Ceftaroline Fosamil for the Treatment of Methicillin-Resistant Staphylococcus Aureus Bacteremia: A Real-World Comparative Clinical Outcomes Study.

BACKGROUND AND OBJECTIVE: Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia results in substantial morbidity and mortality. As current treatments often lead to unsatisfactory outcomes, evidence guiding alternative treatment options is needed. This study evaluated real-world clinical outcomes of ceftaroline fosamil for the treatment of MRSA bacteremia. METHODS: This retrospective study included adults hospitalized with MRSA bacteremia between 2011 and 2019. Patients were classified according to treatment with ceftaroline fosamil (ceftaroline), vancomycin, or daptomycin: Group 1, ceftaroline; Group 2, vancomycin or daptomycin (without ceftaroline); Group 3, combination therapy with ≥ 2 of these three agents. Clinical outcomes were compared using propensity-score-adjusted odds ratios (ORs) from logistic regression models. RESULTS: Overall, 24,479 patients were included (Group 1, n = 532; Group 2, n = 21,555; Group 3, n = 2392). Mean age was 59.6, 60.8, and 57.4 years in Groups 1, 2, and 3, respectively. Mean post-index treatment length of stay was 8.8, 8.8, and 8.0 days, respectively. The most frequent line of therapy was ceftaroline first-line (42.1%), vancomycin or daptomycin first-line (95.4%), and combination therapy third-line or later (67.8%) in Groups 1, 2, and 3, respectively. Compared with Group 2, Groups 1 and 3 had similar favorable clinical responses {odds ratio [OR] = 1.18 [95% confidence interval (CI) 0.98-1.44], p = 0.08; OR = 1.20 [95% CI 0.97-1.47], p = 0.09, respectively} and were less likely to switch treatment (both p < 0.001). Compared with Group 2, Group 1 was more likely to undergo 30-day all-cause readmission [OR = 1.38 (95% CI 1.06-1.80), p = 0.02], whereas this was less likely for Group 3 [OR = 0.77 (95% CI 0.58-1.00), p = 0.05]. CONCLUSIONS: Patients receiving ceftaroline more often had favorable clinical responses than those receiving vancomycin or daptomycin monotherapy. In the absence of large-scale randomized controlled trials, these real-world data provide insights into the potential role of ceftaroline for treating MRSA bacteremia.

Subphenotypes of self-reported symptoms and outcomes in long COVID: a prospective cohort study with latent class analysis.

OBJECTIVE: To characterise subphenotypes of self-reported symptoms and outcomes (SRSOs) in postacute sequelae of COVID-19 (PASC). DESIGN: Prospective, observational cohort study of subjects with PASC. SETTING: Academic tertiary centre from five clinical referral sources. PARTICIPANTS: Adults with COVID-19 ≥20 days before enrolment and presence of any new self-reported symptoms following COVID-19. EXPOSURES: We collected data on clinical variables and SRSOs via structured telephone interviews and performed standardised assessments with validated clinical numerical scales to capture psychological symptoms, neurocognitive functioning and cardiopulmonary function. We collected saliva and stool samples for quantification of SARS-CoV-2 RNA via quantitative PCR. OUTCOMES MEASURES: Description of PASC SRSOs burden and duration, derivation of distinct PASC subphenotypes via latent class analysis (LCA) and relationship with viral load. RESULTS: We analysed baseline data for 214 individuals with a study visit at a median of 197.5 days after COVID-19 diagnosis. Participants reported ever having a median of 9/16 symptoms (IQR 6-11) after acute COVID-19, with muscle-aches, dyspnoea and headache being the most common. Fatigue, cognitive impairment and dyspnoea were experienced for a longer time. Participants had a lower burden of active symptoms (median 3 (1-6)) than those ever experienced (p<0.001). Unsupervised LCA of symptoms revealed three clinically active PASC subphenotypes: a high burden constitutional symptoms (21.9%), a persistent loss/change of smell and taste (20.6%) and a minimal residual symptoms subphenotype (57.5%). Subphenotype assignments were strongly associated with self-assessments of global health, recovery and PASC impact on employment (p<0.001) as well as referral source for enrolment. Viral persistence (5.6% saliva and 1% stool samples positive) did not explain SRSOs or subphenotypes. CONCLUSIONS: We identified three distinct PASC subphenotypes. We highlight that although most symptoms progressively resolve, specific PASC subpopulations are impacted by either high burden of constitutional symptoms or persistent olfactory/gustatory dysfunction, requiring prospective identification and targeted preventive or therapeutic interventions.

