Assessing quality of care is essential for improving the management of patients experiencing traumatic brain injury (TBI). This study aimed at devising a rigorous framework to evaluate the quality of TBI care provided by intensive care units (ICUs) and applying it to the Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe (CREACTIVE) consortium, which involved 83 ICUs from seven countries. The performance of the centers was assessed in terms of patients' outcomes, as measured by the 6-month Glasgow Outcome Scale-Extended (GOS-E). To account for the between-center differences in the characteristics of the admitted patients, we developed a multinomial logistic regression model estimating the probability of a four-level categorization of the GOS-E: good recovery (GR), moderate disability (MD), severe disability (SD), and death or vegetative state (D/VS). A total of 5928 patients admitted to the participating ICUs between March 2014 and March 2019 were analyzed. The model included 11 predictors and demonstrated good discrimination (area under the receiver operating characteristic [ROC] curve in the validation set for GR: 0.836, MD: 0.802, SD: 0.706, D/VS: 0.890) and calibration, both overall (Hosmer-Lemeshow test p value: 0.87) and in several subgroups, defined by prognostically relevant variables. The model was used as a benchmark for assessing quality of care by comparing the observed number of patients experiencing GR, MD, SD, and D/VS to the corresponding numbers expected in each category by the model, computing observed/expected (O/E) ratios. The four center-specific ratios were assembled with polar representations and used to provide a multidimensional assessment of the ICUs, overcoming the loss of information consequent to the traditional dichotomizations of the outcome in TBI research. The proposed framework can help in identifying strengths and weaknesses of current TBI care, triggering the changes that are necessary to improve patient outcomes.

Abstract Objective: Studies using the dietary inflammatory index often perform complete case analyses (CCA) to handle missing data, which may reduce the sample size and increase the risk of bias. Furthermore, population-level socio-economic differences in the energy-adjusted dietary inflammatory index (E-DII) have not been recently studied. Therefore, we aimed to describe socio-demographic differences in E-DII scores among American adults and compare the results using two statistical approaches for handling missing data, i.e. CCA and multiple imputation (MI). Design: Cross-sectional analysis. E-DII scores were computed using a 24-hour dietary recall. Linear regression was used to compare the E-DII scores by age, sex, race/ethnicity, education and income using both CCA and MI. Setting: USA. Participants: This study included 34 547 non-Hispanic White, non-Hispanic Black and Hispanic adults aged ≥ 20 years from the 2005–2018 National Health and Nutrition Examination Survey. Results: The MI and CCA subpopulations comprised 34 547 and 23 955 participants, respectively. Overall, 57 % of the American adults reported 24-hour dietary intakes associated with inflammation. Both methods showed similar patterns wherein 24-hour dietary intakes associated with high inflammation were commonly reported among males, younger adults, non-Hispanic Black adults and those with lower education or income. Differences in point estimates between CCA and MI were mostly modest at ≤ 20 %. Conclusions: The two approaches for handling missing data produced comparable point estimates and 95 % CI. Differences in the E-DII scores by age, sex, race/ethnicity, education and income suggest that socio-economic disparities in health may be partially explained by the inflammatory potential of diet.

PURPOSE:It is unclear whether common maternal infections during pregnancy are risk factors for adverse birth outcomes. We assessed the association between self-reported infections during pregnancy with preterm birth and small-for-gestational-age (SGA) in an international cohort consortium. METHODS:Data on 120,507 pregnant women were obtained from six population-based birth cohorts in Australia, Denmark, Israel, Norway, the UK and the USA. Self-reported common infections during pregnancy included influenza-like illness, common cold, any respiratory tract infection, vaginal thrush, vaginal infections, cystitis, urinary tract infection, and the symptoms fever and diarrhoea. Birth outcomes included preterm birth, low birth weight and SGA. Associations between maternal infections and birth outcomes were first assessed using Poisson regression in each cohort and then pooled using random-effect meta-analysis. Risk ratios (RR) and 95% confidence intervals (CI) were calculated, adjusted for potential confounders. RESULTS:Vaginal infections (pooled RR, 1.10; 95% CI, 1.02-1.20) and urinary tract infections (pooled RR, 1.17; 95% CI, 1.09-1.26) during pregnancy were associated with higher risk of preterm birth. Similar associations with low birth weight were also observed for these two infections. Fever during pregnancy was associated with higher risk of SGA (pooled RR, 1.07; 95% CI, 1.02-1.12). No other significant associations were observed between maternal infections/symptoms and birth outcomes. CONCLUSION:Vaginal infections and urinary infections during pregnancy were associated with a small increased risk of preterm birth and low birth weight, whereas fever was associated with SGA. These findings require confirmation in future studies with laboratory-confirmed infection diagnosis.

