forked from child-growth/Co-occurrence
-
Notifications
You must be signed in to change notification settings - Fork 0
/
1a_3b_co_cuminc_agerange.R
456 lines (398 loc) · 20 KB
/
1a_3b_co_cuminc_agerange.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
#-----------------------------------
# Co-occurence analysis
# Objective 1a
# Calculate cumulative incidence (ever stunted) at
# 3, 6, 12, 18, and 24 mo of age
# Cumulative incidence pooled using random effects
# Wasting and stunting can occur at any time in the age range
# They can occur at different times in the age range.
#-----------------------------------
library(dplyr)
library(ggplot2)
library(tidyr)
library(metafor)
theme_set(theme_bw())
# load base functions
source("U:/Scripts/Stunting/2-analyses/0_st_basefunctions.R")
load("U:/Data/Co-occurrence/co-occurrence_data.RData")
# define age windows
d = d %>%
#filter(agedays>1) %>%
mutate(agecat=ifelse(agedays<=3*30.4167,"3 months",
ifelse(agedays>3*30.4167 & agedays<=6*30.4167,"6 months",
ifelse(agedays>6*30.4167 & agedays<=12*30.4167,"12 months",
ifelse(agedays>12*30.4167 & agedays<=18*30.4167,"18 months",
ifelse(agedays>12*30.4167& agedays<=24*30.4167,"24 months","")))))) %>%
mutate(agecat=factor(agecat,levels=c("3 months","6 months","12 months","18 months","24 months")))
# check age categories
d %>%
group_by(agecat) %>%
summarise(n=sum(!is.na(agedays)),
min=min(agedays/30.4167),
mean=mean(agedays/30.4167),
max=max(agedays/30.4167))
# identify children ever stunted + wasted at the same time
evs = d %>%
filter(!is.na(agecat)) %>%
group_by(studyid,country,subjid) %>%
arrange(studyid,subjid) %>%
#create variable with minhaz by age category, cumulatively
mutate(minhaz=ifelse(agecat=="3 months",min(haz[agecat=="3 months"]),
ifelse(agecat=="6 months",min(haz[agecat=="3 months" | agecat=="6 months"]),
ifelse(agecat=="12 months",min(haz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"]),
ifelse(agecat=="18 months",min(haz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"|agecat=="18 months"]),
min(haz)))))) %>%
mutate(minwhz=ifelse(agecat=="3 months",min(whz[agecat=="3 months"]),
ifelse(agecat=="6 months",min(whz[agecat=="3 months" | agecat=="6 months"]),
ifelse(agecat=="12 months",min(whz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"]),
ifelse(agecat=="18 months",min(whz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"|agecat=="18 months"]),
min(whz)))))) %>%
# create indicator for whether the child was ever stunted and wasted
# by age category
group_by(studyid,country,agecat,subjid) %>%
summarise(minhaz=min(minhaz), minwhz=min(minwhz)) %>%
mutate(ever_stunted=ifelse(minhaz< -2,1,0), ever_wasted=ifelse(minwhz< -2,1,0), ever_co=as.numeric(ever_co==1 & ever_wasted==1))
# count incident cases per study by age
# exclude time points if number of measurements per age
# in a study is <50
cuminc.data= evs%>%
group_by(studyid,country,agecat) %>%
summarise(
nchild=length(unique(subjid)),
nstudy=length(unique(studyid)),
ncases=sum(ever_co),
N=sum(length(ever_co))) %>%
filter(N>=50)
cuminc.data
