Search

Rationale: Many patients with suspected chronic obstructive pulmonary disease (COPD) do not undergo spirometry to confirm the diagnosis. Underutilization is often attributed to barriers to accessing spirometry. Objective: Our objective wasto identify factors associated with spirometry underutilization for patients who are less likely to face access barriers related to travel, insurance, and availability of spirometry. Methods: A retrospective analysis was conducted of patients enrolled in the Veterans Health Administration and living in urban areas with a new diagnosis of COPD between 2012 to 2015, reducing out-of-pocket cost and travel barriers, respectively. We included only patients whose primary care clinic was located in an academically affiliated tertiary level facility with spirometry available. We used logistic regression to estimate associations between patient characteristics and receipt of spirometry within 2 years before or after COPD diagnosis. Results: Of 24,300 patients, 59.7% had spirometry. Compared to patients

BackgroundMortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes.ObjectivesEvaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients.Research designRetrospective cohort study.SubjectsAll ICU patients in five hospitals from October 2017 through September 2019.MeasuresWe used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots.ResultsThe cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with ConclusionsModels incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

ImportanceMany patients do not receive recommended services. Drive time to health care services may affect receipt of guideline-recommended care, but this has not been comprehensively studied.ObjectiveTo assess associations between drive time to care and receipt of guideline-recommended screening, diagnosis, and treatment interventions.Design, Setting, and ParticipantsThis cohort study used administrative data from the National Veterans Health Administration (VA) data merged with Medicare data. Eligible participants were patients using VA services between January 2016 and December 2019. Women ages 65 years or older without underlying bone disease were assessed for osteoporosis screening. Patients with new diagnosis of chronic obstructive pulmonary disease (COPD) indicated by at least 2 encounter codes for COPD or at least 1 COPD-related hospitalization were assessed for receipt of diagnostic spirometry. Patients hospitalized for ischemic heart disease were assessed for cardiac rehabilitation treatment.ExposuresDrive time from each patient’s residential address to the closest VA facility where the service was available, measured using geocoded addresses.Main Outcomes and MeasuresBinary outcome at the patient level for receipt of osteoporosis screening, spirometry, and cardiac rehabilitation. Multivariable logistic regression models were used to assess associations between drive time and receipt of services.ResultsOf 110 780 eligible women analyzed, 36 431 (32.9%) had osteoporosis screening (mean [SD] age, 66.7 [5.4] years; 19 422 [17.5%] Black, 63 403 [57.2%] White). Of 281 130 patients with new COPD diagnosis, 145 249 (51.7%) had spirometry (mean [SD] age, 68.2 [11.5] years; 268 999 [95.7%] men; 37 834 [13.5%] Black, 217 608 [77.4%] White). Of 73 146 patients hospitalized for ischemic heart disease, 11 171 (15.3%) had cardiac rehabilitation (mean [SD] age, 70.0 [10.8] years; 71 217 [97.4%] men; 15 213 [20.8%] Black, 52 144 [71.3%] White). The odds of receiving recommended services declined as drive times increased. Compared with patients with a drive time of 30 minutes or less, patients with a drive time of 61 to 90 minutes had lower odds of receiving osteoporosis screening (adjusted odds ratio [aOR], 0.90; 95% CI, 0.86-0.95) and spirometry (aOR, 0.90; 95% CI, 0.88-0.92) while patients with a drive time of 91 to 120 minutes had lower odds of receiving cardiac rehabilitation (aOR, 0.80; 95% CI, 0.74-0.87). Results were similar in analyses restricted to urban patients or patients whose primary care clinic was in a tertiary care center.Conclusions and RelevanceIn this retrospective cohort study, longer drive time was associated with less frequent receipt of guideline-recommended services across multiple components of care. To improve quality of care and health outcomes, health systems and clinicians should adopt strategies to mitigate travel burden, even for urban patients.

Objective: Robust disease and syndromic surveillance tools are underdeveloped in the United States, as evidenced by limitations and heterogeneity in sociodemographic data collection throughout the COVID-19 pandemic. To monitor the COVID-19 pandemic in Minnesota, we developed a federated data network in March 2020 using electronic health record (EHR) data from 8 multispecialty health systems. Materials and Methods: In this serial cross-sectional study, we examined patients of all ages who received a COVID-19 polymerase chain reaction test, had symptoms of a viral illness, or received an influenza test from January 3, 2016, through November 7, 2020. We evaluated COVID-19 testing rates among patients with symptoms of viral illness and percentage positivity among all patients tested, in aggregate and by zip code. We stratified results by patient and area-level characteristics. Results: Cumulative COVID-19 positivity rates were similar for people aged 12-64 years (range, 15.1%-17.6%) but lower for adults aged ≥65 years (range, 9.3%-10.7%). We found notable racial and ethnic disparities in positivity rates early in the pandemic, whereas COVID-19 positivity was similarly elevated across most racial and ethnic groups by the end of 2020. Positivity rates remained substantially higher among Hispanic patients compared with other racial and ethnic groups throughout the study period. We found similar trends across area-level income and rurality, with disparities early in the pandemic converging over time. Practice Implications: We rapidly developed a distributed data network across Minnesota to monitor the COVID-19 pandemic. Our findings highlight the utility of using EHR data to monitor the current pandemic as well as future public health priorities. Building partnerships with public health agencies can help ensure data streams are flexible and tailored to meet the changing needs of decision makers.

Using vaccine data combined with electronic health records, we report that mRNA boosters provide greater protection than a two-dose regimen against SARS-CoV-2 infection and related hospitalizations. The benefit of a booster was more evident in the elderly and those with comorbidities. These results support the case for COVID-19 boosters.

