-
Notifications
You must be signed in to change notification settings - Fork 0
/
1_physical_activity.R
466 lines (364 loc) · 22.4 KB
/
1_physical_activity.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
453
454
455
456
457
458
459
460
461
462
463
464
465
466
####*1.physical activity(PA)*####
#The change of physical activity in travel mode change
####***********************Calculation of MET-mins**************************####
####1.The MET-min before modal shift####
###(1)Total mileage divided by gender and age ratio####
##mileage ratio in gender###
bikepo$milmr <- bikepo$milm/bikepo$mil
bikepo$milfr <- bikepo$milf/bikepo$mil
##gender-age distribution(10 type)
#male
bikepo$mil25mr <- bikepo$mil25m/bikepo$mil
bikepo$mil2530mr <- bikepo$mil2530m/bikepo$mil
bikepo$mil3135mr <- bikepo$mil3135m/bikepo$mil
bikepo$mil3640mr <- bikepo$mil3640m/bikepo$mil
bikepo$mil40mr <- bikepo$mil40m/bikepo$mil
#female
bikepo$mil25fr <- bikepo$mil25f/bikepo$mil
bikepo$mil2530fr <- bikepo$mil2530f/bikepo$mil
bikepo$mil3135fr <- bikepo$mil3135f/bikepo$mil
bikepo$mil3640fr <- bikepo$mil3640f/bikepo$mil
bikepo$mil40fr <- bikepo$mil40f/bikepo$mil
###(2)Time spent in different travel modes before modal shift####
##Total
bikepo$walkt <- bikepo$milew*10000/(5/60)
#The walking speed was set as 5km/h,
#10000 is the distance unit(10 thousand km), 60 is the conversion of hour and minute in speed
bikepo$biket <- bikepo$mileb*10000/(bikepo$mil/bikepo$wtt)
#The riding speed was extract from actual data
bikepo$cart <- bikepo$milec*10000/(30/60)
#The driving speed was set as 30km/h
##gender-age distribution
#walk
bikepo$walkt25m <- bikepo$milew*bikepo$mil25mr*10000/(5/60)
bikepo$walkt2530m <- bikepo$milew*bikepo$mil2530mr*10000/(5/60)
bikepo$walkt3135m <- bikepo$milew*bikepo$mil3135mr*10000/(5/60)
bikepo$walkt3640m <- bikepo$milew*bikepo$mil3640mr*10000/(5/60)
bikepo$walkt40m <- bikepo$milew*bikepo$mil40mr*10000/(5/60)
bikepo$walkt25f <- bikepo$milew*bikepo$mil25fr*10000/(5/60)
bikepo$walkt2530f <- bikepo$milew*bikepo$mil2530fr*10000/(5/60)
bikepo$walkt3135f <- bikepo$milew*bikepo$mil3135fr*10000/(5/60)
bikepo$walkt3640f <- bikepo$milew*bikepo$mil3640fr*10000/(5/60)
bikepo$walkt40f <- bikepo$milew*bikepo$mil40fr*10000/(5/60)
#riding
bikepo$biket25m <- bikepo$milew*bikepo$mil25mr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket2530m <- bikepo$milew*bikepo$mil2530mr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket3135m <- bikepo$milew*bikepo$mil3135mr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket3640m <- bikepo$milew*bikepo$mil3640mr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket40m <- bikepo$milew*bikepo$mil40mr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket25f <- bikepo$milew*bikepo$mil25fr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket2530f <- bikepo$milew*bikepo$mil2530fr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket3135f <- bikepo$milew*bikepo$mil3135fr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket3640f <- bikepo$milew*bikepo$mil3640fr*10000/(bikepo$mil/bikepo$wtt)
bikepo$biket40f <- bikepo$milew*bikepo$mil40fr*10000/(bikepo$mil/bikepo$wtt)
#driving
bikepo$cart25m <- bikepo$milew*bikepo$mil25mr*10000/(30/60)
bikepo$cart2530m <- bikepo$milew*bikepo$mil2530mr*10000/(30/60)
