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From Averages to Heterogeneity : A Plain-Language Guide to MAIHDA in Epidemiology

Merlo, Juan LU orcid ; Kaufman, Jay S and Leckie, George LU (2026) In International journal of social determinants of health and health services
Abstract

Many studies compare health averages between groups such as neighbourhoods or social categories. Averages are simple but can be misleading, since individuals within the same group often differ widely. We present MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) as a general framework to study how context structures health differences. MAIHDA is not a new statistical model but a way to reorganize standard multilevel analysis to look beyond averages. It integrates three perspectives: (1) Specific Contextual Effects (mean differences between groups), (2) General Contextual Effects (how strongly outcomes cluster within groups, eg the variance partition coefficient), and (3) Discriminatory Accuracy (how... (More)

Many studies compare health averages between groups such as neighbourhoods or social categories. Averages are simple but can be misleading, since individuals within the same group often differ widely. We present MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) as a general framework to study how context structures health differences. MAIHDA is not a new statistical model but a way to reorganize standard multilevel analysis to look beyond averages. It integrates three perspectives: (1) Specific Contextual Effects (mean differences between groups), (2) General Contextual Effects (how strongly outcomes cluster within groups, eg the variance partition coefficient), and (3) Discriminatory Accuracy (how well group membership classifies individuals according to the outcome). Interpreting these dimensions together shows to which degree a context shapes outcome and whether interventions should be universal or targeted. Although intersectional studies have recently popularized MAIHDA, the framework predates its intersectional applications. It was first developed within contextual epidemiology to study geographical and institutional settings, and later extended to intersectionality and multicategorical analyses, which added visibility. By shifting attention from averages to heterogeneity and clustering, MAIHDA helps avoid group stigmatization and guides equitable strategies such as proportionate universalism. It offers a practical, theory-agnostic way to understand how contexts structure individual inequalities.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
International journal of social determinants of health and health services
article number
27551938261423042
publisher
SAGE Publications
external identifiers
  • pmid:41734011
  • scopus:105030995431
ISSN
2755-1938
DOI
10.1177/27551938261423042
language
English
LU publication?
yes
id
56a9030c-b940-4d22-9d8b-6ae21f7296a0
date added to LUP
2026-03-02 10:43:27
date last changed
2026-03-17 05:41:34
@article{56a9030c-b940-4d22-9d8b-6ae21f7296a0,
  abstract     = {{<p>Many studies compare health averages between groups such as neighbourhoods or social categories. Averages are simple but can be misleading, since individuals within the same group often differ widely. We present MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) as a general framework to study how context structures health differences. MAIHDA is not a new statistical model but a way to reorganize standard multilevel analysis to look beyond averages. It integrates three perspectives: (1) Specific Contextual Effects (mean differences between groups), (2) General Contextual Effects (how strongly outcomes cluster within groups, eg the variance partition coefficient), and (3) Discriminatory Accuracy (how well group membership classifies individuals according to the outcome). Interpreting these dimensions together shows to which degree a context shapes outcome and whether interventions should be universal or targeted. Although intersectional studies have recently popularized MAIHDA, the framework predates its intersectional applications. It was first developed within contextual epidemiology to study geographical and institutional settings, and later extended to intersectionality and multicategorical analyses, which added visibility. By shifting attention from averages to heterogeneity and clustering, MAIHDA helps avoid group stigmatization and guides equitable strategies such as proportionate universalism. It offers a practical, theory-agnostic way to understand how contexts structure individual inequalities.</p>}},
  author       = {{Merlo, Juan and Kaufman, Jay S and Leckie, George}},
  issn         = {{2755-1938}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{SAGE Publications}},
  series       = {{International journal of social determinants of health and health services}},
  title        = {{From Averages to Heterogeneity : A Plain-Language Guide to MAIHDA in Epidemiology}},
  url          = {{http://dx.doi.org/10.1177/27551938261423042}},
  doi          = {{10.1177/27551938261423042}},
  year         = {{2026}},
}