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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.

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.

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.

Introduction Position dependent obstructive sleep apnea (PdOSA) is highly prevalent, but the implication of PdOSA in cardiovascular (CV) outcomes has not been studied. We hypothesized that PdOSA is associated with lower risk of future CV events than those without PdOSA. Methods We included patients who underwent diagnostic polysomnography (PSG) at a single academic center. The study included patients with ≥2 hours of total sleep time, 30 minutes in both supine and non-supine sleep, and OSA (apnea hypopnea index [AHI]≥5/hr). We defined PdOSA as either POSA (overall AHI≥5/hr accompanied by supine AHI (sAHI) at least twice as high as non-supine AHI (ns-AHI)), or ePOSA (POSA criteria + ns-AHI < 5/hr (i.e., normalized)). Blood pressure (BP) was derived by averaging multiple measures of clinic BP within 1 year of PSG. All patients were followed up to 11 years to identify incident CV events (including acute coronary syndrome, stroke, and new heart failure). We examined the association of PdOSA with CV events with a logistic regression, adjusting for age, sex, race, BMI, systolic BP, and OSA severity (mild [AHI 5-15] vs. moderate to severe [≥15]). Results A total of 1750 patients (mean age: 54 years, female 60%) were included. POSA and ePOSA were present in 47% and 23% of the cohort. Patients with either POSA or ePOSA were less obese and had less severe OSA compared with those without POSA and ePOSA. Patients with ePOSA had lower BP (systolic BP 128.8 vs 131.2 mmHg, p=0.003) than those without ePOSA. Over a median 8.3 years of follow-up, a total of 340 CV events occurred. In multivariable analysis, both POSA and ePOSA were associated with lower odds of future CV events (OR 0.8, 95% CI [0.64-0.99], p=0.044; OR 0.69 [0.52-0.89], p=0.0061, respectively). Analysis stratified by OSA severity showed that the ePOSA association was present only in those with mild OSA (OR 0.67 [0.48-0.94], p=0.021). Conclusion Among patients with OSA, PdOSA is associated with more favorable CV outcomes than those without PdOSA. The benefit appears to be limited to those with mild OSA. Future studies should compare the CV risks between PdOSA patients and those without OSA. Support (if any)

Background Despite remarkable achievements in ensuring health equity, racial/ethnic disparities in sleep still persist and are emerging as a major area of concern. Accumulating evidence has not yet been well characterized from a broad perspective. We conducted a scoping review of studies on sleep disparities by race/ethnicity to summarize characteristics of existing studies and identify evidence gaps. Methods We searched PubMed, CINAHL, PsycINFO, and Web of Science databases for studies of racial/ethnic disparities in sleep. Studies that met inclusion criteria were retrieved and organized in a data charting form by study design, sleep measuring methods, sleep features, and racial/ethnic comparisons. Results One hundred sixteen studies were included in this review. Most studies focused on disparities between Whites and Blacks. Disproportionately fewer studies examined disparities for Hispanic, Asian, and other racial/ethnic groups. Self-reported sleep was most frequently used. Sleep duration, overall sleep quality, and sleep disordered breathing were frequently studied, whereas other features including sleep efficiency, latency, continuity, and architecture were understudied, particularly in racial minority groups in the US. Current study findings on racial/ethnic disparities in most of sleep features is mixed and inconclusive. Conclusions This review identified significant evidence gaps in racial/ethnic disparities research on sleep. Our results suggest a need for more studies examining diverse sleep features using standardized and robust measuring methods for more valid comparisons of sleep health in diverse race/ethnicity groups.

Objective: Annual Wellness Visits (AWVs) are yearly visits for Medicare beneficiaries that include discussing preventive care needs and scheduling future preventive care services. Our prior work found that AWVs are associated with increased utilization of preventive care for diabetes patients, including uptake of foot exams. Our objective is to determine whether AWVs are associated with a reduced risk of major lower-extremity amputations (MLEAs) for patients in the Diabetes Belt. Methods: Our analysis used a sample of approximately 900,000 Medicare Fee-for-Service beneficiaries from 2015 that lived in the Diabetes Belt and surrounding areas. The Diabetes Belt is a region of 644 counties in the Appalachian and deep south areas of the US identified by the CDC in 20 that continues to have a high prevalence. We used multivariable logistic regression to examine the association between AWVs and MLEAs in 2015, adjusting for demographic factors, comorbidities, major risk factors including diabetic foot ulcers, neuropathy, peripheral vascular disease, and access to care factors. Results: The rate of MLEAs was significantly lower among patients in the Diabetes Belt who had an AWV in the last year. The odds of MLEAs were 36% lower in patients who used AWVs in the same year compared to those who did not, adjusting for all covariates. We also found that patients in the Diabetes Belt had about 27% higher odds of having an MLEA compared to those residing in the surrounding areas. Discussion: Patients who used AWVs may have had their diabetic foot examined sooner to prevent a major adverse outcome than those who did not. Policy makers should prioritize incentives for having patients participate in AWVs to help reduce MLEAs. Disclosure J.M.Lobo: n/a. S.Kim: None. H.Kang: None. M.Sohn: None. Funding NIH/NIDDK R01DK113295

