Willingness to Know the Cause of Death and Hypothetical Acceptability of the Minimally Invasive Autopsy in Six Diverse African and Asian Settings: A Mixed Methods Socio-Behavioural Study
Bibliographic record
Abstract
BACKGROUND: The minimally invasive autopsy (MIA) is being investigated as an alternative to complete diagnostic autopsies for cause of death (CoD) investigation. Before potential implementation of the MIA in settings where post-mortem procedures are unusual, a thorough assessment of its feasibility and acceptability is essential. METHODS AND FINDINGS: We conducted a socio-behavioural study at the community level to understand local attitudes and perceptions related to death and the hypothetical feasibility and acceptability of conducting MIAs in six distinct settings in Gabon, Kenya, Mali, Mozambique, and Pakistan. A total of 504 interviews (135 key informants, 175 health providers [including formal health professionals and traditional or informal health providers], and 194 relatives of deceased people) were conducted. The constructs "willingness to know the CoD" and "hypothetical acceptability of MIAs" were quantified and analysed using the framework analysis approach to compare the occurrence of themes related to acceptability across participants. Overall, 75% (379/504) of the participants would be willing to know the CoD of a relative. The overall hypothetical acceptability of MIA on a relative was 73% (366/504). The idea of the MIA was acceptable because of its perceived simplicity and rapidity and particularly for not "mutilating" the body. Further, MIAs were believed to help prevent infectious diseases, address hereditary diseases, clarify the CoD, and avoid witchcraft accusations and conflicts within families. The main concerns regarding the procedure included the potential breach of confidentiality on the CoD, the misperception of organ removal, and the incompatibility with some religious beliefs. Formal health professionals were concerned about possible contradictions between the MIA findings and the clinical pre-mortem diagnoses. Acceptability of the MIA was equally high among Christian and Islamic communities. However, in the two predominantly Muslim countries, MIA acceptability was higher in Mali than in Pakistan. While the results of the study are encouraging for the potential use of the MIA for CoD investigation in low-income settings, they remain hypothetical, with a need for confirmation with real-life MIA implementation and in populations beyond Health and Demographic Surveillance System areas. CONCLUSIONS: This study showed a high level of interest in knowing the CoD of a relative and a high hypothetical acceptability of MIAs as a tool for CoD investigation across six distinct settings. These findings anticipate potential barriers and facilitators, both at the health facility and community level, essential for local tailoring of recommendations for future MIA implementation.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".