asthma_01_population {nemsqar} | R Documentation |
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
)
Arguments
df |
A data.frame or tibble containing EMS data. Default is |
patient_scene_table |
A data.frame or tibble containing at least
ePatient and eScene fields as a fact table. Default is |
response_table |
A data.frame or tibble containing at least the
eResponse fields needed for this measure's calculations. Default is |
situation_table |
A data.frame or tibble containing at least the
eSituation fields needed for this measure's calculations. Default is
|
medications_table |
A data.frame or tibble containing at least the
eMedications fields needed for this measure's calculations. Default is
|
erecord_01_col |
The column representing the EMS record unique
identifier. Default is |
incident_date_col |
Column that contains the incident date. This
defaults to |
patient_DOB_col |
Column that contains the patient's date of birth. This
defaults to |
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. |
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(s)
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