Using Data to Identify Health Inequities: Examples from Community and Public Health Practices

1. Using Data to Identify Health Inequities: Examples from Community and Public HealthPractices Prepared by: Heidi Schaeffer and Kate Mulligan, Alliance for Healthier Communities

2. Public Health Equitable System Change Training Collaborative A Program Advisory Committee (PAC) of individuals from PHUs in different regions, other health and social system intermediaries, and experts in diversity, equity and training guide the program. http://phesc.ca/

3. Boards of Health Roles: Assess and Report Modify/Orient programs and services Engage in community and multi-sectoral collaboration Lead, support and participate with other to address policies Health Equity Standards Include 4 Requirements

4. Prerequisite for this webinar: Assessing and Reporting on Health Inequities Webinars Introduction to health equity Assessing and reporting on health inequities Approaches to developing equitable public health interventions and strategies Moving upstream: Working across sectors to decrease health inequities Policy development and advocacy to improve health equity Racial health equity: Embracing decolonial, anti-racist practice You are here http://phesc.ca/

5. Indigenous-Specific Health Equity

6. Learning Objectives • Briefly consider the importance and challenges with using data to understand health inequities; • Understand the Community Health Centre’s Disaggregated and Intersectional Analysis (DIA) framework; • Explore levels and types of data that are important to collect, analyze and use with a DIA framework to identify health inequities; • Learn about the ways that Public Health Units and Community Health Centres are currently using data to identify health inequities Front Line Staff Management Epidemiologists and Data Analysts Senior Leaders

7. A word about health equity “indicators” in this webinar Health equity indicators provide a way for Ontario local public health agencies to monitor their efforts to assess and improve health equity at the population, organization and program level. Health equity indicators in this webinar also include health status indicators and stratifiers, which are sets of quantitative data that provide information on the health status of individuals, groups or populations.

8. Intersectionality and “equity stratifiers” Intersectionality Introduced by legal scholar Kimberlé Crenshaw, intersectionality is a framework for understanding the ways that the multiple aspects of our identities intersect, influence one another, and compound to create unique experiences. The concept is regularly used to describe the ways that societal privilege and oppression is complicated by the different parts of our identity that are marginalized or privileged in society.

9. Why a webinar on the Disaggregated Intersectional Analysis (DIA) framework used by Community Health Centres?This webinar builds capacity for moving from having data and evidence to doing something with the data and evidence to advance health equity.

10. How do we look at data with a health equity lens? How do we see the bigger strategy and know what data is useful and how to use and/or collect it. How do we collect quality data? And understand the limitations/gaps in the data? For example, 30% of people don’t want to report their income How do we address what the data we have is telling us? Common challenges with using data

11. Examples from Community Health Centres: Essential Considerations for Using Data for Health Equity • To advance health equity, we first need to understand health inequities - in all their complexities and variations. • Specifically, we need to examine data on: • the nature, level, and types of health inequities (e.g. is the inequity at the level of access to health, quality and experience or population health and health program outcomes); • the causes of these health inequities (e.g. is inequity caused by barriers to the determinants of health ?); • how different groups are differentially and disproportionately impacted by these inequities.

12. Examples from Community Health Centres: Essential Considerations for Using Data for Health Equity • It is not necessary for everyone to have advanced analysis skills. • More important is having the right organizational commitment, capacity and culture and the right “analytical framework” to produce, make sense of, and use data related to health equity/inequity. • Epidemiologists and colleagues with analysis skills can support and conduct the analysis needed.

13. Examples from Community Health Centres: Essential Considerations for Using Data for Health Equity Community Health Centres use an analytical framework to ensure that they are: • Collecting or accessing the right type of data related to equity/inequity. • Collecting high quality data related to equity/inequity. • Appreciating and valuing this equity data. • Having the right language and tools to discuss, make sense of, and utilize this data. • Working with the organizational commitment to mobilize actions/solutions to overcome these inequities.

14. DIA - Disaggregated andIntersectional Analysis To understand and overcome health inequities the Disaggregated and Intersectional Analysis (DIA) framework, is used by community health centres. It is comprised of two interlinked steps: • Break down and pull apart “aggregate” (whole population level) data) into sub-populations or by different demographic indicators = “disaggregated data” • Explore how different demographics/ indicators link and intersect to produce multiple or varied inequities = “intersectional analysis” Intersectionality Class gender Race

15. DIA - Disaggregated and Intersectional Analysis Indicators of social demography, social and economic status and social environment are equity stratifiers. They are needed for doing DIA and can include: • Socio-cultural backgrounds (things that you cannot really change, e.g. gender, race/ethnicity, country of birth, sexual orientation, birthplace, language. • Structural/economic determinants (can be changed/improved), e.g. income/class, education level, employment status, official language fluency, and social isolation.

16. DIA - Disaggregated and Intersectional Analysis Indicators in community health for doing DIA can also include: 3. Organizational processes and practices within healthcare institutions, e.g. client eligibility criteria, hours of service, whether and what language interpretation services are offered, diversity of staff. 4. Health issues as determinants - having certain health issues (for eg. disability, mental health issue, or chronic health condition) may in turn be risk factor for other health issues or may be a barrier to healthcare access.

