Highlights
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Neurosurgery patients in Alberta are experiencing longer hospital stays than expected.
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There were significant differences in ALOS/ELOS between geographical zones, and patient-level factors account for differences in ALOS/ELOS.
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Three main themes influencing neurosurgery inpatient flow: interdisciplinary care pathways, introducing new roles and identification of risk factors.
Introduction
Healthcare systems worldwide, including those in Sweden, Singapore, the UK and parts of Canada such as Ontario and Alberta, continue to prioritize and work to improve inpatient flow, which remains a significant, consistent health system challenge. Reference Farrokhnia and Göransson1–5 Inpatient flow is the movement of patients through a healthcare facility, from admission to discharge. Poorly managed inpatient flow can lead to adverse health outcomes, including increased mortality and readmission rates. Reference Burgan and Embury-Young6,7 Improving inpatient flow benefits patients, healthcare professionals and the healthcare system. Key benefits include reducing hospital wait times, improving clinical outcomes and patient experiences and saving on overtime and waitlist costs through system efficiency. 8 In neurosurgery, timely and complex interventions highlight the importance of efficient inpatient flow.
Across diverse care settings, many patient-, unit- and system-level factors influence hospital inpatient flow. An emergency department study found that staffing levels, lack of inpatient beds and high patient demands influenced inpatient flow. Reference Jarvis9 Red days are days when an acute or community inpatient receives little or no beneficial medical treatment or attention for their condition. Reference Irvine, Awan and Chharawala10 They are a metric of inpatient flow, and addressing their causes advances the healthcare system. Reference Irvine, Awan and Chharawala10 A study from UK neurosurgical unit (n = 529) found that 63% of total red days were attributed to patients awaiting a bed in a local hospital. Reference Irvine, Awan and Chharawala10 Other contributing factors included patients with complex discharge needs, those awaiting decisions from non-neurosurgical teams, individuals awaiting social care assessments and care introductions and those awaiting neurosurgery consultant decisions or reviews. Reference Irvine, Awan and Chharawala10 The factors influencing inpatient flow in neurosurgery are not as well defined as other areas such as emergency and critical care.
Select studies investigated strategies and interventions to enhance inpatient flow. Reference Burgan and Embury-Young6,Reference Marco, Simpson, Shanks, Krenkel, Simon and Bellows11–Reference Ducharme, Alder, Pelletier, Murray and Tepper13 For example, in response to high average length of stay (ALOS)/expected length of stay (ELOS), a measure of inpatient flow that compares actual patient stays to expected stays based on factors like age, diagnosis and interventions, a cardiology unit in Canada completed a quality improvement project utilizing Plan-Do-Study-Act to develop a standardized approach for daily rounding, communication and discharge. Reference Marco, Simpson, Shanks, Krenkel, Simon and Bellows11 The study showed improved effectiveness of rounds, enhanced communication and an 11.4% decrease in ALOS/ELOS rates compared to the previous year. Reference Marco, Simpson, Shanks, Krenkel, Simon and Bellows11 The applicability of these findings to neurosurgery inpatient flow is unclear.
Alberta Health Services (AHS) is the single health authority for the Canadian province of Alberta, providing health services to ∼ 4.8 million people. 14 Alberta was the first province-wide integrated health system, with zonal variation across five zones (two urban, three rural). Neurosurgical procedures are conducted province-wide, but primarily in two urban centers. Reference McKenzie, O’Connell and Neurosciences15 Preliminary analysis revealed high ALOS/ELOS rates across Alberta neurosurgery populations, which led AHS to further investigation of the data to understand strategies for improvement.
The purpose of the study was to evaluate the existing state of inpatient flow in Alberta’s neurosurgical population and explore potential strategies for improving flow. Our research questions were as follows: (1) What are the interventions/factors that impact inpatient flow for adult neurosurgery patients in different jurisdictions? (2) Are there differences in ALOS/ELOS rates among the five AHS zones (three rural, two urban)? (3) Are ALOS/ELOS rates associated with comorbidity complexity, neurosurgical procedure, age, AHS zone or sex in adult inpatient neurosurgical care in Alberta?