Objective: Evidence-based preventive care services are crucial for people with diabetes to improve their health outcomes, but the utilization of the services among older adults is suboptimal. Our objective is to compare relative performance of the Diabetes Belt and surrounding counties in the uptake of preventive services among Medicare beneficiaries with diabetes given available health care resources in the counties. The Diabetes Belt includes 644 counties in the Appalachian and deep south areas of the US that have high diabetes prevalence. Methods: We used data envelopment analysis (DEA) to compute technical efficiency of counties for 2009-2015. Technical efficiency measures how many outputs a decision-making unit produces given a set of inputs. Inputs included the number of physicians and nurses per 1000 adults in the counties, and outputs included the number of foot exams, a1c tests, and microalbumin tests per 1000 Medicare beneficiaries with diabetes. The Diabetes Belt includes counties in the Appalachian and deep south areas of the US that have high diabetes prevalence. We performed t-tests to compare efficiency between the Diabetes Belt and surrounding counties. We excluded counties with 0 doctors or 0 nurses for >1 year in 2009-2015. Results: Of 1057 counties, 861 (496 in the Belt and 365 in surrounding areas) were included. During the study period, the average technical efficiency of the counties ranged from 79% to 83%, with only 2.- 3.4% of counties achieving 100% technical efficiency. Diabetes Belt counties had consistently lower efficiency than surrounding counties during the study period (p Discussion: Our results suggest that the degree of inefficiencies was higher in the Diabetes Belt compared to surrounding counties. Given relatively low available resources in these areas, more attention to improve diabetes preventive care is needed. Disclosure H.Kang: None. S.Kim: None. J.M.Lobo: n/a. M.Sohn: None. Funding National Institutes of Health (1R01DK113295-01A1)

Objective: Medicare beneficiaries are eligible to receive free Annual Wellness Visits (AWVs) for assessing preventive care needs, identifying care resources, and scheduling visits. We previously reported that AWVs are associated with increased use of preventive care for patients with diabetes. In this study, our objective is to conduct a case-control study to examine whether AWVs are associated more strongly with preventive care use in the Diabetes Belt than in surrounding areas. Methods: We used a sample of ∼900,000 Medicare Fee-for-Service beneficiaries who lived in the Diabetes Belt and surrounding areas in 2014 - 2015. We identified patients who have used an annual foot exam, eye exam, A1c test, and microalbuminuria (MA) test and those who did not in 2015. We used the dates of service receipt and the last day of the year (12/31) as the event date for users and non-users, respectively. We looked one full year before the event date to identify any AWVs. We used propensity-score stratification to estimate log odds of 4 preventive care use of annual foot exam, eye exam, A1c test, and MA test. Results: For all four services, AWV users had significantly higher preventive care use compared to non-users. In 3 preventive services, AWVs were more strongly associated with preventive care use in the Belt than in the surrounding areas (foot exam, ORs = 2.38 vs 2.16, p < 0.001; A1c test, ORs = 1.44 vs. 1.36, p = 0.002; MA test, ORs = 1.70 vs. 1.44, p < 0.001; eye exam, ORs 1.22 vs. 1.19, p=0.063) . Discussion: AWVs appear to have stronger effects on preventive care use for patients with diabetes in the more healthcare resource-poor Diabetes Belt than in the surrounding areas. AWVs may help to reduce disparities in the use of recommended preventive care for patients with diabetes. Disclosure M.Sohn: None. T.L.Mcmurry: None. H.Kang: None. S.Kim: None. J.M.Lobo: n/a. Funding NIDDK R01DK113295

