Geographical access to care at birth in Ghana: a barrier to safe motherhood
© Gething et al.; licensee BioMed Central Ltd. 2012
Received: 25 June 2012
Accepted: 12 November 2012
Published: 16 November 2012
Appropriate facility-based care at birth is a key determinant of safe motherhood but geographical access remains poor in many high burden regions. Despite its importance, geographical access is rarely audited systematically, preventing integration in national-level maternal health system assessment and planning. In this study, we develop a uniquely detailed set of spatially-linked data and a calibrated geospatial model to undertake a national-scale audit of geographical access to maternity care at birth in Ghana, a high-burden country typical of many in sub-Saharan Africa.
We assembled detailed spatial data on the population, health facilities, and landscape features influencing journeys. These were used in a geospatial model to estimate journey-time for all women of childbearing age (WoCBA) to their nearest health facility offering differing levels of care at birth, taking into account different transport types and availability. We calibrated the model using data on actual journeys made by women seeking care.
We found that a third of women (34%) in Ghana live beyond the clinically significant two-hour threshold from facilities likely to offer emergency obstetric and neonatal care (EmONC) classed at the ‘partial’ standard or better. Nearly half (45%) live that distance or further from ‘comprehensive’ EmONC facilities, offering life-saving blood transfusion and surgery. In the most remote regions these figures rose to 63% and 81%, respectively. Poor levels of access were found in many regions that meet international targets based on facilities-per-capita ratios.
Detailed data assembly combined with geospatial modelling can provide nation-wide audits of geographical access to care at birth to support systemic maternal health planning, human resource deployment, and strategic targeting. Current international benchmarks of maternal health care provision are inadequate for these purposes because they fail to take account of the location and accessibility of services relative to the women they serve.
Despite the prominence of international targets for maternal mortality reduction, around a third of a million women continue to die annually from complications of pregnancy or childbirth and many more suffer prolonged or permanent post-partum ill health or disability [1–3]. The greatest share of this burden, around 90%, is borne by developing nations of sub-Saharan Africa and South Asia [2, 4]. Limited access to health facilities staffed by appropriately trained personnel and offering midwifery competencies and life-saving obstetric interventions at birth is known to be a key driver of maternal mortality [5, 6]. Unlike the vertical programmes of intervention that can be effective in combating other global health challenges [7–9], improving access to effective care at birth relies on strengthening health systems and this partly explains the slow pace of improvement in access and resulting mortality reductions relative to some other global health targets [10, 11].
Access to care at birth is determined by a diverse set of factors related to both the services offered (such as their availability, quality and cost) and the population being served (such as their wealth, education, and culturally-mediated perceptions) [10, 12, 13]. Linking population and health system factors is geography: the physical accessibility of facilities to women in labour. These factors interact in potentially complex ways and in some settings, such as urban areas with robust health infrastructures , geographical distance may play only a minor role in determining levels of access and subsequent health outcomes. However, where service provision is sparse, transport infrastructures weak, and populations predominately poor, geography often presents a fundamental and insurmountable barrier to accessing adequate care at birth and therefore plays a central role in sustaining high maternal mortality [15–22]. This is manifest both in those women who die having not sought facility-based care as well as in the significant number that die en route to health facilities, en route from one hospital to another with more appropriate resources, or after arriving too late [20, 23, 24]. The delays in (i) making the decision to seek care, (ii) reaching an adequate health facility and (iii) receiving the needed care within a facility limits uptake of emergency obstetric care [11, 20, 25]. While delays (i) and (iii) have received much research attention, delay (ii) has not been studied systematically [26–28].
Given the importance of geographical access, its measurement should arguably form a central component of maternal health system assessment and strategic planning, as well as providing a key development target indicator. Measuring geographical access robustly is, however, fraught with data and methodological challenges and the result is that policy-makers typically revert to crude alternatives such as regional facility-population ratios [29, 30]. These ratios are potentially biased and inadequate proxies for auditing the true number of women able to access care at birth and therefore hamper progress towards solutions.
Assessing geographical accessibility over large regions is problematic for several reasons. First, the data requirements are considerable and rarely met in developing countries. Comprehensive data are required on the geographical distribution of both the population and the health facilities to which mothers must travel to access care. Such data sets at the required level of spatial detail, contemporariness, and completeness are the exception rather than rule in SSA . Second, it is well established that straight-line distances act as a poor proxy for the actual cost (distance, time, expense) of journeys [32–35]. This precludes straightforward Euclidean analysis of access and necessitates that the actual landscape across which journeys are made, and the availability of different means of transport, must be known with some degree of detail [36, 37]. These factors combine to mean that, whilst the importance of distance to maternal and newborn health services has been demonstrated by many small-scale studies [15–22], the detailed measurement of geographical access to maternity care at birth across national populations that might support country-level strategies for scaling up care is rare. Of only two published studies at a national level that are known to the authors, one focuses exclusively on emergency referrals rather than population access to care  and the other uses a simple distance measure that is not necessarily representative of actual journeys faced by women in labour .
