Prevalence and associated determinants of malaria parasites among Kenyan children
Bibliographic record
Abstract
Approximately 80% of deaths attributed to malaria worldwide occurred mainly in Africa in 2015. Kenya is one of the major malaria endemic countries, making malaria the leading public health concern in this country. This study intended to document the prevalence of malaria and determine associated factors including socioeconomic status among children aged 6 months to 14 years in Kenya. This study analyzed the secondary data extracted from the 2015 Kenya Malaria Indicator Survey (KMIS), a cross-sectional country representative survey. Associations of demographic, socioeconomic, community-based, and behavioral factors with the prevalence of malaria in children were analyzed using multivariable logistic regression analysis. Data from 7040 children aged 6 months to 14 years were analyzed. The prevalence of malaria showed an upward trend in terms of age, with the highest prevalence among children aged 11–14 years. Prevalence was also higher among rural children (10.16%) compared to urban children (2.93%), as well as poor children (11.05%) compared to rich children (3.23%). The likelihood of having malaria was higher among children aged 10–14 years (AOR = 4.47, 95% CI = 3.33, 6.02; P < 0.001) compared with children aged under 5 years. The presence of anemia (AOR = 3.52, 95% CI = 2.78, 4.45; P < 0.001), rural residence (AOR = 1.71, 95% CI = 1.31, 2.22; P < 0.001), lack of a hanging mosquito net (AOR = 2.38, 95% CI = 1.78, 3.19; P < 0.001), primary education level of the household head (AOR = 1.15, 95% CI = 1.08, 2.25; P < 0.05), and other factors, such as the household having electricity and access to media such as television or radio, were also associated with the likelihood of infection. This study demonstrated the need to focus on awareness programs to prevent malaria and to use existing knowledge in practice to control the malaria burden in Kenya. Furthermore, this study suggests that improving the information available through the mass media and introducing behavior change communication and intervention program specifically for those of poor socioeconomic status will help to reduce malaria cases.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".