# Access to care: analysis of the geographical distribution of healthcare
using Linked Open Data

SSelene Báez Santamaría  EEmmanouil Manousogiannis  GGuusje Boomgaard  LPLinh
P. Tran  ZZoltán Szlávik  RJRobert-Jan Sips  Vrije Universiteit Amsterdam, De
Boelelaan 1111, 1081 HN Amsterdam, The Netherlands  MyTomorrows, Anthony
Fokkerweg 61, 1059 CP Amsterdam, The Netherlands

###### Abstract

Background: Access to medical care is strongly dependent on resource
allocation, such as the geographical distribution of medical facilities.
Nevertheless, this data is usually restricted to country official
documentation, not available to the public. While some medical facilities’
data is accessible as semantic resources on the Web, it is not consistent in
its modeling and has yet to be integrated into a complete, open, and
specialized repository. This work focuses on generating a comprehensive
semantic dataset of medical facilities worldwide containing extensive
information about such facilities’ geo-location.
Results: For this purpose, we collect, align, and link various open-source
databases where medical facilities’ information may be present. This work
allows us to evaluate each data source along various dimensions, such as
completeness, correctness, and interlinking with other sources, all critical
aspects of current knowledge representation technologies.
Conclusions: Our contributions directly benefit stakeholders in the biomedical
and health domain (patients, healthcare professionals, companies, regulatory
authorities, and researchers), who will now have a better overview of the
access to and distribution of medical facilities.

Data Integration,

Semantic Web,

Knowledge Base Construction,

Access to Healthcare,

###### keywords:

Research

[id=n1]Work produced while affiliated at MyTomorrows

## Background

Equal access to healthcare is a fundamental, irrevocable human right and is
protected by international law [1]. Access to primary healthcare has been
studied along various dimensions, such as the geographic spread of specialized
healthcare sites [2, 3], racial and/or ethnic factors [4, 5] and gender [6].
The aforelisted confirm that despite legislation and good intentions,
healthcare is not universally accessible.

Yet another factor in determining the quality of healthcare is access to novel
treatments. Several studies indicate that participation in clinical trials can
result in improved health conditions and lower-mortality rates in rare or
life-threatening conditions [7, 8, 9]. However, the above is not the only
reason why analyzing the geo-location of clinical research is of significant
importance. Genome-related diseases do not spread equally across ethnicity and
geography [10]; while diseases that were bound to a specific geographical
location are nowadays spreading more widely due to climate change and
globalization [11]. In some cases, effectively targeting the right population
might provide a solution to the issues hampering healthcare research. For
example, Stensland et al. [12] estimate that about 39% of cancer trials fail
because of low accrual, and only $\approx 5\%$ of patients referred are
eventually able to access the trial.

Despite the importance of monitoring and studying (disparities in) access to
healthcare, we lack a comprehensive knowledge base (KB)containing medical
facilities that offers the following: a) high geographic coverage (e.g., cover
various countries), b) complete location information (e.g., geo-coordinates),
c) validity (e.g., excludes outdated or inaccurate information), and d) rich
interlinking with external data sources for machine-readability. The World
Health Organization reports related aggregated metrics, for example, the
number of hospitals per 100,000 people in a given country [13]; yet their
latest report refers to information from 2014. Meanwhile, other open-source
initiatives [14] have focused on curating and visualizing data from a single
source (i.e. OpenStreetMaps), which inherently contains biased data. Detailed
medical facility location data is currently distributed and does not come in a
standardized and machine-recognizable format, thus making it challenging to
explore, use, and analyze.

In this paper, i) we evaluate the coverage and quality of various data sources
with regards to medical facilities and their geo-location, and provide
recommendations for future use of these data sources, especially the Linked
Open Data (LOD)repositories; ii) we create an aligned database of worldwide
medical facilities and illustrate its potential to investigate access to
healthcare by analyzing the facilities’ geographical distribution; ii) we
perform simple entity linking to match clinical trial sites to medical
facilities in the aforementioned database and we further investigate the
hurdles with regards to geographical search of pre-approval treatments.

### Related Work

### Linked Open Data

The idea of LOD came with the purpose of making data accessible and
interconnected across sources and domains. The LOD Cloud collects all datasets
that are present and interlinked on the Web. As of March 2020, the LOD Cloud
contained 1,260 datasets and 16,187 links.111https://lod-cloud.net/#about.
Farber et al. made an in-depth study comparing five of the largest open and
accessible knowledge graphs: DBpedia, Freebase, OpenCyc, Wikidata, and Yet
another great ontology (YAGO)[15]. The study defines dimensions along which we
can evaluate a KB and metrics to assess it quantitatively. Among these, we may
emphasize interlinking, defined as ’the extent to which entities representing
the same concept are linked to one another’.

Previous work has explored the intersection between LOD and biomedical
sciences222https://lod-cloud.net/clouds/life-sciences-lod.svg. For example,
Cuzzola et al. [16] worked on linking domain-specific KB s like the Unified
Medical Language System (UMLS)[17] to general-domain LOD resources like
DBpedia; while Zehra et al. [18] focused their efforts on datasets in the sub-
domain of cancer genomics. Some of these initiatives illustrate the impact LOD
resources may have on improving access to health care. Hassanzadeh et al. [19]
created an Resource Description Framework (RDF)representation of
ClinicalTrials.gov (CTGov)333https://clinicaltrials.gov/ to allow the
inclusion of this resource to the LOD landscape. Bukhari et al. [20] published
the Canadian health census as Linked Open Data. Zaveri et al. [21] focused on
quantifying disease-research disparity using open Web resources.

Special attention to geographical information in the LOD Cloud has been
explored in use cases such as delineation of functional urban areas [22] and
identification of locations in historical data [23].

#### Treatment search

Searching for treatments in development remains a challenging and time-
consuming task since the current tools for searching clinical trials are sub-
optimal [24]. In the context of this work, we observe that these interfaces
are not flexible or usable when it comes to searching the hospitals involved
in clinical trials. For example, CTGov allows users to search by country or
alternatively by location keywords in their advanced search; the European
Clinical Trials Database
(EudraCT)444https://www.clinicaltrialsregister.eu/ctr-search/search advanced
search only allows to filter by country; while the WHO International Clinical
Trials Registry Platform
(ICTRP)555http://apps.who.int/trialsearch/default.aspx does not support
geographic search.

