programas cribado cancer

ACTUALIZACIÓN BIBLIOGRÁFICA

Nota Bibliográfica

Esta Nota es una recopilación de publicaciones (artículos, informes, libros) sobre cribado de cáncer resultado de una revisión no sistemática de la literatura.

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Josep A Espinás. Pla Director d'Oncología de Catalunya.
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Nota bibliográfica cribado miscelánea 2014-06

Wald NJ, Bestwick JP. Is the area under an ROC curve a valid measure of the performance of a screening or diagnostic test? J Med Screen. 2014;21(1):51–6.
 Available from: http://msc.sagepub.com/content/21/1/51.abstract. oi: 10.1177/0969141313517497.
 
The AUC is an unreliable measure of screening performance because in practice the standard deviation of a screening or diagnostic test in affected and unaffected individuals can differ. The problem is avoided by not using AUC at all, and instead specifying DRs for given FPRs or FPRs for given DRs

 

Nota bibliográfica cribado miscelánea 2014-05

Lacruz AIG, Gorgemans S, Lacruz MG. Female Preventive Practices: Breast and Smear Tests. Health Policy (New York). 2014;(0). Available from: http://www.sciencedirect.com/science/article/pii/S0168851014001110. doi: http://dx.doi.org/10.1016/j.healthpol.2014.04.012.

Breast cancer and cervical cancer are the most common female cancers in Spain and in many developed countries. The main goal of this paper is to identify the determinants of individual decisions on breast screening and smear testing, that is to say, the decision to take a test for the first time and the decision to test with suitable regularity. To that end, we have combined analyses of micro and macro data (the Spanish National Health Survey and Spanish Regional Social Indicators) and employed multilevel estimation models. Among the main results, we highlight the fact that regional public screening programmes improve individual decisions on screening (more women testing for the first time and more women testing regularly) and, furthermore, they generate positive synergies; for example, regional public programmes for smear testing improve individual decisions on both cervical and breast cancer screening. In addition, we conclude that it is not only important to know if the numbers of women undergoing breast screening and smear testing are increasing, it is also important to know if they are testing regularly.

Escoffery C, Rodgers KC, Kegler MC, Haardörfer R, Howard DH, Liang S, et al. A systematic review of special events to promote breast , cervical and colorectal cancer screening in the United States. 2014;14:274. doi: 10.1186/1471-2458-14-274.

Conclusions: Special events found in the review varied and used evidence-based strategies. Screening data suggest that some special events can lead to increases in cancer screening, especially if they provide onsite screening services. However, there is insufficient evidence to demonstrate that special events are effective in increasing cancer screening. The heterogeneity of populations served, event activities, outcome variables assessed, and the reliance on self-report to measure screening limit conclusions. This study highlights the need for further research to determine the effectiveness of special events to increase cancer screening
 
Zahl P-H, Jørgensen KJ, Gøtzsche PC. Lead-Time Models Should Not Be Used to Estimate Overdiagnosis in Cancer Screening.J Gen Intern Med. 2014; Available from: http://www.ncbi.nlm.nih.gov/pubmed/24590736. doi: 10.1007/s11606-014-2812-2. PMID: 24590736.

Lead-time can mean two different things: Clinical lead-time is the lead-time for clinically relevant tumors; that is, those that are not overdiagnosed. Model-based lead-time is a theoretical construct where the time when the tumor would have caused symptoms is not limited by the person’s death. It is the average time at which the diagnosis is brought forward for both clinically relevant and overdiagnosed cancers. When screening for breast cancer, clinical lead-time is about 1 year, while model-based lead-time varies from 2 to 7 years. There are two different methods to calculate overdiagnosis in cancer screening-the excess-incidence approach and the lead-time approach-that rely on two different lead-time definitions. Overdiagnosis when screening with mammography has varied from 0 to 75 %. We have explained that these differences are mainly caused by using different definitions and methods and not by variations in data. High levels of overdiagnosis of cancer have usually been explained by detection of many slow-growing tumors with long lead-times. This theory can be tested by studying if slow-growing tumors accumulate in the absence of screening, which they don't. Thus, it is likely that the natural history of many subclinical cancers is spontaneous regression.

Etzioni R, Gulati R. Oversimplifying Overdiagnosis [editorial]. J Gen Intern Med. 2014; Available from: http://link.springer.com/10.1007/s11606-014-2867-0.

In general, we agree with Zahl et al. that when disease can regress, application of lead-time models may represent a simplification of disease biology and may produce biased results, or at least results that are contingent on other inputs to the analysis. But this does not mean that the empirical excess-incidence approach, with all of its known deficiencies, should be used instead. In this endeavor to estimate the unobservable, we stand by a well-known quote attributed to Albert

Einstein: “Everything should be made as simple as possible, but not simpler.”

Sherman M. Screening for liver cancer: another piece of the puzzle? Hepatology. 2014;59(5):1673–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24254319. doi: 10.1002/hep.26936. PMID: 24254319

 

Nota bibliográfica cribado c miscelánea 2014-03

Freidlin B, Korn EL. A Model Too Far. J Natl Cancer Inst. 2014;106(2). Available from: http://jnci.oxfordjournals.org/content/106/2/djt368.short. doi: 10.1093/jnci/djt368.

However, the level of evidence that can be generated by modeling is more suited for augmentation of questions directly addressed in a randomized screening trial rather than as a primary source for guiding public health policy

   

Nota bibliográfica cribado c miscelánea 2013-10

Areia M, Carvalho R, Cadime AT, Rocha Gonçalves F, Dinis-Ribeiro M. Screening for gastric cancer and surveillance of premalignant lesions: a systematic review of cost-effectiveness studies. Helicobacter. 2013;18(5):325–37. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23566268. doi: 10.1111/hel.12050.
PMID: 23566268.

CONCLUSIONS: The available evidence shows that Helicobacter pylori serology or endoscopic population screening is cost-effective, while endoscopic surveillance of premalignant gastric lesions presents conflicting results. Better implementation of published guidelines and accomplishment of systematic detailed reviews are needed

 

Nota bibliográfica cribado c miscelánea 2013-09

Wald NJ. The press, press releases, and raising unwarranted expectations. J Med Screen. 2013;20(2):55–6.
 Available from: http://msc.sagepub.com/content/20/2/55.short. doi: 10.1177/0969141313492150.

Liu Z (Amy), Hanley JA, Strumpf EC. Projecting the yearly mortality reductions due to a cancer screening programme. J Med Screen. 2013; Available from: http://msc.sagepub.com/content/early/2013/09/17/0969141313504088.abstract. doi: 10.1177/0969141313504088.
The decision on whether to implement a 20-year screening programme for a cancer requires weighing the harms and costs against the health benefits (such as the number of cancer deaths averted every year). The evidence of the benefits is often based on a single-number summary, such as the mortality reduction over the entire follow-up time in a single trial, or an average of such one-number measures from a meta-analysis of several trials. There are several problems associated with using the traditional one-number summaries from trials to deduce the yearly mortality reductions expected from a sustained screening programme. We here propose using a rate ratio curve, and its complement (a mortality reduction curve), to address the mortality impact (timing, magnitude, and duration) of a screening programme. This curve is easy to interpret, as it shows when mortality reductions begin, how big they are, and how long they last. We illustrate when and how such rate ratio curves from screening trials could be computed, and how they could be used to compare reduction patterns expected with different screening regimens. We encourage trialists to report the necessary data to arrive at such projections

   

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