Publications by authors named "Anita D Misra-Hebert"

48 Publications

Primary Care Health Care Use for Patients With Type 2 Diabetes During the COVID-19 Pandemic.

Diabetes Care 2021 Jul 14. Epub 2021 Jul 14.

Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland, OH.

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http://dx.doi.org/10.2337/dc21-0853DOI Listing
July 2021

Shared Medical Appointments and Prediabetes: The Power of the Group.

Ann Fam Med 2021 May-Jun;19(3):258-261

Center for Value-Based Care Research Cleveland Clinic, Cleveland, Ohio.

Shared medical appointments, which allow greater access to care and provide peer support, may be an effective treatment modality for prediabetes. We used a retrospective propensity-matched cohort analysis to compare patients attending a prediabetes shared medical appointment to usual care. Primary outcome was patient's weight change over 24 months. Secondary outcomes included change in hemoglobin A, low density lipoprotein, and systolic blood pressure. The shared medical appointments group lost more weight (2.88 kg vs 1.29 kg, = .003), and achieved greater reduction in hemoglobin A (-0.87% vs +0.87%, = .001) and systolic blood pressure (-4.35 mmHg vs +0.52 mmHg, = .044). The shared medical appointment model can be effective in treating prediabetes.
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http://dx.doi.org/10.1370/afm.2647DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118487PMC
November 2019

Treatment of Patients with Prediabetes in a Primary Care Setting 2011-2018: an Observational Study.

J Gen Intern Med 2021 04 15;36(4):923-929. Epub 2021 Jan 15.

Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA.

Background: Over one third of American adults are at high risk for developing diabetes, which can be delayed or prevented using interventions such as medical nutrition therapy (MNT) or metformin. Physicians' self-reported rates of prediabetes treatment are improving, but patterns of actual referral, prescription, and MNT visits are unknown.

Objective: To characterize treatment of prediabetes in primary care.

Design: We conducted a retrospective cohort study using electronic health record data. We described patterns of treatment and used multivariable logistic regression to evaluate the association of patient factors and PCP-specific treatment rate with patient treatment.

Patients: We included overweight or obese outpatients who had a first prediabetes-range hemoglobin A1c (HbA1c) during 2011-2018 and had primary care provider (PCP) follow-up within a year.

Main Measures: We collected patient characteristics and the following treatments: metformin prescription; referral to MNT, diabetes education, endocrinology, or bariatric medicine; and MNT visit. We did not capture within-visit physician counseling.

Key Results: Of 16,713 outpatients with prediabetes, 20.4% received treatment, including metformin prescriptions (7.8%) and MNT referrals (11.3%), but only 7.4% of referred patients completed a MNT visit. The strongest predictor of treatment was the patient's PCP's treatment rate. Some PCPs never treated prediabetes, but two treated more than half of their patients; 62% had no patients complete a MNT visit. Being younger or female and having higher body mass index or HbA1c were also positively associated with treatment. Compared to white patients, black patients were more likely to receive MNT referral and less likely to receive metformin.

Conclusions: Almost 80% of patients with new prediabetes never received treatment, and those who did receive referrals had very poor visit completion. Treatment rates appear to reflect provider rather than patient preferences.
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http://dx.doi.org/10.1007/s11606-020-06354-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041989PMC
April 2021

Healthcare utilization and patient and provider experience with a home visit program for patients discharged from the hospital at high risk for readmission.

Healthc (Amst) 2021 Mar 4;9(1):100518. Epub 2021 Jan 4.

Center for Community Health Integration, Case Western Reserve University, Cleveland, OH, USA.

Background: Home visits after hospital discharge may reduce future healthcare utilization. We assessed the association of home visits by advanced practice registered nurses (APRN) and paramedics with healthcare utilization and mortality, and provider and patient experience.

Methods: We conducted a retrospective cohort study using convergent mixed methods in one health system including adult medical patients discharged to home from November 2017-September 2019. We assessed outcomes for home visit vs. matched comparison patients at 30, 90, and 180 days, including hospital admission, emergency department (ED) use, and death: Phase 1 (APRN or paramedic visits assigned by geographic location) and Phase 2 (APRN and paramedic visit teams assigned to patients). Patients declining home visits and those accepting were also compared. Semi-structured interviews were conducted with home visit patients and providers, primary care providers, and nurse care coordinators.

Results: In Phase 1, the 101 home visit matched to 303 comparison patients showed no differences in readmissions, ED visits, or death at 30, 90, and 180 days. In Phase 2, 157 home visit matched to 471 comparison patients had fewer 30-day readmissions (19.1% vs. 28.7%, p 0.024) and no differences in other outcomes. Compared with patients declining home visits, patients accepting had lower odds of 30-day readmission. In 44 interviews, themes of Medication Understanding, Knowledge Gap after Discharge, Patient Medical Complexity, Social Context, and Patient Engagement/Need for Reassurance emerged.

Conclusion: Post-discharge home visits by APRNs and paramedics working together were associated with reduced 30-day readmissions. Identified themes could inform strategies to improve patient support.
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http://dx.doi.org/10.1016/j.hjdsi.2020.100518DOI Listing
March 2021

Impact of the COVID-19 Pandemic on Healthcare Workers' Risk of Infection and Outcomes in a Large, Integrated Health System.

J Gen Intern Med 2020 11 1;35(11):3293-3301. Epub 2020 Sep 1.

Healthcare Delivery and Implementation Science Center, Cleveland Clinic, Cleveland, OH, USA.

Background: Understanding the impact of the COVID-19 pandemic on healthcare workers (HCW) is crucial.

