Examining the temporal relationships between childhood obesity and asthma

Childhood obesity has become an issue of increasing concern to health researchers and policymakers in the United States. One important chronic health condition linked to obesity is pediatric asthma. Although researchers have speculated that both conditions may have common origins, the majority of research in this area has focused on a unidirectional relationship between obesity and later asthma. However, much of the literature is limited by its reliance on cross-sectional data and its failure to examine the possibility that asthma may influence weight fluctuations through changes in physical and sedentary activity. Using data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K), I explore the bidirectional relationships between childhood obesity and asthma. The results in this paper suggest that past asthma levels are positively correlated with changes in BMI and the onset of obesity. However, only new onset asthma is positively correlated with subsequent changes in BMI. The potential mechanisms are unclear, as I find little evidence that asthma is structurally related to changes in physical or sedentary activity over time. When testing the prevailing hypothesis that obesity is related to subsequent asthma, I find that lagged weight status is strongly related to asthma prevalence levels but that the onset of overweight or obesity is not associated with the subsequent onset of asthma. These results suggest that the onset of asthma may be related to subsequent weight gain over time.     

Adjusting body mass for measurement error with invalid validation data

We propose a new method for using validation data to correct self-reported weight and height in surveys that do not measure respondents. The standard correction in prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of measured weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples. This makes our correction more robust to differences in measurement error across surveys as long as both surveys represent the same population. We examine three nationally representative datasets and find that misreporting appears to be sensitive to differences in survey context. When we compare predicted BMI distributions using the two validation approaches, we find that the standard correction is affected by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates.     

The influence of liver fluke infection on production in sheep and cattle: a meta-analysis

Liver flukes (Fasciola spp.) are important parasites of ruminant livestock worldwide, causing profound damage to animal health and productivity. Many reviews have discussed the results of decades of research on the impact of fluke on livestock traits such as weight gain and milk production, but there have been no known attempts to collate previous research in a quantitative manner or to determine the factors that vary between studies that find substantial effects of fluke and others concluding that effects of fluke are negligible. Here, we use meta-analysis to provide quantitative “global” estimates of the impact of liver fluke on animal performance, and to identify elements of study design (“moderators”) that influence variation between studies in their outcome. A literature search provided 233 comparisons of performance in fluke-infected and uninfected animals. We standardised these data as log response ratios and calculated effect size variances to weight studies by the accuracy of their estimates. We performed multi-level meta-analysis to estimate effects of fluke infection on daily weight gain, live weight, carcass weight, total weight gain and milk production. There were statistically-supported negative effects of fluke infection on daily weight gain, live weight and carcass weight (9%, 6% and 0.6% reductions, respectively), but not on total weight gain or milk production. A mixed-effects meta-analysis revealed that studies of younger animals found more severe effects of fluke on weight gain and that effects on live weight increased with time since infection. Limitations to the data that could be analysed, including a lack of statistical reporting in older papers and variation in the outcome variables measured, may have explained the relatively limited influence of modifiers that we detected. Our results provide, to our knowledge, the first quantitative estimate of the impact of liver fluke on performance across studies and highlight some elements of study design that can influence conclusions.     

(Co)Variance components and genetic parameter estimates for growth traits in Moghani sheep

Genetic parameters were estimated for birth weight (BW), weaning weight (WW), yearling weight (YW), average daily gain from birth to weaning (ADG1) and average daily gain from weaning to yearling (ADG2) in Moghani sheep. Maximum number of data was 4237 at birth, but only 1389 records at yearling were investigated. The data was collected from 1995 to 2007 at the Breeding Station of Moghani sheep in Jafarabad, Moghan, Iran. (Co)Variance components and genetic parameters were estimated with different models which including direct effects, with and without maternal additive genetic effects as well as maternal permanent environmental effects using restricted maximum likelihood (REML) method. The most appropriate model for each trait was determined based on likelihood ratio tests and Akaike's Information Criterion (AIC). Maternal effects were important only for pre-weaning traits. Direct heritability estimates for BW, ADG1, WW, ADG2 and YW were 0.07, 0.08, 0.09, 0.09 and 0.17, respectively. Fractions of variance due to maternal permanent environmental effects on phenotypic variance were 0.08 for ADG1. Maternal heritability estimates for BW and WW were 0.18 and 0.06, respectively. Multivariate analysis was performed using the most appropriate models obtained in univariate analysis. Direct genetic correlations among studied traits were positive and ranged from 0.37 for BW–ADG2 to 0.85 for ADG1–YW. Maternal genetic correlation estimate between BW and WW was 0.33. Phenotypic and environmental correlation estimates were generally lower than those of genetic correlation. Low direct heritability estimates imply that mass selection for these traits results in slow genetic gain.     

