Background: Although effective medication for hayfever (seasonal allergic rhinitis) is available, treatment outcomes are often be poor. Patient beliefs influence outcomes in many other diseases. Assessing patients’ beliefs about their illness and medication may identify targets for intervention to optimize self management and lessen disease impact.
Objective: The application of validated health-related analytical models (Leventhal’s illness representations and Horne’s beliefs about medications) to explore patients’ understanding and experience of hayfever and its treatment.
Methods: Cross-sectional postal questionnaire sent to 20% sample of adults attending four General Practices in South England and prescribed medication for hayfever symptoms in the previous two years. Measures included the Revised Illness Perception Questionnaire and the Beliefs about Medicines Questionnaire.
Results: 316/586 questionnaires were returned (54%). Cluster analysis identified two patient groups; those with negative beliefs (n=132) and those with more positive beliefs about hayfever and its treatment (n=182). Those with negative beliefs were more likely to believe that their hayfever would last for a long time, that they have little personal control over their illness and that their treatment is not effective. Conversely, they reported greater consequences, greater emotional impact, less understanding of hayfever and more medication concerns than those with more positive beliefs.
Conclusions and clinical relevance: Patients with hayfever fall into two distinct groups: nearly half (41% of those sampled) have negative beliefs about their condition. Eliciting patient beliefs during the consultation may reveal assumptions that differ from those of healthcare professionals. Such beliefs should be considered when negotiating treatment plans.
Keywords: Beliefs about medication; General practice; Hayfever; Ill-ness representations
SAR: Seasonal Allergic Rhinitis; GPs: General Practitioners; ICD9: International Classification of Disease version 9; SRM: Leventhal’s Self Regulatory Model of illness or Self Regulatory Model; IPQ-R: Revised Illness Perception Questionnaire; BMQ-specific: Beliefs about Medicines Questionnaire –specific version; BMQ-hayfever: Beliefs about Medicines Questionnaire -hayfever version; SPSS: Statistical Package for the Social Sciences
Seasonal allergic rhinitis (SAR), commonly known as hayfever, is a common and growing challenge in Primary Care. Increasing numbers of patients present with symptoms  and suboptimal use of medication is widespread . The reported incidence of SAR is between 2-15%, depending on the diagnostic criteria and population . The prevalence of SAR in UK school children has increased since the 1960s [1,4,5] and the lifetime prevalence of SAR also continues to increase in adults . This rising prevalence has been reflected in increased use of health services. Consultations with general practitioners (GPs) have increased : the number of patients of all ages consulting GPs for SAR in England and Wales (ICD9-477) doubled between 1971-1991 . Between 1991 and 2004, Primary Care prescriptions for nasal allergy increased by over 60% (from 2.7 to 4.5 million/year) and ocular antiinflammatory prescriptions increased by 50% to 1.4 million/year . While increases in prevalence statistics may reflect increased patient awareness or altered diagnostic practice, the increase in prescriptions does confirm that more people are seeking treatment for SAR .
Common symptoms of SAR include sneezing, runny or blocked nose and itchy eyes [2,8]. Despite the availability of a wide range of symptom-relieving medications, SAR can significantly affect sufferers’ perceived health and quality of life [9-14]. Only a minority of SAR patients report good symptom control, although this statistic may be skewed because many people with SAR (44%) do not consult a clinician . Suboptimal use of medication and poor symptom control may result from not taking medications as instructed , which in other illnesses has been shown to be influenced by patients’ beliefs [16,17].
Leventhal’s Common Sense Model of self regulation of health and illness (SRM), provides a three-stage framework for understanding how symptom-based and psychological factors combine to form patients’ own model of illness and how this influences coping strategies and outcomes [18,19]. Firstly, the person constructs cognitive and emotional representations of the health threat. These reflect internal cues (symptoms), and/or external cues (information from friends, family and healthcare professionals). Five cognitive representations are described: identity: the signs and symptoms and the arbitrary label given to the condition; cause: the individual’s perceived cause of the condition, which may differ from healthcare professionals’ views; time-line: beliefs about the likely duration of the condition; consequences: beliefs about the consequences of the condition for the individual and his/her life; curability/controllability: beliefs about whether the condition can be cured or controlled, including how much the individual can control this. Alongside these cognitive representations, the SRM includes emotional representations which reflect the emotional responses to the condition.
