No large studies have evaluated whether the human oral microbiome is directly associated with mortality. We evaluated prospective associations between the oral microbiome, measured using 16S rRNA gene sequencing, from participants aged 20-69 years in the 2009-2012 cycles of the National Health and Nutrition Examination Survey (NHANES) and all-cause mortality (N=7,721, representing ∼194 million individuals). Alpha diversity was inversely associated with mortality, and some significant associations were observed with the beta diversity matrices. Higher relative abundances of Granulicatella and Lactobacillus were associated with increased risk, while Bacteroides was associated with decreased all-cause mortality at the genus level. Results suggest oral bacterial communities may be important contributors to health and disease.

IMPORTANCE: The oral microbiome likely plays key roles in human health. Yet, population-representative characterizations are lacking. OBJECTIVE: To characterize the composition, diversity, and correlates of the oral microbiome in US adults. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed data from the population-representative National Health and Nutrition Examination Survey (NHANES) from 2009 to 2012. Microbiome data were made publicly available in 2024. NHANES participants were aged 18 to 69 years and provided oral rinse samples in 1 of 2 consecutive NHANES cycles (2009-2010 and 2011-2012). EXPOSURES: Demographic, socioeconomic, behavioral, anthropometric, metabolic, and clinical characteristics. MAIN OUTCOMES AND MEASURES: Oral microbiome measures, characterized through 16S ribosomal RNA gene sequencing, included α diversity (observed amplicon sequence variants [ASVs], Faith phylogenetic diversity, Shannon-Weiner Index, and Simpson Index); β diversity (unweighted UniFrac, weighted UniFrac, and Bray-Curtis dissimilarity); and prevalence and relative abundance at phylum level through genus level. Analyses accounted for the NHANES complex sample design. RESULTS: This study included 8237 US adults aged 18 to 69 years, representing 202 314 000 individuals (102 813 000 men [50.8%]; mean [SD] age, 42.3 [14.4] years; 9.3% self-reported as Mexican American, 12.1% as non-Hispanic Black, 64.7% as non-Hispanic White, 5.9% as other Hispanic, and 8.1% as other non-Hispanic individuals). The oral microbiome encompassed 37 bacterial phyla, 99 classes, 212 orders, 446 families, and 1219 genera. Five phyla (Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Fusobacteria) and 6 genera (Veillonella, Streptococcus, Prevotella 7, Rothia, Actinomyces, and Gemella) were present in nearly all US adults (weighted prevalence, >99%). These genera were the most abundant, accounting for 65.7% of total abundance. Observed ASVs showed a quadratic pattern with age (peak at 30 years), were similar by sex, significantly lower among non-Hispanic White individuals, and increased with greater body mass index (BMI), alcohol use, and periodontal disease severity. All covariates together accounted for a modest proportion of oral microbiome variability as measured by β diversity: R2 = 8.7% (95% CI, 8.4%-9.1%) for unweighted UniFrac, R2 = 7.2% (95% CI, 6.6%-7.7%) for weighted UniFrac, and R2 = 6.3% (95% CI, 3.1%-6.7%) for Bray-Curtis matrices. By contrast, relative abundance of a few genera explained a high percentage of variability in β diversity for weighted UniFrac: Aggregatibacter (R2 = 22.4%; 95% CI, 22.1%-22.8%), Lactococcus (R2 = 21.6%; 95% CI, 20.9%-22.3%), and Haemophilus (R2 = 18.4%; 95% CI, 18.1%-18.8%). Prevalence and relative abundance of numerous genera were associated with age, race and ethnicity, smoking, BMI categories, alcohol use, and periodontal disease severity. CONCLUSIONS AND RELEVANCE: This cross-sectional study of the oral microbiome in US adults showed that a few genera were universally present and a different set of genera explained a high percentage of oral microbiome diversity across the population. This comprehensive characterization provides a contemporary reference standard for future studies.