Using vaccine data combined with electronic health records, we report that mRNA boosters provide greater protection than a 2-dose regimen against SARS-CoV-2 infection and related hospitalizations. The benefit of a booster was more evident in the elderly and those with comorbidities.

Introduction/objectives Pneumocystis jirovecii pneumonia (PJP) is a rare but potentially fatal opportunistic infection; however, consensus varies around which conditions or medications confer a level of risk sufficient to justify antibiotic prophylaxis for PJP. We used electronic health record (EHR) data to assess the current patterns of PJP prophylaxis, PJP outcomes, and prophylaxis-related adverse events among patients with rheumatic diseases who were receiving high-risk immunosuppressant drugs. Methods Data derive from the EHR of a large health system. We included new immunosuppressant users with diagnoses of vasculitis, myositis, or systemic lupus erythematosus. We calculated the proportion of patients who received PJP prophylaxis for each diagnosis and drug combination. We also calculated the number of PJP infections and the number of antibiotic adverse drug events (ADEs) per patient-year of exposure. Results We followed 316 patients for 23.2 + /- 14.2 months. Overall, 124 (39%) of patients received prophylactic antibiotics for PJP. At least 25% of patients with the highest risk conditions (e.g. vasculitis) or highest risk immunosuppressants (e.g. cyclophosphamide) did not receive PJP prophylaxis. We found no cases of PJP infection over 640 patient-years of follow up, including among those not receiving prophylaxis, and an overall incidence rate of ADEs of 2.2% per patient-year. Conclusions PJP prophylaxis for patients with rheumatic conditions is inconsistent, with one quarter of patients who have high risk conditions or high risk immunosuppressants not receiving prophylaxis. However, given extremely low rates of PJP infection, but detectable ADEs to prophylactic antibiotics, our findings suggest that evidence to guide more personalized risk assessments are needed to inform PJP prophylaxis.

Background Opioids and multimodal analgesia are widely administered to manage postoperative pain. However, little is known on how improvements in inpatient pain control are correlated with high-risk (> 90 daily OME) discharge opioid prescriptions for opioid naïve surgical patients. Methods We conducted a retrospective observational study of adult opioid-naïve patients undergoing surgery from June 2012 through December 2018 at a large academic medical center. We used multivariate logistic regression to assess whether multimodal analgesic drugs consumed in the 24 h prior to discharge was associated with a reduction in high-risk opioid discharge prescriptions. We identified other risk factors for receiving a high-risk discharge opioid prescription. Results Among the 32,511 patients, 83% of patients were discharged with an opioid prescription. In 2013, 34.1% of patients with a discharge opioid prescription received a high-risk prescription and this declined to 17.7% by 2018. Use of multimodal analgesic agents during the final 24 h of hospitalization increased each year, with over 80% receiving at least one multimodal analgesic agent by 2018. The median OME consumed in the 24 h prior to discharge peaked in 2013 at 31 and steadily decreased to 19.8 by 2018. There was a significant association between the use of acetaminophen in the 24 h prior to discharge and a high-risk prescription at discharge (p < 0.01). OMEs consumed in the 24 h prior to discharge was a significant predictor of receiving a high-risk discharge prescription, even at low doses. Other factors associated with receipt of a high-risk discharge opioid prescription included male gender, race, history of anxiety disorder, and discharge service. Discussion Use of multimodal analgesia regimens in hospitalized surgical patients in the 24 h prior to hospital discharge increased between 2012 and 2018. Simultaneously, opioid use prior to hospital discharge decreased. Despite these gains, approximately one in five discharge prescriptions was high-risk (> 90 daily OME). In addition, we found that prescribing of discharge opioids above inpatient opioid requirements remains common in opioid naive surgical patients. Conclusion Providers should account for pre-discharge opioid consumption and use of multimodal analgesia when considering the total and daily OME’s that may be appropriate for an individual surgical patient on the discharge opioid prescription.

Supplemental Digital Content is available in the text., OBJECTIVES: To evaluate whether different approaches in note text preparation (known as preprocessing) can impact machine learning model performance in the case of mortality prediction ICU. DESIGN: Clinical note text was used to build machine learning models for adults admitted to the ICU. Preprocessing strategies studied were none (raw text), cleaning text, stemming, term frequency-inverse document frequency vectorization, and creation of n-grams. Model performance was assessed by the area under the receiver operating characteristic curve. Models were trained and internally validated on University of California San Francisco data using 10-fold cross validation. These models were then externally validated on Beth Israel Deaconess Medical Center data. SETTING: ICUs at University of California San Francisco and Beth Israel Deaconess Medical Center. SUBJECTS: Ten thousand patients in the University of California San Francisco training and internal testing dataset and 27,058 patients in the external validation dataset, Beth Israel Deaconess Medical Center. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Mortality rate at Beth Israel Deaconess Medical Center and University of California San Francisco was 10.9% and 7.4%, respectively. Data are presented as area under the receiver operating characteristic curve (95% CI) for models validated at University of California San Francisco and area under the receiver operating characteristic curve for models validated at Beth Israel Deaconess Medical Center. Models built and trained on University of California San Francisco data for the prediction of inhospital mortality improved from the raw note text model (AUROC, 0.84; CI, 0.80–0.89) to the term frequency-inverse document frequency model (AUROC, 0.89; CI, 0.85–0.94). When applying the models developed at University of California San Francisco to Beth Israel Deaconess Medical Center data, there was a similar increase in model performance from raw note text (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.72) to the term frequency-inverse document frequency model (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.83). CONCLUSIONS: Differences in preprocessing strategies for note text impacted model discrimination. Completing a preprocessing pathway including cleaning, stemming, and term frequency-inverse document frequency vectorization resulted in the preprocessing strategy with the greatest improvement in model performance. Further study is needed, with particular emphasis on how to manage author implicit bias present in note text, before natural language processing algorithms are implemented in the clinical setting.