bikepo$cart3135m <- bikepo$milew*bikepo$mil3135mr*10000/(30/60)
bikepo$cart3640m <- bikepo$milew*bikepo$mil3640mr*10000/(30/60)
bikepo$cart40m <- bikepo$milew*bikepo$mil40mr*10000/(30/60)
bikepo$cart25f <- bikepo$milew*bikepo$mil25fr*10000/(30/60)
bikepo$cart2530f <- bikepo$milew*bikepo$mil2530fr*10000/(30/60)
bikepo$cart3135f <- bikepo$milew*bikepo$mil3135fr*10000/(30/60)
bikepo$cart3640f <- bikepo$milew*bikepo$mil3640fr*10000/(30/60)
bikepo$cart40f <- bikepo$milew*bikepo$mil40fr*10000/(30/60)
###(3)The change of MET-mins between modal shift####
##MET-min取值
#A.We obtain METs for walking and cycling from previous literature
# are 3.6 and 5.4 respectively
#non-travel MET
#Male-age 25:4218.6; 26-30:6303.6; 31-35:7620.9; 36-40:7278.9; 40+:4690.1
#Femle-age 25:2885.0; 26-30:4745.7; 31-35:5470.6; 36-40:5719.4; 40+:2622.6
## MET-mins before travel mode shift per week
bikepo$metnoshared <- ((bikepo$wau*10000*bikepo$ager25m*4218.6+bikepo$walkt25m*3.6+bikepo$biket25m*5.4)+
(bikepo$wau*10000*bikepo$ager2630m*6303.6+bikepo$walkt2530m*3.6+bikepo$biket2530m*5.4)+
(bikepo$wau*10000*bikepo$ager3135m*7620.9+bikepo$walkt3135m*3.6+bikepo$biket3135m*5.4)+
(bikepo$wau*10000*bikepo$ager3640m*7278.9+bikepo$walkt3640m*3.6+bikepo$biket3640m*5.4)+
(bikepo$wau*10000*bikepo$ager41m*4690.1+bikepo$walkt40m*3.6+bikepo$biket40m*5.4)+
(bikepo$wau*10000*bikepo$ager25f*2885.0+bikepo$walkt25f*3.6+bikepo$biket25f*5.4)+
(bikepo$wau*10000*bikepo$ager2630f*4745.7+bikepo$walkt2530f*3.6+bikepo$biket2530f*5.4)+
(bikepo$wau*10000*bikepo$ager3135f*5470.6+bikepo$walkt3135f*3.6+bikepo$biket3135f*5.4)+
(bikepo$wau*10000*bikepo$ager3640f*5719.4+bikepo$walkt3640f*3.6+bikepo$biket3640f*5.4)+
(bikepo$wau*10000*bikepo$ager41f*2622.6+bikepo$walkt40f*3.6+bikepo$biket40f*5.4))/(bikepo$wau*10000)/60
mean(bikepo$metnoshared,na.rm = T)
##Total MET-mins after travel mode shift per week
bikepo$metshared <- (bikepo$wau*10000*(bikepo$ager25m*4218.6+bikepo$ager2630m*6303.6+bikepo$ager3135m*7620.9+
bikepo$ager3640m*7278.9+bikepo$ager41m*4690.1+bikepo$ager25f*2885.0+
bikepo$ager2630f*4745.7+bikepo$ager3135f*5470.6+bikepo$ager3640f*5719.4+
bikepo$ager41f*2622.6)+bikepo$wtt*10000*5.4)/(bikepo$wau*10000)/60
mean(bikepo$metshared,na.rm = T)
####(3)Average weekly MET-mins in every province####
#wmetnoshared --met-mins before travel mode shift per week;
#wmetshared --met-mins after travel mode shift per week
wmetnoshared <- aggregate(bikepo["metnoshared"],by=list(area=bikepo$area),mean,na.rm=T)
wmetshared <- aggregate(bikepo["metshared"],by=list(area=bikepo$area),mean,na.rm=T)
padata <- merge(wmetnoshared,wmetshared,by="area")
####2.Average weekly user in every Province####
###(1)population assumption####
#According to the ratio of daily average mileage:
# 0-3 walking; 3-5 cycling; 5+ driving
bikepo$acw <- (bikepo$dmilr1+bikepo$dmilr13)*bikepo$wau*10000 #原来走路
bikepo$acb <- bikepo$dmilr35*bikepo$wau*10000 #原来骑车
bikepo$acc <- (bikepo$dmilr57+bikepo$dmilr710+bikepo$dmilr10)*bikepo$wau*10000 #原来开车
###(2)The average number of user####
padata <- aggregate.data.frame(bikepo["wau"]*10000,by=list(area=bikepo$area),mean,na.rm=T) %>%
merge(padata,by="area")
####3.Obtained reduced health risks from PA ####
#due to the non-liner relationship between PA and health,we used a weight coefficient to convert the RR
