High Prevalence of Sickle Cell Disease in Low-Endemic Areas: A Pilot Study in Chunya, Tanzania
Bibliographic record
Abstract
Backround: Tanzania has the fifth highest prevalence of Sickle Cell Disease (SCD) worldwide. Annually, 11,000 children are born with SCD, but only 10% survive to their fifth birthday. Limited screening has led to underestimation of the burden in regions such as the southern highlands. The epidemiology of SCD, just like other diseases, is affected by climate change through increasing migration in search of arable land hence the shifts in the geographical prevalence of SCD from high prevalent areas to low prevalence areas. Early identification of SCD across all regions is therefore essential to improve survival, quality of life, mental health, reduce stigma, and alleviate the financial burden. Study Objective: The objective of the study was to assess the prevalence of SCD and SCT in Chunya district, Mbeya Region, Tanzania and to identify demographical factors associated with the risk of SCT among community members. Methods: A cross-sectional study on SCD was conducted in Chunya district, Mbeya Region, between 21st and 22nd February 2020. A total of 523 villagers were selected and screened for SCD and sickle cell trait (SCT) using rapid test (SICKLE SCAN (®). Results: The study revealed a notably high prevalence of SCD in southern highlands of Tanzania which highlighted the need for early screening and community-based awareness programs. The prevalence of SCD in the tested population was 1.91% and the prevalence of SCT was 8.41% of which the majority of the SCD patient were five years and below P= .02. Having a mother from Southern Zone was a protective factor (OR 0.2) against acquiring SCT while having a father from Northern Zone was a risk factor (OR 10), P value <.005. Conclusion: To reduce the burden of SCD, new strategies of screening should be developed to enable timely diagnosis and management of the disease.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".