Abdolvahab Baghbanian

Resource Allocation and Economic Evaluation in Australia's Healthcare System

OBJECTIVE To explore dimensions and varieties of economic evaluations that healthcare decision-makers do or do not use. DESIGN Web-based survey. SETTING AND PARTICIPANTS A purposive sample of Australian healthcare decision-makers was recruited by direct invitation through email. All were invited to complete an online questionnaire derived from the EUROMET 2004 survey. RESULTS A total of 91 questionnaires were analysed. Almost all participants were involved in financial resource allocations. Most commonly, participants based their decisions on patient needs, effectiveness of interventions, cost of interventions or overall budgetary effect, and policy directives. Evidence from cost-effectiveness analysis was used by half of the participants. Timing, ethical issues and lack of knowledge about economic evaluation were the most significant barriers to the use of economic evaluations in resource allocation decisions. Most participants reported being moderately to very familiar with the cost-effectiveness analysis. There was a general impression that evidence from economic evaluations should play a larger role in the future. CONCLUSIONS Evidence from health economic evaluations may provide valuable information in some decisions; however, at present, it is not central to many decisions. The study suggests that, for economic evaluation to be helpful in real-life policy decisions, it has to be placed into context - a context which is complex, political and often resistant to voluntary change.

Association between social capital, health-related quality of life, and mental health: a structural-equation modeling approach

Aim To explore the association(s) between demographic factors, socioeconomic status (SES), social capital, health-related quality of life (HRQoL), and mental health among residents of Tehran, Iran. Methods The pooled data (n = 31 519) were extracted from a population-based survey Urban Health Equity Assessment and Response Tool-2 (Urban HEART-2) conducted in Tehran in 2011. Mental health, social capital, and HRQoL were assessed using the 28-item General Health Questionnaire (GHQ-28), social capital questionnaire, and Short-Form Health Survey (SF-12), respectively. The study used a multistage sampling method. Social capital, HRQoL, and SES were considered as latent variables. The association between these latent variables, demographic factors, and mental health was determined by structural-equation modeling (SEM). Results The mean age and mental health score were 44.48 ± 15.87 years and 23.33 ± 11.10 (range, 0-84), respectively. The prevalence of mental disorders was 41.76% (95% confidence interval 41.21-42.30). The SEM model showed that age was directly associated with social capital (P = 0.016) and mental health (P = 0.001). Sex was indirectly related to mental health through social capital (P = 0.018). SES, HRQoL, and social capital were associated both directly and indirectly with mental health status. Conclusion This study suggests that changes in social capital and SES can lead to positive changes in mental health status and that individual and contextual determinants influence HRQoL and mental health.