Implementation of Interventions for the Control of Typhoid Fever in Low- and Middle-Income Countries
Bibliographic record
Abstract
Past research has focused on typhoid fever surveillance with little attention to implementation methods or effectiveness of control interventions. This study purposefully sampled key informants working in public health in Chile, India, Pakistan, Bangladesh, Thailand, Vietnam, South Africa, and Nigeria to 1) scope typhoid-relevant interventions implemented between 1990 and 2015 and 2) explore contextual factors perceived to be associated with their implementation, based on the Consolidated Framework for Implementation Research (CFIR). We used a mixed methods design and collected quantitative data (CFIR questionnaire) and qualitative data (interviews with 34 public health experts). Interview data were analyzed using a deductive qualitative content analysis and summary descriptive statistics are provided for the CFIR data. Despite relatively few typhoid-specific interventions reportedly implemented in these countries, interventions for diarrheal disease control and regulations for food safety and food handlers were common. Most countries implemented agricultural and sewage treatment practices, yet few addressed the control of antibiotic medication. Several contextual factors were perceived to have influenced the implementation of typhoid interventions, either as enablers (e.g., economic development) or barriers (e.g., limited resources and habitual behaviors). Consolidated Framework for Implementation Research factors rated as important in the implementation of typhoid interventions were remarkably consistent across countries. The findings provide a snapshot of typhoid-relevant interventions implemented over 25 years and highlight factors associated with implementation success from the perspective of a sample of key informants. These findings can inform systematic investigations of the implementation of typhoid control interventions and contribute to a better understanding of the direct effects of implementation efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".