BACKGROUND: Chronic respiratory diseases (CRDs) are the third leading cause of death worldwide. Data of the associations between specific volatile organic compounds (VOCs), a major component of air pollution and tobacco smoke, and subsequent CRD mortality in the general population are scarce. METHODS: In a case-cohort analysis within the population-based Golestan cohort study (n = 50045, aged 40-75 years, 58% women, enrollment: 2004-2008, northeastern Iran), we included all participants who died from CRD during follow-up through 2018 (n = 242) as cases and stratified them into 16 strata defined by age, sex, residence, and tobacco smoking. Subcohort participants (n = 610) were randomly selected from all eligible cohort participants in each stratum, and sampling fractions were calculated. Baseline urine samples were used to measure 20 VOCs using ultra high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry. After excluding participants with previous history of CRDs, we used stratified Cox regression models weighted by the inverse sampling fractions (i.e. inverse probability weighting) adjusted for potential confounders, including urinary cotinine and pack-years of smoking, to calculate hazard ratios (HR) for the associations between biomarker tertiles and CRD mortality. RESULTS: Data from 545 non-case, sub-cohort participants and 149 cases (69.1% chronic obstructive pulmonary disease, 13.4% asthma, 17.5% other CRDs) were assessed in this study. During a follow-up of 10.5 years, associations [2nd and 3rd vs. 1st tertiles, HR (95% confidence interval), p for trend] were observed between metabolites of acrolein [1.56 (0.64,3.79), 3.53 (1.53,8.16), 0.002] and styrene/ethylbenzene [1.17 (0.53,2.60), 3.24 (1.37,7.66), 0.005] and CRD mortality, which persisted after excluding the first four years of follow-up. CONCLUSION: Our findings support prior research suggesting respiratory toxicity of VOCs. Further investigation and monitoring of these compounds, especially acrolein and styrene/ethylbenzene, as CRD risk factors, are recommended.

BackgroundHealthcare personnel (HCP) are at high risk for respiratory infections through occupational exposure to respiratory viruses.AimWe used data from a prospective influenza vaccine effectiveness study in HCP to quantify the incidence of acute respiratory infections (ARI) and their associated presenteeism and absenteeism.MethodsAt the start and end of each season, HCP at two Israeli hospitals provided serum to screen for antibodies to influenza virus using the haemagglutination inhibition assay. During the season, active monitoring for the development of ARI symptoms was conducted twice a week by RT-PCR testing of nasal swabs for influenza and respiratory syncytial virus (RSV). Workplace presenteeism and absenteeism were documented. We calculated incidences of influenza- and RSV-associated ARI and applied sampling weights to make estimates representative of the source population.ResultsThe median age of 2,505 participating HCP was 41 years, and 70% were female. Incidence was 9.1 per 100 person-seasons (95% CI: 5.8-14.2) for RT-PCR-confirmed influenza and 2.5 per 100 person-seasons (95% CI: 0.9-7.1) for RSV illness. Each season, 18-23% of unvaccinated and influenza-negative HCP seroconverted. The incidence of seroconversion or RT-PCR-confirmed influenza was 27.5 per 100 person-seasons (95% CI: 17.8-42.5). Work during illness occurred in 92% (95% CI: 91-93) of ARI episodes, absence from work in 38% (95% CI: 36-40).ConclusionInfluenza virus and RSV infections and associated presenteeism and absenteeism were common among HCP. Improving vaccination uptake among HCP, infection control, and encouraging sick HCP to stay home are important strategies to reduce ARI incidence and decrease the risk of in-hospital transmission.

BACKGROUND: Primary congenital glaucoma (PCG) affects approximately 1 in 10,000 live born infants in the United States (U.S.). PCG has a autosomal recessive inheritance pattern, and variable expressivity and reduced penetrance have been reported. Likely causal variants in the most commonly mutated gene, CYP1B1, are less prevalent in the U.S., suggesting that alternative genes may contribute to the condition. This study utilized exome sequencing to investigate the genetic architecture of PCG in the U.S. and to identify novel genes and variants. METHODS: We studied 37 family trios where infants had PCG and were part of the National Birth Defects Prevention Study (births 1997-2011), a U.S. multicenter study of birth defects. Samples underwent exome sequencing and sequence reads were aligned to the human reference sample (NCBI build 37/hg19). Variant filtration was conducted under de novo and Mendelian inheritance models using GEMINI. RESULTS: Among candidate variants, CYP1B1 was most represented (five trios, 13.5%). Twelve probands (32%) had potentially pathogenic variants in other genes not previously linked to PCG but important in eye development and/or to underlie Mendelian conditions with potential phenotypic overlap (e.g., CRYBB2, RXRA, GLI2). CONCLUSION: Variation in the genes identified in this population-based study may help to further explain the genetics of PCG.

