OHESI releases new factsheet to mark World AIDS Day 2018

World AIDS Day provides an opportunity to remember those who lost their lives to HIV/AIDS, educate people about the impacts and prevention of HIV infection, celebrate our accomplishments, and support those in the continued fight against the pandemic. Today we release a new OHESI factsheet to commemorate World AIDS Day.

Download 2017 Testing and Diagnosis factsheet.

Over the course of the Ontario epidemic, HIV infections have declined and outcomes have improved, but new infections persist. Our continued effort is necessary to reduce the number of people affected by HIV and the disproportionate burden experienced by those at greatest risk (priority populations). The Ontario HIV Epidemiology and Surveillance Initiative (OHESI) plays a critical role by providing information that informs effective policies and programs in Ontario.

This factsheet includes an update for 2017 testing and diagnosis data. In summary, HIV testing in Ontario continues to increase with testing rates similar for males and females. There were 916 new diagnoses in Ontario; these diagnoses include a number of individuals originally diagnosed (and who likely became infected) elsewhere and later moved to Ontario and tested again. Excluding those previously diagnosed out of province, there were 797 new diagnoses. Regional breakdowns and breakdowns for males and females, by age and priority population, are included on the factsheet.

World AIDS Day brings us together to remember those who have passed on, to share knowledge, and to support the response to HIV/AIDS. We support this event with reinvigorated optimism and a strategy for the future.

Please join the OHESI mailing list to have access to the most up to date information on HIV/AIDS in Ontario.

OHESI releases new report presenting HIV indicators by public health unit

OHESI is pleased to announce the release of a new technical report: HIV in Ontario by public health unit: Testing, new diagnoses and care cascade.

Download the report on “HIV in Ontario by public health unit.”

Timely, relevant HIV epidemiological data are critical for public health units (PHUs) to monitor their local HIV epidemic, and to plan and evaluate local health promotion and prevention programs. PHU-level data is not only important for the health units themselves, but also for AIDS Service Organizations and other community-based organizations whose catchment areas may be better represented by these smaller geographic boundaries.

This newly released report is the first OHESI knowledge product to present HIV indicators at the PHU level. The data included in the report span the breadth of the HIV prevention, engagement and care cascade and include testing and diagnosis, as well as later care cascade indicators (i.e., the percent of diagnosed individuals who are in care, on antiretroviral treatment and virally suppressed). These indicators were derived from databases at the laboratory at Public Health Ontario.

A few key findings of the report include:

  • Diagnoses were distributed unevenly across Ontario and there were relatively small numbers in most PHUs. Between 2013 and 2017, the cumulative number of diagnoses ranged from 2,220 in Toronto to zero in Huron County, and there were fewer than 25 cumulative diagnoses in 21 PHUs.
  • Toronto and Middlesex-London had the highest diagnosis and positivity rates, followed by Ottawa, Hamilton and Windsor-Essex (not necessarily in that order).
  • While test rates were generally similar by sex in each PHU, diagnosis rates were higher for males than females in almost all PHUs. The overall diagnosis rate was four times higher for males than females.
  • PHUs with the largest numbers of diagnosed people living with HIV (for example, Toronto, Ottawa, Peel, Hamilton, Middlesex-London) generally ranked in the middle to lower end in terms of the measures of engagement in the HIV care cascade (i.e., the percent who were in care, on antiretroviral treatment or virally suppressed).

We hope you find the data in this report useful. Moving forward, OHESI aims to produce similar reports in the future.

OHESI releases factsheet summarizing Ontario’s HIV care cascade

This new knowledge exchange product is OHESI’s first factsheet and summarizes data from two previously published OHESI technical reports titled ‘HIV care cascade in Ontario‘ and ‘HIV care cascade in Ontario by sex, age and health region‘. These cascade data include the number of people who are living with diagnosed HIV in Ontario and the percent who are in care, on antiretroviral treatment and virally suppressed. Click here to check out the factsheet.