Visit‐to‐visit blood pressure (BP) variability (BPV) is an independent risk factor of cardiovascular disease (CVD). Sleep architecture characterizes the distribution of different stages of sleep and may be important in CVD development. We examined the association between visit‐to‐visit BPV and sleep architecture using in‐lab polysomnographic data from 3,565 patients referred to an academic sleep center. BPV was calculated using the intra‐individual coefficient of variation of BP measures collected 12 months before the sleep study. We conducted multiple linear regression analyses to assess the association of systolic and diastolic BPV with sleep architecture—rapid eye movement (REM) and non‐rapid eye movement (NREM) sleep duration. Our results show that systolic BPV was inversely associated with REM sleep duration (p = .058). When patients were divided into tertile groups based on their BPV, those in the third tertile (highest variability) spent 2.7 fewer minutes in REM sleep than those in the first tertile (lowest variability, p = .032), after adjusting for covariates. We did not find an association of systolic BPV with other measures of sleep architecture. Diastolic BPV was not associated with sleep architecture either. In summary, our study showed that greater systolic BPV was associated with lower REM sleep duration. Future investigation is warranted to clarify the directionality, mechanism, and therapeutic implications.

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.

Background: Cannabis use today is the highest it has been in three decades, approaching 36.5% prevalence for past year use (Schulenberg et al., 2017). From a 2014 nationwide sample of Veterans over 18, approximately 9% reported past year cannabis use (Davis et al., 2018). It also showed that in states where medical cannabis was legal, 41% of Veterans who used cannabis in the past year reported doing so for medical purposes. Modern research findings continue to point to medical cannabis as a potentially effective alternative to prescription medications (i.e., opioids and benzodiazepines) for treating a broad range of medical conditions. Aims: The goal of our larger study was to develop a deeper understanding of cannabis use in US older Veterans (60 years +) who are using cannabis as a substitute or complement for opioids and/or benzodiazepines. While research exists on the use levels of cannabis, to our knowledge, limited research on the perceived stigma of using cannabis among older Veterans exists. For the current study, we sought to develop an understanding of stigma associated with older Veterans using cannabis. Methodology: We surveyed 121 older 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 who completed the initial online survey. From November 2020 to February 2021, two researchers conducted 30-minute audiotaped semi-structured interviews. Participants represented diversity regarding the age of cannabis initiation, type of cannabis user, military branch, type of healthcare provider, and race/ethnicity. Interview topics included (1) use of cannabis, opioids, and benzodiazepines, (2) interactions with medical providers, (3) stigma regarding cannabis use, and (4) educational materials for older Veterans. For the current study, we present findings from the third topic regarding stigma associated with using cannabis. The interviews were transcribed verbatim for data analysis purposes. Weekly meetings among two coders ensued to debrief on coding procedures, reflect on biases and interpretations, and reach consensus regarding coding discrepancies. The final codebook reached an 87% inter-rater reliability. Then, the two coders independently coded the transcripts and employed a rigorous thematic analysis approach using NVivo12 QSR. A narrative was woven together with exemplary quotes to illustrate major themes. Findings: We identified three stigma focused themes: (1) stereotypes regarding people who use cannabis, (2) hesitation of disclosing cannabis use with others, and (3) media portrayal (i.e., movies, television shows) of cannabis users. Implications: Stigma creates situations in which older Veterans are hesitant to disclose their use of cannabis with physicians and friends/family which can be dangerous and also socially isolating. Additionally, older Veterans may benefit from shared experiences about cannabis use for medical purposes, but this often does not occur. The empirically-based insights gained from this work have the potential to inform public health leaders, healthcare administrators, and public messaging regarding the use of medical cannabis. Additional research is needed to expand upon our findings with more generalizable methods and a representative sample of older Veterans.

Introduction:We describe and demonstrate an efficient method for assigning clinic days to urology providers in academic and large urology group practices given their numerous scheduling con...

