Author’s information (optional)
Url Link
The hyperlink to my paper’s website.
Methods
We extracted all ED admissions for MBD from the National Ambulatory Care Reporting System (NACRS) database administered by CIHI that occurred between March 1st, 2004 and December 31st, 2020 in 5 health regions in the province of Alberta and 35 health regions in the province of Ontario (Gibson et al., 2008). We included ED visits regardless if these resulted or not in subsequent hospital admission.
The researchers collected all emergency department visits related to mental and behavioural disorders from a national Canadian health database. These records covered patients from 5 health regions in Alberta and 35 health regions in Ontario from March 2004 to December 2020, including visits that did not lead to hospital admission.
Introduction
Ambient extreme temperatures have been found to be significantly associated with total and cause-specific morbidities (Ye et al., 2012; Xu et al., 2020). A growing number of studies have also reported associations between extreme temperatures and the psychotic exacerbation of core symptoms for many mental and behavior disorders (MBDs).
Extreme hot and cold weather is strongly linked to overall illness as well as specific types of health conditions. Many studies have also shown that extreme temperatures can worsen psychotic symptoms and core symptoms of various mental and behavioural disorders.
Results
In total, there were 9,958,759 ED visits for MBDs in Alberta and Ontario between March 1st, 2004 and December 31st, 2020. Most cases were observed in Ontario, were 30-49 years of age (33.8%) and were females (53.2%).
From March 2004 to December 2020, almost 10 million people went to the emergency department in Alberta and Ontario for mental and behavioural disorders. Most of the patients were from Ontario, were between 30 and 49 years old, and more than half of them were female.
Discussion
We also found that the associations between extreme heat and MBD ED visits were stronger among those aged 30-49 years and among those with the highest level of neighborhood deprivation. Residential exposure to increasing tree canopy coverage appeared to play a beneficial role in mitigating the effect of exposure to extreme heat while higher daily levels of NO2 and O3 appeared to enhance the risk of heat on MBD ED visits.
The relationship between extreme heat and mental health emergency visits was stronger among people aged 30 to 49 and among those living in highly deprived neighbourhoods. Living near more tree canopy helped reduce the impact of heat, while higher levels of air pollution such as NO2 and O3 increased the risk of heat-related mental health emergency visits.
Future Directions
Future research could build on this study by expanding data collection beyond Alberta and Ontario. Although these two provinces were selected because they have a well-established and mandatory emergency department reporting system, future studies could explore alternative national data sources or develop new methods to include other provinces and territories.
Difficult Material
I found the statistical analysis to be the most challenging part to fully understand. In particular, I had difficulty fully understanding how the distributed lag non-linear models (DLMNs) measured the delayed effects of temperature over time and how to interpret the odd ratios (ORs).
URL link:
· Paste the hyperlink to the website containing the original posting about the paper – NOT to the scientific paper itself (either the public posting webpage, or the Moodle forum webpage):
https://www.sciencedirect.com/science/article/pii/S001393512202326X
Additional Translation:
· From which section of the paper is this passage?
Methods: Environmental and Contextual Factors
· Paste quoted text on the next line. Do not include quotation marks or a bullet mark:
Health-region level information were captured in order to reduce and explain levels of heteregoneity that may exist across health regions. These were subsequently used in a meta-regression model. These factors, derived from census data, included information on population density, percent of the population in the health region with income less than the low-income cutoff (LICO) in Canada, percent of the population in the health region self-identified as Black and percent of the population in the health region living in an urban area.
· Write your translation on the next line:
Information was gathered from the various health authorities that were examined as part of the study, and an advanced statistical technique was applied to the data that looked at how diversity factors (like population density or declared income of a population) in different regions have more of an effect on influencing mental health and behaviour when exposed to short-term variations in temperature.
Additional Future Directions:
· What future research do you think should follow up on this work?
This research could also look at whether access to appropriate artificial weather control measures, like access to air conditioning in the summer or heated buildings in the winter, has a mitigating effect on MDB ER visit numbers compared to access to high tree canopy coverage levels. The access to tree canopy coverage areas may be a confounding factor, so it’s important to compare against an alternative factor that can also massively affect deprived neighbourhoods with large populations at a low socioeconomic level.
Difficult Material (from original poster or subsequent student):
· What did the previous poster state was difficult to understand? (please copy and paste their statement here):
I found the statistical analysis to be the most challenging part to fully understand. In particular, I had difficulty fully understanding how the distributed lag non-linear models (DLMNs) measured the delayed effects of temperature over time and how to interpret the odd ratios (ORs).
· Please try to explain the difficult materials to the original poster, as best as you can. (This is where you can help them understand what they found difficult.)
This study analyzed data having to do with extreme temperature events in the different health areas. A distributed lag non-linear model is a statistical tool that comes from the understanding that things like temperature do not necessarily have an immediate effect. Thus, a 5-day buffer was added so the researchers would be sure of capturing mental health ER visits that stemmed from the extreme temperature event. An odd ratio looks to measure the association between an exposure (extreme temperature event) and an outcome (mental health related ER visit). If odds are greater than 1, there is an increased chance of an outcome happening, while lesser than 1 is a decreased chance of the outcome occurring. A higher odd ratio indicates a stronger association between temperature events and metal health issues.
New Difficult Material (according to you):
· What did you not understand about this paper, that someone else can help with? If you understood everything, then what did you find most challenging to understand?
It took a few read throughs to make sense of how the collected data was analyzed. A lot of references to mega-regressive models and statistical analyses like Cochran’s Q-test and I^2 statistic which made things a bit more difficult to understand. There were a huge amount of graphs that mapped out the odd ratio effects of temperature events on various mental health conditions, that didn’t seem to link any of the other factors studied (socioeconomic status, age, tree canopy access, etc)