Stay tuned for additional reports and factsheets that include testing and diagnosis data up to 2017.

Refining HIV surveillance on new HIV diagnoses in Ontario

Summary

  • In Ontario, the number of new HIV diagnoses has increased in recent years. Challenges to interpreting diagnosis trends include:
    • The double-counting of individuals diagnosed through anonymous testing (duplicate diagnoses).
    • The inclusion of people who were diagnosed outside of the province and later moved to Ontario and tested again (‘out-of-province’ diagnoses).
  • In recent analyses led by OHESI, removing duplicates decreased the number of diagnoses in 2017 from 935 to 916. Also removing ‘out-of-province’ diagnoses further reduced this number to 797.
  • These analyses also suggest that the overall increase in new diagnoses between 2016 and 2017 was the result of an increase in ‘out-of-province’ diagnoses, rather than an increase in new HIV transmissions in the province.

Surveillance data on new HIV diagnoses are often used by front-line service providers and policy makers to inform their work. Information collected on diagnoses, such as sociodemographics and HIV risk factors, is commonly used to:

  • guide the planning and delivery of appropriate HIV care.
  • inform HIV prevention initiatives and evaluate their success (as new diagnoses are often used as an indirect way of measuring new infections).

This blog post describes the challenges of using Ontario surveillance data for the above purposes, OHESI’s recent work in refining these data and the impact of these refinements on recent trends.

Information collected during HIV testing in Ontario

Before discussing refinements to HIV surveillance, it is important to understand the HIV testing process and how information on newly diagnosed individuals is collected in Ontario.

When a person is tested for HIV in the province, the health care provider ordering the test fills out an HIV test requisition form. This form collects information on the person tested, including sex, date/year of birth, HIV risk factors and either the person’s name (nominal testing) or an anonymous code (anonymous testing).

When a person tests positive for HIV, Public Health Ontario sends a second form – the Laboratory Enhancement Program (LEP) questionnaire – to the health care provider who ordered the initial test. The purpose of this second form is to supplement the HIV test requisition and provide a more comprehensive understanding of who is being diagnosed with HIV in Ontario. Data collected on the LEP form includes some of the same information documented on the requisition form, as well as other information, such as race/ethnicity, country of birth and HIV testing history.[1]

Double-counting of diagnoses

In Ontario, a new diagnosis is defined as an individual’s first HIV-positive test result in the province. This means that if a person receives more than one HIV-positive test in Ontario (see Box below), only the first test is counted as a new diagnosis in order to avoid double-counting. Duplicate diagnoses are identified and removed when test information is entered into the laboratory surveillance databases at Public Health Ontario.

Why might a second HIV diagnostic test be conducted?

There are several possible reasons. For example, some physicians order an HIV test to confirm an individual’s diagnosis when the person first enters care.

Anonymous testing provides an important option for people concerned about privacy; however, it complicates the accurate collection of surveillance information and can result in an individual being counted twice in the data. With anonymous testing, duplicate diagnoses are difficult to identify due to the lack of identifying information collected on the person tested. For example, someone who initially received an HIV-positive diagnosis through anonymous testing, and later had a nominal HIV test when entering care, may be counted twice as a new diagnosis.[2] Individuals who receive more than one anonymous HIV-positive test may also be counted twice.

When people are double-counted, the number of new diagnoses included in Ontario surveillance reports is higher than the actual number of diagnoses.

‘Out-of-province’ diagnoses

Interpretation of diagnosis trends is also complicated by individuals who were initially diagnosed outside of Ontario and then moved to the province and tested for HIV again (for example, as part of the immigration process or when entering care). These ‘out-of-province’ diagnoses are counted as a new diagnosis in Ontario and their inclusion means that trends can be influenced by migration patterns to the province, in addition to other factors. This makes it difficult to interpret trends. For example, an increase in new diagnoses could be due to more HIV transmissions occurring in Ontario, more HIV-diagnosed individuals moving to and being re-tested in Ontario, or a combination of both.

