Biopsychosocial vs. Biomedical Model

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Patient Preference and Adherence 2016:10 669–681

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Open Access Full Text Article

http://dx.doi.org/10.2147/PPA.S96241

An ontology for factors affecting tuberculosis treatment adherence behavior in sub-Saharan Africa

Olukunle Ayodeji Ogundele1

Deshendran Moodley1

Anban w Pillay1

Christopher J Seebregts1,2

1UKZN/CSiR Meraka Centre for Artificial intelligence Research and Health Architecture Laboratory, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, KwaZulu-Natal, 2Jembi Health Systems NPC, Cape Town, South Africa

Purpose: Adherence behavior is a complex phenomenon influenced by diverse personal, cultural, and socioeconomic factors that may vary between communities in different regions.

Understanding the factors that influence adherence behavior is essential in predicting which

individuals and communities are at risk of nonadherence. This is necessary for supporting

resource allocation and intervention planning in disease control programs. Currently, there is no

known concrete and unambiguous computational representation of factors that influence tuber-

culosis (TB) treatment adherence behavior that is useful for prediction. This study developed

a computer-based conceptual model for capturing and structuring knowledge about the factors

that influence TB treatment adherence behavior in sub-Saharan Africa (SSA).

Methods: An extensive review of existing categorization systems in the literature was used to develop a conceptual model that captured scientific knowledge about TB adherence behav-

ior in SSA. The model was formalized as an ontology using the web ontology language. The

ontology was then evaluated for its comprehensiveness and applicability in building predictive

models.

Conclusion: The outcome of the study is a novel ontology-based approach for curating and structuring scientific knowledge of adherence behavior in patients with TB in SSA. The ontology

takes an evidence-based approach by explicitly linking factors to published clinical studies.

Factors are structured around five dimensions: factor type, type of effect, regional variation,

cross-dependencies between factors, and treatment phase. The ontology is flexible and extend-

able and provides new insights into the nature of and interrelationship between factors that

influence TB adherence.

Keywords: tuberculosis, treatment adherence behavior, influencing factor, conceptual model, ontology

Introduction Poor adherence or nonadherence of patients with tuberculosis (TB) to prescribed

treatment is a major contributor to treatment failure.1–3 Treatment adherence behavior

(TAB) is defined as the extent to which a person’s practice of taking medication, follow-

ing a diet, and/or executing lifestyle changes corresponds with agreed recommendations

from a health care provider.4 Thus, poor adherence is the failure of patients with TB

to take medication or follow a diet and lifestyle in accordance with the prescription

given by a health worker.5 Patients with TB who exhibit poor adherence to treatment

over a period of time have a high risk of becoming resistant to prescribed drugs that

may eventually become life-threatening.

Adherence is a complex and dynamic phenomenon with a wide range of interacting

socioeconomic factors impacting on a patient’s adherence behavior.6 These factors vary

in both granularity and the extent of their effects on adherence behavior across different

Correspondence: Olukunle Ayodeji Ogundele UKZN/CSIR Meraka Centre for Artificial intelligence Research and Health Architecture Laboratory, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, H1 Block, westville Campus, University Road, Durban 3629, South Africa Tel +27 78 617 0144 email [email protected]

Journal name: Patient Preference and Adherence Article Designation: Review Year: 2016 Volume: 10 Running head verso: Ogundele et al Running head recto: An ontology for factors affecting TB TAB in sub-Saharan Africa DOI: http://dx.doi.org/10.2147/PPA.S96241

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Ogundele et al

socioeconomic statuses and geographical regions and can

sometimes have opposite effects under different circum-

stances. Understanding adherence behavior in patients with

TB is important for effective and efficient treatment planning,

and improved understanding of fluctuations in treatment

outcomes in disease-monitoring programs.4 Knowledge of

the pattern of influencing factors and adherence behavior is

also useful for decision support in TB disease control pro-

grams. Taking patients’ subjective treatment experiences into

consideration can facilitate patient-centered interventions

and become a tool to better promote treatment adherence.6

Structured and systematic synthesis of qualitative research

can contribute to improved understanding, interpretation, and

comparison of the growing volume of studies about patients’

adherence to treatment.6

A number of systems have emerged to analyze, structure,

and compare knowledge about influencing factors contribut-

ing to adherence in patients with TB.4,6,7 These systems are

limited in terms of their comprehensiveness and represen-

tational support; some categories are vague and ambiguous,

and there are fundamental semantic differences between the

classification systems, which make them incompatible with

each other. Transforming the current systems into a holistic

formal and computational model is a step toward specifying

a common, consistent, and unambiguous vocabulary and

structure for consolidating the current knowledge around TB

adherence behavior. This consolidated knowledge, or knowl-

edge repository, can form the basis for building adherence

risk predication models for specific communities to identify

knowledge gaps and inform further research studies into

adherence behavior.

