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Abstract Guideline concordant opioid therapy is a key part of the concerted effort to address the opioid crisis in the United States. The study aimed to compare the rates of guideline concordant care between veterans who solely used VA services (mono users) and veterans who used both VA services and community care (dual-system users). We used electronic health record data from the Washington DC and Baltimore VA Medical Centers from 2015 to 2019. We provided descriptive statistics as well as generalized estimating equations models to find associations between mono vs. dual-system users and each guideline outcome, controlling for demographic factors and comorbid conditions. The study found that overall rates of guideline concordant care were high in both mono and dual-system users with over 90% adherence rates for the majority of recommendations. However, there were variations in adherence to specific guidelines, with urine drug screening at initiation being the least commonly followed recommendation (8.9% of mono-user opioid initiators and 11.2% of dual-user initiators). This study also found that there was no consistent pattern of higher guideline adherence in mono vs. dual-system users but did show that through the course of this study (2015–2019) overall rates of guideline concordance increased. Future research will explore additional guideline recommendations and potential coordination issues among dual-system users.

BackgroundThe COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic. ObjectiveWe studied the impact of the COVID-19 pandemic on in-person health care usage and telehealth adoption across chronic diseases to understand differences in telehealth adoption across disease cohorts and patient demographics (such as the Social Vulnerability Index [SVI]). MethodsWe conducted a retrospective cohort study of 6 different disease cohorts (anxiety: n=67,578; depression: n=45,570; diabetes: n=81,885; kidney failure: n=29,284; heart failure: n=21,152; and cancer: n=35,460). We used summary statistics to characterize changes in usage and regression analysis to study how patient characteristics relate to in-person health care and telehealth adoption and usage during the first 12 months of the pandemic. ResultsWe observed a reduction in in-person health care usage across disease cohorts (ranging from 10% to 24%). For most diseases we study, telehealth appointments offset the reduction in in-person visits. Furthermore, for anxiety and depression, the increase in telehealth usage exceeds the reduction in in-person visits (by up to 5%). We observed that younger patients and men have higher telehealth usage after accounting for other covariates. Patients from higher SVI areas are less likely to use telehealth; however, if they do, they have a higher number of telehealth visits, after accounting for other covariates. ConclusionsThe COVID-19 pandemic affected health care usage across diseases, and the role of telehealth in replacing in-person visits varies by disease cohort. Understanding these differences can inform current practices and provides opportunities to further guide modalities of in-person and telehealth visits. Critically, further study is needed to understand barriers to telehealth service usage for patients in higher SVI areas. A better understanding of the role of social determinants of health may lead to more support for patients and help individual health care providers improve access to care for patients with chronic conditions.

BackgroundCardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions. ObjectiveThis study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes. MethodsRetrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm’s learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR. ResultsIn total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days. ConclusionsThis study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities.

IntroductionEfforts to achieve opioid guideline concordant care may be undermined when patients access multiple opioid prescription sources. Limited data are available on the impact of dual-system sources of care on receipt of opioid medications.ObjectiveWe examined whether dual-system use was associated with increased rates of new opioid prescriptions, continued opioid prescriptions and diagnoses of opioid use disorder (OUD). We hypothesized that dual-system use would be associated with increased odds for each outcome.MethodsThis retrospective cohort study was conducted using Veterans Administration (VA) data from two facilities from 2015 to 2019, and included active patients, defined as Veterans who had at least one encounter in a calendar year (2015–2019). Dual-system use was defined as receipt of VA care as well as VA payment for community care (non-VA) services. Mono users were defined as those who only received VA services. There were 77,225 dual-system users, and 442,824 mono users. Outcomes were three binary measures: new opioid prescription, continued opioid prescription (i.e., received an additional opioid prescription), and OUD diagnosis (during the calendar year). We conducted a multivariate logistic regression accounting for the repeated observations on patient and intra-class correlations within patients.ResultsDual-system users were significantly younger than mono users, more likely to be women, and less likely to report white race. In adjusted models, dual-system users were significantly more likely to receive a new opioid prescription during the observation period [Odds ratio (OR) = 1.85, 95% confidence interval (CI) 1.76–1.93], continue prescriptions (OR = 1.24, CI 1.22–1.27), and to receive an OUD diagnosis (OR = 1.20, CI 1.14–1.27).DiscussionThe prevalence of opioid prescriptions has been declining in the US healthcare systems including VA, yet the prevalence of OUD has not been declining at the same rate. One potential problem is that detailed notes from non-VA visits are not immediately available to VA clinicians, and information about VA care is not readily available to non-VA sources. One implication of our findings is that better health system coordination is needed. Even though care was paid for by the VA and presumably closely monitored, dual-system users were more likely to have new and continued opioid prescriptions.

