Glioblastoma carries a severe prognosis with a median survival of approximately 12 months . The primary treatment of glioblastoma is surgical resection most often followed by chemotherapy and radiotherapy. Due to the intracerebral location of the tumour, surgical resection as the primary treatment is carefully balanced against the risk of complications in terms of impaired physical or cognitive function with a possible severe impact on the patient’s quality of life. When surgical resection is not feasible, tissue-based diagnostics is performed by stereotactic biopsy [2, 3]. When the patient’s condition is poor, no tissue diagnosis is obtained and palliative treatment is initiated. In Denmark, the treatment of glioblastoma is provided according to national guidelines .
The treatment of gliomas in Denmark is centralised to four hospitals located in four of the five Danish geographical regions: Rigshospitalet (Capital Region), Odense University Hospital (Region of Southern Denmark), Aarhus University Hospital (Central Denmark Region) and Aalborg University Hospital (North Denmark Region). Surgery, radiotherapy and chemotherapy are conducted in all four hospitals. Patients who live in Region Zealand are typically referred to Rigshospitalet for diagnostic workup and treatment.
In Denmark, healthcare and hospital services are provided to the residents by each of the five Danish administrative regions, and the evaluation of mortality rates across regions may reflect regional differences in treatment intensity or quality. Furthermore, it is of interest to examine the contribution of socio-economic factors to survival after a diagnosis of glioblastoma in the tax-financed Danish healthcare system .
The aim of this study was to examine regional differences in overall survival after a histologically verified diagnosis of glioblastoma in Denmark, and to evaluate the influence of the use of surgical resection and socio-economic factors on the prognosis.
We collected data on all patients with histologically verified gliomas diagnosed in the 2013-2018 period registered in the Danish Neuro-oncology Registry (n = 2,435) (DNOR) . Patients with missing data on tumour grade or socio-economic variables were excluded. These exclusions left 2,360 patients. The analysis was further restricted to 1,731 (73%) patients with grade IV glioma (glioblastoma) (SNOMED morphology codes: M94403; M94423). The date of diagnosis was the earliest date of diagnostic biopsy or surgical resection with corresponding histologically verified glioblastoma.
Data sources were the DNOR , the Danish Civil Registration System (CRS)  and the Danish National Patient Registry (NPR) . Information about education and household income was extracted from the national registries at Statistics Denmark [9, 10]. The unique Danish ten-digit personal identification number (PIN) was used to link data across registers. The study was registered in accordance with the General Data Protection Regulation in the Central Denmark Region (Reg. no.: 1-16-02-823-17).
Region of residence at diagnosis, marital status and date of death were extracted from the CRS . The Capital Region contributed with the highest number of patients and was used as the reference region in the analyses. Marital status was defined as 1) married or in a registered partnership, 2) other cohabiting persons and 3) single or widowed.
The highest attained formal education for each patient was classified as 1) basic school education (the compulsory school education only), 2) professional education (including, e.g., apprenticeships and including high-school only), 3) shorter further education and 4) longer further education.
Household income in the year before the cancer diagnosis was grouped into quartiles of the income distribution for the total population of glioblastoma patients.
Comorbidity up to three months prior to the date of diagnosis was calculated as the Charlson Comorbidity Index based on hospital discharge diagnoses in the NPR  from ten years before the date of diagnosis.
Tumour grade was defined according to the contemporary World Health Organization (WHO) Classification of Tumors of the Central Nervous System .
Surgical resection was a binary variable indicating that a resection (NOMESCO classification code AAB00 (gross total resection) or AAB10 (partial resection))  was the primary treatment. Surgical resection registered within 90 days of an initial diagnostic biopsy was classified as the primary treatment.
The statistical analysis was conducted as a time-to-event analysis using death from any cause as outcome. Patients were followed up from the date of diagnosis until death, emigration or end-of-follow-up on July 15, 2019,whichever occurred first. The follow-up period ranged from six months to six years, depending on the time of diagnosis. We used a multivariable Cox regression model and reported the results as adjusted hazard ratios (HRs) with corresponding 95% confidence interval (95% CI). The basic model (Model 1) included age (quadratic function), sex, year of diagnosis and comorbidity as covariates. Each of the remaining covariates were then added to Model 1 separately in order to identify their individual contribution to the variation in mortality.
Trial registration: not relevant.
