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- Materials and Methods
Purpose: To evaluate patients’ maximum acceptable waiting time (MAWT) and to assess the determinants of patient perceptions of MAWT.
Methods: A total of 500 consecutive patients with cataract were asked to fill out a preoperative questionnaire, addressing patients’ MAWT to undergo cataract surgery. Patients’ visual impairment (VF-14 score), education, profession and social status were evaluated, and an ophthalmologic examination was performed. Univariate analysis included Spearman’s correlation test, unpaired Student’s t-test and the Mann–Whitney U test. Univariate and multivariate associations were calculated using unconditional logistic regression.
Results: The mean MAWT was 3.17 ± 2.12 months. The mean VF-14 score was 72.10 ± 22.54. Between VF-14 score and MAWT, there was a significant correlation (r = 0.180, p = 0.004). Patients with higher education (high school, university) accepted significantly longer MAWT (3.92 ± 2.38 months versus 3.02 ± 2.00 months, p = 0.009). Patients who had self-noticed visual impairment were nearly four times (OR: 3.88, 95% CI = 2.07–7.28, p < 0.001) more likely to accept only MAWT of <3 months.
Conclusions: Patients with low tolerance for waiting had greater self-reported difficulty with vision. Patients’ acceptance of waiting was not associated with clinical visual acuity measures. Education, ability to work, living independently and taking care of dependents were also strong predictors from patients’ perspective. Considering the implementation of standards for waiting lists, these facts should be taken into account.
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- Materials and Methods
Cataract is the most common cause of surgical remediable poor vision in older adults (Woodcock et al. 2004; Conner-Spady et al. 2007). Cataract surgery is one of the most common elective surgical procedures undertaken in elderly people worldwide (Crabtree et al. 1999; Friedman et al. 2002; Pager & McCluskey 2004; Smith & Ross 2004). With greater life expectancies and increasing demand for improved quality of life, the number of people receiving cataract surgery has been projected to grow substantially in the coming years (McCarty 2002; Conner-Spady et al. 2005). The relative number of cataracts will double over the next 50 years as a result of the ageing of the population (Richter-Mueksch et al. 2001). In addition, improved surgical techniques and better surgical outcomes have significantly changed the attitude of ophthalmologists over the past 20 years: cataract surgery is now recommended at an earlier stage of the disease (Wong 2001; Conner-Spady et al. 2005). Another reason for the increasing surgical volume is the higher frequency of second eye surgery (Lundstrom et al. 2006). With increasing demand, the waiting list for surgery also expands, and long waiting times for cataract surgery have become an important issue in many countries with public-funded health care systems (Wong 2001; Conner-Spady et al. 2005). Unfortunately, lengthy waiting times are linked to a decline in visual acuity (Desai et al. 1999; Riley et al. 2001). There is evidence that individuals with cataracts have an increased risk of falls and related hip fractures and are more likely to be involved in car accidents (Gilhotra et al. 2001; Ivers et al. 2002, 2003). This calls for long waiting times to be reduced and for services to be redesigned from the patients’ point of view.
In Austria, currently 90% of cataract surgeries are performed as inpatient procedures with a hospital stay of one or two nights. In the last years, waiting times amount up to 4–6 months, in some hospitals up to 9–12 months, from the time the patient is referred to the hospital to the date of surgery. The fact that cataract surgery is still carried out mostly as inpatient procedure in Austria explains that surgical volume could not be raised according to increasing request in the last years. To reduce waiting lists, an expansion of day-case surgery is planned in most hospitals in Austria.
In the Austrian public healthcare system, a patient with cataract is referred by a general ophthalmologist for examination by a hospital surgeon. People in Austria cannot choose their social health insurance fund. They are assigned to social insurances according to their occupation or profession. By paying a monthly compulsory contribution to social health insurance funds, people acquire entitlements to treatment as set out in the current general social security provisions. A total of 99% of the population have health insurance cover. One third of the population has a supplementary private insurance to cover the costs of more comfortable rooms in the hospital or to finance visits to physicians not under contract with the patient’s health insurance fund. Only 4.6% of the overall capacity of beds are operated by private entities (Hofmarcher & Rack 2001).
Materials and Methods
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- Materials and Methods
The study was performed at the Department of Ophthalmology and Optometry, KH Hietzing, Vienna, Austria.
All participants have been recruited from the outpatient department at the Department of Ophthalmology, KH Hietzing, Vienna, where 2000 cataract surgeries are performed every year.
A total of 500 consecutive patients with cataract presenting for initial consultation and to schedule a date for cataract surgery from February 2007 – September 2007 have been included in the study. All patients received surgery with inpatient admission. Patients with incomplete files were excluded from the analysis.
During the initial consultation, an ophthalmologic examination of each patient was performed including refraction, slit lamp and fundus examination. Best corrected visual acuity (VA) was obtained using Snellen chart.
