Medication Adherence of Older Adults with Hypertension: A Systematic Review.

Objective: Hypertension (HTN) significantly increases the risk of stroke and heart disease, which are the leading causes of death and disability globally, particularly among older adults. Antihypertensive medication is a proven treatment for blood pressure control and preventing complications. However, medication adherence rates in older adults with HTN are low. In this review, we systematically identified factors influencing medication adherence in older adults with HTN. Methods: We applied the PRISMA guidelines and conducted systematic searches on PubMed, MEDLINE, and Google Scholar in July 2022 to identify preliminary studies reporting factors influencing medication adherence among older adults with HTN. The convergent integrated analysis framework suggested by the Joanna Briggs Institute for systematic reviews was adopted for data synthesis. Results: Initially, 448 articles were identified, and after title and abstract screening, 16 articles qualified for full-text review. During this phase, three articles were excluded for reporting on irrelevant populations or focusing on issues beyond the review's aim, leaving thirteen studies in the final review. After data synthesis, fifteen themes were extracted from the key findings of the included studies. The most prevalent themes included the number of medications used (53.9%, n=7 studies), financial status (38.5%, n=5), sex (38.5%, n=5), age (30.1%, n=4), duration of disease (23.1%, n=3), comorbidities (23.1%, n=3), and health compliance (23.1%, n=3). Other themes, such as education, health literacy, health belief, medication belief, perception of illness, patient-physician relationship, self-efficacy, and social support, were also identified. Conclusion: The findings of this review highlight critical areas for developing innovative, evidence-based programs to improve medication adherence in hypertensive older adults. Insights from this review can contribute to improving medication adherence and preventing future health complications.

Understanding the Utility of Less Than Six-Month Prognosis Using Administrative Data Among U.S. Nursing Home Residents With Cancer.

Background: There is a dearth of studies evaluating the utility of reporting prognostication among nursing home (NH) residents with cancer. Objective: To study factors associated with documented less than six-month prognosis, and its relationship with end-of-life (EOL) care quality measures among residents with cancer. Methods: The Surveillance, Epidemiology, and End Results linked with Medicare, and the Minimum Data Set databases was used to identify 20,397 NH residents in the United States with breast, colorectal, lung, pancreatic, or prostate cancer who died between July 2016 and December 2018. Of these, 2205 residents (10.8%) were documented with less than six-month prognosis upon NH admission. Main outcomes were more than one hospitalization, more than one emergency department visit, and any intensive care unit admission within the last 30 days of life as aggressive EOL care markers, as well as admission to hospice, receipt of advance care planning and palliative care, and survival. Specificity and sensitivity of prognosis were assessed using six-month mortality as the outcome. Propensity score matching adjusted for selection biases, and logistic regression examined association. Results: Specificity and sensitivity of documented less than six-month prognosis for mortality were 94.2% and 13.7%, respectively. Residents with documented less than six-month prognosis had greater odds of being admitted to hospice than those without (adjusted odds ratio: 3.27, 95% confidence interval: 2.86-3.62), and lower odds to receive aggressive EOL care. Conclusion: In this cohort study, documented less than six-month prognosis was associated with less aggressive EOL care. Despite its high specificity, however, low sensitivity limits its utility to operationalize care on a larger population of residents with terminal illness.

Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review.

Objective: To evaluate the evidence of artificial neural network (NNs) techniques in diagnosing ischemic stroke (IS) in adults. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilized as a guideline for this review. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched to identify studies published between 2018 and 2022, reporting using NNs in IS diagnosis. The Critical Appraisal Checklist for Diagnostic Test Accuracy Studies was adopted to evaluate the included studies. Results: Nine studies were included in this systematic review. Non-contrast computed tomography (NCCT) (n = 4 studies, 26.67%) and computed tomography angiography (CTA) (n = 4 studies, 26.67%) are among the most common features. Five algorithms were used in the included studies. Deep Convolutional Neural Networks (DCNNs) were commonly used for IS diagnosis (n = 3 studies, 33.33%). Other algorithms including three-dimensional convolutional neural networks (3D-CNNs) (n = 2 studies, 22.22%), two-stage deep convolutional neural networks (Two-stage DCNNs) (n = 2 studies, 22.22%), the local higher-order singular value decomposition denoising algorithm (GL-HOSVD) (n = 1 study, 11.11%), and a new deconvolution network model based on deep learning (AD-CNNnet) (n = 1 study, 11.11%) were also utilized for the diagnosis of IS. Conclusion: The number of studies ensuring the effectiveness of NNs algorithms in IS diagnosis has increased. Still, more feasibility and cost-effectiveness evaluations are needed to support the implementation of NNs in IS diagnosis in clinical settings.