## Methods

### Requirements for Knowledge Base Construction

To serve the use case of geography-centered search for (approved or pre-
approved) treatment options, we set the goal to create a KB that gathers
location information for medical facilities. The requirements for our KB are
as follows:

  1. Requirement 1:

To gather healthcare facilities and potential clinical trial sites.

  2. Requirement 2:

To gather the entities’ names and alternative names in various languages.

  3. Requirement 3:

To gather the entities’ location details (country, state, city, zip, latitude,
and longitude coordinate pairs) as semantic resources themselves.

  4. Requirement 4:

To validate the entities’ properties as valid locations and names.

To generate such a KB we follow the steps below:

  1. Step 1:

Relevant data source and resource identification

  2. Step 2:

Data querying and gathering

  3. Step 3:

Resource and ontology alignment

To satisfy Requirement 1, we identify three different categories for potential
trial sites: hospitals, universities, and pharmaceutical companies. These are
sites where clinical research and trials may take place. To address these
categories in more detail, the category of hospitals in our case includes
clinics and research institutes that have any relation to diseases.
Furthermore, the concept of a university is not the same in every country.
Therefore, we broaden this category to higher educational institutions.
Finally, pharmaceutical companies include businesses in the pharmaceutical
industry, biotechnology, and healthcare industry. An important notion here is
that there could be overlaps between the categories.

To satisfy Requirement 2, we include labels and alternative labels of the
locations in at least 30 different languages. During the exploration of the
data sources, we noted that most of them link to semantic resources from the
GeoNames vocabulary. Therefore, to satisfy Requirement 3 we collect location
data mapped to GeoNames when possible and attempt to link other string
representations of locations to the GeoNames vocabulary.

Finally, to satisfy Requirement 4 we implement a ‘blacklisting’ strategy to
avoid retrieving invalid resources such as broad or abstract concepts.

### Data sources

We integrate four different LOD resources: DBpedia [25]666http://dbpedia.org/,
Wikidata [26]777https://www.wikidata.org/, YAGO 2 [27]888https://www.mpi-
inf.mpg.de/departments/databases-and-information-systems/research/yago-
naga/yago/, and GeoNames [28]999https://www.geonames.org/. We also use the
UMLS, due to its significance in the biomedical domain. We disregard LOD
resources that would pose a challenge in terms of data validity and up-to-date
information. For example, Freebase was shut down in 2016 while OpenCyc’s last
available version dates back to 2012. We also disregard OpenStreetMaps as
other initiatives have thoroughly studied this resource.

#### DBpedia

DBpedia is a data source that contains extracted data from Wikipedia info-
boxes and that includes abstracts in 11 different languages [29]. According to
the latest statistics published (v. 2016-10), DBpedia contained about 6
million entities, including 275K organizations (e.g. 67K companies and 53K
educational institutions) [30]. Dbpedia provides mappings to Wikidata, Yago2
and GeoNames as well as numerous other data sources outside the focus of this
paper [15]. Unfortunately, DBpedia does not provide a unique numerical
identifier for a concept, other than its Internationalized Resource Identifier
(IRI), structured as:
http://dbpedia.org/resource/<Title_of_Wikipedia_Article>. Therefore, we
created an identifier for DBpedia items based on their page name (e.g.
‘Miles_Laboratories’).

#### Wikidata

Wikidata is a collaboratively created linked dataset hosted by the Wikimedia
Foundation101010https://wikimediafoundation.org/. It currently contains about
68 million items (Nov 2019) [31] and it is built upon Wikipedia, including
different language-specific Wikipedia variants. A concept is identified by a
unique number, prefixed with the letter Q, known as a “QID”. Because of this
language-independent identifier, unlike DBpedia, the IRIs of items in Wikidata
are consistent among different languages:
http://www.wikidata.org/entity/<QID>. This is an important difference between
the two databases since the language-dependent IRI of DBpedia has caused
inconsistencies between the property values of items among versions in
different languages. Wikidata contains mappings to GeoNames and sometimes to
UMLS or other medical vocabularies like Medical Subject Headings (MeSH)[32].

#### Yago

Yet another great ontology (YAGO)is developed at the Max Planck Institute for
Computer Science111111https://www.mpi-inf.mpg.de. Just like DBpedia and
Wikidata, YAGO is also extracted from Wikipedia. Additionally, it uses a
categorization system based on WordNet [33], and it contains enriched
geographic data based on GeoNames. While DBpedia provides links between the
different language-specific knowledge graphs, YAGO instead aims at
multilingual knowledge fusion [29]. This data source is presumed to contain
the largest amount of organizations, followed by Wikidata and DBpedia [15].
YAGO 2, the second large iteration of this ontology, provides mappings to
DBpedia.

#### Geonames

GeoNames is a geographical database containing over 11 million concepts. It
integrates location information such as latitude and longitude coordinate
pairs, as well as postal codes, administrative divisions, and countries.
GeoNames mostly consists of data provided by official public sources and
crowd-sourced modifications to it. GeoNames does not provide a SPARQL Protocol
and RDF Query Language (SPARQL)endpoint to query it, but its ontology is
available in OWL while the vocabulary is available as either a RDF or Comma-
separated values (CSV)dump [34]. Through its alternative names property,
GeoNames provides mappings to Wikidata.

#### UMLS

The Unified Medical Language System (UMLS)is a KB that unifies many health and
biomedical vocabularies and standards. Because of the enormous scope of the
UMLS Metathesaurus, it reflects the many views of the world present in its
source vocabularies [35]. Therefore, it may be experienced as a rich yet noisy
data source. As the UMLS aligns, merges, and relates medical taxonomies and
ontologies, it allows researchers and informaticians to navigate the complex
biomedical domain and translate concepts across terminologies. Though mostly
focused on modeling medical entities (e.g., chemical compounds, diseases,
genes), the UMLS also contains some information regarding medical
organizations. For institutions, under the semantic group ‘ORGA’, the UMLS
provides concepts for Healthcare Related Organizations (semantic type T093),
Professional Societies (semantic type T094), Self-help, or Relief
Organizations (semantic type T095), and a general Organization category
(semantic type T092). The UMLS may provide unique concepts and labels for
organizations, but no location data. Yet, due to its importance and heavy
adoption in the biomedical domain, mappings to this data source are extremely
valuable.

