Life expectancy at 65 in England (Local Authority highs and lows, with some asides about line charts)

Life expectancy at 65 has increased over time in England for both males and females: male life expectancy at 65 increased from 16.2 years for 2000-02 to 18.5 years for 2011-13, whilst female life expectancy over the same period rose from 19.2 years to 21.0 years. Male life expectancy at 65 increased faster than female life expectancy, reducing the gender gap from three years (for 2000-02) to 2.5 years (2011-13).  

Overall, this is an encouraging picture. Nevertheless, beneath the England level figures, substantial variation is found at local local authority (LA) level (and local authority district (LAD) level for two tier authorities).  Figures 1 and 2 show the time series data for female and male life expectancy at 65 for the LAs/LADs with the highest and lowest life expectancy as of 2011-13. 

Figure 1 shows that the highest female life expectancy at 65 were found in Chiltern (a rural LAD in Buckinghamshire) and Camden (in London): the lowest life expectancy of 18.8 years was found in Halton Borough Council (which covers Widnes and Runcorn).  Camden's life expectancy increased rapidly, by 2.9 years, between 2004-06 and 2008-10.  Female life expectancy at 65 for Halton has actually decreased in each of the last two available years: from 19.5 in 2009-11 to 18.8 in 2011-13. I'd expect that there are some public health people in Halton who are pretty unhappy about that.

Figure 1:  Female life expectancy at age 65: highest and lowest local authorities / local authority districts as of 2013

Notes: Upper and lower 95% confidence limits are shown in faint lines for local authority/local authority district life expectancy. Life expectancy is calculated using a rolling average over a three year period.

Figure 2 shows that for 2011-13, Harrow (in Greater London) had the highest male life expectancy at 65 (21.1 years). Manchester, the LA area covering central Manchester, had the lowest life expectancy (16.0 years).  This male life expectancy gap of 5.1 years is similar to that seen for women (5.2 years).


Figure 2: Male life expectancy at age 65: highest and lowest local authorities as of 2013

Notes: Upper and lower 95% confidence limits are shown in faint lines for local authority/local authority district life expectancy. Life expectancy is calculated using a rolling average over a three year period.

Whilst these charts show that the life expectancy gap for these particular LAs / LADs has increased over time, it doesn't show anything about what the LA/LAD level life expectancy gap has done in general over this period (Harrow and Manchester are at the extremes of the range in 2011-13, but not necessarily for other years). I'll be pulling together some charts that look at how the life expectancy gap has changed over time for another post.

About the charts

These are fairly simple Excel-based time series charts (primarily made as mock-ups for a data explorer I'm making in SSRS). If you like this design, there are a few things that may be worth noting about how to build these.

Y axis scales

Y-axis scales for time series charts don't have to start at zero. The main purpose of these (and similar) charts is to examine change over time. Starting the y-axis at zero would make the changes too difficult to see clearly - and make the chart unhelpful to users. But - if you are going to ask your readers to compare different charts, make sure the charts use a common format:

• Apply the same y-axis range to all the charts that will be compared. To do this in Excel 2016, select the y-axis and look under  'Format Axis - Axis options - Axis options.' The option needed is 'Bounds - Minimum and Maximum'  
• Ensure that the charts to be compared are the same size.  This is straightforward in Excel 2016: under 'Format chart area - Size and properties' check under properties that 'Don't size or move with charts' is selected (this keeps the chart from re-sizing if you expand a column or row) and then  under 'Size', set the chart height and width to the desired dimensions
• Ensure that colour use is as consistent as possible across charts. In Figures 1 and 2, for example, 'blue' denotes the LA with the lowest life expectancy; teal is used for England-level life expectancy and 'orange' is used for high life expectancy (gray is also used in Figure 1, where two local authorities had the same female life expectancy at 65 for 2011-13).

Replace legends with data labels
Using data labels instead of legends makes it easier for readers to interpret charts quickly.
Ordinarily for charts like these, I'd include both the series name and the final data value as a data label at the right of the chart. This won't work for Figure 1 as Camden and Chiltern share the same final data value. Whilst both series names and final values could still be displayed to the right (just offset up and down), it would be unclear which label related to which data series. I got around this in Figure 1 by adding a blank cell to the start of the x-axis series and to each data series. This provided space to include data series names at the start of the data series, without overlapping the y-axis.

Legends slow readers down, forcing them to look away from the actual chart for extra information. Keep them in reserve for cases where there are no viable alternatives (for example, where data series start and end on the same values).

Line weights combined with tints/shades can be used to emphasise or deemphasise data 

This is a pain in Excel (each series has to be set manually, and chart templates don't seem to consistently solve the problem, unless one is very particular about data set up), but pleasantly straightforward in SSRS.  I've used thin line weights and tints to show the upper and lower 95% confidence limits for the the LA/LAD results. I've also moved the confidence limit series to the top of the data series list (in the 'Select Data' pane), meaning that data series showing life expectancy will always cross in front of confidence limit series.