nemsqar (version 1.1.0)

asthma_01_population: Asthma-01 Populations

Description

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

Identifies key categories 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.

Usage

asthma_01_population(
  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
)

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. Default is NULL.

patient_scene_table

A data.frame or tibble containing at least ePatient and eScene fields as a fact table. Default is NULL.

response_table

A data.frame or tibble containing at least the eResponse fields needed for this measure's calculations. Default is NULL.

situation_table

A data.frame or tibble containing at least the eSituation fields needed for this measure's calculations. Default is NULL.

medications_table

A data.frame or tibble containing at least the eMedications fields needed for this measure's calculations. Default is NULL.

erecord_01_col

The column representing the EMS record unique identifier. Default is NULL.

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 representing the patient's numeric age agnostic of unit.

epatient_16_col

Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minute").

eresponse_05_col

Column that contains eResponse.05.

esituation_11_col

Column that contains eSituation.11 provider primary impression data.

esituation_12_col

Column that contains all eSituation.12 values as (possible a single comma-separated list), provider secondary impression data.

emedications_03_col

Column that contains all eMedications.03 values as a single comma-separated list.

Author

Nicolas Foss, Ed.D., MS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

# patient table
patient_table <- tibble::tibble(

  erecord_01 = 1:3,
  incident_date = as.Date(c("2025-01-01", "2025-01-05", "2025-02-01")),
  patient_dob = as.Date(c("2000-01-01", "2020-01-01", "2023-01-01")),
  epatient_15 = c(25, 5, 2),
  epatient_16 = c("years", "years", "months")

)

# response table
response_table <- tibble::tibble(

  erecord_01 = 1:3,
  eresponse_05 = c("2205001", "2205009", "2205003")

)

# situation table
situation_table <- tibble::tibble(

  erecord_01 = 1:3,
  esituation_11 = c("weakness", "asthma", "bronchospasm"),
  esituation_12 = c("asthma", "weakness", "weakness")
)

# medications table
medications_table <- tibble::tibble(

  erecord_01 = 1:3,
  emedications_03 = c("albuterol", "levalbuterol", "metaproterenol")

)

# test the success of the function
result <- asthma_01_population(patient_scene_table = patient_table,
                               response_table = response_table,
                               situation_table = situation_table,
                               medications_table = medications_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,
                               emedications_03_col = emedications_03
                               )

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