Examination of Prediabetes and Diabetes Testing Among US Pediatric Patients With Overweight or Obesity Using an Electronic Health Record.

Background: Youth with excess weight are at risk of developing type 2 diabetes (T2DM). Guidelines recommend screening for prediabetes and/or T2DM after 10 years of age or after puberty in youth with excess weight who have ≥1 risk factor(s) for T2DM. Electronic health records (EHRs) offer an opportunity to study the use of tests to detect diabetes in youth. Methods: We examined the frequency of (1) diabetes testing and (2) elevated test results among youth aged 10-19 years with at least one BMI measurement in an EHR from 2019 to 2021. We examined the presence of hemoglobin A1C (A1C), fasting plasma glucose (FPG), or oral glucose tolerance test (2-hour plasma glucose [2-hrPG]) results and, among those tested, the frequency of elevated values (A1C ≥6.5%, FPG ≥126 mg/dL, or 2-hrPG ≥200 mg/dL). Patients with pre-existing diabetes (n = 6793) were excluded. Results: Among 1,024,743 patients, 17% had overweight, 21% had obesity, including 8% with severe obesity. Among patients with excess weight, 10% had ≥1 glucose test result. Among those tested, elevated values were more common in patients with severe obesity (27%) and obesity (22%) than in those with healthy weight (8%), and among Black youth (30%) than White youth (13%). Among patients with excess weight, >80% of elevated values fell in the prediabetes range. Conclusions: In youth with excess weight, the use of laboratory tests for prediabetes and T2DM was infrequent. Among youth with test results, elevated FPG, 2hrPG, or A1C levels were most common in those with severe obesity and Black youth.

Using Real-World Electronic Health Record Data to Assess Chronic Disease Screening in Children: A Case Study of Non-Alcoholic Fatty Liver Disease.

Background: Data sources for assessing pediatric chronic diseases and associated screening practices are rare. One example is non-alcoholic fatty liver disease (NAFLD), a common chronic liver disease prevalent among children with overweight and obesity. If undetected, NAFLD can cause liver damage. Guidelines recommend screening for NAFLD using alanine aminotransferase (ALT) tests in children ≥9 years with obesity or those with overweight and cardiometabolic risk factors. This study explores how real-world data from electronic health records (EHRs) can be used to study NAFLD screening and ALT elevation. Research Design: Using IQVIA's Ambulatory Electronic Medical Record database, we studied patients 2-19 years of age with body mass index ≥85th percentile. Using a 3-year observation period (January 1, 2019 to December 31, 2021), ALT results were extracted and assessed for elevation (≥1 ALT result ≥22.1 U/L for females and ≥25.8 U/L for males). Patients with liver disease (including NAFLD) or receiving hepatotoxic medications during 2017-2018 were excluded. Results: Among 919,203 patients 9-19 years of age, only 13% had ≥1 ALT result, including 14% of patients with obesity and 17% of patients with severe obesity. ALT results were identified for 5% of patients 2-8 years of age. Of patients with ALT results, 34% of patients 2-8 years of age and 38% of patients 9-19 years of age had ALT elevation. Males 9-19 years of age had a higher prevalence of ALT elevation than females (49% vs. 29%). Conclusions: EHR data offered novel insights into NAFLD screening: despite screening recommendations, ALT results among children with excess weight were infrequent. Among those with ALT results, ALT elevation was common, underscoring the importance of screening for early disease detection.

Prevalence of Testing for Diabetes Among US Adults With Overweight or Obesity, 2016-2019.

