Task sharing in elective inguinal hernia surgery in Ghana: a workforce model comparing surgeons and physicians
Bibliographic record
Abstract
Over one million inguinal hernias were untreated in Ghana between 2012 and 2022 due to a shortage of surgeons and a lack of surgical care close to patients’ communities1,2. Task sharing is a process whereby specific roles are moved from one cadre of the workforce to another; however, both cadres provide the service to maximize population benefits. This model has shown potential for expanding access to safe surgical care in low-resource settings3. Task sharing between surgeons and non-surgeon physicians for inguinal hernia repair is being explored in Ghana to reduce unmet need2,4. An RCT is soon to be launched across 18 district hospitals in Ghana to assess the safety and clinical impact of this change in practice. In anticipation of this study, a decision-analytic model was built using published data and informed by site visits to five district hospitals. A healthcare provider perspective was adopted, and costs were estimated using a top-down approach. It was assumed that non-surgeon physicians would conduct hernia repairs for up to six years after completion of training, and anticipated volume was estimated from the data available. The cost of a hernia repair was taken from a published study comparing hernia repair undertaken by non-surgeon physicians and surgeons5. Resources for training one surgeon were calculated to equate to the resources for training 12 non-surgeon physicians at simple hernia repair (both for time and cost). The analysis estimated the cumulative number of hernia repairs performed over time by 12 non-surgeon physicians compared with one surgeon, alongside the costs of the surgery performed. The reported costs include the average cost of surgical training (€14 720), the average cost of training non-surgeon physicians (€1 225), the cost of hernia repair, and cost of recurrence and reoperation. After 6 years of repairing hernias the 12 non-surgeon physicians performed 4625 hernia repairs at a cost of €624 543(Euros), compared with 784 hernia repairs performed by a surgeon at a cost of € 109,921. As such, the non-surgeon physicians performed 3841 more hernia repairs at an additional cost of € 514,622 (Fig. 1). The cost per hernia repair, inclusive of training, was comparable (non-surgeon physicians €135, surgeon €140). In a sensitivity analysis, even if the dropout rate of non-surgeon physicians reaches 100% between 3 and 6 years, the number of hernia repairs completed will still be almost 4-fold that achieved by the surgeon and the per-procedure costs remain comparable (Fig. 1). Hernia repairs performed by 12 non-surgeon physicians (NSPs) and a surgeon, and associated costs Expanding training for surgeons in Ghana cannot address the current unmet need for inguinal hernia repair; however, short- and medium-term access can be improved by training non-surgeon physicians with comparable procedure costs. Non-surgeon physicians are not a replacement for surgeons but can be cost-effective in meeting medium-term capacity needs for specific procedures in a task-sharing capacity. One limitation of this analysis is that it did not consider the productivity gains associated with increasing access to surgery. Expanding access to hernia repair will require more resources including operating theatres and consumables. The next step is to evaluate the clinical and patient benefits of such a development, and the impact this model may have beyond the borders of Ghana. This research was funded by the National Institute for Health Research (NIHR) Global Health Research Unit on Global Surgery Grant (NIHR 16.136.79). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed are those of the authors and not necessarily those of the, the National Institute for Health Research. Mwayi Kachapila (Conceptualization, Data curation, Methodology, Formal analysis, Writing-original draft, Writing—review & editing), Stephen Tabiri (Conceptualization, Data curation, Funding acquisition, Methodology, Resources, Supervision, Writing—review & editing), Mark Monahan (Conceptualization, Data curation, Formal analysis, Methodology, Writing—original draft, Writing—review & editing), Francis Abantanga (Conceptualization, Writing—review & editing), Anita Agbeko (Writing—review & editing), Fareeda Agyei (Writing—review & editing), Aneel Bhangu (Conceptualization, Funding acquisition, Resources, Writing—review & editing), Dion Morton (Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Validation, Writing—original draft, Writing—review & editing), Tracy Roberts (Conceptualization, Methodology, Supervision, Validation, Writing—original draft, Writing—review & editing), Virginia Ledda (Writing—review & editing), Mike Ohene-Yeboah (Writing—review & editing), and Raymond Oppong (Conceptualization, Methodology, Supervision, Writing—original draft, Writing—review & editing) The authors declare no conflicts of interests. The data set used in this study is not publicly accessible but can be made available when a reasonable data request has been made to the authors.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".