Substantial health and economic burden of COVID-19 during the year after acute illness among US adults at high risk of severe COVID-19.

BACKGROUND: Post-COVID conditions encompass a range of long-term symptoms after SARS-CoV-2 infection. The potential clinical and economic burden in the United States is unclear. We evaluated diagnoses, medications, healthcare use, and medical costs before and after acute COVID-19 illness in US patients at high risk of severe COVID-19. METHODS: Eligible adults were diagnosed with COVID-19 from April 1 to May 31, 2020, had ≥ 1 condition placing them at risk of severe COVID-19, and were enrolled in Optum's de-identified Clinformatics® Data Mart Database for ≥ 12 months before and ≥ 13 months after COVID-19 diagnosis. Percentages of diagnoses, medications, resource use, and costs were calculated during baseline (12 months preceding diagnosis) and the post-acute phase (12 months after the 30-day acute phase of COVID-19). Data were stratified by age and COVID-19 severity. RESULTS: The cohort included 19,558 patients (aged 18-64 y, n = 9381; aged ≥ 65 y, n = 10,177). Compared with baseline, patients during the post-acute phase had increased percentages of blood disorders (16.3%), nervous system disorders (11.1%), and mental and behavioral disorders (7.7%), along with increases in related prescriptions. Overall, there were substantial increases in inpatient and outpatient healthcare utilization, along with a 23.0% increase in medical costs. Changes were greatest among older patients and those admitted to the intensive care unit for acute COVID-19 but were also observed in younger patients and those who did not require COVID-19 hospitalization. CONCLUSIONS: There is a significant clinical and economic burden of post-COVID conditions among US individuals at high risk for severe COVID-19.

Substantial health and economic burden of COVID-19 during the year after acute illness among US adults not at high risk of severe COVID-19.

BACKGROUND: Patients recovering from SARS-CoV-2 infection and acute COVID-19 illness can experience a range of long-term post-acute effects. The potential clinical and economic burden of these outcomes in the USA is unclear. We evaluated diagnoses, medications, healthcare utilization, and medical costs before and after acute COVID-19 illness in US patients who were not at high risk of severe COVID-19. METHODS: This study included eligible adults who were diagnosed with COVID-19 from April 1 to May 31, 2020, who were 18 - 64 years of age, and enrolled within Optum's de-identified Clinformatics® Data Mart Database for 12 months before and 13 months after COVID-19 diagnosis. Patients with any condition or risk factor placing them at high risk of progression to severe COVID-19 were excluded. Percentages of diagnoses, medications, healthcare utilization, and costs were calculated during baseline (12 months preceding diagnosis) and the post-acute phase (12 months after the 30-day acute phase of COVID-19). Data were stratified into 3 cohorts according to disposition during acute COVID-19 illness (i.e., not hospitalized, hospitalized without intensive care unit [ICU] admission, or admitted to the ICU). RESULTS: The study included 3792 patients; 56.5% of patients were men, 44% were White, and 94% did not require hospitalization. Compared with baseline, patients during the post-acute phase had percentage increases in the diagnosis of the following disorders: blood (166%), endocrine and metabolic (123%), nervous system (115%), digestive system (76%), and mental and behavioral (75%), along with increases in related prescriptions. Substantial increases in all measures of healthcare utilization were observed among all 3 cohorts. Total medical costs increased by 178% during the post-acute phase. Those who were hospitalized with or without ICU admission during the acute phase had the greatest increases in comorbidities and healthcare resource utilization. However, the burden was apparent across all cohorts. CONCLUSIONS: As evidenced by resource use in the post-acute phase, COVID-19 places a significant long-term clinical and economic burden among US individuals, even among patients whose acute infection did not merit hospitalization.

