MétaCan
Menu
Back to cohort
Record W2902109863 · doi:10.1093/ofid/ofy209.080

964. Impact of Doxycycline as Malaria Prophylaxis on Risk of Influenza Like Illness Among International Travelers

2018· article· en· W2902109863 on OpenAlexaboutno aff
Kathryn Lago, Kalyani Telu, David R. Tribble, Anuradha Ganesan, Anjali Kunz, Charla Geist, Jamie Fraser, Indrani Mitra, Heather C. Yun

Bibliographic record

VenueOpen Forum Infectious Diseases · 2018
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMalaria prophylaxisMalariaDoxycyclineIncidence (geometry)Poisson regressionSore throatPre-exposure prophylaxisDemographyEnvironmental healthSurgeryImmunologyAntibioticsPopulation

Abstract

fetched live from OpenAlex

Abstract Background International travelers are often at risk for both influenza like illness (ILI) and malaria. Doxycycline is active against many pathogens causing ILI and is routinely used for malaria prophylaxis. We evaluated the incidence of and risk factors for ILI, and whether the choice of malaria prophylaxis was associated with ILI. Methods TravMil is a prospective observational study enrolling subjects presenting to 6 military travel clinics. We analyzed pre- and post-travel surveys from travelers to regions outside of the continental United States, Western or Northern Europe, Canada, or New Zealand between July 2010 and August 2018. ILI was defined as subjective fever associated with either a sore throat or cough. Characteristics of trip and traveler and the use of malaria prophylaxis were analyzed to determine association with development of ILI. Poisson regression models with robust error variance were used to estimate relative risk of ILI. Results A total of 3,227 travelers were enrolled: 62.1% male, median age of 39 (IQR 27, 59), median travel duration 19 days (IQR 12, 49). 32% traveled to Africa, 40% to Asia, and 27% to the Caribbean, Mexico, and Central or South America. Military travel (46%) and vacation (40%) were most common reasons for travel. Twenty percent took doxycycline for malaria prophylaxis, 50% other prophylaxis (89% atovaquone–proguanil), and 30% took none. 8.7% developed ILI. Compared with those on no or other prophylaxis, doxycycline was associated with decreased risk of ILI [RR 0.65 (0.43–0.99), P = 0.046], as was military travel [RR 0.30 (0.21–0.43), P < 0.01]. Increased risk of ILI was associated with female gender [RR 1.57 (1.24–1.98), P < 0.01], travel to Asia [RR 1.37 (1.08–1.75), P = 0.01], cruises [RR 2.21 (1.73–2.83), P < 0.01], and longer duration of travel [RR 1.01 (1.00–1.01, P < 0.01]. Conclusion The use of doxycycline is associated with a decreased risk of ILI compared with taking no or other malaria prophylaxis. The reasons for this are unclear but may be related to anti-inflammatory effects, activity against bacterial respiratory pathogens, effects on disease transmission in closed populations (e.g., military deploying groups), or other unmeasured factors. With few proven strategies for decreasing ILI risk in travelers, these findings bear further investigation. Disclosures All authors: No reported disclosures.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.354
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueOpen Forum Infectious DiseasesSame topicTravel-related health issuesFrench-language works237,207