Objective: Utilizing a health system COVID-19 research registry, we assessed HCW risk for COVID-19 infection, hospitalization, and intensive care unit (ICU) admission.

Design: Retrospective cohort study with overlap propensity score weighting.

Participants: Individuals tested for SARS-CoV-2 infection in a large academic healthcare system (N = 72,909) from March 8-June 9, 2020, stratified by HCW and patient-facing status.

Main Measures: SARS-CoV-2 test result, hospitalization, and ICU admission for COVID-19 infection.

Key Results: Of 72,909 individuals tested, 9.0% (551) of 6145 HCW tested positive for SARS-CoV-2 compared to 6.5% (4353) of 66,764 non-HCW. The HCW were younger than the non-HCW (median age 39.7 vs. 57.5, p < 0.001) with more females (proportion of males 21.5 vs. 44.9%, p < 0.001), higher reporting of COVID-19 exposure (72 vs. 17%, p < 0.001), and fewer comorbidities. However, the overlap propensity score weighted proportions were 8.9 vs. 7.7 for HCW vs. non-HCW having a positive test with weighted odds ratio (OR) 1.17, 95% confidence interval (CI) 0.99-1.38. Among those testing positive, weighted proportions for hospitalization were 7.4 vs. 15.9 for HCW vs. non-HCW with OR of 0.42 (CI 0.26-0.66) and for ICU admission: 2.2 vs. 4.5 for HCW vs. non-HCW with OR of 0.48 (CI 0.20-1.04). Those HCW identified as patient facing compared to not had increased odds of a positive SARS-CoV-2 test (OR 1.60, CI 1.08-2.39, proportions 8.6 vs. 5.5), but no statistically significant increase in hospitalization (OR 0.88, CI 0.20-3.66, proportions 10.2 vs. 11.4) and ICU admission (OR 0.34, CI 0.01-3.97, proportions 1.8 vs. 5.2).

Conclusions: In a large healthcare system, HCW had similar odds for testing SARS-CoV-2 positive, but lower odds of hospitalization compared to non-HCW. Patient-facing HCW had higher odds of a positive test. These results are key to understanding HCW risk mitigation during the COVID-19 pandemic.
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http://dx.doi.org/10.1007/s11606-020-06171-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462108PMC
November 2020

Impact of the COVID-19 pandemic on healthcare workers risk of infection and outcomes in a large, integrated health system.

Res Sq 2020 Aug 19. Epub 2020 Aug 19.

Cleveland Clinic.

Understanding the impact of the COVID-19 pandemic on healthcare workers (HCW) is crucial. Utilizing a health system COVID-19 research registry, we assessed HCW risk for COVID-19 infection, hospitalization and intensive care unit (ICU) admission. Retrospective cohort study with overlap propensity score weighting. Individuals tested for SARS-CoV-2 infection in a large academic healthcare system (N=72,909) from March 8-June 9 2020 stratified by HCW and patient-facing status. SARS-CoV-2 test result, hospitalization, and ICU admission for COVID-19 infection. Of 72,909 individuals tested, 9.0% (551) of 6,145 HCW tested positive for SARS-CoV-2 compared to 6.5% (4353) of 66,764 non-HCW. The HCW were younger than non-HCW (median age 39.7 vs. 57.5, p<0.001) with more females (proportion of males 21.5 vs. 44.9%, p<0.001), higher reporting of COVID-19 exposure (72 vs. 17 %, p<0.001) and fewer comorbidities. However, the overlap propensity score weighted proportions were 8.9 vs. 7.7 for HCW vs. non-HCW having a positive test with weighted odds ratio (OR) 1.17, 95% confidence interval (CI) 0.99-1.38. Among those testing positive, weighted proportions for hospitalization were 7.4 vs.15.9 for HCW vs. non-HCW with OR of 0.42 (CI 0.26-0.66) and for ICU admission: 2.2 vs.4.5 for HCW vs. non-HCW with OR of 0.48 (CI 0.20 -1.04). Those HCW identified as patient-facing compared to not had increased odds of a positive SARS-CoV-2 test (OR 1.60, CI 1.08-2.39, proportions 8.6 vs. 5.5), but no statistically significant increase in hospitalization (OR 0.88, CI 0.20-3.66, proportions 10.2 vs. 11.4) and ICU admission (OR 0.34, CI 0.01-3.97, proportions 1.8 vs. 5.2). In a large healthcare system, HCW had similar odds for testing SARS-CoV-2 positive, but lower odds of hospitalization compared to non-HCW. Patient-facing HCW had higher odds of a positive test. These results are key to understanding HCW risk mitigation during the COVID-19 pandemic.
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http://dx.doi.org/10.21203/rs.3.rs-61235/v1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444292PMC
August 2020

Assessment of Physician Priorities in Delivery of Preventive Care.

JAMA Netw Open 2020 07 1;3(7):e2011677. Epub 2020 Jul 1.

Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio.

Importance: Primary care physicians have limited time to discuss preventive care, but it is unknown how they prioritize recommended services.

Objective: To understand primary care physicians' prioritization of preventive services.

Design, Setting, And Participants: This online survey was administered to primary care physicians in a large health care system from March 17 to May 12, 2017. Physicians were asked whether they prioritize preventive services and which factors contribute to their choice (5-point Likert scale). Results were analyzed from July 8, 2017, to September 19, 2019.

Exposures: A 2 × 2 factorial design of 2 hypothetical patients: (1) a 50-year-old white woman with hypertension, type 2 diabetes, hyperlipidemia, obesity, a 30-pack-year history of smoking, and a family history of breast cancer; and (2) a 45-year-old black man with hypertension, hyperlipidemia, obesity, a 30-pack-year history of smoking, and a family history of colorectal cancer. Two visit lengths (40 minutes vs 20 minutes) were given. Each patient was eligible for at least 11 preventive services.