Table 1 characterises the study population of 1,731 glioblastoma patients by region of residence. The patients in the Capital Region were more often women, were better educated and had a higher income than patients in the other regions. Region Zealand had the highest proportion (12%) of patients with a high comorbidity (Charlson Comorbidity Index > 3). The highest proportion of patients of older age (≥ 70 years), lower education (school-based education only) and low income was seen in the Region of Southern Denmark. Surgical resection was initiated more frequently in the Region of Southern Denmark (83%) than in the other four regions. The lowest resection rate was observed in the North Denmark Region (59%).
Table 1also includes results of the Cox regression analyses for each of the included covariates in the study. The basic model included adjustment for age, sex, year of diagnosis and comorbidity. Mortality increased significantly with higher age and high comorbidity but did not differ significantly between men and women; nor according to education or income. Patients with no surgical resection had significantly higher mortality.
Table 2 shows the regional variation in mortality using different adjustments. With adjustment for age, sex, year of diagnosis and comorbidity (Model 1), the Region of Southern Denmark had the lowest mortality, HR = 0.79 (95% CI: 0.68-0.91)); and the Capital Region had the highest (HR = 1.00; the baseline category in the model). The results were robust to further separate adjustment for civil status, education and income. When adjusted for surgical resection, the regional differences in mortality were attenuated, indicating that the variation in Model 1 was partly attributable to the varying use of surgical resection.
Table 3 shows the adjusted hazard ratio estimates of mortality by household income. Model 1 showed no significant variation in mortality between income groups, and estimates were robust to the separate adjustment for included covariates.
We examined regional differences in mortality after a histologically confirmed glioblastoma diagnosis in 1,731 patients in Denmark. The median overall survival was 11.2 months.
When adjustment was made for year of diagnosis, age, sex and comorbidity, a 21% difference was observed in patient mortality between Southern Denmark (lowest) and the Capital Region (highest). In the age group 60-69 years (32% of the study population), the observed regional difference in mortality corresponded to a difference in median survival between Southern Denmark and the Capital Region of 2.6 months (Log-rank, p = 0.51). In the age group ≥ 70 years, the difference in median survival was 2.3 months (log-rank, p = 0.11), whereas in the age group 18-59 years the difference was only 0.2 months in favour of the Capital Region (log-rank, p = 0.36) (Figure 1).
The variation between the five Danish regions in socio-economic variables did not explain the observed regional differences in mortality as revealed by the robust HR estimate for Southern Denmark after separate adjustments. The results were sensitive to adjustment for surgical resection, which varied from 83% in Southern Denmark to 59% in Northern Denmark. Surgical resection had an attenuating effect on the observed regional variation.
Only around 15% of glioblastoma patients survive two years after diagnosis . Therefore, quality of life is of utmost importance when deciding the treatment strategy. The choice of treatment strategy also depends on comorbidity and performance status, and on disease-related neurological deficits such as cognitive impairment, memory loss, apraxia and language disorders. Furthermore, the risk of adverse effects from surgery and other interventions needs to be considered to balance the expected survival benefit.
The resection rate ranged from 59% to 83% between the five Danish regions, although all treatment is centralised to four high-volume academic centres that follow the same national guidelines to perform resection as safely as possible . This difference may be a result of patient selection because patients are selected for resection according to age, performance status and infiltration of the tumour into eloquent brain tissue. Moreover, with extended state or tumour growth at symptom presentation, some patients are not offered surgery and were thus not included in this analysis.
A national consensus exists on guidelines based on systematic reviews which conclude that a total resection improves survival , although the extent of resection remains controversial due to few randomised controlled trials . Hence, surgical resection is the standard treatment in glioblastomas , but preventing neurological deficits has top priority when aiming to perform resection as safely as possible since postoperative deficits may reduce patient survival and quality of life . Selection for surgery is therefore a complex risk-adapted strategy influenced by evaluation of prognostic data, patient-preferences and usability of surgical adjuncts. We do not have data on the regional variation in these surgical adjuncts, such as stereotactic navigation systems, intraoperative MRI, tumour visualisation by fluorescent aminolevulinic acid and awake monitoring with cortical stimulation, which can optimise the achievement of minimal residual postoperative tumour volume. The risk-adapted selection strategy is of utmost importance and may have influenced the regional variation in resection rate.
