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Record W2603430634 · doi:10.1371/journal.pntd.0005475

Dengue seroprevalence, seroconversion and risk factors in Dhaka, Bangladesh

2017· article· en· W2603430634 on OpenAlexafffund
Parnali Dhar‐Chowdhury, Kishor Kumar Paul, C. Emdad Haque, Shakhawat Hossain, L. Robbin Lindsay, Antonia Dibernardo, W. Abdullah Brooks, Michael Drebot

Bibliographic record

VenuePLoS neglected tropical diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of WinnipegUniversity of ManitobaPublic Health Agency of Canada
FundersInternational Development Research CentrePublic Health AgencyPublic Health Agency of CanadaManitoba Health Research Council
KeywordsSeroprevalenceDengue feverSeroconversionDengue virusMedicinePopulationSerologyEnvironmental healthDemographyVirologyImmunologyVirusAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Dengue virus (DENV) activity has been reported in Dhaka, Bangladesh since the early 1960s with the greatest burden of dengue fever and dengue hemorrhagic fever cases observed in 2000. Since this time, the intensity of dengue activity has varied from year to year, and its determining factors remained relatively unknown. In light of such gaps in knowledge, the main objectives of this study were to determine the magnitude of seroprevalence and seroconversion among the surveyed population, and establish the individual/household level risk factors for the presence of DENV antibodies among all age groups of target populations in the city of Dhaka. METHODOLOGY/PRINCIPAL FINDINGS: Considering the lack of fine scale investigations on the factors driving dengue activity in Bangladesh, a prospective cohort study involving serological surveys was undertaken with participant interviews and blood donation across the city of Dhaka in 2012. Study participants were recruited from 12 of 90 wards and blood samples were collected during both the pre-monsoon (n = 1125) and post-monsoon (n = 600) seasons of 2012. The findings revealed that the seroprevalence in all pre-monsoon samples was 80.0% (900/1125) while the seropositivity in the pre-monsoon samples that had paired post-monsoon samples was 83.3% (503/600). Of the 97 paired samples that were negative at the pre-monsoon time point, 56 were positive at the post-monsoon time point. This resulted in a seroprevalence of 93.2% (559/600) among individuals tested during the post-monsoon period. Seroprevalence trended higher with age with children exhibiting a lower seropositivity as compared to adults. Results from this study also indicated that DENV strains were the only flaviviruses circulating in Dhaka in 2012. A multivariate analysis revealed that age, possession of indoor potted plants, and types of mosquito control measures were significant factors associated with DENV seroprevalence; while attendance in public/mass gatherings, and use of mosquito control measures were significantly associated with DENV seroconversion after adjusting for all other variables. CONCLUSIONS/SIGNIFICANCE: Our study suggests that there is a high level of endemic dengue virus circulation in the city of Dhaka which has resulted in significant DENV seroprevalence among its residents. Seropositivity increased with age, however, a substantial proportion of children are at risk for DENV infections. Our serological analysis also documents considerable DENV seroconversion among study participants which indicates that a large proportion of the population in the city of Dhaka were newly exposed to DENV during the study period (pre-and post-monsoon 2012). High levels of seroconversion suggest that there was an intense circulation of DENV in 2012 and this may have resulted in a significant risk for viral associated illness. Findings of our study further indicated that home-based interventions, such as removing indoor potted plants and increased bed net use, in addition to vector control measures in public parks, would reduce exposure to DENV and further decrease risk of viral associated disease.

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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.251
Teacher spread0.238 · 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 teacher head, 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

Citations100
Published2017
Admission routes2
Has abstractyes

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