Main Outcomes And Measures: Physicians rated their likelihood of discussing each service during the visit and reported their top 3 priorities for patients 1 and 2. Physician choices were compared with the preventive services most likely to improve life expectancy, using a previously published mathematical model.

Results: Of 241 physicians, 137 responded (57%), of whom 74 (54%) were female and 85 (62%) were younger than 50 years. Physicians agreed they prioritized preventive services (mean score, 4.27 [95% CI, 4.12-4.42] of 5.00), mostly by ability to improve quality (4.56 [95% CI, 4.44-4.68] of 5.00) or length (4.53 [95% CI, 4.40-4.66] of 5.00) of life. Physicians reported more prioritization in the 20- vs 40-minute visit, indicating that they were likely to discuss fewer services during the shorter visit (median, 5 [interquartile range {IQR}, 3-8] vs 11 [IQR, 9-13] preventive services for patient 1, and 4 [IQR, 3-6] vs 9 [IQR, 8-11] for patient 2). Physicians reported similar top 3 priorities for both patients: smoking cessation, hypertension control, and glycemic control for patient 1 and smoking cessation, hypertension control, and colorectal cancer screening for patient 2. Physicians' top 3 priorities did not usually include diet and exercise or weight loss (ranked in their top 3 recommendations for either patient by only 48 physicians [35%]), although these were among the 3 preventive services most likely to improve life expectancy based on the mathematical model.

Conclusions And Relevance: In this survey study, physicians prioritized preventive services under time constraints, but priorities did not vary across patients. Physicians did not prioritize lifestyle interventions despite large potential benefits. Future research should consider whether physicians and patients would benefit from guidance on preventive care priorities.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.11677DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103855PMC
July 2020

Late Diagnosis of COVID-19 in Patients Admitted to the Hospital.

J Gen Intern Med 2020 09 15;35(9):2829-2831. Epub 2020 Jun 15.

Center for Value-Based Care Research, Cleveland Clinic Community Care, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.

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http://dx.doi.org/10.1007/s11606-020-05949-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295323PMC
September 2020

The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model.

Diabetes Care 2020 08 11;43(8):1910-1919. Epub 2020 Jun 11.

Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH.

Objective: To assess patient characteristics and treatment factors associated with uncontrolled type 2 diabetes (T2D) and the probability of hemoglobin A (A1C) goal attainment.

Research Design And Methods: This was a retrospective cohort study using the electronic health record at Cleveland Clinic. Patients with uncontrolled T2D (A1C >9%) were identified on the index date of 31 December 2016 ( = 6,973) and grouped by attainment ( = 1,653 [23.7%]) or nonattainment ( = 5,320 [76.3%]) of A1C <8% by 31 December 2017, and subgroups were compared on a number of demographic and clinical variables. On the basis of these variables, a nomogram was created for predicting probability of A1C goal attainment.

Results: For the entire population, median age at index date was 57.7 years (53.3% male), and the majority were white (67.2%). Median A1C was 10.2%. Obesity (50.6%), cardiovascular disease (46.9%), and psychiatric disease (61.1%) were the most common comorbidities. Metformin (62.7%) and sulfonylureas (38.7%) were the most common antidiabetes medications. Only 1,653 (23.7%) patients achieved an A1C <8%. Predictors of increased probability of A1C goal attainment were older age, white/non-Hispanic race/ethnicity, Medicare health insurance, lower baseline A1C, higher frequency of endocrinology/primary care visits, dipeptidyl peptidase 4 inhibitor use, thiazolidinedione use, metformin use, glucagon-like peptide 1 receptor agonist use, and fewer classes of antidiabetes drugs. Factors associated with lower probability included insulin use and longer time in the T2D database (both presumed as likely surrogates for duration of T2D).

Conclusions: A minority of patients with an A1C >9% achieved an A1C <8% at 1 year. While most identified predictive factors are nonmodifiable by the clinician, pursuit of frequent patient engagement and tailored drug regimens may help to improve A1C goal attainment.
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http://dx.doi.org/10.2337/dc19-0968DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372043PMC
August 2020

Association between shared medical appointments and weight loss outcomes and anti-obesity medication use in patients with obesity.

Obes Sci Pract 2020 Jun 25;6(3):247-254. Epub 2020 Feb 25.

Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio.

Objective: In shared medical appointments (SMAs), multiple patients with a similar clinical diagnosis are seen by a multidisciplinary team for interactive group sessions. Very few studies have specifically studied SMAs and weight loss in patients with obesity. This study compared weight loss outcomes and anti-obesity medication (AOM) access between patients with obesity managed through (SMAs) versus individual appointments.

Methods: Retrospective study of adults seen for obesity between September 2014 and February 2017 at Cleveland Clinic Institute of Endocrinology and Metabolism. Percent weight loss from baseline was compared between two propensity score-matched populations: patients who attended ≥1 SMA and patients managed with individual medical appointments.

Results: From all eligible patients identified (n=310 SMA, n=1,993 non-SMA), 301 matched pairs were evaluated for weight loss. The SMA group (n=301) lost a mean of 4.2%, 5.2% and 3.8% of baseline weight over 6, 12 and 24 months; the non-SMA group (n=301) lost significantly less weight (1.5%, 1.8% and 1.6%, respectively) (paired -test, <.05). All patients were eligible for US Food and Drug Administration-approved AOMs based on obesity diagnosis; however, 49.8% (150/301) of matched SMA patients were prescribed an AOM versus 12.3% (37/301) of matched non-SMA patients.

