Filters data down to the target populations for Safety-04, and categorizes records to identify needed information for the calculations.
Identifies key categories related to a 911 request or interfacility request for patients less than 8 years of age during which patients are transported using a pediatric restraint device. This function segments the data by age into adult and pediatric populations.
Usage
safety_04_population(
df = NULL,
patient_scene_table = NULL,
response_table = NULL,
arrest_table = NULL,
injury_table = NULL,
procedures_table = NULL,
disposition_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
eresponse_05_col,
earrest_01_col,
einjury_03_col,
eprocedures_03_col,
edisposition_14_col,
transport_disposition_col
)Arguments
- df
A data frame or tibble containing EMS data where each row represents an individual observation.
- patient_scene_table
A data frame or tibble containing fields from epatient and escene needed for this measure's calculations.
- response_table
A data frame or tibble containing fields from eresponse needed for this measure's calculations.
- arrest_table
A data frame or tibble containing fields from earrest needed for this measure's calculations.
- injury_table
A data frame or tibble containing fields from einjury needed for this measure's calculations.
- procedures_table
A data frame or tibble containing fields from eprocedures needed for this measure's calculations.
- disposition_table
A data frame or tibble containing fields from edisposition needed for this measure's calculations.
- erecord_01_col
The column containing unique record identifiers for each encounter.
- incident_date_col
Column that contains the incident date. This defaults to
NULLas it is optional in case not available due to PII restrictions.- patient_DOB_col
Column that contains the patient's date of birth. This defaults to
NULLas it is optional in case not available due to PII restrictions.- epatient_15_col
Column name indicating the patient age.
- epatient_16_col
Column name for the unit of age (e.g., "Years," "Months").
- eresponse_05_col
Column containing response transport codes.
- earrest_01_col
Column with cardiac arrest status information.
- einjury_03_col
Column describing traumatic injuries, expected as a list or text-separated entries.
- eprocedures_03_col
Column listing procedures, assumed to contain multiple procedure codes/texts in each cell.
- edisposition_14_col
Column for transport dispositions.
- transport_disposition_col
Columns for primary and secondary transport dispositions.
Value
A list that contains the following:
a tibble with counts for each filtering step,
a tibble for each population of interest
a tibble for the initial population
a tibble for the total dataset with computations
Examples
# create tables to test correct functioning
# patient table
patient_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
incident_date = as.Date(c("2025-01-01", "2025-01-05",
"2025-02-01", "2025-01-01",
"2025-06-01")
),
patient_dob = as.Date(c("2000-01-01", "2020-01-01",
"2023-02-01", "2023-01-01",
"1970-06-01")
),
epatient_15 = c(25, 5, 2, 2, 55), # Ages
epatient_16 = c("Years", "Years", "Years", "Years", "Years")
)
# response table
response_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
eresponse_05 = rep(2205001, 5)
)
# disposition table
disposition_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
edisposition_14 = rep(4214001, 5),
edisposition_30 = rep(4230001, 5),
)
# arrest table
arrest_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
earrest_01 = rep("No", 5)
)
# injury table
injury_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
einjury_03 = rep("non-injury", 5)
)
# procedures table
procedures_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
eprocedures_03 = rep("other response", 5)
)
# test the success of the function
result <- safety_04_population(patient_scene_table = patient_table,
response_table = response_table,
arrest_table = arrest_table,
injury_table = injury_table,
procedures_table = procedures_table,
disposition_table = disposition_table,
erecord_01_col = erecord_01,
incident_date_col = incident_date,
patient_DOB_col = patient_dob,
epatient_15_col = epatient_15,
epatient_16_col = epatient_16,
eresponse_05_col = eresponse_05,
earrest_01_col = earrest_01,
einjury_03_col = einjury_03,
edisposition_14_col = edisposition_14,
transport_disposition_col = edisposition_30,
eprocedures_03_col = eprocedures_03
)
#> Running `safety_04_population()` [Working on 1 of 13 tasks] ●●●───────────────…
#> Running `safety_04_population()` [Working on 2 of 13 tasks] ●●●●●●────────────…
#> Running `safety_04_population()` [Working on 3 of 13 tasks] ●●●●●●●●──────────…
#> Running `safety_04_population()` [Working on 4 of 13 tasks] ●●●●●●●●●●────────…
#> Running `safety_04_population()` [Working on 5 of 13 tasks] ●●●●●●●●●●●●●─────…
#> Running `safety_04_population()` [Working on 6 of 13 tasks] ●●●●●●●●●●●●●●●───…
#> Running `safety_04_population()` [Working on 7 of 13 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `safety_04_population()` [Working on 8 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()` [Working on 9 of 13 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()` [Working on 10 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()` [Working on 11 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()` [Working on 12 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_04_population()` [Working on 13 of 13 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 10 × 2
#> filter count
#> <chr> <int>
#> 1 Transport runs 5
#> 2 Interfacility runs 0
#> 3 Cardiac arrest calls 0
#> 4 Severe injury calls 0
#> 5 Calls involving long board 0
#> 6 Calls involving an airway procedure 0
#> 7 Car seat used 5
#> 8 Peds denominator 3
#> 9 Initial population 3
#> 10 Total dataset 5