The World Health Organization’s CoDEdit electronic tool is intended to help producers of cause-of-death statistics in strengthening their capacity to perform routine checks on their data. This package ports the original tool built using Microsoft Access into R. The aim is to leverage the utility and function of the original tool into a usable application program interface (API) that can be used for building more universal tools or for creating programmatic scientific workflows aimed at routine, automated, and large-scale monitoring of cause-of-death data.
What does codeditr do?
The codeditr package provides functions for implementing data quality checks on cause-of-death records. It is built upon the same heuristics and algorithms that the WHO CoDEdit v2.0 electronic tool uses to evaluate quality of cause-of-death data produced and used by country level statisticians in charge of mortality reporting.
Through this package, end users will have the ability to programmatically create scientific workflows for routine monitoring and evaluation and/or research on cause-of-death data and their quality. They can also build more open tools or applications for routine monitoring of cause-of-death data without having to rely on the proprietary Microsoft Access software.
Currently, the codeditr package supports the following use cases:
- Cause-of-death dataset preparation for use in the CoDEdit tool
To be able to use WHO’s CoDEdit tool built on Microsoft Access, the user can either enter their cause-of-death data into the tool itself using a spreadsheet style input system with very specific input fields or through uploading of a Microsoft .xlsx file that is structured in a specific way required by the tool.
The codeditr package has a set of functions that support in checking that specific required variables are formatted to be compatible for the CoDEdit tool and structures these variables into a dataset that is consistent with what is required for uploading to the CodEdit tool.
This use case are for those what would still prefer to use WHO’s CoDEdit tool using Microsoft Access (either for continuity purposes or for consistency with organisational policy) but would like to have the functionality of converting their existing cause-of-death dataset into a CoDEdit tool-compatible format and structure.
- Cause-of-death dataset checking to identify data quality issues
The codeditr package has a set of functions that performs all the checks that the CoDEdit tool performs in addition to other general data quality checks. These functions allow for a similar output as the CoDEdit tool
This use case are for those that prefer not to use Microsoft Access (either because they don’t already own a copy of this software or that their purpose for data quality checks is for large-scale datasets) and would like a completely programmatic approach to performing routine and potentially large scale cause-of-death data quality checks.
Installation
codeditr is not yet on CRAN but can be installed through the Oxford iHealth R Universe with:
install.packages(
"codeditr",
repos = c("https://oxfordihtm.r-universe.dev", "https://cloud.r-project.org")
)then load codeditr
Usage
Support to usage of CoDEdit tool
- Perform checks on existing input data for CoDEdit tool
Using the icd10_example dataset which is a dataset already formatted into a compatible structure required by the CoDEdit tool, we can perform a check on this dataset to see possible issues in its formatting and structure before using with the CoDEdit tool.
cod_check_codedit_input(icd10_example)
#> # A tibble: 3,613 × 8
#> sex_check sex_check_note age_check age_check_note code_check
#> <int> <fct> <int> <fct> <int>
#> 1 0 No issues with sex value 0 No issues with age… 0
#> 2 0 No issues with sex value 0 No issues with age… 0
#> 3 0 No issues with sex value 0 No issues with age… 0
#> 4 0 No issues with sex value 0 No issues with age… 0
#> 5 0 No issues with sex value 0 No issues with age… 0
#> 6 0 No issues with sex value 0 No issues with age… 0
#> 7 0 No issues with sex value 0 No issues with age… 0
#> 8 0 No issues with sex value 0 No issues with age… 0
#> 9 0 No issues with sex value 0 No issues with age… 0
#> 10 0 No issues with sex value 0 No issues with age… 0
#> # ℹ 3,603 more rows
#> # ℹ 3 more variables: code_check_note <chr>, dod_check <int>,
#> # dod_check_note <fct>- Structure raw cause-of-death data for input into CoDEdit tool
Using the cod_data_raw_example dataset, we can format it into a compatible structure required by the CoDEdit tool.
