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NHANES_part3.R
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NHANES_part3.R
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library(fields)
###############################
#Preprocess mortality data from fits by race, sex, age, period and cohort, informed by BMI quintile from
#NHANES longitudinal followup and save as tables for use in BMI history generator
#Decennial (period) Life Tables based on data from the National Center for Health Statistics
#for 3-year periods around the decennial censuses from 1950 - 1990.
#www.ced.gov/nchs/data/nvsr51/nvsr51_03.pdf (Tables 5-8 for year 2000)
#www.ced.gov/nchs/data/nvsr51/nvsr64_11.pdf (Tables 5-8 for year 2011)
#White males life tables
wmlt5090=read.table('../Basecase3/Lifetables/w.m.lt.1x3',header=TRUE)
wmlt2000=read.csv('../Basecase3/Lifetables/LifeTable2000_WM_Table05.csv',header=TRUE)
wmlt2010=read.csv('../Basecase3/Lifetables/LifeTable2010_WM_Table05.csv',header=TRUE)
#White females life tables
wflt5090=read.table('../Basecase3/Lifetables/w.f.lt.1x3',header=TRUE)
wflt2000=read.csv('../Basecase3/Lifetables/LifeTable2000_WF_Table06.csv',header=TRUE)
wflt2010=read.csv('../Basecase3/Lifetables/LifeTable2010_WF_Table06.csv',header=TRUE)
#Black males life tables (Data missing for blacks for 1950, 1960 - use non-white data below
bmlt7090=read.table('../Basecase3/Lifetables/b.m.lt.1x3',header=TRUE)
bmlt2000=read.csv('../Basecase3/Lifetables/LifeTable2000_BM_Table08.csv',header=TRUE)
bmlt2010=read.csv('../Basecase3/Lifetables/LifeTable2010_BM_Table08.csv',header=TRUE)
#Black females life tables (Data missing for blacks for 1950, 1960 - use non-white data below
bflt7090=read.table('../Basecase3/Lifetables/b.f.lt.1x3',header=TRUE)
bflt2000=read.csv('../Basecase3/Lifetables/LifeTable2000_BF_Table09.csv',header=TRUE)
bflt2010=read.csv('../Basecase3/Lifetables/LifeTable2010_BF_Table09.csv',header=TRUE)
#Non-White males life tables (Use for blacks for 1950, 1960
nwmlt5090=read.table('../Basecase3/Lifetables/nw.m.lt.1x3',header=TRUE)
#Non-White females life tables (Use for blacks for 1950, 1960
nwflt5090=read.table('../Basecase3/Lifetables/nw.f.lt.1x3',header=TRUE)
#Period Life Tables for the Social Security Area by Calendar Year and Sex (Not race)
# <https://www.ssa.gov/OACT/NOTES/as120/LifeTables_Tbl_6_2030.html>
amflt1900=read.table('../Basecase3/Lifetables/SSATable6_1900Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 1900
xmam00 <- matrix(NA,100,2)
xmam00[,1] <- amflt1900[2:101,1]
xmam00[,2] <- rep(1900,100)
ymam00 <- amflt1900[2:101,2]
#All females 1900
xmaf00 <- xmam00
ymaf00 <- amflt1900[2:101,9]
amflt1910=read.table('../Basecase3/Lifetables/SSATable6_1910Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 1910
xmam10 <- matrix(NA,100,2)
xmam10[,1] <- amflt1910[2:101,1]
xmam10[,2] <- rep(1910,100)
ymam10 <- amflt1910[2:101,2]
#All females 1910
xmaf10 <- xmam10
ymaf10 <- amflt1910[2:101,9]
amflt1920=read.table('../Basecase3/Lifetables/SSATable6_1920Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 1920
xmam20 <- matrix(NA,100,2)
xmam20[,1] <- amflt1920[2:101,1]
xmam20[,2] <- rep(1920,100)
ymam20 <- amflt1920[2:101,2]
#All females 1920
xmaf20 <- xmam20
ymaf20 <- amflt1920[2:101,9]
amflt1930=read.table('../Basecase3/Lifetables/SSATable6_1930Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 1930
xmam30 <- matrix(NA,100,2)
xmam30[,1] <- amflt1930[2:101,1]
xmam30[,2] <- rep(1930,100)
ymam30 <- amflt1930[2:101,2]
#All females 1930
xmaf30 <- xmam30
ymaf30 <- amflt1930[2:101,9]
amflt1940=read.table('../Basecase3/Lifetables/SSATable6_1940Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 1940
xmam40 <- matrix(NA,100,2)
xmam40[,1] <- amflt1940[2:101,1]
xmam40[,2] <- rep(1940,100)
ymam40 <- amflt1940[2:101,2]
#All females 1940
xmaf40 <- xmam40
ymaf40 <- amflt1940[2:101,9]
amflt1950=read.table('../Basecase3/Lifetables/SSATable6_1950Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 1950
xmam50 <- matrix(NA,100,2)
xmam50[,1] <- amflt1950[2:101,1]
xmam50[,2] <- rep(1950,100)
ymam50 <- amflt1950[2:101,2]
#All females 1950
xmaf50 <- xmam50
ymaf50 <- amflt1950[2:101,9]
amflt2010=read.table('../Basecase3/Lifetables/SSATable6_2010Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2010
xmam2010 <- matrix(NA,100,2)
xmam2010[,1] <- amflt2010[2:101,1]
xmam2010[,2] <- rep(2010,100)
ymam2010 <- amflt2010[2:101,2]
#All females 2010
xmaf2010 <- xmam2010
ymaf2010 <- amflt2010[2:101,9]