#The weight coefficient were obtain from previous literature
#Formula from: https://doi.org/10.1371/journal.pone.0051462
# Reduction in health outcome as a change of trval mode shift = 1 – RR^(a/b).
# a = Scenario MET hours per weeks^Transformation of exposure.
# b = Reference MET hours per week^Transformation of exposure
##A.all-cause mortality
#RR=0.81,weight coefficient=0.25
padata$RRt <- (1-0.81^((padata$metshared^0.25)/(padata$metnoshared^0.25)))/(0.81^((padata$metshared^0.25)/(padata$metnoshared^0.25))) #骑车在前，无骑车在后
padata$RRtL <- (1-0.76^((padata$metshared^0.25)/(padata$metnoshared^0.25)))/(0.76^((padata$metshared^0.25)/(padata$metnoshared^0.25)))
padata$RRtH <- (1-0.85^((padata$metshared^0.25)/(padata$metnoshared^0.25)))/(0.85^((padata$metshared^0.25)/(padata$metnoshared^0.25)))
##B.cause-specific mortality
padata$cvdRRt <- (1-0.84^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.84^((padata$metshared^0.5)/(padata$metnoshared^0.5)))###心血管疾病,中风，缺血性心脏病###
padata$cvdRRtL <- (1-0.79^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.79^((padata$metshared^0.5)/(padata$metnoshared^0.5)))
padata$cvdRRtH <- (1-0.90^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.90^((padata$metshared^0.5)/(padata$metnoshared^0.5)))
padata$respRRt <- (1-0.88^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.88^((padata$metshared^0.5)/(padata$metnoshared^0.5)))###呼吸系统疾病，COPD，哮喘###
padata$respRRtL <- (1-0.77^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.77^((padata$metshared^0.5)/(padata$metnoshared^0.5)))
padata$respRRtH <- (1-0.99^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.99^((padata$metshared^0.5)/(padata$metnoshared^0.5)))
padata$t2dmRRt <- (1-0.85^((padata$metshared^0.375)/(padata$metnoshared^0.375)))/(0.85^((padata$metshared^0.375)/(padata$metnoshared^0.375)))###糖尿病###
padata$t2dmRRtL <- (1-0.79^((padata$metshared^0.375)/(padata$metnoshared^0.375)))/(0.79^((padata$metshared^0.375)/(padata$metnoshared^0.375)))
padata$t2dmRRtH <- (1-0.91^((padata$metshared^0.375)/(padata$metnoshared^0.375)))/(0.91^((padata$metshared^0.375)/(padata$metnoshared^0.375)))
padata$depRRt <- (1-0.83^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.83^((padata$metshared^0.5)/(padata$metnoshared^0.5)))###抑郁###
padata$depRRtL <- (1-0.79^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.79^((padata$metshared^0.5)/(padata$metnoshared^0.5)))
padata$depRRtH <- (1-0.87^((padata$metshared^0.5)/(padata$metnoshared^0.5)))/(0.87^((padata$metshared^0.5)/(padata$metnoshared^0.5)))
padata$demRRt <- (1-0.72^((padata$metshared^0.5)/(padata$metshared^0.5)))/(0.72^((padata$metshared^0.5)/(padata$metshared^0.5)))###阿兹海默（痴呆）###
