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

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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,

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farther facility. But as a first approximation, this method gives at least an accurate picture of how facilities are placed in terms of nearby populations.

Community-facility distance As described above, each community was associated with the closest facility and the distance between the two was calculated. That distance became the measure of how far someone in that community lives from a facility. Its calculation required use of spherical geometry, using the expression given here, where (LatC , LonC) and (LatF , LonF) are the latitude and longitude of the community and facility, respectively, all in radians:

cos [sin( ) × sin( ) + cos( ) × cos( ) × cos ( )] × 6371. −1 Calculation of𝐿𝐿𝐿𝐿𝐿𝐿 the𝐶𝐶 distances𝐿𝐿𝐿𝐿𝐿𝐿 required𝐹𝐹 surmounting𝐿𝐿𝐿𝐿𝐿𝐿𝐶𝐶 𝐿𝐿𝐿𝐿𝐿𝐿 another𝐹𝐹 obstacle:𝐿𝐿𝐿𝐿𝐿𝐿𝐹𝐹 −while𝐿𝐿𝐿𝐿𝐿𝐿 𝐶𝐶the facility position coordinates (obtained from the MOHSW facility database) were given in decimal degrees, the community coordinates (obtained from LISGIS) were in meters, using a UTM projection. The community coordinates were converted into decimal degrees using a complicated Excel spreadsheet available at http://www.uwgb.edu/dutchs/UsefulData/HowUseExcel.HTM .

Various statistics were calculated for the distances determined as above, both for Liberia as a whole and for individual counties: • Based on the number of communities th th o Mean, median, 10 percentile, and 90 percentile distance o Proportion of communities within 2 km, 5km, 10km, 15km, and 20 km of facilities • Based on the population of communities th th o Mean, median, 10 percentile, and 90 percentile distance o Proportion of people living within 2 km, 5km, 10km, 15km, and 20 km of facilities. Those statistics were calculated using Stata/IC 11.1, where the second set of statistics were derived by weighting each community by its population (using Stata’s fweight option).

District maps For high-level planning purposes, raw numbers are difficult to process. Maps are much easier for visualizing large mounds of data at a glance. For that reason, various statistics were calculated for each district in the country: total population and statistics related to the distance from communities to the nearest facility (mean, median, 10th percentile, and 90th percentile). Maps of Liberia were created using Quantum GIS software (open source, free software) that shade each district according the statistic of interest. That allows checking, at a glance, where in the country people live on average far from facilities, suggesting where county planners need to focus their efforts. Annex 1 gives the complete list of 137 districts with the statistics described above.

Limitations While this analysis was done carefully and rigorously, it is intended to be only a first step, sufficient for informing the 10-year planning process, but needing substantial refinement in the more leisurely future. Several issues need to be considered when interpreting the results to follow: Page 5 of 28

• The pseudo-catchment populations described here represent a compromise: a reasonable estimate but not a substitute for true catchment populations, which underlie calculation of coverage indicators. RBHS will work with the Ministry, CHTs, and the Ministry’s other NGO partners to push forward the process of calculating accurate catchment populations. • The distance as calculated here is only a rough approximation of the reality of how far people live from facilities. A much better indicator of how away facilities are would be measured in units of time, not distance: how long it takes people to get from their home to the nearest facility. RBHS has conducted quarterly “dipstick” surveys for the past year to assess exposure in RBHS-supported counties to the Take-Cover campaign advocating use of mosquito nets, and the past three surveys have included a question addressing exactly that indicator. Of 440 respondents, 46% reported living one hour or less from a facility, while 10% reported living more than 3 hours away. Those results, of course, are highly subjective, but a more objective result would be prohibitively expensive and time-consuming to obtain. • The version of the Ministry’s facility database used here had a number of inaccuracies. If the database were updated, it would be very easy to revise this analysis and calculate new results. o Some position coordinates were clearly wrong (despite this being the database that was cleaned just last year, including verification of positions). Those were corrected for facilities in RBHS counties, but no attempt was made to check the accuracy of other facilities’ positions. o Only facilities marked as “Public” in the database were used in this analysis, though in at least a couple of cases, facilities marked “Private” were actually public (and were therefore used). It seemed clear from facility names that some facilities marked “Public” were actually private, and those were discarded (e.g., facilities named “FPAL Clinic”), but others may remain. Annex 2 contains a list of all facilities used in this analysis with the average distance from each of its pseudo-catchment communities. o The facility database also includes a field indicating whether a facility is functioning or not, but in many cases, facilities were marked as non- functional, yet were being supported by an NGO or the Pool Fund, clear evidence of being functional. Because of such issues, the functionality of facilities according to the database was not considered for this analysis.

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Results To answer the first question posed above – are facilities Liberia facilities pseudo-catchment populations at the same level serving more or less the same 70 63

populations? – the pseudo- 60 47 48 catchment population of 50 43 40 each facility was calculated. 39 38 40 32 Figure 1 shows a histogram 30 with the height of each bar 18 19 20 16 giving the number of 11 facilities falling into the Number of facilities 10 pseudo-catchment 0 population range shown on the horizontal axis.

Clearly the range of catchment populations is Catchment population enormous. One might speculate that the problem Figure 1 is that Figure 1 includes results from all types of facilities – health centers and hospitals as well as clinics – which might be expected to have larger catchment Liberia clinics pseudo-catchment populations populations. But in fact, 70 essentially the same 59 60 distribution holds when 50 45 45 health centers and hospitals 40 are removed from the 40 36 36 calculations, as shown in 30 27 19 Figure 2; the only effect has 16 20 13 15 been to halve the size of the of Number clinics tail (facilities with pseudo- 10 4 catchment populations 0 greater than 20,000). One might also speculate that Montserrado County is influencing the right-hand side of the graph, with many Catchment population facilities having large pseudo-catchment Figure 2 populations, but in fact if Montserrado County facilities are removed, the distribution is nearly identical to Figure 2. Overall, 47% of facilities have pseudo-catchment populations of less than 5,000; excluding Montserrado, that figure rises slightly to 53%. In any case, approximately half the facilities in Liberia serve fewer than 5,000 people.