A full medical history was obtained, ocular comorbidities were recorded, and the patients were asked to fill out a questionnaire. The main question addressed the patient’s MAWT (‘What is your personal maximum acceptable waiting time?’) to undergo cataract surgery. The waiting time following consultation to surgery was recorded but did not include the time following referral to the ophthalmic surgeon (i.e. ‘actual waiting time’). Before receiving an appointment with a general ophthalmologist, patients in Austria have to wait 1–3 months, and after referral to hospital, there is no additional waiting time until they are seen by an ophthalmic surgeon. (Fig. 1)
The questionnaire included the Visual Function Index (VF-14), a series of questions about common visual tasks (e.g. driving, reading small print, watching TV) where the respondent was asked to rank his/her degree of impairment in five grades ranging from no difficulty (score of 4) to unable to perform (score of 0) (Steinberg et al. 1994). The VF-14 is scaled from 0 to 100, with 0 indicating that the patient is unable to perform any applicable activities and 100 meaning that the patient can perform all applicable activities without difficulty.
To find out possible influencing factors, age, gender, patients’ education, professional status and social network were evaluated.
Patients were also asked who had had influence on their decision-making to undergo cataract surgery. The question ‘Who had influence on your decision-making to undergo cataract-surgery?’ could be answered with a multiple answer set: (i) ‘I have noticed worsening of my visual acuity’, (ii) ‘My ophthalmologist suggested to undergo surgery’, (iii) ‘My family suggested to undergo surgery’ and (iv) ‘I need the surgery for my job’.
Written, informed consent was obtained from all participants, and the study was in adherence to the tenets of the Declaration of Helsinki. We certify that all applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed during this research.
Mean visual acuity was determined by calculating the geometric mean with standard deviation after conversion to logMAR format. For better readability, the scores were reconverted to Snellen format.
Univariate analysis included evaluation of correlations using the Spearman’s correlation test and comparisons of means using unpaired Student’s t-test when measurements showed normal distribution. For nonparametric data, the Mann–Whitney U test and the Kruskal–Wallis test were applied. Odds ratios and 95% confidence intervals for univariate and multivariate associations were calculated using uncon-ditional logistic regression. All p-values were two sided, and p-values <0.05 were considered statistically significant. The spss 14.0 (IBM, Chicago, IL, USA) for Windows computer program was used for all analyses.
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- Materials and Methods
Of the total number of 500 patients, the response rate was 78.8% (394 questionnaires), and 137 questionnaires with incomplete data were excluded from evaluation. Consequently, 256 questionnaires were left for statistical analysis.
Baseline and ophthalmologic characteristics are shown in Table 1.
Table 1. Baseline and ophthalmologic characteristics.
| ||Mean ± SD|
|MAWT (months)||3.2 ± 2.1|
|Age (years)||74.6 ± 9.3|
|VA better eye (Snellen)||0.60 ± 0.23|
|VA worse eye (Snellen)||0.39 ± 0.18|
|Actual waiting time (months)||5.3 ± 2.1|
|VF-14-Score||72.1 ± 22.5|
Of the total number of 256 participants, 168 (65.6%) were women and 88 (34.4%) were men. Men tended to accept longer waiting times (3.3 ± 2.2 months versus 3.1 ± 2.1 months, respectively). This difference was not statistically significant (p = 0.416, unpaired t-test).
The mean age of women was 75.4 ± 9.2 years and of men 73.2 ± 9.5 years; the difference was not statistically significant (p = 0.088, unpaired t-test).
Sixteen patients had a VF-14 score of 100, and two patients had a VF-14 score of 0. Correlations are summarized in Table 2.
Table 2. Correlations (Spearman’s correlation test).
|MAWT – Age||−0.660||0.297|
|MAWT – VA better eye||0.080||0.204|
|MAWT – VA worse eye||0.093||0.141|
|MAWT – VF-14-Score||0.180||0.004*|
|VF-14-Score – VA better eye||0.290||<0.001*|
|VF-14-Score – VA worse eye||0.188||0.003*|
|VF-14-Score – actual waiting time||0.840||0.185|
|MAWT – actual waiting time||0.235||<0.001*|
Differences in patients who were scheduled for one eye or for both eyes surgery are shown in Table 3.
