Comparing percentages

I am doing a study, and have gathered the following data:

A patient population was divided into 3 categories:
Young patients (n=19) / Middle aged patients (n=66) / Old patients (n =20)

Based on a questionairre, we have categorized each patient into 2 categories: "Yes family history of diabetes" or "no family history of diabetes".

The data was as follows:
Young patients (n=19):
Yes Family Hx = 7
No Family Hx = 12
So, % of patients with "Yes Family Hx" = 37%

Middle aged patients (n=66):
Yes Family Hx = 21
No Family Hx = 45
So, % of patients with "Yes Family Hx" = 32%

Old patients (n =20):
Yes Family Hx = 1
No Family Hx = 19
So, % of patients with "Yes Family Hx" = 5%

I have used Chi square table to test whether the differences between the 3 groups is statistically significant.

**My question is:
Is there any way I compare these 3 groups using the PRECENTAGE data above? I am asking because I want to represent this data in a graph as follows:
Y axis = % of patients
X axis = Each of the 3 categories of ages as bar graphs.

Most importantly, I want to indicate on this graph whether the % in each age group DIFFER FROM EACH OTHER STATISTICALLY.

A graph similar to what I want to do is found in Figure 1 of this paper:
you can use 'Chi square test for trend in proportions'. you can get detailed procedure for computing in the book entitled"Statistical Methods" by Snedecor G.W. and Cochran W. G. (Oxford & IBH publishing company, 1967). This will help to decide whether the there is an increasing trend in proportions of patirnts as age increases/from low to high age group. become


Your chi-squared test already tells you that the proportions (percentages) differ between the 3 groups. In the figure you cited, all they did was an overall chi-squared test (as you've done) and then repeat the chi-squared test for group 1 vs group 2 and group 2 vs group 3. I wouldn't recommend doing that - I would go with Medha's advice, which is effectively running a linear regression with family history as a binary outcome and age group as the sole covariate; or alternatively run a logistic regression with family history as the binary outcome and age group (or even better, age) as the covariate.