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  • Age standardized incidence rates for time series data

    Hello all,
    I have time series data for years 1970-2013.Variables I have in my data sets are female population all ages, male population all ages, male cases, female cases,crude incidence rates for female, crude incidence rates for male. They are in wide format. I have also created them in long format.
    I would like to have direct age and sex standarization using European standard population. I would like to take 1976 and 2013 European standard population.

    Any help would be appreciated. Thank you.

  • #2
    -help dstdize-

    Comment


    • #3
      I am not getting in to it as I am not using European standard population for 1970-2013. I am just using 2 years ie. 1976 and 2013. So, I am wondering how could I manage it?

      Comment


      • #4
        So, what does it mean that you are using two years, 1976 and 2013. Do you want to do two standardizations, one based on 1976 and the other based on 2013. Or were you thinking about somehow combining the 1976 and 2013 age-specific populations? Just what do you have in mind here?

        Comment


        • #5
          Let me simplify it.
          I have 4 different age groups 0-19, 20-39, 40-59, 60+ years.I have crude incidence rates according to years (1970-2013),age(4 broad categories) and sex. Now, I would like to have age standardized incidence rates using two years European Standard population(1976 and 2013).

          Comment


          • #6
            I am thinking to use mean population (1976 and 2013). But I do not know If it is correct.

            Comment


            • #7
              or what if i use just the recent ones?

              Comment


              • #8
                Well, you can use anything you want as the standard population. The results will differ somewhat depending on what you choose. The main thing is that whatever you use be understandable to your audience, and that it make sense as a standard to which you refer the observed incidence rates. So, if you are dealing with a chronic disease that mostly affects midlife and elderly people, it would not make sense to standardize to the population of a country whose median age is 15! But here you have two different years of the European population, and you are looking at incidence rates from a range of years that includes those two years, so appropriateness of reference would not be a problem with either. Averaging them would create a synthetic age distribution that does not correspond to any actual population's age distribution--but that wouldn't be a problem either provided whoever you are going to ultimately explain this too can understand it and doesn't object.

                That said, I don't know much about European demographics. I know that Europe has aged considerably between 1976 and 2013. I don't know if Europe had a post-war baby boom similar to that of the United States or if the aging is attributable just to very low birth rates (which I know has happened, at least in some European countries).

                I think that in choosing a reference age distribution, you want to try to use one where the bulk of the distribution is close to general age range where the age-specific incidence peaks.

                Comment


                • #9
                  Thank you so much Clyde for taking so much time to explain.
                  I have seen several examples of age standardization but they are just for one years with several age categories. But in my case, I have original data sets for whole 43 years as It is trend analysis as I have mentioned from 1970-2013. Could you give some hint how could I calculate age standardized rates using standard population let's take 2013 European standard population.

                  Comment


                  • #10
                    The details of how you would code this depend a great deal on exactly how your data is organized. It sounds like you have 43 data files, one for each year 1970-2013 (actually, that would be 43 data years: is there a missing year somewhere?) And you have another file with the 2013 European standard population age distribution. To help you concretely I need to see examples of the data in those files.

                    If you don't have -dataex- installed, already, run -ssc install dataex-. Open one of the 43 yearly data files. (I will assume that they all look like whichever one you show me.) Run -help dataex- and follow the simple instructions for using -dataex-. Then use -dataex- and post back what Stata gives you. Then open the 2013 European standard population data set, and do it again. When I can re-create both of those data sets, I can give you specific instructions.

                    Please be sure to use -dataex- for this. Do not post screen shots, nor the output that Stata gives you from -list-, and do not attach any spreadsheet files.

