Prescription patterns and drug use among pregnant women with febrile Illnesses in Uganda: a survey in out-patient clinics
Bibliographic record
Abstract
Malaria is a public health problem in Uganda; affecting mainly women and children. Effective treatment has been hampered by over-diagnosis and over-treatment with anti-malarial drugs among patients presenting with fever. In order to understand the effect of drug pressure on sulfadoxine-pyrimethamine (SP) resistance in pregnancy, a sample of pregnant women presenting with fever in out–patient clinics was studied. The main objective was to assess prescription patterns and drug use in pregnancy especially SP; and draw implications on the efficacy of SP for intermittent preventive treatment of malaria in pregnancy (IPTp). A total of 998 pregnant women with a history of fever were interviewed and blood samples taken for diagnosis of malaria and HIV infections. Data were captured on the drugs prescribed for the current febrile episode and previous use of drugs especially SP, anti-retroviral drugs (ARVs) and cotrimoxazole. Few pregnant women, 128 (12.8%) were parasitaemic for P.falciparum; and of these, 72 (56.3%) received first-line treatment with Artemether-lumefantrine (Coartem®) 14 (10.9%) SP and 33 (25.8%) quinine. Of the parasite negative patients (non-malarial fevers), 186 (21.4%) received Coartem, 423 (48.6%) SP and 19 (2.1%) cotrimoxazole. Overall, malaria was appropriately treated in 35.5% of cases. Almost all febrile pregnant women, 91.1%, were sleeping under a mosquito net. The majority of them, 911 (91.3%), accepted to have an HIV test done and 92 (9.2%) were HIV positive. Of the HIV positive women, 23 (25.0%) were on ARVs, 10 (10.9%) on cotrimoxazole and 30 (32.6%) on SP. A significant proportion of women, 40 (43.5%), were on both SP and cotrimoxazole. Age and occupation were associated with diagnosis and treatment of malaria and HIV infections. There is inappropriate treatment of malaria and non-malarial fevers among pregnant women in these facilities. This is due to non-adherence to the guidelines. Over-prescription and use of anti-malarial drugs, especially SP may have implications on resistance against SP for malaria prevention in pregnancy. The policy implications of these findings are to evaluate SP efficacy as IPTp; and the need to enforce adherence to the current clinical treatment guidelines.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".