Objective: Describe NICU admission rate variation among hospitals in infants with birthweight ≥2500g and low illness acuity and describe factors that predict NICU admission. Study Design: Retrospective study from the Vizient Clinical Data Base/Resource Manager®. Support vector machine methodology was used to develop statistical models using 1) patient characteristics 2) only the indicator for the inborn hospital and 3) patient characteristics plus indicator for the inborn hospital. Results: NICU admission rates of 427,449 infants from 154 hospitals ranged from 0–28.6%. C-statistics for the patient characteristics model: 0.64 (Confidence Interval (CI) 0.62–0.65), hospital only model: 0.81 (CI, 0.81–0.82) and patient characteristic plus hospital variable model: 0.84 (CI, 0.83–0.84). Conclusion/Relevance: There is wide variation in NICU admission rates in infants with low acuity diagnoses. In all cohorts, birth hospital better predicted NICU admission than patient characteristics alone.

This cross-sectional study uses data from the IBM MarketScan Commercial Database to examine the association between increases in wholesale list prices of branded medications and amounts paid by patients and insurers for the same drugs., Key Points Question How are increases in wholesale list prices for medications distributed among increases in patient out-of-pocket costs, insurer payments, and changes in rebates and discounts? Findings In this cross-sectional analysis of 14.4 million pharmacy claims made by 1.8 million patients for the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers in 2014, the median drug wholesale list price (as defined by Average Wholesale Price) increased by 129% from 2010-2016, while median patient out-of-pocket costs increased by 53% and median insurance payments after rebates and discounts increased by 64%. Meaning This study’s findings suggest that, after adjusting for inflation, increases in drug list prices are associated with increased patient out-of-pocket costs, which may have implications for cost-related nonadherence, and insurer payments., Importance High out-of-pocket drug costs can cause patients to skip treatment and worsen outcomes, and high insurer drug payments could increase premiums. Drug wholesale list prices have doubled in recent years. However, because of manufacturer discounts and rebates, the extent to which increases in wholesale list prices are associated with amounts paid by patients and insurers is poorly characterized. Objective To determine whether increases in wholesale list prices are associated with increases in amounts paid by patients and insurers for branded medications. Design, Setting, and Participants Cross-sectional retrospective study analyzing pharmacy claims for patients younger than 65 years in the IBM MarketScan Commercial Database and pricing data from SSR Health, LLC, between January 1, 2010, and December 31, 2016. Pharmacy claims analyzed represent claims of employees and dependents participating in employer health benefit programs belonging to large employers. Rebate data were estimated from sales data from publicly traded companies. Analysis focused on the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers nationwide in 2014. Data were analyzed from July 2017 to July 2020. Exposures Calendar year. Main Outcomes and Measures Changes in inflation-adjusted amounts paid by patients and insurers for branded medications. Results In this analysis of 14.4 million pharmacy claims made by 1.8 million patients from 2010-2016, median drug wholesale list price increased by 129% (interquartile range [IQR], 78%-133%), while median insurance payments increased by 64% (IQR, 28%-120%) and out-of-pocket costs increased by 53% (IQR, 42%-82%). The mean percentage of wholesale list price accounted for by discounts increased from 17% in 2010 to 21% in 2016, and the mean percentage of wholesale list price accounted for by rebates increased from 22% in 2010 to 24% in 2016. For specialty medications, median patient out-of-pocket costs increased by 85% (IQR, 73%-88%) from 2010 to 2016 after adjustment for inflation and 42% (IQR, 25%-53%) for nonspecialty medications. During that same period, insurer payments increased by 116% for specialty medications (IQR, 100%-127%) and 28% for nonspecialty medications (IQR, 5%-34%). Conclusions and Relevance This study’s findings suggest that drug list prices more than doubled over a 7-year study period. Despite rising manufacturer discounts and rebates, these price increases were associated with large increases in patient out-of-pocket costs and insurer payments.

Background Neurosurgical spine specialists receive considerable amounts of industry support that may impact the cost of care. The aim of this study was to evaluate the association between industry payments received by spine surgeons and the total hospital and operating room (OR) costs of an anterior cervical discectomy and fusion (ACDF) procedure among Medicare beneficiaries. Methods All ACDF cases were identified among the Medicare carrier files from January 1, 2013, to December 31, 2014, and matched to the Medicare inpatient baseline file. The total hospital and OR charges were obtained for these cases. Charges were converted to cost using year-specific cost-to-charge ratios. Surgeons were identified among the Open Payments database, which is used to quantify industry support. Analyses were performed to examine the association between industry payments received and ACDF costs. Results Matching resulting in the inclusion of 2209 ACDF claims from 2013–2014. In 2013 and 2014, the mean total cost for an ACDF was $21,798 and $21,008, respectively; mean OR cost was $5878 and $6064, respectively. Mann-Whitney U test demonstrated no significant differences in the mean total or OR cost for an ACDF based on quartile of general industry payment received (P = 0.21 and P = 0.54), and linear regression found no association between industry general payments, research support, or investments on the total hospital cost (P = 0.41, P = 0.13, and P = 0.25, respectively), or OR cost for an ACDF (P = 0.35, P = 0.24, and P = 0.40, respectively). Conclusions This study suggests that spine surgeons performing ACDF surgeries may receive industry support without impacting the cost of care.

