Filters data down to the target populations for Seizure-02, 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.
Usage
seizure_02_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 where each row is an observation, containing all necessary columns for analysis.
- patient_scene_table
A data frame or tibble containing only epatient and escene fields as a fact table. Default is
NULL.- response_table
A data frame or tibble containing only the eresponse fields needed for this measure's calculations. Default is
NULL.- situation_table
A data.frame or tibble containing only the esituation fields needed for this measure's calculations. Default is
NULL.- medications_table
A data.frame or tibble containing only the emedications fields needed for this measure's calculations. Default is
NULL.- erecord_01_col
The column containing unique record identifiers for each encounter.
- 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 name for patient age in numeric form.
- epatient_16_col
Column name for age unit (e.g.,
"Years"or"Months").- eresponse_05_col
Column name for response codes; "911" call codes are filtered.
- esituation_11_col
Column name for primary impressions.
- esituation_12_col
Column name for secondary impressions.
- emedications_03_col
Column name for medications administered; ideally a list column or string with comma-separated values.
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
# 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 = rep("G40", 5),
esituation_12 = rep("r56", 5),
)
# medications table
medications_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
emedications_03 = rep(3322, 5)
)
# test the success of the function
result <- seizure_02_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 `seizure_02_population()` [Working on 1 of 10 tasks] ●●●●─────────────…
#> Running `seizure_02_population()` [Working on 2 of 10 tasks] ●●●●●●●──────────…
#> Running `seizure_02_population()` [Working on 3 of 10 tasks] ●●●●●●●●●●───────…
#> Running `seizure_02_population()` [Working on 4 of 10 tasks] ●●●●●●●●●●●●●────…
#> Running `seizure_02_population()` [Working on 5 of 10 tasks] ●●●●●●●●●●●●●●●●─…
#> Running `seizure_02_population()` [Working on 6 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_population()` [Working on 7 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_population()` [Working on 8 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_population()` [Working on 9 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_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 5
#> 2 Seizure cases 5
#> 3 Benzodiazepine cases 5
#> 4 Adults denominator 2
#> 5 Peds denominator 3
#> 6 Initial population 5
#> 7 Total dataset 5