R包的安装
在使用Norpred包进行发生/死亡率的预测前，我们需要安装Norpred
这里提供两种安装方法，请移步我的外部教程链接GBD视频教程——安装预测包的2种方法
下面我们来演示第一种R语言命令直接安装Norpred包
install.packages("remotes")
library(remotes)
remotes::install_github("haraldwf/nordpred") 装载R包及数据读取
library(nordpred)
library(data.table)
library(tidyverse)
library(ggplot2)
library(epitools)
library(reshape2)
source('function_sum_year.R')读取标准人口数据
EC <- read.csv('EC_predict.csv')
age_stand <- read.csv('GBD2019 world population age standard.csv')[-c(1:3),]
head(EC)课程示例来源 measure location sex age cause metric year
课程示例来源 1 Deaths Global Male 20-24 years Esophageal cancer Number 1990
课程示例来源 2 Deaths Global Female 20-24 years Esophageal cancer Number 1990
课程示例来源 3 Deaths Global Both 20-24 years Esophageal cancer Number 1990
课程示例来源 4 Deaths Global Male 20-24 years Esophageal cancer Rate 1990
课程示例来源 5 Deaths Global Female 20-24 years Esophageal cancer Rate 1990
课程示例来源 6 Deaths Global Both 20-24 years Esophageal cancer Rate 1990
课程示例来源 val upper lower
课程示例来源 1 204.06728160 233.17885537 147.87989318
课程示例来源 2 211.56655514 251.61840857 148.46760027
课程示例来源 3 415.63383675 473.45283772 308.49779248
课程示例来源 4 0.08218287 0.09390681 0.05955484
课程示例来源 5 0.08657739 0.10296743 0.06075600
课程示例来源 6 0.08436255 0.09609826 0.06261680rownames(age_stand) <- 1:nrow(age_stand)
knitr::kable(age_stand,digits=2,align = 'c')| age | std_population |
|---|---|
| <1 year | 2.03 |
| 1 to 4 | 7.91 |
| 5 to 9 | 9.57 |
| 10 to 14 | 8.99 |
| 15 to 19 | 8.32 |
| 20 to 24 | 7.87 |
| 25 to 29 | 7.63 |
| 30 to 34 | 7.33 |
| 35 to 39 | 6.81 |
| 40 to 44 | 6.14 |
| 45 to 49 | 5.51 |
| 50 to 54 | 4.92 |
| 55 to 59 | 4.35 |
| 60 to 64 | 3.68 |
| 65 to 69 | 2.99 |
| 70 to 74 | 2.27 |
| 75 to 79 | 1.61 |
| 80 to 84 | 1.11 |
| 85 to 89 | 0.62 |
| 90 to 94 | 0.26 |
| 95 plus | 0.08 |
我比较喜欢把age换成原来的格式
首先来看下新版本的数据库提供的年龄格式
unique(EC$age)课程示例来源 [1] "20-24 years" "25-29 years" "30-34 years" "35-39 years"
课程示例来源 [5] "40-44 years" "45-49 years" "50-54 years" "55-59 years"
课程示例来源 [9] "60-64 years" "65-69 years" "70-74 years" "75-79 years"
课程示例来源 [13] "All ages" "Age-standardized" "80-84 years" "85-89 years"
课程示例来源 [17] "90-94 years" "95+ years" "<5 years" "5-9 years"
课程示例来源 [21] "10-14 years" "15-19 years"接着我们对年龄数据格式转换，以配对人口数据
EC <- EC %>% mutate(age=sub('-',replacement = ' to ', age)) %>%
mutate(age=sub(' years',replacement = '', age)) %>%
mutate(age=sub(' year',replacement = '', age)) %>%
mutate(age=sub('95\\+',replacement = '95 plus', age)) %>%
mutate(age=sub('Age to standardized',replacement = 'Age-standardized', age)) %>%
mutate(age=sub('<5',replacement = 'Under 5', age)) %>%
filter(val>0)
unique(EC$age)课程示例来源 [1] "20 to 24" "25 to 29" "30 to 34" "35 to 39"
课程示例来源 [5] "40 to 44" "45 to 49" "50 to 54" "55 to 59"
课程示例来源 [9] "60 to 64" "65 to 69" "70 to 74" "75 to 79"
课程示例来源 [13] "All ages" "Age-standardized" "80 to 84" "85 to 89"
课程示例来源 [17] "90 to 94" "95 plus"构建3种年龄向量用于提取不同数据
#### 疾病真实的年龄结构
ages <- c("20 to 24", "25 to 29",
"30 to 34", "35 to 39", "40 to 44", "45 to 49", "50 to 54", "55 to 59",
"60 to 64", "65 to 69", "70 to 74", "75 to 79", "80 to 84", "85 to 89",
"90 to 94", "95 plus")
#### 调取标准人口百分比用
ages_2 <- c("<1 year","1 to 4", "5 to 9","10 to 14", "15 to 19","20 to 24", "25 to 29",
"30 to 34", "35 to 39", "40 to 44", "45 to 49", "50 to 54", "55 to 59",
"60 to 64", "65 to 69", "70 to 74", "75 to 79", "80 to 84", "85 to 89",
"90 to 94", "95 plus")
#### 预测的年龄结构
ages_3 <- c("0 to 14", "15 to 19","20 to 24", "25 to 29",
"30 to 34", "35 to 39", "40 to 44", "45 to 49", "50 to 54", "55 to 59",
"60 to 64", "65 to 69", "70 to 74", "75 to 79", "80 to 84", "85 to 89",
"90 to 94", "95 plus")构建标准人口结构数据
wstand <- c(age_stand$std_population[1:4] %>% as.numeric() %>% sum(),
age_stand$std_population[5:21])/sum(age_stand$std_population[1:21])
wstand课程示例来源 [1] 0.2849456501 0.0832436219 0.0786645018 0.0763291734 0.0733151112
课程示例来源 [6] 0.0681105500 0.0613679818 0.0550949597 0.0492182257 0.0434563307
课程示例来源 [11] 0.0368447375 0.0299123972 0.0227248755 0.0160737166 0.0111303460
课程示例来源 [16] 0.0061707008 0.0025500807 0.0008470394分别提取男性和女性食管癌发病数据
首先提取男性食管癌发病数据
### for incidence for Male and female
EC_Male_incidence <- EC %>% filter(age %in% ages &
sex == 'Male' &
metric == 'Number' &
measure == 'Incidence' &
location== 'Global')
EC_Male_incidence_n <- dcast(data = EC_Male_incidence, age~year, value.var = "val")##### 扩展数据，将其年龄扩展成18个年龄段
EC_Male_incidence_n['0 to 14',] <- c('0 to 14',rep(0,ncol(EC_Male_incidence_n)-1))
EC_Male_incidence_n['15 to 19',] <- c('15 to 19',rep(0,ncol(EC_Male_incidence_n)-1))
EC_Male_incidence_n <- EC_Male_incidence_n %>%
mutate(age=factor(age,levels = ages_3,ordered = T)) %>%
arrange(age)
rownames(EC_Male_incidence_n) <- EC_Male_incidence_n$age
EC_Male_incidence_n <- EC_Male_incidence_n[,-1]
EC_Male_incidence_n <- apply(EC_Male_incidence_n, c(1,2), as.numeric) %>% as.data.frame()knitr::kable(EC_Male_incidence_n,digits=2,align = 'c')| 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 to 14 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 15 to 19 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 20 to 24 | 291.16 | 294.54 | 294.16 | 293.67 | 289.08 | 283.38 | 277.81 | 275.62 | 274.28 | 274.55 | 276.50 | 275.86 | 278.47 | 283.18 | 291.01 | 298.79 | 302.92 | 311.04 | 320.38 | 322.40 | 321.93 | 310.12 | 296.38 | 287.99 | 281.50 | 273.44 | 271.49 | 274.38 | 281.51 | 283.28 |
| 25 to 29 | 417.29 | 435.25 | 447.92 | 458.53 | 466.94 | 472.57 | 477.78 | 481.32 | 483.88 | 491.74 | 496.69 | 491.91 | 488.84 | 487.88 | 487.82 | 484.06 | 473.57 | 469.25 | 478.62 | 490.02 | 497.55 | 496.21 | 493.23 | 494.38 | 490.62 | 486.26 | 483.95 | 483.03 | 492.78 | 495.10 |
| 30 to 34 | 996.76 | 1005.15 | 1019.96 | 1058.30 | 1104.83 | 1157.45 | 1205.53 | 1239.62 | 1261.60 | 1298.24 | 1343.12 | 1376.10 | 1402.31 | 1449.34 | 1457.52 | 1399.62 | 1306.94 | 1249.47 | 1231.80 | 1235.18 | 1220.03 | 1169.31 | 1133.13 | 1130.62 | 1145.72 | 1153.53 | 1185.77 | 1236.57 | 1307.84 | 1357.20 |
| 35 to 39 | 2821.14 | 2898.91 | 2927.26 | 2912.83 | 2856.17 | 2816.42 | 2789.21 | 2838.51 | 2981.90 | 3206.49 | 3461.59 | 3674.43 | 3897.11 | 4065.61 | 4175.55 | 4116.40 | 3983.14 | 3851.37 | 3719.86 | 3537.76 | 3370.09 | 3127.45 | 2929.77 | 2845.18 | 2834.58 | 2795.37 | 2796.19 | 2875.91 | 3026.56 | 3123.22 |
| 40 to 44 | 6998.61 | 7457.42 | 7782.45 | 8202.08 | 8438.49 | 8874.21 | 9041.55 | 9082.69 | 8959.71 | 8809.44 | 8670.65 | 8642.01 | 8927.17 | 9354.80 | 10039.65 | 10568.23 | 10861.14 | 10909.69 | 10491.06 | 9788.08 | 9354.40 | 8786.69 | 8265.25 | 7941.06 | 7739.44 | 7454.65 | 7325.89 | 7341.90 | 7444.92 | 7497.33 |
| 45 to 49 | 12361.37 | 12705.75 | 13228.37 | 13808.00 | 14650.88 | 15195.83 | 15978.15 | 16506.97 | 17310.78 | 18163.79 | 19540.09 | 20189.74 | 20733.27 | 20970.75 | 20675.75 | 19719.71 | 18597.38 | 18226.77 | 18718.87 | 19550.29 | 20020.65 | 20044.81 | 20051.58 | 19467.13 | 18552.04 | 18046.09 | 17788.11 | 17448.25 | 17351.53 | 17484.03 |
| 50 to 54 | 22640.81 | 22667.50 | 22683.20 | 22842.62 | 22853.15 | 23002.25 | 23514.94 | 24580.73 | 25769.60 | 27799.67 | 29376.27 | 31450.89 | 33285.61 | 35169.30 | 36622.04 | 38218.46 | 37659.83 | 36533.55 | 34585.20 | 31927.56 | 30171.27 | 29135.85 | 28871.64 | 29029.35 | 29641.31 | 30536.89 | 31216.91 | 32022.98 | 33011.73 | 33649.57 |
