A CONCEPTUAL DATA MODEL FOR DISEASE

SURVEILLANCE, MONITORING AND PREDICTION IN

NIGERIA

Peter Idowu, Dan Cornford and Lucy Bastin

Knowledge Engineering Group, School of Appplied Science and Engineering, Aston University

Birmingham,B4 7ET, U.K.

Keywords: Epidemiology, HIV/AIDS, Developing Countries, GIS, GML.

Abstract: Despite the fact that Sub-Saharan Africa is a region characterised by high rates of several deadly diseases,

there is relatively little consistent or reliable data that can be used for surveillance, monitoring and

management of these diseases in the region. In order to alleviate the problem of patchy and inconsistent

epidemiological data, a well structured, interoperable spatial data model for diseases surveillance and

monitoring is proposed in this paper. The model is motivated by HIV/AIDS monitoring and prediction in

Nigeria. We initially review some of the existing health data models which we modify and extend to

develop a conceptual data model for disease surveillance, monitoring, management and, potentially,

prediction. The data model captures information required for the development of diseases surveillance

systems. The model is developed using the Unified Modelling Language and we aim to make the model an

open standard in order to promote collaboration and encourage researchers in developing nations to

contribute to the maintenance of the data model. The model will be implemented in XML, and will be

applied to a system using service oriented architecture with a focus on HIV/AIDS surveillance and

monitoring in Nigeria.

1 INTRODUCTION

Currently, there are almost no easily accessible,

open data standards for disease surveillance, health

monitoring and management in developing countries.

In this paper, we describe a proposed data model for

disease surveillance, monitoring, management and

prediction for Nigeria, a country which has high

incidences of diseases such as Human

Immunodeficiency Virus/Acquired Immuno-

deficiency Syndrome (HIV/AIDS), malaria,

tuberculosis, etc. It is designed such that it can be

adopted by any country within sub-Saharan Africa.

Indeed it has the potential to be used globally.

HIV/AIDS has been a destructive epidemic and

threatens to continue to create health, social,

economic and developmental problems for

developing nations. This incurable disease is one of

the major causes of poverty in Africa, which, with

around 10% of the world’s population has over 75%

of the people living with HIV/AIDS (UNAIDS,

2004), and 72% of the world’s mortalities from

HIV/AIDS. An estimated 2.8 million Africans

became infected with HIV in 2006 alone - more than

all other regions of the world (UNAIDS, 2007).

Since the first case of HIV/AIDS in Nigeria was

reported in 1986, the prevalence rate has increased

steadily from 3.8% in 1991 to 5.8% in 2001 (Pyke

and Ali-Akpajiak, 2003) with a slight decline to

4.4% in 2007. Despite the decline, Nigeria still has

the largest HIV/AIDS epidemic in sub-Saharan

Africa. According to latest statistics on HIV/AIDS,

Nigeria, now ranks second in the world with disease

counts of over 3.0 million (UNAIDS, 2007) and

almost half a million annual deaths (Adegoke, 2008).

Some Nigerian states have a prevalence rate as high

as 10% (Federal Ministry of Health Nigeria, 2006;

Utulu & Lawoyin, 2007) but epidemiological data is

patchy and inconsistent (Lawoyin & Adewole, 2004).

Coping with recent HIV/AIDS increases in

Nigeria is consuming a large portion of the national

health budget, and threatens the health sector (FMH

and NACA, 2002). In 2000, Nigeria overall health

care system performance was ranked 187th out of

442

Idowu P., Cornford D. and Bastin L. (2009).

A CONCEPTUAL DATA MODEL FOR DISEASE SURVEILLANCE, MONITORING AND PREDICTION IN NIGERIA.

In Proceedings of the International Conference on Health Informatics, pages 442-449

DOI: 10.5220/0001538604420449

 SciTePress

the 191 World Health Organisation (WHO) member

states (WHO, 2000) and this shows that the Nigerian

health care system is weak. ICT facilities such as

email, Internet, and electronic surveillance systems

are vital for healthcare management and exchange of

information. ICT has been identified as the

backbone of health services to prevent, diagnose and

monitor diseases (WHO, 2004) and reduce the cost

of running hospitals (Remlex, 2007). However, there

is almost no existing ICT infrastructure in any

Nigerian hospital. The country faces a number of

obstacles in the use of ICT and its implementation in

the health sector, including an ‘epileptic’ electric

power supply, inadequate telecommunication system,

high cost of ICT equipment and the lack of reliable

Internet facilities (Idowu et al, 2008).

