Multiple imputation of more than one environmental exposure with nondifferential measurement error.

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population.

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467-474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty.

Harnessing causal forests for epidemiologic research: key considerations.

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7):1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research.

Constructing Residential Histories in a General Population-Based Representative Sample.

Research on neighborhoods and health typically measures neighborhood context at a single point in time. However, neighborhood exposures accumulate over the life course, influenced by both residential mobility and neighborhood change, with potential implications for estimating the impact of neighborhoods on health. Commercial databases offer fine-grained longitudinal residential address data that can enrich life-course spatial epidemiology research, and validated methods for reconstructing residential histories from these databases are needed. Our study draws on unique data from a geographically diverse, population-based representative sample of adult Wisconsin residents and the LexisNexis (New York, New York) Accurint, a commercial personal profile database, to develop a systematic and reliable methodology for constructing individual residential histories. Our analysis demonstrated that creating residential histories across diverse geographical contexts is feasible, and it highlights differences in the information obtained from available residential histories by age, education, race/ethnicity, and rural/urban/suburban residency. Researchers should consider potential address data availability and information biases favoring socioeconomically advantaged individuals and their implications for studying health inequalities. Despite these limitations, LexisNexis data can generate varied residential exposure metrics and be linked to contextual data to enrich research into the contextual determinants of health at varied geographic scales.

Sporadic cases of chronic wasting disease in old moose - an epidemiological study.

Transmissible spongiform encephalopathies or prion diseases comprise diseases with different levels of contagiousness under natural conditions. The hypothesis has been raised that the chronic wasting disease (CWD) cases detected in Nordic moose (Alces alces) may be less contagious, or not contagious between live animals under field conditions. This study aims to investigate the epidemiology of CWD cases detected in moose in Norway, Sweden and Finland using surveillance data from 2016 to 2022.In total, 18 CWD cases were detected in Nordic moose. All moose were positive for prion (PrPres) detection in the brain, but negative in lymph nodes, all were old (mean 16 years; range 12-20) and all except one, were female. Age appeared to be a strong risk factor, and the sex difference may be explained by few males reaching high age due to hunting targeting calves, yearlings and males.The cases were geographically scattered, distributed over 15 municipalities. However, three cases were detected in each of two areas, Selbu in Norway and Arjeplog-Arvidsjaur in Sweden. A Monte Carlo simulation approach was applied to investigate the likelihood of such clustering occurring by chance, given the assumption of a non-contagious disease. The empirical P-value for obtaining three cases in one Norwegian municipality was less than 0.05, indicating clustering. However, the moose in Selbu were affected by different CWD strains, and over a 6 year period with intensive surveillance, the apparent prevalence decreased, which would not be expected for an ongoing outbreak of CWD. Likewise, the three cases in Arjeplog-Arvidsjaur could also indicate clustering, but management practices promotes a larger proportion of old females and the detection of the first CWD case contributed to increased awareness and sampling.The results of our study show that the CWD cases detected so far in Nordic moose have a different epidemiology compared to CWD cases reported from North America and in Norwegian reindeer (Rangifer tarandus tarandus). The results support the hypothesis that these cases are less contagious or not contagious between live animals under field conditions. To enable differentiation from other types of CWD, we support the use of sporadic CWD (sCWD) among the names already in use.

A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes.

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.

Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects.

Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant Staphylococcus aureus , and codeine has no effect on pain in individuals with certain polymorphisms in the CYP2D6 enzyme. Subgroups where a treatment is ineffective are often called negative control populations or placebo groups. They are leveraged to detect bias in different disciplines. Here we present formal criteria that justify the use of negative control populations to rule out unmeasured confounding and mechanistic (direct) causal effects. We further argue that negative control populations, satisfying our formal conditions, are available in many settings, spanning from clinical studies of infectious diseases to epidemiologic studies of public health interventions. Negative control populations can also be used to rule out placebo effects in unblinded randomized experiments. As a case study, we evaluate the effect of mobile stroke unit dispatches on functional outcomes at discharge in individuals with suspected stroke, using data from a large trial. Our analysis supports the hypothesis that mobile stroke units improve functional outcomes in these individuals.

Estimating the impact of bias in causal epidemiological studies: the case of health outcomes following assisted reproduction.