OBJECTIVE To examine trends in uninsured rates between 2012 and 2016 among low-income adults aged RESEARCH DESIGN AND METHODS Data for 3,129 U.S. counties, obtained from the Small Area Health Insurance Estimates and Area Health Resources Files, were used to analyze trends in uninsured rates among populations with a household income ≤138% of the federal poverty level. Multivariable analysis adjusted for the percentage of county populations aged 50–64 years, the percentage of women, Distressed Communities Index value, and rurality. RESULTS In 2012, 39% of the population in the Diabetes Belt and 34% in non-Belt counties were uninsured (P < 0.001). In 2016 in states where Medicaid was expanded, uninsured rates declined rapidly to 13% in Diabetes Belt counties and to 15% in non-Belt counties. Adjusting for county demographic and economic factors, Medicaid expansion helped reduce uninsured rates by 12.3% in Diabetes Belt counties and by 4.9% in non-Belt counties. In 2016, uninsured rates were 15% higher for both Diabetes Belt and non-Belt counties in the nonexpansion states than in the expansion states. CONCLUSIONS ACA-driven Medicaid expansion was more significantly associated with reduced uninsured rates in Diabetes Belt than in non-Belt counties. Initial disparities in uninsured rates between Diabetes Belt and non-Belt counties have not existed since 2014 among expansion states. Future studies should examine whether and how Medicaid expansion may have contributed to an increase in the use of health services in order to prevent and treat diabetes in the Diabetes Belt.

For patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from the 2010 Behavioral Risk Factor Surveillance System. 1112 US counties were analyzed. Cluster analysis was used to place counties into three similar groups in terms of economic wellbeing and population characteristics. Group 1 consisted of metropolitan counties with prosperous or comfortable economic levels. Group 2 mostly consisted of non-metropolitan areas between distress and mid-tier levels, while Group 3 were mostly prosperous or comfortable counties in metropolitan areas. We used data enveopement analysis to assess efficiencies within each group. The majority of counties had modest efficiency in providing diabetes preventive care; 36 counties (57.1%), 345 counties (61.1%), and 263 counties (54.3%) were inefficient (efficiency scores

STUDY OBJECTIVES: Sleep quality in patients studied with laboratory-based polysomnography may differ from sleep quality in patients studied at home but remains clinically relevant and important to describe. We assessed objective sleep quality and explored factors associated with poor sleep in patients undergoing laboratory-based polysomnography. METHODS: We reviewed diagnostic polysomnography studies from a 10-year period at a single sleep center. Total sleep time (TST) and sleep efficiency (SE) were assessed as markers of sleep quality. Poor sleep was defined as TST ≤ 4 hours or SE ≤ 50%. Multivariable analysis was performed to determine associations between objective sleep quality as an outcome and multiple candidate predictors including age, sex, race, body mass index, comorbidities, severity of obstructive sleep apnea, and central nervous system medications. RESULTS: Among 4957 patients (age 53 ± 15 years), average TST and median SE were 5.8 hours and 79%, respectively. There were 556 (11%) and 406 (8%) patients who had poor sleep based on TST and SE, respectively. In multivariable analysis, those who were older (per 10 years: 1.48 [1.34, 1.63]), male (1.38 [1.14,1.68]), and had severe obstructive sleep apnea (1.76 [1.28, 2.43]) were more likely to have short sleep. Antidepressant use was associated with lower odds of short sleep (0.77 [0.59,1.00]). Older age (per 10 years: 1.48 [1.34, 1.62]), male sex (1.34 [1.07,1.68]), and severe obstructive sleep apnea (2.16 [1.47, 3.21]) were associated with higher odds of poor SE. CONCLUSIONS: We describe TST and SE from a single sleep center cohort. Multiple demographic characteristics were associated with poor objective sleep in patients during laboratory-based polysomnography. CITATION: Harrison EI, Roth RH, Lobo JM, et al. Sleep time and efficiency in patients undergoing laboratory-based polysomnography. J Clin Sleep Med. 2021;17(8):1591–1598.

Background: The Affordable Care Act created annual wellness visits (AWVs) for Medicare beneficiaries to have one yearly visit to primary care physicians and discuss preventive care for the upcoming year. We assessed whether AWVs were associated with increased use of these preventive services in Medicare patients with diabetes living in the Diabetes Belt and surrounding counties. Methods: We used claims files from a longitudinal sample of ∼1,000,000 Medicare patients with diabetes living in the diabetes belt or surrounding counties, covering years 2014-2015. The Diabetes Belt was identified by the CDC as 644 counties across Appalachian and southeastern counties in the US that had diabetes prevalence ≥ 11% in 2008. We used these data to examine associations between AWVs and preventive service use in the next year. We examined four preventive services recommended annually for patients with diabetes by the ADA, including A1c tests, foot exam, eye exam, and microalbuminuria (MA) tests. Results: Of all patients, only 19.6% used AWVs in 2015. Eye exams (61% vs. 56%), foot exams (92% vs. 83%), A1C tests (78% vs. 73%), and MA tests (42% vs. 31%) were more common among patients who had an AWV in the preceding year compared with those who did not. These differences remained after adjusting for patient demographics, comorbidities, county level medical resources, and geographic factors: eye exam (Odds Ratio 1.13, 95% CI 1.12-1.15); A1c Test (1.28, CI 1.26-1.31); Foot Exam (2.06, CI 2.01-2.11); MA Test (1.45, CI 1.42-1.47). Conclusions: This study shows that AWVs were significantly associated with increased recommended preventive care use among patients with diabetes living in and near the diabetes belt. The low rates of AWV utilization by patients with diabetes in and around the Diabetes Belt is concerning and should receive more policy attention. Disclosure T. L. Mcmurry: None. S. Kim: None. J. M. Lobo: None. H. Kang: None. M. Sohn: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK113295)

