% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pediatrics_03b.R
\name{pediatrics_03b}
\alias{pediatrics_03b}
\title{Pediatrics-03B Calculation}
\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{
\item{df}{A dataframe or tibble contianing EMS data where each row represents
an observation and columns represent features.}

\item{patient_scene_table}{A data.frame or tibble containing at least
ePatient, and eScene as a fact table.}

\item{response_table}{A data.frame or tibble containing at least the
eResponse fields needed for this measure's calculations.}

\item{exam_table}{A data.frame or tibble containing only the eExam fields
needed for this measure's calculations. Default is \code{NULL}.}

\item{medications_table}{A data.frame or tibble containing at least the
eMedications fields needed for this measure's calculations. Default is
\code{NULL}.}

\item{erecord_01_col}{The column representing the EMS record unique
identifier.}

\item{incident_date_col}{Column that contains the incident date. This
defaults to \code{NULL} as it is optional in case not available due to PII
restrictions.}

\item{patient_DOB_col}{Column that contains the patient's date of birth. This
defaults to \code{NULL} as it is optional in case not available due to PII
restrictions.}

\item{epatient_15_col}{Column representing the patient's numeric age agnostic
of unit.}

\item{epatient_16_col}{Column representing the patient's age unit ("Years",
"Months", "Days", "Hours", or "Minutes").}

\item{eresponse_05_col}{Column that contains eResponse.05 or the response
type.}

\item{eexam_01_col}{Column containing estimated patient body weight in
kilograms.}

\item{eexam_02_col}{Column containing data on length based tape measure for
patients.}

\item{emedications_03_col}{Column that contains all medication administered
to the patient (eMedications.03) values as a single comma-separated list per
distinct eRecord.01 ID.}

\item{emedications_04_col}{Column indicating route medication was
administered to the patient.}

\item{confidence_interval}{Logical. If \code{TRUE}, the function calculates a
confidence interval for the proportion estimate.}

\item{method}{Character. Specifies the method used to calculate confidence
intervals. Options are \code{"wilson"} (Wilson score interval) and
\code{"clopper-pearson"} (exact binomial interval). Partial matching is supported,
so \code{"w"} and \code{"c"} can be used as shorthand.}

\item{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.}

\item{correct}{Logical. If \code{TRUE}, applies a continuity correction to the
Wilson score interval when \code{method = "wilson"}. Defaults to \code{TRUE}.}

\item{...}{optional additional arguments to pass onto \code{dplyr::summarize}.}
}
\value{
A data.frame summarizing results for two population groups (Peds)
with the following columns:
\itemize{
\item \code{pop}: Population type (Peds).
\item \code{numerator}: Count of incidents meeting the measure.
\item \code{denominator}: Total count of included incidents.
\item \code{prop}: Proportion of incidents meeting the measure.
\item \code{prop_label}: Proportion formatted as a percentage with a specified number
of decimal places.
\item \code{lower_ci}: Lower bound of the confidence interval for \code{prop}
(if \code{confidence_interval = TRUE}).
\item \code{upper_ci}: Upper bound of the confidence interval for \code{prop}
(if \code{confidence_interval = TRUE}).
}
}
\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.
}
\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
)

}
\author{
Nicolas Foss, Ed.D., MS
}
