Abstract
Background
Pre-hospital delays in patients experiencing acute coronary syndromes (ACS) remain unacceptably long.
Aims
To examine simultaneously a wide range of clinical, sociodemographic and situational factors associated with total pre-hospital delay and its two components.
Methods
Pre-hospital delay data were collected from 228 patients with ACS using patient's medical notes and semi-structured interviews. Total pre-hospital delay (symptom onset to hospital admission) was divided into 2 components: decision time (symptom onset to call for medical help), and home-to-hospital delay (call for help to hospital admission).
Results
Shorter total pre-hospital delays and decision times were associated with ST segment myocardial infarction (STEMI), recognizing symptoms as cardiac in origin, being married, symptom onset outside the home and the presence of a bystander. Shorter home-to-hospital delays were more likely among younger patients, those experiencing an STEMI, and patients reporting a greater number of symptoms. Initial contact with emergency medical services was related to shorter total delays and decision times.
Conclusions
Different factors were associated with shorter times in the 2 component phases. Greater understanding of the factors impacting on the component phases may help target interventions more effectively and reduce pre-hospital delays.
Keywords: Acute coronary syndromes, Pre-hospital delay
1. Introduction
Rapid medical intervention is essential to the successful treatment of patients suffering from acute coronary syndromes (ACS) because of the need for prompt thrombolysis and measures to reduce risk of fatal arrhythmias [1]. Although it has been known for more than 20 years that delays between symptom onset and treatment of less than 60 min are desirable [2], pre-hospital delays remain unacceptably long, with median intervals averaging 2 to 4 h [3–6], while interventions to reduce delays have met with limited success [7]. A greater understanding of the contributing factors may stimulate new approaches to reducing delays. A number of sociodemographic, clinical, social and proximal factors have been associated with pre-hospital delay [1,4,6,8–11]. The total pre-hospital delay period consists of two components: time taken by patients to recognise recognize that their symptoms are serious and to contact medical help (decision time) and the time taken from requesting help to hospital admission (home-to-hospital delay). Different factors may affect these 2 components [12] so we carried out a study of factors associated with total pre-hospital delay and its components. Most previous studies have focused on one or two specific areas, but this study investigated a range of sociodemographic, clinical, social and proximal factors simultaneously in the same sample. We set out to discover what are the characteristics of patients who have long pre-hospital delays, and what factors are specifically associated with the two components: decision time and home-to-hospital delay. Previous research also suggests that consultation with a physician or family member may lead to longer pre-hospital delays in comparison with direct contact with emergency medical services (EMS) [1,13]. Additional analyses of factors associated with contacting the EMS were therefore conducted.
2. Methods
2.1. Participants
Participants were 228 patients admitted with ACS to one of four London hospitals. Patients were recruited on 3 days per week rather than consecutively due to staffing constraints. ACS was diagnosed on the basis of chest pain verified by electrocardiographic criteria and/or troponin T measurement [14]. Other inclusion criteria included ability to recall the time symptoms started, time of calling for medical help and events prior to hospital admission, absence of comorbid conditions (renal failure, cancer, stroke, ongoing infection or inflammatory conditions such as inflammatory bowel disease) that might influence symptom presentation, mood or troponin positivity [15], and ability to complete an interview in English. The study was approved by the University College London/University College London Hospitals Ethics Committee and ethics committees of participating hospitals, and the investigation conforms with the principles outlined in the Declaration of Helsinki [16]. Data were collected between 2001 and 2004 in the context of a larger longitudinal observational cohort study of emotional and behavioral triggers of ACS [17]. A total of 375 patients were potentially eligible for this study; of these, 48 (12.8%) were discharged or transferred before the interview could take place, information regarding delay was incomplete for a further 67 (17.9%) due to initial admission to/transfer from non-participating hospitals, and 32 (8.5%) declined participation. The effective response rate was therefore 88%. All participants gave informed written consent.