Investigating the Influence of Periodontal Disease on the Association Between Complex Multimorbidity and Health: A Cross-Sectional Study.

Aims and Objectives: The aim of this study was to evaluate whether periodontal disease (PD) moderates the association between complex multimorbidity (MM) and fair/poor general health in a US representative sample of older individuals. Materials and Methods: This study identified 937 participants who were at least 60 years of age from the 2013-2014 National Health and Nutrition Examination Survey. A multivariable logistic regression model was used to estimate the association between complex MM (including chronic conditions, functional limitations, and geriatric syndromes) and self-reported fair/poor general health. Subsequently, we specified interactions between complex MM and PD to determine whether PD moderates the effect of complex MM on fair/poor general health. Results: Nineteen percent of participants reported fair/poor general health. Complex MM was associated with 2.2 times greater odds of self-reported fair/poor health. However, PD was neither independently associated with fair/poor health nor an effect modifier for the association between complex MM and fair/poor general health. Age, white race, those with at least a high-school education level, a family income-to-poverty ratio of at least 2, former smoking status, and being married were significantly associated with fair/poor general health. Conclusion: Complex MM, but not PD, was associated with greater odds to report fair/poor general health, and PD did not moderate the association between complex MM and fair/poor general health. People with complex MM are more likely to have worse general health; however, PD did not strengthen this association. Further studies are needed to evaluate whether treatment for PD for people vulnerable to the development of complex MM has a positive effect on their general health.

Aggressive end-of-life care across gradients of cognitive impairment in nursing home patients with metastatic cancer.

BACKGROUND: Studies examining end-of-life (EOL) care in older cancer patients are scarce, and prior studies have not accounted for gradients of cognitive impairment (COG-I). We examine EOL care patterns across COG-I gradients, hypothesizing that greater COG-I severity is associated with lower odds of receiving aggressive EOL care. METHODS: Using data from the linked Surveillance Epidemiology and End Results (SEER) -Medicare -Minimum Data Set (MDS) 3.0, we identified patients with nursing facility stays (NFS) and who died with metastatic cancer from 2013 to 2017. Markers of aggressive EOL care were: cancer-directed treatment, intensive care unit admission, >1 emergency department visit, or >1 hospitalization in the last 30 days of life, hospice enrollment in the last 3 days of life, and in-hospital death. In addition to descriptive analysis, we conducted multivariable logistic regression analysis to evaluate the independent association between COG-I severity and receipt of aggressive EOL care. RESULTS: Of the 40,833 patients in our study population, 49.2% were cognitively intact; 24.4% had mild COG-I; 19.7% had moderate COG-I; and 6.7% had severe COG-I. The percent of patients who received aggressive EOL care was 62.6% and 74.2% among those who were cognitively intact and those with severe COG-I, respectively. Compared with cognitively intact patients, those with severe COG-I had 86% higher odds of receiving any type of aggressive EOL care (adjusted odds ratio (aOR): 1.86 (95% confidence interval: 1.70-2.04)), which were primarily associated with higher odds of in-hospital death. The odds of in-hospital death associated with severe COG-I were higher among those with short- than with long-term stays (aOR:2.58 (2.35-2.84) and aOR:1.40 (1.17-1.67), respectively). CONCLUSIONS: Contrary to our hypothesis, aggressive EOL care in older metastatic cancer patients with NFS was highest among those suffering severe COG-I. These findings can inform the development of interventions to help reduce aggressive EOL care in this patient population.

Cancer burden in women with HIV on Medicaid: A nationwide analysis.