Risk factors and outcomes of maternal obesity and excessive weight gain during pregnancy

Objective:

The prevalence of overweight and obesity among women of reproductive age is increasing. We aimed to determine risk factors and maternal, fetal and childhood consequences of maternal obesity and excessive gestational weight gain.

Design and Methods:

The study was embedded in a population‐based prospective cohort study among 6959 mothers and their children. The study was based in Rotterdam, The Netherlands (2001–2005).

Results:

Maternal lower educational level, lower household income, multiparity, and FTO risk allel were associated with an increased risk of maternal obesity, whereas maternal European ethnicity, nulliparity, higher total energy intake, and smoking during pregnancy were associated with an increased risk of excessive gestational weight gain (all p‐values <0.05). As compared to normal weight, maternal obesity was associated with increased risks of gestational hypertension (OR 6.31 (95% CI 4.30, 9.26)), preeclampsia (OR (3.61, (95% CI 2.04, 6.39)), gestational diabetes (OR 6.28 (95%CI 3.01, 13.06)), caesarean delivery (OR 1.91 (95% CI 1.46, 2.50)), delivering large size for gestational age infants (OR 2.97 (95% CI 2.16, 4.08)), and childhood obesity (OR 5.02 (95% CI:2.97, 8.45)). Weaker associations of excessive gestational weight gain with maternal, fetal and childhood outcomes were observed, with the strongest effects for first trimester weight gain.

Conclusions:

Our study shows that maternal obesity and excessive weight gain during pregnancy are associated with socio‐demographic, lifestyle, and genetic factors and with increased risks of adverse maternal, fetal and childhood outcomes. As compared to prepregnancy overweight and obesity, excessive gestational weight gain has a limited influence on adverse pregnancy outcomes.     

Operator bias in software‐aided bat call identification

Software-aided identification facilitates the handling of large sets of bat call recordings, which is particularly useful in extensive acoustic surveys with several collaborators. Species lists are generated by “objective” automated classification. Subsequent validation consists of removing any species not believed to be present. So far, very little is known about the identification bias introduced by individual validation of operators with varying degrees of experience. Effects on the quality of the resulting data may be considerable, especially for bat species that are difficult to identify acoustically. Using the batcorder system as an example, we compared validation results from 21 volunteer operators with 1–26 years of experience of working on bats. All of them validated identical recordings of bats from eastern Austria. The final outcomes were individual validated lists of plausible species. A questionnaire was used to enquire about individual experience and validation procedures. In the course of species validation, the operators reduced the software''s estimate of species richness. The most experienced operators accepted the smallest percentage of species from the software''s output and validated conservatively with low interoperator variability. Operators with intermediate experience accepted the largest percentage, with larger variability. Sixty-six percent of the operators, mainly with intermediate and low levels of experience, reintroduced species to their validated lists which had been identified by the automated classification, but were finally excluded from the unvalidated lists. These were, in many cases, rare and infrequently recorded species. The average dissimilarity of the validated species lists dropped with increasing numbers of recordings, tending toward a level of ˜20%. Our results suggest that the operators succeeded in removing false positives and that they detected species that had been wrongly excluded during automated classification. Thus, manual validation of the software''s unvalidated output is indispensable for reasonable results. However, although application seems easy, software-aided bat call identification requires an advanced level of operator experience. Identification bias during validation is a major issue, particularly in studies with more than one participant. Measures should be taken to standardize the validation process and harmonize the results of different operators.     

Analyzing self‐controlled case series data when case confirmation rates are estimated from an internal validation sample

Vaccine safety studies are often electronic health record (EHR)‐based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self‐controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power.     

