Timeliness of cancer care in a regional Victorian health service: A comparison of high‐volume (Lung) and low‐volume (oesophagogastric) tumour streams

Abstract Background Timeliness of cancer care is vital for improved survival and quality of life of patients. Service and care centralisation at larger‐volume centres has been associated with improved outcomes. However, there is a lack of systematic data on the impact of tumour stream volume on timeliness of care. Aims To investigate and compare timeliness of care for lung cancer, a high‐volume (more commonly diagnosed) tumour stream, and oesophagogastric (OG) cancer, a low‐volume (less commonly diagnosed) tumour stream, at a regional health service in Victoria, Australia. Methods A retrospective cohort study comprising random samples of 75 people newly diagnosed with lung cancer (International Classification of Diseases and Related Health Problems‐10 [ICD‐10] diagnosis codes C34 in the Victorian Cancer Registry [VCR]) and 50 people newly diagnosed with OG cancer (ICD‐10 diagnosis codes C15 or C16 in VCR) at one regional Victorian health service between 2016 and 2017. Binary logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between patient factors and suboptimal timeliness of care. Results In comparison to OG cancer patients, lung cancer patients had reduced odds of suboptimal timeliness of care in reference to times outside OCP for referral to diagnosis (OR [95% CI] = 0.34 [0.14 to 0.83]) but increased odds of suboptimal timeliness for diagnosis to treatment (OR [95% CI] = 2.48 [1.01 to 6.09]). Conclusion In the low‐volume OG cancer stream, patients had longer wait times from referral to an MDM, where treatment decisions occur, but shorter time to commencement of first treatment. Conversely in the high‐volume lung cancer group, there was delayed initiation of first treatment following presentation at MDM. There is need to explore ways to fast‐track MDM presentation and commencement of therapy among people diagnosed with low‐volume and high‐volume cancers, respectively.

However, treatment at larger treatment centres has been shown to culminate in better survival for both lung 18,19 and OG 20-22 cancer patients. This evidence may suggest that the larger treatment centres have more streamlined care services than smaller centres, highlighting the benefits of service centralisation. 4 In regional areas, however, transport is a major barrier to accessing treatment. The need for regional patients to travel long distances has been associated with poor outcomes and worse quality of life. 23 A robust, data-driven approach to quality improvement may help to optimise services and timeliness of care in smaller, non-metropolitan centres, with a view to overcoming the deficit relative to larger centres. Assessing the patient pathway for high-volume tumour streams may help identify aspects of timeliness of care that are improved when centralisation is employed.
We, therefore, investigated and compared timeliness of care for lung cancer, a high-volume tumour stream, and OG cancer, a low-volume tumour stream, at a regional health service in Victoria, Australia. 2 | METHODS

| Design and setting
We conducted a retrospective cohort study among patients with lung or OG cancer at a regional health service providing cancer care in the Redmond, Washington). The region in which the study is set, the LMR makes up almost a quarter of Victoria's area yet is home to only approximately 331 000 (5% of the state's population) residents. 24 In this region, the annual incidence of cancer is approximately 2200 cases, including 200 lung cancer cases and fewer than 100 OG cancer cases.
Using paper medical records and electronic hospital systems, data were collected on demographic variables, clinical variables and dates corresponding to optimal timeframes specified in the lung and OG cancer OCPs. 12,13 These included dates of referral receipt at the health service derived from patient letters, first specialist appointment from the clinics record, diagnosis date from the pathology results, and MDM and commencement of first treatment from the treatment logs in the patient file. Due to the exploratory nature of the study on factors associated with suboptimal timelines, all patient demographic and clinical covariates were collected. Comorbidities were categorised into the body systems affected and other cancers if present.