BACKGROUND: Cancer is the leading cause of death in people living with HIV. In the United States, nearly 1 in 4 people living with HIV are women, more than half of whom rely on Medicaid for healthcare coverage. OBJECTIVE: The objective of this study is to evaluate the cancer burden of women living with HIV on Medicaid. DESIGN: We conducted a cross-sectional study of women 18-64 years of age enrolled in Medicaid during 2012, using data from Medicaid Analytic eXtract files. METHODS: Using International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes, we identified women living with HIV (n = 72,508) and women without HIV (n = 17,353,963), flagging the presence of 15 types of cancer and differentiating between AIDS-defining cancers and non-AIDS-defining cancers. We obtained adjusted prevalence ratios and 95% confidence intervals for each cancer and for all cancers combined, using multivariable log-binomial models, and additionally stratifying by age and race/ethnicity. RESULTS: The highest adjusted prevalence ratios were observed for Kaposi's sarcoma (81.79 (95% confidence interval: 57.11-117.22)) and non-Hodgkin's lymphoma (27.69 (21.67-35.39)). The adjusted prevalence ratios for anal and cervical cancer, both of which were human papillomavirus-associated cancers, were 19.31 (17.33-21.51) and 4.20 (3.90-4.52), respectively. Among women living with HIV, the adjusted prevalence ratio for all cancer types combined was about two-fold higher (1.99 (1.86-2.14)) in women 45-64 years of age than in women 18-44 years of age. For non-AIDS-defining cancers but not for AIDS-defining cancers, the adjusted prevalence ratios were higher in older than in younger women. There was no significant difference in the adjusted prevalence ratios for all cancer types combined in the race/ethnicity-stratified analyses of the women living with HIV cohort. However, in cancer type-specific sub-analyses, differences in adjusted prevalence ratios between Hispanic versus non-Hispanic women were observed. For example, the adjusted prevalence ratio for Hispanic women for non-Hodgkin's lymphoma was 2.00 (1.30-3.07) and 0.73 (0.58-0.92), respectively, for breast cancer. CONCLUSION: Compared to their counterparts without HIV, women living with HIV on Medicaid have excess prevalence of cervical and anal cancers, both of which are human papillomavirus related, as well as Kaposi's sarcoma and lymphoma. Older age is also associated with increased burden of non-AIDS-defining cancers in women living with HIV. Our findings emphasize the need for not only cancer screening among women living with HIV but also for efforts to increase human papillomavirus vaccination among all eligible individuals.

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.

The Arthroplasty Surgeon Growth Indicator: A Tool for Monitoring Supply and Demand Trends in the Orthopaedic Surgeon Workforce from 2020 to 2050.

BACKGROUND: Orthopaedic practices in the U.S. face a growing demand for total joint arthroplasties (TJAs), while the orthopaedic workforce size has been stagnant for decades. This study aimed to estimate annual TJA demand and orthopaedic surgeon workforce supply from 2020 to 2050, and to develop an arthroplasty surgeon growth indicator (ASGI), based on the arthroplasty-to-surgeon ratio (ASR), to gauge nationwide supply and demand trends. METHODS: National Inpatient Sample and Association of American Medical Colleges data were reviewed for individuals who underwent primary TJA and for active orthopaedic surgeons (2010 to 2020), respectively. The projected annual TJA volume and number of orthopaedic surgeons were modeled using negative binominal and linear regression, respectively. The ASR is the number of actual (or projected) annual total hip (THA) and/or knee (TKA) arthroplasties divided by the number of actual (or projected) orthopaedic surgeons. ASGI values were calculated using the 2017 ASR values as the reference, with the resulting 2017 ASGI defined as 100. RESULTS: The ASR calculation for 2017 showed an annual caseload per orthopaedic surgeon (n = 19,001) of 24.1 THAs, 41.1 TKAs, and 65.2 TJAs. By 2050, the TJA volume was projected to be 1,219,852 THAs (95% confidence interval [CI]: 464,808 to 3,201,804) and 1,037,474 TKAs (95% CI: 575,589 to 1,870,037). The number of orthopaedic surgeons was projected to decrease by 14% from 2020 to 2050 (18,834 [95% CI: 18,573 to 19,095] to 16,189 [95% CI: 14,724 to 17,655]). This would yield ASRs of 75.4 THAs (95% CI: 31.6 to 181.4), 64.1 TKAs (95% CI: 39.1 to 105.9), and 139.4 TJAs (95% CI: 70.7 to 287.3) by 2050. The TJA ASGI would double from 100 in 2017 to 213.9 (95% CI: 108.4 to 440.7) in 2050. CONCLUSIONS: Based on historical trends in TJA volumes and active orthopaedic surgeons, the average TJA caseload per orthopaedic surgeon may need to double by 2050 to meet projected U.S. demand. Further studies are needed to determine how the workforce can best meet this demand without compromising the quality of care in a value-driven health-care model. However, increasing the number of trained orthopaedic surgeons by 10% every 5 years may be a potential solution.