To this end, we developed a conceptual model for struc-

turing, curating, and uncovering scientific knowledge about

factors influencing TB adherence. The study presents an

evidence-based model that is essential for clear identifica-

tion and understanding of community-specific factors that

influence TB patients’ adherence and identify communities

at risk.

The conceptual model is formalized as an ontology

and expressed in the web ontology language8 (OWL). An

ontology is an explicit specification of a conceptualization.9

Ontologies have been used successfully to represent concepts

in the public health domain.10–12 OWL is the most widely used

language for expressing and sharing ontologies. It is designed

to represent rich and complex knowledge about things,

groups of things, and relations between things (http://www.

w3.org/2001/sw/wiki/OWL). SNOMED CT (Systematized

Nomenclature of Medicine – Clinical Terms) is represented

as an ontology with OWL.13

The ontology is extendable, can be navigated and queried,

and is useful for computer-based prediction. The ontology

was evaluated for its effectiveness in representing and clas-

sifying factors associated with adherence to TB treatment in

sub-Saharan African (SSA) countries.

The remainder of the paper is organized as follows: the

methods followed in the study are detailed in the “Methodol-

ogy” section. Existing categorization models are reviewed

in the “Review of existing categorization models” section.

The conceptual model is presented in the “Development of

a conceptual model and ontology” section, and the ontology

that is evaluated is described in the “Evaluation of the con-

ceptual model (ontology)” section. Finally, the “Discussion

and conclusion” section is given.

Methodology Three process steps were used to develop the TB TAB ontol-

ogy. The first step, knowledge acquisition, entailed a review

of the literature on treatment adherence of patients with TB to

identify existing dimensions for classifying influencing factors.

The second step, model development, involved the develop-

ment of a conceptual model using the information extracted

from the literature review and expressing this as an OWL

ontology. The ontology was then evaluated in the final step.

A review of the literature was conducted to provide back-

ground knowledge required for the ontology development

process. The repositories searched included Google Scholar,

Science Direct (Elsevier), SCOPUS, Web of Science,

EBSCO, and PubMed. Keywords such as “Tuberculosis

Treatment Adherence Predictors” OR “Tuberculosis

Medication Adherence Factors” were used to carry out

searches for related literature. The word “treatment” was

also substituted with “drugs” and “medication”. The word

“adherence” was substituted for “compliance”, and the word

“factor” was substituted for “predictor”. Some of the search

phrases used for the search include the following:

• Factors influencing (medication/treatment) (compliance/ adherence) behavior of tuberculosis patient

• Factors influencing tuberculosis patient (poor/non) (compliance/adherence) with prescribed (medication/

drug)

• Predictors of (drug/medication/treatment) (compliance/ adherence) behavior of tuberculosis patient

• Predictors of tuberculosis patient (poor/non) (compliance/ adherence) to prescribed (drug/medication/treatment)

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An ontology for factors affecting TB TAB in sub-Saharan Africa

Scientific papers were collated and analyzed iteratively

as base knowledge for developing the model. A total of 66

papers were initially identified in the review. Twenty-one

of these were excluded because they did not focus on deter-

mining the influencing factors (predictors) of TB TAB. The

remaining 45 papers were classified into clinical studies or

review papers.

Eight review papers were selected and used as a basis

for formulating the classification dimensions. Five papers

explicitly proposed categorization systems or identified

categories while the remaining three papers supplemented

the general formulation of the final categories.

Thirty-seven papers that reported on clinical studies were

used to identify factors that influence adherence for specific

communities that can be included in the model. Six of these

papers were excluded because they did not focus on factors

that influence TB patients’ adherence. Of the remaining

28 papers, only 14 focused on patients with TB in SSA

countries. These 14 were used to evaluate the model.

The development of the conceptual model involved the

consolidation of the existing categorization systems and identi-

fication of dimensions for representing and structuring factors.

Categorization dimensions were extracted from published

papers through a manual process. A conceptual model that

effectively represents the complexity of factors and objectively

captures existing domain knowledge (from the literature) was

developed using an iterative process. The conceptual model

was formalized into an OWL ontology by following a rigor-

ous ontology engineering method that was adapted from the

Unified Process for Ontology Building14 methodology.

The correctness and comprehensiveness of the ontol-

ogy in capturing and extending knowledge of factors that

influence treatment adherence of patients with TB in SSA

were evaluated and validated. First, a comparative analysis

with the existing categorization was carried out to verify the

representativeness of the model. Second, we validated

the effectiveness of the model in representing the nuances of

the influencing factors by using the model to capture scientific

publications that provide information about patients with TB

in SSA. Finally, we validated the use of the ontology for

building predictive models by using it to construct a Bayesian

decision network model for SSA TB communities.

