Prevalence and determinants of adolescent childbearing: comparative analysis of 2017–18 and 2014 Bangladesh Demographic Health Survey

ObjectivesBangladesh has one of the highest adolescent childbearing rates in South Asia, which prevent women from realizing their full potential in life. This study aimed to compare the prevalence and determinants of adolescent childbearing in Bangladesh using data from the 2014 and 2017–18 Bangladesh Demographic and Health Survey (BDHS).MethodsNationally representative surveys of respondents were selected using a two-stage sampling process. The study recruited 2,023 and 1,951 ever-married women aged 15–19 from 2014 and 2017–18 BDHS surveys, respectively, from rural and urban settings from all eight geographic divisions of Bangladesh. Univariate and multivariate logistic regression models were fit to determine the factors associated with adolescent childbearing.ResultThe adolescent childbearing prevalence rate was 30.8% in 2014 BDHS and 27.6% in 2017–18 BDHS. Marriage at age 13 years or less also reduced significantly in 2017–18 compared to 2014 (12.7% vs. 17.4%, respectively). Significantly higher odds of adolescent childbearing were found in 2014 among women in the Sylhet Division (adjusted odds ratio (AOR) = 3.0; 95% confidence interval (CI): 1.6–6.1) and the Chittagong Division (AOR = 1.8; 95% CI: 1.8–2.7) compared to the Barisal Region; however, in 2017, there were no significant differences was found across the geographic Divisions. Compared to women in the lowest wealth quintile, women in all other quintiles had lower odds of adolescent childbearing, with the lowest odds found among women in the wealthiest quintile (AOR = 0.3; 95% CI: 0.2–0.6). Women who married at age 14–17 had 60% lower odds of adolescent childbearing compared to the women who married at age 10–13.ConclusionNearly one-third of married adolescents in Bangladesh were pregnant or had at least one child in 2014, and it was reduced only marginally in 2017–18. Marriage at an early age and income inequalities among families were significant predictors of adolescent childbearing in Bangladesh. This study highlighted change in the magnitude and determinants of adolescent childbearing in Bangladesh taken data from two nationally representative surveys conducted 4 years apart

Hypertension and sex related differences in mortality of COVID-19 infection: A systematic review and Meta-analysis

Background: Hypertension is the leading cause of cardiovascular diseases and premature deaths. Hypertension plays a striking role in mortality and morbidity in case of Coronavirus Disease 2019 (COVID-19) infection; however, numerous studies have reported contradictory findings. Objective: To assess the relationship of hypertensive disease and mortality of COVID-19 infection and to assess the sex and age differentials on the association. Methods: We have conducted a systematic review of published literatures that identified the relationship between hypertension and mortality of COVID-19 infections. Nineteen articles were selected following structured inclusion and exclusion criteria for systematic review and analyses. A total of 21,684 hospital admitted COVID-19 patients were included in this review and meta-analysis from 19 studies. The studies covered the six months of the pandemic from December 2019 to May 2020. Results: In the pooled analysis, the median age of patients was 58 years, and the proportion of male patients was 58.8%. In contrast, we estimated 33.26% of hypertensive and 19.16% of diabetes mellitus patients in the studies. Hypertension was found to be associated with COVID-19 mortality (“Risk ratio (RR) = 1.45, [95% confidence interval (CI): 1.35 - 1.55]; I2 = 77.1%, p - value < 0.001”). The association in the meta-regression was affected by sex (p - value = 0.050). The association was found to be stronger in the studies with males ≥ 55% and age ≥ 55 years (“RR = 1.65, [95% CI: 1.52 - 1.78]; I2 = 77.1%, p - value < 0.001”) compared to male < 55% or age < 55 years (“RR = 1.11, [95% CI: 0.94 - 1.28]; I2 = 72.2%, p - value < 0.001”). Conclusion: Hypertension was significantly strong associated with COVID-19 mortality which may account for the contradiction in the many studies. The association between hypertension and mortality was affected by sex and there were significantly higher fatalities among older male patients.&nbsp

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns

<div><p>Background</p><p>Identifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum <i>β</i>-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.</p><p>Results</p><p>The proposed method relies on a <i>β</i>-weight function, which produces values between 0 and 1. The <i>β</i>-weight function with <i>β</i> = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the <i>β</i>-weights is used to calculate the cut-off point, which is compared to the observed <i>β</i>-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA.</p><p>Conclusion</p><p>Analyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for <i>m</i> = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for <i>m</i> > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression.</p></div

Predicted distribution of <i>β</i> weights.

<p>Predicted (solid curve) and simulated (histogram) observed distributions of the <i>β</i> weights of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138810#pone.0138810.e015" target="_blank">Eq (5)</a>: (a) without outlying gene expressions and (b) with 5% outlying gene expressions.</p

Pairwise comparison analysis by all 4 methods with their corresponding selected significance DE genes.