The control of any disease, in any country,

requires that the spatial and temporal rates and

trends of the disease must be determined. This

information will assist public health officials and

stakeholders to determine the locations and areas on

which to focus their attention (Myers et al, 2000). In

Nigeria at present, there is neither an electronic

surveillance system nor any electronic national

database for disease monitoring. As in most other

African nations, the monitoring and surveillance of

disease especially HIV/AIDS in Nigeria is limited to

biennial sentinel surveys at less than 100 sites which

focus on pregnant women between the ages 15 to 49

years attending antenatal clinics in health facilities

across the country (FMOH, 2006). The absence of a

reliable national database on HIV/AIDS compounds

the challenges facing the management of HIV/AIDS

in the country (USAID, 2002).

There is therefore a need for an effective and

efficient spatio-temporal health data model which

can be used as a guide for the systematic capture of

health related data, to provide the impetus for the

development of a national database that can be used

in the monitoring and management of disease,

especially HIV/AIDS in Nigeria. This is the focus of

this paper.

2 SCOPE OF DATA MODEL

A central requirement within any disease

surveillance system is the effective management of

patient information, diseases and location, for which

a good data model is imperative in order to capture

useful information. The immediate scope of the data

model is to:

• identify different types of information needed

for disease surveillance activities and the

corresponding entities

• represent and document the information

required for disease surveillance activities and

entities

• develop a formal Unified Modelling Language

(UML) description to show the relationship and

association between the entities

• provide enhanced support for flexible spatial

and spatio-temporal data

The future scope is to develop a disease surveillance

database system from the data model that will allow

easy query of pattern and distribution of diseases

based on geographical location such as city/town,

local government area and states and to make the

model open standard so as to encourage other

researchers to contribute, use, modify and extend the

system in order to have a standard disease

surveillance model for sub-Saharan Africa.

To achieve the immediate intention of the model,

we hope to build on existing data models that are

relevant to diseases surveillance and introduce

enhanced spatial support into the model. The model

is developed in UML, with the intention to automate

the generation of the XML schema, allowing easier

maintenance of the data model. An interesting issue

is the governance model for the data model. In the

Geospatial domain a strong governance mechanism

is provided by the Open Geospatial Consortium,

however in the health field it is less clear.

3 REVIEW OF EXISTING DATA

MODELS

A data model may be defined as a formal structured

representation of real world entities, focused on the

definition of an object and its associated attributes

(BIS, 2004). There are a number of existing health

data models such as EHR (Electronic Health

Record) and openEHR (Open Electronic Health

Record), DICOM (Digital Imaging and

Communication in Medicine); and Health Level 7

(HL7). Some of the models however, are not without

problems: both EHR and open EHR are still not

fully developed though EHR has made a significant

contribution to health data models by introducing

archetypes. DICOM, though fully developed,

focuses on medical imaging which is out of context

of the proposed data model. HL7 is also fully

developed and widely used by many vendors. HL7 is

an extensive, comprehensive data model that focuses

on general health care system, with a unique

specification of messages between health care

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application systems. The HL7 messaging protocol is

widely adopted and implemented by several health

data models. Several health data models have built

parts of their model on HL7 not only because of the

messaging protocol, but because it is a widely

recognised and supported standard and many

commercial software vendors actively orient their

product development efforts to this model. In

addition, most international health data model

development organisations are using HL7 to

harmonize their standards effort. Two particularly

relevant data models built on HL7 are the Public

Health Conceptual Data Model and the Canadian

Conceptual Health Data Model which are reviewed

in the following section.

