Nothing
R/pediatrics_03b.R
In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations
Defines functions
pediatrics_03b
Documented in
pediatrics_03b
#' @title 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.
#'
#' @param df A data frame or tibble containing emergency response records.
#' Default is `NULL`.
#' @param patient_scene_table A data.frame or tibble containing only ePatient
#' and eScene fields as a fact table. Default is `NULL`.
#' @param response_table A data.frame or tibble containing only the eResponse
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param exam_table A data.frame or tibble containing only the eExam fields
#' needed for this measure's calculations. Default is `NULL`.
#' @param medications_table A data.frame or tibble containing only the
#' eMedications fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param erecord_01_col Column for unique EMS record identifiers.
#' @param 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.
#' @param 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.
#' @param epatient_15_col Column giving the calculated age value.
#' @param epatient_16_col Column giving the provided age unit value.
#' @param eresponse_05_col Column containing the EMS response codes.
#' @param eexam_01_col Column containing documented weight information.
#' @param eexam_02_col Another column for weight documentation, if applicable.
#' @param emedications_03_col Column indicating medication administration.
#' @param emedications_04_col Column listing medications administered.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("experimental")`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.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#' level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#' confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return 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`).
#'
#' @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
#'
#' @export
#'
pediatrics_03b <- function(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,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
if(all(
!is.null(patient_scene_table),
!is.null(response_table),
!is.null(exam_table),
!is.null(medications_table)
)
&& is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Pediatrics-03b")
# header
cli::cli_h2("Gathering Records for Pediatrics-03b")
# gather the population of interest
pediatrics03b_populations <- pediatrics_03b_population(patient_scene_table = patient_scene_table,
response_table = response_table,
exam_table = exam_table,
medications_table = medications_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
eexam_01_col = {{ eexam_01_col }},
eexam_02_col = {{ eexam_02_col }},
emedications_03_col = {{ emedications_03_col }},
emedications_04_col = {{ emedications_04_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Pediatrics-03b")
# summary
pediatrics.03b <- results_summarize(total_population = NULL,
adult_population = NULL,
peds_population = pediatrics03b_populations$initial_population,
measure_name = "Pediatrics-03b",
population_names = "peds",
numerator_col = DOCUMENTED_WEIGHT,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(pediatrics.03b$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(pediatrics.03b)
} else if(
all(
is.null(patient_scene_table),
is.null(response_table),
is.null(exam_table),
is.null(medications_table)
)
&& !is.null(df)
)
# utilize a dataframe to analyze the data for the measure analytics
{
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Pediatrics-03b")
# header
cli::cli_h2("Gathering Records for Pediatrics-03b")
pediatrics03b_populations <- pediatrics_03b_population(df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
eexam_01_col = {{ eexam_01_col }},
eexam_02_col = {{ eexam_02_col }},
emedications_03_col = {{ emedications_03_col }},
emedications_04_col = {{ emedications_04_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Pediatrics-03b")
# summary
pediatrics.03b <- results_summarize(total_population = NULL,
adult_population = NULL,
peds_population = pediatrics03b_populations$initial_population,
measure_name = "Pediatrics-03b",
population_names = "peds",
numerator_col = DOCUMENTED_WEIGHT,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(pediatrics.03b$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(pediatrics.03b)
}
}
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Defines functions pediatrics_03b
Documented in pediatrics_03b
#' @title 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.
#'
#' @param df A data frame or tibble containing emergency response records.
#' Default is `NULL`.
#' @param patient_scene_table A data.frame or tibble containing only ePatient
#' and eScene fields as a fact table. Default is `NULL`.
#' @param response_table A data.frame or tibble containing only the eResponse
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param exam_table A data.frame or tibble containing only the eExam fields
#' needed for this measure's calculations. Default is `NULL`.
#' @param medications_table A data.frame or tibble containing only the
#' eMedications fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param erecord_01_col Column for unique EMS record identifiers.
#' @param 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.
#' @param 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.
#' @param epatient_15_col Column giving the calculated age value.
#' @param epatient_16_col Column giving the provided age unit value.
#' @param eresponse_05_col Column containing the EMS response codes.
#' @param eexam_01_col Column containing documented weight information.
#' @param eexam_02_col Another column for weight documentation, if applicable.
#' @param emedications_03_col Column indicating medication administration.
#' @param emedications_04_col Column listing medications administered.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("experimental")`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.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#' level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#' confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return 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`).
#'
#' @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
#'
#' @export
#'
pediatrics_03b <- function(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,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
if(all(
!is.null(patient_scene_table),
!is.null(response_table),
!is.null(exam_table),
!is.null(medications_table)
)
&& is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Pediatrics-03b")
# header
cli::cli_h2("Gathering Records for Pediatrics-03b")
# gather the population of interest
pediatrics03b_populations <- pediatrics_03b_population(patient_scene_table = patient_scene_table,
response_table = response_table,
exam_table = exam_table,
medications_table = medications_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
eexam_01_col = {{ eexam_01_col }},
eexam_02_col = {{ eexam_02_col }},
emedications_03_col = {{ emedications_03_col }},
emedications_04_col = {{ emedications_04_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Pediatrics-03b")
# summary
pediatrics.03b <- results_summarize(total_population = NULL,
adult_population = NULL,
peds_population = pediatrics03b_populations$initial_population,
measure_name = "Pediatrics-03b",
population_names = "peds",
numerator_col = DOCUMENTED_WEIGHT,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(pediatrics.03b$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(pediatrics.03b)
} else if(
all(
is.null(patient_scene_table),
is.null(response_table),
is.null(exam_table),
is.null(medications_table)
)
&& !is.null(df)
)
# utilize a dataframe to analyze the data for the measure analytics
{
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Pediatrics-03b")
# header
cli::cli_h2("Gathering Records for Pediatrics-03b")
pediatrics03b_populations <- pediatrics_03b_population(df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
eresponse_05_col = {{ eresponse_05_col }},
eexam_01_col = {{ eexam_01_col }},
eexam_02_col = {{ eexam_02_col }},
emedications_03_col = {{ emedications_03_col }},
emedications_04_col = {{ emedications_04_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Pediatrics-03b")
# summary
pediatrics.03b <- results_summarize(total_population = NULL,
adult_population = NULL,
peds_population = pediatrics03b_populations$initial_population,
measure_name = "Pediatrics-03b",
population_names = "peds",
numerator_col = DOCUMENTED_WEIGHT,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(pediatrics.03b$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(pediatrics.03b)
}
}
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