The waiting time for surgery is a substantial health policy concern in several countries that are members of the Organization for Economic Cooperation and Development (OECD) . Setting priorities in a scenario of finite resources is considered a key element for improving the equity of wait-list management by ensuring that patients with the greatest need are treated first [10, 17]. To reduce wait times, some countries, such as Canada and New Zealand, have implemented or proposed guidelines for acceptable wait times for care where defined criteria and tools are taken in account to properly manage the time patients wait for surgery [3, 18, 21]. A review of initiatives for using priority criteria shows the benefits of their application, such as greater transparency and increased equity of the procedure, driven by clinical need and under physician control .
THA is an elective surgery performed for patients with a wide range of symptoms, disabilities, and sociodemographic factors. The ability of THA to improve function and reduce pain is well documented, and priority criteria for management of waiting lists for this surgery have been proposed [13, 17]. Nevertheless, these criteria often are not followed in clinical practice [19, 31], which often results in unclear wait-list management procedures. Whether waiting times influence the quality of life and health outcomes, such as pain, functional capacity, and social function, also is unclear .
Therefore, we asked: (1) when no prioritization criteria are implemented, what factors affect the wait time for THA, and (2) does wait time influence pain and function after THA?
Patients and Methods
We obtained data through a prospective observational study performed in seven public teaching hospitals of the Basque Health Service (Osakidetza). Osakidetza provides universal public health coverage to 2 million residents of the Basque Country, an autonomous region in northern Spain. All participating hospitals have similar populations and offer similar levels of technical performance. The participating health professionals were blinded to the study’s objectives and goals. All 1495 patients on wait lists to undergo primary THA attributable to hip OA between March 1999 and March 2000 were eligible (Fig. 1). We excluded patients with severe comorbidities, such as cancer, terminal diseases, or psychiatric conditions, which rendered them unable to participate or to complete the questionnaires. We also excluded patients whose main diagnosis was not hip or knee OA, or who did not undergo surgical intervention 1 year after study enrollment. Of the 1495 patients, 991 fulfilled the inclusion criteria, and 817 (82.4%) agreed to participate in the study. The clinical records of 799 of the 817 patients (97.8%) were accessible. Information regarding waiting times was available for 695 (88.2%) patients, of whom 527 (75.8%) responded to the followup questionnaire (Fig. 1). The mean wait time was 5.07 months (SD, 2.98) (Table 1). This study was approved by the ethics review boards of the participating institutions.
We assessed all patients preoperatively and at 6-month followup, the time during which most improvements occur . We collected data from the patients’ hospital medical records. To retrieve data from the medical records, we developed specific data-collection questionnaires that included the length of time on a surgical wait list, sociodemographic data, main clinical diagnosis, comorbidities (all those included in the Charlson comorbidity index) , previous bone disorders (eg, Perthes disease, coxa vara, congenital subluxation), bone quality (defined by the classification of Singh et al. in two categories: normal, 4°, 5°, and 6°; deficient, 1°-3° ), and the American Society of Anesthesiologists (ASA) surgical risk . Recognizing the complexity of assessing waiting time and to assure consistency of the data, we did not account for the time from when the symptoms began or the time from initiation of primary care to secondary care, but rather considered only the time spent on the wait list. All patients on the wait list for THA received a letter with a brief explanation of the study and requested their voluntary participation. Those who agreed to participate were mailed the SF-36  and WOMAC questionnaires . Patients who did not respond after 15 days received a reminder phone call, and those who were still nonresponders received a reminder letter including the questionnaires. Six months after surgery, patients received the same questionnaires. The followup for nonresponders was the same as described previously.
The SF-36  is a generic, health-related instrument for measuring the quality of life. The 36 items cover eight domains (physical function, physical role, bodily pain, general health, vitality, social function, emotional role, and mental health) and can be incorporated into two physical and mental summary scales. The SF-36 scores range from 0 to 100, with a higher score indicating better health status. The SF-36 was translated into Spanish and validated in Spanish populations. Alonso et al. described the measurement properties .
The WOMAC , a disease-specific, self-administered questionnaire was developed to study patients with hip or knee OA. It has a multidimensional scale comprising 24 items grouped into three dimensions: pain (five items), stiffness (two items), and physical function (17 items). The final WOMAC score is determined by adding the aggregate scores for each dimension. Data are standardized to a range of values from 0 to 100, with 0 representing the best health status and 100 the worst health status. A reduction in the overall score represents an improvement. The original questionnaire is reliable, valid, and sensitive to the changes in the health status of patients with hip or knee OA [2, 18]. The WOMAC questionnaire was translated and validated in Spain .