3.1 Summary of Existing Health Data

Models

In this section we present a summary of our review

based on scope, strengths and limitations of the most

relevant data models. The Public Health Conceptual

Data Model (PHCDM) focuses on data needs for

public health at all levels generally, while Canadian

Conceptual Data Model (CHDM) focuses on data

concepts that must be captured to meet the needs of

key stakeholders in the Canadian health system. The

two models develop conceptual models to

encapsulate the data needs of the health activities

they represent. PHCDM aims to develop a high level

process model that can be used in public health

while the CHDM aims to develop a process to

maintain and refine the Canadian model in order to

influence international health data models.

The two models support interoperability but

neither of the models is an open standard. CHDM

incorporates governance data which allows the

building of mechanisms to support accountability for

the use of data and for the processes that use the data.

CHDM does not include relationships and

associations between the entities in the model nor

does the specification discuss details of the attributes

of the classes that make up the data model, in

contrast to the PHCDM. The major inadequacy in

the two models, with respect to our requirements is

lack of formal support for spatial features. The major

contribution of this proposed model to existing

health data models is to provide a more structured

representation of the spatio-temporal aspects of

health data. We adopted, modified and extended the

PHCDM because it is more relevant to our proposed

model and focuses on public health systems and

diseases surveillance.

3.2 Geography Markup Language

Geography Mark-up Language (GML) is an XML

based language used to describe spatial and spatio-

temporal objects (Lake et al, 2004). GML is an

Open Geospatial Consortium (OGC) specification

that defines an XML encoding for geographic

information. GML is an international standard

designed to represent common spatial features and

describe spatial objects (including their geometry),

map projections, topology, time, etc (OGC, 2007).

It uses XML schema to define the geometry

elements needed to encode the geographic features.

GML is used to allow the interoperable exchange

of geographic data. It is mostly used in web feature

services as a mechanism for interaction with a

geospatial database (that is to send features between

servers and clients). In this work we employ GML to

provide the spatial embedding of the data. The use

of GML facilitates easy use of the data in a GIS

system, making the data more easily interoperable

with existing web GIS models. We note that in this

work we do not propose our model as a GML

application schema, since the primary issue we wish

to capture in the model is the health data, however it

seems likely that such an application schema would

have a potentially large impact, and allow much

easier interoperability with existing GIS systems.

The existing data model would require quite

significant revision to formulate as a GML

application schema.

4 THE PROPOSED MODEL

A conceptual data model gives the representation of

the real world phenomena in the context of a

database. The conceptual data model is designed to

describe relevant features and attributes of the

information, the methods from the user perspective

that will be stored in the database. The success of

any information system based project depends on

efficiency and effectiveness of conceptual data

model. There are different approaches that can be

used in developing conceptual data model including

semantic, entity relationship, and object oriented

approaches. In our model, we use an object oriented

approach encoding entities and relationships in the

domain. The main advantage of an object oriented

approach is that it allows representation and

definition of objects which provides a clearer

understanding of the conceptual data model. It also

allows easy representation of spatial information.

The formal language of the object oriented design is

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typically UML (Hay, 1999).

4.1 Modelling using UML

UML is a standard, graphical language for object

modeling. UML is a general-purpose tool and

industry standard modelling language for specifying,

visualizing and documenting the artifacts of a

system intensive process. It offers standard methods

to create data models, database schemas, and

reusable software components amongst other things.

UML is used to develop the proposed data model for

diseases surveillance and monitoring in Nigeria.

4.2 Core Components

This model comprises of a number of classes and the

classes are grouped into three core components or

subject areas namely party, location and health

activity. These core components deal with health

activities, parties that are involved in the health

activities and the location where the parties reside

and health activity takes place. The core components

of the model are discussed below.

4.2.1 Party

This contains information about a person, groups

and any features that are of interest to the health

system. Examples of parties include physicians,

epidemiologists, public health workers, hospitals,

laboratories, patients, association of laboratory

scientists, people living with HIV/AIDS. All these

interact within the health system. The party

component of the model also captures information

about relationship within the parties. For example,

physicians employed in a particular hospital,

laboratory scientists in a particular hospital receiving

treatment in another hospital or the same hospital or

group of sex workers being counsel by public health

officers. It may also represent public health workers

telling people in a particular location how to prevent

disease in their locality. The classes that form the

party component of the model are shown in Figure 1.