| 55 to 59 | 33332.86 | 33706.69 | 33878.18 | 34357.70 | 34432.14 | 34472.78 | 34230.51 | 34022.15 | 34222.19 | 34834.81 | 35790.92 | 37234.88 | 39642.57 | 41442.38 | 44492.76 | 46137.90 | 47259.23 | 47462.59 | 47785.79 | 46845.51 | 47614.28 | 47397.33 | 46997.85 | 45104.02 | 43271.33 | 42493.53 | 42225.01 | 42791.70 | 44654.77 | 47251.18 |
| 60 to 64 | 38051.56 | 38929.72 | 39380.31 | 40090.14 | 40226.18 | 40540.11 | 40873.75 | 41294.35 | 41910.54 | 42552.07 | 43464.84 | 44120.39 | 44923.87 | 46017.54 | 46905.63 | 47214.53 | 47369.54 | 49170.20 | 51035.92 | 53899.94 | 54817.57 | 56031.73 | 56621.51 | 57104.25 | 57046.14 | 58722.04 | 59005.86 | 58628.74 | 58159.95 | 57763.37 |
| 65 to 69 | 35744.33 | 37154.19 | 38433.98 | 39977.21 | 40990.70 | 42088.55 | 42699.29 | 43204.81 | 44049.62 | 45059.43 | 46833.65 | 48242.51 | 49637.40 | 51596.00 | 52477.27 | 52224.95 | 50610.73 | 49845.83 | 50094.89 | 50664.77 | 51413.02 | 52113.69 | 53824.73 | 54290.70 | 56051.44 | 56946.65 | 59153.12 | 60241.40 | 62624.51 | 65056.34 |
| 70 to 74 | 28990.44 | 30192.04 | 31305.85 | 32690.49 | 34047.85 | 35500.84 | 36729.00 | 38034.87 | 39840.59 | 41704.07 | 43960.30 | 45971.26 | 47672.53 | 49652.28 | 51313.89 | 51967.84 | 50958.14 | 50309.13 | 49921.26 | 49378.82 | 49223.93 | 48465.96 | 48027.45 | 47911.23 | 48230.81 | 48771.22 | 50250.74 | 53337.83 | 56460.79 | 60786.27 |
| 75 to 79 | 19201.81 | 19784.53 | 20224.69 | 20733.53 | 21095.70 | 21955.87 | 22883.93 | 23952.63 | 25455.29 | 27158.51 | 28981.75 | 30878.12 | 32596.66 | 34523.80 | 36269.50 | 37464.04 | 37224.11 | 37250.11 | 37770.63 | 38495.73 | 39028.79 | 39087.57 | 39310.56 | 39371.77 | 39470.67 | 39762.68 | 40205.55 | 40929.90 | 42346.83 | 43764.21 |
| 80 to 84 | 9864.95 | 10302.88 | 10662.60 | 11122.36 | 11531.22 | 11978.89 | 12343.76 | 12766.53 | 13401.63 | 14017.54 | 14920.70 | 16118.79 | 17262.94 | 18462.03 | 19884.67 | 21079.11 | 21466.90 | 22093.93 | 23024.09 | 24003.50 | 25028.53 | 25023.29 | 24973.79 | 25578.45 | 26573.39 | 27062.04 | 27717.03 | 28749.72 | 30034.84 | 31011.76 |
| 85 to 89 | 3603.34 | 3813.94 | 4002.65 | 4206.72 | 4385.76 | 4592.04 | 4805.14 | 5038.14 | 5343.52 | 5664.64 | 5928.13 | 6288.57 | 6602.23 | 6844.51 | 7157.09 | 7644.92 | 7994.89 | 8482.29 | 9046.22 | 9617.65 | 10128.24 | 10551.40 | 11061.50 | 11551.48 | 12004.67 | 12601.45 | 13203.13 | 13708.38 | 14352.32 | 15285.71 |
| 90 to 94 | 647.29 | 690.21 | 734.75 | 793.89 | 849.96 | 905.75 | 959.29 | 1018.34 | 1080.38 | 1144.51 | 1203.52 | 1285.71 | 1369.04 | 1450.36 | 1534.64 | 1632.07 | 1688.56 | 1753.44 | 1824.08 | 1884.19 | 1992.86 | 2118.55 | 2241.07 | 2334.92 | 2437.07 | 2592.51 | 2761.45 | 2930.07 | 3164.80 | 3447.78 |
| 95 plus | 93.06 | 98.00 | 104.22 | 109.44 | 115.22 | 121.35 | 130.71 | 139.70 | 152.68 | 166.23 | 181.59 | 195.81 | 213.28 | 229.88 | 242.35 | 260.08 | 274.34 | 293.68 | 313.18 | 334.25 | 361.08 | 384.85 | 400.14 | 415.50 | 434.56 | 458.51 | 482.21 | 505.46 | 533.56 | 570.88 |
同理整理女性数据
EC_Female_incidence <- EC %>% filter(age %in% ages &
sex == 'Female' &
metric == 'Number' &
measure == 'Incidence' &
location== 'Global')
EC_Female_incidence_n <- dcast(data = EC_Female_incidence, age~year, value.var = "val")
##### 扩展数据，将其年龄扩展成18个年龄段
EC_Female_incidence_n['0 to 14',] <- c('0 to 14',rep(0,ncol(EC_Female_incidence_n)-1))
EC_Female_incidence_n['15 to 19',] <- c('15 to 19',rep(0,ncol(EC_Female_incidence_n)-1))
EC_Female_incidence_n <- EC_Female_incidence_n %>%
mutate(age=factor(age,levels = ages_3,ordered = T)) %>%
arrange(age)
rownames(EC_Female_incidence_n) <- EC_Female_incidence_n$age
EC_Female_incidence_n <- EC_Female_incidence_n[,-1]
EC_Female_incidence_n <- apply(EC_Female_incidence_n, c(1,2), as.numeric) %>% as.data.frame()
head(EC_Female_incidence_n)课程示例来源 1990 1991 1992 1993 1994 1995 1996
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 314.1757 315.3922 318.0992 317.6092 332.4554 344.0477 344.9262
课程示例来源 25 to 29 452.4966 462.8614 477.4293 482.8070 534.5153 573.5990 582.7055
课程示例来源 30 to 34 631.3312 635.2006 656.1672 674.9703 757.8403 838.0751 883.6074
课程示例来源 35 to 39 1188.6218 1209.0521 1234.8500 1237.0466 1242.4567 1239.2947 1245.1759
课程示例来源 1997 1998 1999 2000 2001 2002 2003
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 351.3694 359.5428 362.8672 362.2437 358.2691 344.5795 331.6477
课程示例来源 25 to 29 600.7503 629.7969 641.4846 642.5608 632.4197 576.4692 528.2847
课程示例来源 30 to 34 933.5899 989.7285 1014.0689 1034.8725 1030.6553 937.0606 875.2940
课程示例来源 35 to 39 1289.7061 1353.0632 1438.7904 1545.9526 1628.8055 1663.3665 1651.8056
课程示例来源 2004 2005 2006 2007 2008 2009 2010
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 333.8988 336.5320 333.4119 333.5738 336.0658 334.0830 334.5831
课程示例来源 25 to 29 514.7908 508.7874 490.0063 474.8055 477.9697 486.9705 497.1143
课程示例来源 30 to 34 864.8827 828.1417 786.9706 750.4158 733.3955 718.8268 710.3124
课程示例来源 35 to 39 1646.4349 1627.1979 1569.2407 1500.6156 1442.1869 1389.9362 1349.7532
课程示例来源 2011 2012 2013 2014 2015 2016 2017
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 327.2933 308.1487 293.5200 278.0433 268.6533 266.2743 266.7284
课程示例来源 25 to 29 492.1826 476.2211 476.3743 465.1776 457.7798 458.4295 462.3410
课程示例来源 30 to 34 692.5068 665.1585 665.3052 661.9004 662.7321 674.9041 696.1207
课程示例来源 35 to 39 1300.3991 1256.9669 1244.4920 1218.7966 1219.8465 1232.4696 1258.5939
课程示例来源 2018 2019
课程示例来源 0 to 14 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000
课程示例来源 20 to 24 274.9388 277.8970
课程示例来源 25 to 29 472.8733 473.5171
课程示例来源 30 to 34 728.0291 748.2529
课程示例来源 35 to 39 1297.5730 1329.3401读取人口学数据：
需要注意的是：人口学数据中地名Geogria有两个归属，一个属于美国的佐治亚州，另外一个是格鲁吉亚国家，所以204国家数据预测的时候，需要将佐治亚州滤过，通过变量location_id !=533进行筛选
##### 读取人口学数据
dirname <- dir("GBD_Population")
file <- paste0(getwd(),"/GBD_Population/",dirname)
var_name <- c('location_id',"location_name","sex_name","year_id","age_group_name","val")
GBD_population <- as.data.frame(matrix(nrow=0,ncol=length(var_name)))
names(GBD_population)=var_name
for (a in file) {
data <- fread(a) %>% as.data.frame() %>% select(var_name) %>%
filter(age_group_name %in% ages_2 & location_id !=533)
GBD_population <- rbind(GBD_population,data)
}
GBD_population <- GBD_population %>% mutate(sex_name = case_when(
sex_name == "both" ~ "Both",
sex_name == "male" ~ "Male",
sex_name == "female" ~ "Female")) %>%
select(-1)
head(GBD_population)课程示例来源 location_name sex_name year_id age_group_name val
课程示例来源 1 Global Male 1990 1 to 4 257580225
课程示例来源 2 Global Male 1990 5 to 9 300528886
课程示例来源 3 Global Male 1990 10 to 14 274770756
课程示例来源 4 Global Male 1990 15 to 19 264074872
课程示例来源 5 Global Male 1990 20 to 24 248308772
课程示例来源 6 Global Male 1990 25 to 29 222733930#### 读取预测人口数据
prediction_var_name <- c("location_name","sex","year_id","age_group_name","val")
GBD_population_prediction <- fread('IHME_POP_2017_2100_POP_REFERENCE_Y2020M05D01.csv') %>% as.data.frame() %>%
select(prediction_var_name) %>%
filter(year_id %in% 2020:2044)
names(GBD_population_prediction) <- var_name[-1]
