Introduction
Overview
The concept of symptom clusters has recently become an important concept in symptom related nursing research, especially in cancer (Donovan & Jacobsen, 2007: Fan, Hadi & Chow, 2007: Given, Given, Sikorskii & Hadar, 2007: Given, Given, Azzouz, Kozachik & Stommel, 2001: Kirkova, Aktas, Walsh, Rybicki & Davis, 2010a: Kirkova, Walsh, Aktas & Davis, 2010b). The concept of symptom clusters was initially developed in psychology and psychiatry, and then developed to general medicine. It has been extensively utilized in these disciplines for many years now.
However, the concept of symptom clusters is comparatively new to the nursing discipline. Even though concurrent symptoms are frequently reported in clinical practice, surprisingly, symptom management research has not reflected this reality (Dodd, Miaskowski & Lee, 2004: Aktas, Walsh & Rybicki, 2010: Barsevick, 2007a)). According to Miaskowski, Dodd & Lee (2004), symptom clusters is the new frontier in symptom management research. Researchers suggest that specific symptom clusters have a cooperative effect on the patient outcomes and prediction of morbidity (Dodd et al, 2004: Barsevick, 2007b).
The purpose of this paper is to explore the concept of symptom cluster using the Schwartz-Barcott and Kim’s hybrid model of concept development. The review of literature in five different disciplines will serve as the theoretical phase of the process of exploration. These fields include business, astrology, psychiatry and psychology, nursing and general medicine. This will be followed by observation of people clusters in the field, and finally ending with the analytical phase.
Theoretical Phase
The main focus in this phase is to develop a foundation for subsequent phases through thorough analysis and concept modification. It starts with identification of an initial definition, searching for literature and identification of the essential elements of definition and measurement. This phase will culminate in the identification of a working definition for the fieldwork (Schwartz-Barcott & Kim, 2000: Barsevick et al, 2006).
Review of Literature
Cluster in Business Literature
The concept of cluster is an essential concept in business. In 1990, Porter introduced the term of “business cluster” (Porter, 1990: Porter, 1996). Porter (1998a) defines business cluster as a geographic concentration of interrelated businesses, suppliers, and associated institutions in a specific field. It has been shown through literature that this term has been interchangeably used with other terms such as industry cluster, competitive cluster, or Porterian cluster.
However, in earlier definitions, geographical concentration was not seen as a major characteristic of a cluster. Czamanski and Ablas (1979) refer to clusters as “a group of industries connected by important flows of goods and services”. Porter’s contribution to this issue defines an industrial cluster as a set of industries related through buyer-supplier relationships, common technologies, common buyers or distribution channels and common labor pools (Porter, 1990).
Porter (1998b) provides a simple definition of two types of clusters: vertical clusters and horizontal clusters. Vertical clusters are made up of industries that are linked through buyer-seller relationships, whereas horizontal clusters include industries in which the other kind of commonalities such as market, technology and labor force prevails. Geographic proximity emphasizes advantages of industrial clusters but is not a prerequisite to their identification (Zimmerman, 2010).
Rosenfeld (1997) identifies the advantages of close proximity. It allows people to transact cheaply and easily, it enables them to resolve their problems quickly and efficiently and learn in advance and directly about new and innovative technologies and practices. A classic example of the most evident appearance of clustering is Europe’s industrial districts and America’s industry agglomerations, both of which have attracted researchers and policy planners for the better part of this century (Goodman & Bamford, 1989: Pyke & Sengenberger, 1992: Hall, 1988).
The geographic concentration as a key feature in the definition of clusters appears later in the works of Redman (1994). This scholar defines cluster as pronounced geographic concentration of production chains for one product or a range of similar products, as well as linked institutions that influence the competitiveness of these concentrations. This is for example education, infrastructure and research programs. Rosenfeld (1995) stressed in his definition the concept of geographical concentration, identifying a cluster as a loose, geographically bound agglomeration of similar, related firms that together are able to achieve synergy. Firms “self-select” into clusters based on their mutual interdependencies in order to increase economic activity and facilitate business transactions (Holzemer et al, 1999: Honea et al, 2007).
Jacobs & DeMan (1996) present an in-depth discussion of the different definitions of industry clusters. This is despite the fact that these authors use the original definitions by Porter concerning vertical and horizontal clusters as the basis for their works (Lang et al, 2006: Lindgren et al, 2008).
Jacobs & DeMan (1996) argue that there is no single correct definition of the cluster concept because different dimensions are involved. They expand on the definitions of vertical and horizontal industry clusters to identify key dimensions that may be used to define clusters. These include:
- Geographic or spatial clustering of economic activity
- Horizontal and vertical relationships between industry sectors
- Use of common technology
- The presence of a central actor such as a large firm or a research centre
- The quality of the firm network or firm cooperation.