amflt2020=read.table('../Basecase3/Lifetables/SSATable6_2020Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2020
xmam2020 <- matrix(NA,100,2)
xmam2020[,1] <- amflt2020[2:101,1]
xmam2020[,2] <- rep(2020,100)
ymam2020 <- amflt2020[2:101,2]
#All females 2020
xmaf2020 <- xmam2020
ymaf2020 <- amflt2020[2:101,9]
amflt2030=read.table('../Basecase3/Lifetables/SSATable6_2030Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2030
xmam2030 <- matrix(NA,100,2)
xmam2030[,1] <- amflt2030[2:101,1]
xmam2030[,2] <- rep(2030,100)
ymam2030 <- amflt2030[2:101,2]
#All females 2030
xmaf2030 <- xmam2030
ymaf2030 <- amflt2030[2:101,9]
amflt2040=read.table('../Basecase3/Lifetables/SSATable6_2040Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2040
xmam2040 <- matrix(NA,100,2)
xmam2040[,1] <- amflt2040[2:101,1]
xmam2040[,2] <- rep(2040,100)
ymam2040 <- amflt2040[2:101,2]
#All females 2040
xmaf2040 <- xmam2040
ymaf2040 <- amflt2040[2:101,9]
amflt2050=read.table('../Basecase3/Lifetables/SSATable6_2050Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2050
xmam2050 <- matrix(NA,100,2)
xmam2050[,1] <- amflt2050[2:101,1]
xmam2050[,2] <- rep(2050,100)
ymam2050 <- amflt2050[2:101,2]
#All females 2050
xmaf2050 <- xmam2050
ymaf2050 <- amflt2050[2:101,9]
amflt2060=read.table('../Basecase3/Lifetables/SSATable6_2060Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2060
xmam2060 <- matrix(NA,100,2)
xmam2060[,1] <- amflt2060[2:101,1]
xmam2060[,2] <- rep(2060,100)
ymam2060 <- amflt2060[2:101,2]
#All females 2060
xmaf2060 <- xmam2060
ymaf2060 <- amflt2060[2:101,9]
amflt2070=read.table('../Basecase3/Lifetables/SSATable6_2070Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2070
xmam2070 <- matrix(NA,100,2)
xmam2070[,1] <- amflt2070[2:101,1]
xmam2070[,2] <- rep(2070,100)
ymam2070 <- amflt2070[2:101,2]
#All females 2070
xmaf2070 <- xmam2070
ymaf2070 <- amflt2070[2:101,9]
amflt2080=read.table('../Basecase3/Lifetables/SSATable6_2080Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2080
xmam2080 <- matrix(NA,100,2)
xmam2080[,1] <- amflt2080[2:101,1]
xmam2080[,2] <- rep(2080,100)
ymam2080 <- amflt2080[2:101,2]
#All females 2080
xmaf2080 <- xmam2080
ymaf2080 <- amflt2080[2:101,9]
amflt2090=read.table('../Basecase3/Lifetables/SSATable6_2090Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2090
xmam2090 <- matrix(NA,100,2)
xmam2090[,1] <- amflt2090[2:101,1]
xmam2090[,2] <- rep(2090,100)
ymam2090 <- amflt2090[2:101,2]
#All females 2090
xmaf2090 <- xmam2090
ymaf2090 <- amflt2090[2:101,9]
amflt2100=read.table('../Basecase3/Lifetables/SSATable6_2100Male_FemaleProjectedLifeTables.txt',header=TRUE)
#All males 2100
xmam2100 <- matrix(NA,100,2)
xmam2100[,1] <- amflt2100[2:101,1]
xmam2100[,2] <- rep(2100,100)
ymam2100 <- amflt2100[2:101,2]
#All females 2100
xmaf2100 <- xmam2100
ymaf2100 <- amflt2100[2:101,9]
#White males 1950-1990
xmwm5090 <- matrix(NA,dim(wmlt5090)[1],2)
xmwm5090[,1] <- wmlt5090[,2] #age
xmwm5090[,2] <- wmlt5090[,1] #year 1950, 1960, ... 1990
ymwm5090 <- wmlt5090$qx #annual death probability
#White males 1950 (use to extrapolate backward using ratio to all males)
xmwm50 <- xmwm5090[2:101,]
ymwm50 <- ymwm5090[2:101]
#White males 2000
xmwm2000 <- matrix(NA,dim(wmlt2000)[1],2)
xmwm2000[,1] <- wmlt2000[,1] #age
xmwm2000[,2] <- rep(2000,dim(wmlt2000)[1]) #age
ymwm2000 <- wmlt2000$qx #annual death probability
#White males 2010 (use to extrapolate forward using ratio to all males)
xmwm2010 <- matrix(NA,dim(wmlt2010)[1],2)
xmwm2010[,1] <- wmlt2010[,1] #age
xmwm2010[,2] <- rep(2010,dim(wmlt2010)[1]) #age
ymwm2010 <- wmlt2010$qx #annual death probability -
#Ratio white to all males 1950, use ratio to project white mortality back to 1900
yw2am50 <- ymwm50/ymam50
ymwm40 <- yw2am50 * ymam40
ymwm30 <- yw2am50 * ymam30
ymwm20 <- yw2am50 * ymam20
ymwm10 <- yw2am50 * ymam10
ymwm00 <- yw2am50 * ymam00
xmwm40 <- xmam40
xmwm30 <- xmam30
xmwm20 <- xmam20
xmwm10 <- xmam10
xmwm00 <- xmam00
#Ratio white to all males 2010, use ratio to project white mortality to 2100
yw2am2010 <- ymwm2010/ymam2010
ymwm2020 <- yw2am2010 * ymam2020
ymwm2030 <- yw2am2010 * ymam2030
ymwm2040 <- yw2am2010 * ymam2040
ymwm2050 <- yw2am2010 * ymam2050
ymwm2060 <- yw2am2010 * ymam2060
ymwm2070 <- yw2am2010 * ymam2070
ymwm2080 <- yw2am2010 * ymam2080
ymwm2090 <- yw2am2010 * ymam2090
ymwm2100 <- yw2am2010 * ymam2100
xmwm2020 <- xmam2020
xmwm2030 <- xmam2030
xmwm2040 <- xmam2040
xmwm2050 <- xmam2050
xmwm2060 <- xmam2060
xmwm2070 <- xmam2070
xmwm2080 <- xmam2080
xmwm2090 <- xmam2090
xmwm2100 <- xmam2100
