Modeling the impact of genetic screening technologies on healthcare: theoretical model for asthma in children

Intensity of Bilateral Contacts in Social Network Analysis

The approach presented here introduces the use of directed and weighted graph indicators in order to incorporate the intensity of bilateral contacts. The indicators are tested on a reference email network, and their applicability in explaining the role of each individual in the organization is explored. The results suggest that directional indicators have high explicatory relevance and can add value to conventional Social Network Analysis (SNA) approaches.

Factors Affecting the Uptake of Hybrid and Electric Vehicles in the European Union

This paper explores the factors affecting the uptake of hybrid and electric vehicles in the European Union (EU) using data from two extensive cross-sectional surveys. Each survey consists of 26,500 responses to a questionnaire that combines socio-economic and behavioral aspects. The share of respondents across the EU stating that they would certainly or probably consider purchasing a hybrid or a battery-powered electric vehicle (H&EV) in the near future rose from 32% in 2014 to 37.4% in 2018. There is, however, a high variability among EU member states, as well as across different socio-economic groups. Propensity is highly correlated with income, educational attainment, and urbanization level. In order to address the high degree of collinearity, we applied a machine learning classification model to analyze and explain the interaction between the variables that affected the expressed propensity to purchase such a vehicle. The findings highlight something largely missing from the literature, namely that local conditions and regional variation are a major, if not decisive, factor regarding purchasing choices. Seen from a policy perspective, this conclusion may provide guidance regarding how to support the take up of H&EVs through measures that are tailored to the specific needs at the local level.

Cost-effectiveness of primary prevention of paediatric asthma: a decision-analytic model

BACKGROUND

Many children stand to benefit from being asthma-free for life with primary (i.e., prenatally started) prevention addressing one environmental exposure in a unifaceted (UF) approach or at least two in a multifaceted (MF) approach. We assessed the cost-effectiveness of primary prevention programmes for Dutch children in a decision-analytic framework.

METHODS

A decision-analytic tree model analysing healthcare costs and asthma cases prevented was developed to compare usual care (UC) with two UF and three MF programmes on the primary prevention of asthma amongst children. Programmes were evaluated through incremental cost-effectiveness ratios and net monetary benefits. Decision and parameter uncertainty were subjected to value-of-information analyses.

RESULTS

The current UC and one of three MF programmes dominated the other alternatives. The MF programme was more costly but also more effective than UC at an incremental cost-effectiveness ratio of <euro>8,209.20/additional asthma case prevented. The value of perfect information to reduce uncertainty was <euro>291.6M at its lowest. Most of the uncertainty in the cost-effectiveness threshold was attributable to the probability and cost estimates for low-risk children.

CONCLUSION

This study supports the feasibility of a structured programme that simultaneously addresses exposure to house dust mites, pet dander, environmental tobacco, and breast-feeding as a cost-effective alternative to UC in the primary prevention of asthma amongst children.

State-Transition Modeling

State-transition modeling (STM) is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling, including both Markov model cohort simulation as well as individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, STM is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. STMs have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs.

State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--3

State-transition modeling is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling including both Markov model cohort simulation and individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, state-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. State-transition models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs. The goal of this article was to provide consensus-based guidelines for the application of state-transition models in the context of health care. We structured the best practice recommendations in the following sections: choice of model type (cohort vs. individual-level model), model structure, model parameters, analysis, reporting, and communication. In each of these sections, we give a brief description, address the issues that are of particular relevance to the application of state-transition models, give specific examples from the literature, and provide best practice recommendations for state-transition modeling. These recommendations are directed both to modelers and to users of modeling results such as clinicians, clinical guideline developers, manufacturers, or policymakers.

Application of classic utilities to published pediatric cost-utility studies

OBJECTIVE

Economic analyses, such as cost-utility analyses (CUAs), are dependent on the quality of the data used. Our objective was to test how health utility values (measurements of patient preference) assessed by recommended methods (classic utilities) would impact the conclusions in published pediatric CUAs.

METHODS

Classic utilities for pediatric health states were obtained by recommended utility assessment methods, time trade-off, and standard gamble in 4016 parent interviews. To test the impact of these utilities on published studies, we obtained a sample of published pediatric CUAs by searching Medline, EMBASE, EconLit, Health Technology Assessment Database, Cochrane Database on Systematic Reviews, Database of Abstracts of Reviews of Effects, and the Cost Effective Analysis (CEA) Registry at Tufts Medical Center, using search terms for cost-utility analysis. Articles were included when results were presented as cost per quality adjusted life-years (QALYs), the interventions were for children <18 years of age and included at least one of the following health states: attention deficit hyperactivity disorder, asthma, gastroenteritis, hearing loss, mental retardation, otitis media, seizure disorder, or vision loss. Studies that did not include these or equivalent health states were excluded. For each CUA, we determined utilities (values for patient preference), the utility assessment method used, and presence of one-way sensitivity analyses (SAs) on utilities. When one-way SAs were conducted, we determined if using our classic utilities would change the result of the CUA. When an SA was not presented, we determined if using our classic utilities would tend to support or not support the published conclusions.

