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(Philips et al., 2004) was available to assess the quality of economic evaluations<br />

based on a synthesis of effectiveness data from a range of papers and the<br />

construction of different <strong>for</strong>ms of economic evaluation (Appendix 2.8).<br />

2.3.2 Overall approach to and process of synthesis<br />

The approach to synthesis was driven by the research question, the types and quality<br />

of studies and the data included in the review. Heterogeneity of study design,<br />

population and intervention precluded statistical meta-analysis. The most<br />

appropriate approach – a textual narrative synthesis – was conducted. Because our<br />

review question involves two linked but distinct populations – mothers and infants –<br />

we have structured our review accordingly. For a subset of high-quality items, we<br />

conducted a causal chain analysis, also separated according to whether the outcome<br />

was maternal or infant mortality.<br />

2.3.3 Selection of studies <strong>for</strong> synthesis<br />

The narrative synthesis includes all eligible studies on the basis of the<br />

inclusion/exclusion criteria. The causal change analyses are restricted to studies<br />

identified as high quality.<br />

2.3.4 Selection of outcome data <strong>for</strong> synthesis<br />

Outcomes included maternal and infant health outcomes: maternal, infant,<br />

neonatal, perinatal, post-neonatal mortality, and still-birth; and severe acute<br />

maternal morbidity (SAMM) (haemorrhage, dystocia, hypertension, sepsis,<br />

incomplete abortion, Caesarean section (CS), hysterectomy and blood transfusion)<br />

(World Health Organization, 2011b).<br />

This review focuses on maternal and infant mortality outcomes. Process indicators,<br />

such as skilled care at birth, are often used as substitutes <strong>for</strong> mortality outcomes<br />

whose measurement poses considerable challenges. However, mortality outcomes<br />

are not necessarily improved where gains in individual process indicators are not<br />

accompanied by broader changes (Koblinsky et al., 1999). A focus on mortality<br />

outcomes is thus crucial, particularly in areas where the mortality burden is high<br />

(Campbell and Graham, 2006; Bhutta et al., 2008; Lawn et al., 2009).<br />

2.3.5 Synthesis of evidence<br />

All eligible studies were included within the qualitative analysis to establish a<br />

detailed picture of the available evidence. We first analysed the data using EPPI-<br />

Reviewer 4 and generated comprehensive tables <strong>for</strong> each code. All tables were then<br />

exported and merged in Excel 13 to produce a unique results table.<br />

Narrative synthesis was the principal approach used. Eligible studies were grouped<br />

into type of interventions – broadly grouped into clinical and non-clinical – as<br />

described later on in the report. Findings were analysed within each category <strong>for</strong> the<br />

two main target populations: mothers and infants, including neonates. While most<br />

systematic reviews focus on synthesising evidence on effectiveness from quantitative<br />

research, we wanted to develop the contribution that can be made by different<br />

types of evidence, including that involving qualitative data.<br />

In addition, causal chain analysis was per<strong>for</strong>med on included studies identified as<br />

high quality to allow the extraction and illustration of the casual path emerging from<br />

our review, linking reasoning <strong>for</strong> intervention, with experience, implementation and<br />

sustainability. We used an approach based on White’s (2009) theory-based impact<br />

evaluation, and drawing on the realist review approach of Pawson et al. (2005),<br />

13 http://office.microsoft.com/en-us/excel/<br />

Methods<br />

What are the effects of different models of delivery <strong>for</strong> improving maternal and infant health<br />

outcomes <strong>for</strong> poor people in urban areas in low income and lower middle income countries? 20

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