The next question of interest is how far communities – and people – are from facilities. Table 1 addresses the question of communities, showing, for each county, the percentage of communities within 2, 5, 10, 15, and 20 km from the nearest facility. The Page 7 of 28 table is sorted so that the counties with communities farther away (more than 10 km) are at the top. The national total is shown in the middle of the table to make more explicit how each county matches up with the national statistics.

Table 1: % of communities within X km of catchment health facility County ≤ 2 km ≤ 5 km ≤ 10 km ≤ 15 km ≤ 20 km Bong 11 31 67 88 96.5 Grand Kru 26 37 68 84 94.1 Grand Bassa 12 34 75 90 93.8 Gbarpolu 13 34 77 93 99.5 Nimba 14 40 77 89 93.5 Grand Gedeh 30 51 78 88 95.9 Grand Cape Mount 15 49 80 92 96.4 Sinoe 16 51 81 95 98.3 Total 24 50 82 94 97.4 River Gee 23 48 82 93 99.1 River Cess 11 46 86 99 99.8 Margibi 18 56 89 98 99.5 Bomi 11 43 90 100 100 Lofa 24 57 90 98 99.9 Maryland 38 68 92 99 100 Montserrado 83 96 99 99.8 99.99

Figure 3 displays a different statistic, graphically comparing each county and – for each of the 414 facilities included in the analysis – the average of the distances from the facilities to the communities in its pseudo-catchment area.

Distance from communities to facilities

9.0 8.5 8.2 8.0 8.0 8.0 7.1 6.8 6.9 7.0 6.3 5.9 6.1 6.1 5.5 6.0 5.1 5.0 4.0 facility (km) distance facility

- 4.0 2.8 3.0 2.0 1.0 0.0 Average community Average

Figure 3 Page 8 of 28

Figure 4 combines all counties together to show the full distribution of community- facility distances for all 16,753 communities in the country. Each bar represents the percentage of communities with a to-facility distance that falls in the range shown on the horizontal axis.

Distances from communities to health facilities All counties 25

20

15

10

Percentage of communities of Percentage 5

0 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 Distance to catchment facility (km) Number of communities=16,753; Mean=6.3 km; Median=5.0 km; 10%ile=0.8 km; 90%ile=12.9 km

Figure 4

While an analysis of communities and facilities is of interest in its own right, more critical for planning purposes is the population of those communities. For example, if many communities are far from facilities, but have just a handful of inhabitants, then the problem is not as severe as if those communities were highly populated. Table 2 therefore shows data similar to Table 1, but now looking at how far individual people live from facilities, meaning that large communities are weighted more than small ones.

Table 2: % of population within X km of catchment health facility County ≤ 2 km ≤ 5 km ≤ 10 km ≤ 15 km ≤ 20 km Bomi 16 45 92 100 100 Bong 27 47 77 91 97.4 Gbarpolu 29 45 81 95 99.8 Grand Cape Mount 29 64 86 94 98.0 Grand Bassa 31 51 79 93 95.1 Grand Gedeh 45 59 78 88 96.1 Grand Kru 37 52 81 94 98.8 Lofa 44 68 92 98 99.7 Margibi 31 67 88 99 99.9 Maryland 53 78 96 99.6 100 Page 9 of 28

Table 2: % of population within X km of catchment health facility County ≤ 2 km ≤ 5 km ≤ 10 km ≤ 15 km ≤ 20 km Montserrado 61 83 96 99 99.9 Nimba 23 50 83 93 97.3 River Cess 17 47 87 98 99.98 River Gee 42 63 87 93 97.7 Sinoe 32 54 77 94 99.0 Total 48 69 89 96 98.7

Figure 5 graphically compares the 15 counties for the percentage of population living within 5 km of a facility. In the figure and in Table 5, the counties are not sorted, but displayed in alphabetical order. Still, from the figure one can easily see that Bomi and Gbarpolu have the lowest proportion of people living with 5 km of a facility, while Montserrado has the highest.

Population within 5 km of health facility 100 90 83 78 80 68 67 70 64 63 59 60 51 52 54 47 50 47 50 45 45 40

% of population % of 30 20 10 0

Figure 5

Figure 6 is similar to Figure 4 above, but showing populations, not communities; since large communities tend to be near facilities, the distribution is shifted well to the left.

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Distances to health facilities All Counties 50

40

30

20 Percentage of population of Percentage 10

0 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 Distance to catchment facility (km) Total population=3,476,608; Mean=4.3 km; Median=2.2 km; 10%ile=0.4 km; 90%ile=10.5 km

Figure 6

As observed earlier, the statistics presented so far are fine as far as they go, but it is difficult to get an overall perspective of the country and where gaps are. Figure 7 shows a map of the country with county and district boundaries, and with each district colored according to its population. There are three different colors, each assigned to a district depending on what percentile that county’s average community-facility distance falls into: green=bottom third (least populous), yellow=middle third, and red=upper third (most populous); that is, each color represents one third of the country’s districts.

Taken alone, Figure 7 says nothing new, Figure 7: District populations and in fact says nothing at all about facilities. However, Figure 8 takes the next and final step, using a similar format to show how far people live from facilities, allowing identification of areas with poor coverage. The same colors are used, but this time to show districts that have an average community distance of less than 5 km (green), between 5 and 10 km (yellow), and more than 10 km (red). The figure vividly shows that about half Liberia’s counties have districts in which an average community lies more than 10 km from any government

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health facility. Moreover, every county – including Montserrado – has at least one district with an average community distance of greater than 5 km.

Figure 8: Average distance from communities to facilities, by district

While these results quantify the distribution of facilities in Liberia, the question becomes how to take the next step, how to rationally distribute facilities? There is no automated process that can definitively answer that question, but technology can assist planners, giving them tools to see the effect of facility placement and graphically demonstrating for each county where gaps in facility coverage lie.

