Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Residential Racial Isolation and Spatial Patterning of Hypertension in Durham, North Carolina

Introduction - Neighborhood characteristics such as racial segregation may be associated with hypertension, but studies have not examined these relationships using spatial models appropriate for geographically patterned health outcomes. The objectives of our study were to 1) evaluate the geographic heterogeneity of hypertension; 2) describe whether and how patient-level risk factors and racial isolation relate to geographic heterogeneity in hypertension; and 3) examine cross-sectional associations of hypertension with racial isolation. Methods - We obtained electronic health records from the Duke Medicine Enterprise Data Warehouse for 2007–2011. We linked patient data with data on racial isolation determined by census block of residence. We constructed a local spatial index of racial isolation for non-Hispanic black patients; the index is scaled from 0 to 1, with 1 indicating complete isolation. We used aspatial and spatial Bayesian models to assess spatial variation in hypertension and estimate associations with racial isolation. Results - Racial isolation ranged from 0 (no isolation) to 1 (completely isolated). A 0.20-unit increase in racial isolation was associated with 1.06 (95% credible interval, 1.03–1.10) and 1.11 (95% credible interval, 1.07–1.16) increased odds of hypertension among non-Hispanic black and non-Hispanic white patients, respectively. Across Durham, census block-level odds of hypertension ranged from 0.62 to 1.88 among non-Hispanic black patients and from 0.32 to 2.41 among non-Hispanic white patients. Compared with spatial models that included patient age and sex, residual heterogeneity in spatial models that included age, sex, and block-level racial isolation was 33% lower for non-Hispanic black patients and 20% lower for non-Hispanic white patients. Conclusion - Racial isolation of non-Hispanic black patients was associated with increased odds of hypertension among both non-Hispanic black and non-Hispanic white patients. Further research is needed to identify latent spatially patterned factors contributing to hypertension

Prescribing of evidence-based diabetes pharmacotherapy in patients with metabolic dysfunction-associated steatohepatitis

Introduction Metabolic dysfunction-associated steatohepatitis (MASH) is highly prevalent in type 2 diabetes (T2D). Pioglitazone and glucagon-like peptide-1 receptor agonists (GLP-1RA) are medications used in T2D that can resolve MASH and should be considered in all patients with T2D and MASH. We assessed prescription rates of evidence-based T2D pharmacotherapy (EBP) in MASH, and ascertained racial/ethnic disparities in prescribing.Research design and methods We conducted a cross-sectional study on patients in Duke University Health System with diagnosis codes for T2D and MASH between January 2019 and January 2021. Only patients with ≥1 primary care or endocrinology encounter were included. The primary outcome was EBP, defined as ≥1 prescription for pioglitazone and/or a GLP-1RA during the study period. A multivariable logistic regression model was used to examine the primary outcome.Results A total of 847 patients with T2D and MASH were identified; mean age was 59.7 (SD 12) years, 61.9% (n=524) were female, and 11.9% (n=101) and 4.6% (n=39) were of Black race and Latino/a/x ethnicity, respectively. EBP was prescribed in 34.8% (n=295). No significant differences were noted in the rates of EBP use across racial/ethnic groups (Latino/a/x vs White patients: adjusted OR (aOR) 1.82, 95% CI 0.78 to 4.28; Black vs White patients: aOR 0.76, 95% CI 0.44 to 1.33, p=0.20).Conclusions EBP prescriptions, especially pioglitazone, are low in patients with T2D and MASH, regardless of race/ethnicity. These data underscore the need for interventions to close the gap between current and evidence-based care

Recent Clinical Trials in Osteoporosis: A Firm Foundation or Falling Short?

<div><p>The global burden of osteoporotic fractures is associated with significant morbidity, mortality, and healthcare costs. We examined the ClinicalTrials.gov database to determine whether recently registered clinical trials addressed prevention and treatment in those at high risk for fracture. A dataset of 96,346 trials registered in ClinicalTrials.gov was downloaded on September 27, 2010. At the time of the dataset download, 40,970 interventional trials had been registered since October 1, 2007. The osteoporosis subset comprised 239 interventional trials (0.6%). Those trials evaluating orthopedic procedures were excluded. The primary purpose was treatment in 67.0%, prevention in 20.1%, supportive care in 5.8%, diagnostic in 2.2%, basic science in 3.1%, health services research in 0.9%, and screening in 0.9%. The majority of studies (61.1%) included drug-related interventions. Most trials (56.9%) enrolled only women, 38.9% of trials were open to both men and women, and 4.2% enrolled only men. Roughly one fifth (19.7%) of trials excluded research participants older than 65 years, and 33.5% of trials excluded those older than 75 years. The funding sources were industry in 51.0%, the National Institutes of Health in 6.3%, and other in 42.7%. We found that most osteoporosis-related trials registered from October 2007 through September 2010 examined the efficacy and safety of drug treatment, and fewer trials examined prevention and non-drug interventions. Trials of interventions that are not required to be registered in ClinicalTrials.gov may be underrepresented. Few trials are specifically studying osteoporosis in men and older adults. Recently registered osteoporosis trials may not sufficiently address fracture prevention.</p></div