2.2. Measures
Information concerning sociodemographic and socioeconomic factors, cardiovascular history and risk factors, patient's attribution of symptoms, clinical and proximal factors, and time of admission, symptom onset and call for assistance was collected using medical records and semi-structured interviews 2.6 ± 1.5 days after hospital admission. Socioeconomic position was assessed using educational qualifications which were categorized into 3 groups; high school/university qualification, below high school, or no formal educational qualifications of any kind. Patients were asked whether they initially attributed the pain to a heart attack or to a non-cardiac cause (e.g. indigestion, muscle strain, flu). Clinical data included type of ACS, which was classified as ST elevation myocardial infarction (STEMI), non-ST elevation myocardial infarction (NSTEMI) or unstable angina (UA); intensity of chest pain, rated on a 10 point scale (0 = none to 10 = excruciating); number and type of pain symptoms reported in addition to chest pain (i.e. pain in arms, shoulders, jaw, back) and non-pain symptoms (i.e. breathlessness, nausea/vomiting, sweating, flu-like, dizziness/fainting); cardiac history and risk factors. Proximal factors included time/day of onset, presence of a bystander and location, and a record was made of whether they initially contacted the EMS or another source of help such as a physician, family or friends.
2.3. Statistical analysis
The distribution of all time periods (total pre-hospital delay, decision time and home-to-hospital delay) was skewed, so analyses were based on binary divisions of each interval. The factors associated with total pre-hospital delay were analyzed by comparing the characteristics of patients with delays longer or shorter than the median of 120 min. Decision time was analyzed by comparing individuals with decision times ≤ 60 or > 60 min. This criterion was selected because reperfusion therapy is most effective if initiated within the first hour [2]. Home-to-hospital delay was also analyzed by comparing individuals with delays ≤ 60 or > 60 min since the target set by the UK National Service Framework for administration of thrombolysis to eligible patients is within 1 h of calling for help [18]. Other studies investigating pre-hospital delay have used similar time cut offs [11]. Groups were compared using χ2 tests, and significant effects were subsequently entered into multivariate analyses using logistic regression with age and gender as covariates. Results are presented as percentages and odds ratios (OR) adjusted for age and gender with 95% confidence intervals (C.I.).
3. Results
Patients were predominantly male with a mean age of 59.0 ± 11.2 years, white European, married and poorly educated (Table 1). Only a quarter of participants initially recognized their symptoms as a heart attack. Most patients were diagnosed with STEMI and more than a third gave high ratings of pain intensity. Most patients experienced additional symptoms as well as chest pain (such as pain in arms, shoulders, jaw, back and non-pain symptoms including breathlessness, nausea/vomiting, sweating, dizziness/fainting). A bystander was present in just over half of cases and most patients were at home rather than somewhere else (such as their workplace, outside, in a car, at a recreational venue) when their symptoms started. Only half of patients contacted the EMS directly with the remainder first accessing the advice of physicians, friends or family.
Table 1.
Characteristics of patients (N = 228)
| N(%) or mean ± SD | |
|---|---|
| Sociodemographic factors | |
| Men/women | 178/50 |
| Age (years) | 59.0 ± SD 11.2 |
| Ethnicity: White European | 180 (78.9) |
| Married | 146 (64.0) |
| Socioeconomic factors | |
| Educational status | |
| High School/University qualification | 77 (33.8) |
| Below High School qualification | 53 (23.2) |
| No educational qualifications | 98 (43.0) |
| Cardiac history and risk factors | |
| Previous myocardial infarction | 26 (11.5) |
| Hypertensive | 107 (46.9) |
| Hypercholesterolemic | 104 (47.1) |
| Diabetic | 33 (13.5) |
| Current smoker | 97 (42.5) |
| Body Mass Index (kg/m2) | 27.2 ± SD 4.5 |
| Physical activity | |
| None | 149 (65.6) |
| Up to 2 times per week | 44 (19.4) |
| > 2 times per week | 34 (15.0) |
| Attribution | |
| Symptoms attributed to heart attack | 58 (25.6) |
| Clinical factors | |
| Type of acute coronary syndrome | |
| UAa/NSTEMIb | 67 (29.4) |
| STEMIc | 161 (70.6) |
| Number of pain symptoms (in addition to chest pain) | |
| None | 35 (15.4) |
| 1–3 | 127 (55.7) |
| 4–8 | 66 (28.9) |
| Number of non-pain symptoms | |
| None | 47 (20.6) |
| 1–2 | 108 (47.4) |
| 3–6 | 73 (32.0) |
| Intensity of pain (0–10) | |
| < 6 | 45 (30.4) |
| 6–8 | 45 (30.4) |
| 9–10 | 58 (39.2) |
| Proximal factors | |
| Time of symptom onset (h) | |
| 0001–1200 | 122 (53.5) |
| 1201–2400 | 106 (46.5) |
| Day of onset: weekday | 145 (63.6) |
| Bystander present | 100 (54.6) |
| Location: at home | 147 (64.8) |
| Emergency medical services first contact for help | 77/171 (45.0) |
Unstable angina.