Introduction: Screening for prediabetes and type 2 diabetes may allow earlier detection, diagnosis, and treatment. The US Preventive Services Task Force recommends screening every 3 years for abnormal blood glucose among adults aged 40 to 70 years with overweight or obesity. Using IQVIA Ambulatory Electronic Medical Records, we estimated the proportion of adults aged 40 to 70 years with overweight or obesity who received blood glucose testing within 3 years from baseline in 2016. Methods: We identified 1,338,509 adults aged 40 to 70 years with overweight or obesity in 2016 and without pre-existing diabetes. We included adults whose records were present in the data set for at least 2 years before their index body mass index (BMI) in 2016 and 3 years after the index BMI (2017-2019), during which we examined the occurrence of blood glucose testing. We calculated the unadjusted and adjusted prevalence of receiving blood glucose testing. Results: The unadjusted prevalence of receiving blood glucose testing was 33.4% when it was defined as having a hemoglobin A1c or fasting plasma glucose measure. The unadjusted prevalence was 74.3% when we expanded the definition of testing to include random plasma glucose and unspecified glucose measures. Adults with obesity were more likely to receive the test than those with overweight. Men (vs women) and adults aged 50 to 59 years (vs other age groups) had higher testing rates. Conclusion: Our findings could inform clinical and public health promotion efforts to improve screening for blood glucose levels among adults with overweight or obesity.

The effects of policy changes and human mobility on the COVID-19 epidemic in the Dominican Republic, 2020-2021.

Recent advances in technology can be leveraged to enhance public health research and practice. This study aimed to assess the effects of mobility and policy changes on COVID-19 case growth and the effects of policy changes on mobility using data from Google Mobility Reports, information on public health policy, and COVID-19 testing results. Multiple bivariate regression analyses were conducted to address the study objectives. Policies designed to limit mobility led to decreases in mobility in public areas. These policies also decreased COVID-19 case growth. Conversely, policies that did not restrict mobility led to increases in mobility in public areas and led to increases in COVID-19 case growth. Mobility increases in public areas corresponded to increases in COVID-19 case growth, while concentration of mobility in residential areas corresponded to decreases in COVID-19 case growth. Overall, restrictive policies were effective in decreasing COVID-19 incidence in the Dominican Republic, while permissive policies led to increases in COVID-19 incidence.

Leveraging Electronic Health Records to Construct a Phenotype for Hypertension Surveillance in the United States.

BACKGROUND: Hypertension is an important risk factor for cardiovascular diseases. Electronic health records (EHRs) may augment chronic disease surveillance. We aimed to develop an electronic phenotype (e-phenotype) for hypertension surveillance. METHODS: We included 11,031,368 eligible adults from the 2019 IQVIA Ambulatory Electronic Medical Records-US (AEMR-US) dataset. We identified hypertension using three criteria, alone or in combination: diagnosis codes, blood pressure (BP) measurements, and antihypertensive medications. We compared AEMR-US estimates of hypertension prevalence and control against those from the National Health and Nutrition Examination Survey (NHANES) 2017-18, which defined hypertension as BP ≥130/80 mm Hg or ≥1 antihypertensive medication. RESULTS: The study population had a mean (SD) age of 52.3 (6.7) years, and 56.7% were women. The selected three-criteria e-phenotype (≥1 diagnosis code, ≥2 BP measurements of ≥130/80 mm Hg, or ≥1 antihypertensive medication) yielded similar trends in hypertension prevalence as NHANES: 42.2% (AEMR-US) vs. 44.9% (NHANES) overall, 39.0% vs. 38.7% among women, and 46.5% vs. 50.9% among men. The pattern of age-related increase in hypertension prevalence was similar between AEMR-US and NHANES. The prevalence of hypertension control in AEMR-US was 31.5% using the three-criteria e-phenotype, which was higher than NHANES (14.5%). CONCLUSIONS: Using an EHR dataset of 11 million adults, we constructed a hypertension e-phenotype using three criteria, which can be used for surveillance of hypertension prevalence and control.

Probability of 5% or Greater Weight Loss or BMI Reduction to Healthy Weight Among Adults With Overweight or Obesity.

