Disease diagnosis in primary care in Uganda
Bibliographic record
Abstract
BACKGROUND: The overall burden of disease (BOD) especially for infectious diseases is higher in Sub-Saharan Africa than other regions of the world. Existing data collected through the Health Management Information System (HMIS) may not be optimal to measure BOD. The Infectious Diseases Capacity Building Evaluation (IDCAP) cooperated with the Ugandan Ministry of Health to improve the quality of HMIS data. We describe diagnoses with associated clinical assessments and laboratory investigations of outpatients attending primary care in Uganda. METHODS: IDCAP supported HMIS data collection at 36 health center IVs in Uganda for five months (November 2009 to March 2010) prior to implementation of the IDCAP interventions. Descriptive analyses were performed on a cross-sectional dataset of 209,734 outpatient visits during this period. RESULTS: Over 500 illnesses were diagnosed. Infectious diseases accounted for 76.3% of these and over 30% of visits resulted in multiple diagnoses. Malaria (48.3%), cough/cold (19.4%), and intestinal worms (6.6%) were the most frequently diagnosed illnesses. Body weight was recorded for 36.8% of patients and less than 10% had other clinical assessments recorded. Malaria smears (64.2%) and HIV tests (12.2%) accounted for the majority of 84,638 laboratory tests ordered. Fewer than 30% of patients for whom a laboratory investigation was available to confirm the clinical impression had the specific test performed. CONCLUSIONS: We observed a broad range of diagnoses, a high percentage of multiple diagnoses including true co-morbidities, and underutilization of laboratory support. This emphasizes the complexity of illnesses to be addressed by primary healthcare workers. An improved HMIS collecting timely, quality data is needed. This would adequately describe the burden of disease and processes of care at primary care level, enable appropriate national guidelines, programs and policies and improve accountability for the quality of care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".