ترکیب تداخلات حقوق صاحبان سهام، بهره وری در هزینه اثربخشی تجزیه و تحلیل سه روش برای کنترل سرطان پستان

Background The past decade, medical technology assessment focused on cost-effectiveness analysis, yet there is an increasing need to consider equity implications of health interventions as well. This article addresses three equity–efficiency trade-off methods proposed in the literature. Moreover, it demonstrates their impact on cost-effectiveness analyses in current breast cancer control options for women of different age groups. Methods We adapted an existing breast cancer model to estimate cost-effectiveness and equity effects of breast cancer interventions. We applied three methods to quantify the equity–efficiency trade-offs: 1) targeting specific groups, comparing disparities at baseline and in different intervention scenarios; 2) equity weighting, valuing low and high health gains differently; and 3) multicriteria decision analysis, weighing multiple equity and efficiency criteria. We compared the resulting composite league tables of all approaches. Results The approaches show that a comprehensive breast cancer program, including screening, for women below 75 years of age was most attractive in both the group targeting approach and the equity weighting approach. Such control programs would reduce disparities with 56% and at €1908 per equity quality-adjusted life-year gained. In the multicriteria approach, a comprehensive treatment program for women below 75 years of age and treatment in stage III breast cancer were most attractive, with both an 82% selection probability, followed by screening programs for the two age groups. Conclusion In the three equity weighing approaches, targeting women below 75 years of age was more cost-effective and led to more equitable distributions of health. This likely is similar in other fatal diseases with similar age distributions. The approaches may lead to different outcomes in nonfatal disease.

The distribution of the disease burden [1,2] and treatment benefits [3,4] are frequently an area of health economics research. In breast cancer, control studies reveal differences in disease burden by race [2], urbanization [5], socioeconomic status [6], and insurance status [7]. These studies typically report disparities in incidence, prevalence, stage distribution, and disease mortality. In some studies, differences in quality-adjusted life expectancy are calculated [8]. One may distinguish three ways of equity reporting [9]. First, one may observe differences in health outcomes, such as life expectancy, quality of life, and incidence of a condition. Second, disparities may be reported in the provision of health care with those with a more severe condition receiving less, i.e., vertical equity. Third, inequities may be related to dissimilar use of health care for individuals with the same health, i.e., horizontal equity [9]. These three types of equity are interrelated, as utilization of health care is related to health outcomes, and both are related to difference in access. In all cases, inequalities may be reduced through the provision of additional health care to underprivileged groups, for example, by differential reimbursement of health packages [10]. Descriptive and distinct information about health disparities and cost-effectiveness estimates in relation to health interventions may be available and may give insight. Yet, due to the descriptive nature, its use in health policy, addressing equity and efficiency, is limited. Such a broad approach to evidence-based priority setting in health programming would use efficiency information on available strategies, as well as their potential for reducing existing disparities. Without this, reduction of inequalities as a policy goal remains a matter of intuition and debate, rather than of systematic evaluation. If so, still, interventions may have differential effects on the distribution of health depending on the way health inequalities are actually defined, measured, and addressed. Methodological studies on the use of equity considerations in cost-effectiveness analysis and its effect on health inequalities are reported [11–15]. Nevertheless, comparisons of the impact of these methods in economic evaluation have, so far, not been done, and any application in breast cancer control is absent. We distinguished three different methods that can be potentially beneficial in priority setting: targeting specific groups, equity weighting [13], and multicriteria decision analysis [11,16]. The aim of the article is to show the potential and the impact of these approaches in the use of cost-effectiveness analysis, e.g., by government agencies responsible for the selection of health benefit packages. Such processes may have yet to become more explicit, transparent, and thorough if equity implications are to be considered similarly as and parallel to cost-effectiveness analyses. Our perspective is societal and governmental, given the nature of any operational equity–efficiency approach.We explain three approaches in the method section and relate them to the underlying theory. Subsequently, we demonstrate their application in cost-effectiveness analyses, aiming at a rank order of optional interventions. We apply the equityincorporating approaches for breast cancer control evaluations using an existing breast cancer life table model [17], addressing the existing controversy in breast cancer control options by age groups. Differentiating breast cancer control options by age is subject to debate [18–20].We explain three approaches in the method section and relate them to the underlying theory. Subsequently, we demonstrate their application in cost-effectiveness analyses, aiming at a rank order of optional interventions. We apply the equityincorporating approaches for breast cancer control evaluations using an existing breast cancer life table model [17], addressing the existing controversy in breast cancer control options by age groups. Differentiating breast cancer control options by age is subject to debate [18–20].

