=Paper=
{{Paper
|id=Vol-2247/poster9
|storemode=property
|title=Is the Location of Public Health Units in Curitiba Meeting the Citizen's Needs?
|pdfUrl=https://ceur-ws.org/Vol-2247/poster9.pdf
|volume=Vol-2247
|authors=Filipe Lautert,Tatiane Lautert,Nadia P. Kozievitch,Luiz Gomes-Jr.
|dblpUrl=https://dblp.org/rec/conf/vldb/LautertLKG18
}}
==Is the Location of Public Health Units in Curitiba Meeting the Citizen's Needs?==
https://ceur-ws.org/Vol-2247/poster9.pdf
Is the location of Public Health Units in Curitiba meeting
the citizens’ needs?
Filipe Lautert, Luiz Gomes-Jr., Nadia P. Kozievitch, Tatiane A. M. Lautert
Universidade Tecnológica Federal do Paraná (UTFPR)
{filipe,tatiane}lautert@gmail.com,
gomesjr@dainf.ct.utfpr.edu.br, nadiap@utfpr.edu.br
Abstract. Guaranteeing adequate health services to the population is a chal-
lenge, especially in developing countries where limited resources must be opti-
mized in order to reach a larger portion of the population. To properly assess
the adequacy of health services and prioritize new investments, it is important
to gather a large amount of relevant data, integrated from various sources.
This paper presents an ongoing research focusing on Curitiba, one of the
largest cities in Brazil. We have aggregated socio-political, geographical, trans-
portation and health data from open repositories in order to understand the dy-
namics of how citizens choose their health units when required, as well as veri-
fy the availability of bus stops close to these units. The paper reports findings
from our exploratory analysis, highlighting the cases where the city's admin-
istration is on the right track, but also the areas which require more investment.
More specifically, using GIS and Data Analysis tools we analyze the occur-
rence of medical appointments made outside of the citizens’ residential neigh-
borhood and the most frequent diseases they had. We also detail which health
units do not have a bus stop in a determined radius.
Keywords: Open Data, Urban Planning, Health Units, Georeferenced Data.
1 Introduction
As the city grows and health units, bus lines, and its bus stops are implemented, it is
important to assess whether the infrastructure can cope with the increasing demand of
its population; what was a good distribution of services in terms of urban planning in
the recent past may no longer satisfy the citizens’ needs. Geographic Information
Systems (SIGs) can assist in this type of analysis, as long as there is sufficient and
reliable data being generated by the responsible institutions.
According to [ZIVIANI et al 2015], there are many mobility models that are used
to describe or predict urban mobility, but most of them use a single source of data to
do such analysis. Therefore in this article, we bring a new analysis based on different
data sources, being them: health units’ location, patients’ residence location and
availability of bus stops near the health units.
Copyright held by the author(s)
2
Curitiba is the capital and the largest city of the Brazilian state of Paraná, one of
the three states that comprise Brazil’s South Region. It is also the eighth most popu-
lous city in the country. As highlighted at [NAKONETCHNEI et al 2017], Curitiba
has been participating in open data initiatives along with several government stake-
holders, such as Instituto de Planejamento de Curitiba (IPPUC)1 and the Municipality
of Curitiba through its Open Data Portal2. Open data information is collected, pro-
cessed and maintained by the city administration to be freely used by the society and
republish as desired, without restrictions from copyright, patents or other mechanisms
of control. In this scenario, we explore the data about medical appointments from
public Health Units of Curitiba and correlate with the city’s georeferenced data. More
specifically, regarding the neighborhood where the patients reside versus the neigh-
borhood of the health units chosen for their medical appointments and procedures,
categorizing the most frequent diseases these patients had when the medical appoint-
ment took place. We also review the availability of bus stops close to the public
health units. The source of the georeferenced data regarding bus stops, bus stations,
and health units’ location is IPPUC.
The remaining work in this paper is organized as follows: Section 2 details the data
and tools used. Location analysis of the Health Units is presented in Section 3, Limi-
tations are described in Section 4, and finally, conclusion and future work are present-
ed in Section 5.
2 Data and Tools
This Section details the analyzed data, where it was found and the challenges faced to
put it together, going through the tools used to review it.