Conclusion: This study suggests that SMAs may offer a promising alterative for obesity management and one that may facilitate greater utilization of AOMs. In propensity score-matched cohorts, SMAs were associated with greater weight loss outcomes when compared to usual care facilitated through individual medical appointments alone.
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http://dx.doi.org/10.1002/osp4.406DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278906PMC
June 2020

Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

Diabetes Care 2020 08 15;43(8):1937-1940. Epub 2020 May 15.

Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.

Objective: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH).

Research Design And Methods: From 2005 to 2017, we identified NSH events by diagnosis codes and NLP. We then built an SH prediction model.

Results: There were 204,517 patients with type 2 diabetes and no diagnosis codes for NSH. Evidence of NSH was found in 7,035 (3.4%) of patients using NLP. We reviewed 1,200 of the NLP-detected NSH notes and confirmed 93% to have NSH. The SH prediction model (C-statistic 0.806) showed increased risk with NSH (hazard ratio 4.44; < 0.001). However, the model with NLP did not improve SH prediction compared with diagnosis code-only NSH.

Conclusions: Detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.
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http://dx.doi.org/10.2337/dc19-1791DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372042PMC
August 2020

Use of Antihyperglycemic Medications in U.S. Adults: An Analysis of the National Health and Nutrition Examination Survey.

Diabetes Care 2020 06 31;43(6):1227-1233. Epub 2020 Mar 31.

Center for Value-Based Care Research, Cleveland Clinic, Cleveland, OH.

Objective: ) To examine trends in the use of diabetes medications and ) to determine whether physicians individualize diabetes treatment as recommended by the American Diabetes Association (ADA).

Research Design And Methods: We conducted a retrospective, cross-sectional analysis of 2003-2016 National Health and Nutrition Examination Survey (NHANES) data. We included people ≥18 years who had ever been told they had diabetes, had an HbA >6.4%, or had a fasting plasma glucose >125 mg/dL. Pregnant women and patients aged <20 years receiving only insulin were excluded. We assessed trends in use of ADA's seven preferred classes from 2003-2004 to 2015-2016. We also examined use by hypoglycemia risk (sulfonylureas, insulin, and meglitinides), weight effect (sulfonylureas, thiazolidinediones [TZDs], insulin, and meglitinides), cardiovascular benefit (canagliflozin, empagliflozin, and liraglutide), and cost (brand-name medications and insulin analogs).

Results: The final sample included 6,323 patients. The proportion taking any medication increased from 58% in 2003-2004 to 67% in 2015-2016 ( < 0.001). Use of metformin and insulin analogs increased, while use of sulfonylureas, TZDs, and human insulin decreased. Following the 2012 ADA recommendation, the choice of drug did not vary significantly by older age, weight, or presence of cardiovascular disease. Patients with low HbA, or HbA <6%, and age ≥65 years were less likely to receive hypoglycemia-inducing medications, while older patients with comorbidities were more likely. Insurance, but not income, was associated with the use of higher-cost medications.

Conclusions: Following ADA recommendations, the use of metformin increased, but physicians generally did not individualize treatment according to patients' characteristics. Substantial opportunities exist to improve pharmacologic management of diabetes.
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http://dx.doi.org/10.2337/dc19-2424DOI Listing
June 2020

Trends in Age at Diagnosis of Type 2 Diabetes Among US Adults from 2001 to 2016.

J Gen Intern Med 2021 04 19;36(4):1144-1146. Epub 2020 Mar 19.

Center for Value-based Care Research, Cleveland Clinic, Cleveland, OH, USA.

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http://dx.doi.org/10.1007/s11606-020-05767-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042053PMC
April 2021

Risk prediction for severe hypoglycemia in a type 2 diabetes population with previous non-severe hypoglycemia.

J Diabetes Complications 2020 01 12;34(1):107490. Epub 2019 Nov 12.

Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, United States of America.

Background/aim: Episodes of non-severe hypoglycemia can be captured through diagnoses documented in the electronic medical record. We aimed to create a clinically useful prediction model for a severe hypoglycemia event, requiring an emergency department visit or hospitalization, in patients with Type 2 diabetes with a history of non-severe hypoglycemia.

Methods: Using electronic medical record data from 50,439 patients with Type 2 diabetes in one health system, number of severe hypoglycemia events and associated patient characteristics from 2006 to 2015 were previously defined. Using the landmarking method, a dynamic prediction model was built using the subset of 1876 patients who had a documented non-severe hypoglycemia diagnosis code, using logistic regression to obtain landmark-specific odds of severe hypoglycemia in this group. For model performance, the bootstrap procedure was employed for internal validation and area under the curve (AUC) and index of prediction accuracy (IPA) were calculated.

Results: Glycosylated hemoglobin (HbA1c) less than 7% (53 mmol/mol) was associated with increased odds ratio (OR) of severe hypoglycemia at 3 months (OR 1.92 95% Confidence Interval (CI) 1.19-3.10 at HbA1c 5% (31 mmol/mol) and OR 1.21, CI 1.03-1.41 at HbA1c 6%(42 mmol/mol).) History of non-severe hypoglycemia within the past 3 months increased odds for severe hypoglycemia (OR 2.58 95% CI 1.80-3.70) as did Black race, insulin use with the past 3 months, and comorbidities. Metformin and sulfonlylurea use in the past 3 months, increasing age and body mass index had lower odds of a future severe hypoglycemia event. For the prediction model for 3 month risk of severe hypoglycemia, the AUC was 0.890 (CI 0.843-0.907) and the IPA was 10.8% (CI 4.4% - 12.4%).

