Economic Evaluation of Environmental Interventions: Reflections on Methodological Challenges and Developments

Evaluation of the costs and outcomes associated with environmental policies and interventions is often required to inform public policy and allocate scarce resources. Methods to conduct assessments of cost-effectiveness have been developed in the context of pharmaceuticals, but have more recently been applied in public health, diagnostics, and other more complex interventions. The suitability of existing economic evaluation methodology has been explored in many contexts, however, this is yet to be undertaken for interventions and policies pertaining to the natural environment, such as urban green spaces and strategies to reduce indoor and outdoor air pollution. To make significant inroads into the evaluation of interventions and policies relating to the natural environment requires an understanding of the challenges faced in this context. Many of these challenges may be practical (data-related), however, a number are also methodological, and thus have implications for the appropriate framework for economic evaluation. This paper considers some of the challenges faced when conducting cost-effectiveness analyses in this context and explores what solutions have been proposed thus far. The intention is to help pave the way for consideration of which existing framework is most appropriate for the evaluation of natural environment (NE) interventions, or if a distinct framework is required. Environmental policies and interventions relating to the built environment, for example, housing, are not explicitly included here

Non-invasive imaging software to assess the functional significance of coronary stenoses : a systematic review and economic evaluation

Background: QAngio® XA 3D/QFR® (three-dimensional/quantitative flow ratio) imaging software (Medis Medical Imaging Systems BV, Leiden, the Netherlands) and CAAS® vFFR® (vessel fractional flow reserve) imaging software (Pie Medical Imaging BV, Maastricht, the Netherlands) are non-invasive technologies to assess the functional significance of coronary stenoses, which can be alternatives to invasive fractional flow reserve assessment. Objectives: The objectives were to determine the clinical effectiveness and cost-effectiveness of QAngio XA 3D/QFR and CAAS vFFR. Methods: We performed a systematic review of all evidence on QAngio XA 3D/QFR and CAAS vFFR, including diagnostic accuracy, clinical effectiveness, implementation and economic analyses. We searched MEDLINE and other databases to January 2020 for studies where either technology was used and compared with fractional flow reserve in patients with intermediate stenosis. The risk of bias was assessed with quality assessment of diagnostic accuracy studies. Meta-analyses of diagnostic accuracy were performed. Clinical and implementation outcomes were synthesised narratively. A simulation study investigated the clinical impact of using QAngio XA 3D/QFR. We developed a de novo decision-analytic model to estimate the cost-effectiveness of QAngio XA 3D/QFR and CAAS vFFR relative to invasive fractional flow reserve or invasive coronary angiography alone. Scenario analyses were undertaken to explore the robustness of the results to variation in the sources of data used to populate the model and alternative assumptions. Results: Thirty-nine studies (5440 patients) of QAngio XA 3D/QFR and three studies (500 patients) of CAAS vFFR were included. QAngio XA 3D/QFR had good diagnostic accuracy to predict functionally significant fractional flow reserve (≤ 0.80 cut-off point); contrast-flow quantitative flow ratio had a sensitivity of 85% (95% confidence interval 78% to 90%) and a specificity of 91% (95% confidence interval 85% to 95%). A total of 95% of quantitative flow ratio measurements were within 0.14 of the fractional flow reserve. Data on the diagnostic accuracy of CAAS vFFR were limited and a full meta-analysis was not feasible. There were very few data on clinical and implementation outcomes. The simulation found that quantitative flow ratio slightly increased the revascularisation rate when compared with fractional flow reserve, from 40.2% to 42.0%. Quantitative flow ratio and fractional flow reserve resulted in similar numbers of subsequent coronary events. The base-case cost-effectiveness results showed that the test strategy with the highest net benefit was invasive coronary angiography with confirmatory fractional flow reserve. The next best strategies were QAngio XA 3D/QFR and CAAS vFFR (without fractional flow reserve). However, the difference in net benefit between this best strategy and the next best was small, ranging from 0.007 to 0.012 quality-adjusted life-years (or equivalently £140–240) per patient diagnosed at a cost-effectiveness threshold of £20,000 per quality-adjusted life-year. Limitations: Diagnostic accuracy evidence on CAAS vFFR, and evidence on the clinical impact of QAngio XA 3D/QFR, were limited. Conclusions: Quantitative flow ratio as measured by QAngio XA 3D/QFR has good agreement and diagnostic accuracy compared with fractional flow reserve and is preferable to standard invasive coronary angiography alone. It appears to have very similar cost-effectiveness to fractional flow reserve and, therefore, pending further evidence on general clinical benefits and specific subgroups, could be a reasonable alternative. The clinical effectiveness and cost-effectiveness of CAAS vFFR are uncertain. Randomised controlled trial evidence evaluating the effect of quantitative flow ratio on clinical and patient-centred outcomes is needed. Future work: Studies are required to assess the diagnostic accuracy and clinical feasibility of CAAS vFFR. Large ongoing randomised trials will hopefully inform the clinical value of QAngio XA 3D/QFR. Study registration: This study is registered as PROSPERO CRD42019154575. Funding: This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 25, No. 56. See the NIHR Journals Library website for further project information