#White males life tables plate splines to smooth data
xmwm <-rbind( xmwm00, xmwm10, xmwm20, xmwm30, xmwm40,
xmwm5090,xmwm2000,xmwm2010,xmwm2020,xmwm2030,
xmwm2040,xmwm2050,xmwm2060,xmwm2070,
xmwm2080,xmwm2090,xmwm2100)
ymwm <-c( ymwm00, ymwm10, ymwm20, ymwm30, ymwm40,
ymwm5090,ymwm2000,ymwm2010,ymwm2020,ymwm2030,
ymwm2040,ymwm2050,ymwm2060,ymwm2070,
ymwm2080,ymwm2090,ymwm2100)
fitltwm<-Tps(xmwm,ymwm)
#grid.list<- list( x= 1:100, y=1900:2100)
#ltwm <- matrix(predict(fitltwm,xg),100,201)
cnlt <-as.character(c(1900:2100))
rnlt <-as.character(c(12:100))
grid.list<- list( x= 12:100, y=1900:2100)
xg<- make.surface.grid(grid.list)
ltwm <- matrix(predict(fitltwm,xg),89,201)
colnames(ltwm) <- cnlt
rownames(ltwm) <- rnlt
save(ltwm,file = "ltwm.RData")
######################
#Non-White males life tables using thin plate splines to smooth data
xmnwm5090 <- matrix(NA,dim(nwmlt5090)[1],2)
xmnwm50 <- xmnwm5090[1:100,]
xmnwm5090[,1] <- nwmlt5090[,2] #age
xmnwm5090[,2] <- nwmlt5090[,1] #year 1950, 1960, ... 1990
ymnwm5090 <- nwmlt5090$qx #annual death probability
ymnwm50 <- ymnwm5090[1:100]
xmnwm5060 <- xmnwm5090[1:202,]
ymnwm5060 <- ymnwm5090[1:202]
#Black information not available before 1970 - use non-white files back to 1950, project before that
#Ratio non-white to all males 1950, use ratio to project non-white mortality back to 1900
ynw2am50 <- ymnwm50/ymam50
ymnwm40 <- ynw2am50 * ymam40
ymnwm30 <- ynw2am50 * ymam30
ymnwm20 <- ynw2am50 * ymam20
ymnwm10 <- ynw2am50 * ymam10
ymnwm00 <- ynw2am50 * ymam00
xmnwm40 <- xmam40
xmnwm30 <- xmam30
xmnwm20 <- xmam20
xmnwm10 <- xmam10
xmnwm00 <- xmam00
#Black males life tables 1970-1990
xmbm7090 <- matrix(NA,dim(bmlt7090)[1],2)
xmbm7090[,1] <- bmlt7090[,2] #age
xmbm7090[,2] <- bmlt7090[,1] #year 1950, 1960, ... 1990
ymbm7090 <- bmlt7090$qx #annual death probability
#Black males life tables 2000
xmbm2000 <- matrix(NA,dim(bmlt2000)[1],2)
xmbm2000[,1] <- bmlt2000[,1] #age
xmbm2000[,2] <- rep(2000,dim(bmlt2000)[1]) #age
ymbm2000 <- bmlt2000$qx #annual death probability
#Black males life tables 2010
xmbm2010 <- matrix(NA,dim(bmlt2010)[1],2)
xmbm2010[,1] <- bmlt2010[,1] #age
xmbm2010[,2] <- rep(2010,dim(bmlt2010)[1]) #age
ymbm2010 <- bmlt2010$qx #annual death probability
#Ratio black to all males 2010, use ratio to project black mortality to 2100
yb2am2010 <- ymbm2010/ymam2010
ymbm2020 <- yb2am2010 * ymam2020
ymbm2030 <- yb2am2010 * ymam2030
ymbm2040 <- yb2am2010 * ymam2040
ymbm2050 <- yb2am2010 * ymam2050
ymbm2060 <- yb2am2010 * ymam2060
ymbm2070 <- yb2am2010 * ymam2070
ymbm2080 <- yb2am2010 * ymam2080
ymbm2090 <- yb2am2010 * ymam2090
ymbm2100 <- yb2am2010 * ymam2100
xmbm2020 <- xmam2020
xmbm2030 <- xmam2030
xmbm2040 <- xmam2040
xmbm2050 <- xmam2050
xmbm2060 <- xmam2060
xmbm2070 <- xmam2070
xmbm2080 <- xmam2080
xmbm2090 <- xmam2090
xmbm2100 <- xmam2100
#Black males life tables using thin plate splines to smooth data
xmbm <-rbind( xmnwm00, xmnwm10, xmnwm20, xmnwm30, xmnwm40,
xmnwm5060,xmbm7090,xmbm2000,xmbm2010,xmbm2020,xmbm2030,
xmbm2040,xmbm2050,xmbm2060,xmbm2070,
xmbm2080,xmbm2090,xmbm2100)
ymbm <- c( ymnwm00, ymnwm10, ymnwm20, ymnwm30, ymnwm40,
ymnwm5060,ymbm7090,ymbm2000,ymbm2010,ymbm2020,ymbm2030,
ymbm2040,ymbm2050,ymbm2060,ymbm2070,
ymbm2080,ymbm2090,ymbm2100)
fitltbm<-Tps(xmbm,ymbm)
#grid.list<- list( x= 1:100, y=1900:2100)
#xg<- make.surface.grid(grid.list)
#ltbm <- matrix(predict(fitltbm,xg),100,201)
cnlt <-as.character(c(1900:2100))
rnlt <-as.character(c(12:100))
grid.list<- list( x= 12:100, y=1900:2100)
xg<- make.surface.grid(grid.list)
ltbm <- matrix(predict(fitltbm,xg),89,201)
colnames(ltbm) <- cnlt
rownames(ltbm) <- rnlt
save(ltbm,file = "ltbm.RData")
####################
#White females 1950-1990
xmwf5090 <- matrix(NA,dim(wflt5090)[1],2)
xmwf5090[,1] <- wflt5090[,2] #age
xmwf5090[,2] <- wflt5090[,1] #year 1950, 1960, ... 1990
ymwf5090 <- wflt5090$qx #annual death probability
#White females 1950 (use to extrapolate backward using ratio to all females)
xmwf50 <- xmwf5090[1:100,]
ymwf50 <- ymwf5090[1:100]
#White females 2000
xmwf2000 <- matrix(NA,dim(wflt2000)[1],2)
xmwf2000[,1] <- wflt2000[,1] #age
xmwf2000[,2] <- rep(2000,dim(wflt2000)[1]) #age
ymwf2000 <- wflt2000$qx #annual death probability
#White females 2010 (use to extrapolate forward using ratio to all females)
xmwf2010 <- matrix(NA,dim(wflt2010)[1],2)
xmwf2010[,1] <- wflt2010[,1] #age
xmwf2010[,2] <- rep(2010,dim(wflt2010)[1]) #age
ymwf2010 <- wflt2010$qx #annual death probability -
#Ratio white to all females 1950, use ratio to project white mortality back to 1900