Conclusion: In patients with a documented diagnosis of non-severe hypoglycemia, a dynamic prediction model identifies patients with Type 2 diabetes with 3-month increased risk of severe hypoglycemia, allowing for preventive efforts, such as medication changes, at the point of care.
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http://dx.doi.org/10.1016/j.jdiacomp.2019.107490DOI Listing
January 2020

Attitudes of High Versus Low Antibiotic Prescribers in the Management of Upper Respiratory Tract Infections: a Mixed Methods Study.

J Gen Intern Med 2020 04 19;35(4):1182-1188. Epub 2019 Oct 19.

Department of Internal Medicine, Cleveland Clinic, Cleveland, OH, USA.

Importance: Inappropriate antibiotic use for upper respiratory tract infections (URTIs) is an ongoing problem in primary care. There is extreme variation in the prescribing practices of individual physicians, which cannot be explained by clinical factors.

Objective: To identify factors associated with high and low prescriber status for management of URTIs in primary care practice.

Design And Participants: Exploratory sequential mixed-methods design including interviews with primary care physicians in a large health system followed by a survey. Twenty-nine physicians participated in the qualitative interviews. Interviews were followed by a survey in which 109 physicians participated.

Main Measures: Qualitative interviews were used to obtain perspectives of high and low prescribers on factors that influenced their decision making in the management of URTIs. A quantitative survey was created based on qualitative interviews and responses compared to actual prescribing rates. An assessment of self-prescribing pattern relative to their peers was also conducted.

Results: Qualitative interviews identified themes such as clinical factors (patient characteristics, symptom duration, and severity), nonclinical factors (physician-patient relationship, concern for patient satisfaction, preference and expectation, time pressure), desire to follow evidence-based medicine, and concern for adverse effects to influence prescribing. In the survey, reported concern regarding antibiotic side effects and the desire to practice evidence-based medicine were associated with lower prescribing rates whereas reported concern for patient satisfaction and patient demand were associated with high prescribing rates. High prescribers were generally unaware of their high prescribing status.

Conclusions And Relevance: Physicians report that nonclinical factors frequently influence their decision to prescribe antibiotics for URTI. Physician concerns regarding antibiotic side effects and patient satisfaction are important factors in the decision-making process. Changes in the health system addressing both physicians and patients may be necessary to attain desired prescribing levels.
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http://dx.doi.org/10.1007/s11606-019-05433-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7174444PMC
April 2020

Testosterone replacement therapy and the risk of adverse cardiovascular outcomes and mortality.

Basic Clin Androl 2019 29;29. Epub 2019 Mar 29.

3Quantitative Health Sciences, Cleveland Clinic, Desk JJN3, Cleveland, Ohio 44195 USA.

Background: The risk of adverse cardiovascular events and mortality associated with testosterone replacement therapy is controversial. The purpose of this report was to evaluate the effect of testosterone replacement therapy (TRT) in men with secondary hypogonadism on the risk of myocardial infarction (MI), stroke (CVA) or all-cause mortality.

Methods: A retrospective cohort study was conducted using the Cleveland Clinic's electronic health record. Men ≥40 years of age, with at least two testosterone levels < 220 ng/dL, with one level obtained between 7 am and 10 am, were identified. Men with primary hypogonadism, secondary hypogonadism related to overt hypothalamic pituitary pathology, human immunodeficiency virus infection, metastatic cancer, and select contraindications to TRT, were excluded. Men exposed to TRT were matched to controls that were not exposed. A survival analysis was performed on the composite outcome of MI, CVA, or all-cause mortality.

Results: One hundred sixty-five patients exposed to TRT (treatment group) were matched with 210 not exposed to TRT (comparison group). The prevalence of established cardiovascular disease (CVD) was 20.0% in the treatment group vs. 17.1% in the comparison group ( = 0.478). The median [interquartile range (IQR)] age (years) and BMI (kg/m) were 55 (49, 62) and 35.6 (32.1, 40.1) in the treatment group, and 55 (49, 61.7) and 36.3 (32.1, 40.8) in the comparison group, respectively. There were 12 (7.3%) events observed in the treatment group, and 16 (7.6%) in the comparison group. The median time (years) to the composite event was 2.1 (IQR 0.9, 4.6) and 1.8 (IQR 0.6, 3.4) for treatment and comparison groups, respectively. No difference in the risk of the combined cardiovascular endpoint was observed between the treatment group vs the comparison group, hazard ratio (HR) 0.81 (95% Confidence Interval [CI]: 0.38-1.71;  = 0.57).

Conclusion: In hypogonadal men with a modest prevalence of established CVD, TRT was not observed to confer a protective or adverse effect on the risk of MI, CVA or all-cause mortality.
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http://dx.doi.org/10.1186/s12610-019-0085-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440106PMC
March 2019

Physician Empathy Is Not Associated with Laboratory Outcomes in Diabetes: a Cross-sectional Study.

J Gen Intern Med 2019 01 7;34(1):75-81. Epub 2018 Nov 7.

Center for Value-Based Care Research, Cleveland Clinic, Cleveland, OH, USA.

Background: One widely cited study suggested a link between physician empathy and laboratory outcomes in patients with diabetes, but its findings have not been replicated. While empathy has a positive impact on patient experience, its impact on other outcomes remains unclear.

Objective: To assess associations between physician empathy and glycosylated hemoglobin (HgbA1c) as well as low-density lipoprotein (LDL) levels in patients with diabetes.

Design: Retrospective cross-sectional study.

Participants: Patients with diabetes who received care at a large integrated health system in the USA between January 1, 2011, and May 31, 2014, and their primary care physicians.

