Health equity means that everybody in society can be the healthiest they can be, and that people are not disadvantaged from being at their healthiest because of their race, ethnicity, religion, gender, social class, income level, or other socially determined situation.  A health inequity exists when a certain social group experiences a lower level of health than the most advantaged social group.  Health inequities are also known as health gaps. They are systematic and considered to be unfair and avoidable. The term health gap is used throughout this interactive dashboard. Sometimes gaps in health are not considered unjust and are referred to as health inequalities. However, we have decided to use the term health inequities here so that this perspective does not get lost in interpretation of results.

The Shared Health Equity Dashboard (SHED) currently contains multiple dashboards related to a variety of topics. One dashboard contains indicators related to emergency department (ED) visits, hospitalizations, and premature mortality; another infectious diseases and a third self-reported measures. Additional dashboards with other indicators remain in development. Within each interactive dashboard there are multiple topics. For example, the self-reported measures dashboard include the topics Body Mass Index, vegetable and fruit consumption, and physical activity.  Each dashboard also consists of multiple pages.

Each page has one or more interactive graphs. These graphs are best viewed in Chrome, Firefox or Edge. Not all features will work in Internet Explorer. Hovering over data points will show information about them. For health gap measures, this includes the interpretation. Hovering over the graph titles will bring up brief definitions of the terms.

Click here for demos on how to use the dashboard.

The dashboard provides indicators that measure how often health-related outcomes occur in groups of people (incidence or prevalence). In the dashboard, groups of people are defined by social characteristics such as sex, income, education, immigration status, etc. These are called stratifiers. Measures of the difference or gap in how often the health-related outcome occurs between two groups are also provided in the dashboard.

There are several ways to measure health gaps. The rate ratio (RR) and rate difference (RD) are two ways that compare how often health-related outcomes occur in two groups of people. These are the health gap measures used in this dashboard. The RR measures the relative size of the health gap– it shows us how many times higher or lower the rate of an outcome is in a particular social group (a group with the same social characteristic, such as living in low income) compared to a reference group. The RD is a measure of the absolute size of the health gap. It shows the potential public health burden of being in a particular social group – the number of cases of the outcome that could potentially be avoided or gained if a particular social group had the same rate as the reference group.

The RR and RD show different types of information about health gaps. Both should be used when measuring health gaps. A detailed explanation of how to interpret RRs and RDs can be found here.  A video further explaining RRs and RDs and why they are both important can be found  here.

Click here for definitions of the terms used in the dashboard.

KFL&A Public Health (KFL&A), Hastings Prince Edward Public Health (HPEPH), Leeds Grenville & and Lanark District Health Unit (LGLDHU) have come together to develop an interactive dashboard that measures health gaps in the region. The aim is to identify areas where the public health, health care, and community partners can work together to reduce these gaps.

Building and maintaining the SHED is a dynamic process where we are constantly asking ourselves and our partners:

• What do these results mean? What do they mean for my community, for my clients or target population?
• Where are there gaps in the data? What are we missing? Who might not be represented? What other forms of data are needed to explain what we see in the dashboard?
• Can we collect/provide data to address these gaps?
• Where might the data not be applicable or not accurate for us or the groups we serve?
• Is this a priority problem for us, for others, for the groups we serve?
• What are ways to best translate these results for different audiences?
• What can we do with these results? Can the results be used to minimize health inequities? Is sufficient action being taken once results are available?

We also recognize that while quantitative data is critical to making informed decision about public health across our communities, it is just one of many ways of knowing and are working with our partners to ensure ways of knowing are used as we plan for and deliver public health. Continually evaluating SHED means that we always welcome your feedback to these questions and on other aspects of SHED. This reflective process enables us to continually engage with and strive to meet the needs of users, and advocate for changes in data collection and management, while operating within the limitations of currently available data sources. To provide feedback on SHED, please complete the form at the bottom of this page.

Data Sources: Health Administration Databases (the National Ambulatory Care Reporting System, Discharge Abstract Database and Vital Statistics (IntelliHEALTH Ontario), integrated Public Health Information System and Case and Contact Management System) available from the Ontario Ministry of Health (2013-2020). Canadian Community Health Survey Share Files, Statistics Canada, available from the Ontario Ministry of Health and Long-Term Care (2013/2014, 2015/2016, 2017/18, 2019/2020).

Regions: In the dashboards, five regions are used where available: Ontario, Hastings Prince Edward Public Health (HPEPH), Kingston, Frontenac, and Lennox & Addington Public Health (KFL&A), Leeds, Grenville and Lanark District Health Unit (LGLDHU) and the southeastern region, defined as the combined region of the three health units (HPEPH, KFL&A, and LGLDHU).

Rounding: In the dashboard, all rates and RDs are reported to one decimal place, while rate ratios (RRs) are reported to two decimal places. The calculations for RRs and RDs (from the rates) are calculated before rounding and so may appear to be one digit different.

Postal codes and neighbourhood level estimates:

• Quality of postal code data may vary over time, by region, and by data source.
• Neighbourhood-level equity stratifiers like deprivation may underestimate inequities compared to individual-level measures; however, they are the best alternative when individual socioeconomic data is unavailable, as is the case with most health administrative data. These measures may also reflect underlying area-based socioeconomic constructs in their own right, which are separate from individual-level constructs.
• Incidence estimates from health administrative data that use postal code-derived equity stratifiers should not be considered representative of the population for several reasons:
  • denominators are from the 2016 census, not yearly estimates or projections,
  • cases without valid postal codes are excluded,
  • health-related outcomes only include dissemination areas (DAs) with valid deprivation index (DI) scores.  This effectively takes out cases that live in neighbourhoods with a high number of institutions, and also decreases the overall regional denominator – in KFL&A for example, 15 DAs are excluded, so the overall 2016 denominator is lower (about 4%) than it would be compared to when all DAs are included,
  • rounding error. Population counts by DA and then by age and sex can become small. To protect privacy and confidentiality, Statistics Canada has rounded DA total, age, and sex counts to within 5 of the actual value.

Please refer to the SHED Technical Notes for more information on methodology. Click here to download.