Importance: Information on the probability of weight loss among US adults with overweight or obesity is limited. Objective: To assess the probability of 5% or greater weight loss, 10% or greater weight loss, body mass index (BMI) reduction to a lower BMI category, and BMI reduction to the healthy weight category among US adults with initial overweight or obesity overall and by sex and race. Design, Setting, and Participants: This cohort study obtained data from the IQVIA ambulatory electronic medical records database. The sample consists of US ambulatory patients 17 years or older with at least 3 years of BMI information from January 1, 2009, to February 28, 2022. Minimum age was set at 17 years to allow for the change in BMI or weight starting at 18 years. Maximum age was censored at 70 years. Exposures: Initial BMI (calculated as weight in kilograms divided by height in meters squared) category was the independent variable of interest, and the categories were as follows: lower than 18.5 (underweight), 18.5 to 24.9 (healthy weight), 25.0 to 29.9 (overweight), 30.0 to 34.9 (class 1 obesity), 35.0 to 39.9 (class 2 obesity), and 40.0 to 44.9 and 45.0 or higher (class 3 or severe obesity). Main Outcomes and Measures: The 2 main outcomes were 5% or greater weight loss (ie, a ≥5% reduction in initial weight) and BMI reduction to the healthy weight category (ie, BMI of 18.5-24.9). Results: The 18 461 623 individuals in the sample had a median (IQR) age of 54 (40-66) years and included 10 464 598 females (56.7%) as well as 7.7% Black and 72.3% White patients. Overall, 72.5% of patients had overweight or obesity at the initial visit. Among adults with overweight and obesity, the annual probability of 5% or greater weight loss was low (1 in 10) but increased with higher initial BMI (from 1 in 12 individuals with initial overweight to 1 in 6 individuals with initial BMI of 45 or higher). Annual probability of BMI reduction to the healthy weight category ranged from 1 in 19 individuals with initial overweight to 1 in 1667 individuals with initial BMI of 45 or higher. Both outcomes were generally more likely among females than males and were highest among White females. Over the 3 to 14 years of follow-up, 33.4% of persons with overweight and 41.8% of persons with obesity lost 5% or greater of their initial weight. At the same time, 23.2% of persons with overweight and 2.0% of persons with obesity reduced BMI to the healthy weight category. Conclusions and Relevance: Results of this cohort study indicate that the annual probability of 5% or greater weight loss was low (1 in 10) despite the known benefits of clinically meaningful weight loss, but 5% or greater weight loss was more likely than BMI reduction to the healthy weight category, especially for patients with the highest initial BMIs. Clinicians and public health efforts can focus on messaging and referrals to interventions that are aimed at clinically meaningful weight loss (ie, ≥5%) for adults at any level of excess weight.

Body Mass Index and Associated Medical Expenditures in the US Among Privately Insured Individuals Aged 2 to 19 Years in 2018.

Importance: Nearly 40% of US youth aged 2 to 19 years do not have a body mass index (BMI) in the healthy weight category. However, there are no recent estimates for BMI-associated expenditures using clinical or claims data. Objective: To estimate medical expenditures among US youth across all BMI categories along with sex and age groups. Design, Setting, Participants: This cross-sectional study used IQVIA's ambulatory electronic medical records (AEMR) data set linked with IQVIA's PharMetrics Plus Claims database from January 2018 through December 2018. Analysis was performed from March 25, 2022, through June 20, 2022. It included a convenience sample of a geographically diverse patient population from AEMR and PharMetrics Plus. The study sample included privately insured individuals with a BMI measurement in 2018 and excluded patients with pregnancy-related visits. Exposure: BMI categories. Main Outcomes and Measures: Total medical expenditures were estimated using generalized linear model regression with γ distribution and log-link function. For out-of-pocket (OOP) expenditures, a 2-part model was used that included logistic regression to estimate the probability of positive expenditures followed by generalized linear model. Estimates were shown with and without accounting for sex, race and ethnicity, payer type, geographic region, age interacted with sex and BMI categories, and confounding conditions. Results: The sample included 205 876 individuals aged 2 to 19 years; 104 066 were male (50.5%) and the median age was 12 years. Compared with those with healthy weight, total and OOP expenditures were higher for all other BMI categories. Differences in total expenditures were highest for those with severe obesity ($909; 95% CI, $600-$1218) followed by underweight ($671; 95% CI, $286-$1055) compared with healthy weight. Differences in OOP expenditures were highest for those with severe obesity ($121; 95% CI, $86-$155) followed by underweight ($117; 95% CI, $78-$157) compared with healthy weight. Having underweight was associated with higher total expenditures at ages 2 to 5 years and 6 to 11 years by $679 (95% CI, $228-$1129) and $1166 (95% CI, $632-$1700), respectively; having severe obesity was associated with higher total expenditures at ages 2 to 5 years, 6 to 11 years, and 12 to 17 years by $1035 (95% CI, $208-$1863), $821 (95% CI, $414-$1227), and $1088 (95% CI, $594-$1582), respectively. Conclusions and Relevance: The study team found medical expenditures to be higher for all BMI categories when compared with those with healthy weight. These findings may indicate potential economic value of interventions or treatments aimed at reducing BMI-associated health risks.