padata$demRRtL <- (1-0.60^((padata$metshared^0.5)/(padata$metshared^0.5)))/(0.60^((padata$metshared^0.5)/(padata$metshared^0.5)))
padata$demRRtH <- (1-0.86^((padata$metshared^0.5)/(padata$metshared^0.5)))/(0.86^((padata$metshared^0.5)/(padata$metshared^0.5)))
####4.Calculation of mortality effects and YLL####
###(1)mortality effects####
#mortality rate
dierate <- read.csv("./data/pop-Incidence-mortality.csv",stringsAsFactors = F)
names(dierate)
disdierate <- dierate[,c(1,2,12:20)]
###convert the mortality from year(‰) to week(‰)
names(disdierate)
disdierate[,c(3:11)] <- disdierate[,c(3:11)]/100/365*7
names(disdierate)[3:11] <- paste("w",names(disdierate)[3:11],sep = "")
names(disdierate)
#combind the mortality into phsicial activity data
padata <- left_join(padata,disdierate,by="area")
names(padata)
###Calculate reduced deaths###
#all-cause
padata$benphall <- padata$wau/1000*padata$wtotalm*109*padata$RRt #1000为将人数转为每千人
padata$benphallL <- padata$wau/1000*padata$wtotalm*109*padata$RRtH
padata$benphallH <- padata$wau/1000*padata$wtotalm*109*padata$RRtL
padata$benphallse <- (padata$benphallH-padata$benphallL)/(2*1.96) #根据正态分布假设，计算se
#CVD
padata$benphcvd <- padata$wau/1000*padata$wcvdm*109*padata$cvdRRt
padata$benphcvdL <- padata$wau/1000*padata$wcvdm*109*padata$cvdRRtH
padata$benphcvdH <- padata$wau/1000*padata$wcvdm*109*padata$cvdRRtL
padata$benphcvdse <- (padata$benphcvdH-padata$benphcvdL)/(2*1.96)
#stork
padata$benphstro <- padata$wau/1000*padata$wstrorm*109*padata$cvdRRt
padata$benphstroL <- padata$wau/1000*padata$wstrorm*109*padata$cvdRRtH
padata$benphstroH <- padata$wau/1000*padata$wstrorm*109*padata$cvdRRtL
padata$benphstrose <- (padata$benphstroH-padata$benphstroL)/(2*1.96)
#IHD
padata$benphihd <- padata$wau/1000*padata$wihdrm*109*padata$cvdRRt
padata$benphihdL <- padata$wau/1000*padata$wihdrm*109*padata$cvdRRtH
padata$benphihdH <- padata$wau/1000*padata$wihdrm*109*padata$cvdRRtL
padata$benphihdse <- (padata$benphihdH-padata$benphihdL)/(2*1.96)
#RESP
padata$benphresp <- padata$wau/1000*padata$wrespm*109*padata$respRRt
padata$benphrespL <- padata$wau/1000*padata$wrespm*109*padata$respRRtH
padata$benphrespH <- padata$wau/1000*padata$wrespm*109*padata$respRRtL
padata$benphrespse <- (padata$benphrespH-padata$benphrespL)/(2*1.96)
#COPD
padata$benphcopd <- padata$wau/1000*padata$wcopdm*109*padata$respRRt
padata$benphcopdL <- padata$wau/1000*padata$wcopdm*109*padata$respRRtH
padata$benphcopdH <- padata$wau/1000*padata$wcopdm*109*padata$respRRtL
padata$benphcopdse <- (padata$benphcopdH-padata$benphcopdL)/(2*1.96)
#asthma
padata$benphastma <- padata$wau/1000*padata$washm*109*padata$respRRt
padata$benphastmaL <- padata$wau/1000*padata$washm*109*padata$respRRtH
padata$benphastmaH <- padata$wau/1000*padata$washm*109*padata$respRRtL
