nemsqar (version 1.0.0)

respiratory_01_population: Respiratory-01 Populations

Description

The respiratory_01_population function filters and analyzes data related to emergency 911 respiratory distress incidents, providing the adult, pediatric, and initial populations. This function uses specific data columns for 911 response codes, primary and secondary impressions, and vital signs to filter a dataset down to the populations of interest.

Usage

respiratory_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  vitals_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,
  evitals_12_col,
  evitals_14_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 containing incident data with each row representing an observation.

patient_scene_table

A data.frame or tibble containing at least epatient and escene fields as a fact table.

response_table

A data.frame or tibble containing at least the eresponse fields needed for this measure's calculations.

situation_table

A data.frame or tibble containing at least the esituation fields needed for this measure's calculations.

vitals_table

A data.frame or tibble containing at least the evitals fields needed for this measure's calculations.

erecord_01_col

Unique Patient ID

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 giving the calculated age value.

epatient_16_col

Column giving the provided age unit value.

eresponse_05_col

Column name for 911 response codes (e.g., 2205001, 2205003, 2205009).

esituation_11_col

Column name for primary impression codes related to respiratory distress.

esituation_12_col

Column name for secondary impression codes related to respiratory distress.

evitals_12_col

Column name for the first vital sign measurement.

evitals_14_col

Column name for the second vital sign measurement.

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)

)

# situation table
situation_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  esituation_11 = c(rep("J80", 3), rep("I50.9", 2)),
  esituation_12 = c(rep("J80", 2), rep("I50.9", 3))
)

# vitals table
vitals_table <- tibble::tibble(

  erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
  evitals_12 = c(60, 59, 58, 57, 56),
  evitals_14 = c(16, 15, 14, 13, 12)

)

# Run the function
result <- respiratory_01_population(patient_scene_table = patient_table,
                              response_table = response_table,
                              situation_table = situation_table,
                              vitals_table = vitals_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,
                              esituation_11_col = esituation_11,
                              esituation_12_col = esituation_12,
                              evitals_12_col = evitals_12,
                              evitals_14_col = evitals_14
                             )

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