Neighborhood-level factors associated with COVID-19 vaccination rates: a case study in Chicago.

INTRODUCTION: Chicago's deeply-rooted racial and socioeconomic residential segregation is a pattern mirrored in other major cities, making it a prototype for studying the uptake of public health interventions across the US. Residential segregation is related to availability of primary care, sense of community, and trust in the healthcare system, components which are essential in the response to crises like Covid-19 in which vaccine rollout was primarily community-based. We aimed to evaluate the association between rates of access to primary care and community-belonging with Covid-19 vaccination within Chicago's neighborhoods. METHODS: Data from Chicago Department of Public Health (12/2020-6/2022) on Covid-19 vaccination rates, race/ethnicity (% Black and % Hispanic/Latinx residents), age (% >65), gender (% female), socioeconomic status (% below the federal poverty line), access to needed care rate, and rate of self-reported sense of community-belonging on the neighborhood level were analyzed. Linear mixed models (LMMs) were used to study the impact of variables on vaccination; each neighborhood was added as a random effect to account for with-community association. RESULTS: The average Covid-19 vaccination rates across Chicago's neighborhoods was 79%, ranging from 37 to 100%, with median 81%. We found that Covid-19 vaccination rates were positively correlated with access to needed care (p < 0.001) and community-belonging (p < 0.001). Community areas that had lower vaccination rates had greater percentage of Black residents (p < 0.0001) and greater poverty rates (p < 0.0001). After adjusting for poverty, race, gender and age in the models, the association between vaccination rates and access to care or community-belonging were no longer significant, but % Black residents and poverty remained significant. CONCLUSIONS: Though access to needed primary care and community-belonging are correlated with vaccination rates, this association was not significant when controlling for demographic factors. The association between poverty, race and vaccination status remained significant, indicating that socioeconomic and racial disparities across Chicago drive Covid-19 vaccine recommendation adherence regardless of care access. Understanding how poverty, and its intersectional relation to race and primary care access, affects vaccination should be a priority for public health efforts broadly.

Financial, Social, and Health Impacts from the COVID-19 Pandemic: Findings from the Healthy Chicago Survey.

BACKGROUND: This study examines self-reported impacts of the COVID-19 pandemic among Chicago residents and disparities of these impacts across demographic characteristics. Six logistic regression models were developed to identify demographic and socioeconomic factors associated with each COVID-19 impact. METHOD: The study used de-identified and weighted data from the 2020 Healthy Chicago Survey (HCS). HCS is an annual survey using an address-based random sampling method administered by the Chicago Department of Public Health (CDPH) across the city of Chicago on adults aged 18 or older living between July 17 and November 11, 2020, and received 4517 responses. RESULTS: Chicago residents with lower socioeconomic status had a higher likelihood of food and housing insecurity. Non-Hispanic (NH) Black residents were more likely to report being unable to obtain food (OR: 2.996; 95% CI: 2.268-2.324); being unable to pay for rent, mortgage, or bills (OR: 2.352; 95% CI: 2.325-2.380); and grief from a loss of someone to COVID-19 (OR: 2.037; 95% CI: 2.013-2.061) compared to NH White residents. NH White residents were more likely than other racial/ethnic groups to report loss of social connections, worsened mental health, and canceling or postponing medical care. CONCLUSIONS: The analysis showed higher odds of social contact loss and worsened mental health from COVID-19 in NH White and higher education populations. By implementing strategies to address specific challenges faced by different racial groups, Chicago may effectively mitigate pandemic's adverse effects. These strategies can promote a more inclusive approach to distributing COVID investments for programs and policies.

Racial disparities in opioid administration and prescribing in the emergency department for pain.

