Multilevel Analyses of Individual Heterogeneity in Public health: questioning past evidence with new conceptual and methodological approaches

  • Merlo, Juan (Principal Investigator)
  • Leckie, George B (Principal Investigator)
  • Mulinari, Shai (Co-Investigator)
  • Subramanian, SV (Co-Investigator)
  • Austin, Peter C. (Principal Investigator)

Project Details

Description

A growing number of “average” socioeconomic, ethnic and geographic differences in health overwhelms Public Health. However, the importance of these average disparities needs be critically questioned, based on their capacity to disentangle sick and healthy individuals. If this capacity of is weak, promotion of population-level interventions based only on differences between group averages may lead to ineffective interventions and stigmatization of groups of individuals. Against this background, we are developing an innovative conceptual framework denominated Multilevel Analyses of Individual Heterogeneity (MAIH). In MAIH, population/group health is not properly evaluated by studying differences between group averages alone but rather, by quantifying the share of the individual differences in risk (i.e., variance) that exists at the group level. Joining theoretical, methodological and didactical interests, we aim to integrate MAIH in Public Health. Our project will revisit established epidemiological knowledge and suggest improved concepts and analytical tools for decision-making. Having access to surveys, cohort studies and large Swedish databases with rich information across the life course, we will study a variety of contexts and health outcomes of strong significance for the population. We will identify socioeconomic, geographical and biomedical categorizations that are able to discriminate with accuracy sick from healthy individuals across the life course.
StatusFinished
Effective start/end date1/01/1831/12/21

Research Groups and Themes

  • SoE Centre for Multilevel Modelling

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.