The value of integrated care through comprehensive, coordinated, and family-centered services has been increasingly recognized for improving health outcomes of children with special health care needs (CSHCN). In a randomized controlled trial (RCT), the integrated care provided through the Children’s Treatment Network (CTN) was compared with usual care in improving the psychosocial health of target CSHCN. In this paper, we aimed to estimate the effect of CTN care by conducting multiple analyses to handle noncompliance in the trial.
The trial recruited target children in Simcoe County and York Region, ON, Canada. Children were randomized to receive CTN or usual care and were followed for 2 years. The CTN group received integrated services through multiple providers to address their specific needs while the usual care group continued to receive care directed by their parents. The outcome was change in psychosocial quality of life at 2 years. We conducted intention-to-treat, as-treated, per-protocol, and instrumental variable analyses to analyze the outcome.
The trial randomized 445 children, with 229 in the intervention group and 216 in the control group. During follow-up, 52% of children in the intervention group did not receive complete CTN care for various reasons. At 2 years, we did not find a significant improvement in psychosocial quality of life among the children receiving CTN care compared with usual care (intention-to-treat mean difference 1.50, 95% confidence interval −1.49 to 4.50; P = 0.32). Other methods of analysis yielded similar results.
Although the effect of CTN care was not significant, there was evidence showing benefits of integrated care for CSHCN. More RCTs are needed to demonstrate the magnitude of such an effect. The CTN study highlights the key challenges in RCTs when assessing interventions involving integrated care, and informs further RCTs including similar evaluations.
children with special health care needs; chronically ill; family-centered care; randomized controlled trial; noncompliance
Tuberculosis (TB) disease affects survival among HIV co-infected patients on antiretroviral therapy (ART). Yet, the magnitude of TB disease on mortality is poorly understood.
Using a prospective cohort of 22,477 adult patients who initiated ART between August 2000 and June 2009 in Uganda, we assessed the effect of active pulmonary TB disease at the initiation of ART on all-cause mortality using a Cox proportional hazards model. Propensity score (PS) matching was used to control for potential confounding. Stratification and covariate adjustment for PS and not PS-based multivariable Cox models were also performed.
A total of 1,609 (7.52%) patients had active pulmonary TB at the start of ART. TB patients had higher proportions of being male, suffering from AIDS-defining illnesses, having World Health Organization (WHO) disease stage III or IV, and having lower CD4 cell counts at baseline (p < 0.001). The percentages of death during follow-up were 10.47% and 6.38% for patients with and without TB, respectively. The hazard ratio (HR) for mortality comparing TB to non-TB patients using 1,686 PS-matched pairs was 1.37 (95% confidence interval [CI]: 1.08 – 1.75), less marked than the crude estimate (HR = 1.74, 95% CI: 1.49 – 2.04). The other PS-based methods and not PS-based multivariable Cox model produced similar results.
After controlling for important confounding variables, HIV patients who had TB at the initiation of ART in Uganda had an approximate 37% increased hazard of overall mortality relative to non-TB patients.
Antiretroviral therapy; HIV; Tuberculosis; Propensity score methods; Uganda; Prospective cohort study
The standard approach to determine unique or shared genetic factors across populations is to identify risk alleles in one population and investigate replication in others. However, since populations differ in DNA sequence information, allele frequencies, effect sizes, and linkage disequilibrium patterns, SNP association using a uniform stringent threshold on p values may not be reproducible across populations. Here, we developed rank-based methods to investigate shared or population-specific loci and pathways for childhood asthma across individuals of diverse ancestry. We performed genome-wide association studies on 859,790 SNPs genotyped in 527 affected offspring trios of European, African, and Hispanic ancestry using publically available asthma database in the Genotypes and Phenotypes database.