yw2af50 <- ymwf50/ymaf50
ymwf40 <- yw2af50 * ymaf40
ymwf30 <- yw2af50 * ymaf30
ymwf20 <- yw2af50 * ymaf20
ymwf10 <- yw2af50 * ymaf10
ymwf00 <- yw2af50 * ymaf00
xmwf40 <- xmaf40
xmwf30 <- xmaf30
xmwf20 <- xmaf20
xmwf10 <- xmaf10
xmwf00 <- xmaf00
#Ratio white to all females 2010, use ratio to project white mortality to 2100
yw2af2010 <- ymwf2010/ymaf2010
ymwf2020 <- yw2af2010 * ymaf2020
ymwf2030 <- yw2af2010 * ymaf2030
ymwf2040 <- yw2af2010 * ymaf2040
ymwf2050 <- yw2af2010 * ymaf2050
ymwf2060 <- yw2af2010 * ymaf2060
ymwf2070 <- yw2af2010 * ymaf2070
ymwf2080 <- yw2af2010 * ymaf2080
ymwf2090 <- yw2af2010 * ymaf2090
ymwf2100 <- yw2af2010 * ymaf2100
xmwf2020 <- xmaf2020
xmwf2030 <- xmaf2030
xmwf2040 <- xmaf2040
xmwf2050 <- xmaf2050
xmwf2060 <- xmaf2060
xmwf2070 <- xmaf2070
xmwf2080 <- xmaf2080
xmwf2090 <- xmaf2090
xmwf2100 <- xmaf2100
#White females life tables plate splines to smooth data
xmwf <-rbind( xmwf00, xmwf10, xmwf20, xmwf30, xmwf40,
xmwf5090,xmwf2000,xmwf2010,xmwf2020,xmwf2030,
xmwf2040,xmwf2050,xmwf2060,xmwf2070,
xmwf2080,xmwf2090,xmwf2100)
ymwf <-c( ymwf00, ymwf10, ymwf20, ymwf30, ymwf40,
ymwf5090,ymwf2000,ymwf2010,ymwf2020,ymwf2030,
ymwf2040,ymwf2050,ymwf2060,ymwf2070,
ymwf2080,ymwf2090,ymwf2100)
fitltwf<-Tps(xmwf,ymwf)
#grid.list<- list( x= 1:100, y=1900:2100)
grid.list<- list( x= 12:100, y=1900:2100)
xg<- make.surface.grid(grid.list)
#ltwf <- matrix(predict(fitltwf,xg),100,201)
ltwf <- matrix(predict(fitltwf,xg),89,201)
cnlt <-as.character(c(1900:2100))
rnlt <-as.character(c(12:100))
colnames(ltwf) <- cnlt
rownames(ltwf) <- rnlt
save(ltwf,file = "ltwf.RData")
######################
#Non-White females life tables using thin plate splines to smooth data
xmnwf5090 <- matrix(NA,dim(nwflt5090)[1],2)
xmnwf50 <- xmnwf5090[1:100,]
xmnwf5090[,1] <- nwflt5090[,2] #age
xmnwf5090[,2] <- nwflt5090[,1] #year 1950, 1960, ... 1990
ymnwf5090 <- nwflt5090$qx #annual death probability
ymnwf50 <- ymnwf5090[1:100]
xmnwf5060 <- xmnwf5090[1:202,]
ymnwf5060 <- ymnwf5090[1:202]
#Black information not available before 1970 - use non-white files back to 1950, project before that
#Ratio non-white to all females 1950, use ratio to project non-white mortality back to 1900
ynw2af50 <- ymnwf50/ymaf50
ymnwf40 <- ynw2af50 * ymaf40
ymnwf30 <- ynw2af50 * ymaf30
ymnwf20 <- ynw2af50 * ymaf20
ymnwf10 <- ynw2af50 * ymaf10
ymnwf00 <- ynw2af50 * ymaf00
xmnwf40 <- xmaf40
xmnwf30 <- xmaf30
xmnwf20 <- xmaf20
xmnwf10 <- xmaf10
xmnwf00 <- xmaf00
#Black females life tables 1970-1990
xmbf7090 <- matrix(NA,dim(bflt7090)[1],2)
xmbf7090[,1] <- bflt7090[,2] #age
xmbf7090[,2] <- bflt7090[,1] #year 1950, 1960, ... 1990
ymbf7090 <- bflt7090$qx #annual death probability
#Black females life tables 2000
xmbf2000 <- matrix(NA,dim(bflt2000)[1],2)
xmbf2000[,1] <- bflt2000[,1] #age
xmbf2000[,2] <- rep(2000,dim(bflt2000)[1]) #age
ymbf2000 <- bflt2000$qx #annual death probability
#Black females life tables 2010
xmbf2010 <- matrix(NA,dim(bflt2010)[1],2)
xmbf2010[,1] <- bflt2010[,1] #age
xmbf2010[,2] <- rep(2010,dim(bflt2010)[1]) #age
ymbf2010 <- bflt2010$qx #annual death probability
#Ratio black to all females 2010, use ratio to project black mortality to 2100
yb2af2010 <- ymbf2010/ymaf2010
ymbf2020 <- yb2af2010 * ymaf2020
ymbf2030 <- yb2af2010 * ymaf2030
ymbf2040 <- yb2af2010 * ymaf2040
ymbf2050 <- yb2af2010 * ymaf2050
ymbf2060 <- yb2af2010 * ymaf2060
ymbf2070 <- yb2af2010 * ymaf2070
ymbf2080 <- yb2af2010 * ymaf2080
ymbf2090 <- yb2af2010 * ymaf2090
ymbf2100 <- yb2af2010 * ymaf2100
xmbf2020 <- xmaf2020
xmbf2030 <- xmaf2030
xmbf2040 <- xmaf2040
xmbf2050 <- xmaf2050
xmbf2060 <- xmaf2060
xmbf2070 <- xmaf2070
xmbf2080 <- xmaf2080
xmbf2090 <- xmaf2090
xmbf2100 <- xmaf2100
#Black females life tables using thin plate splines to smooth data
xmbf <-rbind( xmnwf00, xmnwf10, xmnwf20, xmnwf30, xmnwf40,
xmnwf5060,xmbf7090,xmbf2000,xmbf2010,xmbf2020,xmbf2030,
xmbf2040,xmbf2050,xmbf2060,xmbf2070,
xmbf2080,xmbf2090,xmbf2100)
ymbf <- c( ymnwf00, ymnwf10, ymnwf20, ymnwf30, ymnwf40,
ymnwf5060,ymbf7090,ymbf2000,ymbf2010,ymbf2020,ymbf2030,
ymbf2040,ymbf2050,ymbf2060,ymbf2070,
ymbf2080,ymbf2090,ymbf2100)
fitltbf<-Tps(xmbf,ymbf)
#grid.list<- list( x= 1:100, y=1900:2100)
grid.list<- list( x= 12:100, y=1900:2100)
xg<- make.surface.grid(grid.list)
#ltbf <- matrix(predict(fitltbf,xg),100,201)
ltbf <- matrix(predict(fitltbf,xg),89,201)
cnlt <-as.character(c(1900:2100))
rnlt <-as.character(c(12:100))
colnames(ltbf) <- cnlt
rownames(ltbf) <- rnlt
save(ltbf,file = "ltbf.RData")