Take Bomi County as an example. It is a small county, but Table 2 shows that it has the lowest proportion of communities within 5 km of a facility. However, it has among the higher proportions of communities within 10 km of a facility, and in fact all four districts have an average community distance of less than 10 km. That suggests that it would not require many resources to improve coverage. Consider the following map, Figure 9. It shows all communities in the county, as well as all government health facilities. Around each health facility is drawn a 5-km shaded circle; any community not covered by a circle is more than 5 km from a facility, and clusters of such communities should be a focus of county planners. However, two other factors must come into play. One is the population of such communities, with more populated communities earning higher priority. The other is the terrain near the communities and the presence of roads and rivers. In the figure, populations are shown with colors and symbols as defined in the legend. Rivers and roads are shown in the map, but are difficult to see here. However, one can see that most of Suehn Mecca District, in the east of Bomi, has a facility gap, but there is a north-south road that might be useful. By contrast, in the northern part of Klay and Suehn Mecca districts, there are a number of poorly covered facilities, but no roads, making planning more difficult.

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<31 people

31-121 people >121 people

Figure 9: Comparison of health facility placement and communities in Bomi County

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Conclusions As expected, people living in Montserrado have easy access to government health facilities, at least in terms of distance. The experience in other counties is not nearly so consistent, as Figure 8 clearly shows. Both Grand Gedeh and Nimba Counties, to take a northeast example, have some districts in which people live far from facilities, while in other districts they live much closer. There are other counties, such as Bong, where most districts have people living far from facilities, except for the far north of the county.

This analysis reveals gaps in both physical placement of facilities (e.g., one district in with no facility at all) and staffing of facilities that is too uniform (as evidenced by a wide variation in catchment populations). Certainly it provides fodder for careful consideration at the county level to allocation of resources, whether that means building new facilities or shifting staff from facilities with low catchment populations to those serving many more people.

Such policy implications are well beyond the scope of this paper. The intent here is to provide hard data that can inform 10-year planning at both the national and county levels. It is also intended to establish – or at least propose – a methodology for analyzing the tricky issues at the table. RBHS stands ready to modify the analysis as necessary to suit the immediate and long-term needs of planners, including providing more detailed data in spreadsheet form that were too bulky to include in this document. Tools such as those described above can be tailored to help county planners investigate the rational distribution of facilities within their counties.

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Annex 1: Distance statistics by district

County District Population Mean Median 10th %ile 90th %ile Bomi Dowein 13,188 3.7 3.6 1.2 6.5 Bomi Klay 23,397 5.6 5.3 1.2 10.8 Bomi Senjeh 30,027 6.8 7.2 3.9 9.9 Bomi Suehn Mecca 17,507 6.9 6.8 2.6 10.5 Bong Boinsen 8,210 7.8 8.6 2.0 12.9 Bong Fuamah 28,823 12.2 10.2 2.3 25.6 Bong Jorquelleh 79,129 8.7 8.4 1.4 16.9 Bong Kokoyah 3,702 20.6 19.4 14.2 26.4 Bong Kpaai 25,949 5.2 4.9 1.3 9.5 Bong Panta 16,473 4.3 3.6 0.4 8.2 Bong Salala 43,617 6.6 6.6 1.9 10.9 Bong Sanoyeah 30,330 9.5 9.4 3.2 15.6 Bong Suakoko 29,180 10.2 10.4 2.8 16.8 Bong Tukpahblee 11,731 7.0 5.8 2.2 15.2 Bong Yeallequelleh 36,097 7.9 7.5 2.5 13.7 Bong Zota 20,240 5.6 5.0 1.3 9.1 Gbarpolu Belleh 15,257 7.6 6.2 0.4 15.3 Gbarpolu Bokomu 9,873 6.3 6.4 2.8 10.1 Gbarpolu Bopolu 17,719 7.4 6.8 1.3 13.1 Gbarpolu Gbarma 15,851 5.8 5.8 1.5 10.1 Gbarpolu Gounwolaila 11,196 6.6 6.8 0.4 13.2 Gbarpolu Kongba 4,162 4.9 5.0 0.4 11.2 Gbarpolu Koninga 9,330 10.1 9.5 2.3 18.6 Grand Bassa Commonwealth-B 34,893 0.7 0.7 0.3 1.2 Grand Bassa District # 1 24,612 8.3 7.6 3.1 14.8 Grand Bassa District # 2 25,722 15.2 11.0 3.6 32.9 Grand Bassa District # 3 49,525 7.3 7.2 1.8 12.3 Grand Bassa District # 4 30,454 9.7 9.3 4.1 15.2 Grand Bassa Neekreen 32,563 4.6 4.6 1.6 7.9 Grand Bassa Owensgrove 13,666 5.0 4.7 1.8 8.6 Grand Bassa St. John River City 10,010 6.3 6.5 2.5 9.9 Grand Cape Mount Commonwealth-C 1,792 0.5 0.5 0.1 0.9 Grand Cape Mount Garwula 26,936 5.5 5.2 1.2 10.4 Grand Cape Mount Golakonneh 23,518 10.8 9.3 2.6 20.8 Grand Cape Mount Porkpa 42,615 7.3 5.8 1.0 15.2 Grand Cape Mount Tewor 27,460 3.6 3.4 0.3 6.5 Grand Gedeh B'hai 10,367 6.1 6.7 1.1 10.3 Grand Gedeh Cavala 13,314 5.4 4.8 0.6 9.6 Grand Gedeh Gbao 12,324 4.7 4.1 0.6 10.3 Grand Gedeh Gboe-Ploe 6,271 27.6 24.9 14.2 44.0 Grand Gedeh Glio-Twarbo 9,030 11.7 13.7 3.7 18.8 Grand Gedeh Konobo 24,705 10.4 10.5 0.8 18.7

