Does heat tolerance actually predict animals' geographic thermal limits?

The "climate extremes hypothesis" is a major assumption of geographic studies of heat tolerance and climatic vulnerability. However, this assumption remains vastly untested across taxa, and multiple factors may contribute to uncoupling heat tolerance estimates and geographic limits. Our dataset includes 1000 entries of heat tolerance data and maximum temperatures for each species' known geographic limits (hereafter, Tmax). We gathered this information across major animal taxa, including marine fish, terrestrial arthropods, amphibians, non-avian reptiles, birds, and mammals. We first tested if heat tolerance constrains the Tmax of sites where species could be observed. Secondly, we tested if the strength of such restrictions depends on how high Tmax is relative to heat tolerance. Thirdly, we correlated the different estimates of Tmax among them and across species. Restrictions are strong for amphibians, arthropods, and birds but often weak or inconsistent for reptiles and mammals. Marine fish describe a non-linear relationship that contrasts with terrestrial groups. Traditional heat tolerance measures in thermal vulnerability studies, like panting temperatures and the upper set point of preferred temperatures, do not predict Tmax or are inversely correlated to it, respectively. Heat tolerance restricts the geographic warm edges more strongly for species that reach sites with higher Tmax for their heat tolerance. These emerging patterns underline the importance of reliable species' heat tolerance indexes to identify their thermal vulnerability at their warm range edges. Besides, the tight correlations of Tmax estimates across on-land microhabitats support a view of multiple types of thermal challenges simultaneously shaping ranges' warm edges for on-land species. The heterogeneous correlation of Tmax estimates in the ocean supports the view that fish thermoregulation is generally limited, too. We propose new hypotheses to understand thermal restrictions on animal distribution.

Household Food Insecurity Is Associated With Physical Activity in Youth and Young Adults With Diabetes: A Cross-Sectional Study.

BACKGROUND: Physical activity (PA) is essential for optimal diabetes management. Household food insecurity (HFI) may negatively affect diabetes management behaviors. The purpose of this study was to cross-sectionally examine the association between HFI and PA in youth and young adults (YYA) with type 1 (N = 1998) and type 2 (N = 391) diabetes from the SEARCH for Diabetes in Youth Study. METHODS: HFI was measured with the US Household Food Security Survey Module. PA was measured with the International Physical Activity Questionnaire Short Form. Walking, moderate-intensity PA (excluding walking), vigorous-intensity PA, moderate- to vigorous-intensity PA, and total PA were estimated as minutes per week, while time spent sitting was assessed in minutes per day. All were modeled with median regression. Meeting PA guidelines or not was modeled using logistic regression. RESULTS: YYA with type 1 diabetes who experienced HFI spent more time walking than those who were food secure. YYA with type 2 diabetes who experienced HFI spent more time sitting than those who were food secure. CONCLUSIONS: Future research should examine walking for leisure versus other domains of walking in relation to HFI and use objective PA measures to corroborate associations between HFI and PA in YYA with diabetes.

A retrospective analysis of the factors associated with surgical checklist compliance using data from a local health unit in Italy, 2018-2021.

RATIONALE: Since its publication, the World Health Organization Surgical Safety Checklist (SSC) has been progressively adopted by healthcare providers around the world to monitor and safeguard the delivery of surgeries. In one Italian region's health system, the SSC and other two surgery-specific checklists were supplemented by a document that records any non-conformity (NC) arising from the safety checks. AIMS AND OBJECTIVES: In this study, we investigated the factors associated with NCs using data from a local health unit (LHU). The secondary aim of this study was to explore the potential impact of the coronavirus crisis on surgical checklist compliance. METHODS: We used data on surgical activity from the Modena LHU between 2018 and 2021 and the accompanying NC documents. The primary goal was to estimate the relative risk (RR) of NCs according to several factors, including checklist incompleteness and surgery class (elective, urgent or emergency), using Poisson regression. A similar analysis was performed separately for 2018-2019 and 2020-2021 to assess the COVID-19 potential impact. RESULTS AND CONCLUSIONS: Checklist compliance in the LHU was 95%, with the presence of NCs in about 7% of surgeries. The factors that increased the RR were incompleteness of the checklist (adjusted RR = 3.12; 95% confidence interval [CI] = 2.86-3.40), urgent surgeries (adjusted RR [aRR] = 1.59; 95% CI = 1.47-1.72), emergencies (aRR = 2.09; 95% CI = 1.15-3.79), and surgeries with more than four procedures (aRR = 1.64; 95% CI = 1.41-1.92). Most notably, the RR for incomplete checklists showed a negative association with NCs before the COVID-19 outbreak but positive afterwards. Checklist compliance was overall satisfactory, though the observation of noncompliant checklists of about 1000 per year suggests there is still room for improvement. Moreover, attention to the checklist best practices and organization of outpatient workload may have been affected by the exceptional circumstances of the pandemic.

