Looking beyond the Millennium Development Goal deadline for women and newborns: Evidence is needed for action on implementation of quality care
Bibliographic record
Abstract
This issue of the International Journal of Gynecology and Obstetrics (IJGO) introduces a new set of pages on “Evidence for Action” for maternal and newborn health (MNH) and survival. Given the significant increase in births in facilities and with skilled providers, quality of care has never been so important in the last push toward the Millennium Development Goals’ (MDGs) deadline and beyond. Without improvements in quality of care, avoidable deaths will continue. Without better evidence, and use of evidence on quality of care, the implementation of known interventions at scale cannot be realized. While these pages are, to a degree, modelled on the Averting Maternal Death and Disability (AMDD) pages published in IJGO between 2000 and 2011, the new pages will give greater attention to political context and the process of implementing improvements in care. Although globally MDGs 4 and 5 are unlikely to be achieved, after a quarter century of the safe motherhood movement, levels of maternal and newborn mortality have decreased and are continuing to decrease. However, gaps are increasing between Sub-Saharan Africa and other countries. Thirteen percent of the world's population live in Sub-Saharan Africa, but 62% of maternal deaths [1] and a quarter (28%) of newborn deaths occur in this region [2]; and 38 of the 48 Sub-Saharan African countries have maternal mortality ratios greater than 300 deaths per 100 000 live births [1]. For many of these countries progress has been slow since 1990 and both neonatal mortality and stillbirth rates remain high [3]. This is due to continuing poor quality of care at birth for newborns. The majority of these maternal and newborn deaths are avoidable and can be tackled by improving care for both women and newborns around the time of birth. Despite this, too little progress has been made over the last 25 years. There is a growing consensus that lack of political will and focus, weak governance, and lack of effective accountability mechanisms at all levels have contributed to poor progress in this region. In spite of great strides in understanding why these deaths occur and how to prevent them, a lag in implementation remains and knowledge has not produced markedly improved outcomes. Although the means of improvement have been known for decades, it is widely believed that countries lack the political will to implement the knowledge to improve the well-being of a population with little voice—women and children. It is in this context that the UK Government has funded Evidence for Action (E4A), a five-year program (2012 − 2016) in six African countries with the worst MNH outcomes in the region (Ethiopia, Ghana, Malawi, Nigeria, Sierra Leone, and Tanzania). E4A's approach is markedly different from previous international development investments by the UK Government and presents further opportunity for implementation science. At the heart of the program's design is the hypothesis that strategically packaged evidence will inspire and fuel effective and targeted advocacy and accountability efforts that will result in decisions and actions to change the level, distribution, and use of resources and so improve the availability and quality of MNH services necessary to save maternal and newborn lives. Evidence of success in other sectors within health—such as smoking [4], tuberculosis, polio, and malaria [5]—strongly suggests that inputs aimed at strengthening the interaction between evidence, accountability, and advocacy can achieve the changes in political will and priority necessary to result in decisions and actions. The E4A series of papers in IJGO will present the means to increase implementation of proven methods in MNH. Selected sets of articles that will be included over the next two years, twice yearly, will contain articles highlighting the use of evidence to fuel implementation through advocacy or accountability mechanisms. This will also include a few shorter pieces from the E4A country offices presenting documented examples of how evidence is being mobilized to move political will and improve quality of care. In this current issue, the articles introduce readers to the E4A program's theoretical framework and activities [6], present the methods used to collect baseline monitoring data on evidence of political will and the use of evidence [7], the use of scorecards to assess facility readiness for delivering emergency obstetric and newborn care [8], and the establishment of a country level accountability mechanism [9]. Articles in future issues will focus on issues such as quality of care, human resources, and accountability in maternal health. Contributors to these selected papers will be invited from E4A teams and other collaborating international and in-country organizations including government, UN agencies, academics, civil society actors, and service providers. In the face of competing priorities, both globally and nationally it is important not to lose focus on the unmet MDGs 4 and 5. The Partnership for Maternal, Newborn and Child Health at the global level and the Campaign on Accelerated Reduction of Maternal, Newborn and Child Mortality in Africa (CARMMA) at the regional level are making concerted efforts to ensure the survival of mothers and newborns. The efficiency of global resource use for these improvements has to be fueled by evidence that is powerful, and can inform and stimulate action.
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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 | 0.031 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".