padata$benphastmase <- (padata$benphastmaH-padata$benphastmaL)/(2*1.96)
#T2DM
padata$benpht2dm <- padata$wau/1000*padata$wt2dmrm*109*padata$t2dmRRt
padata$benpht2dmL <- padata$wau/1000*padata$wt2dmrm*109*padata$t2dmRRtH
padata$benpht2dmH <- padata$wau/1000*padata$wt2dmrm*109*padata$t2dmRRtL
padata$benpht2dmse <- (padata$benpht2dmH-padata$benpht2dmL)/(2*1.96)
#Alzheimer (Dementia)
padata$benphdem <- padata$wau/1000*padata$wdemrm*109*padata$demRRt
padata$benphdemL <- padata$wau/1000*padata$wdemrm*109*padata$demRRtH
padata$benphdemH <- padata$wau/1000*padata$wdemrm*109*padata$demRRtL
padata$benphdemse <- (padata$benphdemH-padata$benphdemL)/(2*1.96)
###View the cumulative death benefit of PA
names(padata)
apply(padata[c(33,37,41,45,49,53,57,61,65)],2, sum)
#Save the result to "padata1"
padata1 <- padata[c(1,33:68)]
###(2)Calculate YLL benefit from mortality benefit####
ylldata <- read.xlsx("./data/YLLdata.xlsx",sheet = 1)
names(ylldata)
#combined the YLL data
padata1 <- left_join(padata1,ylldata,by="area")
#Formula:
#YLL benefit=reduced death*YLL per death
#YLL per death=YLL per 100 thousand people in a year/mortality per 100 thousand people in a year
#all-cause YLL
padata1$yllall <- padata1$benphall*padata1$totyll/(padata$wtotalm/7*365*100)
padata1$yllallL <- padata1$benphallL*padata1$totyll/(padata$wtotalm/7*365*100)
padata1$yllallH <- padata1$benphallH*padata1$totyll/(padata$wtotalm/7*365*100)
padata1$yllallse <- (padata1$yllallH-padata1$yllallL)/(2*1.96)
#CVD YLL
padata1$yllcvd <- padata1$benphcvd*padata1$cvdyll/(padata$wcvdm/7*365*100)
padata1$yllcvdL <- padata1$benphcvdL*padata1$cvdyll/(padata$wcvdm/7*365*100)
padata1$yllcvdH <- padata1$benphcvdH*padata1$cvdyll/(padata$wcvdm/7*365*100)
padata1$yllcvdse <- (padata1$yllcvdH-padata1$yllcvdL)/(2*1.96)
#stork YLL
padata1$yllstro <- padata1$benphstro*padata1$storyll/(padata$wstrorm/7*365*100)
padata1$yllstroL <- padata1$benphstroL*padata1$storyll/(padata$wstrorm/7*365*100)
padata1$yllstroH <- padata1$benphstroH*padata1$storyll/(padata$wstrorm/7*365*100)
padata1$yllstrose <- (padata1$yllstroH-padata1$yllstroL)/(2*1.96)
#IHD YLL
padata1$yllihd <- padata1$benphihd*padata1$ihdyll/(padata$wihdrm/7*365*100)
padata1$yllihdL <- padata1$benphihdL*padata1$ihdyll/(padata$wihdrm/7*365*100)
padata1$yllihdH <- padata1$benphihdH*padata1$ihdyll/(padata$wihdrm/7*365*100)
padata1$yllihdse <- (padata1$yllihdH-padata1$yllihdL)/(2*1.96)
#resp YLL
padata1$yllresp <- padata1$benphresp*padata1$respyll/(padata$wrespm/7*365*100)
padata1$yllrespL <- padata1$benphrespL*padata1$respyll/(padata$wrespm/7*365*100)
padata1$yllrespH <- padata1$benphrespH*padata1$respyll/(padata$wrespm/7*365*100)
padata1$yllrespse <- (padata1$yllrespH-padata1$yllrespL)/(2*1.96)
