Malaria knowledge and utilization of chemoprophylaxis in the UK population and in UK passengers departing to malaria-endemic areas
Bibliographic record
Abstract
BACKGROUND: The burden of imported malaria is predominantly in travellers visiting friends and relatives (VFR) in sub-Saharan Africa. The failure of this group to use chemoprophylaxis is recognized as the most important risk factor for the high incidence of disease. Understanding the reasons for failure to follow national recommendations may relate to knowledge, risk perception, cost, and peer pressure. Research into these variables is critical to understand and change practices in this group and this study was designed to explore whether knowledge, risk perception and prophylaxis use differs between travellers' to various destinations and the rest of the UK population. METHODS: Two face-to-face questionnaire surveys were conducted to collect information on demographics, malaria knowledge, source, and quality of pre-travel advice, past travel experience and perceived malaria threat. One was an IPSOS survey of individuals representative of the UK population. The other was a departure lounge survey (Civil Aviation Authority (CAA)) of passengers departing to malarious regions detailing destinations and use of chemoprophylaxis. RESULTS: Around a quarter of the 1,991 UK population surveyed had previously travelled to a malarious area. Five-hundred departing passengers were interviewed, of which 80% travelled for leisure (56% VFR's) and 42% were travelling to West Africa. Malaria knowledge among the UK population (score 58.6) was significantly lower than that of individuals who had previously travelled or were travelling (63.8 and 70.7 respectively). Malaria knowledge was similar in individuals who had and had not sought pre-travel advice and travellers using and not using chemoprophylaxis for their journey. Leisure travellers to Ghana and Nigeria were predominantly VFRs (74%), whilst 66% of travellers to Kenya were tourists. Despite similar high knowledge scores and perceived (>90%) threat of the lethality of malaria in the three groups, chemoprophylaxis use in Nigerians (50%) was substantially lower than in passengers departing to Kenya (78%) and Ghana (82%). More frequent annual return visits were made to Nigeria (72%) than to Ghana (38%) or Kenya (23%). CONCLUSION: Travellers had more malaria knowledge than the non-travelled UK population. Malaria knowledge, perceived threat, travel experience, and quality of pre-travel advice appear unrelated to the use of chemoprophylaxis in passengers. Reducing malaria in VFR travellers will require strategies other than improving malaria knowledge and enhancing malaria risk awareness.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".