Abstract Objectives The study utilized a cross‐sectional survey to determine the short‐term effects of the COVID‐19 pandemic on dental care practices. The authors hypothesized that the effects of the pandemic would indicate differences based on the ethnicity of the participating dentist. Materials and Methods The survey was available online between June 1, 2020 and July 10, 2020, a period when many dental offices remained closed, and for the most part, unable to provide non‐emergency dental care. The link to the survey was made available to dentists through outreach to several national dental organizations. Descriptive statistics summarized the characteristics of the entire sample and Fisher's exact test was used to examine respondents' answers stratified by ethnicity using frequencies and percentages. Results All ethnic groups reported decreased revenue and African American dentists were the least likely to report a decrease in revenue compared to White and Other ethnic groups (84.2%, 87.2% and 92.9%). African American dentists were the most likely to report willingness to contribute to a task force to address the new challenges resulting from COVID‐19 when compared to White and Other ethnic groups (46.4%, 18.8%, and 29.6%, respectively). African American dentists were more likely to indicate a need for a stronger connection to academic programs as compared to White or Other dentists in order to address current and future challenges (12.3%, 0.0%, and 9.1%). Conclusion The COVID‐19 pandemic has affected dental practices differently, highlighting racial disparities, and strategies that factor in the race or ethnicity of the dentist and the communities in which they practice need to be considered to ensure that underserved communities receive needed resources.

BackgroundHealth care data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes. However, the differences that exist in each individual’s health records, combined with the lack of health data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. Although these problems exist throughout health care, they are especially prevalent within maternal health and exacerbate the maternal morbidity and mortality crisis in the United States. ObjectiveThis study aims to demonstrate that patient records extracted from the electronic health records (EHRs) of a large tertiary health care system can be made actionable for the goal of effectively using ML to identify maternal cardiovascular risk before evidence of diagnosis or intervention within the patient’s record. Maternal patient records were extracted from the EHRs of a large tertiary health care system and made into patient-specific, complete data sets through a systematic method. MethodsWe outline the effort that was required to define the specifications of the computational systems, the data set, and access to relevant systems, while ensuring that data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for their use by a proprietary risk stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. ResultsPatient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients. ConclusionsUpon acquiring data, including their concatenation, anonymization, and normalization across multiple EHRs, the use of an ML-based tool can provide early identification of cardiovascular risk in pregnant patients.

Background: Per- and polyfluoroalkyl substances (PFAS) emissions from a plastic coating industrial source in southern New Hampshire (NH) have contaminated at least 65 square miles of drinking water. Prior research indicates that high levels of PFAS are associated with a variety of adverse health outcomes, including an increased risk of cancer. Reports indicate that mean blood serum levels of perfluorooctanoic acid (PFOA), one type of PFAS, in residents of the exposed community are more than 2 times greater than the mean blood serum level in the US. Merrimack public water supply customers also have higher average blood levels of perfluorooctane sulfonic acid (PFOS) and perfluorohexane sulfonic acid (PFHxS) than the time—matched US average. A 2018 report concludes that the incidence rate of cancer in Merrimack does not exceed the incidence rate of cancer in NH in general. However, prior reporting on the risk of cancer in Merrimack is compared only to a state-wide metric influenced by the Merrimack cancer incidence. Methods: Our ecological study compared the risk in Merrimack, NH residents for 24 types of cancer between 2005 and 2014, targeted in a previous study, and all-cause cancers, to US national cancer rates and cancer rates in demographically similar towns in New England. Four New England “unexposed towns” were chosen based on demographic similarity to Merrimack, with no documented PFAS exposure in water supplies. We utilized unadjusted logistical regression to approximate risk ratios (RR) and 95% confidence intervals (CI) assessing the risk of cancer in Merrimack NH to each of the 4 comparator communities, the pooled comparator variable, and national average incidence. Results: Residents of Merrimack, NH experienced a significantly higher risk of thyroid cancer (RR = 1.47, 95% CI 1.12-1.93), bladder cancer (RR = 1.45, 95% CI 1.17-1.81), esophageal cancer (RR = 1.71, 95% CI 1.1-2.65), and mesothelioma (RR = 2.41, 95% CI 1.09-5.34), compared to national averages. Our work also suggests that Merrimack residents experienced a significantly higher risk of thyroid cancer (RR = 1.69, 95% CI 1.19-2.39), and prostate cancer (RR = 1.36, 95% CI 1.15, 1.6) compared with similarly exposed New England communities. Our results indicate that residents of Merrimack may also have a significantly lower risk of some site-specific cancers compared to national averages, including lower risk of prostate cancer (RR = 0.57, 95% CI 0.5-0.66), female breast cancer (RR = 0.60, 95% CI 0.52-0.68), ovarian cancer (RR = 0.52, 95% CI 0.33-0.84) and cervical cancer (RR = 0.29, 95% CI 0.12-0.69). Conclusion: Merrimack residents experienced a significantly higher risk of at least 4 types of cancer over 10 years between 2005 and 2014. Merrimack is a community with documented PFAS contamination of drinking water in public and private water sources. Results indicate that further research is warranted to elucidate if southern NH residents experience increased risk for various types of cancer due to exposure to PFAS contamination.