Table 3. Differences in patients scheduled for one eye or both eyes surgery (unpaired t-test).
| ||One eye||Both eyes||p-value|
|MAWT (months)||3.1 ± 2.1||3.2 ± 2.1||0.631|
|VA better eye (Snellen)||0.76 ± 0.23||0.52 ± 0.17||<0.001*|
|VA worse eye (Snellen)||0.39 ± 0.21||0.39 ± 0.17||0.828|
Patients were also asked who had had influence on their decision-making to undergo cataract surgery. Most patients chose to be influenced by their ophthalmologist (n = 105), to have self-noticed visual impairment (n = 41) or both of these options (n = 106). Patients with self-noticed visual impairment, patients who were influenced by their families and patients needing surgery for job-related reasons were less tolerant for longer waiting times (2.9 ± 1.9 months, 2.3 ± 1.7 months and 2.1 ± 1.1 months). Patients who preferred the physician to make the final decision tended to accept longer waiting times (3.3 ± 2.2). The Kruskal–Wallis test showed no statistically significant difference (p = 0.131).
Highest school degree earned, professional status and social network are summarized in Table 4. Patients with higher education (high school, university) accepted significantly longer MAWT (p = 0.009) (Fig. 2). Patients with higher education had slightly better VA in the better (0.63 ± 0.25 versus 0.59 ± 0.22) but not in the worse eye (0.38 ± 0.20 versus 0.39 ± 0.17), but these differences were not statistically significant (p = 0.365 and p = 0.865, respectively). The VF-14-score was a little better in patients with higher education, but this difference was also not significant (75.9 ± 21.7 versus 71.4 ± 22.4, p = 0.207).
Table 4. Education, social network and professional status.
| ||Distribution (%)||MAWT (months)|
|Elementary school||11||3.0 ± 2.0|
|Junior high school||61|
|High school||16||3.9 ± 2.4|
|Retired||96||3.2 ± 1.4|
|Living alone||40||2.9 ± 2.1|
|Living together with a partner||52||3.4 ± 2.2|
|Living together with children||10||3.9 ± 2.2|
|Relatives close-by||23||3.1 ± 1.6|
|Friends in the neighbourhood||22||3.1 ± 1.6|
|Nursing case at home||4||2.8 ± 1.9|
The questionnaire also included questions regarding the professional status and the social network; here, multiple answers were possible (Table 4).
No meaningful statistical analysis could be made concerning patients’ professional status because more than 95% of patients were retired.
Patients living together with a partner or children had a higher acceptance for longer waiting times than patients living alone and those who had to take care of a nursing case (Table 4). Patients living alone accepted significantly shorter MAWT than patients living together with a partner (p = 0.047, Mann–Whitney U).
Of the 137 excluded incomplete questionnaires, 110 had completed all questions except: ‘What is your personal maximum acceptable waiting time?’ There was no significant difference in age, sex, VA, actual waiting time, influence on decision-making and VF-14 score between patients with complete questionnaires (responders) and patients with incomplete questionnaires (nonresponders). Responders were statistically significant better educated than nonresponders (high school/university as highest degree earned in 22.7% versus 9.1%, p = 0.002).
After adjusting for sex and age, each of the variables was analysed in multivariate logistic analysis, using backwards stepwise model with MAWT as dependent variable. In the logistic regression model, the associations between self-noticed degree of visual acuity, being retired or living together with children and mean MAWT were statistically significant. The strongest association was found between self-noticed visual impairment and mean MAWT. Patients who noticed decrease in visual acuity were nearly four times more likely to tolerate MAWT of <3 months. Patients with high school or university degree and patients with VF-14 score better than the mean tended to tolerate longer MAWT. Retired patients and patients living together with their children also tended to accept longer MAWT (Table 5). However, the broad range of the 95% confidence intervals suggests that these associations may have weak reliability.
Table 5. Logistic regression model (MAWT < 3 months).
| ||OR||95% CI||p-value|
|Self-noticed visual impairment||3.88||2.07–7.28||<0.001*|
|Lower than mean VF-14 score||1.23||1.04–1.45||0.018*|
|Living with children||0.29||0.11–0.79||0.015*|
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- Materials and Methods
Patients’ acceptance of waiting time is strongly correlated with anticipated waiting time (Dunn et al. 1997). Length of wait is also a significant predictor of the patients’ acceptance of waiting time in other elective surgery disciplines (Lofvendahl et al. 2005).
The mean MAWT in our study was 3.2 months. The actual waiting time was 5.3 months, compared to <1–25 months in different studies (Dunn et al. 1997; Churchill et al. 2000; Wong 2001; Conner-Spady et al. 2004a,b). Acceptance of 3 months or less is described in literature (Dunn et al. 1997; Conner-Spady et al. 2004a,b; Pager & McCluskey 2004). Patients who wait more than 6 months for cataract surgery may experience negative outcomes during the wait period, including vision loss, a reduced quality of life and an increased rate of falls (Hodge et al. 2007). In patients awaiting elective total hip arthroplasty, clinically important losses in health-related quality of life and mobility occur in patients waiting more than 6 months (Mahon et al. 2002). The lengthy wait for elective surgery represents a burden in terms of living with the unrelieved symptoms and poor health-related quality of life (Derrett et al. 1999). A significant decrease in physical and social functioning, both before and after surgery, for patients waiting more than 3 months for coronars artery bypass grafting was found (Sampalis et al. 2001).
