pediatrics_03b: Pediatrics-03B Calculation

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

Pediatrics-03B Calculation

Description

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 NULL as 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 NULL as 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	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 when method = "wilson". Defaults to TRUE.
...	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 for prop (if confidence_interval = TRUE).

• upper_ci: Upper bound of the confidence interval for prop (if confidence_interval = TRUE).

Author(s)

Nicolas Foss, Ed.D., MS

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
)

nemsqar documentation built on Aug. 8, 2025, 6:15 p.m.