### Data querying and gathering

#### Query complexity

The main type of restrictions to cover the three selected medical facility
categories are shown in Table 2. There is a noticeable difference in the
complexity of the SPARQL queries constructed across the three distinct LOD
repositories. This makes query portability difficult across the Semantic Web.
As for GeoNames and UMLS, a filter on the concepts’ semantic type (or feature
code in GeoNames) is sufficient.

Beyond the difference across data sources, in some cases retrieving
information within a single data source was not straightforward. As studied by
[15], one of the challenges when extracting information from DBpedia is the
variety in predicates that express the same relation, which inherently drives
towards more complex SPARQL queries to obtain the needed information. This is
particularly evident with geo-location information. For example, to extract
the country where an institution is, four different triple patterns exist
(Table 1).

#### Data validation: facility concepts

We explicitly blacklist some concepts that do not represent a specific medical
facility (see Table 2, last row). The choice of concepts was driven by
heuristics based on preliminary experiments. For example, DBpedia concepts
with the name ‘Hospital’ or ‘University’ represent information about the
general concept of what a hospital or university is. By using rdf:type
dbo:Hospital or rdf:type dbo:University in DBpedia, these entities
unintentionally get queried as well because they are specified with these
properties. Similarly, UMLS includes items for abstract concepts such as
‘clinic’ that had to be excluded. DBpedia blacklisted items represent 0.02% of
its total retrieved concepts, while UMLS items correspond to 0.73%.

#### Data validation: location of concepts

Most location information (i.e. country, state or city) in the original data
sources are already mapped to GeoNames (123,851 mappings, 32.81%). However,
some location descriptions provided a name but no semantic ID. We attempt to
map these to GeoNames as a standardized vocabulary, thus transforming this
into semantic information. Textual information was linked by string matching
techniques (12,220 mappings, 3.23%). 2.18% of location information remains un-
linked (i.e., no Geonames id could be retrieved for the given text), and
61.75% remains unidentified (i.e., NULL values).

Furthermore, in Wikidata no specific information about the state or city is
present. Instead, a general ‘administrative territory’ predicate captures this
information. Therefore, every territory with a GeoNames ID found in Wikidata
was compared against the list of cities (populated places with more than 500
inhabitants) and states (administrative divisions level 1) provided by
GeoNames to represent location details in a standardized way.

#### Source coverage and quality

Figure 1 summarizes the coverage per data source: the total number of facility
concepts retrieved, and the number of concepts for which we could gather
location information at either country, state, city, zip, or geo-coordinates
level. Wikidata leads the way by containing the most amount of concepts
(61,003), nearly all located at the country level (59,236). However, it
suffers from low coverage in the state (6,325) or city (162) levels, arguably
due to its joint ‘administrative territory’ property. GeoNames is second place
in retrieving concepts (35,788), almost tied with DBpedia (34,415). Finally,
YAGO takes the third spot (29,742), followed by UMLS covering the least amount
of concepts (15,954) and no locations.

### Alignment and knowledge base construction

Once the data has been gathered, we merge nodes before adding them to the KB.
To merge nodes, the equivalency of two concepts across data sources needs to
be established. One way to do so is to take the owl:sameAs mappings most LOD
resources provide. Still, the density of the mappings in this sub-domain is
poor, as shown in Figure 5. Thus, we perform additional alignment as a means
to discover new mappings. For simplicity, we enforce a one-to-one mapping
among data sources (i.e. one DBpedia concept may correspond to one and only
one GeoNames id, one Wikidata id, etc.).

The final steps for merging and constructing the KB are:

  1. Step 1:

Add concepts that are owl:sameAs mapped to all other resources in the LOD
Cloud.

  2. Step 2:

Add concepts that are partially owl:sameAs mapped across UMLS Cloud resources.

  3. Step 3:

Add concepts that are mapped by (approximate) string matching.

  4. Step 4:

Add all unmapped concepts

During Step 2 we needed to impose an order over data sources. This decision is
important due to the one-to-one mapping requirement. To decide which data
source to add first we look at the analysis in Figure 1 and select the
following order: Wikidata $\rightarrow$ GeoNames $\rightarrow$ DBpedia
$\rightarrow$ YAGO.

For Step 3, we first generate candidate entities for aligning a given medical
facility by performing an approximate nearest neighbors search. We search over
the set of medical facilities’ aliases and output the entities associated with
the nearest K. Names are encoded using a vector of term frequency–inverse
document frequency (TF-IDF)scores of character tri-grams, which appears in 10
or more entity aliases (i.e., document frequency at least 10). In a second
processing step, we use the geographical nature of the data and map the
concepts heuristically, using three emperically-derived rules, after manual
inspection of the data:

  1. 1.

If we know latitude and longitude for both entities, we merge them if their
cosine similarity is at least $0.75$ and their geographical distance is at
most $1$ km,

  2. 2.

If we do not know latitude and longitude for both entities, we merge them if
they have the same city and country name and have a cosine similarity of at
least $0.9$,

  3. 3.

Finally, if we do not know the city name, we merge the entities if they have
at least three aliases with a cosine similarity of at least $0.99$ and are
within the same country.

## Results

### Knowledge Base Evaluation: Coverage

#### Type distribution

Table 3 shows the distribution of concepts in the KB per semantic type.
Healthcare-related organizations (e.g. hospitals) is the category with the
most concepts (68,896), followed by Educational Institutions, such as
universities (56,667).

#### Size in nodes

We identified 68,896 medical facilities distributed in 202 countries. The
contribution and mapping per data source are shown in Figure 2. After merging,
we observe that we managed to map the majority of concepts into Wikidata,
DBpedia and YAGO respectively, as can be seen in (Figure 1).

### Knowledge Base Evaluation: Completeness per node

#### Location

Table 3 shows the percentage of located concepts in the KB per semantic type.
A concept is considered to be located if we have registered a valid value
(i.e., not NULL and not (0,0)) for latitude and longitude. Healthcare-related
organizations (e.g., hospitals) are the category with the highest percentage
of located concepts, which should aid in identifying clinical trial
facilities.

#### Language variety

As per Requirement 2, we have gathered multilingual labels per concept,
reaching a total of 115 languages. Figure 3 compares the total number of
concepts and the average number of languages per concept for each data source.
YAGO has the highest language coverage, with an average of 2.76 languages per
concept. As a result of the alignment, a concept in our KB contains 2.09
languages on average.