Background: The BMI z-score is a standardized measure of weight status and weight change in children and adolescents. BMI z-scores from various growth references are often considered comparable, and differences among them are underappreciated. Methods: This study reanalyzed data from a weight management clinical study of liraglutide in pubertal adolescents with obesity using growth references from CDC 2000, CDC Extended, World Health Organization (WHO), and International Obesity Task Force. Results: BMI z-score treatment differences varied 2-fold from -0.13 (CDC 2000) to -0.26 (WHO) overall and varied almost 4-fold from -0.05 (CDC 2000) to -0.19 (WHO) among adolescents with high baseline BMI z-score. Conclusions: Depending upon the growth reference used, BMI z-score endpoints can produce highly variable treatment estimates and alter interpretations of clinical meaningfulness. BMI z-scores cited without the associated growth reference cannot be accurately interpreted.

Background: Volatile organic compounds (VOCs) are major components of air pollution and tobacco smoke, two known risk factors for cardiovascular diseases. VOCs are ubiquitous in the environment and originate from a wide range of sources, including the burning of biomass, fossil fuels, and consumer products. Direct evidence for associations between specific VOCs and ischemic heart disease (IHD) mortality in the general population is scarce. Methods: In a case-cohort study (stratified by age groups, sex, residence, and tobacco smoking), nested within the population-based Golestan cohort study (n = 50,045, 40–75 years, 58% women, enrollment: 2004–2008) in northeastern Iran, we measured urinary concentrations of 20 smoking-related VOC biomarkers using ultra high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry. We calculated hazard ratio (HR) and 95% confidence interval (CI) for their associations with IHD mortality during follow-up to 2018, using Cox regression models adjusted for age, ethnicity, education, marital status, body mass index, physical activity, wealth, and urinary cotinine. Results: There were 575 non-cases from random subcohort and 601 participants who died from IHD, mean (standard deviation) age, 58.2 (9.3) years, with a median of 8.4 years follow-up. Significant associations [3rd vs. 1st tertile, HR (95% CI), P for trend] were observed between biomarkers of acrylamide [1.68(1.05,2.69), 0.025], acrylonitrile [2.06(1.14,3.72), 0.058], acrolein [1.98(1.30,3.01), 0.003 and 2.44(1.43,4.18), 0.002], styrene/ethylbenzene [1.83(1.19,2.84), 0.007 and 1.44(1.01,2.07), 0.046], dimethylformamide/methylisocyanate [2.15(1.33,3.50), 0.001], and 1,3butadiene [2.35(1.52,3.63),<0.001] and IHD mortality. These associations were independent of tobacco smoking, and they were only present in the non-smoking subgroup. Conclusion: Our findings provide direct evidence for associations between exposure to several VOCs with widespread household and commercial use and IHD mortality many years after these exposures. These results highlight the importance of VOC exposure in the general population as a risk factor for cardiovascular diseases and underline the importance of bio-monitoring non-tobacco VOC exposure. © 2024