Jacobs & DeMan (1996) consider the presence of a central actor as a key feature for a cluster. This represents quite an exception in the literature.
Rosenfeld (1997) adds other criteria in defining a cluster including the size of the cluster, its economic or strategic importance, range of products produced or services used and the use of common inputs. However, Rosenfeld (1997) does not encourage defining clusters exclusively by the size of the consisting industries, emphasizing that many effective clusters are located in small inter-related industries that do not necessarily have distinct employment concentrations.
According to Rosenfeld (1997), an industry cluster is a geographically bound concentration of similar, related or complementary businesses with active channels for business transactions, communications and dialogue that share specialized infrastructure, labor markets and services and which are faced by common opportunities and threats. This definition clearly emphasizes the importance of the role of social inter- action and firm cooperation in determining the nature of a cluster (Miaskowski et al, 2007).
Recent contributions strengthen the feature of the geographic concentration and emphasize the importance of regional perspective in defining clusters (Porter, 1998a: Swann, Prevezer & Stout, 1998: Cooke, 2000: Feser and Bergman, 2000). Businesses cluster in all sorts of regions and around many core interests.
Rosenfeld (1997) points out that the benefits to business are so fundamental that clustering is ordinary and frequent, not exceptional and rare. Rosenfeld (1997) is of the view that the term ‘cluster’ is used to describe companies that are geographically located and have the ability to contribute to each other, without the necessity of an outstanding employment scale.
The concept of clusters has emerged as a central idea in competitiveness and economic development in the last decade. Drawing on a long tradition of literature, the reasons for cluster formation and its benefits on productivity and innovation are becoming better known (Porter, 2008: Barsevick et al, 2006).
The understanding of the concept cluster in business is a well-established component of national and regional economic development plans. Many cluster initiatives have been carried out worldwide. These clusters are part of economic development. However, there is a gap in the literature and little is known about the structure of these clusters and their outcomes. There is an urgent need to understand best practices as far as they are concerned (Sölvell, Lindqvist & Ketels, 2003: Bassett, Bury & Honer, 1994). Today, clusters in business demonstrate a new way of thinking about national, state and local economies. This gives companies, governments and other institutions new roles in enhancing competitiveness (Porter, 2000: Porter, 2003).
In summary, the key components of a business cluster are formal input-output relationships, the buyer-seller linkages, geographic connections of companies and shared expert infrastructures (Porter, 2008: Porter, 1996).
Cluster in Astrology Literature
There are similarities between star clusters and symptom clusters, which could be enlightening for the concept of development of symptom clusters. Stars do not occur in space at arbitrary places. Some, such as the Sun, are single field stars, but others are members of pairs or form multiple-star systems (Chiaroni & Chiesa, 2006).
Multiple-star systems are called clusters. These are formed in various types and sizes. All of them are condensed from clouds of gas and dust. Two main types of star clusters are discernible. These are:
Globular Clusters
These are tight groups of hundreds of thousands of very old stars which are gravitationally bound. These clusters contain thousands to millions of old stars packed within a region of only tens of light years across (Maccarone, Kundu, Zepf, & Rhode, 2007). Their high stellar densities make it very probable that their member stars will interact or collide (Maccarone, Kundu, Zepf, & Rhode, 2007). 2).
Open Clusters
These are more loosely clustered group of stars which generally contains less than a few hundred members. They are often very young (Zwart, McMillan, Hut & Makino, 2001). Open clusters become disrupted over time by the gravitational influence of giant molecular clouds as they move through the galaxy. However, cluster members will continue to move in broadly the same direction through space even though they are no longer gravitationally bound. They are then referred to as a stellar association (Zwart, McMillan, Hut & Makino, 2001).
The stars within a cluster follow a velocity distribution which is established through two-body encounters, just like a gas or plasma does through collisions of particles. Therefore, it is possible to assign a dynamical temperature (T) to the stars in a cluster. In this framework, the cluster is treated as an ideal gas with point-particles interacting only through collisions. Although this analogy demonstrates some errors in view of the fact that interactions between stars are not limited to the short moment of collision, valuable quantitative results can be obtained out of it (Kupper, Kroupa & Baumgardt, 2008).
In summary, in astrology literature, only numbers and sequences are addressed. However, this is different in psychology and psychiatry
Symptom Cluster in Psychology and Psychiatry Literature
The pathophysiology of associated symptoms is reasonably well understood and causal relationships are established in many known diseases. On the other hand, it is well known that it is not easy to identify etiologies of most mental disorders. More often, an agreement on specific symptoms is recommended for a common etiology, which is then regarded as sufficient enough to recognize a psychological syndrome (Collen, 2008).