QALY gain and health care resource impacts of air pollution control: A Markov modelling approach

This paper proposes a novel complementary approach to evaluate the public health benefits of air pollution control, where the joint impact on individuals’ quality and length of life is fully quantified using Markov modelling. A Markov model which captures, for the first time: (i) air pollution's influence on population individuals’ quality of life and life expectancy at baseline and (ii) dynamics in individuals’ susceptibility to air pollution exposure, is developed. In order to represent the body of epidemiological evidence on the cardio-respiratory effects of long-term exposure to fine particulate air pollution, the model is structured around three diseases: chronic obstructive pulmonary disease, coronary heart disease and lung cancer. Application of the model provides the first estimates of age and gender-specific quality-adjusted life years (QALY) gains from air quality improvement in the UK. Reducing mean PM2.5 concentrations by 1 μg/m3 in London and in England and Wales is expected to yield more than 63,000 and 540,000 QALYs respectively, to adults aged 40 and above over their remaining lifetime, discounting at 3.5% p.a. At a WTP value for a QALY of £65,000, which is in line with recommendations for the UK, the expected discounted monetary benefit of the intervention amounts to £4 billion in London and £34 billion in England and Wales

Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions : A Bayesian Belief Network Application

Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development

Typology of nodes in BBN modeling, according to their modeling role and position in the network.

<p>Typology of nodes in BBN modeling, according to their modeling role and position in the network.</p

Mutually exclusive aquaculture and land management scenarios used in BBN modeling.

<p><i>Notes</i>:</p><p>- The last three scenarios combine intertidal land conversion with mangroves replanting and conservation and were designed to capture non-linearity in mangroves provision of ecosystem services <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Barbier1" target="_blank">[12]</a>.</p><p>- The “aquasilviculture” and “restore forest+closed-system” scenarios were associated with a 100% probability of “high” level of coastal protection, while “BAU” and “BMP” scenarios were associated with a 100% probability of “low” level of coastal protection.</p

Convention adopted to graphically represent each type of node in a Bayesian Belief Network.

<p>Convention adopted to graphically represent each type of node in a Bayesian Belief Network.</p

Locations of onsite surveys in Thailand.

<p>1: Surat Thani (Surat Thani Province) representing conventional shrimp farming. 2: Samroyiot (Prachap Kiri Khan Province) representing small-scale shrimp farming.</p

Aquaculture's main impacts on the environment, the economy and livelihoods and related user-conflicts.

<p>ES = Ecosystem Services. ^a Benefits should be distinguished from final ES since they are often a product of final ES and human inputs (e.g. fishing gear) and economic valuation of ecosystem should apply to ecosystem benefits only <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Fisher1" target="_blank">[13]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Boyd1" target="_blank">[54]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Fisher2" target="_blank">[55]</a>. Figure based on information from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Troell1" target="_blank">[40]</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Brander1" target="_blank">[8]</a>, relying on the ecosystem services classification by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Fisher1" target="_blank">[13]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075956#pone.0075956-Fisher2" target="_blank">[55]</a>.</p

Sensitivity analyses for the node “Locals' subsistence capacity.”

<p>Assessment of the predictive influence of the variables “quantity of wood for locals”, “quantity of fish for locals” and “local employment” on the posterior probability distribution of the node “locals' subsistence capacity”.</p