Electronic problem list documentation of chronic kidney disease and quality of care

Background: Chronic kidney disease (CKD) is increasingly common and under-recognized in primary care clinics, leading to low rates of stage-appropriate monitoring and treatment. Our objective was to determine whether electronic problem list documentation of CKD is associated with monitoring and treatment. Methods: This is a cross-sectional observational study of patients with stage 3 or 4 CKD, defined as two past estimated glomerular filtration rates (eGFR) 15-60 mL/min/1.73 m2 separated by 90 days and collected between 2007-2008. We examined the association of problem list documentation with: 1) serum eGFR monitoring test, 2) urine protein or albumin monitoring test, 3) an angiotensin converting enzyme inhibitor or angiotensin receptor blocker (ACE/ARB) prescription, 4) mean systolic blood pressure (BP), and 5) BP control. Results: Out of 3,149 patients with stage 3 or 4 CKD, only 16% of patients had CKD documented on the problem list. After adjustment for eGFR, gender, and race/ethnicity and after clustering by physician, problem list documentation of CKD was associated with serum eGFR testing (97% with problem list documentation vs. 94% without problem list documentation, p = 0.02) and urine protein testing (47% with problem list documentation vs. 40% without problem list documentation, p = 0.04). After adjustment, problem list documentation was not associated with ACE/ARB prescription, mean systolic BP, or BP control. Conclusions: Documentation of CKD on the electronic problem list is rare. Patients with CKD documentation have better stage-appropriate monitoring of the disease, but do not have higher rates of blood pressure treatment or better blood pressure control. Interventions aimed at increasing documentation of CKD on the problem list may improve stage-appropriate monitoring, but may not improve clinical outcomes

Visit complexity, diagnostic uncertainty, and antibiotic prescribing for acute cough in primary care: a retrospective study

Background: Guidelines and performance measures recommend avoiding antibiotics for acute cough/acute bronchitis and presume visits are straightforward with simple diagnostic decision-making. We evaluated clinician-assigned diagnoses, diagnostic uncertainty, and antibiotic prescribing for acute cough visits in primary care. Methods: We conducted a retrospective analysis of acute cough visits – cough lasting ≤21 days in adults 18–64 years old without chronic lung disease – in a primary care practice from March 2011 through June 2012. Results: Of 56,301 visits, 962 (2%) were for acute cough. Clinicians diagnosed patients with 1, 2, or ≥ 3 cough-related diagnoses in 54%, 35%, and 11% of visits, respectively. The most common principal diagnoses were upper respiratory infection (46%), sinusitis (10%), acute bronchitis (9%), and pneumonia (8%). Clinicians prescribed antibiotics in 22% of all visits: 65% of visits with antibiotic-appropriate diagnoses and 4% of visits with non-antibiotic-appropriate diagnoses. Clinicians expressed diagnostic uncertainty in 16% of all visits: 43% of visits with antibiotic-appropriate diagnoses and 5% of visits with non-antibiotic-appropriate diagnoses. Clinicians expressed uncertainty more often when prescribing antibiotics than when not prescribing antibiotics (30% vs. 12%; p < 0.001). As the number of visit diagnoses increased from 1 to 2 to ≥ 3, clinicians were more likely to express diagnostic uncertainty (5%, 25%, 40%, respectively; p < 0.001) and prescribe antibiotics (16%, 25%, 41%, respectively; p < 0.001). Conclusions: Acute cough may be more complex and have more diagnostic uncertainty than guidelines and performance measures presume. Efforts to reduce antibiotic prescribing for acute cough should address diagnostic complexity and uncertainty that clinicians face

Does probiotic consumption reduce antibiotic utilization for common acute infections? A systematic review and meta-analysis.