It is evident in review of psychology and psychiatry literature that symptom clusters have long been the basis of disease diagnosis of psychological disorders. Several themes have been addressed in the literature review with regard to the concept of symptom cluster. These themes include empirical methods and factor analysis, the associative relationships among symptoms in a cluster, basic aspects, common etiology of psychological disorders and symptom construction expressed by symptom clusters and clinical implication of the concept of symptom clusters (Elson, Hut & Inagaki, 1987: Fernandez-Herlihy, 1988: Kupper et al, 2008).
In a recent study by Hybels, Blazer, Pieper, Landerman & Steffens (2009), the researchers explored the basic aspects of symptom presentation in older adults with major depression by identifying homogeneous clusters of individuals based on symptom profiles. It was a secondary data analysis using latent class cluster analysis. In another classic study by Asmundson, Frombach, McQuaid, Pedrelli, Lenox & Stein (2000), the researchers described symptom clusters as corresponding to basic aspects of posttraumatic stress disorder (PTSD). But the researchers questioned whether PTSD symptom clusters derived by experts were truly corresponding to the basic aspects of PTSD and then proceeded to verify this assumption statistically by using factor analysis (Parker et al, 2005: Schmalig & Bell, 1997).
However, there has been controversy over the appropriate way to define symptom clusters for PTSD. Amdur & Liberzon (2001) tested the factor structure of the Impact of Event Scale (IES) in a sample of 195 male combat veterans with chronic PTSD by using confirmatory factor analysis. They found that the two-factor model including Intrusion and Avoidance deviated significantly from being a good fit. In spite of this, a four-factor model, including Intrusion and Effortful Avoidance subscales-as well as Sleep Disturbance and Emotional Numbing subscales-was significantly a better fit (Shevlin et al, 2007b: Shevlin et al, 2007a). They concluded that essential behavior became visible in the symptoms of a cluster. Conceptually, one can look at psychological disorder as a group of symptoms that may be constructed into precise symptom clusters, which distinguish characteristics of a specific disorder. Subsequently, these symptom clusters present the basis for diagnosis and classification of mental disorders and syndromes.
Statistical analyses were used to classify syndromes, while operational diagnostic criteria were used for mental disorders. Both Asmundson et al (2000) and Breslau, Reboussin, Anthony & Storr (2005) used factor and cluster analysis to describe clusters in posttraumatic stress disorder. Previous research suggested that psychosis is better described as a continuum rather than a dichotomous entity.
Shevlin, Dorahy, Adamson & Murphy (2007a) conducted a study to describe the distribution of positive psychosis-like symptoms in the general population by means of latent class analysis. They used latent class analysis to identify homogeneous sub-types of psychosis-like experiences. The latent class analysis showed that psychosis-like symptoms at the population level could be best explained by four groups that appeared to represent an underlying continuum (Taub et al, 1995: Vaira et al, 2001: Werdmuller et al, 1998).
Shevlin, Murphy, Dorahy & Adamson (2007b) conducted another study that examined the types of borderline personality profiles, associated psychological disorders and stressful life-events. They used data from the British Psychiatric Morbidity Survey to examine homogeneous subtypes of participants based on their responses to nine borderline personality disorder criteria (Westbrook & Talley, 2002: Williams, 2007).
In psychology and psychiatry, symptom cluster is described using the associative relationships between symptoms (Amdur & Liberzon, 2001). Amdur & Liberzon (2001) acknowledged the strong relationship between symptoms within a cluster. Other properties of symptom clusters include the nature or type of symptoms in a cluster and the number of symptoms in a cluster.
The number of symptoms in a symptom cluster does not seem to be important. In a classic study, Rusch, Guastello & Mason (1992) attempted to delineate symptom clusters that may be considered most distinctive of patients diagnosed with borderline personality disorder (BPD). Medical records were examined to assess the extent to which each of the eight DSM-III-R BPD criteria was present in 89 psychiatric in-patients diagnosed with BPD. Structural analysis revealed three symptom clusters that can explain symptomatology for a majority of the sample.
It is also evident from literature review that researchers are studying the etiology of psychological disorders and they are investigating symptom construction expressed by symptom clusters. For example, Dunn et al (2002) determined clustering of depressive symptoms in a combined group of unipolar and patients with bipolar disorder using Principle Components Analysis of the Beck Depression Inventory. They also compared unipolars and bipolar. These symptom clusters were examined for interrelationships, and for relationships to regional cerebral metabolism for glucose measured by positron emission tomography.