BackgroundHeart failure (HF) is a serious health condition, associated with high health care costs, and poor outcomes. Patient empowerment and self-care are a key component of successful HF management. The emergence of telehealth may enable providers to remotely monitor patients' statuses, support adherence to medical guidelines, improve patient wellbeing, and promote daily awareness of overall patients' health.ObjectiveTo assess the feasibility of a voice activated technology for monitoring of HF patients, and its impact on HF clinical outcomes and health care utilization.MethodsWe conducted a randomized clinical trial; ambulatory HF patients were randomized to voice activated technology or standard of care (SOC) for 90 days. The system developed for this study monitored patient symptoms using a daily survey and alerted healthcare providers of pre-determined reported symptoms of worsening HF. We used summary statistics and descriptive visualizations to study the alerts generated by the technology and to healthcare utilization outcomes.ResultsThe average age of patients was 54 years, the majority were Black and 45% were women. Almost all participants had an annual income below $50,000. Baseline characteristics were not statistically significantly different between the two arms. The technical infrastructure was successfully set up and two thirds of the invited study participants interacted with the technology. Patients reported favorable perception and high comfort level with the use of voice activated technology. The responses from the participants varied widely and higher perceived symptom burden was not associated with hospitalization on qualitative assessment of the data visualization plot. Among patients randomized to the voice activated technology arm, there was one HF emergency department (ED) visit and 2 HF hospitalizations; there were no events in the SOC arm.ConclusionsThis study demonstrates the feasibility of remote symptom monitoring of HF patients using voice activated technology. The varying HF severity and the wide range of patient responses to the technology indicate that personalized technological approaches are needed to capture the full benefit of the technology. The differences in health care utilization between the two arms call for further study into the impact of remote monitoring on health care utilization and patients' wellbeing.

Abstract Background Programmed cell death ligand-1 (PD-L1) expression on tumor cells (TCs) is associated with improved survival in patients with head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy, although its role as a prognostic factor is controversial. This study investigates whether tumoral expression of PD-L1 is a prognostic marker in patients with recurrent and/or metastatic (R/M) HNSCC treated with standard chemotherapy. Methods This retrospective, multicenter, noninterventional study assessed PD-L1 expression on archival R/M HNSCC tissue samples using the VENTANA PD-L1 (SP263) Assay. PD-L1 high was defined as PD-L1 staining of ≥ 25% TC, with exploratory scoring at TC ≥ 10% and TC ≥ 50%. The primary objective of this study was to estimate the prognostic value of PD-L1 status in terms of overall survival (OS) in patients with R/M HNSCC. Results 412 patients (median age, 62.0 years; 79.9% male; 88.2% Caucasian) were included from 19 sites in seven countries. 132 patients (32.0%) had TC ≥ 25% PD-L1 expression; 199 patients (48.3%) and 85 patients (20.6%) had TC ≥ 10% and ≥ 50%, respectively. OS did not differ significantly across PD-L1 expression (at TC ≥ 25% cutoff median OS: 8.2 months vs TC