Rank-based analyses showed that there are shared genetic factors for asthma across populations, more at the gene and pathway levels than at the SNP level. Although the top 1,000 SNPs were not shared, 11 genes (RYR2, PDE4D, CSMD1, CDH13, ROBO2, RBFOX1, PTPRD, NPAS3, PDE1C, SEMA5A, and CTNNA2) mapped by these SNPs were shared across populations. Ryanodine receptor 2 (RYR2, a statin response-related gene) showed the strongest association in European (p value = 2.55 × 10−7) and was replicated in African (2.57 × 10−4) and Hispanic (1.18 × 10−3) Americans. Imputation analyses based on the 1000 Genomes Project uncovered additional RYR2 variants associated with asthma. Network and functional ontology analyses revealed that RYR2 is an integral part of dermatological or allergic disorder biological networks, specifically in the functional classes involving inflammatory, eosinophilic, and respiratory diseases.
Our rank-based genome-wide analysis revealed for the first time an association of RYR2 variants with asthma and replicated previously discovered PDE4D asthma gene across human populations. The replication of top-ranked asthma genes across populations suggests that such loci are less likely to be false positives and could indicate true associations. Variants that are associated with asthma across populations could be used to identify individuals who are at high risk for asthma regardless of genetic ancestry.
Asthma; GWAS; Ancestry; Trans-ancestral analysis; Rank analysis; Imputation; dbGaP; 1000 Genomes project; Networks/pathways, RYR2
The effect of fructose on cardiometabolic risk in humans is controversial. We conducted a systematic review and meta-analysis of controlled feeding trials to clarify the effect of fructose on glycemic control in individuals with diabetes.
RESEARCH DESIGN AND METHODS
We searched MEDLINE, EMBASE, and the Cochrane Library (through 22 March 2012) for relevant trials lasting ≥7 days. Data were aggregated by the generic inverse variance method (random-effects models) and expressed as mean difference (MD) for fasting glucose and insulin and standardized MD (SMD) with 95% CI for glycated hemoglobin (HbA1c) and glycated albumin. Heterogeneity was assessed by the Cochran Q statistic and quantified by the I2 statistic. Trial quality was assessed by the Heyland methodological quality score (MQS).
Eighteen trials (n = 209) met the eligibility criteria. Isocaloric exchange of fructose for carbohydrate reduced glycated blood proteins (SMD −0.25 [95% CI −0.46 to −0.04]; P = 0.02) with significant intertrial heterogeneity (I2 = 63%; P = 0.001). This reduction is equivalent to a ∼0.53% reduction in HbA1c. Fructose consumption did not significantly affect fasting glucose or insulin. A priori subgroup analyses showed no evidence of effect modification on any end point.
Isocaloric exchange of fructose for other carbohydrate improves long-term glycemic control, as assessed by glycated blood proteins, without affecting insulin in people with diabetes. Generalizability may be limited because most of the trials were <12 weeks and had relatively low MQS (<8). To confirm these findings, larger and longer fructose feeding trials assessing both possible glycemic benefit and adverse metabolic effects are required.
It is not known whether children with acute promyelocytic leukemia (APL) have an infection risk similar to non- APL acute myeloid leukemia. The objective was to describe infectious risk in children with newly diagnosed APL and to describe factors associated with these infections.
We conducted a retrospective, population-based cohort study that included children ≤ 18 years of age with de novo APL treated at 15 Canadian centers. Thirty-three children with APL were included; 78.8% were treated with APL -specific protocols.
Bacterial sterile site infection occurred in 12 (36.4%) and fungal sterile site infection occurred in 2 (6.1%) children. Of the 127 chemotherapy courses, 101 (79.5%) were classified as intensive and among these, the proportion in which a sterile site microbiologically documented infection occurred was 14/101 (13.9%). There was one infection-related death.
One third of children with APL experienced at least one sterile site bacterial infection throughout treatment and 14% of intensive chemotherapy courses were associated with a microbiologically documented sterile site infection. Infection rates in pediatric APL may be lower compared to non- APL acute myeloid leukemia although these children may still benefit from aggressive supportive care during intensive chemotherapy.