According to data from the Health Department3, the Unified Health System of Cu-
ritiba has a network of 216 units distributed as described in Table 1 across its 75
neighborhoods. For this paper, we obtained the dataset of medical services performed
in three types of health units, namely: Emergency Care Units, Basic Health Units, and
Medical Specialty Centers.
Table 1. Types of Health Units from Curitiba.
Unit Type N. of Units
Basic Health Units (BHU) 109
Health Spaces 68
Psychosocial Attention Centers (PSAC) 12
Emergency Care Units (ECU) 9
Medical Specialty Centers 5
1
http://ippuc.org.br/ Last accessed 10-May-2018
2
http://www.curitiba.pr.gov.br/DADOSABERTOS/ Last accessed 10-May-2018
3
http://www.saude.curitiba.pr.gov.br/a-secretaria/historico-da-secretaria.html, last accessed
18-April-2018.
3
Therapeutic Residences 5
Dental Unit Centers 3
Hospitals 2
Clinical Analyses Laboratory 1
Central of Vaccines 1
Zoonose Centre 1
Using data from the same Health Department that is available on Curitiba Open Data
Portal, from January-2017 to December-2017, except for February-2017 due to the
data being incorrectly formatted (apparently due to an export error), it was found that
3,086,518 medical appointments were performed from 23 different medical proce-
dures. Figure 1 shows the numbers of appointments by month, on average there were
280,525 appointments each month in 2017. January was the month with the lowest
number of appointments and October the month with the highest number, but in gen-
eral, the number of appointments is quite constant throughout the year.
Figure 1. Number of medical appointments by month in 2017
Also according to information from the Department of Transportation4, Curitiba has
250 bus lines. As prospected by [KOZIEVITCH et al 2016], the city has 9,940 bus
stops within the stationary data. It also has an additional bus stop category named
Tube Bus Stations (in Portuguese, Estação Tubo). The tube stations (officially 342)
are bus stops which look like tubes, for specific bus routes, such as Expresso and
Linha Direta. The neighborhoods named CIC and Centro have the majority of them,
4
https://www.urbs.curitiba.pr.gov.br/institucional/urbs-em-numeros, last accessed 08-April-
2018.
4
with a total of 1,628 and 667 each one. It also has 23 bus terminals and one intercity/
interstate terminal which also offers train services.
As we reviewed the data for the medical appointments in order to understand the
origin of the people that use the medical system, we discovered that it contains only
the neighborhood of the patient. Based on that, we decided to use this field as the
person’s origin location, even though some neighborhoods are quite large such as
CIC. In Table 2 we describe the most important fields available on the medical ap-
pointment source files - in total it has 37 fields so we suppressed the ones that are not
relevant for this study.
Table 2. Database table atendimento_unidade_saude.
Column Name Description
Dt_atendimento Date when the medical appointment occurred.
Cod_unidade Code of the Health Unit
Desc_unidade Description of the Health Unit
Cod_procedimento Code of the medical procedure executed
Desc_procedimento Description of the procedure executed
Cod_cbo Code of the professional occupation
Desc_cbo Description of the professional occupation
Cod_cid International Classification of Diseases Code
Desc_cid International Classification of Diseases Description
Cid_internamento International Classification of Diseases of hospitaliza-
tion
Municipio City of the patient
Bairro Neighborhood of the patient
Table 3. Database table unidade_de_saude.
Column Name Description
Cd_equip Code of the health unit
Nome_abrev Short name of the health unit
Nome_mapa Nome of the health unit on the map
Cd_bairro Code of the neighborhood
Bairro Neighborhood
Quadr_equi Block of the health unit
Cd_regiona Code of the Regional
Regional Regional
Func_manha Whether or not it is open in the morning
Func_tarde Whether or not it is open in the afternoon
Func_noite Whether or not it is open at night
Func_24hr Whether or not it is open 24 hours a day
Desativado Whether or not it is disabled
Coord_e Coordinate e
Coord_n Coordinate n
Geom Geometry
5
Table 4. Database table atendimento_join_unidades_de_saude.
Column Name Description
Cd_equip Code of the health unit
Desc_unidade Description of the health unit
Cod_unidade Code of the health unit
The data was imported to a table created on a PostgreSQL5 server with spatial ex-
tension PostGIS6, in which georeferenced data provided by IPPUC from the public
health units of Curitiba was already stored, as well as information of the city’s bus
stops and terminals.
