CMGPD-LNMethodological Lecture

Day 7Health and Mortality

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

0.0

5.1

.15

.2.2

5P

rob

abili

ty o

f dyi

ng in

nex

t 3 y

ear

s

0 20 40 60 80 100AGE

Males Females

. 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

| SexAge 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

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

Life tablesA crude approach

keep if AGE_IN_SUI > 0 & AGE_IN_SUI <= 75 & NEXT_3 & PRESENT & SEX > 0

* Divide into five year age groupsreplace AGE_IN_SUI =

5*int((AGE_IN_SUI-1)/5)+1tab AGE_IN_SUI SEXcollapse NEXT_DIE, by(AGE_IN_SUI SEX)sort SEX AGE_IN_SUI

. tab AGE_IN_SUI SEX

| SexAge 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

Example of a crude life table

SEXAGE_IN_SUI NEXT_DIE mx 5px lx

Female 1 0.110824 0.039153 0.822204 1 4.555511 50.20073Female 6 0.047963 0.016384 0.921346 0.822204 3.949347 45.64522Female 11 0.030478 0.010317 0.949722 0.757535 3.692455 41.69588Female 16 0.036621 0.012436 0.939713 0.719447 3.488803 38.00342Female 21 0.038381 0.013046 0.936854 0.676074 3.273641 34.51462Female 26 0.040006 0.01361 0.934216 0.633382 3.062746 31.24098Female 31 0.042988 0.014647 0.929385 0.591716 2.854119 28.17823Female 36 0.04774 0.016306 0.921707 0.549932 2.642018 25.32411Female 41 0.049033 0.016759 0.919622 0.506876 2.432524 22.68209Female 46 0.05236 0.017927 0.914265 0.466134 2.230759 20.24957Female 51 0.064616 0.022266 0.894644 0.42617 2.018601 18.01881Female 56 0.087951 0.030687 0.857756 0.38127 1.770768 16.00021Female 61 0.120243 0.042703 0.807739 0.327037 1.477993 14.22944Female 66 0.177129 0.064985 0.722581 0.26416 1.137594 12.75145Female 71 0.227646 0.086104 0.650171 0.190877 11.61386 11.61386

Example of a crude life table

SEX AGE_IN_SUI NEXT_DIE mx 5px lx eMale 1 0.075437 0.026145 0.87746 1 4.69365 56.08813Male 6 0.025836 0.008725 0.957312 0.87746 4.293658 51.39448Male 11 0.018216 0.006128 0.969825 0.840003 4.136648 47.10082Male 16 0.018684 0.006287 0.969055 0.814656 4.010255 42.96417Male 21 0.020036 0.006746 0.96683 0.789446 3.881767 38.95392Male 26 0.021479 0.007238 0.964458 0.763261 3.748484 35.07215Male 31 0.02662 0.008994 0.956028 0.736133 3.599742 31.32367Male 36 0.03458 0.011731 0.943033 0.703764 3.41859 27.72392Male 41 0.045686 0.015588 0.925022 0.663673 3.193961 24.30533Male 46 0.060921 0.020952 0.90054 0.613912 2.91691 21.11137Male 51 0.079247 0.027521 0.871442 0.552852 2.586578 18.19446Male 56 0.10896 0.038455 0.825079 0.481779 2.198212 15.60788Male 61 0.140271 0.050379 0.777325 0.397506 1.766243 13.40967Male 66 0.200569 0.074618 0.688603 0.308991 1.304409 11.64343Male 71 0.251858 0.096721 0.616557 0.212772 10.33902 10.33902

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

------------------------------------------------------------------------------ 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------------------------------------------------------------------------------

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

• Polynomialgenerate age2 = age^2generate age3 = age^3logit NEXT_DIE age age2 age3

• Hybrid– Include age group categories and linear term for age– To capture variation in risks within age groups

Other notes on mortality analysis

• Since many of the ‘immortals’ were tao at 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.

Using the disability variables

• Basic contents• Time trends• Age patterns• Working with the original disabilities

– And positions…

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, sortrun "C:\Users\Cameron Campbe\Documents\Dropbox\Lee-Campbell group

(Dropbox shares)\SJTU Dongbei Zhongxin\SJTU Summer Class\strip_disability.do“

tab new_CONDITION_PINYIN, sortgenerate byte lao_zheng = index(new_CONDITION_PINYIN,"lao zheng") > 0tab lao_zheng

.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'","",.)

