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Crude percentages (A) and adjusted percent differences in prevalence compared with 2020 (B) of adolescents aged 12–17 years with obesity who received an obesity medication prescription — IQVIA Ambulatory <t>Electronic</t> Medical Records, United States, 2018–2023 Abbreviations: AAP = American Academy of Pediatrics; BMI = body mass index; FDA = Food and Drug Administration. * Adjusted percent differences in prescription prevalence in each year (compared with 2020) were obtained from a generalized linear model with log link and binomial distribution. The adjusted model controls for sex, age category, and obesity class. Obesity was defined as BMI ≥95th percentile for age and sex. 95% CIs indicated by bars. † In November 2020, FDA approved setmelanotide (Imcivree) for treating obesity in persons with monogenic or syndromic obesity aged ≥6 years. In December 2020, FDA approved liraglutide (Saxenda) for treating obesity in adolescents aged ≥12 years. In June 2022, FDA approved phentermine-topiramate (Qsymia) for treating obesity in adolescents aged ≥12 years. In December 2022, FDA approved semaglutide (Wegovy) for treating obesity in adolescents aged ≥12 years. In January 2023, a new AAP clinical practice guideline recommended that clinicians offer obesity medications as part of evidence-based multicomponent treatment for adolescents aged 12–17 years with obesity ( AAP Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents With Obesity ). § The sample included 526,973 U.S. adolescents aged 12–17 years with obesity who had a total of 789,057 annual BMI measurements during 2018–2023.

Ambulatory Electronic Medical Records (Emr), supplied by IQVIA Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "Prescriptions for Obesity Medications Among Adolescents Aged 12–17 Years with Obesity — United States, 2018–2023"

Article Title: Prescriptions for Obesity Medications Among Adolescents Aged 12–17 Years with Obesity — United States, 2018–2023

Journal: Morbidity and Mortality Weekly Report

doi: 10.15585/mmwr.mm7420a1

Figure Legend Snippet: Crude percentages (A) and adjusted percent differences in prevalence compared with 2020 (B) of adolescents aged 12–17 years with obesity who received an obesity medication prescription — IQVIA Ambulatory Electronic Medical Records, United States, 2018–2023 Abbreviations: AAP = American Academy of Pediatrics; BMI = body mass index; FDA = Food and Drug Administration. * Adjusted percent differences in prescription prevalence in each year (compared with 2020) were obtained from a generalized linear model with log link and binomial distribution. The adjusted model controls for sex, age category, and obesity class. Obesity was defined as BMI ≥95th percentile for age and sex. 95% CIs indicated by bars. † In November 2020, FDA approved setmelanotide (Imcivree) for treating obesity in persons with monogenic or syndromic obesity aged ≥6 years. In December 2020, FDA approved liraglutide (Saxenda) for treating obesity in adolescents aged ≥12 years. In June 2022, FDA approved phentermine-topiramate (Qsymia) for treating obesity in adolescents aged ≥12 years. In December 2022, FDA approved semaglutide (Wegovy) for treating obesity in adolescents aged ≥12 years. In January 2023, a new AAP clinical practice guideline recommended that clinicians offer obesity medications as part of evidence-based multicomponent treatment for adolescents aged 12–17 years with obesity ( AAP Clinical Practice Guideline for the Evaluation and Treatment of Children and Adolescents With Obesity ). § The sample included 526,973 U.S. adolescents aged 12–17 years with obesity who had a total of 789,057 annual BMI measurements during 2018–2023.

Techniques Used: Medications

Adjusted prevalence ratios for receiving an obesity medication prescription among adolescents aged 12–17 years with obesity, by selected demographic characteristics and obesity class — IQVIA Ambulatory Electronic Medical Records, United States, 2023 Abbreviations: BMI = body mass index; Ref = referent. * 95% CIs indicated by bars. † Obesity was defined as BMI ≥95th percentile for age and sex. § A generalized linear model with log link and binomial distribution (model 1) was used to estimate characteristics associated with the outcome of receiving an obesity medication prescription in 2023: age (12–14 years [Ref] and 15–17 years), sex (male [Ref], female), obesity class (class 1 [Ref], class 2, and class 3), and U.S. Census Bureau region (Northeast [Ref], South, Midwest, and West). Model 2 was restricted to adolescents who were Black or African American (Black) or White and included the same covariates as model 1, with an additional covariate of race (Black/White). Obesity classes were as follows: class 1 obesity or BMI ≥95th percentile to BMI <120% of the 95th percentile [Ref], class 2 obesity or BMI of 120% to <140% of the 95th percentile, and class 3 obesity or BMI ≥140% of the 95th percentile. Classes 2 and 3 represented severe obesity. Estimates of association from the model were expressed as adjusted prevalence ratios and plotted on a log(10) scale.