                    Comment


                    • #11
                      Hello Clyde,
                      Below I have provided data for female population for 2 years(1970 and 1971).Similalry, I have continuous data sets till 2013 for female.Then, I have male data sets.
                      Code:
                      * Example generated by -dataex-. To install: ssc install dataex
                      clear
                      input int dg_y str5 age_group str6 sex byte allglioma long pop byte(agecat1 agecat2) str4 year int CAT byte(year5 CAT2)
                      1970 "0-4"   "Female"  6 166273 1 1 "1970" 19701 1 11
                      1970 "5-9"   "Female"  6 186729 1 1 "1970" 19701 1 11
                      1970 "10-14" "Female"  4 194975 1 1 "1970" 19701 1 11
                      1970 "15-19" "Female"  1 205481 1 1 "1970" 19701 1 11
                      1970 "20-24" "Female"  5 216751 2 2 "1970" 19702 1 12
                      1970 "25-29" "Female"  2 161956 2 2 "1970" 19702 1 12
                      1970 "30-34" "Female"  3 142458 2 2 "1970" 19702 1 12
                      1970 "35-39" "Female"  4 135708 2 2 "1970" 19702 1 12
                      1970 "40-44" "Female"  4 147804 3 3 "1970" 19703 1 13
                      1970 "45-49" "Female"  4 148699 3 3 "1970" 19703 1 13
                      1970 "50-54" "Female" 11 132245 3 3 "1970" 19703 1 13
                      1970 "55-59" "Female"  5 137817 3 3 "1970" 19703 1 13
                      1970 "60-64" "Female"  9 132893 4 4 "1970" 19704 1 14
                      1970 "65-69" "Female"  5 105405 4 4 "1970" 19704 1 14
                      1970 "70-74" "Female"  1  78686 4 4 "1970" 19704 1 14
                      1970 "75-79" "Female"  0  48244 4 4 "1970" 19704 1 14
                      1970 "80-84" "Female"  0  24895 5 4 "1970" 19704 1 14
                      1970 "85+"   "Female"  0  11332 5 4 "1970" 19704 1 14
                      1971 "0-4"   "Female"  3 160437 1 1 "1971" 19711 1 11
                      1971 "5-9"   "Female"  6 184936 1 1 "1971" 19711 1 11
                      1971 "10-14" "Female"  3 191684 1 1 "1971" 19711 1 11
                      1971 "15-19" "Female"  2 206166 1 1 "1971" 19711 1 11
                      1971 "20-24" "Female"  0 214936 2 2 "1971" 19712 1 12
                      1971 "25-29" "Female"  5 170361 2 2 "1971" 19712 1 12
                      1971 "30-34" "Female"  5 151511 2 2 "1971" 19712 1 12
                      1971 "35-39" "Female"  2 134865 2 2 "1971" 19712 1 12
                      1971 "40-44" "Female"  6 146578 3 3 "1971" 19713 1 13
                      1971 "45-49" "Female"  7 147702 3 3 "1971" 19713 1 13
                      1971 "50-54" "Female"  7 135970 3 3 "1971" 19713 1 13
                      1971 "55-59" "Female"  6 134712 3 3 "1971" 19713 1 13
                      1971 "60-64" "Female"  8 134335 4 4 "1971" 19714 1 14
                      1971 "65-69" "Female"  3 108591 4 4 "1971" 19714 1 14
                      1971 "70-74" "Female"  4  81468 4 4 "1971" 19714 1 14
                      1971 "75-79" "Female"  0  49820 4 4 "1971" 19714 1 14
                      1971 "80-84" "Female"  1  25830 5 4 "1971" 19714 1 14
                      1971 "85+"   "Female"  0  11973 5 4 "1971" 19714 1 14
                      end

