Similarities and differences between WHO criteria and two other approaches for maternal near miss diagnosis

Menezes, Filipe Emanuel Fonseca; Galvão, Larissa Paes Leme; Mendonça, Caio Menezes Machado de; Góis, Kaique Andre do Nascimento; Ribeiro, Ruy Farias; Santos, Victor Santana; Gurgel, Ricardo Queiróz

doi:10.1111/tmi.12568

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…Process research could compare the cascade with others methods for measuring health system readiness using cost, cost-effectiveness or item response burden analyses. Outcome studies could test the cascade’s ability to predict facility-based morbidity, mortality, severe maternal outcomes or labor-related health outcomes for neonates [ 63 – 67 ]. The cascade logic could be applied to other obstetric situations [ 41 , 68 – 79 ] including perinatal emergencies or routine clinical care for uncomplicated deliveries [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Beyond signal functions in global obstetric care: Using a clinical cascade to measure emergency obstetric readiness

et al. 2018

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BackgroundGlobally, the rate of reduction in delivery-associated maternal and perinatal mortality has been slow compared to improvements in post-delivery mortality in children under five. Improving clinical readiness for basic obstetric emergencies is crucial for reducing facility-based maternal deaths. Emergency readiness is commonly assessed using tracers derived from the maternal signal functions model.Objective-methodWe compare emergency readiness using the signal functions model and a novel clinical cascade. The cascades model readiness as the proportion of facilities with resources to identify the emergency (stage 1), treat it (stage 2) and monitor-modify therapy (stage 3). Data were collected from 44 Kenyan clinics as part of an implementation trial.FindingsAlthough most facilities (77.0%) stock maternal signal function tracer drugs, far fewer have resources to practically identify and treat emergencies. In hypertensive emergencies for example, 38.6% of facilities have resources to identify the emergency (Stage 1 readiness, including sphygmomanometer, stethoscope, urine collection device, protein test). 6.8% have the resources to treat the emergency (Stage 2, consumables (IV Kit, fluids), durable goods (IV pole) and drugs (magnesium sulfate and hydralazine). No facilities could monitor or modify therapy (Stage 3). Across five maternal emergencies, the signal functions overestimate readiness by 54.5%. A consistent, step-wise pattern of readiness loss across signal functions and care stage emerged and was profoundly consistent at 33.0%.SignificanceComparing estimates from the maternal signal functions and cascades illustrates four themes. First, signal functions overestimate practical readiness by 55%. Second, the cascade’s intuitive indicators can support cross-sector health system or program planners to more precisely measure and improve emergency care. Third, adding few variables to existing readiness inventories permits step-wise modeling of readiness loss and can inform more precise interventions. Fourth, the novel aggregate readiness loss indicator provides an innovative and intuitive approach for modeling health system emergency readiness. Additional testing in diverse contexts is warranted.

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Section: Discussionmentioning

confidence: 99%

Beyond signal functions in global obstetric care: Using a clinical cascade to measure emergency obstetric readiness

et al. 2018

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“…A recent study by Menezes et al states that the WHO criteria perform well [ 12 ]. In this study, conducted in two Brazilian reference hospitals, 77 out of 1196 (6.4%) women were identified as having life-threatening conditions based on the WHO MNM tool, compared to 33.8% and 80.2% by using Waterstone’s or other literature-based criteria respectively.…”

Section: Discussionmentioning

confidence: 99%

Validating the WHO maternal near miss tool: comparing high- and low-resource settings