Trends in Resources for Neonatal Intensive Care at Delivery Hospitals for Infants Born Younger Than 30 Weeks' Gestation, 2009-2020.

Importance: In an ideal regionalized system, all infants born very preterm would be delivered at a large tertiary hospital capable of providing all necessary care. Objective: To examine whether the distribution of extremely preterm births changed between 2009 and 2020 based on neonatal intensive care resources at the delivery hospital. Design, Setting, and Participants: This retrospective cohort study was conducted at 822 Vermont Oxford Network (VON) centers in the US between 2009 and 2020. Participants included infants born at 22 to 29 weeks' gestation, delivered at or transferred to centers participating in the VON. Data were analyzed from February to December 2022. Exposures: Hospital of birth at 22 to 29 weeks' gestation. Main Outcomes and Measures: Birthplace neonatal intensive care unit (NICU) level was classified as A, restriction on assisted ventilation or no surgery; B, major surgery; or C, cardiac surgery requiring bypass. Level B centers were further divided into low-volume (<50 inborn infants at 22 to 29 weeks' gestation per year) and high-volume (≥50 inborn infants at 22 to 29 weeks' gestation per year) centers. High-volume level B and level C centers were combined, resulting in 3 distinct NICU categories: level A, low-volume B, and high-volume B and C NICUs. The main outcome was the change in the percentage of births at hospitals with level A, low-volume B, and high-volume B or C NICUs overall and by US Census region. Results: A total of 357 181 infants (mean [SD] gestational age, 26.4 [2.1] weeks; 188 761 [52.9%] male) were included in the analysis. Across regions, the Pacific (20 239 births [38.3%]) had the lowest while the South Atlantic (48 348 births [62.7%]) had the highest percentage of births at a hospital with a high-volume B- or C-level NICU. Births at hospitals with A-level NICUs increased by 5.6% (95% CI, 4.3% to 7.0%), and births at low-volume B-level NICUs increased by 3.6% (95% CI, 2.1% to 5.0%), while births at hospitals with high-volume B- or C-level NICUs decreased by 9.2% (95% CI, -10.3% to -8.1%). By 2020, less than half of the births for infants at 22 to 29 weeks' gestation occurred at hospitals with high-volume B- or C-level NICUs. Most US Census regions followed the nationwide trends; for example, births at hospitals with high-volume B- or C-level NICUs decreased by 10.9% [95% CI, -14.0% to -7.8%) in the East North Central region and by 21.1% (95% CI, -24.0% to -18.2%) in the West South Central region. Conclusions and Relevance: This retrospective cohort study identified concerning deregionalization trends in birthplace hospital level of care for infants born at 22 to 29 weeks' gestation. These findings should serve to encourage policy makers to identify and enforce strategies to ensure that infants at the highest risk of adverse outcomes are born at the hospitals where they have the best chances to attain optimal outcomes.

Joint regression modelling of intensity and timing of accelerometer counts.