STUDY OBJECTIVE: To investigate the holistic characteristics of patients administered or prescribed opioids to treat pain in the emergency department (ED). METHODS: We used National Hospital Ambulatory Medical Care Survey (NHAMCS) data for 2018 to examine the administration and prescribing of opioids for pain-related ED visits. Weighted logistic regression models were developed to evaluate the association between opioid administration and prescribing (OAP) in the ED and patients' pain/severity of conditions, demographic/socioeconomic factors, behavioral factors, contextual factors, and organizational factors. Then, subgroup analyses were conducted by type of pain. RESULTS: Nearly 55% of the ED visits in 2018 involved pain as a main reason for visiting the ED. The odds of receiving opioids were 45% less in black patients than in white patients when other covariates were adjusted (OR: 0.55; CI: 0.430-0.703). Compared to patients with private insurance, Medicaid beneficiaries and uninsured/self-pay patients had a 45% (OR: 0.55; CI: 0.423-0.706) and 44% (OR: 0.56; CI: 0.386-0.813) lower chance of receiving or being prescribed opioids for a pain-related ED visit when all covariates were adjusted. Other significant predictors of OAP for pain in EDs included older age, higher pain level, ED arrival by ambulance, admission to hospital, ED arrival during a night shift, geographic region of the ED. Behavioral factors, such as ED return within 72 h and whether a patient has substance/alcohol abuse or dependence, were not significantly associated with OAP. The subgroup analysis indicated that black patients had lower odds of OAP than their white counterparts only for certain pain categories. CONCLUSION: Despite increasing awareness of potential implicit bias in managing pain in the ED, racial disparities in OAP still existed. More education and training on implicit bias would help with reduce the disparities. Also, our study result indicated that non-clinical factors may play a role in emergency physicians' decision making in OAP. Increased recognition of the variation and systemic efforts to address factors affecting the variability are needed.

Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio.

BACKGROUND: Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly. METHODS: We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley's K function and the local Moran's I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment. RESULTS: The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources. CONCLUSIONS: Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates.

Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach.

Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times can be significant for cancer patients due to their physical and emotional vulnerability. In this paper, a mixed integer programming infusion appointment scheduling (IAS) mathematical model is developed based on patient appointment data, obtained from a cancer center of an academic hospital in Central Virginia. This model minimizes the weighted sum of the total wait times of patients, the makespan and the number of beds used through the planning horizon. A mixed integer programming robust slack allocation (RSA) mathematical model is designed to find the optimal patient appointment schedules, considering the fact that infusion time of patients may take longer than expected. Since the models can only handle a small number of patients, a robust scheduling heuristic (RSH) is developed based on the adaptive large neighborhood search (ALNS) to find patient appointments of real size infusion centers. Computational experiments based on real data show the effectiveness of the scheduling models compared to the original scheduling system of the infusion center. Also, both robust approaches (RSA and RSH) are able to find more reliable schedules than their deterministic counterparts when infusion time of patients takes longer than the scheduled infusion time.

Biopsychosocial factors and health outcomes associated with cannabis, opioids and benzodiazepines use among older veterans.

Background: Older Veterans may experience injuries that result in chronic pain and mental health conditions. Given the increasing availability of medical cannabis, it is important to examine if it serves as a viable or undesirable form of care relative to existing approaches.Objectives: We compared cannabis, prescription opioids, and benzodiazepines use between older Veteran and non-Veterans, and identified outcomes of cannabis use among Veterans. Because of the physical and mental conditions experienced by older Veterans we expected Veterans to report higher use of opioids and benzodiazepines compared to non-Veterans.Methods: We collected surveys from individuals aged 60 and older enrolled in the Illinois Medical Cannabis Patient Program and conducted logistic regression to identify factors associated with cannabis, opioids and benzodiazepines use between Veterans (N = 514, 90.2% male) and Non-Veterans (N = 2758, 41.1% male) across biopsychosocial factors.Results: Both groups reported similar levels of pain, quality of life, social satisfaction, and sleep quality. Veterans were more likely to use cannabis for mental health conditions (p = <.001) while they reported lower use for pain-related conditions (p = <.001) than non-Veterans. Veterans were less likely to use opioids (p = .013) and benzodiazepines (p < .01) compared to non-Veterans. Veterans also reported desirable health outcomes of cannabis use for pain, sleep quality, health conditions, and quality of life.Conclusions: Our work provides insights for clinicians and policy makers to consider whether cannabis can be a viable option to reduce or replace opioid and benzodiazepine use by older Veterans with chronic physical and mental health conditions.