Hypothesis Testing for an Exposure–Disease Association in Case–Control Studies Under Nondifferential Exposure Misclassification in the Presence of Validation Data: Bayesian and Frequentist Adjustments

In epidemiologic studies, measurement error in the exposure variable can have a detrimental effect on the power of hypothesis testing for detecting the impact of exposure in the development of a disease. To adjust for misclassification in the hypothesis testing procedure involving a misclassified binary exposure variable, we consider a retrospective case–control scenario under the assumption of nondifferential misclassification. We develop a test under Bayesian approach from a posterior distribution generated by a MCMC algorithm and a normal prior under realistic assumptions. We compared this test with an equivalent likelihood ratio test developed under the frequentist approach, using various simulated settings and in the presence or the absence of validation data. In our simulations, we considered varying degrees of sensitivity, specificity, sample sizes, exposure prevalence, and proportion of unvalidated and validated data. In these scenarios, our simulation study shows that the adjusted model (with-validation data model) is always better than the unadjusted model (without validation data model). However, we showed that exception is possible in the fixed budget scenario where collection of the validation data requires a much higher cost. We also showed that both Bayesian and frequentist hypothesis testing procedures reach the same conclusions for the scenarios under consideration. The Bayesian approach is, however, computationally more stable in rare exposure contexts. A real case–control study was used to show the application of the hypothesis testing procedures under consideration.     

Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence

Background

State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity.

Methods

Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods.

Results

Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%–in contrast, most states were below 30% in CDC maps.

Conclusions

Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.     

Estimating haplotype relative risks on human survival in population-based association studies

Association-based linkage disequilibrium (LD) mapping is an increasingly important tool for localizing genes that show potential influence on human aging and longevity. As haplotypes contain more LD information than single markers, a haplotype-based LD approach can have increased power in detecting associations as well as increased robustness in statistical testing. In this paper, we develop a new statistical model to estimate haplotype relative risks (HRRs) on human survival using unphased multilocus genotype data from unrelated individuals in cross-sectional studies. Based on the proportional hazard assumption, the model can estimate haplotype risk and frequency parameters, incorporate observed covariates, assess interactions between haplotypes and the covariates, and investigate the modes of gene function. By introducing population survival information available from population statistics, we are able to develop a procedure that carries out the parameter estimation using a nonparametric baseline hazard function and estimates sex-specific HRRs to infer gene-sex interaction. We also evaluate the haplotype effects on human survival while taking into account individual heterogeneity in the unobserved genetic and nongenetic factors or frailty by introducing the gamma-distributed frailty into the survival function. After model validation by computer simulation, we apply our method to an empirical data set to measure haplotype effects on human survival and to estimate haplotype frequencies at birth and over the observed ages. Results from both simulation and model application indicate that our survival analysis model is an efficient method for inferring haplotype effects on human survival in population-based association studies.     

Asthma and Maternal Body Mass Index Are Related to Pediatric Body Mass Index and Obesity: Results from the Third National Health and Nutrition Examination Survey

Objective: Clinical research has shown an increased prevalence of obesity in children with asthma. This study was designed to assess the relationship between asthma and pediatric body mass index (BMI) in a national database and to examine factors that may modify this relationship. Design: The cross‐sectional relationship between asthma and pediatric BMI and obesity (BMI ≥ 85th percentile) was studied. Variables that may influence the relationship between asthma and pediatric BMI, such as race/ethnicity and television watching were included in the model for the total sample. A smaller sample of 3009 white and African American youth were studied in regression models including maternal BMI. Study Population: A nationally representative crosssectional sample of 5154 children and adolescents of 6 to 16 years of age from the Third National Health And Nutrition Examination Survey. Results: In the full sample, asthma and television watching were related to BMI, accounting for 3% of the variance in BMI. When maternal BMI was included in the nonHispanic sample, television watching, maternal BMI, and the interaction of maternal BMI and asthma were related to youth BMI, accounting for 15% of the variance. The standardized BMI z‐score for those youth without asthma and no maternal obesity was 0.06, which increased to 0.33 if the youth had asthma, to 0.70 if the youth did not have asthma but the mother was obese, and to 1.71 if the youth had asthma and the mother was obese. Asthma, television watching, and maternal BMI were independent predictors of youth obesity. Conclusions: BMI and prevalence of obesity is higher in youth with asthma. Pediatric BMI, but not obesity, is also related to the interaction of asthma and maternal BMI in white and African American youth. Comorbidity of asthma and obesity may complicate treatment of either condition, and prevention of obesity should be encouraged for asthmatic children.     

