RESUMEN
Objectives: Bangladesh 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). Methods: Nationally 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. Result: The 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. Conclusion: Nearly 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.
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Renta , Adolescente , Femenino , Humanos , Embarazo , Bangladesh/epidemiología , Encuestas Epidemiológicas , Prevalencia , Factores SocioeconómicosRESUMEN
BACKGROUND: 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 ß-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. RESULTS: The proposed method relies on a ß-weight function, which produces values between 0 and 1. The ß-weight function with ß = 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 ß-weights is used to calculate the cut-off point, which is compared to the observed ß-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. CONCLUSION: 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 m = 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 m > 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.
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Análisis de Varianza , Perfilación de la Expresión Génica/métodos , Neoplasias del Colon/genética , Simulación por Computador , Regulación Neoplásica de la Expresión Génica , Humanos , Neoplasias Pancreáticas/genética , Tamaño de la MuestraRESUMEN
BACKGROUND: Microarray data enables the high-throughput survey of mRNA expression profiles at the genomic level; however, he data presents a challenging statistical problem because of the large number of transcripts with small sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayes hierarchical models have been developed. However, because of the complexity of the microarray data, no model can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression that are not expected by the usual statistical gene by gene models. RESULTS: As an extension of empirical Bayes (EB) procedures, we have developed the ß-empirical Bayes (ß-EB) approach based on a ß-likelihood measure which can be regarded as an 'evidence-based' weighted (quasi-) likelihood inference. The weight of a transcript t is described as a power function of its likelihood, fß(yt|θ). Genes with low likelihoods have unexpected expression patterns and low weights. By assigning low weights to outliers, the inference becomes robust. The value of ß, which controls the balance between the robustness and efficiency, is selected by maximizing the predictive ß0-likelihood by cross-validation. The proposed ß-EB approach identified six significant (p<10â»5) contaminated transcripts as differentially expressed (DE) in normal/tumor tissues from the head and neck of cancer patients. These six genes were all confirmed to be related to cancer; they were not identified as DE genes by the classical EB approach. When applied to the eQTL analysis of Arabidopsis thaliana, the proposed ß-EB approach identified some potential master regulators that were missed by the EB approach. CONCLUSIONS: The simulation data and real gene expression data showed that the proposed ß-EB method was robust against outliers. The distribution of the weights was used to scrutinize the irregular patterns of expression and diagnose the model statistically. When ß-weights outside the range of the predicted distribution were observed, a detailed inspection of the data was carried out. The ß-weights described here can be applied to other likelihood-based statistical models for diagnosis, and may serve as a useful tool for transcriptome and proteome studies.