Different depressive symptom clusters may have different neural substrates in unipolars, but clusters and their substrates are convergent in bipolars (Dun et al, 2002). These researchers have contributed essentially to the knowledge of brain regions involved in the expression of depressive symptoms.
Symptom Cluster in General Medicine Literature
In medicine, the concept of symptom cluster has been used to explore symptom categorization. Siegel, Myers & Dineen (1987) evaluated premenstrual symptoms in a group of women with severe premenstrual tension syndrome. They performed a factor analysis to establish the nature of symptom clusters in their selected sample. Similar to clinical observations reported earlier, their results revealed two distinct clusters of emotional and behavioral symptoms and two of physical symptoms.
Symptom clusters would possibly help clinicians when looking at etiology of general medical disorders. For example, Cowey & Hardy (2006) defined metabolic syndrome as composed of cardiovascular risk factors including increased body mass index and waist circumference, blood pressure, plasma glucose and triglycerides, as well as decreased high-density lipoprotein cholesterol. The researchers noted that essence of the metabolic syndrome lies in the clustering of these risk factors which are associated with cardiovascular disease.
Nock, Li, Larkin, Patel & Redline (2009) describes Syndrome Z which involves individual components of Syndrome X (the metabolic syndrome). They performed a factor analysis that revealed five syndrome components that included insulin resistance, obesity, hypertension, dyslipidemia and sleep disturbance.
Other researchers suggested that symptom clusters could be used to investigate the etiology in congestive heart failure patients (Martin & Pinkerton, 1983). They recommend that congestive heart failure in adults should be conceptualized as a clinical syndrome. They explain that patients with congestive heart failure exhibit clusters of symptoms that define sets of systemic congestion, pulmonary congestion and inadequate cardiac output. Some have potentially correctable anatomic or metabolic defects. Others have myocardial failure while some have both as underlying causes of the syndrome.
Eslick, Howell, Hammer & Talley (2004) conducted a study to determine how clusters of patients with symptoms compare to a clinical diagnosis in patients with irritable bowel syndrome and non-ulcer dyspepsia. They used a Factor analysis and a k-means cluster analysis. The factor analysis identified nine symptom factors. These are diarrhea, constipation, dysmotility, dyspepsia or reflux, nausea and vomiting, bowel, meal-related pain, weight loss and abdominal pain. The k-means cluster analysis identified seven distinct subject groups which included an undifferentiated group.
Symptom cluster can also be used to plan treatment. In a study by Jurgens et al (2009), the researchers identified the number, type and combination of symptoms in hospitalized HF patients. They also identified the contribution of comorbid illness and age to symptom clusters. Three conceptually unique symptom clusters were recognized in individuals with heart failure. These are:
- Acute volume overload cluster which includes shortness of breath, fatigue and poor sleep
- Emotional cluster which includes depression, memory problems and worry
- Chronic volume overload clusters which includes swelling, increased need to rest and dyspnea on exertion
The knowledge of symptom clusters may improve the ability to recognize symptoms appropriately and make symptom monitoring more meaningful for patients (Jurgens et al, 2009). This example demonstrates the clinical application of the concept of cluster in complicated illnesses.
It has been shown through literature review that factor analysis and cluster analysis identifies different symptom clusters in different diseases, such as gastrointestinal (GI) syndromes (Eslick et al, 2004: Talley, Boyce & Jones, 1998). Talley et al. (1998) conducted a study to determine whether distinct symptom groupings exist in the community of Sydney residents in Penrith, Australia. In total, 60% of the population reported four or more gastrointestinal symptoms. There was considerable overlap of irritable bowel syndrome (IBS) with dyspepsia and among the dyspepsia subgroups by application of the Rome criteria. Independently, 10 symptom groupings were identified by factor analysis.
Another example is people with chronic hepatitis C infection. Quality of life has been shown to be poor among people living with chronic hepatitis C. However, it is not clear how this relates to the presence of symptoms and their severity. Lang et al (2006) conducted a study to describe the typology of a broad array of symptoms that were attributed to hepatitis C viral (HCV) infection. Principal components analysis identified four symptom clusters of neuropsychiatric basis which include mental tiredness, poor concentration, forgetfulness, depression, irritability, physical tiredness and poor sleep.