#COPD YLL
padata1$yllcopd <- padata1$benphcopd*padata1$copdyll/(padata$wcopdm/7*365*100)
padata1$yllcopdL <- padata1$benphcopdL*padata1$copdyll/(padata$wcopdm/7*365*100)
padata1$yllcopdH <- padata1$benphcopdH*padata1$copdyll/(padata$wcopdm/7*365*100)
padata1$yllcopdse <- (padata1$yllcopdH-padata1$yllcopdL)/(2*1.96)
#ashma YLL
padata1$yllastma <- padata1$benphastma*padata1$astyll/(padata$washm/7*365*100)
padata1$yllastmaL <- padata1$benphastmaL*padata1$astyll/(padata$washm/7*365*100)
padata1$yllastmaH <- padata1$benphastmaH*padata1$astyll/(padata$washm/7*365*100)
padata1$yllastmase <- (padata1$yllastmaH-padata1$yllastmaL)/(2*1.96)
#T2DM YLL
padata1$yllt2dm <- padata1$benpht2dm*padata1$t2dmyll/(padata$wt2dmrm/7*365*100)
padata1$yllt2dmL <- padata1$benpht2dmL*padata1$t2dmyll/(padata$wt2dmrm/7*365*100)
padata1$yllt2dmH <- padata1$benpht2dmH*padata1$t2dmyll/(padata$wt2dmrm/7*365*100)
padata1$yllt2dmse <- (padata1$yllt2dmH-padata1$yllt2dmL)/(2*1.96)
#Alzheimer (Dementia) YLL
padata1$ylldem <- padata1$benphdem*padata1$demyll/(padata$wdemrm/7*365*100)
padata1$ylldemL <- padata1$benphdemL*padata1$demyll/(padata$wdemrm/7*365*100)
padata1$ylldemH <- padata1$benphdemH*padata1$demyll/(padata$wdemrm/7*365*100)
padata1$ylldemse <- (padata1$ylldemH-padata1$ylldemL)/(2*1.96)
names(padata1)
padata2 <- padata1[c(1,2:37,47:82)]
write.csv(padata2,"./result/PA-death&YLL.csv",row.names=F)
####5.Effect of PA on morbidity and YLD####
###(1)Calculation of morbidity effects####
###cause-specific morbidity
dismorrate <- read.csv("./data/pop-Incidence-mortality.csv",stringsAsFactors = F)
names(dismorrate)
dismorrate <- dismorrate[c(1:11)]
dismorrate[,3:11] <- dismorrate[,3:11]/100/365*7
names(dismorrate)[3:11] <- paste("w",names(dismorrate)[3:11],sep = "")
##combind the morbidity into phsicial activity data
names(padata)
padatai <- padata[1:22] #RR
padatai <- left_join(padatai,dismorrate,by="area")
names(padatai)
##Calculate reduced incident cases
#Formula:reduced incident cases=average weekly users*morbidity*reduced health risks*number of week
#CVD
padatai$benphcvdi <- padatai$wau/1000*padatai$wcvdi*109*padatai$cvdRRt
padatai$benphcvdiL <- padatai$wau/1000*padatai$wcvdi*109*padatai$cvdRRtH
padatai$benphcvdiH <- padatai$wau/1000*padatai$wcvdi*109*padatai$cvdRRtL
padatai$benphcvdise <- (padatai$benphcvdiH-padatai$benphcvdiL)/(2*1.96)
#strok
padatai$benphstroi <- padatai$wau/1000*padatai$wstrori*109*padatai$cvdRRt
padatai$benphstroiL <- padatai$wau/1000*padatai$wstrori*109*padatai$cvdRRtH
padatai$benphstroiH <- padatai$wau/1000*padatai$wstrori*109*padatai$cvdRRtL
padatai$benphstroise <- (padatai$benphstroiH-padatai$benphstroiL)/(2*1.96)
#IHD
padatai$benphihdi <- padatai$wau/1000*padatai$wihdri*109*padatai$cvdRRt
