safety_02_population {nemsqar} | R Documentation |
Safety-02 Populations
Description
Filters data down to the target populations for Safety-02, and categorizes records to identify needed information for the calculations.
Identifies key categories related to a 911 request during which lights and sirens were not used during patient transport. This function segments the data by age into adult and pediatric populations.
Usage
safety_02_population(
df = NULL,
patient_scene_table = NULL,
response_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,
edisposition_18_col,
edisposition_28_col,
transport_disposition_cols
)
Arguments
df |
A data frame where each row is an observation, and each column represents a feature. |
patient_scene_table |
A data.frame or tibble containing only epatient and escene fields as a fact table. |
response_table |
A data.frame or tibble containing only the eresponse fields needed for this measure's calculations. |
disposition_table |
A data.frame or tibble containing only the edisposition fields needed for this measure's calculations. |
erecord_01_col |
The column representing the EMS record unique identifier. |
incident_date_col |
Column that contains the incident date. This
defaults to |
patient_DOB_col |
Column that contains the patient's date of birth. This
defaults to |
epatient_15_col |
Column giving the calculated age value. |
epatient_16_col |
Column giving the provided age unit value. |
eresponse_05_col |
Column giving response codes, identifying 911 responses. |
edisposition_18_col |
Column giving transport mode descriptors, including possible lights-and-sirens indicators. |
edisposition_28_col |
Column giving patient evaluation and care categories for the EMS response. |
transport_disposition_cols |
One or more unquoted column names (such as edisposition.12, edisposition.30) containing transport disposition details. |
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
Author(s)
Nicolas Foss, Ed.D., MS
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_18 = rep(4218015, 5),
edisposition_28 = rep(4228001, 5),
edisposition_30 = rep(4230001, 5)
)
# test the success of the function
result <- safety_02_population(patient_scene_table = patient_table,
response_table = response_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,
edisposition_18_col = edisposition_18,
edisposition_28_col = edisposition_28,
transport_disposition_cols = edisposition_30
)
# show the results of filtering at each step
result$filter_process