Full Report - Research for Development - Department for ...

Conclusions and recommendations
very little. Only one paper (de Bernis et al., 2000) (+)/(+) looked specifically at the
link between service delivery and maternal mortality and morbidity for urban
populations with contrasting health care service provision. This study, from
Senegal, showed that an area where women were more likely to deliver in hospitals
with trained midwives had lower levels of maternal mortality than an area where
most women gave birth in district health centres, usually assisted by TBAs,
implying that access to skilled health care is fundamental. Given that the
distribution of women by socio-economic groups was very similar in both
communities, most of the effect could be attributed to these differentials in
service provision.
Our searches and analyses encountered major problems in identifying ‘poor’
populations. Whilst we took an inclusive strategy and included measures that are
frequently used as proxies for ‘poor’ (e.g., material ownership indices), much of
the evidence uncovered by our searches treated terms such as ‘poor’ and ‘poverty’
as unproblematic – especially that produced in the medical literature. There is a
need for greater clarity about the definitions used for common measures (e.g.,
urban, poor, poverty) in the reporting of interventions in the literature. Without
this clarity, the potential usefulness of high-quality studies for understanding what
works to improve the health of the urban poor is greatly reduced.
Secondary analyses of datasets such as the DHS and MICS are widely used in the
study of the urban poor (and many were identified in our initial searches but
excluded from the review). Such datasets represent an important source of
descriptive information, but their explanatory power is very limited, even when
data are disaggregated to identify the urban poor in wealth quintiles based on
material ownership. The evidence base, where it does disaggregate and identify
(however loosely) the urban poor, tends to compare and contrast with rural
populations. Whilst this approach is intuitive and relatively easy to operationalise,
especially for secondary analyses of large-scale datasets such as the DHS, we need
to remember that ‘the urban health goal is not simply to be better on average than
rural areas. The ambition must be to attain good health for all absolutely’ (Dye,
2008, p.768).
What, then, are the methodological issues involved in studying the health of poor
urban people? Our review found few studies for inclusion that acknowledged and
studied the urban poor as an individual (highly heterogeneous) group. Many
descriptive studies either compare urban poor with urban non-poor and/or rural
populations. Poor urban settings can introduce methodological issues that make
intervention delivery and evaluation difficult, above and beyond those limitations
that might exist in a LIC setting. However, it is important to remember that poor
urban populations do not just live in slums or informal settlements. There are many
poor urbanites who do not live within slums. Non-slum dwelling urban poor are just
as ‘invisible’ in the evidence our search produced.
There is a lag between researchers’ (and funders’) intervention research interests,
but also, we would argue, a degree of inertia. Long-term funding for non-urban
poor settings that evaluate health interventions make compelling arguments for
continued funding, in part to protect time series data. Many longitudinal study
sites in more rural settings have made efforts to capture population movements to
and from urban areas (Collinson and Adazu, 2006). However, the continued
concentration of, for example, Demographic Surveillance Sites in LICs and LMCs in
largely rural settings means that data with explanatory power continue to be
absent for many urban poor.
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? 56