At stage two, the individual adopts an action plan or coping procedure linked to their beliefs and emotions. Personal strategies may aim to achieve and maintain symptomatic control by seeking advice and medication (problem-focused coping) or may involve avoiding going outdoors, taking time off work, or reducing anxiety by meditating (emotion-focused coping). The third stage is coping appraisal, which involves evaluating the effectiveness of the coping strategy in dealing with the threat (in this case the symptoms of SAR). This evaluation feeds back to influence illness representations and coping strategies. The underlying aim is to restore the status quo, cognitively, physically and emotionally. If the patient is prescribed medication, their beliefs about medication will probably impact on coping mechanisms and outcomes  so it is useful to measure these in addition to illness beliefs [16,21].
The SRM has been used to study patient beliefs in many illnesses [22-24] including asthma [16,25,26], leading to better understanding of how patients perceive and cope with chronic conditions and how beliefs affect health outcomes such as depression and quality of life. In patients with myocardial infarction (MI) interventions to change inappropriate cognitions regarding their illness have facilitated faster recovery rates  and provided patients with a greater sense of control over their illness . Research on self-managed illnesses such as asthma has addressed the impact of illness and medication beliefs on adherence to medication [16,25,26] but these findings still await translation into effective patient interventions.
The illness representations of patients with SAR have not been studied, although one recent study looked at allergies in general . The present study aimed to explore the beliefs that SAR patients hold regarding their illness and treatments, and to determine whether patients could be usefully classified according to the configuration of their illness beliefs.
Cross-sectional postal questionnaire survey.
Adult patients (aged 17-65 years) who had consulted with SAR at least once in the preceding two years were identified from computerised records of four General Practices in Southern England.
Questionnaires were distributed to a 20% random sample of patients with SAR in August, at the end of the grass pollen season. Selfcompletion packs were distributed with a covering letter from the GP, and a freepost reply envelope.
Demographic and descriptive data
Age, gender, current symptoms and age of symptom onset were collected by questionnaire.
The following self-completed measures were used to elicit illness representations and medication beliefs:
Revised Illness Perception Questionnaire (IPQ-R):
The revised Illness Perception Questionnaire (IPQ-R)  was chosen to assess patient’s beliefs and understanding of their illness, as it has proven validity and reliability across a range of illness groups. It provides a quantitative measure of the nature and strength of patient cognitions and emotional representations on nine subscales.
The illness identity scale assesses the number and nature of symptoms that patients endorse as part of their condition. Participants are asked to state whether they have experienced any of 14 general symptoms (e.g. fatigue, sore eyes, headaches, sleep difficulties) since their illness began. They are then asked whether they believe the symptom to be specifically due to hayfever (yes/no) which are then summed to give the illness identity subscale score. The causal scale assesses personal perceptions of the likely cause of the illness and contains a core set of 18 belief items (e.g. pollution, diet, overwork). For this study ‘pollen’ was added as an additional causal item.
Illness cognitions are assessed through five subscales: perceived consequences of the illness (6 items, score 6-30) concerned with the expected effects and outcomes of the illness e.g. ‘my hayfever causes difficulties for those that are close to me’; how much personal control the patient feels they have over their illness (6 items, score 6-30) e.g. ‘what I do can determine whether my hayfever gets better or worse’; timeline-chronic assesses how long the patient thinks their illness will last (6 items, score 6-30) e.g. ‘I expect to have hayfever for the rest of my life’; timeline -cyclical assesses perceptions about the pattern of their symptoms (4 items, score 4-20) e.g. ‘my symptoms come and go in cycles’; and treatment control assesses the impact of treatment on control of the condition (5 items, score 5-25) e.g. ‘my treatment can control my hayfever’. The emotional representation the patient holds towards their illness (6 items, score 6-30) e.g. ‘I get depressed when I think about my hayfever’ and illness coherence/understanding which assesses whether patients’ comprehend their illness (5 items, score 5-25) e.g. ‘I have a clear picture or understanding of my hayfever’ are also assessed. These seven subscales are computed from responses to a series of statements to which the respondent indicates their agreement on a 5-point Likert scale, ranging from 1=strongly disagree to 5=strongly agree. Higher scores indicate stronger beliefs.
Beliefs about Medicines Questionnaire-Specific (BMQ-specific):
Patients’ views about the medication prescribed for their hayfever were assessed using the Specific version of the Beliefs about Medicines Questionnaire (BMQ), which has been validated for use in a range of clinical conditions and has been shown to have good internal consistency and test-retest reliability . The BMQ-specific measure comprises two 5-item scales assessing personal beliefs about the necessity of the specifically prescribed medication for controlling their illness and concerns about the potential adverse consequences of taking it. In the BMQ-HF devised for this study, the word ‘medicines’ was replaced with ‘antihistamine tablets’, ‘nasal spray’ and ‘eye drops’, to create three versions of the specific measure. Respondents indicate their degree of agreement with each of the 10 items on a 5-point Likert scale, (1=strongly disagree; 5=strongly agree). Items are summed to give a necessity and concerns scale scores (each ranging from 5-25). Higher scores indicate stronger beliefs in necessity and greater concerns about the particular medication.