We examined relationships between thyroid hormone (TH) metabolites in humans by measuring 3,5-diiodothyronine (3,5-T2) and 3-iodothyronamine (3-T1AM) levels after liothyronine administration. In secondary analyses, we measured 3,5-T2 and 3-T1AM concentrations in stored samples from two clinical trials. In 12 healthy volunteers, THs and metabolites were documented for 96 h after a single dose of 50 mcg liothyronine. In 18 patients treated for hypothyroidism, levothyroxine therapy was replaced by daily dosing of 30–45 mcg liothyronine. Analytes were measured prior to the administration of liothyronine weekly for 6 weeks, and then hourly for 8 h after the last liothyronine dose of the study. In the weekly samples from the hypothyroid patients, 3,5-T2 was higher by 0.033 nmol/L with each mcg/dL increase in T4 and 0.24 nmol/L higher with each ng/dL increase in FT4 (p-values = 0.007, 0.0365). In hourly samples after the last study dose of liothyronine, patients with T3 values higher by one ng/dL had 3-T1AM values that were lower by 0.004 nmol/L (p-value = 0.0473); patients with 3,5-T2 higher by one nmol/L had 3-T1AM values higher by 2.45 nmol/L (p-value = 0.0044). The positive correlations between weekly trough levels of 3,5-T2 and T4/FT4 during liothyronine therapy may provide insight into 3,5-T2 production, possibly supporting some production of 3,5-T2 from endogenous T4, but not from exogenous liothyronine. In hourly sampling after liothyronine administration, the negative correlation between T3 levels and 3-T1AM, but positive correlation between 3,5-T2 levels and 3-T1AM could support the hypothesis that 3-T1AM production occurs via 3,5-T2 with negative regulation by T3.

Summary: Background: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Funding: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research.

Objective: Hypothyroid patients frequently request specific therapies from their physicians. Combination therapy is vigorously discussed at professional meetings. We wished to determine if physician prescribing patterns for hypothyroidism changed during 2017 after specific educational events.Methods: A survey addressing treatment of hypothyroidism was emailed to American Thyroid Association (ATA) members on three occasions in 2017. The Spring emails were sent prior to a satellite symposium addressing hypothyroidism, and prior to the annual Endocrine Society and ATA meetings; the December emails were sent after these events. Physicians were presented with thirteen theoretical patients and chose from 6 therapeutic options, including levothyroxine, synthetic combination therapy, thyroid extract, and liothyronine monotherapy. The patient scenarios successively incorporated factors potentially providing reasons for considering combination therapy. Multivariate repeated measures logistic regression analyses first examined effects of physician characteristics on prescribing the various therapies. Then, analyses also incorporated timing, by comparing prescribing patterns in February, March, and December.Results: In analyses of prescribing levothyroxine monotherapy vs. any T3 therapy, there was a trend of borderline significance (p = 0.053) for T3 therapy to be prescribed more in December compared with February-March combined. When multivariate analyses were performed controlling for time and physician characteristics, choice of therapy was only significantly affected by country of practice (OR 1.7, CI 1.3–2.2). Physician choice of therapies was also examined for the options of continuing (1) levothyroxine, vs. (2) increasing levothyroxine, (3) adding liothyronine either with or without levothyroxine reduction, or (4) replacing levothyroxine with desiccated thyroid extract or liothyronine. When multivariate analyses incorporating time and physician characteristics were performed, respondents in December (OR 1.5, CI 1.0–2.3) and those practicing in North America (OR 1.8, CI 1.2–2.6) were more likely to prescribe liothyronine.Conclusions: This survey shows that although current North American guidelines do not recommend combination therapy, such therapy is being prescribed more over time and is also more commonly prescribed in North America. It is possible our guidelines are failing to incorporate evidence that physicians are considering when prescribing combination therapy. Such evidence could include data about patient preferences, and this needs to be a focus of future studies.

We investigated the effects of transcranial alternating stimulation (tACS) in patients with insomnia. Nine patients with chronic insomnia underwent two in-laboratory polysomnography, 2 weeks apart, and were randomized to receive tACS either during the first or second study. The stimulation was applied simultaneously and bilaterally at F3/M1 and F4/M2 electrodes (0.75 mA, 0.75 Hz, 5-minute). Sleep onset latency and wake after sleep onset dropped on the stimulation night but they did not reach statistical significance; however, there were significant improvements in spontaneous and total arousals, sleep quality, quality of life, recall memory, sleep duration, sleep efficiency, and daytime sleepiness.

Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS). Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989-1991), 2 (1993-1995), and 3 (1998-1999) was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results.