Researchers interested in causal questions must deal with two sources of error: random error (random deviation from the true mean value of a distribution), and bias (systematic deviance from the true mean value due to extraneous factors). For some causal questions, randomization is not feasible, and observational studies are necessary. Bias poses a substantial threat to the validity of observational research and can have important consequences for health policy developed from the findings. The current piece describes bias and its sources, outlines proposed methods to estimate its impacts in an observational study, and demonstrates how these methods may be used to inform debate on the causal relationship between medically assisted reproduction (MAR) and health outcomes, using cancer as an example. In doing so, we aim to enlighten researchers who work with observational data, especially regarding the health effects of MAR and infertility, on the pitfalls of bias, and how to address them. We hope that, in combination with the provided example, we can convince readers that estimating the impact of bias in causal epidemiologic research is not only important but necessary to inform the development of robust health policy and clinical practice recommendations.

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model.

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.

Characterization of male sexual assault in the state of São Paulo, Brazil: an epidemiological study from 2014 to 2017.

Sexual violence is a pervasive global issue that affects individuals of all genders. However, the experiences of male survivors have often been marginalized and inadequately represented. Male rape, which encompasses several forms of sexual violence against men, remains a sensitive and under-discussed topic in academic literature and public discourse. This study presents a descriptive cross-sectional analysis based on data collected from the Legal Medicine Institute (IML-São Paulo, Brazil) between 2014 and 2017. The analysis includes 7386 reports of sexological examinations performed on male victims of alleged rape. The analysis reveals that a significant majority of rape reports involved victims under the age of 12 or 14, which is considered vulnerable rape by the Brazilian legislation. Regarding the examination of reported cases of abuse against men, it was observed that only the minority of these cases exhibited visible injuries consistent with rape or tested positive for the presence of spermatozoa in the perianal region. Since the absence of visible injuries or spermatozoa does not negate the possibility of rape, this work highlights the challenges in obtaining conclusive evidence, necessitating a comprehensive approach to investigate and prosecute these crimes, creating a more inclusive and supportive environment for all survivors of rape, irrespective of their gender.

High-Resolution Spatiotemporal Modeling for PM2.5 Major Components in the Pearl River Delta and Its Implications for Epidemiological Studies.

Distinguishing the effects of different fine particulate matter components (PMCs) is crucial for mitigating their effects on human health. However, the sparse distribution of locations where PM is collected for component analysis makes it challenging to investigate the relevant health effects. This study aimed to investigate the agreement between data-fusion-enhanced exposure assessment and site monitoring data in estimating the effects of PMCs on gestational diabetes mellitus (GDM). We first improved the spatial resolution and accuracy of exposure assessment for five major PMCs (EC, OM, NO3-, NH4+, and SO42-) in the Pearl River Delta region by a data fusion model that combined inputs from multiple sources using a random forest model (10-fold cross-validation R2: 0.52 to 0.61; root mean square error: 0.55 to 2.26 µg/m3). Next, we compared the associations between exposures to PMCs during pregnancy and GDM in a hospital-based cohort of 1148 pregnant women in Heshan, China, using both site monitoring data and data-fusion model estimates. The comparative analysis showed that the data-fusion-based exposure generated stronger estimates of identifying statistical disparities. This study suggests that data-fusion-enhanced estimates can improve exposure assessment and potentially mitigate the misclassification of population exposure arising from the utilization of site monitoring data.

A unified framework for assessing interaction effects among environmental exposures in epidemiologic studies: A case study on temperature, air pollution, and kidney-related conditions in New York state.

BACKGROUND: There are various methods to assess interaction effects. However, current methods have limitations, and quantification of interaction effects is rarely performed. This study aimed to develop a unified quantitative framework for assessing interaction effects. METHODS: We proposed a novel framework using log-linear models with a product term(s) across the exposures that generates parametric bi-variate association and interaction effect surfaces and allows flexible functional forms for exposures in the interaction term(s). In a case study, we assessed the interaction effects between temperature and air pollution (i.e., PM2.5, NO2, and O3) on risk for kidney-related conditions in New York State (2007-2016) using a case-crossover design with conditional logistic models. Our measures of exposure were the moving averages at lag 0-5 days for air pollution (linear) and daytime mean outdoor wet-bulb globe temperature (WBGT; using a natural cubic spline). RESULTS: We derived closed-form expressions for the magnitude of multiplicative interaction effects (the joint relative risk divided by the product of the two conditional relative risks) and their uncertainties. In the case study, we found a Bonferroni-corrected significant multiplicative interaction effect (IE) between outdoor WBGT at the 99th percentile (median as the reference) and (1) PM2.5 (per 5 µg/m3 increase, IE = 1.052; 95 % confidence interval [CI]: 1.019, 1.087) for acute kidney failure and (2) O3 (per 5 ppb increase; IE = 1.022; 95 % CI: 1.008, 1.036) for urolithiasis (the latter being inconclusive based on the sensitivity analysis). CONCLUSIONS: Our framework allows different functional forms of exposure variables in the interaction term, quantifies the magnitudes of entire-exposure-range (in addition to discrete exposure level) multiplicative interaction effects and their uncertainties in a categorical or continuous (linear or non-linear) manner, and harmonizes the two-way evaluation of effect modification. The case study underscores co-consideration of heat and air pollution when estimating health burden and designing heat/pollution alert systems.

