safety_04: Safety-04 Calculation

Description

The safety_04 function processes EMS incident data for specific safety and transport criteria, filtering by patient age and incident type to identify cases that meet specified exclusion or inclusion criteria. This function accommodates data with various EMS-specific codes, age descriptors, and procedure identifiers.

Usage

safety_04(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  injury_table = NULL,
  procedures_table = NULL,
  disposition_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  earrest_01_col,
  einjury_03_col,
  eprocedures_03_col,
  edisposition_14_col,
  transport_disposition_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Value

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).

Arguments

df: A data frame or tibble containing EMS data where each row represents an individual observation.
patient_scene_table: A data frame or tibble containing fields from epatient and escene needed for this measure's calculations.
response_table: A data frame or tibble containing fields from eresponse needed for this measure's calculations.
arrest_table: A data frame or tibble containing fields from earrest needed for this measure's calculations.
injury_table: A data frame or tibble containing fields from einjury needed for this measure's calculations.
procedures_table: A data frame or tibble containing fields from eprocedures needed for this measure's calculations.
disposition_table: A data frame or tibble containing fields from edisposition needed for this measure's calculations.
erecord_01_col: The column containing unique record identifiers for each encounter.
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 name indicating the patient age.
epatient_16_col: Column name for the unit of age (e.g., "Years," "Months").
eresponse_05_col: Column containing response transport codes.
earrest_01_col: Column with cardiac arrest status information.
einjury_03_col: Column describing traumatic injuries, expected as a list or text-separated entries.
eprocedures_03_col: Column listing procedures, assumed to contain multiple procedure codes/texts in each cell.
edisposition_14_col: Column for transport dispositions.
transport_disposition_col: Columns for primary and secondary transport dispositions.
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.

Author

Nicolas Foss, Ed.D., MS

Examples

Run this code


# 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),
    earrest_01 = rep("No", 5),
    einjury_03 = rep("non-injury", 5),
    edisposition_14 = rep(4214001, 5),
    edisposition_30 = rep(4230001, 5),
    eprocedures_03 = rep("other response", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  safety_04(
    df = test_data,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    earrest_01_col = earrest_01,
    einjury_03_col = einjury_03,
    edisposition_14_col = edisposition_14,
    transport_disposition_col = edisposition_30,
    eprocedures_03_col = eprocedures_03,
    confidence_interval = TRUE
  )

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