Main Measures: The main independent measure was physician empathy, as measured by the Jefferson Scale of Empathy (JSE). The JSE is scored on a scale of 20-140, with higher scores indicating greater empathy. Dependent measures included patient HgbA1c and LDL. Mixed-effects linear regression models adjusting for patient sociodemographic characteristics, comorbidity index, and physician characteristics were used to assess the association between physician JSE scores and their patients' HgbA1c and LDL.

Key Results: The sample included 4176 primary care patients who received care with one of 51 primary care physicians. Mean physician JSE score was 118.4 (standard deviation (SD) = 12). Median patient HgbA1c was 6.7% (interquartile range (IQR) = 6.2-7.5) and median LDL concentration was 83 (IQR = 66-104). In adjusted analyses, there was no association between JSE scores and HgbA1c (β = - 0.01, 95%CI = - 0.04, 0.02, p = 0.47) or LDL (β = 0.41, 95%CI = - 0.47, 1.29, p = 0.35).

Conclusion: Physician empathy was not associated with HgbA1c or LDL. While interventions to increase physician empathy may result in more patient-centered care, they may not improve clinical outcomes in patients with diabetes.
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http://dx.doi.org/10.1007/s11606-018-4731-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6318196PMC
January 2019

Implementing team-based primary care models: a mixed-methods comparative case study in a large, integrated health care system.

J Gen Intern Med 2018 11 6;33(11):1928-1936. Epub 2018 Aug 6.

Center for Community Health Integration, and Departments of Family Medicine and Community Health, Population and Quantitative Health Sciences, Oncology and Sociology, Case Western Reserve University, Cleveland, OH, USA.

Background: Successful implementation of new care models within a health system is likely dependent on contextual factors at the individual sites of care.

Objective: To identify practice setting components contributing to uptake of new team-based care models.

Design: Convergent mixed-methods design.

Participants: Employees and patients of primary care practices implementing two team-based models in a large, integrated health system.

Main Measures: Field observations of 9 practices and 75 interviews, provider and staff surveys to assess adaptive reserve and burnout, analysis of quality metrics, and patient panel comorbidity scores. The data were collected simultaneously, then merged, thematically analyzed, and interpreted by a multidisciplinary team.

Key Results: Based on analysis of observations and interviews, the 9 practices were categorized into 3 groups-high, partial, and low uptake of new team-based models. Uptake was related to (1) practices' responsiveness to change and (2) flexible workflow as related to team roles. Strength of local leadership and stable staffing mediated practices' ability to achieve high performance in these two domains. Higher performance on several quality metrics was associated with high uptake practices compared to the lower uptake groups. Mean Adaptive Reserve Measure and Maslach Burnout Inventory scores did not differ significantly between higher and lower uptake practices.

Conclusion: Uptake of new team-based care delivery models is related to practices' ability to respond to change and to adapt team roles in workflow, influenced by both local leadership and stable staffing. Better performance on quality metrics may identify high uptake practices. Our findings can inform expectations for operational and policy leaders seeking to implement change in primary care practices.
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http://dx.doi.org/10.1007/s11606-018-4611-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206362PMC
November 2018

Testing of a Tool for Prostate Cancer Screening Discussions in Primary Care.

Front Oncol 2018 28;8:238. Epub 2018 Jun 28.

Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States.

Background: As prostate cancer (PCa) screening decisions often occur in outpatient primary care, a brief tool to help the PCa screening conversation in busy clinic settings is needed.

Methods: A previously created 9-item tool to aid PCa screening discussions was tested in five diverse primary care clinics. Fifteen providers were recruited to use the tool for 4 weeks, and the tool was revised based upon feedback. The providers then used the tool with a convenience sample of patients during routine clinic visits. Pre- and post-visit surveys were administered to assess patients' knowledge of the option to be screened for PCa and of specific factors to consider in the decision. McNemar's and Stuart-Maxwell tests were used to compare pre-and post-survey responses.

Results: 14 of 15 providers completed feedback surveys and had positive responses to the tool. All 15 providers then tested the tool on 95 men aged 40-69 at the five clinics with 2-10 patients each. The proportion of patients who strongly agreed that they had the option to choose to screen for PCa increased from 57 to 72% ( = 0.018) from the pre- to post-survey, that there are factors in the personal or family history that may affect PCa risk from 34 to 47% ( = 0.012), and that their opinions about possible side effects of treatment for PCa should be considered in the decision from 47 to 61% ( = 0.009).

Conclusion: A brief conversation tool for the PCa screening discussion was well received in busy primary-care settings and improved patients' knowledge about the screening decision.
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http://dx.doi.org/10.3389/fonc.2018.00238DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6031706PMC
June 2018

Patient Perspectives on Clinical Scribes in Primary Care.

J Gen Intern Med 2018 11;33(11):1859-1861

Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, 9500 Euclid Avenue, G10, Cleveland, OH, 44195, USA.

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http://dx.doi.org/10.1007/s11606-018-4573-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6206358PMC
November 2018

Physicians' Views of Self-Monitoring of Blood Glucose in Patients With Type 2 Diabetes Not on Insulin.

Ann Fam Med 2018 07;16(4):349-352

Center for Value-Based Care Research, Cleveland Clinic, Cleveland, Ohio.

This qualitative study examines to what extent and why physicans still prescribe self-monitoring of blood glucose (SMBG) in patients with non-insulin-treated type 2 diabetes (NITT2D) when the evidence shows it increases cost without improving hemoglobin A (HbA), general well being, or health-related quality of life. Semistructured phone interviews with 17 primary care physicians indicated that the majority continue to recommend routine self-monitoring of blood glucose due to a compelling belief in its ability to promote the lifestyle changes needed for glycemic control. Targeting physician beliefs about the effectiveness of self-monitoring of blood glucose, and designing robust interventions accordingly, may help reduce this practice.
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http://dx.doi.org/10.1370/afm.2244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6037524PMC
July 2018

Prostate Cancer Screening-A New Recommendation for Meaningful Physician-Patient Conversations.

