Health inequality monitoring foundations:  Health data disaggregation

Disaggregated health data show health indicator estimates by population subgroup, describing, for example, health intervention coverage across subgroups with different education levels or economic status. In this course, learners will examine how disaggregated health data are integral to the process of health inequality monitoring, and gain skills in assessing and reporting disaggregated data.

Photo credits: WHO / Blink Media - Mustafa Saeed

En mode autodidacte
Langue: English
Not disease specific

Informations sur le cours

Overview: Considerations related to health data disaggregation are present across the steps of health inequality monitoring, from selecting health indicators and dimensions of inequality to sourcing data, analyzing data, reporting results and developing evidence-informed approaches to promote health equity. An in-depth understanding of disaggregated health data and related issues is part of building and strengthening capacity for health inequality monitoring.

This course is an exploration of health data disaggregation, aiming to build both theoretical understanding and practical skills. Drawing on examples, the course begins by defining disaggregated data and demonstrating how they are relevant to health inequality monitoring. It closely examines the selection of health indicators and dimensions of inequality, and considerations for measuring categorizing dimensions of inequality. The course then addresses strategies and best practices for reporting disaggregated health data. The target audience is monitoring and evaluation officers, researchers and analysts, though the course is suitable for anyone with a general interest in health data and inequality monitoring.

This course is part of the Health Inequality Monitoring Foundations series, featuring the following courses: (1) Overview, (2) Data Sources, (3) Health Data Disaggregation, (4) Summary Measures of Health Inequality and (5) Reporting.

Course duration: Approximately 1.5 hours

Certificates: A Record of Achievement will be available to participants who score at least 80% of the total points available in the final assessment. Participants who receive a Record of Achievement can also download an Open Badge for this course. Click here to learn how.

Ce que vous apprendrez

  • How disaggregated health data are used across the steps of health inequality monitoring
  • Considerations for the selection of health indicators and dimensions of inequality
  • Approaches and considerations for measuring and categorizing dimensions of inequality
  • Characteristic patterns of inequality in disaggregated health data
  • Best practices for reporting disaggregated health data

Contenu du cours

  • Introduction:

    This module defines disaggregated data and describes how disaggregated data are relevant across the five steps of the health inequality monitoring cycle. It then introduces the forthcoming components of the course.
  • Module 1: Health indicators and dimensions of inequality:

    Module 1 addresses health indicators and dimensions of inequality. By the end of the module, you will: define health indicators, including proxy and composite indicators; discuss considerations for selecting and measuring health indicators; define and give examples of dimensions of inequality; discuss considerations for selecting relevant dimensions of inequality, and explain the concepts of double and multiple disaggregation.
  • Module 2: Measuring dimensions of inequality:

    Module 2 discusses considerations and strategies for the measurement of dimensions of inequality. By the end of the module, you will: provide examples of different criteria for measuring dimensions of inequality; discuss the use of individual, household and small-area units for measuring dimensions of inequality; discuss considerations for selecting direct or proxy measures of economic status, and understand the role of data availability in measuring dimensions of inequality.
  • Module 3: Categorizing dimensions of inequality:

    Module 3 covers approaches to categorizing dimensions of inequality. By the end of this module, you will: describe considerations for determining subgroup composition and number, including granularity and sample size; describe resolution issues related to the number of subgroups, and discuss contextual considerations for categorizing dimensions of inequality.
  • Module 4: Reporting disaggregated health data:

    Module 4 describes key considerations and strategies for reporting disaggregated health data. By the end of the module, you will: discuss considerations and best practices for reporting disaggregated health data; identify characteristic patterns of inequality across disaggregated data with ordered subgroups, and describe the use of text, tables, figures, maps and interactive visuals for reporting disaggregated data.
  • Final assessment

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Certificate Requirements

  • Obtenez un certificat de réussite en gagnant plus de 80% du nombre maximal de points pour la somme de toutes les tâches hebdomadaires.