Diagnostic Accuracy of Cord Bilirubin to Predict the Need for Phototherapy in Healthy Neonates >35-Week Gestational Age: A Systematic Review and Meta-Analysis

Objective

Early discharge puts neonates at risk of delayed detection of jaundice and resulting neurological injury. In these neonates, we can use cord bilirubin to make predictions. In this meta-analysis, we assessed the diagnostic accuracy of cord bilirubin in predicting the need for phototherapy (AAP-2004 or NICE-2010 charts).

Methods

We searched the databases of PubMed, Embase, Cochrane Library, Google Scholar, and Index Medicus for Southeast Asian Region. We included all observational studies that assessed the diagnostic accuracy of cord bilirubin. A bivariate model was used to pool the data in prespecified range of cord bilirubin levels (<1.5 mg/dl, 1.5-2.0 mg/dl, 2.0-2.5 mg/dl, 2.5-3.0 mg/dl, and >3.0 mg/dl). Data were pooled separately for studies including all neonates (no risk stratification), high-risk neonates (Rh and/or ABO incompatibility only), and low-risk neonates (excluded Rh and ABO incompatibility).

Results

Of the 1990 unique records, we studied 153 full texts and included 54 studies in the meta-analysis. For all the three groups of studies, the highest diagnostic odds ratio was noted for a cord bilirubin cut-off of 2.5-3.0 mg/dl (all neonates: 22.5, 95% CI: 21.1, 22.9; high-risk neonates: 75.5, 95% CI: 63, 85.7; low-risk neonates: 91.9; 95% CI: 64, 134.14). Using the same cut-off, the studies including all neonates without risk stratification had a pooled sensitivity of 0.31 (95% CI: 0.18, 0.47) and a pooled specificity of 0.98 (0.96, 0.99) in predicting the need for phototherapy. In studies on high-risk neonates, the pooled sensitivity was 0.8 (0.39, 0.96) and pooled specificity was 0.95 (0.78, 0.99). In studies on low-risk neonates, the pooled sensitivity was 0.74 (0.39, 0.93) and pooled specificity of 0.97 (0.91, 0.99). We noted significant heterogeneity and a high risk of bias in the index test's conduct.

Conclusion

A cord bilirubin cut-off of 2.5-3 mg/dl has good diagnostic accuracy in predicting the need for phototherapy in neonates.

Registration number

CRD42020196216.

Theoretical Model and Actual Characteristics of Air Pollution Affecting Health Cost: A Review

BACKGROUND

The impact of environmental pollution (such as air pollution) on health costs has received a great deal of global attention in the last 20 years.

METHODS

This review aims to summarize the theoretical analysis model of air pollution affecting health costs, and further explore the actual characteristics of the impact of air pollution on health costs. The following main databases were taken into account: Web of Science Core Collection, Medline, SCOPUS, PubMed, and CNKI (China). As of 30 March 2021, we retrieved a total of 445 papers and ended up with 52 articles.

RESULTS

This review mainly expounds clarification of the concept of air pollution and health costs, the theoretical model and the actual characteristics of air pollution affecting health costs. In addition, it also discusses other related factors affecting health costs.

CONCLUSION

Our conclusion is that, while academic research on the relationship between air pollution and health costs has made some progress, there are still some shortcomings, such as insufficient consideration of individual avoidance behavior and rural-urban and international mobility. Therefore, the simple use of the original data obtained in the statistical yearbook of the health cost caused by air pollution is also the reason for the errors in the empirical results. In addition, the choice of proxy variables of environmental pollution by scholars is relatively simple, mainly focusing on air pollutants, while the impact of water quality or soil pollution safety on health costs is becoming increasingly prominent, and will become the focus of future research.

Optimal dose-finding for efficacy-safety models

Dose-finding is an important part of the clinical development of a new drug. The purpose of dose-finding studies is to determine a suitable dose for future development based on both efficacy and safety. Optimal experimental designs have already been used to determine the design of this kind of studies, however, often that design is focused on efficacy only. We consider an efficacy-safety model, which is a simplified version of the bivariate Emax model. We use here the clinical utility index concept, which provides the desirable balance between efficacy and safety. By maximizing the utility of the patients, we get the estimated dose. This desire leads us to locally c -optimal designs. An algebraic solution for c -optimal designs is determined for arbitrary c vectors using a multivariate version of Elfving's method. The solution shows that the expected therapeutic index of the drug is a key quantity determining both the number of doses, the doses itself, and their weights in the optimal design. A sequential design is proposed to solve the complication of parameter dependency, and it is illustrated in a simulation study.