Accelerometers are commonly used in human medical and public health research to measure physical movement, which is relevant in a wide range of studies, from physical activity and sleep behaviours studies, to identification of movement patterns in people affected by diseases of the locomotor system and prediction of risk of injury in high performance sports. The accelerometer output provides the intensity (activity count) and timing (timestamp) of the movement, which can be used to define bouts of activity (periods of sustained movement of a given intensity). In some contexts, it may be important to include both dimensions to obtain a broader and deeper understanding of the phenomenon under study. Such is the case of a large-scale epidemiological investigation on the daily and weekly physical activity behaviours of school-aged children enrolled in the UK Millennium Cohort Study, which has motivated the present article. I present a statistical approach to joint modelling of intensity and timing of activity bouts that takes advantage of the circular nature of the timing. The model, which accounts for the longitudinal structure of the observations, is remarkably simple to implement using standard statistical software.

Household food insecurity is associated with diabetic ketoacidosis but not severe hypoglycemia or glycemic control in youth and young adults with youth-onset type 2 diabetes.

OBJECTIVE: To examine the association between household food insecurity (HFI), glycemic control, severe hypoglycemia and diabetic ketoacidosis (DKA) among youth and young adults (YYA) with youth-onset type 2 diabetes. RESEARCH DESIGN AND METHODS: This cross-sectional study included 395 YYA with type 2 diabetes from the SEARCH for Diabetes in Youth Study (2015-2019). HFI was reported by young adult participants or parents of minor participants via the US Household Food Security Survey Module. Glycemic control was assessed by HbA1c and analyzed as a continuous and categorical variable (optimal: <7.0%, suboptimal: ≥7.0%-9.0%, poor: >9.0%). Acute complications included self-reported severe hypoglycemia or DKA in the last 12 months. Adjusted logistic and linear regression were used for binary and continuous outcomes, respectively. RESULTS: Approximately 31% reported HFI in the past 12 months. Mean HbA1c among those with HFI was 9.2% compared to 9.5% without HFI. Of those with HFI, 56% had an HbA1c >9.0% compared to 55% without HFI. Adjusted models showed no associations between HFI and glycemic control. Of those with HFI, 14.4% reported experiencing DKA and 4.7% reported severe hypoglycemia. YYA with HFI had 3.08 times (95% CI: 1.18-8.06) the odds of experiencing DKA as those without HFI. There was no association between HFI and severe hypoglycemia. CONCLUSIONS: HFI was associated with markedly increased odds of DKA but not with glycemic control or severe hypoglycemia. Future research among YYA with type 2 diabetes should evaluate longitudinally whether alleviating HFI reduces DKA.

Estimation of sparse functional quantile regression with measurement error: a SIMEX approach.

Quantile regression is a semiparametric method for modeling associations between variables. It is most helpful when the covariates have complex relationships with the location, scale, and shape of the outcome distribution. Despite the method's robustness to distributional assumptions and outliers in the outcome, regression quantiles may be biased in the presence of measurement error in the covariates. The impact of function-valued covariates contaminated with heteroscedastic error has not yet been examined previously; although, studies have investigated the case of scalar-valued covariates. We present a two-stage strategy to consistently fit linear quantile regression models with a function-valued covariate that may be measured with error. In the first stage, an instrumental variable is used to estimate the covariance matrix associated with the measurement error. In the second stage, simulation extrapolation (SIMEX) is used to correct for measurement error in the function-valued covariate. Point-wise standard errors are estimated by means of nonparametric bootstrap. We present simulation studies to assess the robustness of the measurement error corrected for functional quantile regression. Our methods are applied to National Health and Examination Survey data to assess the relationship between physical activity and body mass index among adults in the United States.

Mid-quantile regression for discrete responses.

We develop quantile regression methods for discrete responses by extending Parzen's definition of marginal mid-quantiles. As opposed to existing approaches, which are based on either jittering or latent constructs, we use interpolation and define the conditional mid-quantile function as the inverse of the conditional mid-distribution function. We propose a two-step estimator whereby, in the first step, conditional mid-probabilities are obtained nonparametrically and, in the second step, regression coefficients are estimated by solving an implicit equation. When constraining the quantile index to a data-driven admissible range, the second-step estimating equation has a least-squares type, closed-form solution. The proposed estimator is shown to be strongly consistent and asymptotically normal. A simulation study shows that our estimator performs satisfactorily and has an advantage over a competing alternative based on jittering. Our methods can be applied to a large variety of discrete responses, including binary, ordinal, and count variables. We show an application using data on prescription drugs in the United States and discuss two key findings. First, our analysis suggests a possible differential medical treatment that worsens the gender inequality among the most fragile segment of the population. Second, obesity is a strong driver of the number of prescription drugs and is stronger for more frequent medications users. The proposed methods are implemented in the R package Qtools. Supplemental materials for this article, including a brief R tutorial, are available as an online supplement.