padatai$benphihdiL <- padatai$wau/1000*padatai$wihdri*109*padatai$cvdRRtH
padatai$benphihdiH <- padatai$wau/1000*padatai$wihdri*109*padatai$cvdRRtL
padatai$benphihdise <- (padatai$benphihdiH-padatai$benphihdiL)/(2*1.96)
#RESP
padatai$benphrespi <- padatai$wau/1000*padatai$wrespi*109*padatai$respRRt
padatai$benphrespiL <- padatai$wau/1000*padatai$wrespi*109*padatai$respRRtH
padatai$benphrespiH <- padatai$wau/1000*padatai$wrespi*109*padatai$respRRtL
padatai$benphrespise <- (padatai$benphrespiH-padatai$benphrespiL)/(2*1.96)
#COPD
padatai$benphcopdi <- padatai$wau/1000*padatai$wcopdi*109*padatai$respRRt
padatai$benphcopdiL <- padatai$wau/1000*padatai$wcopdi*109*padatai$respRRtH
padatai$benphcopdiH <- padatai$wau/1000*padatai$wcopdi*109*padatai$respRRtL
padatai$benphcopdise <- (padatai$benphcopdiH-padatai$benphcopdiL)/(2*1.96)
#Ashma
padatai$benphastmai <- padatai$wau/1000*padatai$washi*109*padatai$respRRt
padatai$benphastmaiL <- padatai$wau/1000*padatai$washi*109*padatai$respRRtH
padatai$benphastmaiH <- padatai$wau/1000*padatai$washi*109*padatai$respRRtL
padatai$benphastmaise <- (padatai$benphastmaiH-padatai$benphastmaiL)/(2*1.96)
#T2DM
padatai$benpht2dmi <- padatai$wau/1000*padatai$wt2dmi*109*padatai$t2dmRRt
padatai$benpht2dmiL <- padatai$wau/1000*padatai$wt2dmi*109*padatai$t2dmRRtH
padatai$benpht2dmiH <- padatai$wau/1000*padatai$wt2dmi*109*padatai$t2dmRRtL
padatai$benpht2dmise <- (padatai$benpht2dmiH-padatai$benpht2dmiL)/(2*1.96)
#Alzheimer (Dementia)
padatai$benphdemi <- padatai$wau/1000*padatai$wdemri*109*padatai$demRRt
padatai$benphdemiL <- padatai$wau/1000*padatai$wdemri*109*padatai$demRRtH
padatai$benphdemiH <- padatai$wau/1000*padatai$wdemri*109*padatai$demRRtL
padatai$benphdemise <- (padatai$benphdemiH-padatai$benphdemiL)/(2*1.96)
#depression
padatai$benphdepi <- padatai$wau/1000*padatai$wdepri*109*padatai$depRRt
padatai$benphdepiL <- padatai$wau/1000*padatai$wdepri*109*padatai$depRRtH
padatai$benphdepiH <- padatai$wau/1000*padatai$wdepri*109*padatai$depRRtL
padatai$benphdepise <- (padatai$benphdepiH-padatai$benphdepiL)/(2*1.96)
###数据整理3###
names(padatai)
apply(padatai[c(33,37,41,45,49,53,57,61,65)],2, sum) #合计发病收益数
padatai1 <- padatai[c(1,33:68)]
###(2)Calculate YLD benefit from morbidity benefit####
ylddata <- read.xlsx("./data/YLDdata.xlsx",sheet = 1)
names(ylddata)
padatai1 <- left_join(padatai1,ylddata,by="area")
sum(is.na(padatai1))
#The calculation idea:
#Every disease means a certain amount of YLD
#and the reduced incident cases will save the corresponding YLD
#CVD YLD
padatai1$yldcvd <- padatai1$benphcvdi*padatai1$cvdyld/(padatai$wcvdi/7*365*100)
padatai1$yldcvdL <- padatai1$benphcvdiL*padatai1$cvdyld/(padatai$wcvdi/7*365*100)
