Meta-analysis under imbalance in measurement of confounders in cohort studies using only summary-level data.

BACKGROUND: Cohort collaborations often require meta-analysis of exposure-outcome association estimates across cohorts as an alternative to pooling individual-level data that requires a laborious process of data harmonization on individual-level data. However, it is likely that important confounders are not all measured uniformly across the cohorts due to differences in study protocols. This imbalance in measurement of confounders leads to association estimates that are not comparable across cohorts and impedes the meta-analysis of results. METHODS: In this article, we empirically show some asymptotic relations between fully adjusted and unadjusted exposure-outcome effect estimates, and provide theoretical justification for the same. We leverage these results to obtain fully adjusted estimates for the cohorts with no information on confounders by borrowing information from cohorts with complete measurement on confounders. We implement this novel method in CIMBAL (confounder imbalance), which additionally provides a meta-analyzed estimate that appropriately accounts for the dependence between estimates arising due to borrowing of information across cohorts. We perform extensive simulation experiments to study CIMBAL's statistical properties. We illustrate CIMBAL using National Children's Study (NCS) data to estimate association of maternal education and low birth weight in infants, adjusting for maternal age at delivery, race/ethnicity, marital status, and income. RESULTS: Our simulation studies indicate that estimates of exposure-outcome association from CIMBAL are closer to the truth than those from commonly-used approaches for meta-analyzing cohorts with disparate confounder measurements. CIMBAL is not too sensitive to heterogeneity in underlying joint distributions of exposure, outcome and confounders but is very sensitive to heterogeneity of confounding bias across cohorts. Application of CIMBAL to NCS data for a proof-of-concept analysis further illustrates the utility and advantages of CIMBAL. CONCLUSIONS: CIMBAL provides a practical approach for meta-analyzing cohorts with imbalance in measurement of confounders under a weak assumption that the cohorts are independently sampled from populations with the same confounding bias.

Multivariate analysis of the impact of weather and air pollution on emergency department visits for unprovoked seizure among children: A retrospective clinical observational study.

BACKGROUND: An unprovoked seizure is a seizure or a cluster of seizures occurring within 24 h in a patient older than 1 month of age without precipitating factors. Recent studies have reported that extrinsic factors, such as meteorological conditions and air pollutants, may be important in seizure occurrence. Thus, this study aimed to examine the association between the number of visits to the emergency department (ED) by children for nighttime unprovoked seizures and exposure to multi-faceted factors, such as meteorological conditions and air pollution. METHODS: We conducted a clinical observational analysis and reviewed consecutive patients younger than 16 years of age who visited the primary ED center in Kobe City, Japan, during nighttime (7:30 p.m.-7:00 a.m.) between January 1, 2011 and December 31, 2015. We investigated the effects of meteorological factors and air pollutants on the number of patients with unprovoked seizures using multivariate analysis of Poisson regression estimates. RESULTS: In total, 151,119 children visited the ED, out of which 97 patients presented with unprovoked seizures. The mean age of the patients was 4.7 years (range, 1 month to 15.3 years), and 54.6% of them were boys. The total number of patients with unprovoked seizures showed no significant changes with the seasons; however, there were dominant peaks during the fall and fewer visits during the summer. The multivariate analysis of Poisson regression estimates revealed a significant positive relationship between the number of patients presenting with unprovoked seizures and precipitation (+1 patient/87 mm; p = 0.03) and methane (+1 patient/0.14 ppm; p = 0.03) levels and a negative relationship between the number of patients presenting with unprovoked seizures and nitrogen dioxide level (-1 patient/0.02 ppm; p = 0.04). CONCLUSIONS: The present study is the first to evaluate the association between the number of children who presented to the ED with nighttime unprovoked seizures and environmental factors after controlling for confounding factors.