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County District Population Mean Median 10th %ile 90th %ile Grand Gedeh Putu 16,426 5.0 5.0 0.3 8.3 Grand Gedeh Tchien 32,821 3.5 1.5 0.5 8.7 Grand Kru Barclayville 11,573 5.6 3.6 0.6 11.1 Grand Kru Bleebo 1,710 3.8 5.4 0.3 6.8 Grand Kru Bolloh 1,917 7.6 7.6 3.1 12.1 Grand Kru Buah 643 17.6 16.5 12.4 22.8 Grand Kru Dorbor 2,364 7.3 6.8 3.3 12.2 Grand Kru Dweh 928 20.0 18.8 15.9 25.0 Grand Kru Felo-Jekwi 2,011 2.7 0.6 0.3 11.0 Grand Kru Fenetoe 1,696 1.7 1.3 0.0 4.1 Grand Kru Forpoh 1,545 8.7 8.1 6.4 12.6 Grand Kru Garraway 61,225 3.1 4.9 1.7 13.0 Grand Kru Gee 2,543 14.8 14.4 13.7 16.2 Grand Kru Grand Cess Wedabo 10,809 4.2 3.3 0.4 9.6 Grand Kru Kpi 1,597 17.5 17.0 11.3 23.3 Grand Kru Lower Jloh 1,285 14.1 14.4 9.6 19.4 Grand Kru Nrokwia-Wesldow 1,876 10.6 11.4 1.3 14.6 Grand Kru Trenbo 3,631 4.9 5.6 0.4 7.0 Grand Kru Upper Jloh 1,573 5.6 5.7 3.8 8.0 Grand Kru Wlogba 687 7.6 7.6 7.3 7.9 Lofa Foya 73,312 4.2 3.6 1.1 8.7 Lofa Kolahun 60,557 5.4 4.1 0.8 11.7 Lofa Quardu Boundi 18,785 5.6 5.3 0.5 12.3 Lofa Salayea 23,578 4.9 4.3 0.5 8.9 Lofa Vahun 17,137 7.5 7.3 1.0 14.0 Lofa 42,790 4.6 4.2 0.6 8.6 Lofa Zorzor 40,704 6.5 6.3 0.8 11.4 Margibi Firestone 62,236 7.7 8.1 2.5 13.7 Margibi Gibi 14,250 6.7 6.2 1.9 12.0 Margibi 88,704 4.8 4.0 1.5 8.4 Margibi Mambah Kaba 44,981 5.6 6.0 1.1 10.3 Maryland Gwelekpoken 10,060 4.6 4.1 0.6 7.3 Maryland Harper 38,024 2.0 1.3 0.4 4.5 Maryland Karluway#1 8,494 5.8 6.0 0.6 10.2 Maryland Karluway#2 17,159 6.1 5.5 1.3 12.9 Maryland Nyorken 10,057 3.6 3.5 0.3 6.8 Maryland Pleebo/Sodoken 43,223 3.8 3.0 0.4 9.0 Maryland Whojah 8,921 7.5 7.6 0.7 16.4 Montserrado Greater 970,824 1.1 1.0 0.3 2.1 Nimba Boe & Quilla 18,262 6.6 6.9 2.2 10.7 Nimba Buu-Yao 40,007 4.8 4.5 0.7 8.4 Nimba Doe 35,918 13.7 14.3 2.7 25.5 Nimba Garr-Bain 9,525 6.2 2.5 0.3 7.6 Nimba Gbehlay-Geh 32,176 3.2 2.7 0.5 6.2

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County District Population Mean Median 10th %ile 90th %ile Nimba Gbi & Doru 8,131 39.3 34.9 14.5 68.0 Nimba Gbor 10,875 5.3 5.4 2.1 8.5 Nimba Kparblee 11,424 4.7 4.5 0.6 9.0 Nimba Leewehpea-Mahn 24,747 4.8 4.6 2.1 8.0 Nimba Meinpea-Mahn 24,157 6.8 7.6 2.9 9.8 Nimba Mahn 25,370 5.8 5.3 1.2 11.1 Nimba Twan River 37,479 7.7 7.7 2.8 12.7 Nimba Wee-Gbehyi-Mahn 32,934 5.0 4.8 1.0 10.2 Nimba Yarmein 22,718 8.8 8.7 2.8 13.6 Nimba Yarpea Mahn 21,647 5.5 5.1 2.0 8.7 Nimba Yarwein Mehnsonnoh 25,584 8.1 8.2 3.5 12.7 Nimba Zoe-Gbao 29,372 5.5 5.1 1.4 9.3 River Gee Chedepo 10,518 3.6 2.1 0.3 9.2 River Gee Gbeapo 10,934 1.8 1.0 0.2 5.0 River Gee Glaro 4,992 8.9 7.2 0.7 18.6 River Gee Karforh 5,956 4.7 3.2 2.1 6.9 River Gee Nanee 6,002 14.1 15.6 6.1 22.4 River Gee Nyenawliken 5,159 5.2 4.1 0.8 9.7 River Gee Nyenebo 5,703 4.8 5.2 0.3 8.7 River Gee Potupo 7,337 5.9 6.6 0.8 10.2 River Gee Sarbo 5,320 3.6 3.3 0.4 8.0 River Gee Tuobo 4,868 7.9 7.9 1.4 12.9 Rivercess Beawor 3,854 9.6 9.5 4.8 14.4 Rivercess Central Rivercess 8,303 7.9 7.2 2.0 14.1 Rivercess Doedain 13,041 7.1 6.2 2.2 12.1 Rivercess Fen River 12,630 5.0 4.6 2.1 8.0 Rivercess Jo River 8,921 6.3 5.3 2.0 11.5 Rivercess Norwein 13,900 4.9 4.5 1.7 8.9 Rivercess Sam Gbalor 3,714 5.1 5.5 1.3 7.6 Rivercess Zarflahn 7,146 6.3 6.6 1.4 10.0 Rural Montserrado Careysburg 29,712 3.9 3.8 1.3 7.3 Rural Montserrado Commonwealth 16,631 3.1 2.4 0.8 6.2 Rural Montserrado St. Paul River 71,831 5.5 4.4 1.0 12.3 Rural Montserrado Todee 33,998 6.4 5.8 2.2 11.6 Sinoe Bodae 3,539 10.3 10.5 6.7 12.4 Sinoe Bokon 4,373 2.4 2.7 0.3 4.9 Sinoe Butaw 3,432 6.0 5.6 2.2 9.7 Sinoe Dugbe River 9,239 7.2 6.7 3.4 12.5 Sinoe Greenville 15,715 2.4 2.7 0.4 4.6 Sinoe Jaedae 3,539 5.1 3.8 1.0 11.5 Sinoe Jeadepo 7,895 3.6 3.2 0.4 7.7 Sinoe Juarzon 6,088 10.4 10.9 6.4 13.2 Sinoe Kpayan 10,661 5.1 4.0 1.3 11.5 Sinoe Kulu Shaw Boe 8,555 6.1 4.6 1.2 13.1