Relative Effectiveness of Coronavirus Disease 2019 Vaccination and Booster Dose Combinations Among 18.9 Million Vaccinated Adults During the Early Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Period-United States, 1 January 2022 to 31 March 2022.

BACKGROUND: Small sample sizes have limited prior studies' ability to capture severe COVID-19 outcomes, especially among Ad26.COV2.S vaccine recipients. This study of 18.9 million adults aged ≥18 years assessed relative vaccine effectiveness (rVE) in three recipient cohorts: (1) primary Ad26.COV2.S vaccine and Ad26.COV2.S booster (2 Ad26.COV2.S), (2) primary Ad26.COV2.S vaccine and mRNA booster (Ad26.COV2.S+mRNA), (3) two doses of primary mRNA vaccine and mRNA booster (3 mRNA). METHODS: We analyzed two de-identified datasets linked using privacy-preserving record linkage (PPRL): insurance claims and retail pharmacy COVID-19 vaccination data. We assessed the presence of COVID-19 diagnosis during January 1-March 31, 2022 in: (1) any claim, (2) outpatient claim, (3) emergency department (ED) claim, (4) inpatient claim, and (5) inpatient claim with intensive care unit (ICU) admission. rVE for each outcome comparing three recipient cohorts (reference: two Ad26.COV2.S doses) was estimated from adjusted Cox proportional hazards models. RESULTS: Compared with two Ad26.COV2.S doses, Ad26.COV2.S+mRNA and three mRNA doses were more effective against all COVID-19 outcomes, including 57% (95% CI: 52-62) and 62% (95% CI: 58-65) rVE against an ED visit; 44% (95% CI: 34-52) and 54% (95% CI: 48-59) rVE against hospitalization; and 48% (95% CI: 22-66) and 66% (95% CI: 53-75) rVE against ICU admission, respectively. CONCLUSIONS: This study demonstrated that Ad26.COV2.S + mRNA doses were as good as three doses of mRNA, and better than two doses of Ad26.COV2.S. Vaccination continues to be an important preventive measure for reducing the public health impact of COVID-19.

Direct Medical Costs Associated With Post-COVID-19 Conditions Among Privately Insured Children and Adults.

INTRODUCTION: SARS-CoV-2, the virus that causes COVID-19, has caused more than 100.2 million infections and more than 1 million deaths in the US as of November 2022, yet information on the economic burden associated with post-COVID-19 conditions is lacking. We estimated the possible economic burden associated with post-COVID-19 conditions by comparing direct medical costs among patients younger than 65 years with and without COVID-19 in the postacute period. METHODS: Commercially insured children and adults with a COVID-19 diagnosis (cases) during April-August 2020 were matched to those without COVID-19 (controls) on a 1:4 ratio. Direct medical costs represented 1-, 3-, and 6-month total expenditures per person starting 31 days after the diagnosis date. We used a 2-part model to evaluate cost differences among individuals with and without COVID-19, adjusted for patient characteristics. RESULTS: Costs were higher among cases compared with controls. Direct medical costs among child cases were 1.82, 1.72, and 1.70 times higher than controls over 1, 3, and 6 months, respectively. Direct medical costs among adult cases were 1.69, 1.54, and 1.46 times higher than costs among controls over 1, 3, and 6 months, respectively. Relative differences in costs were highest among adults aged 50 to 64 years. In a subset of people with COVID-19, costs were higher among hospitalized cases compared with nonhospitalized cases. CONCLUSION: Our findings suggest a considerable economic burden of COVID-19 even after the resolution of acute illness, highlighting the importance of prevention and mitigation measures to reduce the economic impact of COVID-19 on the US health care system.