BackgroundOverall reduction of antibiotic use is a widely adopted public health goal. Given evidence that consuming probiotics reduce the incidence, duration and/or severity of certain types of common acute infections, we hypothesized that probiotics are associated with reduced antibiotic use. This systematic review of randomized controlled trials (RCTs) assessed the impact of probiotic supplementation (any strain, dose or duration), compared to placebo, on antibiotic utilization for common, acute infections in otherwise healthy people of all ages.MethodsWe searched 13 electronic databases including MEDLINE, Embase and CENTRAL from inception to 17th January 2017. Backward and forward citation searches were also conducted. Two reviewers independently selected studies for inclusion and extracted study data. We assessed risk of bias for individual studies using criteria adapted from the Centre for Reviews and Dissemination, and the quality of evidence for each outcome was assessed using the GRADE system. Studies that evaluated similar outcomes were pooled statistically in meta-analyses using a random-effects model.ResultsWe screened 1533 citations, and of these, 17 RCTs met our predefined inclusion criteria. All 17 were conducted in infants and/or children with a primary aim of preventing acute respiratory tract infections, acute lower digestive tract infections or acute otitis media. Included studies used 13 probiotic formulations, all comprising single or combination Lactobacillus and Bifidobacterium delivered in a range of food or supplement products. Mean duration of probiotic supplementation ranged from 4 days to 9 months. Trial quality was variable. Meta-analysis demonstrated that infants and children who received probiotics to prevent acute illnesses had a lower risk of being prescribed antibiotics, relative to those who received placebo (Pooled Relative Risk = 0.71, 95% CI: 0.54-0.94). When restricted to five studies with a low risk of bias, the pooled relative risk was 0.46 (95% CI: 0.23-0.97). Significant statistical heterogeneity was present in effect size estimates, which appeared to be due to one trial which could partly be considered as an outlier.ConclusionsProbiotics, provided to reduce the risk for common acute infections, may be associated with reduced antibiotic use in infants and children. Additional well-designed studies are needed to substantiate these findings in children and explore similar findings in other population groups

Galaxy Peculiar Velocities From Large-Scale Supernova Surveys as a Dark Energy Probe

Upcoming imaging surveys such as the Large Synoptic Survey Telescope will repeatedly scan large areas of sky and have the potential to yield million-supernova catalogs. Type Ia supernovae are excellent standard candles and will provide distance measures that suffice to detect mean pairwise velocities of their host galaxies. We show that when combining these distance measures with photometric redshifts for either the supernovae or their host galaxies, the mean pairwise velocities of the host galaxies will provide a dark energy probe which is competitive with other widely discussed methods. Adding information from this test to type Ia supernova photometric luminosity distances from the same experiment, plus the cosmic microwave background power spectrum from the Planck satellite, improves the Dark Energy Task Force Figure of Merit by a factor of 1.8. Pairwise velocity measurements require no additional observational effort beyond that required to perform the traditional supernova luminosity distance test, but may provide complementary constraints on dark energy parameters and the nature of gravity. Incorporating additional spectroscopic redshift follow-up observations could provide important dark energy constraints from pairwise velocities alone. Mean pairwise velocities are much less sensitive to systematic redshift errors than the luminosity distance test or weak lensing techniques, and also are only mildly affected by systematic evolution of supernova luminosity.Comment: 18 pages; 4 figures; 4 tables; replaced to match the accepted versio

Primary care clinicians’ perceptions about antibiotic prescribing for acute bronchitis: a qualitative study

Background: Clinicians prescribe antibiotics to over 65% of adults with acute bronchitis despite guidelines stating that antibiotics are not indicated. Methods: To identify and understand primary care clinician perceptions about antibiotic prescribing for acute bronchitis, we conducted semi-structured interviews with 13 primary care clinicians in Boston, Massachusetts and used thematic content analysis. Results: All the participants agreed with guidelines that antibiotics are not indicated for acute bronchitis and felt that clinicians other than themselves were responsible for overprescribing. Barriers to guideline adherence included 6 themes: (1) perceived patient demand, which was the main barrier, although some clinicians perceived a recent decrease; (2) lack of accountability for antibiotic prescribing; (3) saving time and money; (4) other clinicians’ misconceptions about acute bronchitis; (5) diagnostic uncertainty; and (6) clinician dissatisfaction in failing to meet patient expectations. Strategies to decrease inappropriate antibiotic prescribing included 5 themes: (1) patient educational materials; (2) quality reporting; (3) clinical decision support; (4) use of an over-the-counter prescription pad; and (5) pre-visit triage and education by nurses to prevent visits. Conclusions: Clinicians continued to cite patient demand as the main reason for antibiotic prescribing for acute bronchitis, though some clinicians perceived a recent decrease. Clinicians felt that other clinicians were responsible for inappropriate antibiotic prescribing and that better pre-visit triage by nurses could prevent visits and change patients’ expectations. Electronic supplementary material The online version of this article (doi:10.1186/s12875-014-0194-5) contains supplementary material, which is available to authorized users

