Early childhood growth trajectory and later cognitive ability:Evidence from a large prospective birth cohort of healthy term-born children

BACKGROUND: Most studies of associations between child growth and cognitive ability were based on size at one or two ages and a single measure of cognition. We aimed to characterize different aspects of early growth and their associations with cognitive outcomes in childhood through adolescence. METHODS: In a sample of 12 368 Belarusian children born at term, we examined associations of length/height and weight trajectories over the first 6.5 years of life with cognitive ability at 6.5 and 16 years and its change over time. We estimated growth trajectories using two random-effects models—the SuperImposition by Translation and Rotation to model overall patterns of growth and the Jenss-Bayley to distinguish growth in infancy from post infancy. Cognitive ability was measured using the Wechsler Abbreviated Scales of Intelligence at 6.5 years and the computerized NeuroTrax test at 16 years. RESULTS: Higher length/height between birth and 6.5 years was associated with higher cognitive scores at 6.5 and 16 years {2.7 points [95% confidence interval (CI): 2.1, 3.2] and 2.5 points [95% CI: 1.9, 3.0], respectively, per standard deviation [SD] increase}. A 1-SD delay in the childhood height-growth spurt was negatively associated with cognitive scores [–2.4 (95% CI: –3.0, –1.8) at age 6.5; –2.2 (95% CI: –2.7, –1.6) at 16 years]. Birth size and post-infancy growth velocity were positively associated with cognitive scores at both ages. Height trajectories were not associated with the change in cognitive score. Similar results were observed for weight trajectories. CONCLUSIONS: Among term infants, the overall size, timing of the childhood growth spurt, size at birth and post-infancy growth velocity were all associated with cognitive ability at early-school age and adolescence

Perceived access and barriers to care among illicit drug users and hazardous drinkers: findings from the Seek, Test, Treat, and Retain data harmonization initiative (STTR).

BACKGROUND: Illicit drug use (DU) and hazardous drinking (HD) among marginalized populations may be associated with greater barriers to care. METHODS: We used baseline data on the participants of the Seek, Test, Treat, and Retain data harmonization initiative. DU includes use of any illicit drugs within the past 6 months. HD was defined as scores ≥8 for men and ≥ 7 for women on Alcohol Use Disorders Identification Test within the past 12 months. Social support scores were assigned by summing scores from individual questions related to social support. Two outcomes for multivariable regression models and mediation analysis were perceived access to care and perceived barriers to care scores, calculated from summated points from individual questions within each domain. All models were adjusted for age, gender, race/ethnicity, and social support and stratified by HIV status. RESULTS: Among 1403 illicit drug users and 4984 non-drug users, the mean age was 39.6 ± 12.2 years old, 71% were male, 57% African Americans, and 39% Hispanic/Latinos. Over 25% reported difficulties in covering medical costs and finding transportation to health care facilities and greater proportions of drug users and hazardous drinkers reported these issues than non-DU/non-HD. In multivariable models, DU and HD were both independently associated with having greater barriers to care (β: 0.49 (95% confidence interval: 0.19 to 0.79) p \u3c 0.01; 0.31 (0.18 to 0.45) \u3c 0.01) in HIV-negative participants. Neither DU nor HD was strongly associated with barriers to care for HIV-positive participants. Social support was associated with better perceived access to care and fewer barriers to care in the HIV-negative participants. CONCLUSION: The current study found that financial burdens of care, logistical difficulties in accessing care, and low social support were common challenges among individuals using illicit drugs and/or drinking hazardously. Addressing structural barriers and strengthening social support may be important strategies to improve health care among marginalized populations, regardless of HIV status

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Bayesian hierarchical model for the study of clustered data with cluster-level sources of measurement