Review of existing categorization models Several categorizations of factors contributing to adherence

behavior have been published.4,6,15 These earlier studies

carried out an assessment of these factors for the purpose of

providing a better understanding of the relationship between

the factors and patients’ adherence, and for proposing appro-

priate intervention strategies. These studies include the World

Health Organization (WHO) study,4 a systematic review and

study by Munro et al,6 and a quantitative literature review

by Jin et al.15 These three studies presented dimensions for

categorizing influencing factors. Additional categorization

concepts that are not evidence based but, nonetheless, are

useful for categorizing influencing factors have been pro-

posed, eg, temporal variation proposed by Castelnuovo16

and Kruk et al.17

The wHO model A study by the WHO was aimed at structuring appropri-

ate intervention plans for several infectious and chronic

diseases.4 This is the earliest known attempt to consolidate

knowledge about influencing factors for comprehensive

intervention plans for different types of diseases. The study

draws on several qualitative and quantitative studies to

present a categorization with five major categories: patient-

related, socioeconomic, health system, therapy-related, and

condition-related. Second, two categories were presented

based on the type of effect: positive factors that stimulate

patients to adhere more and negative factors that cause a

decrease in adherence.4

Munro et al’s model Munro et al6 conducted a systematic review of the literature

from 1999 to 2005 and developed a model for categorizing

TB influencing factors. The review was aimed at under-

standing which factors are considered important by patients

with TB, caregivers, and health care providers. A total of

44 articles drawn from different regions of the world were

reviewed. From the study, four main categorization themes

were developed. The four themes are as follows: structural

factors, personal factors, social context factors, and health

service factors.

Jin et al’s model Jin et al15 identified some categorizations for representing

influencing factors through a systematic review of 102

articles that focused on all types of therapy for several

chronic and infectious diseases. The study examined common

factors causing therapeutic nonadherence from the patient’s

perspective and identified three dimensions for classifying

these factors.

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First, they presented five categories based on factor type:

patient-centered, therapy-related, health care system, social

and economic, and disease-related. Second, they presented

three categories based on the type of effect: compliance

increment, compliance decrement, and no effect. Third,

they presented three categories based on difficulties encoun-

tered in measuring the effect and counter intervention of

the factors. They are hard factors, whose impacts are more

quantifiable, and soft factors, whose effects are difficult to

measure and counter.

Temporal concept Two categories were identified through a review of six

studies carried out by Castelnuovo16 to depict the period

of effect of factors. The categories relate to the treatment

phases of an anti-TB treatment plan. The first is the “intensive

phase”, which is the first 2 months of anti-TB treatment

after the patients are diagnosed with TB. The second is the

“continuation phase”, which starts immediately after the

intensive phase and continues for 4–6 months.16 Other tem-

poral representations are the weekly and monthly categoriza-

tions introduced by Kruk et al.17 They reviewed 14 studies

that focused on the timing of default in low-income countries’

TB treatment.

Challenges of the existing categorization Variations in the models presented in existing studies pose

challenges for the common and shareable representation of

factors. For instance, the factor type categories identified

across the papers may appear similar, but the description

of the categories and the factors belonging to each category

vary. There are variations in the number of categories

presented under the same dimensions. The WHO4 study

proposed five categories, Munro et al6 developed a model

of four categories, and Jin et al15 identified five categories,

which are similar to the WHO’s categories. Similarly, the

type of effect proposed by the WHO and Jin et al is differ-

ent. Although the WHO proposed three categories, Jin et al

proposed two categories. A comparison of the different

categorization systems is given in Table 1.

Additionally, the naming and definition of existing cat-

egories are inconsistent. There are no generally accepted

names for the categories. For instance, patient-related fac-

tors have different names and meanings across the three

models. They are named as personal factors in Munro et al

and patient-related factors in the WHO and Jin et al. The

WHO’s patient-related-factor category focuses on patient

demographic information and excludes certain lifestyle and

psychological attributes included in Jin et al’s category.

There is also no uniformity in the classification hierar-

chy; some of the existing models introduce subcategories,

while others do not. In the absence of subcategories, fac-

tors are directly grouped under the main categories. Jin

et al introduced two subcategories in their classification

only for the “patient-centered” category, and they are the

demographic and psychological factor categories. Munro et

al used the eight themes as the intermediate groups, but the

relationships with the four themes are not clearly defined.

The WHO report did not provide any subcategories in its

classification.

Finally, none of the categorization systems represent all

the categorization dimensions identified in Table 1. While

some represent more than one dimension in their studies,

others concentrate only on one dimension. Three of the five

studies, WHO,4 Munro et al,6 and Jin et al,15 focused on cat-

egorizing factors, ie, the factor type dimension. Two studies

classified factors according to the type of effect. Two studies

focused solely on the period of effect.