| Statistical analysis
The percentage capture of each date was calculated for each tumour stream separately as well as both combined. For patients in each cancer group with relevant dates available, date pairs were used to calculate the following times in days: referral receipt to first specialist appointment, referral receipt to diagnosis, referral receipt to first treatment, MDM to first treatment, diagnosis to MDM and diagnosis to first treatment. For each time in each tumour group, the proportion of patients who did not meet the optimal timeframe published in the relevant OCP 12,13 (ie, had suboptimal timeliness of care) was calculated and expressed as a percentage. Patients who were presented at the MDM before their diagnosis were excluded from the analysis. Numbers less than 5 have been reported as "<5" to maintain patient confidentiality, in line with VCR requirements.
Patient characteristics were compared between the lung and OG cancer groups using either the χ 2 test for independence (categorical variables) or the independent samples t-test (continuous variables).
Continuous variables, including times with optimal timeframes specified in OCPs, were compared between lung and OG cancers using the Kruskal-Wallis test. Factors associated with suboptimal timeliness of care (Table 3) were analysed by univariable binary logistic regression.
This involved the calculation of odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Multivariable binary logistic regression models, including all assessed demographic and clinical factors as comorbidities, stage of disease and Eastern Cooperative Oncology Group (ECOG) performance status, 25 were also fit to determine if any factors were independently associated with suboptimal timeliness of care. In all analyses, missing values were excluded case-by-case and a P-value less than .05 or a 95% CI excluding 1.00 were considered to be indicative of a statistically significant result. All statistical analyses were carried out using SPSS Version 23 (SPSS Inc., Chicago, Illinois).

| RESULTS
The median ages for lung and OG cancer patients were 63.3 and 69.5 years, respectively. The older OG cancer patients were also likely to be residents of Greater Bendigo, the city in which the regional hospital is located, and had significantly more males and a significantly smaller proportion of patients with comorbidities ( score of more than 2, a diagnosis of metastatic disease and their management options considered at an MDM ( Table 1). None of the treatment types (ie, chemotherapy, chemoradiation therapy, radiotherapy of surgery) were significantly associated with suboptimal timeframes from MDM to treatment date (P = 0.651) when both cancer types are analysed together. However, the median times were longer for lung cancer across all treatment types except surgery ( Figure 1). Due to small numbers in treatment type groups, statistical difference could not be calculated. Although the proportions of patientsvaried slightly for the different timelines, none were statistically significant ( Table 2).
Lung and OG cancer patients were grouped together when analysing factors associated with receiving care outside optimal timeframes (ie, suboptimal timeliness of care). times greater odds of a suboptimal time from referral to diagnosis.
The odds of a suboptimal time from referral to diagnosis were 3.8 times greater for those with metastatic disease relative to those with non-metastatic disease. The odds of a suboptimal time from referral to first treatment were 3.2 times greater for those patients who presented with cardiovascular and gastrointestinal disorders, and 2.5 times higher in those presenting with metabolic disorders. (Table 3).
Factors associated with suboptimal timeliness of care were also assessed for each cancer stream individually. Among OG cancer patients, no factors were found to be associated with having suboptimal timeliness of care for any of the optimal timeframes. In lung cancer, factors associated with the odds of suboptimal care times included age at diagnosis, living within the local government area of the City of Greater Bendigo, respiratory disease, cardiovascular disease, gastrointestinal disease and presenting with an ECOG score of less than 3. The odds of suboptimal wait times from referral to first appointment were 80% lower in lung cancer patients who were residents of Greater Bendigo. However, the odds of suboptimal time from referral to diagnosis was increased in lung cancer patients presenting with metastatic disease by a factor of 7.2. The odds of suboptimal timelines from referral to first treatment were increased in lung cancer patients presenting with cardiovascular comorbidity by a factor of 3.9 and reduced 6% with an increasing age of diagnosis. The odds of suboptimal times from diagnosis to MDM were increased by a factor 5 in

| DISCUSSION
In this retrospective cohort study set in regional Australia, we show that the low-volume OG cancer stream was associated with suboptimal timeliness from referral to diagnosis compared to the highvolume lung cancer stream while the high-volume lung cancer was associated with suboptimal timeliness from diagnosis to treatment.
n/a n/a n/a n/a n/a Alleviating the effects of healthcare saturation on initiatives to improve the quality of cancer care may present its own challenges. 28 Previously, we have shown that several sub-regional nurse-led oncology services with existing memorandums of understanding with the regional cancer center 29 could be scaled up to reduce wait times and travel distances of regional Victorian cancer patients. Such services have the potential to negate the effects of hospital saturation on quality of care for high-volume tumours.

| CONCLUSION
In the low-volume OG cancer stream, patients had longer times from referral to diagnosis but shorter time to commencement of first treatment. Conversely, in the high-volume lung cancer group, there was delayed initiation of first treatment following presentation at MDM.
There is a need to explore ways to fast track diagnoses, MDM presentations and commencement of therapy among people diagnosed with low and high-volume cancers, respectively.

CONFLICT OF INTEREST
Authors declare no conflict of interests. visualization; writing-original draft; writing-review and editing.