Statistical analysis was conducted using SPSS v14 for Windows. Missing items from IPQ-R scales were addressed by mean imputation as advised by the scale developer: if more than half of the subscale items were completed, the missing items were assigned values equal to the average of the completed items.
Relationships between illness and medication belief subscales and demographic factors were explored using Spearman’s rank correlation coefficients (Mann-Whitney U test for gender). Patient subgroups were identified by hierarchical agglomerative cluster analysis of IPQ-R subscale scores, using Ward’s method , taking as the resemblance coefficient, the squared Euclidean distance between illness beliefs as measured by the seven cognitive and emotional subscales of the IPQR . As IPQ-R subscales contain differing numbers of items, values were standardised by transformation into Z scores. The agglomeration schedule was examined to determine the most appropriate number of clusters in the data. Independent sample t-tests (95% confidence intervals and equal variance assumed), were conducted to determine whether there were any significant differences between clusters in medication beliefs, age, duration of illness and age of SAR onset. Cluster differences in gender and experience of symptoms was investigated using Chi2 tests.
Of the 586 questionnaires sent, 316 were returned (54% response rate). 61% of respondents were female (61%); mean age 40 years (SD 12.47). 81% reported moderate or severe symptoms and 76% reported symptoms ≥ 4 days/week for more than 4 weeks in the preceding hayfever season. 63% reported using oral antihistamines regularly, 37% used nasal sprays regularly and 22% used eye drops regularly (Table 1).
|Current age (years) :
|Experience of hay fever|
|Age developed hay fever (years):
|Duration of symptoms (years):
|Self-reported severity of symptoms:||N||(%)|
|Experiencing symptoms ≥ 4 days per week for more than 4 weeks||201||(76)|
|Oral antihistamines: Regular||198||(65)|
|Nasal spray: Regular||116||(39)|
|Eye drops: Regular||71||(24)|
|No. of different medications used: 1||83||(27)|
†Data missing from all categories
Table 1: Characteristics of respondents†.
All illness and treatment belief subscales demonstrated acceptable internal consistency with Cronbach’s Alpha ranging from 0.70-0.91 (Table 2).
|Mean (SD)||Median||Min-max score||Cronbach’s α|
|Illness identity (n=237)||6.73
|Timeline cyclical (n=315)||12.78
|Personal control (n=314)||18.88
|Treatment control (n=314)||14.64
|Illness coherence (n=314)||17.85
|Emotional representations (n=314)||11.57
|BMQ-Specific(n reflects number reporting use of medication)|
Table 2: Means (SD), medians, ranges and Cronbach’s Alpha values for illness perceptions (IPQ-R) and medication beliefs (BMQ).
The most common symptoms attributed to SAR were sore eyes (91%), sore throat (66%), wheeziness (63%), fatigue (55%), breathlessness (55%), sleep difficulties (53%) and headaches (49%). Some participants blamed hay fever for symptoms not commonly associated with SAR, e.g. upset stomach, loss of strength, pain, and nausea.
As causes of SAR, patients mainly endorsed immune and risk factors beyond their control, such as pollen (93%), pollution (73%), inherited condition (43%), altered immunity (32%). Fewer respondents selected psychological attributions such as stress/worry (16%), or emotional state (9%) (Table 3) .
|Cause||N||% of sample endorsing item (agreeing/ strongly agreeing)|
|Pollution in the environment||232||73|
|Hereditary - ‘it runs in my family’||136||43|
|Chance or bad luck||60||19|
|Stress or worry†||51||16|
|Diet or eating habits||47||15|
|My own behaviour†||43||14|
|My emotional state e.g. feeling down, lonely, anxious, empty†||30||9|
|A germ or virus||29||9|
|My mental attitude e.g. thinking about life negatively†||10||3|
|Poor medical care in my past||9||3|
|Family problems or worries caused my illness†||8||3|
|Accident or injury||5||2|
†Emotional casual attributions 
Table 3: Causal attributions of hay fever.
Relationships between patient beliefs and demographic factors
Females were more likely than males to believe that their hayfever was permanent or would last a long time (p=0.025). With increasing age, perceptions of personal controllability of their illness increased (r=0.13; p=0.025) as did beliefs about needing to take antihistamines (r=0.15; p=0.015) and concerns about nasal sprays (r=0.19; p=0.009).
Later onset of SAR correlated positively with stronger perceptions of controllability of illness by treatment (r=0.14; n=297; p=0.019), more concerns about antihistamines (r=0.17; p=0.01) and nasal sprays (r=0.15; p=0.047), and weaker beliefs that hayfever would last a long time or be permanent (r=-0.15; p=0.008).