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County District Population Mean Median 10th %ile 90th %ile Sinoe Plahn Nyarn 6,677 5.4 5.1 0.4 8.9 Sinoe Pynes Town 3,067 6.4 6.0 0.3 12.5 Sinoe Sanquin Dist# 1 2,118 5.2 5.5 1.4 8.9 Sinoe Sanquin Dist# 2 3,152 18.1 17.4 9.6 25.0 Sinoe Sanquin Dist# 3 3,256 5.6 5.4 3.1 9.0 Sinoe Seekon 7,024 9.7 9.1 4.0 16.4 Sinoe Wedjah 4,061 10.8 12.4 3.7 16.1

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Annex 2: Health facilities with pseudo-catchment populations and average community distances

County Facility Population Avg distance Bomi Beafinnie Community Clinic 2,170 6.1 Bomi Beh Town Clinic 2,134 6.3 Bomi Bonjeh Community Clinic 1,833 2.6 Bomi Dagweh Community Clinic 2,262 3.3 Bomi Fefeh Clinic 2,012 1.8 Bomi Goghen Clinic 6,147 7.8 Bomi Gonjeh Clinic 1,582 4.9 Bomi Gonzipo Clinic 6,918 6.4 Bomi Jenneh #3 Clinic 2,117 1.9 Bomi Liberia Government Hospital 18,788 7.6 Bomi Malema Community Clinic 7,828 5.5 Bomi Mecca Community Clinic 5,498 7.4 Bomi Sackie Town Clinic 1,071 4.5 Bomi Sasstown Clinic 4,613 3.3 Bomi Suehn Community Clinic 8,560 6.2 Bomi Vortor Community Clinic 1,498 3.3 Bomi Weawolo Community Clinic 2,397 4.5 Bomi Yomo Town Clinic 1,404 7.4 Bomi Zordee Community Clinic 5,287 6.1 Bong Bah-ta Clinic 9,855 7.2 Bong Belefanai Clinic 4,375 5.6 Bong Bellemu Clinic 3,404 3.1 Bong Bong Mines OPD 15,675 10.8 Bong Botota Clinic 8,420 11.3 Bong C.B. Dunbar Health Center 60,717 9.9 Bong Fenutoli Clinic 6,862 8.9 Bong Foequelleh Clinic 5,346 4.0 Bong Garmu Clinic 6,422 4.5 Bong Gbalatuah Clinic 3,376 4.6 Bong Gbansu Sulonmah Clinic 1,525 6.1 Bong Gbarnla Clinic 6,392 7.9 Bong Gbartala Clinic 18,111 8.0 Bong Gbecohn Health Center 7,013 12.4 Bong Gbonota Clinic 9,556 7.2 Bong Haindii Clinic 13,148 13.5 Bong Janyea Clinic 3,708 5.3 Bong Jorwah Clinic 1,919 3.0 Bong Kpaai Clinic 944 2.0 Bong Naama Clinic 7,298 8.4 Bong Palala Clinic 12,291 6.4 Bong Phebe OPD 28,769 10.0

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County Facility Population Avg distance Bong Salala Clinic 20,700 6.1 Bong Samay Clinic 10,996 7.9 Bong Sanoyea Health Center 18,717 10.1 Bong Shankpalai Clinic 3,694 4.4 Bong Tokpaipolu Clinic 1,802 5.8 Bong Totota Clinic 21,152 7.2 Bong Yila Clinic 2,822 4.7 Bong Zeanzue Clinic 9,155 7.2 Bong Zebay Clinic 3,739 3.8 Bong Zowienta Clinic 5,578 4.9 Gbarpolu Ante/ORT 1,174 8.4 Gbarpolu Bambuta Clinic 2,428 6.7 Gbarpolu Camp Alph 158 11.5 Gbarpolu Chief Jallahlone Medical Hospital 25,835 2.6 Gbarpolu Fassama Bade Clinic 7,750 8.1 Gbarpolu Gatima 4,135 10.6 Gbarpolu Gbaayama Clinic 3,613 8.7 Gbarpolu Gbangay 2,739 6.5 Gbarpolu Gbarma Clinic 9,644 7.6 Gbarpolu Gumgbeta 6,526 6.7 Gbarpolu Henry Town Clinic 3,104 5.0 Gbarpolu Kologbandi 3,530 6.8 Gbarpolu Kparyeakwele Clinic 4,053 6.8 Gbarpolu Kungbor Community Clinic 4,841 6.2 Gbarpolu Nomofama 2,523 6.4 Gbarpolu Palakwell 4,502 6.2 Gbarpolu Tarkpoima Clinic 1,975 5.0 Gbarpolu Timba 2,791 10.5 Gbarpolu Toigli 3,607 7.4 Gbarpolu Totoquelleh Clinic 1,215 6.0 Gbarpolu Weasua Clinic 2,847 5.0 Gbarpolu Yangayah Clinic 1,767 4.7 Gbarpolu Zuei 4,464 8.9 Grand Bassa Barseegiah Clinic 12,924 15.9 Grand Bassa Boeglay Town 11,537 9.8 Grand Bassa Bokay Town Clinic 9,494 6.8 Grand Bassa Camphor Mission Clinic 6,872 5.4 Grand Bassa Civil Compound Clinic 3,578 4.9 Grand Bassa Compound # 3 Clinic 18,411 5.5 Grand Bassa Compound # 4 7,991 8.1 Grand Bassa Compound #2 8,272 7.9 Grand Bassa Desoe Town Clinic 2,975 6.1 Grand Bassa Edina Clinic 2,217 5.5 Grand Bassa Foster Town Clinic 3,602 7.5

