joint strategic needs assessment foundation profile - JSNA

Interative Hull Atlas: www.hullpublichealth.org/Pages/hull_atlas.htm More information: www.jsnaonline.org and www.hullpublichealth.org
of deaths is very small, in fact, there is only one extra death in area B. Even if these two
areas had exactly the same underlying mortality rate, one would not expect that exactly
the same number of deaths to occur every single year in each area; there will be some
natural variation over time and between the two areas. So it is reasonable to expect the
number of deaths to vary over time in the two areas, and be zero, one or two in the
areas for most years. Presenting confidence intervals (CIs) (see section 12.5 on page
775) are useful in virtually all cases, but CIs are even more useful when there is a
problem with small numbers. In this example, one would find that the CIs are wide, and
this would indicate that the numbers are too small to provide a good estimate of the
underlying statistic. In the above case the 95% confidence intervals are 1 to 464 deaths
per 100,000 persons for area A and 19 to 602 deaths per 100,000 persons for area B.
This represents a very wide confidence interval, and means that the estimate is not
useful (it is likely that one would have guessed that the estimate fell between 1 and 464
deaths per 100,000 persons in area A before any analysis of the data was completed).
Therefore, even if a mortality rate appears to be substantially higher in one area
compared to another, the number of deaths should be considered (and the width of the
95% confidence interval if presented). If the numbers of deaths are relatively small, then
the results should be interpreted very cautiously.
12.8 Percentiles, Quartiles, Quintiles and Medians
Percentiles divide a distribution of ordered numerical values into groups. The 10 th
percentile is the value of a numerical variable for which 10% of the people or sample of
values fall below. For example, if from a survey of employees at a particular company
the 10 th percentile for annual income is £10,000, then this would mean that 10% of the
employees for this particular company were earning £10,000 or less. Quintiles and
quartiles are alternative names for specific percentiles. The quintiles divide the sample
or observations or people into five groups whereas the quartiles divide the observations
into four groups. The Index of Multiple Deprivation is frequently divided into quintiles
usually based on the national distribution of all the IMD scores across the entire lower
layer super output areas (LLSOAs; geographical areas – with a mean of 1,500 residents
– on which the IMD scores are calculated). As Hull is much more deprived than
England with none of the 163 LLSOAs within the least deprived national quintile and
very few in the second least deprived national quintile, the IMD scores are often divided
into local quintiles for Hull. Thus, the most deprived quintile of areas represents the
20% most deprived areas. The quartiles divide the observations into four groups, and
the cut-offs are generally referred to as the lower quartile, median and upper quartile.
Thus 25% of all the observations have a value equal to the lower quartile or less, 25%
between the lower quartile and the median, etc, and half of the observations have a
value of the median or less (or the median or more). The median is frequently used to
illustrate the „typical‟ or „middle‟ value if the observations have a skewed distribution
where there are a small number of observations that have a particularly high value. The
mean (arithmetic average) is not the best measure of the „typical‟ value if the
Joint Strategic Needs Assessment Foundation Profile – Hull Health Profile: Release 3. March 2011. 777