                      European standard pop

                      Code:
                      * Example generated by -dataex-. To install: ssc install dataex
                      clear
                      input str8 agegroup str3 sex str7 ESP2013
                      "agegroup" "sex" "ESP2013"
                      "0-4"      "M"   "5000"   
                      "0-4"      "F"   "5000"   
                      "5-9"      "M"   "5500"   
                      "5-9"      "F"   "5500"   
                      "10-14"    "M"   "5500"   
                      "10-14"    "F"   "5500"   
                      "15-19"    "M"   "5500"   
                      "15-19"    "F"   "5500"   
                      "20-24"    "M"   "6000"   
                      "20-24"    "F"   "6000"   
                      "25-29"    "M"   "6000"   
                      "25-29"    "F"   "6000"   
                      "30-34"    "M"   "6500"   
                      "30-34"    "F"   "6500"   
                      "35-39"    "M"   "7000"   
                      "35-39"    "F"   "7000"   
                      "40-44"    "M"   "7000"   
                      "40-44"    "F"   "7000"   
                      "45-49"    "M"   "7000"   
                      "45-49"    "F"   "7000"   
                      "50-54"    "M"   "7000"   
                      "50-54"    "F"   "7000"   
                      "55-59"    "M"   "6500"   
                      "55-59"    "F"   "6500"   
                      "60-64"    "M"   "6000"   
                      "60-64"    "F"   "6000"   
                      "65-69"    "M"   "5500"   
                      "65-69"    "F"   "5500"   
                      "70-74"    "M"   "5000"   
                      "70-74"    "F"   "5000"   
                      "75-79"    "M"   "4000"   
                      "75-79"    "F"   "4000"   
                      "80-84"    "M"   "2500"   
                      "80-84"    "F"   "2500"   
                      "85-89"    "M"   "1500"   
                      "85-89"    "F"   "1500"   
                      "90+"      "M"   "1000"   
                      "90+"      "F"   "1000"   
                      end

                      Comment


                      • #12
                        OK. GIven the layout of your data, it is probably easier to just calculate it than to use -dstdize-. The example data you sent is not entirely suitable: you can't do age-sex standardization with just female data. So, to show how it would work, I have just copied the female data over and labeled it male. Obviously you will use your real male data. And you will also start by appending together all your years for both sexes.

                        The first step is to manage the standard population data so that it can be merged with the glioma incidence data. That requires assuring that the key variables, age_group and sex have the same names in both files and are coded the same in both files.

                        The next step is to bring in the glioma data and calculate age-sex stratum-specific incidence rates. To make the numbers easier to read, I have denominated incidence per 100,000 population.

                        The third step is to merge the European standard population data in.

                        The final step is to calculate European standard population-weighted averages of the stratum specific incidence rates by year.

                        Code:
                        //    CLEAN THE EURO STANDARD POPULATION
                        //    AND MODIFY ITS CONTENT TO SUPPORT
                        //    MATCHING WITH THE OTHER DATA
                        * Example generated by -dataex-. To install: ssc install dataex
                        clear
                        input str8 agegroup str3 sex str7 ESP2013
                        "agegroup" "sex" "ESP2013"
                        "0-4"      "M"   "5000"   
                        "0-4"      "F"   "5000"   
                        "5-9"      "M"   "5500"   
                        "5-9"      "F"   "5500"   
                        "10-14"    "M"   "5500"   
                        "10-14"    "F"   "5500"   
                        "15-19"    "M"   "5500"   
                        "15-19"    "F"   "5500"   
                        "20-24"    "M"   "6000"   
                        "20-24"    "F"   "6000"   
                        "25-29"    "M"   "6000"   
                        "25-29"    "F"   "6000"   
                        "30-34"    "M"   "6500"   
                        "30-34"    "F"   "6500"   
                        "35-39"    "M"   "7000"   
                        "35-39"    "F"   "7000"   
                        "40-44"    "M"   "7000"   
                        "40-44"    "F"   "7000"   
                        "45-49"    "M"   "7000"   
                        "45-49"    "F"   "7000"   
                        "50-54"    "M"   "7000"   
                        "50-54"    "F"   "7000"   
                        "55-59"    "M"   "6500"   
                        "55-59"    "F"   "6500"   
                        "60-64"    "M"   "6000"   
                        "60-64"    "F"   "6000"   
                        "65-69"    "M"   "5500"   
                        "65-69"    "F"   "5500"   
                        "70-74"    "M"   "5000"   
                        "70-74"    "F"   "5000"   
                        "75-79"    "M"   "4000"   
                        "75-79"    "F"   "4000"   
                        "80-84"    "M"   "2500"   
                        "80-84"    "F"   "2500"   
                        "85-89"    "M"   "1500"   
                        "85-89"    "F"   "1500"   
                        "90+"      "M"   "1000"   
                        "90+"      "F"   "1000"   
                        end
                        