CONTEXT:Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community.PROGRAM:Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020.IMPLEMENTATION:The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard.EVALUATION:This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring.DISCUSSION:Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.

OBJECTIVE:Tracheostomy is performed for various indications ranging from prolonged ventilation to airway obstruction. Many factors may play a role in the incidence of complications in the immediate post-operative period including patient-related factors. Chronic obstructive pulmonary disease and asthma are some of the most common pulmonary pathologies in the United States. The relationship between obstructive pulmonary diseases and acute post-tracheostomy complications has been incompletely studied. DESIGN:A retrospective chart review was designed in order to answer these objectives. Medical records were reviewed for the technique used, complications, and contributing patient factors. Post-operative complications were defined as any tracheostomy-related adverse event occurring within 14 days. SETTING:The study took place at an academic comprehensive cancer. PARTICIPANTS:Inclusion criteria included patients from January 2017 through December 2018 who underwent a tracheostomy. Exclusion criteria included presence of stomaplasty, total laryngectomy, and tracheostomies performed at outside hospitals. MAIN OUTCOME MEASURES:Patient factors examined included demographics, comorbidities, and body mass index with the primary outcome measured being the rate of tracheostomy complications. RESULTS:The most common indication for tracheostomy among the 321 patients that met inclusion criteria was airway obstruction or a head and neck cancer surgical procedure. Obstructive sleep apnea was associated with acute complications in bivariate analysis (29.4% complications, p = .003). Chronic obstructive pulmonary disease and asthma were not associated with acute complications in bivariate analysis (11.6% complications, p = .302). Among the secondary outcomes measured, radiation was associated with early complications occurring in post-operative days 0-6 (1.1%, p = .029). CONCLUSION:Patients with obstructive sleep apnea may have a higher risk of acute post-tracheostomy complications that might be due to the patient population at risk for obstructive sleep apnea. Patients with obstructive pulmonary pathologies such as asthma or chronic obstructive pulmonary disorder did not have an elevated risk of complications which is clinically significant when considering the utility of ventilation and tracheostomy in the management of acute respiratory failure secondary to these conditions.

Importance While the relationship between persistent elevations in intracranial pressure (ICP) and poorer outcomes is well established for patients with traumatic brain injury (TBI), there is no consensus on how ICP measurements should drive treatment choices, and the effectiveness of ICP monitoring remains unknown. Objective To evaluate the effectiveness of ICP monitoring on short- and mid-term outcomes of patients with TBI. Design, Setting, and Participants CREACTIVE was a prospective cohort study that started in March 2014 and lasted 5 years. More than 8000 patients with TBI were enrolled at 83 intensive care units (ICUs) from 7 countries who joined the CREACTIVE Consortium. Patients with TBI who met the Brain Trauma Foundation guidelines for ICP monitoring were selected for the current analyses, which were performed from January to November 2022. Exposure Patients who underwent ICP monitoring within 2 days of injury (exposure group) were propensity score–matched to patients who were not monitored or who underwent monitoring 2 days after the injury (control group). Main Outcome and Measure Functional disability at 6 months as indicated by Glasgow Outcome Scale–Extended (GOS-E) score. Results A total of 1448 patients from 43 ICUs in Italy and Hungary were eligible for analysis. Of the patients satisfying the ICP-monitoring guidelines, 503 (34.7%) underwent ICP monitoring (median [IQR] age: 45 years [29-61 years]; 392 males [77.9%], 111 females [22.1%]) and 945 were not monitored (median [IQR] age: 66 years [48-78 years]; 656 males [69.4%], 289 females [30.6%]). After matching to balance the variables, worse 6-month recovery was observed for monitored patients compared with nonmonitored patients (death/vegetative state: 39.2% vs 40.6%; severe disability: 33.2% vs 25.4%; moderate disability: 15.7% vs 14.9%; good recovery: 11.9% vs 19.1%, respectively; P = .005). Monitored patients received medical therapies significantly more frequently. Conclusions and Relevance In this cohort study, ICP monitoring was associated with poorer recovery and more frequent medical interventions with their relevant adverse effects. Optimizing the value of ICP monitoring for TBI requires further investigation on monitoring indications, clinical interventions, and management protocols.

Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of nonignorable non-response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%-2.0%), corresponding to similar to 85,00 0 prevalent infections (95% credible interval: 6,30 0-177,00 0) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%-2.7%), corresponding to similar to 118,0 00 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts. (C) 2021ElsevierInc. Allrightsreserved.

Background Previous epidemiological studies have found positive associations between maternal infections and childhood leukaemia; however, evidence from prospective cohort studies is scarce. We aimed to examine the associations using large-scale prospective data. Methods Data were pooled from six population-based birth cohorts in Australia, Denmark, Israel, Norway, the UK and the USA (recruitment 1950s-2000s). Primary outcomes were any childhood leukaemia and acute lymphoblastic leukaemia (ALL); secondary outcomes were acute myeloid leukaemia (AML) and any childhood cancer. Exposures included maternal self-reported infections [influenza-like illness, common cold, any respiratory tract infection, vaginal thrush, vaginal infections and urinary tract infection (including cystitis)] and infection-associated symptoms (fever and diarrhoea) during pregnancy. Covariate-adjusted hazard ratio (HR) and 95% confidence interval (CI) were estimated using multilevel Cox models. Results Among 312 879 children with a median follow-up of 13.6 years, 167 leukaemias, including 129 ALL and 33 AML, were identified. Maternal urinary tract infection was associated with increased risk of any leukaemia [HR (95% CI) 1.68 (1.10-2.58)] and subtypes ALL [1.49 (0.87-2.56)] and AML [2.70 ([0.93-7.86)], but not with any cancer [1.13 (0.85-1.51)]. Respiratory tract infection was associated with increased risk of any leukaemia [1.57 (1.06-2.34)], ALL [1.43 (0.94-2.19)], AML [2.37 (1.10-5.12)] and any cancer [1.33 (1.09-1.63)]; influenza-like illness showed a similar pattern but with less precise estimates. There was no evidence of a link between other infections and any outcomes. Conclusions Urinary tract and respiratory tract infections during pregnancy may be associated with childhood leukaemia, but the absolute risk is small given the rarity of the outcome.

OBJECTIVE:This study investigated the effects of two temporal response properties of the auditory nerve (i.e., neural adaptation and recovery from neural adaptation) on speech perception performance in postlingually deafened adult cochlear implant (CI) users. DESIGN:Study participants included 18 postlingually deafened adults who were Cochlear Nucleus device users with a full electrode array insertion in the test ear(s). Neural adaptation and adaptation recovery of the auditory nerve (AN) were evaluated using electrophysiological measures of the electrically evoked compound action potential (eCAP). The amount of neural adaptation was quantified by the adaptation index within three time windows: 0 to 8.89 (window 1), 44.44 to 50.00 (window 2), and 94.44 to 100.00 ms (window 3). The speed of neural adaptation was estimated using a two-parameter power law function. To evaluate adaptation recovery of the AN, eCAPs to the last pulse of the 100-ms pulse train were recorded at masker-probe-intervals ranging from 1.054 to 256 ms in logarithmic steps. The amount of adaptation recovery was quantified by the adaptation recovery ratio. The time-constant of adaptation recovery was estimated using an exponential function with up to three components. Speech perception performance was evaluated by measuring consonant-nucleus-consonant (CNC) word scores presented in quiet and in speech-shaped noise at a signal-to-noise ratio (SNR) of +10 dB. One-tailed Pearson Product Moment correlation tests were used (1) to assess the associations among parameters of neural adaptation and adaptation recovery and (2) to evaluate the strength of association between these parameters and CNC word scores measured in quiet and in noise. The contributions of different parameters quantifying neural adaptation and adaptation recovery on speech perception scores were evaluated using multivariable linear regression analyses. RESULTS:The Pearson Product Moment correlation coefficient demonstrated a moderate, negative correlation between the speed of adaptation recovery and CNC word scores measured in quiet and in noise. The speed of adaptation recovery accounted for 14.1% of variability in CNC word scores measured in quiet and 16.7% of variability in CNC word scores measured in noise. The correlation strengths between CNC word scores and the adaptation index, the adaptation recovery ratio and the speed of neural adaptation ranged from negligible to weak. CONCLUSIONS:The speed of adaptation recovery plays a more important role than other features of neural adaptation and adaptation recovery of the AN in speech perception in postlingually deafened adult CI users. Patients with prolonged adaptation recovery tend to show poorer speech perception performance.