JAMA Oncol 2018 08;4(8):1049-1050

Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio.

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http://dx.doi.org/10.1001/jamaoncol.2018.1492DOI Listing
August 2018

Provider Variation in Antibiotic Prescribing and Outcomes of Respiratory Tract Infections.

South Med J 2018 04;111(4):235-242

From the Department of Internal Medicine, Medicine Institute, Cleveland Clinic, and the Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio.

Objectives: Inappropriate antibiotic use for respiratory tract infection (RTI) is an ongoing problem linked to the emergence of drug resistance and other adverse effects. Less is known about the prescribing practices of individual physicians or the impact of physician prescribing habits on patient outcomes. We studied the prescribing practices of providers for acute RTIs in an integrated health system, identified patient factors associated with receipt of an antibiotic and assessed the relation between providers' adjusted prescribing rates and a number of patient outcomes.

Methods: This was a retrospective analysis of adults with an RTI visit to any primary care providers across the Cleveland Clinic Health System in 2011-2012. Patients with a history of chronic obstructive pulmonary disease or immunocompromised status were excluded. Logistic regression was used to examine patient factors associated with receipt of an antibiotic.

Results: Of 31,416 patients with an RTI, 54.8% received an antibiotic. Patient factors associated with antibiotic prescribing included white race (odds ratio [OR] 1.35, < 0.001), presence of fever (OR 1.66, < 0.001), and a diagnosis of bronchitis (OR 10.98, < 0.001) or sinusitis (OR 33.85, < 0.001). Among 290 providers with ≥10 RTI visits, adjusted antibiotic prescribing rates ranged from 0% to 100% (mean 49%). Antibiotics were prescribed more often for sinusitis (OR 33.85, < 0.001), bronchitis (OR 10.98, < 0.001), or pharyngitis (OR 1.76, < 0.001) compared with upper respiratory tract infection. Patients who were prescribed antibiotics at the index visit were more likely to return for RTI within 1 year (adjusted OR 1.26, < 0.001). Emergency department visits for respiratory complications were rare and not associated with antibiotic receipt.

Conclusions: Antibiotic prescribing for RTI varies widely among physicians and cannot be explained by patient factors. Patients prescribed antibiotics for RTI were more likely to return for RTI.
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http://dx.doi.org/10.14423/SMJ.0000000000000795DOI Listing
April 2018

Antidiabetic treatment patterns and specialty care utilization among patients with type 2 diabetes and cardiovascular disease.

Cardiovasc Diabetol 2018 04 10;17(1):54. Epub 2018 Apr 10.

Department of Endocrinology, Endocrinology and Metabolism Institute, Cleveland Clinic, 9500 Euclid Avenue, Desk F-20, Cleveland, OH, USA.

Background: To evaluate real-world patient characteristics, medication use, and health care utilization patterns in patients with type 2 diabetes with established cardiovascular disease (CVD).

Methods: Cross-sectional analysis of patients with type 2 diabetes seen at Cleveland Clinic from 2005 to 2016, divided into two cohorts: with-CVD and without-CVD. Patient demographics and antidiabetic medications were recorded in December 2016; department encounters included all visits from 1/1/2016 to 12/31/2016. Comorbidity burden was assessed by the diabetes complications severity index (DCSI) score.

Results: Of 95,569 patients with type 2 diabetes, 40,910 (42.8%) were identified as having established CVD. Patients with CVD vs. those without were older (median age 69.1 vs. 58.2 years), predominantly male (53.8% vs. 42.6%), and more likely to have Medicare insurance (69.4% vs. 35.3%). The with-CVD cohort had a higher proportion of patients with a DCSI score ≥ 3 than the without-CVD cohort (65.0% vs. 10.3%). Utilization rates of glucagon-like peptide-1 receptor agonists and sodium-glucose co-transporter-2 inhibitors were low in both with-CVD (4.1 and 2.5%) and without-CVD cohorts (5.4 and 4.1%), respectively. The majority of patient visits (75%) were seen by a primary care provider. During the 1-year observation period, 81.9 and 62.0% of patients with type 2 diabetes and CVD were not seen by endocrinology or cardiology, respectively.

Conclusions: These data indicated underutilization of specialists and antidiabetic medications reported to confer CV benefit in patients with type 2 diabetes and CVD. The impact of recently updated guidelines and cardiovascular outcome trial results on management patterns in such patients remains to be seen.
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http://dx.doi.org/10.1186/s12933-018-0699-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5892008PMC
April 2018

Patient Characteristics Associated With Severe Hypoglycemia in a Type 2 Diabetes Cohort in a Large, Integrated Health Care System From 2006 to 2015.

Diabetes Care 2018 06 16;41(6):1164-1171. Epub 2018 Mar 16.

Department of Endocrinology, Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH.

Objective: To identify severe hypoglycemia events, defined as emergency department visits or hospitalizations for hypoglycemia, in patients with type 2 diabetes receiving care in a large health system and to identify patient characteristics associated with severe hypoglycemia events.

Research Design And Methods: This was a retrospective cohort study from January 2006 to December 2015 using the electronic medical record in the Cleveland Clinic Health System (CCHS). Participants included 50,439 patients with type 2 diabetes receiving care in the CCHS. Number of severe hypoglycemia events and associated patient characteristics were identified.