Ghana is representative of most countries in sub-Saharan Africa (SSA) in that rates of maternal mortality remain unacceptably high, with estimates for 2008 of 350 (range of uncertainty 210–630) maternal deaths per 100,000 live births . It is also typical in that (i) a significant proportion of births continue to occur at home without a professionally trained health worker, (ii) the fraction that give birth at home increases dramatically with distance from main urban centres, and (iii) progress in reducing maternal deaths remains slow [6, 40]. Geographical access plays a characteristically large role in limiting uptake of maternity care services in Ghana, especially at the time of birth, and was the most commonly cited reason for non-attendance in a recent national survey . Further, a review of 322 maternal deaths occurring in Ghanaian health facilities in 2011 found delay in arrival at a health facility to be a contributing factor in nearly half (46%) of cases. Distance, rather than decision making at home, is strongly implicated in many of these delays .
In this study, we use the example of Ghana to present the first detailed national-scale assessment of geographical access to maternity care at birth in a high burden country based on a calibrated journey-time model. This represents one outcome of a four-year study that has included the assembly of comprehensive population, health facility, and landscape data, augmented by multiple existing national and sub-national population sample surveys, and the development of a geospatial framework for modelling realistic journey-times. We use these components to demonstrate the current geographical accessibility of three levels of care at birth in Ghana and identify populations where this is dangerously inadequate. We also compare these results to existing metrics of access, based on regional facilities-per-capita ratios, currently in use by decision-makers.
To maximise the realism of the model for mechanised journey-times, we carried out an initial calibration stage in which survey data on actual journey-times made by women in labour were used to find optimum model parameters which were then applied nationally. By combining the resulting per-pixel map of journey-times with a high-resolution population map for WoCBA, we generated estimates of the proportion of WoCBA able to access successive levels of care within two hours, within four hours, or more than four hours journey-time. Each of these components is now described in more detailed.
Establishing a geospatial database of health facilities
We compiled national lists of health facilities from four main sources. First, a list was obtained from the Ghana Ministry of Health containing records of 2,021 facilities of all types nationwide that included for each a description of services offered and the region (first administrative level), district (second administrative level), and town in which each facility was located. Second, a list of geo-referenced facilities was compiled by the Centre for Remote Sensing and Geographic Information Services (CERSGIS), University of Ghana that contained listings of 1,915 facilities nationwide. These two lists were combined, cross-checked and reconciled. The Ghana Ministry of Health also maintains listings of health facilities by district on a web resource. These were cross-checked with the formal listings and any additional facilities added to our database. Facilities without latitude and longitude data were geo-referenced by manual matching of listed town names to mapped locations on Google Earth and, in the remaining unresolved cases, by telephone contact with district health offices to confirm locations. Facilities that do not offer maternity services were excluded (e.g. psychiatric hospitals, supplementary feeding centres, nursing training colleges and administrative offices). Finally, a recent project by the Ghana Ministry of Health and Ghana Health Service carried out an audit of maternity facilities nationwide with accurate assessments of the level of emergency obstetric and neonatal care (EmONC) that each offers. Using an established set of nine 'signal-functions'  of potentially life-saving birth care services, all government facilities were classified as non-, partial-, basic- or comprehensive-EmONC depending on the number of signal functions available . All hospitals offering partial-EmONC or higher were extracted from this report, cross-referenced with our existing database, and geo-referenced using the process described above. Where more than one facility was listed at a single site (either because they shared the same building, or because they were geo-referenced using a village location), we retained only the highest order facility for subsequent analysis, thus avoiding any potential duplicated facility listings.
Grading facilities that provide care at birth
We focused on those health facilities providing care at childbirth, rather than antenatal or postnatal/postpartum care, as this is the crucial period within the continuum of care chain when most mortality occurs, both for women and their newborn babies . We stratified our analysis of geographical access to facilities providing three tiers of care. First, we considered a broad categorisation of 1,864 facilities of all types listed as offering any standard of care at birth (hereafter denoted as any-birth-care facilities, ABC). These spanned the complete spectrum of care from large tertiary hospitals to the most basic peripheral facilities including maternity homes as well as Community-based Health Planning and Services Initiative (CHPS) facilities. Measurement of access to this mixed level of care will overestimate true service availability because many people will be forced to bypass the simpler facilities, many of which offer only rudimentary care and no 24-h staffing. Indeed, even relatively well equipped hospitals in Ghana can offer less than 24-h cover . We included this broad category of health facilities as the theoretical point-of-entry to the health system that also represents a hypothetical best-case scenario were all such facilities fully functional and offering robust referral services.
Second, and representing the other extreme, we considered those 76 hospitals nationwide assessed as offering comprehensive-EmONC services (hereafter C-EmONC). This is a much more stringent designation, denoting hospitals providing all nine signal functions, including the availability of blood transfusion and surgical/caesarean section capability that are typically absent from other facilities. The reality for labouring women in Ghana often lies between these two extremes: a wider set of hospitals offer partial- or basic-EmONC services (where six/seven or eight signal functions are provided, respectively) that nevertheless represent a much higher degree of service than non-EmONC facilities and are able to respond appropriately to a range of birth complications. We therefore assessed access to a third intermediate category that included these partial- and basic- as well as comprehensive-EmONC hospitals (hereafter PBC-EmONC), representing 157 facilities nationwide with heterogeneous levels of care. The final geo-referenced facility database was imported into a GIS (ArcGIS 10.0) as a point shapefile for analysis.