Association of obesity with arterial stiffness: The Multi-Ethnic Study of Atherosclerosis (MESA).

Arterial stiffness (AS) and obesity are recognized as important risk factors of cardiovascular disease (CVD). The purpose of this study was to investigate the relationship between AS and obesity. AS was defined as high augmentation index (AIx) and low elasticity (C1, large artery elasticity; C2, small artery elasticity) in participants enrolled in the Multi-Ethnic Study of Atherosclerosis at baseline. We compared AIx, C1, and C2 by body mass index (BMI) (< 25, 25-29.9, 30-39.9, ⩾ 40 kg/m2) and waist-hip ratio (WHR) (< 0.85, 0.85-0.99, ⩾ 1). The obesity-AS association was tested across 10-year age intervals. Among 6177 participants (62 ± 10 years old, 52% female), a significant inverse relationship was observed between obesity and AS. After adjustments for CVD risk factors, participants with a BMI > 40 kg/m2 had 5.4% lower AIx (mean difference [Δ] = -0.82%; 95% CI: -1.10, -0.53), 15.4% higher C1 (Δ = 1.66 mL/mmHg ×10; 95% CI: 1.00, 2.33), and 40.2% higher C2 (Δ = 1.49 mL/mmHg ×100; 95% CI: 1.15, 1.83) compared to those with a BMI < 25 kg/m2 (all p for trend < 0.001). Participants with a WHR ⩾ 1 had 5.6% higher C1 (∆ = 0.92 mL/mmHg ×10; 95% CI: 0.47, 1.37) compared to those with a WHR < 0.85. The WHR had a significant interaction with age on AIx and C2, but not with BMI; the inverse relationships of the WHR with AIx and C2 were observed only in participants < 55 years between the normal (WHR < 0.85) and the overweight (0.85 ⩽ WHR < 0.99) groups. Different associations of WHR and BMI with arterial stiffness among older adults should be further investigated.

A comparison of the effect of weather and climate on emergency department visitation in Roanoke and Charlottesville, Virginia.

Compared with mortality, the impact of weather and climate on human morbidity is less well understood, especially in the cold season. We examined the relationships between weather and emergency department (ED) visitation at hospitals in Roanoke and Charlottesville, Virginia, two locations with similar climates and population demographic profiles. Using patient-level data obtained from electronic medical records, each patient who visited the ED was linked to that day's weather from one of 8 weather stations in the region based on each patient's ZIP code of residence. The resulting 2010-2017 daily ED visit time series were examined using a distributed lag non-linear model to account for the concurrent and lagged effects of weather. Total ED visits were modeled separately for each location along with subsets based on gender, race, and age. The relationship between the relative risk of ED visitation and temperature or apparent temperature over lags of one week was positive and approximately linear at both locations. The relative risk increased about 5% on warm, humid days in both cities (lag 0 or lag 1). Cold conditions had a protective effect, with up to a 15% decline on cold days, but ED visits increased by 4% from 2 to 5 days after the cold event. The effect of thermal extremes tended to be larger for non-whites and the elderly, and there was some evidence of a greater lagged response for non-whites in Roanoke. Females in Roanoke were more impacted by winter cold conditions than males, who were more likely to show a lagged response at high temperatures. In Charlottesville, males sought ED attention at lower temperatures than did females. The similarities in the ED response patterns between these two hospitals suggest that certain aspects of the response may be generalizable to other locations that have similar climates and demographic profiles.

Obstructive sleep apnea and electrocardiographic P-wave morphology.