Non-ST elevation myocardial infarction.
ST elevation myocardial infarction.
The median total pre-hospital delay was 120 min, ranging from 10 min to 4.3 days, with a mean delay of 6.1 h (± 12.9 h). Decision time constituted 60% of the total pre-hospital delay period, while home-to-hospital delay accounted for 40%. The median patient decision time was 50 min (mean 3.6 h, ± 8.0 h), ranging from 0 to 2.7 days, and patient decision times ≤ 60 min were observed in 60.8% of patients. The median home-to-hospital delay was 58 min (mean 2.4 h ± 8.9 h), and 53.7% had home-to-hospital delays ≤ 60 min.
3.1. Factors associated with short total pre-hospital delays (≤ 120 min)
Married patients were more likely than patients who were not married to have a short total pre-hospital delay (p = 0.048). There were no other significant associations between short total pre-hospital delays and sociodemographic or socioeconomic factors, but clinical presentation was important (Table 2). Patients who had an STEMI rather than NSTEMI or UA were more likely to have short total pre-hospital delay (p = 0.028), as were those who experienced a greater number of symptoms (p = 0.007). Patients who experienced 3 or more non-pain symptoms (including breathlessness, nausea/vomiting, dizziness) were also more likely to have short home-to-hospital delays (p = 0.002). Patients who had suffered a previous myocardial infarction (MI) were more likely to have a short total pre-hospital delay (p = 0.023), as were those who attributed symptoms to a heart attack rather than another cause (p = 0.039). There were no significant associations with other cardiac risk factors such as diabetes, hypertension, hypercholesterolemia, smoking, or level of physical activity.
Table 2.
Predictors of short total pre-hospital delays
| Delay ≤ 120 min (%) | Delay > 120 min (%) | p-value (χ2) | Odds ratioa | 95% Confidence interval | p-value | |
|---|---|---|---|---|---|---|
| Sociodemographic factors | ||||||
| Marital status | ||||||
| Not married | 29.7 | 41.9 | 0.056 | 1 | ||
| Married | 70.3 | 58.1 | 1.77 | (1.01 to 3.11) | 0.048 | |
| Clinical factors | ||||||
| Type of acute coronary syndrome | ||||||
| UAb/NSTEMIc | 22.5 | 35.9 | 0.027 | 1 | (1.03 to 3.90) | 0.028 |
| STEMId | 77.5 | 64.1 | 1.93 | |||
| Number of pain symptoms: (in addition to chest pain) | ||||||
| None | 11.7 | 18.8 | 0.005 | 1 | ||
| 1–3 | 49.5 | 61.5 | 1.34 | (0.62 to 2.93) | 0.460 | |
| 4–8 | 38.7 | 19.7 | 3.03 | (1.39 to 7.86) | 0.007 | |
| Number of non-pain symptoms | ||||||
| None | 15.3 | 25.6 | 0.001 | 1 | ||
| 1–2 | 41.4 | 53.0 | 1.33 | (0.65 to 2.73) | 0.429 | |
| 3–6 | 43.2 | 21.4 | 3.49 | (1.60 to 7.58) | 0.002 | |
| Cardiac risk factors | ||||||
| Previous myocardial infarction | ||||||
| No | 83.8 | 93.1 | 0.028 | 1 | ||
| Yes | 16.2 | 6.9 | 2.80 | (1.15 to 6.82) | 0.023 | |
| Attribution | ||||||
| Symptoms attributed to heart attack | ||||||
| No | 68.2 | 80.3 | 0.036 | 1 | ||
| Yes | 31.8 | 19.7 | 1.90 | (1.03 to 3.49) | 0.039 | |
| Proximal factors | ||||||
| Location at onset | ||||||
| Home | 55.9 | 73.3 | 0.006 | 1 | ||
| Other | 44.1 | 26.7 | 2.23 | (1.25 to 3.95) | 0.006 | |
| Presence of a bystander | ||||||
| Absent | 35.5 | 55.6 | 0.006 | 1 | ||
| Present | 64.5 | 44.4 | 2.26 | (1.26 to 4.15) | 0.006 | |
| Time of onset | ||||||
| 0001–1200 | 44.1 | 62.4 | 0.006 | 1 | ||
| 1201–2400 | 55.9 | 37.6 | 2.08 | (1.23 to 3.54) | 0.007 | |
Adjusted for age and gender.
Unstable angina.
Non Non-ST elevation myocardial infarction.
ST elevation myocardial infarction.
