README.md
In stephenbalogun/tidyndr: Analysis of the Nigeria National Data Repository (NDR)
Tidyndr
The goal of {tidyndr} is to provide a specialized, simple and easy to
use functions that wrap around existing functions in R for
manipulation of the NDR patient
line-list file allowing the user to focus on the tasks to be completed
rather than the code/formula details.
The functions presented are similar to the PEPFAR Monitoring Evaluation
and Reporting Indicators and are currently grouped into four categories:
-
The read_ndr function for reading the patient-level line-list
downloaded from the front-end of the NDR in ‘csv’ format.
-
The PEPFAR treatment group of indicators that can be performed on
the NDR line-list.
-
The ‘Viral Load’ indicators (tx_vl_eligible(), tx_pvls_den()
tx_pvls_num() and tx_vl_unsuppressed()).
-
The summary functions (summarise_ndr() and disaggregrate())
provides a tabular summary for the tasks that have been completed
using any of the functions above.
Installation
You can install the released version of {tidyndr} from
CRAN with:
install.packages("tidyndr")
Or the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("stephenbalogun/tidyndr",
build_vignette = TRUE)
Usage
library(tidyndr)
read_ndr
read_ndr() reads the downloaded “.csv” file into
R using
vroom::vroom() behind the scene and
passing appropriate column types to the col_types argument. It also
formats the variable names using the
snakecase style.
## read from a local file path (not run)
# file_path <- system.file("extdata", "ndr_example.csv", package = "tidyndr")
# read_ndr(file_path, time_stamp = "2021-02-15")
### read line-list available on the internet
path <- "https://raw.githubusercontent.com/stephenbalogun/example_files/main/ndr_example.csv"
ndr_example <- read_ndr(path, time_stamp = "2021-02-20")
Treatment Indicators
The functions included in this group are:
-
tx_new()
-
tx_curr()
-
tx_ml() and tx_ml_outcomes()
-
tx_rtt()
-
Other supporting functions are: tx_mmd(), tx_regimen() and
tx_appointment()
## Subset "TX_NEW"
tx_new(ndr_example, from = "2021-01-01", to = "2021-03-31")
#> # A tibble: 1,556 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… M State … 0003 1990-02-07 31
#> 2 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0003 1988-05-05 27
#> 4 IP_name State… LGA0… Facili… datim_… F State … 0003 1992-01-01 NA
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0008 1996-01-01 38
#> 6 IP_name State… LGA0… Facili… datim_… M State … 0007 2002-01-01 37
#> 7 IP_name State… LGA0… Facili… datim_… M State … 0002 1980-01-11 31
#> 8 IP_name State… LGA0… Facili… datim_… F State … 00035 1983-01-01 30
#> 9 IP_name State… LGA0… Facili… datim_… F State … 00042 1995-09-14 41
#> 10 IP_name State… LGA0… Facili… datim_… M State … 0001 1987-01-01 32
#> # … with 1,546 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
## Generate line-list of clients with medication refill in October 2021
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-01-31"
)
#> # A tibble: 3,512 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… F State … 0002 1980-03-27 40
#> 2 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0002 1971-02-04 27
#> 4 IP_name State… LGA0… Facili… datim_… F State … 0001 1973-02-02 35
#> 5 IP_name State… LGA0… Facili… datim_… M State … 0004 1965-05-13 23
#> 6 IP_name State… LGA0… Facili… datim_… M State … 0007 2002-01-01 37
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0009 1992-10-24 34
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0006 1980-05-02 70
#> 9 IP_name State… LGA0… Facili… datim_… F State … 0005 1990-01-01 39
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0003 1981-08-08 24
#> # … with 3,502 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
## Generate list of clients who were active at the beginning of October 2021 but became inactive at the end of December 2021.
tx_ml(new_data = ndr_example,
from = "2021-01-01",
to = "2021-03-31")
#> # A tibble: 10,307 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… F State … 0002 1980-03-27 40
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0002 1984-07-14 35
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0002 1980-01-01 37
#> 4 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0004 1972-01-01 NA
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0001 1980-01-01 NA
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0002 1971-02-04 27
#> 8 IP_name State… LGA0… Facili… datim_… F State … 0001 1973-02-02 35
#> 9 IP_name State… LGA0… Facili… datim_… M State … 0004 1965-05-13 23
#> 10 IP_name State… LGA0… Facili… datim_… M State … 0008 1988-01-01 26
#> # … with 10,297 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
Viral Suppression Indicators
The tx_vl_eligible(), tx_pvls_den() and the tx_pvls_num()
functions come in handy when you need to generate the line-list of
clients who are eligible for viral load test at a given point for a
given facility/state, those who have a valid viral load result (not more
than 1 year for people aged 20 years and above and not more than 6
months for paediatrics and adolescents less or equal to 19 years), and
those who are virally suppressed (out of those with valid viral load
results). When the sample = TRUE attribute is supplied to the
tx_vl_eligible() function, it generates the line-list of only those
who are due for a viral load test out of all those who are eligible.