Incidence of Aggressive End-of-Life Care Among Older Adults With Metastatic Cancer Living in Nursing Homes and Community Settings.

Importance: Nearly 10% of the 1.5 million persons residing in nursing homes (NHs) have received or will receive a diagnosis of cancer. Although aggressive end-of-life (EOL) care is common among community-dwelling patients with cancer, little is known about such patterns of care among NH residents with cancer. Objective: To compare markers of aggressive EOL care between older adults with metastatic cancer who are NH residents and their community-dwelling counterparts. Design, Setting, and Participants: This cohort study used the Surveillance, Epidemiology, and End Results database linked with the Medicare database and the Minimum Data Set (including NH clinical assessment data) for deaths occurring from January 1, 2013, to December 31, 2017, among 146 329 older patients with metastatic breast, colorectal, lung, pancreas, or prostate cancer, with a lookback period in claims data through July 1, 2012. Statistical analysis was conducted between March 2021 and September 2022. Exposures: Nursing home status. Main Outcomes and Measures: Markers of aggressive EOL care were cancer-directed treatment, intensive care unit admission, more than 1 emergency department visit or more than 1 hospitalization in the last 30 days of life, hospice enrollment in the last 3 days of life, and in-hospital death. Results: The study population included 146 329 patients 66 years of age or older (mean [SD] age, 78.2 [7.3] years; 51.9% men). Aggressive EOL care was more common among NH residents than community-dwelling residents (63.6% vs 58.3%). Nursing home status was associated with 4% higher odds of receiving aggressive EOL care (adjusted odds ratio [aOR], 1.04 [95% CI, 1.02-1.07]), 6% higher odds of more than 1 hospital admission in the last 30 days of life (aOR, 1.06 [95% CI, 1.02-1.10]), and 61% higher odds of dying in the hospital (aOR, 1.61 [95% CI, 1.57-1.65]). Conversely, NH status was associated with lower odds of receiving cancer-directed treatment (aOR, 0.57 [95% CI, 0.55-0.58]), intensive care unit admission (aOR, 0.82 [95% CI, 0.79-0.84]), or enrollment in hospice in the last 3 days of life (aOR, 0.89 [95% CI, 0.86-0.92]). Conclusions and Relevance: Despite increased emphasis to reduce aggressive EOL care in the past several decades, such care remains common among older persons with metastatic cancer and is slightly more prevalent among NH residents than their community-dwelling counterparts. Multilevel interventions to decrease aggressive EOL care should target the main factors associated with its prevalence, including hospital admissions in the last 30 days of life and in-hospital death.

Sociodemographic and clinical factors associated with receipt of biomarker testing in patients with metastatic colorectal cancer.

BACKGROUND: Standard clinical practice and national guidelines dictate somatic testing of metastatic colorectal cancer (mCRC) tumors to guide appropriate therapy; however, previous studies suggest that not all patients are tested. The objective of this study was to investigate potential differences in testing for mCRC by demographic and clinical factors. METHODS: We performed a retrospective review of de-identified patient data derived from electronic health records (EHRs) of 25,469 patients diagnosed with mCRC between the years 2013 and 2020. Our outcome was a receipt of the following tests: (a) biomarker testing (BRAF, KRAS, NRAS, MMR/MSI) and (b) next-generation sequencing (NGS). We interrogated our data using the machine-learning algorithm Classification and Regression Tree (CART), a unique approach to identifying combinations of, rather than individual demographic and clinical characteristics associated with receipt of testing. RESULTS: A total of 25,469 patients were identified with mCRC. Of these, 21,133 (83%) received either biomarker testing only (n = 12,485) or any testing (biomarker + NGS) (n = 8648). The proportion of patients who received any testing increased over calendar time for all age, race, and sex categories. Receipt of any testing was highest (90%) among younger and patients with better performance status, and there was no difference in receipt of any testing by race. The highest percentage of NGS testing was among those with better performance status, <70 years old, commercial or other governmental program payers, and low comorbidity burden; however, those who were Black or Hispanic had a lower prevalence of NGS testing than those who were White. CONCLUSIONS AND RELEVANCE: Considerable variations exist in somatic biomarker testing across subgroups of the population. Identification of genomic alterations can aid in determining targeted treatment and improving clinical outcomes; therefore, equitable use of these testing strategies, particularly NGS, is necessary.