unique(GBD_population_prediction$age_group_name)课程示例来源 [1] "Early Neonatal" "Late Neonatal" "Post Neonatal" "1 to 4"
课程示例来源 [5] "5 to 9" "10 to 14" "15 to 19" "20 to 24"
课程示例来源 [9] "25 to 29" "30 to 34" "35 to 39" "40 to 44"
课程示例来源 [13] "45 to 49" "50 to 54" "55 to 59" "60 to 64"
课程示例来源 [17] "65 to 69" "70 to 74" "75 to 79" "All Ages"
课程示例来源 [21] "80 to 84" "85 to 89" "90 to 94" "95 plus"由于预测人口数据没有<1的年龄数据，因此我们需要进行转换，将Early Neonatal,Late Neonatal,
Post Neonatal,合并成<1的年龄数据
GBD_1year <- GBD_population_prediction %>%
filter(age_group_name %in% c("Early Neonatal","Late Neonatal", "Post Neonatal")) %>%
group_by(location_name,sex_name,year_id) %>%
summarise(val=sum(val)) %>%
mutate(age_group_name="<1 year") %>%
select(var_name[-1])
GBD_population_prediction <- GBD_population_prediction %>% filter(!(age_group_name %in% c("Early Neonatal","Late Neonatal", "Post Neonatal"))) %>%
rbind(GBD_1year)
unique(GBD_population_prediction$age_group_name)课程示例来源 [1] "1 to 4" "5 to 9" "10 to 14" "15 to 19" "20 to 24" "25 to 29"
课程示例来源 [7] "30 to 34" "35 to 39" "40 to 44" "45 to 49" "50 to 54" "55 to 59"
课程示例来源 [13] "60 to 64" "65 to 69" "70 to 74" "75 to 79" "All Ages" "80 to 84"
课程示例来源 [19] "85 to 89" "90 to 94" "95 plus" "<1 year"人口数据和预测人口数据进行合并
##### 合并现人口数+预测人口数
GBD <- rbind(GBD_population,GBD_population_prediction)
#### 合并0-14
GBD_age14 <- GBD %>% filter(age_group_name %in% c("<1 year","1 to 4","5 to 9","10 to 14")) %>%
group_by(location_name,sex_name,year_id) %>%
summarize(val=sum(val)) %>% mutate(age_group_name='0 to 14') %>% select(c(1:3,5,4))
GBD <- rbind(GBD,GBD_age14)
GBD <- GBD %>% filter(age_group_name %in% ages_3) %>%
mutate(age_group_name=factor(age_group_name,levels=ages_3,ordered = T)) %>%
arrange(age_group_name)
unique(GBD$age_group_name)课程示例来源 [1] 0 to 14 15 to 19 20 to 24 25 to 29 30 to 34 35 to 39 40 to 44 45 to 49
课程示例来源 [9] 50 to 54 55 to 59 60 to 64 65 to 69 70 to 74 75 to 79 80 to 84 85 to 89
课程示例来源 [17] 90 to 94 95 plus
课程示例来源 18 Levels: 0 to 14 < 15 to 19 < 20 to 24 < 25 to 29 < 30 to 34 < ... < 95 plus提取对应国家的人口学数据
## 提取对应国家的人口学数据
GBD_Global_Male <- GBD %>% filter(location_name=='Global' & sex_name == 'Male')
GBD_Global_Female <- GBD %>% filter(location_name=='Global' & sex_name == 'Female')
GBD_Global_Male_n <- dcast(data = GBD_Global_Male,
age_group_name ~ year_id,
value.var = c("val")) %>%
select(-1)
GBD_Global_Female_n <- dcast(data = GBD_Global_Female,
age_group_name ~ year_id,
value.var = c("val")) %>%
select(-1)将5年数据相加成5年为一组的数据
GBD_Global_Male_n <- apply(GBD_Global_Male_n, c(1,2), as.numeric) %>% as.data.frame()
GBD_Global_Female_n <- apply(GBD_Global_Female_n, c(1,2), as.numeric) %>% as.data.frame()
EC_Male_incidence_n <- apply(EC_Male_incidence_n, c(1,2), as.numeric) %>% as.data.frame()
EC_Female_incidence_n <- apply(EC_Female_incidence_n, c(1,2), as.numeric) %>% as.data.frame()
EC_Male_incidence_g <- function_sum_year5(EC_Male_incidence_n,1990,2019,2019)
EC_Female_incidence_g <- function_sum_year5(EC_Female_incidence_n,1990,2019,2019)
GBD_Global_Male_g <- function_sum_year5(GBD_Global_Male_n,1990,2044,2019)
GBD_Global_Female_g <- function_sum_year5(GBD_Global_Female_n,1990,2044,2019)
rownames(EC_Male_incidence_g) <- ages_3
rownames(EC_Female_incidence_g) <- ages_3
rownames(GBD_Global_Female_g) <- ages_3
rownames(GBD_Global_Male_g) <- ages_3
GBD_Global_Male_g <- apply(GBD_Global_Male_g, c(1,2), as.numeric) %>% as.data.frame()
GBD_Global_Female_g <- apply(GBD_Global_Female_g, c(1,2), as.numeric) %>% as.data.frame()
EC_Male_incidence_g <- apply(EC_Male_incidence_g, c(1,2), as.numeric) %>% as.data.frame()
EC_Female_incidence_g <- apply(EC_Female_incidence_g, c(1,2), as.numeric) %>% as.data.frame()模型拟合
Norpred预测模型参数详解
cuttrend:预测过程中drift的减缓率
Norperiods：是指距离从现在往前用多少个period进行预测，我这里选择4:6,软件可以根据计算选择最佳的period,
startestage：指纳入模型进行回归分析的最年轻的年龄组，
startuseage：指的是用来预测的最年轻的年龄组，未纳入模型的年龄组
Linkfunc：可以选择power5或者poisson功能。
由于男性和女性的发病率有所不同，个人认为比较严谨的方法是采用分开预测的方法进行预测，因此这里展示采用分开预测的方法进行计算。
Male_res <- nordpred(EC_Male_incidence_g, GBD_Global_Male_g,
noperiods = 4:6, startestage = 3, startuseage = 3,
cuttrend = c(0, .25, .5, .75, .75), linkfunc = "power5", recent = NULL)
Female_res <- nordpred(EC_Female_incidence_g, GBD_Global_Female_g,
noperiods = 4:6, startestage = 3, startuseage = 3,
cuttrend = c(0, .25, .5, .75, .75), linkfunc = "power5", recent = NULL)展示预测的数据
## 不同年龄段的发病率
round(nordpred.getpred(Male_res, incidence = TRUE, standpop = NULL), 2)课程示例来源 1990-1994 1995-1999 2000-2004 2005-2009 2010-2014 2015-2019 2020-2024
课程示例来源 0-4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
课程示例来源 5-9 0.00 0.00 0.00 0.00 0.00 0.00 0.00
课程示例来源 10-14 0.12 0.11 0.11 0.11 0.10 0.09 0.08
课程示例来源 15-19 0.19 0.19 0.19 0.18 0.17 0.16 0.16
课程示例来源 20-24 0.51 0.53 0.57 0.51 0.44 0.43 0.42
课程示例来源 25-29 1.55 1.45 1.66 1.55 1.20 1.11 1.10
课程示例来源 30-34 4.92 4.92 4.63 4.65 3.48 3.00 2.95
课程示例来源 35-39 10.61 10.94 11.70 10.00 8.99 7.52 6.70
课程示例来源 40-44 20.80 21.12 23.15 21.65 16.27 15.33 13.59
课程示例来源 45-49 35.44 34.00 36.12 34.94 29.49 25.59 23.75
课程示例来源 50-54 48.26 47.47 48.44 48.26 44.32 39.63 33.52
课程示例来源 55-59 62.63 61.86 65.42 61.41 57.65 52.68 48.53
课程示例来源 60-64 77.35 77.80 83.36 79.72 69.18 67.48 64.01
课程示例来源 65-69 79.57 83.46 90.62 87.81 80.32 75.31 71.90
课程示例来源 70-74 76.59 84.36 96.08 97.13 89.88 87.22 78.80
课程示例来源 75-79 73.61 78.28 89.47 95.00 92.86 91.43 85.15
课程示例来源 80-84 52.83 55.30 60.33 65.81 65.53 63.72 62.70
课程示例来源 85+ 36.75 37.31 40.32 43.13 45.84 45.29 46.34
课程示例来源 2025-2029 2030-2034 2035-2039 2040-2044
课程示例来源 0-4 0.00 0.00 0.00 0.00
课程示例来源 5-9 0.00 0.00 0.00 0.00
课程示例来源 10-14 0.07 0.07 0.06 0.06
课程示例来源 15-19 0.14 0.14 0.13 0.13
课程示例来源 20-24 0.42 0.40 0.39 0.38
课程示例来源 25-29 1.11 1.14 1.12 1.09
课程示例来源 30-34 2.98 3.05 3.16 3.11
课程示例来源 35-39 6.58 6.73 6.97 7.17
课程示例来源 40-44 12.39 12.35 12.75 13.14
课程示例来源 45-49 21.18 19.69 19.84 20.43
课程示例来源 50-54 32.11 29.12 27.48 27.68
课程示例来源 55-59 41.85 40.54 37.30 35.30
课程示例来源 60-64 58.76 51.43 50.35 46.48
课程示例来源 65-69 68.57 63.57 56.26 55.09
课程示例来源 70-74 77.20 74.30 69.56 61.70
课程示例来源 75-79 78.54 77.60 75.32 70.53
课程示例来源 80-84 59.63 55.17 54.95 53.22
课程示例来源 85+ 45.03 43.08 40.03 39.86## 标准发病率
round(nordpred.getpred(Male_res, incidence = TRUE, standpop = wstand), 2)课程示例来源 1990-1994 1995-1999 2000-2004 2005-2009 2010-2014 2015-2019 2020-2024 2025-2029
课程示例来源 11.78 11.89 12.72 12.30 10.93 10.11 9.27 8.61
课程示例来源 2030-2034 2035-2039 2040-2044
课程示例来源 8.10 7.78 7.55## 不同年龄段的发病数
round(nordpred.getpred(Male_res, incidence = FALSE, standpop = NULL), 2)课程示例来源 1990-1994 1995-1999 2000-2004 2005-2009 2010-2014 2015-2019 2020-2024
课程示例来源 0-4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
课程示例来源 5-9 0.00 0.00 0.00 0.00 0.00 0.00 0.00
课程示例来源 10-14 1462.61 1385.65 1405.03 1555.52 1497.92 1384.11 1251.01
课程示例来源 15-19 2225.92 2407.29 2453.15 2395.52 2471.99 2441.12 2404.65
课程示例来源 20-24 5184.99 6162.44 7028.39 6423.01 5798.81 6240.91 6431.73
课程示例来源 25-29 14416.32 14632.53 19274.29 19208.54 15107.07 14617.25 16034.44
课程示例来源 30-34 38879.05 44767.59 45634.27 52618.21 42086.85 37064.69 38552.13
课程示例来源 35-39 66754.37 83155.53 102109.60 94813.01 98136.20 88118.01 81693.63
课程示例来源 40-44 113687.26 124667.19 165904.11 178924.61 146849.43 160438.07 155525.58