padatai1$yldcvdH <- padatai1$benphcvdiH*padatai1$cvdyld/(padatai$wcvdi/7*365*100)
padatai1$yldcvdse <- (padatai1$yldcvdH-padatai1$yldcvdL)/(2*1.96)
padatai1$yldstro <- padatai1$benphstroi*padatai1$storyld/(padatai$wstrori/7*365*100)
padatai1$yldstroL <- padatai1$benphstroiL*padatai1$storyld/(padatai$wstrori/7*365*100)
padatai1$yldstroH <- padatai1$benphstroiH*padatai1$storyld/(padatai$wstrori/7*365*100)
padatai1$yldstrose <- (padatai1$yldstroH-padatai1$yldstroL)/(2*1.96)
padatai1$yldihd <- padatai1$benphihdi*padatai1$ihdyld/(padatai$wihdri/7*365*100)
padatai1$yldihdL <- padatai1$benphihdiL*padatai1$ihdyld/(padatai$wihdri/7*365*100)
padatai1$yldihdH <- padatai1$benphihdiH*padatai1$ihdyld/(padatai$wihdri/7*365*100)
padatai1$yldihdse <- (padatai1$yldihdH-padatai1$yldihdL)/(2*1.96)
padatai1$yldresp <- padatai1$benphrespi*padatai1$respyld/(padatai$wrespi/7*365*100)
padatai1$yldrespL <- padatai1$benphrespiL*padatai1$respyld/(padatai$wrespi/7*365*100)
padatai1$yldrespH <- padatai1$benphrespiH*padatai1$respyld/(padatai$wrespi/7*365*100)
padatai1$yldrespse <- (padatai1$yldrespH-padatai1$yldrespL)/(2*1.96)
padatai1$yldcopd <- padatai1$benphcopdi*padatai1$copdyld/(padatai$wcopdi/7*365*100)
padatai1$yldcopdL <- padatai1$benphcopdiL*padatai1$copdyld/(padatai$wcopdi/7*365*100)
padatai1$yldcopdH <- padatai1$benphcopdiH*padatai1$copdyld/(padatai$wcopdi/7*365*100)
padatai1$yldcopdse <- (padatai1$yldcopdH-padatai1$yldcopdL)/(2*1.96)
padatai1$yldastma <- padatai1$benphastmai*padatai1$astyld/(padatai$washi/7*365*100)
padatai1$yldastmaL <- padatai1$benphastmaiL*padatai1$astyld/(padatai$washi/7*365*100)
padatai1$yldastmaH <- padatai1$benphastmaiH*padatai1$astyld/(padatai$washi/7*365*100)
padatai1$yldastmase <- (padatai1$yldastmaH-padatai1$yldastmaL)/(2*1.96)
padatai1$yldt2dm <- padatai1$benpht2dmi*padatai1$t2dmyld/(padatai$wt2dmi/7*365*100)
padatai1$yldt2dmL <- padatai1$benpht2dmiL*padatai1$t2dmyld/(padatai$wt2dmi/7*365*100)
padatai1$yldt2dmH <- padatai1$benpht2dmiH*padatai1$t2dmyld/(padatai$wt2dmi/7*365*100)
padatai1$yldt2dmse <- (padatai1$yldt2dmH-padatai1$yldt2dmL)/(2*1.96)
padatai1$ylddem <- padatai1$benphdemi*padatai1$demyld/(padatai$wdemri/7*365*100)
padatai1$ylddemL <- padatai1$benphdemiL*padatai1$demyld/(padatai$wdemri/7*365*100)
padatai1$ylddemH <- padatai1$benphdemiH*padatai1$demyld/(padatai$wdemri/7*365*100)
padatai1$ylddemse <- (padatai1$ylddemH-padatai1$ylddemL)/(2*1.96)
padatai1$ylddep <- padatai1$benphdepi*padatai1$depyld/(padatai$wdepri/7*365*100)
padatai1$ylddepL <- padatai1$benphdepiL*padatai1$depyld/(padatai$wdepri/7*365*100)
padatai1$ylddepH <- padatai1$benphdepiH*padatai1$depyld/(padatai$wdepri/7*365*100)
padatai1$ylddepse <- (padatai1$ylddepH-padatai1$ylddepL)/(2*1.96)
names(padatai1)