Infection; Acute promyelocytic leukemia; Bacteremia; Sepsis; Acute myeloid leukemia
Basal-like breast cancers (BLBC) express a luminal progenitor gene signature. Notch receptor signaling promotes luminal cell fate specification in the mammary gland, while suppressing stem cell self-renewal. Here we show that deletion of Lfng, a sugar transferase that prevents Notch activation by Jagged ligands, enhances stem/progenitor cell proliferation. Mammary-specific deletion of Lfng induces basal-like and claudin-low tumors with accumulation of Notch intracellular domain fragments, increased expression of proliferation-associated Notch targets, amplification of the Met/Caveolin locus, and elevated Met and Igf-1R signaling. Human BL breast tumors, commonly associated with JAGGED expression, elevated MET signaling, and CAVEOLIN accumulation, express low levels of LFNG. Thus, reduced LFNG expression facilitates JAG/NOTCH luminal progenitor signaling and cooperates with MET/CAVEOLIN basal-type signaling to promote BLBC.
The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE)) and cluster-specific (i.e. random-effects logistic regression (RELR)) models for analyzing data from cluster randomized trials (CRTs) with missing binary responses.
In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI) and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE), and coverage probability.
GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small) is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF) <3; within-cluster MI for CRTs with VIF≥3 and cluster size>50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied.
GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.
Marginal model; Population-averaged model; Cluster-specific model; Multiple imputation; Cluster randomized trial; Covariate dependent missingness; Generalized estimating equations; Random-effects logistic regression
To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.
We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.
Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.
Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications.
Glycemic index (GI) and glycemic load (GL) have been associated with coronary heart disease (CHD) risk in some but not all cohort studies. We therefore assessed the association of GI and GL with CHD risk in prospective cohorts.
Methods and Results
We searched MEDLINE, EMBASE, and CINAHL (through April 5, 2012) and identified all prospective cohorts assessing associations of GI and GL with incidence of CHD. Meta-analysis of observational studies in epidemiology (MOOSE) methodologies were used. Relative measures of risk, comparing the group with the highest exposure (mean GI of cohorts=84.4 GI units, range 79.9 to 91; mean GL of cohorts=224.8, range 166 to 270) to the reference group (mean GI=72.3 GI units, range 68.1 to 77; mean GL=135.4, range 83 to 176), were pooled using random-effects models, expressed as relative risk (RR) with heterogeneity assessed by χ2 and quantified by I2. Subgroups included sex and duration of follow-up. Ten studies (n=240 936) were eligible. Pooled analyses showed an increase in CHD risk for the highest GI quantile compared with the lowest, with RR=1.11 (95% confidence interval [CI] 0.99 to 1.24) and for GL, RR=1.27 (95% CI 1.09 to 1.49), both with evidence of heterogeneity (I2>42%, P<0.07). Subgroup analyses revealed only a significant modification by sex, with the female cohorts showing significance for GI RR=1.26 (95% CI 1.12 to 1.41) and for GL RR=1.55 (95% CI 1.18 to 2.03).
High GI and GL diets were significantly associated with CHD events in women but not in men. Further studies are required to determine the relationship between GI and GL with CHD in men.
coronary heart disease; glycemic index and load; meta-analysis; nutrition; prospective cohort
Intraclass correlation coefficients (ICCs) are used in a wide range of applications. However, most commonly used estimators for the ICC are known to be subject to bias.
Using second order Taylor series expansion, we propose a new bias-corrected estimator for one type of intraclass correlation coefficient, for the ICC that arises in the context of the balanced one-way random effects model. A simulation study is performed to assess the performance of the proposed estimator. Data have been generated under normal as well as non-normal scenarios.
Our simulation results show that the new estimator has reduced bias compared to the least square estimator which is often referred to as the conventional or analytical estimator. The results also show marked bias reduction both in normal and non-normal data scenarios. In particular, our estimator outperforms the analytical estimator in a non-normal setting producing estimates that are very close to the true ICC values.
The proposed bias-corrected estimator for the ICC from a one-way random effects analysis of variance model appears to perform well in the scenarios we considered in this paper and can be used as a motivation to construct bias-corrected estimators for other types of ICCs that arise in more complex scenarios. It would also be interesting to investigate the bias-variance trade-off.
