Objectives: The urban environment can have a significant impact on mental and physical health. Health impact appraisal of new developments should address these issues. However, transferable economic valuation evidence for urban planners in the United Kingdom is thin, especially around mental health, making it harder to estimate the cost-efficiency of public health interventions to address these conditions. A further complication is that mental health may be perceived differently from physical health. This study examines willingness to pay (WTP) to avoid depression and lower back pain.
Methods: WTP estimates were obtained by applying contingent valuation tasks in an online survey with a representative sample in the United Kingdom (N = 1553). Interval regression models were used to estimate the effects of disease severity, payment frequency, and respondent characteristics on WTP.
Results: Respondents' WTP to avoid both conditions was relatively high (around 5%-6% of stated income to return to current health state). Depression was rated as being twice as burdensome on quality of life than pain, and bids to avoid depression were 20% to 30% more than pain. Analysis of motivation responses suggests mental health treatment is perceived as less easy to access and less effective than the equivalent for pain, and respondents expect a larger burden on their family and relationships as they try to manage their condition themselves.
Conclusions: Results suggest that depression bids may be affected by uncertainty around access to effective treatment in the healthcare system. This has implications for how mental illness may be prioritized in resource allocation toward public health interventions.
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http://dx.doi.org/10.1016/j.jval.2024.06.009 | DOI Listing |
J Med Internet Res
January 2025
Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Background: Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process, called smart sensing, allows a fine-grained assessment of various features (eg, time spent at home based on the GPS sensor). Based on its prevalence and impact, depression is a promising target for smart sensing.
View Article and Find Full Text PDFJ Prim Care Community Health
January 2025
Instituto de Investigación Biomédica de Málaga, Málaga, Spain.
Aim: To investigate the detection and initial management of first psychotic episodes, as well as established schizophrenia, within the primary care of the Andalusian Health System.
Background: Delay in detecting and treating psychosis is associated with slower recovery, higher relapse risk, and poorer long-term outcomes. Often, psychotic episodes go unnoticed for years before a diagnosis is established.
Personal Disord
January 2025
Laboratoire sur les Interactions Cognition, Action, Émotion (LICAE), UFR STAPS, Universite Paris-Nanterre.
This study aimed to assess measurement invariance for the Five-Factor Inventory for (Oltmanns & Widiger, 2020) across nine national samples from four continents ( = 6,342), and to validate a French translation in seven French-speaking national samples. All were convenience samples of adults. Exploratory factor analyses supported a four-factor structure in the French-speaking Western samples (Belgium, Canada, France, and Switzerland) while a three-factor structure was preferred in the French-speaking African samples (Burkina Faso and Togo), and no adequate structure was found in the Indian sample.
View Article and Find Full Text PDFPersonal Disord
January 2025
Department of Psychological Science, Kent State University.
Antagonism is a personality domain located in most major trait models and is central to multiple personality disorders. This construct has been linked to many societally harmful externalizing behaviors (e.g.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!