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
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 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
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
)
#> Running `safety_02_population()` [Working on 1 of 11 tasks] ●●●●──────────────…
#> Running `safety_02_population()` [Working on 2 of 11 tasks] ●●●●●●────────────…
#> Running `safety_02_population()` [Working on 3 of 11 tasks] ●●●●●●●●●─────────…
#> Running `safety_02_population()` [Working on 4 of 11 tasks] ●●●●●●●●●●●●──────…
#> Running `safety_02_population()` [Working on 5 of 11 tasks] ●●●●●●●●●●●●●●●───…
#> Running `safety_02_population()` [Working on 6 of 11 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `safety_02_population()` [Working on 7 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()` [Working on 8 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()` [Working on 9 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()` [Working on 10 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `safety_02_population()` [Working on 11 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 8 × 2
#> filter count
#> <chr> <int>
#> 1 911 calls 5
#> 2 Patients evaluated and care provided 5
#> 3 Transport runs 5
#> 4 No lights and sirens 5
#> 5 Adults denominator 2
#> 6 Peds denominator 3
#> 7 Initial population 5
#> 8 Total dataset 5