4.2.2 Health Activity

A health activity is the provision of a specific health

service to a health service recipient by a service

provider at a given place during a particular period

of time. It often intends to affect, or report on, the

health of a person or group. A health activity

component contains information about all the

activities that occur between patient and health

provider. The core health activities in this data

model include observation, diagnosis, laboratory test,

treatment, (which may result in admission or

referral) and intervention. For example, it can

capture information on how a patient is diagnosed

by a physician with a blood or urine sample.

Intervention which is a means of preventing diseases

or providing care is also part of the model.

Intervention includes educational and media

campaigns about the spread of diseases and how to

prevent this. For example, the distribution of

_Party

+PartyID

+PartyDescription

Person

+OccupationCode

+RaceCode

+EthnicityCode

+EducLevelCode

+GenderCode

+BirthDate

+DeathDate

_Group

+OrganizationName

Informal Group

+GroupCode

Formal Group

+IndustryCode

PartyRelationship

+RelationshipDTime

+RelationshipType

0..*

ActorParticipation

+ActorType

+ActorTimeRange

0..*

TargetP articipation

+TargetType

+TargetTimeRange

0..1

0..*

ActorParticipation

(from Health Activity)

TargetParticipation

(from Health Activity)

PartyLocationParticipation

(from Location)

Contact Details

+E-MailAddr

+Telephone

+Website

0..1

Figure 1: The party component of the data model. Classes begin with an underscore represent abstract classes.

A CONCEPTUAL DATA MODEL FOR DISEASE SURVEILLANCE, MONITORING AND PREDICTION IN

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445

_Health Activity

+ActCriticalTime

+ActTime

+ActId

+ActDescription

+ActStatus

+ActMethod

+ActType

+Confidentiality

Diagnosis

+PhysicianId

+PhysicianName

+DiaRoomNo

+DiaStatusCode

+DiseaseTypeCode

+DetectMethodCode

+TransMethodCode

+DiseaseImportCode

+DiseaseStatus

+DiseaseConfirmDTime

+Comment

LaboratoryTest

+PhysicianId

+TypeofTest

+TestResult

+RequestDTime

+ResultDTime

+Description

Treatment

+DrugDispense

+Quantity

+Procedure

+Counselling

+DTime

Intervention

+IntervCode

+IntervQty

+IntervReason

+IntervStrength

+IntervDTime

Admission

+AccCode

+LengthOfStay

+PatientStatus

+DeathDischargedDTime

Referral

+ReferalCode

+ReferalDescriptn

_Observation

+DTime

+ObservationCode

ActorParticipation

+ActorType

+ActorTimeRange

0..*

TargetParticipation

+TargetType

+TargetTimeRange

0..*

0..1

0..*

ActivityRelationship

+ActivityRelationshipType

+ActiityRelationshipTime

0..*

0..1

0..*

Party

(from Party)

Location

(from Location)

Figure 2: The health activity component of the data model.

_gml:Geometry SpatialReferenceSystem

Point

Surface

Curve

_GeometryCollection

LineString

Line LinearRing

MultiSurface MultiCurve

MultiPoint

MultiPolygon

MultiLineString

1..*

-2..*

Polygon

1..*

Location

+LocationId

+LocationCode

+LocationType

+LocationGranularityCode

+LocationDescriptn

Address

+HouseNo

+StreetName

+CityTown

+PostalCode

+LGA

+State

+Country

0..1

1..*

LocationRelationship

+LocationRelationshipTypeCode

+LocationRelationshipDateTimeRange

PartyLocationParticipation

+ParticipationDateTimeRange

+PartcipationTypeCode

+CurrentStatusCode

+CurrentStatusEffectiveDate

Party

(from Party)

TargetParticipation

(from Health Activity)

0..*

0..1

Figure 3: The location component of the data model.

condoms by public health workers or diseases

agencies among some groups of people (such as sex

workers, bus drivers, etc) and encouraging them to

avoid unprotected sex can be represented as a health

activity.