## Generate list of clients who are eligible for VL (i.e. expected to have a documented VL result)
ndr_example %>%
tx_vl_eligible(ref = "2021-12-31")
#> # A tibble: 27,020 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… M State … 0001 1988-06-05 25
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0001 1975-05-15 22
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-03-23 46
#> 4 IP_name State… LGA0… Facili… datim_… M State … 0002 1957-05-11 18
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-12-22 30
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-06-10 NA
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0001 1960-05-19 25
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0003 1990-02-07 31
#> 9 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-01-01 22
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0004 1983-06-01 NA
#> # … with 27,010 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
## Generate list of clients that will be expected to have a viral load test done by March 2022
ndr_example %>%
tx_vl_eligible("2022-03-31",
sample = TRUE)
#> # A tibble: 27,020 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… M State … 0001 1988-06-05 25
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0001 1975-05-15 22
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-03-23 46
#> 4 IP_name State… LGA0… Facili… datim_… M State … 0002 1957-05-11 18
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-12-22 30
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-06-10 NA
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0001 1960-05-19 25
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0003 1990-02-07 31
#> 9 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-01-01 22
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0004 1983-06-01 NA
#> # … with 27,010 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
### Calculate the Viral Load Coverage as of December 2021
no_of_vl_results <- tx_pvls_den(ndr_example,
ref = "2021-12-31") %>%
nrow()
no_of_vl_eligible <- tx_vl_eligible(ndr_example,
ref = "2021-12-31") %>%
nrow()
vl_coverage <- scales::percent(no_of_vl_results / no_of_vl_eligible)
print(vl_coverage)
#> [1] "2%"
For all the ‘Treatment’ and ‘Viral Suppression’ indicators (except
tx_ml_outcomes(), which should be use with tx_ml()), you have
control over the level of action (state or facility) by supplying to the
states and/or facilities arguments the values of interest . For more
than one state or facility, combine the values with the c() e.g.
## subset clients that have medication appointment in between January and March of 2021 in
## and are also due for viral load
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-03-31",
) %>%
tx_vl_eligible(sample = TRUE)
#> # A tibble: 7,038 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-06-10 NA
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0001 1960-05-19 25
#> 3 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 4 IP_name State… LGA0… Facili… datim_… F State … 0004 1972-01-01 NA
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0001 1980-01-01 NA
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0005 1990-05-25 32
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0002 1971-02-04 27
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0005 1976-01-26 26
#> 9 IP_name State… LGA0… Facili… datim_… M State … 0007 2002-01-01 37
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0002 1997-06-01 21
#> # … with 7,028 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
Summarising your Indicators
You might want to generate a summary table of all the indicators you
have pulled out. The summarise_ndr() (or summarize_ndr()) allows you
to do this with ease. It accepts all the line-lists you are interested
in creating a summary table for, the level at which you want the summary
to be created (country/ip, state or facility), and the names you want to
give to each of your summary column.
## generates line-list of TX_NEW between July and December 2021
new <- tx_new(ndr_example, from = "2021-01-01", to = "2021-03-31")
## generates line-list of currently active clients
curr <- tx_curr(ndr_example)
## generates line-list of clients who were active at the beginning of the October but inactive at end of December 2021
ml <- tx_ml(new_data = ndr_example, from = "2021-01-01", to = "2021-03-31")
summarise_ndr(new, curr, ml,
level = "state",
names = c("tx_new", "tx_curr", "tx_ml"))
#> # A tibble: 4 × 5
#> ip state tx_new tx_curr tx_ml
#> <chr> <chr> <int> <int> <int>
#> 1 IP_name State 1 272 5647 2595
#> 2 IP_name State 2 300 7931 4152
#> 3 IP_name State 3 984 13446 3560
#> 4 Total - 1556 27024 10307
The disaggregate() allows you to summarise an indicator of interest
into finer details based on “current_age”, “sex” “pregnancy_status”,
“art_duration”, “months_dispensed (of ARV)” or “age_sex”. These are
supplied to the by parameter of the function. The default
disaggregates the variable of interest at the level of “states” but can
also do this at “country/ip”, “lga” or “facility” level when any of this
is supplied to the level parameter.