Proximal factors were also important. Patients who were away from home when their symptoms started were more likely to have a short pre-hospital delay than patients who were at home (p = 0.006), as were those who were accompanied by a bystander at symptom onset rather than being alone (p = 0.006). If symptoms began after midday, patients were more likely to have short pre-hospital delays than if symptoms started earlier (p = 0.007). In multivariate logistic regression entering all the factors found to be significant in Table 2, effects remained significant for previous history of MI (p = 0.008), being away from home at symptom onset (p = 0.004) and having a bystander present (p = 0.001).
3.2. Factors associated with short decision times (≤ 60 min)
There were no associations between decision time ≤ 60 min and sociodemographic and socioeconomic factors apart from marital status (Table 3); married patients were more likely to have decision times ≤ 60 min than patients who were not married (p = 0.034). Patients who had an STEMI rather than NSTEMI or UA were more likely to have short decision times (p = 0.006), as were those who attributed symptoms to a heart attack rather than some other cause (p = 0.001). Patients whose symptoms started in the presence of a bystander rather than being alone were more likely to have a short decision time (p = 0.006). The variables that were associated with short decision times were not independent of one another, since symptom onset in the presence of a bystander was strongly associated with marital status. Patients who were away from home when their symptoms started had shorter decision times than those who were at home (p = 0.005). In multivariate logistic regression, including all variables found to be significant in Table 3, attribution of symptoms to a heart attack (p < 0.001), presence of a bystander (p = 0.010) and being away from home (p = 0.003) remained significant independent predictors of short decision times.
Table 3.
Predictors of short patient decision times
| Decision time ≤ 60 min (%) | Decision time > 60 min (%) | p-value (χ2) | Odds ratioa | 95% Confidence interval | p-value | |
|---|---|---|---|---|---|---|
| Sociodemographic factors | ||||||
| Marital status | ||||||
| Not married | 31.2 | 43.8 | 0.053 | 1 | ||
| Married | 68.8 | 56.2 | 1.85 | (1.05 to 3.28) | 0.034 | |
| Clinical factors | ||||||
| Type of acute coronary syndrome | ||||||
| UAb/NSTEMIc | 22.5 | 39.3 | 0.006 | 1 | ||
| STEMId | 77.5 | 60.7 | 2.26 | (1.26 to 4.06) | 0.006 | |
| Attribution | ||||||
| Symptoms attributed to heart attack | ||||||
| No | 66.4 | 86.5 | 0.001 | 1 | ||
| Yes | 33.6 | 13.5 | 3.24 | (1.60 to 6.56) | 0.001 | |
| Proximal factors | ||||||
| Presence of a bystander | ||||||
| Absent | 37.3 | 58.3 | 0.005 | 1 | ||
| Present | 62.7 | 41.7 | 2.35 | (1.28 to 4.31) | 0.006 | |
| Location at onset | ||||||
| Home | 58.4 | 75.3 | 0.009 | 1 | ||
| Other | 41.6 | 24.7 | 2.42 | (1.32 to 4.47) | 0.005 | |
Adjusted for age and gender.
Unstable angina.
Non-ST elevation myocardial infarction.
ST elevation myocardial infarction.
3.3. Factors associated with short home-to-hospital delays (≤ 60 min)
Younger patients were more likely to have short home-to-hospital delays. Table 4 shows that 66.4% of the patients who had a short home-to-hospital delay were aged 60 or less, compared with 42.9% of those with longer delays. There were no associations with socioeconomic factors but clinical factors were important. Patients who had an STEMI rather than a NSTEMI/UA were more likely to have home-to-hospital delay of ≤ 60 min (p = 0.002), as were those who experienced a greater number of pain symptoms (p = 0.028), and non-pain symptoms (p = 0.012). Patients who were away from home when symptoms started were also more likely to have a short decision delay (p = 0.049).
Table 4.
