| DAuid |
tot_pop |
tot_male |
0_4male |
5_9male |
10_14male |
15_19male |
20_24male |
25_29male |
30_34male |
35_39male |
40_44male |
45_49male |
50_54male |
55_59male |
60_64male |
60_69male |
70_74male |
75_79male |
80_84male |
85_overmale |
total_female |
0-4female |
5_9female |
10_14female |
15_19female |
20-24female |
25_29female |
30_34female |
35_39female |
40_44female |
45_49female |
50_54female |
55_59female |
60_64female |
65_69female |
70_74female |
75_79female |
80_84female |
85_over_female |
| 10010165 |
540 |
245 |
5 |
10 |
15 |
20 |
20 |
15 |
10 |
15 |
15 |
25 |
35 |
25 |
20 |
5 |
5 |
5 |
0 |
0 |
295 |
15 |
15 |
10 |
25 |
35 |
20 |
15 |
10 |
30 |
30 |
30 |
25 |
15 |
5 |
5 |
0 |
0 |
5 |
| 10010166 |
374 |
175 |
5 |
10 |
5 |
20 |
20 |
10 |
10 |
5 |
15 |
15 |
25 |
15 |
10 |
5 |
0 |
0 |
0 |
0 |
200 |
5 |
10 |
15 |
15 |
20 |
10 |
5 |
10 |
15 |
25 |
25 |
15 |
15 |
0 |
5 |
0 |
5 |
0 |
| 10010167 |
511 |
250 |
15 |
15 |
15 |
15 |
40 |
25 |
10 |
10 |
15 |
25 |
25 |
15 |
20 |
5 |
0 |
0 |
5 |
0 |
260 |
10 |
5 |
10 |
25 |
30 |
25 |
15 |
15 |
15 |
30 |
30 |
25 |
15 |
0 |
5 |
5 |
0 |
0 |
| 10010168 |
595 |
285 |
5 |
10 |
10 |
20 |
25 |
20 |
10 |
25 |
20 |
20 |
20 |
25 |
25 |
20 |
15 |
5 |
5 |
5 |
315 |
10 |
15 |
10 |
25 |
25 |
30 |
15 |
25 |
15 |
20 |
30 |
30 |
30 |
10 |
15 |
10 |
0 |
5 |
| 10010169 |
326 |
160 |
5 |
10 |
15 |
15 |
10 |
10 |
10 |
15 |
15 |
20 |
10 |
10 |
5 |
5 |
0 |
0 |
0 |
0 |
170 |
5 |
5 |
10 |
20 |
15 |
10 |
10 |
10 |
20 |
15 |
15 |
10 |
5 |
5 |
5 |
0 |
5 |
0 |
| 10010170 |
453 |
215 |
10 |
10 |
15 |
20 |
25 |
20 |
5 |
15 |
15 |
20 |
20 |
20 |
15 |
0 |
0 |
5 |
5 |
0 |
235 |
5 |
10 |
15 |
20 |
20 |
15 |
5 |
20 |
25 |
15 |
25 |
20 |
15 |
5 |
5 |
5 |
5 |
5 |
| 10010171 |
563 |
260 |
10 |
15 |
20 |
30 |
20 |
20 |
15 |
10 |
25 |
30 |
25 |
10 |
15 |
5 |
5 |
5 |
5 |
0 |
300 |
5 |
20 |
25 |
20 |
35 |
20 |
10 |
15 |
35 |
30 |
20 |
15 |
20 |
10 |
5 |
5 |
0 |
0 |
| 10010172 |
246 |
120 |
5 |
5 |
5 |
5 |
15 |
15 |
10 |
5 |
10 |
10 |
15 |
10 |
5 |
0 |
5 |
0 |
0 |
0 |
125 |
5 |
10 |
10 |
10 |
5 |
10 |
10 |
10 |
15 |
5 |
10 |
10 |
0 |
10 |
5 |
5 |
0 |
0 |
| 10010173 |
984 |
465 |
20 |
40 |
45 |
40 |
40 |
30 |
35 |
35 |
50 |
50 |
35 |
15 |
15 |
10 |
10 |
5 |
0 |
0 |
515 |
30 |
35 |
40 |
40 |
45 |
50 |
35 |
35 |
60 |
60 |
30 |
20 |
15 |
10 |
10 |
10 |
0 |
0 |
|
|
Hi All
I have a agregate data in the above format. DAUID is a census dissemination area. The figures in each cell indicate number of individuals in each category. tot_pop=total population, tot_male= total male population and 0_4male: number of male between 0 and 4 years and so on.
I want to combine this data with people with certain disease condition(confidential data, so I could not share here) to look at the effect of age and sex (among other variables) on the ocurrence of disease. The confidential data has information(age, sex) on people with disease only.
I am trying to make a dataset like the below using the above dataset, where 0 indicates male, and 1 female (male_female) and age_gp (0 to 4 =1, 5-9=2, and so on).
I am hoping that once I combine the disease dataset with controls, the below dataset by using DAUID (dissemination area) variable, I can see the effect of age, and sex on disease ocurrence.
Can you please let me know the STATA data set to do this. I am using STATA 15.1.
many thanks
Yuba
| DAUID |
male_female |
age_gp |
| 10010165 |
0 |
1 |
| 10010165 |
1 |
2 |
| 10010165 |
1 |
6 |
| 10010165 |
1 |
8 |
| 10010165 |
0 |
3 |
| 10010165 |
1 |
2 |
| 10010165 |
1 |
5 |
| 10010165 |
1 |
6 |
| 10010165 |
1 |
6 |
| 10010165 |
1 |
7 |
| 10010165 |
1 |
8 |
| 10010165 |
1 |
9 |
| 10010165 |
1 |
1 |
| 10010165 |
0 |
4 |
| 10010165 |
0 |
5 |
| 10010165 |
1 |
6 |
| 10010165 |
1 |
7 |
| 10010166 |
1 |
5 |
| 10010166 |
1 |
3 |
| 10010166 |
1 |
3 |
| 10010166 |
1 |
3 |
| 10010166 |
0 |
3 |
| 10010166 |
0 |
3 |
| 10010166 |
0 |
3 |
|
0 Response to converting agregate data to individual data
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