Figure Legend Snippet: Adjusted prevalence ratios for receiving an obesity medication prescription among adolescents aged 12–17 years with obesity, by selected demographic characteristics and obesity class — IQVIA Ambulatory Electronic Medical Records, United States, 2023 Abbreviations: BMI = body mass index; Ref = referent. * 95% CIs indicated by bars. † Obesity was defined as BMI ≥95th percentile for age and sex. § A generalized linear model with log link and binomial distribution (model 1) was used to estimate characteristics associated with the outcome of receiving an obesity medication prescription in 2023: age (12–14 years [Ref] and 15–17 years), sex (male [Ref], female), obesity class (class 1 [Ref], class 2, and class 3), and U.S. Census Bureau region (Northeast [Ref], South, Midwest, and West). Model 2 was restricted to adolescents who were Black or African American (Black) or White and included the same covariates as model 1, with an additional covariate of race (Black/White). Obesity classes were as follows: class 1 obesity or BMI ≥95th percentile to BMI <120% of the 95th percentile [Ref], class 2 obesity or BMI of 120% to <140% of the 95th percentile, and class 3 obesity or BMI ≥140% of the 95th percentile. Classes 2 and 3 represented severe obesity. Estimates of association from the model were expressed as adjusted prevalence ratios and plotted on a log(10) scale.

Techniques Used:

other:

Article Title: Lipid Screening in Children and Adolescents-A Missed Opportunity to Improve Lifelong Health.
Article Snippet: Lipid Screening by Test Type and Result Category, IQVIA Ambulatory Electronic Medical Records (2018-2021) 0 100 80 60 40 20 Lipid screening test type M ea su re m en t r es ul ts , % Abnormal Lipid level result category Borderline Healthy Overall TC Triglycerides LDL-C Non–HDL–C VLDL-C Lipid measurements were considered elevated if 1 or more of the following results were obtained: total cholesterol (TC), 170 mg/dL or higher; low-density lipoprotein cholesterol (LDL-C), 110 mg/dL or higher; very lowdensity lipoprotein cholesterol (VLDL-C), 31 mg/dL or higher, non–high-density lipoprotein cholesterol (HDL-C) , 120 mg/dL or higher; or triglycerides, 75 mg/dL or higher (9 years) or 90 mg/dL or higher (10-21 years).

Article Title: The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to Remicade ®
Article Snippet: Fig. 1 Empirical equipoise, covariate balance, empirical calibration validity diagnostics and representativeness for IBD primary analysis Key: Amb EMR = IQVIA Ambulatory Electronic Medical Records; ASMD = absolute standardized mean difference; CCAE = MerativeTM MarketScan ® Commercial Database; CI = Confidence Interval; Clinformatics ® = Optum ® De-Identified Clinformatics ® Data Mart Database; EASE = expected absolute systematic error; HR = Hazard ratio; IBD = irritable bowel diseases (Crohn’s disease or ulcerative colitis); IBD comparator = golimumab, certolizumab pegol, ustekinumab, or vedolizumab; Optum ® EHR = Optum ® De-Identified Electronic Health Record; Pharmetrics = IQVIA Adjudicated Health Plan Claims Data; Remicade ® (m) = Remicade ® exposure; Target covariate prevalence = prevalence of baseline covariates in the initial Remicade ® exposure cohort before study design restrictions were applied; Analytic covariate prevalence = prevalence of baseline covariates in Remicade ® exposure cohort after study design restrictions were applied (i.e., PS matching) Fig. 2 Empirical equipoise, covariate balance, empirical calibration validity diagnostics and representativeness for RA primary analysis Key – Target: patients with inflammatory bowel diseases newly exposed to Remicade ® , Comparator: patients with inflammatory bowel diseases newly exposed to [golimumab, certolizumab pegol, ustekinumab, or vedolizumab], CCAE: MerativeTM MarketScan ® Commercial Database, Optum ® EHR = Optum ® De-Identified Electronic Health Record; Pharmetrics = IQVIA Adjudicated Health Plan Claims Data Figure depicts the same diagnostics information as Fig. but for the comparisons in the RA study population.

Article Title: Lipid Screening in Children and Adolescents-A Missed Opportunity to Improve Lifelong Health.
Article Snippet: Abnormal Lipid Screening Results by Test Type and Age Group, IQVIA Ambulatory Electronic Medical Records (2018-2021) 0 40 30 20 10 Lipid screening test type M ea su re m en t r es ul ts , % 17-21 Age group, y 12-16 9-11 TC Triglycerides LDL-C Non–HDL–C VLDL-C See the caption to Figure 1 for expansions of the abbreviations and for definitions of abnormal lipid measurements and the lipid level categories.