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1, N2, and E) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman r = 0.70, p < 0.05), with the N2 gene being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Whole-genome sequencing result showed that those detected SARS-CoV-2 variants of concern from the wastewater matched with the clinical isolates from the communities. The findings from this study suggest that wastewater surveillance is effective in COVID-19 trend tracking and variant emergence and transmission within a community.

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1 and N2 gene regions, and E gene) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman's r = 0.70, p < 0.05), with the N2 gene region being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Viral sequencing showed that shifts in strain-defining variants of SARS-CoV-2 in wastewater samples matched those in clinical isolates from the same time periods. The findings from this study support that wastewater surveillance is effective in COVID-19 trend tracking and provide sentinel warning of variant emergence and transmission within various types of communities.

Persistent postoperative pain (PPP) is a prevalent complication after inguinal hernia repair. The aim of this study was to develop and validate a preoperative risk score for PPP. We developed the risk score based on a cohort of 2,508 Danish men, who answered a questionnaire six months after inguinal hernia repair performed 2015–2016. PPP was defined as a numerical rating scale score ≥ 2 during activity six months postoperatively. Logistic regression analyses were undertaken to determine statistically significant predictors of PPP. Univariable analysis selected potential predictors with a p value ≤ 0.20, and a subsequent multivariable model was built using backward elimination with a criterion of p value < 0.10. We created a risk score based on the β coefficients of the multivariable model. The risk score was validated internally using Hosmer–Lemeshow goodness of fit test, calibration belt test, and receiver operating characteristic curve analyses with 95% confidence intervals based on the bootstrap analysis. External validation was performed in a cohort of 293 men recruited preoperatively. Predictors of PPP were age 18–49 and 50–59 (versus ≥ 60) years (p < 0.001), total load lifted > 1,000 kg/day (p = 0.001), working in a bent-over position > 1 h/day (p < 0.001), leisure-time physical activity < 2 h/week (p = 0.009), increasing body mass index (per unit) (p < 0.003), and repair of recurrent hernia (p = 0.001).The preoperative risk score predicted risks of 6–61% in the development population. The model showed good internal and external validity. The results suggest that the risk of PPP after inguinal hernia repair can be predicted using a preoperative risk score.

Abstract Comparing outcomes across different levels of trauma centers is vital in evaluating regionalized trauma care. With observational data, it is critical to adjust for patient characteristics to render valid causal comparisons. Propensity score matching is a popular method to infer causal relationships in observational studies with two treatment arms. Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. We fill the gap by developing an iterative matching algorithm for the three-group setting. Our algorithm outperforms the nearest neighbor algorithm and is shown to produce matched samples with total distance no larger than twice the optimal distance. We implement the evidence factors method for binary outcomes, which includes a randomization-based testing strategy and a sensitivity analysis for hidden bias in three-group matched designs. We apply our method to the Nationwide Emergency Department Sample data to compare emergency department mortality among non-trauma, level I, and level II trauma centers. Our tests suggest that the admission to a trauma center has a beneficial effect on mortality, assuming no unmeasured confounding. A sensitivity analysis for hidden bias shows that unmeasured confounders, moderately associated with the type of care received, may change the result qualitatively. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Currently, it is unclear how inflammatory skin diseases impact hospital outcomes for cancer patients receiving chemotherapy. Our objective was to assess the impact of inflammatory dermatoses on hospitalization outcomes for cancer patients undergoing inpatient chemotherapy treatment using national discharge data from the 2014 U.S. Nationwide Readmission Database. We utilized patient discharge diagnoses for chemotherapy administration (ICD 9 CM code V58.1) to identify our primary study population. The subset of patients with inflammatory dermatoses were identified by diagnoses codes for other inflammatory conditions of skin and subcutaneous tissue, urticaria, and symptoms involving skin and other integumentary tissue (ICD 9 CM 690-698, 708, and 782, respectively). In total, 20,848 patient discharges met inclusion criteria. We performed multiple stepwise linear regression to predict adjusted hospital length of stay from inflammatory dermatoses diagnosis, while controlling for other covariates of interest. After adjusting for age, sex, operating room procedures, elective procedures, number of yearly admissions, number of chronic conditions, hospital admittance, and whether the patient had a solid organ or hematologic cancer, we determined an inflammatory dermatitis diagnosis was positively associated with adjusted hospital length of stay (P<.001). Adjusted hospital length of stay increased 3.91 days for inflammatory dermatitis diagnosis when controlling for all other covariates (B = 3.91, 95% CI [3.57-4.26]). Limitations of the study include evaluating a range of ICD-9 codes, including non-specific skin disease (ICD 9 CM 782) diagnoses, in only a single year. Based on this data, Inflammatory dermatoses negatively impact hospital outcomes for cancer patients receiving inpatient chemotherapy. Further research is needed to evaluate skin morphologies and diseases most responsible for these effects.

