Published in

Oxford University Press, European Journal of Public Health, 6(29), p. 1103-1107, 2019

DOI: 10.1093/eurpub/ckz024

Links

Tools

Export citation

Search in Google Scholar

The influence of patient case mix on public health area statistics for cancer stage at diagnosis: a cross-sectional study

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Abstract Background Summary statistics comparing the stage at diagnosis of geographically defined populations of cancer patients are increasingly used in public reporting to monitor geographical inequalities but may be confounded by patient case mix. We explore the impact of case-mix adjustment on a publicly reported measure of early stage at diagnosis in England. Methods We analyzed data used for publicly reported statistics about the stage of patients diagnosed with 1 of 11 solid tumours in 2015 in England, including information on cancer site (bladder, breast, colon, rectum, kidney, lung, melanoma, non-Hodgkin lymphoma, ovarian, prostate, endometrial), age, gender, income deprivation and population-based commissioning organization. We investigated how cancer site and other patient characteristics influence organizational comparisons and attainment of early-stage targets (≥60% of all cases diagnosed in TNM stages I–II). Results Adjusting for patient case mix reduced between-organization variance by more than 50%, resulting in appreciable discordance in organizational ranks (Kendall’s tau = 0.53), with 18% (37/207) of organizations being reclassified as meeting/failing the early-stage target due to case mix. Conclusion Summary statistics on stage of cancer diagnosis for geographical populations currently used as public health surveillance tools to monitor organizational inequalities need to account for patient sociodemographic characteristics and cancer site case mix.