Objective: Rates for non-traumatic lower-extremity amputations (LEA) have been declining, but concerns exist over disparities in rates between races/ethnicities. Our objective is to track changes in major LEA (MLEA) rates before and after the Affordable Care Act (ACA) among Medicare beneficiaries residing in the Diabetes Belt compared to those in the surrounding areas. Methods: We used Medicare claims files for a sample of ~1 million Fee-for-Service beneficiaries in 2006 - 2015 to compute rates per 1000 patients with diabetes. The Diabetes Belt was identified by the CDC as 644 counties across Appalachian and southeastern counties in the US that had diabetes prevalence ≥ 11% in 2008. Results: MLEA rates for the Diabetes Belt and surrounding counties as well as rates for Non-Hispanic (NH) Black and NH White are in Figure. Patients in the Belt experienced one full MLEA more per 1000 patients than those in the surrounding area. Although amputation rates declined rapidly in both areas, NH blacks consistently had > 3 times higher rates than NH whites in the Belt. Trends did not show noticeable changes in MLEAs before and after the ACA. Discussion: Our data show persistent disparities in MLEA rates between the Diabetes Belt and surrounding counties. Racial disparities were much larger in the Belt. Targeted policies to prevent MLEAs among NH black patients are needed to reduce the disparities in this region. Disclosure J. M. Lobo: None. S. Kim: None. H. Kang: None. T. L. Mcmurry: None. M. Sohn: None. Funding National Institutes of Health (R01DK113295)

Objective: Preventive care has been shown to reduce both the incidence and progression of diabetic complications. American Diabetes Association treatment guidelines recommend annual preventive care such as annual doctor visits and A1c tests. Our objective is to compare utilization rates of four annually recommended preventive services for persons with diabetes in the Diabetes Belt and those in the surrounding area. Methods: We used 2015 Medicare claims data to identify preventive service utilization using ICD9/10 and CPT/HCPCS codes. Diabetes Belt consists of 644 counties in the Appalachian region and southeastern part of US that had diabetes prevalence over 11% in 2008 compared to ~8.5% in the rest of the country. Surrounding counties include 118 counties that are adjacent but not contiguous to the Diabetes Belt counties. Results: Patients in the Belt had significantly lower utilization than those in surrounding counties for all four preventive services: foot exam, 81% vs. 88%; eye exam, 53% vs. 58%; microalbuminuria test, 31% vs. 34%; one or more A1c tests, 73% vs. 74%. After adjusting for age, sex, and race/ethnicity, patients in the Diabetes Belt had 38%, 18%, 16%, and 11% lower odds of using foot exams, microalbuminuria tests, eye exams, and A1c tests, respectively, compared to those in the surrounding counties (p for all comparisons < 0.001). Even after adjusting for socioeconomic, access-to-care factors, and service availability, patients in the Diabetes Belt had consistently lower rates of utilization for these four services than surrounding counties. Conclusion: Patients with diabetes in the Diabetes Belt are doubly jeopardized for diabetic complications because of higher prevalence diabetes and lower preventive care utilization than surrounding counties. Health policy makers may want to pay more attention to the Diabetes Belt in reducing the regional differences in the utilization of these services. Disclosure M. Sohn: None. H. Kang: None. T. L. Mcmurry: None. S. Kim: None. J. M. Lobo: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK113295)

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 ...