课程示例来源 45-49 169707.57 171782.44 198603.51 235491.03 230384.81 219416.19 238521.15
课程示例来源 50-54 196677.91 207170.82 225432.26 248690.13 281621.19 292279.96 270102.52
课程示例来源 55-59 192300.40 217101.70 248786.84 253441.16 267693.58 304022.02 323667.23
课程示例来源 60-64 157226.67 191809.38 238570.27 252535.20 241859.38 269606.84 315965.69
课程示例来源 65-69 101040.26 121406.24 163249.83 188204.62 196269.37 207009.18 228931.40
课程示例来源 70-74 53484.02 64508.36 86649.13 111667.53 127177.45 144575.40 151896.26
课程示例来源 75-79 20012.40 25443.48 32820.54 42785.97 55297.28 69150.98 80361.16
课程示例来源 80-84 3716.10 5108.26 6843.27 8782.34 11124.47 14896.61 20926.52
课程示例来源 85+ 519.94 710.67 1062.92 1475.53 1996.14 2550.61 3783.96
课程示例来源 2025-2029 2030-2034 2035-2039 2040-2044
课程示例来源 0-4 0.00 0.00 0.00 0.00
课程示例来源 5-9 0.00 0.00 0.00 0.00
课程示例来源 10-14 1171.35 1134.03 1117.72 1068.50
课程示例来源 15-19 2253.73 2200.45 2214.90 2193.61
课程示例来源 20-24 6377.42 6223.46 6297.78 6378.62
课程示例来源 25-29 16825.93 17012.79 17087.03 17377.36
课程示例来源 30-34 42697.62 45492.88 46576.04 46973.79
课程示例来源 35-39 84370.02 94702.04 102030.68 103994.18
课程示例来源 40-44 146670.30 153846.15 174584.57 187585.98
课程示例来源 45-49 232141.01 223358.64 237300.47 269026.70
课程示例来源 50-54 302123.98 299739.13 293184.26 311954.86
课程示例来源 55-59 304950.50 346790.92 350173.70 344520.39
课程示例来源 60-64 338632.10 326168.93 377413.70 384369.24
课程示例来源 65-69 273523.30 298755.53 294054.69 343592.85
课程示例来源 70-74 175549.30 215211.84 240219.59 240618.75
课程示例来源 75-79 88019.51 105690.71 133881.39 152028.37
课程示例来源 80-84 24789.99 28321.16 35551.48 46370.21
课程示例来源 85+ 5301.65 6892.65 8540.00 11398.73将Norpred预测的5年一组数据转换成单年一组数据
关于如何解决Norpred只能预测5年为一组的预测值的问题
以下为我的个人理解：
Norpred模型的本质其实就是广义线性模型，模型的理论基础是，发病率或死亡率和年龄结构和人口规模有关联。挖掘这种关联的基础还是广义线性模型（GLM）。
5年数据本质上是5年中位数的数据，比如2020-2024年的结果可以算作2022的数据。我们可以根据线性模型的线性关系将5年数据进行分解为单年的数据
预测的结果中5组5年预测数据组成：
中间3年：可以根据线性关系进行换算
第一组5年数据：拿前一组5年即2015-2019（2017）与2020-2024（2022）计算2020-2021年的数据。
最后一组5年数据：根据线性模型，人为计算出最后一组+1的数据，即2*最后一组5年数据-最后第二组5年数据，再根据根据线性关系进行换算。
Norpred模型预测ASR的原理是先计算每一年龄段的发病率数据后再根据ASR的原理计算预测的ASR，因此我们做的是根据得到的5年一组的每一年龄段的发病率根据线性关系换算成每一年的每一年龄段的发病率，再计算预测的ASR
我们来看下代码如何实现
## producing the end periods rates:
Male_Age_rate <- nordpred.getpred(Male_res, incidence = TRUE, standpop = NULL)
## 提取预测的率的数据
Male_Age_rate_proj <- Male_Age_rate[,7:ncol(Male_Age_rate)]
head(Male_Age_rate_proj)课程示例来源 2020-2024 2025-2029 2030-2034 2035-2039 2040-2044
课程示例来源 0-4 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 5-9 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 10-14 0.07960918 0.07195209 0.06718343 0.06489586 0.06267102
课程示例来源 15-19 0.15780868 0.14450346 0.13614350 0.13211120 0.12817501
课程示例来源 20-24 0.42108056 0.42222379 0.40242120 0.39280254 0.38336869
课程示例来源 25-29 1.10323371 1.11363498 1.13826221 1.11612357 1.09433075## 提取头之前之前的数据
rc1 <- Male_Age_rate[, 6]
rc1课程示例来源 [1] 0.00000000 0.00000000 0.09081849 0.15886189 0.42844721 1.10871601
课程示例来源 [7] 3.00178577 7.51558076 15.32746643 25.59217849 39.62813206 52.68000931
课程示例来源 [13] 67.48178432 75.30569416 87.21791764 91.42758570 63.71799813 45.28597548## 计算尾部+1的数据
rc2 <- 2*Male_Age_rate_proj[, ncol(Male_Age_rate_proj)] - Male_Age_rate_proj[, (ncol(Male_Age_rate_proj)-1)]
rc2课程示例来源 [1] 0.00000000 0.00000000 0.06044619 0.12423883 0.37393484 1.07253792
课程示例来源 [7] 3.05642884 7.37315288 13.52591305 21.02126073 27.87881961 33.29243813
课程示例来源 [13] 42.61710393 53.93050505 53.83228649 65.74926967 51.49455984 39.69224184##合并预测的数据
full_rate_proj <- cbind(rc1, Male_Age_rate_proj, rc2)
head(full_rate_proj)课程示例来源 rc1 2020-2024 2025-2029 2030-2034 2035-2039 2040-2044
课程示例来源 0-4 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 5-9 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 10-14 0.09081849 0.07960918 0.07195209 0.06718343 0.06489586 0.06267102
课程示例来源 15-19 0.15886189 0.15780868 0.14450346 0.13614350 0.13211120 0.12817501
课程示例来源 20-24 0.42844721 0.42108056 0.42222379 0.40242120 0.39280254 0.38336869
课程示例来源 25-29 1.10871601 1.10323371 1.11363498 1.13826221 1.11612357 1.09433075
课程示例来源 rc2
课程示例来源 0-4 0.00000000
课程示例来源 5-9 0.00000000
课程示例来源 10-14 0.06044619
课程示例来源 15-19 0.12423883
课程示例来源 20-24 0.37393484
课程示例来源 25-29 1.07253792annual_Male_Age_rate_proj <- matrix(NA, 18, 5*ncol(Male_Age_rate_proj))
for (i in 2:(5+1)){
annual_Male_Age_rate_proj[,(i-2)*5+1] <- (2/5)*full_rate_proj[,i-1] + (3/5)*full_rate_proj[,i]
annual_Male_Age_rate_proj[,(i-2)*5+2] <- (1/5)*full_rate_proj[,i-1] + (4/5)*full_rate_proj[,i]
annual_Male_Age_rate_proj[,(i-2)*5+3] <- (0/5)*full_rate_proj[,i-1] + (5/5)*full_rate_proj[,i]
annual_Male_Age_rate_proj[,(i-2)*5+4] <- (1/5)*full_rate_proj[,i+1] + (4/5)*full_rate_proj[,i]
annual_Male_Age_rate_proj[,(i-2)*5+5] <- (2/5)*full_rate_proj[,i+1] + (3/5)*full_rate_proj[,i]
}
annual_Male_Age_rate_proj <- annual_Male_Age_rate_proj %>% as.data.frame()
names(annual_Male_Age_rate_proj) <- 2020:(2020+ncol(annual_Male_Age_rate_proj)-1)
rownames(annual_Male_Age_rate_proj) <- ages_3这样，我们就得到了2020-2044年的每个年龄段的发病率数据
knitr::kable(annual_Male_Age_rate_proj,digits=2,align = 'c')| 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 | 2031 | 2032 | 2033 | 2034 | 2035 | 2036 | 2037 | 2038 | 2039 | 2040 | 2041 | 2042 | 2043 | 2044 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 to 14 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 15 to 19 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 20 to 24 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 | 0.06 |
| 25 to 29 | 0.16 | 0.16 | 0.16 | 0.16 | 0.15 | 0.15 | 0.15 | 0.14 | 0.14 | 0.14 | 0.14 | 0.14 | 0.14 | 0.14 | 0.13 | 0.13 | 0.13 | 0.13 | 0.13 | 0.13 | 0.13 | 0.13 | 0.13 | 0.13 | 0.13 |
| 30 to 34 | 0.42 | 0.42 | 0.42 | 0.42 | 0.42 | 0.42 | 0.42 | 0.42 | 0.42 | 0.41 | 0.41 | 0.41 | 0.40 | 0.40 | 0.40 | 0.40 | 0.39 | 0.39 | 0.39 | 0.39 | 0.39 | 0.39 | 0.38 | 0.38 | 0.38 |
| 35 to 39 | 1.11 | 1.10 | 1.10 | 1.11 | 1.11 | 1.11 | 1.11 | 1.11 | 1.12 | 1.12 | 1.13 | 1.13 | 1.14 | 1.13 | 1.13 | 1.12 | 1.12 | 1.12 | 1.11 | 1.11 | 1.10 | 1.10 | 1.09 | 1.09 | 1.09 |
| 40 to 44 | 2.97 | 2.96 | 2.95 | 2.95 | 2.96 | 2.97 | 2.97 | 2.98 | 2.99 | 3.01 | 3.02 | 3.04 | 3.05 | 3.07 | 3.09 | 3.11 | 3.14 | 3.16 | 3.15 | 3.14 | 3.13 | 3.12 | 3.11 | 3.10 | 3.09 |
| 45 to 49 | 7.03 | 6.86 | 6.70 | 6.68 | 6.65 | 6.63 | 6.61 | 6.58 | 6.61 | 6.64 | 6.67 | 6.70 | 6.73 | 6.78 | 6.83 | 6.87 | 6.92 | 6.97 | 7.01 | 7.05 | 7.09 | 7.13 | 7.17 | 7.21 | 7.25 |
| 50 to 54 | 14.28 | 13.93 | 13.59 | 13.35 | 13.11 | 12.87 | 12.63 | 12.39 | 12.38 | 12.38 | 12.37 | 12.36 | 12.35 | 12.43 | 12.51 | 12.59 | 12.67 | 12.75 | 12.83 | 12.91 | 12.98 | 13.06 | 13.14 | 13.22 | 13.29 |
| 55 to 59 | 24.49 | 24.12 | 23.75 | 23.24 | 22.72 | 22.21 | 21.69 | 21.18 | 20.88 | 20.58 | 20.28 | 19.99 | 19.69 | 19.72 | 19.75 | 19.78 | 19.81 | 19.84 | 19.96 | 20.08 | 20.20 | 20.31 | 20.43 | 20.55 | 20.67 |
| 60 to 64 | 35.97 | 34.75 | 33.52 | 33.24 | 32.96 | 32.67 | 32.39 | 32.11 | 31.51 | 30.91 | 30.31 | 29.71 | 29.12 | 28.79 | 28.46 | 28.13 | 27.80 | 27.48 | 27.52 | 27.56 | 27.60 | 27.64 | 27.68 | 27.72 | 27.76 |
