CMGPD-LN Methodological Lecture

1. CMGPD-LNMethodological Lecture Day 7 Health and Mortality

2. Mortality outcomes • Until age 75, recording of mortality appears plausible • Age patterns resemble other historical populations, model life tables • After age 75, mortality record is problematic • Many immortals were taoding at some point, so for mortality analysis perhaps safest to throw out all records of anyone who was taoding • Rates below age 5 appear normal, but representativeness of registered children is unclear • Large numbers of deaths allow for fine-grained analysis of mortality determinants

4. . use "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from > ICPSR\ICPSR_27063\DS0001\27063-0001-Data.dta", clear (China Multi-Generational Panel Dataset, Liaoning (CMGPD-LN) > , 1749-1909, Liaoning) . recode AGE_IN_SUI min/0=. 1/15=1 16/55=16 56/max=56 (AGE_IN_SUI: 1478270 changes made) . keep if NEXT_DIE >= 0 & NEXT_3 & PRESENT (653682 observations deleted) . keep if SEX >= 1 (1 observation deleted) . tab AGE_IN_SUI SEX if NEXT_DIE | Sex Age in Sui | Female Male | Total -----------+----------------------+---------- 1 | 1,189 5,132 | 6,321 16 | 11,160 10,721 | 21,881 56 | 11,342 11,923 | 23,265 -----------+----------------------+---------- Total | 23,691 27,776 | 51,467

5. Analyzing mortality • Life tables • Remember, ages are in sui • Probability of death in next three years (3qx) • Need to be converted to mx to put into a life table • One crude conversion: mx= -ln(1- 3qx)/3 • More sophisticated conversions are appropriate at early ages when rates are changing fast • Discrete-time event-history analysis • Logistic regression • Complementary log-log regression

6. Life tablesA crude approach keep if AGE_IN_SUI > 0 & AGE_IN_SUI <= 75 & NEXT_3 & PRESENT & SEX > 0 * Divide into five year age groups replace AGE_IN_SUI = 5*int((AGE_IN_SUI-1)/5)+1 tab AGE_IN_SUI SEX collapse NEXT_DIE, by(AGE_IN_SUI SEX) sort SEXAGE_IN_SUI

7. . tab AGE_IN_SUI SEX | Sex Age in Sui | Female Male | Total -----------+----------------------+---------- 1 | 5,026 37,223 | 42,249 6 | 7,881 53,337 | 61,218 11 | 8,334 51,932 | 60,266 16 | 20,835 47,582 | 68,417 21 | 35,747 46,067 | 81,814 26 | 37,344 44,648 | 81,992 31 | 34,870 40,533 | 75,403 36 | 32,342 37,912 | 70,254 41 | 30,347 35,131 | 65,478 46 | 27,330 30,170 | 57,500 51 | 24,282 26,714 | 50,996 56 | 20,898 22,568 | 43,466 61 | 16,949 17,566 | 34,515 66 | 13,143 12,664 | 25,807 71 | 9,014 8,072 | 17,086 -----------+----------------------+---------- Total | 324,342 512,119 | 836,461

8. Example of a crude life table

9. Example of a crude life table

10. Event-history analysis keep if AGE_IN_SUI > 0 & AGE_IN_SUI <= 75 & NEXT_3 & PRESENT & SEX > 0 replace AGE_IN_SUI = 5*int((AGE_IN_SUI-1)/5)+1 xi:logit NEXT_DIE i.AGE_IN_SUI i.SEX i.REGION

11. ------------------------------------------------------------------------------------------------------------------------------------------------------------ NEXT_DIE | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _IAGE_IN_~_6 | -1.077269 .0301595 -35.72 0.000 -1.136381 -1.018158 _IAGE_IN_~11 | -1.453427 .0342583 -42.43 0.000 -1.520572 -1.386282 _IAGE_IN_~16 | -1.275694 .0307841 -41.44 0.000 -1.33603 -1.215358 _IAGE_IN_~21 | -1.134171 .0279815 -40.53 0.000 -1.189014 -1.079328 _IAGE_IN_~26 | -1.068992 .0274992 -38.87 0.000 -1.122889 -1.015094 _IAGE_IN_~31 | -.9322853 .0271684 -34.32 0.000 -.9855344 -.8790363 _IAGE_IN_~36 | -.7535797 .0264842 -28.45 0.000 -.8054878 -.7016715 _IAGE_IN_~41 | -.5966655 .0259978 -22.95 0.000 -.6476202 -.5457108 _IAGE_IN_~46 | -.4034962 .0257241 -15.69 0.000 -.4539145 -.353078 _IAGE_IN_~51 | -.1480721 .0250983 -5.90 0.000 -.1972639 -.0988803 _IAGE_IN_~56 | .194831 .0244138 7.98 0.000 .1469809 .2426811 _IAGE_IN_~61 | .5058013 .024371 20.75 0.000 .4580351 .5535676 _IAGE_IN_~66 | .9441143 .024353 38.77 0.000 .8963834 .9918453 _IAGE_IN_~71 | 1.246485 .0257523 48.40 0.000 1.196011 1.296958 _ISEX_2 | -.107873 .0102132 -10.56 0.000 -.1278905 -.0878555 _IREGION_2 | .0075932 .0117758 0.64 0.519 -.015487 .0306734 _IREGION_3 | -.1400285 .0138099 -10.14 0.000 -.1670953 -.1129616 _IREGION_4 | -.2427861 .017067 -14.23 0.000 -.2762367 -.2093354 _cons | -2.300452 .0209234 -109.95 0.000 -2.341461 -2.259443 ------------------------------------------------------------------------------