# cohort specific results
ci.cohort=lapply(list("3 months","6 months","12 months","18 months","24 months"),function(x)
fit.escalc(data=cuminc.data,ni="N", xi="ncases",age=x,meas="PR"))
ci.cohort=as.data.frame(do.call(rbind, ci.cohort))
ci.cohort=cohort.format(ci.cohort,y=ci.cohort$yi,
lab= c("3 months","6 months","12 months","18 months","24 months"))
# estimate random effects, format results
ci.res=lapply(list("3 months","6 months","12 months","18 months","24 months"),function(x)
fit.rma(data=cuminc.data,ni="N", xi="ncases",age=x,measure="PR",nlab=" measurements"))
ci.res=as.data.frame(do.call(rbind, ci.res))
ci.res[,4]=as.numeric(ci.res[,4])
ci.res = ci.res %>%
mutate(est=est*100, lb=lb*100, ub=ub*100)
ci.res$agecat.f=as.factor(ifelse(ci.res$agecat=="3 months","0-3 months",
ifelse(ci.res$agecat=="6 months","4-6 months",
ifelse(ci.res$agecat=="12 months","7-12 months",
ifelse(ci.res$agecat=="18 months","13-18 months","19-24 months")))))
ci.res$agecat.f=factor(ci.res$agecat.f,levels=c("0-3 months","4-6 months",
"7-12 months","13-18 months","19-24 months"))
ci.res$ptest.f=sprintf("%0.0f",ci.res$est)
ci.res
# plot cohort incidence
pdf("U:/Figures/stunting-cuminc-africa.pdf",width=11,height=5,onefile=TRUE)
ggplot(ci.cohort[ci.cohort$region=="Africa",],
aes(y=y,x=age.f))+
geom_point(size=2)+facet_wrap(~cohort)+
geom_linerange(aes(ymin=ci.lb,ymax=ci.ub),
size=2,alpha=0.3) +
scale_y_continuous(limits=c(0,100))+
xlab("Age category")+
ylab("Cumulative incidence (95% CI)")+
ggtitle("Cohort-specific cumulative incidence of stunting and wasting co-occurence - Africa")
dev.off()
pdf("U:/Figures/co-occurence-cuminc-latamer-eur.pdf",width=11,height=5,onefile=TRUE)
ggplot(ci.cohort[ci.cohort$region=="Latin America"|
ci.cohort$region=="Europe",],
aes(y=y,x=age.f))+
geom_point(size=2)+facet_wrap(~cohort)+
geom_linerange(aes(ymin=ci.lb,ymax=ci.ub),
size=2,alpha=0.3) +
scale_y_continuous(limits=c(0,100))+
xlab("Age category")+
ylab("Cumulative incidence (95% CI)")+
ggtitle("Cohort-specific cumulative incidence of stunting and wasting co-occurence - Latin America & Europe")
dev.off()
pdf("U:/Figures/co-occurence-cuminc-asia.pdf",width=17,height=7,onefile=TRUE)
ggplot(ci.cohort[ci.cohort$region=="Asia",],
aes(y=y,x=age.f))+
geom_point(size=2)+facet_wrap(~cohort)+
geom_linerange(aes(ymin=ci.lb,ymax=ci.ub),
size=2,alpha=0.3) +
scale_y_continuous(limits=c(0,100))+
xlab("Age category")+
ylab("Cumulative incidence (95% CI)")+
ggtitle("Cohort-specific cumulative incidence of stunting and wasting co-occurence - Asia")
dev.off()
# plot pooled cumulative incidence
pdf("U:/Figures/co-occurence-cuminc-pool.pdf",width=9,height=3.5,onefile=TRUE)
ggplot(ci.res,aes(y=est,x=agecat.f))+
geom_point(size=3)+
geom_errorbar(aes(ymin=lb,ymax=ub),width=0.05) +
scale_y_continuous(limits=c(0,80))+
xlab("Age category")+
ylab("Cumulative incidence per 100 children (95% CI)")+
annotate("text",x=ci.res$agecat.f,y=5,label=ci.res$nmeas.f,size=3)+
annotate("text",x=ci.res$agecat.f,y=1,label=ci.res$nstudy.f,size=3)+
annotate("text",label=ci.res$ptest.f,x=ci.res$agecat.f,
y=ci.res$est,hjust=-0.75,size=3)+
