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Record W4401812362 · doi:10.1186/s41043-024-00625-0

Risk factors for non-communicable diseases in Afghanistan: insights of the nationwide population-based survey in 2018

2024· article· en· W4401812362 on OpenAlexaff
Omid Dadras, Muhammad Haroon Stanikzai, Massoma Jafari, Essa Tawfiq

Bibliographic record

VenueJournal of Health Population and Nutrition · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineEnvironmental healthOverweightPublic healthPopulationEpidemiologyDemographyObesityCross-sectional studyNon-communicable diseaseGerontologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Noncommunicable diseases (NCDs) account for a substantial number of deaths in Afghanistan. Understanding the prevalence and correlates of major NCD risk factors could provide a benchmark for future public health policies and programs to prevent and control NCDs. Therefore, this study aimed to examine the prevalence and correlates of NCD risk factors among adults aged 18-69 years in Afghanistan. METHODS: We used data from the Afghanistan STEPS Survey 2018. The study population were 3650 (1896 males and 1754 females) adults aged 18-69 years sampled from all 34 provinces through a multistage cluster sampling process. Information on behavioural and biological risk factors was collected. We used STATA (version 18.0) for data analysis. RESULTS: Of the total participants, 42.8% were overweight or obese, 8.6% were current smokers, 26.9% had insufficient physical activities, 82.6% had low consumption of fruits and vegetables, and only 0.5% had ever consumed alcohol. Approximately 15% of participants had a high salt intake, while 25% and 8% had elevated blood pressure and blood glucose levels, respectively. Similarly, around 18% had elevated total cholesterol. The study revealed a lower prevalence of current smoking among females [AOR = 0.17, 95%CI (0.09-0.30)] compared with males, but a higher prevalence in those who had higher education levels [1.95 (1.13-3.36)] compared with those with no formal education. Insufficient physical activity was higher in participants aged 45-69 years [1.96 (1.39-2.76)], females [4.21 (1.98-8.84)], and urban residents [2.38 (1.46-3.88)] but lower in those with higher education levels [0.60 (0.37-0.95)]. Participants in the 25th to 75th wealth percentiles had higher odds of low fruit and vegetable consumption [2.11 (1.39-3.21)], while those in the > 75th wealth percentile had lower odds of high salt intake [0.63 (0.41-0.98)]. Being overweight/obese was more prevalent in participants aged 45-69 years [1.47 (1.03-2.11)], females [1.42 (0.99-2.01)], currently married [3.56 (2.42-5.21)] or ever married [5.28 (2.76-10.11)], and urban residents [1.39 (1.04-1.86)]. Similarly, high waist circumference was more prevalent in participants aged 45-69 years [1.86 (1.21-2.86)], females [5.91 (4.36-8.00)], those being currently married [4.82 (3.12-7.46)], and those being in 25th to 75th wealth percentile [1.76 (1.27-2.43)]. A high prevalence of elevated blood pressure was observed in participants aged 45-69 years [3.60 (2.44-5.31)] and currently married [2.31 (1.24-4.31)] or ever married [6.13 (2.71-13.8)] participants. Elevated blood glucose was more prevalent in older adults ([1.92 (1.09-3.39)] for 45-69 and [3.45 (2.44-5.31)] for 30-44 years), urban residents [2.01 (1.33-3.03)], and ever-married participants [4.89 (1.48-16.2)]. A higher prevalence of elevated cholesterol was observed in females [2.68 (1.49-4.82)] and those currently married [2.57 (1.17-5.63)] or ever married [4.24 (1.31-13.73)]. CONCLUSION: This study used up-to-date available data from a nationally representative sample and identified the prevalence of NCDs and associated risk factors in Afghanistan. Our findings have the potential to inform and influence health policies by identifying people at high risk of developing NCDs and can assist policymakers, health managers, and clinicians to design and implement targeted health interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.342
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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