Obtaining unexpectedly large p-value (chi square) i have no much experience coding nor with statistics so sorry and thank you in advance.
I'm trying to find out if two variables (living in urban or rural areas) and using medicine A or B (due to accessibility) are related. I have a data frame with many other variables and thousands of cases on which i'm doing the analysis. This is the grouped data
$data
           Outcome (medicine)

Predictor 1    2    Total

Urban     1412 1792  3204
Middle    885  1167  2052
Rural     485  662   1147
Total     2782 3621  6403 

I've tried using both chisq.test, and oddsratio.wald, obtaining with both a p.value of around 0.54, thus i can't reject the Null hypothesis and should assume that both may not be related at all, nevertheless if we look at the percentages we can see that there is a trend, in which the more rural the area, the less percentage use medicine 1.
          % use of Total
Urban     44.07% 
Middle    43.13%  
Rural     42.28%

So shouldn't i be obtaining a very low (<0.05) p value? am i doing something wrong?
I've used:
chiurba<-chisq.test(dfbi$TIPOANTICONCEP,dfbi$DEGURBA,simulate.p.value = TRUE)

and
ORurba<-oddsratio.wald(dfbi$DEGURBA,dfbi$TIPOANTICONCEP)

Thank you in advance

A crude but effective way of estimating uncertainty in those kind of problems is  by taking the square root of the number of observations (which is approximately the standard deviation of a Binomial distribution).
So for example the percentage of using medicine 1 in the rural case would be estimated as
$$ \frac{485 \pm \sqrt{485}}{1147} \approx (42.3 \pm 1.9)\% $$
which is less than 1 standard deviation away from the urban case. So, despite the suggestive trend, it is not very significant.