Previous studies concerning older adults have focused on whether cannabis use leads to positive or negative outcomes. In this study, we identified clusters of negative health outcomes associated with medical cannabis use. In total, we examined eight health outcomes: pain, sleep, falls, memory, digestive issues, mental health conditions, exercise, and general productivity reported by 2,968 persons over 60 who participated in the Illinois Medical Cannabis Program. We used association analysis to simultaneously identify groups of negative outcomes reported by participants. The distribution of non-positive outcomes shows a bell-shaped curve: 1.4% of participants responded that cannabis use improved all outcomes, while 4.1% of participants answered that cannabis use did not. When looking at negative outcomes, 86% of participants reported none worsened, and 11% reported one of the outcomes was affected. Only a small fraction of the participants (3%) claimed more than one negative outcomes after cannabis use.

OBJECTIVE SBP variability may be a target for mitigating end-organ damage associated with vascular disease. We evaluated the relationship between increased SBP variability and risk of incident diabetic foot ulceration. METHODS Using a nested case-control design, we followed patients diagnosed with diabetes and treated within the US Department of Veterans Affairs Healthcare system for development of a diabetic foot ulcer (event) between 2006 and 2010. Each case was randomly matched to up to five controls based on age, sex, race/ethnicity, and calendar time. SBP variability was computed using at least three blood pressure measurements from the year preceding the event. The association between SBP variability and foot ulceration was examined using conditional logistic regression. Potential protective effects of calcium channel blockers, which blunt SBP variability, were also explored. RESULTS The study sample included 51 111 cases and 129 247 controls. Compared with those in quartile 1 (lowest variability), patients in quartiles 2-4 had higher adjusted odds ratios for diabetic foot ulcer development: 1.11 (95% CI 1.07-1.16), 1.20 (95% CI 1.15-1.25), 1.29 (95% CI 1.24-1.34) (P for trend

PURPOSE: Electronic health records (EHRs) have been mainly used to analyze bottlenecks in care processes of outpatient oncology clinics. However, EHR data lead to some limitations in understanding patient flow because they are manually entered and not updated in real time. Data generated from a real-time location system (RTLS) can supplement EHR data. This study aims to demonstrate how RTLS data combined with EHR data can be used to evaluate potential interventions to improve patient flow in an outpatient cancer center. METHODS: EHR and RTLS data obtained from a large cancer center in central Virginia were analyzed to estimate process times and determine the various patient paths patients follow during their visit for infusion. Using the input data, we developed a discrete-event simulation (DES) model and assessed 5 what-if scenarios involving changes in staff scheduling and care processes. RESULTS: Raw RTLS data including > 3.5 million observations were preprocessed to remove noise and extract meaningful information. The DES results showed that new nursing schedules for the infusion center and improved pharmacy processes have positive impacts on reducing patient waiting times by approximately 20% and overall length of stay by approximately 3.4% to 4.6%, compared with the current system. CONCLUSION: Combining EHR and RTLS data, we were able to capture dynamic aspects of patient flow more realistically. DES models that represent a complex system based on accurate input data can help decision making on determining operational changes to improve patient flow.

BackgroundWe present a method of randomly drawing a longitudinal sample of Medicare patients to 1) conduct longitudinal analysis of care use and outcomes over a ten-year follow-up; 2) provide a representative cross-sectional sample in each year during this time period; and 3) provide adequate precision for estimates in comparisons that involve minority patients at the county level. This method was applied to patients with diabetes in the Diabetes Belt (a region in the Appalachian and southern US with higher rates of diabetes) and surrounding counties.MethodsWe used the Medicare Master Beneficiary Summary Files (A/B/C/D and Chronic Conditions segments) to identify eligible patients for each year. We targeted a sample of just under 900,000 patients per year. The 2006 sample is stratified by county and white/minority status, and targeted at least 250 patients in each stratum with the remaining sample allocated proportional to county size with oversampling of the minority population. 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 were replaced with a sample of patients in their first year of Medicare FFS (e.g., new enrollees) or patients who moved into a sampled county during the previous year.ResultsThe resulting sample contains an average of 899,266 patients each year and closely matches population demographics and chronic conditions. For all years in the sample, the weighted sample average age and the population age differ by < 0.01 years; the proportion white is within 0.01%; and the proportion female is within 0.08%. No difference was statistically significant at the α = 0.05 level.ConclusionsThis carefully constructed survey sample will allow us to to perform longitudinal and cross-sectional analysis on healthcare utilization and outcomes. This sampling strategy can be easily adapted to other projects that require random samples of Medicare beneficiaries for longitudinal follow-up with possible oversampling of some sub-populations.