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County Facility Population Avg distance Grand Bassa Garduor Clinic 6,561 10.5 Grand Bassa Harmonville Clinic 5,061 5.9 Grand Bassa Island Mission Clinic 1,374 3.4 Grand Bassa Jacob Lateh Clinic 8,736 9.6 Grand Bassa Liberia Govt Hospital 11,920 0.8 Grand Bassa Little Bassa Clinic 3,475 6.7 Grand Bassa Little Kola Clinic 9,733 8.0 Grand Bassa Lloydsville Clinic 10,651 7.2 Grand Bassa Neor Town Tubmanville 7,113 6.0 Grand Bassa Owensgrove Clinic 3,003 2.6 Grand Bassa Own your Own 483 2.1 Grand Bassa Red Cross 1,112 3.4 Grand Bassa SATMH - Mittal Steel Hosp 619 1.2 Grand Bassa Senyah Community Clinic 14,505 17.5 Grand Bassa St. John Clinic 7,340 5.9 Grand Bassa Sue Clinic 3,726 7.5 Grand Bassa Upper Buchanan/ORT 11,403 0.7 Grand Bassa Well Baby Clinic 27,005 2.4 Grand Cape Mount Bamballa Clinic 10,463 9.9 Grand Cape Mount Bangorma Community Clinic 3,004 3.7 Grand Cape Mount Bendaja Community Clinic 12,226 4.0 Grand Cape Mount Bendu Clinic 2,720 5.0 Grand Cape Mount Bo Waterside Clinic 3,337 2.5 Grand Cape Mount Bomboja Clinic 2,417 4.9 Grand Cape Mount Damballa Health Center 9,141 4.0 Grand Cape Mount Diah Community Clinic 4,516 4.7 Grand Cape Mount Fahnja Clinic 1,098 3.9 Grand Cape Mount Fanti Town Clinic 1,792 0.5 Grand Cape Mount Gondama Clinic 2,270 3.2 Grand Cape Mount Gonelor Community Clinic 1,028 1.9 Grand Cape Mount Jene-wonde Clinic 1,261 1.3 Grand Cape Mount Jundu Community Clinic 1,983 4.0 Grand Cape Mount Karnga Clinic 1,041 2.1 Grand Cape Mount Kawelahun Clinic 3,854 11.9 Grand Cape Mount Kongo Clinic 5,949 6.8 Grand Cape Mount Kpeneji Clinic 1,788 2.9 Grand Cape Mount Kulangor Community Clinic 2,809 5.0 Grand Cape Mount Lofa Bridge Clinic 8,092 15.4 Grand Cape Mount Madina Community Clinic 6,988 5.9 Grand Cape Mount Mambo Community Clinic 2,253 3.4 Grand Cape Mount M'baloma Clinic 4,848 10.0 Grand Cape Mount Sembelehun Clinic 1,560 6.4 Grand Cape Mount Sinje Health Center 8,654 6.1 Grand Cape Mount St. Timothy Hospital 1,729 0.8

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County Facility Population Avg distance Grand Cape Mount Tahn Gola Konneh Clinic 5,941 9.3 Grand Cape Mount Tahn Mafa Clinic 3,064 3.5 Grand Cape Mount Talla Community Clinic 1,529 6.2 Grand Cape Mount Tiene Community Clinic 4,220 3.2 Grand Cape Mount Varguaye clinic 4,458 8.4 Grand Cape Mount Zaway Community Clinic 1,043 6.8 Grand Gedeh Beh Town Clinic 1,955 6.3 Grand Gedeh Gboleken Clinic 2,633 4.7 Grand Gedeh Gorbowrogba Clinic 423 4.9 Grand Gedeh Janzon Clinic 4,740 5.8 Grand Gedeh Jarwodee Clinic 6,480 19.9 Grand Gedeh Karlorwleh Clinic 4,356 7.3 Grand Gedeh Kumah Town Clinic 1,530 3.3 Martha Tubman Memorial Grand Gedeh Hospital 9,064 3.6 Grand Gedeh Pennizon Clinic 756 1.4 Grand Gedeh Pennoken Clinic 7,307 5.4 Grand Gedeh Polar Clinic 3,467 7.4 Grand Gedeh Putu Jarwodee Clinic 5,437 5.4 Grand Gedeh Tarwroken 8,680 10.8 Grand Gedeh Toe Town Clinic 8,390 6.8 Grand Gedeh Toffoi Town Clinic 3,568 8.4 Grand Gedeh Tuzon Clinic 1,545 3.9 Grand Gedeh Zai Town Clinic 8,566 8.3 Grand Gedeh Ziah Town Clinic 21,897 10.0 Grand Gedeh Zleh Town Clinic 10,548 5.9 Grand Kru Barclayville Health Center 6,292 4.5 Grand Kru Barforwin 2,057 6.8 Grand Kru Behwan Health Center 3,705 4.6 Grand Kru Buah Clinic 9,321 13.6 Grand Kru Garraway Clinic 3,514 2.1 Grand Kru Gbaken Clinic 5,075 7.2 Grand Kru Gblebo Health Clinic 4,426 9.3 Grand Kru Genoyah 2,293 2.7 Grand Kru Juduken 3,085 4.6 Grand Kru Niful 2,513 10.6 Grand Kru Nyankupo 4,359 9.4 Grand Kru Picnicess Clinic 2,903 1.9 Grand Kru Rally Time Hospital 3,840 2.4 Grand Kru Sass Town Health Center 2,450 3.0 Grand Kru Wilsonville 2,080 3.7 Lofa Balagwalazu Clinic 2,501 12.0 Lofa Balakpalasu Clinic 1,273 2.2 Lofa Barkedu Clinic 12,372 6.0

