safety_02 | R Documentation |
The safety_02
function calculates the Safety-02 metric, evaluating the
proportion of emergency medical calls involving transport where no lights and
sirens were used. This function categorizes the population into adult and
pediatric groups based on their age, and summarizes results with a total
population count as well.
safety_02(
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,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...
)
A data.frame summarizing results for two population groups (All, Adults and Peds) with the following columns:
pop
: Population type (All, Adults, and Peds).
numerator
: Count of incidents meeting the measure.
denominator
: Total count of included incidents.
prop
: Proportion of incidents meeting the measure.
prop_label
: Proportion formatted as a percentage with a specified number
of decimal places.
lower_ci
: Lower bound of the confidence interval for prop
(if confidence_interval = TRUE
).
upper_ci
: Upper bound of the confidence interval for prop
(if confidence_interval = TRUE
).
Nicolas Foss, Ed.D., MS
# 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),
edisposition_18 = rep(4218015, 5),
edisposition_28 = rep(4228001, 5),
edisposition_30 = rep(4230001, 5)
)
# Run the function
# Return 95% confidence intervals using the Wilson method
safety_02(
df = test_data,
erecord_01_col = erecord_01,
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,
confidence_interval = TRUE
)
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