Accurate health information is the cornerstone of effective decision-making and reliable assessment of disease burden and resource needs 123. Efforts to tackle the enormous burden of ill-health in low-income countries are hampered by the lack of functioning health information structures to provide reliable health statistics 45678910. Central to a fully operational Health Information Systems (HIS) is a basic inventory of all functioning health facilities and the services they provide. Such an inventory requires a spatial dimension, allowing facilities to be linked to the populations they serve and other proximate determinants of health such as environment, poverty and education. This spatial linkage can be provided by geographic information systems (GIS). The use of GIS for health services planning is widespread in developed countries 111213 but there are few examples of their development and operational use in resource poor settings in Africa 14151617.

In Kenya, a map of health facilities was produced in 1959 18 but not up-dated until 2003 15. Since then, there has been an expansion in funding and resources in the health sector following the election of a new government in 2002 that promoted the establishment of a constituency development fund (CDF) to fund local development projects 19 including the building of new health centres and dispensaries where need was defined by the constituency. To reflect these changes and map the equity of expanded service provision since 2003, we have updated the spatial audit of public health facilities in Kenya against high resolution population density maps projected to 2008.

The 2003 national health facility database recorded 3,512 public health facilities providing care to ambulatory patients including 232 hospitals; 694 health centres; 2,541 dispensaries; and 45 unspecified clinics (Table 1). The MoH managed 59% of these facilities; 33% by the mission/NGO sector and 3% by local authorities (Table 1). Following a careful triangulation of commodity distribution lists, revised health facility lists and facilities identified during the national roads mapping exercise, a total of 5,334 public health facilities providing care to ambulatory patients were identified as operational in 2008. Of these facilities the MoH was responsible for the management of 3,574 (67%), mission/NGO managed 1,484 (28%); and local authorities managed 101 (2%) while the remainder belonged to employers and other public institutions. A total of 40 unspecified clinics managed by employers and other ministries that were functioning in 2003 had closed by 2008.

Overall 1,862 more public health facilities providing out-patient services were identified in 2008 compared to a similar exercise in 2003 (Table 1) representing an overall five-year increase of 35%. Among these new facilities, 1,690 (91%) were dispensaries, representing a 37% five-year increase in this lowest level of out-patient care nationwide (Table 1). Increases in hospital (24%) and health centre (29%) level care were less dramatic and largely a result of up-grading of existing facilities to new levels of service provision rather than new constructions. The largest increases in facilities were among those managed by the MoH (43% over five years) with far fewer being found in the Mission/NGO sector (23% growth over five years) (Table 1).

In 2003 we were unable to geo-locate 267 (15%) of public health facilities and 2,292 (64%) were positioned using crude sources such as hand-drawn maps or 1:50,000 topographical maps. Very few (21%, n = 693), were located using GPS sources (Noor et al 2004). Significant improvements have been made on the precise spatial positioning of both facilities identified in 2003 and new facilities established after 2003. Through a combination of mapping methods, 93% of the MoH and 90% of the mission/NGO facilities were spatially positioned in the 2008 database. GPS coordinates were used to identify 2,048 (38%) of public health facilities and only 314 (6%) and 215 (4.3%) were identified through the use of 1:50,000 maps hand-drawn maps, respectively. By 2008, 390 (7%) of the public health facilities remained un-positioned, which would require direct consultation with personnel at district levels or bespoke field surveys to locate.

The highest percentage increase between 2003 and 2008 in public health facilities occurred in North Eastern province (70%), followed by Nyanza (40%) and Eastern (39%) with Nairobi showing the lowest increase of less than a quarter (22%) (Table 2). Overall, 89% of the population was within the national target of 5 km distance to a public health facility in 2008 compared to 71% in 2003. This improvement, however, varied widely across the country (Figure 2). Although North Eastern province registered the highest growth in public health facilities (68%), it remained the least well served with only 29% of the population within 5 km of a public health service provider in 2008, an increase of about 10% from 2003 (Table 2 & Figure 2B &2C). Of the 4 million people who were outside the 5 km benchmark in 2008, approximately 3.3 million (83%) were from the northern, predominantly pastoralist, areas of the Rift Valley, North Eastern and Eastern provinces (Table 2 & Figure 2D).

The Kenya health sector has experienced dramatic increases in the number of service providers over the last five years but their documentation, in terms of the number of health facilities and their location has been fragmented, programme specific, and difficult to integrate. It is difficult to imagine any effective service planning without an inventory of current providers spatially configure in relation to the distribution of the human population. Despite attempts in 2003 to reconcile all available information on the location, level and management of health services in Kenya 15 there remains no centralized authoritative spatially-defined inventory of health service providers in 2008. The 2003 database was provided to the MoH in 2004 with the intention that the resource would serve as the platform for future planning. In the absence of an updated, centralized database we have repeated the audit of service providers in 2008. We estimate that there has been a 34% expansion in public health facilities providing out-patient care since 2003, the majority supported by the MoH.