}

. 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

. tab new_CONDITION_PINYIN, sort

new_CONDITION_PINYIN | Freq. Percent Cum.--------------------------------------+----------------------------------- chen tao | 1,238 10.93 10.93 lao zheng | 741 6.54 17.48 chen lao zheng | 574 5.07 22.55 yan xia | 462 4.08 26.62 chen xia | 388 3.43 30.05 can ji | 307 2.71 32.76 chen tao you an | 300 2.65 35.41 tu xie | 272 2.40 37.81 xia zi | 260 2.30 40.11 tui que | 234 2.07 42.18 tui tong | 190 1.68 43.85 chen tui que | 178 1.57 45.43 tui huai | 167 1.47 46.90 er long | 166 1.47 48.37 lao bing tu xie | 159 1.40 49.77 yan ji | 154 1.36 51.13 yao huai | 148 1.31 52.44 lou chuang | 121 1.07 53.51 ge bo huai | 113 1.00 54.50 lao tui | 108 0.95 55.46

. generate byte lao_zheng = index(new_CONDITION_PINYIN,"lao zheng") > 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

Preceding birth intervaluse "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from

ICPSR\ICPSR_27063\DS0001\27063-0001-Data.dta", cleardrop if MOTHER_ID == "-99" | BIRTHYEAR < 0 | (SEX == 1 &

MARITAL_STATUS != 2)bysort PERSON_ID: keep if _n == 1bysort MOTHER_ID (BIRTHYEAR): generate pbi = BIRTHYEAR -

BIRTHYEAR[_n-1]bysort MOTHER_ID (BIRTHYEAR): generate firstborn = _n == 1* Basically force firstborn and twin into separate categories

represented by the dummy variablesbysort MOTHER_ID (BIRTHYEAR): replace pbi = 0 if firstbornrecode pbi 15/max=15tab pbikeep PERSON_ID pbi firstbornsave pbi

pbi | Freq. Percent Cum.------------+----------------------------------- 0 | 76,026 51.57 51.57 1 | 4,385 2.97 54.54 2 | 10,152 6.89 61.43 3 | 9,843 6.68 68.11 4 | 7,615 5.17 73.27 5 | 6,238 4.23 77.50 6 | 5,478 3.72 81.22 7 | 4,339 2.94 84.16 8 | 3,569 2.42 86.58 9 | 3,138 2.13 88.71 10 | 2,669 1.81 90.52 11 | 2,063 1.40 91.92 12 | 1,945 1.32 93.24 13 | 1,456 0.99 94.23 14 | 1,292 0.88 95.11 15 | 7,215 4.89 100.00------------+----------------------------------- Total | 147,423 100.00

use "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from ICPSR\ICPSR_27063\DS0001\27063-0001-Data.dta", clear

merge m:1 PERSON_ID using pbi, keep(match master)keep if SEX == 2bysort PERSON_ID (YEAR): keep if AGE_IN_SUI[1] > 0 &

AGE_IN_SUI[1] <= 10keep if AT_RISK_DIE == 1 & NEXT_3 == 1 & PRESENT == 1generate short_pbi = firstborn == 0 & (pbi == 0 | pbi == 1 |

pbi == 2)generate age_group = 1+5*int((AGE_IN_SUI-1)/5)xi:clogit NEXT_DIE i.age_group firstborn short_pbi if

age_group >= 56 & age_group <= 75, group(MOTHER_ID)

. xi:clogit NEXT_DIE i.age_group firstborn short_pbi if age_group >= 56 & age_group <= 75, group(MOTHER_ID)

i.age_group _Iage_group_1-166 (naturally coded; _Iage_group_1 omitted)note: multiple positive outcomes within groups encountered.note: 8860 groups (19131 obs) dropped because of all positive or all negative outcomes.

Conditional (fixed-effects) logistic regression Number of obs = 9902 LR chi2(5) = 2041.71 Prob > chi2 = 0.0000Log likelihood = -2389.4246 Pseudo R2 = 0.2993

------------------------------------------------------------------------------ NEXT_DIE | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------_Iage_gro~_6 | (omitted)…_Iage_gro~51 | (omitted)_Iage_gr~_56 | -4.255857 .1234024 -34.49 0.000 -4.497721 -4.013993_Iage_gro~61 | -2.764736 .1071336 -25.81 0.000 -2.974714 -2.554758_Iage_gr~_66 | -1.425874 .0934471 -15.26 0.000 -1.609027 -1.242721_Iage_gro~71 | (omitted)…_Iage_gr~166 | (omitted) firstborn | -.2756034 .1105682 -2.49 0.013 -.4923131 -.0588938 short_pbi | .300082 .1539756 1.95 0.051 -.0017047 .6018687------------------------------------------------------------------------------

Age at which father last seen aliveuse "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from ICPSR\