One issue that we experienced was that the medical appointments table and the
medical units table do not share the same identifier. The medical units table stores the
geospatial location of the units and is detailed in Table 3. Based on that we decided to
use the unit names to link both, however, the names were not matching due to issues
with accentuation and small variations of the name. Due to that we had to create an
association table as detailed in Table 4 and link them both mostly manually. It was
also found that one unit was missing as it had been inaugurated recently - thus we had
to georeference it and add to the table. When georeferencing it we found out that the
column used to store the spatial data (column named geom) is configured using pro-
jection ESPG 4326, but the data on it is really on ESPG 291927 projection, so we had
to convert the data that we had to suit that.
Subsequently, for the integration and visualization of the georeferenced data, we
used QGIS8 software, versions 2.14.11 (Essen) and 2.18.18(Las Palmas). Additional-
ly, to generate a complex network graph we used Gephi9 0.9.2 software.
3 Location analysis of the Health Units
In our initial study, we analyzed the distribution of the public health units by neigh-
borhoods in the city of Curitiba, and it was verified that of the 75 existing neighbor-
hoods in Curitiba, 33 of them do not have a health unit. Most of these 33 neighbor-
hoods, in which there are no health units, are those with the highest income per
household, according to data presented in the economic profile report published by
Curitiba Agency10. On the other hand, in 29 neighborhoods there are 2 or more health
5
https://www.postgresql.org, last accessed 08-April-2018.
6
https://postgis.net, last accessed 08-April-2018.
7
http://spatialreference.org/ref/epsg/sad69-utm-zone-22s-2/, last accessed 15-May-2018.
8
https://www.qgis.org/en/site/, last accessed 14-May-2018.
9
https://gephi.org/, last accessed 14-May-2018.
10
http://www.agencia.curitiba.pr.gov.br/arquivos/regionais/perfil-economico-regional-
matriz.pdf, last accessed 08-April-2018.
6
units, and the Industrial District of Curitiba (CIC) has the highest number of health
units (a total of 16).
Using heat maps it is possible to confirm the distribution of the health units across
the city and relate it to the neighborhoods and income. In Figure 2 it is possible to see
the distribution of the population according to data from the 2010 census11. As a
comparison, on Figure 3 the heat map shows the medical appointments per health
units, and there is a notable overlap between Figures 2 and 3. Additionally, there is an
empty space around the center of them that is filed in Figure 4, which shows a heat
map of income per household. Therefore, by looking at those maps it is confirmed
that the appointments at health units and the units themselves are located closer to the
population with the lowest income.
Figure 2. Population by Figure 3. Total medical Figure 4. Higher Income per
neighborhood appointments per health units household
Regarding the existence of bus stops near the health units, a radius of 200 meters was
taken into account for this analysis; it was found that 6 health units neither have a bus
stop nor a bus station within the radius of 200 meters. These 6 health units are BHU
Umbará, BHU Vila Leão, BHU Luiz L. Lazof / Vila Esperança, BHU Pompéia , ECU
Boqueirão and ECU Pinheirinho. They are highlighted as yellow triangles in Figure 5,
all other health units are shown as red triangles and the bus stops near them are pre-
sented as black spots. Figure 6 shows a histogram with the distance of the health units
to bus stops.
11
http://www.ippuc.org.br/nossobairro/nosso_bairro.htm, last accessed 20/05/2018.
7
Figure 5. Health Units and bus stops within a radius of 200 meters.
Figure 6. Histogram showing the distance of the HU to the bus stops in meters
8
It was verified that 12.34% of the 3,086,518 medical appointments made during the
analyzed period were performed outside the neighborhood where the patients reside –
it means that a daily average of 1,130 people went to another neighborhood to have a
medical appointment. Figure 7 shows the displacement made by the patients from
their residential neighborhood to the 5 most visited health units by patients from out-
side the neighborhood where the unit is located. These 5 most visited health units are
emergency care units (ECU) that provide service 24 hours a day, being: Cajuru,
Boqueirão, Boa Vista, Campo Comprido and Sítio Cercado. Figure 7 shows a com-
plex network graph which highlights the intensity of the flow for those patients who
had their medical procedures outside their residential neighborhood - thicker lines
represent a greater flow of people. Looking at the numbers of those 5 units, a total of
28.64% of the medical appointments (184,392 appointments out of 643,764) were
performed by people from other neighborhoods.