### Knowledge Base Evaluation: Connectivity

We identified 23.43% mappings across concepts that were not registered in the
LOD Cloud. Figure 5 illustrates the lack of alignment between datasets
(247,201 unidirectional mappings) before the string matching approach, while
Figure 5 shows the result after merging nodes (350,919 unidirectional
mappings, resulting in 701,838 bidirectional mappings). As expected, the most
benefits are reflected in linking UMLS to YAGO repositories (Figure 5, bottom
row). Surprisingly, though GeoNames is a standard vocabulary to which DBpedia,
Wikidata, and YAGO link when modeling a facility’s location, almost no
alignment among medical facilities themselves is available. Our approach
succeeded in partially mapping GeoNames and other resources, but further
efforts are needed.

The most overlap is observed between DBpedia, Wikidata and YAGO (17,828),
followed by the intersection of DBpedia and Wikidata (11,296) (see Figure 2).

### Knowledge Base Description and Usage

#### Modelling

Figure 6 shows the schema of our KB. We model four types of semantic
resources: medical facilities and three location information levels, i.e.,
country, state, and city. These semantic resources are associated with data
values for name, geo-coordinates and postal code (the latter only being
applicable to the clinical trial facility itself). We provide access to the KB
as relational data as well as RDF triples.

Relational data is provided as a single CSV file of 128,506 rows. The CSV is a
flattened representation of the database, where each entry represents a unique
concept for a potential clinical facility, associated with its location
details (latitude, longitude, city, zip, state, country) and identifiers in
the source data. RDF triples are provided as a turtle file consisting of
1,094,278 statements. The modeling closely follows practices by DBpedia and
Wikidata in that it separates vocabulary resources from ontological terms. We
use the namespaces mto: http://research.mytomorrows.com/ontology/ and mtp:
http://research.mytomorrows.com/property/ to refer to classes and properties
in the ontology, while the namespace mtr:
http://research.mytomorrows.com/resource/ refers to the specific instances in
the vocabulary.

In order to improve the reusability and interoperability of our KB as a linked
data resource, our modeling is mapped to SCHEMA.ORG121212http://schema.org/
and GeoNames. On the one hand, SCHEMA.ORG is selected because the DBpedia,
Wikidata, and GeoNames ontologies are consistently mapped to it. On the other
hand, GeoNames is selected as the main LOD resource for geographical data.
Additionally, resource names are modeled using Resource Description Framework
Schema (RDFS)131313http://www.w3.org/2000/01/rdf-schema# and Simple Knowledge
Organization System (SKOS)141414http://www.w3.org/2004/02/skos/core# for
preferred and alternative names. Finally, looking back to Requirement 1, we
decided to model location semantic resources under the GeoNames conventions,
thus assigning a feature code and feature class.

#### Querying

The RDF representation of the KB can be loaded to any triple store. It can be
queried as follows:

⬇

PREFIX mto: <http://research.mytomorrows.com/ontology/>

PREFIX mtp: <http://research.mytomorrows.com/property/>

SELECT DISTINCT ?site ?site_name ?country_name ?state_name ?city_name

?lat ?long WHERE {

?site rdf:type mto:TrialFacility .

?site rdfs:label ?site_name .

?site mtp:country ?country .

?country rdfs:label ?country_name .

?site mtp:state ?state .

?state rdfs:label ?state_name .

?site mtp:city ?city .

?city rdfs:label ?city_name .

?site mtp:latitude ?lat .

?site mtp:longitude ?long .

}

This sample query shows all facility sites in the KB, including their property
country, state, and city. By using rdfs:label, a human-readable version of the
resource’s name is provided in the result. Furthermore, one can filter the
result by country for instance, by adding a specific label to the filter
statement in the query as follows: FILTER(?country_name=”United Kingdom”@en).

We can also exploit the geographical properties of our KB using GEOSPARQL. The
recommended triple stores are GraphDB or Stardog as they support GEOSPARQL.
The following query retrieves information about trial facilities and their
distance to a given point.

⬇

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>

PREFIX mto: <http://research.mytomorrows.com/ontology/>

PREFIX geo: <http://www.opengis.net/ont/geosparql#>

PREFIX geof: <http://www.opengis.net/def/function/geosparql/>

PREFIX uom: <http://www.opengis.net/def/uom/OGC/1.0/>

PREFIX mtp: <http://research.mytomorrows.com/property/>

PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT DISTINCT ?site ?distance ?country ?state ?city ?coor WHERE {

?site rdf:type mto:TrialFacility;

geo:asWKT ?coor.

BIND(”POINT(52.381683␣4.807050)”^^geo:wktLiteral AS ?mycoor)

BIND(geof:distance(?coor, ?mycoor, uom:metre) AS ?distance)

OPTIONAL {

?site mtp:country ?co.

?co rdfs:label ?country.

}

OPTIONAL {

?site mtp:state ?st.

?st rdfs:label ?state.

}

OPTIONAL {

?site mtp:city ?ci.

?ci rdfs:label ?country.

}

}

ORDER BY (?distance)

## Discussion

Our knowledge base constitutes two main contributions in terms of data and the
method via which it is obtained, i.e. medical facility coverage and recency,
and facilities identified as clinical trial sites.

### Knowledge Base Coverage and Recency

Through the merging of various frequently updated individual data sources such
as Wikidata, our knowledge base offers recent data and more extensive coverage
of facilities than that of any of the individual sources used. Considering
that the World Health Organization (WHO)has last reported the number of
medical facilities per 100,000 inhabitants in every country in 2013/2014 [13],
recency and coverage may be quite valuable to those who rely on this WHO data.

Our knowledge base combined, for instance, with disease incidence data per
country [36] or with hospital capacity (a property present in some LOD
resources), may also offer a starting point for policymakers to make better-
informed decisions regarding the local organization of healthcare.

As illustrated in Figures 8 and 8 showing a geographical representation of
hospitals compared to the WHO, our knowledge base is higher in coverage.
However, what comes with this gain is a lower level of validation of the data,
which needs to be taken into consideration when using the knowledge base. For
instance, our exact definition of what a hospital is may differ significantly
from that of the WHO. Indeed, some countries in the WHO dataset only consider
public hospitals, and others also include private medical facilities, while
our KB includes both private and public hospitals to a greater extent. As
shown in the figures linked above, hospital density information and other
knowledge covered by our knowledge base should be critically examined upon
usage.

