Incidence and Determinants of Ventilation Tubes in Denmark

<div><p>Background and objectives</p><p>Many children are treated for recurrent acute otitis media and middle ear effusion with ventilation tubes (VT). The objectives are to describe the incidence of VT in Denmark during 1997–2011 from national register data, furthermore, to analyze the determinants for VT in the Copenhagen Prospective Studies on Asthma in Childhood₂₀₁₀ (COPSAC₂₀₁₀) birth cohort.</p><p>Methods</p><p>The incidence of VT in all children under 16 years from 1997–2011 were calculated in the Danish national registries. Determinants of VT were studied in the COPSAC₂₀₁₀ birth cohort of 700 children.</p><p>Results</p><p>Nationwide the prevalence of VT was 24% in children aged 0 to 3 three years, with a significant increase over the study period. For all children 0–15 years, the incidence of VT was 35/1,000. In the VT population, 57% was male and 43% females. In the COPSAC₂₀₁₀ birth cohort, the prevalence of VT during the first 3 years of life was 29%. Determinants of VT were: maternal history of middle ear disease; aHR 2.07, 95% CI [1.45–2.96] and siblings history of middle ear disease; aHR 3.02, [2.11–4.32]. Paternal history of middle ear disease, presence of older siblings in the home and diagnosis of persistent wheeze were significant in the univariate analysis but the association did not persist after adjustment.</p><p>Conclusion</p><p>The incidence of VT is still increasing in the youngest age group in Denmark, demonstrating the highest incidence recorded in the world. Family history of middle ear disease and older siblings are the main determinants for VT.</p></div

Age distribution of children 0–15 years who received ventilation tubes between 1997–2011 in Denmark.

<p>Age distribution of children 0–15 years who received ventilation tubes between 1997–2011 in Denmark.</p

Incidence of ventilation tubes in Denmark.

<p>The incidence (ventilation tubes/1000 children 0–3 years of age) from 1997–2011 in Denmark.</p

Determinants of ventilation tubes.

<p>Determinants of ventilation tubes.</p

Additional file 7: Figure S6. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

A. False positive rate distributions for datasets A1s–A3s and A1m–A3m. Violin plot of distributions of false positive rate (FPR) in 150 iterations for datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). FPR is defined as the fraction of OTUs with p < 0.05. P values were not corrected for multiple testing. Black dots represent medians in each distribution. B. Area under the curve distributions for multiplicative spike-ins in datasets A1s–A3s and A1m–A3m. Violin plot of distributions of area under the receiver operating characteristic curve (AUC) for spiked vs non-spiked p values from differential relative abundance (DA) tests. AUC distributions from 150 iterations for each multiplicative spike-in magnitude in datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). Black dots represent medians in each distribution. C. Area under the curve distributions for additive spike-ins in datasets A1s–A3s and A1m–A3m. Violin plot of distributions of area under the receiver operating characteristic curve (AUC) for spiked vs non-spiked p values from differential relative abundance (DA) tests. AUC distributions from 150 iterations for each additive spike-in magnitude in datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). Black dots represent medians in each distribution. D. Area under the curve distributions for mixed multiplicative spike-ins in datasets A1s–A3s and A1m–A3m. Violin plot of distributions of area under the receiver operating characteristic curve (AUC) for spiked vs non-spiked p values from differential relative abundance (DA) tests. AUC distributions from 150 iterations for mixed multiplicative spike-in magnitudes in datasets A1s–A3s and A1m–A3m (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). Black dots represent medians in each distribution. (ZIP 4027 kb

Additional file 3: Figure S2. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

A. False positive rate distributions for datasets A1–A3. Violin plot of distributions of false positive rate (FPR) in 150 iterations for each case proportion in datasets A1–A3 (vertical panels), analyzed with all differential relative abundance methods (horizontal panels). FPR is defined as the fraction of OTUs with p < 0.05. P values were not corrected for multiple testing. Black dots represent medians in each distribution. B. False positive rate distributions for dataset A4. Violin plot of distributions of false positive rate (FPR) in 150 iterations dataset A4, analyzed with all differential relative abundance methods (horizontal panels). FPR is defined as the fraction of OTUs with p < 0.05. P values were not corrected for multiple testing. Black dots represent medians in each distribution. (ZIP 649 kb

Additional file 9: Figure S8. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

A. Spike-in retrieval as a function of number of positive samples, by dataset size. Aggregated results across 150 iterations of multiplicative spike-ins of magnitude 5 with 50% cases, in datasets A1, A1s, and A1m. Each dot represents a spiked OTU. The Y-axis displays its p value quantile (0 is lowest p value, 1 is highest p value) within that DA run, and the X axis shows how many samples are positive (nonzero) for that OTU. Results from the three datasets are overlaid with different colors and faceted by statistical method. B. Spike-in retrieval as a function of number of positive samples, by case proportion. Aggregated results across 150 iterations of multiplicative spike-ins of magnitude 5 with 10, 25, or 50% cases, in dataset A1. Each dot represents a spiked OTU. The Y-axis displays its p value quantile (0 is lowest p value, 1 is highest p value) within that DA run, and the X axis shows how many samples are positive (nonzero) for that OTU. Results from the three case proportions are overlaid with different colors, and faceted by statistical method. C. Spike-in retrieval as a function of number of positive samples, by spike-in magnitude. Aggregated results across 150 iterations of multiplicative spike-ins of magnitudes 0.5, 2, 5, 10, and 20 with 50% cases, in dataset A1. Each dot represents a spiked OTU. The Y-axis displays its p value quantile (0 is lowest p value, 1 is highest p value) within that DA run, and the X axis shows how many samples are positive (nonzero) for that OTU. Results from the different spike-in magnitudes are overlaid with different colors, and faceted by statistical method. (ZIP 16398 kb

Additional file 14: Table S2. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

Chimeras removed from dataset B3. (XLSX 49 kb

Additional file 1: Table S1. of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

Overview of the datasets used in the study. Sampling and data characteristics of the seven datasets used in the study, A1–A4 for the false positive rate and spike-in retrieval tests and B1–B3 for the beta-diversity optimization tests. (XLSX 5 kb

Additional file 13: of Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies

Supplementary methods. (DOCX 15 kb