The Influence of Overweight and Obesity on Longitudinal Trends in Maternal Serum Leptin Levels During Pregnancy

Maternal obesity influences a number of metabolic factors that can affect the course of pregnancy. Among these factors, leptin plays an important role in energy metabolism and fetal development during pregnancy. Our objective was to estimate the influence of maternal overweight/obesity on variation in the maternal serum leptin profile during pregnancy. In a prospective cohort of 143 adult gravidas with singleton pregnancies presenting for general prenatal care, we measured serum leptin levels at 6–10, 10–14, 16–20, 22–26, and 32–36 weeks' gestation. The longitudinal effects of maternal prepregnancy BMI, categorized as nonoverweight (≤26.0 kg/m²) and overweight/obese (>26.0 kg/m²), on serum leptin concentration were analyzed using linear mixed models. Overweight/obese women had significantly higher serum leptin concentrations than their nonoverweight counterparts throughout pregnancy (P < 0.01). Although these concentrations increased significantly across gestation for both groups, the rate of increase was significantly smaller for overweight/obese women (P < 0.05). To investigate whether these differences merely reflected differences in weight‐gain patterns between the two groups, we examined an index of leptin concentration per unit body weight (leptin (ng/ml)/weight (kg)). Overweight/obese women had a significantly higher index throughout pregnancy (P < 0.01). However, although this index increased significantly across pregnancy for nonoverweight women, it actually decreased significantly for overweight/obese women (P < 0.01). Our results suggest that factors other than fat mass alone influence leptin concentrations in overweight/obese women compared to normal‐weight women during pregnancy. Such factors may contribute to differences in the intrauterine environment and its influence on pregnancy outcomes in the two groups.     

Sexual dimorphism and direct and maternal genetic effects on body weight in mice

Summary Genetic and phenotypic parameters for three-, six- and eight-week body weight and for weight gain between three and six weeks of age were estimated from data collected over 14 generations in a randombred control population. Genetic parameters were also estimated for sexual dimorphism in body weight and gain. Heritability estimates were substantial for body weight at all ages and for body weight gain. Additive maternal variances were also large. Estimates of the covariance between direct and maternal genetic effects were negative and substantial for three- and six-week weights and gain. Also the covariance between maternal effects on weaning weight and direct genetic effects on six- and eight-week weights were negative. These results indicate a consistent antagonism between maternal and direct genetic effects in this population.The analysis of sexual dimorphism yielded estimates of 0.87±.09 and 0.71±.14 for the correlation between additive direct effects on males and females for six-week weight and body weight gain respectively. Corresponding heritability estimates were 0.07±.09 and 0.11±.09. Heritability estimates for sexual dimorphism in three- and eight-week weights were negative.Journal Paper No. 3687 of the North Carolina State University Agricultural Experiment Station. This investigation was supported in part by NIH Grant No. GM11546.     

Psychological workload and weight gain among women with and without familial obesity

Objective : High job demands and low job influence may be associated with subsequent weight gain. Predisposition to obesity may further modify such associations. The purpose of the study was to determine whether familial predisposition to obesity modified associations between psychological workload and 6‐year weight changes among nurses. Research Methods and Procedures : A total of 6404 Danish nurses 45 to 65 years old, who belonged to the workforce in both 1993 and 1999, answered a questionnaire on psychological workload, body weight, and familial obesity. Women were considered to be predisposed to obesity if they were overweight and had at least one obese parent. Parents’ body shape was reported using pictograms. Results : An increased psychological workload, reflected by high job demands and low influence in job, was associated with an increased body weight. This was particularly the case for nurses being predisposed to obesity, suggesting a synergy between familial obesity predisposition and the psychological workload environment. An interaction test among job demands, familial predisposition to obesity, and weight gain on adjusted data was made. The test showed p = 0.05. The adjusted interaction test among influence in job, familial predisposition to obesity, and weight gain showed p = 0.02. Predisposed nurses who were busy in their job gained 4.4 kg, whereas other nurses gained only 3.2 kg during the 6 years. Similarly, nurses predisposed to obesity with low influence in job had a higher body weight gain (5.4 vs. 3.2 kg) compared with other nurses. Discussion : High psychological workload due to high job demands and low influence in job seems to predict weight gain in general and, in particular, among those nurses with a familial predisposition to obesity.     

