Evidence map›Paper›PMID 41749265›Full record

ArticleBMC medicine2026

Unveiling disparities in lung cancer care: a joint spatio-temporal analysis of multidisciplinary meeting presentation, supportive care screening, and diagnostic timeliness in Victoria.

Getayeneh Antehunegn Tesema, Rob G Stirling, Zemenu Tadesse Tessema, Arul Earnest

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Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Getayeneh Antehunegn TesemaSchool of Public Health and Preventive Medicine, Monash University, Room 415, Level 4, 553 St Kilda Road, Melbourne, VIC, 3004, Australia. Getayeneh.tesema@monash.edu.
Rob G StirlingCentral Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3168, Australia.
Zemenu Tadesse TessemaSchool of Public Health and Preventive Medicine, Monash University, Room 415, Level 4, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.
Arul EarnestSchool of Public Health and Preventive Medicine, Monash University, Room 415, Level 4, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer remains the most diagnosed malignancy and the leading cause of cancer-related mortality worldwide. Improving key clinical quality indicators (CQIs), including multidisciplinary meeting (MDM) presentation, supportive care screening, and timely diagnosis is essential for optimising patient outcomes. These indicators often co-occur, yet evidence on their shared spatio-temporal structure and the utility of joint modelling approaches remains limited. Thus, we aimed to explore the joint spatio-temporal patterns of these CQIs across Victoria, Australia.

methodsWe analysed data from 12,970 lung cancer cases recorded in the Victorian Lung Cancer Registry (VLCR). A Bayesian hierarchical joint spatio-temporal shared-component model was employed to estimate the joint magnitude of MDM presentation, supportive care screening, and timely diagnosis across space (local government area) and time (year). To improve the precision of our estimates, information across the three outcomes was combined using both shared and outcome-specific components within the shared-component modelling framework. Models were estimated using Integrated Nested Laplace Approximation (INLA) in R-INLA and were compared using the Deviance Information Criterion (DIC).

resultsApproximately 35% of the total variation across CQIs was explained by shared spatial and temporal components. The shared temporal trend exhibited a modest but consistent increase over the study period, whereas outcome-specific temporal trends displayed distinct patterns. Shared spatial risk patterns closely paralleled CQI-specific distributions, highlighting regions with persistent geographic inequities in the delivery of lung cancer care.

conclusionsSubstantial geographic and temporal disparities in lung cancer care were observed, with marked shared patterns across CQIs. These findings highlight the need for integrated, geographically targeted strategies to strengthen MDM presentation, supportive care screening, and timely diagnosis, thereby improving coordination and reducing inequities in lung cancer management.

Indexed as

Healthcare DisparitiesLung NeoplasmsAgedBayes TheoremEarly Detection of CancerFemaleHumansMaleMiddle AgedRegistriesSpatio-Temporal AnalysisVictoriaBayesian modellingHealth inequitiesJoint spatio-temporal analysisLung cancer careShared component model

Identifiers

PMID41749265
PMCPMC13049978

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.