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Record W2107350356 · doi:10.3402/gha.v2i0.1984

Using the INDEPTH HDSS to build capacity for chronic non-communicable disease risk factor surveillance in low and middle-income countries

2009· article· en· W2107350356 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Health Action · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsMedicineNon-communicable diseaseEnvironmental healthSampling frameWaistDisease burdenEpidemiological transitionStratified samplingEpidemiologyCross-sectional studyPublic healthGerontologyDemographyObesityPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic non-communicable diseases (NCDs) are the leading cause of morbidity, mortality, and disability worldwide. More than 80% of chronic disease deaths occur in low-income and middle-income countries. Epidemiological data on the burden of chronic NCD and the risk factors which predict them are lacking in most low-income countries. The INDEPTH Network (http://www.indepth-network.org) which includes the Health and Demographic Surveillance System (HDSS) with many surveillance sites in low-middle income countries provided an opportunity to establish surveillance of the major chronic NCD risk factors in 2005 using a standardised approach. OBJECTIVE: This paper presents the conceptual framework and research design of the chronic NCD risk factor surveillance within nine rural INDEPTH HDSS settings in Asia. METHODS: This multi-site study was designed as a baseline cross-sectional survey with sufficient sample size to measure trends over time. In each of nine HDSS sites in five Asian countries, a sample of 2,000 men and women aged 25-64 years, using the WHO STEPwise approach to Surveillance (http://who.int/chp/steps), was selected using stratified random sampling (in each 10-year interval) from the HDSS sampling frame. RESULTS: A total of 18,494 men and women from the nine sites were interviewed with an overall response rate of 98%. The major NCDs risk factors included self-reported information on tobacco and alcohol consumption, fruit and vegetable intake, physical activity patterns, and measured body weight, height, waist circumference, and blood pressure. A series of training sessions were conducted for research scientists, supervisors, and surveyors in each site. Data quality was ensured through spot check, re-check, and data validation procedures, including accuracy and completeness of data obtained. Standardised data entry programme, created using the EPIDATA software, was used to ensure uniform database structure across sites. The data merging and analysis were done using STATA Version 10. CONCLUSION: This multi-site study confirmed the feasibility of conducting chronic NCD risk factor surveillance in the low and middle-income settings by integrating the chronic NCDs risk factor surveillance into an existing HDSS data collection and management setting. This collaborative work has provided reliable epidemiological data as a basis for developing chronic NCD prevention and control activities.

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.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.084
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.062
GPT teacher head0.367
Teacher spread0.305 · 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