ggtitle("Pooled cumulative incidence of stunting and wasting co-occurence")
dev.off()
#--------------------------------------
# Sensitivity analysis excluding birth from CI
# only among birth cohort studies
#--------------------------------------
# birth cohorts
bc= d %>% group_by(studyid,country) %>%
summarise(minage=min(agedays)) %>%
# filter(minage==1) %>%
mutate(birthcohort=ifelse(minage==1,1,0)) %>%
select(-minage)
# identify ever stunted children
# excluding birth in birth cohort studies
evs.sens.nobirth = left_join(d, bc, by=c("studyid","country")) %>%
filter(birthcohort==1 & !is.na(agecat) & agedays>1) %>%
group_by(studyid,country,subjid) %>%
arrange(studyid,subjid) %>%
#create variable with minhaz by age category, cumulatively
mutate(minhaz=ifelse(agecat=="3 months",min(haz[agecat=="3 months"]),
ifelse(agecat=="6 months",min(haz[agecat=="3 months" | agecat=="6 months"]),
ifelse(agecat=="12 months",min(haz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"]),
ifelse(agecat=="18 months",min(haz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"|agecat=="18 months"]),
min(haz)))))) %>%
# create indicator for whether the child was ever stunted
# by age category
group_by(studyid,country,agecat,subjid) %>%
summarise(minhaz=min(minhaz), minwhz=min(minwhz)) %>%
mutate(ever_stunted=ifelse(minhaz< -2,1,0), ever_wasted=ifelse(minwhz< -2,1,0), ever_co=as.numeric(ever_co==1 & ever_wasted==1))
# count incident cases per study by age
# exclude time points if number of measurements per age
# in a study is <50
cuminc.data.nobirth= evs.sens.nobirth%>%
group_by(studyid,country,agecat) %>%
summarise(
nchild=length(unique(subjid)),
nstudy=length(unique(studyid)),
ncases=sum(ever_co),
N=sum(length(ever_co))) %>%
filter(N>=50)
cuminc.data.nobirth
# estimate random effects, format results
ci.res.nobirth=lapply(list("3 months","6 months","12 months","18 months","24 months"),function(x)
fit.rma(data=cuminc.data.nobirth,ni="N", xi="ncases",age=x,measure="PR",nlab=" measurements"))
ci.res.nobirth=as.data.frame(do.call(rbind, ci.res.nobirth))
ci.res.nobirth[,4]=as.numeric(ci.res.nobirth[,4])
ci.res.nobirth = ci.res.nobirth %>%
mutate(est=est*100, lb=lb*100, ub=ub*100)
ci.res.nobirth$agecat.f=as.factor(ifelse(ci.res.nobirth$agecat=="3 months","0-3 months",
ifelse(ci.res.nobirth$agecat=="6 months","4-6 months",
ifelse(ci.res.nobirth$agecat=="12 months","7-12 months",
ifelse(ci.res.nobirth$agecat=="18 months","13-18 months","19-24 months")))))
ci.res.nobirth$agecat.f=factor(ci.res.nobirth$agecat.f,levels=c("0-3 months","4-6 months",
"7-12 months","13-18 months","19-24 months"))
ci.res.nobirth$ptest.f=sprintf("%0.0f",ci.res.nobirth$est)
ci.res.nobirth
# plot pooled cumulative incidence
pdf("U:/Figures/co-occurence-cuminc-pool-bc-nobirth.pdf",width=9,height=3.5,onefile=TRUE)
ggplot(ci.res.nobirth,aes(y=est,x=agecat.f))+
geom_point(size=3)+
geom_errorbar(aes(ymin=lb,ymax=ub),width=0.05) +
scale_y_continuous(limits=c(0,80))+
xlab("Age category")+
ylab("Cumulative incidence per 100 children (95% CI)")+
