A simulation study of three methods for affecting disease clusters

Background Cluster detection is an important part of spatial epidemiology because it can help identifying environmental factors associated with disease and thus guide investigation of the aetiology of diseases. In this article we study three methods suitable for detecting local spatial clusters: (1) a spatial scan statistic (SaTScan), (2) generalized additive models (GAM) and (3) Bayesian disease mapping (BYM). We conducted a simulation study to compare the methods. Seven geographic clusters with different shapes were initially chosen as high-risk areas. Different scenarios for the magnitude of the relative risk of these areas as compared to the normal risk areas were considered. For each scenario the performance of the methods were assessed in terms of the sensitivity, specificity, and percentage correctly classified for each cluster. Results The performance depends on the relative risk, but all methods are in general suitable for identifying clusters with a relative risk larger than 1.5. However, it is difficult to detect clusters with lower relative risks. The GAM approach had the highest sensitivity, but relatively low specificity leading to an overestimation of the cluster area. Both the BYM and the SaTScan methods work well. Clusters with irregular shapes are more difficult to detect than more circular clusters. Conclusion Based on our simulations we conclude that the methods differ in their ability to detect spatial clusters. Different aspects should be considered for appropriate choice of method such as size and shape of the assumed spatial clusters and the relative importance of sensitivity and specificity. In general, the BYM method seems preferable for local cluster detection with relatively high relative risks whereas the SaTScan method appears preferable for lower relative risks. The GAM method needs to be tuned (using cross-validation) to get satisfactory results

Maternal Anxiety and Infants Birthweight and Length of Gestation. A sibling design

Background The overall aim of this study is to examine the effect of prenatal maternal anxiety on birthweight and gestational age, controlling for shared family confounding using a sibling comparison design. Methods The data on 77,970 mothers and their 91,165 children from the population-based Mother, Father and Child Cohort Study and data on 12,480 pairs of siblings were used in this study. The mothers filled out questionnaires for each unique pregnancy, at 17th and 30th week in pregnancy. Gestational age and birth weight was extracted from the Medical Birth Registry of Norway (MBRN). Associations between prenatal maternal anxiety (measured across the 17th and 30th weeks) and birth outcomes (birthweight and gestational age) were examined using linear regression with adjustment for shared-family confounding in a sibling comparison design. Results In the population level analysis the maternal anxiety score during pregnancy was inversely associated with new-born’s birthweight (Beta = -63.8 95% CI: -92.6, -35.0) and gestational age (Beta = -1.52, 95% CI: -2.15, -0.89) after adjustment for several covariates. The association of the maternal anxiety score with birthweight was no longer significant, but remained for maternal anxiety at 30th week with gestational age (Beta = -1.11, 95% CI: -1.82, -0.4) after further adjusting for the shared-family confounding in the sibling comparison design. Conclusion No association was found for maternal prenatal anxiety with birth weight after multiple covariates and family environment were controlled. However, there was an association between prenatal maternal anxiety at 30th week only with gestational age, suggesting a timing effect for maternal anxiety in third trimester

The blood microbiome and its association to cardiovascular disease mortality: case-cohort study

Background Little is known about the association between bacterial DNA in human blood and the risk of cardiovascular disease (CVD) mortality. Methods A case-cohort study was performed based on a 9 ½ year follow-up of the Oslo II study from 2000. Eligible for this analysis were men born in 1923 and from 1926 to 1932. The cases were men (n = 227) who had died from CVD, and the controls were randomly selected participants from the same cohort (n = 178). Analysis of the bacterial microbiome was performed on stored frozen blood samples for both cases and controls. Association analyses for CVD mortality were performed by Cox proportional hazard regression adapted to the case-cohort design. We used the Bonferroni correction due to the many bacterial genera that were identified. Results Bacterial DNA was identified in 372 (82%) of the blood samples and included 78 bacterial genera from six phyla. Three genera were significantly associated with CVD mortality. The genera Kocuria (adjusted hazard ratio (HR) 8.50, 95% confidence interval (CI) (4.05, 17.84)) and Enhydrobacter (HR 3.30 (2.01, 5.57)) indicate an association with CVD mortality with increasing levels. The genera Paracoccus (HR 0.29 (0.15, 0.57)) was inversely related. Significant predictors of CVD mortality were: the feeling of bad health; and the consumption of more than three cups of coffee per day. The following registered factors were borderline significant, namely: a history of heart failure; increased systolic blood pressure; and currently taking antihypertensive drugs now, versus previously. Conclusions The increasing levels of two bacterial genera Kocuria (skin and oral) and Enhydrobacter (skin) and low levels of Paracoccus (soil) were associated with CVD mortality independent of known risk factors for CVD

Adjusting for unmeasured confounding using validation data: Simplified two-stage calibration for survival and dichotomous outcomes

In epidemiology, one typically wants to estimate the risk of an outcome associated with an exposure after adjusting for confounders. Sometimes, outcome and exposure and maybe some confounders are available in a large data set, whereas some important confounders are only available in a validation data set that is typically a subset of the main data set. A generally applicable method in this situation is the two-stage calibration (TSC) method. We present a simplified easy-to-implement version of the TSC for the case where the validation data are a subset of the main data. We compared the simplified version to the standard TSC version for incidence rate ratios, odds ratios, relative risks, and hazard ratios using simulated data, and the simplified version performed better than our implementation of the standard version. The simplified version was also tested on real data and performed well

Adjusting for unmeasured confounding using validation data: Simplified two-stage calibration for survival and dichotomous outcomes

In epidemiology, one typically wants to estimate the risk of an outcome associated with an exposure after adjusting for confounders. Sometimes, outcome and exposure and maybe some confounders are available in a large data set, whereas some important confounders are only available in a validation data set that is typically a subset of the main data set. A generally applicable method in this situation is the two-stage calibration (TSC) method. We present a simplified easy-to-implement version of the TSC for the case where the validation data are a subset of the main data. We compared the simplified version to the standard TSC version for incidence rate ratios, odds ratios, relative risks, and hazard ratios using simulated data, and the simplified version performed better than our implementation of the standard version. The simplified version was also tested on real data and performed well