Assessing the impact of energy and fuel poverty on health: a European scoping review.

BACKGROUND: The burden of energy and fuel poverty (EFP) in Europe is increasing in the face of the cost-of-living crisis, the Russian invasion of Ukraine, the coronavirus disease 2019 (COVID-19) pandemic and the climate emergency. While the health impacts of EFP are often the driving reason for addressing it, EFP's association with health is poorly delineated. This review aims to scope the evidence of EFP's association with health in Europe. METHODS: A scoping review based on Arksey and O'Malley's framework was conducted using search terms relevant to EFP, health and Europe. Five databases were searched, in addition to hand searching. Review selection was performed by two independent reviewers, and articles were thematically analyzed. RESULTS: Thirty-five articles published between January 2000 and March 2022 were included. The literature varied in definitions and measurements of EFP and in the health indicators examined. The review revealed a negative association between EFP and health, specifically, general unspecified poor health (9 articles), excess winter mortality (3 articles), communicable diseases (3 articles), non-communicable diseases (11 articles), mental health (15 articles) and well-being (12 articles). While women were reported to be at a higher risk of EFP than men, children and older adults were identified as particularly vulnerable to EFP's adverse health repercussions. CONCLUSIONS: This scoping review illustrates a significant and complex association between EFP and various domains of health. Though heterogeneity across research makes it difficult to compare findings, our review supports the use of health as a justification to address EFP and urges public health to be more involved in EFP mitigation.

Doctor decision making with time inconsistent patients.

Non-adherence to treatments is prevalent. The aim of this paper is to model how doctors should adapt their medical treatment decisions if non-adherence is due to present-bias in the patient population, and to test the predictions of this model in a lab experiment. Under certain conditions, a rational doctor should adapt to non-adherence by choosing a treatment all patients complete (though less effective) when the probability of a patient being present-biased is sufficiently large. This is explored in a lab experiment where we test whether students in the doctor role adapt their behaviour as they learn about the distribution of non-adherence (due to present bias) in the patient population over the rounds of the experiment. We test the model prediction when we align individual incentives with the goal of maximising overall patient welfare. The results show that, on average, participants adapt to non-adherence as they learn about the probability of non-adherence (due to present-bias). However, a proportion of participants do not adapt to the optimal choice. The rate of adaptation was similar for the first 5 rounds under both individual incentives and salary. However, participants continued to adapt after round 5 under individual incentives whilst adaptation plateaued under salary. The adaptation to non-adherence may indicate that adherence can be improved by providing doctors with information about the probability of non-adherence (due to present-bias) in their patients.

Identifying Priorities, Targets, and Actions for the Long-term Social and Ecological Management of Invasive Non-Native Species.

Formulating effective management plans for addressing the impacts of invasive non-native species (INNS) requires the definition of clear priorities and tangible targets, and the recognition of the plurality of societal values assigned to these species. These tasks require a multi-disciplinary approach and the involvement of stakeholders. Here, we describe procedures to integrate multiple sources of information to formulate management priorities, targets, and high-level actions for the management of INNS. We follow five good-practice criteria: justified, evidence-informed, actionable, quantifiable, and flexible. We used expert knowledge methods to compile 17 lists of ecological, social, and economic impacts of lodgepole pines (Pinus contorta) and American mink (Neovison vison) in Chile and Argentina, the privet (Ligustrum lucidum) in Argentina, the yellow-jacket wasp (Vespula germanica) in Chile, and grasses (Urochloa brizantha and Urochloa decumbens) in Brazil. INNS plants caused a greater number of impacts than INNS animals, although more socio-economic impacts were listed for INNS animals than for plants. These impacts were ranked according to their magnitude and level of confidence on the information used for the ranking to prioritise impacts and assign them one of four high-level actions-do nothing, monitor, research, and immediate active management. We showed that it is possible to formulate management priorities, targets, and high-level actions for a variety of INNS and with variable levels of available information. This is vital in a world where the problems caused by INNS continue to increase, and there is a parallel growth in the implementation of management plans to deal with them.

A comparison of professional and private time preferences of General Practitioners.

In a perfect agency relationship, doctors consider all information and select the patient's 'utility maximising' option given the patient's preferences. The patient's time preferences are important as treatments vary in the timing and length of their benefits. However, doctors often do not have full information on patients' preferences and may apply their own preferences. This has generated empirical interest in estimating doctors' time preferences. However, these studies generally elicit doctors' private preferences (preferences for their own health) rather than professional preferences (preferences for the patient). We hypothesise that private and professional preferences may differ. Professional time preferences may be 'taught' in medical school or learned through repeated interactions with patients. If preferences differ then estimates of doctors' private preferences are less informative for medical decision-making. This study compares private and professional time preferences for health in a national sample of General Practitioners, using a between sample design. Time discounting is explored using exponential and quasi-hyperbolic models. We elicit time preferences using multiple price lists. We find no significant difference between the time preference for the self or the patient. This result holds for axiomatic discounting classification and maximum likelihood estimates. We do not find evidence of present-bias. There are a high proportion of increasingly impatient GPs, potentially implying a maximum 'willingness to wait' for treatment benefits. GPs value the health state differently between themselves or for a patient. These results suggest that we can use estimates of private preferences from doctors to inform medical decision-making.

Decision heuristic or preference? Attribute non-attendance in discrete choice problems.

This paper investigates if respondents' choice to not consider all characteristics of a multiattribute health service may represent preferences. Over the last decade, an increasing number of studies account for attribute non-attendance (ANA) when using discrete choice experiments to elicit individuals' preferences. Most studies assume such behaviour is a heuristic and therefore uninformative. This assumption may result in misleading welfare estimates if ANA reflects preferences. This is the first paper to assess if ANA is a heuristic or genuine preference without relying on respondents' self-stated motivation and the first study to explore this question within a health context. Based on findings from cognitive psychology, we expect that familiar respondents are less likely to use a decision heuristic to simplify choices than unfamiliar respondents. We employ a latent class model of discrete choice experiment data concerned with National Health Service managers' preferences for support services that assist with performance concerns. We present quantitative and qualitative evidence that in our study ANA mostly represents preferences. We also show that wrong assumptions about ANA result in inadequate welfare measures that can result in suboptimal policy advice. Future research should proceed with caution when assuming that ANA is a heuristic.

Bayesian and classical estimation of mixed logit: An application to genetic testing.

Discrete choice experiments (DCEs) in health economics have recently used the mixed logit (MXL) model to incorporate preference heterogeneity. These studies typically use a classical approach to estimation or have specified normal distributions for the attributes. Specifying normal distributions can lead to erroneous interpretation; non-normal distributions may cause problems with convergence to the global maximum of the simulated log-likelihood function. Hierarchical Bayes (HB) of MXL is an alternative estimation approach that may alleviate problems of convergence. We investigated Bayesian and classical approaches to MXL estimation using a DCE that elicited preferences for a genetic technology. The classical approach produced unrealistic results in one of the econometric specifications, which led to an erroneous willingness to pay estimate. The HB procedure produced reasonable results for both specifications and helped ascertain that the classical procedures were converging at a local maximum.