Multi-Institutional Sharing of Electronic Health Record Data to Assess Childhood Obesity

Objective

To evaluate the validity of multi-institutional electronic health record (EHR) data sharing for surveillance and study of childhood obesity.

Methods

We conducted a non-concurrent cohort study of 528,340 children with outpatient visits to six pediatric academic medical centers during 2007–08, with sufficient data in the EHR for body mass index (BMI) assessment. EHR data were compared with data from the 2007–08 National Health and Nutrition Examination Survey (NHANES).

Results

Among children 2–17 years, BMI was evaluable for 1,398,655 visits (56%). The EHR dataset contained over 6,000 BMI measurements per month of age up to 16 years, yielding precise estimates of BMI. In the EHR dataset, 18% of children were obese versus 18% in NHANES, while 35% were obese or overweight versus 34% in NHANES. BMI for an individual was highly reliable over time (intraclass correlation coefficient 0.90 for obese children and 0.97 for all children). Only 14% of visits with measured obesity (BMI ≥95%) had a diagnosis of obesity recorded, and only 20% of children with measured obesity had the diagnosis documented during the study period. Obese children had higher primary care (4.8 versus 4.0 visits, p<0.001) and specialty care (3.7 versus 2.7 visits, p<0.001) utilization than non-obese counterparts, and higher prevalence of diverse co-morbidities. The cohort size in the EHR dataset permitted detection of associations with rare diagnoses. Data sharing did not require investment of extensive institutional resources, yet yielded high data quality.

Conclusions

Multi-institutional EHR data sharing is a promising, feasible, and valid approach for population health surveillance. It provides a valuable complement to more resource-intensive national surveys, particularly for iterative surveillance and quality improvement. Low rates of obesity diagnosis present a significant obstacle to surveillance and quality improvement for care of children with obesity.     

Prenatal Parental Separation and Body Weight,Including Development of Overweight and Obesity Later in Childhood

Background

Early parental separation may be a stress factor causing a long-term alteration in the hypothalamic-pituitary-adrenal-axis activity possibly impacting on the susceptibility to develop overweight and obesity in offspring. We aimed to examine the body mass index (BMI) and the risk of overweight and obesity in children whose parents lived separately before the child was born.

Methods

A follow-up study was conducted using data from the Aarhus Birth Cohort in Denmark and included 2876 children with measurements of height and weight at 9-11-years-of-age, and self-reported information on parental cohabitation status at child birth and at 9-11-years-of-age. Quantile regression was used to estimate the difference in median BMI between children whose parents lived separately (n = 124) or together (n = 2752) before the birth. We used multiple logistic regression to calculate odds ratio (OR) for overweight and obesity, adjusted for gender, parity, breast feeding status, and maternal pre-pregnancy BMI, weight gain during pregnancy, age and educational level at child birth; with and without possible intermediate factors birth weight and maternal smoking during pregnancy. Due to a limited number of obese children, OR for obesity was adjusted for the a priori confounder maternal pre-pregnancy BMI only.

Results

The difference in median BMI was 0.54 kg/m² (95% confidence intervals (CI): 0.10; 0.98) between children whose parents lived separately before birth and children whose parents lived together. The risk of overweight and obesity was statistically significantly increased in children whose parents lived separately before the birth of the child; OR 2.29 (95% CI: 1.18; 4.45) and OR 2.81 (95% CI: 1.05; 7.51), respectively. Additional, adjustment for possible intermediate factors did not substantially change the estimates.

Conclusion

Parental separation before child birth was associated with higher BMI, and increased risk of overweight and obesity in 9-11-year-old children; this may suggest a fetal programming effect or unmeasured difference in psychosocial factors between separated and non-separated parents.     

Dose-response curve estimation: a semiparametric mixture approach

In the estimation of a dose-response curve, parametric models are straightforward and efficient but subject to model misspecifications; nonparametric methods are robust but less efficient. As a compromise, we propose a semiparametric approach that combines the advantages of parametric and nonparametric curve estimates. In a mixture form, our estimator takes a weighted average of the parametric and nonparametric curve estimates, in which a higher weight is assigned to the estimate with a better model fit. When the parametric model assumption holds, the semiparametric curve estimate converges to the parametric estimate and thus achieves high efficiency; when the parametric model is misspecified, the semiparametric estimate converges to the nonparametric estimate and remains consistent. We also consider an adaptive weighting scheme to allow the weight to vary according to the local fit of the models. We conduct extensive simulation studies to investigate the performance of the proposed methods and illustrate them with two real examples.     