While there is some consensus on methods for investigating statistical and methodological heterogeneity, little attention has been paid to clinical aspects of heterogeneity. The objective of this study is to summarize and collate suggested methods for investigating clinical heterogeneity in systematic reviews.
We searched databases (Medline, EMBASE, CINAHL, Cochrane Library, and CONSORT, to December 2010) and reference lists and contacted experts to identify resources providing suggestions for investigating clinical heterogeneity between controlled clinical trials included in systematic reviews. We extracted recommendations, assessed resources for risk of bias, and collated the recommendations.
One hundred and one resources were collected, including narrative reviews, methodological reviews, statistical methods papers, and textbooks. These resources generally had a low risk of bias, but there was minimal consensus among them. Resources suggested that planned investigations of clinical heterogeneity should be made explicit in the protocol of the review; clinical experts should be included on the review team; a set of clinical covariates should be chosen considering variables from the participant level, intervention level, outcome level, research setting, or others unique to the research question; covariates should have a clear scientific rationale; there should be a sufficient number of trials per covariate; and results of any such investigations should be interpreted with caution.
Though the consensus was minimal, there were many recommendations in the literature for investigating clinical heterogeneity in systematic reviews. Formal recommendations for investigating clinical heterogeneity in systematic reviews of controlled trials are required.
Integration involves the coordination of services provided by autonomous agencies and improves the organization and delivery of multiple services for target patients. Current measures generally do not distinguish between agencies’ perception and expectation. We propose a method for quantifying the agencies’ service integration. Using the data from the Children’s Treatment Network (CTN), we aimed to measure the degree of integration for the CTN agencies in York and Simcoe.
Theory and methods
We quantified the integration by the agreement between perceived and expected levels of involvement and calculated four scores from different perspectives for each agency. We used the average score to measure the global network integration and examined the sensitivity of the global score.
Most agencies’ integration scores were <65%. As measured by the agreement between every other agency’s perception and expectation, the overall integration of CTN in Simcoe and York was 44% (95% CI: 39%–49%) and 52% (95% CI: 48%–56%), respectively. The sensitivity analysis showed that the global scores were robust.
Our method extends existing measures of integration and possesses a good extent of validity. We can also apply the method in monitoring improvement and linking integration with other outcomes.
integration measure; perception; expectation; collaboration agreement
Hyperuricemia is linked to gout and features of metabolic syndrome. There is concern that dietary fructose may increase uric acid concentrations. To assess the effects of fructose on serum uric acid concentrations in people with and without diabetes, we conducted a systematic review and meta-analysis of controlled feeding trials. We searched MEDLINE, EMBASE, and the Cochrane Library for relevant trials (through August 19, 2011). Analyses included all controlled feeding trials ≥7 d investigating the effect of fructose feeding on uric acid under isocaloric conditions, where fructose was isocalorically exchanged with other carbohydrate, or hypercaloric conditions, and where a control diet was supplemented with excess energy from fructose. Data were aggregated by the generic inverse variance method using random effects models and expressed as mean difference (MD) with 95% CI. Heterogeneity was assessed by the Q statistic and quantified by I2. A total of 21 trials in 425 participants met the eligibility criteria. Isocaloric exchange of fructose for other carbohydrate did not affect serum uric acid in diabetic and nondiabetic participants [MD = 0.56 μmol/L (95% CI: −6.62, 7.74)], with no evidence of inter-study heterogeneity. Hypercaloric supplementation of control diets with fructose (+35% excess energy) at extreme doses (213–219 g/d) significantly increased serum uric acid compared with the control diets alone in nondiabetic participants [MD = 31.0 mmol/L (95% CI: 15.4, 46.5)] with no evidence of heterogeneity. Confounding from excess energy cannot be ruled out in the hypercaloric trials. These analyses do not support a uric acid-increasing effect of isocaloric fructose intake in nondiabetic and diabetic participants. Hypercaloric fructose intake may, however, increase uric acid concentrations. The effect of the interaction of energy and fructose remains unclear. Larger, well-designed trials of fructose feeding at “real world” doses are needed.
