Feasibility and effectiveness of electronic vs. paper partograph on improving birth outcomes: A prospective crossover study design
Bibliographic record
Abstract
BACKGROUND: The partograph has been endorsed by World Health Organization (WHO) since 1994 which presents an algorithm for assessing maternal and foetal conditions and labor progression. Monitoring labour with a partograph can reduce adverse pregnancy outcomes such as prolonged labor, emergency C-sections, birth asphyxia and stillbirths. However, partograph use is still very low, particularly in low and middle income countries (LMICs). In Bangladesh the reported partograph user rate varies from 1.4% to 33.0%. Recently, an electronic version of the partograph, with the provision of online data entry and user aid for emergency clinical support, has been tested successfully in different settings. With this proven evidence, we conducted and operations research to test the feasibility and effectiveness of implementing an e-partograph, for the first time, in 2 public hospitals in Bangladesh. METHODS: We followed a prospective crossover design. Two secondary level referral hospitals, Jessore and Kushtia District Hospital (DH) were the study sites. All pregnant women who delivered in the study hospitals were the study participants. All nurse-midwives working in the labor ward of study hospitals were trained on appropriate use of both types of partograph along with standard labour management guidelines. Collected quantitative data was analyzed using SPSS 23 statistical software. Discrete variables were expressed as percentages and presented as frequency distribution and cross tabulations. Chi square tests were employed to test the association between exposure and outcome variables. Potential confounding factors were adjusted using multivariate binary logistic regression methods. Ethical approval was obtained from the institutional review board of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b). FINDINGS: In total 2918 deliveries were conducted at Jessore DH and 2312 at Kushtia DH during one-year study period. Of them, 1012 (506 in each facility) deliveries were monitored using partograph (paper or electronic). The trends of facility based C-section rates was downwards in both the hospitals; 43% to 37% in Jessore and from 36% to 25% in Kushtia Hospital. There was a significant reduction of prolonged labour with e-partograph use. In Kushtia DH, the prolonged labour rate was 42% during phase 1 with the paper version which came down to 29% during phase-2 with the e-partograph use. The similar result was observed in Jessore DH where the prolonged labour rate reduced to 7% with paper partograph from the reported 30% prolonged labour with e-partograph. The e-partograph user rate was higher than the paper partograph during both phases (phase 1: 3.31, CI: 2.04-5.38, p < .001 and in phase 2: 15.20 CI: 6.36-36.33, p < .001) after adjusting for maternal age, parity, gestational age, religion, mother's education, husband's education, and fetal sex. CONCLUSION: The partograph user rate has significantly improved with the e- partograph and was associated with an overall reduction in cesarean births. Use of the e-partograph was also associated with reduced rates of prolonged labour. This study has added to the growing body of evidence on the positive impact of e-partograph use. We recommend implementing e-partograph intervention at scale in both public and private hospitals in Bangladesh. TRIAL REGISTRATION: ClinicalTrials.gov NCT03509103.
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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 | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".