### Sites linked to clinical trials

By connecting facilities to clinical trials listed in CTGov using various
approaches, we believe we may be facilitating several critical use cases, some
of which we are discussing below, backed up by data from our knowledge base
whenever we are able to do so.

First of all, a search engine’s ability to connect trials to facility entities
may be a tremendous help for patients in need. Better linkage may enable the
retrieval/filtering of trials that are within a specified range (such as
within 50km from someone’s home) with higher recall (as opposed to having
relevant trials not being returned because their location string is not linked
to a known entity with latitude/longitude/city data). Also, linked data may
offer fewer false positives, resulting from loose matching of strings employed
in various current trial search engines.

As a proof-of-principle, we performed entity linking of our KB to a snapshot
of clinical trials collected from WHO on January 12th 2021\. The download
contains 309,658 trials where at least one trial site is mentioned, with an
average of 7 trial sites per clinical trial and 871,570 distinct combinations
of clinical trial site name and location (city, zip, state, country). We link
the clinical trial sites to our KB using an approximate nearest neighbor
algorithm, similar to the (approximate) string matching approach described for
aligning concepts in our KB. However, this time, we filter matches on cosine
similarity (greater than 0,7) and location. We create 206,218 one-to-one
matches between distinct clinical trial sites and medical facilities in our
KB, corresponding to 46% (1,027,955) of the clinical trial sites mentioned in
CTGov.

Table 4 gives an overview of the top 5 medical facilities linked to the most
clinical trials and the top 5 clinical trial site mentions we failed to match.
An interesting occurrence is that of a pharmaceutical company (Boehringer
Ingelheim) matched to 4,062 trials as a clinical trial site. Upon closer
inspection of the dataset, this is an example of a particular phenomenon that
can be found in clinical trial documents: For several trial documents,
clinical trial site names are vague, either because they are generic (e.g.
“Hospital”) or because they are intentionally hidden (e.g. “Novartis Facility
#1”). String matching based approaches will not identify these locations
correctly, excluding these sites from query results. To tackle this, we
created specific patterns to identify vague clinical trial site names, as
shown in Table 5. These patterns identify 75,664 unique clinical trial site
names (16.56% of the total clinical trial site names, and 21.81% of the
unidentified clinical trial site names). Since the names described by these
patterns do not represent specific medical facilities, these clinical trial
site names must follow alternative linking approaches that go beyond the KB
medical facilities’ names quality. An extension to our work would be to look
into deanonymizing these locations for our use cases using clinical trial
sites’ location properties and those of medical facilities in our KB.

As an illustration of the use of such a semantic clinical trial site linking,
we performed a location-based search in four of the largest clinical trial
search engines: CTGov, EudraCT, ICTRP, and a clinical trial search engine
powered by our generated semantic database. As a query, we aim to find trials
running in the “University of Michigan”. Screenshots of the search engines are
provided in Figure 9. CTGov retrieves the most related studies (3,211) to
these keywords. However, the results are not precise as they relate to each
token independently and retrieve studies performed in any ‘University’. On the
opposite side of the spectrum, EudraCT retrieves the lowest amount of
documents (16), including trials that are only running in the United Kingdom
but are retrieved due to similar reasons as CTGov. ICTRP lies in the middle
range in retrieving relevant trials (1,176) together with our search engine
(1,120). Upon close examination, we see that the ICTRP results include studies
sponsored by the University of Michigan but taking place in different
locations. We do not have the ground-truth to validate the accuracy
(precision/recall) of the retrieval, but still, given the ‘findability’ aspect
of our use case, we may see that bringing semantics into this domain would
provide clarity regarding what each entity represents and the role each entity
has in the clinical trial.

A knowledge base such as ours may also be helpful while choosing suitable
clinical trial locations. Looking at accurate historical records of clinical
trials at locations, as well as locating expertise via such data, may be a
valuable supporting step for this choice. Furthermore, it has been established
that a large percentage of trials fail due to the inability to recruit
patients, rather than clinical reasons such as toxicity or the lack of
efficacy of the medicine [37, 38]. A major reason for patients to be rejected
from clinical trial participation is that they live too far away from a trial
site [39]. Thus, a high-coverage, recent knowledge base tied to past or
current clinical trials – perhaps combined with other relevant data sources –
may ultimately lead to an efficiency increase in the drug development process.

## Conclusions

The LOD initiative has the vision of making data accessible and interlinked
(“With linked data, when you have some of it, you can find other, related,
data.”[40]). Yet, in the domain of location of medical sites the problem of
poor connectivity limits the usability of any of these resources. This could
mean that we are not serving society well, since significant effort is needed
to identify medical facilities thus interfering with a basic human right:
access to health care.

While tackling the challenge of creating a unified KB for medical facilities,
we learned several lessons:

  1. 1.

Extensive exploration is needed to query each individual data source due to
the lack of query portability across repositories.

  2. 2.

The set of mappings across linked data repositories is not complete; and the
coverage of location data on each repository differs greatly. These two
factors present the perfect use case for ontology alignment benefits to be
maximized. On the one hand, distinct nodes increase the overall coverage of
the knowledge graph. On the other hand, overlapping nodes enrich each other by
containing different information per source (e.g., labels in different
languages and different location information).

  3. 3.

When data sources do not separate ontological concepts from vocabulary
resources, some general entities like ‘University’ may be wrongly fetched. A
‘blacklisting’ step benefits the data quality of the KB. The items to
blacklist are gathered during the exploration phase for every data source.

  4. 4.

Counter-intuitively, aligning resources may hurt some KB evaluation metrics.
For example, we observe a compromise between increasing KB coverage (adding
more nodes) and decreasing completeness (the lack of information available per
node).

Our analysis may shed some light on future research efforts. For example, our
connectivity analysis illustrates that there is still work to be done on
linking GeoNames concepts to other LOD sources, specifically outside the
country/city semantic types. On a similar note, our completeness analysis
brings to attention that Wikidata has a surprisingly low language coverage in
this domain, despite its efforts for being a highly multilingual LOD source.

One of our KB’s largest limitations is the validity of the data, as it
directly depends on the quality of the original data sources. We performed in-
house expert validation due to the access to health care professionals we have
in our lab, but an approach as in-depth as [36] is extremely costly. In the
future, we could leverage the acquired knowledge to design more general and
less expensive techniques. Furthermore, the current implementation of the
medical facilities KB is heavily focused towards gathering multilingual labels
and location properties. In the future, we plan on accounting for relations
between organizations for example a hospital as part of a university (e.g.
Academic Medical Center and University of Amsterdam).