| 65 to 69 | 50.19 | 49.36 | 48.53 | 47.20 | 45.86 | 44.52 | 43.19 | 41.85 | 41.59 | 41.33 | 41.07 | 40.80 | 40.54 | 39.89 | 39.24 | 38.60 | 37.95 | 37.30 | 36.90 | 36.50 | 36.10 | 35.70 | 35.30 | 34.89 | 34.49 |
| 70 to 74 | 65.40 | 64.71 | 64.01 | 62.96 | 61.91 | 60.86 | 59.81 | 58.76 | 57.29 | 55.83 | 54.36 | 52.89 | 51.43 | 51.21 | 51.00 | 50.78 | 50.56 | 50.35 | 49.57 | 48.80 | 48.03 | 47.25 | 46.48 | 45.71 | 44.94 |
| 75 to 79 | 73.26 | 72.58 | 71.90 | 71.23 | 70.57 | 69.90 | 69.23 | 68.57 | 67.57 | 66.57 | 65.57 | 64.57 | 63.57 | 62.11 | 60.65 | 59.18 | 57.72 | 56.26 | 56.03 | 55.79 | 55.56 | 55.33 | 55.09 | 54.86 | 54.63 |
| 80 to 84 | 82.17 | 80.48 | 78.80 | 78.48 | 78.16 | 77.84 | 77.52 | 77.20 | 76.62 | 76.04 | 75.46 | 74.88 | 74.30 | 73.35 | 72.40 | 71.46 | 70.51 | 69.56 | 67.99 | 66.42 | 64.84 | 63.27 | 61.70 | 60.12 | 58.55 |
| 85 to 89 | 87.66 | 86.41 | 85.15 | 83.83 | 82.51 | 81.19 | 79.86 | 78.54 | 78.35 | 78.16 | 77.97 | 77.79 | 77.60 | 77.14 | 76.69 | 76.23 | 75.77 | 75.32 | 74.36 | 73.40 | 72.45 | 71.49 | 70.53 | 69.58 | 68.62 |
| 90 to 94 | 63.11 | 62.91 | 62.70 | 62.09 | 61.47 | 60.86 | 60.25 | 59.63 | 58.74 | 57.85 | 56.95 | 56.06 | 55.17 | 55.12 | 55.08 | 55.04 | 54.99 | 54.95 | 54.60 | 54.26 | 53.91 | 53.57 | 53.22 | 52.88 | 52.53 |
| 95 plus | 45.92 | 46.13 | 46.34 | 46.07 | 45.81 | 45.55 | 45.29 | 45.03 | 44.64 | 44.25 | 43.86 | 43.47 | 43.08 | 42.47 | 41.86 | 41.25 | 40.64 | 40.03 | 39.99 | 39.96 | 39.93 | 39.89 | 39.86 | 39.83 | 39.79 |
annual_Male_Age_rate_ob <- EC_Male_incidence_n/GBD_Global_Male_n[,1:30]*100000
Male_Age_rate <- cbind(annual_Male_Age_rate_ob,annual_Male_Age_rate_proj)
Male_Age_count <- Male_Age_rate*GBD_Global_Male_n/100000
rownames(Male_Age_rate) <- ages_3
rownames(Male_Age_count) <- ages_3
## 每个年龄段的发病率
head(Male_Age_rate)课程示例来源 1990 1991 1992 1993 1994 1995 1996
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1172577 0.1172053 0.1161043 0.1153998 0.1134671 0.1113437 0.1094017
课程示例来源 25 to 29 0.1873471 0.1897218 0.1903727 0.1912269 0.1920114 0.1921580 0.1921752
课程示例来源 30 to 34 0.5099405 0.5072882 0.5042377 0.5078725 0.5124141 0.5194022 0.5260961
课程示例来源 35 to 39 1.5760143 1.5803043 1.5635869 1.5341092 1.4896554 1.4549466 1.4203647
课程示例来源 1997 1998 1999 2000 2001 2002 2003
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1086706 0.1079916 0.1075435 0.1072887 0.1055914 0.1047849 0.1045220
课程示例来源 25 to 29 0.1920918 0.1922547 0.1950853 0.1971521 0.1956316 0.1945945 0.1938734
课程示例来源 30 to 34 0.5286056 0.5291393 0.5379695 0.5509262 0.5582149 0.5637271 0.5790106
课程示例来源 35 to 39 1.4143661 1.4407985 1.4963420 1.5624292 1.6134502 1.6728710 1.7168612
课程示例来源 2004 2005 2006 2007 2008 2009 2010
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1052891 0.1059000 0.1049182 0.1051462 0.1058912 0.1046518 0.1032698
课程示例来源 25 to 29 0.1928052 0.1895636 0.1829711 0.1781989 0.1783565 0.1792053 0.1784046
课程示例来源 30 to 34 0.5801779 0.5563286 0.5194335 0.4959935 0.4873195 0.4857523 0.4754761
课程示例来源 35 to 39 1.7415923 1.6985601 1.6239461 1.5542206 1.4901682 1.4111062 1.3413203
课程示例来源 2011 2012 2013 2014 2015 2016
课程示例来源 0 to 14 0.00000000 0.0000000 0.00000000 0.00000000 0.00000000 0.0000000
课程示例来源 15 to 19 0.00000000 0.0000000 0.00000000 0.00000000 0.00000000 0.0000000
课程示例来源 20 to 24 0.09901558 0.0947570 0.09258192 0.09116926 0.08921786 0.0890295
课程示例来源 25 to 29 0.17401471 0.1690357 0.16583450 0.16165746 0.15821752 0.1566457
课程示例来源 30 to 34 0.45005053 0.4294623 0.42112025 0.41897640 0.41361493 0.4159047
课程示例来源 35 to 39 1.24420128 1.1647347 1.12808207 1.11748418 1.09203253 1.0789419
课程示例来源 2017 2018 2019 2020 2021 2022
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.09019953 0.09259019 0.09307148 0.0840929 0.08185104 0.07960918
课程示例来源 25 to 29 0.15651521 0.16053781 0.16244653 0.1582300 0.15801932 0.15780868
课程示例来源 30 to 34 0.42403293 0.43910411 0.44766302 0.4240272 0.42255389 0.42108056
课程示例来源 35 to 39 1.09312363 1.13089951 1.14596644 1.1054266 1.10433017 1.10323371
课程示例来源 2023 2024 2025 2026 2027 2028
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.07807776 0.07654634 0.07501493 0.07348351 0.07195209 0.07099836
课程示例来源 25 to 29 0.15514764 0.15248659 0.14982555 0.14716450 0.14450346 0.14283147
课程示例来源 30 to 34 0.42130920 0.42153785 0.42176650 0.42199514 0.42222379 0.41826327
课程示例来源 35 to 39 1.10531397 1.10739422 1.10947447 1.11155473 1.11363498 1.11856043
课程示例来源 2029 2030 2031 2032 2033 2034
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.0000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.0000000
课程示例来源 20 to 24 0.07004462 0.06909089 0.06813716 0.06718343 0.06672591 0.0662684
课程示例来源 25 to 29 0.14115948 0.13948748 0.13781549 0.13614350 0.13533704 0.1345306
课程示例来源 30 to 34 0.41430275 0.41034224 0.40638172 0.40242120 0.40049747 0.3985737
课程示例来源 35 to 39 1.12348588 1.12841132 1.13333677 1.13826221 1.13383449 1.1294068
课程示例来源 2035 2036 2037 2038 2039 2040
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.06581088 0.06535337 0.06489586 0.06445089 0.06400592 0.06356096
课程示例来源 25 to 29 0.13372412 0.13291766 0.13211120 0.13132396 0.13053673 0.12974949
课程示例来源 30 to 34 0.39665001 0.39472627 0.39280254 0.39091577 0.38902900 0.38714223
课程示例来源 35 to 39 1.12497903 1.12055130 1.11612357 1.11176501 1.10740644 1.10304788
课程示例来源 2041 2042 2043 2044
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.06311599 0.06267102 0.06222606 0.06178109
课程示例来源 25 to 29 0.12896225 0.12817501 0.12738778 0.12660054
课程示例来源 30 to 34 0.38525546 0.38336869 0.38148192 0.37959515
课程示例来源 35 to 39 1.09868931 1.09433075 1.08997218 1.08561362## 每个年龄段的发病数
head(Male_Age_count)课程示例来源 1990 1991 1992 1993 1994 1995 1996
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 291.1612 294.5433 294.1617 293.6652 289.0831 283.3790 277.8131
课程示例来源 25 to 29 417.2857 435.2500 447.9225 458.5289 466.9366 472.5735 477.7830
课程示例来源 30 to 34 996.7558 1005.1520 1019.9554 1058.2955 1104.8272 1157.4490 1205.5276
课程示例来源 35 to 39 2821.1391 2898.9136 2927.2640 2912.8349 2856.1667 2816.4215 2789.2080
课程示例来源 1997 1998 1999 2000 2001 2002 2003
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 275.6204 274.2784 274.5544 276.5031 275.8566 278.4735 283.1843
课程示例来源 25 to 29 481.3208 483.8762 491.7413 496.6934 491.9130 488.8430 487.8812
课程示例来源 30 to 34 1239.6210 1261.5975 1298.2436 1343.1208 1376.0994 1402.3089 1449.3437
课程示例来源 35 to 39 2838.5101 2981.8965 3206.4889 3461.5883 3674.4302 3897.1092 4065.6149
课程示例来源 2004 2005 2006 2007 2008 2009 2010
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 291.0089 298.7858 302.9173 311.0375 320.3839 322.3960 321.9275
课程示例来源 25 to 29 487.8165 484.0605 473.5674 469.2453 478.6198 490.0228 497.5462
课程示例来源 30 to 34 1457.5184 1399.6160 1306.9396 1249.4750 1231.7976 1235.1783 1220.0337
课程示例来源 35 to 39 4175.5505 4116.4047 3983.1449 3851.3705 3719.8586 3537.7618 3370.0857
课程示例来源 2011 2012 2013 2014 2015 2016 2017
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 310.1169 296.3840 287.9895 281.5013 273.4434 271.4938 274.3779
课程示例来源 25 to 29 496.2135 493.2275 494.3823 490.6223 486.2592 483.9461 483.0344
课程示例来源 30 to 34 1169.3072 1133.1288 1130.6182 1145.7181 1153.5283 1185.7714 1236.5707
课程示例来源 35 to 39 3127.4508 2929.7740 2845.1797 2834.5814 2795.3665 2796.1868 2875.9133