cod_structure_input(
df = cod_data_raw_example,
sex = "sex", dob = "dob", dod = "dod", code = "code", id = "id"
)
#> # A tibble: 20 × 6
#> FreeId Sex `Age Value` `Age Type` Code `Death Date`
#> <int> <int> <int> <chr> <chr> <chr>
#> 1 4136 1 1318 Y NE84&XA6KU8 2023
#> 2 4137 2 1318 Y 2B6D&XS9R 2023
#> 3 4138 1 1318 Y 2C82&XS9R 2023
#> 4 4139 1 1318 Y CA40.Z&XK9J 2023
#> 5 4140 2 1318 Y 6C40.3&XS25 2023
#> 6 4141 1 1318 Y 6C40.3&XS25 2023
#> 7 4142 1 1318 Y DB94.1&XT8W 2023
#> 8 4143 2 1318 Y BD40.Z 2023
#> 9 4144 2 1318 Y 2C76.Z&XA8QA8 2023
#> 10 4145 1 1318 Y 6C40.3&XS25 2023
#> 11 4146 2 1318 Y 8B11.5Z 2023
#> 12 4147 1 1318 Y 2B90.Y&XH74S1 2023
#> 13 4148 1 1318 Y BD10&XT5R 2023
#> 14 4149 1 1318 Y 1G41 2023
#> 15 4150 1 1318 Y BD10&XT5R 2023
#> 16 4151 2 1318 Y CA40.Z&XB25 2023
#> 17 4152 2 1318 Y BA01 2023
#> 18 4153 1 1318 Y 1G41 2023
#> 19 4154 2 1318 Y BB40 2023
#> 20 4155 1 1318 Y 1B91 2023The output is a data.frame that can then be saved as an .xlsx file for use as input into the CoDEdit tool.
CoDEdit tool replacement workflow
- Perform all checks on cause-of-death data
The cod_check_code() function performs all the checks implemented by the CoDEdit tool.
cod_check_code(
cod_data_raw_example$code, version = "icd11",
sex = cod_data_raw_example$sex, age = cod_data_raw_example$age
)
#> # A tibble: 20 × 12
#> cod_check_structure cod_check_note_structure cod_check_ill_defined
#> <int> <fct> <int>
#> 1 0 No issues found in CoD code 0
#> 2 0 No issues found in CoD code 0
#> 3 0 No issues found in CoD code 0
#> 4 0 No issues found in CoD code 0
#> 5 0 No issues found in CoD code 0
#> 6 0 No issues found in CoD code 0
#> 7 0 No issues found in CoD code 0
#> 8 0 No issues found in CoD code 0
#> 9 0 No issues found in CoD code 0
#> 10 0 No issues found in CoD code 0
#> 11 0 No issues found in CoD code 0
#> 12 0 No issues found in CoD code 0
#> 13 0 No issues found in CoD code 0
#> 14 0 No issues found in CoD code 0
#> 15 0 No issues found in CoD code 0
#> 16 0 No issues found in CoD code 0
#> 17 0 No issues found in CoD code 0
#> 18 0 No issues found in CoD code 0
#> 19 0 No issues found in CoD code 0
#> 20 0 No issues found in CoD code 0
#> # ℹ 9 more variables: cod_check_note_ill_defined <fct>,
#> # cod_check_unlikely <int>, cod_check_note_unlikely <fct>,
#> # cod_check_sex <int>, cod_check_note_sex <fct>, cod_check_age <int>,
#> # cod_check_note_age <fct>, cod_check_code <dbl>, cod_check_code_note <fct>Results of the per row cause-of-death checks can also be summarised to give a count of issues found in the dataset.
