GIS at work in mapping RoCs
Distribution answering the “WHERE”
question
Where are our
Recipients of
Care and how
best can we
serve them?
DEAN FRED MWENYA
M&EA FHI360
FOR CONFIDENTIALITY REASONS, SOME FIELDS HAVE BEEN REDACTED
Table of Contents
1.0 Introduction................................................................................................................................................... 2
2.0 Objective (s) .................................................................................................................................................. 2
3.0 Process .......................................................................................................................................................... 3
4.0 Output Map.................................................................................................................................................... 7
5.0 Discussion/Conclusion.............................................................................................................................. 8
6.0 Recommendations .................................................................................................................................... 11
7.0 References .................................................................................................................................................. 12
8.0 About the Author ....................................................................................................................................... 12
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1.0 Introduction
The tremendous potential of GIS to benefit the health care industry is just now beginning to be realized.
Both public and private sectors are developing innovative ways to harness the data integration and
spatial visualization power of GIS. The types of companies and organizations adopting GIS span the
health care spectrum--from public health departments and public health policy and research
organizations to hospitals, medical centers, and health insurance organizations (1).
In its most basic use, GIS answers the question of “Where?” (2). This can mean questions such as
“Where are people living?” “Where are diseases starting?”, in this case “where are our Recipients of
Care and how best can we serve them?”
FHI360, as a TA partner to Eastern Provincial Health Office CoAG, in particular Petauke District
Hospital, which is one of the high volume sites managing PLWHIV (Tx-Curr as at 31st July, 2021 was
5483), leveraged the use of GIS to map Recipients of Care distribution in the whole of Petauke and
Lusangazi district with Petauke District Hospital as the reference point. The primary reason for doing
so was to study the RoCs distribution patterns with respect to retention on treatment. The findings from
the output map were then used to make recommendations targeting the improvement of Retention.
2.0 Objective (s)
1) To generate a Tx-Curr list, i.e. a line list of all RoCs active on treatment as at 13/07/2021
using the PEPFAR QAQI.
2) To cluster the RoCs address to known wards in Petauke District
3) To use the Tx-Curr ward data and append to an existing ward attribute table in QGIS.
4) To create 10km, 20km, 30km and 40km buffer zones around Petauke District Hospital for
the proximity analysis.
5) To generate an output map showing the distribution of RoCs based on Tx-Curr in
respective wards.
6) To make appropriate recommendations that would greatly benefit the RoCs.
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3.0 Process
The chart below shows the process that was used to achieve the above stated objectives.
Prepare
Datasets
Tx-Curr +
RoCs
Addresses
Shapefiles
+ CSV Data
Load ZMB
Admin, Wards,
Health Facilities
Append Tx-Curr
to Ward Table in
GIS
Create
choropleth Map
based on TX-Curr
by Ward
Select Hospital
Create 10Km
Buffer Zones
around Hospital
Create
Output Map
3
Cluster
Addresses by
Wards
FOR CONFIDENTIALITY REASONS, SOME FIELDS HAVE BEEN REDACTED
The first step was to prepare the datasets to use in the QGIS system. The version of QGIS which was
used was v.3.18. The Tx-Curr line list was generated using the PEPFAR QAQI from SmartCare as at
13/07/2021, however this report does not list the addresses of the RoCs and so other reports with
addresses such as due for Viral Load were used to index match the two reports in order to add the
address field using the NUPN as the Primary Key.
All duplicates were removed before doing this process and sorted out manually, then later added to the
master list. Note that this process was done before the coming of the Admin tool. In short, the admin
tool can output a list of RoCs active on Treatment with their addresses and other fields such as their
phone numbers. It is highly recommended for this process as doing it the other way has its own
challenges, for instance, not all clients were index matched as the matching reports didn’t have them
and so their addresses had to be added in manually (about 800-plus such cases).
PEPFAR QAQI SNIP (Source: SmartCare Legacy v.5.21.0721).
The other datasets which had to be prepared where GIS shapefiles for administrative boundaries of
Zambia and inner provincial and town boundaries, and also a shapefile showing all wards in Petauke
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District. A list of health facilities in Zambia in CSV format was also prepared. All these datasets were
obtained via online open sources. It must be noted that, herein also lies a challenge on how recent the
datasets are, however, for the purpose intended they worked just fine.
The next key step was clustering addresses based on wards. The purpose of doing so was to come up
with a complete set of Tx-Cur distribution to be represented in the output map as choropleth map rather
than showing the RoCs as Points, which would have crowded the map. The clustering was done with
the help of a DAPP Field Officer (FO), Mr. Michael Mwale (credits to him) who was worked at PDH
since 2013, i.e. for 8 years and knows the areas and wards quite well. He provided what is called
Volunteered Geographical Information (VGI) in GIS terms. However, the limitation here is that there
were a few addresses (251) that could not be mapped to any ward because the FO did not recognize
them. In addition 275 RoCs had no addresses listed in the system and so they could not be mapped to
any ward. A sample of the clustering is shown in a snip table on the next page.
Sample Snip: Clustering of Addresses by Ward
After loading the shapefiles in GIS and doing some clipping in order to only remain with Petauke District
and its wards, Facility data as CSV was added and necessary labels were also added. The attribute
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table (as shown on the next page) for the Petauke Wards Shapefile was edited in order to append TxCurr Data per ward after consolidating the figures in excel after the clustering process.
The Tx-Curr appended to the attribute table was used as a basis to create a choropleth map which
assigns a colours to a range of figures, in this case the Tx-Curr. Buffer zones were created in steps of
10km around Petauke District Hospital up to 40km.
Finally, map elements were added and an output map was created as shown in 4.0 Output Map
Petauke Wards Attribute Table showing Tx-Curr data Appended to it
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4.0 Output Map
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5.0 Discussion/Conclusion
Petauke has 22 wards (note that the map used was before Lusangazi was made into a district as well).
