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. 2022 Dec 7;29(12):9640-9659.
doi: 10.3390/curroncol29120757.

Characterizing Regional Variability in Lung Cancer Outcomes across Ontario-A Population-Based Analysis

Monica L Mullin  1 , Jonas Shellenberger  2 , Marlo Whitehead  2 , Michael Brundage  3 , Elizabeth A Eisenhauer  3 , M Diane Lougheed  1   4 , Christopher M Parker  1   4 , Geneviève C Digby  1   4
Affiliations

Characterizing Regional Variability in Lung Cancer Outcomes across Ontario-A Population-Based Analysis

Monica L Mullin et al. Curr Oncol. .

Abstract

Background: Lung cancer (LC) is the leading cause of cancer-related mortality. In Ontario, Canada, there are significant survival differences for patients with newly diagnosed LC across the 14 provincial regions. Methods: A population-based retrospective cohort study using ICES databases from 01/2007-12/2017 identified patients with newly diagnosed LC through the Ontario Cancer Registry and those with LC as the cause of death. Descriptive data included patient, disease, and system characteristics. The primary outcome was 5-year survival by region. Results: 178,202 patient records were identified; 101,263 met inclusion criteria. LC incidence varied by region (5.6-14.6/10,000), as did histologic subtype (adenocarcinoma: 27.3-46.1%). Five-year cancer-specific survival was impacted by age, rurality, pathologic subtype, stage at diagnosis, and income quintile. Timely care was inversely related to survival (fastest quintile: HR 3.22, p < 0.0001). Adjusted 5-year cancer-specific survival varied across regions (24.1%, HR 1.12; 34.0%, HR 0.89, p < 0.001). Conclusions: When adjusting for confounders, differences in survival by health region persisted, suggesting a complex interplay between patient, disease, and system factors. A single approach to improving patient care is likely to be ineffective across different systems. Quality improvement initiatives to improve patient outcomes require different approaches amongst health regions to address local disparities in care.

Keywords: health equity; lung cancer; population outcomes.

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Conflict of interest statement

M.L. Mullin, J. Shellenberger, M. Whitehead, M. Brundage, and E.A. Eisenhauer. G.C. Digby do not have any relevant disclosures. M.D. Lougheed has received research funding from MaRS/Merck & Co Inc and from Pfizer Inc, unrelated to this publication, as well as an honorarium from Merck & Co Inc for a speaking engagement and from AstraZeneca for participation in a working group. She has also received grants outside the submitted work paid directly to Queen’s University from the Ontario Lung Association and Ontario Thoracic Society. She serves as the Canadian Thoracic Society Choosing Wisely Canada Working Group chair. C. Parker has received research funding from MaRS/Merck & Co Inc, unrelated to this publication. MD Lougheed has received grants outside the submitted work paid directly to Queen’s University from the Canadian Institutes of Health Research (sub-grant from Ottawa Health Research Institute), Manitoba Workers Compensation Board, Ontario Lung Association, Ontario Thoracic Society, the Government of Ontario’s Innovation Fund, Queen’s University, Astra Zeneca and GlaxoSmithKline; and honoraria from the Canadian Thoracic Society for co-development and co-presentation of as Severe Asthma PREP course, from MDBreifcase for co-development of an accredited CME module on Severe Asthma; and from AstraZeneca for participation in the Precision Program Advisory Board. She has also served as a member and past-chair of the Canadian Thoracic Society (CTS) Asthma Clinical Assembly, member of the CTS Asthma Clinical Assembly Steering Committee, CTS representative on the Lung Association’s Board of Directors, CTS representative to the European Respiratory Society, and member of Health Quality Ontario’s Asthma in Adults and Asthma in Children Quality Standard Advisory Committee.

Figures

Figure 1
Figure 1
Study Cohort inclusion and exclusion criteria. This figure is a graphical representation of the flow of patient exclusion for our study, including the exclusion criteria, the total number of patients at each step (N), and the number of patients removed for each criterion.
Figure 2
Figure 2
Proportion of histologic subtypes of lung cancer, by LHIN. This graph demonstrates the breakdown of pathologic subtype for patients diagnosed with LC in each LHIN throughout the study period. There are differences between LHINs, with some (LHIN 5–8) having higher proportions of more favorable pathology, such as adenocarcinoma with others (LHIN4, 10, 13–14) having higher proportions of less favorable pathology such as small cell, poorly differentiated and unknown.
Figure 3
Figure 3
Stage of disease at lung cancer diagnosis, by LHIN. This graph demonstrates the stage of LC at diagnosis for patients in each LHIN throughout the study period. There are differences between LHINs, with some having higher proportions of early stage (LHIN 5–8, 11) and others having more patients with stage unknown or stage IV (LHIN 3, 12, 14).
Figure 4
Figure 4
Timeliness of care from first abnormal imaging to LC treatment. This graph shows the average number of days from the first abnormal imaging to the first treatment (including radiation, surgery, or systemic therapy) for lung cancer across LHINs. *denotes significance by pairwise t-tests for each of the LHINs compared to LHIN 7, using Šidák correction for multiple comparisons, p < 0.05.
Figure 5
Figure 5
Adjusted hazard ratios for cancer-specific mortality by LHIN. This graph shows the HRs for cancer-specific mortality calculated by Cox modeling and adjusting for patient age, sex, income quintile, rurality index, distance to the nearest hospital, stage, histology, co-morbidity status, timeliness of care, and access to specialty assessment. After adjusting for these variables, the performance of some LHINs improved, and while there was less disparity amongst LHINs, significant differences remained.
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References

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