4.2.3 Location

The location component contains information about

the addresses and spatial positions associated with

the other two core components (Party and Health

Activity). Figure 3 below shows different types of

geometry that can be used to represent location in

the model. Location may be used to represent the

position of a range of parties and activities including,

for example, hospitals, buildings, cities, or local

government areas where patients reside. Address

(which contains information such as House No,

Street Name, City/Town, Postal Code, logical

government area, etc) and geometry are aggregated

to location. Geometry is represented using the GML

abstract geometry base element, which is

substitutable for a wide range of geometry types and

will allow easy query of diseases based on

geographical location.

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4.3 Structure Of The Model

In our proposed model, we adopt, modify and extend

PHCDM as it is applicable to the need of diseases

surveillance in order to represent the data needs of

diseases surveillance.

Figure 1 above depicts party component which

provides information about data required in party

component. Party component contains information

about person and group. In order to provide useful

information for the database designer, it is important

to provide information about what to be stored

within the party component. Party components store

information about persons, groups, and, contact

details. A person include patient, physician, diseases

agent and person may be a member of more than one

group without necessarily knowing. For example, a

physician that just contacted AIDS and contact

details are aggregated to party in order to provide

more information about the parties such as website

address, e-mail address and telephone number.

Patients, physicians, hospitals, group of people may

have website that provide more information about

them and or their interest, for example National

Agency for the Control of AIDS in Nigeria

(www.naca.gov.ng).

The group party represents formal and informal

organization; it is formal when the group has an

administrative and functional structure with

common objectives such as Association of

Midwives, Nigeria Association of Resident Doctors.

It is informal group when the group is casual such as

group of sex workers.

In health activity of our model, classes like

notification or outbreak are not included as in

PHCDM because the model aims to be used by

diseases agent to query the pattern and distribution

of a particular disease based on spatial location and

other demographic data. So, there is no need to

notify any party about any disease, the stakeholder

will fetch necessary data about any disease from

database. Diagnosis, laboratory report and treatment

are included in our model since we focus on diseases

surveillance and it will assist the stakeholders to

know the type of disease that is prevalent in a

particular location, at a particular period of time. For

example, a particular location may be prone to a

particular disease in a particular time of the year

such as case of malaria during raining session.

Location is an important component in any

disease surveillance system because the occurrence

of any diseases will be based on location and the

stakeholders will query the diseases surveillance

database based on location so as to know the pattern

and distribution of any diseases and where

intervention is needed. Location component of this

model is unique as shown in Figure 3 compared with

location in PHCDM or any other health data models

because a spatial feature is included and aggregated

to location.

In order to show the relationship between the

three core classes or components, we use class

relationship methods so as to give clear

understanding of the model and the methods are

super class/sub class relationship, relationship

association and participation association.

Super class is a class from which other classes

are derived. It is also known as parent class and sub

class inherit from super class. The three core

components in the model have super class and the

sub class associated with them. The relationship

association is used in the model to show the

relationship between the super class and the sub

class. The symbols 1, 0..1 and 0..* on the association

line shows the multiplicity of the association

between the main class and relationship class. A

single health activity may be associated with zero or

more activity relationship relating it to another

health activity. For example diagnosis of chronic

typhoid fever in a particular patient may lead to

admission of such patient.

The participation association is used to show the

relationship between the main classes. Each of the

main classes has a many to many relationship to

other main classes and each of the class has

attributes that describe data items that can be

collected for a given class in the model. For example

roles play by physician in diagnosis HIV/AIDS and

role play by public health workers in distribution of

condom.

In the model, we use party relationship, actor

participation, target participation, party location

participation, activity relationship, and location

relationship to describe relationship between the

components.

Party relationship gives information about the

relationship that exists between parties in the health

activity. Example of party relationship includes a

relationship between health worker and patient,

health organization and a particular community,

disease agency and people living with a particular

disease such as relationship between National

Agency for the Control of AIDS (NACA) and

Network of People Living With AIDS in Nigeria

(NEPWHAN).