BACKGROUND: Asymptomatic SARS-CoV-2 infection in children is highly prevalent but its acute and chronic implications have been minimally described. METHODS: In this controlled case-ascertained household transmission study, we recruited asymptomatic children <18 years with SARS-CoV-2 nucleic acid testing performed at 12 tertiary care pediatric institutions in Canada and the United States. We attempted to recruit all test-positive children and 1 to 3 test-negative, site-matched controls. After 14 days' follow-up we assessed the clinical (ie, symptomatic) and combined (ie, test-positive, or symptomatic) secondary attack rates (SARs) among household contacts. Additionally, post-COVID-19 condition (PCC) was assessed in SARS-CoV-2-positive participating children after 90 days' follow-up. RESULTS: A total of 111 test-positive and 256 SARS-CoV-2 test-negative asymptomatic children were enrolled between January 2021 and April 2022. After 14 days, excluding households with co-primary cases, the clinical SAR among household contacts of SARS-CoV-2-positive and -negative index children was 10.6% (19/179; 95% CI: 6.5%-16.1%) and 2.0% (13/663; 95% CI: 1.0%-3.3%), respectively (relative risk = 5.4; 95% CI: 2.7-10.7). In households with a SARS-CoV-2-positive index child, age <5 years, being pre-symptomatic (ie, developed symptoms after test), and testing positive during Omicron and Delta circulation periods (vs earlier) were associated with increased clinical and combined SARs among household contacts. Among 77 asymptomatic SARS-CoV-2-infected children with 90-day follow-up, 6 (7.8%; 95% CI: 2.9%-16.2%) reported PCC. CONCLUSIONS: Asymptomatic SARS-CoV-2-infected children, especially those <5 years, are important contributors to household transmission, with 1 in 10 exposed household contacts developing symptomatic illness within 14 days. Asymptomatic SARS-CoV-2-infected children may develop PCC.

BACKGROUND: Sodium and potassium measured in 24-hour urine collections are often used as reference measurements to validate self-reported dietary intake instruments. OBJECTIVE: To evaluate whether collection and analysis of a limited number of urine voids at specified times during the day ("timed voids") can provide alternative reference measurements, and to identify their optimal number and timing. METHODS: We used data from a urine calibration study among 441 adults aged 18-39 years. Participants collected each urine void in a separate container for 24 hours and recorded collection time. For the same day, they reported dietary intake using a 24-hour recall. Urinary sodium and potassium were analyzed in a 24-hour composite sample and in four timed voids (morning, afternoon, evening and overnight). Using linear regression models, equations predicting log-transformed 24-hour urinary sodium or potassium level were developed using each of the four single timed voids, six pairs and four triples. The equations also included age, sex, race, body mass index and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-hour levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-hour dietary recall. These estimates were compared. RESULTS: Optimal combinations found were: single voids - Evening; paired voids - Afternoon+Overnight (sodium) and Morning+Evening (potassium); triple voids - Morning+Evening+Overnight (sodium) and Morning+Afternoon+Evening (potassium). Predicted 24-hour urinary levels estimated 24-hour recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-hour urinary levels. To recover lost precision, it was estimated that sample sizes need to be larger by about 2.6-2.7 times for a single void, 1.7-2.1 times for paired voids and 1.5-1.6 times for triple voids. CONCLUSIONS: Our results lay the basis for further development of new reference biomarkers based on timed voids. CLINICAL TRIAL REGISTRY: Home ClinicalTrials.govas NCT01631240.

OBJECTIVES: To examine the prevalence and trends in severe obesity among 16.6 million children aged 2 to 4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) from 2010 to 2020. METHODS: Severe obesity was defined as a sex-specific BMI for age ≥120% of the 95th percentile on the Centers for Disease Control and Prevention growth charts or BMI ≥35 kg/m2. Joinpoint regression was used to identify when changes occurred in the overall trend. Logistic regression was used to compute the adjusted prevalence differences between years controlling for sex, age, and race and ethnicity. RESULTS: The prevalence of severe obesity significantly decreased from 2.1% in 2010 to 1.8% in 2016 and then increased to 2.0% in 2020. From 2010 to 2016, the prevalence decreased significantly among all sociodemographic subgroups except for American Indian/Alaska Native (AI/AN) children. The largest decreases were among 4-year-olds, Asian/Pacific Islander and Hispanic children, and children from higher-income households. However, from 2016 to 2020, the prevalence increased significantly overall and among sociodemographic subgroups, except for AI/AN and non-Hispanic white children. The largest increases occurred in 4-year-olds and Hispanic children. Among 56 WIC agencies, the prevalence significantly declined in 17 agencies, and 1 agency (Mississippi) showed a significant increase from 2010 to 2016. In contrast, 21 agencies had significant increases, and only Alaska had a significant decrease from 2016 to 2020. CONCLUSIONS: Although severe obesity prevalence in toddlers declined from 2010 to 2016, recent trends are upward. Early identification and access to evidence-based family healthy weight programs for at-risk children can support families and child health.

