Last updated: 2022-08-23

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Knit directory: Vaccination_COVID/

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Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/.Rhistory
    Ignored:    code/.DS_Store

Unstaged changes:
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Staged changes:
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development. 

knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE, out.width = "100%", root.dir = rprojroot::find_rstudio_root_file())

library(data.table)
library(dplyr)
Warning: package 'dplyr' was built under R version 4.1.2

Attaching package: 'dplyr'
The following objects are masked from 'package:data.table':

    between, first, last
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(tidyr)
Warning: package 'tidyr' was built under R version 4.1.2
library(lubridate)

Attaching package: 'lubridate'
The following objects are masked from 'package:data.table':

    hour, isoweek, mday, minute, month, quarter, second, wday, week,
    yday, year
The following objects are masked from 'package:base':

    date, intersect, setdiff, union
library(ggplot2)
library(gtsummary)
Warning: package 'gtsummary' was built under R version 4.1.2
library(ggsci)

datap <- file.path("~", "Downloads", "updated_dataset")

bcg <- readRDS(file.path(datap, "bcg_haiduong.rds"))
bcg <- data.table(bcg)
bcg <- bcg[which(bcg$shot == 1),]

hepb <- readRDS(file.path(datap, "hepb_haiduong.rds"))
hepb <- data.table(hepb)
hepb <- hepb[which(hepb$shot == 1),]

measle_all <- readRDS(file.path(datap, "measles_haiduong.rds"))
measle_all <- data.table(measle_all)

time_step <- "month"
bcg$vacym <- floor_date(bcg$vacdate, time_step)
hepb$vacym <- floor_date(hepb$vacdate, time_step)

Hep B

Number of days from Hep B vaccination to date of birth 

    Var1  Freq
212    0 96114
213    1 93316
214    2  8599
215    3  2099
216    4   907
217    5   520
218    6   331
219    7   177
220    8   114
221    9    98
222   10   199
223   11   121
224   12    62
225   13    41
226   14    53

Only keep children vaccinated within 3 days after birth (should we?)

BCG

Number of days from BCG vaccination to date of birth 

    Var1 Freq
137    0  861
138    1 1368
139    2 2127
140    3 2398
141    4 3966
142    5 3687
143    6 3617
144    7 4160
145    8 4379
146    9 4746
147   10 5069
148   11 5362
149   12 5188
150   13 5550
151   14 5764

Compare Hep B and BCG

BCG in Hep B


 FALSE   TRUE 
 48813 190988

Hep B in BCG


 FALSE   TRUE 
  9140 190988

Hep B and BCG

Hep B, BCG and Measles

Vaccine coverage

All children born in Jan 2019 (use Hep B)

tmp1 <- hepb %>% 
  filter(
    month(dob) == 1,
    year(dob) == 2019
  )

nrow(tmp1)
[1] 2730

Also all children born in Jan 2019 got the measles shot

tmp2 <- measle_all %>% 
  filter(
    month(dob) == 1,
    year(dob) == 2019,
    vacname2 == "Measles"
  ) %>%
  distinct(., pid, .keep_all = T)
nrow(tmp2)
[1] 2729
table(tmp1$pid %in% tmp2$pid)

FALSE  TRUE 
  415  2315 
2315 / 2729
[1] 0.8482961

These children in measles dataset get Measles shot in October 2019

tmp2 <- tmp2 %>%
  filter(
    vacmonth == 10,
    vacyear == 2019,
  )
nrow(tmp2)
[1] 1360

So the vaccine coverage

nrow(tmp2) / nrow(tmp1)
[1] 0.4981685

Cumulative coverage of children born in Jan - Feb 2019

tmp1 <- hepb %>% 
  filter(
    month(dob) %in% c(1, 2),
    year(dob) == 2019
  )

tmp2 <- measle_all %>% 
  filter(
    month(dob) %in% c(1, 2),
    year(dob) == 2019,
    vacname2 == "Measles"
  ) %>%
  distinct(., pid, .keep_all = T)

nrow(tmp1)
[1] 4847
nrow(tmp2)
[1] 4773
tmp2 <- tmp2 %>%
  filter(
    vacmonth %in% c(10, 11),
    vacyear == 2019,
  )

nrow(tmp2) / nrow(tmp1)
[1] 0.6769136

Cumulative coverage of children born in Jan - Feb - March 2019

tmp1 <- hepb %>% 
  filter(
    month(dob) %in% c(1, 2, 3),
    year(dob) == 2019
  )

tmp2 <- measle_all %>% 
  filter(
    month(dob) %in% c(1, 2, 3),
    year(dob) == 2019,
    vacmonth %in% c(10, 11, 12),
    vacyear == 2019,
    vacname2 == "Measles"
  ) %>%
  distinct(., pid, .keep_all = T)

nrow(tmp2) / nrow(tmp1)
[1] 0.7470621

R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggsci_2.9         gtsummary_1.6.1   ggplot2_3.3.5     lubridate_1.8.0  
[5] tidyr_1.2.0       dplyr_1.0.9       data.table_1.14.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.2    xfun_0.32           bslib_0.4.0        
 [4] purrr_0.3.4         colorspace_2.0-3    vctrs_0.4.1        
 [7] generics_0.1.3      htmltools_0.5.3     yaml_2.3.5         
[10] utf8_1.2.2          rlang_1.0.4         jquerylib_0.1.4    
[13] later_1.3.0         pillar_1.8.1        glue_1.6.2         
[16] withr_2.5.0         DBI_1.1.3           plyr_1.8.7         
[19] lifecycle_1.0.1     stringr_1.4.1       munsell_0.5.0      
[22] gtable_0.3.0        workflowr_1.7.0     evaluate_0.16      
[25] labeling_0.4.2      knitr_1.39          fastmap_1.1.0      
[28] httpuv_1.6.5        fansi_1.0.3         highr_0.9          
[31] Rcpp_1.0.9          promises_1.2.0.1    scales_1.2.1       
[34] cachem_1.0.6        jsonlite_1.8.0      farver_2.1.0       
[37] fs_1.5.2            digest_0.6.29       stringi_1.7.8      
[40] rprojroot_2.0.3     grid_4.1.1          cli_3.3.0          
[43] tools_4.1.1         magrittr_2.0.3      sass_0.4.2         
[46] tibble_3.1.8        crayon_1.5.1        pkgconfig_2.0.3    
[49] broom.helpers_1.8.0 assertthat_0.2.1    gt_0.6.0           
[52] rmarkdown_2.15      rstudioapi_0.13     R6_2.5.1           
[55] git2r_0.30.1        compiler_4.1.1