Patients with low tolerance for waiting had greater self-reported difficulty with vision, as assessed by the VF-14 score. Patients’ acceptance of waiting was not associated with clinical visual acuity measures; these results are supported by previous studies (Dunn et al. 1997; Frost & Sparrow 2000). Most surgeons test routinely only monocular distance visual acuity in clinical decision-making (Frost & Sparrow 2000). VF-14 gives additional information and has been shown to be the best predictor of gain in patient satisfaction (Steinberg et al. 1994) and is used as prioritization tool (Wong 2001). However, some symptoms reported by patients, such as glare or difficulty with artificial light as traditional symptoms associated with cataract, are not part of the VF-14. Therefore, patients with no symptoms in the VF-14 could also benefit from surgery, as shown in the study performed by Bellan (2005). In a british study, although visual function (VF-14) scores were unchanged or deteriorated in 25% of patients after surgery, 93.1% rated the results of the operation as ‘good,’‘very good’ or ‘excellent,’ and 93.5% felt their eye problem was ‘better’ (Black et al. 2009).
MAWT was also significantly correlated with the actual waiting time, which is an interesting finding, because the questionnaire was completed prior to the consultation with the surgeon. Patients are scheduled with a first come first served booking system, but patient’s meaningful description of the visual impairment could play a role in prioritizing some patients.
Better educated patients were significantly more likely to accept longer waiting times, despite they had neither better VA nor higher VF-14 scores in the statistical analysis. Better knowledge of the public health system and better understanding of the pathogenesis of cataract could be a possible explanation in these better informed patients. Education may play a role, pointing to the potential role of patient expectations in determining the acceptability of waits for specialized services (Sanmartin et al. 2007).
Men were more likely to accept longer waiting times. This result stands in contrast to the findings of Conner-Spady et al. (2005), who found men to be more likely to rate their MAWT as shorter. In our study, men had a significantly higher VF-14 score (76.9 ± 21.9 versus 69.6 ± 22.5, p = 0.015), implicating lower subjective problems. These findings are supported by Monestam & Wachtmeister (1998) with women rating their preoperative visual impairment significantly higher and therefore not tolerating longer waiting times.
Not surprisingly, patients with self-noticed visual impairment or patients, who needed the surgery for job-related reasons, had shorter MAWT. Impairment in visual function and ability to work are strong predictors of physician MAWT in literature (Wong 2001; Edwards et al. 2003). Patients with influence of their families to undergo surgery also had lower acceptance for longer waiting times, which maybe owing to applied pressure by their caring family members. Longer MAWT was accepted, if the ophthalmologist took the final decision for surgery, referring to lower subjective problems of the patients.
Concerning the social network, patients who had family support (living together with partner or children) accepted longer MAWT. Patients who had to take care of themselves and thus needing to live independently and patients who had to take care of dependents had shorter MAWT. Ability to live independently and care for a nursing case were strong predictors of surgeon MAWT in studies referring to physicians’ priority criteria (Wong 2001; Edwards et al. 2003).
A limiting factor of our study might be the large number of incomplete questionnaires. A total of 27 patients missed to answer multiple questions; 110 skipped the question: ‘What is your personal maximum acceptable waiting time?’ The accustomed role of our elderly patients seeing the doctor as traditional power figure could be an explanation why one-fifth of all participants skipped this single question only. With an attitude of ‘My doctor knows best’, an answer to this question might not be meaningful to these patients.
Patients with elementary school or junior high school as highest school degree earned were significantly more likely to be nonresponders. Higher educated patients might prefer to be involved in decisions. Lower educated patients, in contrast, might want less responsibility for treatment decisions. They may tend to rely more on the expertise or responsibility of others, and so they do not dare to answer to this question.
Prioritization of waiting lists for elective surgery represents a major issue in public systems in view of the fact that patients often suffer from consequences of long waiting times (Derrett et al. 2003; Valente et al. 2009). Validity of priority criteria tools for elective surgery is described in literature (Conner-Spady et al. 2004a,b). Physical symptoms and impairments in work have high impact on priority on surgical waiting lists (Oudhoff et al. 2007).
In conclusion, we found the VF-14 to have wide influence on MAWT. Education, self-noticed visual impairment, ability to work, living independently and taking care of dependents are also strong predictors from patients’ perspective. Considering the implementation of standards for waiting lists, these facts should be taken into account. It is important to have comprehensible criteria to guarantee a transparent system. The priority lists should include the VF-14-questionnaire and should allow prioritizing patients who need faster surgery to maintain the ability to work, to live independently or to take care of dependents.