annotate("text",x=ci.res.nobirth$agecat.f,y=5,label=ci.res.nobirth$nmeas.f,size=3)+
annotate("text",x=ci.res.nobirth$agecat.f,y=1,label=ci.res.nobirth$nstudy.f,size=3)+
annotate("text",label=ci.res.nobirth$ptest.f,x=ci.res.nobirth$agecat.f,
y=ci.res.nobirth$est,hjust=-0.75,size=3)+
ggtitle("Pooled cumulative incidence of stunting and wasting co-occurence - birth cohorts only - CI excludes birth")
dev.off()
# identify ever stunted children
# including birth in birth cohort studies
evs.sens.birth = left_join(d, bc, by=c("studyid","country")) %>%
filter(birthcohort==1 & !is.na(agecat)) %>%
group_by(studyid,country,subjid) %>%
arrange(studyid,subjid) %>%
#create variable with minhaz by age category, cumulatively
mutate(minhaz=ifelse(agecat=="3 months",min(haz[agecat=="3 months"]),
ifelse(agecat=="6 months",min(haz[agecat=="3 months" | agecat=="6 months"]),
ifelse(agecat=="12 months",min(haz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"]),
ifelse(agecat=="18 months",min(haz[agecat=="3 months" | agecat=="6 months"|agecat=="12 months"|agecat=="18 months"]),
min(haz)))))) %>%
# create indicator for whether the child was ever stunted
# by age category
group_by(studyid,country,agecat,subjid) %>%
summarise(minhaz=min(minhaz), minwhz=min(minwhz)) %>%
mutate(ever_stunted=ifelse(minhaz< -2,1,0), ever_wasted=ifelse(minwhz< -2,1,0), ever_co=as.numeric(ever_co==1 & ever_wasted==1))
# count incident cases per study by age
# exclude time points if number of measurements per age
# in a study is <50
cuminc.data.birth= evs.sens.birth%>%
group_by(studyid,country,agecat) %>%
summarise(
nchild=length(unique(subjid)),
nstudy=length(unique(studyid)),
ncases=sum(ever_co),
N=sum(length(ever_co))) %>%
filter(N>=50)
cuminc.data.birth
# estimate random effects, format results
ci.res.birth=lapply(list("3 months","6 months","12 months","18 months","24 months"),function(x)
fit.rma(data=cuminc.data.birth,ni="N", xi="ncases",age=x,measure="PR",nlab=" measurements"))
ci.res.birth=as.data.frame(do.call(rbind, ci.res.birth))
ci.res.birth[,4]=as.numeric(ci.res.birth[,4])
ci.res.birth = ci.res.birth %>%
mutate(est=est*100, lb=lb*100, ub=ub*100)
ci.res.birth$agecat.f=as.factor(ifelse(ci.res.birth$agecat=="3 months","0-3 months",
ifelse(ci.res.birth$agecat=="6 months","4-6 months",
ifelse(ci.res.birth$agecat=="12 months","7-12 months",
ifelse(ci.res.birth$agecat=="18 months","13-18 months","19-24 months")))))
ci.res.birth$agecat.f=factor(ci.res.birth$agecat.f,levels=c("0-3 months","4-6 months",
"7-12 months","13-18 months","19-24 months"))
ci.res.birth$ptest.f=sprintf("%0.0f",ci.res.birth$est)
ci.res.birth
# plot pooled cumulative incidence
pdf("U:/Figures/co-occurence-cuminc-pool-bc-birth.pdf",width=9,height=3.5,onefile=TRUE)
ggplot(ci.res.birth,aes(y=est,x=agecat.f))+
geom_point(size=3)+
geom_errorbar(aes(ymin=lb,ymax=ub),width=0.05) +
scale_y_continuous(limits=c(0,80))+
xlab("Age category")+
ylab("Cumulative incidence per 100 children (95% CI)")+
annotate("text",x=ci.res.birth$agecat.f,y=5,label=ci.res.birth$nmeas.f,size=3)+
annotate("text",x=ci.res.birth$agecat.f,y=1,label=ci.res.birth$nstudy.f,size=3)+