ACES: A new framework for the application of the 2018 periodontal status classification scheme to epidemiological survey data.

AIM: To propose a framework for consistently applying the 2018 periodontal status classification scheme to epidemiological surveys (Application of the 2018 periodontal status Classification to Epidemiological Survey data, ACES). PROPOSED FRAMEWORK: We specified data requirements and workflows for either completed or planned epidemiological surveys, utilizing commonly collected measures of periodontal status (clinical attachment levels [CAL], probing depths, bleeding on probing), as well as additional necessary variables for the implementation of the 2018 periodontal status classification (tooth loss due to periodontitis and complexity factors). Following detailed instructions and flowcharts, survey participants are classified as having periodontal health, gingivitis or periodontitis. Rates of edentulism must also be reported. In cases of periodontitis, instructions on how to compute the stage and extent are provided. Assessment of grade can be derived from CAL measurements (or from radiographic alveolar bone loss data) in relation to root length and the participant's age. CONCLUSIONS: ACES is a framework to be used in epidemiological studies of periodontal status that (i) have been completed, and in which stage and grade according to the 2018 classification are inferred retroactively, or (ii) are being planned. Consistent use of the proposed comprehensive approach will facilitate the comparability of periodontitis prevalence estimates across studies.

The association between body weight indices, behavioral factors, and type 2 diabetes mellitus in Africa: A systematic review and meta-analysis of population-based epidemiological studies.

BACKGROUND AND AIM: Type 2 diabetes mellitus (T2DM) is a significant public health concern in Africa. While the associations between modifiable risk factors and T2DM are likely to be Africa-specific, their overall estimations have not been published. This study aimed to use systematic and meta-analytic methods to examine the strength of associations between modifiable risk factors and T2DM in Africa. METHODS AND RESULTS: A systematic search of literature published between January 2000 to March 2022 was conducted. The review included only population-based studies and data extracted from 57 studies. Of these, unadjusted data from 50 studies were included in meta-analysis. With considerable heterogeneity between studies, random-effect models were calculated to ascertain the odds ratios (OR) and 95% confidence intervals (CI) for the associations between obesity (OB) and overweight (OV), defined by BMI; central obesity (waist circumference (OB-WC), waist-to-hip-ratio (OB-WHR)), alcohol, fruit and vegetable consumption, smoking, physical activity (PA) and T2DM. Moderator effects of age, African regions, and urban/rural location were assessed. Risk factors associated with T2DM include BMI-OB [OR = 3.05, 95% CI: (2.58, 3.61)], BMI-OV [OR = 2.38, 95% CI: (1.51, 3.75)], and BMI-OV/OB [OR = 2.07, 95% CI: (1.82, 2.34)]; OB-WC [OR = 2.58, 95% CI: (2.09, 3.18)] and OB-WHR [OR = 2.22, 95% CI: (1.69, 2.92)]; PA [OR = 1.85, 95% CI: (1.50, 2.30)]. Significant moderator effects were not observed. CONCLUSION: Obesity defined by BMI and central obesity, but not behavioral risk factors were most strongly associated with T2DM in African populations, emphasizing the need for obesity prevention to limit the rise of T2DM. REGISTRATION: The PROSPERO registration number is CRD42016043027.

Epidemiological study of the subtype frequency of systemic amyloidosis listed in the Annual of the Pathological Autopsy Cases in Japan.

