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Safety-01 Calculation

Source: R/safety_01.R
safety_01.Rd

The safety_01 function calculates the proportion of 911 responses where "lights and sirens" were not used in an EMS dataset. It generates age-based population summaries, calculating the count and proportion of "lights and sirens" responses among all incidents, and within adult and pediatric groups.

Usage

safety_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eresponse_24_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A data frame or tibble containing EMS data.

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.

erecord_01_col

Column name containing the unique patient record identifier.

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 containing age.

epatient_16_col

Column for age units.

eresponse_05_col

Column containing response mode codes (e.g., 911 response codes).

eresponse_24_col

Column detailing additional response descriptors as text.

confidence_interval

[Experimental] Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.

method

[Experimental]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

[Experimental]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

[Experimental]Logical. If TRUE, applies a continuity correction to the Wilson score interval when method = "wilson". Defaults to TRUE.

...

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 for prop (if confidence_interval = TRUE).

  • upper_ci: Upper bound of the confidence interval for prop (if confidence_interval = TRUE).

Author

Nicolas Foss, Ed.D., MS

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),
    eresponse_24 = rep("No Lights or Sirens", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  safety_01(
    df = test_data,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    eresponse_24_col = eresponse_24,
    confidence_interval = TRUE
  )
#> 
#> ── Safety-01 ───────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Safety-01 ──
#> 
#> Running `safety_01_population()`  [Working on 1 of 9 tasks] ●●●●───────────────…
#> Running `safety_01_population()`  [Working on 2 of 9 tasks] ●●●●●●●●───────────…
#> Running `safety_01_population()`  [Working on 3 of 9 tasks] ●●●●●●●●●●●────────…
#> Running `safety_01_population()`  [Working on 4 of 9 tasks] ●●●●●●●●●●●●●●─────…
#> Running `safety_01_population()`  [Working on 5 of 9 tasks] ●●●●●●●●●●●●●●●●●●─…
#> Running `safety_01_population()`  [Working on 6 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#> Running `safety_01_population()`  [Working on 7 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#> Running `safety_01_population()`  [Working on 8 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#> Running `safety_01_population()`  [Working on 9 of 9 tasks] ●●●●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Safety-01 ──
#> 
#> 
#> ✔ Function completed in 0.14s.
#> 
#> 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 Safety-01 Adults         3           3     1 100%         0.310         1
#> 2 Safety-01 Peds           1           1     1 100%         0.0546        1
#> 3 Safety-01 All            5           5     1 100%         0.463         1