Refining Ontario’s HIV surveillance data

OHESI (a collaboration between Public Health Ontario and the Ontario HIV Treatment Network, AIDS Bureau of the Ontario Ministry of Health and Long-Term Care, and Public Health Agency of Canada) is dedicated to providing the best possible provincial surveillance numbers for Ontario. By refining the new diagnosis data, we may be able to better estimate the actual number of people who receive a first time HIV-positive diagnosis in Ontario, as well as improve our ability to interpret trends.

Recently, OHESI conducted new analyses to determine how information collected on HIV testing history (on the LEP form) could help improve these data. This LEP-based information includes when and where an individual has previously tested positive for HIV.[3]

In these new analyses:

  • diagnoses with history of a previous HIV-positive test result within Ontario were removed in order to reduce double-counting (as these diagnoses are duplicates and would have already been counted as a new diagnosis with their first positive test in Ontario).
  • individuals with history of a previous HIV-positive test result outside of Ontario (‘out-of-province diagnoses) were removed in order to better assess trends in new HIV infections that occurred in the province (as these individuals were most likely infected with HIV outside of Ontario).

Note: Diagnoses with history of a previous HIV-positive test result are referred to as ‘previous positives’ in the remainder of this post.

What is the impact of removing previous positives?

Trends in the number of new diagnoses in Ontario between 2008 and 2017 are shown in the figure below, along with the impact of excluding previous positives.

 

Line graph showing new diagnoses with previous positives removed (over time)

The top line displays the trend in new diagnoses when the LEP is not used to remove any previous positives. This line shows a decrease in diagnoses in the earlier part of the past decade, followed by an increase between 2013 and 2017.

When the LEP form is used to address double-counting and remove duplicate diagnoses with history of a previous positive test result within Ontario (the middle line), the trend is identical but there are an average of 22 fewer diagnoses each year.

When ‘out-of-province’ diagnoses are also removed (the bottom line), the difference is more noticeable. In this scenario, the trend is identical until 2016 and then – instead of continuing to increase – the number of new diagnoses in Ontario decreases to 797 in 2017.

Taken together, these data suggest that the increase in new diagnoses between 2016 and 2017 (as observed in the top and middle lines) was the result of an increase in ‘out-of-province’ diagnoses, rather than an increase in new HIV transmissions in the province. Further, these data suggest that the number of new HIV infections occurring in Ontario in recent years may be closer to 800 than 900, and possibly lower (information on HIV testing history is missing for about half of diagnoses each year).[4]

Implications

Moving forward, OHESI will use information collected on HIV testing history to refine Ontario diagnosis data.

Future OHESI knowledge exchange products will exclude diagnoses with history of a previous positive within Ontario to reduce double-counting. Importantly, OHESI will continue to include ‘out-of-province’ diagnoses to provide an accurate picture of how many people and who require HIV care in the province. However, in separate tables and figures, OHESI will also exclude ‘out-of-province’ diagnoses in order to better understand trends in new HIV infections and guide HIV prevention priorities.

We hope these refinements will enhance the usefulness of surveillance data for people working in HIV.

Footnotes

  1. Race/ethnicity, country of birth and HIV testing history were added to the HIV test requisition in 2018.
  2. In Ontario, an average of 115 people are diagnosed with HIV through anonymous testing each year. However, it is unclear how many of these individuals also receive an additional anonymous and/or nominal HIV-positive diagnostic test and are double-counted.
  3. Information on HIV testing history has been collected on the LEP since the questionnaire was introduced in 1999, but is not available for every diagnosis. Approximately 50% of new HIV diagnoses have both 1) an LEP questionnaire returned, and 2) the HIV testing history section of the questionnaire completed.
  4. Additional caution is needed when using new HIV diagnoses as an indirect measure for new HIV infections. This is because many people are not diagnosed in the year they become infected with HIV. OHESI is currently working with mathematical modelers to better estimate the number of new HIV infections.