Children's rates of BMI change during pre-pandemic and two COVID-19 pandemic periods, IQVIA Ambulatory Electronic Medical Record, January 2018 Through November 2021.

OBJECTIVE: Many US youth experienced accelerated weight gain during the early COVID-19 pandemic. Using an ambulatory electronic health record data set, the authors compared children's rates of BMI change in three periods: pre-pandemic (January 2018-February 2020), early pandemic (March-December 2020), and later pandemic (January-November 2021). METHODS: This study used mixed-effects models to examine differences in rates of change in BMI, weight, and obesity prevalence among the three periods. Covariates included time as a continuous variable, a variable indicating in which period each BMI was taken, sex, age, and initial BMI category. RESULTS: In a longitudinal cohort of 241,600 children aged 2 through 19 years with ≥4 BMI measurements, the monthly rates of BMI change (kilograms per meters squared) were 0.056 (95% CI: 0.056-0.057) in the pre-pandemic period, 0.104 (95% CI: 0.102-0.106) in the early pandemic, and 0.035 (95% CI: 0.033-0.036) in the later pandemic. The estimated prevalence of obesity in this cohort was 22.5% by November 2021. CONCLUSIONS: In this large, geographically diverse cohort of US youth, accelerated rates of BMI change observed during 2020 were largely attenuated in 2021. Positive rates indicate continued weight gain rather than loss, albeit at a slower rate. Childhood obesity prevalence remained high, which raises concern about long-term consequences of excess weight and underscores the importance of healthy lifestyle interventions.

Post-COVID-19 Symptoms and Conditions Among Children and Adolescents - United States, March 1, 2020-January 31, 2022.

Post-COVID-19 (post-COVID) symptoms and conditions* are new, recurring, or ongoing health problems that occur 4 or more weeks after infection with SARS-CoV-2 (the virus that causes COVID-19). Previous studies have characterized and estimated the incidence of post-COVID conditions among adults (1,2), but data among children and adolescents are limited (3-8). Using a large medical claims database, CDC assessed nine potential post-COVID signs and symptoms (symptoms) and 15 potential post-COVID conditions among 781,419 U.S. children and adolescents aged 0-17 years with laboratory-confirmed COVID-19 (patients with COVID-19) compared with 2,344,257 U.S. children and adolescents without recognized COVID-19 (patients without COVID-19) during March 1, 2020-January 31, 2022. The analysis identified several symptoms and conditions with elevated adjusted hazard ratios among patients with COVID-19 (compared with those without). The highest hazard ratios were recorded for acute pulmonary embolism (adjusted hazard ratio [aHR] = 2.01), myocarditis and cardiomyopathy (1.99), venous thromboembolic event (1.87), acute and unspecified renal failure (1.32), and type 1 diabetes (1.23), all of which were rare or uncommon in this study population. Conversely, symptoms and conditions that were most common in this study population had lower aHRs (near or below 1.0). Patients with COVID-19 were less likely than were patients without to experience respiratory signs and symptoms, symptoms of mental conditions, muscle disorders, neurological conditions, anxiety and fear-related disorders, mood disorders, and sleeping disorders. COVID-19 prevention strategies, including vaccination for all eligible children and adolescents, are critical to prevent SARS-CoV-2 infection and subsequent illness, including post-COVID symptoms and conditions (9).

Weight gain among US adults during the COVID-19 pandemic through May 2021.

OBJECTIVE: There have been conflicting reports concerning weight gain among adults during the COVID-19 epidemic. Although early studies reported large weight increases, several of these analyses were based on convenience samples or self-reported information. The objective of the current study is to examine the pandemic-related weight increase associated with the pandemic through May 2021. METHODS: A total of 4.25 million adults (18 to 84 years) in an electronic health record database who had at least two weight measurements between January 2019 and February 2020 and one after June 2020 were selected. Weight changes before and after March 2020 were contrasted using mixed-effects regression models. RESULTS: Compared with the pre-pandemic weight trend, there was a small increase (0.1 kg) in weight in the first year of the pandemic (March 2020 through March 2021). Weight changes during the pandemic varied by sex, age, and initial BMI, but the largest mean increase across these characteristics was < 1.3 kg. Weight increases were generally greatest among women, adults with BMI of 30 or 35 kg/m2 , and younger adults. CONCLUSIONS: The results indicate that the mean weight gain among adults during the COVID-19 pandemic may be small.