Residential Segregation and Emergency Department Utilization Among an Underserved Urban Emergency Department Sample in North Carolina.

BACKGROUND Residential segregation is a spatial manifestation of structural racism. Racial disparities in emergency department (ED) utilization mirror social inequity in the larger community. We evaluated associations between residential segregation and ED utilization in a community with known disparities and geographically concentrated social and health risk.METHODS Cross-sectional data were collected from electronic medical records of 101 060 adult ED patients living in Mecklenburg County, North Carolina in 2017. Community context was measured as residential segregation using the dissimilarity index, categorized into quintiles (Q1-Q5) using 2013-2017 American Community Survey estimates, and residency in a public health priority area (PHPA). The outcome was measured as total ED visits during the study period. Associations between community context and ED utilization were modeled using Anderson's behavioral model of health service utilization, and estimated using negative binomial regression, including interaction terms by race.RESULTS Compared to areas with the lowest proportions of Black residents (Q1), living in Q4 was associated with higher rates of ED utilization among Black/Other (AME = 0.11) and White (AME = 0.23) patients, while associations with living in Q5 were approximately equivalent (AME = 0.12). PHPA residency was associated with higher rates of ED utilization among Black/Other (AME = 0.10) and White patients (AME = 0.22).LIMITATIONS Associations should not be interpreted as causal, or be generalized to the larger community without ED utilization. Health system leakage is possible but limited.CONCLUSIONS Residential segregation is associated with higher rates of ED utilization, as are PHPA residency and other individual-level determinants.

Changes in hospital quality at hospitals serving black and hispanic newborns below 30 weeks' gestation.

OBJECTIVE: Examine whether the quality of Black and Hispanic serving (BHS) compared with not BHS (NBHS) NICUs has changed differentially over time. STUDY DESIGN: Infants 24-29 weeks' gestation born at U.S. Vermont Oxford Network centers (2006-2018) were studied. We calculated adjusted hospital quality scores as the predicted probabilities of composite in-hospital mortality and morbidities from a logistic model. We regressed hospital quality scores on birth year to estimate the linear temporal slope by BHS-serving status for hospitals within each Census division. RESULTS: Hospital quality improved similarly over time for BHS and NBHS hospitals across all divisions except West South Central where a mean change in the composite score was -18.8 (95% CI: -24.1, -13.5) for NBHS and -9.3 (95% CI: -14.1, -4.6) for BHS hospitals (p-value = 0.009). CONCLUSION: Hospital quality improved similarly for BHS and NBHS hospitals across most divisions. Variation within and between divisions should be a focus for quality improvement.

Differences in ​the proportion of children meeting behavior guidelines ​between summer ​and school by socioeconomic status and race.

OBJECTIVE: Children who fail to meet activity, sleep, and screen-time guidelines are at increased risk for obesity. Further, children who are Black are more likely to have obesity when compared to children who are White, and children from low-income households are at increased risk for obesity when compared to children from higher-income households. The objective of this study was to evaluate the proportion of days meeting obesogenic behavior guidelines during the school year compared to summer vacation by race and free/reduced priced lunch (FRPL) eligibility. METHODS: Mixed-effects linear and logistic regressions estimated the proportion of days participants met activity, sleep, and screen-time guidelines during summer and school by race and FRPL eligibility within an observational cohort sample. RESULTS: Children (n = 268, grades = K - 4, 44.1%FRPL, 59.0% Black) attending three schools participated. Children's activity, sleep, and screen-time were collected during an average of 23 school days and 16 days during summer vacation. During school, both children who were White and eligible for FRPL met activity, sleep, and screen-time guidelines on a greater proportion of days when compared to their Black and non-eligible counterparts. Significant differences in changes from school to summer in the proportion of days children met activity (-6.2%, 95CI = -10.1%, -2.3%; OR = 0.7, 95CI = 0.6, 0.9) and sleep (7.6%, 95CI = 2.9%, 12.4%; OR = 2.1, 95CI = 1.4, 3.0) guidelines between children who were Black and White were observed. Differences in changes in activity (-8.5%, 95CI = -4.9%, -12.1%; OR = 1.5, 95CI = 1.3, 1.8) were observed between children eligible versus uneligible for FRPL. CONCLUSIONS: Summer vacation may be an important time for targeting activity and screen-time of children who are Black and/or eligible for FRPL.