课程示例来源 2018 2019 2020 2021 2022 2023 2024
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 281.5108 283.2800 260.7896 255.5174 250.1847 247.0028 243.8197
课程示例来源 25 to 29 492.7773 495.0997 483.4408 480.4855 479.2913 472.4632 466.5091
课程示例来源 30 to 34 1307.8406 1357.2007 1290.0083 1292.3201 1290.9980 1289.7841 1284.1811
课程示例来源 35 to 39 3026.5642 3123.2195 3106.6270 3168.0706 3217.6224 3264.7772 3305.2133
课程示例来源 2025 2026 2027 2028 2029 2030 2031
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 240.6314 237.4291 234.2017 232.8715 231.5569 230.1822 228.6759
课程示例来源 25 to 29 461.1020 455.9330 450.7125 448.4837 446.3028 444.1638 442.0552
课程示例来源 30 to 34 1277.1955 1271.8385 1271.1342 1262.6432 1256.5585 1252.0498 1248.3195
课程示例来源 35 to 39 3338.0733 3362.4557 3377.4373 3387.6220 3386.1419 3380.8427 3379.7218
课程示例来源 2032 2033 2034 2035 2036 2037 2038
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 226.9686 226.7383 226.3432 225.7610 224.9658 224.2022 222.4613
课程示例来源 25 to 29 439.9587 440.7452 441.6135 442.4222 443.0296 443.2959 443.2636
课程示例来源 30 to 34 1244.5943 1247.0399 1249.7371 1252.6791 1255.8394 1259.1737 1262.9609
课程示例来源 35 to 39 3390.9299 3387.2057 3390.0755 3397.4092 3407.1105 3417.1060 3427.1511
课程示例来源 2039 2040 2041 2042 2043 2044
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 220.4464 218.2544 215.9504 213.3827 211.4960 209.4653
课程示例来源 25 to 29 442.9130 442.2011 441.0764 440.0184 436.9478 433.3345
课程示例来源 30 to 34 1267.0081 1270.9081 1274.2522 1276.6340 1277.9862 1278.4403
课程示例来源 35 to 39 3437.9582 3449.5014 3461.6967 3474.4167 3488.1025 3502.5605## 总发病数
Male_sum_year <- apply(Male_Age_count, 2, sum) %>% as.data.frame()
Male_sum_year$year <- 1990:(1990+nrow(Male_sum_year)-1)
names(Male_sum_year) <- c("number","year")
head(Male_sum_year)课程示例来源 number year
课程示例来源 1990 216056.8 1990
课程示例来源 1991 222136.7 1991
课程示例来源 1992 227110.5 1992
课程示例来源 1993 233657.5 1993
课程示例来源 1994 238334.3 1994
课程示例来源 1995 243958.3 1995## 计算标准发病率
Male_Age_standardized <- matrix(nrow =0, ncol = 2) %>% as.data.frame()
names(Male_Age_standardized) <- c("ASR","year")
for (i in 1:ncol(Male_Age_count)) {
asr = ageadjust.direct(count = Male_Age_count[,i], pop = GBD_Global_Male_n[,i],
stdpop = wstand)
Male_Age_standardized[i,1:2] <- c(round(100000*asr, 2)[2],names(Male_Age_count)[i]) ##rate per 100,000 per year
}
head(Male_Age_standardized)课程示例来源 ASR year
课程示例来源 1 11.71 1990
课程示例来源 2 11.77 1991
课程示例来源 3 11.76 1992
课程示例来源 4 11.83 1993
课程示例来源 5 11.81 1994
课程示例来源 6 11.83 1995同理计算女性数据
Female_Age_rate <- nordpred.getpred(Female_res, incidence = TRUE, standpop = NULL)
Female_Age_rate_proj <- Female_Age_rate[,7:ncol(Female_Age_rate)]
rc1 <- Female_Age_rate[, 6]
rc2 <- 2*Female_Age_rate_proj[, ncol(Female_Age_rate_proj)] - Female_Age_rate_proj[, (ncol(Female_Age_rate_proj)-1)]
full_rate_proj <- cbind(rc1, Female_Age_rate_proj, rc2)
# producing annual age-specific rates:
annual_Female_Age_rate_proj <- matrix(NA, 18, 5*ncol(Female_Age_rate_proj))
for (i in 2:(5+1)){
annual_Female_Age_rate_proj[,(i-2)*5+1] <- (2/5)*full_rate_proj[,i-1] + (3/5)*full_rate_proj[,i]
annual_Female_Age_rate_proj[,(i-2)*5+2] <- (1/5)*full_rate_proj[,i-1] + (4/5)*full_rate_proj[,i]
annual_Female_Age_rate_proj[,(i-2)*5+3] <- (0/5)*full_rate_proj[,i-1] + (5/5)*full_rate_proj[,i]
annual_Female_Age_rate_proj[,(i-2)*5+4] <- (1/5)*full_rate_proj[,i+1] + (4/5)*full_rate_proj[,i]
annual_Female_Age_rate_proj[,(i-2)*5+5] <- (2/5)*full_rate_proj[,i+1] + (3/5)*full_rate_proj[,i]
}
annual_Female_Age_rate_proj <- annual_Female_Age_rate_proj %>% as.data.frame()
names(annual_Female_Age_rate_proj) <- 2020:(2020+ncol(annual_Female_Age_rate_proj)-1)
rownames(annual_Female_Age_rate_proj) <- ages_3
annual_Female_Age_rate_ob <- EC_Female_incidence_n/GBD_Global_Female_n[,1:30]*100000
Female_Age_rate <- cbind(annual_Female_Age_rate_ob,annual_Female_Age_rate_proj)
Female_Age_count <- Female_Age_rate*GBD_Global_Female_n/100000
rownames(Female_Age_rate) <- ages_3
rownames(Female_Age_count) <- ages_3
Female_sum_year <- apply(Female_Age_count, 2, sum) %>% as.data.frame()
Female_sum_year$year <- 1990:(1990+nrow(Female_sum_year)-1)
names(Female_sum_year) <- c("number","year")
Female_Age_standardized <- matrix(nrow =0, ncol = 2) %>% as.data.frame()
names(Female_Age_standardized) <- c("ASR","year")
for (i in 1:ncol(Female_Age_count)) {
asr = ageadjust.direct(count = Female_Age_count[,i], pop = GBD_Global_Female_n[,i],
stdpop = wstand)
Female_Age_standardized[i,1:2] <- c(round(100000*asr, 2)[2],names(Female_Age_count)[i]) ##rate per 100,000 per year
}
head(Female_Age_rate)课程示例来源 1990 1991 1992 1993 1994 1995 1996
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1285672 0.1279918 0.1282682 0.1274204 0.1328378 0.1370568 0.1372056
课程示例来源 25 to 29 0.2055781 0.2044285 0.2059846 0.2048255 0.2239549 0.2378944 0.2391944
课程示例来源 30 to 34 0.3319933 0.3289697 0.3322790 0.3312806 0.3591024 0.3838996 0.3931173
课程示例来源 35 to 39 0.6841474 0.6802768 0.6818124 0.6743417 0.6707781 0.6620017 0.6541716
课程示例来源 1997 1998 1999 2000 2001 2002 2003
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1395197 0.1422899 0.1427363 0.1411361 0.1377018 0.1302922 0.1231990
课程示例来源 25 to 29 0.2443608 0.2541536 0.2571603 0.2562934 0.2516296 0.2289144 0.2091188
课程示例来源 30 to 34 0.4053906 0.4222954 0.4271104 0.4312460 0.4248548 0.3826157 0.3545222
课程示例来源 35 to 39 0.6613265 0.6712075 0.6879572 0.7136266 0.7298394 0.7274195 0.7099374
课程示例来源 2004 2005 2006 2007 2008 2009 2010
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1218310 0.1205358 0.1169439 0.1144195 0.1128954 0.1103267 0.1091758
课程示例来源 25 to 29 0.2026273 0.1984970 0.1886914 0.1799146 0.1780022 0.1782768 0.1787850
课程示例来源 30 to 34 0.3480697 0.3318236 0.3147864 0.2997544 0.2922483 0.2850719 0.2793515
课程示例来源 35 to 39 0.6987266 0.6835382 0.6523398 0.6181114 0.5895341 0.5648522 0.5462630
课程示例来源 2011 2012 2013 2014 2015 2016
课程示例来源 0 to 14 0.0000000 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.1063314 0.1003351 0.09622419 0.09196482 0.08966389 0.08947122
课程示例来源 25 to 29 0.1734881 0.1643497 0.16108350 0.15456890 0.15025122 0.14979319
课程示例来源 30 to 34 0.2689449 0.2543737 0.25016396 0.24462813 0.24054034 0.24001113
课程示例来源 35 to 39 0.5254784 0.5073629 0.50108229 0.48814072 0.48423167 0.48293618
课程示例来源 2017 2018 2019 2020 2021 2022
课程示例来源 0 to 14 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000 0.0000000
课程示例来源 20 to 24 0.0899865 0.09293322 0.09395515 0.08495025 0.08286852 0.0807868
课程示例来源 25 to 29 0.1513954 0.15589449 0.15747522 0.15419918 0.15461761 0.1550360
课程示例来源 30 to 34 0.2423043 0.24822764 0.25062245 0.25506628 0.25860295 0.2621396
课程示例来源 35 to 39 0.4854662 0.49194746 0.49521697 0.48535747 0.48445610 0.4835547
课程示例来源 2023 2024 2025 2026 2027 2028
课程示例来源 0 to 14 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.07935396 0.07792113 0.0764883 0.07505547 0.07362263 0.07272527
课程示例来源 25 to 29 0.15261153 0.15018703 0.1477625 0.14533803 0.14291353 0.14138326
课程示例来源 30 to 34 0.26190348 0.26166732 0.2614312 0.26119501 0.26095886 0.25847548
课程示例来源 35 to 39 0.48925494 0.49495514 0.5006554 0.50635556 0.51205577 0.51383468
课程示例来源 2029 2030 2031 2032 2033 2034