Individual differences in behaviour explain variation in survival: a meta-analysis

Research focusing on among-individual differences in behaviour ('animal personality') has been blooming for over a decade. Central theories explaining the maintenance of such behavioural variation posits that individuals expressing greater "risky" behaviours should suffer higher mortality. Here, for the first time, we synthesize the existing empirical evidence for this key prediction. Our results did not support this prediction as there was no directional relationship between riskier behaviour and greater mortality; however there was a significant absolute relationship between behaviour and survival. In total, behaviour explained a significant, but small, portion (5.8%) of the variance in survival. We also found that risky (vs. "shy") behavioural types live significantly longer in the wild, but not in the laboratory. This suggests that individuals expressing risky behaviours might be of overall higher quality but the lack of predation pressure and resource restrictions mask this effect in laboratory environments. Our work demonstrates that individual differences in behaviour explain important differences in survival but not in the direction predicted by theory. Importantly, this suggests that models predicting behaviour to be a mediator of reproduction-survival trade-offs may need revision and/or empiricists may need to reconsider their proxies of risky behaviours when testing such theory.

Bivariate threshold models for genetic evaluation of susceptibility to and ability to recover from mastitis in Danish Holstein cows

Mastitis in dairy cows is an unavoidable problem and genetic variation in recovery from mastitis, in addition to susceptibility, is therefore of interest. Genetic parameters for susceptibility to and recovery from mastitis were estimated for Danish Holstein-Friesian cows using data from automatic milking systems equipped with online somatic cell count measuring units. The somatic cell count measurements were converted to elevated mastitis risk, a continuous variable [on a (0-1) scale] indicating the risk of mastitis. Risk values >0.6 were assumed to indicate that a cow had mastitis. For each cow and lactation, the sequence of health states (mastitic or healthy) was converted to a weekly transition: 0 if the cow stayed within the same state and 1 if the cow changed state. The result was 2 series of transitions: one for healthy to diseased (HD, to model mastitis susceptibility) and the other for diseased to healthy (DH, to model recovery ability). The 2 series of transitions were analyzed with bivariate threshold models, including several systematic effects and a function of time. The model included effects of herd, parity, herd-test-week, permanent environment (to account for the repetitive nature of transition records from a cow) plus two time-varying effects (lactation stage and time within episode). In early lactation, there was an increased risk of getting mastitis but the risk remained stable afterwards. Mean recovery rate was 45% per lactation. Heritabilities were 0.07 [posterior mean of standard deviations (PSD) = 0.03] for HD and 0.08 (PSD = 0.03) for DH. The genetic correlation between HD and DH has a posterior mean of -0.83 (PSD = 0.13). Although susceptibility and recovery from mastitis are strongly negatively correlated, recovery can be considered as a new trait for selection.

Performance of methods for meta-analysis of diagnostic test accuracy with few studies or sparse data

Hierarchical models such as the bivariate and hierarchical summary receiver operating characteristic (HSROC) models are recommended for meta-analysis of test accuracy studies. These models are challenging to fit when there are few studies and/or sparse data (for example zero cells in contingency tables due to studies reporting 100% sensitivity or specificity); the models may not converge, or give unreliable parameter estimates. Using simulation, we investigated the performance of seven hierarchical models incorporating increasing simplifications in scenarios designed to replicate realistic situations for meta-analysis of test accuracy studies. Performance of the models was assessed in terms of estimability (percentage of meta-analyses that successfully converged and percentage where the between study correlation was estimable), bias, mean square error and coverage of the 95% confidence intervals. Our results indicate that simpler hierarchical models are valid in situations with few studies or sparse data. For synthesis of sensitivity and specificity, univariate random effects logistic regression models are appropriate when a bivariate model cannot be fitted. Alternatively, an HSROC model that assumes a symmetric SROC curve (by excluding the shape parameter) can be used if the HSROC model is the chosen meta-analytic approach. In the absence of heterogeneity, fixed effect equivalent of the models can be applied.