We used data from the population-based Danish national health registries and registries from Statistics Denmark that are renerally considered to be of high quality in terms of completeness and validity [7-10]. Since 2015, the DNOR has been based on registrations in the NPR combined with clinical validation of data and supplementary, manually registered variables that are not possible to obtain through the Danish registries. A previous validation study conducted before the DNOR was register based (2009-2014) showed that the population of glioma patients was nearly complete (92%) and the data of selected clinically important variables was of good validity .
Household income in the year before diagnosis was not a predictor of survival, a very positive finding in the Danish healthcare setting. Furthermore, socio-economic covariates did not contribute to explaining the variation in survival between the five Danish regions.
We found 21% variation in age-, sex- and comorbidity-adjusted patient mortality between the five Danish regions in patients with a histologically verified glioblastoma diagnosis. The observed differences in surgical treatment across the five Danish regions may explain part of the variation in mortality. Household income was not associated with survival, nor did socio-economic factors explain regional differences in survival after a glioblastoma diagnosis.
Correspondence Henriette Engberg. E-mail: email@example.com
Accepted 15 December 2021
Conflicts of interest none. Disclosure forms provided by the authors are available with the article at ugeskriftet.dk/dmj
Acknowledgements The authors take this opportunity to extend their gratitude to the following experts for their contribution to the project advisory group: Professor Michael Borre, Danish Multidisciplinary Cancer, Groups (DMCG), Dr. Lone Susanne Jensen, Danish EsophagoGastric Cancer Group, Linda Aagaard Thomsen, Danish Cancer Society & Dr. Claus Wilki Fristrup, Danish Pancreatic Cancer Group.
References can be found with the article at ugeskriftet.dk/dmj
Cite this as Dan Med J 2022;69(3):A08210673
Rasmussen BK, Hansen S, Laursen RJ et al. Epidemiology of glioma: clinical characteristics, symptoms, and predictors of glioma patients grade I-IV in the the Danish Neuro-Oncology Registry. J Neurooncol 2017 Dec;135(3):571-9.
Wen PY, Weller M, Lee EQ et al. Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. Neuro Oncol. 2020 Aug 17;22(8):1073-1113.
Weller M, van den Bent M, Preusser M et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol 2020 Mar;18(3):170-86.
The Danish Neuro-Oncology Group. Kliniske retningslinjer. Gliomer hos voksne Version 2.0. 2020. www.dmcg.dk/siteassets/kliniske-retningslinjer---skabeloner-og-vejledninger/kliniske-retningslinjer-opdelt-pa-dmcg/dnog/dnog_gliomer_voksne_admgodk181220.pdf (1 May 2021).
The Danish Cancer Society. Social Ulighed i Kræft i Danmark. Hvidbog 2019. Copenhagen: Danish Cancer Society, 2019.
Hansen S, Nielsen J, Laursen RJ et al. The Danish Neuro-Oncology Registry: establishment, completeness and validity. BMC Res Notes 2016 Aug 30;9(1):425.
Schmidt M, Pedersen L, Sorensen HT. The Danish Civil Registration System as a tool in epidemiology. Eur J Epidemiol 2014 Aug;29(8):541-9.
Schmidt M, Schmidt SAJ, Sandegaard JL et al. The Danish National Patient Registry: a review of content, data quality, and research potential. Clin Epidemiol 2015 Nov 17;7:449-90.
Jensen VM, Rasmussen AW. Danish Education Registers. Scand J Public Health 2011 Jul;39(suppl 7):91-4.
Baadsgaard M, Quitzau J. Danish registers on personal income and transfer payments. Scand J Pub Health 2011 Jul; 39(suppl 7):103-5.
Charlson ME, Pompei P, Ales KL et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40(5):373-83.
Louis DN, Perry A, Reifenberger G et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016 Jun;131(6):803-20.
Nordic Medico-Statistical Committee (NOMESCO). NOMESCO Classification of Surgical Procedures (NCSP), Version 1.14. Copenhagen: NOMESCO, 2009.
Brown TJ, Brennan MC, Li M et al. Association of the extent of resection with survival in glioblastoma: a systematic review and meta-analysis. JAMA Oncol 2016 Nov 1;2(11):1460-9.
Hart MG, Grant GR, Solyom EF et al. Biopsy versus resection for high-grade glioma. Cochrane Database Syst Rev 2019 Jun 6;(6):CD002034.
Gulati S, Jakola AS, Nerland US et al. The risk of getting worse: surgically acquired deficits, perioperative complications, and functional outcomes after primary resection of glioblastoma. World Neurosurg. 2011 Dec;76(6):572-9.