression models and Cox-proportional hazards models were used to study definitive treatment patterns and mortality, respectively. RESULTS: For low-risk prostate cancer, dialysis patients (N = 83) had lower but not statistically significant odds (OR, 0.74; 95% CI: 0.48-1.16) of receiving definitive treatment than non-ESKD patients (N = 24,935). For those with intermediate-risk prostate cancer, dialysis patients (N = 254) had lower odds to receive definitive treatment (OR, 0.54; 95% CI: 0.42-0.72) than non-ESKD patients (N = 60,883). From 2004-2010 to 2011-2015, for patients with low-risk prostate cancer, while the receipt of definitive treatment for non-ESKD patients trended down from 72% to 48%, it trended up for dialysis patients from 55% to 65%. Kidney transplant patients (N = 33 for low-risk and N = 91 for intermediate-risk) had lower rates of definitive treatment for low-risk and similar rates of treatment for intermediate-risk prostate cancer compared to non-ESKD patients. CONCLUSIONS: The disparity in definitive treatment rates for low-risk prostate cancer among dialysis patients exists despite their high mortality, compared to the general population.

Variation in and Factors Associated With US County-Level Cancer Mortality, 2008-2019.

Importance: The association between cancer mortality and risk factors may vary by geography. However, conventional methodological approaches rarely account for this variation. Objective: To identify geographic variations in the association between risk factors and cancer mortality. Design, Setting, and Participants: This geospatial cross-sectional study used county-level data from the National Center for Health Statistics for individuals who died of cancer from 2008 to 2019. Risk factor data were obtained from County Health Rankings & Roadmaps, Health Resources and Services Administration, and Centers for Disease Control and Prevention. Analyses were conducted from October 2021 to July 2022. Main Outcomes and Measures: Conventional random forest models were applied nationwide and by US region, and the geographical random forest model (accounting for local variation of association) was applied to assess associations between a wide range of risk factors and cancer mortality. Results: The study included 7 179 201 individuals (median age, 70-74 years; 3 409 508 women [47.5%]) who died from cancer in 3108 contiguous US counties during 2008 to 2019. The mean (SD) county-level cancer mortality rate was 177.0 (26.4) deaths per 100 000 people. On the basis of the variable importance measure, the random forest models identified multiple risk factors associated with cancer mortality, including smoking, receipt of Supplemental Nutrition Assistance Program (SNAP) benefits, and obesity. The geographical random forest model further identified risk factors that varied at the county level. For example, receipt of SNAP benefits was a high-importance factor in the Appalachian region, North and South Dakota, and Northern California; smoking was of high importance in Kentucky and Tennessee; and female-headed households were high-importance factors in North and South Dakota. Geographic areas with certain high-importance risk factors did not consistently have a corresponding high prevalence of the same risk factors. Conclusions and Relevance: In this cross-sectional study, the associations between cancer mortality and risk factors varied by geography in a way that did not correspond strictly to risk factor prevalence. The degree to which other place-specific characteristics, observed and unobserved, modify risk factor effects should be further explored, and this work suggests that risk factor importance may be a preferable paradigm for selecting cancer control interventions compared with risk factor prevalence.

Post-Affordable Care Act Improvements in Cancer Stage Among Ohio Medicaid Beneficiaries Resulted From an Increase in Stable Coverage.

BACKGROUND: The mechanisms underlying improvements in early-stage cancer at diagnosis following Medicaid expansion remain unknown. We hypothesized that Medicaid expansion allowed for low-income adults to enroll in Medicaid before cancer diagnosis, thus increasing the number of stably-enrolled relative to those who enroll in Medicaid only after diagnosis (emergently-enrolled). METHODS: Using data from the 2011-2017 Ohio Cancer Incidence Surveillance System and Medicaid enrollment files, we identified individuals diagnosed with incident invasive breast (n=4850), cervical (n=1023), and colorectal (n=3363) cancer. We conducted causal mediation analysis to estimate the direct effect of pre- (vs. post-) expansion on being diagnosed with early-stage (-vs. regional-stage and distant-stage) disease, and indirect (mediation) effect through being in the stably- (vs. emergently-) enrolled group, controlling for individual-level and area-level characteristics. RESULTS: The percentage of stably-enrolled patients increased from 63.3% to 73.9% post-expansion, while that of the emergently-enrolled decreased from 36.7% to 26.1%. The percentage of patients with early-stage diagnosis remained 1.3-2.9 times higher among the stably-than the emergently-enrolled group, both pre-expansion and post-expansion. Results from the causal mediation analysis showed that there was an indirect effect of Medicaid expansion through being in the stably- (vs. emergently-) enrolled group [risk ratios with 95% confidence interval: 1.018 (1.010-1.027) for breast cancer, 1.115 (1.064-1.167) for cervical cancer, and 1.090 (1.062-1.118) for colorectal cancer. CONCLUSION: We provide the first evidence that post-expansion improvements in cancer stage were caused by an increased reliance on Medicaid as a source of stable insurance coverage.

Factors affecting timely breast cancer treatment among black women in a high-risk urban community: a qualitative study.

BACKGROUND: Black women diagnosed with breast cancer in the U.S. tend to experience significantly longer waits to begin treatment than do their white counterparts, and such treatment delay has been associated with poorer survival. We sought to identify the factors driving or mitigating treatment delay among Black women in an urban community where treatment delay is common. METHODS: Applying the SaTScan method to data from Ohio's state cancer registry, we identified the community within Cuyahoga County, Ohio (home to Cleveland) with the highest degree of breast cancer treatment delay from 2010 through 2015. We then recruited breast cancer survivors living in the target community, their family caregivers, and professionals serving breast cancer patients in this community. Participants completed semi-structured interviews focused on identifying barriers to and facilitators of timely breast cancer treatment initiation after diagnosis. RESULTS: Factors reported to impact timely treatment fell into three primary themes: informational, intrapersonal, and logistical. Informational barriers included erroneous beliefs and lack of information about processes of care; intrapersonal barriers centered on mistrust, fear, and denial; while logistical barriers involved transportation and financial access, as well as patients' own caregiving obligations. An informational facilitator was the provision of objective and understandable disease information, and a common intrapersonal facilitator was faith. Logistical facilitators included financial counseling and mechanisms to assist with Medicaid enrollment. Crosscutting these themes, and mentioned frequently, was the centrality of both patient navigators and support networks (formal and, especially, informal) as critical lifelines for overcoming barriers and leveraging facilitating factors. CONCLUSIONS: The present study describes the numerous hurdles to timely breast cancer treatment faced by Black women in a high-risk urban community. These hurdles, as well as corresponding facilitators, can be classified as informational, intrapersonal, and logistical. Observing similar results on a larger scale could inform the design of interventions and policies to reduce race-based disparities in processes of cancer care.