## generates line-list of TX_NEW between July and September 2021
new_clients <- tx_new(ndr_example, from = "2021-01-01", to = "2021-03-30")
disaggregate(new_clients,
by = "current_age", pivot_wide = FALSE)
#> # A tibble: 49 × 4
#> ip state current_age number
#> <chr> <chr> <chr> <int>
#> 1 IP_name State 1 <1 0
#> 2 IP_name State 1 1-4 2
#> 3 IP_name State 1 5-9 0
#> 4 IP_name State 1 10-14 0
#> 5 IP_name State 1 15-19 11
#> 6 IP_name State 1 20-24 37
#> 7 IP_name State 1 25-29 83
#> 8 IP_name State 1 30-34 63
#> 9 IP_name State 1 35-39 36
#> 10 IP_name State 1 40-44 24
#> # … with 39 more rows
## disaggregate 'TX_CURR' by sex
ndr_example %>%
tx_curr() %>%
disaggregate(by = "sex")
#> # A tibble: 4 × 5
#> ip state Male Female unknown
#> <chr> <chr> <int> <int> <int>
#> 1 IP_name State 1 1662 3985 0
#> 2 IP_name State 2 2335 5596 0
#> 3 IP_name State 3 5894 7552 0
#> 4 Total - 9891 17133 0
Code of Conduct
Please note that the {tidyndr} project is released with a Contributor
Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
stephenbalogun/tidyndr documentation built on Feb. 6, 2023, 11:35 p.m.
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Tidyndr
The goal of {tidyndr} is to provide a specialized, simple and easy to
use functions that wrap around existing functions in R for
manipulation of the NDR patient
line-list file allowing the user to focus on the tasks to be completed
rather than the code/formula details.
The functions presented are similar to the PEPFAR Monitoring Evaluation and Reporting Indicators and are currently grouped into four categories:
-
The
read_ndrfunction for reading the patient-level line-list downloaded from the front-end of the NDR in ‘csv’ format. -
The PEPFAR treatment group of indicators that can be performed on the NDR line-list.
-
The ‘Viral Load’ indicators (
tx_vl_eligible(),tx_pvls_den()tx_pvls_num()andtx_vl_unsuppressed()). -
The summary functions (
summarise_ndr()anddisaggregrate()) provides a tabular summary for the tasks that have been completed using any of the functions above.
Installation
You can install the released version of {tidyndr} from CRAN with:
install.packages("tidyndr")
Or the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("stephenbalogun/tidyndr",
build_vignette = TRUE)
Usage
library(tidyndr)
read_ndr
read_ndr() reads the downloaded “.csv” file into
R using
vroom::vroom() behind the scene and
passing appropriate column types to the col_types argument. It also
formats the variable names using the
snakecase style.
## read from a local file path (not run)
# file_path <- system.file("extdata", "ndr_example.csv", package = "tidyndr")
# read_ndr(file_path, time_stamp = "2021-02-15")
### read line-list available on the internet
path <- "https://raw.githubusercontent.com/stephenbalogun/example_files/main/ndr_example.csv"
ndr_example <- read_ndr(path, time_stamp = "2021-02-20")
Treatment Indicators
The functions included in this group are:
-
tx_new() -
tx_curr() -
tx_ml()andtx_ml_outcomes() -
tx_rtt() -
Other supporting functions are:
tx_mmd(),tx_regimen()andtx_appointment()
## Subset "TX_NEW"
tx_new(ndr_example, from = "2021-01-01", to = "2021-03-31")
#> # A tibble: 1,556 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… M State … 0003 1990-02-07 31
#> 2 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0003 1988-05-05 27
#> 4 IP_name State… LGA0… Facili… datim_… F State … 0003 1992-01-01 NA
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0008 1996-01-01 38
#> 6 IP_name State… LGA0… Facili… datim_… M State … 0007 2002-01-01 37
#> 7 IP_name State… LGA0… Facili… datim_… M State … 0002 1980-01-11 31
#> 8 IP_name State… LGA0… Facili… datim_… F State … 00035 1983-01-01 30
#> 9 IP_name State… LGA0… Facili… datim_… F State … 00042 1995-09-14 41
#> 10 IP_name State… LGA0… Facili… datim_… M State … 0001 1987-01-01 32
#> # … with 1,546 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
## Generate line-list of clients with medication refill in October 2021
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-01-31"
)
#> # A tibble: 3,512 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… F State … 0002 1980-03-27 40
#> 2 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0002 1971-02-04 27
#> 4 IP_name State… LGA0… Facili… datim_… F State … 0001 1973-02-02 35
#> 5 IP_name State… LGA0… Facili… datim_… M State … 0004 1965-05-13 23
#> 6 IP_name State… LGA0… Facili… datim_… M State … 0007 2002-01-01 37
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0009 1992-10-24 34
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0006 1980-05-02 70
#> 9 IP_name State… LGA0… Facili… datim_… F State … 0005 1990-01-01 39
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0003 1981-08-08 24
#> # … with 3,502 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
## Generate list of clients who were active at the beginning of October 2021 but became inactive at the end of December 2021.