Real-World Evidence of the Top 100 Prescribed Drugs in the USA and Their Potential for Drug Interactions with Nirmatrelvir; Ritonavir.

Nirmatrelvir (coadministered with ritonavir as PAXLOVIDTM) reduces the risk of COVID-19-related hospitalizations and all-cause death in individuals with mild-to-moderate COVID-19 at high risk of progression to severe disease. Ritonavir is coadministered as a pharmacokinetic enhancer. However, ritonavir may cause drug-drug interactions (DDIs) due to its interactions with various drug-metabolizing enzymes and transporters, including cytochrome P450 (CYP) 3A, CYP2D6, and P-glycoprotein transporters. To better understand the extent of DDIs (or lack thereof) of nirmatrelvir; ritonavir in a clinical setting, this study used real-world evidence (RWE) from the Optum Clinformatics Data Mart database to identify the top 100 drugs most commonly prescribed to US patients at high risk of progression to severe COVID-19 disease. The top 100 drugs were identified based on total counts associated with drugs prescribed to high-risk patients (i.e., ≥ 1 medical condition associated with an increased risk of severe COVID-19) who were continuously enrolled in the database throughout 2019 and had ≥ 1 prescription claim. Each of the 100 drugs was then assessed for DDI risk based on their metabolism, excretion, and transport pathways identified from available US prescribing and medical literature sources. Seventy drugs identified were not expected to have DDIs with nirmatrelvir; ritonavir, including many cardiovascular agents, anti-infectives, antidiabetic agents, and antidepressants. Conversely, 30 drugs, including corticosteroids, narcotic analgesics, anticoagulants, statins, and sedatives/hypnotics, were expected to cause DDIs with nirmatrelvir; ritonavir. This RWE analysis is complementary to the prescribing information and other DDI management tools for guiding healthcare providers in managing DDIs.

Use of the Postacute Sequelae of COVID-19 Diagnosis Code in Routine Clinical Practice in the US.

Importance: A new International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code (U09.9 Post COVID-19 condition, unspecified) was introduced by the Centers for Disease Control and Prevention on October 1, 2021. Objective: To examine the use of the U09.9 code and describe concurrently diagnosed conditions to understand physician use of this code in clinical practice. Design, Setting, and Participants: This cohort study of US patients with an ICD-10-CM code for post-COVID-19 condition used deidentified patient-level claims data aggregated by HealthVerity. Children and adolescents (aged 0-17 years) and adults (aged 18-64 and ≥65 years) with a post-COVID-19 condition code were identified between October 1, 2021, and January 31, 2022. To identify a prior COVID-19 diagnosis, 3 months of continuous enrollment (CE) before the post-COVID-19 diagnosis date was required. Main Outcomes and Measures: Presence of the ICD-10-CM U09.9 code. Results: There were 56 143 patients (7723 female patients [61.2%]; mean [SD] age, 47.6 [19.2] years) with a post-COVID-19 diagnosis code, with cases increasing in mid-December 2021 following the trajectory of the Omicron case wave by 3 to 4 weeks. The analysis cohort included 12 622 patients after the 3-month preindex CE criteria was applied. Among this cohort, the median (IQR) age was 49 (35-61) years; however, 1080 (8.6%) were pediatric patients. The U09.9 code was used most often in the outpatient setting, although 305 older adults (14.0%) were inpatients. Only 698 patients (5.5%) had at least 1 of the 5 codes listed as possible concurrent conditions in the coding guidance. Only 8879 patients (70.4%) had a documented acute COVID-19 diagnosis code (569 [52.7%] among children), and the median (IQR) time between acute COVID-19 and post-COVID-19 diagnosis codes was 56 (21-200) days. The most common concurrently coded conditions varied by age; children experienced COVID-19-like symptoms (eg, 207 [19.2%] had cough and 115 [10.6%] had breathing abnormalities), while 459 older adults aged 65 years or older (21.1%) experienced respiratory failure and 189 (8.7%) experienced viral pneumonia. Conclusions and Relevance: This retrospective cohort study found patients with a post-COVID-19 ICD-10-CM diagnosis code following the acute phase of COVID-19 disease among patients of all ages in clinical practice in the US. The use of the U09.9 code encompassed a wide range of conditions. It will be important to monitor how the use of this code changes as the pandemic continues to evolve.