INTRODUCTION: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder with important cardiovascular implications. Left atrial abnormality can be identified by electrocardiographic P-wave morphology and is considered an important risk for atrial fibrillation (AF) and stroke, both of which have been associated with OSA. We hypothesized that severity of OSA would be associated with more abnormal electrocardiographic P-wave morphology as indicated by P-wave terminal force in V1 (PTFV1 ) and P-wave area in V1 (PWAV1 ). METHODS: Patients who underwent clinically indicated polysomnography and had 12-lead ECG were identified through medical record review. Logistic regression was used to determine the associations between the measures of OSA severity (apnea hypopnea index [AHI] and mean nocturnal oxygen [O2 ] saturation) and abnormal PTFV1 and PWAV1 (defined by >75% percentile value of the studied cohort) adjusting for age, sex, body mass index, and hypertension. RESULTS: A total of 261 patients (mean age: 57 years old, male: 52%) were included in the study. Multivariate analysis showed that AHI was associated with abnormal PTFV1 (>7,280 µV ms) and PWAV1 (>1,000 µV ms; OR: 1.5; 95% CI [1.1, 2.0], p = 0.008; OR: 1.5 [1.1, 2.1], p = 0.005 per 1 SD increase in AHI, respectively). Mean O2 saturation was associated with abnormal PWAV1 (OR: 0.72 [0.54, 0.98], p = 0.03). Results remained unchanged after excluding patients taking AV nodal blocking agents. CONCLUSION: In a sleep clinic cohort, there was significant association between OSA severity and ECG-defined left atrial abnormality.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

Patient flow in the emergency department: a classification and analysis of admission process policies.

STUDY OBJECTIVE: We investigate the effect of admission process policies on patient flow in the emergency department (ED). METHODS: We surveyed an advisory panel group to determine approaches to admission process policies and classified them as admission decision is made by the team of providers (attending physicians, residents, physician extenders) (type 1) or attending physicians (type 2) on the admitting service, team of providers (type 3), or attending physicians (type 4) in the ED. We developed discrete-event simulation models of patient flow to evaluate the potential effect of the 4 basic policy types and 2 hybrid types, referred to as triage attending physician consultation and remote collaborative consultation on key performance measures. RESULTS: Compared with the current admission process policy (type 1), the alternatives were all effective in reducing the length of stay of admitted patients by 14% to 26%. In other words, patients may spend 1.4 to 2.5 hours fewer on average in the ED before being admitted to internal medicine under a new admission process policy. The improved flow of admitted patients decreased both the ED length of stay of discharged patients and the overall length of stay by up to 5% and 6.4%, respectively. These results are framed in context of teaching mission and physician experience. CONCLUSION: An efficient admission process can reduce waiting times for both admitted and discharged ED patients. This study contributed to demonstrating the potential value of leveraging admission process policies and developing a framework for pursuing these policies.

A sampling strategy for longitudinal and cross-sectional analyses using a large national claims database.

Importance: The United States (US) Medicare claims files are valuable sources of national healthcare utilization data with over 45 million beneficiaries each year. Due to their massive sizes and costs involved in obtaining the data, a method of randomly drawing a representative sample for retrospective cohort studies with multi-year follow-up is not well-documented. Objective: To present a method to construct longitudinal patient samples from Medicare claims files that are representative of Medicare populations each year. Design: Retrospective cohort and cross-sectional designs. Participants: US Medicare beneficiaries with diabetes over a 10-year period. Methods: Medicare Master Beneficiary Summary Files were used to identify eligible patients for each year in over a 10-year period. We targeted a sample of ~900,000 patients per year. The first year's sample is stratified by county and race/ethnicity (white vs. minority), and targeted at least 250 patients in each stratum with the remaining sample allocated proportional to county population size with oversampling of minorities. Patients who were alive, did not move between counties, and stayed enrolled in Medicare fee-for-service (FFS) were retained in the sample for subsequent years. Non-retained patients (those who died or were dropped from Medicare) were replaced with a sample of patients in their first year of Medicare FFS eligibility or patients who moved into a sampled county during the previous year. Results: The resulting sample contains an average of 899,266 ± 408 patients each year over the 10-year study period and closely matches population demographics and chronic conditions. For all years in the sample, the weighted average sample age and the population average age differ by <0.01 years; the proportion white is within 0.01%; and the proportion female is within 0.08%. Rates of 21 comorbidities estimated from the samples for all 10 years were within 0.12% of the population rates. Longitudinal cohorts based on samples also closely resembled the cohorts based on populations remaining after 5- and 10-year follow-up. Conclusions and relevance: This sampling strategy can be easily adapted to other projects that require random samples of Medicare beneficiaries or other national claims files for longitudinal follow-up with possible oversampling of sub-populations.