RESULTS

We evaluated 39 articles. Eighteen articles presented results of one-way SAs on utilities. Seven articles presented SAs over a range that included our classic utilities. In 4 of the 7, using classic utilities would change the conclusion of the study. For the 32 articles where no one-way SA were presented (n = 21), or where the classic utilities fell outside the range tested (n =11), a change to classic utility would tend against the study conclusion in 12 articles (31%).

CONCLUSIONS

More than a third of published CUA studies could change if pediatric utilities obtained by recommended, classic methods were used. One-way SAs on utilities are often not presented, making comparison between studies challenging.

Economic value of home-based, multi-trigger, multicomponent interventions with an environmental focus for reducing asthma morbidity a community guide systematic review

CONTEXT

A recent systematic review of home-based, multi-trigger, multicomponent interventions with an environmental focus showed their effectiveness in reducing asthma morbidity among children and adolescents. These interventions included home visits by trained personnel to assess the level of and reduce adverse effects of indoor environmental pollutants, and educate households with an asthma client to reduce exposure to asthma triggers. The purpose of the present review is to identify economic values of these interventions and present ranges for the main economic outcomes (e.g., program costs, benefit-cost ratios, and incremental cost-effectiveness ratios).

EVIDENCE ACQUISITION

Using methods previously developed for Guide to Community Preventive Services economic reviews, a systematic review was conducted to evaluate the economic efficiency of home-based, multi-trigger, multicomponent interventions with an environmental focus to improve asthma-related morbidity outcomes. A total of 1551 studies were identified in the search period (1950 to June 2008), and 13 studies were included in this review. Program costs are reported for all included studies; cost-benefit results for three; and cost-effectiveness results for another three. Information on program cost was provided with varying degrees of completeness: six of the studies did not provide a list of components included in their program cost description (limited cost information), three studies provided a list of program cost components but not a cost per component (partial cost information), and four studies provided both a list of program cost components and costs per component (satisfactory cost information).

EVIDENCE SYNTHESIS

Program costs per participant per year ranged from $231-$14,858 (in 2007 U.S.$). The major factors affecting program cost, in addition to completeness, were the level of intensity of environmental remediation (minor, moderate, or major), type of educational component (environmental education or self-management), the professional status of the home visitor, and the frequency of visits by the home visitor. Benefit-cost ratios ranged from 5.3-14.0, implying that for every dollar spent on the intervention, the monetary value of the resulting benefits, such as averted medical costs or averted productivity losses, was $5.30-$14.00 (in 2007 U.S.$). The range in incremental cost-effectiveness ratios was $12-$57 (in 2007 U.S.$) per asthma symptom-free day, which means that these interventions achieved each additional symptom-free day for net costs varying from $12-$57.

CONCLUSIONS

The benefits from home-based, multi-trigger, multicomponent interventions with an environmental focus can match or even exceed their program costs. Based on cost-benefit and cost-effectiveness studies, the results of this review show that these programs provide a good value for dollars spent on the interventions.

Asthma genetics: personalizing medicine

Asthma is a chronic inflammatory lung disease that leads to significant morbidity, mortality, and economic burden. The clinical symptoms, which are a result of airway inflammation and reversible airway obstruction, have led to the mainstay of therapies for asthma: anti-inflammatory medications and bronchodilators. However, the efficacies of the various classes of medications are not equal among all patients and may be affected by asthma phenotypes, environmental exposures, and genetic differences. Similarly, the risk for developing asthma and the natural history of the disease show great inter-individual variability due to these same factors. Over the past few decades, much effort has been focused on the genetics of asthma, and investigators have identified more than one hundred potential asthma susceptibility genes, of which at least ten have been replicated in numerous independent studies. In parallel, researchers have also identified genetic factors that impact the pharmacotherapeutic responses to the major classes of asthma medications. While the results of previous studies have been promising, future investigations need to combine genetics, pharmacogenetics, accurate disease phenotyping, and environmental exposures to build the foundation for personalized and predictive medicine for the 21st century. The ultimate goal is to enable physicians to identify those at risk for asthma, intervene to prevent or attenuate the disease, and select the optimal medical regimen for each individual patient. If successful, the resulting paradigm shift in medical practice will lead to improved clinical outcomes and decreased health care expenditures.