Risk factors in people with mold infections that have spread to different parts of the body: a plain language summary.

WHAT IS THIS SUMMARY ABOUT?: This is a summary of a study originally published in ClinicoEconomics and Outcomes Research. Mold infections spread from one to other parts of the body and can infect other body parts. We need to understand what makes people more likely to get this type of mold infection (called invasive mold infection). This summary may help doctors to understand the risks that can relate to invasive mold infections. WHAT WERE THE RESULTS?: The main risks in people with invasive aspergillosis (shortened to IA) and invasive mucormycosis (shortened to IM) were: ○diabetes (high blood sugar and associated conditions), ○lung disease (such as tuberculosis, chronic obstructive pulmonary disorder), ○blood-related cancers (such as leukemia, lymphoma), and ○solid organ transplant (removing an organ from one person and placing in another person). WHAT DO THE RESULTS OF THE STUDY MEAN?: People with the risks listed above may be more likely to get invasive mold infections. People with these risks should talk to their doctor about invasive mold infections. Being aware of these risks may help doctors to be aware of which people are at risk of invasive mold infections.

Characterizing patients with rare mucormycosis infections using real-world data.

BACKGROUND: Invasive mucormycosis (IM) is a rare and often life-threatening fungal infection, for which clinical and epidemiological understanding is lacking. Electronic health record (EHR) data can be utilized to elucidate large populations of patients with IM to address this unmet need. This study aimed to descriptively assess data on patients with IM using the Optum® EHR dataset. METHODS: US patient data from the Optum® deidentified EHR dataset (2007-2019) were analyzed to identify patients with IM. Patients with hematologic malignancies (HM), at high risk of IM, were selected and sorted by IM diagnosis (ICD9 117.7; ICD10 B46). Demographics, comorbidities/other diagnoses, and treatments were analyzed in patients with IM. RESULTS: In total, 1133 patients with HM and IM were identified. Most were between 40 and 64 years of age, Caucasian, and from the Midwest. Essential primary hypertension (50.31%) was the most common comorbidity. Of the 1133 patients, only 33.72% were prescribed an antifungal treatment. The most common antifungal treatments were fluconazole (24.27%) and posaconazole (16.33%), which may have been prophylactic, and any AmB (15.62%). CONCLUSIONS: A large population of patients with IM were identified, highlighting the potential of analyzing EHR data to investigate epidemiology, diagnosis, and the treatment of apparently rare diseases.

Pandemic-related declines in hospitalization for non-COVID-19-related illness in the United States from January through July 2020.

BACKGROUND: The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially impacted healthcare utilization worldwide. The objective of this retrospective analysis of a large hospital discharge database was to compare all-cause and cause-specific hospitalizations during the first six months of the pandemic in the United States with the same months in the previous four years. METHODS: Data were collected from all hospitals in the Premier Healthcare Database (PHD) and PHD Special Release reporting hospitalizations from January through July for each year from 2016 through 2020. Hospitalization trends were analyzed stratified by age group, major diagnostic categories (MDCs), and geographic region. RESULTS: The analysis included 286 hospitals from all 9 US Census divisions. The number of all-cause hospitalizations per month was relatively stable from 2016 through 2019 and then fell by 21% (57,281 fewer hospitalizations) between March and April 2020, particularly in hospitalizations for non-respiratory illnesses. From April onward there was a rise in the number of monthly hospitalizations per month. Hospitalizations per month, nationally and in each Census division, decreased for 20 of 25 MDCs between March and April 2020. There was also a decrease in hospitalizations per month for all age groups between March and April 2020 with the greatest decreases in hospitalizations observed for patients 50-64 and ≥65 years of age. CONCLUSIONS: Rates of hospitalization declined substantially during the first months of the COVID-19 pandemic, suggesting delayed routine, elective, and emergency care in the United States. These lapses in care for illnesses not related to COVID-19 may lead to increases in morbidity and mortality for other conditions. Thus, in the current stage of the pandemic, clinicians and public-health officials should work, not only to prevent SARS-CoV-2 transmission, but also to ensure that care for non-COVID-19 conditions is not delayed.