The 2020 adult immunization schedule from the Advisory Committee on Immunization Practices (1) contained an inaccuracy regarding the efficacy of PCV13. The Revised Content and Graphics section states that “PCV13 is a safe and potentially effective vaccine for older adults.” The word “potentially” is inaccurate. The sentence should state: “PCV13 is a safe and effective vaccine for older adults.”

Background. Benzene is a known human carcinogen. Human exposure to benzene can be assessed by measuring trans, trans-muconic acid (MUCA) in urine. Golestan Province in northeastern Iran has been reported to have high incidence of esophageal cancer linked to the use of tobacco products. This manuscript evaluates the urinary MUCA concentrations among the participants of the Golestan Cohort Study (GCS).Methods. We analyzed MUCA concentration in 177 GCS participants' urine samples and performed nonparametric pairwise multiple comparisons to determine statistically significant difference among six different product use groups. Mixed effects model was fitted on 22 participants who exclusively smoked cigarette and 51 participants who were classified as nonusers. The urinary MUCA data were collected at the baseline and approximately five years later, and intraclass correlation coefficient (ICC) was calculated from the model.Results. Compared with nonusers, tobacco smoking was associated with higher urinary MUCA concentrations. Based on the nonparametric test of pairwise multiple comparisons, MUCA concentrations among participants who smoked combusted tobacco products were statistically significantly higher compared to nonusers. Urinary MUCA collected five years apart from the same individuals showed moderate reliability (ICC = 0.41), which was expected given the relatively short half-life (∼6 hrs) of MUCA.Conclusion. Our study revealed that tobacco smoke was positively associated with increased levels of urinary MUCA concentration, indicating that it is a significant source of benzene exposure among GCS participants.

BACKGROUND: Studying carcinogens in tobacco and non-tobacco sources may be key to understanding the pathogenesis and geographic distribution of esophageal cancer. METHODS: Golestan Cohort Study (GCS) has been conducted since 2004 in a region with high rates of esophageal squamous cell carcinoma (ESCC). For this nested study, the cases comprised of all incident cases by Jan 1, 2018; controls were matched to the case by age, sex, residence, time in cohort, and tobacco use. We measured urinary concentrations of 33 exposure biomarkers of nicotine, polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and tobacco-specific nitrosamines (TSNAs). We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (95%CI) for associations between the 90th versus the 10th percentiles of the biomarker concentrations and incident ESCC. RESULTS: Among individuals who did not currently use tobacco (148 cases/163 controls), two acrolein metabolites, two acrylonitrile metabolites, one propylene oxide metabolite and one 1,3-butadiene metabolite were significantly associated with incident ESCC (adjusted ORs between 1.8 and 4.3). Among tobacco users (57 cases/63 controls), metabolites of two other VOCs (styrene and xylene) were associated with ESCC (ORs= 6.2 and 9.0). In tobacco users, two TSNAs (NNN and N'-Nitrosoanatabine) were also associated with ESCC. Suggestive associations were seen with some PAHs (especially 2-hydroxynaphthalene) in non-users of tobacco products and other TSNAs in tobacco users. CONCLUSION: These novel associations based on individual-level data and samples collected many years before cancer diagnosis, from a population without occupational exposure, have important public health implications.