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County Facility Population Avg distance Lofa Barziwen Clinic 2,288 6.1 Lofa Bazagizia Clinic 1,147 2.7 Lofa Bolahun Health Center 4,723 2.7 Lofa Bondi Clinic 1,997 4.4 Lofa Borkeza Clinic 7,110 4.8 Lofa Duogomai Clinic 2,507 5.1 Lofa Fangoda Clinic 13,096 4.0 Lofa Fassawolu Health Clinic 1,346 2.9 Lofa Fissebu Clinic 9,827 6.4 Lofa Foya Community Clinic 32,461 3.9 Lofa Foya Tengia Clinic 3,583 2.5 Lofa Ganglota Clinic 4,049 4.8 Lofa Gbanway Clinic 1,137 0.4 Lofa Gbonyea Clinic 2,460 11.9 Lofa Gondolahun Clinic 4,900 10.9 Lofa Gorlu Clinic 3,416 3.1 Lofa Kamatahun Clinic 4,711 5.1 Lofa Kiantahun Health Clinic 3,778 3.9 Lofa Kolahun Hospital 14,053 6.7 Lofa Konia Health Center 8,729 6.7 Lofa Korworhun Clinic 5,042 4.0 Lofa Kpademai Clinic 1,000 4.8 Lofa Kpaiyea Clinic 2,118 2.9 Lofa Kpakamai Clinic 1,644 3.9 Lofa Kpotomai Clinic 1,329 6.3 Lofa Lawalazu Clinic 6,771 5.4 Lofa Leingbamba Clinic 3,638 3.3 Lofa Lukasa Health Clinic 3,761 8.2 Lofa Luyeama Clinic 1,407 3.1 Lofa Nyandemoilahun Clinic 3,202 4.6 Lofa Popatahun Health Clinic 2,789 2.5 Lofa Porluma Clinic 7,181 3.3 Lofa Salayea Clinic 5,449 2.8 Lofa Sarkonedu Clinic 6,564 3.9 Lofa Shello Clinic 9,895 6.1 Lofa Sorlumba Clinic 10,107 5.1 Lofa Sucromu Clinic 2,551 1.8 Lofa Tellewoyan Hospital 19,027 3.1 Lofa Vahun Clinic 17,137 7.5 Lofa Vezala Clinic 3,295 5.3 Lofa Worsonga Clinic 2,745 3.1 Lofa Yarpuah Clinic 2,951 3.7 Lofa Yeala Clinic 1,944 4.8 Lofa Yekpedu 3,261 2.7

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County Facility Population Avg distance Lofa Zenalormai Clinic 2,246 7.5 Lofa Zolowo Clinic 6,345 7.5 Margibi C.H. Rennie Hospital 49,008 4.5 Margibi Charlesville Clinic 3,369 3.5 Margibi Dolo's Town 66,687 5.2 Margibi Gbaye Town 1,911 2.9 Margibi Lakayta Town 7,277 4.5 Margibi Marshall 2,950 4.9 Margibi Peter's Town 4,203 4.9 Margibi S. Rubber C. 7,071 3.9 Margibi Schifflin 10,989 7.6 Margibi Tucker's Town Clinic 6,159 3.4 Margibi Unification Town Clinic 638 1.6 Margibi Velleta Clinic 3,061 4.2 Margibi Wohm Clinic 8,716 7.3 Margibi Wolola 6,765 4.1 Margibi WRC Clinic 11,530 6.6 Margibi Yarnwullie Clinic 4,847 4.3 Margibi Yeamen 4,858 8.7 Margibi Zeeworth 9,884 6.8 Maryland Barraken Clinic 3,417 3.2 Maryland Boniken Clinic 5,936 4.8 Maryland Cavalla Kunokadi 1,536 5.9 Maryland Cavalla Clinic 3,974 2.2 Maryland Feloken Clinic 3,804 2.9 Maryland Fish Town Clinic 1,978 1.1 Maryland Gbawiliken Clinic 3,718 2.6 Maryland Glofarken Clinic 8,205 6.4 Maryland J. J. Dossen Hospital 24,278 1.1 Maryland Juluken #1 Clinic 8,587 5.0 Maryland Karloken Clinic 7,535 6.1 Maryland Little Wlebo Clinic 587 3.8 Maryland Newaken Clinic 5,982 6.7 Maryland Plebo Health Center 34,829 3.3 Maryland Pougbaken Clinic 4,417 4.7 Maryland Pullah Clinic 2,551 3.7 Maryland Rock Town Clinic 2,204 0.5 Maryland Rock Town Kunokudi Clinic 1,339 2.7 Maryland Sedoken 3,448 4.3 Maryland Yediaken Clinic 7,613 7.1 Montserrado A F Russel 5,337 1.9 Montserrado Bardnersville H C 12,625 1.4 Montserrado Benson Hospital 24,411 0.8 Montserrado Central Prison 10,590 0.8

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County Facility Population Avg distance Montserrado Clara Town Clinic 57,741 0.7 Montserrado Duport Road Clinic 51,107 1.3 Montserrado E.J. Goodridge H C 31,070 1.0 Montserrado Gardnersville 13,449 0.9 Montserrado Good Samaritan 56,377 1.4 Montserrado Grey Stone (TB) 33,528 0.6 Montserrado Hydro Clinic 54,094 0.9 Montserrado Island Hospital 19,221 0.9 Montserrado Jaimaca Road Clinic 75,318 1.0 Montserrado JFK Medical Center 70,034 1.3 Montserrado Johnsoville Clinic 4,880 1.9 Montserrado Kenedeja Clinic 36,005 2.1 Montserrado Morris Farm Clinic 46,323 1.5 Montserrado New Georgia Clinic 31,721 1.6 Montserrado New Kru Town Clinic 53,217 0.7 Montserrado Paynesville MERCI 36,125 0.9 Montserrado Pippeline Comm. 31,253 1.6 Montserrado PUCC 13,485 0.7 Montserrado R C Marshall 2,319 3.3 Montserrado R H Ferguson H C 60,326 1.4 Montserrado Red Cross Clinic 14,722 0.4 Montserrado Redemption 12,515 0.5 Montserrado Rehab Community Clinic 32,557 1.3 Montserrado Slipway 34,794 0.8 Montserrado Soko Sackor 4,964 0.6 Montserrado Sonnowein H'th Center 8,278 0.9 Montserrado TB Hospital 40,859 1.4 Nimba Bahn 22,127 5.2 Nimba Beadatuo 7,651 5.2 Nimba Beo Yoolar 12,255 6.0 Nimba Bonlay 2,915 4.4 Nimba Bunadin 7,186 5.6 Nimba Buutuo 10,598 5.3 Nimba Diallah 26,645 21.5 Nimba Duo 7,479 7.8 Nimba Duo Tiayee 14,351 4.9 Nimba Duoplay 4,485 2.6 Nimba Duoyee 6,193 4.1 Nimba Flumpa 13,050 5.3 Nimba Ganta Comm. 61,002 6.2 Nimba Gbeivonwea 6,863 7.3 Nimba Gblarlay 8,362 4.3 Nimba Gbloulay 6,671 3.5 Nimba Goagortuo 3,287 4.2

