Most common reasons for primary care visits in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
With the Covid-19 pandemic and the introduction of the WHO's Essential Diagnostics List (EDL), increasing global attention is focused on the crucial role of diagnostics in achieving universal health coverage. To create national EDLs and to aid health system planning, it is vital to understand the most common conditions with which people present at primary care health facilities. We undertook a systematic review of the most common reasons for primary care visits in low- and middle-income countries. Six databases were searched for articles published between January 2009 and December 2019, with the search updated on MEDLINE to January 2021. Data on the most common patient reasons for encounter (RFEs) and provider diagnoses were collected. 17 of 22,279 screened articles were included. Most studies used unvalidated diagnostic classification systems or presented provider diagnosis data grouped by organ system, rather than presenting specific diagnoses. No studies included data from low-income countries. Only four studies (from Brazil, India, Nigeria and South Africa) using the ICPC-2 classification system contained RFE and provider diagnosis data and could be pooled. The top five RFEs from the four studies were headache, fever, back or low back symptom, cough and pain general/multiple sites. The top five diagnoses were uncomplicated hypertension, upper respiratory tract infection, type 2 diabetes, malaria and health maintenance/prevention. No psychological symptoms were among the top 10 pooled RFEs. There was more variation in top diagnoses between studies than top RFEs, showing the importance of creating location-specific lists of essential diagnostics for primary care. Future studies should aim to sample primary care facilities from across their country of study and use ICPC-3 to report both patient RFEs and provider diagnoses.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".