####################
### Use data from: Prospective Studies Collaboration, Body-mass index and cause-specific mortality
# in 900,000 adults: collabortive analyses of 57 prospective studies. Lancet 2009; 373:1083-96
# Supplementary webappendix, Webfigure 1. a) males, and b) females.
#Males
xmhaz <- matrix(NA,90,2)
xmhazbmi <- c(16.25,18.75,21.25,23.75,26.25,28.75,31.25,33.75,36.25)
xmhazage <- c(47,64.5,74.5,84.5)
xmhaz5 <- rep(5,9) #assume male annual (floating) morality hazard by BMI ~ same for age 5 - 59
xmhaz15 <- rep(15,9)
xmhaz25 <- rep(25,9)
xmhaz35 <- rep(35,9)
xmhaz45 <- rep(45,9)
xmhaz55 <- rep(55,9)
xmhaz65 <- rep(xmhazage[2],9)
xmhaz75 <- rep(xmhazage[3],9)
xmhaz85 <- rep(xmhazage[4],9)
xmhaz95 <- rep(95,9) #assume male annual (floating) morality hazard by BMI ~ same for age 90 - 100
xmhaz[,1] <- c( xmhaz5, xmhaz15, xmhaz25, xmhaz35, xmhaz45, xmhaz55,
xmhaz65,xmhaz75,xmhaz85,xmhaz95)
xmhaz[,2] <- rep(xmhazbmi,10)
ymhaz45 <- c(12.3, 7.2, 5.8, 5.4, 5.8, 6.4, 8.3, 9.2,12.0)
ymhaz65 <- c(46.2,33.9,28.8,25.3,26.5,29.8,36.5,42.7,48.4)
ymhaz75 <- c(110,80.0,70.0,65.6,69.6,76.7,85.8,91.1,114.4)
ymhaz85 <- c(257,186.6,172.1,176.9,180.3,184.2,240.5,245.5,239.6)
ymhaz <- c( rep(ymhaz45,6), ymhaz65,ymhaz75,rep(ymhaz85,2))
fitmbmihaz<-Tps(xmhaz,ymhaz)
bmirange=12:50
grid.list<- list( x= 1:100, y=bmirange)
xgbmi<- make.surface.grid(grid.list)
mhazbmi <- matrix(predict(fitmbmihaz,xgbmi),100,39)
fitwmbmihaz=fitmbmihaz #assume white and black males follow the same relative mortality risk
fitbmbmihaz=fitmbmihaz
#Females
xfhaz <- matrix(NA,90,2)
xfhazbmi <- c(16.25,18.75,21.25,23.75,26.25,28.75,31.25,33.75,36.25)
xfhazage <- c(47,64.5,74.5,84.5)
xfhaz5 <- rep(5,9) #assume male annual (floating) morality hazard by BMI ~ same for age 5 - 59
xfhaz15 <- rep(15,9)
xfhaz25 <- rep(25,9)
xfhaz35 <- rep(35,9)
xfhaz45 <- rep(45,9)
xfhaz55 <- rep(55,9)
xfhaz65 <- rep(xfhazage[2],9)
xfhaz75 <- rep(xfhazage[3],9)
xfhaz85 <- rep(xfhazage[4],9)
xfhaz95 <- rep(95,9) #assume male annual (floating) morality hazard by BMI ~ same for age 90 - 99
xfhaz[,1] <- c( xfhaz5, xfhaz15, xfhaz25, xfhaz35, xfhaz45, xfhaz55,
xfhaz65,xfhaz75,xfhaz85,xfhaz95)
xfhaz[,2] <- rep(xfhazbmi,10)
yfhaz45 <- c(6.8, 3.4, 3.0, 3.0, 3.0, 3.4, 3.6, 5.1,5.7)
yfhaz65 <- c(22.0,16.5,14.3,12.3,13.9,14.6,16.9,21.9,26.4)
yfhaz75 <- c(55.3,40.4,38.5,38.7,37.2,45.5,50.3,53.4,68.6)
yfhaz85 <- c(148.8,131.8,127.5,116.5,121.7,135.0,140.7,135.2,175.7)
yfhaz <- c( rep(yfhaz45,6), yfhaz65,yfhaz75,rep(yfhaz85,2))
fitfbmihaz<-Tps(xfhaz,yfhaz)
fitwfbmihaz=fitfbmihaz #assume white and black males follow the same relative mortality risk
fitbfbmihaz=fitfbmihaz
bmirange=12:50
grid.list<- list( x= 1:100, y=bmirange)
xgbmi<- make.surface.grid(grid.list)
fhazbmi <- matrix(predict(fitwfbmihaz,xgbmi),100,39)
uspop2014 <- read.csv('./USPopulationData/USPop2014.csv',header=TRUE)
uswmpop12_84 =uspop2014$WM2014pop[12:84]
uswfpop12_84 =uspop2014$WF2014pop[12:84]
usbmpop12_84 =uspop2014$BM2014pop[12:84]
usbfpop12_84 =uspop2014$BF2014pop[12:84]
###############################
#BMI fits by race, sex, quintile saved as tables (part 1) for use in BMI history generator
#load("bmiwm.RData")
#load("bmiwf.RData")
#load("bmibm.RData")
#load("bmibf.RData")
load("fitwmbmi.RData")
load("fitwfbmi.RData")
load("fitbmbmi.RData")
load("fitbfbmi.RData")
##########################################################
cnlt <-as.character(c(1900:2100)) #colnames
rnlt <-as.character(c(12:100)) #rownames
#White males
#life tables, pop, and fractions projected into the future from long data resampled to fit BMI dist
ixwm <- matrix(0,89,201); colnames(ixwm) <- cnlt;rownames(ixwm) <- rnlt
cltwm <- matrix(0,89,201); colnames(cltwm) <- cnlt;rownames(cltwm) <- rnlt
ltwm_obese <- matrix(0,89,201); colnames(ltwm_obese) <- cnlt;rownames(ltwm_obese) <- rnlt
frwm_obese <- matrix(0,89,201); colnames(frwm_obese) <- cnlt;rownames(frwm_obese) <- rnlt
ltwm_nonob <- matrix(0,89,201); colnames(ltwm_nonob) <- cnlt;rownames(ltwm_nonob) <- rnlt
frwm_nonob <- matrix(0,89,201); colnames(frwm_nonob) <- cnlt;rownames(frwm_nonob) <- rnlt
