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.
Usage
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_col,
transport_disposition_cols = lifecycle::deprecated(),
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...
)Arguments
- df
A dataframe or tibble contianing EMS data where each row represents an observation and columns represent features.
- patient_scene_table
A data.frame or tibble containing at least ePatient, and eScene as a fact table.
- response_table
A data.frame or tibble containing at least 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 representing the patient's numeric age agnostic of unit.
- epatient_16_col
Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minutes").
- eresponse_05_col
Column that contains eResponse.05 or the response type.
- edisposition_18_col
Column giving documentation of transport mode techniques for this EMS response.
- edisposition_28_col
Column giving patient disposition for an EMS event identifying whether a patient was evaluated and care or services were provided.
- transport_disposition_col
One or more unquoted column names (such as edisposition.12, edisposition.30) containing transport disposition for an EMS event identifying whether a transport occurred and by which unit.
- transport_disposition_cols
![[Deprecated]](figures/lifecycle-deprecated.svg)
Use
transport_disposition_colinstead.- confidence_interval
Logical. If
TRUE, the function calculates a confidence interval for the proportion estimate.- method
Character. Specifies the method used to calculate confidence intervals. Options are
"wilson"(Wilson score interval) and"clopper-pearson"(exact binomial interval). Partial matching is supported, so"w"and"c"can be used as shorthand.- conf.level
Numeric. The confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.
- correct
Logical. If
TRUE, applies a continuity correction to the Wilson score interval whenmethod = "wilson". Defaults toTRUE.- ...
optional additional arguments to pass onto
dplyr::summarize.
Value
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 forprop(ifconfidence_interval = TRUE).upper_ci: Upper bound of the confidence interval forprop(ifconfidence_interval = TRUE).
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),
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,
incident_date_col = NULL,
patient_DOB_col = NULL,
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_col = edisposition_30,
confidence_interval = TRUE
)
#>
#> ── Safety-02 ───────────────────────────────────────────────────────────────────
#>
#> ── Gathering Records for Safety-02 ──
#>
#> 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] ●●●●●●●●●●●●●●●●●…
#>
#>
#>
#> ── Calculating Safety-02 ──
#>
#>
#> ✔ Function completed in 0.17s.
#>
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 3 × 8
#> measure pop numerator denominator prop prop_label lower_ci upper_ci
#> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <dbl>
#> 1 Safety-02 Adults 3 3 1 100% 0.310 1
#> 2 Safety-02 Peds 1 1 1 100% 0.0546 1
#> 3 Safety-02 All 5 5 1 100% 0.463 1