Witteveen

Bezstarosti

Koning

et al. 2017

BMC Pregnancy Childbirth

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BackgroundWHO proposed the WHO Maternal Near Miss (MNM) tool, classifying women according to several (potentially) life-threatening conditions, to monitor and improve quality of obstetric care. The objective of this study is to analyse merged data of one high- and two low-resource settings where this tool was applied and test whether the tool may be suitable for comparing severe maternal outcome (SMO) between these settings.MethodsUsing three cohort studies that included SMO cases, during two-year time frames in the Netherlands, Tanzania and Malawi we reassessed all SMO cases (as defined by the original studies) with the WHO MNM tool (five disease-, four intervention- and seven organ dysfunction-based criteria). Main outcome measures were prevalence of MNM criteria and case fatality rates (CFR).ResultsA total of 3172 women were studied; 2538 (80.0%) from the Netherlands, 248 (7.8%) from Tanzania and 386 (12.2%) from Malawi. Total SMO detection was 2767 (87.2%) for disease-based criteria, 2504 (78.9%) for intervention-based criteria and 1211 (38.2%) for organ dysfunction-based criteria. Including every woman who received ≥1 unit of blood in low-resource settings as life-threatening, as defined by organ dysfunction criteria, led to more equally distributed populations. In one third of all Dutch and Malawian maternal death cases, organ dysfunction criteria could not be identified from medical records.ConclusionsApplying solely organ dysfunction-based criteria may lead to underreporting of SMO. Therefore, a tool based on defining MNM only upon establishing organ failure is of limited use for comparing settings with varying resources. In low-resource settings, lowering the threshold of transfused units of blood leads to a higher detection rate of MNM. We recommend refined disease-based criteria, accompanied by a limited set of intervention- and organ dysfunction-based criteria to set a measure of severity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12884-017-1370-0) contains supplementary material, which is available to authorized users.

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“…Despite MD and MNM having two separate outcomes, MNM almost always accounts for the most characteristics of MD. Equally, in many cases, it tends to occur immediately before MD [8,12]. Studying all of them under the same umbrella will, therefore, increase the indepth understanding of the issues behind maternal and newborn health in a low-resource setting such as Tanzania.…”

Section: Introductionmentioning

confidence: 99%

Incidence and Predictors of Maternal and Perinatal Mortality among Women with Severe Maternal Outcomes: A Tanzanian Facility-Based Survey for Improving Maternal and Newborn Care

Lilungulu

Bintabara

Mujungu

et al. 2020

Obstetrics and Gynecology International

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Introduction. Maternal and perinatal mortality is still a major public health challenge in Tanzania, despite the ongoing government efforts to improve maternal and newborn care. Among the contributors to these problems is the high magnitude of severe maternal outcomes (maternal near-miss). The current study, therefore, aimed to identify the magnitude and predictors of maternal and perinatal mortality among women with severe maternal outcomes admitted to Dodoma Regional Referral Hospital. Methods. A retrospective cross-sectional study was conducted from October 2015 to January 2016 at Dodoma Regional Referral Hospital in Dodoma City. All maternal deaths and maternal near-misses based on WHO criteria were included in this study. Three outcome variables have been identified: maternal mortality, perinatal mortality, and neonatal complications. To examine the predictors for the three predetermined outcome variables, the three logit models each containing unadjusted and adjusted findings were fitted. A P-value less than 0.05 was considered indicative of statistically significant. Results. A total of 3600 pregnant women were admitted for obstetric reasons during the mentioned period. 140 of them were diagnosed with severe maternal outcomes; hence, they were included in this study. The severe maternal outcome incidence ratio was 40.23 per 1000 live births, the institutional maternal mortality ratio was 459.77 per 100000 live births, and the perinatal mortality rate was 10.83 per 1000 total births. Most of the maternal morbidity and mortality were due to direct causes in which postpartum hemorrhage and hypertensive disorders were the leading causes. In adjusted analysis, per-protocol management, maternal age, and mode of birth were predictors of maternal mortality, perinatal mortality, and neonatal complications, respectively. Conclusion. Establishing and strengthening obstetric ICUs will help reduce maternal mortality as the response time from the onset of obstetric complications, while the provision of high-quality care will be substantially reduced. Furthermore, the study recommends regular provision of in-service refresher training to emphasize the practice and compliance of per-protocol case management through a team approach in order to reduce the burden of maternal and perinatal mortality in Tanzania.

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Similarities and differences between WHO criteria and two other approaches for maternal near miss diagnosis

Cited by 11 publications

References 19 publications

Beyond signal functions in global obstetric care: Using a clinical cascade to measure emergency obstetric readiness

Beyond signal functions in global obstetric care: Using a clinical cascade to measure emergency obstetric readiness

Validating the WHO maternal near miss tool: comparing high- and low-resource settings

Incidence and Predictors of Maternal and Perinatal Mortality among Women with Severe Maternal Outcomes: A Tanzanian Facility-Based Survey for Improving Maternal and Newborn Care

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