Neal Yuan,1 Christy Boscardin,2 Nadra E Lisha,2 R Adams Dudley,3,4 Grace A Lin2 1Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; 2Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA, USA; 3Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, USA; 4School of Public Health, University of Minnesota, Minneapolis, MN, USACorrespondence: Neal YuanSmidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Davis 1015, Los Angeles, CA 90048, USAEmail Neal.Yuan@cshs.orgBackground: In stable coronary artery disease (CAD), shared decision-making (SDM) is encouraged when deciding whether to pursue percutaneous coronary intervention (PCI) given similar cardiovascular outcomes between PCI and medical therapy. However, it remains unclear whether improving patient-provider communication and patient knowledge, the main tenets of SDM, changes patient preferences or the treatment chosen. We explored the relationships between patient-provider communication, patient knowledge, patient preferences, and the treatment received.Methods: We surveyed stable CAD patients referred for elective cardiac catheterization at seven hospitals from 6/2016 to 9/2018. Surveys assessed patient-provider communication, medical knowledge, and preferences for treatment and decision-making. We verified treatments received by chart review. We used linear and logistic regression to examine relationships between patient-provider communication and knowledge, knowledge and preference, and preference and treatment received.Results: Eighty-seven patients completed the survey. More discussion of the benefits and risks of both medical therapy and PCI associated with higher patient knowledge scores (β=0.28, p< 0.01). Patient knowledge level was not associated with preference for PCI (OR=0.78, 95% CI 0.57– 1.03, p=0.09). Black patients had more than four times the odds of preferring medical therapy to PCI (OR=4.49, 1.22– 18.45, p=0.03). Patients preferring medical therapy were not significantly less likely to receive PCI (OR=0.67, 0.16– 2.52, p=0.57).Conclusions: While communicating the risks of PCI may improve patient knowledge, this knowledge may not affect patient treatment preferences. Rather, other factors such as race may be significantly more influential on a patient’s treatment preferences. Furthermore, patient preferences are still not well reflected in the treatment received. Improving shared decision-making in stable CAD therefore may require not only increasing patient education but also better understanding and including a patient’s background and pre-existing beliefs.Keywords: stable coronary artery disease, stable angina, shared decision-making, percutaneous coronary intervention, stent, optimal medical therapy

BackgroundThere is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes.ObjectiveIdentify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.Design, Settings, and ParticipantsRetrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020.MethodsEnsemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes.Main outcomes and measuresPhenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality.ResultsThe database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III.When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (pConclusionIn this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.

Importance Despite past and ongoing efforts to achieve health equity in the United States, persistent disparities in socioeconomic status along with multilevel racism maintain disparate outcomes and appear to be amplified by COVID-19. Objective Measure socioeconomic factors and primary language effects on the risk of COVID-19 severity across and within racial/ethnic groups. Design Retrospective cohort study. Setting Health records of 12 Midwest hospitals and 60 clinics in the U.S. between March 4, 2020 to August 19, 2020. Participants PCR+ COVID-19 patients. Patients missing race or ethnicity data or those diagnosed with COVID-19 during a hospitalization were excluded. Exposures Main exposures included race/ethnicity, area deprivation index (ADI), and primary language. Main Outcomes and Measures The primary outcome was COVID-19 severity using hospitalization within 45 days of diagnosis. Logistic and competing-risk regression models (censored at 45 days and accounting for the competing risk of death prior to hospitalization) assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race effects of ADI and primary language were measured using logistic regression. Results 5,577 COVID-19 patients were included, 866 (n=15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 years, IQR: 45.7-75.9 vs. 40.4 years, IQR: 25.6-58.3, p

This study examines cost sharing for novel second-line diabetes treatment agents under Medicare Part D.

BackgroundDespite past and ongoing efforts to achieve health equity in the United States, persistent disparities in socioeconomic status along with multilevel racism maintain disparate outcomes and appear to be amplified by COVID-19.ObjectiveMeasure socioeconomic factors and primary language effects on the risk of COVID-19 severity across and within racial/ethnic groups.DesignRetrospective cohort study.SettingHealth records of 12 Midwest hospitals and 60 clinics in the U.S. between March 4, 2020 to August 19, 2020.PatientsPCR+ COVID-19 patients.ExposuresMain exposures included race/ethnicity, area deprivation index (ADI), and primary language.Main Outcomes and MeasuresThe primary outcome was COVID-19 severity using hospitalization within 45 days of diagnosis. Logistic and competing-risk regression models (censored at 45 days and accounting for the competing risk of death prior to hospitalization) assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race effects of ADI and primary language were measured using logistic regression.Results5,577 COVID-19 patients were included, 866 (n=15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 vs. 40.4 years, pIndependent of ADI, minority race/ethnicity was associated with COVID-19 severity; Hispanic patients (OR 3.8, 95% CI 2.72–5.30), Asians (OR 2.39, 95% CI 1.74–3.29), and Blacks (OR 1.50, 95% CI 1.15–1.94). ADI was not associated with hospitalization. Non-English speaking (OR 1.91, 95% CI 1.51–2.43) significantly increased odds of hospital admission across and within minority groups.ConclusionsMinority populations have increased odds of severe COVID-19 independent of neighborhood deprivation, a commonly suspected driver of disparate outcomes. Non-English-speaking accounts for differences across and within minority populations. These results support the continued concern that racism contributes to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity across and within minority groups.Key PointsQuestionDoes socioeconomic factors or primary language account for racial disparities in COVID-19 disease severity?FindingsIn this observational study of 5,577 adults, race/ethnicity minorities and non-English as a primary language, independent of neighborhood-level deprivation, are associated with increased risk of severe COVID-19 disease.MeaningSocioeconomic factors do not account for racial/ethnic disparities related to COVID-19 severity which supports further investigation into the racism and highlights the need to focus on our non-English speaking populations.