Establishing a national topographic database
To support geospatial modelling of realistic journey-times to health facilities, detailed topographic datasets were obtained directly from CERSGIS. These included digitised topographic survey data on the national road network and additional tracks and trails, as well as other features such as rivers, lakes, and marshland that may act as a natural barrier to determine the route taken during journeys. These data stem from an unusually detailed national programme of land surveillance carried out by the Water Research Institute, CERSGIS, Department of Feeder Roads, Ghana Survey Department and the Forestry Commission of Ghana between 1995 and 2005. All layers were available as point, polygon, or line feature shapefiles and were imported into ArcGIS for subsequent analysis. These are described in more detailed in Additional file 1.
Developing a calibrated journey-time model
Cost-surface algorithms are increasingly used within GIS software to estimate journey-times across modelled landscapes [41–47]. Users first define a gridded impedance surface in which the value of each grid cell represents the estimated time required to traverse it, taking into account the size of each cell and the type of landscape feature it represents. Low impedance values are assigned to high-speed features such as roads, with much larger values for off-road or rough terrain. Barrier features can be designated as impassable or assigned very large impedance values. Destination features (e.g. health facilities) are located on the modelled landscape, and the cost-surface algorithm computes the shortest cell-by-cell route from each origin cell to its nearest destination feature. The cumulative sum of impedance values along the route provides the estimated journey-time. The accuracy of these journey-time estimates is dependent on detailed landscape data and on appropriate choice of impedance parameter values.
Cost-surface models have been used to estimate journey-time to health facilities in resource-poor settings, but have often focused only on journeys made on foot  or else have dealt simplistically with varying modes of transportation . Pedestrian journeys made without mechanised transport are potentially more straightforward to model because average speeds will tend to be relatively similar across different settings and, because different categories of paths, tracks, and roads offer broadly similar walking speeds, journeys rarely deviate from the most direct route available. We used an established parameter set to represent an average non-mechanised travel speed of 5 kmh-1 on established roads or tracks, and 2.5 kmh-1 elsewhere [32, 49]. Barriers such as rivers and lakes were given higher impedance, meaning journeys were likely to utilise established bridges and crossings where available, but in their absence could be traversed (e.g. by boat), with an appropriate time delay.
Where journeys are made by car, bus, or motorbike, speeds can vary widely according to the type of road or track, and the most direct route is often not the fastest. One approach is to use statutory speed limits as a means of parameterising impedance values for different road categories, but this makes numerous assumptions about road, vehicle, and driver characteristics that may not be valid in many settings. To maximise the realism of our mechanised journey-time model a calibration exercise was undertaken using data on real journeys made by women in labour seeking care in Ghana. Such data have previously been obtained by the IMMPACT  project, originally designed to estimate out of pocket costs for birth care [51, 52] in which a sample of women giving birth at health facilities in two regions in Ghana (Volta and Central) reported their origin (home) and destination (facility) locations, mode of transport, and time taken to make the journey. From these data, we extracted a total of 138 unique origin–destination pairs and, for each pair, an impedance grid was established from the national topographic data to model the surrounding landscape. These grids differentiated five categories of road or track, from the fastest national highways through to minor paved roads or unmade tracks (see Additional file 1: Figure A1.1). Recognising that the smallest tracks connecting households with the road network may not be captured in the database, a category for 'background' grid cells was defined, within which rivers and lakes were also defined as potential barriers (see Additional file 1: Figure A1.2). An automated algorithm was developed using Python 2.6 which allowed a large number of cost-surface models to be run within ArcGIS 10.0 for each origin–destination journey. In each run, a different set of candidate impedance parameters was assigned to the various landscape features and the resulting estimated journey-times were compared to the values reported in the IMMPACT survey. The overall performance of each parameter set was assessed by the median magnitude of errors between predicted and observed journey-times. A total of 1000 parameter sets were assessed in this way using a hierarchical grid-search that spanned the range of plausible values and the set returning the smallest median absolute error was identified. Optimum impedance parameters were identified as an average travel speed of 60 kmh-1 for national roads, 45 kmh-1 for inter-regional and regional roads, 5 kmh-1 for unmade tracks and trails and 1.75 kmh-1 for background pixels.
Implementation of a nationwide journey-time model
The impedance parameters described above were used to define a mechanised and a non-mechanised impedance grid covering all of Ghana at 100 m × 100 m spatial resolution. Cost-surface algorithms were then implemented for both transport modes to calculate journey-times to nearest ABC, PBC-EmONC and C-EmONC facilities, resulting in a total of six journey-time surfaces. Clearly, journeys made by mechanised transport will almost always be substantially faster than those on foot, and so the availability of transport to women in labour is a critically important determinant of their geographical access to care. This availability will itself be influenced by complex socioeconomic factors that will vary from place to place. To estimate the fraction of women likely to be able to use mechanised transport to seek birth care, we first obtained data collected during the Core Welfare Indicator Questionnaire (CWIQ) survey carried out by the Ghana Statistical Service in 2003 . This is a nationally representative survey which sampled 210,170 individuals from 49,003 households from all the 110 districtsa of Ghana and included a question on the mode of transport used in accessing health facilities. This survey was preferred to any direct data on, for example, car ownership, since the journey to seek care is out-of-the-ordinary and may represent a rare occasion when a taxi or bus ride is purchased, or when health facilities themselves may organise transport [6, 29]. We used an SAE approach for a unit-level model  to relate the CWIQ data to a suite of potential correlates (literacy rate; dwelling ownership; marital status; urban population; material of roof, wall and floor; main source of drinking water; type of toilet facility and main fuel used for cooking) that were also available for all enumeration areas (EAs) from the 2000 Ghana Population and Housing Census  and, thereby, impute the fraction of women in each EA likely to make mechanised versus non-mechanised journeys to seek care.