To conclude, in this paper we have described the generation of a KB following
the identification and alignment of several data sources containing medical
facility information. We believe that both the resulting KB and the lessons
learned while creating it might be valuable for researchers and practitioners
needing this, or similar biomedical resources.

## Glossary

## Acronyms

CSV

    Comma-separated values
CTGov

    ClinicalTrials.gov
EudraCT

    European Clinical Trials Database
ICTRP

    WHO International Clinical Trials Registry Platform
IRI

    Internationalized Resource Identifier
KB

    knowledge base
LOD

    Linked Open Data
MeSH

    Medical Subject Headings
RDF

    Resource Description Framework
RDFS

    Resource Description Framework Schema
SKOS

    Simple Knowledge Organization System
SPARQL

    SPARQL Protocol and RDF Query Language
TF-IDF

    term frequency–inverse document frequency
UMLS

    Unified Medical Language System
WHO

    World Health Organization
YAGO

    Yet another great ontology

## Declarations

## Ethics approval and consent to participate

Not applicable

## Consent for publication

Not applicable

## Availability of data and materials

The datasets generated and/or analysed during the current study are available
in the myTomorrows repository, https://research.mytomorrows.com/datasets.

## Competing interests

The authors declare that they have no competing interests.

## Funding

Not applicable

## Author’s contributions

Data gathering: GB, LPT, SBS. Alignment: SBS, RJS. Evaluation design: ZS.
Evaluation: EM, SBS.