Machine learning-based patient classification system for adults with stroke: A systematic review.

OBJECTIVE: To evaluate the existing evidence of a machine learning-based classification system that stratifies patients with stroke. METHODS: The authors carried out a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations for a review article. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched from January 2015 to February 2021. RESULTS: There are twelve studies included in this systematic review. Fifteen algorithms were used in the included studies. The most common forms of machine learning (ML) used to classify stroke patients were the support vector machine (SVM) (n = 8 studies), followed by random forest (RF) (n = 7 studies), decision tree (DT) (n = 4 studies), gradient boosting (GB) (n = 4 studies), neural networks (NNs) (n = 3 studies), deep learning (n = 2 studies), and k-nearest neighbor (k-NN) (n = 2 studies), respectively. Forty-four features of inputs were used in the included studies, and age and gender are the most common features in the ML model. DISCUSSION: There is no single algorithm that performed better or worse than all others at classifying patients with stroke, in part because different input data require different algorithms to achieve optimal outcomes.

Achievement of age-friendly health systems committed to care excellence designation in a convenient care health care system.

OBJECTIVE: To describe the implementation of the age-friendly health systems (AFHS) 4Ms Framework, an evidence-based framework to assess and act on "What Matters, Medication, Mentation and Mobility to deliver Age-Friendly health care for patients 65 and older", to achieve the Institute for Health care Improvement (IHI) Committed to Care Excellence recognition in a convenient care health system and test two novel implementation strategies. SETTING: The study was conducted in over 1100 convenient care clinics in 35 states and DC. MinuteClinics are located in community-based retail pharmacies in rural, suburban, and urban areas and staffed with approximately 3300 nurse practitioners and physician associates. DESIGN: In Year 1, the project used a quality improvement design, and in Year 2, a quasi-experimental implementation research design to pilot two strategies at the provider level (Virtual Clinic and Plan-Do-Study-Act (PDSA)). Statistical process control charts were used to assess changes in 4Ms documentation over time. Mixed-effects Poisson regression was used to assess the effectiveness of the pilot studies. DATA COLLECTION: The electronic health record (EHR) was enhanced to capture documentation of the AFHS 4Ms assessments and actions. A learning platform was created to teach and evaluate provider 4Ms competency, and the two data sources were merged into a registry. A formative evaluation was conducted using Tableau and reporting dashboards. FINDINGS: After 18 months and the implementation of 20 strategies to improve the uptake of the 4Ms, MinuteClinic achieved the IHI Committed to Care Excellence recognition. A significant increase over time in the reliable delivery of all 4Ms and each M component individually was found. For the research, there were significant improvements in the mean number of Ms delivered per visit (M-Score) in the Virtual Clinic (Incident Rate Ratio [IRR]: 2.47, p = 0.001) and PDSA (IRR: 3.08, p = 0.002) strategy intervention groups when compared to controls. CONCLUSIONS: Application of quality improvement and implementation methodologies contributed to the success of implementing age-friendly 4Ms evidence-based practice.

Prevalence of multimorbidity combinations and their association with medical costs and poor health: A population-based study of U.S. adults.

Background: Multimorbidity is common, but the prevalence and burden of the specific combinations of coexisting disease has not been systematically examined in the general U.S. adult population. Objective: To identify and estimate the burden of highly prevalent combinations of chronic conditions that are treated among one million or more adults in the United States. Methods: Cross-sectional analysis of U.S. households in the Medical Expenditure Panel Survey (MEPS), 2016-2019, a large nationally-representative sample of the community-dwelling population. Association rule mining was used to identify the most common combinations of 20 chronic conditions that have high relevance, impact, and prevalence in primary care. The main measures and outcomes were annual treated prevalence, total medical expenditures, and perceived poor health. Logistic regression models with poor health as the outcome and each multimorbidity combination as the exposure were used to calculate adjusted odds ratios and 95% confidence intervals. Results: Frequent pattern mining yielded 223 unique combinations of chronic disease, including 74 two-way (dyad), 115 three-way (triad), and 34 four-way combinations that are treated in one million or more U.S. adults. Hypertension-hyperlipidemia was the most common two-way combination occurring in 30.8 million adults. The combination of diabetes-arthritis-cardiovascular disease was associated with the highest median annual medical expenditures ($23,850, interquartile range: $11,593-$44,616), and the combination of diabetes-arthritis-asthma/COPD had the highest age-race-sex adjusted odds ratio of poor self-rated health (adjusted odd ratio: 6.9, 95%CI: 5.4-8.8). Conclusion: This study demonstrates that many multimorbidity combinations are highly prevalent among U.S. adults, yet most research and practice-guidelines remain single disease focused. Highly prevalent and burdensome multimorbidity combinations could be prioritized for evidence-based research on optimal prevention and treatment strategies.

