Respiratory-01 Populations — respiratory_01

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
)

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.

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

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
                             )
#> Running `respiratory_01_population()`  [Working on 1 of 13 tasks] ●●●──────────…
#> Running `respiratory_01_population()`  [Working on 2 of 13 tasks] ●●●●●●───────…
#> Running `respiratory_01_population()`  [Working on 3 of 13 tasks] ●●●●●●●●─────…
#> Running `respiratory_01_population()`  [Working on 4 of 13 tasks] ●●●●●●●●●●───…
#> Running `respiratory_01_population()`  [Working on 5 of 13 tasks] ●●●●●●●●●●●●●…
#> Running `respiratory_01_population()`  [Working on 6 of 13 tasks] ●●●●●●●●●●●●●…
#> Running `respiratory_01_population()`  [Working on 7 of 13 tasks] ●●●●●●●●●●●●●…
#> Running `respiratory_01_population()`  [Working on 8 of 13 tasks] ●●●●●●●●●●●●●…
#> Running `respiratory_01_population()`  [Working on 9 of 13 tasks] ●●●●●●●●●●●●●…
#> Running `respiratory_01_population()`  [Working on 10 of 13 tasks] ●●●●●●●●●●●●…
#> Running `respiratory_01_population()`  [Working on 12 of 13 tasks] ●●●●●●●●●●●●…
#> Running `respiratory_01_population()`  [Working on 13 of 13 tasks] ●●●●●●●●●●●●…
#> 

# show the results of filtering at each step
result$filter_process
#> # A tibble: 7 × 2
#>   filter                                    count
#>   <chr>                                     <int>
#> 1 Respiratory Distress                          5
#> 2 Pulse Oximetry and Respiratory Rate taken     5
#> 3 911 calls                                     5
#> 4 Adults denominator                            2
#> 5 Peds denominator                              3
#> 6 Initial population                            5
#> 7 Total dataset                                 5