Clinical and Pregnancy Outcomes of Coronavirus Disease 2019 Among Hospitalized Pregnant Women in the United States.

BACKGROUND: Pregnant women with coronavirus disease 2019 (COVID-19) may be at greater risk of poor maternal and pregnancy outcomes. This retrospective analysis reports clinical and pregnancy outcomes among hospitalized pregnant women with COVID-19 in the United States. METHODS: The Premier Healthcare Database-Special Release was used to examine the impact of COVID-19 among pregnant women aged 15-44 years who were hospitalized and who delivered compared with pregnant women without COVID-19. Outcomes evaluated were COVID-19 clinical progression, including the use of supplemental oxygen therapy, intensive care unit admission, critical illness, receipt of invasive mechanical ventilation/extracorporeal membrane oxygenation, maternal death, and pregnancy outcomes, including preterm delivery and stillbirth. RESULTS: Overall, 473 902 hospitalized pregnant women were included, 8584 (1.8%) of whom had a COVID-19 diagnosis (mean age = 28.4 [standard deviation = 6.1] years; 40% Hispanic). The risk of poor clinical and pregnancy outcomes was greater among pregnant women with COVID-19 compared with pregnant women without a COVID-19 diagnosis in 2020; the risk of poor clinical and pregnancy outcomes increased with increasing age. Hispanic and Black non-Hispanic women were consistently observed to have the highest relative risk of experiencing poor clinical or pregnancy outcomes across all age groups. CONCLUSIONS: Overall, COVID-19 had a significant negative impact on maternal health and pregnancy outcomes. These data help inform clinical practice and counseling to pregnant women regarding the risks of COVID-19. Clinical studies evaluating the safety and efficacy of vaccines against severe acute respiratory syndrome coronavirus 2 in pregnant women are urgently needed.

Hospitalization and mortality trends among patients with confirmed COVID-19 in the United States, April through August 2020.

BACKGROUND: The United States has experienced high COVID-19 case counts, hospitalizations, and death rates. This retrospective analysis reports changing trends in the demographics and clinical outcomes of hospitalized US COVID-19 patients between April and August 2020. DESIGN AND METHODS: The Premier Healthcare Database Special Release was used to examine patient demographics of hospitalized COVID-19 patients from all US Census Bureau divisions. Demographics included age, sex, race, and ethnicity. Clinical outcomes included in-hospital mortality, intensive care unit (ICU) admission, and receipt of invasive mechanical ventilation. RESULTS: Overall, 146,491 hospitalized COVID-19 patients were included (mean [SD] age, 61.0 [18.4] years; 51.7% male; 29.6% White non-Hispanic). Monthly total hospitalizations decreased from 44,854 in April to 18,533 in August; ICU admissions increased from 19.8% to 23.6%, and ventilator use and inpatient mortality decreased from 18.6% to 14.5% and 21.0% to 11.4%, respectively. Inpatient mortality was highest in the Middle Atlantic division (20.3%), followed by the New England (19.0%), East North Central (14.2%), and Mountain (13.7%) divisions. Black non-Hispanic patients were overrepresented among hospitalizations (19.0%); this group comprises 12.2% of the US population. Patients aged <65 years made up 53% of hospitalizations and had lower inpatient mortality than those aged ≥65 years. CONCLUSIONS: Hospitalizations, ventilator use, and mortality decreased, while ICU admission rates increased from April to August 2020. Older individuals and Black non-Hispanics were found to be at elevated risk of severe outcomes. These trends could inform ongoing patient care and US public health policies to limit the further spread of SARS-CoV-2.

Patient Characteristics and Risk Factors in Invasive Mold Infections: Comparison from a Systematic Review and Database Analysis.