Table 1 Existing influencing factor categorizations

Dimension WHO4 Munro et al6 Jin et al15 Castelnuovo16 Kruk et al17

Factor type Patient-related factors Personal factors Patient-centered factors Therapy-related factors Health service factors Therapy-related factors Health system factors Social context factors Health care system factors Socioeconomic factors Structural factors Social and economic factors Condition-related factors Disease-related factors

Type of effect Positive factors Compliance increment factors Negative factors Compliance decrement factors

No-effect factors Measurement Hard factors

Soft factors Temporal intensive phase weekly/monthly

Continuation phase

Abbreviation: wHO, world Health Organization.

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An ontology for factors affecting TB TAB in sub-Saharan Africa

Some dimensions are not incorporated across all cat-

egorizations. One of these is the cross-dependency between

influencing factors. Some clinical studies have established

cross-dependencies among factors, ie, a factor’s influence is

dependent on another factor.18

Development of a conceptual model and ontology Restructuring existing categorizations into a common conceptual model The proposed conceptual model is aimed at representing,

collating, and structuring knowledge found in the literature

in a consistent manner for clear understanding and classifi-

cation of the factors. The model is intended to be used as a

formal basis to develop the ontology. Five dimensions were

identified from the review of existing categorizations. They

have been restructured in order to have a complete and unique

representation of the influencing factors and their application

to patients with TB in SSA. The key elements of the clas-

sification as drawn from the review are factor type, type of

effect, treatment phase, region, and cross-dependency.

Factor type Factor type represents the grouping of influencing factors

according to the similarity of common terms as presented

in the literature. This type of grouping enables the creation

of a category, sometimes in a hierarchy, to assist in distin-

guishing terms. It is a common dimension for categorizing

influencing factors.

We used the classifications found in the three existing

studies to develop unique and specific factor type catego-

ries. The existing categories were restructured to eliminate

concept overlaps and misrepresented factors. They were

iteratively checked in terms of their effectiveness to classify

factors found in scientific publications.

The process of restructuring the categories involves

matching of existing categories based on the similarity of

names and meaning. Similar factor type categories were

merged to produce a comprehensive category. In addition,

some of the broad categories that represent heterogeneous

factors were split to produce unique categories without

unnecessary overlap. Through this process, seven factor

types were defined and their boundaries were set to facilitate

the inclusion of factors from scientific evidence. They are

patient-centered, social, economic, therapy-related, health

system, lifestyle, and geographical access.

A hierarchical model was introduced to capture the factor

type in a consistent manner. The top level of the hierarchy

includes the main categories, while the second level represents

subgroups of factors. This second level is generated from

some ad hoc groupings found in existing studies. The lowest

level in the hierarchy will represent concrete and measurable

influencing factors. Table 2 shows the proposed model with

new categories developed from the existing models.

The patient-centered category was created by merging

related categories and was redefined. The term patient-

centered was taken from the study by Jin et al15 as against

“patient-related” in the WHO4 and Munro et al’s6 “personal

character”. The category also reflects the definition given

Table 2 Three-level hierarchy of factors based on the factor type

Top level Middle level Bottom level

Patient-centered Demographic Age group Sex Marital status

Knowledge Knowledge of TB education level

Psychology emotional state Psychiatric condition Depression

economic Finance income class Poverty

employment Job class employment status

Basic amenities Lack of food Homelessness

Social Social network Family support Community network

Stigma-related Perceived stigma experienced stigma

Belief wellness perceived as cured Treatment efficacy belief

Therapy Therapy effect Drug adverse effect Symptoms persistence

Comorbidity Hiv coinfection Treatment Defaulting history

Treatment alternative Health system Health care facility Opening hour favorability

Drug availability Health care staff Staff friendliness

Communication Gap experience

Lifestyle Substance abuse Alcoholism Smoking/tobacco usage Hard drug usage

Healthy living Diet exercise

Geographical access Location Distance to facility Dwelling region

Transportation Travel time Transportation cost

Abbreviations: HIV, human immunodeficiency virus; TB, tuberculosis.

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by Munro et al. The new patient-centered category is defined

as the category of influencing factors based on the demo-

graphic attribute of patients and the attitude that defines the

characteristics of the patients. Our patient-centered category

excludes social-related factors from the definitions presented

by Jin et al and Munro et al, interpretation of wellness and

illness, motivation, and beliefs.6,15 In addition, compliance

history and substance abuse included in Jin et al were

excluded, because they are therapy- and lifestyle-related

factors, respectively.

Economic factors were separated from the social factors

following Munro et al’s classification to create two categories.

This will allow for a unique representation of the factors in a

specific category and reveal the potential of a factor to belong

to more than one category. The “social factor” category rep-

resents the social context and situation of a patient while the

“economic factor” category relates to the economic status

and condition of the patient.