With regard to somatic diseases, researchers found clusters in chronic fibromyalgia patients. Recent evidence points to the likelihood of heterogeneity in the presentation and etiology of fibromyalgia (FM). In order to gain insight regarding this condition, a clear understanding of the symptomatology and consideration of potential FM subtypes is needed. Rutledge, Mouttapa & Wood (2009) conducted a study to determine whether clusters could be identified among 20 symptoms that participants in a prior online study identified and to elucidate the underlying structure of resultant clusters. Factor analysis was used on data from a study sponsored by the National Fibromyalgia Association. Results revealed that in this well-educated, primarily Caucasian sample, morning stiffness, fatigue and not feeling rested in the morning were the symptoms with the highest severity scores.
Another example of somatic diseases is in multiple sclerosis patients. Motl & McAuley (2009) examined the symptom cluster of fatigue, pain and depression and its direct and indirect prediction of physical activity behavior in a sample of individuals with multiple sclerosis (MS). The data analysis indicated that fatigue, depression and pain represented a symptom cluster. Additionally, the symptom cluster had a strong and negative predictive relationship with physical activity behavior.
Recently, symptom clusters has been used in general medicine as a statistical method to describe the relationships between symptoms. For the purposes of this paper, statistical associations may be essential in defining symptom clusters. On the contrary, a small number of researchers have clearly described relationships between symptoms when they defined symptom clusters. Hunter, Battersby & Whitehead (2008) provides a detailed analysis of the relationships between menopausal status and psychological and somatic symptoms. They used a principal components analysis to examine the relationships between symptoms.
Kotagal et al (1995) analyzed 91 psychomotor seizures from 31 patients, seizure free at least one year after temporal lobectomy. The researchers explored fifty symptoms in every seizure and noted the time of onset and ending. They used statistical analysis to define symptom clusters and to identify the order of appearance of symptoms. They found that the eighteen most common symptoms they examined form a tight cluster showing a high degree of correlation. They recommended that this high correlation is essential in defining symptom clusters.
In another study, Kay et al (1996) tried to assess the clustering of abdominal symptoms in a random population. Data from a cohort study of a 70-year-old Danish population were analyzed. They indicated that clusters’ defined level of significance was set at 1%. Their results revealed that in this 70-year-old population, abdominal symptoms occur in clusters comparable to clusters in younger populations.
There is no evidence in the literature regarding statistical opinions and patients’ real symptom experience except for one example regarding asthma symptoms and coping. Kinsman et al (1973) explored characteristics of subjective symptomatology of asthma within a group of 100 asthma inpatients. Researchers suggested that complex patterning of subjective symptomatology is common in asthma. Symptom patterns described across each of their identified 5 symptom clusters may help to define coping styles related to the role of emotions in asthma and the course of illness.
Another evident aspect in the general medicine literature is the underlying dimension in defining symptom clusters. In factor analysis, the relationship between each symptom and factor is essential. Barrett et al (2002)- in an attempt to develop a sensitive, reliable, responsive and easy-to-use instrument for assessing the severity and functional impact of the common cold using a factor analysis- identified 4 underlying symptom dimensions. These are cough, throat, nasal and fever aches. In another study, Alvir & Thys-Jacobs (1991) explored the effect of calcium therapy on peri-menstrual symptom clusters in a randomized, double blinded, crossover trial of calcium supplementation. Using a factor analysis, they identified 4 symptom clusters. Internal consistency was high for scales based on these factors which were negative affect, water retention, food and pain. Correlations between the scores ranged from.35 to.69. Scores were low during the inter-menstrual phase and much higher during both luteal and menstrual phases. They also looked at dimensions of symptoms that were affected by calcium treatment. They found that calcium supplementation reduced negative affect, water retention and pain during the luteal phase and pain during the menstrual phase.
Another important aspect in defining symptom cluster is concurrence of symptoms within a cluster as a criterion in defining symptom. However, there is little evidence in the literature to support this essential aspect. Groppel, Kapitany & Baumgartner (2000) and Kotagal et al. (1995) defined seizure related symptom clusters in their research as symptoms that occurred together. However, they didn’t address concurrence in relation to statistical methods; neither did they discuss the timing of coexisting occurrence for symptoms to form a cluster.
Regarding the number of symptoms involved in a cluster, the existence of several symptoms appears to be necessary for symptom clusters to develop. More so, there is no restriction in the number of symptoms that can be involved in a cluster. For example, Alvir & Thys-Jacobs (1991) performed a factor analysis in order to investigate peri-menstrual symptoms. They identified two symptom clusters where each cluster contained two symptoms. The first cluster is food, which includes increased appetite and craving for sweets. The second cluster is pain, which includes abdominal cramps and back pain.
Hammer et al (2003) performed cluster analysis and factor analysis in order to investigate gastrointestinal symptoms in a sub sample of patients with diabetes mellitus. The researchers identified only one cluster which included two symptoms; nausea and vomiting. Groppel et al (2000) performed a cluster analysis of clinical seizure of psychogenic non-epileptic seizures. They identified three clusters. Two of those clusters contained seven symptoms while the remaining one contained only one symptom clusters. Collectively, these results propose that there is no specific number of symptoms restricted in a cluster.