Role of Interleukin(IL)-6 in NK Activity to Hypoxic-Induced Highly Invasive Hepatocellular Carcinoma(HCC) Cells.

Natural killer (NK) cell dysfunctions against hepatocellular carcinoma (HCC) in a hypoxic environment. Many solid tumors are present in a hypoxic condition, which changes the effector function of various immune cells. The transcription of hypoxic-inducible factors (HIFs) in cancer cells make it possible to adapt to their hypoxic environment and to escape the immune surveillance of NK cells. Recently, the correlation between the transcription of HIF-1α and pro-inflammatory cytokines has been reported. Interleukin (IL)-6 is higher in cancers with a highly invasive ability, and is closely related to the metastasis of cancers. This study showed that the expression of HIF-1α in HCC cells was associated with the presence of IL-6 in the environment of HCC-NK cells. Blocking of IL-6 by antibody in the HCC-NK interaction changed the production of several cytokines including TGF-ß, IL-1, IL-18 and IL-21. Interestingly, in a co-culture of HIF-1α-expressed HCC cells and NK cells, blocking of IL-6 increased the production of IL-21 in their supernatants. In addition, the absence of IL-6 significantly enhanced the cytotoxic ability and the expression of the activating receptors (NKG2D, NKp44, and NKG2C) in NK cells to HIF-1α-expressed HCC cells. These effects might be made by the decreased expression of HIF-1α in HCC cells through the inhibited phosphorylation of STAT3. In conclusion, the absence of IL-6 in the interaction of HIF-1α-expressed HCC cells and NK cells could enhance the antitumor activity of NK cells to HCC cells.

Weeding Out the Stigma: Older Veterans in Illinois Share Their Experiences Using Medical Cannabis.

Many U.S. Veterans are using cannabis for medical purposes. Modern research findings continue to point to medical cannabis as a potentially effective alternative to prescription medications for treating a range of medical conditions. While research exists on the use levels of cannabis, limited research can be found on the perceived stigma of using cannabis, especially among older Veterans. We surveyed 121 older U.S. Veterans who were enrolled in the Illinois Medical Cannabis Patient Program during Fall 2020. We then used maximum variation sampling to select a subset of 32 Veterans to partake in a phone interview. Two researchers conducted and qualitatively coded 30-minute audiotaped semi-structured interviews. Interview topics included (1) the use of cannabis, opioids, and benzodiazepines; (2) interactions with medical providers; (3) stigma regarding cannabis use; and (4) educational materials. We share findings from stigma. We identified three themes: (1) stereotypes regarding people who use cannabis, (2) media portrayal of cannabis users, and (3) hesitation in disclosing cannabis use. Stigma creates situations in which older Veterans may be hesitant to disclose their use of cannabis with physicians and friends/family, which can be dangerous and socially isolating. Additional research is needed to expand upon our findings with more generalizable methods.

Offering an Alternative to Persons with Chronic Pain: How Access to Cannabis May Provide an Off-Ramp from Undesired Prescription Opioid Use.