cod_check_code(
cod_data_raw_example$code, version = "icd11",
sex = cod_data_raw_example$sex, age = cod_data_raw_example$age
) |>
cod_check_code_summary()
#> $`Code structure`
#> # A tibble: 65 × 2
#> cod_check_note n
#> <fct> <int>
#> 1 No issues found in CoD code 20
#> 2 CoD code has a period (`.`) character in the wrong place 0
#> 3 CoD code starts with `O` or `I` 0
#> 4 CoD code has a period (`.`) character in the wrong place; CoD code s… 0
#> 5 CoD code has a number as its second value 0
#> 6 CoD code has a period (`.`) character in the wrong place; CoD code h… 0
#> 7 CoD code starts with `O` or `I`; CoD code has a number as its second… 0
#> 8 CoD code has a period (`.`) character in the wrong place; CoD code s… 0
#> 9 CoD code has `O` or `I` as its second value 0
#> 10 CoD code has a period (`.`) character in the wrong place; CoD code h… 0
#> # ℹ 55 more rows
#>
#> $`Ill-defined code`
#> # A tibble: 2 × 2
#> cod_check_note n
#> <fct> <int>
#> 1 No issues found in CoD code 20
#> 2 CoD code is an ill-defined code 0
#>
#> $`Unlikely cause-of-death code`
#> # A tibble: 2 × 2
#> cod_check_note n
#> <fct> <int>
#> 1 No issues found in CoD code 20
#> 2 CoD code is an unlikely cause-of-death 0
#>
#> $`Code not appropriate for sex`
#> # A tibble: 2 × 2
#> cod_check_note n
#> <fct> <int>
#> 1 No issues found in CoD code 20
#> 2 CoD code is not appropriate for person's sex 0
#>
#> $`Code not appropriate for age`
#> # A tibble: 2 × 2
#> cod_check_note n
#> <fct> <int>
#> 1 No issues found in CoD code 20
#> 2 CoD code is not appropriate for person's age 0
#>
#> $Overall
#> # A tibble: 2 × 2
#> cod_check_note n
#> <fct> <int>
#> 1 No issues found in CoD code 20
#> 2 Issues found in CoD code 0- Perform specific check types on cause-of-death data
The family of cod_check_code_* functions can be used to perform specific check types on the cause-of-death data.
### Perform code structure check on cause-of-death data ----
cod_check_code_structure_icd10(icd10_example$Code)
#> # A tibble: 3,613 × 2
#> cod_check cod_check_note
#> <int> <fct>
#> 1 0 No issues found in CoD code
#> 2 0 No issues found in CoD code
#> 3 0 No issues found in CoD code
#> 4 0 No issues found in CoD code
#> 5 0 No issues found in CoD code
#> 6 0 No issues found in CoD code
#> 7 0 No issues found in CoD code
#> 8 0 No issues found in CoD code
#> 9 0 No issues found in CoD code
#> 10 0 No issues found in CoD code
#> # ℹ 3,603 more rows
### Perform check for ill-defined codes on cause-of-death data ----
cod_check_code_ill_defined_icd11(cod_data_raw_example$code)
#> # A tibble: 20 × 2
#> cod_check cod_check_note
#> <int> <fct>
#> 1 0 No issues found in CoD code
#> 2 0 No issues found in CoD code
#> 3 0 No issues found in CoD code
#> 4 0 No issues found in CoD code
#> 5 0 No issues found in CoD code
#> 6 0 No issues found in CoD code
#> 7 0 No issues found in CoD code
#> 8 0 No issues found in CoD code
#> 9 0 No issues found in CoD code
#> 10 0 No issues found in CoD code
#> 11 0 No issues found in CoD code
#> 12 0 No issues found in CoD code
#> 13 0 No issues found in CoD code
#> 14 0 No issues found in CoD code
#> 15 0 No issues found in CoD code
#> 16 0 No issues found in CoD code
#> 17 0 No issues found in CoD code
#> 18 0 No issues found in CoD code
#> 19 0 No issues found in CoD code
#> 20 0 No issues found in CoD code
### Perform check for unlikely cause-of-death codes ----
cod_check_code_unlikely_icd11(cod_data_raw_example$code)
#> # A tibble: 20 × 2
#> cod_check cod_check_note
#> <int> <fct>
#> 1 0 No issues found in CoD code
