Factors influencing severity of recurrent malaria in a conflict-affected state of South Sudan: an unmatched case-control study
Bibliographic record
Abstract
BACKGROUND: The burden of malaria remains the highest in sub-Saharan Africa and South Sudan is not an exception. The country has borne the brunt of years of chronic warfare and remains endemic of malaria, with increasing mortality and morbidity. Limited data still exists on factors influencing the recurrence of severe malaria, especially in emergency contexts such as South Sudan, affected by various conflicts and humanitarian situations. This study therefore aimed to investigate factors influencing severity of occurrence malaria in selected primary healthcare centres in South Sudan. This would assist and guide in malaria prevention, treatment, and eradication efforts. METHODS: We conducted an unmatched case-control study using routinely collected clinic data for individuals aged 1 year and above who received a diagnosis of severe malaria at 3 primary healthcare centres (PHCC); Malual Bab PHCC, Matangai PHCC and Malek PHCC between September 15, 2019 to December 15, 2019 in South Sudan. Patient characteristics were analyzed using simple descriptive statistics. Inferential statistics were also conducted to identify the associated factors influencing recurrence of severe malaria. All analyses were conducted using R Version 3.6.2. RESULTS: A total of 289 recurrent malaria cases were included in this study. More than half of the participants were female. Overall, the prevalence of severe recurrent malaria was 66.1% (191) while 74.4% (215) did not complete malaria treatment. Among those who did not complete malaria treatment, 76.7% (165) had severe recurrent malaria, while among those who completed malaria treatment 35.1% (26) had severe recurrent malaria (p < 0.001). There is a significant association between marital status (OR 0.33, 95% CI 0.19-0.56, p < 0.001), employment status (OR 0.35, 95% CI 0.14-0.87, p = 0.024), the use of preventive measures (OR 3.82, 95% CI 1.81-8.43, p < 0.001) and nutrition status (OR 0.22, 95% CI 0.13-0.37, p < 0.001). When adjusted for employment, marital status, nutritional and prevention measures in turns using Mantel-Haenszel test of association, this effect remained statistically significant. CONCLUSIONS: Our study showed that there is a high prevalence of severe recurrent malaria in South Sudan and that a significant relationship exists between severe recurrent malaria and antimalarial treatment dosage completion influenced by certain personal and social factors such as marital status, employment status, the use of preventive measures and nutrition status. Findings from our study would be useful for effective response to control and prevent malaria in endemic areas of South Sudan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".