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
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
NULLas 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
NULLas 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.
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
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
)
#> Running `asthma_01_population()` [Working on 1 of 10 tasks] ●●●●──────────────…
#> Running `asthma_01_population()` [Working on 2 of 10 tasks] ●●●●●●●───────────…
#> Running `asthma_01_population()` [Working on 3 of 10 tasks] ●●●●●●●●●●────────…
#> Running `asthma_01_population()` [Working on 4 of 10 tasks] ●●●●●●●●●●●●●─────…
#> Running `asthma_01_population()` [Working on 5 of 10 tasks] ●●●●●●●●●●●●●●●●──…
#> Running `asthma_01_population()` [Working on 6 of 10 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `asthma_01_population()` [Working on 7 of 10 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `asthma_01_population()` [Working on 8 of 10 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `asthma_01_population()` [Working on 9 of 10 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `asthma_01_population()` [Working on 10 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 7 × 2
#> filter count
#> <chr> <int>
#> 1 911 calls 3
#> 2 Asthma cases 2
#> 3 Beta agonist cases 3
#> 4 Adults denominator 1
#> 5 Peds denominator 1
#> 6 Initial population 2
#> 7 Total dataset 3