Continuum Halos in Nearby Galaxies -- an EVLA Survey (CHANG-ES) -- II: First Results on NGC 4631

We present the first results from the CHANG-ES survey, a new survey of 35 edge-on galaxies to search for both in-disk as well as extra-planar radio continuum emission. The motivation and science case for the survey are presented in a companion paper (Paper I). In this paper (Paper II), we outline the observations and data reduction steps required for wide-band calibration and mapping of EVLA data, including polarization, based on C-array test observations of NGC 4631. With modest on-source observing times (30 minutes at 1.5 GHz and 75 minutes at 6 GHz for the test data) we have achieved best rms noise levels of 22 and 3.5 $\mu$Jy beam$^{-1}$ at 1.5 GHz and 6 GHz, respectively. New disk-halo features have been detected, among them two at 1.5 GHz that appear as loops in projection. We present the first 1.5 GHz spectral index map of NGC 4631 to be formed from a single wide-band observation in a single array configuration. This map represents tangent slopes to the intensities within the band centered at 1.5 GHz, rather than fits across widely separated frequencies as has been done in the past and is also the highest spatial resolution spectral index map yet presented for this galaxy. The average spectral index in the disk is $\bar\alpha_{1.5 GHz}\,=\,-0.84\,\pm\,0.05$ indicating that the emission is largely non-thermal, but a small global thermal contribution is sufficient to explain a positive curvature term in the spectral index over the band. Two specific star forming regions have spectral indices that are consistent with thermal emission. Polarization results (uncorrected for internal Faraday rotation) are consistent with previous observations and also reveal some new features. On broad scales, we find strong support for the notion that magnetic fields constrain the X-ray emitting hot gas.Comment: Accepted to the Astronomical Journal, Version 2 changes: Added acknowledgement to NRA

A new role for primary care teams in the United States after “Obamacare:” Track and improve health insurance coverage rates

Maintaining continuous health insurance coverage is important. With recent expansions in access to coverage in the United States after “Obamacare,” primary care teams have a new role in helping to track and improve coverage rates and to provide outreach to patients. We describe efforts to longitudinally track health insurance rates using data from the electronic health record (EHR) of a primary care network and to use these data to support practice-based insurance outreach and assistance. Although we highlight a few examples from one network, we believe there is great potential for doing this type of work in a broad range of family medicine and community health clinics that provide continuity of care. By partnering with researchers through practice-based research networks and other similar collaboratives, primary care practices can greatly expand the use of EHR data and EHR-based tools targeting improvements in health insurance and quality health care

Prescription Opioid Use Patterns, Use Disorder Diagnoses, and Addiction Treatment Receipt after the 2014 Medicaid Expansion in Oregon

Background/Aims: Evidence suggests Medicaid beneficiaries in the USA are prescribed opioids more frequently than are people who are privately‐insured, but little is known about opioid prescribing patterns among Medicaid enrollees who gained coverage via the Affordable Care Act Medicaid expansions. This study compared the prevalence of receipt of opioid prescriptions and opioid‐use‐disorder (OUD), along with time from OUD diagnosis to medication‐assisted treatment (MAT) receipt between Oregon residents who had been continuously insured by Medicaid, were newly insured after Medicaid expansion in 2014, or returned to Medicaid coverage after expansion. Design: Cross‐sectional study using inverse‐propensity weights to adjust for differences among insurance groups. Setting: Oregon. Participants: 225,295 Oregon Medicaid adult beneficiaries insured 2014‐2015 and either: 1) newly enrolled, 2) returning in 2014 after a \u3e 12‐month gap, or 3) continuously insured between 2013 and 2015. We excluded patients in hospice care or with cancer diagnoses. Measurements: Any opioid dispensed, chronic (≥90‐day) and high dose (≥ 90 daily morphine milligram equivalence) opioid use, documented OUD diagnosis, and MAT receipt. Findings: Compared with the continuously insured, newly and returning insured enrollees were less likely to be dispensed opioids [newly: 42.3%, 95% confidence interval (95%CI) 42.0‐42.7%; returning: 49.3%, 95%CI 48.8‐49.7%; continuously: 52.5%, 95%CI 52.0‐53.0%], use opioids chronically (newly: 12.8%, 95%CI 12.4‐13.1%; returning: 11.9%, 95%CI 11.5‐12.3%, continuously: 15.8%, 95%CI 15.4‐16.2%), have OUD diagnoses (newly: 3.6%, 95%CI 3.4‐3.7%; returning: 3.9%, 95%CI 3.8‐4.1%, continuously: 4.7%, 95%CI 4.5‐4.9%), and receive MAT after OUD diagnosis [Hazard Ratio newly: 0.57, 95%CI 0.53‐0.61; Hazard Ratio returning: 0.60, 95%CI 0.56‐0.65 (REF: continuously)]. Conclusions: Residents of Oregon, USA who enrolled or re‐enrolled in Medicaid health insurance after expansion of coverage in 2014 as a result of the Affordable Care Act were less likely than those already covered to receive opioids, use them chronically, or receive medication‐assisted treatment for opioid use disorder

Patient-Reported Social Risks and Clinician Decision Making: Results of a Clinician Survey in Primary Care Community Health Centers