Methods
ALOS/ELOS
ALOS/ELOS is a critical measure of inpatient flow that compares the ALOS to the ELOS. ALOS is the total number of days that a patient spends in the hospital from the date of admission to discharge. 16 ELOS is calculated for typical patients considering the factors for hospitalization, age, comorbidity and complications. 16 Typical patients exclude transfers, deaths, voluntary discharge and extreme ALOS cases, as established by the Canadian Institute for Health Information (CIHI). 17 While ELOS provides an expected duration of stay for patients undergoing specific procedures, it is not a fixed value for every individual and can vary significantly based on patient-specific factors. An ALOS/ELOS ratio <1 indicates that patients are hospitalized for a shorter duration than expected, while a ratio >1 signifies that patients are hospitalized for longer than expected.
Study design
The study employed a convergent parallel mixed-methods design, involving a rapid scoping review and a retrospective cohort study. These methodologies were integrated during data analysis, where review findings contextualized and enriched the cohort analysis.
Phase 1: rapid scoping review
The rapid scoping review of peer-reviewed and gray literature used the PRISMA framework. The review addressed research question 1 to understand the interventions or factors that influence inpatient flow for adult neurosurgery inpatients across jurisdictions.
Search strategy
The rapid scoping review used the PICO framework. The population of interest included adult (>18 years) neurosurgery inpatients. Any interventions or strategies were included, which aimed to improve inpatient flow. Studies with or without comparators were included. The outcomes of interest were the results of interventions or factors aimed at improving inpatient flow. Study eligibility was not limited by study outcomes.
A health librarian executed the search strategy using a comprehensive list of search terms across three databases (CINAHL, EMBASE and MEDLINE). Gray literature was also searched (Supplemental material). The search terms sought the key concepts: inpatient flow, neurosurgical procedures and interventions.
Inclusion and exclusion criteria
All empirical study designs published in peer-reviewed and gray literature were included. Study designs included quantitative designs (e.g., randomized-controlled trials and quasi-experimental studies), observational studies (e.g., cohort and case-control studies), mixed-methods studies and qualitative study designs (e.g., ethnography and observation). All studies were published in the English language between 2013 and 2023. Excluded articles included case studies, theses and abstracts.
Data extraction
A three-stage screening and extraction process was employed. First, titles/abstracts were screened using the eligibility criteria. Three independent reviewers screened the first 10 articles to ensure consistent PICO-criteria application and to mitigate biases. Second, full-text screening was completed. Eligibility decisions were recorded using standardized coding on Covidence (2024). Third, data extraction was completed with an a priori form (e.g., study information, population, intervention, outcomes) in Microsoft Excel.
Data analysis
A structured narrative synthesis of the extracted data summarized the (a) scope of the interventions, strategies and factors, (b) known impacts of the different types of interventions and (c) quality of the evidence using the Mixed Methods Appraisal Tool (MMAT). Reference Hong, Fàbregues and Bartlett18
One team member used the MMAT to evaluate study quality, assessing various types (e.g., qualitative descriptive, mixed methods and randomized-controlled studies) during systematic mixed studies’ reviews. Reference Hong, Fàbregues and Bartlett18 Lower MMAT scores suggest limitations that affect study validity. Reference Hong, Fàbregues and Bartlett18 Assessment results were recorded, but studies were not excluded based on scores.
Phase 2: retrospective cohort study
A retrospective cohort study addressed research questions 2 and 3 to investigate the potential associations between patient- and unit-level factors and ALOS/ELOS rates in neurosurgical populations in Alberta.
Population
CIHI developed case-mix groups (CMGs) as a system to categorize inpatients discharged from acute care into statistically and clinically similar groups using clinical and administrative data. 19 CMGs broadly fall under major clinical categories (MCCs): broad groupings of diagnoses that classify patients. In neurosurgery, 16 CMGs are grouped into 4 MCCs: MCC 1 (Diseases and Disorders of the Nervous System); MCC 8 (Diseases and Disorders of the Musculoskeletal System and Connective Tissue); MCC 10 (Diseases and Disorders of the Endocrine System, Nutrition and Metabolism); and MCC 19 (Trauma Injury Poison and Toxic Effects of Drugs). 20 The inclusion criteria were identified using CMGs and included patients who had undergone cranial or spinal neurosurgery performed by neurosurgeons, all of whom are based in Edmonton and Calgary but may travel to rural Alberta to provide care (MCCs 1, 8, 10 and 19), adult patients (>18 years) and those treated between 2009 and 2019.
