Chapter 2 LITERATURE REVIEW
2.2 Factors that Influence Treatment Adherence Behaviour
Despite the fact that adherence behaviour measurement provides useful information for managing patients and predicting treatment outcomes, it is still very difficult to measure. Brown and Bussell state that identification of non-adherence is challenging and requires specific interviewing skills [4]. This is because adherence is a complex and dynamic phenomenon [10]. Adherence information cannot be provided by outcome-monitoring alone [1] but also from further insight into the behaviour of patients under treatment.
Measuring treatment adherence behaviour is challenging; it is costly and requires patient information that is not included in clinical data collection [1]. Adherence behaviour is difficult to understand and it is even more challenging for health workers to identify potential treatment defaulters [1] because adherence behaviour in patients is caused by complex factors [3], [1]. These factors, which have been classified into categories such as social, economic, clinical, biological, patient-related condition-related and so on [4], [1], [10], [12] interconnect to motivate the autonomous and dynamic behaviours of patients receiving treatment. They vary in granularities and also vary across socio-economic regions and degrees of influence. The complexity of the cause-effect relationship that exists between the factors and poor adherence eventually leads to patients’ refusal to take drugs, defaulting treatment and dropping out of treatment plans.
Most of the factors that have been identified from various studies have been shown to cause poor adherence in patients. These factors include adverse effect of drugs, poverty, substance abuse, stigmatisation, wellness perceived as disease cured, lack of belief in treatment efficacy and lack
of knowledge about the illness and its treatment [6], [12], [29]. These factors, however, can also be linked to good adherence behaviour, depending on the state and perception of patients.
The above factors and their effects on patients are related to the patient’s state, perception or experiences. Patients’ physical and mental state, perception and experience of these factors is linked with poor or good adherence to treatment. For example, the adverse effect of drugs is based on the patient’s experience with medications; negative experiences are seen to cause poor adherence. Also, a strong belief in treatment efficacy seems to promote good adherence, while the lack of belief is regarded as a negative influence on adherence behaviour.
2.2.1
Existing Categorisation for the Factors
Earlier studies carried out an assessment of factors that influence adherence behaviour for the purpose of providing a better understanding of the relationship between the factors and patients’
adherence, and for proposing appropriate intervention strategies. These studies include a World Health Organization (WHO) study by Sabate, 2003 [1], a systematic review by Munro et al [8], Jin et al [28], Brown and Bussell [4], Castelnuovo [39], and Kruk et al [40]. While studies by Munro et al [8], Castelnuovo [39] and Kruk et al [40] focused on TB, the WHO study by Sabate, 2003 [1], Jin et al [28] and Brown and Bussells [4] focused on adherence and considered multiple diseases. Some of these studies published categorisation systems through which some dimensions for categorizing the factors were established. The dimensions identified from the studies include factor type, type of effect, degree of effect, regional and temporal variation. These categorisations will be briefly described in this section and the detail assessment will be presented in Chapter three.
A study by the WHO was aimed at structuring appropriate intervention plans for several infectious and chronic diseases [1]. This is the earliest known attempt to consolidate knowledge about influencing factors from several qualitative and quantitative studies for the purpose of proposing comprehensive intervention plans for different types of diseases. The study presented a
categorization with five major categories based on factor type dimension; these are patient- related, socioeconomic, health system, therapy-related, condition-related. It also presented another categorisation based on the type of effect; namely, positive factors and negative factors Munro et al conducted a systematic review of the literature from 1999 to 2005 and developed a model for categorizing the factors [8]. The review was aimed at understanding which factors are considered important by TB patients, caregivers and healthcare providers. A total of 44 articles drawn from different regions of the world were reviewed. From the study, four main categorization themes were developed based on factor type dimension: structural factors, personal factors, social context factors and health service factors.
Jin et al. [28] 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 non-adherence from the patient’s perspective and identified 3 dimensions for classifying these factors. Firstly, they presented five categories based on factor type: patient-centred, therapy-related, healthcare system, social and economic, and disease-related. Secondly, they presented three categories based on the type of effect: compliance increment, compliance decrement and no-effect. Thirdly, they presented three categories based on difficulties encountered in measuring the effect and counter intervention of the factors: hard factors and soft factors.
Two categories were identified through a review of six studies carried out by Castelnuovo [39] to depict the period of effect of factors. The categories relate to the treatment phases of an anti-TB treatment plan. They are the intensive phase of anti-TB treatment after the patients are diagnosed with TB and the continuation phase, which starts immediately after the intensive phase.
Other categorisations include temporal representations, such as the weekly and monthly categorizations introduced by Kruk et al [40] based on a review of 14 studies that focused on the timing of default in low income countries’ TB treatment. Another categorisation is the study of
Brown and Bussell [4] that identified 3 broad categories based on factor type through a review of 127 papers. The three categories are patient-related, physician-related and the health system- related factors.
2.2.2
Challenges with Existing Categorization Systems
It is imperative that stakeholders in the health sector support the disease programmes regarding resource allocation and intervention planning. However, the mandate can only be effectively achieved if they understand which factors influence treatment adherence. The knowledge of these factors is essential and useful for predicting which individuals and communities are at a high risk of non-adherence. Conversely, there exists no computational representation of knowledge about these factors, which can be used to curate and share the factors among human experts and predictive modelling tools.
There are some challenges with the existing systems that make them unfit as a concrete computational representation of factors that influence adherence behaviour. Some will be highlighted here to show the gap that is required to be filled by this study. The detailed discussion of these shortcomings will be elaborated on in chapter three as a pre-analysis for the construction of a conceptual model for representing the factors.
There are large variations in the systems presented in existing studies and this is a challenge for a common and sharable representation of the factors. For instance, the categories identified across the papers may appear similar, but the description of the categories and the factors belonging to each category vary
There is inconsistency in the naming and definition of existing categorization systems as there are no generally accepted names for the categories. For instance, patient-related factors have different names and meanings across the systems that have included it in their categorisation
There is also no uniformity in the classification hierarchy as some of the existing systems introduce sub-categories while others do not. In the systems that do not have sub- categories, factors are directly grouped under the main categories
None of the categorization systems represent all the categorization dimensions. While some represent more than one dimension in their studies, others concentrate only on one dimension. Also, some dimensions are not included in any of the categorizations. One of these is the cross-dependency between influencing factors; some clinical studies have established cross-dependencies among factors, that is, a factor’s influence is dependent on another factor [41]
Lastly, none of these categorisations are concretely defined. There is no concrete definition of the dimensions and elements of the categorisation system.