Exploring the concept of surgical transition: surgical activity in the light of economic development in Sierra Leone, Liberia, Ghana and India
Bibliographic record
Abstract
Introduction: The surgical volume indicator measures surgical activity within a population, but it does not fully untangle the details behind the statistical indicator. As health systems evolve and countries develop economically, the types of surgeries performed, providers, and levels of healthcare facilities may provide a richer understanding of changes in surgical activity. This research studied surgical activity in four diverse settings by analyzing initial data to assess trends in patient characteristics, surgical staff, case distribution, level of care, and anesthesia practices, forming the basis for a "surgical transition" framework. Methods: We conducted a secondary analysis of surgical volume data from four studies in Sierra Leone, Liberia, Ghana, and India, to assess trends in surgical distribution. Descriptive statistics were used to compare surgical volumes by population subgroups, surgical providers, case distribution, level of care, and anesthesia. Results: Findings show that countries with higher GDP per capita had greater surgical volumes, more specialist providers, and a broader, more advanced case mix. Increases in surgical volume were most notable among older age groups, gender disparities in access diminished as systems developed. In lower-income settings, a large share of surgeries were cesarean sections or other procedures for women of reproductive age, while there were more surgeries in the older population in more advanced economies. The proportion of essential surgeries, including for example surgeries for obstetric complications, abdominal emergencies and injuries, remained stable between low- and lower-middle-income countries, decreasing only with further economic development. Specialist-performed procedures increased with economic growth, resulting in greater surgical variety and complexity. Discussion: Changes in surgical volume must be understood within the broader context of societal and economic development as well as the health system. The concept of "surgical transition" highlights how demographic and socioeconomic progress is reflected in the quantity, diversity, and complexity of surgical services. As countries advance, internal priorities, such as healthcare policies, financing, infrastructure, and service delivery mechanisms, also evolve. These factors influence surgical care delivery. Each phase of the surgical transition presents different challenges and needs. Recognizing the phase of surgical transition can help guide strategies and establish realistic interim targets for the global surgical indicators, making them more actionable tools for measuring progress and comparing systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".