Change in knee contact force with simulated change in body weight

The relationship between obesity, weight gain and progression of knee osteoarthritis is well supported, suggesting that excessive joint loading may be a mechanism responsible for cartilage deterioration. Examining the influence of weight gain on joint compressive forces is difficult, as both muscles and ground reaction forces can have a significant impact on the forces experienced during gait. While previous studies have examined the relationship between body weight and knee forces, these studies have used models that were not validated using experimental data. Therefore, the objective of this study was to evaluate the relationship between changes in body weight and changes in knee joint contact forces for an individual's gait pattern using musculoskeletal modeling that is validated against known internal compressive forces. Optimal weighting constants were determined for three subjects to generate valid predictions of knee contact forces (KCFs) using in vivo data collection with instrumented total knee arthroplasty. A total of five simulations per walking trial were generated for each subject, from 80% to 120% body weight in 10% increments, resulting in 50 total simulations. The change in peak KCF with respect to body weight was found to be constant and subject-specific, predominantly determined by the peak force during the baseline condition at 100% body weight. This relationship may be further altered by any change in kinematics or body mass distribution that may occur as a result of a change in body weight or exercise program.     

Estimation of country-level incidence of early-onset invasive Group B Streptococcus disease in infants using Bayesian methods

Neonatal invasive disease caused by Group B Streptococcus (GBS) is responsible for much acute mortality and long-term morbidity. To guide development of better prevention strategies, including maternal vaccines that protect neonates against GBS, it is necessary to estimate the burden of this condition globally and in different regions. Here, we present a Bayesian model that estimates country-specific invasive GBS (iGBS) disease incidence in children aged 0 to 6 days. The model combines different types of epidemiological data, each of which has its own limitations: GBS colonization prevalence in pregnant women, risk of iGBS disease in children born to GBS-colonized mothers and direct estimates of iGBS disease incidence where available. In our analysis, we present country-specific maternal GBS colonization prevalence after adjustment for GBS detection assay used in epidemiological studies. We then integrate these results with other epidemiological data and estimate country-level incidence of iGBS disease including in countries with no studies that directly estimate incidence. We are able to simultaneously estimate two key epidemiological quantities: the country-specific incidence of early-onset iGBS disease, and the risk of iGBS disease in babies born to GBS-colonized women. Overall, we believe our method will contribute to a more comprehensive quantification of the global burden of this disease, inform cost-effectiveness assessments of potential maternal GBS vaccines and identify key areas where data are necessary.     

Hepatic 5'-deiodinase activity of Japanese quail using reverse-T3 as substrate: assay validation, characterization, and developmental studies

Using rT3 as substrate, an in vitro 5'D assay was validated for use with liver tissue from adult Japanese quail, by defining conditions under which activity is proportional to enzyme (protein) concentration and is linear with incubation time. Activity was measured as the release of 125I from labeled rT3. Using validated assay conditions we found the following 5'D characteristics: maximal activity from 10 to 50 mM dithiothreitol (cofactor), an apparent Km of 0.52 microM rT3, pH optimum of 7.6-8.5, complete inhibition by 1 mM propylthiouracil and by 1.0 mM iopanoic acid, and substrate "preference" of rT3 greater than T4 greater than T3. Based on these characterizations the quail hepatic 5'D activity is like the Type I 5'D activity found in mammalian liver and kidney and embryonic chicken liver. To determine how previous unvalidated assays, that used high tissue and relatively low substrate (T4) concentrations, influenced 5'D studies we reevaluated 5'D development using an assay validated for each developmental stage with rT3 as substrate. We found extreme quantitative differences in the activities measured and in the proportional relationships between stages, and only limited qualitative similarity in the pattern of 5'D development when unvalidated T4 assay results were compared with validated rT3 assay results. Our data in this paper show good correspondence between whole liver 5'D activity per unit body weight and plasma T3/T4 ratios for the developmental stages sampled.