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.07182791 0.07093055 0.07003318 0.06913582 0.06870385 0.06827189
课程示例来源 25 to 29 0.13985300 0.13832274 0.13679248 0.13526221 0.13452206 0.13378191
课程示例来源 30 to 34 0.25599209 0.25350871 0.25102533 0.24854195 0.24733622 0.24613050
课程示例来源 35 to 39 0.51561359 0.51739250 0.51917141 0.52095032 0.51876784 0.51658535
课程示例来源 2035 2036 2037 2038 2039 2040
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.06783992 0.06740796 0.06697599 0.06655489 0.06613378 0.06571268
课程示例来源 25 to 29 0.13304177 0.13230162 0.13156147 0.13083761 0.13011375 0.12938989
课程示例来源 30 to 34 0.24492477 0.24371905 0.24251332 0.24133111 0.24014890 0.23896668
课程示例来源 35 to 39 0.51440287 0.51222039 0.51003790 0.50789215 0.50574639 0.50360064
课程示例来源 2041 2042 2043 2044
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.06529158 0.06487048 0.06444937 0.06402827
课程示例来源 25 to 29 0.12866603 0.12794217 0.12721831 0.12649445
课程示例来源 30 to 34 0.23778447 0.23660225 0.23542004 0.23423783
课程示例来源 35 to 39 0.50145488 0.49930913 0.49716337 0.49501761head(Female_Age_count)课程示例来源 1990 1991 1992 1993 1994 1995 1996
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 314.1757 315.3922 318.0992 317.6092 332.4554 344.0477 344.9262
课程示例来源 25 to 29 452.4966 462.8614 477.4293 482.8070 534.5153 573.5990 582.7055
课程示例来源 30 to 34 631.3312 635.2006 656.1672 674.9703 757.8403 838.0751 883.6074
课程示例来源 35 to 39 1188.6218 1209.0521 1234.8500 1237.0466 1242.4567 1239.2947 1245.1759
课程示例来源 1997 1998 1999 2000 2001 2002 2003
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 351.3694 359.5428 362.8672 362.2437 358.2691 344.5795 331.6477
课程示例来源 25 to 29 600.7503 629.7969 641.4846 642.5608 632.4197 576.4692 528.2847
课程示例来源 30 to 34 933.5899 989.7285 1014.0689 1034.8725 1030.6553 937.0606 875.2940
课程示例来源 35 to 39 1289.7061 1353.0632 1438.7904 1545.9526 1628.8055 1663.3665 1651.8056
课程示例来源 2004 2005 2006 2007 2008 2009 2010
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 333.8988 336.5320 333.4119 333.5738 336.0658 334.0830 334.5831
课程示例来源 25 to 29 514.7908 508.7874 490.0063 474.8055 477.9697 486.9705 497.1143
课程示例来源 30 to 34 864.8827 828.1417 786.9706 750.4158 733.3955 718.8268 710.3124
课程示例来源 35 to 39 1646.4349 1627.1979 1569.2407 1500.6156 1442.1869 1389.9362 1349.7532
课程示例来源 2011 2012 2013 2014 2015 2016 2017
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 327.2933 308.1487 293.5200 278.0433 268.6533 266.2743 266.7284
课程示例来源 25 to 29 492.1826 476.2211 476.3743 465.1776 457.7798 458.4295 462.3410
课程示例来源 30 to 34 692.5068 665.1585 665.3052 661.9004 662.7321 674.9041 696.1207
课程示例来源 35 to 39 1300.3991 1256.9669 1244.4920 1218.7966 1219.8465 1232.4696 1258.5939
课程示例来源 2018 2019 2020 2021 2022 2023 2024
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 274.9388 277.8970 253.1522 247.3196 241.6785 238.1329 234.7209
课程示例来源 25 to 29 472.8733 473.5171 463.9847 461.4690 460.5565 452.6851 445.3169
课程示例来源 30 to 34 728.0291 748.2529 770.3935 786.4233 799.3766 796.5799 790.2940
课程示例来源 35 to 39 1297.5730 1329.3401 1348.3738 1375.4840 1397.6931 1434.9544 1470.3860
课程示例来源 2025 2026 2027 2028 2029 2030 2031
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 231.4330 228.2573 225.1795 223.9126 222.8124 221.7453 220.5836
课程示例来源 25 to 29 438.3973 431.8748 425.7003 422.4801 419.5194 416.8074 414.3287
课程示例来源 30 to 34 782.6027 775.5800 771.2912 762.8601 755.2681 748.4381 742.2976
课程示例来源 35 to 39 1502.6394 1530.3066 1551.9195 1553.3413 1547.8683 1539.5222 1532.3784
课程示例来源 2032 2033 2034 2035 2036 2037 2038
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 219.2053 219.1123 218.8940 218.4890 217.8339 217.1203 215.5349
课程示例来源 25 to 29 412.0635 412.5594 413.4027 414.3513 415.1671 415.6166 415.7783
课程示例来源 30 to 34 736.7771 735.5742 734.8553 734.6108 734.8221 735.4628 736.8383
课程示例来源 35 to 39 1530.5664 1522.0462 1515.1894 1509.8690 1505.9622 1503.3504 1501.9566
课程示例来源 2039 2040 2041 2042 2043 2044
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 213.6966 211.7263 209.7127 207.5388 205.9379 204.1835
课程示例来源 25 to 29 415.7058 415.2819 414.3850 413.3795 410.6425 407.4206
课程示例来源 30 to 34 738.8421 741.0419 743.0103 744.3282 745.0429 745.3422
课程示例来源 35 to 39 1501.5617 1502.1490 1503.6814 1506.1048 1509.9002 1514.9980head(Female_sum_year)课程示例来源 number year
课程示例来源 1990 103911.9 1990
课程示例来源 1991 106298.3 1991
课程示例来源 1992 108802.2 1992
课程示例来源 1993 111790.2 1993
课程示例来源 1994 113141.8 1994
课程示例来源 1995 114088.0 1995head(Female_Age_standardized)课程示例来源 ASR year
课程示例来源 1 4.91 1990
课程示例来源 2 4.92 1991
课程示例来源 3 4.93 1992
课程示例来源 4 4.96 1993
课程示例来源 5 4.91 1994
课程示例来源 6 4.85 1995通过男性和女性数据计算总体发病情况
GBD_Global_Both_n <- GBD_Global_Female_n + GBD_Global_Male_n
Both_Age_count <- Female_Age_count + Male_Age_count
Both_Age_rate <- Both_Age_count/GBD_Global_Both_n*10^5
Both_Age_standardized <- matrix(nrow =0, ncol = 2) %>% as.data.frame()
names(Both_Age_standardized) <- c("ASR","year")
for (i in 1:ncol(Both_Age_count)) {
asr = ageadjust.direct(count = Both_Age_count[,i], pop = GBD_Global_Both_n[,i],
stdpop = wstand)
Both_Age_standardized[i,1:2] <- c(round(100000*asr, 2)[2],names(Both_Age_count)[i]) ##rate per 100,000 per year
}
Both_sum_year <- (Male_sum_year[,1] + Female_sum_year[,1]) %>% as.data.frame()
colnames(Both_sum_year) <- 'number'
Both_sum_year$year <- rownames(Female_sum_year)
head(Both_Age_rate)课程示例来源 1990 1991 1992 1993 1994 1995 1996
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1228672 0.1225456 0.1221212 0.1213478 0.1230662 0.1241117 0.1232336
课程示例来源 25 to 29 0.1964086 0.1970268 0.1981200 0.1979681 0.2078336 0.2148002 0.2154457
课程示例来源 30 to 34 0.4221901 0.4192808 0.4192911 0.4205690 0.4365802 0.4523477 0.4602478
课程示例来源 35 to 39 1.1367393 1.1374061 1.1300035 1.1116256 1.0872846 1.0651082 1.0432404
课程示例来源 1997 1998 1999 2000 2001 2002 2003
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1240406 0.1250968 0.1251028 0.1241776 0.1216136 0.1175075 0.1138305
课程示例来源 25 to 29 0.2179776 0.2229635 0.2259610 0.2266509 0.2236240 0.2117755 0.2015109
课程示例来源 30 to 34 0.4675566 0.4761757 0.4829912 0.4915587 0.4920703 0.4738772 0.4675385
课程示例来源 35 to 39 1.0432445 1.0610654 1.0970647 1.1427916 1.1761170 1.2045411 1.2178346
课程示例来源 2004 2005 2006 2007 2008 2009 2010
课程示例来源 0 to 14 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 15 to 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
课程示例来源 20 to 24 0.1135252 0.1131795 0.1108932 0.1097491 0.1093648 0.1074649 0.1061976
课程示例来源 25 to 29 0.1977264 0.1940387 0.1858360 0.1790577 0.1781793 0.1787413 0.1785945
课程示例来源 30 to 34 0.4647600 0.4445258 0.4174378 0.3981807 0.3901364 0.3858331 0.3778591
课程示例来源 35 to 39 1.2246797 1.1955829 1.1428626 1.0909617 1.0444022 0.9919293 0.9471124
课程示例来源 2011 2012 2013 2014 2015 2016
课程示例来源 0 to 14 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.1026417 0.09752058 0.09438524 0.09156285 0.08943835 0.08924767
课程示例来源 25 to 29 0.1737521 0.16670086 0.16346855 0.15812809 0.15425168 0.15323561
课程示例来源 30 to 34 0.3599050 0.34231118 0.33604693 0.33226413 0.32760387 0.32856504
课程示例来源 35 to 39 0.8876441 0.83854701 0.81699529 0.80529812 0.79056413 0.78323139
课程示例来源 2017 2018 2019 2020 2021 2022