                        // DATA SET AS PROVIDED INCLUDES VARIABLE NAMES
                        // IN FIRST OBSERVATION.  REMOVE THAT AND
                        // DESTRING THE POPULATION COUNT
                        drop in 1
                        destring ESP2013, replace
                        //    CHANGE CODING OF SEX SO IT WILL
                        //    MATCH THE OTHER DATA FILES
                        replace sex = "Female" if sex == "F"
                        replace sex = "Male" if sex == "M"
                        //    THE GLIOMA DATA HAS AN 85+ CATEGORY
                        //    SO COMBINE THE 85-89 & 90+ CATEGORIES
                        rename agegroup age_group
                        replace age_group = "85+" if inlist(age_group, "85-89", "90+")
                        collapse (sum) ESP2013, by(age_group sex)
                        isid age_group sex, sort
                        tempfile euro_standard
                        save `euro_standard'
                        
                        //    NOW BRING THE GLIOMA DATA IN
                        * Example generated by -dataex-. To install: ssc install dataex
                        clear
                        input int dg_y str5 age_group str6 sex byte allglioma long pop byte(agecat1 agecat2) str4 year int CAT byte(year5 CAT2)
                        1970 "0-4"   "Female"  6 166273 1 1 "1970" 19701 1 11
                        1970 "0-4"   "Male"    6 166273 1 1 "1970" 19701 1 11
                        1970 "10-14" "Female"  4 194975 1 1 "1970" 19701 1 11
                        1970 "10-14" "Male"    4 194975 1 1 "1970" 19701 1 11
                        1970 "15-19" "Female"  1 205481 1 1 "1970" 19701 1 11
                        1970 "15-19" "Male"    1 205481 1 1 "1970" 19701 1 11
                        1970 "20-24" "Female"  5 216751 2 2 "1970" 19702 1 12
                        1970 "20-24" "Male"    5 216751 2 2 "1970" 19702 1 12
                        1970 "25-29" "Female"  2 161956 2 2 "1970" 19702 1 12
                        1970 "25-29" "Male"    2 161956 2 2 "1970" 19702 1 12
                        1970 "30-34" "Female"  3 142458 2 2 "1970" 19702 1 12
                        1970 "30-34" "Male"    3 142458 2 2 "1970" 19702 1 12
                        1970 "35-39" "Female"  4 135708 2 2 "1970" 19702 1 12
                        1970 "35-39" "Male"    4 135708 2 2 "1970" 19702 1 12
                        1970 "40-44" "Female"  4 147804 3 3 "1970" 19703 1 13
                        1970 "40-44" "Male"    4 147804 3 3 "1970" 19703 1 13
                        1970 "45-49" "Female"  4 148699 3 3 "1970" 19703 1 13
                        1970 "45-49" "Male"    4 148699 3 3 "1970" 19703 1 13
                        1970 "5-9"   "Female"  6 186729 1 1 "1970" 19701 1 11
                        1970 "5-9"   "Male"    6 186729 1 1 "1970" 19701 1 11
                        1970 "50-54" "Female" 11 132245 3 3 "1970" 19703 1 13
                        1970 "50-54" "Male"   11 132245 3 3 "1970" 19703 1 13
                        1970 "55-59" "Female"  5 137817 3 3 "1970" 19703 1 13
                        1970 "55-59" "Male"    5 137817 3 3 "1970" 19703 1 13
                        1970 "60-64" "Female"  9 132893 4 4 "1970" 19704 1 14
                        1970 "60-64" "Male"    9 132893 4 4 "1970" 19704 1 14
                        1970 "65-69" "Female"  5 105405 4 4 "1970" 19704 1 14
                        1970 "65-69" "Male"    5 105405 4 4 "1970" 19704 1 14
                        1970 "70-74" "Female"  1  78686 4 4 "1970" 19704 1 14
