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
specific time period. Direct standardisation results in an age-gender standardised rate
of disease (often per 10,000 or 100,000 population).
Indirect standardisation generally 71 results in a standardised mortality (or morbidity) ratio
(SMR). The SMR will take the value of 100 if the sample group has the same mortality
(or morbidity) rate as the „standard‟ population, and an SMR greater (less) than 100 if
the sample group has a greater (lower) mortality rate relative to the standard population.
An SMR of 130 means that there is a 30% higher mortality rate for people in the local
area compared to England even after taking into account the gender and age structure
of the two geographical areas. An SMR of 80 means that the mortality rate locally is
80% of that observed nationally after allowing for differences in the gender and age
structure of the two areas (or equivalently 20% lower locally compared to the standard
population).
12.4 Significance Testing
It is often useful to compare a particular summary parameter (for instance, mean,
median, measure of risk) among different groups. Since there is natural variation
associated with virtually all measurements and since we generally only have a sample
and have not measured the entire population, it is necessary to distinguish between
differences which are close enough together to be explained by chance and differences
which are „unlikely‟ to be explained by chance. Such a comparison can be undertaken
using a statistical test which takes into the account chance variation. When undertaking
a statistical test, we assume that there is no difference in the summary measure among
the groups and then calculate the probability of obtaining the difference we observe in
our sample (i.e. in the data we have). If the calculated probability, or so-called p-value,
is small then this means that there is a small chance of obtaining such a result under the
assumption that there is no difference. Therefore, if the probability is small enough
(generally, less than one in twenty or less than 0.05) then we assume that the original
assumption must be incorrect and that there really is a difference. Since this is based
on probabilities and assumptions, just because a small p-value is observed, it does not
necessarily mean that the original assumption of no difference between the groups is
untrue. However, clearly the smaller the p-value, the more likely it is that the original
assumption is untrue. Similarly, just because you obtain a large p-value and therefore
have no evidence to reject the original assumption, it does not mean that it is actually
true, it could be that there is simply insufficient evidence to show otherwise (for example,
a small number of people or small number of people with a particular event). If a small
p-value is obtained (p