annotate("text",label=ci.res.birth$ptest.f,x=ci.res.birth$agecat.f,
y=ci.res.birth$est,hjust=-0.75,size=3)+
ggtitle("Pooled cumulative incidence of stunting and wasting co-occurence - birth cohorts only - CI includes birth")
dev.off()
#--------------------------------------
# Calculate cumulative incidence of
# co-occurence in certain age ranges only
# including children who had not yet
# become stunted in prior age ranges
#--------------------------------------
#NOTE: NEED TO FIX FOR CO-OCCURANCE OUTCOME
# stunt_ci = d %>% ungroup() %>%
# #filter(!is.na(agecat) ) %>%
# filter(!is.na(agecat) & agedays>1) %>%
# group_by(studyid,country,subjid) %>%
# arrange(studyid,country,subjid,agedays) %>%
# mutate(stunt = as.numeric(haz < -2), stunt_nmeas=cumsum(stunt), stunt_onset= as.numeric(stunt==1 & stunt_nmeas==1)) %>%
# mutate(wast = as.numeric(whz < -2), wast_nmeas=cumsum(wast), wast_onset= as.numeric(wast==1 & wast_nmeas==1)) %>%
# mutate()
# filter(stunt_nmeas<2 & wast_nmeas<2) %>%
# ungroup() %>% group_by(studyid,country, agecat) %>% mutate(N=n()) %>%
# ungroup() %>% group_by(studyid,country,subjid, agecat) %>% arrange(desc(stunt_onset)) %>% slice(1) %>%
# ungroup()
#
#
# group_by(studyid,country,agecat,subjid) %>%
# summarise(minhaz=min(minhaz), minwhz=min(minwhz)) %>%
# mutate(ever_stunted=ifelse(minhaz< -2,1,0), ever_wasted=ifelse(minwhz< -2,1,0), ever_co=as.numeric(ever_co==1 & ever_wasted==1))
#
#
#
# # count incident cases per study by age
# # exclude time points if number of measurements per age
# # in a study is <50
# cuminc.data.agerange <- stunt_ci%>%
# group_by(studyid,country,agecat) %>%
# summarise(
# nchild=length(unique(subjid)),
# nstudy=length(unique(studyid)),
# ncases=sum(stunt_onset),
# N=sum(length(stunt_onset))) %>%
# filter(N>=50)
#
#
#
# # manually calculate incident cases, person-time at risk at each time point
# stunt_ci %>% group_by(studyid,country,agecat) %>% filter(N>=50) %>%
# group_by(agecat) %>%
# summarise(inc.case=sum(stunt_onset),N= n())
#
#
# #cuminc.data %>% group_by(agecat) %>% summarise(mean(ncases/nchild))
# cuminc.data.agerange %>% group_by(agecat) %>% summarize(sum(ncases), sum(N), one=mean(ncases/N), two=sum(ncases)/sum(N))
#
#
# # estimate random effects, format results
# gc()
# ci.res.agerange=lapply(list("3 months","6 months","12 months","18 months","24 months"),function(x)
# fit.rma(data=cuminc.data.agerange,ni="N", xi="ncases",age=x,measure="PR",nlab=" at-risk"))
# ci.res.agerange=as.data.frame(do.call(rbind, ci.res.agerange))
# ci.res.agerange[,4]=as.numeric(ci.res.agerange[,4])
# ci.res.agerange = ci.res.agerange %>%
# mutate(est=est*100, lb=lb*100, ub=ub*100)
# ci.res.agerange$agecat.f=as.factor(ifelse(ci.res.agerange$agecat=="3 months","2 days-3 months",
# ifelse(ci.res.agerange$agecat=="6 months","3-6 months",
# ifelse(ci.res.agerange$agecat=="12 months","6-12 months",
# ifelse(ci.res.agerange$agecat=="18 months","12-18 months","18-24 months")))))
# ci.res.agerange$agecat.f=factor(ci.res.agerange$agecat.f,levels=c("2 days-3 months","3-6 months",
# "6-12 