Calculates the NEMSQA Asthma-01 measure.
Calculates key statistics 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(
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,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...
)Arguments
- df
A dataframe or tibble contianing EMS data where each row represents an observation and columns represent features.
- patient_scene_table
A data.frame or tibble containing at least ePatient, and eScene 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. 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.
- 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 "Minutes").
- eresponse_05_col
Column that contains eResponse.05 or the response type.
- 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 medication administered to the patient (eMedications.03) values as a single comma-separated list per distinct eRecord.01 ID.
- confidence_interval
Logical. If
TRUE, the function calculates a confidence interval for the proportion estimate.- method
Character. Specifies the method used to calculate confidence intervals. Options are
"wilson"(Wilson score interval) and"clopper-pearson"(exact binomial interval). Partial matching is supported, so"w"and"c"can be used as shorthand.- conf.level
Numeric. The confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.
- correct
Logical. If
TRUE, applies a continuity correction to the Wilson score interval whenmethod = "wilson". Defaults toTRUE.- ...
optional additional arguments to pass onto
dplyr::summarize.
Value
A data.frame summarizing results for two population groups (All, Adults and Peds) with the following columns:
pop: Population type (All, Adults, and Peds).numerator: Count of incidents meeting the measure.denominator: Total count of included incidents.prop: Proportion of incidents meeting the measure.prop_label: Proportion formatted as a percentage with a specified number of decimal places.lower_ci: Lower bound of the confidence interval forprop(ifconfidence_interval = TRUE).upper_ci: Upper bound of the confidence interval forprop(ifconfidence_interval = TRUE).
Examples
# Synthetic test data
test_data <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
epatient_15 = c(34, 5, 45, 2, 60), # Ages
epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
eresponse_05 = rep(2205001, 5),
esituation_11 = c("Respiratory Distress", "Respiratory Distress",
"Chest Pain", "Respiratory Distress", "Respiratory Distress"),
esituation_12 = c("Asthma", "Asthma", "Other condition", "Asthma", "Asthma"),
emedications_03 = c("Albuterol", "Albuterol", "Epinephrine", "None",
"Albuterol")
)
# Run the function
# Return 95% confidence intervals using the Wilson method
asthma_01(
df = test_data,
erecord_01_col = erecord_01,
incident_date_col = NULL,
patient_DOB_col = NULL,
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,
confidence_interval = TRUE
)
#>
#> ── Asthma-01 ───────────────────────────────────────────────────────────────────
#>
#> ── Gathering Records for Asthma-01 ──
#>
#> 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] ●●●●●●●●●●●●●●●●●…
#>
#>
#>
#> ── Calculating Asthma-01 ──
#>
#>
#> ✔ Function completed in 0.29s.
#>
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 3 × 8
#> measure pop numerator denominator prop prop_label lower_ci upper_ci
#> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <dbl>
#> 1 Asthma-01 Adults 2 2 1 100% 0.198 1
#> 2 Asthma-01 Peds 1 1 1 100% 0.0546 1
#> 3 Asthma-01 All 3 4 0.75 75% 0.219 0.987