In meta-regression, as the number of trials in the analyses decreases, the risk of false positives or false negatives increases. This is partly due to the assumption of normality that may not hold in small samples. Creation of a distribution from the observed trials using permutation methods to calculate P values may allow for less spurious findings. Permutation has not been empirically tested in meta-regression. The objective of this study was to perform an empirical investigation to explore the differences in results for meta-analyses on a small number of trials using standard large sample approaches verses permutation-based methods for meta-regression.
We isolated a sample of randomized controlled clinical trials (RCTs) for interventions that have a small number of trials (herbal medicine trials). Trials were then grouped by herbal species and condition and assessed for methodological quality using the Jadad scale, and data were extracted for each outcome. Finally, we performed meta-analyses on the primary outcome of each group of trials and meta-regression for methodological quality subgroups within each meta-analysis. We used large sample methods and permutation methods in our meta-regression modeling. We then compared final models and final P values between methods.
We collected 110 trials across 5 intervention/outcome pairings and 5 to 10 trials per covariate. When applying large sample methods and permutation-based methods in our backwards stepwise regression the covariates in the final models were identical in all cases. The P values for the covariates in the final model were larger in 78% (7/9) of the cases for permutation and identical for 22% (2/9) of the cases.
We present empirical evidence that permutation-based resampling may not change final models when using backwards stepwise regression, but may increase P values in meta-regression of multiple covariates for relatively small amount of trials.
Alzheimer's disease (AD) is common among older adults and leads to significant disability. Volatile anesthetic gases administered during general anesthesia (GA) have been hypothesized to be a risk factor for the development of AD. The objective of this study is to systematically review the association between exposure to GA and risk of AD.
We searched electronic databases including MEDLINE, Embase, and Google scholar for observational studies examining the association between exposure to GA and risk of AD. We examined study quality using a modified version of the Newcastle-Ottawa risk of bias assessment for observational studies. We used standard meta-analytic techniques to estimate pooled odds ratios (OR) and 95% confidence intervals (CI). Subgroup and sensitivity analyses were undertaken to evaluate the robustness of the findings.
A total of 15 case-control studies were included in the review. No cohort studies were identified that met inclusion criteria. There was variation in the methodological quality of included studies. There was no significant association between any exposure to GA and risk of AD (pooled OR: 1.05; 95% CI: 0.93 - 1.19, Z = 0.80, p = 0.43). There was also no significant association between GA and risk of AD in several subgroup and sensitivity analyses.
A history of exposure to GA is not associated with an increased risk of AD although there are few high-quality studies in this area. Prospective cohort studies with long-term follow-up or randomized controlled trials are required to further understand the association between GA and AD.
dementia; Alzheimer's disease; anesthesia; surgery; meta-analysis; systematic review
Infection of the CNS is considered to be the major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. Despite being identified worldwide as an important public health concern, studies on encephalitis are very few and often focus on particular types (with respect to causative agents) of encephalitis (e.g. West Nile, Japanese, etc.). Moreover, a number of other infectious and non-infectious conditions present with similar symptoms, and distinguishing encephalitis from other disguising conditions continues to a challenging task.
We used canonical correlation analysis (CCA) to assess associations between set of exposure variable and set of symptom and diagnostic variables in human encephalitis. Data consists of 208 confirmed cases of encephalitis from a prospective multicenter study conducted in the United Kingdom. We used a covariance matrix based on Gini's measure of similarity and used permutation based approaches to test significance of canonical variates.
Results show that weak pair-wise correlation exists between the risk factor (exposure and demographic) and symptom/laboratory variables. However, the first canonical variate from CCA revealed strong multivariate correlation (ρ = 0.71, se = 0.03, p = 0.013) between the two sets. We found a moderate correlation (ρ = 0.54, se = 0.02) between the variables in the second canonical variate, however, the value is not statistically significant (p = 0.68). Our results also show that a very small amount of the variation in the symptom sets is explained by the exposure variables. This indicates that host factors, rather than environmental factors might be important towards understanding the etiology of encephalitis and facilitate early diagnosis and treatment of encephalitis patients.