The “therapy-related” factor was adopted from WHO

and Jin et al. It represents the category of influencing factors

that relate to therapy difficulty faced by patients and clinical

procedures that facilitate or hinder patients from adhering

to treatment. It also consists of part of the disease-related

factor presented by Jin et al and part of the “health service”

category of Munro et al.

The “health system” category consists of influencing

factors that relate to the performance of health care providers

and accessibility of patients to health care service at the health

facilities. The health system category is directly represented

in categorizations by Jin et al and the WHO.

The “lifestyle” factor is a new category that is introduced to

distinctly cover those factors related to a patient’s lifestyle that

are circumstantial habits developed by patients and are subject

to change, eg, substance abuse, diet, and exercise. Jin et al

classified some of these factors as patient-centered, and the

WHO classified them as “condition-related” factors. Separat-

ing these factors into different categories will allow for a clear

identification of the unhealthy lifestyle-related factors.

A “geographical access” category was also introduced to

represent the category of influencing factors that relate to the

location of health care facilities and the house/workplace of

the patients, and accessibility costs in terms of distance, time,

effort, and financial expenses. This will help in understand-

ing both the financial and nonfinancial burden that relate to

a patient’s geographic access to health facilities.

Type of effect This category represents the type of effect a factor has on

patients’ TB adherence, and the degree of effect represents

the intensity of influence on a patient with TB. The type of

effect is based on that of the WHO study. Another type was

included based on the “no-effect” type identified in the study

by Jin et al. The three types of effect included in this model

are positive, negative, and neutral effects.

“Positive influencing factor” represents a group of factors

that show significant motivating influence in the improve-

ment of good adherence behavior. These factors are known

to encourage patients to adhere to medication as prescribed

by a health care officer. This category corresponds to the

positive effect4 and compliance increment.15

“Negative influencing factor” represents factors that show

significant demoralizing influence on patients’ attitudes and

cause poor adherence behavior. This category corresponds to

the negative effect (WHO) and compliance decrement.15

“Neutral influencing factors” are a group of factors that

show no significant effect or correlation on patients’ attitude

toward adhering to treatment. This category corresponds to

the no-effect category in the WHO study.

The patients’ state, perception, or experience in rela-

tion to these factors makes the factors negative or positive.

The sex-related factor is based on whether being a male is

a negative influencing factor or being a female is a posi-

tive influencing factor. Therapy-related factors are mostly

based on patient experience. Drug adverse effect, eg, is

based on the treatment experience of the patients receiving

TB treatment and is seen to cause poor adherence. Belief-

related factors are based on the perception of patients about

circumstances or conditions. An example is a patient who

has a strong belief in treatment efficacy (positive influenc-

ing factor) and the lack of this is regarded as a negative

influencing factor.

Treatment phase The treatment phase factor refers to the stage during which

a factor is influential during treatment. The SSA clinical

cohort studies have considered measuring adherence and the

defaulting rate over different treatment phases. For example,

the two main TB treatment phases are the intensive and

continuation phases of treatment. Previous studies have con-

cluded that there is an increasing trend of poor adherence as

patients go into the continuation treatment phase, and that

more patients tend to default at the continuation than the

intensive phase.17,19,20

Other treatment phases can be included, eg, the “drug

resistance phase factor” represents the category of factors

that are influential during a drug resistance treatment phase

for the treatment of patients resistant to first-line regimen

drugs and can be as long as 2 years.

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An ontology for factors affecting TB TAB in sub-Saharan Africa

Region The regional variation of the influencing factors describes

the existence of a factor with a significant influence, in

particular, on socioeconomic regions. Although, there is no

existing regional model for influencing factor classification,

several studies have used geographic regions for their clas-

sification. The result of several clinical and review studies

revealed that influencing factors can vary across regions.

Regions can be delineated based on socioeconomic or geo-

graphic similarities. The administrative area is commonly

used for classification and represents geographical regions

with internationally recognized administrative boundaries

and governance, eg, country and provinces. The geographical

region is a representation of regions with physical boundaries

or common geographical/physical features. The region does

not have recognized political boundaries and governance and

represents the communities where the clinical studies were

carried out. Finally, the socioeconomic region is a collection

of regions with social and economic similarities.

Cross-dependency Although, cross-dependency relationships between influenc-

ing factors are not represented in current categorizations, they

are common in the findings of clinical studies focusing on

influencing factors. A cross-dependency relationship implies

that a certain factor was found to only influence adherence

behavior if another factor was present. Cross-dependency

relationships are represented in a way that they link the “trig-

ger factor” to the factors that are dependent on the trigger

caused by the trigger factor. A “dependent factor” is only

triggered when another factor is present.