<p>The values reported in the form {x, x, x, x} in this table represent the numbers of downregulated (DR) or upregulated (UR) differentially expressed (DE) genes estimated by the ANOVA, LIMMA, KW and proposed (Bold) methods, respectively. ^<i>a</i>Note that <math><mrow>log<msub><mi>μ</mi><mo>^</mo><mi>i</mi></msub><msub><mi>μ</mi><mo>^</mo><mi>j</mi></msub><mo><</mo><mo>−</mo><mn>1</mn></mrow></math> indicates significant 2-fold downregulation and <math><mrow>log<msub><mi>μ</mi><mo>^</mo><mi>i</mi></msub><msub><mi>μ</mi><mo>^</mo><mi>j</mi></msub><mo>></mo><mo>+</mo><mn>1</mn></mrow></math> indicates significant 2-fold upregulation.</p><p>Pairwise comparison analysis by all 4 methods with their corresponding selected significance DE genes.</p

Performance evaluation in pairwise comparison tests using four methods (ANOVA, LIMMA, KW and Proposed) for the small-sample case.

<p>We generated 300 DE genes out of 20,000 total genes for <i>m</i> = 4 conditions with different patterns for a small-sample case (n1 = n2 = n3 = n4 = 6) and <i>σ</i>² = 0.05, with a 2-fold change in expression between the groups, to investigate the pattern-detection performance of the proposed method in comparison with the others. The values reported in the form {x, x, x, x} in this table represent the numbers of downregulated (DR) or upregulated (UR) differentially expressed (DE) genes estimated by the ANOVA, LIMMA, KW and proposed (Bold) methods, respectively. ^<i>a</i>Note that <math><mrow>log<msub><mi>μ</mi><mo>^</mo><mi>i</mi></msub><msub><mi>μ</mi><mo>^</mo><mi>j</mi></msub><mo><</mo><mo>−</mo><mn>1</mn></mrow></math> indicates significant 2-fold downregulation and <math><mrow>log<msub><mi>μ</mi><mo>^</mo><mi>i</mi></msub><msub><mi>μ</mi><mo>^</mo><mi>j</mi></msub><mo>></mo><mo>+</mo><mn>1</mn></mrow></math> indicates significant 2-fold upregulation.</p><p>Performance evaluation in pairwise comparison tests using four methods (ANOVA, LIMMA, KW and Proposed) for the small-sample case.</p

Venn diagram and outlier gene expression profile for colon cancer data.

<p>Comparison of the results on the colon cancer gene expression dataset. (a) Venn diagram of the top 100 genes estimated by KW, BetaEB and the proposed method. (b) Outlying DE genes detected by the proposed method only. The results for the control group are plotted below the lines, and the results for the cancer group are plotted above the lines.</p

Venn diagram and outlier gene expression profile for pancreatic cancer data.

<p>(a) Venn diagram of the DE genes estimated by all four methods (ANOVA, LIMMA, KW and Proposed) based on pairwise comparisons of CTC vs T, CTC vs P, CTC vs G, T vs P, T vs G and G vs P. (b) Frequency distributions of <i>β</i>-weights for each expression of the 8152 genes in 24 samples. (c) Scatter plot of the smallest <i>β</i>-weight for each of the 8152 genes vs. the gene index, where the smallest value represents the minimum value of 24 <i>β</i>-weights from 24 samples for each gene. The red circles between the two gray lines represent moderate/noisy outliers, whereas the other red circles, corresponding to <i>β</i>-weights of less than 0.2, represent extreme outliers. (d) Plot of ordered smallest <i>β</i>-weights in (c) for 8152 genes. (e) The 80 DE genes detected by the proposed method only, as shown in (a). Seventeen out of 80 DE genes were detected as extreme outlying genes using the <i>β</i>-weight function. The results for the T, P, G and CTC groups are plotted above the lines with four different colors. The outlying samples are indicated by circles above them.</p

Performance evaluation based on Spike gene expression profiles with 2 conditions for the sample case (n₁ = n₂ = 9).

<p>We considered the estimated top 1944 genes for each method and then crossed with the designated ‘DE gene-set’ to calculate the summary statistics (TPR, TNR, FPR, FNR, FDR, MER, AUC and pAUC) for performance evaluation in the Spike gene expression profiles.</p><p>Performance evaluation based on Spike gene expression profiles with 2 conditions for the sample case (n₁ = n₂ = 9).</p

Performance evaluation based on simulated gene expression profiles with <i>m</i> = 2 conditions/groups.

<p>Average performance results of eight methods (ANOVA, SAM, LIMMA, eLNN, EBarrays, BetaEB, KW and Proposed) based on 100 datasets generated using a one-way ANOVA model with <i>m</i> = 2 groups/conditions and <i>σ</i>² = 0.05 for both sample sizes n1 = n2 = 3 and n1 = n2 = 15. Each dataset for each case contained 300 true DE genes, and the remainder were 19700 true EE genes. The performance indices/measures TPR, FPR, TNR, FNR, FDR, MER and AUC were calculated for each method based on the top 300 estimated DE genes, under the assumption that the other estimated genes in each dataset for each case were EE genes for each method. The performance measure ‘pAUC’ was calculated at FPR = 0.2 for each method and for each dataset.</p><p>Performance evaluation based on simulated gene expression profiles with <i>m</i> = 2 conditions/groups.</p