Symptom Cluster in Nursing Literature
In nursing literature, the concept of symptom clusters is a relatively new one. Several approaches to the concept of symptoms have been addressed, including symptom occurrence, symptom distress and unpleasant symptoms. However, there is limited research and publications in literature about the use of the term “symptom clusters” and additionally there are changeable definitions.
Some researchers borrowed this concept from general medicine, psychology and psychiatric disciplines. Others used this term to explain several symptoms appearing together. Hall (1988) invented a very useful method of understanding and teaching about the multiplicity of symptoms of Alzheimer disease (AD). Each person with AD presents many different symptoms that change over time (Richards, 1990). Rather than compile a list of symptoms and losses associated with various stages, Hall (1988) identified four symptom clusters that groups change associated with AD. These are intellectual losses, personality losses, planning losses and progressively lowered stress threshold. She noted that each patient exhibit some symptoms from each category. Richards (1990) discussed that the goal for planning care using this approach is to compensate for the losses and to help the patient function better within their neurological capacity. This approach is promising for practice and research as it is based on existing theories of stress and coping (Richards, 1990).
Earlier works in the oncology nursing literature attempted to address concurrence of symptoms and associative relationships among symptoms presented in oncology patients (Given et al, 2001: Given et al, 2007: Lenz, Pugh, Milligan, Gift & Suppe, 1997: Sarna, 1993: Sarna & Brecht, 1997).
However, although researchers in oncology nursing literature did not specifically relate their findings to the concept of symptom cluster, their contribution have formed the foundation for the newly promising concept of symptom clusters. Recently in oncology nursing literature, there is a fair amount of research that relates oncology patients’ symptoms to the concept of symptom clusters (Armstrong, Cohen, Eriksen & Hickey, 2004: Cheung, Le & Zimmermann, 2009: Donovan& Jacobsen, 2007: Fan et al, 2007: Fox & Lyon, 2007: Gift, 2007: Given et al, 2007: Kim, McGuire, Tulman & Barsevick, 2005: Kirkova et al, 2010a: Kirkova et al, 2010b: Lacasse & Beck, 2007: Liu et al, 2009: Maliski, Kwan, Elashoff & Litwin, 2008: Miaskowski & Aouizerat, 2007: Miaskowski, Aouizerat, Dodd & Cooper, 2007).
Following the previously addressed concept analysis, Armstrong et al (2004) reviewed and analyzed the literature to provide a critical analysis of the state of the science of research on symptom clusters in the general oncology population compared to symptom research in the primary brain tumor population. They addressed symptoms as multidimensional experiences that include perceptions of the frequency, intensity, distress, and meaning as symptoms occur and are expressed. They emphasized that a symptom can influence the occurrence and meaning of other symptoms. They found that symptoms occur in clusters in general oncology patients, and these clusters have been shown to influence functional status. The potential effect of tumor biology on symptom clusters is shown by the cluster of symptoms theorized to be associated with pro-inflammatory cytokine production. Unfortunately, studies of symptom clusters have not been reported for patients with primary brain tumors. They recommended that application of the symptom cluster paradigm to guide research is needed.
With regard to defining the symptom cluster, Dodd et al (2001) conducted a study to determine the effect of the symptom cluster of pain, fatigue and sleep insufficiency on functional status during three cycles of chemotherapy. They defined the concept of symptom cluster as follows: “When three or more concurrent symptoms are related to each other, they are called a symptom cluster. The symptoms within a cluster are not required to share the same etiology” (Dodd et al, 2001: p465). They identified relationship and concurrence as the key attributes of a symptom cluster in cancer patients. However, they did not address the associative relationships or timing of symptoms occurring together (Dodd et al, 2001)
With regard to underlying dimensions of symptoms in defining symptom clusters, Woods et al (1999) identified the clusters of symptoms women experience during the premenstrual and assessed the reliability of the symptom clusters as reported by women with three peri-menstrual symptom patterns. They also compared the levels of severity for the symptom clusters across menstrual cycle phases and by symptom patterns and estimated the stability of the symptom cluster rankings across three menstrual cycle phases. Using a factor analysis, they identified four symptom clusters representing the underlying dimensions of symptoms, which included: turmoil, fluid retention, somatic symptoms and arousal symptoms. With regard to the number of symptoms included in a cluster, it appears to be very diverse in nursing literature. Dodd et al (2001) addresses the number of symptoms in the oncology population as a minimum of 3 symptoms in a cluster.