Data collection and analysis
Health system data custodians gathered and de-identified included data from the Discharge Abstract Database, a secondary data source that captures administrative, clinical and demographic information collected during a patient’s hospital stay in Alberta and other Canadian provinces and territories.
All statistical analyses were performed using SPSS version 25 (Chicago, IL). Descriptive statistics portrayed demographic characteristics. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) determined whether there were statistically significant differences in ALOS/ELOS between the AHS zones. The data was positively skewed, as visually interpreted from normal Q-Q plots. As ANOVA is robust to deviations from normality, especially with large sample sizes, and a non-normal distribution does not affect Type 1 error, the data was not transformed. 21 Linear regression examined the individual relationships between predictor variables (age, comorbidity, sex, AHS zone and type of neurosurgical procedure) and ALOS/ELOS. Multiple regression analysis assessed the combined effects between the predictor variables and ALOS/ELOS. The analyses were adjusted for confounding factors: sex, comorbidity, type of neurosurgical procedure and AHS zone. A p value >0.05 was deemed statistically significant.
Results cross-jurisdictional rapid scoping review
Study selection and characteristics
A total of 391 articles were identified from the databases after duplicate removal (Figure 1). After screening 391 titles and abstracts, 348 articles were excluded. The remaining 43 articles underwent full-text screening, with 34 excluded due to the wrong population (n = 12), wrong intervention (n = 13), wrong setting (n = 8) or full-text unavailable (n = 1). Nine studies were included in the final review. Two types of study design were identified: retrospective cohort (n = 8, 88.9%) and mixed-methods quasi-experimental (n = 1, 11.1%). These studies came from five countries, with the highest representation from the USA (n = 4; 4.4%), followed by China (n = 2; 22.2%), the UK (n = 1; 11.1%), Canada (n = 1; 11.1%) and Sweden (n = 1; 11.1%). Five studies (n = 5; 55.6%) were published between 2015 and 2018, and four studies (n = 4; 44.4%) were published between 2020 and 2023. The sample sizes varied widely, ranging from 66 to 14,598, with a median sample size of 984. The mean age of participants was 59 years. Across all studies, there was a well-balanced distribution of sex, with an average representation of 47% females.
Figure 1. The PRISMA Flow diagram illustrates the identification, screening, eligibility and inclusion process of studies captured by our search strategy.
Study quality
The mean MMAT quality score was 82% (n = 8); scores ranged from 80% to 100%. There was a low risk of bias, as the MMAT scores were moderate. The lack of accounting for confounders was the most common reason for the lower scores.
Interventions affecting inpatient flow
Three themes of interventions affecting inpatient flow emerged: interdisciplinary care pathways, identification of risk factors and introducing new roles.
Interdisciplinary care pathways. Four interventions related to interdisciplinary care pathways (Table 1). Reference Irvine, Awan and Chharawala10,Reference Almqvist, Delgado and Sjöstrand22–Reference Jiang, Zhao and Wang24 These pathways aimed to enhance collaboration among multidisciplinary healthcare professionals involved in the care of cranial and, broadly, neurosurgery patients. These pathways sought to standardize practices, as demonstrated by the Stockholm Stroke Triage System, Reference Almqvist, Delgado and Sjöstrand22 the patient transfer standardization process, Reference Jiang, Zhao and Wang24 and the SAFER patient bundle. Reference Irvine, Awan and Chharawala10 Pathways focused on implementing protocols and algorithms to ensure uniform and effective care delivery. The morning huddle was a unique approach fostering daily team discussions to enhance communication and reduce hospital costs. Reference Chan and Vadera23 Pathways focused on different aspects of the care process: efficiency (patient transfer process standardization) and communication (morning huddle). The positive impact of three interdisciplinary care pathways on inpatient flow is evident in various quantitative measures, including reduced ICU days, increased senior reviews and decreased time between successive operations (Table 1).
Table 1. Interdisciplinary care pathways
Note: This table summarizes studies evaluating the impact of interdisciplinary care pathways on inpatient flow. For each study, it includes the intervention description, setting, population, sample size, study design, aims, key results and whether it had an impact on inpatient flow.
