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Dhaka, Bangladesh

Fottrell E.,University College London | Fottrell E.,Umea University | Hogberg U.,Uppsala University | Ronsmans C.,London School of Hygiene and Tropical Medicine | And 7 more authors.
Emerging Themes in Epidemiology | Year: 2014

Background: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. Results: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. Conclusion: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings. © 2014Fottrell et al.; licensee BioMed Central Ltd. Source


Younes L.,University College London | Houweling T.A.J.,University College London | Houweling T.A.J.,Rotterdam University | Azad K.,Perinatal Care Project | And 2 more authors.
BMC Pregnancy and Childbirth | Year: 2012

Background: Reducing maternal and child mortality requires focused attention on better access, utilisation and coverage of good quality health services and interventions aimed at improving maternal and newborn health among target populations, in particular, pregnant women. Intervention coverage in resource and data poor settings is rarely documented. This paper describes four different methods, and their underlying assumptions, to estimate coverage of a community mobilisation women's group intervention for maternal and newborn health among a population of pregnant women in rural Bangladesh.Methods: Primary and secondary data sources were used to estimate the intervention's coverage among pregnant women. Four methods were used: (1) direct measurement of a proxy indicator using intervention survey data; (2) direct measurement among intervention participants and modelled extrapolation based on routine longitudinal surveillance of births; (3) direct measurement among participants and modelled extrapolation based on cross-sectional measurements and national data; and (4) direct measurement among participants and modelled extrapolation based on published national data.Results: The estimated women's group intervention's coverage among pregnant women ranged from 30% to 34%, depending on method used. Differences likely reflect differing assumptions and methodological biases of the various methods.Conclusion: In the absence of complete and timely population data, choice of coverage estimation method must be based on the strengths and limitations of available methods, capacity and resources for measurement and the ultimate end user needs. Each of the methods presented and discussed here is likely to provide a useful understanding of intervention coverage at a single point in time and Methods 1 and 2 may also provide more reliable estimates of coverage trends.Footnotes: 1Unpublished data from three focus group discussions with women's group members and facilitators participating in the Women's Groups intervention. © 2012 Younes et al.; licensee BioMed Central Ltd. Source


Prost A.,University College London | Colbourn T.,University College London | Seward N.,University College London | Azad K.,Perinatal Care Project | And 28 more authors.
The Lancet | Year: 2013

Background: Maternal and neonatal mortality rates remain high in many low-income and middle-income countries. Different approaches for the improvement of birth outcomes have been used in community-based interventions, with heterogeneous effects on survival. We assessed the effects of women's groups practising participatory learning and action, compared with usual care, on birth outcomes in low-resource settings. Methods: We did a systematic review and meta-analysis of randomised controlled trials undertaken in Bangladesh, India, Malawi, and Nepal in which the effects of women's groups practising participatory learning and action were assessed to identify population-level predictors of effect on maternal mortality, neonatal mortality, and stillbirths. We also reviewed the cost-effectiveness of the women's group intervention and estimated its potential effect at scale in Countdown countries. Findings: Seven trials (119 428 births) met the inclusion criteria. Meta-analyses of all trials showed that exposure to women's groups was associated with a 37% reduction in maternal mortality (odds ratio 0·63, 95% CI 0·32-0·94), a 23% reduction in neonatal mortality (0·77, 0·65-0·90), and a 9% non-significant reduction in stillbirths (0·91, 0·79- 1·03), with high heterogeneity for maternal (I2=58·8%, p=0·024) and neonatal results (I2=64·7%, p=0·009). In the meta-regression analyses, the proportion of pregnant women in groups was linearly associated with reduction in both maternal and neonatal mortality (p=0·026 and p=0·011, respectively). A subgroup analysis of the four studies in which at least 30% of pregnant women participated in groups showed a 55% reduction in maternal mortality (0·45, 0·17-0·73) and a 33% reduction in neonatal mortality (0·67, 0·59-0·74). The intervention was cost effective by WHO standards and could save an estimated 283 000 newborn infants and 41 100 mothers per year if implemented in rural areas of 74 Countdown countries. Interpretation: With the participation of at least a third of pregnant women and adequate population coverage, women's groups practising participatory learning and action are a cost-effective strategy to improve maternal and neonatal survival in low-resource settings. © 2013. World Health Organization. Published by Elsevier Ltd/Inc/BV. All rights reserved. Source