INTRODUCTION: Diagnosis and treatment of invasive mold infections (IMI) can be challenging and IMI is a significant source of morbidity and mortality. Invasive aspergillosis (IA) and invasive mucormycosis (IM) are two of the most common mold infections. A better understanding of patient comorbidities and risk factors that predispose IMI may help clinicians to refine the difficult diagnostic and treatment process. METHODS: A systematic literature review (SLR) was conducted (January 2008-October 2019) for studies reporting comorbidities/risk factors of patients with IA or IM (Phase I), followed by an analysis on the Optum® US EHR database of prominent risk factor cohorts based on SLR findings and expert opinion (Phase II). From the four identified patient cohorts: 1) patients undergoing solid organ transplant (SOT) and patients with 2) hematologic cancers, 3) diabetes, or 4) lung disease, rates of IA, IM, or concurrent IA and IM; patient comorbidities; and Charlson Comorbidity Index (CCI) scores were reported. RESULTS: The SLR included 88 studies, and 46 were used to select comorbidities/risk factors cohorts in IA and IM patients. The most important comorbidities/risk factors in IA and IM patients were diabetes, lung disease, hematological malignances, and SOT. In the Optum database (N=101,340,454 patients), IA rates were highest in lung transplant (10.81%) patients and IM rates were highest in intestine transplant (0.83%) patients, lung transplant (0.43%), and hematopoietic stem cell transplant (0.49%). CCI scores were elevated in all mold infection groups compared to the total Optum cohort. CONCLUSION: The current study describes patient comorbidity and risk factors associated with IA and IM. These data can be used to refine clinical decision-making regarding when to suspect mold infections. Future research should focus on identifying whether patients respond differently to various antifungal treatments to determine if strategic recommendations should be made for certain patient groups.

Health outcomes and economic burden of hospitalized COVID-19 patients in the United States.

OBJECTIVE: The aims of this study were to evaluate health outcomes and the economic burden of hospitalized COVID-19 patients in the United States. METHODS: Hospitalized patients with a primary or secondary discharge diagnosis code for COVID-19 (ICD-10 code U07.1) from 1 April to 31 October 2020 were identified in the Premier Healthcare COVID-19 Database. Patient demographics, hospitalization characteristics, and concomitant medical conditions were assessed. Hospital length of stay (LOS), in-hospital mortality, hospital charges, and hospital costs were evaluated overall and stratified by age groups, insurance types, and 4 COVID-19 disease progression states based on intensive care unit (ICU) and invasive mechanical ventilation (IMV) usage. RESULTS: Of the 173,942 hospitalized COVID-19 patients, the median age was 63 years, 51.0% were male, and 48.5% were covered by Medicare. The most prevalent concomitant medical conditions were cardiovascular disease (73.5%), hypertension (64.8%), diabetes (40.7%), obesity (27.0%), and chronic kidney disease (24.2%). Approximately one-fifth (21.9%) of the hospitalized COVID-19 patients were admitted to the ICU and 16.9% received IMV; most patients (73.6%) did not require ICU admission or IMV, and 12.4% required both. The median hospital LOS was 5 days, in-hospital mortality was 13.6%, median hospital charges were $43,986, and median hospital costs were $12,046. Hospital LOS and in-hospital mortality increased with ICU and/or IMV usage and age; hospital charges and costs increased with ICU and/or IMV usage. Patients with both ICU and IMV usage had the longest median hospital LOS (15 days), highest in-hospital mortality (53.8%), and highest hospital charges ($198,394) and hospital costs ($54,402). LIMITATIONS: This retrospective administrative database analysis relied on coding accuracy and a subset of admissions with validated/reconciled hospital costs. CONCLUSIONS: This study summarizes the severe health outcomes and substantial hospital costs of hospitalized COVID-19 patients in the US. The findings support the urgent need for rapid implementation of effective interventions, including safe and efficacious vaccines.

A Large Cross-Sectional Survey Study of the Prevalence of Alopecia Areata in the United States.