Assessment of population access to care
As a final step, the six national journey-time maps were combined with a population grid detailing the number of WoCBA (defined as 15–49 years) residing in each 100 m × 100 m grid cell (see Additional file 1: Figure A1.3). This surface is a new product produced by the AfriPop project (http://www.afripop.org) that combines high-resolution census data with satellite sensor imagery of settlements to create the most detailed population surfaces available for Africa . We created policy-relevant summary statistics by summing the number of WoCBA within each district and region that fell within three levels of geographical access: less than two hours from a given facility type, greater than two hours, and greater than four hours. These thresholds follow earlier studies and are based on both clinical factors (two hours being the estimated modal time to death for postpartum haemorrhage [57, 58] and empirical analyses showing significant successive increases in maternal case-fatality rates associated with journey-times of greater than two and four hours .
Data access and permissions
All data used in this study were either available on an unrestricted basis in the public domain, or provided with permission from the agencies described above.
Modelled journey-time surfaces
Geographical accessibility at the national level
Summary of geographical access to three levels of facility-based birth care in Ghana, by region
Travel time category
‘ABC’ facilities offering any care at birth
EmONC facilities (partial, basic or comprehensive)
EmONC facilities (comprehensive)
NATIONAL Total = 6,205,703
< 2 hrs
> 2 hrs
> 4 hrs
Ashanti Total = 1,796,118
< 2 hrs
> 2 hrs
> 4 hrs
Brong Ahafo Total = 548,345
< 2 hrs
> 2 hrs
> 4 hrs
Central Total = 496,948
< 2 hrs
> 2 hrs
> 4 hrs
Eastern Total = 624,211
< 2 hrs
> 2 hrs
> 4 hrs
Greater Accra Total = 785,183
< 2 hrs
> 2 hrs
> 4 hrs
Northern Total = 551,093
< 2 hrs
> 2 hrs
> 4 hrs
Upper East Total = 551,093
< 2 hrs
> 2 hrs
> 4 hrs
Upper West Total = 160,357
< 2 hrs
> 2 hrs
> 4 hrs
Volta Total = 472,916
< 2 hrs
> 2 hrs
> 4 hrs
Western Total = 535,956
< 2 hrs
> 2 hrs
> 4 hrs
Geographical accessibility at the regional and district level
Comparison with international metrics of access
Comparison of national and international targets for provision of comprehensive EmONC facilities with estimated levels of geographic access
Population per C-EmONC facility1
Provision of C-EmONC facilities: % of target achieved2
% women with geographical access to C-EmONC facilities
UN target (1:500,000)
National target (1:200,000)
Efforts to reduce maternal mortality by improving the quality and availability of facility-based care during childbirth will have limited impact where long distances, poor infrastructure, and lack of transport mean women are unable to physically access these services within clinically appropriate timeframes. Because geographical access is difficult to measure across entire populations, the international community has defined targets based on facility provision per capita. Here, we have used a uniquely detailed assembly of spatial data on the population, health service and topographic landscape, as well as supporting survey and census data to reconstruct realistic levels of geographical access to life-saving delivery care at a national scale. This has allowed us to audit the number of women in Ghana with dangerously poor levels of access, but also to assess the appropriateness of international targets as a benchmark indicator of adequate service provision.
We have found that geographical access in Ghana is generally poor with long distances to health facilities particularly in the rural areas. Although a high proportion (90%) of women in Ghana have relatively good access to some form of facility offering care at birth, this must be interpreted in the context of low levels of service provision at the majority of these facilities. A substantial proportion of women (around a third) live beyond two hours from any facility likely to offer partial EmONC or above and nearly half live that distance or further from comprehensive EmONC facilities offering life-saving blood transfusion and surgery. Worryingly, nearly a third of women live more than four hours from these top-tier facilities and are, thus, at substantially greater risk of dying in the event of unforeseen complications during childbirth. Our analysis also highlights the marked regional variation in geographical access, and allows detailed identification of the worst-served communities. By comparing our results to international and national targets assessed sub-nationally, we also highlighted how crude per-capita facility provision ratios can mask the actual levels of access for populations living distant from urban centres. This has potentially important consequences that extend beyond the Ghanaian context: in any setting with dispersed rural populations, the meeting of international targets is unlikely to be a guarantee of safe accessibility for large proportions of women.
