Filters data down to the target populations for Safety-01, and categorizes records to identify needed information for the calculations.
Identifies key categories related to 911 responses where "lights and sirens" were not used in an EMS dataset. This function segments the data by age into adult and pediatric populations.
Usage
safety_01_population(
df = NULL,
patient_scene_table = NULL,
response_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
eresponse_05_col,
eresponse_24_col
)Arguments
- df
A data frame or tibble containing EMS data.
- patient_scene_table
A data frame or tibble containing only epatient and escene fields as a fact table. Default is
NULL.- response_table
A data frame or tibble containing only the eresponse fields needed for this measure's calculations. Default is
NULL.- erecord_01_col
Column name containing the unique patient record identifier.
- incident_date_col
Date or POSIXct column indicating the date of the incident.
- patient_DOB_col
Date or POSIXct column for the patient’s date of birth
- epatient_15_col
Column containing age.
- epatient_16_col
Column for age units.
- eresponse_05_col
Column containing response mode codes (e.g., 911 response codes).
- eresponse_24_col
Column detailing additional response descriptors as text.
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),
eresponse_24 = rep("No Lights or Sirens", 5)
)
# Run the function
result <- safety_01_population(patient_scene_table = patient_table,
response_table = response_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,
eresponse_24_col = eresponse_24
)
#> Running `safety_01_population()` [Working on 1 of 9 tasks] ●●●●───────────────…
#> Running `safety_01_population()` [Working on 2 of 9 tasks] ●●●●●●●●───────────…
#> Running `safety_01_population()` [Working on 3 of 9 tasks] ●●●●●●●●●●●────────…
#> Running `safety_01_population()` [Working on 4 of 9 tasks] ●●●●●●●●●●●●●●─────…
#> Running `safety_01_population()` [Working on 5 of 9 tasks] ●●●●●●●●●●●●●●●●●●─…
#> Running `safety_01_population()` [Working on 6 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#> Running `safety_01_population()` [Working on 7 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#> Running `safety_01_population()` [Working on 8 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#> Running `safety_01_population()` [Working on 9 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 6 × 2
#> filter count
#> <chr> <int>
#> 1 911 calls 5
#> 2 No lights and sirens 5
#> 3 Adults denominator 2
#> 4 Peds denominator 3
#> 5 Initial population 5
#> 6 Total dataset 5