Multilevel analyses of related public health indicators: The European Surveillance of Congenital Anomalies (EUROCAT) Public Health Indicators

BACKGROUND:Public health organisations use public health indicators to guide health policy. Joint analysis of multiple public health indicators can provide a more comprehensive understanding of what they are intended to evaluate. OBJECTIVE:To analyse variaitons in the prevalence of congenital anomaly-related perinatal mortality attributable to termination of pregnancy for foetal anomaly (TOPFA) and prenatal diagnosis of congenital anomaly prevalence. METHODS:We included 55 363 cases of congenital anomalies notified to 18 EUROCAT registers in 10 countries during 2008-12. Incidence rate ratios (IRR) representing the risk of congenital anomaly-related perinatal mortality according to TOPFA and prenatal diagnosis prevalence were estimated using multilevel Poisson regression with country as a random effect. Between-country variation in congenital anomaly-related perinatal mortality was measured using random effects and compared between the null and adjusted models to estimate the percentage of variation in congenital anomaly-related perinatal mortality accounted for by TOPFA and prenatal diagnosis. RESULTS:The risk of congenital anomaly-related perinatal mortality decreased as TOPFA and prenatal diagnosis prevalence increased (IRR 0.79, 95% confidence interval [CI] 0.72, 0.86; and IRR 0.88, 95% CI 0.79, 0.97). Modelling TOPFA and prenatal diagnosis together, the association between congenital anomaly-related perinatal mortality and TOPFA prevalence became stronger (RR 0.70, 95% CI 0.61, 0.81). The prevalence of TOPFA and prenatal diagnosis accounted for 75.5% and 37.7% of the between-country variation in perinatal mortality, respectively. CONCLUSION:We demonstrated an approach for analysing inter-linked public health indicators. In this example, as TOPFA and prenatal diagnosis of congenital anomaly prevalence decreased, the risk of congenital anomaly-related perinatal mortality increased. Much of the between-country variation in congenital anomaly-related perinatal mortality was accounted for by TOPFA, with a smaller proportion accounted for by prenatal diagnosis

Mediation analysis.

<p>Microarray analysis was used to identify gene expression markers that are associated with both As exposure and birth weight. Subsequently, a stepwise approach <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092677#pone.0092677-Baron1" target="_blank">[38]</a> was followed to investigate the mediating effects of the candidate gene expression markers in the association between As exposure and birth weight by linear regression models. The method is described for hypothetical marker gene ‘X’. To demonstrate mediation, four requirements must be met: Model 1) Birth weight (dependent variable) should be associated with As exposure (independent variable); model 2) The expression of gene ‘X’ (mediator) should be associated with As exposure; model 3) Birth weight should be associated with the expression of gene ‘X’; and model 4) the expression of gene ‘X’ should be a significant predictor of birth weight, while controlling for As exposure. The estimated change in birth weight related to As exposure in model 4 should be less compared to model 1 to demonstrate partial mediation, and drop to zero to demonstrate full mediation.</p

qPCR analysis of sFLT1.

<p>A) Confirmation of gene expression of sFLT1 (NM_001159920) as measured by microarray with by qPCR for a subset of 30 samples. For each sample, the expression of <i>sFLT1</i> (was calculated as ratio to one given sample (reference sample, open square) and subsequently logarithmically transformed (log2 scale). The Pearson correlation coefficient equals = 0.64 (p = 1.33E−04). B) Correlation between two transcript variants of sFLT1 (NM_001159920 and NM_001160030, N = 30). The pearson correlation coefficient equals 0.87 (p-value = 2.55E−04).</p

Genes associated to ‘embryonal growth’.

<p>This figure shows the potential of genes related to embryonal growth (N = 156) to link the level of As exposure to decreased birth weight for boys (A) and girls (B) separately. For each of the sequences, the significance (−log10(PValue)) of differential regulation between ‘high’ and ‘low’ As is plotted on the horizontal axis, and between ‘low’ and ‘high’ birth weight is plotted on the vertical axis. The direction of regulation (up or down) is included in the plot. The 1^st quadrant is composed of transcripts that show higher expression among the group of high As and the group of low birth weight as compared to the group of low As and the group of high birth weight, respectively. Making the same comparisons, transcripts that show lower expression can be found in the 3^rd quadrant. The dashed threshold lines represent p-value equal to 0.05. For transcripts of which the p-value is smaller than 0.05 in either one of the comparisons, the corresponding gene symbol is included in the plot.</p

Correlation between the level of arsenic measured in cord blood and maternal blood.

<p>The Pearson correlation coefficient r between the natural logarithm (ln-transformed) of the As values in cord blood (horizontal axis) and maternal blood (vertical axis) equals 0.82 (N = 177, p-value <0.0001). The vertical and horizontal dotted line correspond to the geometric mean of cord blood and maternal blood As levels respectively. For downstream analysis, the level of As in cord blood was categorized, i.e. ‘low As’ (filled squares): samples for which As level smaller than geometric mean minus standard deviation; ‘high As’ (filled triangles): higher than geometric mean plus standard deviation; ‘median As’ (open circles): all samples in between.</p

<i>sFLT1</i> gene expression as a mediator in the association between arsenic exposure and birth weight.

<p>N = number of subjects; CI = Confidence Interval; SGA = Small for Gestational Age; g = gram;</p><p>* = p-value <0.05; High arsenic = Arsenic level in cord blood above geomean plus standard deviation (N = 31); Median or low arsenic: arsenic level in cord blood below geomean plus standard deviation (N = 152).</p><p>Interpretation of the estimate (β): <b>Model 1)</b> Difference in birth weight between high arsenic exposed and low to median exposed newborns; <b>Model 2)</b> Log2(fold change) in sFLT1 expression between high arsenic exposed and low to median exposed newborns; <b>Model 3)</b> Difference in birth weight for a duplication in sFLT1 expression; <b>Model 4)</b> For sFLT1: Difference in birth weight for a duplication in sFLT1 expression - For arsenic: Difference in birth weight between high arsenic exposed and low to median exposed newborns.</p><p>The associations in model 1 were adjusted for gender, gestational age, smoking during pregnancy, and parity; The associations in model 2 were adjusted for gender and gestational age; The associations in model 3 and 4 were adjusted for gender, gestational age, smoking during pregnancy, parity, and the interaction between gender and sFLT1.</p

Median amount (25th–75th percentile) of volatile and non-volatile PAHs and benzo<i>(a)</i>pyrene found in house dust (ng/g dust).

<p>Median amount (25th–75th percentile) of volatile and non-volatile PAHs and benzo<i>(a)</i>pyrene found in house dust (ng/g dust).</p

Estimated change (95% CI) in mitochondrial DNA (mtDNA) content in association with PAHs exposure.

a<p>Percentage was calculated for each doubling in PAHs exposure (based on a model with log PAH and log mtDNA-content).</p

Characteristics of selected genes for qPCR.

*<p>Primer efficiency was determined in two different experiments.</p><p>Mitochondrial encoded NADH dehydrogenase 1 (ND-1; Beta actin (β-actin); Acidic ribosomal phosphoprotein P0 (36B4).</p

Characteristics of the study population stratified for winter and summer.

<p>Data are presented as number (%) or arithmetic mean ±SD.</p>*<p>Frequency of consumption of grilled food: data for one person, winter missing.</p