Rising blood pressure in Bangladesh
Bibliographic record
Abstract
Almost a fourth of residents living in a middle-class neighborhood of Bangladesh’s capital city Dhaka were found to have hypertension, reports a study published in Global Cardiology Science & Practice. Researchers in Bangladesh collected demographic, anthropometric and health-related data from 730 residents of a randomly selected neighborhood in Dhaka. Their aim was to assess the prevalence and risk factors for hypertension in urban Bangladesh . They found that nearly a quarter of the study population had hypertension, affecting relatively more men than women. People aged 66 to 74 were found to be significantly more at-risk than those in other age groups. Bangladeshis have a cultural preference to high salt intake, and study participants who consumed more than one teaspoon of salt a day were found to be 1.5 times more at risk for hypertension than others. Smokers and tobacco users were also at a higher risk of developing hypertension. Finally, obese participants, those with a high waist circumference, a family history of stroke or cardiovascular disease, or who consumed less than 2.5 cups of vegetables per day were at a higher risk of developing the disease. High blood pressure is estimated (http://www.who.int/gho/ncd/risk_factors/blood_pressure_prevalence_text/en/) to cause 12.8% of all deaths globally each year. In 2008, it affected 40% of the world’s population over the age of 25. A disproportionately high number of people living with hypertension are in low- and middle-income countries. The 2011 Bangladesh Demographic and Health Survey reported that hypertension affected as much as 34% of adults living in the country. Another study found hypertension was relatively less prevalent in rural areas compared to urban centres. Hypertension and cardiovascular diseases have recently increased in South-East Asia as a result of rapid urbanization, increased life expectancy, and lifestyle changes. The researchers say that policies that target the promotion of a healthy lifestyle are needed in Bangladesh and the wider region. “Population-based intervention programmes and policies for increased awareness about risk factors and lifestyle modifications are essential for prevention of hypertension,” they write.Other InformationPublished in: QScience.com Highlights, Published by Nature Research for Hamad bin Khalifa University Press (HBKU Press) License: http://creativecommons.org/licenses/by/4.0
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".