A Diffusion Network Event History Estimator

Research on the diffusion of political decisions across jurisdictions typically accounts for units’ influence over each other with (1) observable measures or (2) by inferring latent network ties from past decisions. The former approach assumes that interdependence is static and perfectly captured by the data. The latter mitigates these issues but requires analytical tools that are separate from the main empirical methods for studying diffusion. As a solution, we introduce network event history analysis (NEHA), which incorporates latent network inference into conventional discrete-time event history models. We demonstrate NEHA’s unique methodological and substantive benefits in applications to policy adoption in the American states. Researchers can analyze the ties and structure of inferred networks to refine model specifications, evaluate diffusion mechanisms, or test new or existing hypotheses. By capturing targeted relationships unexplained by standard covariates, NEHA can improve models, facilitate richer theoretical development, and permit novel analyses of the diffusion process

Use of Practice-Based Research Network Data to Measure Neighborhood Smoking Prevalence

Introduction: Practice-Based Research Networks (PBRNs) and health systems may provide timely, reliable data to guide the development and distribution of public health resources to promote healthy behaviors, such as quitting smoking. The objective of this study was to determine if PBRN data could be used to make neighborhood-level estimates of smoking prevalence. Methods: We estimated the smoking prevalence in 32 greater Boston neighborhoods (population = 877,943 adults) by using the electronic health record data of adults who in 2009 visited one of 26 Partners Primary Care PBRN practices (n = 77,529). We compared PBRN-derived estimates to population-based estimates derived from 1999–2009 Behavioral Risk Factor Surveillance System (BRFSS) data (n = 20,475). Results: The PBRN estimates of neighborhood smoking status ranged from 5% to 22% and averaged 11%. The 2009 neighborhood-level smoking prevalence estimates derived from the BRFSS ranged from 5% to 26% and averaged 13%. The difference in smoking prevalence between the PBRN and the BRFSS averaged −2 percentage points (standard deviation, 3 percentage points). Conclusion: Health behavior data collected during routine clinical care by PBRNs and health systems could supplement or be an alternative to using traditional sources of public health data

Use of behavioral economics and social psychology to improve treatment of acute respiratory infections (BEARI): rationale and design of a cluster randomized controlled trial [1RC4AG039115-01] - study protocol and baseline practice and provider characteristics

Background: Inappropriate antibiotic prescribing for nonbacterial infections leads to increases in the costs of care, antibiotic resistance among bacteria, and adverse drug events. Acute respiratory infections (ARIs) are the most common reason for inappropriate antibiotic use. Most prior efforts to decrease inappropriate antibiotic prescribing for ARIs (e.g., educational or informational interventions) have relied on the implicit assumption that clinicians inappropriately prescribe antibiotics because they are unaware of guideline recommendations for ARIs. If lack of guideline awareness is not the reason for inappropriate prescribing, educational interventions may have limited impact on prescribing rates. Instead, interventions that apply social psychological and behavioral economic principles may be more effective in deterring inappropriate antibiotic prescribing for ARIs by well-informed clinicians. Methods/design The Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections (BEARI) Trial is a multisite, cluster-randomized controlled trial with practice as the unit of randomization. The primary aim is to test the ability of three interventions based on behavioral economic principles to reduce the rate of inappropriate antibiotic prescribing for ARIs. We randomized practices in a 2 × 2 × 2 factorial design to receive up to three interventions for non-antibiotic-appropriate diagnoses: 1) Accountable Justifications: When prescribing an antibiotic for an ARI, clinicians are prompted to record an explicit justification that appears in the patient electronic health record; 2) Suggested Alternatives: Through computerized clinical decision support, clinicians prescribing an antibiotic for an ARI receive a list of non-antibiotic treatment choices (including prescription options) prior to completing the antibiotic prescription; and 3) Peer Comparison: Each provider’s rate of inappropriate antibiotic prescribing relative to top-performing peers is reported back to the provider periodically by email. We enrolled 269 clinicians (practicing attending physicians or advanced practice nurses) in 49 participating clinic sites and collected baseline data. The primary outcome is the antibiotic prescribing rate for office visits with non-antibiotic-appropriate ARI diagnoses. Secondary outcomes will examine antibiotic prescribing more broadly. The 18-month intervention period will be followed by a one year follow-up period to measure persistence of effects after interventions cease. Discussion The ongoing BEARI Trial will evaluate the effectiveness of behavioral economic strategies in reducing inappropriate prescribing of antibiotics. Trials registration ClinicalTrials.gov: NCT0145494

Inflation and Dark Energy from spectroscopy at z > 2

The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at z < 1, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at 2 < z < 5, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $\sigma(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space