Social and demographic characteristics of frequent or high-charge emergency department users: A quantile regression application.

OBJECTIVE: Heavy users of the emergency department (ED) are a heterogeneous population. Few studies have captured the social and demographic complexity of patients with the largest burden of ED use. Our objective was to model associations between social and demographic patient characteristics and quantiles of the distributions of ED use, defined as frequent and high-charge. METHODS: We conducted a cross-sectional analysis of electronic health and billing records of 99 637 adults residing in an urban North Carolina county who visited an ED within Atrium Health, a large integrated health care system, in 2017. Mid-quantile and standard quantile regression models were used for count and continuous responses, respectively. Frequent and high-charge use outcomes were defined as the median (0.50) and upper quantiles (0.75, 0.95, 0.99) of the outcome distributions for total billed ED visits and associated charges during the study period. Patient characteristic predictors were: insurance coverage (Medicaid, Medicare, private, uninsured), total visits to ambulatory care during the study period (0, 1, >1), and patient demographics: age, gender, race, ethnicity, and living in an underprivileged community called a public health priority area (PHPA). RESULTS: Results showed heterogeneous relationships that were stronger at higher quantiles. Having Medicaid or Medicare insurance was positively associated with ED visits and ED charges at most quantiles. Racial and geographic disparities were observed. Black patients had more ED visits and lower ED charges than their White counterparts at most quantiles of the outcome distributions. Patients living in PHPAs, had lower charges than their counterparts at the median but higher charges at the 0.95 and 0.99 quantiles. CONCLUSIONS: The relationships between patient characteristics and frequent and high-charge use of the ED vary based on the level of use. These findings can be used to inform targeted interventions, tailored policy, and population health management initiatives.

Ambulatory care, insurance, and avoidable emergency department utilization in North Carolina.

OBJECTIVE: To examine whether and how avoidable emergency department (ED) utilization is associated with ambulatory or primary care (APC) utilization, insurance, and interaction effects. DESIGN AND SAMPLE: A cross-sectional analysis of electronic health records from 70,870 adults residing in Mecklenburg County, North Carolina, who visited an ED within a large integrated healthcare system in 2017. METHODS: APC utilization was measured as total visits, categorized as: 0, 1, and > 1. Insurance was defined as the method of payment for the ED visit as: Medicaid, Medicare, private, or uninsured. Avoidable ED utilization was quantified as a score (aED), calculated as the sum of New York University Algorithm probabilities multiplied by 100. Quantile regression models were used to predict the 25th, 50th, 75th, 95th, and 99th percentiles of avoidable ED scores with APC visits and insurance as predictors (Model 1) and with an interaction term (Model 2). RESULTS: Having >1 APC visit was negatively associated with aED at the lower percentiles and positively associated at higher percentiles. A higher aED was associated with having Medicaid insurance and a lower aED was associated with having private insurance, compared to being uninsured. In stratified models, having >1 APC visit was negatively associated with aED at the 25th percentile for the uninsured and privately insured, but positively associated with aED at higher percentiles among the uninsured, Medicaid-insured, and privately insured. CONCLUSIONS: The association between APC utilization and avoidable ED utilization varied based on segments of the distribution of ED score and differed significantly by insurance type.

The Gut Microbial Profile of Preclinical Crohn's Disease Is Similar to That of Healthy Controls.

