nemsqar (version 1.0.0)

safety_01_population: Safety-01 Populations

Description

Filters data down to the target populations for Safety-01, and categorizes records to identify needed information for the calculations.

Identifies key categories related to 911 responses where "lights and sirens" were not used in an EMS dataset. This function segments the data by age into adult and pediatric populations.

Usage

safety_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eresponse_24_col
)

Value

A list that contains the following:

• a tibble with counts for each filtering step,

• a tibble for each population of interest

• a tibble for the initial population

• a tibble for the total dataset with computations

Arguments

df

A data frame or tibble containing EMS data.

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.

erecord_01_col

Column name containing the unique patient record identifier.

incident_date_col

Date or POSIXct column indicating the date of the incident.

patient_DOB_col

Date or POSIXct column for the patient’s date of birth

epatient_15_col

Column containing age.

epatient_16_col

Column for age units.

eresponse_05_col

Column containing response mode codes (e.g., 911 response codes).

eresponse_24_col

Column detailing additional response descriptors as text.

Author

Nicolas Foss, Ed.D., MS

Examples

# create tables to test correct functioning

# patient table
  patient_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    incident_date = as.Date(c("2025-01-01", "2025-01-05",
                              "2025-02-01", "2025-01-01",
                              "2025-06-01")
                              ),
    patient_dob = as.Date(c("2000-01-01", "2020-01-01",
                            "2023-02-01", "2023-01-01",
                            "1970-06-01")
                            ),
    epatient_15 = c(25, 5, 2, 2, 55),  # Ages
    epatient_16 = c("Years", "Years", "Years", "Years", "Years")

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    eresponse_05 = rep(2205001, 5),
    eresponse_24 = rep("No Lights or Sirens", 5)

  )

# Run the function
result <- safety_01_population(patient_scene_table = patient_table,
                              response_table = response_table,
                              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,
                              eresponse_24_col = eresponse_24
                        )

# show the results of filtering at each step
result$filter_process