Identification of risk factors. Three interventions related to the identification of risk factors (Table 2). Reference Di Capua, Somani and Kim25–Reference Lim, King, Low and Ng27 Identification of risk factors aimed to enhance care for spinal and general neurosurgery patients. This included identifying the risk factors associated with patients being discharged to a location other than their home after spinal surgery, analyzing the existing transfer records to identify improvement and identifying predictive markers for readmission. These findings inform predictive factors, particularly measures, for future interventions (e.g., indicators for readmission, discharge and extended transfer times) (Table 2).
Table 2. Identification of risk factors
Note: This table summarizes studies evaluating the impact of the identification of risk factors on inpatient flow. For each study, it includes the intervention description, setting, population, sample size, study design, aims, key results and whether it had an impact on inpatient flow.
Introducing new roles. Two interventions related to introducing new roles (Table 3). Reference Gibson, Whitson, Filson, Grunch and Pack28,Reference Keenan, Mutterback, Velthuizen, Pantalone and Gossack-Keenan29 These roles aimed to expand knowledge of specialized roles in broad neurosurgery settings and enhance care delivery. These roles included physician assistants and advanced practice nurses. Responsibilities, within their scope of practice, included team-based rounding, medication management and bedside procedures. Reference Gibson, Whitson, Filson, Grunch and Pack28,Reference Keenan, Mutterback, Velthuizen, Pantalone and Gossack-Keenan29 The positive impact of both roles on inpatient flow is evident: reduced time for venous thromboembolism prophylaxis (p = 0.0021) and decreased length of stay (LOS) (p = 0.18) (Table 3).
Table 3. Introducing new roles
Note: This table summarizes studies evaluating the impact of introducing new roles on inpatient flow. For each study, it includes the intervention description, setting, population, sample size, study design, aims, key results and whether it had an impact on inpatient flow.
Alberta-focused retrospective cohort study
Study population
A total of 302,413 patients from 2009 to 2019 met the inclusion criteria. The total sample size may have been an overestimate from the MCC methodology. However, this approach is recognized as a feasible way to find this population in these databases. The mean age of patients was 58 years, and 50.2% of the sample was female (Table 4). Spinal surgery accounted for 69.2% of the surgeries, while cranial surgery accounted for 30.8% (Table 4). A Charlson Comorbidity Index of zero, indicating no comorbidities, was reported in 86.3% of patients (Table 4). Alberta is organized into five AHS geographical zones, with three rural and two urban. 30 Most neurosurgical procedures were performed in the two urban zones with populations > one million (Table 4). The mean ALOS/ELOS rate was 1.41 (Table 5).
Table 4. Demographics (2009–2019)
Note: This table summarizes the patient demographics. Values are presented as means (standard deviation) for continuous variables and counts (percentages) for categorical variables.
Table 5. ALOS/ELOS rates (2009–2019)
Note: This table summarizes descriptive statistics for patient ALOS/ELOS rates. ALOS/ELOS = average length of stay/expected length of stay.
Differences in ALOS/ELOS rates across alberta
A one-way Welch ANOVA was conducted to determine if the ALOS/ELOS ratio varied among AHS zones. Participants were classified into five AHS zones based on the site of surgery: Rural Zone 1 (n = 23,575), Rural Zone 2 (n = 13,699), Rural Zone 3 (n = 20,024), Urban Zone 1 (n = 11,4052) and Urban Zone 2 (n = 12,0614). The homogeneity of variances was violated, as assessed by Levene’s test of homogeneity of variance (p < 0.001). The data is presented as mean ± standard deviation. ALOS/ELOS increased from Rural Zone 2 (1.20 ± 0.66) to Urban Zone 2 (1.21 ± 0.75), to Rural Zone 3 (1.23 ± 0.66), to Urban Zone 1 (1.25 ± 0.75) and Rural Zone 1 (1.37 ± 0.71). The results revealed a significant difference in ALOS/ELOS among the five AHS zones, Welch’s F(4, 53,824.91) = 278.33, p <.0.001. Post hoc comparisons using Games-Howell revealed statistically significant differences between zones.
An ANCOVA was run to control for covariates. After adjustment for sex, age and comorbidity complexity, there was a statistically significant difference in ALOS/ELOS between the AHS zones, F(4, 291919) = 165.161, p < 0.001, partial η2 = 0.002. Post hoc comparisons using the Bonferroni adjustment revealed statistically significant differences between zones.
