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 requried or set to none
Add additional variables as needed

ℹ Data uploaded is not stored

Upload your .CSV file.

Deaths column:

Date column:

Temperature column:

Population column:

Country:

Sub-geography column:

Add additional independent variables

Add variable

Disaggregate by sub-geography

Disaggregate

Meta-analysis (pool regional estimates)

Run meta-analysis

Number of years for the calculation of relative risk distribution:

Year to calculate output for:

Number of days in lag:

On this page you can calculate indicators using your own data via the platform. Information about the indicators and their methods will be found under the statistical framework. The underlying code for these indicator calculators is open source and will be available on GitHub.

ℹ Calculator summary: Heat- and cold-related mortality

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 .

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 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.

Select a sub-geography to plot results:

This plot shows the relationship between temperature and relative risk of mortality (RR) in the data for in . 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 in . 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.

Meta-analysis (pool regional estimates)

This calculator returns the following data:

Your CSV data (first 5 rows)

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