## Acknowledgements

Not applicable

## References

  * [1] General comment no. 14: The right to the highest attainable standard of health (art. 12 of the covenant). UN Economic and Social Council (2000)
  * [2] Anderson, R.T., Yang, T.C., Matthews, S.A., Camacho, F., Kern, T., Mackley, H.B., Kimmick, G., Louis, C., Lengerich, E., Yao, N.: Breast cancer screening, area deprivation, and later-stage breast cancer in appalachia: does geography matter? Health services research 49(2), 546–567 (2014). doi:10.1111/1475-6773.12108
  * [3] Lin, C.C., Bruinooge, S.S., Kirkwood, M.K., Hershman, D.L., Jemal, A., Guadagnolo, B.A., James, B.Y., Hopkins, S., Goldstein, M., Bajorin, D., et al.: Association between geographic access to cancer care and receipt of radiation therapy for rectal cancer. International Journal of Radiation Oncology, Biology, Physics 94(4), 719–728 (2016). doi:10.1016/j.ijrobp.2015.12.012
  * [4] Manuel, J.I.: Racial/ethnic and gender disparities in health care use and access. Health services research 53(3), 1407–1429 (2018). doi:10.1111/1475-6773.12705
  * [5] Peipins, L.A., Graham, S., Young, R., Lewis, B., Flanagan, B.: Racial disparities in travel time to radiotherapy facilities in the atlanta metropolitan area. Social science & medicine 89, 32–38 (2013). doi:10.1016/j.socscimed.2013.04.018
  * [6] Pinillos-Franco, S., Somarriba, N.: Examining gender health inequalities in europe using a synthetic health indicator: the role of family policies. European journal of public health 29(2), 254–259 (2018). doi:10.1093/eurpub/cky177
  * [7] Downing, A., Morris, E.J., Corrigan, N., Sebag-Montefiore, D., Finan, P.J., Thomas, J.D., Chapman, M., Hamilton, R., Campbell, H., Cameron, D., Kaplan, R., Parmar, M., Stephens, R., Seymour, M., Gregory, W., Selby, P.: High hospital research participation and improved colorectal cancer survival outcomes: a population-based study. Gut 66(1), 89–96 (2017). doi:10.1136/gutjnl-2015-311308
  * [8] Nijjar, S., D’Amico, M., Wimalaweera, N., Cooper, N., Zamora, J., Khan, K.: Participation in clinical trials improves outcomes in women’s health: a systematic review and meta-analysis. BJOG: An International Journal of Obstetrics & Gynaecology 124(6), 863–871 (2017). doi:10.1111/1471-0528.14528
  * [9] Brennan, M., Gaß, P., Häberle, L., Wang, D., Hartmann, A., Lux, M., Beckmann, M., Untch, M., Fasching, P.: The effect of participation in neoadjuvant clinical trials on outcomes in patients with early breast cancer. Breast Cancer Research and Treatment 171 (2018). doi:10.1007/s10549-018-4829-4
  * [10] Tan, D., Mok, T., Rebbeck, T.R.: Cancer genomics: diversity and disparity across ethnicity and geography. Journal of Clinical Oncology 34(1), 91–101 (2016). doi:10.1200/jco.2015.62.0096
  * [11] WHO: Sustaining the Drive to Overcome the Global Impact of Neglected Tropical Diseases: Second WHO Report on Neglected Diseases vol. WHO/HTM/NTD/2013.1. World Health Organization (2013). doi:10.1163/2210-7975_HRD-9841-3020
  * [12] Stensland, K.D., McBride, R.B., Latif, A., Wisnivesky, J., Hendricks, R., Roper, N., Boffetta, P., Hall, S.J., Oh, W.K., Galsky, M.D.: Adult cancer clinical trials that fail to complete: an epidemic? JNCI: Journal of the National Cancer Institute 106(9) (2014). doi:10.1093/jnci/dju229
  * [13] GHO: Health infrastructure, Data by country. http://apps.who.int/gho/data/node.main.506?lang=en. Accessed: 2020-08-17
  * [14] Healthsites.io. https://healthsites.io/. Accessed: 2020-08-17
  * [15] Färber, M., Bartscherer, F., Menne, C., Rettinger, A.: Linked data quality of dbpedia, freebase, opencyc, wikidata, and yago. Semantic Web 9(1), 77–129 (2018). doi:10.3233/sw-170275
  * [16] Cuzzola, J., Bagheri, E., Jovanovic, J.: Umls to dbpedia link discovery through circular resolution. Journal of the American Medical Informatics Association 25(7), 819–826 (2018). doi:10.1093/jamia/ocy021
  * [17] Bodenreider, O.: The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research 32(suppl_1), 267–270 (2004). doi:10.1093/nar/gkh061
  * [18] Zehra, D., Jha, A., Khan, Y., Hasnain, A., d’Aquin, M., Sahay, R.: A cancer genomics data space within the linked open data (lod) cloud. In: SeWeBMeDa@ ISWC, pp. 31–39 (2019)
  * [19] Hassanzadeh, O., Miller, R.J.: Automatic curation of clinical trials data in linkedct. In: International Semantic Web Conference, pp. 270–278 (2015). doi:10.1007/978-3-319-25010-6_16
  * [20] Bukhari, A.C., Baker, C.J.: The canadian health census as linked open data: towards policy making in public health. In: Data Integration in the Life Sciences (2013)
  * [21] Zaveri, A., Pietrobon, R., Auer, S., Lehmann, J., Martin, M., Ermilov, T.: Redd-observatory: Using the web of data for evaluating the research-disease disparity. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01, pp. 178–185 (2011). doi:10.1109/wi-iat.2011.114. IEEE Computer Society
  * [22] Khalili, A., Van den Besselaar, P., De Graaf, K.A.: Using linked open geo boundaries for adaptive delineation of functional urban areas. In: European Semantic Web Conference, pp. 327–341 (2018). doi:10.1007/978-3-319-98192-5_51
  * [23] Beek, W., Zijdeman, R.: nlgis: A use case in linked historic geodata. In: European Semantic Web Conference, pp. 437–447 (2018). doi:10.1007/978-3-319-98192-5_58
  * [24] Korkontzelos, I., Mu, T., Restificar, A., Ananiadou, S.: Text mining for efficient search and assisted creation of clinical trials. In: Proceedings of the ACM Fifth International Workshop on Data and Text Mining in Biomedical Informatics, pp. 43–50 (2011). doi:10.1145/2064696.2064706
  * [25] Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: Dbpedia: A nucleus for a web of open data. In: The Semantic Web, pp. 722–735 (2007). doi:10.1007/978-3-540-76298-0_52
  * [26] Vrandečić, D.: Wikidata: A new platform for collaborative data collection. In: Proceedings of the 21st International Conference on World Wide Web, pp. 1063–1064 (2012). doi:10.1145/2187980.2188242. ACM
  * [27] Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: Yago2: A spatially and temporally enhanced knowledge base from wikipedia. Artificial Intelligence 194, 28–61 (2013). doi:10.1016/j.artint.2012.06.001
  * [28] Ahlers, D.: Assessment of the accuracy of geonames gazetteer data. In: Proceedings of the 7th Workshop on Geographic Information Retrieval, pp. 74–81 (2013). doi:10.1145/2533888.2533938. ACM
  * [29] Paulheim, H.: Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic web 8(3), 489–508 (2017). doi:10.3233/sw-160218
  * [30] YEAH! We did it again ;) – New 2016-04 DBpedia release. https://blog.dbpedia.org/2016/10/19/yeah-we-did-it-again-new-2016-04-dbpedia-release/. Accessed: 2019-10-30
  * [31] Wikidata: Statistics. https://www.wikidata.org/wiki/Wikidata:Statistics. Accessed: 2019-10-30
  * [32] Lipscomb, C.E.: Medical subject headings (mesh). Bulletin of the Medical Library Association 88(3), 265 (2000)
  * [33] Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995). doi:10.1145/219717.219748
  * [34] About GeoNames. https://www.geonames.org/about.html. Accessed: 2019-10-30
  * [35] National Library of Medicine: UMLS® Reference Manual. NLM Bethesda, MD. Accessed: 2020-10-21 (2009)
  * [36] Ballas, L.K., Elkin, E.B., Schrag, D., Minsky, B.D., Bach, P.B.: Radiation therapy facilities in the united states. International Journal of Radiation Oncology, Biology, Physics 66(4), 1204–1211 (2006). doi:10.1016/j.ijrobp.2006.06.035
  * [37] Stensland, K.D., DePorto, K., Ryan, J., Kaffenberger, S., Reinstatler, L.S., Galsky, M., Canes, D., Skolarus, T.A., Moinzadeh, A.: Estimating the rate and reasons of clinical trial failure in urologic oncology. In: Urologic Oncology: Seminars and Original Investigations (2020). Elsevier
  * [38] Bandari, J., Theisen, K.M., Maganty, A., Davies, B.J., Yabes, J.G., Jacobs, B.L.: Clinical trials in urology: Predictors of successes and failures. The Journal of urology 204(4), 805–810 (2020)
  * [39] Ellis, A., Scroggins, M.: Patient perspectives on clinical trial participation: A pilot survey of gynecologic cancer survivors. Gynecologic Oncology 159, 250–251 (2020)
  * [40] Linked Data: Design issues. https://www.w3.org/DesignIssues/LinkedData.html. Accessed: 2019-10-30

## Figures

Figure 1:  Number of concepts and locations per source. GeoNames is the most
complete data source with geo-coordinates for all concepts and country
location, while UMLS contains no location data. Figure 2:  Final concept
contribution per data source to merged Knowledge Base. Figure 3:  Average
number of languages in alternative names per concept in the Knowledge Base.

Figure 4: Alignment across source databases

Figure 5: Alignment achieved by our approach

Figure 6: TBox for resulting KB.

Figure 7: Hospital density per 100,000
inhabitants. Source: WHO data.
Countries with no data are
marked in grey.

Figure 8: Hospital density per 100,000
inhabitants. Source: Our KB.
Countries with no data, or a population of less than 500,000 are marked in
grey.

Figure 9: Comparison of search results and interfaces when attempting to
retrieve clinical trials or early access programs happening in the University
of Michigan. From top to bottom: CTGOV, EUDRA, ICTRP, our search engine.
Screenshots were produced in October 2019.