Predictors of short home-to-hospital delays
| Home-to-hospital delay ≤ 60 min (%) | Home-to-hospital delay > 60 min (%) | p-value (χ2) | Odds ratioa | 95% Confidence interval | p-value | |
|---|---|---|---|---|---|---|
| Sociodemographic factors | ||||||
| Age | ||||||
| > 70 years | 15.6 | 28.6 | < .005 | 1 | ||
| 61–70 | 18.0 | 28.6 | 1.13 | (0.51 to 2.52) | 0.764 | |
| 51–60 | 37.7 | 24.8 | 2.66 | (1.23 to 5.72) | 0.013 | |
| ≤ 50 years | 28.7 | 18.1 | 2.72 | (1.19 to 6.24) | 0.018 | |
| Clinical factors | ||||||
| Type of acute coronary syndrome | ||||||
| UAb/NSTEMIc | 20.5 | 39.0 | 0.002 | 1 | ||
| STEMId | 79.5 | 61.0 | 2.57 | (1.40 to 4.72) | 0.002 | |
| Number of pain symptoms: (in addition to chest pain) | ||||||
| None | 12.3 | 19.0 | 0.090 | 1 | ||
| 1–3 | 54.9 | 56.2 | 1.85 | (0.85 to 4.06) | 0.124 | |
| 4–8 | 32.8 | 24.8 | 2.65 | (1.11 to 6.35) | 0.028 | |
| Number of non-pain symptoms | ||||||
| None | 17.2 | 24.8 | 0.026 | 1 | ||
| 1–2 | 44.3 | 50.5 | 1.45 | (0.71 to 2.96) | 0.313 | |
| 3–6 | 38.5 | 24.8 | 2.75 | (1.25 to 6.02) | 0.012 | |
| Proximal factors | ||||||
| Location at onset | ||||||
| Home | 57.4 | 74.0 | 0.009 | 1 | ||
| Other | 42.6 | 26.0 | 1.81 | (1.00 to 3.25) | 0.049 | |
Adjusted for age and gender.
Unstable angina.
Non-ST elevation myocardial infarction.
ST elevation myocardial infarction.
3.4. Patients' choice of contact for help following onset of acute symptoms
Following symptom onset, 45% of patients initially decided to call the EMS rather than other sources of help such as their physician, family/friends, or to refer themselves directly to hospital. As predicted, patients who contacted the EMS for help were more likely to have short total pre-hospital delays: 74.0% of patients who contacted the EMS had total pre-hospital delays ≤ 120 min compared with 33.0% of those who contacted another source of help (adjusted OR 5.80, C.I. 2.98 to 11.30). Patients who contacted the EMS also had shorter decision times; 80.3% had a decision time ≤ 60 min compared with 45.7% who contacted another source of help (adjusted OR 4.83, C.I. 2.40 to 9.70). Interestingly, contacting the EMS was not related to the home-to-hospital component of the total pre-hospital delay period but only to decision time.
We investigated the correlates of contacting the EMS following symptom onset. It was not related to age, gender, marital status, education, cardiac history, attribution of symptoms, ACS type, or the presence of a bystander. However, patients who contacted the EMS were likely to have more intense chest pain (adjusted OR 2.05, C.I. 1.01 to 4.61, p = 0.047) and more likely to have an ACS at the weekend rather than a weekday (adjusted OR 1.82, C.I. 0.98 to 3.36, p = 0.057) than those who did not.
4. Discussion
When a wide range of sociodemographic, clinical, social and proximal factors are examined in the same sample population, the results of this study indicate that different factors are associated with the two different components of pre-hospital delay. This may be important in helping to develop interventions which target specific components of delay. Our findings support previous research showing that decision time accounts for almost two thirds of pre-hospital delay [19,20]. Shorter decision times were associated with being married, attributing symptoms to a heart attack, and symptom onset in the presence of a bystander. Home-to-hospital delays were shorter in patients who had other symptoms as well as chest pain, particularly non-pain symptoms. Two factors predicted both short decision times and home-to-hospital delays: the type of ACS and symptom onset away from home. Previous studies showed that both attribution of symptoms to a heart attack and diagnosis of an STEMI predicted shorter decision times and total pre-hospital delays [4,12,21,22] and this combination of factors may help to reduce decision time. Patients who experienced both STEMI and a greater number of non-pain symptoms (nausea, breathlessness etc) which may be more salient than pain, were more likely to have short home-to-hospital delays, a finding that has not been reported before. Home-to-hospital delays were not associated with attribution of symptoms to a heart attack, but to the number and variety of symptoms. Patients who are found to have a STEMI possibly experience symptoms that are perceived as serious, and this may increase their motivation to reach hospital quickly. The type of ACS may therefore be associated with shorter delays in both components but for different reasons.