For example, suppose some study found that being male

contributes to negative adherence behavior only when there

are unfavorable conditions at work,18 then male sex is rep-

resented as a factor that is triggered by unfavorable working

conditions.

An ontology for TB TAB The section “Restructuring existing categorizations into a

common conceptual model” presents an abstract conceptual

model for structuring knowledge around adherence. This

subsection describes the TB adherence behavior ontology,

which provides a concrete, formal, and computer-accessible

representation of the conceptual model.

An ontology is a specification of a conceptualization,

provides an unambiguous logic-based model of some

domain of reality, and allows for the representation of rich

and complex knowledge about things, groups of things, and

relations between things.21 Ontologies not only allow for

explicitly capturing, storing, and sharing expert knowledge

but also enable computers to perform automatic reasoning,

consistency checks, data analysis, and decision support.12

Figure 1 provides an overview of the ontology in the

OWL. Key concepts of the model are represented as classes

in the ontology, eg, influencing factor is represented as a

class. The ontology also incorporates a class for evidence

to represent and link published clinical studies that assert

different adherence factors. Relationships between concepts

(classes) are represented as class properties (the arcs between

the nodes in Figure 1). For instance, the “evidence” class is

linked with “influencing factor” by asserts-influencing factor

“object property”.

The factor type dimension is represented as influencing

factor and is a hierarchy of categories of influencing factors.

The type of effect and treatment phases are represented as a

hierarchical object property that links the evidence with the

Figure 1 Overview of the key concepts and relations in the ontology.

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Ogundele et al

influencing factor class. The region dimension is represented

using the “place” class that is linked to evidence in order

to connect each factor to a specific location. Finally, cross-

dependency is represented as an interdependency class and

linked to the evidence and influencing factor.

The developed ontology facilitates the categorization

of influencing factors. It can be applied in the structuring

of influencing factors of adherence behavior for patients

with TB in SSA. It provides links to the information source,

ie, scientific publications, by representing the type and period

of effect as the object property and linking this to the evidence

class. The ontology also maintains knowledge about where

and when these studies were performed, allowing users to

classify factors that fit the profile of their community.

In order to integrate the knowledge adherence with other

knowledge sources, existing ontologies were incorporated

and reused where possible. The evidence class is based on

the evidence ontology22 and the place class is based on the

Geonames23 ontology.

Using the ontology The rich computational representation of the ontology is

ideally suited to provide a sound basis for developing tools

useful for clinicians and researchers. The ontology was used

to develop a prototype web-based knowledge repository that

allows users to update, navigate, and query the knowledge.

The interface (Figure 2) currently allows users to navigate,

filter, and search for classes and properties in the ontology.

To use the ontology, users navigate or search through

the ontology to discover and select potential factors that are

appropriate for a specific community. A complex search for

an influencing factor can be carried out using a combination

of the classes and class properties in the ontology. Catego-

ries can be navigated to find specific factors that have been

identified by the published literature. Factor properties can

also be filtered, eg, the type of effect can be used to identify

factors that have a specific type of effect, by specifying,

eg, negative influencing factors.

Community-specific influencing factors can be identi-

fied by either specifying a region of interest or describing

the characteristics of the region. Search results will include

factors directly associated with the specified region as well

as those factors that are associated with communities that are

contained within the specified region. For instance, a user

may request for negative factors that can be found in Africa.

By specifying Africa, the repository will include factors

pertaining to communities within countries and geographical

regions within Africa.

Figure 2 The interface for ontology repository.

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An ontology for factors affecting TB TAB in sub-Saharan Africa

The ontology was designed to be extendable. The catego-

ries in the ontology can be extended, a new category can be

defined as equivalent to the collection of existing categories

or factors. This is useful for those users who want to repre-

sent a different classification mechanism or introduce new

categories that are not currently in the ontology. Users can

easily add additional factors and associated scientific papers

to the repository.

Support for Bayesian decision network construction One of the design goals of the ontology is to aid in the building

of predictive models for specific communities. The ontology

allows for automating the construction of a Bayesian decision

network. A Bayesian network is an annotated directed graph

that encodes probabilistic relationships among distinctions

of interest in an uncertain-reasoning problem.24 In a typical

usage scenario, the modeler would search the repository for

and identify factors that are likely to impact on adherence in

a target community. These factors will then be used to auto-

matically generate the causal structure of a decision network

with default conditional probabilities for that community

(Figure 3). The modeler must still use his/her expertise to

refine and set the weightings of the conditional probabilities,

or degree of effect of each factor. The resultant Bayesian

decision network represents the adherence profile applicable

to that community and may even be used to predict adherence

behavior for individual patients in that community.