Inpatient care cost, duration, and acute complications associated with BMI in children and adults hospitalized for COVID-19.

OBJECTIVE: This study aimed to assess the association of BMI with inpatient care cost, duration, and acute complications among patients hospitalized for COVID-19 at 273 US hospitals. METHODS: Children (aged 2-17 years) and adults (aged ≥18 years) hospitalized for COVID-19 during March 2020-July 2021 and with measured BMI in a large electronic administrative health care database were included. Generalized linear models were used to assess the association of BMI categories with the cost and duration of inpatient care. RESULTS: Among 108,986 adults and 409 children hospitalized for COVID-19, obesity prevalence was 53.4% and 45.0%, respectively. Among adults, overweight and obesity were associated with higher cost of care, and obesity was associated with longer hospital stays. Children with severe obesity had higher cost of care but not significantly longer hospital stays, compared with those with healthy weight. Children with severe obesity were 3.7 times (95% CI: 1.4-9.5) as likely to have invasive mechanical ventilation and 62% more likely to have an acute complication (95% CI: 39%-90%), compared with children with healthy weight. CONCLUSIONS: These findings show that patients with a high BMI experience significant health care burden during inpatient COVID-19 care.

Trends in Clinical Severity of Hospitalized Patients With Coronavirus Disease 2019-Premier Hospital Dataset, April 2020-April 2021.

BACKGROUND: Clinical severity of coronavirus disease 2019 (COVID-19) may vary over time; trends in clinical severity at admission during the pandemic among hospitalized patients in the United States have been incompletely described, so a historical record of severity over time is lacking. METHODS: We classified 466677 hospital admissions for COVID-19 from April 2020 to April 2021 into 4 mutually exclusive severity grades based on indicators present on admission (from most to least severe): Grade 4 included intensive care unit (ICU) admission and invasive mechanical ventilation (IMV); grade 3 included non-IMV ICU and/or noninvasive positive pressure ventilation; grade 2 included diagnosis of acute respiratory failure; and grade 1 included none of the above indicators. Trends were stratified by sex, age, race/ethnicity, and comorbid conditions. We also examined severity in states with high vs low Alpha (B.1.1.7) variant burden. RESULTS: Severity tended to be lower among women, younger adults, and those with fewer comorbidities compared to their counterparts. The proportion of admissions classified as grade 1 or 2 fluctuated over time, but these less-severe grades comprised a majority (75%-85%) of admissions every month. Grades 3 and 4 consistently made up a minority of admissions (15%-25%), and grade 4 showed consistent decreases in all subgroups, including states with high Alpha variant burden. CONCLUSIONS: Clinical severity among hospitalized patients with COVID-19 has varied over time but has not consistently or markedly worsened over time. The proportion of admissions classified as grade 4 decreased in all subgroups. There was no consistent evidence of worsening severity in states with higher vs lower Alpha prevalence.

Measuring BMI change among children and adolescents.

BACKGROUND: Weight control programs for children monitor BMI changes using BMI z-scores that adjust BMI for the sex and age of the child. It is, however, uncertain if BMIz is the best metric for assessing BMI change. OBJECTIVE: To identify which of 6 BMI metrics is optimal for assessing change. We considered a metric to be optimal if its short-term variability was consistent across the entire BMI distribution. SUBJECTS: 285 643 2- to 17-year-olds with BMI measured 3 times over a 10- to 14-month period. METHODS: We summarized each metric's variability using the within-child standard deviation. RESULTS: Most metrics' initial or mean value correlated with short-term variability (|r| ~ 0.3 to 0.5). The metric for which the within-child variability was largely independent (r = 0.13) of the metric's initial or mean value was the percentage of the 50th expressed on a log scale. However, changes in this metric between the first and last visits were highly (r ≥ 0.97) correlated with changes in %95th and %50th. CONCLUSIONS: Log %50 was the metric for which the short-term variability was largely independent of a child's BMI. Changes in log %50th, %95th, and %50th are strongly correlated.