PURPOSE: Alopecia areata (AA) is an autoimmune disease characterized by the development of non-scarring alopecia. The prevalence is not well known, and estimates vary considerably with no recent estimates in the United States (US). The objective of this study was to define the current AA point prevalence estimate among the general population in the US overall and by severity. PATIENTS AND METHODS: We administered an online, cross-sectional survey to a representative sample of the US population. Participants self-screening as positive for AA using the Alopecia Assessment Tool (ALTO) also completed the Severity of Alopecia Tool (SALT) to measure the severity of disease as a percent of scalp hair loss. Self-reported AA participants were invited to upload photographs for adjudication of AA by 3 clinicians. RESULTS: The average age of participants was 43 years. Approximately half of the participants (49.2%) were male, and the majority were white (77.1%) and not of Hispanic origin (93.2%). Among the 511 self-reported AA participants, 104 (20.4%) uploaded photographs for clinician evaluation. Clinician-adjudicated point prevalence of AA was 0.21% (95% CI: 0.17%, 0.25%) overall, 0.12% (95% CI: 0.09%, 0.15%) for "mild" disease (≤50% SALT score), and 0.09% (95% CI: 0.06%, 0.11%) for "moderate to severe" disease (>50% SALT score) with 0.04% (95% CI: 0.02%, 0.06%) for the alopecia totalis/alopecia universalis (100% SALT score) "moderate to severe" subgroup. The average SALT score was 44.4% overall, 8.8% for "mild", and 93.4% for "moderate to severe". CONCLUSION: This study suggests that the current AA prevalence in the US is similar to the upper estimates from the 1970s at approximately 0.21% (700,000 persons) with the current prevalence of "moderate to severe" disease at approximately 0.09% (300,000 persons). Given this prevalence and the substantial impact of AA on quality of life, the burden of AA within the US is considerable.

Curating and Integrating Data from Multiple Sources to Support Healthcare Analytics.

As the volume and variety of healthcare related data continues to grow, the analysis and use of this data will increasingly depend on the ability to appropriately collect, curate and integrate disparate data from many different sources. We describe our approach to and highlight our experiences with the development of a robust data collection, curation and integration infrastructure that supports healthcare analytics. This system has been successfully applied to the processing of a variety of data types including clinical data from electronic health records and observational studies, genomic data, microbiomic data, self-reported data from surveys and self-tracked data from wearable devices from over 600 subjects. The curated data is currently being used to support healthcare analytic applications such as data visualization, patient stratification and predictive modeling.

Generic and therapeutic statin switches and disruptions in therapy.

BACKGROUND: The study objective was to compare dose-equivalence, adherence and subsequent switch rates among patients recently switched from a branded to generic version of the same statin (generic substitution, GS) vs. those switched from branded statin to generic version of a different statin (therapeutic substitution, TS). METHODS: In a retrospective cohort analysis among adult enrollees in over 90 US health plans, the authors identified adult patients who switched from a branded to generic statin from July-December 2006 (simvastatin became generic in June 2006). Patients were classified by type of statin switch: GS (e.g., branded simvastatin --> generic simvastatin), and TS (e.g., branded atorvastatin --> generic simvastatin). Demographic and clinical data were collected from claims before switch through 6 months follow-up. Separate outcomes of interest included proportion of patients that switched to a less potent daily dose, that switched back to previous branded statin after switch, and that were at least 80% adherent during the 6 months after initial switch. Significant predictors of each clinical outcome were identified using multivariable logistic regression models, adjusting for differences between groups in covariates and potential confounders. RESULTS: The 6-month TS (n = 3807) and GS (n = 40,165) groups were generally similar demographically. Compared to GS, TS patients were significantly more likely to be switched to a less potent dose (26.2% vs. 0.5%, adjusted odds ratio [AOR] in patients with high-potency index medication = 83.4, p < 0.0001); 33% less likely to be adherent in the 6 months after switch (67.7% vs. 75.9%, AOR in patients with no switch in first 6 months follow-up = 0.67, p < 0.0001); and four times more likely to switch back to previous branded statin (11.3% vs. 2.9%, AOR = 4.1, p < 0.0001). LIMITATIONS: This study did not account for co-payment changes, lipid measurements, or changes in pill burden. CONCLUSIONS: While this study did not have data on why patients had TS (e.g., for cost or clinical reasons), TS was more likely to involve a subsequent disruption to statin therapy than GS. TS could potentially lead to adverse impacts on patients' outcomes, and should be studied further.