Evaluation of the conceptual model (ontology) Comparative analysis with existing categorizations Table 3 compares the adherence ontology in terms of its

coverage with existing categorizations. The developed ontology

is more comprehensive than the existing categorizations. It

Table 3 Coverage of the ontology compared with existing categorizations

Dimensions WHO4 Munro et al6 Jin et al15 Castelnuovo16 The ontology

Factor type     Type of effect    Treatment phase   Region  (gp)  (exp) Difficulty of measurement  (imp) Cross-dependency  (imp)  (exp) Total dimensions covered 2 2 4 1 5

Abbreviations: exp, explicit; gp, geopolitical; imp, implicit; WHO, World Health Organization.

Figure 3 A Bayesian decision network for predicting TB TAB. Abbreviations: TAB, treatment adherence behavior; TB, tuberculosis.

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Ogundele et al

includes five out of six identified dimensions for influencing

factor categorization extracted from the extensive literature

review. Jin et al’s categorization covers four of the dimen-

sions. Both the WHO’s categorization and Munro et al’s

models cover two dimensions. Castelnuovo’s categorization

only covers the treatment phase category.

One important feature that makes the ontology more com-

prehensive than the existing categorizations is the explicit

representation of the region and the cross-dependency dimen-

sions. Both the geographical region and the interdependency

between factors have not been explicitly modeled by existing

categorizations.

Representing findings in SSA communities We tested the comprehensiveness and effectiveness of the

conceptual model in representing the “nuances” of factors

found in communities in SSA. Factors and their charac-

teristics were extracted from clinical cohort studies that

focused on adherence in TB communities in SSA. A total

of 14 clinical studies found in the SSA region were used in

the identification of factors, which were then classified and

captured in the ontology. The coverage of these factors by

the model was analyzed.

Factor type The new categories provide a comprehensive range of

factors identified in SSA. First, the newly created patient-

centered category covers ten (71%) of the factors identi-

fied in relation to patients with TB in SSA. This matches

the personal character category defined by Munro et al,

although named differently. This is because our definition

of patient-centered factors is similar to personal character

as it includes demographic and psychological factors.

Patient-centered6 covers 86% of the factors, which is higher

than the new category. Patient-related4 categories only

cover 43% and show a very narrow representation of the

category (Table 4).

The new economic and social categories have a wider

coverage than the “socioeconomic” category presented by the

WHO and Jin et al. Eighty-six percent of the studies identified

factors belonging to these classes. Economic-related

factors are identified in six studies, even with the exclu-

sion of transportation-related factors. The socioeconomic4

covers 64%, the social and economic15 covers 71% of the

factors, while the social context6 covers 14%. The newly

created social category covers 43% of the factors. Simi-

larly, the newly created “economic” category covers 43%,

which makes it lower than condition-related4 (71%) and

“structural”6 factors (64%). This is due to the fact that most

factors in the structural- and condition-related4 categories

are incorporated into the two new categories: geographic

access and lifestyle.

The new health system category covers 26% of the

factors. It covers less than Jin et al’s “health care system”15

which is 43%. This is because not all factors in Jin et al’s

category are represented in the new category. For instance,

lack of accessibility to a health care facility was included

under health care system15 and under geographic access but

was excluded from the new category. The new category

covers more factors than both the health system4 (21%) and

health service6 (14%) categories.

The coverage of therapy-related matches those from

the two studies, which cover 57% of the factors extracted

from SSA studies. Geographic access category has 36%

coverage on influencing factors identified for SSA. Lifestyle

category has 43% coverage on influencing factors identified

for SSA.

The new factor type categorization offers a more com-

plete representation than the existing ones. The categories

are distinct from one another and cover the factors uniquely.

However, certain factors from SSA studies such as the

existence of a direct observation therapy center within the

district,25 false/unknown address,26 and outpatient method20

did not fit into any of the new categories.

Table 4 Analysis of existing and new factor type categories

Influencing factor classifications No of studies (14) in sub-Saharan Africa, n (%)

Patient-relateda 6 (43) Personal factorb 10 (71) Patient-centeredc 12 (86) Patient-centeredd 10 (71) Socioeconomica 9 (64) Social contextb 2 (14) Social and economicc 10 (71) Sociald 6 (43) Condition-relateda 10 (71) Structuralb 9 (64) economicd 6 (43) Therapy-relateda 8 (57) Therapy-relatedc 8 (57) Clinical-relatedd 8 (57) Health systema 3 (21) Health serviceb 2 (14) Health care systemc 6 (43) Health systemd 4 (26) Disease-relatedc 2 (14) Lifestyled 6 (43) Geographic accessd 5 (36)

Notes: aWHO;4 bMunro et al;6 cJin et al1;5 dthe ontology. Abbreviation: wHO, world Health Organization.