tx_ml(new_data = ndr_example,
from = "2021-01-01",
to = "2021-03-31")
#> # A tibble: 10,307 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… F State … 0002 1980-03-27 40
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0002 1984-07-14 35
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0002 1980-01-01 37
#> 4 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0004 1972-01-01 NA
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0001 1980-01-01 NA
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0002 1971-02-04 27
#> 8 IP_name State… LGA0… Facili… datim_… F State … 0001 1973-02-02 35
#> 9 IP_name State… LGA0… Facili… datim_… M State … 0004 1965-05-13 23
#> 10 IP_name State… LGA0… Facili… datim_… M State … 0008 1988-01-01 26
#> # … with 10,297 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
Viral Suppression Indicators
The tx_vl_eligible(), tx_pvls_den() and the tx_pvls_num()
functions come in handy when you need to generate the line-list of
clients who are eligible for viral load test at a given point for a
given facility/state, those who have a valid viral load result (not more
than 1 year for people aged 20 years and above and not more than 6
months for paediatrics and adolescents less or equal to 19 years), and
those who are virally suppressed (out of those with valid viral load
results). When the sample = TRUE attribute is supplied to the
tx_vl_eligible() function, it generates the line-list of only those
who are due for a viral load test out of all those who are eligible.
## Generate list of clients who are eligible for VL (i.e. expected to have a documented VL result)
ndr_example %>%
tx_vl_eligible(ref = "2021-12-31")
#> # A tibble: 27,020 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… M State … 0001 1988-06-05 25
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0001 1975-05-15 22
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-03-23 46
#> 4 IP_name State… LGA0… Facili… datim_… M State … 0002 1957-05-11 18
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-12-22 30
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-06-10 NA
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0001 1960-05-19 25
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0003 1990-02-07 31
#> 9 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-01-01 22
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0004 1983-06-01 NA
#> # … with 27,010 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
## Generate list of clients that will be expected to have a viral load test done by March 2022
ndr_example %>%
tx_vl_eligible("2022-03-31",
sample = TRUE)
#> # A tibble: 27,020 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… M State … 0001 1988-06-05 25
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0001 1975-05-15 22
#> 3 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-03-23 46
#> 4 IP_name State… LGA0… Facili… datim_… M State … 0002 1957-05-11 18
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-12-22 30
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-06-10 NA
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0001 1960-05-19 25
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0003 1990-02-07 31
#> 9 IP_name State… LGA0… Facili… datim_… F State … 0002 1982-01-01 22
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0004 1983-06-01 NA
#> # … with 27,010 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
### Calculate the Viral Load Coverage as of December 2021
no_of_vl_results <- tx_pvls_den(ndr_example,
ref = "2021-12-31") %>%
nrow()
no_of_vl_eligible <- tx_vl_eligible(ndr_example,
ref = "2021-12-31") %>%
nrow()
vl_coverage <- scales::percent(no_of_vl_results / no_of_vl_eligible)
print(vl_coverage)
#> [1] "2%"
For all the ‘Treatment’ and ‘Viral Suppression’ indicators (except
tx_ml_outcomes(), which should be use with tx_ml()), you have
control over the level of action (state or facility) by supplying to the
states and/or facilities arguments the values of interest . For more
than one state or facility, combine the values with the c() e.g.