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County Facility Population Avg distance Nimba Graie 5,269 4.3 Nimba GW Harley 24,572 5.8 Nimba Karnplay 18,178 5.2 Nimba Karnwee 7,868 4.4 Nimba Kpain 8,735 6.5 Nimba Kpaytuo 2,414 4.6 Nimba Kwendin 11,178 12.0 Nimba Lepula 6,314 5.0 Nimba Loguatuo 6,438 6.9 Nimba Lugbeyee 22,548 9.3 Nimba Mehnla 9,091 7.7 Nimba New Yourpea 7,808 4.8 Nimba Payee 12,951 6.2 Nimba Saclepea 31,137 6.0 Nimba Toweh Town 9,937 6.9 Nimba Vayenglay 13,290 6.5 Nimba Wehplay 5,071 3.9 Nimba Younlay 4,066 2.7 Nimba Zekepa 10,650 8.3 Nimba Zodru 4,321 6.4 Nimba Zorgowee 7,566 4.5 Nimba Zuaplay 8,246 8.7 Nimba Zuolay 3,258 4.2 River Gee Cheboken Clinic 6,447 5.0 River Gee Fish Town Health Center 4,541 5.1 River Gee Gbeapo Health Center 16,445 8.4 River Gee Jarkaken Clinic 5,394 3.9 River Gee Jayproken Clinic 1,683 6.5 River Gee Jimmyville Clinic 4,023 6.1 River Gee Juwelpo Clinic 1,001 6.5 River Gee Killepo Clinic 2,334 4.0 River Gee Nyaaken Clinic 396 2.4 River Gee Nyenebo Clinic 1,420 4.8 River Gee Pronoken Clinic 5,159 5.2 River Gee Putuken Clinic 2,790 1.9 River Gee River Gbeh Clinic 3,587 3.9 River Gee Sarbo Health Center 2,181 4.7 River Gee Tuobo Clinic 4,396 7.6 River Gee U-Bor Clinic 4,992 8.8 Rivercess Bodowhea Clinic 5,251 5.9 Rivercess Boegeesay Clinic 5,140 6.5 Rivercess Charile Town Clinic 7,155 5.1 Rivercess Dorbor Clinic 4,363 6.6 Rivercess Gbediah 3,553 4.3

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County Facility Population Avg distance Rivercess Gblossoe 3,054 4.3 Rivercess Gozohn Clinic 2,997 6.1 Rivercess ITI Clinic 1,822 7.5 Rivercess Kangbo/OR 4,962 8.9 Rivercess Kayay Clinic 4,807 6.4 Rivercess Larkpazee Clinic 3,039 5.5 Rivercess Neezuin Clinic 2,440 4.5 Rivercess Rock Cess 3,842 6.0 Rivercess Sahyah Clinic 3,550 6.4 Rivercess St. Francis H C 4,944 7.7 Rivercess Timbo Compound Clinic 5,317 4.7 Rural Montserrado Arthington 5,585 6.1 Rural Montserrado Banjor Comm. 15,195 0.9 Rural Montserrado HC 6,238 4.7 Rural Montserrado Blamacee Community 4,467 0.9 Rural Montserrado Bromely Community 661 1.4 Rural Montserrado Careysburg Clinic 7,252 3.8 Rural Montserrado Cooper Health Care Center 3,198 4.2 Rural Montserrado Crozierville Clinic 1,983 3.5 Rural Montserrado Dagmon Clinic 1,553 2.7 Rural Montserrado Goba Town 7,326 4.9 Rural Montserrado Harrisburg Clinic 2,249 2.4 Rural Montserrado Imani House Clinic 8,212 4.7 Rural Montserrado Kingsville # 7 Clinic 10,179 4.5 Rural Montserrado Koon Town 6,666 6.2 Rural Montserrado Kpallah 23,188 9.3 Rural Montserrado Louisiana Clinic 2,165 2.8 Rural Montserrado MZB H C 7,031 0.9 Rural Montserrado Nyehn Comm Clinic 7,574 5.4 Rural Montserrado Providence Clinic 6,882 3.9 Rural Montserrado Yarkpa Town 4,565 3.2 Rural Montserrado Zanna Town Clinic 7,161 8.1 Sinoe BOTC Clinic 2,235 8.6 Sinoe Butaw 3,361 5.3 Sinoe Chebioh's Town 1,675 10.4 Sinoe Dejija Kilo Town 5,071 4.8 Sinoe Diyankpo 3,592 6.9 Sinoe Doodwicken 2,304 6.1 Sinoe Dorbor/ORT 1,879 2.6 Sinoe Ducorfree 1,677 9.6 Sinoe Edward Memorial 1,075 5.9 Sinoe ENI Clinic 12,118 10.7 Sinoe F. J. Grante 10,753 1.3 Sinoe Gbarteken 519 2.4

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County Facility Population Avg distance Sinoe Geetroh 2,360 5.9 Sinoe Govt Camp Clinic 4,675 5.2 Sinoe Jacksonville 5,481 3.9 Sinoe Juarzon 642 6.2 Sinoe Karquekpo 822 2.8 Sinoe Kayjeyken 753 2.1 Sinoe Kenyon's Town 230 3.5 Sinoe Kingyon Village 4,166 14.5 Sinoe kwitatuzon 3,785 7.9 Sinoe Nyennawlicken 2,774 5.5 Sinoe Panama 1,043 3.4 Sinoe Payne Town 2,288 5.6 Sinoe Plasken C 1,971 8.3 Sinoe Popakea 274 3.6 Sinoe Quittatuaon 705 3.4 Sinoe Saywon Town 322 4.0 Sinoe Seesee's 1,692 3.6 Sinoe St. Joseph Catholic 3,506 2.4 Sinoe Tubmanville 907 2.6 Sinoe Tuzon 2,283 4.9 Sinoe Voogbardee 5,166 7.7 Sinoe Wiah Town 7,309 7.9

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