Recommendations for action
A more positive interpretation of our results is that, taking into account the predicted use of mechanised versus non-mechanised transport, 90% of WoCBA in Ghana are within two hours journey-time of their nearest birthing facility of some kind. Although most of these facilities do not meet formal standards of care, many do have the capacity - or the potential, if strengthened - to deal with some complications and refer women if necessary. The Millennium Development Goals Acceleration Framework (MAF, ), as planned for 2012–2015 by the Ghana Health Services with the support of a large range of stakeholders has the potential to strengthen those facilities that currently lack the capability to deal with such complications, as well as to build up capacity for referral. The results presented in this study can support the optimum targeting of this initiative to maximise improvements in geographical access to underserved populations. Weak referral infrastructure is a key bottleneck identified by the MAF and actions should be designed to strengthen transport solutions such as ambulances and taxi drivers unions, as well as communications technologies. These initiatives must be set against the context of a growing demographic challenge in Ghana, as the number of births continues to grow due to the persistence of high fertility rates .
Limitations, caveats, and generalisability
We chose to focus in this study on access to facility-based care at birth. Whilst provision of services for safer home-based birth can form an important part of care provision in some settings, a facility-centred intrapartum-care strategy is increasingly advocated  and reflects the fact that an estimated 10-15% of women will develop unexpected and potentially life-threatening complications requiring emergency obstetric care [4, 30, 61]. We also do not consider in this analysis the potential role of antenatal care in identifying potentially high risk deliveries, but recognise that this may form an important part of an improved access strategy.
Modelling journeys accurately is a particularly complex challenge. A huge variety of factors and decision-making processes contribute to the circumstances of each individual journey made to seek care. Although we have compiled an unusually comprehensive set of data describing the landscape, it is also true that it will never be possible to capture local details that may in some cases be important. One simplification we have made is to assume a static landscape. Clearly, the quality of unmade road surfaces can vary dramatically between wet and dry seasons, and this is not captured directly in our model. The survey data on actual journey-times used to calibrate the model were, however, obtained during a 12-month period and therefore captured a spread of journey-times across all seasons . We also make some simplifying assumptions regarding travel speed and traffic flows. In urban areas and, in particular, the large conurbations of Accra and Kumasi, journey-times are often determined not by the quality of roads, but by heavy traffic which can make even short journeys extremely slow. Our calibration data were from predominately rural areas and so these effects were not captured in our model.
The analytical framework we have presented in this study can, in principle, be envisaged in any country as part of an evidence-based approach to assessing geographical access to care at birth or indeed to other facility-based health services. The extent of generalisability to new settings is likely to be determined almost entirely by the availability of adequate data, including detailed geospatial data on landscape features and facility service audits linked to georeferenced facility locations. The increasing availability of such data sources bodes well for the application of similar work across a broader swathe of countries.
The journey to an adequate health facility can represent an insurmountable barrier to women during childbirth. As the international community seeks to accelerate efforts to reduce maternal mortality, the measurement of this barrier becomes increasingly important as a platform for evidence-based strategies to improve access to care. We show here that detailed country-wide measurement of geographical access to care can be achieved in a high burden country, and that such analysis has revealed for Ghana the nature of accessibility to their evolving network of facilities. We also demonstrate how a GIS study of this type can significantly enhance the systemic information available to plan services, deploy human resources, and provide strategic intelligence to a range of policymaking efforts including the effective targeting of poorly accessible services for strengthening. Clearly, international benchmarks of service provision are inadequate for these purposes.
aThe districts in this study refer to the 110 districts created as part of the political decentralisation of Ghana in 1988 and adopted for the 2000 Ghana Population and Housing Census. Additional districts have been created recently.
Centre for Remote Sensing and Geographic Information Services
Community-based Health Planning and Services Initiative
Core Welfare Indicator Questionnaire
Emergency obstetric and neonatal care
Partial- basic- or comprehensive-EmONC
Geographical information system
Millennium-Development-Goals Acceleration Framework
Women of childbearing age.