With regard to shared etiology between symptom clusters, Dodd et al (2001) noted that symptoms in a cluster are not required to share the same etiology. On the other hand, Gulick (1989), in an attempt to validate a multiple sclerosis related symptom checklist, made an assumption that symptoms would cluster together according to neurological functional systems affected by multiple sclerosis. Using a factor analysis, the author tested this hypothesis and the results supported this hypothesis. Mitchell & Woods (1996) conducted a study to describe the type and stability of symptoms experienced by midlife women. They recommended that within the 5 different symptom clusters they identified, it is possible that the underlying etiology of each symptom cluster may be diverse.
Measurements of Symptom Cluster
Symptom clusters were initially identified by clinical impressions and expert agreement (Porter, 1996: Asmundson et al, 2000). However, these approaches were considered subjective and illogical. Therefore, empirical methods for identifying symptom clusters were introduced. Factor analysis was historically used to identify symptom clusters. The usage of factor analysis resulted in symptom clusters. Symptom clusters have often been identified using factor analysis, a statistical method to study or validate the basic construction of variables. Symptom clusters are defined on the foundation of factor analysis and every factor develops a symptom cluster that corresponds to underlying dimensions of symptoms. These underlying dimensions describe precise characteristics of symptoms.
Choosing a Working Definition
Symptom cluster is one or more symptoms related to each other. The symptoms in a cluster have associative relationships and are not required to share the same etiology. They may have adverse effects on patient outcomes and may have synergistic effects and predict patient morbidity.
Fieldwork Phase
The fieldwork phase in concept development is intended to enhance and confirm the concept by broadening and integrating the analysis of the concept with empirical observations Empirical data was collected by observing a cluster of people in four different places. These were Harre Union at Valparaiso University (VU), The Christopher Center Library Information Resources at VU, the Dune Park Train Station and the Millennium Park Train Station. These selection sites were relevant to collect empirical data for understanding the concept of symptom clusters. Specifically, they were selected in order to identify if the term cluster is the right term used to identify a symptom cluster.
According to Stanghellini (2001), three criteria should guide the selection of fieldwork. These are the likelihood of observations of the phenomenon under study, appropriateness of participant observation as a method of gathering data and the likelihood that the researcher will be able to create and sustain a participant-observation role in the setting.
The population and selection sites of commuters and students clustering will likely maintain all three criteria to complete the fieldwork phase. The fieldwork began with the investigator observing the phenomenon under study. The investigator wrote down observational notes by observing and listening to individual conversations in all four setting. The following section will briefly describe each observation in all four settings.
Harre Union
In the Harre Union, observation took place in one of the biggest dining areas. The investigator observed different samples of student clusters such as a group of 12 undergrad male students talking about partying and gossiping about friends, a group of 3 undergrad female students gossiping about other friends and talking about shopping and make up, brands and fashion, another group of 3 students who are not even communicating with each other, just eating. It seems like they know each other but not so close. A group of six graduate students wearing suits having an intellectual discussion about careers and jobs. Lastly, a group of 2 guys just talking about the food offered in the cafeteria.
Christopher Center Library
The second observation is in Christopher Center Library and Information Resources. The investigator observed different student clusters; a group of five students working on a project, haphazardly communicating and discussing ideas but mostly laughing, having fun and eating crackers. Another group of 4 students working on a project as well, but with this group they are having a rich discussion with a leader who is dividing, planning and organizing the work and the other three are efficiently and effectively communicating. A group of 2 students sitting together but working separately, they just said hi and byes. Ten small clusters that consist of only one person working on their laptop quietly were observed. Lastly, 5 clusters of private tutoring sessions were also observed.
The Two Train Stations
Lastly, the two train stations (Dune Park Station and the Millennium Park Station) are both part of the South Shore Line at the Northern Indiana commuter transportation district. The Dune Park observation was during morning rush hours while the Millennium Park Station observation was at night. Those two observations will obviously have different examples due to the difference in timing. For the Dune Park observation, mostly people were well dressed and heading to work. Some clusters were forming because the same people commute everyday for work. One specific cluster of two people was formed because most likely they work in the same company and their conversation was mainly about new frustrations of the new managerial change. Another interesting cluster was a group of females with suitcases. It seems like they are heading to the airport.
Mostly, there were many clusters of one person who is not communicating with other clusters or other individuals, reading a book, waiting for the train vigilantly or talking on the phone.
