LIBERIA REBUILDING BASIC HEALTH SERVICES (RBHS) GEOGRAPHIC AND DEMOGRAPHIC DISTRIBUTION OF HEALTH FACILITIES IN LIBERIA 20 November 2010 Chip Barnett, M&E Director The Rebuilding Basic Health Services (RBHS) Project is funded by the United States Agency for International Development through Cooperative Agreement No: 669-A-00-09- 00001-00 and is implemented by JSI Research and Training Institute, Inc., in collaboration with Jhpiego, the Johns Hopkins University Center for Communication Programs (JHU/CCP), and Management Sciences for Health (MSH). This document is made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of JSI Research and Training Institute, Inc., and do not necessarily reflect the views of USAID or the United States Government. Executive summary Liberia’s Basic Package of Health Services (BPHS) was explicitly designed to reach all Liberians through an equitable provision of health services. While much progress has been made, a rational distribution of health facilities to provide those services remains a difficult objective to state clearly, much less achieve. This paper presents an analysis of geographic and demographic data from the 2008 Census, proposing how to measure “rational distribution”, with results that quantify the current facility distribution. One way of looking at rational distribution involves ensuring a Liberia facilities catchment populations uniform distribution of people 60 55 55 52 served by facilities; facilities should 49 50 serve approximately the same 40 number of people, having essentially 40 35 the same catchment populations. 30 25 25 27 One can also look at rational 19 18 20 distribution through the distance 14 between populations and facilities Number of facilities 10 intended to serve them. In fact, as 0 shown by the histogram at right, catchment populations vary enormously throughout the country, with 19 facilities serving fewer than 1,000 people, and 25 serving more Catchment population than 30,000 (mostly clinics). One would hope that health workers in facilities would be assigned based on population served, but in general they are not. Beyond simply the number of people that facilities serve is the question of how far people have to walk to be served by facilities. Ideally statistics would specify how far – in terms of time – people have to go to reach facilities, but in general those statistics do not exist, except from limited household surveys. The measure used in this paper is the distance in kilometers between each community in the country and the nearest facility. In the figure to the right, those distances are summarized in a map of the country with each district colored according to the average distance from communities to facilities. This paper presents both a methodology and evidence for national and county planners; it is a first step, with limitations, but a step that Average distance from communities to facilities, should be sufficient for planning purposes. by district Page 2 of 28 Introduction Since coming out of 14 years of civil war seven years ago, Liberia has made remarkable advances in a number of sectors – including health care – driven by a visionary national health policy and five-year strategic plan (2007-2011). The policy and plan laid out the establishment of the Basic Package of Health Services (BPHS) as the “cornerstone of the national health care delivery strategy” and an explicit objective to increase “equitable access to quality health care services” for the people of Liberia. The BPHS was intended to be “delivered at each level of the health system, from the lowest to the highest level of technical sophistication.” 1 While the BPHS explicitly applies to community-based as well as facility-based services, certainly the focus to date has been on fixed facilities. That focus is beginning to broaden, with a systematic effort to actively involve communities and recruit and train community volunteers to provide health education, clinical treatment for childhood illnesses, and family planning commodities. Nonetheless, no matter how many services are delivered at the community level, facilities will remain key to the BPHS, since they will serve not only as critical referral centers, but as a source of supervision and supply for community volunteers. Ensuring a rational distribution of facilities throughout the country is therefore crucial to implementation of the BPHS. The Policy envisioned such a process: “The Ministry will establish objective planning criteria… Densely populated areas will be served by larger health facilities, so as to deliver better services and attain economies of scale. Sparely settled areas will be served by many small health facilities… County health authorities will be responsible for planning the number and spatial distribution of health facilities…”2 However, it has been clear at an anecdotal level that distribution of facilities remains less than rational, with clinics staffed at the same level but serving widely varying populations. In some relatively sparely populated areas it has seemed that facilities are packed close together, whereas in others facilities are far apart. The Ministry is now developing a new 10-year strategic plan, and it is the perfect time to pursue the issue of a rational distribution of facilities. Unfortunately, it is not at all obvious how exactly to define “rational” or how to quantify “distribution.” This paper proposes several methods for addressing those dilemmas and then applies them to all communities and government health facilities in Liberia to come up with a quantitative description of the current state of facility placement. 1 National Health Policy (2007), Ministry of Health and Social Welfare (Liberia). 2 National Health Policy (2007) Page 3 of 28 Methodology There are two key characteristics of a rational distribution of facilities: facilities at the same level (e.g., clinics) would serve more or less the same population with the same numbers of staff; and no one in the country would live far from a facility. The first characteristic should be easy enough to measure, because it should be simply the catchment population of a facility. However, accurately determining a facility’s catchment population has proved difficult, and is still ongoing in Liberia. Determining how far someone lives from a facility would also seem easy to determine, giving the geographic position of all facilities and their catchment communities. Again, the reality is not so simple, both because catchment areas have not yet been defined for much of the country and because distance as the crow flies may often diverge greatly from the distance a person has to walk or ride. For the purposes of this rough analysis, two indicators were used: pseudo-catchment population of facilities, and simple Euclidean distance between communities and facilities. Details of their precise definitions and calculations are given below. Pseudo-catchment population In October and November 2009, a series of workshops were conducted in each county outside Montserrado jointly by RBHS, the MOHSW, and LISGIS, in which all (or most) OICs from government facilities huddled together with lists of communities and – in principle – assigned every community to a single facility. The plan was that LISGIS would map the resulting catchment areas of each facility, and together with community populations from the 2008 Census, a definitive catchment population for each facility could be calculated. Unfortunately, a number of obstacles impeded that plan, including incomplete data collected in some counties, a delay of many months in obtaining population data from LISGIS, and the inability of LISGIS to provide the necessary maps for validation of the workshop assignments. RBHS took on the chore of mapping communities and facilities to edit the workshop community-facility assignments, but prioritized the five main RBHS counties: Grand Cape Mount, Bong, Lofa, Nimba, and River Gee. For those five counties, accurate catchment populations are available for all government facilities in each county, though they have yet to be validated by CHTs and OICs; any changes that occur during validation are expected to be minor and should not change the populations much. For the remaining 10 counties, much more work remains, and results will take too long to inform this 10-year planning process. A new measure called “pseudo-catchment” population was therefore defined as follows: For the five main RBHS-supported counties, the pseudo-catchment population was identical to the standard catchment population; within each of the other 10 counties, a distance was calculated from each community to each government facility in the county. A community was assigned to the pseudo-catchment area of that unique facility to which the community was closest. The pseudo-catchment population of a facility was then defined as the sum of the populations of all communities to which the facility was closest. This technique ignores any geographical features such as roads and rivers that might affect which facility is most convenient for people. The presence of a road might make one facility much easier to reach than the closest facility. Conversely, the presence of a river might make the closest facility impossible to reach, diverting people to another, Page 4 of 28 farther facility. But as a first approximation, this method gives at least an accurate picture of how facilities are placed