Factors associated with ALOS/ELOS rates
For each MCC, linear regression analysis explored the individual relationships between ALOS/ELOS and comorbidity complexity, age, sex, AHS zone and neurosurgical procedure. Despite the linear regression results, all variables were included in the final multiple regression models. Researchers argue that while statistical significance is one method, the value of a variable should also be assessed by its regression coefficient. 21 The written results herein only present MCC 8 as an exemplar.
For MCC 8 (limited to spinal surgery), significant relationships were revealed between each predictor variable and ALOS/ELOS. Patient comorbidity complexity (β = 0.036, p = <0.001) and age (β = 0.272, p = <0.001) demonstrated positive correlations with ALOS/ELOS. Patients in Rural Zone 1 (β = 0.14, p = <0.001) and Urban Zone 1 (β = 0.04, p = <0.001) demonstrated higher ALOS/ELOS rates. Urban Zone 2 (β = −0.05, p = <0.001), Rural Zone 3 (β = −0.04, p = <0.001) and Rural Zone 2 (β = −0.73, p = <0.001) demonstrated that patients in these zones tend to have lower ALOS/ELOS rates. Sex demonstrated a negative correlation with ALOS/ELOS (β = −0.107, p = <0.001), indicating that females generally experience higher ALOS/ELOS rates compared to men.
Multiple regression analyses were conducted to explore the combined associations between ALOS/ELOS and sex, age, AHS Zone, comorbidity complexity and neurosurgical procedure for each of the MCCs. For MCC 8, the multiple regression model statistically significantly accounted for the difference in ALOS/ELOS, F(8, 201737) = 2341.24, p < 0.001, ΔR2 = 0.085 (Table 6). Sex, comorbidity, age, Rural Zone 1, Urban Zone 1 and Urban Zone 2 added statistically significantly to the model (p < 0.001). The ΔR2 for the model was 0.085, indicating that 8.5% of the difference in ALOS/ELOS rates was attributed to the statistically significant variables in the model.
Table 6. Multiple regression results for MCC 8: Disorders and Diseases of the Musculoskeletal System and Connective Tissue
Note: This table summarizes the results of the multiple regression for MCC 8: Disorders and Diseases of the Musculoskeletal System and Connective Tissue. B = unstandardized regression coefficient; CI = confidence interval; SE B = standard error of the coefficient; β = standardized coefficient; R2 = coefficient of determination; ΔR2 = adjusted coefficient of determination.
MCC 1, MCC 10 and MCC 19 had lower ΔR2 values compared to MCC 8. For all MCCs, sex, comorbidity complexity and age added statistically significantly to the associations, p < 0.001. For MCC 1, the multiple regression model statistically significantly accounted for the difference in ALOS/ELOS, F(8, 81,498) = 127.47, p < 0.001, ΔR2 = 0.012. For MCC 10, the model statistically significantly accounted for the difference in ALOS/ELOS, F(8, 3310) = 21.32, p < 0.001, ΔR2 = 0.047. For MCC 19, the model statistically significantly accounted for the difference in ALOS/ELOS, F(7, 5854) = 22.49, p < 0.001, ΔR2 = 0.025.
Discussion and Conclusions
Results synthesis
Study findings indicated that neurosurgical patients in Alberta were staying longer in hospitals than expected; there were significant differences in ALOS/ELOS between the geographical Zones; patient-level factors account for differences in ALOS/ELOS.
The results revealed significant differences in ALOS/ELOS rates across the AHS zones. The mean ALOS/ELOS rates for each zone were greater than one, indicating a province-wide need to improve. Rural Zone 1 had the highest mean ALOS/ELOS, suggesting a priority area for further investigation. We speculated that Rural Zone 1 had the highest ALOS/ELOS rates given its rural location and lack of neurosurgery infrastructure and resources, posing challenges in neurosurgery patient treatment and postoperative care. Previous research aligns with this finding, highlighting the discrepancies in access to acute care services for those living in rural versus urban areas. Reference Hameed, Schuurman, Razek and Boone31 The finding that ALOS/ELOS rates in Urban Zone 2 and Rural Zone 2 were almost equal to one suggests that they are operating relatively effectively, warranting further investigation to share their successful practices with other zones.