Results: The incidence proportion of severe hypoglycemia increased from 0.12% in 2006 to 0.31% in 2015 ( = 0.01). Compared with patients who did not experience severe hypoglycemia, those with severe hypoglycemia had similar median glycosylated hemoglobin (HbA) levels. More patients with severe hypoglycemia versus those without had a prior diagnosis of nonsevere hypoglycemia (9% vs. 2%, < 0.001). Logistic regression confirmed an increased odds for severe hypoglycemia with insulin, sulfonylureas, increased number of diabetes medications, history of nonsevere hypoglycemia (odds ratio [OR] 3.01, < 0.001), HbA <6% (42 mmol/mol) (OR 1.95, < 0.001), black race, and increased Charlson comorbidity index. Lower odds of severe hypoglycemia were noted with higher BMI and use of metformin, dipeptidyl peptidase 4 inhibitors, and glucagon-like peptide 1 agonists.

Conclusions: In this retrospective study of patients with type 2 diabetes with severe hypoglycemia, patient characteristics were identified. Patients with severe hypoglycemia had previous nonsevere hypoglycemia diagnoses more frequently than those without. Identifying patients at high risk at the point of care can allow for change in modifiable risk factors and prevention of severe hypoglycemia events.
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http://dx.doi.org/10.2337/dc17-1834DOI Listing
June 2018

Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system.

BMJ Open 2017 Nov 16;7(11):e017583. Epub 2017 Nov 16.

Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, Ohio, USA.

Objective: To determine the prevalence of obesity and its related comorbidities among patients being actively managed at a US academic medical centre, and to examine the frequency of a formal diagnosis of obesity, via International Classification of Diseases, Ninth Revision (ICD-9) documentation among patients with body mass index (BMI) ≥30 kg/m.

Design: The electronic health record system at Cleveland Clinic was used to create a cross-sectional summary of actively managed patients meeting minimum primary care physician visit frequency requirements. Eligible patients were stratified by BMI categories, based on most recent weight and median of all recorded heights obtained on or before the index date of 1July 2015. Relationships between patient characteristics and BMI categories were tested.

Setting: A large US integrated health system.

Results: A total of 324 199 active patients with a recorded BMI were identified. There were 121 287 (37.4%) patients found to be overweight (BMI ≥25 and <29.9), 75 199 (23.2%) had BMI 30-34.9, 34 152 (10.5%) had BMI 35-39.9 and 25 137 (7.8%) had BMI ≥40. There was a higher prevalence of type 2 diabetes, pre-diabetes, hypertension and cardiovascular disease (P value<0.0001) within higher BMI compared with lower BMI categories. In patients with a BMI >30 (n=134 488), only 48% (64 056) had documentation of an obesity ICD-9 code. In those patients with a BMI >40, only 75% had an obesity ICD-9 code.

Conclusions: This cross-sectional summary from a large US integrated health system found that three out of every four patients had overweight or obesity based on BMI. Patients within higher BMI categories had a higher prevalence of comorbidities. Less than half of patients who were identified as having obesity according to BMI received a formal diagnosis via ICD-9 documentation. The disease of obesity is very prevalent yet underdiagnosed in our clinics. The under diagnosing of obesity may serve as an important barrier to treatment initiation.
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http://dx.doi.org/10.1136/bmjopen-2017-017583DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702021PMC
November 2017

Determining the optimal screening interval for type 2 diabetes mellitus using a risk prediction model.

PLoS One 2017 14;12(11):e0187695. Epub 2017 Nov 14.

Medicine Institute, Cleveland Clinic, Cleveland OH, United States of America.

Background: Progression to diabetes mellitus (DM) is variable and the screening time interval not well defined. The American Diabetes Association and US Preventive Services Task Force suggest screening every 3 years, but evidence is limited. The objective of the study was to develop a model to predict the probability of developing DM and suggest a risk-based screening interval.

Methods: We included non-diabetic adult patients screened for DM in the Cleveland Clinic Health System if they had at least two measurements of glycated hemoglobin (HbA1c), an initial one less than 6.5% (48 mmol/mol) in 2008, and another between January, 2009 and December, 2013. Cox proportional hazards models were created. The primary outcome was DM defined as HbA1C greater than 6.4% (46 mmol/mol). The optimal rescreening interval was chosen based on the predicted probability of developing DM.

Results: Of 5084 participants, 100 (4.4%) of the 2281 patients with normal HbA1c and 772 (27.5%) of the 2803 patients with prediabetes developed DM within 5 years. Factors associated with developing DM included HbA1c (HR per 0.1 units increase 1.20; 95%CI, 1.13-1.27), family history (HR 1.31; 95%CI, 1.13-1.51), smoking (HR 1.18; 95%CI, 1.03-1.35), triglycerides (HR 1.01; 95%CI, 1.00-1.03), alanine aminotransferase (HR 1.07; 95%CI, 1.03-1.11), body mass index (HR 1.06; 95%CI, 1.01-1.11), age (HR 0.95; 95%CI, 0.91-0.99) and high-density lipoproteins (HR 0.93; 95% CI, 0.90-0.95). Five percent of patients in the highest risk tertile developed DM within 8 months, while it took 35 months for 5% of the middle tertile to develop DM. Only 2.4% percent of the patients in the lowest tertile developed DM within 5 years.

Conclusion: A risk prediction model employing commonly available data can be used to guide screening intervals. Based on equal intervals for equal risk, patients in the highest risk category could be rescreened after 8 months, while those in the intermediate and lowest risk categories could be rescreened after 3 and 5 years respectively.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0187695PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685604PMC
December 2017
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