In the Millennium Park observation, it seemed like people were tired and are going back home. Several clusters were forming with different characteristics such as; a cluster of two couples fighting, another cluster of two couples not communicating and each one is just reading a book on their own. Additionally, small clusters that consist of one person either reading, eating, sleeping or simply waiting were observed. Lastly, an interesting cluster formed of two separate clusters; one consisted of a female in her mid twenties reading a novel and the other was a female in her mid thirties eating. Those two females seems like they come from totally different backgrounds because of the way they were dressed. But the common thing between those two females was the novel one of them was reading. These two females were talking for half an hour about the author, other novels by the same other, thoughts and ideas derived from the novel. The investigator was so interested in the link between the two clusters. Both started as a single cluster of one person and then they joined to be one cluster. This could add to the concept of symptom cluster.
Analytical Phase
The final phase of developing the concept is to re-examine it by comparing and contrasting the data to produce a definition of what the concept of symptom clustering means in nursing. Collectively, the investigator identified five themes of clusters during these observations which are relevant to symptom cluster in literature. These are necessity of categorization, numbers necessary for clustering, reason of clustering and timing and relationships and association themes.
Necessity of Categorization
From the examples observed, we can divide the conversions to three different categories about people, things and idea. Both conversations about people and things can come from low intellectual perspective while ideas come from a higher intellectual perspective which leads us to the necessity of categorization of those different conversations. It broadly leads to the necessity of categorizing symptom clusters such as sickness behavior symptom cluster, cardiac related symptom clusters such as acute or chronics volume overload cluster and emotional cluster. Clinicians need to be more precise about categorizing symptom clusters.
Numbers Necessary for Clusters
Empirically, it was shown that clusters can range from one person to a group of people. Theoretically, the inclusion of 1 or more symptoms in several symptom clusters simultaneously is an unresolved issue in symptom clusters. Westbrook & Talley (2002) conducted a factor analysis to find clinically meaningful dyspepsia subgroups and produced symptom clusters in which no symptom loaded at 0.5 or greater on more than one. This indicated that each symptom appeared in only one cluster at a time. Kay et al (1996) found that one symptom (abdominal pain) appeared in the 3 abdominal symptom clusters that were defined by discrete graphic modeling. This indicated that one symptom appeared in several symptom clusters simultaneously.
Reason for Clustering and Timing
Empirically, it was evident through observations that in each setting, people were clustering for mainly the same reason. However, there could be other sub reasons for the clusters within the big cluster to form. For example, the people in the train were all at the station for the main reason of traveling to another area, but their stops were different. The sub reasons and the purpose of traveling were different. More so, when the train station is observed at different timings, again the purpose was the same in the two settings (the morning and the night observation) but the sub reasons for the same person if there was one traveling on both is different, meaning they could be going to work in the morning and going back home in the evening or vise versa.
Relationships and Association
Empirically, relationships and associations were observed in all four setting on different levels. In the train stations, the relationship between people was that they were commuter and even though they did not know each other very well, most of them were traveling everyday together; they even had relationships with the conductor of the train. Specifically, with the two females’ cluster that formed from two clusters mentioned earlier, there is no relationship between the two but the novel has formed the relationship. Therefore, other factors can contribute and link clusters together. The two examples given about the different group studies (students who were working on a project) had different relationships. The group that was having more fun was more interactive, maybe not as organized as the other one, but seemed like a stronger relationship.
Also in the Union, relationships and strength associations were observed and varied between clusters. Theoretically, literature suggested that disciplines of nursing, psychology and psychiatry and general medicine addressed the theme of relationships and associations as essential components of a symptom cluster. However, there is no mention of the strength of relationships between symptoms or clusters. Therefore, it is it essentially important that researchers clarify what they consider to be a clinically significant level of relationship. It is also obvious that relationships among symptoms within a cluster should be greater than relationships among symptoms across separate clusters. The relative independence in relationships between clusters appears to be assumed, particularly in psychology and psychiatry because otherwise dimensions could not be identified. However, as noted earlier in the psychology and psychiatry literature, the pattern of relationships among symptom clusters can reveal different ways to understand diseases or symptoms, as found in depressive symptom clusters between bipolar patients and unipolar patients (Dun et al, 2002: Given et al, 2001a: Given, 2001b).
Conclusion
Further investigation through derivation of empirically or theoretically meaningful symptom clusters is needed. Theoretically, major indicators of cluster are associative relationships, underlying dimensions and common etiology. Empirically, major indicators of clusters are reason of clustering which could correlates with underlying dimensions and etiology and associative relationships. The theoretical and empirical data were combined to produce a definition of symptom cluster in nursing: “symptom cluster is a group of one or more symptoms that have an associative relationship, may have a common etiology, and could be related to underlying dimensions”. The hybrid model has been a useful method for this concept development.
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