To develop a method for calculating rates of testing for breast cancer recurrence in patients who have already undergone initial treatment for breast cancer, we calculated rates in a cohort of Medicare breast cancer patients and an age-matched non-cancer cohort. We first used only tests with claims including diagnosis codes indicating invasive breast cancer and then used all tests regardless of diagnosis code. For each method, we calculated testing rates in the breast cancer cohort above the background rate in the non-cancer population. The two methods provided similar estimates of testing prevalence and frequency, with exception of prevalence of CT.

Evidence suggests that hydroxychloroquine (HCQ) retinal toxicity is more common than previously thought. Adhering to careful weight-based dosing can significantly reduce the risk of this adverse event and is recommended in recent guidelines. We used electronic health record data from a large health system to examine HCQ dosing over a 5-year period and identify risk factors associated with higher dosage of HCQ. We constructed a longitudinal, retrospective cohort of patients with HCQ prescriptions (1681 patients with 3490 prescribing events) between 2012 and 2016. We measured HCQ dosing patterns relative to guidelines (

OBJECTIVE: Delaying cataract surgery is associated with an increased risk of falls, but it is unknown whether routine preoperative testing delays cataract surgery long enough to cause clinical harm. We sought to determine whether the use of routine preoperative testing leads to harm in the form of delayed surgery and falls in Medicare beneficiaries awaiting cataract surgery. DESIGN: Retrospective observational cohort study using 2006-2014 Medicare claims PARTICIPANTS: Medicare beneficiaries age 66+ with a CPT claim for ocular biometry METHODS: We measured the mean and median number of days between ocular biometry and cataract surgery, calculated the proportion of patients waiting >30 days or >90 days for surgery, and determined the odds of having a fall within 90 days of biometry among patients of high-testing physicians (testing performed in ≥75% of their patients) compared to patients of low-testing physicians. We estimated the number of days of delay attributable to high-testing physicians relative to other factors that may determine surgical scheduling. MAIN OUTCOME MEASURES: Incidence of falls occurring between biometry and surgery, odds of falling within 90 days of biometry, and estimated delay in days associated with physician testing behavior . RESULTS: Of 248,345 beneficiaries, 16.4% were patients of high-testing physicians. More patients of high-testing physicians waited >30 days and >90 days to have cataract surgery (31.4% and 8.2% versus 25.0% and 5.5%, respectively, p

Introduction: Opioids are routinely prescribed to manage acute postoperative pain, but changes in postoperative opioid prescribing associated with the marketing of long-acting opioids such as OxyContin have not been described in the surgical cohort. Methods: Using a large commercial claims data set, we studied postoperative opioid prescribing after selected common surgical procedures between 1994 and 2014. For each procedure and year, we calculated the mean postoperative morphine milligram equivalents (MME) filled on the index prescription and assessed the proportion of patients who filled a high-dose prescription (≥350 MME). We reported changes in postoperative opioid prescribing over time and identified predictors of filling a high-dose postoperative opioid prescription. Results: We identified 1,321,264 adult patients undergoing selected common surgical procedures between 1994 and 2014, of whom 80.3% filled a postoperative opioid prescription. One in five surgery patients filled a high-dose postoperative opioid prescription. Between 1994 and 2014, the mean MME filled increased by 145%, 84%, and 85% for lumbar laminectomy/laminotomy, total knee arthroplasty, and total hip arthroplasty, respectively. The procedures most likely to be associated with a high-dose opioid fill were all orthopaedic procedures (AOR 5.20 to 7.55, P < 0.001 for all). Patients whose postoperative opioid prescription included a long-acting formulation had the highest odds of filling a prescription that exceeded 350 MME (AOR 32.01, 95% CI, 30.23–33.90). Discussion: After the US introduction of long-acting opioids such as OxyContin, postoperative opioid prescribing in commercially insured patients increased in parallel with broader US opioid-prescribing trends, most notably among patients undergoing orthopaedic surgical procedures. The increase in the mean annual MME filled starting in the late 1990s was driven in part by the higher proportion of long-acting opioid formulations on the index postoperative opioid prescription filled by orthopaedic surgery patients.

This study examines the insurance coverage and out-of-pocket costs to Medicare beneficiaries for the angiotensin receptor–neprilysin inhibitor sacubitril/valsartan.

Objective Electronic health records (EHRs) were expected to yield numerous benefits. However, early studies found mixed evidence of this. We sought to determine whether widespread adoption of modern EHRs in the US has improved clinical care. Methods We studied hospitals reporting performance measures from 2008–2015 in the Centers for Medicare and Medicaid Services Hospital Compare database that also reported having an EHR in the American Hospital Association 2015 IT supplement. Using interrupted time-series analysis, we examined the association of EHR implementation, EHR vendor, and Meaningful Use status with 11 process measures and 30-day hospital readmission and mortality rates for heart failure, pneumonia, and acute myocardial infarction. Results A total of 1246 hospitals contributed 8222 hospital-years. Compared to hospitals without EHRs, hospitals with EHRs had significant improvements over time on 5 of 11 process measures. There were no substantial differences in readmission or mortality rates. Hospitals with CPSI EHR systems performed worse on several process and outcome measures. Otherwise, we found no substantial improvements in process measures or condition-specific outcomes by duration of EHR use, EHR vendor, or a hospital’s Meaningful Use Stage 1 or Stage 2 status. Conclusion In this national study of hospitals with modern EHRs, EHR use was associated with better process of care measure performance but did not improve condition-specific readmission or mortality rates regardless of duration of EHR use, vendor choice, or Meaningful Use status. Further research is required to understand why EHRs have yet to improve standard outcome measures and how to better realize the potential benefits of EHR systems.

