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and birthing care services, including the costs of promoting behaviour change to
encourage the use of skilled care in different districts of Tanzania and Kenya
(Boulenger and Dmytraczenko, 2007).
Synthesis results
It is also possible to use economic modelling techniques to look at the economic
case for scaling up access to a package of appropriate services. One excellent
example of a high-quality modelling study, by Goldie et al. (2010), sought to look at
this across rural and urban areas in India. While it does not indicate methods by
which access would be increased, nor look specifically at the urban poor population,
it does illustrate that scaling up services can be very cost effective. Reducing the
level of unmet need for family planning services alone, or coupled with the
elimination of unmet need for safe abortion services, would be very cost effective,
reducing the lifetime risk of death due to maternal complications in urban India from
1 in 119 to 1 in 155 or 1 in 173 respectively. Projected costs avoided for a single
cohort of 15-year-old women over their lifetimes could be as much as US$ 120
million. A package of measures to scale up fully integrated services, including
intrapartum care as well as family planning and abortion services, would have an
incremental cost per year of life saved of between US$200 and $900. This would be
considered cost effective in an Indian context. Darmstadt et al. (2008) also modelled
the cost-effectiveness of scaling up 16 interventions to tackle infant mortality in 60
low income countries in sub-Saharan Africa and south-east Asia. Scaling up of
intrapartum care, in particular given the more limited availability of services in
these countries was shown in this economic modelling exercise to be the most cost
effective strategy if a step-wise approach to scaling up was required due to limited
resources. Similarly Adam et al. (2005), in observing that there are a number of cost
effective maternal and child health interventions, suggested that scaling these up to
95 percent population coverage would halve neonatal and maternal deaths.
The evidence base might also be strengthened by looking at literature from
countries that were excluded on the grounds of their World Bank classification, but
that nonetheless have large disparities in income and access to maternal and infant
health services. For example, Horton et al. (1996) looked at the cost-effectiveness
of breastfeeding promotion programmes in Brazil and Mexico, concluding that they
are among the best value buys for policy makers who wished to reduce the incidence
and number of diarrhoea-related deaths. As this study noted, ‘maternity services
that have already eliminated formula [through promotion of breast feeding] can, by
investing from $2 to $3 per birth, prevent diarrhoea cases and deaths for $3.50 to
$6.75 per case, and $550 to $800 per death respectively, with DALYs gained at $12
to $19 each’. The results of this economic analysis might be adapted to low and
middle income country settings to provide further information on the potential costeffectiveness
of promotion programmes.
4.2 Synthesis of evidence: causal chain analysis
The causal chain analysis included papers of high quality only (n=21). These were
studies that scored ++ in both the internal and external validity checklist. For the
reasons highlighted in Section 3.2, we will only conduct a narrative causal chain
analysis based on these 21 studies. They are subdivided for neonatal/infant and
maternal interventions.
What are the effects of different models of delivery for improving maternal and infant health
outcomes for poor people in urban areas in low income and lower middle income countries? 45