The authors are grateful to colleagues in the Ghan a Statistical Service, the Centre for Remote Sensing & Geographic Information Services (CERSGIS), University of Ghana and the Ghana Health Service for assistance in accessing data. We thank Andy Tatem for advice and provision of the AfriPop population surfaces. We also thank the referees of this study who provided useful feedback and advice for improvement. The findings, interpretations and conclusions are those of the authors as individuals and do not necessarily reflect the views of the organisations they represent. The authors are grateful to the UK Department for International Development (DfID) and the Economic & Social Research Council (ESRC) who funded this research (# Res-167-25-0343). PWG is supported by a Wellcome Trust Senior Research Fellowship held by Professor Simon Hay (#095066). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- Hogan MC, Foreman KJ, Naghavi M, Ahn SY, Wang M, Makela SM, Lopez AD, Lozano R, Murray CJL: Maternal mortality for 181 countries, 1980–2008: a systematic analysis of progress towards Millennium Development Goal 5. Lancet. 2010, 375 (9726): 1609-1623. 10.1016/S0140-6736(10)60518-1.View ArticlePubMedGoogle Scholar
- WHO, UNICEF, UNFPA, World Bank: Trends in maternal mortality: 1990 to 2010: estimates developed by WHO, UNICEF, UNFPA and the World Bank. 2012, Geneva: World Health Organization, 59-Google Scholar
- AbouZahr C: New estimates of maternal mortality and how to interpret them: choice or confusion?. Reprod Health Matters. 2011, 19 (37): 117-128. 10.1016/S0968-8080(11)37550-7.View ArticlePubMedGoogle Scholar
- van den Broek NR, Falconer AD: Maternal mortality and Millennium Development Goal 5. Br Med Bull. 2011, 99 (1): 25-38. 10.1093/bmb/ldr033.View ArticlePubMedGoogle Scholar
- WHO, ICM, FIGO: Making pregnancy safer: the critical role of the skilled attendant. A joint statement by WHO, ICM and FIGO. 2004, Geneva: World Health Organisation, 18-Google Scholar
- Ghana Statistical Service (GSS), Ghana Health Service (GHS), Macro International: Ghana Maternal Health Survey 2007. 2009, Calverton: GSS, GHS, and Macro International, 221-Google Scholar
- Lim SS, Stein DB, Charrow A, Murray CJL: Tracking progress towards universal childhood immunisation and the impact of global initiatives: a systematic analysis of three-dose diphtheria, tetanus, and pertussis immunisation coverage. Lancet. 2008, 372 (9655): 2031-2046. 10.1016/S0140-6736(08)61869-3.View ArticlePubMedGoogle Scholar
- Noor AM, Mutheu JJ, Tatem AJ, Hay SI, Snow RW: Insecticide-treated net coverage in Africa: mapping progress in 2000–07. Lancet. 2009, 373 (9657): 58-67. 10.1016/S0140-6736(08)61596-2.View ArticlePubMedGoogle Scholar
- Palella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, Aschman DJ, Holmberg SD, Investigators HIVOS: Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. N Engl J Med. 1998, 338 (13): 853-860. 10.1056/NEJM199803263381301.View ArticlePubMedGoogle Scholar
- Lule E, Ramana GNV, Ooman N, Epp J, Huntington D, JE R: Achieving the millennium development goal of improving maternal health: determinants, interventions and challenges. HNP discussion paper. 2005, Washington, DC: The World BankGoogle Scholar
- Campbell OMR, Graham WJ: Strategies for reducing maternal mortality: getting on with what works. Lancet. 2006, 368 (9543): 1284-1299. 10.1016/S0140-6736(06)69381-1.View ArticlePubMedGoogle Scholar
- Filippi V, Ronsmans C, Campbell OMR, Graham WJ, Mills A, Borghi J, Koblinsky M, Osrin D: Maternal survival 5 - Maternal health in poor countries: the broader context and a call for action. Lancet. 2006, 368 (9546): 1535-1541. 10.1016/S0140-6736(06)69384-7.View ArticlePubMedGoogle Scholar
- Buor D, Bream K: An analysis of the determinants of maternal mortality in sub-Saharan Africa. Journal of Womens Health. 2004, 13 (8): 926-938. 10.1089/jwh.2004.13.926.View ArticleGoogle Scholar
- Matthews Z, Channon A, Neal S, Osrin D, Madise N, Stones W: Examining the “urban advantage” in maternal health care in developing countries. PLoS Med. 2010, 7 (9): e1000327-10.1371/journal.pmed.1000327.View ArticlePubMedPubMed CentralGoogle Scholar
- Gabrysch S, Cousens S, Cox J, Campbell OMR: The influence of distance and level of care on delivery place in rural zambia: a study of linked national data in a geographic information system. PLoS Med. 2011, 8 (1): e1000394-10.1371/journal.pmed.1000394.View ArticlePubMedPubMed CentralGoogle Scholar
- De Allegri M, Ridde V, Louis VR, Sarker M, Tiendrebeogo J, Ye M, Mueller O, Jahn A: Determinants of utilisation of maternal care services after the reduction of user fees: A case study from rural Burkina Faso. Health Policy. 2011, 99 (3): 210-218. 10.1016/j.healthpol.2010.10.010.View ArticlePubMedGoogle Scholar
- Titaley CR, Dibley MJ, Roberts CL: Factors associated with underutilization of antenatal care services in Indonesia: results of Indonesia Demographic and Health Survey 2002/2003 and 2007. BMC Publ Health. 2010, 10: 485-10.1186/1471-2458-10-485.View ArticleGoogle Scholar