The construction of new health facilities has led to significant improvement in geographic access by patients to service providers from 71% of population within the national target of 5 km of public health service providers in 2003 to 89% in 2008. In spite of these improvements, sparsely populated, predominantly pastoralist, areas in the north of the country contained between 100,000 and 500,000 people outside of the required geographic access to public health care (Figure 2C) and accounted for over 80% of the approximately 4 million people living beyond 5 km from a public health service provider in 2008. It is unlikely that any construction of new health facilities will yield significant improvements in geographic access in Central, Nairobi, Nyanza and Western provinces, all of which have 97% to 100% of their population within 5 km of a public health facility. The Coast, Eastern and Rift Valley provinces already have more than 80% of their populations with appropriate geographic access to public health care and only a few strategically located additional health service providers are required to increase access, particularly in their remote sparsely populated reaches. North Eastern province, however, lags considerably behind other provinces in terms of geographic access and significant investment in new health service providers is required to bridge this gap.

In addition to monitoring geographic access, the applications of a spatially referenced inventory of service providers are manifold because they can be linked to other geo-referenced datasets. For example we have shown elsewhere that where only a minority of health facilities report their health statistics, these can be used, together with the full dataset, and with appropriate geo-statistical methods, to estimate complete disease burden coverage with acceptable accuracies at national and provincial levels 2829.Such approaches have been adapted to examine temporal changes in out-patient malaria burdens 30 and the estimation of national anti-malarial drug requirements 31. At management levels a facility inventory should provide the basis for financial planning and monitoring, stock management, personnel deployment and regular quality assurance sampling. Whether modeling disease risks or planning commodity distribution it is critical that sources of data or recipients of drugs are positioned spatially.

The development of a database of health service providers is a continuous process and its long-term success depends on a number of technical, financial and management issues. In this study, it became clear that the quality and completeness of existing health facility lists are driven by programme-specific interests to the extent that key departments within the Ministry of Health rely on different facility lists, each believed to be the 'universe' of health services in the country. In compiling the lists into a single database a major challenge has been the lack of a consistent naming of health facilities and the absence of a unique and common health facility code making data integration difficult. In addition, although 93% of the public health facilities in the 2008 database are now spatially-referenced, only 38% were mapped using 'gold-standard' GPS method, albeit an 18% increase from those in 2003. Given that most of the facilities are located in the densely populated middle belt of the country (Figure 1) and are located close to each other, it is important that the proportion of facilities mapped using GPS is increased to ensure they can be linked accurately to the populations they serve using newly available high resolution maps 24.

With publication of this paper we have released all the spatially configured data we have been able to assemble into the public domain. It is available as a database with longitude and latitude and along with metadata describing how these were established 25 along with KMZ files suitable for export to Google Earth 22. The MoH is currently up-grading its facility listings 32, however this inventory does not have facility coordinates and cannot be used in any mapping or spatial analysis. We hope that the release of our geo-referenced data will assist those concerned with the measurement of equity and service provision. This database should serve as a template for future work and we still recommend a nationwide census of public health service providers, similar to the national school mapping exercise sponsored by the USAID 21, to serve as a confirmatory exercise and improve the quality-of-care and human resource attribute data necessary to transform a population-to-provider platform into a truly valuable planning tool. An efficient system for regular updating of the health service database also needs to be put in place by the Ministry of Health. Possibilities of expanding such an exercise to the important but poorly regulated and prolific private health sector in Kenya must be explored.

CDF: Constituency Development Fund; GPS: Global positioning system; HIS: Health information system; KEMRI: Kenya Medical Research Institute; LA: Local Authority; MAP: Malaria Atlas Project; MoH: Ministry of Health; MRPW: Ministry of Roads and Public Works; NGO: Non-governmental organization; USAID: United States Agency for International Aid.

The authors declare that they have no competing interests.

AMN was responsible for study design, data cleaning, analysis, interpretation and production of the final manuscript. VAA contributed to data cleaning and analysis and production of final manuscript. PWG contributed to data, analysis, interpretation and production of the final manuscript. RWS was responsible for overall scientific management, analysis, interpretation and preparation of the final manuscript.

This study received financial support from The Wellcome Trust, UK (#058922), Roll Back Malaria Initiative, AFRO (AFRO/WHO/RBM # AF/ICP/CPC/400/XA/00); Management Sciences for Health (MSH: Cooperative Agreement #GHN-A-00-07-00002-00) and the Kenya Medical Research Institute. AMN is supported by the Wellcome Trust as a Research Training Fellow (#081829). RWS is supported by the Wellcome Trust as Principal Research Fellow (#079080). Both AMN and RWS acknowledge the support of the Kenyan Medical Research Institute.