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An ontology for factors affecting TB TAB in sub-Saharan Africa

Regional variation Regional classification of the influencing factors was car-

ried out using countries in SSA with the aim of identifying

influencing factors specific to each of these regions. This

classification revealed knowledge about varying predomi-

nant influencing factors for different countries (Table 5).

Although, there is wide variation in the range of factors

identified for different countries, the most common categories

across all countries are the patient-centered, therapy, and

social-related factors.

Discussion and conclusion Using a rigorous ontology engineering methodology, we

developed an ontology, the TAB-influencing factors ontol-

ogy, for representing knowledge about factors that influence

TAB in patients with TB. The underlying conceptual model

was developed by reformulating existing categorization

systems from the literature. It incorporates more dimensions

than any of the current categorization systems and was suc-

cessfully used to capture most of the factors that influence

TB adherence behavior in SSA found in the literature.

The ontology takes an evidence-based approach by

explicitly relating each factor to published clinical studies: an

important consideration for health practitioners. It presents the

potential for capturing details of diverse multifaceted influ-

encing factors and their interrelationships and complexities

beyond normal human abstraction, simplification, and compre-

hension. For instance, the diametrically opposing influencing

effects that a specific factor can have under different circum-

stances can be effectively represented in the ontology.

The usefulness of the TAB-IF ontology was demonstrated

in an open, shareable, and extendable web-based knowledge

repository. The ontology formed the computational model

that underpinned the repository and provided advanced

navigation, search, and filtering capabilities. The repository

can be used by program officers to navigate and find potential

factors affecting TB adherence emanating from clinical

studies in similar communities, and to profile communities

and generate risk indices that will help simplify TB patient

monitoring and follow-up activities. The ontology also pro-

vided the basis for the development of a predictive model, a

Bayesian decision network that may be integrated in clinical

decision support tools.

The study presents a novel ontology-based approach for

consolidating and structuring knowledge about TB adherence

behavior. However, a number of limitations of the study should

be noted. Adherence behavior is broad, complex, and difficult

to assess. The current ontology does not claim to be an exhaus-

tive representation of factors that influence TB adherence

behavior. However, the ontology was designed to be extend-

able to reflect custom views and a changing body of knowledge

around TB adherence behavior. Although the conceptual

model contains more dimensions than existing categorization

systems, additional dimensions can be incorporated into the

ontology. The ontology was based on knowledge extracted

from scientific publications, which may not exhaustively reflect

all factors and categorizations experienced in practice.

Possible future research work premised on this study

could be an extension of the ontology to incorporate other

dimensions that are not currently included or supported by the

ontology, eg, “difficulty of measurement”. Further research

is required to qualify the “degree of influence” in a form

that is useful to further categorize and structure influencing

factors. Although the ontology focused on the knowledge

of TB adherence factors in SSA, the approach is potentially

applicable to other diseases and regions where adherence is

Table 5 Regional comparison of predominant influencing factors

Regions Influencing factors category

Burkina Faso27 Alcoholism; defaulting history; TB knowledge Cameroon20 Stigmatization; wellness perceived as cured ethiopia19,28,29 Wellness perceived as cured; age group; geographic access; education level; drug adverse effect; social network (family

support); TB knowledge; finance related; alternative treatment Kenya7 Health care system related; social and economic factor; patient-related factor; alcoholism; therapy-related Madagascar26 Transportation time; TB knowledge; sex; communication gap experienced Namibia2 Distance to health care facility; wellness perceived as cured; sex; marital status; education level (literacy); social network (family

support); TB knowledge; drug adverse effect; symptoms persistence; long waiting time; lack of food; substance abuse; lifestyle Nigeria18 Coinfection (HIV); sex; unfavorable working condition South Africa30–32 Stigmatization; wellness perceived as cured; alcoholism; tobacco usage (smoking); poverty; incentive expectation at clinic;

symptoms persistence; drug adverse effect; sex; coinfection; psychological distress Tanzania25 Sex; age group; distance to facility; geographic access Zambia33 Wellness perceived as cured; TB knowledge; drug availability; drug adverse effect

Abbreviation: TB, tuberculosis.

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Ogundele et al

a significant factor. The proposed ontology can also be used

as a basis to analyze adherence in other diseases such as HIV

and can be extended beyond SSA.

Acknowledgments This work, including support for the Health Architecture

Laboratory (HeAL) project as well as for DM, CJS, and AWP

and a PhD scholarship to OAO, was funded by grants from

the Rockefeller Foundation (establishing a health enterprise

architecture laboratory, a research laboratory focused on the

application of enterprise architecture and health informatics

to resource-limited settings, grant number: 2010 THS 347)

and the International Development Research Centre (HeAL,

grant number: 106452-001).

Disclosure The funders had no role in study design and data collec-

tion. The authors report no other conflicts of interest in

this work.

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