Nothing
R/asthma_01.R
In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations
Defines functions
asthma_01
Documented in
asthma_01
#' @title Asthma-01 Calculation
#'
#' @description
#'
#' Calculates the NEMSQA Asthma-01 measure.
#'
#' Calculates key statistics related to asthma-related incidents in an EMS
#' dataset, specifically focusing on cases where 911 was called for respiratory
#' distress, and certain medications were administered. This function segments
#' the data by age into adult and pediatric populations, computing the
#' proportion of cases that received beta-agonist treatment.
#'
#' @param df A data.frame or tibble containing EMS data. Default is `NULL`.
#' @param patient_scene_table A data.frame or tibble containing at least
#' ePatient and eScene fields as a fact table. Default is `NULL`.
#' @param response_table A data.frame or tibble containing at least the
#' eResponse fields needed for this measure's calculations. Default is `NULL`.
#' @param situation_table A data.frame or tibble containing at least the
#' eSituation fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param medications_table A data.frame or tibble containing at least the
#' eMedications fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier. Default is `NULL`.
#' @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 representing the patient's numeric age agnostic
#' of unit.
#' @param epatient_16_col Column representing the patient's age unit ("Years",
#' "Months", "Days", "Hours", or "Minute").
#' @param eresponse_05_col Column that contains eResponse.05.
#' @param esituation_11_col Column that contains eSituation.11.
#' @param esituation_12_col Column that contains all eSituation.12 values as a
#' single comma-separated list.
#' @param emedications_03_col Column that contains all eMedications.03 values as
#' a single comma-separated list.
#' @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 (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 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"),
#' epatient_15 = c(34, 5, 45, 2, 60), # Ages
#' epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
#' eresponse_05 = rep(2205001, 5),
#' esituation_11 = c("Respiratory Distress", "Respiratory Distress",
#' "Chest Pain", "Respiratory Distress", "Respiratory Distress"),
#' esituation_12 = c("Asthma", "Asthma", "Other condition", "Asthma", "Asthma"),
#' emedications_03 = c("Albuterol", "Albuterol", "Epinephrine", "None",
#' "Albuterol")
#' )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#' asthma_01(
#' df = test_data,
#' erecord_01_col = erecord_01,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' esituation_11_col = esituation_11,
#' esituation_12_col = esituation_12,
#' emedications_03_col = emedications_03,
#' confidence_interval = TRUE
#' )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
asthma_01 <- function(df = NULL,
patient_scene_table = NULL,
response_table = NULL,
situation_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,
esituation_11_col,
esituation_12_col,
emedications_03_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"))
# utilize applicable tables to analyze the data for the measure
if(
all(!is.null(patient_scene_table),
!is.null(response_table),
!is.null(situation_table),
!is.null(medications_table)
) && is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Asthma-01")
# header
cli::cli_h2("Gathering Records for Asthma-01")
# gather the population of interest
asthma_01_populations <- asthma_01_population(patient_scene_table = patient_scene_table,
response_table = response_table,
situation_table = situation_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 }},
esituation_11_col = {{ esituation_11_col }},
esituation_12_col = {{ esituation_12_col }},
emedications_03_col = {{ emedications_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Asthma-01")
# summary
asthma.01 <- results_summarize(total_population = asthma_01_populations$initial_population,
adult_population = asthma_01_populations$adults,
peds_population = asthma_01_populations$peds,
measure_name = "Asthma-01",
numerator_col = beta_agonist_check,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(asthma.01$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(asthma.01)
} else if(all(is.null(patient_scene_table), is.null(response_table), is.null(situation_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("Asthma-01")
# header
cli::cli_h2("Gathering Records for Asthma-01")
# gather the population of interest
asthma_01_populations <- asthma_01_population(df = df,
patient_scene_table = patient_scene_table,
response_table = response_table,
situation_table = situation_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 }},
esituation_11_col = {{ esituation_11_col }},
esituation_12_col = {{ esituation_12_col }},
emedications_03_col = {{ emedications_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Asthma-01")
# summary
asthma.01 <- results_summarize(total_population = asthma_01_populations$initial_population,
adult_population = asthma_01_populations$adults,
peds_population = asthma_01_populations$peds,
measure_name = "Asthma-01",
numerator_col = beta_agonist_check,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(asthma.01$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(asthma.01)
}
}
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Defines functions asthma_01
Documented in asthma_01
#' @title Asthma-01 Calculation
#'
#' @description
#'
#' Calculates the NEMSQA Asthma-01 measure.