Figure 7. The 5 Health Units most visited by patients from other neighborhoods.
9
It is interesting to note that ECU Boqueirão is one of the top 5 that most receive peo-
ple from other neighborhoods but it does not have a bus stop in the 200 meter area.
We also reviewed the top 5 International Classification of Diseases (ICD) codes for
these medical visits performed outside the patients' residential neighborhood, compar-
ing them to the 5 ICD codes of all the medical visits present in the data set for com-
parison purposes:
Table 5. Comparison of top 5 ICD between all the medical visits and medical visits performed
outside the patient’s residential neighborhood.
All medical visits Total Top-5 outside the residential Total
neighborhood
General Medical Examina- 309366 Acute upper respiratory infections, 14512
tion unspecified
Issue of Repeat Prescription 174749 Acute tonsillitis, unspecified 9229
Acute upper respiratory in- 124942 Acute nasopharyngitis [common 8969
fections , unspecified cold]
Essential (primary) hyper- 108797 General Medical Examination 8309
tension
Acute nasopharyngitis 95349 Other gastroenteritis and colitis of 8098
[common cold] infectious and unspecified origin
Based on this information, it is possible to notice that the great majority of the medi-
cal visits made outside the patients’ residential neighborhood consist of complications
of the respiratory tract, which usually get aggravated at night. To confirm this infor-
mation, the time of these medical visits were checked as seen in Figure 8. The num-
bers on the left represent the number of medical appointments in the blue line and the
numbers on the right represent the number of medical appointments in the red line. In
summary, this analysis points out that the number of medical visits is constant from
14:00 to 23:00 in the top 5 health units visited by patients from other neighborhood,
while the number of general medical appointments drops sharply after this period.
Figures 9 shows the number of appointments by hour per day of the week for all
health units while Figure 10 shows the same view but only for the top 5 health units
visited by patients from other neighborhoods.
10
Figure 8. Time comparison of general medical visits and top 5 health units visited by patients
from other neighborhood.
Figure 9. Number of appointments by hour per day of the week for all health units.
Figure 10. Number of appointments by hour per day of the week for top 5 health units visited
by patients from other neighborhoods.
11
4 Limitations
A number of issues were encountered throughout this work, especially in terms of
data consistency and integrity. The first issue was with the open data set of medical
appointments for the month of February 2017 - as mentioned in Section 2; a great
amount of the data for this specific month had missing columns which prevented us
from using it.
As stated in Section 2 another issue was that the open data of the medical appoint-
ments and the database table containing the health units’ geolocation did not share the
same identifier; therefore it was required extensive manual work to correlate both data
sets using the name of the health units: the first data set was not using accent and the
second was, additionally there were a few names mismatch as well.
Regarding the bus stop data used, although the data showed there was no bus stop
in the 200 meters radius of ECU Pinheirinho, google maps shows a bus stop almost in
front of the ECU Pinheirinho.
In summary, the main challenge faced throughout this work was regarding data
consistency among the different sources of data.
5 Conclusion
The objective of this research was to understand the medical appointments at public
health units in Curitiba and relate this information to their locations, bus stops and
displacements performed by the citizens to arrive at the health units.
In a preliminary analysis, it can be understood that the bus stops are well located
and allow citizens to arrive at the majority of health units without the need of long
walks, fulfilling its function of being located where the population needs. But it is
also observed that there is a relevant amount of displacements from outside the citi-
zens’ neighborhood to the top 5 ECU, especially looking for treatment of respiratory
problems – it is required further research on related works and with the municipality
of Curitiba to understand if those numbers are expected and why.
In future work it would be possible to validate whether not only the destination is
in the correct location but whether the origin and bus routes are addressing this need
of the population who are more distant from health units - or even assess whether
these health units are in the correct location and the reason why citizens are looking
for them instead of going to the nearest units. Additionally, it is intended to use
Google Map review data about the health units and analyze the data using Collective
intelligence approaches.
Curitiba municipal government could use the results of this research to assess
whether the different data sources administered by the city can be normalized in order
to facilitate future researches. Other studies using data from different cities could use
the numbers from this paper as a baseline for comparison.
12
Acknowledgments
The authors would like to thank EU-BR EUBra-BigSea project (MCTI/RNP 3rd Co-
ordinated Call) for the support and the Municipality of Curitiba and IPPUC for
providing the necessary data for this research.
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