Clinical presentation of systemic amyloidosis differs among subtypes, and accurate subtype classification is important for choosing the treatment. Amyloid transthyretin (ATTR) amyloidosis was the predominant among the recently consulted amyloidosis cases in Japan. To reveal the latest subtype frequency of systemic amyloidosis among autopsy cases in Japan. We analyzed systemic amyloidosis cases autopsied from January 2017 to December 2018, that were listed in the Annuals of the Pathological Autopsy Cases in Japan, Volumes 60 and 61. When the subtype was unclear, we performed a questionnaire survey, immunohistochemistry with in-house rabbit polyclonal anti-κ116 - 133 , anti-λ118 -134 , and anti-transthyretin115 -124 antibodies, and proteomic analysis. Out of 481 systemic amyloidosis cases listed in the Annuals, 411 cases were available for analysis (85.4%). We classified 399 of these systemic amyloidosis cases. ATTR was the most common subtype (44.4%, n = 177), followed by amyloid immunoglobulin light chain (AL) (38.8%, n = 155). Amyloid A and amyloid ß2 -microglobulin were 9.3% (n = 37) and 6.0% (n = 24), respectively. Double deposition of amyloid was identified in 1.6% (n = 6). In 168 cases (42.1%), systemic amyloidosis was the main cause of death. Of these cases, AL was the most common subtype (47.6%, n = 80), followed by ATTR (41.1%, n = 69). ATTR is the most predominant subtype among the current autopsy cases in Japan.

Trachoma: an underdiagnosed disease revealed by a survey carried out at Jaú, São Paulo.

BACKGROUND: Trachoma is a notifiable disease in the state of São Paulo- Brazil. However, in Jaú, a municipality located in this state, in the last 10 years there are no records of cases. This study purpose is to assess if there are cases of inflammatory trachoma in schoolchildren aged 1 to 9 years in the municipality of Jaú as well if it is possible to detect clusters areas of the disease to establish elimination programs. METHODS: An epidemiological study was performed in 2018, involving a stratified random sample of schoolchildren aged 1- to 9-year-old, from public day care centers and elementary schools in the municipality of Jaú. A trachoma screening following the criteria of the World Health Organization (WHO) and the distribution of cases was assessed using geoprocessing. RESULTS: Four thousand-six hundred-nineteen children from 44 elementary schools were examined, and 126 children with active trachoma were detected, with an adjusted prevalence rate of 2.65%. The prevalence was higher (3.01%) in children aged 6- to 9-year-old compared to children aged 1-to 5-year-old (2.42%). There were clusters with a higher concentration of positive cases of the disease in three schools located in the neighborhoods with lower socioeconomic conditions. CONCLUSION: Inflammatory trachoma still present in children aged 1- to 9-year-old in the city of Jaú. The positive cases were located mainly in areas with low socioeconomic conditions. Health promotion with active search and periodical treatment must be planned to fight this important blinding cause, that persists in our municipality.

Increasing Incidence and Decreasing Mortality of Melanoma in Elderly Adults: An Epidemiologic Study in Olmsted County, Minnesota.

BACKGROUND AND OBJECTIVE: Primary cutaneous melanoma incidence is increasing in elderly individuals. This population-based cohort examines incidence and mortality rates among adults aged 61 years and older with cutaneous melanoma. MATERIALS AND METHODS: Using the Rochester Epidemiology Project, patients aged 61 years of age or older with a first lifetime diagnosis of cutaneous melanoma between January 1, 1970 and December 31, 2020 were identified. RESULTS: The age- and sex-adjusted incidence rate increased from 16.4 (95% CI, 8.2-24.6) per 100,000 person-years in 1970 to 1979 to 201.5 (95% CI, 185.1-217.8) per 100,000 person-years in 2011 to 2020 (12.3-fold increase). There was a 16.0x increase in males and an 8.5× increase in females. Melanoma incidence has stabilized in males (1.2-fold increase, p = .11) and continues to significantly increase in females (2.7-fold increase, p < .001). Older age at diagnosis was significantly associated with an increased risk of death (HR 1.23 per 5-year increase in age at diagnosis, 95% CI, 1.02-1.47). CONCLUSION: Melanoma incidence continues to increase since 1970. The incidence has risen in elderly females, but has stabilized in males. Mortality has decreased throughout this period.

Drug utilization in patients starting haemodialysis with a focus on cardiovascular and antidiabetic medications: an epidemiological study in the Lazio region (Italy), 2016-2020.