For the statistical analysis, the unit of study was the patient. When two interventions were performed in one patient during the recruitment period, we selected the first THA performed. The descriptive statistics included frequency tables, means, medians, and SDs. We compared the time spent on the wait list with sociodemographic and clinical characteristics, and the baseline WOMAC and SF-36 domains using the nonparametric Wilcoxon or Kruskal-Wallis tests. We initially used univariable general linear models to identify sociodemographic and clinical factors and the baseline WOMAC and SF-36 domains associated with waiting times. Owing to the nonnormal distribution of the dependent variable “waiting time”, we performed a log transformation. After adjusting by hospital, we performed a multilevel multivariable analysis with mixed models to identify the importance of each factor. Independent variables included in the multivariable analysis were those identified as having a p value less than 0.15 in the univariable analysis and hospital. To interpret the previous parameters estimated, we performed exponential transformation. We used the general linear model to study the effect of waiting times on changes in WOMAC domains from preoperatively to 6 months postoperatively, adjusting for corresponding preoperative scores and clinical characteristics. For these analyses, we categorized waiting times into three groups: less than 3 months, 3 to 6 months, and greater than 6 months. We performed multivariable logistic regression models to evaluate the effect of time on a wait list with the likelihood of exceeding the minimal clinically important difference (MCID) for WOMAC domains, adjusting for corresponding baseline scores and clinical characteristics. MCID reflects the smallest change in the level of function that signifies an important improvement for the individual patient.
The MCID improvement, implying relief or improvement, in the WOMAC pain and function domains has been defined as a decrease of 30 points or more, whereas the MCID improvement in the WOMAC stiffness domain has been defined as an increase of 25 points or more . We performed statistical analyses using SAS for Windows statistical software, version 9.1 (SAS Institute Inc, Cary, NC, USA).
We found differences in the length of the waiting time regarding the degree of pain (p = 0.005), function (p = 0.002), and stiffness (p = 0.016). Patients with lower levels of severity had longer waiting times. Patients with better physical function (p = 0.041), physical role (p < 0.001), and social function (p = 0.003) waited longer for surgery. We observed differences (p < 0.001) in waiting times regarding the hospital in which the surgery was performed, with times varying from 2 months (SD, 0.92) to 6 months (SD, 3.48). The number of comorbidities and other social factors had no relationship to the wait times (Table 2). After adjusting for hospital in the multivariable model, only pain and function level remained significant in determining the waiting time. Thus, patients with the least pain and better function at the time of inclusion waited longer. After performing the exponential transformation of the beta parameter to facilitate interpretation, patients with pain levels from 0 to 40 points on the WOMAC scale spent 1.13 times longer (p = 0.029) on a wait list than patients with a pain level exceeding 40 points; patients with better function (0-40 points in functional limitation item of the WOMAC scale) spent 1.22 times longer (p = 0.020) than patients with worse function (greater than 40 points) (Table 3).
The functional capacity was poorer (p = 0.025) among patients who waited longer than 6 months for surgery (Table 4). This remained significant after adjusting for the baseline values of the analyzed variable and other covariables that could affect the final health outcome of the surgery, such as age and bone quality. A progressive reduction in the percentage of patients surpassing the MCID associated with the increase of waiting time was observed. Among patients waiting less than 3 months, 87 (74%) surpassed the MCID, patients waiting between 3 and 6 months had a lower MCID of 68% (146), and patients waiting more than 6 months had an even lower MCID of 52% (94) (p < 0.001). The likelihood of perceiving a gain greater than the MCID was lower (OR, 0.47; p = 0.006) for patients waiting more than 6 months when compared with the MCID for patients waiting less than 3 months. We observed no effects of other WOMAC domains.
Wait times before surgery are a major health-policy concern. Setting priorities is considered a key element for improving the equity of wait-list management. The questions we addressed were: (1) when no prioritization criteria are implemented, what factors affect the waiting time for THA, and (2) does waiting time influence pain and function after THA?