ICPSR_27063\DS0001\27063-0001-Data.dta", clearmerge 1:1 RECORD_NUMBER using "C:\Users\Cameron Campbe\Documents\Baqi\

CMGPD-LN from ICPSR\ICPSR_27063\DS0003\27063-0003-Data.dta"keep if SEX == 2bysort PERSON_ID (YEAR): keep if AGE_IN_SUI[1] <= 10 & AGE_IN_SUI[1] >= 1drop if FATHER_ALIVE < 0drop if AGE_IN_SUI < 0bysort PERSON_ID (FATHER_ALIVE YEAR): generate father_last_alive = AGE_IN_SUI[_N]bysort PERSON_ID (FATHER_ALIVE YEAR): replace father_last_alive = 0 if

FATHER_ALIVE[_N] == 0recode father_last_alive 1/5=1 6/10=6 11/15=11 16/max=16generate ever_married = MARITAL_STATUS != 2tab father_last_alive if SEX == 2 & AGE_IN_SUI >= 26 & AGE_IN_SUI <= 30,

sum(ever_married)tab father_last_alive if SEX == 2 & AGE_IN_SUI >= 26 & AGE_IN_SUI <= 30 &

HAS_POSITION >= 0, sum(HAS_POSITION)

. tab father_last_alive if SEX == 2 & AGE_IN_SUI >= 26 & AGE_IN_SUI <= 30, sum(ever_married)

father_last | Summary of ever_married _alive | Mean Std. Dev. Freq.------------+------------------------------------ 0 | .72305186 .4475514 3683 1 | .6979405 .45925601 2185 6 | .72417511 .44697719 4637 11 | .71541591 .45126712 4424 16 | .74787225 .43424031 35601------------+------------------------------------ Total | .73888779 .43924531 50530

. tab father_last_alive if SEX == 2 & AGE_IN_SUI >= 26 & AGE_IN_SUI <= 30 & HAS_POSITION >= 0, sum(HAS_POSITION)

father_last | Summary of Has Official Position _alive | Mean Std. Dev. Freq.------------+------------------------------------ 0 | .01466196 .12021194 3683 1 | .01464531 .12015586 2185 6 | .00841061 .09133275 4637 11 | .0187613 .13569627 4424 16 | .01949383 .1382547 35601------------+------------------------------------ Total | .01785078 .13241027 50530

Another approach to identifying age at last time father was observed

use "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from ICPSR\ICPSR_27063\DS0001\27063-0001-Data.dta", clear

keep if SEX == 2 & PRESENT == 1 & AGE_IN_SUI > 0bysort PERSON_ID (YEAR): keep if _n == _Nkeep PERSON_ID YEARrename PERSON_ID FATHER_IDrename YEAR father_last_yearsave father_last_year, replaceuse "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from ICPSR\ICPSR_27063\DS0001\27063-0001-

Data.dta", clearkeep if FATHER_ID != "-99"keep if SEX == 2merge m:1 FATHER_ID using father_last_year, keep(match master)drop if father_last_year == .keep if BIRTHYEAR > 0generate age_at_father_last_year = father_last_year - BIRTHYEARrecode age_at_father_last_year min/-11=-99 -10/0=0 1/5=1 6/10=6 11/15=11 16/max=16tab age_at_father_last_year if HAS_POSITION >= 0 & AGE_IN_SUI >= 31 & AGE_IN_SUI <= 35, sum(HAS_POSITION)generate ever_married = MARITAL_STATUS != 2tab age_at_father_last_year if MARITAL_STATUS >= 1 & AGE_IN_SUI >= 31 & AGE_IN_SUI <= 35,

sum(ever_married)

. tab age_at_father_last_year

age_at_fath |er_last_yea | r | Freq. Percent Cum.------------+----------------------------------- -99 | 26,808 3.24 3.24 0 | 37,958 4.58 7.82 1 | 53,882 6.51 14.32 6 | 70,491 8.51 22.83 11 | 82,367 9.94 32.78 16 | 556,793 67.22 100.00------------+----------------------------------- Total | 828,299 100.00