12. Accounting for age and sex • We generally analyze childhood, working ages, and old age separately • Since relevant variables vary, as do their effects • We often, but not always, analyze males and females separately • Because effects of key variables may vary by sex • Categorical variable for age group • See previous example • Polynomial generate age2 = age^2 generate age3 = age^3 logit NEXT_DIE age age2 age3 • Hybrid • Include age group categories and linear term for age • To capture variation in risks within age groups

13. Other notes on mortality analysis • Since many of the ‘immortals’ were taoat some point in their life, maybe worthwhile to throw out observations of anyone who was ever tao, even if they aren’t tao right now. • Regional differences in mortality rates suggest inclusion of REGION as a basic control variable.

14. Using the disability variables • Basic contents • Time trends • Age patterns • Working with the original disabilities • And positions…

19. Working with the original disabilities use "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from ICPSR\ICPSR_27063\DS0001\27063-0001-Data.dta", clear merge 1:1 RECORD_NUMBER using "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from ICPSR\ICPSR_27063\DS0003\27063-0003-Data.dta" merge m:1 DATASET DISABILITY_CODE using "C:\Users\Cameron Campbe\Documents\Baqi\extracts\CMGPD-LN Disability for SJTU class",keep(match master) tab CONDITION_PINYIN, sort run "C:\Users\Cameron Campbe\Documents\Dropbox\Lee-Campbell group (Dropbox shares)\SJTU DongbeiZhongxin\SJTU Summer Class\strip_disability.do“ tab new_CONDITION_PINYIN, sort generate byte lao_zheng = index(new_CONDITION_PINYIN,"laozheng") > 0 tab lao_zheng

20. .do file to clean up generate new_CONDITION_PINYIN = CONDITION_PINYIN local for_removal "1 2 3 4 5 6 7 8 9" foreach x of local for_removal { replace new_CONDITION_PINYIN = subinstr(new_CONDITION_PINYIN,"`x'","",.) }

21. . tab CONDITION_PINYIN, sort Disease | Freq. Percent Cum. --------------------------------------+----------------------------------- chen2 tao2 | 1,238 10.93 10.93 lao2 zheng4 | 741 6.54 17.48 chen2 lao2 zheng4 | 574 5.07 22.55 yan3 xia1 | 462 4.08 26.62 chen2 xia1 | 388 3.43 30.05 chen2 tao2 you3 an4 | 300 2.65 32.70 can2 ji2 | 297 2.62 35.32 tu3 xie3 | 267 2.36 37.68 xia1 zi5 | 259 2.29 39.97 tui3 que2 | 234 2.07 42.03 tui3 tong4 | 190 1.68 43.71 chen2 tui3 que2 | 178 1.57 45.28 tui3 huai4 | 167 1.47 46.76 er3 long2 | 166 1.47 48.23 lao2 bing4 tu3 xie3 | 159 1.40 49.63 yan3 ji2 | 154 1.36 50.99 yao1 huai4 | 148 1.31 52.30 lou4 chuang1 | 121 1.07 53.36 lao3 tui4 | 108 0.95 54.32 chen2 tu3 xie3 | 107 0.94 55.26 xia1 yan3 yan3 ji2 | 107 0.94 56.21 yang2 gao1 feng1 | 107 0.94 57.15

22. . tab new_CONDITION_PINYIN, sort new_CONDITION_PINYIN | Freq. Percent Cum. --------------------------------------+----------------------------------- chentao | 1,238 10.93 10.93 laozheng | 741 6.54 17.48 chenlaozheng | 574 5.07 22.55 yanxia | 462 4.08 26.62 chenxia | 388 3.43 30.05 can ji | 307 2.71 32.76 chentao you an | 300 2.65 35.41 tuxie | 272 2.40 37.81 xiazi | 260 2.30 40.11 tuique | 234 2.07 42.18 tui tong | 190 1.68 43.85 chentuique | 178 1.57 45.43 tuihuai | 167 1.47 46.90 er long | 166 1.47 48.37 laobingtuxie | 159 1.40 49.77 yanji | 154 1.36 51.13 yaohuai | 148 1.31 52.44 louchuang | 121 1.07 53.51 gebohuai | 113 1.00 54.50 laotui | 108 0.95 55.46

23. . generate byte lao_zheng = index(new_CONDITION_PINYIN,"laozheng") > 0 . tab lao_zheng lao_zheng | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,511,910 99.90 99.90 1 | 1,447 0.10 100.00 ------------+----------------------------------- Total | 1,513,357 100.00