BACKGROUND: To assist clinicians with identifying children at risk of severe outcomes, we assessed the association between laboratory findings and severe outcomes among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected children and determined if SARS-CoV-2 test result status modified the associations. METHODS: We conducted a cross-sectional analysis of participants tested for SARS-CoV-2 infection in 41 pediatric emergency departments in 10 countries. Participants were hospitalized, had laboratory testing performed, and completed 14-day follow-up. The primary objective was to assess the associations between laboratory findings and severe outcomes. The secondary objective was to determine if the SARS-CoV-2 test result modified the associations. RESULTS: We included 1817 participants; 522 (28.7%) SARS-CoV-2 test-positive and 1295 (71.3%) test-negative. Seventy-five (14.4%) test-positive and 174 (13.4%) test-negative children experienced severe outcomes. In regression analysis, we found that among SARS-CoV-2-positive children, procalcitonin ≥0.5 ng/mL (adjusted odds ratio [aOR], 9.14; 95% CI, 2.90-28.80), ferritin >500 ng/mL (aOR, 7.95; 95% CI, 1.89-33.44), D-dimer ≥1500 ng/mL (aOR, 4.57; 95% CI, 1.12-18.68), serum glucose ≥120 mg/dL (aOR, 2.01; 95% CI, 1.06-3.81), lymphocyte count <1.0 × 10(9)/L (aOR, 3.21; 95% CI, 1.34-7.69), and platelet count <150 × 10(9)/L (aOR, 2.82; 95% CI, 1.31-6.07) were associated with severe outcomes. Evaluation of the interaction term revealed that a positive SARS-CoV-2 result increased the associations with severe outcomes for elevated procalcitonin, C-reactive protein (CRP), D-dimer, and for reduced lymphocyte and platelet counts. CONCLUSIONS: Specific laboratory parameters are associated with severe outcomes in SARS-CoV-2-infected children, and elevated serum procalcitonin, CRP, and D-dimer and low absolute lymphocyte and platelet counts were more strongly associated with severe outcomes in children testing positive compared with those testing negative.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence, the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association (STREGA) studies initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

In December 2022, the Centers for Disease Control and Prevention (CDC) released Extended BMI-for-age growth charts1,2 for children and adolescents with high BMI values. These charts extend to a BMI of 60 and add 4 growth curves (98th, 99th, 99.9th, and 99.99th percentiles). Obesity among children and adolescents is defined as BMI ≥95th percentile of BMI-for-age and severe obesity as BMI ≥120% of the 95th percentile or ≥35.3 The recent American Academy of Pediatrics guideline for the treatment of obesity recommends using percentages of the 95th percentile of BMI-for-age to indicate different levels of severe obesity.4 This analysis compares CDC extended BMI-for-age percentiles with 120% and 140% of the 95th percentile and illustrates the differences between the prevalence of US children and adolescents 2 to 19 years of age with a BMI ≥ extended 98th percentile using the newly defined curve and those ≥120% of the 95th percentile using 2017 to March 2020 National Health and Nutritional Examination (NHANES) data.

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.

BACKGROUND: There are large and persistent disparities in life expectancy among racial-ethnic groups in the USA, but the extent to which these patterns vary geographically on a local scale is not well understood. This analysis estimated life expectancy for five racial-ethnic groups, in 3110 US counties over 20 years, to describe spatial-temporal variations in life expectancy and disparities between racial-ethnic groups. METHODS: We applied novel small-area estimation models to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual sex-specific and age-specific mortality rates stratified by county and racial-ethnic group (non-Latino and non-Hispanic White [White], non-Latino and non-Hispanic Black [Black], non-Latino and non-Hispanic American Indian or Alaska Native [AIAN], non-Latino and non-Hispanic Asian or Pacific Islander [API], and Latino or Hispanic [Latino]) from 2000 to 2019. We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and then constructed abridged life tables to estimate life expectancy at birth. FINDINGS: Between 2000 and 2019, trends in life expectancy differed among racial-ethnic groups and among counties. Nationally, there was an increase in life expectancy for people who were Black (change 3·9 years [95% uncertainty interval 3·8 to 4·0]; life expectancy in 2019 75·3 years [75·2 to 75·4]), API (2·9 years [2·7 to 3·0]; 85·7 years [85·3 to 86·0]), Latino (2·7 years [2·6 to 2·8]; 82·2 years [82·0 to 82·5]), and White (1·7 years [1·6 to 1·7]; 78·9 years [78·9 to 79·0]), but remained the same for the AIAN population (0·0 years [-0·3 to 0·4]; 73·1 years [71·5 to 74·8]). At the national level, the negative difference in life expectancy for the Black population compared with the White population decreased during this period, whereas the negative difference for the AIAN population compared with the White population increased; in both cases, these patterns were widespread among counties. The positive difference in life expectancy for the API and Latino populations compared with the White population increased at the national level from 2000 to 2019; however, this difference declined in a sizeable minority of counties (615 [42·0%] of 1465 counties) for the Latino population and in most counties (401 [60·2%] of 666 counties) for the API population. For all racial-ethnic groups, improvements in life expectancy were more widespread across counties and larger from 2000 to 2010 than from 2010 to 2019. INTERPRETATION: Disparities in life expectancy among racial-ethnic groups are widespread and enduring. Local-level data are crucial to address the root causes of poor health and early death among disadvantaged groups in the USA, eliminate health disparities, and increase longevity for all. FUNDING: National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Science Research, US National Institutes of Health.

