Prostate-specific antigen testing patterns and prostate cancer stage at diagnosis in older Ohio cancer patients.

BACKGROUND: Prostate cancer (PCa) screening recommendations do not support prostate-specific antigen (PSA) screening for older men. Such screening often occurs, however. It is, therefore, important to understand how frequently and among which subgroups screening occurs, and the extent of distant stage PCa diagnoses among screened older men. METHODS: Using the 2014-2016 linked Ohio Cancer Incidence Surveillance System (OCISS) and Medicare administrative database, we identified men 68 and older diagnosed with PCa and categorized their PSA testing in the three years preceding diagnosis as screening or diagnostic. We conducted multivariable logistic regression analysis to identify correlates of screening PSA and to determine whether screening PSA is independently associated with distant stage disease. RESULTS: Our study population included 3034 patients (median age: 73 years). 62.1% of PCa patients underwent at least one screening-based PSA in the three years preceding diagnosis. Older age (75-84 years: aOR [95% CI]: 0.84 [0.71, 0.99], ≥ 85: aOR: 0.27 [0.19, 0.38]), and frailty (aOR: 0.51 [0.37, 0.71]) were associated with lower screening. Screening was associated with decreased odds of distant stage disease (aOR: 0.55 [0.42, 0.71]). However, older age (75-84 years: aOR: 2.43 [1.82, 3.25], ≥ 85: aOR: 10.57 [7.05, 15.85]), frailty (aOR: 5.00 [2.78, 9.31]), and being separated or divorced (aOR: 1.64 [1.01, 2.60]) were associated with increased distant stage PCa. CONCLUSION: PSA screening in older men is common, though providers appear to curtail PSA screening as age and frailty increase. Screened older men are diagnosed at earlier stages, but the harms of screening cannot be assessed.

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.

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.

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.

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.

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.

An informatics infrastructure to catalyze cancer control research and practice.

PURPOSE: A disconnect often exists between those with the expertise to manage and analyze complex, multi-source data sets, and the clinical, social services, advocacy, and public health professionals who can pose the most relevant questions and best apply the answers. We describe development and implementation of a cancer informatics infrastructure aimed at broadening the usability of community cancer data to inform cancer control research and practice; and we share lessons learned. METHODS: We built a multi-level database known as The Ohio Cancer Assessment and Surveillance Engine (OH-CASE) to link data from Ohio's cancer registry with community data from the U.S. Census and other sources. Space-and place-based characteristics were assigned to individuals according to residential address. Stakeholder input informed development of an interface for generating queries based on geographic, demographic, and disease inputs and for outputting results aggregated at the state, county, municipality, or zip code levels. RESULTS: OH-CASE contains data on 791,786 cancer cases diagnosed from 1/1/2006 to 12/31/2018 across 88 Ohio counties containing 1215 municipalities and 1197 zip codes. Stakeholder feedback from cancer center community outreach teams, advocacy organizations, public health, and researchers suggests a broad range of uses of such multi-level data resources accessible via a user interface. CONCLUSION: OH-CASE represents a prototype of a transportable model for curating and synthesizing data to understand cancer burden across communities. Beyond supporting collaborative research, this infrastructure can serve the clinical, social services, public health, and advocacy communities by enabling targeting of outreach, funding, and interventions to narrow cancer disparities.

Is Medicaid Expansion Narrowing Gaps in Surgical Disparities for Low-Income Breast Cancer Patients?

BACKGROUND: The objective of this study is to understand the effect of Medicaid expansion under the Affordable Care Act (ACA) on patterns of surgical care among low-income breast cancer patients. Emerging literature suggests cancer patients in Medicaid expansion states are presenting with earlier stages of disease. However, less is known regarding the implications of Medicaid expansion on patterns of surgical care in low-income women. PATIENTS AND METHODS: We compared nonmetastatic 30-64-year-old uninsured or Medicaid-insured Ohio breast cancer patients diagnosed 4 years before and 4 years after the state's 2014 Medicaid expansion (study group); the control group was the privately insured. Time-to-surgery (TTS) was defined as days from diagnosis to surgery. Demographic and treatment variables before and after expansion were examined in multivariate analysis. RESULTS: There was a 10.4% point increase in breast conservation therapy (BCT) in the study group (pre-ACA 26.3%, post-ACA 36.7%; p < 0.01) compared with a 5.8% point increase in the control group (pre-ACA 36.0%, post-ACA 41.8%; p < 0.01). Disparities in reconstruction narrowed between the study (pre-ACA 21.4%, post-ACA 34.5%; p < 0.01) and the control (37.0% pre-ACA, 44.1% post-ACA group p < 0.01) groups. There was no statistically significant change in mean TTS in the study group (pre-ACA 42.1 days, post-ACA 43.1 days p = 0.18) but there was an increase in TTS in the control group (pre-ACA 35.0 days, post ACA 37.0 days; p < 0.01). CONCLUSIONS: Medicaid expansion appears to have narrowed disparities in the utilization of BCT and reconstruction in low-income women. However, there was no improvement in surgical delay.

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.

Medicaid Expansion Associated With Reduction in Geospatial Breast Cancer Stage at Diagnosis Disparities.

CONTEXT: Prior studies demonstrate that Medicaid expansion has been associated with earlier-stage breast cancer diagnosis among women with low income, likely through increased access to cancer screening services. However, how this policy change has impacted geospatial disparities in breast cancer stage at diagnosis is unclear. OBJECTIVE: To examine whether there were reductions in geospatial disparities in advanced stage breast cancer at diagnosis in Ohio after Medicaid expansion. DESIGN: The study included 33 537 women aged 40 to 64 years diagnosed with invasive breast cancer from the Ohio Cancer Incidence Surveillance System between 2010 and 2017. The space-time scan statistic was used to detect clusters of advanced stage at diagnosis before and after Medicaid expansion. Block group variables from the Census were used to describe the contextual characteristics of detected clusters. RESULTS: The percentage of local stage diagnosis among women with breast cancer increased from 60.2% in the pre-expansion period (2010-2013) to 62.6% in the post-expansion period (2014-2017), while the uninsured rate among those women decreased from 13.7% to 7.5% during the same period. Two statistically significant ( P < .05) and 6 nonsignificant spatial clusters ( P > .05) of advanced stage breast cancer cases were found in the pre-expansion period, while none were found in the post-expansion period. These clusters were in the 4 largest metropolitan areas in Ohio, and individuals inside the clusters were more likely to be disadvantaged along numerous socioeconomic factors. CONCLUSIONS: Medicaid expansion has played an important role in reducing geospatial disparities in breast cancer stage at diagnosis, likely through the reduction of advanced stage disease among women living in socioeconomically disadvantaged communities.

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.

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.

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.

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.