Association of Infant Eczema with Childhood and Adult Asthma: Analysis of Data from the 1958 Birth Cohort Study.

The influence of early life exposures on later life disease has for some time provided clues to modifiable risk factors of disease. The “atopic march” is thought to play a role in the progression of allergic diseases and may offer an opportunity to lower asthma’s health and socioeconomic burden, although evidence remains controversial. We aimed to examine the relationship between early life eczema and asthma later in life. Using the National Child Development Study, we examined infant eczema and childhood and adult asthma. Data related to asthma or wheezing bronchitis were available for 13,503 (73%; 95% CI 72⁻74), 11,503 (61%; 95% CI 60⁻61), 12,524 (68%; 95% CI 67⁻69), 11,194 (60%; 95% CI 60⁻60), 9377 (51%; 95% CI 51⁻51), and 9760 (53%; 95% CI 52⁻53) subjects at ages 11, 16, 23, 33, 44, and 50 years, respectively. Logistic regression models were fitted to examine each wave separately before and after adjusting for a range of potential confounders. Generalised estimating equation (GEE) methods were undertaken to examine the associations after pooling all data from questionnaires. The prevalence of self-reported asthma in those that had previously reported infant eczema ranged from 1.0%; 95% CI 0.9⁻1.4 (age 44 years) to 2.2%; 95% CI 2.1⁻2.3 (age 33 years). Participants with infant eczema had a 2⁻3-fold increased risk of reporting asthma in childhood and adulthood; this was 1.6 times at age 44 years when using spirometry measures. Similar effect sizes were observed in the GEE models when considering all participants (OR 2.9; 95% CI 2.6⁻3.2). Childhood and adult asthma were consistently associated with infant eczema both by using the self-reported data and lung measures.

High urinary tungsten concentration is associated with stroke in the National Health and Nutrition Examination Survey 1999-2010.

BACKGROUND: In recent years there has been an exponential increase in tungsten demand, potentially increasing human exposure to the metal. Currently, the toxicology of tungsten is poorly understood, but mounting evidence suggests that both the elemental metal and its alloys have cytotoxic effects. Here, we investigate the association between tungsten and cardiovascular disease (CVD) or stroke using six waves of the National Health and Nutrition Examination Survey (NHANES). METHODS: We investigated associations using crude and adjusted logistic regression models in a cohort of 8614 adults (18-74 years) with 193 reported stroke diagnoses and 428 reported diagnoses of CVD. We also stratified our data to characterize associations in a subset of younger individuals (18-50 years). RESULTS: Elevated tungsten concentrations were strongly associated with an increase in the prevalence of stroke, independent of typical risk factors (Odds Ratio (OR): 1.66, 95% Confidence Interval (95% CI): 1.17, 2.34). The association between tungsten and stroke in the young age category was still evident (OR: 2.17, 95% CI: 1.33, 3.53). CONCLUSION: This study represents the most comprehensive analysis of the human health effects of tungsten to date. Individuals with higher urinary tungsten concentrations have double the odds of reported stroke. We hypothesize that the pathological pathway resulting from tungsten exposure may involve oxidative stress.

Individual participant data meta-analyses should not ignore clustering.

OBJECTIVES: Individual participant data (IPD) meta-analyses often analyze their IPD as if coming from a single study. We compare this approach with analyses that rather account for clustering of patients within studies. STUDY DESIGN AND SETTING: Comparison of effect estimates from logistic regression models in real and simulated examples. RESULTS: The estimated prognostic effect of age in patients with traumatic brain injury is similar, regardless of whether clustering is accounted for. However, a family history of thrombophilia is found to be a diagnostic marker of deep vein thrombosis [odds ratio, 1.30; 95% confidence interval (CI): 1.00, 1.70; P = 0.05] when clustering is accounted for but not when it is ignored (odds ratio, 1.06; 95% CI: 0.83, 1.37; P = 0.64). Similarly, the treatment effect of nicotine gum on smoking cessation is severely attenuated when clustering is ignored (odds ratio, 1.40; 95% CI: 1.02, 1.92) rather than accounted for (odds ratio, 1.80; 95% CI: 1.29, 2.52). Simulations show models accounting for clustering perform consistently well, but downwardly biased effect estimates and low coverage can occur when ignoring clustering. CONCLUSION: Researchers must routinely account for clustering in IPD meta-analyses; otherwise, misleading effect estimates and conclusions may arise.

Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?

BACKGROUND: A fundamental aspect of epidemiological studies concerns the estimation of factor-outcome associations to identify risk factors, prognostic factors and potential causal factors. Because reliable estimates for these associations are important, there is a growing interest in methods for combining the results from multiple studies in individual participant data meta-analyses (IPD-MA). When there is substantial heterogeneity across studies, various random-effects meta-analysis models are possible that employ a one-stage or two-stage method. These are generally thought to produce similar results, but empirical comparisons are few. OBJECTIVE: We describe and compare several one- and two-stage random-effects IPD-MA methods for estimating factor-outcome associations from multiple risk-factor or predictor finding studies with a binary outcome. One-stage methods use the IPD of each study and meta-analyse using the exact binomial distribution, whereas two-stage methods reduce evidence to the aggregated level (e.g. odds ratios) and then meta-analyse assuming approximate normality. We compare the methods in an empirical dataset for unadjusted and adjusted risk-factor estimates. RESULTS: Though often similar, on occasion the one-stage and two-stage methods provide different parameter estimates and different conclusions. For example, the effect of erythema and its statistical significance was different for a one-stage (OR = 1.35, [Formula: see text]) and univariate two-stage (OR = 1.55, [Formula: see text]). Estimation issues can also arise: two-stage models suffer unstable estimates when zero cell counts occur and one-stage models do not always converge. CONCLUSION: When planning an IPD-MA, the choice and implementation (e.g. univariate or multivariate) of a one-stage or two-stage method should be prespecified in the protocol as occasionally they lead to different conclusions about which factors are associated with outcome. Though both approaches can suffer from estimation challenges, we recommend employing the one-stage method, as it uses a more exact statistical approach and accounts for parameter correlation.

Individual participant data meta-analysis of prognostic factor studies: state of the art?

BACKGROUND: Prognostic factors are associated with the risk of a subsequent outcome in people with a given disease or health condition. Meta-analysis using individual participant data (IPD), where the raw data are synthesised from multiple studies, has been championed as the gold-standard for synthesising prognostic factor studies. We assessed the feasibility and conduct of this approach. METHODS: A systematic review to identify published IPD meta-analyses of prognostic factors studies, followed by detailed assessment of a random sample of 20 articles published from 2006. Six of these 20 articles were from the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials in traumatic brain injury) collaboration, for which additional information was also used from simultaneously published companion papers. RESULTS: Forty-eight published IPD meta-analyses of prognostic factors were identified up to March 2009. Only three were published before 2000 but thereafter a median of four articles exist per year, with traumatic brain injury the most active research field. Availability of IPD offered many advantages, such as checking modelling assumptions; analysing variables on their continuous scale with the possibility of assessing for non-linear relationships; and obtaining results adjusted for other variables. However, researchers also faced many challenges, such as large cost and time required to obtain and clean IPD; unavailable IPD for some studies; different sets of prognostic factors in each study; and variability in study methods of measurement. The IMPACT initiative is a leading example, and had generally strong design, methodological and statistical standards. Elsewhere, standards are not always as high and improvements in the conduct of IPD meta-analyses of prognostic factor studies are often needed; in particular, continuous variables are often categorised without reason; publication bias and availability bias are rarely examined; and important methodological details and summary results are often inadequately reported. CONCLUSIONS: IPD meta-analyses of prognostic factors are achievable and offer many advantages, as displayed most expertly by the IMPACT initiative. However such projects face numerous logistical and methodological obstacles, and their conduct and reporting can often be substantially improved.