## subset clients that have medication appointment in between January and March of 2021 in
## and are also due for viral load
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-03-31",
) %>%
tx_vl_eligible(sample = TRUE)
#> # A tibble: 7,038 × 52
#> ip state lga facil…¹ datim…² sex patie…³ hospi…⁴ date_of_…⁵ age_a…⁶
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr> <date> <dbl>
#> 1 IP_name State… LGA0… Facili… datim_… F State … 0001 1985-06-10 NA
#> 2 IP_name State… LGA0… Facili… datim_… F State … 0001 1960-05-19 25
#> 3 IP_name State… LGA0… Facili… datim_… M State … 0003 1986-04-06 39
#> 4 IP_name State… LGA0… Facili… datim_… F State … 0004 1972-01-01 NA
#> 5 IP_name State… LGA0… Facili… datim_… F State … 0001 1980-01-01 NA
#> 6 IP_name State… LGA0… Facili… datim_… F State … 0005 1990-05-25 32
#> 7 IP_name State… LGA0… Facili… datim_… F State … 0002 1971-02-04 27
#> 8 IP_name State… LGA0… Facili… datim_… M State … 0005 1976-01-26 26
#> 9 IP_name State… LGA0… Facili… datim_… M State … 0007 2002-01-01 37
#> 10 IP_name State… LGA0… Facili… datim_… F State … 0002 1997-06-01 21
#> # … with 7,028 more rows, 42 more variables: current_age <dbl>,
#> # art_start_date <date>, art_start_date_source <fct>,
#> # last_drug_pickup_date <date>, last_drug_pickup_date_q1 <date>,
#> # last_drug_pickup_date_q2 <date>, last_drug_pickup_date_q3 <date>,
#> # last_drug_pickup_date_q4 <date>, last_regimen <fct>,
#> # last_clinic_visit_date <date>, days_of_arv_refill <dbl>,
#> # pregnancy_status <fct>, current_viral_load <dbl>, …
Summarising your Indicators
You might want to generate a summary table of all the indicators you
have pulled out. The summarise_ndr() (or summarize_ndr()) allows you
to do this with ease. It accepts all the line-lists you are interested
in creating a summary table for, the level at which you want the summary
to be created (country/ip, state or facility), and the names you want to
give to each of your summary column.
## generates line-list of TX_NEW between July and December 2021
new <- tx_new(ndr_example, from = "2021-01-01", to = "2021-03-31")
## generates line-list of currently active clients
curr <- tx_curr(ndr_example)
## generates line-list of clients who were active at the beginning of the October but inactive at end of December 2021
ml <- tx_ml(new_data = ndr_example, from = "2021-01-01", to = "2021-03-31")
summarise_ndr(new, curr, ml,
level = "state",
names = c("tx_new", "tx_curr", "tx_ml"))
#> # A tibble: 4 × 5
#> ip state tx_new tx_curr tx_ml
#> <chr> <chr> <int> <int> <int>
#> 1 IP_name State 1 272 5647 2595
#> 2 IP_name State 2 300 7931 4152
#> 3 IP_name State 3 984 13446 3560
#> 4 Total - 1556 27024 10307
The disaggregate() allows you to summarise an indicator of interest
into finer details based on “current_age”, “sex” “pregnancy_status”,
“art_duration”, “months_dispensed (of ARV)” or “age_sex”. These are
supplied to the by parameter of the function. The default
disaggregates the variable of interest at the level of “states” but can
also do this at “country/ip”, “lga” or “facility” level when any of this
is supplied to the level parameter.
## generates line-list of TX_NEW between July and September 2021
new_clients <- tx_new(ndr_example, from = "2021-01-01", to = "2021-03-30")
disaggregate(new_clients,
by = "current_age", pivot_wide = FALSE)
#> # A tibble: 49 × 4
#> ip state current_age number
#> <chr> <chr> <chr> <int>
#> 1 IP_name State 1 <1 0
#> 2 IP_name State 1 1-4 2
#> 3 IP_name State 1 5-9 0
#> 4 IP_name State 1 10-14 0
#> 5 IP_name State 1 15-19 11
#> 6 IP_name State 1 20-24 37
#> 7 IP_name State 1 25-29 83
#> 8 IP_name State 1 30-34 63
#> 9 IP_name State 1 35-39 36
#> 10 IP_name State 1 40-44 24
#> # … with 39 more rows
## disaggregate 'TX_CURR' by sex
ndr_example %>%
tx_curr() %>%
disaggregate(by = "sex")
#> # A tibble: 4 × 5
#> ip state Male Female unknown
#> <chr> <chr> <int> <int> <int>
#> 1 IP_name State 1 1662 3985 0
#> 2 IP_name State 2 2335 5596 0
#> 3 IP_name State 3 5894 7552 0
#> 4 Total - 9891 17133 0
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