Population health surveillance using mobile telephone surveys in low- and middle-income countries: methodology and sample representativeness of a behavioural risk factor survey of live poultry exposure in Bangladesh
Bibliographic record
Abstract
Abstract BackgroundIn low- and middle-income countries (LMICs), population-based health surveys are typically conducted using face-to-face household interviews. However, telephone-based surveys are cheaper, faster, and can provide greater access to hard to reach or remote populations. The rapid growth in mobile telephone ownership in LMICs provides a unique opportunity to implement novel data collection methods for population health surveys. This study describes the methodology, development, and population representativeness of a mobile telephone survey measuring live poultry exposure in urban Bangladesh.MethodsA population-based cross-sectional mobile telephone survey was conducted between September and November 2019 in North and South Dhaka City Corporations (DCC), Bangladesh to measure live poultry exposure using a stratified probability sampling design. Data were collected using a computer-assisted telephone interview (CATI) platform. Call operational data were summarized, and participant data were weighted by age, sex, and education to the 2011 census. Demographic distributions of the weighted sample were compared with external sources to assess population representativeness.ResultsA total of 5486 unique mobile telephone numbers were dialled, with 1047 respondents completing the survey. The survey had an overall response rate of 52.4% and a cooperation rate of 89.0%. Initial results comparing the socio-demographic profile of the survey sample to the census population showed that mobile telephone sampling slightly underrepresented older individuals and overrepresented those with higher secondary education. After weighting, the demographic profile of the sample population was well matched to the latest DCC census population profile. ConclusionsProbability-based mobile telephone survey sampling and data collection methods produced a population-representative sample with minimal adjustment in DCC, Bangladesh. Mobile telephone-based surveys can offer an efficient, economic, and robust way to conduct surveillance for population health outcomes, which has important implications for improving population health surveillance in LMICs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".