Evaluating the Effectiveness of the Mozambique-Canada Maternal Health Abstraction Tool (MCMH Tool) in the Identification of Maternal Near Miss (MNM) Events
Bibliographic record
Abstract
Background: Maternal morbidity and mortality has long been of great developmental concern globally. In 2005, the WHO defined Maternal Near-Miss (MNM) as a woman who nearly dies from obstetrical complications during pregnancy or up to 42 days after birth but survives the event. It also developed an abstraction tool that identifies these events. The tool is divided into 3 criteria (Disease, Intervention, and Organ-dysfunction criteria). Earlier studies suggested that the Organ-dysfunction criterion was the best yardstick for identifying MNMs. However, growing research shows that this criterion is not as effective within LMICs due to the lack of necessary laboratory capacity and skilled personnel to diagnose organ system failures. Instead, countries are increasingly relying on the disease-based criterion and have adapted the original WHO tool to suit their local needs. The Mozambique-Canada Maternal Health Project near-miss abstraction tool (MCMH near-miss tool) was tailored for the local resource availability in Mozambique as part of a wider initiative aimed at reducing maternal and neonatal morbidity. The tool contains all three (3) criteria of the WHO tool in addition to two (2) additional clinical criteria, namely, “Expanded Disease” and “Co-morbidities”. It also contains important socio-demographic indicators concerning MNM patients. It is important to examine if the added clinical criteria improve the ability of the original disease criterion to identify MNMs. \nPurpose: The purpose of this study was to determine how the additional clinical criteria, namely, the “Expanded Disease” and “Co-morbidities” criteria of the MCMH abstraction tool improve the capacity of the Original WHO Disease criterion in the identification of MNM cases in the Inhambane province of Mozambique. It also aimed to examine how specific health system, geographic, and socio-demographic factors influence the identification of MNMs in Inhambane, Mozambique.\nMethods: The study utilized data obtained from the MNM 1.0 study, which was conducted across two (2) hospitals in the Inhambane province in Mozambique between August 2021 and February 2022 by researchers in the Mozambique-Canada Maternal Health Project. Approximately 2057 respondent samples were analyzed for this study. To estimate the association between the additional clinical criteria and the original disease criterion, both chi-square test of independence and kappa estimates were performed. Furthermore, multivariable logistic regression was performed to determine the \nassociation between various socio-demographic factors and the identification of MNMs based on all 3 clinical criteria.\nResults: Generally, the additional clinical criteria identified more MNMs than the original WHO Disease group. There were stronger associations between the Expanded Disease criterion markers and the WHO disease category. Out of this, hypertension was the most strongly associated and was the only marker with a moderate level of agreement with the original disease group. Contrastingly, the Co-morbidities group showed weak or no associations with the original disease group. Of note, HIV/AIDs had no significant overlap with the original WHO Disease criterion although it contributed the most to the Co-morbidities category. Concerning the socio-demographic indicators, distance from the health facility was consistently associated with MNMs regardless of the clinical criterion. Other factors like education, age, and type of hospital showed varying levels of association with MNMs depending on the clinical criterion. No associations were observed between MNMs and profession or religion. \nConclusion: In conclusion, the Expanded Disease criterion can be a useful category in expanding the ability of the original WHO Disease criterion to identify MNMs. Additionally, the study provides evidence that factors such as distance from the hospital, type of hospital, and age, could be strong predictors for recognizing MNMs especially in rural areas. Overall, this study provides information to help assess the effectiveness of MCMH near-miss tool within the Inhambane province of Mozambique. Further research is however needed to understand its usefulness across different provinces throughout Mozambique.
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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 | 0.030 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".