This study examines the ambulatory health care use and costs of adults with congenital heart disease in the US.

Importance The 2019 federal Ending the HIV Epidemic initiative requires a vast expansion of access to antiretroviral therapy (ART) and preexposure prophylaxis (PrEP) for HIV treatment and prevention. However, high prices for ART and PrEP can reduce their affordability and use. Medicare covers 1 in 4 persons living with HIV, and the Medicare Part D drug benefit imposes complicated cost-sharing between patients and other stakeholders. Objective To determine how the Medicare Part D design distributes the cost burden for ART and PrEP between patients, insurance plans, manufacturers, and Medicare. Design and Setting Nationwide cross-sectional analyses of first quarter 2019 Medicare formulary and pricing files for 3326 Part D plans were performed. These files contain drug benefit data, including prices and cost-sharing requirements. Main Outcomes and Measures For 18 ART and 2 PrEP regimens, the out-of-pocket costs for patients and the cost borne by plans, manufacturers, and Medicare were projected for 1 year of treatment or prevention under a 2019 standard Medicare Part D insurance plan. Analyses assumed that patients used the ART or PrEP regimen and no other medications. Results In 2019, ART prices ranged from $24 010 to $46 770 annually (median price, $35 780), with patients projected to pay 9% to 14% of the cost ($3270-$4350), insurance plans 18% to 24% ($5340-$8450), manufacturers 6% to 11% ($2370-$2750), and Medicare 53% to 67% ($12 770-$31 270). The price of PrEP was $20 570 annually, with patients contributing 15% ($2990), insurance plans 22% ($4570), manufacturers 13% ($2750), and Medicare 50% ($10 260). For beneficiaries with low-income subsidies that cover all patient cost-sharing, Medicare would assume 67% to 76% of ART costs and 65% of PrEP costs. Conclusions and Relevance Medicare Part D mandates universal ART and PrEP coverage, but high prices (>$35 000 annually for ART and>$20 000 annually for PrEP) and the design of Part D can jeopardize affordability for patients and place most of the cost burden on taxpayers. Under a standard Medicare Part D benefit, patients pay $3000 to $4000 out-of-pocket yearly, unless they qualify for low-income subsidies, and half to two-thirds of the cost of ART and PrEP is borne by Medicare rather than insurance plans or manufacturers. To end the HIV epidemic by 2030, it appears that policies must address both high drug prices and revamp Medicare Part D cost-sharing.

This study estimates mean 2017 total and out-of-pocket costs for infliximab and its biosimilar infliximab-dyyb under Medicare Part D.

INTRODUCTION: Dementia is among the costliest of medical conditions, but it is not known how these costs vary by dementia subtype. METHODS: The effect of dementia diagnosis subtype on direct healthcare costs and utilization was estimated using 2015 California Medicare fee-for-service data. Potential drivers of increased costs in Lewy body dementia, in comparison to Alzheimer’s disease, were tested. RESULTS: 3,001,987 Medicare beneficiaries were identified, of which 8.2% had a dementia diagnosis. Unspecified dementia was the most common diagnostic category (59.6%), followed by Alzheimer’s disease (23.2%). Lewy body dementia was the costliest subtype to Medicare, on average, followed by vascular dementia. The higher costs in Lewy body dementia were explained in part by falls, urinary incontinence or infection, depression, anxiety, dehydration, and delirium. DISCUSSION: Dementia subtype is an important predictor of healthcare costs. Earlier identification and targeted treatment might mitigate the costs associated with co-occurring conditions in Lewy body dementia.

Risk adjustment models for intensive care outcomes have yet to realize the full potential of data unlocked by the increasing adoption of EHRs. In particular, they fail to fully leverage the information present in longitudinal, structured clinical data - including laboratory test results and vital signs - nor can they infer patient state from unstructured clinical narratives without lengthy manual abstraction. A fully electronic ICU risk model fusing these two types of data sources may yield improved accuracy and more personalized risk estimates, and in obviating manual abstraction, could also be used for real-time decision-making. As a first step towards fully “electronic” ICU models based on fused data, we present results of generalized additive modeling applied to a sample of over 36,000 ICU patients. Our approach outperforms those based on the SAPS and OASIS systems (A UC: 0.908 vs. 0.794 and 0.874), and appears to yield more granular and easily visualized risk estimates.

© Health Research and Educational Trust. Objectives. Narrative review of the impact of pay-for-performance (P4P) and public reporting (PR) on health care outcomes, including spillover effects and impact on disparities. Principal Findings. The impact of P4P and PR is dependent on the underlying payment system (fee-for-service, salary, capitation) into which these schemes are introduced. Both have the potential to improve care, but they can also have substantial unintended consequences. Evidence from the behavioral economics literature suggests that individual physicians will vary in how they respond to incentives. We also discuss issues to be considered when including patient-reported outcome measures (PROMs) or patient-reported experience measures into P4P and PR schemes. Conclusion. We provide guidance to payers and policy makers on the design of P4P and PR programs so as to maximize their benefits and minimize their unintended consequences. These include involving clinicians in the design of the program, taking into account the payment system into which new incentives are introduced, designing the structure of reward programs to maximize the likelihood of intended outcomes and minimize the likelihood of unintended consequences, designing schemes that minimize the risk of increasing disparities, providing stability of incentives over some years, and including outcomes that are relevant to patients' priorities. In addition, because of the limitations of PR and P4P as effective interventions in their own right, it is important that they are combined with other policies and interventions intended to improve quality to maximize their likely impact.