There is no standard laboratory diagnostic strategy for investigation of encephalitis and even experienced physicians are often uncertain about the cause, appropriate therapy and prognosis of encephalitis. Exploration of human encephalitis data using advanced multivariate statistical modelling approaches that can capture the inherent complexity in the data is, therefore, crucial in understanding the causes of human encephalitis. Moreover, application of multivariate exploratory techniques will generate clinically important hypotheses and offer useful insight into the number and nature of variables worthy of further consideration in a confirmatory statistical analysis.
The timely provision of critical care to hospitalised patients at risk for cardiopulmonary arrest is contingent upon identification and referral by frontline providers. Current approaches require improvement. In a single-centre study, we developed the Bedside Paediatric Early Warning System (Bedside PEWS) score to identify patients at risk. The objective of this study was to validate the Bedside PEWS score in a large patient population at multiple hospitals.
We performed an international, multicentre, case-control study of children admitted to hospital inpatient units with no limitations on care. Case patients had experienced a clinical deterioration event involving either an immediate call to a resuscitation team or urgent admission to a paediatric intensive care unit. Control patients had no events. The scores ranged from 0 to 26 and were assessed in the 24 hours prior to the clinical deterioration event. Score performance was assessed using the area under the receiver operating characteristic (AUCROC) curve by comparison with the retrospective rating of nurses and the temporal progression of scores in case patients.
A total of 2,074 patients were evaluated at 4 participating hospitals. The median (interquartile range) maximum Bedside PEWS scores for the 12 hours ending 1 hour before the clinical deterioration event were 8 (5 to 12) in case patients and 2 (1 to 4) in control patients (P < 0.0001). The AUCROC curve (95% confidence interval) was 0.87 (0.85 to 0.89). In case patients, mean scores were 5.3 at 20 to 24 hours and 8.4 at 0 to 4 hours before the event (P < 0.0001). The AUCROC curve (95% CI) of the retrospective nurse ratings was 0.83 (0.81 to 0.86). This was significantly lower than that of the Bedside PEWS score (P < 0.0001).
The Bedside PEWS score identified children at risk for cardiopulmonary arrest. Scores were elevated and continued to increase in the 24 hours before the clinical deterioration event. Prospective clinical evaluation is needed to determine whether this score will improve the quality of care and patient outcomes.
Several randomized, controlled trials (RCTs) have tested strategies to prevent sexual acquisition of HIV infection, but their quality has been variable. We aimed to identify, describe, and evaluate the quality of RCTs studying biomedical interventions to prevent HIV acquisition by sexual transmission.
We conducted a systematic review to identify all RCTs evaluating the efficacy of biomedical HIV prevention interventions. We assessed seven generic and content-specific quality components important in HIV prevention trials, factors influencing study power, co-interventions provided, and trial ethics.
We identified 26 eligible RCTs. The median number of quality components judged to be inadequate or unclear was 3 (range, 1-4) in 1992-1998, 3 (range, 1-4) in 1999-2003, and 0 (range 0-2) in 2004-2008 (p < 0.001). Common problems that may have biased results included low retention (median 84%), poor adherence to interventions requiring ongoing use (median ≤78%), and lower HIV incidence than expected a priori (in 8 of 11 trials where evaluable).
Reporting of trials of biomedical HIV prevention interventions has improved over time. However, quality improvement is needed in several key areas that influence study power, including participant retention, adherence to interventions, and estimation of expected HIV incidence.
HIV; primary prevention; clinical trial; systematic review; research methodology; statistical bias
Encephalitis is an acute clinical syndrome of the central nervous system (CNS), often associated with fatal outcome or permanent damage, including cognitive and behavioural impairment, affective disorders and epileptic seizures. Infection of the central nervous system is considered to be a major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. However, a large proportion of cases have unknown disease etiology.