High-risk diagnosis combinations in patients undergoing interhospital transfer: a retrospective observational study.

BACKGROUND: There is limited research on individual patient characteristics, alone or in combination, that contribute to the higher levels of mortality in post-transfer patients. The purpose of this work is to identify significant combinations of diagnoses that identify subgroups of post-interhospital transfer patients experiencing the highest levels of mortality. METHODS: This was a retrospective cross-sectional study using structured electronic health record data from a regional health system between 2010-2017. We employed a machine learning approach, association rules mining using the Apriori algorithm to identify diagnosis combinations. The study population includes all patients aged 21 and older that were transferred within our health system from a community hospital to one of three main receiving hospitals. RESULTS: Overall, 8893 patients were included in the analysis. Patients experiencing mortality post-transfer were on average older (70.5 vs 62.6 years) and on average had more diagnoses in 5 of the 6 diagnostic subcategories. Within the diagnostic subcategories, most diagnoses were comorbidities and active medical problems, with hypertension, atrial fibrillation, and acute respiratory failure being the most common. Several combinations of diagnoses identified patients that exceeded 50% post-interhospital transfer mortality. CONCLUSIONS: Comorbid burden, in combination with active medical problems, were most predictive for those experiencing the highest rates of mortality. Further improving patient level prognostication can facilitate informed decision making between providers and patients to shift the paradigm from transferring all patients to higher level care to only transferring those who will benefit or desire continued care, and reduce futile transfers.

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.

Combinations of chronic conditions, functional limitations and geriatric syndromes associated with periodontal disease.

OBJECTIVE: To identify complex multimorbid conditions, including chronic conditions, functional limitations and geriatric syndromes, associated with the presence and severity of periodontal disease (PD), after accounting for a series of demographic and behavioural characteristics. DESIGN: This cross-sectional study used secondary data from a nationally representative sample, classification and regression tree analysis and random forest identified combinations of specific conditions constituting complex multimorbidity associated with the presence and severity of PD. SETTING: US National Health and Nutritional Examination Survey (2013-2014). PARTICIPANTS: Individuals 60 years of age or older who completed a periodontal examination. RESULTS: Among 937 participants aged 60 and over, the prevalence of PD was 72.6%. PD was associated with sociodemographic factors and limitations in instrumental activities of daily living. Male sex and non-white race were the two most critical predictors of stage III/IV PD. Other important factors included age, education level and the federal poverty level. CONCLUSIONS: Rather than chronic conditions or geriatric syndromes, PD was associated with sociodemographic factors and functional limitations. Accounting for the co-occurrence of sociodemographic and functional limitations will help recognise older adults who are at an increased vulnerability to the severity of PD.

Comparing the association between multiple chronic conditions, multimorbidity, frailty, and survival among older patients with cancer.

INTRODUCTION: The high prevalence of multiple chronic conditions (MCC), multimorbidity, and frailty may affect treatment and outcomes for older adults with cancer. The goal of this study was to use three conceptually distinct measures of morbidity to examine the association between these measures and mortality. MATERIALS AND METHODS: Using Medicare claims data linked with the 2012-2016 Ohio Cancer Incidence Surveillance System we identified older adults with incident primary cancer sites of breast, colorectal, lung, or prostate (n = 29,140). We used claims data to identify their Elixhauser comorbidities, Multimorbidity-Weighted Index (MWI), and Claims Frailty Index (CFI) as measures of MCC, multimorbidity, and frailty, respectively. We used Cox proportional hazard models to examine the association between these measures and survival time since diagnosis. RESULTS: Lung cancer patients had the highest levels of MCC, multimorbidity, and frailty. There was a positive association between all three measures and a greater hazard of death after adjusting for age, sex (colorectal and lung only), and stage. Breast cancer patients with 5+ comorbidities had an adjusted hazard ratio (aHR) of 1.63 (95% confidence interval [CI]: 1.38, 1.93), and those with mild frailty had an aHR of 3.38 (95% CI; 2.12, 5.41). The C statistics for breast cancer were 0.79, 0.78, and 0.79 for the MCC, MWI, and CFI respectively. Similarly, lung cancer patients who were moderately or severely frail had an aHR of 1.82 (95% CI: 1.53, 2.18) while prostate cancer patients had an aHR of 3.39 (95% CI: 2.12, 5.41) and colorectal cancer patients had an aHR of 4.51 (95% CI: 3.23, 6.29). Model performance was nearly identical across the MCC, multimorbidity, and frailty models within cancer type. The models performed best for prostate and breast cancer, and notably worse for lung cancer. The frailty models showed the greatest separation in unadjusted survival curves. DISCUSSION: The MCC, multimorbidity, and frailty indices performed similarly well in predicting mortality among a large cohort of older cancer patients. However, there were notable differences by cancer type. This work highlights that although model performance is similar, frailty may serve as a clearer indicator in risk stratification of geriatric oncology patients than simple MCCs or multimorbidity.

