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This diagnostic/prognostic study describes the use of cell-free transcriptomics, urine metabolomics, and plasma proteomics for identifying the biological measurements associated with preterm birth.

ObjectiveTo improve the accuracy of the prediction of gestational age (GA) before birth with the standardised measurement of symphysis-fundal height (SFH), estimation of uterine volume, and statistical modelling including maternal anthropometrics and other factors.DesignProspective pregnancy cohort study.SettingRural communities in Sylhet, Bangladesh.Participants1516 women with singleton pregnancies with early pregnancy ultrasound dating (<20 weeks); 1486 completed follow-up.MethodsSFH and abdominal girth were measured at subsequent antenatal care (ANC) visits by community health workers at 24 to 28, 32 to 36, and/or >37 weeks gestation. An estimated uterine volume (EUV) was calculated from these measures. Data on pregnancy characteristics and other maternal anthropometrics were also collected.Primary outcome measureGA at subsequent ANC visits, as defined by early ultrasound dating.Results1486 (98%) women had at least one subsequent ANC visit, 1102 (74%) women had two subsequent ANC visits, and 748 (50%) had three visits. Using the common clinical practice of approximating the GA (in weeks) with the SFH measurement (cm), SFH systematically underestimated GA in late pregnancy (mean difference −4.4 weeks, 95% limits of agreement −12.5 to 3.7). For the classification of GA <28 weeks, SFH <26 cm had 85% sensitivity and 81% specificity; and for GA <34 weeks, SFH <29 cm had 83% sensitivity and 71% specificity. EUV had similar diagnostic accuracy. Despite rigorous statistical modelling of SFH, accounting for repeated longitudinal measurements and additional predictors, the best model without including a known last menstrual period predicted 95% of pregnancy dates within ±7.4 weeks of early ultrasound dating.ConclusionsWe were unable to predict GA with a high degree of accuracy before birth using maternal anthropometric measures and other available maternal characteristics. Efforts to improve GA dating in low- and middle-income countries before birth should focus on increasing coverage and training of ultrasonography.Trial registration numberNCT01572532

Aims Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction. Methods This work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy. Results The average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight. Conclusion This research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh’s U5 children.

ObjectivesLow-income and middle-income countries are undergoing epidemiological transition, however, progression is varied. Bangladesh is simultaneously experiencing continuing burden of communicable diseases and emerging burden of non-communicable diseases (NCDs). For effective use of limited resources, an increased understanding of the shifting burden and better characterisation of risk factors of NCDs, including hypertension is needed. This study provides data on prevalence and factors associated with hypertension among males and females 35 years and older in rural Bangladesh.MethodsThis is a population-based cross-sectional study conducted in Zakiganj and Kanaighat subdistricts of Sylhet district of Bangladesh. Blood pressure was measured and data on risk factors were collected using STEPS instrument from 864 males and 946 females aged 35 years and older between August 2017 and January 2018. Individuals with systolic blood pressure of ≥140 mm Hg or diastolic blood pressure of ≥90 mm Hg or taking antihypertensive drugs were considered hypertensive. Bivariate and multivariate analyses were performed to identify factors associated with hypertension.ResultsThe prevalence of hypertension was 18.8% (95% CI 16.3 to 21.5) and 18.7% (95% CI 16.3 to 21.3) in adult males and females, respectively. Among those who were hypertensive, the prevalence of controlled, uncontrolled and unaware/newly identified hypertension was 23.5%, 25.9% and 50.6%, respectively among males and 38.4%, 22.6% and 39.0%, respectively among females. Another 22.7% males and 17.8% females had prehypertension. Increasing age and higher waist circumference (≥90 cm for males and ≥80 cm for females) were positively associated with hypertension both in males (OR 4.0, 95% CI 2.5 to 6.4) and females (OR 2.8, 95% CI 2.0 to 4.1).ConclusionsIn view of the high burden of hypertension and prehypertension, a context-specific scalable public health programme including behaviour change communications, particularly to increase physical activity and consumption of healthy diet, as well as identification and management of hypertension needs to be developed and implemented.