BACKGROUND: Entering dialysis is a critical moment in patients' healthcare journey, and little is known about drug therapy around it. A study funded by the Italian Medicines Agency offered the opportunity to leverage data from the Lazio Regional Dialysis and Transplant Registry (RRDTL) and perform an observational study on drug use patterns before and after initiating chronic dialysis. METHODS: Individuals initiating dialysis in 2016-2020 were identified from RRDTL, excluding patients with prior renal transplantation, stopping dialysis early, or dying within 12 months. Use of study drugs, predefined by clinicians, in the two years around the index date was retrieved from the drug claims register and described by semester. For each drug group, proportions of users (min 2 claims in 6 months) by semester, and intensity of treatment in terms of Defined Daily Doses (DDDs) for cardiovascular and antidiabetic agents were compared across semesters, stratifying by sex and age. RESULTS: In our cohort of 3,882 patients we observed a general increase in drug use after initiating dialysis, with the mean number rising from 5.5 to 6.2. Cardiovascular agents accounted for the highest proportions, along with proton pump inhibitors and antithrombotics over all semesters. Dialysis-specific therapies showed the most evident increase, in particular anti-anaemics (iron 4-fold, erythropoietins almost 2-fold), anti-parathyroids (6-fold), and chelating agents (4-fold). Use of cardiovascular and antidiabetic drugs was characterised by significant variations in terms of patterns and intensity, with some differences between sexes and age groups. CONCLUSIONS: Entering dialysis is associated with increased use of specific drugs and goes along with adaptations of chronic therapies.

Transitivity, coherence, and reliability of network meta-analyses comparing proximal humerus fracture treatments: a meta-epidemiological study.

BACKGROUND: Network meta-analyses can be valuable for decision-makers in guiding clinical practice. However, for network meta-analysis results to be reliable, the assumptions of both transitivity and coherence must be met, and the methodology should adhere to current best practices. We aimed to assess whether network meta-analyses of randomized controlled trials (RCTs) comparing interventions for proximal humerus fractures provide reliable estimates of intervention effects. METHODS: We searched PubMed, EMBASE, The Cochrane Library, and Web of Science for network meta-analyses comparing interventions for proximal humerus fractures. We critically assessed the methodology regarding the development of a protocol, search strategy, trial inclusion, outcome extraction, and the methods used to conduct the network meta-analyses. We assessed the transitivity and coherence of the network graphs for the Constant score (CS), Disabilities of the Arm, Shoulder, and Hand score (DASH), and additional surgery. Transitivity was assessed by comparing probable effect modifiers (age, gender, fracture morphology, and comorbidities) across intervention comparisons. Coherence was assessed using Separating Indirect from Direct Evidence (SIDE) (Separating Indirect from Direct Evidence) and the design-by-treatment interaction test. We used CINeMA (Confidence in Network Meta-analyses) to assess the confidence in the results. RESULTS: None of the three included network meta-analyses had a publicly available protocol or data-analysis plan, and they all had methodological flaws that could threaten the validity of their results. Although we did not detect incoherence for most comparisons, the transitivity assumption was violated for CS, DASH, and additional surgery in all three network meta-analyses. Additionally, the confidence in the results was 'very low' primarily due to within-study bias, reporting bias, intransitivity, imprecision, and heterogeneity. CONCLUSIONS: Current network meta-analyses of RCTs comparing interventions for proximal humerus fractures do not provide reliable estimates of intervention effects. We advise caution in using these network meta-analyses to guide clinical practice. To improve the utility of network meta-analyses to guide clinical practice, journal editors should require that network meta-analyses are done according to a predefined analysis plan in a publicly available protocol and that both coherence and transitivity have been adequately assessed and reported.

Neonatal Outcomes of Critical Congenital Heart Defects: A Multicenter Epidemiological Study of Turkish Neonatal Society : Neonatal Outcomes of CCHD.

Critical congenital heart disease (CCHD) is one of the leading causes of neonatal and infant mortality. We aimed to elucidate the epidemiology, spectrum, and outcome of neonatal CCHD in Türkiye. This was a multicenter epidemiological study of neonates with CCHD conducted from October 2021 to November 2022 at national tertiary health centers. Data from 488 neonatal CCHD patients from nine centers were entered into the Trials-Network online registry system during the study period. Transposition of great arteria was the most common neonatal CHD, accounting for 19.5% of all cases. Sixty-three (12.9%) patients had extra-cardiac congenital anomalies. A total of 325 patients underwent cardiac surgery. Aortic arch repair (29.5%), arterial switch (25.5%), and modified Blalock-Taussig shunt (13.2%). Overall, in-hospital mortality was 20.1% with postoperative mortality of 19.6%. Multivariate analysis showed that the need of prostaglandin E1 before intervention, higher VIS (> 17.5), the presence of major postoperative complications, and the need for early postoperative extracorporeal membrane oxygenation were the main risk factors for mortality. The mortality rate of CCHD in our country remains high, although it varies by health center. Further research needs to be conducted to determine long-term outcomes for this vulnerable population.