Health equity and the post COVID‑19 condition
After catching COVID-19, some people do not fully recover, but continue to experience symptoms for weeks or months. So far there is not much reliable evidence, but this post-COVID-19 condition, also called “long COVID” or “long-haul COVID,” may affect up to ten percent of COVID patients. As with COVID‑19 itself, some people seem to be more affected than others, but it is not yet clear what social factors (such as sex, age, ethnicity, and income) might be involved. Another area where evidence is lacking is how other health conditions, such as diabetes, asthma or depression, may interact with COVID‑19 to worsen or even prevent long COVID. Ontario collects data on health care use that can be analysed to produce evidence about long COVID. However, doing these kinds of analyses, where there are a lot of variables involved, is complicated and requires some special methods. Artificial intelligence is well suited to this kind of research, because it is very good at classifying and categorizing data with a lot of different features. Our research project will use artificial intelligence to look at the health care use of people in Ontario who have been diagnosed with COVID-19, to see what kind of relationship there is between social factors, existing health conditions, and long COVID. We will engage with community groups and patient groups to help us interpret our findings, and post them in visual form on a publicly available website. Our findings will give policymakers and healthcare decision-makers evidence they need to come up with interventions that are better at preventing and managing COVID-19.