ltwm_ovwgt <- matrix(0,89,201); colnames(ltwm_ovwgt) <- cnlt;rownames(ltwm_ovwgt) <- rnlt
frwm_ovwgt <- matrix(0,89,201); colnames(frwm_ovwgt) <- cnlt;rownames(frwm_ovwgt) <- rnlt
ltwm_nmwgt <- matrix(0,89,201); colnames(ltwm_nmwgt) <- cnlt;rownames(ltwm_nmwgt) <- rnlt
frwm_nmwgt <- matrix(0,89,201); colnames(frwm_nmwgt) <- cnlt;rownames(frwm_nmwgt) <- rnlt
#Counterfactual pop and fracs assuming constant obese, non-obse frac continue in future as in 2015
cixwm <- matrix(0,89,201); colnames(cixwm) <- cnlt;rownames(cixwm) <- rnlt
cvfracob<- vector(mode="numeric", length=89)
cvfracnob<- vector(mode="numeric", length=89)
crr<- vector(mode="numeric", length=89)
crr=1
wmprojlytot=0
wmcfaclytot=0
wmcfacyears=0
cumprobl = (0:1000)/1000
for (i in 1:89) { #ages 12-100
age=i+11
for (j in 1:200) { #years 1900-2100
yr <- 1899 +j
byr=yr-age #byrk=1 for 84 year old in 2014; 73 for 12 yr old
byrk=byr-1929 #byrk=1 for 84 year old in 2014; 73 for 12 yr old
zbmip <- list( x= age, y=yr, z=cumprobl) #quintile 1 median
xbmig<- make.surface.grid(zbmip)
fbmip<- predict( fitwmbmi, xbmig)
zhazp <- list( x= age, z=fbmip) #quintile 1 median
xhazg<- make.surface.grid(zhazp)
fhazbmi<- predict(fitwmbmihaz,xhazg)
nhazbmi<- fhazbmi/sum(fhazbmi)
fracob <- length(fbmip[fbmip>=30])/length(fbmip)
hazob <- sum(nhazbmi[fbmip>=30])/fracob
ltwm_obese[i,j] <- ltwm[i,j] * hazob
frwm_obese[i,j] <- fracob
nonob <- fbmip[fbmip<30]
fracnob <- length(nonob)/length(fbmip)
haznobi <- nhazbmi[fbmip<30]
haznob <- sum(haznobi)/fracnob
ltwm_nonob[i,j] <- ltwm[i,j] * haznob
frwm_nonob[i,j] <- fracnob
fracow <- length(nonob[nonob>=25])/length(fbmip)
hazow <- sum(haznobi[nonob>=25])/fracow
ltwm_ovwgt[i,j] <- ltwm[i,j] * hazow
frwm_ovwgt[i,j] <- fracow
fracnw <- length(nonob[nonob<25])/length(fbmip)
haznw <- sum(haznobi[nonob<25])/fracnw
ltwm_nmwgt[i,j] <- ltwm[i,j] * haznw
frwm_nmwgt[i,j] <- fracnw
if(yr <= 2014) { #counterfactual vectors by age of BMI fractions assumed to remain as in 2015
cvfracob[i]=fracob
cvfracnob[i]=fracnob
crr[i]=1
}
if(yr == 2014){
ixwm[1:73,j]=uswmpop12_84
cixwm[1:73,j]=uswmpop12_84
cltwm[1:73,j]=ltwm[i,j]
}
if(yr > 2014){
crr[i]=cvfracob[i]*hazob + cvfracnob[i]*haznob
if (i == 1) {
ixwm[i,j]=ixwm[i,j-1]*(1-ltwm[i,j])
cixwm[i,j]=cixwm[i,j-1]*(1-ltwm[i,j]*crr[i])
}
if (i > 1) {
ixwm[i,j]=ixwm[i-1,j-1]*(1-ltwm[i,j])
cixwm[i,j]=cixwm[i-1,j-1]*(1-ltwm[i,j]*crr[i])
}
}
print(c(i,j,byr,age,yr,crr[i],ixwm[i,j],cixwm[i,j]))
}
wmprojlytot=wmprojlytot+ixwm[i,j]
wmcfaclytot=wmcfaclytot+cixwm[i,j]
wmcfacyears=wmcfacyears+1
print(c(i,wmprojlytot,wmcfaclytot,wmcfacyears))
}
write.csv(ltwm,file='lftb_wm.csv')
write.csv(ixwm,file='pop12_wm.csv')
write.csv(cixwm,file='cfac_pop12_wm.csv')
write.csv(ltwm_obese,file='lftb_wm_obese.csv')
write.csv(frwm_obese,file='frac_wm_obese.csv')
write.csv(ltwm_nonob,file='lftb_wm_nonobese.csv')
write.csv(frwm_nonob,file='frac_wm_nonobese.csv')
write.csv(ltwm_ovwgt,file='lftb_wm_overwght.csv')
write.csv(frwm_ovwgt,file='frac_wm_overwght.csv')
write.csv(ltwm_nmwgt,file='lftb_wm_normwght.csv')
write.csv(frwm_nmwgt,file='frac_wm_normwght.csv')
print(wmprojlytot)
print(wmcfaclytot)
print(wmcfacyears)
wmbcly<- vector(mode="numeric", length=88)
cwmbcly<- vector(mode="numeric", length=88)
wmbcly=0
cwmbcly=0
wmbcly[1:89]=ixwm[,115] #age 12:100 in 2014
cwmbcly[1:89]=cixwm[,115]
for (i in 2:87) {
lastage=90-i
nextyr=114+i
print(c(i,nextyr))
wmbcly[1:lastage]=wmbcly[1:lastage]+ixwm[i:89,nextyr]
cwmbcly[1:lastage]=cwmbcly[1:lastage]+cixwm[i:89,nextyr]
}
write.csv(wmbcly,file='wmbcly.csv')
write.csv(cwmbcly,file='cwmbcly.csv')
> sum(wmbcly)
[1] 4129986658
> sum(cwmbcly)
[1] 4052676646
> sum(wmbcly-cwmbcly)
[1] 77310012
> sum(wmbcly-cwmbcly)/sum(wmbcly)
[1] 0.01871919
[1] 3577184.632 3657893.372 3713178.797 3654170.569 3632862.795 3628071.529
[7] 3656322.373 3708787.817 3705858.854 3528030.229 3392385.252 3224764.829
[13] 2998116.410 2680761.663 2447650.441 2238127.350 2081945.760 1937972.530
[19] 1723086.152 1613525.968 1487912.545 1361873.328 1270820.576 1077762.559
[25] 970360.270 869086.880 775398.342 725220.164 646860.203 591714.112
[31] 577100.603 554212.056 520326.559 446285.550 399348.360 374314.704
