Mapping and the application of GIS in many health related fields is becoming sophisticated and demonstrated as a valuable tool for providing evidence to guide strategies, but its application in MNH is lagging behind. The midwifery workforce assessments sought to integrate the capacity and added value of GIS technology and the approaches outlined here for population, age group, pregnancy, birth and facility mapping to produce gridded surfaces, with each 100 m by 100 m grid cell providing an estimate of the number of people, live births, and pregnancies within it. Such datasets provide exceptional flexibility in terms of enabling summarization to any level required. This may include, for example: (i) summing all grid square estimates within district boundaries to provide district-level estimates of number of pregnancies, (ii) usage of a map of urban areas overlaid onto the gridded data to provide per-city estimates of number of pregnancies, or urban rural proportions, and (iii) integration of the gridded datasets with health facility locations and road network data within travel models to quantify numbers of pregnant women residing at more than two hours from a comprehensive EmONC facility. In each of these example cases, simple national averages that do not account for subnational geography cannot reveal the spatial heterogeneities that exist in this way.
While it has been shown that accounting for sub-national heterogeneity in population attributes likely results in significant improvements in the accuracy of health metrics , it is clear that many sources of uncertainty and error remain. Primarily, it is clear that uncertainties in the output maps increase and accumulate at each stage of the process. While the location and number of total people and women of childbearing age subgroup are known with a relatively good degree of precision, the subnational variations in birth rates are less precisely known, while subnational information on pregnancies, abortions and stillbirths do not currently exist. This means for pregnancies, a reliance on the assumption of no spatial variation within a country on rates of stillbirths and abortions through the use of national-level estimates . Further, all of the census and survey-based data used here are subject to various sources of error and bias, many of which have been well-documented [33, 38, 39], while the underlying WorldPop population datasets also contain uncertainties . It is also clear that the sampling frames used in undertaking the DHS surveys mean that uncertainties exist in defining sub-national urban/rural ASFRs, with sample sizes becoming relatively small when summarizing at such levels. Nevertheless, the resultant ASFRs do still match closely with what is known about regional and urban–rural differences in each of the countries, with lower rates in urban areas and the highest rates in the most isolated and deprived rural areas of each country (Figure 3). Like most other population parameters reported for administrative polygons, the population, age, sex and fertility rate data used here are also subject to the modifiable areal unit problem, i.e. that summary measures are influenced by the administrative boundaries that they are reported at . Finally, as highlighted in the methods section, assessments of geographical access to care could be improved through the modelling of travel times, rather than simple straight-line distances.
There is clearly a need to more rigorously quantify the uncertainties inherent in spatial demographic datasets , such as those presented here to better communicate the spatial variations in reliability of input datasets and guide prioritization of additional data collection, and future work will aim to tackle this. The advancement of theory, increasing availability of computation, and growing recognition of the importance of robust handling of uncertainty have all contributed to the emergence in recent years of new, sophisticated approaches to the large-scale modeling and mapping of disease based on geostatistics (e.g. ) and small area estimation , but such methods have yet to cross over to the spatial demographic databases with which such maps are used. The regular availability of new national household surveys means that more contemporary data is continually becoming available to aid in updating and improving the accuracy of the datasets presented here, and future steps will involve the development of semi-automated systems that can rapidly adapt to new incoming data and integrate them into the output spatial datasets, alongside robust methods to account for temporal differences . Further, the linkage of these pregnancy datasets with the location of health facilities and spatial models of travel time will enable improved estimates of the spatial coverage of healthcare to be made.
Despite the limitations and caveats above the results in all four countries provide new intelligence on disaggregated population needs for MNH services and are informing the policy discourses on the distribution of health facilities and health personnel who provide MNH/midwifery services. However, not all countries maintain an accurate, current list of EmONC facilities with georeferenced codes and even fewer maintain a live database on the number, type, competency and skill levels of health personnel per facility. The absence of this basic information therefore diminishes the ability to conduct detailed analysis of supply-side constraints to respond to population need. Improvements in human resource information systems are critically needed and if linked to facility GIS codes would lead to new insights on accessibility to a skilled and competent health worker. Additionally we recognise that comparing need to supply means that we are missing an important step: women’s demand for and utilisation of MNH/midwifery services. In all four countries, coverage of antenatal care, skilled attendance at birth and postnatal care is variable, with significant difference between urban/rural areas and socio-economic quintiles. Barriers to access care are therefore beyond the geographical location of services and require triangulation with other sources of data to appreciate the realities that women experience in seeking, accessing and affording quality care during pregnancy, birth and the post-natal periods.