PurposeTo assess the extent that patients' social determinants of health (SDOH) influence safety-net primary care clinicians' decisions at the point of care; examine how that information comes to the clinician's attention; and analyze clinician, patient, and encounter characteristics associated with the use of SDOH data in clinical decision making.MethodsThirty-eight clinicians working in 21 clinics were prompted to complete 2 short card surveys embedded in the electronic health record (EHR) daily for 3 weeks. Survey data were matched with clinician-, encounter-, and patient-level variables from the EHR. Descriptive statistics and generalized estimating equation models were used to assess relationships between the variables and the clinician reported use of SDOH data to inform care.ResultsSocial determinants of health were reported to influence care in 35% of surveyed encounters. The most common sources of information on patients' SDOH were conversations with patients (76%), prior knowledge (64%), and the EHR (46%). Social determinants of health were significantly more likely to influence care among male and non-English-speaking patients, and those with discrete SDOH screening data documented in the EHR.ConclusionsElectronic health records present an opportunity to support clinicians integrating information about patients' social and economic circumstances into care planning. Study findings suggest that SDOH information from standardized screening documented in the EHR, combined with patient-clinician conversations, may enable social risk-adjusted care. Electronic health record tools and clinic workflows could be used to support both documentation and conversations. Study results also identified factors that may cue clinicians to include SDOH information in point-of-care decision-making. Future research should explore this topic further

Following Uninsured Patients Through Medicaid Expansion: Ambulatory Care Use and Diagnosed Conditions

PURPOSE The Patient Protection and Affordable Care Act (ACA) has improved access to health insurance, yet millions remain uninsured. Many patients who remain uninsured access care at community health centers (CHCs); however, little is known about their health conditions and health care use. We assessed ambulatory care use and diagnosed health conditions among a cohort of CHC patients uninsured before enactment of the ACA (pre-ACA: January 1, 2012 to December 31, 2013) and followed them after enactment (post-ACA: January 1, 2014 to December 31, 2015). METHODS This retrospective cohort analysis used electronic health record data from CHCs in 11 US states that expanded Medicaid eligibility. We assessed ambulatory care visits and documented health conditions among a cohort of 138,246 patients (aged 19 to 64 years) who were uninsured pre-ACA and either remained uninsured, gained Medicaid, gained other health insurance, or did not have a visit post-ACA. We estimated adjusted predicted probabilities of ambulatory care use using an ordinal logistic mixed-effects regression model. RESULTS Post-ACA, 20.9% of patients remained uninsured, 15.0% gained Medicaid, 12.4% gained other insurance, and 51.7% did not have a visit. The majority of patients had ≥1 diagnosed health condition. The adjusted proportion of patients with high use (≥6 visits over 2 years) increased from pre-ACA to post-ACA among those who gained Medicaid (pre-ACA: 23%, post-ACA: 34%, P CONCLUSIONS A significant percentage of CHC patients remained uninsured; many who remained uninsured had diagnosed health conditions, and one-half continued to have ≥3 visits to CHCs. CHCs continue to be essential providers for uninsured patients

Adjusting for Patient Economic/Access Issues in a Hypertension Quality Measure

IntroductionThe American Heart Association and American College of Cardiology have proposed adjusting hypertension-related care quality measures by excluding patients with economic/access issues from the denominator of rate calculations. No research to date has explored the methods to operationalize this recommendation or how to measure economic/access issues. This study applied and compared different approaches to populating these denominator exceptions.MethodsElectronic health record data from 2019 were used in 2021 to calculate hypertension control rates in 84 community health centers. A total of 10 different indicators of patient economic/access barriers to care were used as denominator exclusions to calculate and then compare adjusted quality measure performance. Data came from a nonprofit health center‒controlled network that hosts a shared electronic health record for community health centers located in 22 states.ResultsA total of 5 of 10 measures yielded an increase in adjusted hypertension control rates in ≥50% of clinics (average rate increases of 0.7-3.71 percentage points). A total of 3 of 10 measures yielded a decrease in adjusted hypertension control rates in >50% of clinics (average rate decreases of 1.33-13.82 percentage points). A total of 5 measures resulted in excluding >50% of the clinic's patient population from quality measure assessments.ConclusionsChanges in clinic-level hypertension control rates after adjustment differed depending on the measure of economic/access issue. Regardless of the exclusion method, changes between baseline and adjusted rates were small. Removing community health center patients experiencing economic/access barriers from a hypertension control quality measure resulted in excluding a large proportion of patients, raising concerns about whether this calculation can be a meaningful measure of clinical performance

Clinic Factors Associated with Utilization of a Pregnancy Intention Screening Tool in Community Health Centers.

Objective Routine pregnancy-intention screening in the primary care setting is a promising practice to help patients achieve their reproductive goals. We aim to describe the utilization of a pregnancy-intention screening tool integrated in the electronic health record (EHR) of a national network of community health centers (CHCs) and identify clinic-level factors associated with tool use. Study design We conducted a clinic-level retrospective observational study to assess tool utilization during the first 3 years after the tool was made available in the EHR (November 2015 to October 2018). We describe characteristics of clinics with higher tool utilization (≥90th percentile) versus lower utilization (\u3c90th percentile) and the types of providers who used the tool. We then employ negative binomial regression to identify independent clinic-level factors associated with tool utilization. Results Across 194 clinics in our study sample which served 289,754 eligible female patients, the tool was used for 113,116 (39%). Medical assistants performed 60.3% of screenings and clinicians performed 11.2%. CHCs with higher tool utilization rates were more likely to be located in rural settings (RR 1.75, 95% CI 1.07–2.87) and serve patient populations with higher proportions of women (RR 1.32, 95% CI 1.24–1.41) and lower proportions of patients with non-English language preference (RR 0.92, 95% CI 0.89–0.95). Conclusions Many health centers utilized pregnancy-intention screening after an EHR-based tool was made available, though overall screening rates were low. Implications Additional study of implementation strategies and effectiveness of pregnancy-intention screening tools is needed. Implementation of future pregnancy-intention screening interventions must be tailored to address clinic-level barriers and facilitators to screening