- Malqvist M, Sohel N, Do TT, Eriksson L, Persson L-A: Distance decay in delivery care utilisation associated with neonatal mortality. A case referent study in northern Vietnam. BMC Publ Health. 2010, 10: 762-10.1186/1471-2458-10-762.View ArticleGoogle Scholar
- Gabrysch S, Campbell OMR: Still too far to walk: literature review of the determinants of delivery service use. BMC Pregnancy Childbirth. 2009, 9: 34-10.1186/1471-2393-9-34.View ArticlePubMedPubMed CentralGoogle Scholar
- Thaddeus S, Maine D: Too far to walk - maternal mortality in context. Soc Sci Med. 1994, 38 (8): 1091-1110. 10.1016/0277-9536(94)90226-7.View ArticlePubMedGoogle Scholar
- Hounton S, Chapman G, Menten J, De Brouwere V, Ensor T, Sombie I, Meda N, Ronsmans C: Accessibility and utilisation of delivery care within a skilled care initiative in rural Burkina Faso. Trop Med Int Health. 2008, 13: 44-52.View ArticlePubMedGoogle Scholar
- Buor D: Analysing the primacy of distance in the utilization of health services in the Ahafo-Ano South district, Ghana. Int J Health Plann Manage. 2003, 18 (4): 293-311. 10.1002/hpm.729.View ArticlePubMedGoogle Scholar
- Zhang LM, Ding H: Analysis of the causes of maternal death in China. Bull World Health Organ. 1988, 66 (3): 387-390.PubMedGoogle Scholar
- Kwast BE, Rochat RW, Kidane-Mariam W: Maternal Mortality in Addis Ababa, Ethiopia. Stud Fam Plann. 1986, 17 (6): 288-301. 10.2307/1966906.View ArticlePubMedGoogle Scholar
- Barnes-Josiah D, Myntti C, Augustin A: The "three delays" as a framework for examining maternal mortality in Haiti. Soc Sci Med. 1998, 46 (8): 981-993. 10.1016/S0277-9536(97)10018-1.View ArticlePubMedGoogle Scholar
- Cham M, Sundby J, Vangen S: Maternal mortality in the rural Gambia, a qualitative study on access to emergency obstetric care. Reprod Heal. 2005, 2 (1): 3-10.1186/1742-4755-2-3.View ArticleGoogle Scholar
- Essendi H, Mills S, Fotso J-C: Barriers to formal emergency obstetric care services' utilization. J Urban Health. 2011, 88: 356-369. 10.1007/s11524-010-9481-1.View ArticleGoogle Scholar
- Killewo J, Anwar I, Bashir I, Yunus M, Chakraborty J: Perceived delay in healthcare-seeking for episodes of serious illness and its implications for safe motherhood interventions in rural Bangladesh. J Health Popul Nutr. 2006, 24 (4): 403-412.PubMedPubMed CentralGoogle Scholar
- Ministry of Health (MOH), Ghana Health Service (GHS), Government of Ghana: National Assessment for Emergency Obstetric and Newborn Care. 2011, Accra: MOH, GHS, Government of Ghana, 248-Google Scholar
- WHO: Monitoring emergency obstetric care: a handbook. 2009, Geneva: World Health Organization, 152-Google Scholar
- Noor AM, Alegana VA, Gething PW, Snow RW: A spatial national health facility database for public health sector planning in Kenya in 2008. Int J Health Geogr. 2009, 8: 13-10.1186/1476-072X-8-13.View ArticlePubMedPubMed CentralGoogle Scholar
- Noor AM, Amin AA, Gething PW, Atkinson PM, Hay SI, Snow RW: Modelling distances travelled to government health services in Kenya. Trop Med Int Health. 2006, 11 (2): 188-196. 10.1111/j.1365-3156.2005.01555.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Gething PW, Noor AM, Zurovac D, Atkinson PM, Hay SI, Nixon MS, Snow RW: Empirical modelling of government health service use by children with fevers in Kenya. Acta Trop. 2004, 91 (3): 227-237. 10.1016/j.actatropica.2004.05.002.View ArticlePubMedPubMed CentralGoogle Scholar
- Costa LS, Nassi CD, Pinheiro RS, Almeida RMVR: Accessibility of selected hospitals and medical procedures by means of aerial and transit network-based measures. Health Serv Manage Res. 2003, 16 (2): 136-140. 10.1258/095148403321591456.View ArticlePubMedGoogle Scholar
- Perry B, Gesler W: Physical access to primary health care in Andean Bolivia. Soc Sci Med. 2000, 50 (9): 1177-1188. 10.1016/S0277-9536(99)00364-0.View ArticlePubMedGoogle Scholar
- The Prevention of Maternal Mortality Network: Barriers to treatment of obstetric emergencies in rural communities of West Africa. The Prevention of Maternal Mortality Network. Stud Fam Plann. 1992, 23 (5): 279-291.View ArticleGoogle Scholar
- Young JC, Garro LY: Variation in the choice of treatment in two Mexican communities. Soc Sci Med. 1982, 16 (16): 1453-1465. 10.1016/0277-9536(82)90060-0.View ArticlePubMedGoogle Scholar
- Dulin MF, Ludden TM, Tapp H, Blackwell J, de Hernandez BU, Smith HA, Furuseth OJ: Using geographic information systems (GIS) to understand a community's primary care needs. J Am Board Fam Med. 2010, 23 (1): 13-21. 10.3122/jabfm.2010.01.090135.View ArticlePubMedGoogle Scholar
- Bailey PE, Keyes EB, Parker C, Abdullah M, Kebede H, Freedman L: Using a GIS to model interventions to strengthen the emergency referral system for maternal and newborn health in Ethiopia. Int J Gynecol Obstet. 2011, 115 (3): 300-309. 10.1016/j.ijgo.2011.09.004.View ArticleGoogle Scholar
