Income level and regional policies, underlying factors associated with unwarranted variations in conservative breast cancer surgery in Spain

<p>Abstract</p> <p>Background</p> <p>Geographical variations in medical practice are expected to be small when the evidence about the effectiveness and safety of a particular technology is abundant. This would be the case of the prescription of conservative surgery in breast cancer patients. In these cases, when variation is larger than expected by need, socioeconomic factors have been argued as an explanation. Objectives: Using an ecologic design, our study aims at describing the variability in the use of surgical conservative versus non-conservative treatment. Additionally, it seeks to establish whether the socioeconomic status of the healthcare area influences the use of one or the other technique.</p> <p>Methods</p> <p>81,868 mastectomies performed between 2002 and 2006 in 180 healthcare areas were studied. Standardized utilization rates of breast cancer conservative (CS) and non-conservative (NCS) procedures were estimated as well as the variation among areas, using small area statistics. Concentration curves and dominance tests were estimated to determine the impact of income and instruction levels in the healthcare area on surgery rates. Multilevel analyses were performed to determine the influence of regional policies.</p> <p>Results</p> <p>Variation in the use of CS was massive (4-fold factor between the highest and the lowest rate) and larger than in the case of NCS (2-fold), whichever the age group. Healthcare areas with higher economic and instruction levels showed highest rates of CS, regardless of the age group, while areas with lower economic and educational levels yielded higher rates of NCS interventions. Living in a particular Autonomous Community (AC), explained a substantial part of the CS residual variance (up to a 60.5% in women 50 to 70).</p> <p>Conclusion</p> <p>The place where a woman lives -income level and regional policies- explain the unexpectedly high variation found in utilization rates of conservative breast cancer surgery.</p

The Population Burden of Cancer: Research Driven by the Catchment Area of a Cancer Center

Cancer centers, particularly those supported by the National Cancer Institute, are charged with reducing the cancer burden in their catchment area. However, methods to define both the catchment area and the cancer burden are diverse and range in complexity often based on data availability, staff resources, or confusion about what is required. This article presents a review of the current literature identifying 4 studies that have defined various aspects of the cancer burden in a defined geographical area and highlights examples of how some cancer centers and other health institutions have defined their catchment area and characterized the cancer burden within it. We then present a detailed case study of an approach applied by the University of California, San Francisco, Helen Diller Family Comprehensive Cancer Center to define its catchment area and its population cancer burden. We cite examples of how the Cancer Center research portfolio addresses the defined cancer burden. Our case study outlines a systematic approach to using publicly available data, such as cancer registry data, that are accessible by all cancer centers. By identifying gaps and formulating future research directions based on the needs of the population within the catchment area, epidemiologic studies and other types of cancer research can be directed to the population served. This review can help guide cancer centers in developing an approach to defining their own catchment area as mandated and applying research findings to this defined population

Poisson Regression for Clustered Data

We compare five methods for parameter estimation of a Poisson regression model for clustered data: (1) ordinary (naive) Poisson regression (OP), which ignores intracluster correlation, (2) Poisson regression with fixed cluster-specific intercepts (FI), (3) a generalized estimating equations (GEE) approach with an equi-correlation matrix, (4) an exact generalized estimating equations (EGEE) approach with an exact covariance matrix, and (5) maximum likelihood (ML). Special attention is given to the simplest case of the Poisson regression with a cluster-specific intercept random when the asymptotic covariance matrix is obtained in closed form. We prove that methods 1-5, except GEE, produce the same estimates of slope coefficients for balanced data (an equal number of observations in each cluster and the same vectors of covariates). All five methods lead to consistent estimates of slopes but have different efficiency for unbalanced data design. It is shown that the FI approach can be derived as a limiting case of maximum likelihood when the cluster variance increases to infinity. Exact asymptotic covariance matrices are derived for each method. In terms of asymptotic efficiency, the methods split into two groups: OP & GEE and EGEE & FI & ML. Thus, contrary to the existing practice, there is no advantage in using GEE because it is substantially outperformed by EGEE and FI. In particular, EGEE does not require integration and is easy to compute with the asymptotic variances of the slope estimates close to those of the ML. Copyright 2007 The Authors. Journal compilation (c) 2007 International Statistical Institute.