Article Title: The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to Remicade ®
Article Snippet: <5 8 1 0.18 0 0.508 1 0.040 1 3 Key – <5: a censored value between 1 and 4, Amb EMR: IQVIA Ambulatory Electronic Medical Records, AS comparator: [certolizumab pegol, golimumab, ixekizumab, or secukinumab], AS: ankylosing spondylitis, ASD: absolute standardized difference, CCAE: IBM® MarketScan® Commercial Claims and Encounters Database, Clinformatics®: Optum® De-Identified Clinformatics® Data Mart Database, EASE: expected absolute systematic error, IBD comparator: [golimumab, certolizumab pegol, ustekinumab, or vedolizumab], IBD: inflammatory bowel diseases (Crohn’s disease or ulcerative colitis), Optum® EHR: Optum® De-Identified Electronic Health Record, Pharmetrics: IQVIA Adjudicated Health Plan Claims Data, PsO/PsA comparator: [golimumab, certolizumab pegol, guselkumab, risankizumab, tildrakizumab, brodalumab, ixekizumab, secukinumab, or ustekinumab], PsO/PsA: psoriatic conditions plaque psoriasis or psoriatic arthritis, RA comparator: certolizumab pegol or tocilizumab, RA: rheumatoid arthritis; Remicade®(m): Remicade® exposure with concurrent methotrexate, Study pop.

Article Title: Retrospective Database Analysis of Liver-Related Clinical Events in Adult and Pediatric Patients with Alpha-1 Antitrypsin Deficiency in the United States
Article Snippet: This was a retrospective, observational analysis of administrative claims data from the IQVIA PharMetrics ® Plus and Ambulatory Electronic Medical Records databases from 2011 to 2022.

Biomarker Discovery:

Article Title: The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to Remicade ®
Article Snippet: .. Table S2: Confusion matrix contingency cell counts and misclassification errors for the broad, narrow, and primary outcome definitions across databases Database NIU outcome definition TP TN FP FN Sensitivity Specificity PPV NPV Amb EMR Broad 945 1412318 516 17 0.98231 0.999635 0.646133 0.999988 Amb EMR Narrow 360 1412617 217 602 0.37461 0.999846 0.623917 0.999574 Amb EMR Primary 496 1412594 241 465 0.516129 0.999829 0.672999 0.99967 Pharmetrics Broad 5800 1965431 3778 2827 0.672192 0.998081 0.60545 0.998563 Pharmetrics Narrow 2091 1968056 1153 6536 0.242379 0.999415 0.644575 0.99669 Pharmetrics Primary 2249 1967965 1244 6378 0.260577 0.999368 0.643573 0.996769 Optum® EHR Broad 2733 1946194 826 1907 0.589009 0.999576 0.767912 0.999021 Optum® EHR Narrow 839 1946842 177 3802 0.180603 0.999909 0.824803 0.998051 Optum® EHR Primary 1369 1946703 317 3271 0.295043 0.999838 0.811981 0.998323 Clinformatics® Broad 8646 1657041 2639 4086 0.679051 0.99841 0.766061 0.99754 Clinformatics® Narrow 3497 1658857 824 9234 0.274684 0.999504 0.809303 0.994464 Clinformatics® Primary 6007 1658007 1673 6725 0.471762 0.998992 0.782031 0.99596 CCAE Broad 6874 1911524 1870 3454 0.665537 0.999023 0.786025 0.998196 CCAE Narrow 2423 1912944 450 7905 0.234531 0.999765 0.843021 0.995885 CCAE Primary 4044 1912446 949 6283 0.391595 0.999504 0.809934 0.996725 Key – Amb EMR: IQVIA Ambulatory Electronic Medical Records, Pharmetrics: IQVIA Adjudicated Health Plan Claims Data, Optum® EHR: Optum® De-Identified Electronic Health Record, Clinformatics®: Optum® De-Identified Clinformatics® Data Mart Database, CCAE: MerativeTM MarketScan® Commercial Database, TP: true positives, TN: true negatives, FP: false positives, FN: false negatives, Sensitivity = TP/(TP+FN), Specificity = TN/(TN+FP), PPV = positive predictive value = TP/(TP+FP); NPV = negative predictive value = TN/(TN+FN), Broad: first occurrence of a NIU code, Narrow: first occurrence of a NIU code with a second NIU code occurrence between 31 days and 365 days relative to first occurrence, Primary: [first occurrence of a NIU code with a second NIU code occurrence between 31 days and 365 days relative to first occurrence] OR [first occurrence of a NIU code during an ophthalmology visit] Page 24 of 31 Three NIU algorithms were analyzed using probabilistic reference standard validation across five databases. ..

Medications:

Article Title: Notes from the Field: Public Health Response to Surveillance for Recent HIV Infections - Malawi, May 2024.
Article Snippet: .. Percentage of adolescents aged 12–17 years with obesity* who were prescribed obesity medications, by selected demographic characteristics, obesity class, and medication type — IQVIA Ambulatory Electronic Medical Records, United States, 2023 Characteristic No. of adolescents with obesity (%) No. of adolescents prescribed obesity medication (%) Total 93,121 (100) 427 (100) Age group, yrs, mean (SD) 14.4 (1.7) 15.2 (1.6) 12–14 49,584 (53.2) 139 (32.6) 15–17 43,537 (46.8) 288 (67.4) ..

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