Moser et al. [1] have argued that social and proximal factors such as being married, having a bystander present and location at symptom onset play an important role in the cognitive aspects of symptom appraisal. Our findings support this, showing that both being married and the presence of a bystander predicted short pre-hospital delay, and are associated specifically with the decision time component. Perhaps not surprisingly, a bystander is more likely to be present if patients are married. Previous studies have shown conflicting findings concerning the role and relationship of the bystander in pre-hospital delay, since although patients tend to have a short decision time if a bystander is present, this is not the case if the bystander is a relative because family members, particularly spouses, often recommend strategies that increase delay [6,8]. Our results support previous findings showing that patients who are at home when symptoms begin are likely to have longer pre-hospital delays [8,23], indicating that the relationship between the context in which cardiac symptoms occur and pre-hospital delay is complicated and warrants detailed investigation. Educational and counseling interventions aimed at reducing decision delay have been found to increase patients knowledge of ACS [24] but it may be useful if future interventions were to incorporate ways of optimizing bystander assistance, particularly among relatives of patients previously identified as at risk of ACS.
Our study highlights the critical role of making initial contact with the EMS in promoting short pre-hospital delays, since the results show that patients who contacted the EMS were more likely to have short decision times. Patients experiencing acute cardiac symptoms are recommended to contact the EMS directly, not only as the quickest mode of transport to hospital but also because the EMS are trained and equipped to treat life threatening cardiac arrhythmias which often accompany acute cardiac symptoms. Patients who use the EMS receive reperfusion therapy more promptly than others [1]. Previous research has shown that patients are often reluctant to use the EMS because they do not believe that their symptoms are serious [5,6,13,25]. In our study, attribution of symptoms to a heart attack was not a predictor of contacting EMS, however, patients who reported greater intensity of pain were more likely to contact the EMS. The experience of intense pain may override doubts about the cause or how serious the symptoms are and patients' reluctance to seek emergency care. We also observed that contact with the EMS was more likely when symptoms began at the weekend, perhaps because patients are unwilling to contact their physicians at the weekend thus limiting their choices. The decision to contact the EMS may therefore reflect broader cognitive and social aspects of symptom appraisal and coping involved in the decision making process.
This study has some limitations. The sample size was relatively small in comparison with some other studies. The average age of patients was younger than in many contemporary clinical cohorts, with a higher proportion of men and patients admitted with STEMI compared with NSTEMI/UA [26,27]. The exclusion of patients with co-morbidities that can affect symptom presentation, mood or troponin measurements is likely to have excluded older patients. Only patients with chest pain were included due to the requirements of the larger study [17], leading to the possibility that a proportion of patients who presented without chest pain were excluded. However, despite these factors, the study cohort remains comparable to similar recent studies of pre-hospital delay [11,12,28]. It was necessary to collect data retrospectively and to restrict analyses to patients who could recall the time of symptom onset. Although every effort was made to interview patients soon after admission, data may have been affected by recall bias. Only survivors could be interviewed thus factors that influenced delay in individuals who did not survive ACS may not have been captured. We carried out our study in the UK where there is no charge for health care at the point of contact and in a densely urban environment, and different factors may operate in rural areas.
4.1. Future recommendations
Pre-hospital delay remains a significant obstacle to improving treatment for ACS. Further work investigating how patients and their relatives appraise symptoms in the two component phases of delay may help in the development of more effectively targeted interventions. Interventions aimed at helping patients and their families appraise symptoms more accurately and seek help more quickly are likely to reduce delay times here. The pattern of symptoms, including both pain and non-pain symptoms played an important part in reducing delay in the home-to-hospital component, suggesting that wider educational interventions aimed at the general public as well as clinical staff in recognizing the atypical symptoms of ACS may help to improve the rapidity of hospital admission. Interventions should focus on challenging unhelpful illness beliefs, and providing information and education concerning atypical patterns of symptoms in relation to ACS and be made more widely available to include patients' families, health care professionals and the general public.
5. Conclusion
In conclusion, the strength of this study is that it examined a range of demographic factors in relation the pre-hospital delay and its components simultaneously. Independent predictors of short decision time included recognizing symptoms as cardiac, being away from home and being with a bystander when they started, where as home-to-hospital delay was predicted by younger age, experiencing an STEMI and a greater number of symptoms and being away from home. It seems likely that the pattern of symptoms and their interpretation, and the social context in which symptoms occur impact on appraisal and coping strategies at different points along the timeline from onset to hospital admission. The interplay between patients' belief that they may be having a heart attack and social context seems to be a salient area for future research if we are to understand more about which specific factors predict the individual components of delay so as to target interventions effectively.
Acknowledgements
This study was supported by the British Heart Foundation, the Medical Research Council and the Economic & Social Research Council. We are grateful to Dr Susan Edwards for participation in data collection and the staff and patients of University College London Hospital, St. George's Hospital, Southend and Kingston Hospitals.
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