Management of many chronic conditions has improved dramatically in the last decade with the advent of novel specialty drugs, which are often both life-changing and costly. This is particularly true for rheumatoid arthritis (RA), a condition affecting 1.3 million people in the United States (1). Prior to the late 1990s, RA was among the most debilitating chronic conditions, with one in three patients permanently disabled within five years (2, 3). In 2014, disease control is possible for many patients with early and aggressive treatment using disease modifying anti-rheumatic drugs (DMARDs), now standard components of guideline-based care (4, 5). However, newer biologic DMARDs can cost over $20,000 annually, and even with insurance, many patients who require them after failing first-line drugs for disease control will face a significant financial burden for treatment (6–9). A national survey of 1,100 adults with RA found that 1 in 6 decreased medication use due to cost, potentially resulting in worse outcomes (10). Because biologic DMARDs are both efficacious and expensive, understanding how to cover and pay for them is an important policy issue. Since RA affects 2.3% of older persons and over 1 in 4 Medicare beneficiaries with RA receive biologic DMARDs, Medicare spending in this area is large, exceeding a billion dollars in 2009 (11, 12). Insurance coverage for biologic DMARDs by Medicare has grown increasingly complex. Biologic DMARDs were historically covered only under Medicare’s medical insurance (Part B) as physician-administered drugs. With the introduction of the Part D pharmacy benefit in 2006, coverage was significantly expanded to include self-administered biologic DMARDs dispensed through a pharmacy. Despite the projected growth in Medicare spending on biologic DMARDs in Part D, no studies have systematically examined how Part D plans nationwide cover biologic DMARDs or the financial implications of current coverage policies. To address this gap, we conducted a nationwide examination of 2013 Part D plans’ coverage of biologic and non-biological DMARDs. We investigate the level of cost sharing for patients and also analyze how Medicare Advantage versus stand-alone drug plans structure cost sharing by using prior authorizations (PA), specialty tiers, and coinsurance versus fixed dollar copayments. Our findings shed light on how Part D currently provides coverage for biologic DMARDs and the potential impact of Part D changes included in the Affordable Care Act.

Background: The objective of this study was to examine the presence and magnitude of US geographic variation in use rates of both recommended and high-cost imaging in young patients with early-stage breast cancer during the 18 month period after surgical treatment of their primary tumor. Methods: Using the Truven Health MarketScan Commercial Database, a descriptive analysis was conducted of geographic variation in annual rates of dedicated breast imaging and high-cost body imaging of 36,045 women aged 18 to 64 years treated with surgery for invasive unilateral breast cancer between 2010 and 2012. Multivariate hierarchical analysis examined the relationship between likelihood of imaging and patient characteristics, with metropolitan statistical area (MSA) serving as a random effect. Patient characteristics included age group, BRCA1/2 carrier status, family history of breast cancer, combination of breast surgery type and radiation therapy, drug therapy, and payer type. All MSAs in the United States were included, with areas outside MSAs within a given state aggregated into a single area for analytic purposes. Results: Descriptive analysis of rates of imaging use and intensity within MSA regions revealed wide geographic variation, irrespective of treatment cohort or age group. Increased probability of recommended postoperative dedicated breast imaging was primarily associated with age and treatment including both surgery and radiation therapy, followed by MSA region (odds ratio, 1.42). Increased probability of PET use-a high-cost imaging modality for which postoperative routine use is not recommended in the absence of specific clinical findings-was primarily associated with surgery type followed by MSA region (odds ratio, 1.82). Conclusions: In patients with breast cancer treated for low-risk disease, geography has effects on the rates of posttreatment imaging, suggesting that some patients are not receiving beneficial dedicated breast imaging, and high-cost nonbreast imaging may not be targeted to those groups most likely to benefit.

OBJECTIVES To determine the occurrence rate of unplanned readmissions to PICUs within 1 year and examine risk factors associated with repeated readmission. DESIGN Retrospective cohort analysis. SETTING Seventy-six North American PICUs that participated in the Virtual Pediatric Systems, LLC (VPS, LLC, Los Angeles, CA). PATIENTS Ninety-three thousand three hundred seventy-nine PICU patients discharged between 2009 and 2010. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Index admissions and unplanned readmissions were characterized and their outcomes compared. Time-to-event analyses were performed to examine factors associated with readmission within 1 year. Eleven percent (10,233) of patients had 15,625 unplanned readmissions within 1 year to the same PICU; 3.4% had two or more readmissions. Readmissions had significantly higher PICU mortality and longer PICU length of stay, compared with index admissions (4.0% vs 2.5% and 2.5 vs 1.6 d; all p < 0.001). Median time to readmission was 30 days for all readmissions, 3.5 days for readmissions during the same hospitalization, and 66 days for different hospitalizations. Having more complex chronic conditions was associated with earlier readmission (adjusted hazard ratio, 2.9 for one complex chronic condition; hazard ratio, 4.8 for two complex chronic conditions; hazard ratio, 9.6 for three or more complex chronic conditions; all p < 0.001 compared no complex chronic condition). Most specific complex chronic condition conferred a greater risk of readmission, and some had considerably higher risk than others. CONCLUSIONS Unplanned readmissions occurred in a sizable minority of PICU patients. Patients with complex chronic conditions and particular conditions were at much higher risk for readmission.

A fundamental task of physicians is to guide patients through illness, a process that involves collecting information and then seeking balance between the risks and benefits of treatments and between the length and quality of life. The work of physicians draws on an expanding base of medical knowledge, and there is always more to learn than time to read. Understandably, help is appreciated. It is easier when educational opportunities are convenient and enjoyable such as when they are combined with meals brought into clinic by pharmaceutical representatives and others.

