Knowledge, attitude and practice (KAP) and risk factors on dengue fever among children in Brazil, Fortaleza: A cross-sectional study
Bibliographic record
Abstract
Abstract Background Dengue fever is a mosquito-borne viral disease that is associated with four serotypes of the dengue virus. Children are vulnerable to infection with the dengue virus and the risk of severe dengue disease is the highest among infants and children, particularly those who have been previously infected with a different dengue serotype. Sufficient knowledge, positive attitudes, and proper practices (KAP) are essential for dengue prevention and control. This study aims to estimate the dengue seropositivity for study participants and to examine the association between households’ dengue-related knowledge, attitudes, and practices (KAP), and children’s risk of dengue seropositivity, while accounting for socioeconomic and demographic differences in Brazil. Methodology/Principal Findings This analysis was based on a cross-sectional study from Fortaleza, Brazil between November 2019, and February 2020. There were 392 households and 483 participant children who provided a sample of sufficient quality for serological analysis. The main exposure was a household’s dengue-related knowledge, attitudes, and practices, assessed through a questionnaire to construct a composite KAP score categorized into three levels: low, moderate and high. The main outcome is dengue immunoglobulin G(IgG) antibodies, collected using dried blood spots and assessed with Panbio Dengue IgG indirect ELISA (enzyme-linked immunosorbent assays) test commercial kits. The estimated crude dengue seroprevalence among participating children (n=483) was 25.1%. 5% (n=20) of households achieved a score of over 75% for KAP, while most households achieved between 50% and 75% of the total scores (n=271, 69%), and 101 households scored lower than 50% of the total score (26%). Each KAP domain was significantly and positively associated with the others. We found high household KAP scores were associated with an increased risk of seropositivity (RR: 2.08, 95% CI: 1.09-3.97, p=0.027). Household adult respondents’ education level of elementary school or higher was negatively associated with children’s relative risk of being seropositive (RR: 0.65, 95% CI: 0.49-0.87, p=0.004). The risk of seropositivity in older children (6-12 years old) was over 6 times that of younger children (2-5 years old) (RR: 6.15, 95% CI: 3.51-10.76, p<0.001). Children living in households with sealed water tanks or no water storage had a lower relative risk of being seropositive (RR: 0.73, 95% CI: 0.55-0.99, p=0.041). Conclusions/Significance Our results provide insight into the prevalence of dengue seropositivity in Fortaleza, Brazil in children, and certain demographic and socioeconomic characteristics associated with children’s risk of being seropositive. They also suggest that KAP may not identify those more at risk for dengue, although understanding and enhancing households’ KAP is crucial for effective community dengue control and prevention initiatives. Author summary Dengue fever has become an increasing threat to public health, with its global expansion and increased presence in dengue endemic countries. This study provides insight into the prevalence of dengue seropositivity in children in Fortaleza, Brazil, a city which has been a hotspot for arbovirus infections. We estimated seroprevalence for certain study population characteristics and identified characteristics that were associated with an increased risk of dengue seropositivity. We also explored the associations of a composite measure of knowledge, attitudes, and practice (KAP) with seropositivity and found an inverse relationship between KAP for dengue control and children’s seropositivity. KAP could change due to previous infection experience, which is a potential limitation of using KAP as a potential predictor of dengue seropositivity in cross-sectional studies. Despite this, KAP remains useful for identifying gaps in knowledge, attitude, and practice that can be used to inform public health measures, such as education campaigns. Measures of social acceptability of interventions should also be considered for inclusion in similar studies, as it would provide an indication of likelihood of adoption of interventions, which provides additional insight for different dengue interventions.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".