Heat- and cold-related mortality Indicator
This calculator returns the relative mortality risk attributable to non-optimal outdoor heat and cold for a given time series. This is estimated using a distributed lag non-linear model (DLNM). Full details of the methodology will be provided in the Heat and cold framework documentation .
This calculator returns the following data:
- Relative risk for the temperature-mortality association per region or aggregated across regions.
- Number of deaths attributable to non-optimal outdoor heat and cold per region or aggregated across regions.
- Deaths per 100,000 population attributable to non-optimal outdoor heat and cold per region or aggregated across regions.
Heat and cold indicator tool instructions
- Select your input data (.csv) file
- Select columns from your file to match the required columns in the dropdowns
- Select a column in sub-geography if required or set to none
- Add additional variables as needed
ℹ Data uploaded is not stored
Please provide data in the following format:
| Date (yyyy-mm-dd) | Region (Area) | Mean Temperature (°C) | Number of Deaths (per day) | Population |
|---|---|---|---|---|
| 2021-01-28 | North West | 20.5 | 190 | 45000 |
| 2021-01-29 | North West | 21.3 | 210 | 45000 |
This plot shows the relationship between temperature and relative risk of mortality (RR) in the data for . RR is calculated from a distributed lag non-linear model (DLNM) and is cumulative across the lag period. RR is the likelihood of an individual dying during or shortly after exposure to extreme cold or hot days (days when the mean daily temperature is below the 2.5th and above the 97.5th percentile over the previous 5-15 years respectively. The shaded area above and below the curve represent 95% confidence intervals. An RR of more than 1 means there is an increase in the likelihood of an individual experiencing the health outcome. The point at which the line changes colour is the minimum mortality temperature; the temperature at which RR is lowest. The optimal temperature range is the temperature range where RR is between 1 and 1.1.
This plot shows the rate of mortality per 100,000 population attributed to extreme cold (blue) and hot days (red) for . Extreme cold and hot days are days when the mean daily temperature is below the 2.5th (cold) and above the 97.5th (hot) percentile over the previous 5-15 years. Note: The threshold for extreme heat and cold varies among regions.
Wildfire smoke PM2.5 related health impact
This tool calculates health impacts of wildfire-related PM2.5 on all cause mortality using a time-stratified case-crossover approach. Full details of the methodology is be provided in the Wildfires framework documentation .
This calculator returns the following data:
- Relative risk of mortality associated with every 10 μg/m3 increase in wildfire related PM2.5 for the input data period
- Number of deaths attributable to wildfire realted smoke PM2.5 per region or aggregated across regions.
- Deaths per 100,000 population attributable to to wildfire realted smoke PM2.5 per region or aggregated across regions.
Wildfires indicator tool instructions
- Select your input data (.csv) file
- Select columns from your file to match the required columns in the dropdowns
- Select a column in Region if required or set to none
ℹ Data uploaded is not stored
Data must contain date, region (optional), temperature, and a health outcome column in the following format:
| Date (yyyy-mm-dd) | Region (Area) | Particulate Matter 2.5 (μg/m³) | Health Outcome (per day) |
|---|---|---|---|
| 2021-01-28 | North West | 35 | 190 |
| 2021-01-29 | North West | 40 | 210 |
This below plots shows the relative risk of mortality (RR) (and 95% confidence intervals) associated with each 10μg/m3 increase in wildfire-related PM2.5 concentration for the input data period, for each lag period.
The RR is the likelihood of an individual dying during, or shortly after, exposure to a 10μg/m3 increase in wildfire-related PM2.5. When the RR is 1, this means there is neither an increase or decrease in the likelihood of the individual dying during, or shortly after, exposure to the increase in wildfire-related PM2.5 exposure. A RR of 1.10 denotes a 10% increase in the risk of dying.
Flooding
This is some sample text for the flooding indicator but will not be displayed as there is no hyperlink to this section.
Mental Health Indicator: Suicides attributed to extreme temperature
This tool calculates the relationship between temperature and suicides using a time-stratified case-crossover approach with a distributed lag non-linear model (DLNM). Full details of the methodology are provided in the Mental health framework documentation .
Please upload your daily time series CSV (date, region optional, temperature, and suicides). You can specify additional model parameters (lag days, degrees of freedom, etc.).
This calculator returns the following data:
- Relative risk for the temperature-suicide association per region or aggregated across regions
Mental Health indicator tool instructions
- Select your input data (.csv) file
- Select columns from your file to match the required columns in the dropdowns
- Select a column in Region if required or set to none
ℹ Data uploaded is not stored
Data must contain columns for date, temperature, suicides, etc.:
| Date (yyyy-mm-dd) | Region (Area) | Mean Temperature (°C) | Suicide related Deaths |
|---|---|---|---|
| 2021-01-28 | North West | 20.5 | 190 |
| 2021-01-29 | North West | 21.3 | 210 |
This plot shows the relationship between temperature and relative risk of suicide (RR) in the data for . RR is calculated from a case-crossover with distributed lag non-linear model (DLNM) and is cumulative across the lag period. RR is the likelihood of an individual dying by suicide during, or shortly, after exposure to a certain daily temperature. The shaded area above and below the curve represent 95% confidence intervals. An RR of more than 1 means there is an increase in the likelihood of an individual dying by suicide.