A comparison of phenotype definitions for diabetes mellitus

OBJECTIVE: This study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions. MATERIALS AND METHODS: Inclusion criteria from seven diabetes phenotype definitions were translated into query algorithms and applied to a population (n=173 503) of adult patients from Duke University Health System. The numbers of patients meeting criteria for each definition and component (diagnosis, diabetes-associated medications, and laboratory results) were compared. RESULTS: Three phenotype definitions based heavily on ICD-9-CM codes identified 9–11% of the patient population. A broad definition for the Durham Diabetes Coalition included additional criteria and identified 13%. The electronic medical records and genomics, NYC A1c Registry, and diabetes-associated medications definitions, which have restricted or no ICD-9-CM criteria, identified the smallest proportions of patients (7%). The demographic characteristics for all seven phenotype definitions were similar (56–57% women, mean age range 56–57 years).The NYC A1c Registry definition had higher average patient encounters (54) than the other definitions (range 44–48) and the reference population (20) over the 5-year observation period. The concordance between populations returned by different phenotype definitions ranged from 50 to 86%. Overall, more patients met ICD-9-CM and laboratory criteria than medication criteria, but the number of patients that met abnormal laboratory criteria exclusively was greater than the numbers meeting diagnostic or medication data exclusively. DISCUSSION: Differences across phenotype definitions can potentially affect their application in healthcare organizations and the subsequent interpretation of data. CONCLUSIONS: Further research focused on defining the clinical characteristics of standard diabetes cohorts is important to identify appropriate phenotype definitions for health, policy, and research

Missing signposts on the roadmap to quality: a call to improve medication adherence indicators in data collection for population research

Purpose: Poor adherence to prescribed medicines is associated with increased rates of poor outcomes, including hospitalization, serious adverse events, and death, and is also associated with increased healthcare costs. However, current approaches to evaluation of medication adherence using real-world electronic health records (EHRs) or claims data may miss critical opportunities for data capture and fall short in modeling and representing the full complexity of the healthcare environment. We sought to explore a framework for understanding and improving data capture for medication adherence in a population-based intervention in four U.S. counties.Approach: We posited that application of a data model and a process matrix when designing data collection for medication adherence would improve identification of variables and data accessibility, and could support future research on medication-taking behaviors. We then constructed a use case in which data related to medication adherence would be leveraged to support improved healthcare quality, clinical outcomes, and efficiency of healthcare delivery in a population-based intervention for persons with diabetes. Because EHRs in use at participating sites were deemed incapable of supplying the needed data, we applied a taxonomic approach to identify and define variables of interest. We then applied a process matrix methodology, in which we identified key research goals and chose optimal data domains and their respective data elements, to instantiate the resulting data model.Conclusions: Combining a taxonomic approach with a process matrix methodology may afford significant benefits when designing data collection for clinical and population-based research in the arena of medication adherence. Such an approach can effectively depict complex real-world concepts and domains by mapping the relationships between disparate contributors to medication adherence and describing their relative contributions to improved outcomes

Association of Unmet Social Needs with Metformin Use Among Patients with Type 2 Diabetes

Objective: To evaluate the relationship between social needs and metformin use among adults with type 2 diabetes (T2D).Research Design and Methods: In a prospective cohort study of adults with T2D (n=722) we linked electronic health record (EHR) and Surescripts (Surescripts, LLC) prescription network data to abstract data on patient reported social needs and to calculate metformin adherence based on expected refill frequency using a proportion of days covered methodology.Results: Adjusting for demographics and clinical complexity, 2 or more social needs (-0.046, 95%CI=-0.089,-0.003), being uninsured (-0.052, 95%CI=-0.095,-0.009) and while adjusting for other needs, being without housing (-0.069 95% CI=-0.121, -0.018), and lack of access to medicine/health care (-0.058 95% CI=-0.115, -0.000) were associated with lower use.Conclusions: We found that overall social need burden and specific needs, particularly housing and healthcare access were associated with clinically significant reductions in metformin adherence among T2D patients.</p