[37] 339545.063 332523.903 307117.214 279419.055 259874.809 236639.887
[43] 220005.405 204931.557 192163.484 180338.704 167055.116 151414.374
[49] 136689.096 126432.673 108530.696 97917.671 90546.376 75778.106
[55] 69147.729 68593.196 41037.242 35421.704 31120.468 25751.774
[61] 18731.139 14526.693 11983.434 8280.479 6445.378 5057.629
[67] 4244.418 3559.207 2786.602 2330.990 1898.107 1609.199
[73] 1269.624 0.000 0.000 0.000 0.000 0.000
[79] 0.000 0.000 0.000 0.000 0.000 0.000
[85] 0.000 0.000 0.000 0.000 0.000
#White females
#life tables, pop, and fractions projected into the future from long data resampled to fit BMI dist
ixwf <- matrix(0,89,201); colnames(ixwf) <- cnlt;rownames(ixwf) <- rnlt
cltwf <- matrix(0,89,201); colnames(cltwf) <- cnlt;rownames(cltwf) <- rnlt
ltwf_obese <- matrix(0,89,201); colnames(ltwf_obese) <- cnlt;rownames(ltwf_obese) <- rnlt
frwf_obese <- matrix(0,89,201); colnames(frwf_obese) <- cnlt;rownames(frwf_obese) <- rnlt
ltwf_nonob <- matrix(0,89,201); colnames(ltwf_nonob) <- cnlt;rownames(ltwf_nonob) <- rnlt
frwf_nonob <- matrix(0,89,201); colnames(frwf_nonob) <- cnlt;rownames(frwf_nonob) <- rnlt
ltwf_ovwgt <- matrix(0,89,201); colnames(ltwf_ovwgt) <- cnlt;rownames(ltwf_ovwgt) <- rnlt
frwf_ovwgt <- matrix(0,89,201); colnames(frwf_ovwgt) <- cnlt;rownames(frwf_ovwgt) <- rnlt
ltwf_nmwgt <- matrix(0,89,201); colnames(ltwf_nmwgt) <- cnlt;rownames(ltwf_nmwgt) <- rnlt
frwf_nmwgt <- matrix(0,89,201); colnames(frwf_nmwgt) <- cnlt;rownames(frwf_nmwgt) <- rnlt
#Counterfactual pop and fracs assuming constant obese, non-obse frac continue in future as in 2015
cixwf <- matrix(0,89,201); colnames(cixwf) <- cnlt;rownames(cixwf) <- rnlt
cvfracob<- vector(mode="numeric", length=89)
cvfracnob<- vector(mode="numeric", length=89)
crr<- vector(mode="numeric", length=89)
crr=1
wfprojlytot=0
wfcfaclytot=0
wfcfacyears=0
cumprobl = (0:1000)/1000
for (i in 1:89) { #ages 12-100
age=i+11
for (j in 1:200) { #years 1900-2100
yr <- 1899 +j
byr=yr-age #byrk=1 for 84 year old in 2014; 73 for 12 yr old
byrk=byr-1929 #byrk=1 for 84 year old in 2014; 73 for 12 yr old
zbmip <- list( x= age, y=yr, z=cumprobl) #quintile 1 median
xbmig<- make.surface.grid(zbmip)
fbmip<- predict( fitwfbmi, xbmig)
zhazp <- list( x= age, z=fbmip) #quintile 1 median
xhazg<- make.surface.grid(zhazp)
fhazbmi<- predict(fitwfbmihaz,xhazg)
nhazbmi<- fhazbmi/sum(fhazbmi)
fracob <- length(fbmip[fbmip>=30])/length(fbmip)
hazob <- sum(nhazbmi[fbmip>=30])/fracob
ltwf_obese[i,j] <- ltwf[i,j] * hazob
frwf_obese[i,j] <- fracob
nonob <- fbmip[fbmip<30]
fracnob <- length(nonob)/length(fbmip)
haznobi <- nhazbmi[fbmip<30]
haznob <- sum(haznobi)/fracnob
ltwf_nonob[i,j] <- ltwf[i,j] * haznob
frwf_nonob[i,j] <- fracnob
fracow <- length(nonob[nonob>=25])/length(fbmip)
hazow <- sum(haznobi[nonob>=25])/fracow
ltwf_ovwgt[i,j] <- ltwf[i,j] * hazow
frwf_ovwgt[i,j] <- fracow
fracnw <- length(nonob[nonob<25])/length(fbmip)
haznw <- sum(haznobi[nonob<25])/fracnw
ltwf_nmwgt[i,j] <- ltwf[i,j] * haznw
frwf_nmwgt[i,j] <- fracnw
if(yr <= 2014) { #counterfactual vectors by age of BMI fractions assumed to remain as in 2015
cvfracob[i]=fracob
cvfracnob[i]=fracnob
crr[i]=1
}
if(yr == 2014){
ixwf[1:73,j]=uswfpop12_84
cixwf[1:73,j]=uswfpop12_84
cltwf[1:73,j]=ltwf[i,j]
}
if(yr > 2014){
crr[i]=cvfracob[i]*hazob + cvfracnob[i]*haznob
if (i == 1) {
ixwf[i,j]=ixwf[i,j-1]*(1-ltwf[i,j])
cixwf[i,j]=cixwf[i,j-1]*(1-ltwf[i,j]*crr[i])
}
if (i > 1) {
ixwf[i,j]=ixwf[i-1,j-1]*(1-ltwf[i,j])
cixwf[i,j]=cixwf[i-1,j-1]*(1-ltwf[i,j]*crr[i])
}
}
print(c(i,j,byr,age,yr,crr[i],ixwf[i,j],cixwf[i,j]))
}
wfprojlytot=wfprojlytot+ixwf[i,j]
wfcfaclytot=wfcfaclytot+cixwf[i,j]
wfcfacyears=wfcfacyears+1
print(c(i,wfprojlytot,wfcfaclytot,wfcfacyears))
}
write.csv(ltwf,file='lftb_wf.csv')
write.csv(ixwf,file='pop12_wf.csv')
write.csv(cixwf,file='cfac_pop12_wf.csv')
write.csv(ltwf_obese,file='lftb_wf_obese.csv')
write.csv(frwf_obese,file='frac_wf_obese.csv')
write.csv(ltwf_nonob,file='lftb_wf_nonobese.csv')
write.csv(frwf_nonob,file='frac_wf_nonobese.csv')
write.csv(ltwf_ovwgt,file='lftb_wf_overwght.csv')