                        1970 "70-74" "Male"    1  78686 4 4 "1970" 19704 1 14
                        1970 "75-79" "Female"  0  48244 4 4 "1970" 19704 1 14
                        1970 "75-79" "Male"    0  48244 4 4 "1970" 19704 1 14
                        1970 "80-84" "Female"  0  24895 5 4 "1970" 19704 1 14
                        1970 "80-84" "Male"    0  24895 5 4 "1970" 19704 1 14
                        1970 "85+"   "Female"  0  11332 5 4 "1970" 19704 1 14
                        1970 "85+"   "Male"    0  11332 5 4 "1970" 19704 1 14
                        1971 "0-4"   "Female"  3 160437 1 1 "1971" 19711 1 11
                        1971 "0-4"   "Male"    3 160437 1 1 "1971" 19711 1 11
                        1971 "10-14" "Female"  3 191684 1 1 "1971" 19711 1 11
                        1971 "10-14" "Male"    3 191684 1 1 "1971" 19711 1 11
                        1971 "15-19" "Female"  2 206166 1 1 "1971" 19711 1 11
                        1971 "15-19" "Male"    2 206166 1 1 "1971" 19711 1 11
                        1971 "20-24" "Female"  0 214936 2 2 "1971" 19712 1 12
                        1971 "20-24" "Male"    0 214936 2 2 "1971" 19712 1 12
                        1971 "25-29" "Female"  5 170361 2 2 "1971" 19712 1 12
                        1971 "25-29" "Male"    5 170361 2 2 "1971" 19712 1 12
                        1971 "30-34" "Female"  5 151511 2 2 "1971" 19712 1 12
                        1971 "30-34" "Male"    5 151511 2 2 "1971" 19712 1 12
                        1971 "35-39" "Female"  2 134865 2 2 "1971" 19712 1 12
                        1971 "35-39" "Male"    2 134865 2 2 "1971" 19712 1 12
                        1971 "40-44" "Female"  6 146578 3 3 "1971" 19713 1 13
                        1971 "40-44" "Male"    6 146578 3 3 "1971" 19713 1 13
                        1971 "45-49" "Female"  7 147702 3 3 "1971" 19713 1 13
                        1971 "45-49" "Male"    7 147702 3 3 "1971" 19713 1 13
                        1971 "5-9"   "Female"  6 184936 1 1 "1971" 19711 1 11
                        1971 "5-9"   "Male"    6 184936 1 1 "1971" 19711 1 11
                        1971 "50-54" "Female"  7 135970 3 3 "1971" 19713 1 13
                        1971 "50-54" "Male"    7 135970 3 3 "1971" 19713 1 13
                        1971 "55-59" "Female"  6 134712 3 3 "1971" 19713 1 13
                        1971 "55-59" "Male"    6 134712 3 3 "1971" 19713 1 13
                        1971 "60-64" "Female"  8 134335 4 4 "1971" 19714 1 14
                        1971 "60-64" "Male"    8 134335 4 4 "1971" 19714 1 14
                        1971 "65-69" "Female"  3 108591 4 4 "1971" 19714 1 14
                        1971 "65-69" "Male"    3 108591 4 4 "1971" 19714 1 14
                        1971 "70-74" "Female"  4  81468 4 4 "1971" 19714 1 14
                        1971 "70-74" "Male"    4  81468 4 4 "1971" 19714 1 14
                        1971 "75-79" "Female"  0  49820 4 4 "1971" 19714 1 14
                        1971 "75-79" "Male"    0  49820 4 4 "1971" 19714 1 14
                        1971 "80-84" "Female"  1  25830 5 4 "1971" 19714 1 14
                        1971 "80-84" "Male"    1  25830 5 4 "1971" 19714 1 14
                        1971 "85+"   "Female"  0  11973 5 4 "1971" 19714 1 14
                        1971 "85+"   "Male"    0  11973 5 4 "1971" 19714 1 14
                        end
                        
                        //    CALCULATE AGE-GROUP SEX SPECIFIC INCIDENCE RATES
                        //    PER 100,000 POPULATION
                        gen inc_rate = 100000*allglioma/pop
                        