BACKGROUND AND AIMS: It is unclear whether microbial dysbiosis plays an etiologic role in Crohn's disease (CD) or is the result of protracted inflammation. Here, we test the hypothesis that dysbiosis predates clinical CD in asymptomatic first-degree relatives (FDRs) of CD patients: normal (FDR1), with borderline inflammation (FDR2), and with frank, very early inflammation (FDR3). METHODS: The gut microbial diversity was tested in ileocecal biopsies through next generation sequencing of the 16S rRNA gene in 10 healthy controls (HCs), 22 patients with active, untreated CD, and 25 FDRs (9 FDR1; 12 FDR2; 4 FDR3). The metagenomic functions of 41 microbiome-related processes were inferred by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. RESULTS: Compared with HCs, alpha diversity in CD patients was decreased, with an observed decrease in Faecalibacterium prausnitzii and increase in Bacteroides fragilis. In FDRs, microbial diversity was unchanged compared with HCs. In Operational Taxonomic Units and PICRUSt Principal coordinates and component analyses, the ellipse centroid of FDRs was diagonally opposed to that of CD patients, but close to the HC centroid. In both analyses, statistically significant differences in terms of beta diversity were found between CD and HC but not between FDR and HC. CONCLUSIONS: In FDRs (including FDR3-who bear preclinical/biologic onset disease), we found that the microbial profile is remarkably similar to HC. If confirmed in larger studies, this finding suggests that clinical CD-associated dysbiosis could result from the changed microenvironment due to disease evolution over time.

Regional and Racial-Ethnic Differences in Perinatal Interventions Among Periviable Births.

OBJECTIVE: To examine whether there are: 1) regional differences in three perinatal interventions that reflect active treatment among periviable gestations and 2) racial-ethnic differences in the receipt of these perinatal interventions after accounting for hospital region. METHODS: We conducted a retrospective study on neonates born at 776 U.S. centers that participated in the Vermont Oxford Network (2006-2017) with a gestational age of 22-25 weeks. The primary outcome was postnatal life support. Secondary outcomes included maternal administration of antenatal corticosteroids and cesarean delivery. We examined rates and 99% CI of the three outcomes by region. We also calculated the adjusted relative risks (aRRs) and 99% CIs for the three outcomes by race and ethnicity within each region using modified Poisson regression models with robust variance estimation. RESULTS: Major regional variation exists in the use of the three interventions at 22 and 23 weeks of gestation but not at 24 and 25 weeks. For example, at 22 weeks of gestation, rates of life support in the South (38.3%; 99% CI 36.3-40.2) and the Midwest (32.7%; 99% CI 30.4-35.0) were higher than in the Northeast (20.2%; 99% CI 17.6-22.8) and the West (22.2%; 99% CI 20.0-24.4). Particularly in the Northeast, black and Hispanic neonates born at 22 or 23 weeks of gestation had a higher provision of postnatal life support than white neonates (at 22 weeks: black: aRR 1.84 [99% CI 1.33-2.56], Hispanic: aRR 1.80 [1.23-2.64]; at 23 weeks: black: aRR 1.14 [99% CI 1.08-1.20], Hispanic: aRR 1.12 [1.05-1.19]). In the West, black and Hispanic neonates born at 23 weeks of gestation also had a higher provision of life support (black: aRR 1.11 [99% CI 1.03-1.19]; Hispanic: aRR 1.10 [1.04-1.16]). CONCLUSION: Major regional variation exists in perinatal interventions when managing 22- and 23-week neonates. In the Northeast and the West regions, minority neonates born at 22 and 23 weeks of gestation had higher provision of postnatal life support.

A family of linear mixed-effects models using the generalized Laplace distribution.

We propose a new family of linear mixed-effects models based on the generalized Laplace distribution. Special cases include the classical normal mixed-effects model, models with Laplace random effects and errors, and models where Laplace and normal variates interchange their roles as random effects and errors. By using a scale-mixture representation of the generalized Laplace, we develop a maximum likelihood estimation approach based on Gaussian quadrature. For model selection, we propose likelihood ratio testing and we account for the situation in which the null hypothesis is at the boundary of the parameter space. In a simulation study, we investigate the finite sample properties of our proposed estimator and compare its performance to other flexible linear mixed-effects specifications. In two real data examples, we demonstrate the flexibility of our proposed model to solve applied problems commonly encountered in clustered data analysis. The newly proposed methods discussed in this paper are implemented in the R package nlmm.

