Seroprevalence of Bordetella pertussis infection in children 1–14 years old: Indonesia basic health research (Riskesdas) 2013 and 2018 data
Bibliographic record
Abstract
Bordetella pertussis infection is a highly contagious respiratory disease that can cause complications such as pneumonia and death. A total of 62,646 cases of pertussis worldwide were reported by WHO in 2022. This study aimed to obtain the pertussis seroprevalence and sociodemographic data in children aged 1-14 years and its association factors in the community based on Riskesdas 2013 and 2018. Bivariate and multivariate analysis was carried out on data from 12,753 children aged 1-14 years collected from Riskesdas 2013 and 2018 in Indonesia. Pertussis serology data was obtained based on the results of the ELISA examination which was categorized as seropositive if anti-pertussis toxin IgG ≥ 100 IU/mL or anti-pertussis IgG > 11 NTU. Pertussis seropositive indicated recent pertussis infection if no pertussis vaccine was received within the last twelve months. Pertussis seroprevalence was found at 9.8% and 33.4% in Riskesdas 2013 and 2018 respectively. While 10.1% of children aged 5-14 years were found pertussis seropositive by excluding the possible effect of vaccination in the last twelve months in Riskesdas 2013. The most important associated factor in seropositive pertussis at ages 1-4 years and 5-14 years was a history of pneumonia in the last month (OR = 2.709, 95%CI: 2.592-2.831 in Riskesdas 2013 and OR = 2.421, 95%CI: 2.299-2.550 in Riskesdas 2018). In the adjusted analysis for respondents' characteristics, low maternal education was the predictive factor that most influenced pertussis seropositivity, especially in the 2013 Riskesdas (APOR = 2.983, 95%CI: 2.670-3.333). In conclusion, the results of this study showed that the seroprevalence of pertussis was high, especially in children aged 5-14 years, so that pertussis vaccine booster administration could be considered. Because the most influencing factor towards pertussis seropositive was low maternal education, the groups of children with low-educated mothers should be targets for strengthening complete vaccination coverage and disease control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".