Background: Chronic pain (CP) is experienced by as many as 50 million Americans and can negatively impact physical and mental health. Prescribing opioids is the most common approach to address moderate to severe CP though these potent analgesics are associated with a significant number of side effects. One alternative some Americans are turning to for CP management is cannabis. In addition to serving as an alternative, many individuals with CP use cannabis in addition to using prescription opioids. This study examined individuals with CP who enrolled in the state of Illinois' opioid diversion program, the Opioid Alternative Pilot Program (OAPP), which offers individuals aged 21 and older a separate pathway to access medical cannabis if they have or could receive a prescription for opioids as certified by a licensed physician. Methods: Cross-sectional survey data were collected from 450 participants. We described participants and compared those who use only cannabis with those who use cannabis and opioids. Results: While 16% of the respondents were cannabis-only users, 84% of the respondents were co-users of opioids and cannabis. Both groups considered opioid use risky (100% cannabis-only, 89% co-users,). The majority (73%) of respondents sought to completely stop or never start using opioids for CP. Cannabis-only users reported lower levels of pain compared to co-users. Co-users (85%) were more likely to have their routine provider as a cannabis certifying physician than cannabis-only users (69%). Conclusion: With increasing clinical evidence, legalization and acceptance, researchers should continue to examine how cannabis may be a viable alternative to reduce the risk of prescription opioid side effects, misuse, or dependence. Our findings also inform health care providers and state policymakers who increasingly are being asked to consider how cannabis may reduce the potential for harmful outcomes among persons with CP who use prescription opioids.

Characteristics of Cannabis and Opioid Users Among Older U.S. Veterans and Their Health Outcomes: A Longitudinal Perspective.

Medical cannabis use among U.S. Veterans has continued to rise. However, data on cannabis use by older Veterans is generally less available. This study aims to understand the characteristics of older Veterans who enrolled in the Medical Cannabis Patient Program in Illinois and analyze their health outcomes and co-use of cannabis and opioids using longitudinal survey data. Overall, participants reported positive outcomes for pain, sleep, and emotional problems because of cannabis use in two survey periods. Approximately, 62% and 85% respondents reported no change in memory and falls, respectively, with only 3% and 1% reporting a negative outcome for the conditions in both surveys. About 20.4% of those who indicated cannabis use only in the initial survey started to co-use opioids in the follow-up survey, while 44.1% of those who indicated the use of both substances in the initial survey reported no longer using opioids in the follow-up survey. However, these changes were not statistically significant (α=0.05). The logistic regression showed that both clinical and contextual factors affected co-use. In conclusion, older U.S. Veterans may be using cannabis to alleviate their pain and other chronic conditions. More research is needed to understand the effect of cannabis use on reducing or substituting opioids.

Prevention, Practice, and Policy: Older US Veterans' Perspectives on Cannabis Use.

BACKGROUND AND OBJECTIVE: Veterans often struggle with disabling physical and mental health conditions that tend to worsen as they age. Current medications used to treat these conditions include opioids and benzodiazepines though they can have negative side effects. Looking for alternatives to these medications, many older Veterans use cannabis for medical purposes. We aimed to develop a deeper understanding of older Veterans' cannabis use. METHODS: We used maximum variation sampling to select 32 Veterans who had completed baseline and follow-up surveys to participate in semi-structured interviews. RESULTS: After applying a thematic analysis, results show older Veterans are using medical cannabis as a means of harm reduction as an adjunct or substitute for other medications and substances with limited guidance from their healthcare providers. Veterans also reported that there exists an inconsistency across the Veterans Health Administration system regarding the interpretation and application of cannabis policies. CONCLUSIONS: Drawing from these findings, we explore medical cannabis as a harm reduction technique and discuss how a lack of physician engagement and current Veterans Health Administration policies discourage older Veterans from discussing and potentially benefiting from the use of medical cannabis.

Charge-Transfer-Induced Self-Assembly of Doped Conjugated Block Copolymer Nanofibers.