Cluster randomized trials are an extensively used tool in health research; their main pitfall arises from within cluster correlation of responses, which prevents the use of traditional methods of design and analysis for individual randomized trials. This issue is overcome at the design stage by adjusting the sample size according to the magnitude of the intraclass correlation coefficient, and at the analysis stage by modeling clustered responses through linear and generalized linear mixed models that include random intercepts accounting for the variability due to clustering. However, there is a disadvantage that is usually overlooked when studying cluster trials, that is, when the measurement of the outcomes is naturally clustered, known as clustered measurement. For outcomes susceptible to non-random error, clustering among individuals measured by the same observer will occur if some observers tend to measure systematically differently than others. A particularly troublesome situation arises when the measurements are clustered within the same groups used as units of randomization, which we call "double clustering". The effect of both measurement issues is to increase the intraclass correlation coefficient due to the introduction of a second source of variability (due to observer) at a cluster level. None of these issues can be addressed at the design stage, and when a second set of random intercepts accounting for variation due to observer is introduced in the models at the analysis stage, the variances at cluster level cannot be estimated separately under double clustering. The Promotion of Breastfeeding Intervention Trial (PROBIT) revealed that double clustering is likely to be present in the measurement of several of its outcomes and, in fact this cluster randomized trial motivated the present research. A strategy to deal with these issues is to conduct a second set of measurements that allows us to obtain separate variance estimates and, in turn, to improve the estimation of the treatment effects; such measurements were taken in PROBIT for other reasons. We present a Bayesian hierarchical model for clustered-measured, and more emphatically for double-clustered outcomes, in which audited measurements are available. Due to the complex nature of the resulting posterior distributions, estimation is carried out through Markov Chain Monte Carlo (MCMC) methods. We conducted a simulation study under different conditions reflecting double clustering/clustered measurement severity for continuous and binary responses, to assess the performance of the treatment effect estimators in terms of bias, precision and coverage. Based on the results from this study, we conduct a comparison between double clustering and clustered measurement situations and ultimately we provide practical recommendations that allow us to design optimal cluster trials when clustered measurement or double clustering are likely to be present in the responses. We illustrate the applicability of our methods with continuous and binary variables from PROBIT.Les essais randomisés en grappes sont un outil largement utilisé en recherche dans le domainede la santé; leur principal faiblesse provient de la corrélation intra-grappe des variables réponses,qui empêche l'usage de méthodes traditionnelles de plani_cation et d'analyse employées dansdes essais randomisés individuels. Ce problème est maîtrisé au stade de la plani_cation selonl'ampleur du coe_cient de corrélation intra-classe, et à l'étape de l'analyse en modélisantles réponses en grappes par des modèles linéaires et des modèles linéaires généralisés mixtesqui comprennent des ordonnées à l'origine aléatoires tenant compte de la variabilité due auxgrappes. Par contre, il y a un désavantage qui passe souvent inaperçu lors de l'étude d'essaisen grappe, c'est-à-dire lorsque la mesure des réponses est regroupée naturellement. Pourdes réponses sujettes à une erreur non-aléatoire, le regroupement d'individus évalués par lemême observateur adviendra si l'évaluation de certains observateurs di_ère systématiquementde celle des autres. Une situation particulièrement problématique advient lorsque lesmesures sont regroupées à l'intérieur même des grappes utilisées comme unités de randomisation,un phénomène que l'on appelle _doubles grappes_. L'e_et de ces problèmes de mesureest d'augmenter le coe_cient de corrélation intra-classe par l'introduction d'une deuxièmesource de variabilité (due à l'observateur) au niveau de la grappe. Aucun de ces problèmesde mesures ne peut être corrigé au stade de la plani_cation, et lorsqu'un second ensembled'ordonnées à l'origine aléatoires tenant compte de la variation due à l'observateur est inclusdans le modèle au stade de l'analyse, les variances au niveau des grappes ne peuventêtre estimées en présence de doubles grappes. L'étude PROBIT (Promotion of Breastfeed-ing Intervention Trial ) a révélé que la présence de doubles grappes pour plusieurs de sesvariables réponses est probable; cet essai randomisé en grappes est la motivation pour letravail de recherche présenté ici. Une stratégie pour corriger ces problèmes est d'obtenirun second ensemble de mesures qui permet d'avoir des valeurs estimées séparées pour lesvariances et, ensuite, améliorer l'estimation des e_ets de traitement; de telles mesures ontété prises dans cadre de l'étude PROBIT pour d'autres raisons. Nous présentons un mod-èle bayésien hiérarchique pour des réponses mesurées en grappes et, en particulier, pour lesréponses à doubles grappes, pour lesquelles des mesures auxiliaires sont disponibles. À causede la complexité des lois a posteriori, l'estimation est e_ectuée par des méthodes de MonteCarlo par chaînes de Markov (MCMC). A_n d'évaluer le comportement des estimateurs dese_ets de traitement en terme de biais, de la précision et de la couverture des intervalles,nous avons e_ectué une étude par simulation sous di_érentes conditions re_étant la sévéritédu phénomène de doubles grappes et de mesures groupées pour des réponses continues etbinaires. En fonction des résultats de cette étude, nous faisons une comparaison entre les situationsde doubles grappes et de mesures groupées et nous formulons des recommandationspratiques pour permettre la plani_cation optimale d'essais en grappes lorsque les doublesgrappes et les mesures groupées sont probables dans la mesure des réponses. Nous illustronsl'application de ces méthodes avec des variables continues et binaires de PROBIT