Interventions aimed at women less than 75 years of age rank higher in all of the equity-including approaches. Treatment scenarios for women of 75 and over lead to larger health disparities between the two age groups; have lower equity-adjusted CERs; and are less likely to have a high probability of selection in applying multiple criteria. The results of the target group approach can be explained by the relative high average loss of healthy life-years at breast cancer diagnoses among ages below 75 in the base-case scenario (i.e., no treatment for either group). Women that received a diagnosis of breast cancer aged 80 already have lived 10 more years in good health than women that received a diagnosis at age 70. Therefore, any intervention aimed at the disadvantaged group (i.e., women of lower ages with breast cancer) in the calculation will result in positive effects on the distribution of health outcomes across all ages. The use of equity weights further increases the attractiveness of treating women below 75 years of age and those of 75 and over, as compared with the regular efficiency approach. This is because equity weights are higher for women below 75 years than for women with higher ages, due to the differences in years lived in good health and the remaining potential life span. The multicriteria decision analysis shows that having a disease at a lower age is an important criterion. There are other contributing factors as the number of potential beneficiaries is higher, the cost-efficiency ratio is lower, and the net individual health benefits are higher in this age group. Hence, interventions aimed at women below the age of 75 are more likely to be on the top of the league table. Although we adapted the model distinguishing different breast cancer stages, there are constraints using the present breast cancer model [17]. Yet we did not incorporate implementation costs. This may vary by group. In addition, the input data used for the analysis (i.e., the epidemiological data, equity weights,and the coefficients in the multicriteria decision analysis) were not all gathered in a similar setting. Consequently, the measured equity impact does not reflect true distributional preferences for a single assessed population. For these reasons, our results should be used with caution. Nevertheless, the significance of this research does not depend on the exact numbers and position in the league tables for the selected interventions. Testing and comparing the potential and its order of magnitude of the three existing equity–efficiency trade-off applications in health technology assessment is the main thrust of our article. Nevertheless, we consider the preference of targeting women in lower age group as rather robust in the case of breast cancer control. The methods applied in this area do not result in large differences between them. Research that addresses interventions for different diseases, among more heterogeneous groups, and distributed differently across age groups, may not show similar patterns and cross-consistency among the three new league tables. This could potentially make the selection of a single equity–efficiency trade-off approach a delicate matter. We defined equity in health in terms of changes of health outcomes given a particular condition and not in terms of healthcare access or net health gains. We believe that equity considerations should be concerned with the presence, severity, and duration of illness as well as with longevity and lifetime benefits, i.e., the fair innings principle. Hence, health should be measured in terms of disability-adjusted life expectancy, health-adjusted life expectancy, or lifetime QALYs. These measures combine severity of illness and the fair innings principle, and may account for the prevailing concepts of equity [29]. We believe that the three approaches used in this article all incorporated (some of) these properties of equity. Nevertheless, the approaches do not deal with equity in the same way; they all have their own strengths and weaknesses, and seem to be more complementary than mutually exclusive. Simply targeting underprivileged groups is the least comprehensive way to go about the equity–efficiency trade-off as it does not require any prior data collection on preferences of the general public or policymakers. In addition, a health gap is an intuitively understandable measure of differences in health between groups as well as the gap-reducing effect of interventions, either in absolute or relative terms. Nevertheless, targeting specific groups does not simultaneously assess the impact that an intervention has on equity and its cost-effectiveness. This means that the trade-off between equity and efficiency remains implicit, albeit it becomes less transparent. Equity weighting explicitly combines those preferences with associated costs and health effects in an easily understandable measure: the equity adjusted CER, incorporating the willingness to give up life-years for equity reasons. This measure can be interpreted as a regular CER. Although it may be easy to interpret equity weights and equity-weighted outcomes, it may be difficult to understand in which way equity weights are measured and calculated. Multicriteria decision analysis incorporates more aspects of health interventions than the other two approaches. Nevertheless, preferences about the relative importance of different criteria are measured in a different context and may be situation determined. Another disadvantage of this approach is the large amount of information that is lost as the performance on each criterion is categorized, weighted in a single outcome measure, i.e., the probability of selection. This limits the possibility to distinguish between different interventions. The number of potential profiles is especially limited if all investigated interventions are aimed at the same disease and same age group as in our example. Our study shows that there are some applications of the equity–efficiency trade-off at the disposal of policymakers. These applications are potentially promising, because they may lead to better informed and more transparent reimbursement decisions. Better information can result in a shift from intuition-based policymaking to more evidence-based policymaking. Various high-income health-care systems are presently shifting toward a process of intervention assessment and appraisal. Typically, the National Institute for Health and Clinical Excellence in the UK has produced an article on social values. In general, governments can only accomplish more of health goals if the consequences of health policies for different goals are known [30]. Nevertheless, increased quantification of knowledge on the impact of health interventions will reduce the autonomy of policymakers to decide which reimbursement scheme is most attractive and a priority. This also may result in reluctance from policymakers toward such explicit applications of the equity– efficiency trade-off. Therefore, it seems most realistic to use the described incorporation of equitability approaches as a potential transparent support of policy with regard to accounting for the equity impact of health interventions. Source of financial support: The Susan G. Komen for the Cure organization funded the study. The funding was unrestricted and the organization had no interference of any kind in the research or in the manuscript writing.