The findings revealed that a small portion of the variance in ALOS/ELOS, ranging from 1.2% to 8.5%, was explained by patient-level factors (age, sex and comorbidity complexity) and geographical setting. These findings align with the rapid scoping review, which found that identifying risk factors is a determinant of inpatient flow. Despite small ΔR2 values, some statistically significant predictors emerged that have practical implications for focusing future healthcare practices. Higher ALOS/ELOS rates were associated with those who were female, had more comorbidities and were older. These findings were consistent with other studies that found that patients who are older and more frail have longer hospital LOS. Reference Hameed, Schuurman, Razek and Boone31,Reference Goel, Kumar, Gulati, Narang and Dey32
The cohort findings were complemented by the rapid scoping review, which explored strategies for improving inpatient flow in neurosurgery. The identification of risk factors and subsequent addressing of risk to improve inpatient flow is a consistent finding from various healthcare studies. Reference Lim, King, Low and Ng27,Reference Gibson, Whitson, Filson, Grunch and Pack28 For instance, creating personalized healthcare treatments by integrating patient comorbidities into patient management may reduce hospital length of stay and costs and improve patient outcomes. Reference Rayman, Akpan and Cowie35 Consistent with the findings, a UK study corroborates the impact of interdisciplinary care pathways on improved inpatient flow. Reference Destino, Bennett and Wood36 The Leeds Improvement Method, a structured approach to continuous improvement and quality management, used in the UK, improved inpatient flow in neurosurgical critical care transition of critical care patients to lower levels of care at night and decreased the number of canceled elective cases. Reference Meacock, Mukherjee and Sheikh12 Several studies provided further support for the theme of introducing new specialized roles to improve inpatient flow. Reference Burgan and Embury-Young6,Reference Ducharme, Alder, Pelletier, Murray and Tepper13 A UK study in a surgical unit assigned a radiologist to assess and report CT scanning, resulting in decreased CT scan and discharge time. Reference Burgan and Embury-Young6 The review results have important implications for researchers, healthcare providers and stakeholders, guiding future interventions, education and policies to enhance inpatient flow in neurosurgical care in Alberta.
Limitations
This study had limitations. First, one researcher completed the three-stage rapid scoping review and quality assessment. Second, a limited number of variables were included in the analysis, and we used administrative data, which does not capture self-identification of gender, income or race. Third, the approach used to identify neurosurgery patients using MCCs may have been overly broad and encompassed non-neurosurgery patients (e.g., orthopedic surgery). Finally, the comparison of ALOS/ELOS rates between rural and urban zones may have been misleading due to their differing neurosurgical populations and the additional resources available to support outflow in urban settings.
Future directions
More research is needed to further understand the relationships between ALOS/ELOS and patient-, unit- and system-level factors including investigation of additional factors that may impact ALOS/ELOS rates such as socioeconomic status and staffing levels. To ensure that Alberta neurosurgery patients are accurately represented, future research should use a narrower method for identifying patients, such as case-mix grouping, intervention codes, diagnosis codes (ICD-10) or neurosurgeons conducting the procedure. Additionally, more research is required to determine whether the increased ALOS/ELOS in females is due to gender (contextual) or sex (physiological) factors. A question arises as to whether care delivery in neurosurgery treats patients differently based on their sex or gender. Finally, a deeper analysis of the differences in ALOS/ELOS rates between the rural and urban zones may identify the reasons for variation and specific areas for improvement.
Conclusions
Neurosurgery patients in Alberta are experiencing longer hospital stays than expected. Several areas requiring more research have been identified, along with potential strategies to enhance patient care and health outcomes.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/cjn.2025.10358.
Acknowledgments
Members of the Provincial NRV Neurosurgery Key Quality Indicator Steering Committee, particularly neurosurgeons Drs. Vivek Mehta, Mark Hamilton and Aaron Hockley.
Author contributions
EB and BM: Critical review of the manuscript.
EP: Conception, statistical analysis planning and critical review of the manuscript.
KPM: Conception and supervision of the study, statistical analysis planning, data interpretation and critical review of the manuscript.
AW: Conception, statistical analysis planning and execution, data collection, data analysis and interpretation, drafting of the first manuscript, critical revision of the manuscript and final approval of the version to be published.
Funding statement
None.
Competing interests
None of the authors have significant disclosures related to this research.
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