Abstract Objective The objective of this study was to create three point-of-care predictive models for very preterm birth using variables available at three different time points: prior to pregnancy, at the end of the first trimester, and mid-pregnancy. Study Design This is a retrospective cohort study of 359,396 Ohio Medicaid mothers from 2008 to 2015. The last baby for each mother was included in the final dataset. Births prior to 22 weeks were excluded. Multivariable logistic regression was used to create three models. These models were validated on a cohort that was set aside and not part of the model development. The main outcome measure was birth prior to 32 weeks. Results The final dataset contained 359,396 live births with 6,516 (1.81%) very preterm births. All models had excellent calibration. Goodness-of-fit tests suggested strong agreement between the probabilities estimated by the model and the actual outcome experience in the data. The mid-pregnancy model had acceptable discrimination with an area under the receiver operator characteristic curve of approximately 0.75 in both the developmental and validation datasets. Conclusion Using data from a large Ohio Medicaid cohort we developed point-of-care predictive models that could be used before pregnancy, after the first trimester, and in mid-pregnancy to estimate the probability of very preterm birth. Future work is needed to determine how the calculator could be used to target interventions to prevent very preterm birth. Key Points We developed predictive models for very preterm birth. All models showed excellent calibration. The models were integrated into a risk calculator.

Objective: The Community Awareness Resources and Education (CARE) project: Reducing Cervical Cancer in Appalachia is part of the NIH Centers for Population Health and Health Disparities initiative. It aims to increase screening rates for cervical cancer, to assist in tobacco cessation, and to determine the role of human papillomavirus (HPV) in contributing to the increased risk of cervical cancer. Methods: Eligible subjects were recruited from 14 clinics in Appalachian Ohio. The CARE cross sectional survey (CS) (571 participants) characterized social, behavioral, and environmental factors of Pap smear screening and smoking. The intervention projects tested programs to increase Pap smear screening (280 participants) or aid in smoking cessation (300 participants). A case control study (1360 participants) assessed social, behavioral, and biologic predictors of abnormal Pap tests. Results: The CS participants tended to be younger, more educated, and were more likely to be employed than the general population of Appalachian women 18 and over. They were similar to the other samples in terms of race, marital status, income level and smoking status. Conclusions: CARE will provide valuable information about the multi-level barriers to obtaining regular Pap smears and quitting smoking, as well as help to identify important biological and social risk factors related to abnormal Pap smears.

Parental occupational exposures to pesticides, animals and organic dust have been associated with an increased risk of childhood cancer based mostly on case–control studies. We prospectively evaluated parental occupational exposures and risk of childhood leukemia and central nervous system (CNS) tumors in the International Childhood Cancer Cohort Consortium. We pooled data on 329,658 participants from birth cohorts in five countries (Australia, Denmark, Israel, Norway and United Kingdom). Parental occupational exposures during pregnancy were estimated by linking International Standard Classification of Occupations‐1988 job codes to the ALOHA+ job exposure matrix. Risk of childhood (<15 years) acute lymphoblastic leukemia (ALL; n = 129), acute myeloid leukemia (AML; n = 31) and CNS tumors ( n = 158) was estimated using Cox proportional hazards models to generate hazard ratios (HR) and 95% confidence intervals (CI). Paternal exposures to pesticides and animals were associated with increased risk of childhood AML (herbicides HR = 3.22, 95% CI = 0.97–10.68; insecticides HR = 2.86, 95% CI = 0.99–8.23; animals HR = 3.89, 95% CI = 1.18–12.90), but not ALL or CNS tumors. Paternal exposure to organic dust was positively associated with AML (HR = 2.38 95% CI = 1.12–5.07), inversely associated with ALL (HR = 0.55, 95% CI = 0.31–0.99) and not associated with CNS tumors. Low exposure prevalence precluded evaluation of maternal pesticide and animal exposures; we observed no significant associations with organic dust exposure. This first prospective analysis of pooled birth cohorts and parental occupational exposures provides evidence for paternal agricultural exposures as childhood AML risk factors. The different risks for childhood ALL associated with maternal and paternal organic dust exposures should be investigated further.