write.csv(frwf_ovwgt,file='frac_wf_overwght.csv')
write.csv(ltwf_nmwgt,file='lftb_wf_normwght.csv')
write.csv(frwf_nmwgt,file='frac_wf_normwght.csv')
print(wfprojlytot)
print(wfcfaclytot)
print(wfcfacyears)
wfbcly<- vector(mode="numeric", length=88)
cwfbcly<- vector(mode="numeric", length=88)
wfbcly=0
cwfbcly=0
wfbcly[1:89]=ixwf[,115] #age 12:100 in 2014
cwfbcly[1:89]=cixwf[,115]
for (i in 2:87) {
lastage=90-i
nextyr=114+i
print(c(i,nextyr))
wfbcly[1:lastage]=wfbcly[1:lastage]+ixwf[i:89,nextyr]
cwfbcly[1:lastage]=cwfbcly[1:lastage]+cixwf[i:89,nextyr]
}
write.csv(wfbcly,file='wfbcly.csv')
write.csv(cwfbcly,file='cwfbcly.csv')
sum(wfbcly)
sum(cwfbcly)
sum(wfbcly-cwfbcly)
sum(wfbcly-cwfbcly)/sum(wfbcly)
#Black males
#life tables, pop, and fractions projected into the future from long data resampled to fit BMI dist
ixbm <- matrix(0,89,201); colnames(ixbm) <- cnlt;rownames(ixbm) <- rnlt
cltbm <- matrix(0,89,201); colnames(cltbm) <- cnlt;rownames(cltbm) <- rnlt
ltbm_obese <- matrix(0,89,201); colnames(ltbm_obese) <- cnlt;rownames(ltbm_obese) <- rnlt
frbm_obese <- matrix(0,89,201); colnames(frbm_obese) <- cnlt;rownames(frbm_obese) <- rnlt
ltbm_nonob <- matrix(0,89,201); colnames(ltbm_nonob) <- cnlt;rownames(ltbm_nonob) <- rnlt
frbm_nonob <- matrix(0,89,201); colnames(frbm_nonob) <- cnlt;rownames(frbm_nonob) <- rnlt
ltbm_ovwgt <- matrix(0,89,201); colnames(ltbm_ovwgt) <- cnlt;rownames(ltbm_ovwgt) <- rnlt
frbm_ovwgt <- matrix(0,89,201); colnames(frbm_ovwgt) <- cnlt;rownames(frbm_ovwgt) <- rnlt
ltbm_nmwgt <- matrix(0,89,201); colnames(ltbm_nmwgt) <- cnlt;rownames(ltbm_nmwgt) <- rnlt
frbm_nmwgt <- matrix(0,89,201); colnames(frbm_nmwgt) <- cnlt;rownames(frbm_nmwgt) <- rnlt
#Counterfactual pop and fracs assuming constant obese, non-obse frac continue in future as in 2015
cixbm <- matrix(0,89,201); colnames(cixbm) <- cnlt;rownames(cixbm) <- rnlt
cvfracob<- vector(mode="numeric", length=89)
cvfracnob<- vector(mode="numeric", length=89)
crr<- vector(mode="numeric", length=89)
crr=1
bmprojlytot=0
bmcfaclytot=0
bmcfacyears=0
cumprobl = (0:1000)/1000
for (i in 1:89) { #ages 12-100
age=i+11
for (j in 1:200) { #years 1900-2100
yr <- 1899 +j
byr=yr-age #byrk=1 for 84 year old in 2014; 73 for 12 yr old
byrk=byr-1929 #byrk=1 for 84 year old in 2014; 73 for 12 yr old
zbmip <- list( x= age, y=yr, z=cumprobl) #quintile 1 median
xbmig<- make.surface.grid(zbmip)
fbmip<- predict( fitbmbmi, xbmig)
zhazp <- list( x= age, z=fbmip) #quintile 1 median
xhazg<- make.surface.grid(zhazp)
fhazbmi<- predict(fitbmbmihaz,xhazg)
nhazbmi<- fhazbmi/sum(fhazbmi)
fracob <- length(fbmip[fbmip>=30])/length(fbmip)
hazob <- sum(nhazbmi[fbmip>=30])/fracob
ltbm_obese[i,j] <- ltbm[i,j] * hazob
frbm_obese[i,j] <- fracob
nonob <- fbmip[fbmip<30]
fracnob <- length(nonob)/length(fbmip)
haznobi <- nhazbmi[fbmip<30]
haznob <- sum(haznobi)/fracnob
ltbm_nonob[i,j] <- ltbm[i,j] * haznob
frbm_nonob[i,j] <- fracnob
fracow <- length(nonob[nonob>=25])/length(fbmip)
hazow <- sum(haznobi[nonob>=25])/fracow
ltbm_ovwgt[i,j] <- ltbm[i,j] * hazow
frbm_ovwgt[i,j] <- fracow
fracnw <- length(nonob[nonob<25])/length(fbmip)
haznw <- sum(haznobi[nonob<25])/fracnw
ltbm_nmwgt[i,j] <- ltbm[i,j] * haznw
frbm_nmwgt[i,j] <- fracnw
if(yr <= 2014) { #counterfactual vectors by age of BMI fractions assumed to remain as in 2015
cvfracob[i]=fracob
cvfracnob[i]=fracnob
crr[i]=1
}
if(yr == 2014){
ixbm[1:73,j]=usbmpop12_84
cixbm[1:73,j]=usbmpop12_84
cltbm[1:73,j]=ltbm[i,j]
}
if(yr > 2014){
crr[i]=cvfracob[i]*hazob + cvfracnob[i]*haznob
if (i == 1) {
ixbm[i,j]=ixbm[i,j-1]*(1-ltbm[i,j])
cixbm[i,j]=cixbm[i,j-1]*(1-ltbm[i,j]*crr[i])
}
if (i > 1) {
ixbm[i,j]=ixbm[i-1,j-1]*(1-ltbm[i,j])
cixbm[i,j]=cixbm[i-1,j-1]*(1-ltbm[i,j]*crr[i])
}
}
print(c(i,j,byr,age,yr,crr[i],ixbm[i,j],cixbm[i,j]))
}
bmprojlytot=bmprojlytot+ixbm[i,j]
bmcfaclytot=bmcfaclytot+cixbm[i,j]
bmcfacyears=bmcfacyears+1
print(c(i,bmprojlytot,bmcfaclytot,bmcfacyears))
}
write.csv(ltbm,file='lftb_bm.csv')
write.csv(ixbm,file='pop12_bm.csv')
write.csv(cixbm,file='cfac_pop12_bm.csv')
write.csv(ltbm_obese,file='lftb_bm_obese.csv')
write.csv(frbm_obese,file='frac_bm_obese.csv')