Socio-economic disparities in access to treatment and their impact on colorectal cancer survival.
ABSTRACT Significant socio-economic disparities have been reported in survival from colorectal cancer in a number of countries, which remain largely unexplained. We assessed whether possible differences in access to treatment among socio-economic groups may contribute to those disparities, using a population-based approach.
We retrospectively studied 71 917 records of colorectal cancer patients, diagnosed between 1997 and 2000, linked to area-level socio-economic information (Townsend index), from three cancer registries in UK. Access to treatment was measured as a function of delay in receipt of treatment. We assessed socio-economic differences in access through logistic regression models. Based on relative survival < or =3 years after diagnosis, we estimated excess hazard ratios (EHRs) of death for different socio-economic groups.
Compared with more affluent patients, deprived patients had poorer survival [EHR = 1.20; 95% confidence interval (CI) 1.16-1.25], were less likely to receive any treatment within 6 months [odds ratio (OR) = 0.87, 95% CI 0.82-0.92] and, if treated, were more likely to receive late treatment. No disparities in survival were detected among patients receiving treatment within 1 month from diagnosis. Disparities existed among patients receiving later or no treatment (EHR = 1.30; 95% CI 1.22-1.39), and persisted after adjustment for age and stage at diagnosis (EHR = 1.15; 95% CI 1.08-1.24).
Tumour stage helped explain socio-economic disparities in colorectal cancer survival. Disparities were also greatly attenuated among patients receiving early treatment. Aspects other than those captured by our measure of access, such as quality of care and patient preferences in relation to treatment, might contribute to a fuller explanation.
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ABSTRACT: Four approaches to estimating a regression model for relative survival using the method of maximum likelihood are described and compared. The underlying model is an additive hazards model where the total hazard is written as the sum of the known baseline hazard and the excess hazard associated with a diagnosis of cancer. The excess hazards are assumed to be constant within pre-specified bands of follow-up. The likelihood can be maximized directly or in the framework of generalized linear models. Minor differences exist due to, for example, the way the data are presented (individual, aggregated or grouped), and in some assumptions (e.g. distributional assumptions). The four approaches are applied to two real data sets and produce very similar estimates even when the assumption of proportional excess hazards is violated. The choice of approach to use in practice can, therefore, be guided by ease of use and availability of software. We recommend using a generalized linear model with a Poisson error structure based on collapsed data using exact survival times. The model can be estimated in any software package that estimates GLMs with user-defined link functions (including SAS, Stata, S-plus, and R) and utilizes the theory of generalized linear models for assessing goodness-of-fit and studying regression diagnostics.Statistics in Medicine 02/2004; 23(1):51-64. · 2.04 Impact Factor
- BMJ 05/2001; 322(7290):830-1. · 14.09 Impact Factor
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ABSTRACT: Achieving equity in healthcare, in the form of equal use for equal need, is an objective of many healthcare systems. The evaluation of equity requires value judgements as well as analysis of data. Previous studies are limited in the range of health and supply variables considered but show a pro-poor distribution of general practitioner consultations and inpatient services and a pro-rich distribution of outpatient visits. We investigate inequality and inequity in the use of general practitioner consultations, outpatient visits, day cases and inpatient stays in England with a unique linked data set that combines rich information on the health of individuals and their socio-economic circumstances with information on local supply factors. The data are for the period 1998-2000, just prior to the introduction of a set of National Health Service (NHS) reforms with potential equity implications. We find inequalities in utilisation with respect to income, ethnicity, employment status and education. Low-income individuals and ethnic minorities have lower use of secondary care despite having higher use of primary care. Ward level supply factors affect utilisation and are important for investigating health care inequality. Our results show some evidence of inequity prior to the reforms and provide a baseline against which the effects of the new NHS can be assessed.Social Science [?] Medicine 04/2005; 60(6):1251-66. · 2.73 Impact Factor