Actor participation is the major roles played by a

party in health activity. Examples include roles

played by a physician in order to diagnose a

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particular disease, or role played by NACA to

distribute condom to hotels in Nigeria. Target

participation on the other hand is the minor roles

played by a party in health activity. For example, if a

person identified as a potential carrier of a disease

(which is a target) is unable to speak or express

himself/herself to a physician probably because of

language barrier, or the intensity of the illness or is

an infant, the person that speak for the potential

carrier (may be interpreter, parent or relative) is an

activity target.

Party location participation shows the

relationship between a location and a party. For

example, a hospital may have different health

facilities such as laboratories, consultation room,

female ward, etc. It may also be diseases agency that

have offices in all the states within the country. The

participation role would be that of the disease

agency that have office at a particular location.

Activity relationship is the relationship between

health activities, for example relationship between

observation and diagnosis, relationship between

diagnosis and treatment. Location relationship deals

with the relationship that exists between locations

and this relationship is important in diseases

surveillance. For example, relationship between

ward and operation room, or relationship between X

ray room and consultation room.

In addition, the model makes use of codes in

order to allow extensibility and flexibility. Codes are

alternative to using free text to describe an attribute

or features of a class. The use of code facilitates data

validation by the system when entered by the users.

Codes are used to allow each of the classes to be

more useful by allowing the class to have type codes

instead of defining new class for minor differences

in the properties of party, health activity or location.

4.4 Model Discussion

The purpose of this model is to document the

information needs of an information system for

effective diseases surveillance, monitoring,

management and prediction.

In the location component of this data model of

which we are aware that explicitly supports

geometry which is represented using the widely

accepted, open GML standard. The GML

representation of the hospitals and party features

allows different geometries such as points, curves,

surfaces and geometry collections which provides

flexibility of encoding.

With GML, user can query a point of interest on

a map in order to ascertain the pattern and

distribution of HIV/AIDS in the vicinity of that

location. A Web Feature Server could also be used

query to fetch the name of locations which has more

than certain prevalent rate for a particular disease,

for example, to fetch the name of state(s) with more

than 5% prevalent rate for HIV/AIDS.

This proposed data model will aid in capturing

comprehensive information about diseases, carriers

of the disease and their location. The model will

assist in developing an understanding of the basic

data required within the health care system in order

to build disease surveillance systems to aid effective

management, monitoring and surveillance of

diseases. It will assist the country to have a good and

reliable epidemiological data and increase the

efficiency of health record unit and this will help the

health policy maker in making favourable health

policies and decision. The model in future may give

birth to electronic health record which will

eventually increase the confidentiality and security

of health record.

This data model will be used to develop a

prototype system which aims to allow users to

spatially query and view data on any diseases in

order to ascertain the patterns, distribution and

prevalent rate of any disease such as HIV/AIDS,

malaria, tuberculosis, etc in any location in Nigeria.

When the system is developed, users will be able to

click on particular point or select a polygon on the

map and the features of the point or polygon such as

the name of the state(s), population at risk and

prevalence rates will be displayed. The prototype

will use aggregated data and focus on HIV/AIDS

because it is only aggregated HIV/AIDS data based

on state level that is currently available for this

research.

It is hoped that in the future when the diseases

surveillance system is fully developed, the

physicians in the hospitals will input patient

information such as demographic data, diseases

associated with each patient and information about

geographical location of each patient into the system

so that epidemiologists, disease agents, policy

makers, and any other authorised users will be able

to query, analyse, view, predict and generate

diseases information based on street, town/city, local

government area, year, population at risk, total

number of cases, prevalence rate, sex, marital status,

educational status and age distribution of disease

carriers in the country.

This system will hopefully aid effective and

efficient intervention in outbreaks of any disease,

which eventually will improve the population health

and reduce the expense on health service provision.

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This will also reduce poverty, which has

characterised the sub-Saharan Africa region. Also,

the system will serve as a means to regularly publish

the patterns and distributions of diseases such as

HIV/AIDS, malaria, etc down to street, town/city,

local government, and state levels and produce

weekly, monthly, bi annual and annual reports as the

case may be on any disease for the use of

researchers, policy makers and stakeholders in hard

copy and on the Internet as against the report which

is published every two years and which only focuses

on sentinel survey in selected centres across the

country.

