The function calculates a pediatric metric focused on EMS responses, specifically targeting responses that involve patients under 18 years of age, where certain weight-based medications were administered. This function filters EMS data to identify relevant 911 responses and further narrows down the dataset to cases involving children, calculating the proportion of cases with documented weight among those where weight-based medications were administered.
Usage
pediatrics_03b(
df = NULL,
patient_scene_table = NULL,
response_table = NULL,
exam_table = NULL,
medications_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
eresponse_05_col,
eexam_01_col,
eexam_02_col,
emedications_03_col,
emedications_04_col,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...
)Arguments
- df
A data frame or tibble containing emergency response records. Default is
NULL.- 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.- exam_table
A data.frame or tibble containing only the eExam fields needed for this measure's calculations. Default is
NULL.- medications_table
A data.frame or tibble containing only the eMedications fields needed for this measure's calculations. Default is
NULL.- erecord_01_col
Column for unique EMS record identifiers.
- 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 containing the EMS response codes.
- eexam_01_col
Column containing documented weight information.
- eexam_02_col
Another column for weight documentation, if applicable.
- emedications_03_col
Column indicating medication administration.
- emedications_04_col
Column listing medications administered.
- confidence_interval
![[Experimental]](figures/lifecycle-experimental.svg)
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 (Peds) with the following columns:
pop: Population type (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"),
incident_date = as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
"2025-06-01", "2025-12-15")),
patient_dob = as.Date(c("2021-01-01", "2020-01-01", "2022-02-01",
"2023-06-01", "2019-12-15")),
epatient_15 = c(4, 5, 3, 2, 6), # Ages
epatient_16 = c("Years", "Years", "Years", "Years", "Years"),
eresponse_05 = rep(2205001, 5),
emedications_03 = rep("stuff", 5),
emedications_04 = c("Inhalation", "pill", "liquid", "pill", "liquid"),
eexam_01 = c(60, 59, 58, 57, 56),
eexam_02 = c("Red", "Purple", "Grey", "Yellow", "Orange")
)
# Run the function
# Return 95% confidence intervals using the Wilson method
pediatrics_03b(
df = test_data,
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,
emedications_03_col = emedications_03,
emedications_04_col = emedications_04,
eexam_01_col = eexam_01,
eexam_02_col = eexam_02,
confidence_interval = TRUE
)
#>
#> ── Pediatrics-03b ──────────────────────────────────────────────────────────────
#>
#> ── Gathering Records for Pediatrics-03b ──
#>
#> Running `pediatrics_03b_population()` [Working on 1 of 9 tasks] ●●●●──────────…
#> Running `pediatrics_03b_population()` [Working on 2 of 9 tasks] ●●●●●●●●──────…
#> Running `pediatrics_03b_population()` [Working on 3 of 9 tasks] ●●●●●●●●●●●───…
#> Running `pediatrics_03b_population()` [Working on 4 of 9 tasks] ●●●●●●●●●●●●●●…
#> Running `pediatrics_03b_population()` [Working on 5 of 9 tasks] ●●●●●●●●●●●●●●…
#> Running `pediatrics_03b_population()` [Working on 6 of 9 tasks] ●●●●●●●●●●●●●●…
#> Running `pediatrics_03b_population()` [Working on 7 of 9 tasks] ●●●●●●●●●●●●●●…
#> Running `pediatrics_03b_population()` [Working on 8 of 9 tasks] ●●●●●●●●●●●●●●…
#> Running `pediatrics_03b_population()` [Working on 9 of 9 tasks] ●●●●●●●●●●●●●●…
#>
#>
#>
#> ── Calculating Pediatrics-03b ──
#>
#>
#> ✔ Function completed in 0.14s.
#>
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 1 × 8
#> measure pop numerator denominator prop prop_label lower_ci upper_ci
#> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <dbl>
#> 1 Pediatrics-03b Peds 4 4 1 100% 0.396 1