#> 2 0 No issues found in CoD code
#> 3 0 No issues found in CoD code
#> 4 0 No issues found in CoD code
#> 5 0 No issues found in CoD code
#> 6 0 No issues found in CoD code
#> 7 0 No issues found in CoD code
#> 8 0 No issues found in CoD code
#> 9 0 No issues found in CoD code
#> 10 0 No issues found in CoD code
#> 11 0 No issues found in CoD code
#> 12 0 No issues found in CoD code
#> 13 0 No issues found in CoD code
#> 14 0 No issues found in CoD code
#> 15 0 No issues found in CoD code
#> 16 0 No issues found in CoD code
#> 17 0 No issues found in CoD code
#> 18 0 No issues found in CoD code
#> 19 0 No issues found in CoD code
#> 20 0 No issues found in CoD code
### Perform check for cause-of-death codes inappropriate for specific sex ----
cod_check_code_sex_icd11(cod_data_raw_example$code, cod_data_raw_example$sex)
#> # A tibble: 20 × 2
#> cod_check cod_check_note
#> <int> <fct>
#> 1 0 No issues found in CoD code
#> 2 0 No issues found in CoD code
#> 3 0 No issues found in CoD code
#> 4 0 No issues found in CoD code
#> 5 0 No issues found in CoD code
#> 6 0 No issues found in CoD code
#> 7 0 No issues found in CoD code
#> 8 0 No issues found in CoD code
#> 9 0 No issues found in CoD code
#> 10 0 No issues found in CoD code
#> 11 0 No issues found in CoD code
#> 12 0 No issues found in CoD code
#> 13 0 No issues found in CoD code
#> 14 0 No issues found in CoD code
#> 15 0 No issues found in CoD code
#> 16 0 No issues found in CoD code
#> 17 0 No issues found in CoD code
#> 18 0 No issues found in CoD code
#> 19 0 No issues found in CoD code
#> 20 0 No issues found in CoD code
### Perform check for cause-of-death codes inappropriate for specific age ----
cod_check_code_age_icd11(cod_data_raw_example$code, cod_data_raw_example$age)
#> # A tibble: 20 × 2
#> cod_check cod_check_note
#> <int> <fct>
#> 1 0 No issues found in CoD code
#> 2 0 No issues found in CoD code
#> 3 0 No issues found in CoD code
#> 4 0 No issues found in CoD code
#> 5 0 No issues found in CoD code
#> 6 0 No issues found in CoD code
#> 7 0 No issues found in CoD code
#> 8 0 No issues found in CoD code
#> 9 0 No issues found in CoD code
#> 10 0 No issues found in CoD code
#> 11 0 No issues found in CoD code
#> 12 0 No issues found in CoD code
#> 13 0 No issues found in CoD code
#> 14 0 No issues found in CoD code
#> 15 0 No issues found in CoD code
#> 16 0 No issues found in CoD code
#> 17 0 No issues found in CoD code
#> 18 0 No issues found in CoD code
#> 19 0 No issues found in CoD code
#> 20 0 No issues found in CoD codeCitation
If you find the codeditr package useful please cite using the suggested citation provided by a call to the citation() function as follows:
citation("codeditr")
#> To cite codeditr in publications use:
#>
#> Anita Makori, Ernest Guevarra (2024). _codeditr: Implementing
#> Cause-of-Death Data Checks Based on World Health Organization's
#> CoDEdit Tool_. R package version 0.0.9000,
#> <https://oxford-ihtm.io/codeditr/>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {codeditr: Implementing Cause-of-Death Data Checks Based on World Health Organization's CoDEdit Tool},
#> author = {{Anita Makori} and {Ernest Guevarra}},
#> year = {2024},
#> note = {R package version 0.0.9000},
#> url = {https://oxford-ihtm.io/codeditr/},
#> }Community guidelines
Feedback, bug reports and feature requests are welcome; file issues or seek support here. If you would like to contribute to the package, please see our contributing guidelines.
This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.