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Breast cancer mortality in Barcelona following implementation of a city breast cancer-screening program.

CIBER de Epidemilogia y Salud Pública (CIBERESP), Agència de Salut Pública de Barcelona, Plaça Lesseps 1, 08023 Barcelona, Spain.
Cancer Detection and Prevention (impact factor: 2.52). 01/2008; 32(2):162-7. DOI:10.1016/j.cdp.2008.05.002 pp.162-7
Source: PubMed

ABSTRACT To assess the impact that the Barcelona city breast cancer-screening program has had in the decline of mortality due to breast cancer among women aged 50-74 years, in the city of Barcelona.
A quasi-experimental study based on breast cancer deaths among women aged between 50 and 74 years residing in Barcelona between 1984 and 2004. The variables used were: age, year, and Primary Health Care District (ABS) grouped into four zones according to the year of implementation of the screening program. We carried out a descriptive analysis of mortality by year and age and fitted Poisson models to calculate the relative risk of dying prior to the existence of the program, after its implementation, and as a function of its degree of implementation. The models are adjusted for ABS socioeconomic level.
Between 1984 and 2004, 3733 women aged between 50 and 74 years died of breast cancer. The mortality rate fluctuated, reaching its highest level in 1991, having declined since. Prior to implementation of the program, mortality was falling by 1% annually (RR=0.99 95 CI%=0.98-0.99), and since then by 5% (RR=0.95 95 CI%=0.92-0.99). There are no significant differences in mortality reduction between zones where the program was implemented earlier and those where it came in later, even though mortality in the final phase of complete implementation is significantly lower by 17%, with respect to the period prior to its introduction.
The results show a reduction in mortality due to breast cancer over the entire period studied, the decline being more marked after the program was introduced. Opportunistic screening and the greater efficacy of the treatment of initial cancers have both influenced the findings. A longer follow up time will be needed in order to obtain more conclusive results.

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    Article: [Evaluation of health policies and plans].
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    ABSTRACT: Evaluation of plans and policies is a key element in their administration and must be performed under real conditions. Such evaluation is complex, as plans and policies include a diverse set of components that operate simultaneously. Moreover, external factors frequently influence those same issues that programs attempt to change. Unless plans and policies are evaluated under real conditions, a policy that effectively reduces the effects of a problem may be deemed ineffective (if the problem increases due to the influence of factors the program does not attempt to affect), or a policy that is unable to influence the problem it attempts to solve may be judged useful (if the magnitude of the problem is being reduced through the influence of factors other than the policy). The present article discusses evaluation of health policies, plans or complex programs, with emphasis on effectiveness assessment, using data from real examples. Among other issues, the need to identify the distinct components of policies and plans is reviewed. This article also describes how to evaluate the outcome or results of a program with indicators from other sources. Aspects related to the timing of evaluation and assessment indicators are analyzed. We discuss situations in which the launch of a new policy or intervention is followed by an increase in the reported magnitude of the problem it attempts to solve. These situations are illustrated by cases in which this increase is attributable to improved detection and by others in which the increase is related to factors external to the intervention. The frequent confusion of the effects of the intervention with other events is covered, with data from some examples. Finally, evaluation of plans that include a wide range of objectives is also addressed.
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Keywords

74 years residing
 
ABS
 
ABS socioeconomic level
 
Barcelona
 
Barcelona city breast cancer-screening program
 
breast cancer
 
breast cancer deaths
 
calculate
 
complete implementation
 
descriptive analysis
 
final phase
 
greater efficacy
 
highest level
 
initial cancers
 
mortality rate fluctuated
 
mortality reduction
 
Poisson models
 
Primary Health Care District
 
quasi-experimental study
 
screening program