                        //    MERGE WITH EUROPEAN STANDARD POPULATION FILE
                        merge m:1 age_group sex using `euro_standard'
                        
                        //    SET STRATUM-SPECIFIC INCIDENCE RATE = 0 IF THERE ARE
                        //    ANY AGE-GROUP SEX STRATA WITH NO DATA IN THE INCIDENCE FILES
                        replace inc_rate = 0 if _merge == 2
                        
                        //    CALCULATE AGE-SEX STANDARDIZED INCIDENCE RATES FOR EACH YEAR
                        collapse (mean) inc_rate [fweight = ESP2013], by(dg_y)
                        label var inc_rate "Age-sex standardized incidence per 100,000"
                        
                        list, noobs clean
                        That should do it.

                        Thank you for using -dataex-. It saved me a lot of time compared to any other way of sharing the data.

                        Comment


                        • #13
                          Hello Clyde,
                          Thank you so much, it is really helpful.
                          I could not merge two files
                          It says
                          .
                          Code:
                           merge m:1 age_group sex using ´euro_standard´
                          age_group is str8 in using data
                          Previously I had used this command for glioma data sets.
                          gen AgeGroup=1 if age_group==4
                          . replace AgeGroup=2 if age_group==59
                          . replace AgeGroup=3 if age_group==1014
                          . replace AgeGroup=4 if age_group==1519
                          . replace AgeGroup=5 if age_group==2024
                          . replace AgeGroup=6 if age_group==2529
                          . replace AgeGroup=7 if age_group==3034
                          . replace AgeGroup=8 if age_group==3539
                          . replace AgeGroup=9 if age_group==4044
                          . replace AgeGroup=10 if age_group==4549
                          . replace AgeGroup=11 if age_group==5054
                          . replace AgeGroup=12 if age_group==5559
                          . replace AgeGroup=13 if age_group==6064
                          . replace AgeGroup=14 if age_group==6569
                          . replace AgeGroup=15 if age_group==7074
                          . replace AgeGroup=16 if age_group==7579
                          . replace AgeGroup=17 if age_group==8084
                          . replace AgeGroup=18 if age_group==85

                          Now, when I tried to use this command,
                          it says
                          [CODE]
                          gen age_group=1 if age_group==4
                          age_group already defined
                          [/CODE
                          I could not figure out what is wrong.

                          Comment


                          • #14
                            In the examples you posted, the variable in the glioma data was called agegroup, and it was a string variable with values like "0-4", "74-79", etc. Based on what you show now, your age group variable is nothing like that at all. If that isn't what you actually have, then why did you post that?

                            In order to do the standardization the agegroup and sex variables have to be named the same way and contain the same values in both the European standard population data and the glioma data.

                            I don't want to invest any more time in this without accurate, representative samples of your data. It is a waste of my time to write code, and a waste of your time to read and try to use code written for data sets that you aren't actually using. Please post back with two or more years of male and female glioma incidence data exactly as it is in your files, and with the European standard population data exactly as it is in your files. Please remember to use -dataex- to do this.