The process of developing the prototype will be

completely based on open standards, and the future

diseases surveillance system is aimed to be fully

open standard, and open source, so that researchers

and anyone interested in developing, using and

evaluating the system will be able to do so at

minimal cost.

5 CONCLUSIONS

This conceptual data model will aid the development

of national database which can be used in

management, monitoring and surveillance of

diseases in Nigeria. The model constitutes a basic

pattern for the design of database structure for

disease surveillance that can be queried easily. The

physical implementation of the data model using

postgreSQL will be discussed in the future.

We hope that the proposed model will form the

basis for the collaboration among researchers

interested in developing health standards for

developing nations and serve as a guide for

development of a standard health data model,

diseases surveillance system, and electronic health

record in Nigeria and sub-Saharan Africa as a whole.

ACKNOWLEDGEMENTS

We hereby acknowledge the referees, Iyinoluwa,

Titilope, Iyinoluwa and Commonwealth Scholarship

Commission

REFERENCES

Adegoke, R., 2008. HIV/AIDS Killed 400,000 in 2007.

Nigeria Tribune (online), Available from http://

www.tribune.com.ng/22042008/news/news10.html

(Accessed 22 April, 2008)

Business Intelligence Solution, 2004. Data Model, Data

Standards and XML, January 2004. Available from

http://www.knowledge-integrity.com/columns/bi2004

01.htm, (Accessed on June 12, 2008).

Federal Ministry of Health Nigeria, 2006. The 2005

National HIV seroprevalence sentinel survey among

pregnant women attending antenatal clinics in Nigeria:

summary position paper, Abuja.

FMH and NACA, 2002. HIV/AIDS: What it means for

Nigeria (Background, Projections, Impact, Interventions,

and Policy) 1

Edition.

Hay. David C. 1999., A Comparison of Data Modelling

Techniques, Essential Strategies, Inc

Idowu, P. Cornford D. and Bastin L., 2008. Health

Informatics Deployment in Nigeria. Journal of Health

Informatics in developing Countries, 2(1) p.20-21.

Lawoyin T., and Adewole D., 2004. Predictors of maternal

HIV/infection at the primary care level in inner city

Ibadan. International Journal of STD and AIDS, 15(3)

p.165-168

Myers M. F., Rogers, D. J., Cox J., Flahault A. and Hay S.

I. 2000. Forecasting Diseases Risk for Increased

Epidemic Preparedness in Public Health. Advances in

Parasitology 47 p. 309-330

Open Geospatial Consortium (2007): OpensGIS,

Geography Markup Language (GML) Encoding Standard.

Available from http://portal.opengeospatial.org/mo-

dules/admin/license_agreement.php?suppress Headers

=0&access_license_id=3&target=http://portal. Open

geospatial.org/files/index.php?artifact_id=20509

(Accessed June 12, 2008)

Pyke T. and Ali-Akpajiak S., 2003. Measuring Poverty in

Nigeria, Oxfam, Oxfam Working Papers.

Remlex 0., 2007. Information and Communication

Technology in chronic diseases care. Medical Care

Research and Review, 64(2) p 123-147.

UNAIDS 2004. Report on the Global AIDS Epidemic.

Geneva.

UNAIDS 2007. AIDS Epidemic Update: December 2007.

UNAIDS/07.27E/JC1322E, Geneva.

USAIDS, 2002. HIV/AIDS in Nigeria: A USAIDS brief.

Available from http://www.aegis.com/files/ syner-

gyaids/nigeria.pdf (Accessed 7 June 2008)

Utulu S, and Lawoyin T., 2007. Epidemiological features

of HIV infection among pregnant women in Makurdi,

Benue State, Nigeria. Journal of Biosocial Science,

39(3), p.297-408.

WHO, 2000. World Health Report Geneva.

WHO, 2004. eHealth for health-care Delivery: Strategy 2-

004-2007. Geneva. Available from http://www.

who.int/eht/en/Backbone.pdf (Accessed 7 June, 2008)

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