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Using social choice theory and acceptability analysis to measure the value of health systems.

ABSTRACT: The Future Health Index (FHI) is developed by the Royal Philips to help determine the readiness of countries to address global health challenges and build sustainable, fit-for-purpose national health systems. The FHI 2018 presents the Value Measure to measure the value of 16 health systems, which is formulated by taking the arithmetic average of Access, Satisfaction and Efficiency. However, this scheme is not the Pareto optimal and loses association with weights. For these reasons, this paper proposes to apply the social choice theory and Stochastic Multicriteria Acceptability Analysis for group decision making (SMAA-2) to measure the value of health systems, by means of re-constructing the Value Measure. Specifically, we begin with considering all possible individual preferences among Access, Satisfaction and Efficiency, which is mathematically represented by ranked weights of them; the pessimistic and optimistic outcomes under certain individual preference are derived in a closed-form manner, according to which an interval decision matrix is then formulated; the SMAA-2 is then lastly applied to compute the holistic acceptability index, which is considered as a revised Value Measure. An empirical study using the data of 16 health systems is conducted to show the effectiveness and superiority of our method. It is demonstrated that our method always outperforms the Value Measure, by means of comparing the Spearman's rank correlation coefficients.

SUBMITTER: Shen H

PROVIDER: S-EPMC7332049 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using social choice theory and acceptability analysis to measure the value of health systems.

Shen Hai H Sui Yubing Y Fu Yelin Y

PloS one 20200702 7

The Future Health Index (FHI) is developed by the Royal Philips to help determine the readiness of countries to address global health challenges and build sustainable, fit-for-purpose national health systems. The FHI 2018 presents the Value Measure to measure the value of 16 health systems, which is formulated by taking the arithmetic average of Access, Satisfaction and Efficiency. However, this scheme is not the Pareto optimal and loses association with weights. For these reasons, this paper pr ...[more]

PMID: 32614864

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Project description:BackgroundSocial class is frequently used as a means of ranking the population to expose inequalities in health, but less often as a means of understanding the social processes of causation. We explored how effectively different social class mechanisms could be measured by longitudinal cohort data and whether those measures were able to explain health outcomes.MethodsUsing a theoretically informed approach, we sought to map variables within the National Child Development Study (NCDS) to five different social class mechanisms: social background and early life circumstances; habitus and distinction; exploitation and domination; location within market relations; and power relations. Associations between the SF-36 physical, emotional and general health outcomes at age 50 years and the social class measures within NCDS were then assessed through separate multiple linear regression models. R2 values were used to quantify the proportion of variance in outcomes explained by the independent variables.ResultsWe were able to map the NCDS variables to the each of the social class mechanisms except 'Power relations'. However, the success of the mapping varied across mechanisms. Furthermore, although relevant associations between exposures and outcomes were observed, the mapped NCDS variables explained little of the variation in health outcomes: for example, for physical functioning, the R2 values ranged from 0.04 to 0.10 across the four mechanisms we could map.ConclusionsThis study has demonstrated both the potential and the limitations of available cohort studies in measuring aspects of social class theory. The relatively small amount of variation explained in the outcome variables in this study suggests that these are imperfect measures of the different social class mechanisms. However, the study lays an important foundation for further research to understand the complex interactions, at various life stages, between different aspects of social class and subsequent health outcomes.

Project description:The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of reliable associations by the overreduction of statistical power. In this report, we examine 2 alternative approaches to improve the statistical power of a whole genome association study to detect reliable genetic associations. The approaches were tested using the Genetic Analysis Workshop 19 (GAW19) whole genome sequencing data. The first tested method estimates the real number of effective independent tests actually being performed in whole genome association project by the use of an extreme value distribution and a set of phenotype simulations. Given the familiar nature of the GAW19 data and the finite number of pedigree founders in the sample, the number of correlations between genotypes is greater than in a set of unrelated samples. Using our procedure, we estimate that the effective number represents only 15 % of the total number of independent tests performed. However, even using this corrected significance threshold, no genome-wide significant association could be detected for systolic and diastolic blood pressure traits. The second approach implements a biological relevance-driven hypothesis tested by exploiting prior computational predictions on the effect of nonsynonymous genetic variants detected in a whole genome sequencing association study. This guided testing approach was able to identify 2 promising single-nucleotide polymorphisms (SNPs), 1 for each trait, targeting biologically relevant genes that could help shed light on the genesis of the human hypertension. The first gene, PFH14, associated with systolic blood pressure, interacts directly with genes involved in calcium-channel formation and the second gene, MAP4, encodes a microtubule-associated protein and had already been detected by previous genome-wide association study experiments conducted in an Asian population. Our results highlight the necessity of the development of alternative approached to improve the efficiency on the detection of reasonable candidate associations in whole genome sequencing studies.

Project description:BackgroundInterest in value-based healthcare, generally defined as providing better care at lower cost, has grown worldwide, and learning health systems (LHSs) have been proposed as a key strategy for improving value in healthcare. LHSs are emerging around the world and aim to leverage advancements in science, technology and practice to improve health system performance at lower cost. However, there remains much uncertainty around the implementation of LHSs and the distinctive features of these systems. This paper presents a conceptual framework that has been developed in Canada to support the implementation of value-creating LHSs.MethodsThe framework was developed by an interdisciplinary team at the Institut national d'excellence en santé et en services sociaux (INESSS). It was informed by a scoping review of the scientific and grey literature on LHSs, regular team discussions over a 14-month period, and consultations with Canadian and international experts.ResultsThe framework describes four elements that characterise LHSs, namely (1) core values, (2) pillars and accelerators, (3) processes and (4) outcomes. LHSs embody certain core values, including an emphasis on participatory leadership, inclusiveness, scientific rigour and person-centredness. In addition, values such as equity and solidarity should also guide LHSs and are particularly relevant in countries like Canada. LHS pillars are the infrastructure and resources supporting the LHS, whereas accelerators are those specific structures that enable more rapid learning and improvement. For LHSs to create value, such infrastructures must not only exist within the ecosystem but also be connected and aligned with the LHSs' strategic goals. These pillars support the execution, routinisation and acceleration of learning cycles, which are the fundamental processes of LHSs. The main outcome sought by executing learning cycles is the creation of value, which we define as the striking of a more optimal balance of impacts on patient and provider experience, population health and health system costs.ConclusionsOur framework illustrates how the distinctive structures, processes and outcomes of LHSs tie together with the aim of optimising health system performance and delivering greater value in health systems.

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Using social choice theory and acceptability analysis to measure the value of health systems.

Publications

Using social choice theory and acceptability analysis to measure the value of health systems.

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