Mason E.,World Health Organization | McDougall L.,Partnership for Maternal | Lawn J.E.,London School of Hygiene and Tropical Medicine | Lawn J.E.,Research and Evidence Division | And 13 more authors.
The Lancet | Year: 2014

Remarkable progress has been made towards halving of maternal deaths and deaths of children aged 1-59 months, although the task is incomplete. Newborn deaths and stillbirths were largely invisible in the Millennium Development Goals, and have continued to fall between maternal and child health efforts, with much slower reduction. This Series and the Every Newborn Action Plan outline mortality goals for newborn babies (ten or fewer per 1000 livebirths) and stillbirths (ten or fewer per 1000 total births) by 2035, aligning with A Promise Renewed target for children and the vision of Every Woman Every Child. To focus political attention and improve performance, goals for newborn babies and stillbirths must be recognised in the post-2015 framework, with corresponding accountability mechanisms. The four previous papers in this Every Newborn Series show the potential for a triple return on investment around the time of birth: averting maternal and newborn deaths and preventing stillbirths. Beyond survival, being counted and optimum nutrition and development is a human right for all children, including those with disabilities. Improved human capital brings economic productivity. Efforts to reach every woman and every newborn baby, close gaps in coverage, and improve equity and quality for antenatal, intrapartum, and postnatal care, especially in the poorest countries and for underserved populations, need urgent attention. We have prioritised what needs to be done differently on the basis of learning from the past decade about what has worked, and what has not. Needed now are four most important shifts: (1) intensification of political attention and leadership; (2) promotion of parent voice, supporting women, families, and communities to speak up for their newborn babies and to challenge social norms that accept these deaths as inevitable; (3) investment for effect on mortality outcome as well as harmonisation of funding; (4) implementation at scale, with particular attention to increasing of health worker numbers and skills with attention to high-quality childbirth care for newborn babies as well as mothers and children; and (5) evaluation, tracking coverage of priority interventions and packages of care with clear accountability to accelerate progress and reach the poorest groups. The Every Newborn Action Plan provides an evidence-based roadmap towards care for every woman, and a healthy start for every newborn baby, with a right to be counted, survive, and thrive wherever they are born. © 2014 Elsevier Ltd. Source


Pagel C.,University College London | Prost A.,University College London | Lewycka S.,University College London | Das S.,Society for Nutrition | And 5 more authors.
Trials | Year: 2011

Background: Public health interventions are increasingly evaluated using cluster-randomised trials in which groups rather than individuals are allocated randomly to treatment and control arms. Outcomes for individuals within the same cluster are often more correlated than outcomes for individuals in different clusters. This needs to be taken into account in sample size estimations for planned trials, but most estimates of intracluster correlation for perinatal health outcomes come from hospital-based studies and may therefore not reflect outcomes in the community. In this study we report estimates for perinatal health outcomes from community-based trials to help researchers plan future evaluations.Methods: We estimated the intracluster correlation and the coefficient of variation for a range of outcomes using data from five community-based cluster randomised controlled trials in three low-income countries: India, Bangladesh and Malawi. We also performed a simulation exercise to investigate the impact of cluster size and number of clusters on the reliability of estimates of the coefficient of variation for rare outcomes.Results: Estimates of intracluster correlation for mortality outcomes were lower than those for process outcomes, with narrower confidence intervals throughout for trials with larger numbers of clusters. Estimates of intracluster correlation for maternal mortality were particularly variable with large confidence intervals. Stratified randomisation had the effect of reducing estimates of intracluster correlation. The simulation exercise showed that estimates of intracluster correlation are much less reliable for rare outcomes such as maternal mortality. The size of the cluster had a greater impact than the number of clusters on the reliability of estimates for rare outcomes.Conclusions: The breadth of intracluster correlation estimates reported here in terms of outcomes and contexts will help researchers plan future community-based public health interventions around maternal and newborn health. Our study confirms previous work finding that estimates of intracluster correlation are associated with the prevalence of the outcome of interest, the nature of the outcome of interest (mortality or behavioural) and the size and number of clusters. Estimates of intracluster correlation for maternal mortality need to be treated with caution and a range of estimates should be used in planning future trials. © 2011 Pagel et al; licensee BioMed Central Ltd. Source

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