Identification and evaluation of risk of generalizability biases in pilot versus efficacy/effectiveness trials: a systematic review and meta-analysis.

BACKGROUND: Preliminary evaluations of behavioral interventions, referred to as pilot studies, predate the conduct of many large-scale efficacy/effectiveness trial. The ability of a pilot study to inform an efficacy/effectiveness trial relies on careful considerations in the design, delivery, and interpretation of the pilot results to avoid exaggerated early discoveries that may lead to subsequent failed efficacy/effectiveness trials. "Risk of generalizability biases (RGB)" in pilot studies may reduce the probability of replicating results in a larger efficacy/effectiveness trial. We aimed to generate an operational list of potential RGBs and to evaluate their impact in pairs of published pilot studies and larger, more well-powered trial on the topic of childhood obesity. METHODS: We conducted a systematic literature review to identify published pilot studies that had a published larger-scale trial of the same or similar intervention. Searches were updated and completed through December 31st, 2018. Eligible studies were behavioral interventions involving youth (≤18 yrs) on a topic related to childhood obesity (e.g., prevention/treatment, weight reduction, physical activity, diet, sleep, screen time/sedentary behavior). Extracted information included study characteristics and all outcomes. A list of 9 RGBs were defined and coded: intervention intensity bias, implementation support bias, delivery agent bias, target audience bias, duration bias, setting bias, measurement bias, directional conclusion bias, and outcome bias. Three reviewers independently coded for the presence of RGBs. Multi-level random effects meta-analyses were performed to investigate the association of the biases to study outcomes. RESULTS: A total of 39 pilot and larger trial pairs were identified. The frequency of the biases varied: delivery agent bias (19/39 pairs), duration bias (15/39), implementation support bias (13/39), outcome bias (6/39), measurement bias (4/39), directional conclusion bias (3/39), target audience bias (3/39), intervention intensity bias (1/39), and setting bias (0/39). In meta-analyses, delivery agent, implementation support, duration, and measurement bias were associated with an attenuation of the effect size of - 0.325 (95CI - 0.556 to - 0.094), - 0.346 (- 0.640 to - 0.052), - 0.342 (- 0.498 to - 0.187), and - 0.360 (- 0.631 to - 0.089), respectively. CONCLUSIONS: Pre-emptive avoidance of RGBs during the initial testing of an intervention may diminish the voltage drop between pilot and larger efficacy/effectiveness trials and enhance the odds of successful translation.

Quantile contours and allometric modelling for risk classification of abnormal ratios with an application to asymmetric growth-restriction in preterm infants.

We develop an approach to risk classification based on quantile contours and allometric modelling of multivariate anthropometric measurements. We propose the definition of allometric direction tangent to the directional quantile envelope, which divides ratios of measurements into half-spaces. This in turn provides an operational definition of directional quantile that can be used as cutoff for risk assessment. We show the application of the proposed approach using a large dataset from the Vermont Oxford Network containing observations of birthweight (BW) and head circumference (HC) for more than 150,000 preterm infants. Our analysis suggests that disproportionately growth-restricted infants with a larger HC-to-BW ratio are at increased mortality risk as compared to proportionately growth-restricted infants. The role of maternal hypertension is also investigated.

Multistate quantile regression models.

We develop regression methods for inference on conditional quantiles of time-to-transition in multistate processes. Special cases include survival, recurrent event, semicompeting, and competing risk data. We use an ad hoc representation of the underlying stochastic process, in conjunction with methods for censored quantile regression. In a simulation study, we demonstrate that the proposed approach has a superior finite sample performance over simple methods for censored quantile regression, which naively assume independence between states, and over methods for competing risks, even when the latter are applied to competing risk data settings. We apply our approach to data on hospital-acquired infections in cirrhotic patients, showing a quantile-dependent effect of catheterization on time to infection.