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.09009439 0.09275936 0.09350699 0.08451304 0.08234835 0.08018348
课程示例来源 25 to 29 0.15396877 0.15822994 0.15997764 0.15622996 0.15633430 0.15643771
课程示例来源 30 to 34 0.33384817 0.34440080 0.34989863 0.33985207 0.34081107 0.34182434
课程示例来源 35 to 39 0.79152651 0.81374536 0.82306029 0.79718051 0.79599699 0.79478588
课程示例来源 2023 2024 2025 2026 2027 2028
课程示例来源 0 to 14 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.0000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.07869903 0.07721455 0.0757301 0.07424569 0.07276138 0.07183451
课程示例来源 25 to 29 0.15389625 0.15135480 0.1488129 0.14627031 0.14372679 0.14212530
课程示例来源 30 to 34 0.34186584 0.34194774 0.3420604 0.34219329 0.34233418 0.33927125
课程示例来源 35 to 39 0.79837139 0.80189096 0.8054373 0.80909550 0.81294667 0.81647326
课程示例来源 2029 2030 2031 2032 2033 2034
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.07090791 0.06998147 0.06905509 0.06812867 0.06768353 0.06723847
课程示例来源 25 to 29 0.14052341 0.13892118 0.13731864 0.13571585 0.13494178 0.13416755
课程示例来源 30 to 34 0.33624004 0.33321749 0.33018173 0.32711213 0.32567474 0.32421857
课程示例来源 35 to 39 0.82015435 0.82395418 0.82783324 0.83174568 0.82907590 0.82653563
课程示例来源 2035 2036 2037 2038 2039 2040
课程示例来源 0 to 14 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.0667934 0.06634823 0.06590283 0.06546936 0.06503592 0.06460259
课程示例来源 25 to 29 0.1333932 0.13261892 0.13184462 0.13108812 0.13033160 0.12957509
课程示例来源 30 to 34 0.3227441 0.32125151 0.31974118 0.31823664 0.31670748 0.31516397
课程示例来源 35 to 39 0.8240376 0.82149708 0.81883270 0.81609674 0.81328853 0.81040902
课程示例来源 2041 2042 2043 2044
课程示例来源 0 to 14 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 15 to 19 0.00000000 0.00000000 0.00000000 0.00000000
课程示例来源 20 to 24 0.06416942 0.06373653 0.06330341 0.06287027
课程示例来源 25 to 29 0.12881859 0.12806212 0.12730562 0.12654911
课程示例来源 30 to 34 0.31361580 0.31207206 0.31052840 0.30897944
课程示例来源 35 to 39 0.80745881 0.80443856 0.80132077 0.79810567head(Both_Age_count)课程示例来源 1990 1991 1992 1993 1994 1995 1996
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 605.3369 609.9355 612.2610 611.2744 621.5385 627.4266 622.7393
课程示例来源 25 to 29 869.7823 898.1115 925.3518 941.3359 1001.4519 1046.1725 1060.4885
课程示例来源 30 to 34 1628.0871 1640.3526 1676.1226 1733.2658 1862.6675 1995.5242 2089.1350
课程示例来源 35 to 39 4009.7609 4107.9657 4162.1140 4149.8815 4098.6233 4055.7162 4034.3839
课程示例来源 1997 1998 1999 2000 2001 2002 2003
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 0.000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 0.000
课程示例来源 20 to 24 626.9898 633.8212 637.4216 638.7468 634.1256 623.053 614.832
课程示例来源 25 to 29 1082.0711 1113.6731 1133.2259 1139.2542 1124.3327 1065.312 1016.166
课程示例来源 30 to 34 2173.2110 2251.3261 2312.3125 2377.9933 2406.7547 2339.370 2324.638
课程示例来源 35 to 39 4128.2162 4334.9597 4645.2794 5007.5409 5303.2357 5560.476 5717.421
课程示例来源 2004 2005 2006 2007 2008 2009 2010
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 624.9077 635.3177 636.3292 644.6113 656.4497 656.4790 656.5106
课程示例来源 25 to 29 1002.6073 992.8479 963.5738 944.0507 956.5895 976.9933 994.6604
课程示例来源 30 to 34 2322.4011 2227.7577 2093.9102 1999.8908 1965.1930 1954.0050 1930.3461
课程示例来源 35 to 39 5821.9853 5743.6026 5552.3856 5351.9861 5162.0455 4927.6980 4719.8389
课程示例来源 2011 2012 2013 2014 2015 2016 2017
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 637.4102 604.5327 581.5095 559.5447 542.0967 537.7681 541.1063
课程示例来源 25 to 29 988.3961 969.4487 970.7566 955.7998 944.0390 942.3756 945.3754
课程示例来源 30 to 34 1861.8140 1798.2873 1795.9233 1807.6185 1816.2604 1860.6755 1932.6914
课程示例来源 35 to 39 4427.8498 4186.7409 4089.6717 4053.3780 4015.2130 4028.6563 4134.5072
课程示例来源 2018 2019 2020 2021 2022 2023 2024
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 556.4496 561.1770 513.9417 502.8370 491.8633 485.1357 478.5406
课程示例来源 25 to 29 965.6506 968.6168 947.4255 941.9545 939.8478 925.1483 911.8260
课程示例来源 30 to 34 2035.8697 2105.4536 2060.4018 2078.7434 2090.3746 2086.3639 2074.4751
课程示例来源 35 to 39 4324.1372 4452.5596 4455.0008 4543.5546 4615.3156 4699.7316 4775.5994
课程示例来源 2025 2026 2027 2028 2029 2030 2031
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 472.0644 465.6864 459.3812 456.7840 454.3693 451.9275 449.2595
课程示例来源 25 to 29 899.4993 887.8078 876.4128 870.9638 865.8222 860.9711 856.3839
课程示例来源 30 to 34 2059.7982 2047.4185 2042.4254 2025.5033 2011.8265 2000.4879 1990.6171
课程示例来源 35 to 39 4840.7127 4892.7623 4929.3568 4940.9633 4934.0102 4920.3649 4912.1002
课程示例来源 2032 2033 2034 2035 2036 2037 2038
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 446.1739 445.8506 445.2373 444.2501 442.7998 441.3224 437.9962
课程示例来源 25 to 29 852.0222 853.3046 855.0162 856.7734 858.1966 858.9125 859.0419
课程示例来源 30 to 34 1981.3714 1982.6142 1984.5924 1987.2899 1990.6615 1994.6365 1999.7992
课程示例来源 35 to 39 4921.4963 4909.2519 4905.2649 4907.2781 4913.0727 4920.4564 4929.1077
课程示例来源 2039 2040 2041 2042 2043 2044
课程示例来源 0 to 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 15 to 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
课程示例来源 20 to 24 434.1430 429.9807 425.6631 420.9215 417.4340 413.6488
课程示例来源 25 to 29 858.6188 857.4830 855.4614 853.3980 847.5903 840.7552
课程示例来源 30 to 34 2005.8503 2011.9500 2017.2625 2020.9623 2023.0291 2023.7825
课程示例来源 35 to 39 4939.5199 4951.6504 4965.3781 4980.5215 4998.0027 5017.5586head(Both_sum_year)课程示例来源 number year
课程示例来源 1 319968.7 1990
课程示例来源 2 328435.0 1991
课程示例来源 3 335912.7 1992
课程示例来源 4 345447.7 1993
课程示例来源 5 351476.1 1994
课程示例来源 6 358046.3 1995head(Both_Age_standardized)课程示例来源 ASR year
课程示例来源 1 8.07 1990
课程示例来源 2 8.1 1991
课程示例来源 3 8.1 1992
课程示例来源 4 8.16 1993
课程示例来源 5 8.12 1994
课程示例来源 6 8.1 1995##双坐标轴图形绘制
####### plot for prediction
Both_sum_year$sex <- 'Both'
Female_sum_year$sex <- 'Female'
Male_sum_year$sex <- 'Male'
Both_Age_standardized$sex <- 'Both'
Female_Age_standardized$sex <- 'Female'
Male_Age_standardized$sex <- 'Male'
ASR <- rbind(Both_Age_standardized,Female_Age_standardized,Male_Age_standardized)
Num <- rbind(Both_sum_year,Female_sum_year,Male_sum_year)
ASR$ASR <- as.numeric(ASR$ASR)
ASR$year <- as.numeric(ASR$year)
Num$number <- as.numeric(Num$number)
Num$year <- as.numeric(Num$year)
ratio <-max(ASR$ASR)/max(Num$number)
p <- ggplot(Num,aes(year,number))+
geom_col(aes(fill=sex),position = 'dodge',width = 0.8)+
labs(title = NULL,x='Year',y='Number of cases') +
theme_bw() +
theme(plot.title=element_text(hjust=0.5),
axis.text.x=element_text(angle=45,size=8,color='black'),
axis.text.y=element_text(size=8,color='black'),
axis.title.y = element_text(size = 10),
axis.title.x = element_text(size = 10),
strip.background.x = element_rect(fill = 'skyblue3'),
title = element_text(size = 10, hjust = 0.5),
legend.position = 'right') +
geom_line(data=ASR,
aes(x=year,y=ASR/ratio,
color=sex)) +
scale_x_continuous(expand=c(0,0))
scale_y_continuous(expand=c(0,0),sec.axis = sec_axis(~.*ratio,
name="Age-standardized rate (per 100000 populations)"))课程示例来源 <ScaleContinuousPosition>
课程示例来源 Range:
课程示例来源 Limits: 0 -- 1