. tab age_at_father_last_year if HAS_POSITION >= 0 & AGE_IN_SUI >= 31 & AGE_IN_SUI <= 35, sum(HAS_POSITION)

age_at_fath |er_last_yea | Summary of Has Official Position r | Mean Std. Dev. Freq.------------+------------------------------------ -99 | .01860465 .13520271 860 0 | .01583435 .12485973 2463 1 | .01697793 .12921048 2945 6 | .01438987 .11910299 5212 11 | .01950475 .13830241 5896 16 | .0251456 .15656902 43785------------+------------------------------------ Total | .022825 .14934653 61161

tab age_at_father_last_year if MARITAL_STATUS >= 1 & AGE_IN_SUI >= 31 & AGE_IN_SUI <= 35, sum(ever_married)

age_at_fath |er_last_yea | Summary of ever_married r | Mean Std. Dev. Freq.------------+------------------------------------ -99 | .80913349 .39321436 854 0 | .78486708 .41099859 2445 1 | .77785396 .41576007 2917 6 | .78262556 .41249944 5157 11 | .78500514 .41085395 5842 16 | .81671433 .38690501 43364------------+------------------------------------ Total | .80749104 .39427379 60579

Prices around time of birthuse "C:\Users\Cameron Campbe\Documents\Baqi\prices\Annual logged low

sorghum.dta"rename YEAR BIRTHYEARsort BIRTHYEARgenerate allosorg5 = allosorg[_n-2]+allosorg[_n-

1]+allosorg+allosorg[_n+1]+allosorg[_n+2]save "Logged low sorghum prices around time of birthyear“use "C:\Users\Cameron Campbe\Documents\Baqi\CMGPD-LN from ICPSR\

ICPSR_27063\DS0001\27063-0001-Data.dta", clearmerge m:1 BIRTHYEAR using "C:\Users\Cameron Campbe\Documents\Baqi\prices\

Logged low sorghum prices around time of birthyear", keep(match master)generate age_group = 5*int((AGE_IN_SUI-1)/5)+1keep if PRESENT == 1 & NEXT_3 == 1 & AT_RISK_DIE == 1 & AGE_IN_SUI >= 1xi:logit NEXT_DIE i.age_group allosorg5 if SEX == 2 & AGE_IN_SUI >= 56 &

AGE_IN_SUI <= 75xi:logit NEXT_DIE i.age_group allosorg5 if SEX == 1 & AGE_IN_SUI >= 56 &

AGE_IN_SUI <= 75

xi:logit NEXT_DIE i.age_group allosorg5 if SEX == 1 & AGE_IN_SUI >= 56 & AGE_IN_SUI <= 75i.age_group _Iage_group_1-201 (naturally coded; _Iage_group_1 omitted)

Iteration 0: log likelihood = -16182.212 Iteration 1: log likelihood = -15874.613 Iteration 2: log likelihood = -15864.592 Iteration 3: log likelihood = -15864.584 Iteration 4: log likelihood = -15864.584

Logistic regression Number of obs = 41779 LR chi2(4) = 635.26 Prob > chi2 = 0.0000Log likelihood = -15864.584 Pseudo R2 = 0.0196

------------------------------------------------------------------------------ NEXT_DIE | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------_Iage_gro~_6 | (omitted) _Iage_gr~_51 | (omitted)_Iage_gr~_56 | -.9623698 .0432435 -22.25 0.000 -1.047125 -.8776141_Iage_gr~_61 | -.6752047 .0438673 -15.39 0.000 -.761183 -.5892265_Iage_gr~_66 | -.2201522 .0438849 -5.02 0.000 -.3061651 -.1341393_Iage_gro~71 | (omitted) _Iage_gr~201 | (omitted) allosorg5 | -.0234833 .0087169 -2.69 0.007 -.0405681 -.0063984 _cons | -1.422218 .0437954 -32.47 0.000 -1.508055 -1.33638------------------------------------------------------------------------------

.

xi:logit NEXT_DIE i.age_group allosorg5 if SEX == 2 & AGE_IN_SUI >= 56 & AGE_IN_SUI <= 75i.age_group _Iage_group_1-201 (naturally coded; _Iage_group_1 omitted)

Iteration 0: log likelihood = -18151.951 Iteration 1: log likelihood = -17845.276 Iteration 2: log likelihood = -17836.418 Iteration 3: log likelihood = -17836.412 Iteration 4: log likelihood = -17836.412

Logistic regression Number of obs = 43633 LR chi2(4) = 631.08 Prob > chi2 = 0.0000Log likelihood = -17836.412 Pseudo R2 = 0.0174

------------------------------------------------------------------------------ NEXT_DIE | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------_Iage_gr~_56 | -.9278634 .0410099 -22.63 0.000 -1.008241 -.8474854_Iage_gr~_61 | -.6300167 .041833 -15.06 0.000 -.7120079 -.5480255_Iage_gr~_66 | -.2499452 .0431567 -5.79 0.000 -.3345308 -.1653597 allosorg5 | -.0302716 .0082515 -3.67 0.000 -.0464442 -.0140989 _cons | -1.305654 .0422039 -30.94 0.000 -1.388373 -1.222936------------------------------------------------------------------------------