As seen in the output map, the bulk of the Tx-Curr of the RoCs (2768) is clustered between a 10km
radius to 20km. However, what is of concern is the fact that Petauke District Hospital, which has no
catchment area since it’s a referral Hospital, has RoCs coming as far as beyond 20Km, and as the
chart shows on the next page, beyond Petauke and beyond Eastern Province. As FHI360 we also ran
a client Experience & Retention Survey from August to September, 2021, from which RoCs who stay
far from PDH were asked if they would like to get a transfer to their nearest facilities; 53.8% said yes,
while 46.2% said no (n=531). Out of the ones that said no, they said they were quite comfortable with
collecting from PDH, while others cited issues of lack of confidentiality at their nearest facilities.
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RoC TX-Curr Distribution (n=5483)
Unknown Ward
Unknown Ward-No Address
Outside Zambia
Outside Eastern Province
Outside Petauke
Chisangu
Ukwimi
Lusangazi
Mawanda
Mateyo-Mzeka
Singozi
Nyakawise
Chilimanyama
Nyika
Nsimbo
Mbala
Kovyane
Ongolwe
Msumbazi
Manjazi
Kaumbwe
Matambazi
Mwangaila
Chingombe
Kapoche
Manyane
Lusinde
251
275
4
Beyond Petauke: 665
19
116
0
51
73
Petauke: 4818
5
563
0
39
272
2768
170
364
0
71
0
0
340
5
0
0
73
24
0
0
500
1000
1500
2000
2500
3000
The chart above shows that only 4818 RoCs stay within Petauke whereas the rest (665) stay beyond
Petauke. 4818 is what was used to generate the output map.
The table below shows additional information which could not be placed on the map, because there
were no suitable shapefiles for, that is, the presence of rivers and farming areas within the wards. This
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information is necessary in order to plan for RoCs for example who stay in wards which are bordered
by rivers, which get flooded and cut them off during the rainy season. These RoCs will miss their
clinical/lab/pharmacy appointments if they get blocked by the flooded rivers and so planning their
schedules well in advance is important, especially making use of the 6MMD DSD Model.
Similarly, knowing where the farms are is also important, as it was discovered through the Client
Experience and Retention Survey that there are some RoCs who are migratory; that is, they have two
physical addresses, and one of those addresses turns out to be a farm far away from Petauke District
Hospital. These RoCs migrate to their farms during the rainy/farming season. Therefore planning for
these RoCs is also important in keeping retention levels high.
Ward Name
Lusinde
TX-Curr
in Ward
Rivers in Ward
0 Lusinde
Manyane
Kapoche
Chingombe
Mwangaila
24
73
0
0
Matambazi
Kaumbwe
Manjazi
Msumbazi
Ongolwe
Kovyane
Mbala
5
340
0
0
71
0
364
Nsimbo
Nyika
Chilimanyama
Nyakawise
Singozi
MateyoMzeka
Mawanda
Lusangazi
Ukwimi
Chisangu
170
2768
272
39
0
Muvuvye
Nil
Nil
Nil
Matambazi
Nil
Nil
Msumbazi
Ongolwe
Nil
Nil
Matonga, Muvuvye,
Msumbazi
Nil
Chilimanyama
Msanzala
Msanzala
Farms in Ward
Farms
Farms in
Mozambique
No data
Farms
No data
Farms in
Mozambique
Farms
No data
No data
No data
No data
Farms
No data
Farms
Farms
Farms
563 Msanzala
Farms
5 Msanzala, Chilimanyama
Farms
73 Msanzala, Chilimanyama
No data
51 Msanzala, Chilimanyama
Farms
0 Nil
No data
Table: Rivers and Farms in Wards & respective Tx-Curr.
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6.0 Recommendations
From the findings from the output map, the following are the recommendations
1) First and foremost, all facilities, especially, the high volume sites in Eastern Province and beyond
should do this mapping as well, for effective planning in improving retention outcomes.
At facility level; for PDH:
1) Since most of the Tx-Curr is clustered between 10km to 20km of Petauke District Hospital, the
forming of UAG groups can be reinforced in order to improve retention outcomes as RoCs in
groups are known to keep appointments better than individuals, as fellow group members keep
track of their friends and remind them of appointments.
2) For RoCs beyond 20km of Petauke District Hospital, they can be encouraged to get transfers to
the nearest facilities, or community ARV dispensation programmes can be initiated by PDH in
order to reach those RoCs and use the nearest health facilities as sites for such distribution.
3) Provide site mentorship to facilities that are near where the RoCs for PDH are so that the RoCs
can feel more comfortable to get transfers to start collecting from those facilities.
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7.0 References
1: https://www.esri.com/news/arcuser/0499/umbrella.html (accessed 16/11/2021)
2: Cromley, Ellen K. and Sara L. McLafferty. (2012). GIS and Public Health. The Guilford Press: New
York, 2nd edition.
GIS Software
https://qgis.org/en/site/forusers/download.html
8.0 About the Author
Public Health Specialist and Computer specialist with 5 plus years of
experience, working in International NGOs in conjunction with Zambia’s
Ministry
of
Health
and
local
NGOs
in
health
promotion
USAID/CDC/DOD/PEPFAR funded projects. Having worked in different health
settings and in different towns in Zambia, using his skills gained for Program
Monitoring Evaluation, Accountability and Learning and Business Intelligence.
Offering a client centred data management approach to influence positive
behavioural change and to track all program deliverables (inputs, outputs,
outcomes, impacts and overall goal attainment).
Contact: +260974886901/+260968723207 | EMAIL: dean.fred.mwenya@gmail.com
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