Risk for Newly Diagnosed Diabetes >30 Days After SARS-CoV-2 Infection Among Persons Aged <18 Years - United States, March 1, 2020-June 28, 2021.

The COVID-19 pandemic has disproportionately affected people with diabetes, who are at increased risk of severe COVID-19.* Increases in the number of type 1 diabetes diagnoses (1,2) and increased frequency and severity of diabetic ketoacidosis (DKA) at the time of diabetes diagnosis (3) have been reported in European pediatric populations during the COVID-19 pandemic. In adults, diabetes might be a long-term consequence of SARS-CoV-2 infection (4-7). To evaluate the risk for any new diabetes diagnosis (type 1, type 2, or other diabetes) >30 days† after acute infection with SARS-CoV-2 (the virus that causes COVID-19), CDC estimated diabetes incidence among patients aged <18 years (patients) with diagnosed COVID-19 from retrospective cohorts constructed using IQVIA health care claims data from March 1, 2020, through February 26, 2021, and compared it with incidence among patients matched by age and sex 1) who did not receive a COVID-19 diagnosis during the pandemic, or 2) who received a prepandemic non-COVID-19 acute respiratory infection (ARI) diagnosis. Analyses were replicated using a second data source (HealthVerity; March 1, 2020-June 28, 2021) that included patients who had any health care encounter possibly related to COVID-19. Among these patients, diabetes incidence was significantly higher among those with COVID-19 than among those 1) without COVID-19 in both databases (IQVIA: hazard ratio [HR] = 2.66, 95% CI = 1.98-3.56; HealthVerity: HR = 1.31, 95% CI = 1.20-1.44) and 2) with non-COVID-19 ARI in the prepandemic period (IQVIA, HR = 2.16, 95% CI = 1.64-2.86). The observed increased risk for diabetes among persons aged <18 years who had COVID-19 highlights the importance of COVID-19 prevention strategies, including vaccination, for all eligible persons in this age group,§ in addition to chronic disease prevention and management. The mechanism of how SARS-CoV-2 might lead to incident diabetes is likely complex and could differ by type 1 and type 2 diabetes. Monitoring for long-term consequences, including signs of new diabetes, following SARS-CoV-2 infection is important in this age group.

Hospitalizations for COVID-19 Among US People Experiencing Incarceration or Homelessness.

Importance: People experiencing incarceration (PEI) and people experiencing homelessness (PEH) have an increased risk of COVID-19 exposure from congregate living, but data on their hospitalization course compared with that of the general population are limited. Objective: To compare COVID-19 hospitalizations for PEI and PEH with hospitalizations among the general population. Design, Setting, and Participants: This cross-sectional analysis used data from the Premier Healthcare Database on 3415 PEI and 9434 PEH who were evaluated in the emergency department or were hospitalized in more than 800 US hospitals for COVID-19 from April 1, 2020, to June 30, 2021. Exposures: Incarceration or homelessness. Main Outcomes and Measures: Hospitalization proportions were calculated. and outcomes (intensive care unit admission, invasive mechanical ventilation [IMV], mortality, length of stay, and readmissions) among PEI and PEH were compared with outcomes for all patients with COVID-19 (not PEI or PEH). Multivariable regression was used to adjust for potential confounders. Results: In total, 3415 PEI (2952 men [86.4%]; mean [SD] age, 50.8 [15.7] years) and 9434 PEH (6776 men [71.8%]; mean [SD] age, 50.1 [14.5] years) were evaluated in the emergency department for COVID-19 and were hospitalized more often (2170 of 3415 [63.5%] PEI; 6088 of 9434 [64.5%] PEH) than the general population (624 470 of 1 257 250 [49.7%]) (P < .001). Both PEI and PEH hospitalized for COVID-19 were more likely to be younger, male, and non-Hispanic Black than the general population. Hospitalized PEI had a higher frequency of IMV (410 [18.9%]; adjusted risk ratio [aRR], 1.16; 95% CI, 1.04-1.30) and mortality (308 [14.2%]; aRR, 1.28; 95% CI, 1.11-1.47) than the general population (IMV, 88 897 [14.2%]; mortality, 84 725 [13.6%]). Hospitalized PEH had a lower frequency of IMV (606 [10.0%]; aRR, 0.64; 95% CI, 0.58-0.70) and mortality (330 [5.4%]; aRR, 0.53; 95% CI, 0.47-0.59) than the general population. Both PEI and PEH had longer mean (SD) lengths of stay (PEI, 9 [10] days; PEH, 11 [26] days) and a higher frequency of readmission (PEI, 128 [5.9%]; PEH, 519 [8.5%]) than the general population (mean [SD] length of stay, 8 [10] days; readmission, 28 493 [4.6%]). Conclusions and Relevance: In this cross-sectional study, a higher frequency of COVID-19 hospitalizations for PEI and PEH underscored the importance of adhering to recommended prevention measures. Expanding medical respite may reduce hospitalizations in these disproportionately affected populations.

Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database.

OBJECTIVE: This study compared the importance of age at adiposity rebound versus childhood BMI to subsequent BMI levels in a longitudinal analysis. METHODS: From the electronic health records of 4.35 million children, a total of 12,228 children were selected who were examined at least once each year between ages 2 and 7 years and reexamined after age 14 years. The minimum number of examinations per child was six. Each child's rebound age was estimated using locally weighted regression (lowess), a smoothing technique. RESULTS: Children who had a rebound age < 3 years were, on average, 7 kg/m2 heavier after age 14 years than were children with a rebound age ≥ 7 years. However, BMI after age 14 years was more strongly associated with BMI at the rebound than with rebound age (r = 0.57 vs. -0.44). Furthermore, a child's BMI at age 3 years provided more information on BMI after age 14 years than did rebound age. In addition, rebound age provided no information on subsequent BMI if a child's BMI at age 6 years was known. CONCLUSIONS: Although rebound age is related to BMI after age 14 years, a child's BMI at age 3 years provides more information and is easier to obtain.

Estimation of Coronavirus Disease 2019 Hospitalization Costs From a Large Electronic Administrative Discharge Database, March 2020-July 2021.

BACKGROUND: Information on the costs of inpatient care for patients with coronavirus disease 2019 (COVID-19) is very limited. This study estimates the per-patient cost of inpatient care for adult COVID-19 patients seen at >800 US hospitals. METHODS: Patients aged ≥18 years with ≥1 hospitalization during March 2020-July 2021 with a COVID-19 diagnosis code in a large electronic administrative discharge database were included. We used validated costs when reported; otherwise, costs were calculated using charges multiplied by cost-to-charge ratios. We estimated costs of inpatient care per patient overall and by severity indicator, age, sex, underlying medical conditions, and acute complications of COVID-19 using a generalized linear model with log link function and gamma distribution. RESULTS: The overall cost among 654673 patients hospitalized with COVID-19 was $16.2 billion. Estimated per-patient hospitalization cost was $24 826. Among surviving patients, estimated per-patient cost was $13 090 without intensive care unit (ICU) admission or invasive mechanical ventilation (IMV), $21 222 with ICU admission alone, and $59 742 with IMV. Estimated per-patient cost among patients who died was $27 017. Adjusted cost differential was higher among patients with certain underlying conditions (eg, chronic kidney disease [$12 391], liver disease [$8878], cerebrovascular disease [$7267], and obesity [$5933]) and acute complications (eg, acute respiratory distress syndrome [$43 912], pneumothorax [$25 240], and intracranial hemorrhage [$22 280]). CONCLUSIONS: The cost of inpatient care for COVID-19 patients was substantial through the first 17 months of the pandemic. These estimates can be used to inform policy makers and planners and cost-effectiveness analysis of public health interventions to alleviate the burden of COVID-19.