Modeling Fetal Weight for Gestational Age: A Comparison of a Flexible Multi-level Spline-based Model with Other Approaches

We present a model for longitudinal measures of fetal weight as a function of gestational age. We use a linear mixed model, with a Box-Cox transformation of fetal weight values, and restricted cubic splines, in order to flexibly but parsimoniously model median fetal weight. We systematically compare our model to other proposed approaches. All proposed methods are shown to yield similar median estimates, as evidenced by overlapping pointwise confidence bands, except after 40 completed weeks, where our method seems to produce estimates more consistent with observed data. Sex-based stratification affects the estimates of the random effects variance-covariance structure, without significantly changing sex-specific fitted median values. We illustrate the benefits of including sex-gestational age interaction terms in the model over stratification. The comparison leads to the conclusion that the selection of a model for fetal weight for gestational age can be based on the specific goals and configuration of a given study without affecting the precision or value of median estimates for most gestational ages of interest.

Coffee consumption is not associated with prevalent subclinical cardiovascular disease (CVD) or the risk of CVD events, in nonalcoholic fatty liver disease: results from the multi-ethnic study of atherosclerosis

BackgroundAtherosclerosis and its clinical sequelae represent the leading cause of mortality among patients with nonalcoholic fatty liver disease (NAFLD). While epidemiologic data support the hepatoprotective benefits of coffee in NAFLD, whether coffee improves NAFLD-associated CVD risk is unknown.MethodsWe examined 3710 ethnically-diverse participants from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort, without history of known liver disease, and with available coffee data from a validated 120-item food frequency questionnaire. All participants underwent baseline non-contrast cardiac CT from which NAFLD was defined by liver:spleen ratio (L:S<1.0), and subclinical CVD was defined by coronary artery calcium (CAC)>0. Major CVD events were defined by the first occurrence of myocardial infarction, cardiac arrest, angina, stroke, or CVD death. We used log-binomial regression to calculate the adjusted prevalence ratio (PR) for CAC>0 by coffee intake and NAFLD status, and events were compared between groups using frequency of events within adjusted Cox proportional hazard regression models.ResultsSeventeen percent (N=637) of participants met criteria for NAFLD. NAFLD participants were more likely to have elevated BMI (mean 31.1±5.5kg/m2 vs. 28.0±5.2kg/m2, p<0.0001), and diabetes (22% vs. 11%, p<0.0001), but did not differ in daily coffee consumption (p=0.97). Among NAFLD participants, coffee consumption was not associated with prevalent, baseline CAC>0 (PR=1.02 [0.98-1.07]). Over 12.8years of follow-up, 93 NAFLD and 415 non-NAFLD participants experienced a CV event. However, coffee intake was not associated with incident CVD events, in either NAFLD (HR=1.05 [0.91-1.21]) or non-NAFLD participants (HR=1.03 [0.97-1.11]).ConclusionIn a large, population-based cohort, coffee consumption was not associated with the prevalence of subclinical CVD, nor did coffee impact the future risk of major CVD events, regardless of underlying NAFLD status

Circulating Interleukin-6 is a biomarker for coronary atherosclerosis in nonalcoholic fatty liver disease: Results from the Multi-Ethnic Study of Atherosclerosis

BackgroundBiomarkers to predict the presence and severity of subclinical cardiovascular disease (CVD) in nonalcoholic fatty liver disease (NAFLD) are lacking.Methods3876 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), without known chronic liver disease underwent baseline non-contrast cardiac CT, with NAFLD defined by validated liver:spleen ratio (L:S) < 1.0, and subclinical CVD defined by coronary artery calcium (CAC) score > 0. Randomly-selected subgroups underwent detailed inflammatory marker testing, including LpPLA2 mass (N = 2951), activity (N = 3020), high-sensitivity C-reactive protein (hsCRP; N = 3849), and interleukin-6 (IL-6; N = 3764). Among those with NAFLD, we estimated the prevalence of CAC > 0 and CAC > 100 for each SD biomarker increase, using multivariable log-binomial regression models adjusted for cardiometabolic risk factors.ResultsSeventeen percent (N = 668) of participants met the criteria for NAFLD. NAFLD participants were younger (mean age 61 ± 10 vs. 63 ± 10 years, p < .0001) but more likely to have an elevated BMI (mean 31.1 ± 5.5 vs. 28.0 ± 5.2 kg/m2, p < .0001), diabetes (22% vs. 11%, p < .0001), and increased inflammatory biomarkers, including LpPLA2 activity, hsCRP and IL-6 (all p < .0001). Among NAFLD participants, IL-6 was the only biomarker independently associated with prevalent CAC > 0 (PR = 1.06 [1.00-1.11]), or CAC > 100 (PR = 1.09 [1.02-1.17]). In contrast, circulating LpPLA2 mass/activity and hsCRP were not associated with either the prevalence or severity of subclinical CVD (all p > .05).ConclusionIn a large, multi-ethnic population with NAFLD, IL-6 is independently associated with the prevalence and severity of subclinical atherosclerosis. Further research into the longitudinal effects of NAFLD on progressive CVD will determine whether IL-6 is a marker or mediator of NAFLD-related atherosclerosis