- Ghana Statistical Service (GSS), Ghana Health Service (GHS), ICF Macro: Demographic and Health Survey 2008. 2009, Accra: GSS, GHS, and ICF Macro, 483-Google Scholar
- Douglas DH: Least-cost Path in GIS Using an Accumulated Cost Surface and Slopelines. Cartographica: The International Journal for Geographic Information and Geovisualization. 1994, 31 (3): 37-51. 10.3138/D327-0323-2JUT-016M.View ArticleGoogle Scholar
- Collischonn W, Pilar JV: A direction dependent least-cost-path algorithm for roads and canals. Int J Geogr Inf Sci. 2000, 14 (4): 397-406. 10.1080/13658810050024304.View ArticleGoogle Scholar
- Teng M, Wu C, Zhou Z, Lord E, Zheng Z: Multipurpose greenway planning for changing cities: A framework integrating priorities and a least-cost path model. Landscape and Urban Planning. 2011, 103 (1): 1-14. 10.1016/j.landurbplan.2011.05.007.View ArticleGoogle Scholar
- Huck M, Jedrzejewski W, Borowik T, Jedrzejewska B, Nowak S, Myslajek RW: Analyses of least cost paths for determining effects of habitat types on landscape permeability: wolves in Poland. Acta Theriol. 2011, 56 (1): 91-101. 10.1007/s13364-010-0006-9.View ArticlePubMedGoogle Scholar
- Howey MCL: Multiple pathways across past landscapes: circuit theory as a complementary geospatial method to least cost path for modeling past movement. J Archaeol Sci. 2011, 38 (10): 2523-2535. 10.1016/j.jas.2011.03.024.View ArticleGoogle Scholar
- Choi Y, Nieto A: Optimal haulage routing of off-road dump trucks in construction and mining sites using Google Earth and a modified least-cost path algorithm. Autom Constr. 2011, 20 (7): 982-997. 10.1016/j.autcon.2011.03.015.View ArticleGoogle Scholar
- Bagli S, Geneletti D, Orsi F: Routeing of power lines through least-cost path analysis and multicriteria evaluation to minimise environmental impacts. Environmental Impact Assessment Review. 2011, 31 (3): 234-239. 10.1016/j.eiar.2010.10.003.View ArticleGoogle Scholar
- Tanser F, Gijsbertsen B, Herbst K: Modelling and understanding primary health care accessibility and utilization in rural South Africa: An exploration using a geographical information system. Soc Sci Med. 2006, 63 (3): 691-705. 10.1016/j.socscimed.2006.01.015.View ArticlePubMedGoogle Scholar
- Langmuir E: Mountain-craft and Leadership. 1984, Leicester: The Scottish Sport Council/MLTB, CordeeGoogle Scholar
- A global research initiative for the evaluation of safe motherhood intervention strategies: Available: http://www.immpact-international.org/ (accessed 17th April 2012)
- Witter S, Adjei S, Armar-Klemesu M, Graham W: Providing free maternal health care: ten lessons from an evaluation of the national delivery exemption policy in Ghana. Health Action. 2009, 2:Google Scholar
- Asante F, Chikwama C, Daniels A, Armar-Klemesu M: Evaluating the economic outcomes of the policy of fee exemption for maternal delivery care in Ghana. Ghana Med J. 2007, 41 (3): 110-117.PubMedPubMed CentralGoogle Scholar
- Ghana Statistical Service: Ghana 2003 Core Welfare Indicators Questionnaire (CWIQ II) Survey Report: Statistical Abstract In. 2005, Accra: Ghana Statistical ServiceGoogle Scholar
- Battese GE, Harter RM, Fuller WA: An error-components model for prediction of county crop areas using survey and satellite data. J Am Stat Assoc. 1988, 83 (401): 28-36. 10.1080/01621459.1988.10478561.View ArticleGoogle Scholar
- Service GS: Population and Housing Census: Summary Report of Final Results. 2000, Accra: Ghana Statistical Service, 2002-Google Scholar
- Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ: Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS One. 2012, 7 (2): e31743-10.1371/journal.pone.0031743.View ArticlePubMedPubMed CentralGoogle Scholar
- LeBacq F, Rietsema A: High maternal mortality levels and additional risk from poor accessibility in two districts of Northern Province, Zambia. Int J Epidemiol. 1997, 26 (2): 357-363. 10.1093/ije/26.2.357.View ArticleGoogle Scholar
- Kwast BE: Postpartum haemorrhage: Its contribution to maternal mortality. Midwifery. 1991, 7 (2): 64-70. 10.1016/S0266-6138(05)80229-3.View ArticlePubMedGoogle Scholar
- Pirkle CM, Fournier P, Tourigny C, Sangare K, Haddad S: Emergency Obstetrical Complications in a Rural African Setting (Kayes, Mali): The Link Between Travel Time and In-Hospital Maternal Mortality. Matern Child Health J. 2011, 15 (7): 1081-1087. 10.1007/s10995-010-0655-y.View ArticlePubMedGoogle Scholar
- Ministry of Health GoG: MDG Acceleration Framework and Country Action Plan: Maternal Health. 2011, Available: http://www.undp-gha.org/site/docs/MAF%20Ghana_MDG5_Low_Web%20(2).pdf. (Accessed April 20th 2012)Google Scholar
- UNICEF, WHO, UNFPA: Guidelines for Monitoring Availability and Use of Obstetric Services. 1997, New York: United Nations Children’s FundGoogle Scholar
- Armar-Klemesu M: Personal communication: Dr Margaret Armar-Klemesu, Noguchi Institute. 2011Google Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/991/prepub
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