Some limitations of this study were related to the study design. First, missing data are a key limitation of a prospective cohort design. Approximately 25% of patients who fulfilled the eligibility criteria and completed the baseline questionnaires did not respond to the followup questionnaires 6 months after THA. These losses occurred despite sending two mailed reminders and contacting nonresponders by telephone. We observed no differences in the relevant variables when we compared the responders and nonresponders. Thus, although a bias may be present owing to missing data, it is likely to be minor, and we believe that the results can be generalized to the entire sample. In addition, we had a relatively large sample of patients who underwent THAs, and we used well-known and validated patient-reported outcomes (the generic, health-related, quality of life SF-36 and the specific WOMAC questionnaire). Second, there are many aspects of a patient’s wait for a surgical procedure, such as the waiting time from referral by the family physician to consultation with the specialist or the time from the decision date to operate to the actual surgery. To ensure accuracy of data, we focused on the second date that was recorded as an administrative procedure. It also is possible that waiting only 6 months to assess the impact of a THA may have introduced bias. Although some studies report that improvement is seen at 6 months [8, 9, 12, 25], some suggest a longer followup period is needed . Third, the WOMAC provides valuable information and is considered the correct instrument for evaluating changes in studies such as ours [15, 26]. However, it also has some inherent problems, such as with ceiling or floor effects. When working with the WOMAC and other instruments, showing clinically meaningful changes, not just statistically significant changes, is crucial. Therefore, we used the MCID of the WOMAC . However, even the MCID must be used carefully and not as an absolute measure, as there are several practical problems in estimating that parameter . Fourth, the patients’ educational status and current job position were addressed directly through a questionnaire; therefore, the data were based on patients’ answers. This is considered a potential limitation in the way these variables were assessed. Fifth, our results may not be generalizable beyond the participating hospitals. The factors affecting waiting time for THA are local findings and cannot be extrapolated to other locations. Nevertheless, a previous study  emphasized the hazards of not having clear, explicit criteria to apply to patients assigned to a wait list for cataract extraction. A similar problem applies to this study. However, our observations and those of Quintana et al.  also emphasize the detrimental effects of assigning patients to a wait list without using explicit criteria. This is an issue of quality of care and equity in patient access to health care.
Patients on wait lists for THA to treat OA had a range of waiting times, with a mean of approximately 6 months for the analyzed period; this score was close to the mean of the OECD countries . Several factors have been reported to explain variations in wait times for elective surgery, including demand factors, quality of life, or pain and disability [5, 22, 32]. We found several factors related to quality of life that decreased when adjusted by other related variables, resulting in the ultimate determination of the waiting time: the degree of pain and function. Pain and function are crucial criteria in the prioritization process for THA . As previously reported [17, 22], the prioritization process should be based on broader criteria. Our findings showed a lack of effect of factors, such as age, comorbidities, and other social aspects (work situation and the availability of help during recovery), which potentially could be highly relevant in determining waiting times. The outstanding differences in the waiting times recorded at the participating hospitals, given the other similarities of the hospitals, probably are related to differences in management procedures of wait lists. In those hospitals, no protocols for managing the wait lists are implemented, and surgeons use various prioritization criteria in their decision-making processes .
Evidence has suggested that pain increases and function deteriorates with longer wait times . A wait time exceeding 12 months from consultation to surgery may adversely affect the 12-month outcomes after THA [13, 14, 24]. Based on our results, long wait times are not free from adverse effects and have irreversible effects on the results of the therapeutic intervention. Our data suggest a relevant effect of the suboptimal management of a wait list for surgery for OA. These procedures somehow have resulted in simplification of the criteria by which patients are prioritized. As a result, only pain and function level, both important factors, are considered in the decision-making process for prioritization, and many relevant factors that determine the appropriateness of the therapeutic intervention, such as those related to patient quality of life and socioeconomic factors, are disregarded. Therefore, implementation of procedures that allow selection and prioritization processes based on good practice criteria for THA surgical wait lists would be in the best interests of patients in the participating hospitals. This study does not offer new insights to improve the prioritization criteria for this type of surgery, but provides evidence that failing to apply some prioritization procedure to surgical wait-list management has consequences for control and functional capacity. Nevertheless, at this time, various priority criteria tools have been published, such as those proposed by Quintana et al.  or Naylor et al. . These management procedures would permit application of the most convenient treatments at the most convenient times based on patients’ physical condition and socioeconomic factors. Optimal patient selection will increase the overall quality of the clinical care, the overall health outcomes of patients after THA, and the equity of the service provided.
We thank I. Vidaurreta, A. Higelmo, and A. Rodriguez for data retrieval and data entry and the Research Committee of the Galdakao Hospital. We are grateful for the support of the staff members of the various services, research, and quality units, and the medical records sections of the participating hospitals. We also acknowledge the editorial assistance provided by Lynda Charters.
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