We thank Rolland-Cachera1 for their letter concerning our article.2 We found that children with an early body mass index (BMI) rebound had a higher BMI after age 10 years, but the BMI value at the age of rebound provided more information. As stated in the text accompanying Table 2, a regression model that included age at rebound accounted for 31% of the subsequent BMI variability. In contrast, including a child’s BMI (rather than age) at rebound accounted for 45% of the variability. As illustrated in Figure 1 and Figure 2 of our article, both the age and BMI at rebound were important predictors of the probability of obesity after age 10 years. |

In October 2020, the Advisory Committee on Immunization Practices (ACIP) voted to approve the Recommended Adult Immunization Schedule for Ages 19 Years or Older, United States, 2021. Following Emergency Use Authorization of Pfizer-BioNTech COVID-19 vaccine by the U.S. Food and Drug Administration, ACIP issued an interim recommendation at its 12 December 2020 emergency meeting for use of Pfizer-BioNTech COVID-19 vaccine in persons aged 16 years or older (1). In addition, ACIP approved an amendment to include COVID-19 vaccine recommendations in the child and adolescent and adult immunization schedules. Following Emergency Use Authorization of Moderna COVID-19 vaccine by the U.S. Food and Drug Administration, ACIP issued an interim recommendation at its 19 December 2020 emergency meeting for use of Moderna COVID-19 vaccine in persons aged 18 years or older (2). The 2021 adult immunization schedule, available at www.cdc.gov/vaccines/schedules/hcp/imz/adult.html, summarizes ACIP recommendations in 2 tables and accompanying notes (Figure). The full ACIP recommendations for each vaccine are available at www.cdc.gov/vaccines/hcp/acip-recs/index.html. The 2021 schedule has also been approved by the director of the Centers for Disease Control and Prevention (CDC) and by the American College of Physicians (www.acponline.org), the American Academy of Family Physicians (www.aafp.org), the American College of Obstetricians and Gynecologists (www.acog.org), the American College of Nurse-Midwives (www.midwife.org), and the American Academy of Physician Assistants

BACKGROUND: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. METHODS: We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. RESULTS: Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. CONCLUSIONS: Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).

Seroprevalence studies provide useful information about the proportion of the population either vaccinated against SARS-CoV-2, previously infected with the virus, or both. Numerous studies have been conducted in the United States, but differ substantially by dates of enrollment, target population, geographic location, age distribution, and assays used. This can make it challenging to identify and synthesize available seroprevalence data by geographic region or to compare infection-induced versus combined infection- and vaccination-induced seroprevalence. To facilitate public access and understanding, the National Institutes of Health and the Centers for Disease Control and Prevention developed the COVID-19 Seroprevalence Studies Hub (COVID-19 SeroHub, https://covid19serohub.nih.gov/ ), a data repository in which seroprevalence studies are systematically identified, extracted using a standard format, and summarized through an interactive interface. Within COVID-19 SeroHub, users can explore and download data from 178 studies as of September 1, 2022. Tools allow users to filter results and visualize trends over time, geography, population, age, and antigen target. Because COVID-19 remains an ongoing pandemic, we will continue to identify and include future studies.