People with earlier age of onset were more likely to report better understanding of their hayfever (illness coherence) than those whose symptoms started later (r=-0.28; n=297; p ≤ 0.001). Longer illness duration was associated with stronger beliefs in personal control (r=0.25; p ≤ 0.001), weaker beliefs in treatment control (r=-0.12; p=0.03), perceptions that the illness would continue (r=0.15; p=0.008) and a better understanding of the illness (illness coherence) (r=0.30; p ≤ 0.001). Although statistically significant, these relationships were all relatively modest.
Relationships between illness and medication beliefs (Table 4)
Moderate associations (r ≥ 0.4) were found between beliefs that SAR has more severe consequences for the individual and concerns about medication. Stronger beliefs in the necessity for antihistamines were related to stronger illness identity and to more negative perceptions of the consequences of SAR. Participants reporting a better understanding of their illness (high illness coherence scores) tended to have fewer concerns about medication (nasal spray, antihistamine tablets and eye drops) than those reporting poor understanding of their SAR (Table 4). Emotional representations were positively associated with all medication beliefs, indicating that those with stronger medication concerns and beliefs in the need for medication were more likely to report being upset or depressed by SAR.
|Beliefs about hay fever (IPQ-R)||Beliefs about hay fever medication (BMQ)|
|Oral antihistamines||Nasal spray||Eye drops|
†Spearman’s correlation coefficients *p ≤ 0.05 **p ≤ 0.005 ***p ≤ 0.001 all others non significant
Table 4: Correlation coefficients† between patient beliefs about hay fever medicines and perceptions about their hay fever.
Identification of two sub-groups of hayfever patients
Cluster analysis of illness representations identified two subgroups of patients (Figure 1). Broadly speaking, 58% had ‘positive’ beliefs and 42% had ‘negative’ beliefs. These two groups were similar in terms of age (t=-0.61; df=311; p>0.05), age of onset (t=-0.03; df=295; p>0.05), gender (χ2=0.49; p>0.05) and duration of illness (t=-0.72; df=295; p>0.05). However, those with negative beliefs were more likely to experience hayfever symptoms ≥ 4 days/week during the season (χ2=20.93; p<0.001) and to perceive their symptoms as more severe (χ2=41.70; p<0.001).
Those with negative beliefs were more likely to perceive their illness as having greater emotional impact, greater consequences for their lives and as lasting longer. This group also reported a poorer understanding of their illness than those holding positive beliefs (Table 5). Consistently, those holding more negative beliefs attributed more symptoms to their hayfever, perceived they had less personal control over their illness and were less likely to believe that treatment could control their hayfever (Table 5).
|Variables||Positive beliefs cluster (n=182†)
|Negative beliefs cluster (n=132†)
|Illness identity||6.14 (3.37) (n=135)||7.56 (3.20) (n=101)||3.27||0.001|
|Timeline‡||19.74 (4.00)||23.59 (3.31)||9.35||<0.001|
|Personal control||19.89 (2.70)||17.49 (2.71)||-7.78||<0.001|
|Treatment control||14.85 (1.83)||14.34 (1.99)||-2.34||0.02|
|Illness coherence||19.21 (4.11)||15.97 (4.35)||-6.72||<0.001|
|Emotional representations‡||9.45 (2.58)||14.48 (3.61)||13.68||<0.001|
|Consequences||13.30 (3.04)||19.17 (3.70)||15.43||<0.001|
|Timeline -cyclical||12.60 (2.99)||13.01 (3.43)||1.11||0.27|
|Nasal spray‡||13.92 (3.90)
|Eye drops||12.73 (4.06)
|Nasal spray||11.37 (3.16)
|Eye drops||9.84 (3.21)
†n as described unless otherwise indicated in the table ‡equal variances not assumed
Table 5: Independent samples t-test results across self-report measures for cluster groups.
Patients with more negative beliefs about their SAR had stronger belief in the necessity of taking antihistamine tablets to improve their health, but there was no difference for nasal sprays or eye drops (Table 5). However, patients with more negative beliefs reported greater concerns about all three types of medication (Table 5).
The authors wish to thank the staff and patients at the four participating general practices for their support. Additional thanks to Raj Meeta for advice on the study design, Louise Brown for co-ordinating the study, Joan Dunleavey and Martine Cross for data entry, and Kate Perry and Matthew Hankins for data cleaning and preliminary analysis.
HS and PW designed the study. All authors (HS, CL, AW, PW & AF) contributed to the interpretation of the data and preparation and revision of the manuscript.