About this toolkit

We developed this toolkit to assist analysts and researchers with measuring and reporting on health inequalities, with a focus on stratifying health indicators. 

Watch Measuring Health Inequalities: An Introduction

The guidelines and resources in this toolkit are organized by phase to help you plan your analysis, analyze your data and report your findings. 

Throughout all phases, it is beneficial to engage stakeholders, partners and subject matter experts. This engagement will help ensure that the health inequalities you measure and report on are relevant, valid, acceptable and actionable to health system decision-makers and the communities* that they serve.

This toolkit may be updated as our work on measuring health inequalities evolves to include new information on data sources and standards, as well as analytical and reporting techniques. 

We would like to acknowledge those who provided input on this toolkit, including participants of the working group meetings held in March and April 2018. 

* The toolkit is not tailored to specific subpopulations in Canada, such as First Nations, Inuit and Métis communities. We acknowledge that subpopulations may be included as part of the measurement of health inequalities; therefore, we stress the importance of working in partnership with subgroup representatives throughout all phases of your work for a more complete understanding of health inequalities. 

Plan your analysis

A. Select relevant equity stratifiers

B. Explore approaches for accessing equity stratifiers

Consider these 3 approaches to access equity stratifier data for your analysis: 

  • Use equity stratifier data embedded within health databases.
  • Link health and equity stratifier data at the area level (e.g., hospital data linked to neighbourhood income level using patient postal codes).
    • Resource: Use the Equity Stratifier Inventory to see which embedded and area-level equity stratifiers are available within selected CIHI and Statistics Canada health data.
    • Resource: Area-Level Equity Stratifiers Using PCCF and PCCF+ tells you which equity stratifiers are available for analysis using Statistics Canada’s Postal CodeOM Conversion File (PCCF) and Postal CodeOM Conversion File Plus (PCCF+) tools.
  • Link health and equity stratifier data at the individual level (e.g., hospital data linked to census information such as household income or education level using personal identifiers).

By the end of the planning phase, you will have the information you need to complete this Analysis Plan Template.  

Analyze your data 

A. Carry out a stratified analysis 

  • Consider the desired direction of the health indicator you are analyzing. For example, higher rates are desirable for the indicator Has a Regular Health Care Provider.
  • Consider the impact of adjusting your health indicator for age, sex or other variables in the context of measuring health inequalities. Consider calculating crude and adjusted rates. 
  • Obtain your equity stratifier data
    • Resource: Depending on the approach you selected for accessing equity stratifiers, you may have to link health and equity stratifier data at the area level. Area-Level Equity Stratifiers Using PCCF and PCCF+ explains how to use these Statistics Canada tools to carry out area-level analysis based on postal code.
  • Categorize your data into population subgroups using the equity stratifier definition you specified in the planning phase. 
  • Calculate stratified indicator rates.
    • Resource: SAS Macros and Methodology Notes contains the formula and a CIHI SAS macro for calculating crude and age-standardized health indicator rates stratified by income quintiles or urban and rural/remote geographic location. 

B. Quantify inequalities using summary measures

  • Select the reference group for each of your equity stratifiers.
  • Use both absolute (difference-based) and relative (ratio-based) summary measures to quantify inequalities between groups. They provide a comprehensive description of health inequality that reduces reporting bias.
  • At a minimum, calculate simple measures of inequality, such as the rate ratio and rate difference. Consider calculating more complex inequality measures such as potential rate reduction and population impact number.
    • Resource: SAS Macros and Methodology Notes contains the formulas and CIHI SAS macro for calculating 4 summary measures of inequality: rate ratio, rate difference, potential rate reduction and population impact number.

Report your findings

A. Interpret results for key findings

  • Review the results of your summary measures alongside the underlying indicator rates to interpret the magnitude of inequality and the patterns across population subgroups. Consider statistical significance to help identify key findings.
  • Use a variety of data visualizations and dashboard to organize and explore your results. 
    • Resource: The Health Inequalities Interactive Tool provides examples of visualizing health indicator data using summary measures and indicator rates. There are different techniques for exploring data over time, and at the national and provincial geographic levels. 

B. Present findings to your audience

  • Review the literature and data to consider the context and impact of your findings. For example,
    • Describe the population subgroups (e.g., the percentage of the population in each subgroup, the mean income in each income quintile).
    • Describe the significance of your health indicator (e.g., the prevalence, financial and personal cost associated with the health indicator).
    • Describe the impact of your health inequality findings, such as the potential for health system improvement (e.g., report on the potential rate reduction and population impact number).
    • Identify opportunities, such as policies and programs, for addressing the inequalities suggested by your key findings. 
  • Tailor your key messages and visualizations to your audience by considering their level of expertise and how they will use the information.  

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