Accuracy of visual inspection with acetic acid and with Lugol's iodine for cervical cancer screening: Meta-analysis

The aim of this review was to provide an updated summary estimation of the accuracy of visual inspection with acetic acid (VIA) and with Lugol's iodine (VILI) in detecting cervical cancer and precancer. Studies on VIA/VILI accuracy were eligible in which VIA/VILI was performed on asymptomatic women who all underwent confirmatory testing of histology, combination of colposcopy and histology, or combination of multiple screening tests, colposcopy and histology, to detect cervical intraepithelial neoplasia grade 2 or worse (CIN2+ or CIN3+). A bivariate model was fitted to estimate the accuracy of VIA/VILI and provide estimates of heterogeneity. Subgroup analysis was used to investigate the source of heterogeneity. A total of 29 studies on VIA and 19 studies on VILI were included finally in the meta-analysis. The summary sensitivity and specificity of VIA for CIN2+ were 73.2% (95%CI: 66.5-80.0%) and 86.7% (95%CI: 82.9-90.4%), respectively, and those for VILI were 88.1% (95%CI: 81.5-94.7%) and 85.9% (95%CI: 81.7-90.0%), respectively. VIA and VILI were both more sensitive in detecting more severe outcome, although there was a slight loss in specificity. Apparent heterogeneity existed in sensitivity and specificity for both VIA and VILI. High sensitivity of both VIA and VILI for CIN2+ was found when a combination of colposcopy and histology was used as disease confirmation. VIA, VILI, even a combination of them in parallel, could be good options for cervical screening in low-resource settings. Significant differences in sensitivity between different gold standards might provide a proxy for optimization of ongoing cervical cancer screening programs.

Genetic and environmental dissections of sub-phenotypes of metabolic syndrome in the Chinese population: a twin-based heritability study

OBJECTIVE

We perform a comprehensive heritability study on multiple phenotypes related to metabolic syndrome using Chinese twins to assess the genetic and environmental effects in determining the variation and covariation of the phenotypes in the Chinese population.

METHODS

The studied sample contains 654 twins collected in the Qingdao municipality. A total of 10 phenotypes covering anthropometric measurements, plasma glucose levels, lipids, blood pressures etc. were examined. Univariate and bivariate structural equation models were fitted for assessing the genetic and environmental contributions.

RESULTS

The AE model combining additive genetic (A) and unique environmental (E) factors produced the best fit for all phenotypes except for triglyceride. Modest to high heritability estimates were obtained in univariate analysis ranging from 0.5 for total cholesterol to 0.78 for weight. The bivariate model estimated high genetic correlations between systolic and diastolic blood pressures, between total cholesterol and low density lipoprotein cholesterol, modest genetic correlations between BMI and blood pressures. No significant common environmental correlation was found between any pair of the phenotypes.

CONCLUSIONS

Our results showed significant genetic contributions to the sub-phenotypes of metabolic syndrome. Although pleiotropic genetic control may exist for some physiologically similar phenotypes, our results do not support a common genetic mechanism among the phenotypes covered in our study.

Bayesian model selection for meta-analysis of diagnostic test accuracy data: Application to Ddimer for deep vein thrombosis

A number of statistical models have been developed for meta-analysis (MA) of diagnostic test (DT) accuracy data. Here we consider these alternative MA models, explore the relationships between them, and consider the use of the deviance information criteria (DIC) to decide which is the most appropriate model for a given dataset. A Bayesian statistical approach is adopted throughout. The alternative MA models are applied to a dataset of 198 assays of Ddimer to diagnose deep vein thrombosis. In this example, based on the DIC, a bivariate random effects model for sensitivity and specificity fitted the data best. When considering the inclusion of study level covariates, allowing sensitivity to vary by study setting further improved the fit of the model. The model fitting approach is then repeated for a subset of the data, which highlights the less decisive results obtained when using the DIC with a more limited dataset. Formal approaches to model selection are often overlooked in an MA context; however, they offer sensible rationale to the analysis, particularly for complex models such as those proposed for DT accuracy. Specifically, the use of the DIC statistic appears to be well suited for deciding between potentially complex mixed-effect MA models, possibly including covariates. Copyright © 2010 John Wiley & Sons, Ltd.