Objective Systemic lupus erythematosus (SLE) has one of the highest hospital readmission rates among chronic conditions. This study was undertaken to identify patient-level, hospital-level, and geographic predictors of 30-day hospital readmissions associated with SLE. Methods Using hospital discharge databases from 5 geographically dispersed states, we studied all-cause readmission of SLE patients between 2008 and 2009. We evaluated each hospitalization as a possible index event leading up to a readmission, our primary outcome. We accounted for clustering of hospitalizations within patients and within hospitals and adjusted for hospital case mix. Using multilevel mixed-effects logistic regression, we examined factors associated with 30-day readmission and calculated risk-standardized hospital-level and state-level readmission rates. Results We examined 55,936 hospitalizations among 31,903 patients with SLE. Of these hospitalizations, 9,244 (16.5%) resulted in readmission within 30 days. In adjusted analyses, age was inversely related to risk of readmission. African American and Hispanic patients were more likely to be readmitted than white patients, as were those with Medicare or Medicaid insurance (versus private insurance). Several clinical characteristics of lupus, including nephritis, serositis, and thrombocytopenia, were associated with readmission. Readmission rates varied significantly between hospitals after accounting for patient-level clustering and hospital case mix. We also found geographic variation, with risk-adjusted readmission rates lower in New York and higher in Florida as compared to California. Conclusion We found that ∼1 in 6 hospitalized patients with SLE were readmitted within 30 days of discharge, with higher rates among historically underserved populations. Significant geographic and hospital-level variation in risk-adjusted readmission rates suggests potential for quality improvement.

The choice of antiplatelet therapy after acute coronary syndrome (ACS) is complicated: Ticagrelor and prasugrel are novel alternatives to clopidogrel, patients with some genotypes may not respond to clopidogrel, and low-cost generic formulations of clopidogrel are available.To determine the most cost-effective strategy for dual antiplatelet therapy after percutaneous coronary intervention for ACS.Decision-analytic model.Published literature, Medicare claims, and life tables.Patients having percutaneous coronary intervention for ACS.Lifetime.Societal.Five strategies were examined: generic clopidogrel, prasugrel, ticagrelor, and genotyping for polymorphisms of CYP2C19 with carriers of loss-of-function alleles receiving either ticagrelor (genotyping with ticagrelor) or prasugrel (genotyping with prasugrel) and noncarriers receiving clopidogrel.Direct medical costs, quality-adjusted life years(QALYs), and incremental cost-effectiveness ratios (ICERs).The clopidogrel strategy produced$179 301 in costs and 9.428 QALYs. Genotyping with prasugrel was superior to prasugrel alone, with an ICER of $35 800 per QALY relative to clopidogrel. Genotyping with ticagrelor was more effective than genotyping with prasugrel ($30 200 per QALY relative to clopidogrel). Ticagrelor was the most effective strategy($52 600 per QALY relative to genotyping with ticagrelor).Stronger associations between genotype and thrombotic outcomes rendered ticagrelor substantially less cost-effective ($104 800 per QALY). Genotyping with prasugrel was the preferred therapy among patients who could not tolerate ticagrelor.No randomized trials have directly compared genotyping strategies or prasugrel with ticagrelor.Genotype-guided personalization may improve the cost-effectiveness of prasugrel and ticagrelor after percutaneous coronary intervention for ACS, but ticagrelor for all patients may bean economically reasonable alternative in some settings.

Background and objectives Little is known about geographic differences in the incidence of AKI. The objective of this study was to determine if regional variation exists in the population incidence of dialysis-requiring AKI in the United States. Design, setting, participants, & methods Data from the Nationwide Inpatient Sample, a US nationally representative sample of hospitalizations, were used to determine the incidence rates of dialysis-requiring AKI between 2007 and 2009 among the four US Census-designated regions. Cases were identified using validated discharge codes. Poisson regression models were used to estimate overall regional rates, accounting for the data's sampling scheme. Results In 2007-2009, the population incidence rates of dialysis-requiring AKI differed across the four Census-designated regions (P=0.04). Incidence was highest in the Midwest (523 cases/million person-yr, 95% confidence interval=483 to 568) and lowest in the Northeast (457 cases/million person-yr, 95% confidence interval=426 to 492). The pattern of regional variation in the incidence of dialysis-requiring AKI was not the same as the pattern of regional variation in the incidence of renal replacement therapy-requiring ESRD (obtained from the US Renal Data System). In-hospital mortality associated with dialysis-requiring AKI differed across the four regions, with the highest case fatality in the Northeast (25.9%) and the lowest case fatality in the Midwest (19.4%). Conclusions Significant regional variation exists in the population incidence of dialysis-requiring AKI in the United States, and additional investigation is warranted to uncover potential causes behind these geographic differences. © 2013 by the American Society of Nephrology.

This study investigated clinicians' perspectives during a quality improvement project to promote breastmilk feedings in premature infants.From 2009 to 2010, 11 hospitals in the California Perinatal Quality Care Collaborative participated in a project to promote breastmilk feedings in premature infants. Audio recordings of monthly meetings held to encourage sharing of ideas were analyzed using qualitative methods to identify common themes related to barriers and solutions to breastmilk feeding promotion.Two broad categories were noted: communication and team composition. Communication subthemes included (1) communication among hospital staff, including consistent documentation, (2) communication with family, and (3) communication between transfer hospitals. Team composition subthemes included (4) importance of physician buy-in and (5) integrated teams designed to empower leaders.Optimizing communication among health professionals and parents and improving team composition may be key components of facilitating breastmilk feeding promotion in premature infants.