We perform hierarchical cluster analysis on a multicenter England encephalitis data set with the aim of identifying sub-groups in human encephalitis. We use the simple matching similarity measure which is appropriate for binary data sets and performed variable selection using cluster heatmaps. We also use heatmaps to visually assess underlying patterns in the data, identify the main clinical and laboratory features and identify potential risk factors associated with encephalitis.
Our results identified fever, personality and behavioural change, headache and lethargy as the main characteristics of encephalitis. Diagnostic variables such as brain scan and measurements from cerebrospinal fluids are also identified as main indicators of encephalitis. Our analysis revealed six major clusters in the England encephalitis data set. However, marked within-cluster heterogeneity is observed in some of the big clusters indicating possible sub-groups. Overall, the results show that patients are clustered according to symptom and diagnostic variables rather than causal agents. Exposure variables such as recent infection, sick person contact and animal contact have been identified as potential risk factors.
It is in general assumed and is a common practice to group encephalitis cases according to disease etiology. However, our results indicate that patients are clustered with respect to mainly symptom and diagnostic variables rather than causal agents. These similarities and/or differences with respect to symptom and diagnostic measurements might be attributed to host factors. The idea that characteristics of the host may be more important than the pathogen is also consistent with the observation that for some causes, such as herpes simplex virus (HSV), encephalitis is a rare outcome of a common infection.
Because of blood lipid concerns, diabetes associations discourage fructose at high intakes. To quantify the effect of fructose on blood lipids in diabetes, we conducted a systematic review and meta-analysis of experimental clinical trials investigating the effect of isocaloric fructose exchange for carbohydrate on triglycerides, total cholesterol, LDL cholesterol, and HDL cholesterol in type 1 and 2 diabetes.
RESEARCH DESIGN AND METHODS
We searched MEDLINE, EMBASE, CINAHL, and the Cochrane Library for relevant trials of ≥7 days. Data were pooled by the generic inverse variance method and expressed as standardized mean differences with 95% CI. Heterogeneity was assessed by χ2 tests and quantified by I2. Meta-regression models identified dose threshold and independent predictors of effects.
Sixteen trials (236 subjects) met the eligibility criteria. Isocaloric fructose exchange for carbohydrate raised triglycerides and lowered total cholesterol under specific conditions without affecting LDL cholesterol or HDL cholesterol. A triglyceride-raising effect without heterogeneity was seen only in type 2 diabetes when the reference carbohydrate was starch (mean difference 0.24 [95% CI 0.05–0.44]), dose was >60 g/day (0.18 [0.00–0.37]), or follow-up was ≤4 weeks (0.18 [0.00–0.35]). Piecewise meta-regression confirmed a dose threshold of 60 g/day (R2 = 0.13)/10% energy (R2 = 0.36). A total cholesterol–lowering effect without heterogeneity was seen only in type 2 diabetes under the following conditions: no randomization and poor study quality (−0.19 [−0.34 to −0.05]), dietary fat >30% energy (−0.33 [−0.52 to −0.15]), or crystalline fructose (−0.28 [−0.47 to −0.09]). Multivariate meta-regression analyses were largely in agreement.
Pooled analyses demonstrated conditional triglyceride-raising and total cholesterol–lowering effects of isocaloric fructose exchange for carbohydrate in type 2 diabetes. Recommendations and large-scale future trials need to address the heterogeneity in the data.
