The safety_01 function calculates the proportion of 911 responses where
"lights and sirens" were not used in an EMS dataset. It generates age-based
population summaries, calculating the count and proportion of "lights and
sirens" responses among all incidents, and within adult and pediatric groups.
Usage
safety_01(
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
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 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.
- ...
arguments passed on to summarize.
Value
A tibble summarizing results for the Adults, Peds, and all records with the following columns:
measure: The name of the measure being calculated.
pop: Population type (Adults, Peds, All).
numerator: Count of 911 responses where "lights and sirens" were not used
in an EMS dataset.
denominator: Total count of incidents.
prop: Proportion of 911 responses where "lights and sirens" were not used
in an EMS dataset.
prop_label: Proportion formatted as a percentage with a
specified number of decimal places.
Examples
# Synthetic test data
test_data <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
epatient_15 = c(34, 5, 45, 2, 60), # Ages
epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
eresponse_05 = rep(2205001, 5),
eresponse_24 = rep("No Lights or Sirens", 5)
)
# Run the function
safety_01(
df = test_data,
erecord_01_col = erecord_01,
epatient_15_col = epatient_15,
epatient_16_col = epatient_16,
eresponse_05_col = eresponse_05,
eresponse_24_col = eresponse_24
)
#>
#> ── Safety-01 ───────────────────────────────────────────────────────────────────
#>
#> ── Gathering Records for Safety-01 ──
#>
#> 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] ●●●●●●●●●●●●●●●●●●●…
#>
#>
#>
#> ── Calculating Safety-01 ──
#>
#>
#> ✔ Function completed in 0.14s.
#>
#> # A tibble: 3 × 6
#> measure pop numerator denominator prop prop_label
#> <chr> <chr> <int> <int> <dbl> <chr>
#> 1 Safety-01 Adults 3 3 1 100%
#> 2 Safety-01 Peds 1 1 1 100%
#> 3 Safety-01 All 5 5 1 100%