Clustering of Social Determinants of Health Among Patients.

INTRODUCTION/OBJECTIVES: Many health systems screen patients for social determinants of health and refer patients with social needs to community organizations for assistance. Understanding how social determinants cluster together may help guide assistance programs. METHODS: This study examined patients screened by The MetroHealth System in Cleveland, Ohio for 9 social determinants, including food insecurity, financial strain, transportation limitations, inability to pay for housing or utilities, intimate partner violence, social isolation, infrequent physical activity, daily stress, and lack of internet access. Clustering analyses were performed to determine which combination of social determinants occurred together more often than would be expected if each determinant were independent of each other. RESULTS: Among 23 161 screened patients, there were 19 dyads, 13 triads, and one tetrad of social determinants that clustered together. The most prevalent triad of food insecurity, social isolation, and inability to pay for housing or utilities occurred among 1095 patients but would be expected to occur among 284 patients, for an observed/expected ratio of 3.85 (95% confidence interval 3.64-4.07). In multivariate analyses, younger, Black, and lower income patients were 2 to 3 times more likely to have this triad compared to older, White, and wealthier patients. CONCLUSIONS: Social determinants of health frequently cluster together, and such clustering is associated with patient demographic characteristics. Further work is needed to determine how social determinant clusters impact health and cost outcomes and to develop programs that can address multiple co-existing social needs.

Excess cancer prevalence in men with HIV: A nationwide analysis of Medicaid data.

BACKGROUND: Cancer is one of the most common comorbidities in men living with HIV (MLWH). However, little is known about the MLWH subgroups with the highest cancer burden to which cancer prevention efforts should be targeted. Because Medicaid is the most important source of insurance for MLWH, we evaluated the excess cancer prevalence in MLWH on Medicaid relative to their non-HIV counterparts. METHODS: In this cross-sectional study using 2012 Medicaid Analytic eXtract data nationwide, we flagged the presence of HIV, 13 types of cancer, symptomatic HIV, and viral coinfections using codes from the International Classification of Diseases, Ninth Revision, Clinical Modification. The study population included individuals administratively noted to be of male sex (men), aged 18 to 64 years, with (n = 82,495) or without (n = 7,302,523) HIV. We developed log-binomial models with cancer as the outcome stratified by symptomatic status, age, and race/ethnicity. RESULTS: Cancer prevalence was higher in MLWH than in men without HIV (adjusted prevalence ratio [APR], 1.84; 95% confidence interval [CI], 1.78-1.90) and was higher among those with symptomatic HIV (APR, 2.74; 95% CI, 2.52-2.97) than among those with asymptomatic HIV (APR, 1.73; 95% CI, 1.67-1.79). The highest APRs were observed for anal cancer in younger men, both in the symptomatic and asymptomatic groups: APR, 312.97; 95% CI, 210.27-465.84, and APR, 482.26; 95% CI, 390.67-595.32, respectively. In race/ethnicity strata, the highest APRs were among Hispanic men for anal cancer (APR, 198.53; 95% CI, 144.54-272.68) and for lymphoma (APR, 9.10; 95% CI, 7.80-10.63). CONCLUSIONS: Given the Medicaid program's role in insuring MLWH, the current findings highlight the importance of the program's efforts to promote healthy behaviors and vaccination against human papillomavirus in all children and adolescents and to provide individualized cancer screening for MLWH.