Perceived access and barriers to care among illicit drug users and hazardous drinkers: findings from the Seek, Test, Treat, and Retain data harmonization initiative (STTR)

Abstract Background Illicit drug use (DU) and hazardous drinking (HD) among marginalized populations may be associated with greater barriers to care. Methods We used baseline data on the participants of the Seek, Test, Treat, and Retain data harmonization initiative. DU includes use of any illicit drugs within the past 6 months. HD was defined as scores ≥8 for men and ≥ 7 for women on Alcohol Use Disorders Identification Test within the past 12 months. Social support scores were assigned by summing scores from individual questions related to social support. Two outcomes for multivariable regression models and mediation analysis were perceived access to care and perceived barriers to care scores, calculated from summated points from individual questions within each domain. All models were adjusted for age, gender, race/ethnicity, and social support and stratified by HIV status. Results Among 1403 illicit drug users and 4984 non-drug users, the mean age was 39.6 ± 12.2 years old, 71% were male, 57% African Americans, and 39% Hispanic/Latinos. Over 25% reported difficulties in covering medical costs and finding transportation to health care facilities and greater proportions of drug users and hazardous drinkers reported these issues than non-DU/non-HD. In multivariable models, DU and HD were both independently associated with having greater barriers to care (β: 0.49 (95% confidence interval: 0.19 to 0.79) p < 0.01; 0.31 (0.18 to 0.45) < 0.01) in HIV-negative participants. Neither DU nor HD was strongly associated with barriers to care for HIV-positive participants. Social support was associated with better perceived access to care and fewer barriers to care in the HIV-negative participants. Conclusion The current study found that financial burdens of care, logistical difficulties in accessing care, and low social support were common challenges among individuals using illicit drugs and/or drinking hazardously. Addressing structural barriers and strengthening social support may be important strategies to improve health care among marginalized populations, regardless of HIV status

Coronary artery calcium and carotid artery intima-media thickness for the prediction of stroke and benefit from statins.

BackgroundCurrent guidelines suggest treatment for many individuals who may never develop a stroke. We hypothesized that a combination of coronary artery calcification (CAC) and carotid artery intima-media thickness (C IMT) data could better individualize risk assessment for ischemic stroke and transient ischemic attack events.MethodsA total of 4720 individuals from the Multi-Ethnic Study of Atherosclerosis were evaluated for ischemic stroke and transient ischemic attack. Cox proportional hazards models for time to incident ischemic stroke/transient ischemic attack were used to examine CAC and CIMT as ischemic stroke/transient ischemic attack predictors in addition to traditional risk factors. We calculated the 10-year number needed to treat by applying the benefit observed in ASCOT-LLA to the observed event rates within CAC and CIMT strata.ResultsMedian follow-up was 13.1 years. Compared with individuals with no CAC and with CIMT ≤ 75th percentile, stroke/transient ischemic attack risk increased progressively with each CAC category (0, 1-100, &gt;100) among individuals with CIMT &gt; 75th percentile. Among participants eligible for statin therapy based on the 2013 atherosclerotic cardiovascular disease (ASCVD) guidelines (ASCVD risk of &gt;5%), 739/2906 (25%) had no CAC and CIMT ≤ 75th percentile and an observed ischemic stroke/transient ischemic attack rate of 2.49 per 1000 person-years. The predicted 10-year number needed to treat was 292 for no CAC and CIMT ≤ 75th percentile and 57 for CAC &gt; 100 and CIMT &gt; 75th percentile.ConclusionThe combination of CIMT and CAC could serve to further refine risk calculation for ischemic stroke/transient ischemic attack prevention and may prioritize those in most need of statin therapy to reduce ischemic stroke/transient ischemic attack risk