Here, we report charge-transfer-driven self-assembly of conjugated block copolymers (BCP) into highly doped conjugated polymer nanofibers. The ground-state integer charge transfer (ICT) between a BCP composed of poly(3-hexylthiophene) and poly(ethylene oxide) (P3HT-b-PEO) and electron-deficient 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) induced spontaneous self-assembly of the donor and the acceptor into well-defined one-dimensional nanofibers. The presence of the PEO block plays an important role for the self-assembly by providing a polar environment that can stabilize nanoscale charge transfer (CT) assemblies. The doped nanofibers were responsive to various external stimuli such as heat, chemical, and light and exhibited efficient photothermal properties in the near-IR region. The CT-driven BCP self-assembly reported here provides a new platform for the fabrication of highly doped semiconductor nanostructures.

Regional and racial disparities in major amputation rates among medicare beneficiaries with diabetes: a retrospective study in the southeastern USA.

Objective: While rates for non-traumatic lower extremity amputations (LEA) have been declining, concerns exist over disparities. Our objectives are to track major LEA (MLEA) rates over time among Medicare beneficiaries residing in a high diabetes prevalence region in the southeastern USA (the diabetes belt) and surrounding areas. Methods: We used Medicare claims files for ~900 000 fee-for-service beneficiaries aged ≥65 years in 2006-2015 to track MLEA rates per 1000 patients with diabetes. We additionally conducted a cross-sectional analysis of data for 2015 to compare regional and racial disparities in major amputation risks after adjusting for demographic, socioeconomic, access-to-care and foot complications and other health factors. The Centers for Disease Control and Prevention defined the diabetes belt as 644 counties across Appalachian and southeastern US counties with high prevalence. Results: MLEA rates were 3.9 per 1000 in the Belt compared with 2.8 in the surrounding counties in 2006 and decreased to 2.3 and 1.6 in 2015. Non-Hispanic black patients had 8.5 and 6.9 MLEAs per 1000 in 2006 and 4.8 and 3.5 in 2015 in the Belt and surrounding counties, respectively, while the rates were similar for non-Hispanic white patients in the two areas. Although amputation rates declined rapidly in both areas, non-Hispanic black patients in the Belt consistently had >3 times higher rates than non-Hispanic whites in the Belt. After adjusting for patient demographics, foot complications and healthcare access, non-Hispanic blacks in the Belt had about twice higher odds of MLEAs compared with non-Hispanic whites in the surrounding areas. Discussion: Our data show persistent disparities in major amputation rates between the diabetes belt and surrounding counties. Racial disparities were much larger in the Belt. Targeted policies to prevent MLEAs among non-Hispanic black patients are needed to reduce persistent disparities in the Belt.

Derivation of a Unique, Algorithm-Based Approach to Cancer Patient Navigator Workload Management.

PURPOSE: Cancer patient navigators (CPNs) can decrease the time from diagnosis to treatment, but workloads vary widely, which may lead to burnout and less optimal navigation. Current practice for patient distribution among CPNs at our institution approximates random distribution. A literature search did not uncover previous reports of an automated algorithm to distribute patients to CPNs. We sought to develop an automated algorithm to fairly distribute new patients among CPNs specializing in the same cancer type(s) and assess its performance through simulation on a retrospective data set. METHODS: Using a 3-year data set, a proxy for CPN work was identified and multiple models were developed to predict the upcoming week's workload for each patient. An XGBoost-based predictor was retained on the basis of its superior performance. A distribution model was developed to fairly distribute new patients among CPNs within a specialty on the basis of predicted work needed. The predicted work included the week's predicted workload from a CPN's existing patients plus that of newly distributed patients to the CPN. Resulting workload unfairness was compared between predictor-informed and random distribution. RESULTS: Predictor-informed distribution significantly outperformed random distribution for equalizing weekly workloads across CPNs within a specialty. CONCLUSION: This derivation work demonstrates the feasibility of an automated model to distribute new patients more fairly than random assignment (with unfairness assessed using a workload proxy). Improved workload management may help reduce CPN burnout and improve navigation assistance for patients with cancer.