## Tables

Table 1: Triple patterns to retrieve country information in DBpedia ?item |  dbo:locationCountry |  ?country
---|---|---
?item |  dbo:country |  ?country
?item |  dbp:country |  ?country
?item |  dbo:location |  ?location .
?location |  dbo:country |  ?country

Table 2: Types of entities retrieved across sources | DBpedia | Wikidata | Yago |  GeoNames | UMLS
---|---|---|---|---|---
|  ?predicate |  ?type |  ?predicate |  ?type |  ?predicate |  ?type |  Feature code |  Semantic Type
| Educational
---
Institutions

rdf:type |  dbo:University, yago:University108286569, yago:WikicatUniversities AndColleges, schema:CollegeOrUniversity, umbel-rc:University |  wdt:P31 /wdt:P279* |  wd:Q38723 |  rdf:type |  yago:wordnet_university_108286569 |  S.UNIV, S.UNIP, S.ITTR, S.STNB |
Hospitals and Medical Centers |  rdf:type |  dbo:Hospital, yago:Hospital103540595, schema:Hospital, yago:MedicalBuilding 103739518, yago:WikicatUniversity Hospitals |  wdt:P31 /wdt:P279* |  wd:Q16917, wd:Q1774898, wd:Q31855 |  rdf:type |  yago:wordnet_medical _building_103739518, yago:wordnet_hospital _103540595, yago:wordnet_clinic _103043274 |  S.HSP, S.HSPC, S.HSPD, S.HSPL, S.CTRM |  T092, T093, T094, T095
dct:subject |  dbc:Hospital_networks, dbc:Medical_and_health _organizations, dbc:Clinics |  ?rel |  [wdt:P31 wd:Q12136] |  |  |  |
dbo:industry |  dbr:Health_care |  |  |  |  |  |
Pharmaceutical companies |  rdf:type |  yago:DrugCompany108002578 |  wdt:P31 /wdt:P279* |  wd:Q4830453 |  rdf:type |  yago:wikicat_Biotechnology _companies, yago:wikicat_Pharmaceutical _companies, yago:wordnet_drug_company _108002578 |  |
|  |  wdt:P452 |  wd:Q507443, wd:Q7108 |  |  |  |
|  |  ?rel |  wd:Q507443, wd:Q15067276 |  |  |  |
Blacklisted items |  IRI |  Hospital, Rehabilitation_hospital, University, School, Community_college, Sixth_form_college, Professional_school, Reform_school |  |  |  |  |  |  C3828423, C0557812, C0027689, C0681333, C1318228, C0871230, C0337962, C0036375, C0557808, C1562642, C0598858, C0587907, C3833703, C3838700, C0020021, C0442592, C3839733, C3810847, C3840771, C0237680, C3834491

Table 3: Percentage of entities with latitude and longitude. Semantic type |  Total concepts |  Concepts with coordinates |  Percentage with coordinates
---|---|---|---
Educational Institution |  47,716 |  25,290 |  53,01 %
Healthcare Related Organization |  64,648 |  58,374 |  90,30%
Pharmaceutical Company |  1,643 |  38 |  2.32%
Table 4: Top 5 facilities and top 5 unmatched sites ranked by amount of trials. Facility |  Trials |  Site mention |  Trials
---|---|---|---
Case Comprehensive Cancer Center |  7,466 |  National Institutes of Health Clinical Center, 9000 Rockville Pike |  2,284
MD Anderson Cancer Center |  5,358 |  Seoul National University Hospital |  562
Memorial Sloan Kettering Cancer Center |  5,036 |  Henry Ford Health System |  465
General Hospital |  4,320 |  Asan Medical Center |  432
Boehringer Ingelheim |  4,062 |  Samsung Medical Center |  409
Table 5: Coverage per pattern to discover vague clinical trial sites. The symbol: % represents a wildcard Pattern |  Total number of clinical trial sites |  Number of distinct name patterns |  Examples
---|---|---|---
‘%Investigational Site%’ |  80,678 |  45,923 |  ‘Pfizer Investigational Site’, ‘Novartis Investigational Site’
‘%Facility #%’ |  71 |  21 |  ‘Facility # 1’, ‘Facility #1’
‘%Investigative Site%’ |  10,312 |  3,175 |  ‘For additional information regarding investigative sites for this trial, contact the Clinical Trials Support Center at 1-877-CTLILLY (1-877-285-4559) or speak with your personal physician.’
‘%Hospital #%’ |  1,691 |  1,103 |  ‘Moscow City Hospital # 29’, ‘St- Petersburg GUZ City Hospital #15’, ‘City Clinical Hospital #8’, ‘NEI HPE Moscow State University of Medicine and Dentistry of FAHSD City Clinical Hospital #70’, ‘City Hospital #31’,
‘%Research Site%’ |  5,400 |  1,676 |  ‘Clinical Research Site’, ‘Research Site’, ‘Research Sites’, ‘Shire Clinical Research Site’, ‘Research Site 1’, ‘Furiex Research Site’, ‘CAPRISA, Vulindlela Clinical Research Site’
‘Site%’ |  33,370 |  23,577 |  ‘Site 128’, ‘Site # 3 OLVG Amsterdam’
‘Local Ins%’ |  933 |  102 |  ‘Local Institution’, ‘Local Instiution’, ‘Local Instiuttion’, ‘Local Institution - Houston’, ‘Local Institute’
‘Local Inv%’ |  3 |  1 |  ‘Local Investigator’
‘Local Hosp%’ |  3 |  2 |  ‘Local Hospitals’, ‘Local hospital at the station Smolensk OAO RZD’
‘Hospital Local%’ |  3 |  3 |  ‘Hospital Local Palos Blancos’, ‘Hospital local de Yopal E.S.E’, ‘Hospital Local’
‘%local%office%’ |  14 |  6 |  ‘Local Medical Information Office’, ‘Pfizer Local Country Office’, ‘US Local Med Info Office’, ‘Local US Medical Information Office’, ‘Local Medical Information Office’, ‘Pfizer Japan Local Country Office’
‘%local%information%’ |  14 |  11 |  ‘Local Medical Information Office’, ‘Pfizer Local Country Office’, ‘US Local Med Info Office’
‘%local%authority of%’ |  11 |  11 |  ‘Local Health Authority of Reggio Emilia-IRCCS’, ‘Uzsoki Hospital of the Local Authority of Budapest, Oncoradiology’, ‘Local Heath Authority of Padova’, ‘Local Heath Authority of Verona’, ‘Local Health Authority of Modena’, ‘Local Health Authority of Pieve di Soligo’, ‘Local Health Authority of Mirano’, ‘Local Heath Authority of Thiene’
‘Azienda%Sanitaria Locale%’ |  78 |  56 |  ‘Azienda Unità Sanitaria Locale di Reggio Emilia’, ‘Azienda Sanitaria Locale Brindisi’, ‘Azienda Unità Sanitaria Locale-IRCCS - Arcispedale Santa Maria Nuova, Ematologia’
‘Local primary school%’ |  5 |  1 |  ‘Local primary schools’