#'
#' Calculates key statistics related to asthma-related incidents in an EMS
#' dataset, specifically focusing on cases where 911 was called for respiratory
#' distress, and certain medications were administered. This function segments
#' the data by age into adult and pediatric populations, computing the
#' proportion of cases that received beta-agonist treatment.
#'
#' @param df A data.frame or tibble containing EMS data. Default is `NULL`.
#' @param patient_scene_table A data.frame or tibble containing at least
#' ePatient and eScene fields as a fact table. Default is `NULL`.
#' @param response_table A data.frame or tibble containing at least the
#' eResponse fields needed for this measure's calculations. Default is `NULL`.
#' @param situation_table A data.frame or tibble containing at least the
#' eSituation fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param medications_table A data.frame or tibble containing at least the
#' eMedications fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier. Default is `NULL`.
#' @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 representing the patient's numeric age agnostic
#' of unit.
#' @param epatient_16_col Column representing the patient's age unit ("Years",
#' "Months", "Days", "Hours", or "Minute").
#' @param eresponse_05_col Column that contains eResponse.05.
#' @param esituation_11_col Column that contains eSituation.11.
#' @param esituation_12_col Column that contains all eSituation.12 values as a
#' single comma-separated list.
#' @param emedications_03_col Column that contains all eMedications.03 values as
#' a single comma-separated list.
#' @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 (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 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"),
#' epatient_15 = c(34, 5, 45, 2, 60), # Ages
#' epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
#' eresponse_05 = rep(2205001, 5),
#' esituation_11 = c("Respiratory Distress", "Respiratory Distress",
#' "Chest Pain", "Respiratory Distress", "Respiratory Distress"),
#' esituation_12 = c("Asthma", "Asthma", "Other condition", "Asthma", "Asthma"),
#' emedications_03 = c("Albuterol", "Albuterol", "Epinephrine", "None",
#' "Albuterol")
#' )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#' asthma_01(
#' df = test_data,
#' erecord_01_col = erecord_01,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' esituation_11_col = esituation_11,
#' esituation_12_col = esituation_12,
#' emedications_03_col = emedications_03,
#' confidence_interval = TRUE
#' )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
asthma_01 <- function(df = NULL,
patient_scene_table = NULL,
response_table = NULL,
situation_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,
esituation_11_col,
esituation_12_col,
emedications_03_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"))
# utilize applicable tables to analyze the data for the measure
if(
all(!is.null(patient_scene_table),
!is.null(response_table),
!is.null(situation_table),
!is.null(medications_table)
) && is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# header
cli::cli_h1("Asthma-01")
# header
cli::cli_h2("Gathering Records for Asthma-01")
# gather the population of interest
asthma_01_populations <- asthma_01_population(patient_scene_table = patient_scene_table,
response_table = response_table,
situation_table = situation_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 }},
esituation_11_col = {{ esituation_11_col }},
esituation_12_col = {{ esituation_12_col }},
emedications_03_col = {{ emedications_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Asthma-01")
# summary
asthma.01 <- results_summarize(total_population = asthma_01_populations$initial_population,
adult_population = asthma_01_populations$adults,
peds_population = asthma_01_populations$peds,
measure_name = "Asthma-01",
numerator_col = beta_agonist_check,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(asthma.01$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(asthma.01)
} else if(all(is.null(patient_scene_table), is.null(response_table), is.null(situation_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("Asthma-01")
# header
cli::cli_h2("Gathering Records for Asthma-01")
# gather the population of interest
asthma_01_populations <- asthma_01_population(df = df,
patient_scene_table = patient_scene_table,
response_table = response_table,
situation_table = situation_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 }},
esituation_11_col = {{ esituation_11_col }},
esituation_12_col = {{ esituation_12_col }},
emedications_03_col = {{ emedications_03_col }}
)
# create a separator
cli::cli_text("\n")
# header for calculations
cli::cli_h2("Calculating Asthma-01")
# summary
asthma.01 <- results_summarize(total_population = asthma_01_populations$initial_population,
adult_population = asthma_01_populations$adults,
peds_population = asthma_01_populations$peds,
measure_name = "Asthma-01",
numerator_col = beta_agonist_check,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(asthma.01$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(asthma.01)
}
}
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