months","12-18 months","18-24 months"))
# ci.res.agerange$ptest.f=sprintf("%0.0f",ci.res.agerange$est)
#
# ci.res.agerange
#
# # plot pooled cumulative incidence
# pdf("U:/Figures/co-occurence-incprop-pool.pdf",width=9,height=3.5,onefile=TRUE)
# ggplot(ci.res.agerange,aes(y=est,x=agecat.f))+
# geom_point(size=3)+
# geom_errorbar(aes(ymin=lb,ymax=ub),width=0.05) +
# scale_y_continuous(limits=c(0,40))+
# xlab("Age category")+
# ylab("Incidence proportion (95% CI)")+
# annotate("text",x=ci.res.agerange$agecat.f,y=5,label=ci.res.agerange$nmeas.f,size=3)+
# annotate("text",x=ci.res.agerange$agecat.f,y=1,label=ci.res.agerange$nstudy.f,size=3)+
# annotate("text",label=ci.res.agerange$ptest.f,x=ci.res.agerange$agecat.f,
# y=ci.res.agerange$est,hjust=-0.75,size=3)+
# ggtitle("Pooled, age-stratified incidence proportion of stunting and wasting co-occurence")
# dev.off()
# #--------------------------------------
# # cohort specific incidence proportion
# #--------------------------------------
#
# ip.cohort=lapply(list("3 months","6 months","12 months","18 months","24 months"),function(x)
# fit.escalc(data=cuminc.data.agerange,ni="N", xi="ncases",age=x,meas="PR"))
# ip.cohort=as.data.frame(do.call(rbind, ip.cohort))
# ip.cohort=cohort.format(ip.cohort,y=ip.cohort$yi,
# lab= c("3 months","6 months","12 months","18 months","24 months"))
#
#
# pdf("U:/Figures/co-occurence-incprop-africa.pdf",width=11,height=5,onefile=TRUE)
# ggplot(ip.cohort[ip.cohort$region=="Africa",],
# aes(y=y,x=age.f))+
# geom_point(size=2)+facet_wrap(~cohort)+
# geom_linerange(aes(ymin=ci.lb,ymax=ci.ub),
# size=2,alpha=0.3) +
# scale_y_continuous(limits=c(0,100))+
# xlab("Age category")+
# ylab("Percent stunted (95% CI)")+
# ggtitle("Cohort-specific incidence proportion of stunting and wasting co-occurence within age ranges - Africa")
#
# dev.off()
#
# pdf("U:/Figures/co-occurence-incprop-latamer-eur.pdf",width=11,height=5,onefile=TRUE)
# ggplot(ip.cohort[ip.cohort$region=="Latin America"|
# ip.cohort$region=="Europe",],
# aes(y=y,x=age.f))+
# geom_point(size=2)+facet_wrap(~cohort)+
# geom_linerange(aes(ymin=ci.lb,ymax=ci.ub),
# size=2,alpha=0.3) +
# scale_y_continuous(limits=c(0,100))+
# xlab("Age category")+
# ylab("Percent stunted (95% CI)")+
# ggtitle("Cohort-specific incidence proportion of stunting and wasting co-occurence within age ranges - Latin America & Europe")
# dev.off()
#
# pdf("U:/Figures/co-occurence-incprop-asia.pdf",width=17,height=7,onefile=TRUE)
# ggplot(ip.cohort[ip.cohort$region=="Asia",],
# aes(y=y,x=age.f))+
# geom_point(size=2)+facet_wrap(~cohort)+
# geom_linerange(aes(ymin=ci.lb,ymax=ci.ub),
# size=2,alpha=0.3) +
# scale_y_continuous(limits=c(0,100))+
# xlab("Age category")+
# ylab("Percent stunted (95% CI)")+
# ggtitle("Cohort-specific incidence proportion of stunting and wasting co-occurence within age ranges - Asia")
# dev.off()
#--------------------------------------
# export data
#--------------------------------------
cuminc=evs %>% select(studyid,country,subjid,agecat,ever_co)
save(cuminc,file="U:/Data/Co-occurence/st_cuminc.RData")