With rapid advances in genotyping technologies in recent years and the growing number of available markers, genome-wide association studies are emerging as promising approaches for the study of complex diseases and traits. However, there are several challenges with analysis and interpretation of such data. First, there is a massive multiple testing problem due to the large number of markers that need to be analyzed, leading to an increased risk of false positives and decreased ability for association studies to detect truly associated markers. In particular, the ability to detect modest genetic effects can be severely compromised. Second, a genetic association of a given single-nucleotide polymorphism as determined by univariate statistical analyses does not typically explain biologically interesting features and often requires subsequent interpretation using a higher unit such as a gene or region, for example as defined by haplotype blocks. Third, missing genotypes in the data set and other data quality issues can pose challenges when comparisons across platforms and replications are planned. Finally, depending on the type of univariate analysis, computational burden can arise as the number of markers continues to grow into the millions. One way to deal with these and related challenges is to consider higher units for the analysis such as genes or regions. This paper summarizes analytical methods and strategies that have been proposed and applied by Group 16 to two genome-wide association data sets made available through the Genetic Analysis Workshop 16.
rheumatoid arthritis; case-control data; family-based study
Objective To determine the relation between overweight and obesity in mothers and preterm birth and low birth weight in singleton pregnancies in developed and developing countries.
Design Systematic review and meta-analyses.
Data sources Medline and Embase from their inceptions, and reference lists of identified articles.
Study selection Studies including a reference group of women with normal body mass index that assessed the effect of overweight and obesity on two primary outcomes: preterm birth (before 37 weeks) and low birth weight (<2500 g).
Data extraction Two assessors independently reviewed titles, abstracts, and full articles, extracted data using a piloted data collection form, and assessed quality.
Data synthesis 84 studies (64 cohort and 20 case-control) were included, totalling 1 095 834 women. Although the overall risk of preterm birth was similar in overweight and obese women and women of normal weight, the risk of induced preterm birth was increased in overweight and obese women (relative risk 1.30, 95% confidence interval 1.23 to 1.37). Although overall the risk of having an infant of low birth weight was decreased in overweight and obese women (0.84, 0.75 to 0.95), the decrease was greater in developing countries than in developed countries (0.58, 0.47 to 0.71 v 0.90, 0.79 to 1.01). After accounting for publication bias, the apparent protective effect of overweight and obesity on low birth weight disappeared with the addition of imputed “missing” studies (0.95, 0.85 to 1.07), whereas the risk of preterm birth appeared significantly higher in overweight and obese women (1.24, 1.13 to 1.37).
Conclusions Overweight and obese women have increased risks of preterm birth and induced preterm birth and, after accounting for publication bias, appeared to have increased risks of preterm birth overall. The beneficial effects of maternal overweight and obesity on low birth weight were greater in developing countries and disappeared after accounting for publication bias.
This study was designed to test the hypothesis that fetal exposure to corticosteroids in the antenatal period is an independent risk factor for the development of asthma in early childhood with little or no effect in later childhood. A population-based cohort study of all pregnant women who resided in Nova Scotia, Canada, and gave birth to a singleton fetus between 1989 and 1998
was undertaken. After a priori specified exclusions, 80,448 infants were available for analysis.
Using linked health care utilization records, incident asthma cases developed after 36 months of
age were identified. Extended Cox proportional hazards models were used to estimate hazard
ratios while controlling for confounders. Exposure to corticosteroids during pregnancy was
associated with a risk of asthma in childhood between 3–5 years of age: adjusted hazard ratio of
1.19 (95% confidence interval: 1.03, 1.39), with no association noted after 5 years of age:
adjusted hazard ratio for 5–7 years was 1.06 (95% confidence interval: 0.86, 1.30)
and for 8 or greater years was 0.74 (95% confidence interval: 0.54, 1.03). Antenatal steroid therapy appears to be an independent risk factor for the development of asthma between 3 and 5 years of age.
We propose the use of latent growth curve model to assess the influence of genetic, environmental, demographic, and lifestyle factors on multiple phenotypes related to coronary heart disease. We model four quantitative traits (systolic blood pressure, high-density lipoprotein, low-density lipoprotein, and triglycerides) simultaneously in a multivariate framework that allows us to study their change over time, assess individual variation, and investigate cross-phenotype relationships. Environmental, demographic, and lifestyle covariates are included at different levels of the model as time-varying or time-invariant, as appropriate. To investigate the change over time attributed to genetic factors, we use candidate markers that have previously been shown to be associated with the quantitative traits. We illustrate our approach using independent observations from the offspring cohort of the Framingham Heart Study data.