                            Comment


                            • #15
                              Hello Clyde,
                              I had posted ESP exactly which i am using now but similar data sets thinking it would work. But now, this is the exact data but it is not complete as I thought it is not wise to post whole data here.
                              Code:
                              * Example generated by -dataex-. To install: ssc install dataex
                              clear
                              input int(dg_y age_group) float sex byte allglioma long pop float(Agecat Agecat2 year Cat year5 Cat2 inc_rate)
                              1971  8 1  2 134865 2 2  1 19712 1 12 1.4829644
                              1976  4 1  3 190273 1 1  6 19761 2 21  1.576682
                              1987 13 1 14 137021 4 4 17 19874 4 44 10.217412
                              1984 13 1 14 138845 4 4 14 19844 3 34 10.083186
                              2005  5 1  2 163226 2 2 35 20052 8 82  1.225295
                              1990 16 1  8  92919 4 4 20 19904 5 54   8.60965
                              1975 17 1  0  30437 5 4  5 19754 2 24         0
                              2005 16 1  5 109611 4 4 35 20054 8 84  4.561586
                              1984 12 1 13 141390 3 3 14 19843 3 33  9.194427
                              1980 11 1 13 144179 3 3 10 19803 3 33   9.01657
                              1985  7 1 13 197817 2 2 15 19852 4 42  6.571731
                              2006  3 1  4 158679 1 1 36 20061 8 81 2.5208125
                              2005 13 1 19 152460 4 4 35 20054 8 84 12.462285
                              1980  6 1  8 196535 2 2 10 19802 3 32  4.070522
                              1976 11 1 12 145267 3 3  6 19763 2 23  8.260651
                              1998  5 1  7 159511 2 2 28 19982 6 62  4.388412
                              2002 17 1  1  74808 5 4 32 20024 7 74 1.3367554
                              1985 15 2  6  65008 4 4 15 19854 4 44  9.229633
                              2009  2 2  2 147367 1 1 39 20091 8 81  1.357156
                              2010 16 2 10  75178 4 4 40 20104 9 94 13.301764
                              2002 12 2 20 181388 3 3 32 20023 7 73 11.026088
                              1989  4 2  1 153928 1 1 19 19891 4 41  .6496544
                              end
                              label values age_group AgeCat
                              label def AgeCat 1 "0-4", modify
                              label def AgeCat 2 "5-9", modify
                              label def AgeCat 3 "10-14", modify
                              label def AgeCat 4 "15-19", modify
                              label def AgeCat 5 "20-24", modify
                              label def AgeCat 6 "25-29", modify
                              label def AgeCat 7 "30-34", modify
                              label def AgeCat 8 "35-39", modify
                              label def AgeCat 9 "40-44", modify
                              label def AgeCat 10 "45-49", modify
                              label def AgeCat 11 "50-54", modify
                              label def AgeCat 12 "55-59", modify
                              label def AgeCat 13 "60-64", modify
                              label def AgeCat 14 "65-69", modify
                              label def AgeCat 15 "70-74", modify
                              label def AgeCat 16 "75-79", modify
                              label def AgeCat 17 "80-84", modify
                              label def AgeCat 18 "85+", modify
                              label values sex sex
                              label def sex 1 "Female", modify
                              label def sex 2 "male", modify
                              label values year years
                              label def years 0 "1970", modify
                              label def years 1 "1971", modify
                              label def years 2 "1972", modify
                              label def years 3 "1973", modify
                              label def years 4 "1974", modify
                              label def years 5 "1975", modify
                              label def years 6 "1976", modify
                              label def years 7 "1977", modify
                              label def years 8 "1978", modify
                              label def years 9 "1979", modify
                              label def years 10 "1980", modify
                              label def years 11 "1981", modify
                              label def years 12 "1982", modify
                              label def years 13 "1983", modify
                              label def years 14 "1984", modify
                              label def years 15 "1985", modify
                              label def years 16 "1986", modify
                              label def years 17 "1987", modify
                              label def years 18 "1988", modify
                              label def years 19 "1989", modify
                              label def years 20 "1990", modify
                              label def years 21 "1991", modify
                              label def years 22 "1992", modify
                              label def years 23 "1993", modify
                              label def years 24 "1994", modify
                              label def years 25 "1995", modify
                              label def years 26 "1996", modify
                              label def years 27 "1997", modify
                              label def years 28 "1998", modify
                              label def years 29 "1999", modify
                              label def years 30 "2000", modify
                              label def years 31 "2001", modify
                              label def years 32 "2002", modify
                              label def years 33 "2003", modify
                              label def years 34 "2004", modify
                              label def years 35 "2005", modify
                              label def years 36 "2006", modify
                              label def years 37 "2007", modify
                              label def years 38 "2008", modify
                              label def years 39 "2009", modify
                              label def years 40 "2010", modify
                              label def years 41 "2011", modify
                              label def years 42 "2012", modify
                              label def years 43 "2013", modify
                              I would like to apologize for wasting your time.

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