Evaluating the goodness of fit of logistic regression models is crucial to ensure the accuracy of the estimated probabilities. Unfortunately, such evaluation is problematic in large samples. Because the power of traditional goodness of fit tests increases with the sample size, practically irrelevant discrepancies between estimated and true probabilities are increasingly likely to cause the rejection of the hypothesis of perfect fit in larger and larger samples. This phenomenon has been widely documented for popular goodness of fit tests, such as the Hosmer-Lemeshow test. To address this limitation, we propose a modification of the Hosmer-Lemeshow approach. By standardizing the noncentrality parameter that characterizes the alternative distribution of the Hosmer-Lemeshow statistic, we introduce a parameter that measures the goodness of fit of a model but does not depend on the sample size. We provide the methodology to estimate this parameter and construct confidence intervals for it. Finally, we propose a formal statistical test to rigorously assess whether the fit of a model, albeit not perfect, is acceptable for practical purposes. The proposed method is compared in a simulation study with a competing modification of the Hosmer-Lemeshow test, based on repeated subsampling. We provide a step-by-step illustration of our method using a model for postneonatal mortality developed in a large cohort of more than 300 000 observations.

Background When the treatment effect on the outcome of interest is influenced by a baseline/demographic factor, investigators say that an interaction is present. In randomized clinical trials (RCTs), this type of analysis is typically referred to as subgroup analysis. Although interaction (or subgroup) analyses are usually stated as a secondary study objective, it is not uncommon that these results lead to changes in treatment protocols or even modify public health policies. Nonetheless, recent reviews have indicated that their proper assessment, interpretation and reporting remain challenging. Results Therefore, this article provides an overview of these challenges, to help investigators find the best strategy for application of interaction analyses on binary outcomes in RCTs. Specifically, we discuss the key points of formal interaction testing, including the estimation of both additive and multiplicative interaction effects. We also provide recommendations that, if adhered to, could increase the clarity and the completeness of reports of RCTs. Conclusion Altogether, this article provides a brief non-statistical guide for clinical investigators on how to perform, interpret and report interaction (subgroup) analyses in RCTs.

Background Historically, cutoff points for childhood and adolescent overweight and obesity have been based on population-specific percentiles derived from cross-sectional data. To obtain cutoff points that might better predict overweight and obesity in young adulthood, we examined the association between childhood body-mass index (BMI) and young adulthood BMI status in a longitudinal cohort. Methods In this study, we used data from the International Childhood Cardiovascular Cohort (i3C) Consortium (which included seven childhood cohorts from the USA, Australia, and Finland) to establish childhood overweight and obesity cutoff points that best predict BMI status at the age of 18 years. We included 3779 children who were followed up from 1970 onwards, and had at least one childhood BMI measurement between ages 6 years and 17 years and a BMI measurement specifically at age 18 years. We used logistic regression to assess the association between BMI in childhood and young adulthood obesity. We used the area under the receiver operating characteristic curve (AUROC) to assess the ability of fitted models to discriminate between different BMI status groups in young adulthood. The cutoff points were then compared with those defined by the International Obesity Task Force (IOTF), which used cross-sectional data, and tested for sensitivity and specificity in a separate, independent, longitudinal sample (from the Special Turku Coronary Risk Factor Intervention Project [STRIP] study) with BMI measurements available from both childhood and adulthood. Findings The cutoff points derived from the longitudinal i3C Consortium data were lower than the IOTF cutoff points. Consequently, a larger proportion of participants in the STRIP study was classified as overweight or obese when using the i3C cutoff points than when using the IOTF cutoff points. Especially for obesity, i3C cutoff points were significantly better at identifying those who would become obese later in life. In the independent sample, the AUROC values for overweight ranged from 0.75 (95% CI 0.70-0.80) to 0.88 (0.84-0.93) for the i3C cutoff points, and the corresponding values for the IOTF cutoff points ranged from 0.69 (0.62-0.75) to 0.87 (0.82-0.92). For obesity, the AUROC values ranged from 0.84 (0.75-0.93) to 0.90 (0.82-0.98) for the i3C cutoff points and 0.57 (0.49-0.66) to 0.76 (0.65-0.88) for IOTF cutoff points. Interpretation The childhood BMI cutoff points obtained from the i3C Consortium longitudinal data can better predict risk of overweight and obesity in young adulthood than can standards that are currently used based on cross-sectional data. Such cutoff points should help to more accurately identify children at risk of adult overweight or obesity. Copyright (C) 2019 Elsevier Ltd. All rights reserved.