17. DIA - Disaggregated and Intersectional Analysis Indicators of focus may vary by type of health inequity For example, when looking at healthcare access the key determinants vary based on what kind of access is being discussed. • Certain people have no OHIP coverage: Those with precarious immigration status, those newly arrived (3 month wait OHIP), and those who are homeless. • Certain healthcare is not covered by OHIP, such as dental care, vision and drug coverage: Those with precarious employment with no extended health benefits, age (18-65), those not eligible for social assistance, land those on ow incomes. • Certain people have OHIP coverage and experience inequity in healthcare access: Based on language barriers race, low, income, sexual orientation and disability.

18. The Three Interconnected Levels of Health Equity Data Population Level Organizational Level Program & Service Level

19. Population Level Data Population-level data: • Can be used to identify which priority sub-groups to target; • Can be used to understand types of inequities faced by your clients/communities compared to the average population; this can help you decide which programs/services to focus on • Can be used to identify policy and advocacy, and building partnerships or coalitions for collective system level solutions Examples: • Food security • Affordable housing • Transportation Specific strategies on Decrease Develop structures

20. Using Disaggregated Data with an Intersectional Approach – Women’s Health in Women’s Hands Community Health Centre Collect quality data & define indicator of interest HIV Diagnosis Disaggregate demographic data Start A Stratify indicator of interest against demographic characteristics B C Design, implement and evaluate strategies D F E Do intersectional analysis Investigate and identify root causes of health inequities

21. New HIV diagnoses, total, Ontario, 2006-2015 ⬇

22. New HIV Diagnoses by Gender, Ontario, 2006-2015 ⬇

23. Percent of HIV diagnoses by Gender and Race, ACB, Ontario, 2009-2015 ⬇

24. Organizational Level Data • Organizational level data • Used to align with Vision, Mission, Values and Strategic Priorities of the organization • Meso level data is essential to shift the Organizational Culture Examples of Organizational Data Sources: • Employment equity survey, Staff Inclusion and Diversity Statistics, Training in Anti-oppression, Anti-racism, Cultural Humility/Cultural Safety • Board Inclusion and Diversity Specific strategies on multiple DOH

25. Evidence-based, pilot-tested indicators to support Ontario local public health agencies to monitor their efforts to assess and improve health equity as an organization. • Is there a human resource strategy in place to consider the workforce diversity (e.g. by age, gender, race/ethnicity, disability, Indigenous/Aboriginal identity) within the public health agency? • If yes, describe how the distribution compares to the overall population diversity of your geographic catchment?

26. Toronto Public Health Organizational Level Health Equity Data Examples Toronto Public Health has a commitment to collecting data to monitor: • Building a Diverse Workforce (Monitoring staff and board diversity); • Enabling Staff Learning for Health Equity (Performance appraisal and monitoring staff and board training in cultural safety, anti-oppression and anti-racism).

27. Stop for Reflection on Organizational Level Data Organizational Equity: To what extent are the staff reflective of populations they serve? Organizational Structures: To what extent are there dedicated staff that promote, lead or address health equity objectives? Organizational Culture: To what extent have staff trained on health equity in the past three years?

28. ProgramLevel Data • Program/Service/Community Member Data; Health Outcomes • Used to inform programming and service planning and delivery • Essential for adopting a culture of quality improvement Examples: • (Access) Waiting days to access chronic disease programs. • (Quality/Experience) Person feeling comfortable and welcome. • (Outcomes) Chronic disease, immunization rates and food security

29. Person-Centered Approaches to Understanding Early Family Risk Healthy Babies Healthy Children (HBHC)

30. Healthy Babies Healthy Children (HBHC)Person-Centred Approach • Person-centered approaches focus on the relations among individuals rather than relations among variables and are helpful to identify families with similar patterns of multiple risk factors • Based on 11 indicators derived from the HBHC Screen, 5 clusters of participants were found in the HBHC program. Two groups had socio-demographic related risks, with one group having more substance use. Two groups had more medical related risks. One group was classified as no low risk.

31. Applying the DIA Framework to Healthy Babies Healthy Children Example First Step:Disaggregate data by the specific demographic indicators /determinants (and/or groups or subgroups) that are of interest. With Healthy Babies Healthy Children example the related sociodemographic risk factors included poverty, family income, marital status, and education level. Second Step:Look at Healthy Babies and Healthy Children participant intersections and links between demographic indicators/determinants from an equity-informed perspective to further disaggregate data based on additional indicators such as race, ethnicity or neighbourhood.

32. Closing Reflection: Using DIA to Understand Inequities • Thinking about a program and the primary client groups served, which are two or three indicators that would be most appropriate to consider using in a disaggregated intersectional analysis plan? I.e. that might best reveal inequities? E.g. income, racial/ethnic group, gender. If you don’t have the data what can be done with the data you have? The Three Interconnected Levels of Health Equity Data: Population Level Organizational Level Program & Service Level

33. Community Health Centre Tools and Templates for Equity-Focused Planning and Evaluation Health Equity Domains Handbook Organizational Health Equity Capacity Profile Health Equity Project Profile for CHCs Equity focused Planning Informed Consent Form Risk Assessment and Mitigation Plan Equity Informed Evaluation Plan Equity Informed Project Charter

34. PHESC.CA Tools and Resources • Epidemiology resources • National Collaborating Centre on the Determinants of Health resources • Practice resources for the Health Equity Standard; Effective Public Health Practice Standard; and Population Health Standard • Community Health Centre equity-focused planning and evaluation resources

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