Cost‐effectiveness of digital mammography screening before the age of 50 in <scp>T</scp>he <scp>N</scp>etherlands

Sankatsing, Valérie D.V.; Heijnsdijk, Eveline A.M.; Luijt, Paula A. van; Ravesteyn, Nicolien T. van; Fracheboud, Jacques; Koning, Harry J. de

doi:10.1002/ijc.29572

Cited by 39 publications

(45 citation statements)

References 46 publications

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“…Probabilities of receiving adjuvant treatment (hormonal therapy, chemotherapy, or a combination of the two) and survival rates are incorporated using data from Dutch regional comprehensive cancer centers (by age, stage, and calendar year) and from the Early Breast Cancer Trialists' Collaborative Group (EBCTCG) meta‐analysis . A detailed description of the model has been published before …”

Section: Methodsmentioning

confidence: 99%

All‐cause mortality versus cancer‐specific mortality as outcome in cancer screening trials: A review and modeling study

Heijnsdijk

Csanádi²,

Gini

et al. 2019

Cancer Medicine

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Background All‐cause mortality has been suggested as an end‐point in cancer screening trials in order to avoid biases in attributing the cause of death. The aim of this study was to investigate which sample size and follow‐up is needed to find a significant reduction in all‐cause mortality. Methods A literature review was conducted to identify previous studies that modeled the effect of screening on all‐cause mortality. Microsimulation modeling was used to simulate breast cancer, lung cancer, and colorectal cancer screening trials. Model outputs were: cancer‐specific deaths, all‐cause deaths, and life‐years gained per year of follow‐up. Results There were large differences between the evaluated cancers. For lung cancer, when 40 000 high‐risk people are randomized to each arm, a significant reduction in all‐cause mortality could be expected between 11 and 13 years of follow‐up. For breast cancer, a significant reduction could be found between 16 and 26 years of follow‐up for a sample size of over 300 000 women in each arm. For colorectal cancer, 600 000 persons in each arm were required to be followed for 15‐20 years. Our systematic literature review identified seven papers, which showed highly similar results to our estimates. Conclusion Cancer screening trials are able to demonstrate a significant reduction in all‐cause mortality due to screening, but require very large sample sizes. Depending on the cancer, 40 000‐600 000 participants per arm are needed to demonstrate a significant reduction. The reduction in all‐cause mortality can only be detected between specific years of follow‐up, more limited than the timeframe to detect a reduction in cancer‐specific mortality.

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Section: Methodsmentioning

confidence: 99%

All‐cause mortality versus cancer‐specific mortality as outcome in cancer screening trials: A review and modeling study

Heijnsdijk

Csanádi²,

Gini

et al. 2019

Cancer Medicine

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“…For screening disutility, almost half of the economic evaluations (29,48,51,54,56,58,60,63,65,66,68,73,74,78,84) used expert VAS utilities derived from a second study in the Netherlands (32), but only three economic evaluations (51,54,73) considered the generalisability of the expert sample to the general population in the model to which this was applied. Other economic evaluations made their own adjustments to local population EQ-5D or SF-6D data (47,49,50,59,69,79,81) applied to reflect this uncertainty in more than half of the economic evaluations, which may bias results (QALYs) toward more frequent screening (29,49,52,54,59).…”

Section: Economic Evaluations Using Cost Per Qalymentioning

confidence: 99%

“…This limitation was justified in five studies due to the lack of robust HSUVs for mammography screening. For the 13 studies (47,50,60,63,65,66,(78)(79)(80)(81)(82)(83)(84) which did attempt to value overdiagnosis in their analysis, an assumption was made that this was captured in the QALYs across screening strategies by including the temporary disutility of diagnosis and treatment without a corresponding gain in life years. However, the utilities applied used sources which had not highlighted that there was a risk the treatment was unnecessary during the valuation process and therefore is unlikely to fully capture the impact of the risk of overdiagnosis on quality of life.…”

Section: Economic Evaluations Using Cost Per Qalymentioning

confidence: 99%

Valuing the health states associated with breast cancer screening programmes: A systematic review of economic measures

Bromley

Petrie

Mann

et al. 2019

Social Science & Medicine

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“…The units of effectiveness measure in CEA can be : number of successful cases treated, number of cases screened or prevented, number of lives saved or number of life years gained [26][27][28][29][30][31][32][33]. For ease of comparing and ranking more than two alternatives in CEA, the notion of costeffectiveness ratio (CER) has been adopted [34,35]. It will be a straight-forward case to reject.…”

Section: Cost-effectiveness Analysis (Cea)mentioning

confidence: 99%

Health Economic Evaluation: A Primer for Healthcare Professionals

Leung¹

2016

Primary Health Care

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Fundamental Concepts Costs, benefits, supply and demandIn any non-utopic society, resources (or goods) are finite (hence scarce) and they are constantly distributed to individuals to satisfy their needs (wants and preferences). Resources have to be produced (by sellers) to replenish consumption by individuals(consumers), who willingly let go of other resources (money) to accrue the resource(s) of their preference (consumer choice). Such transaction (bought and sold) between a consumer and seller is a re-allocation of resources (supplier's good versus consumer's money) at a mutually agreed price, where consumers' willingness to pay meets sellers 'willingness to accept. Here, consumers enjoy a benefit from the good/service by paying sellers the costs of the goods. In any market free from monopoly or regulation, this transaction price indicates equilibrium between supply and demand ( Figure 1).However, when more than one individual consume the same set of resources, competition will arise: as the net resources decrease the equilibrium price will shift, so that only the individual(s) who can now pay a higher price will continue to consume the decreasing resources. On the contrary, when resources become more available than needed, an excess will be left around and hence the demand falls and pulls down the equilibrium price. John Locke, an English philosopher and physician, first described this inverse supply-demand relationship in his writings in 1691, but the actual term "supply and demand" was not coined until 1767 by Denham-Steuart and Adam Smith, in his famous work of "the Wealth of Nations" [1,2]. By definition, supply and demand for any resource or commodity are susceptible to external constraints like weather (under-or over-production) and disasters that will compromise human viability (under-demand of luxurious items and over-demand of basic commodities) (Figure 2). Scenarios when supply and demand curves shift. For a commodity X in a society, there exists an equilibrium where supply curve S1 intersects demand curve D1, at the agreedutility(price) of U3 for a Q4 quantity of X. An epidemic came killing 15% of population and sent the economy to recession, shifting the demand curve for X to the left to D2 (now less people in total and less money earned). If the supply remained the same S1, it will be relatively in excess and hence the agreed utility (price) will fall to U4 for a lesser quantity of X (Q3). Now, if the production of X also fell, the supply curve will be shifted up to S2(harder to produce the same unit of good/service), leading to a higher agreed utility/price of U2 for a much less quantity of X.(Q1). AbstractIn its broadest term, economic evaluation (EE) is a comparative analysis of the input (costs) and the output (consequences, outcomes) of two or more alternatives to see if they are economically beneficial or feasible. The earliest form of economic evaluation took place in mid-19th century and since then; three main forms of EE have evolved which are employed in various settings: cost-benefit ...

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Cost‐effectiveness of digital mammography screening before the age of 50 in The Netherlands

Cited by 39 publications

References 46 publications

All‐cause mortality versus cancer‐specific mortality as outcome in cancer screening trials: A review and modeling study

All‐cause mortality versus cancer‐specific mortality as outcome in cancer screening trials: A review and modeling study

Valuing the health states associated with breast cancer screening programmes: A systematic review of economic measures

Health Economic Evaluation: A Primer for Healthcare Professionals

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