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SEQIC Indicator 2 – Missing Incident Time

Source: R/seqic_2.R
seqic_indicator_2.Rd

[Experimental]

This function calculates System Evaluation and Quality Improvement Committee (SEQIC) Indicator 2. This indicator evaluates the proportion of trauma incidents with missing documented incident time across Level I–IV trauma centers.

Usage

seqic_indicator_2(
  data,
  unique_incident_id,
  level,
  included_levels = c("I", "II", "III", "IV"),
  incident_time,
  groups = NULL,
  calculate_ci = NULL,
  ...
)

Arguments

data

A data frame containing trauma incident records.

unique_incident_id

Unique identifier for each record.

level

Column indicating the trauma center designation level (e.g., I, II, III, IV).

included_levels

Character vector indicating what facility levels to include in the analysis. Defaults to c("I", "II", "III", "IV").

incident_time

The time the patient's injury occurred.

groups

Additional columns passed as a vector of strings to dplyr::summarize() via the .by argument for grouped summaries. Defaults to NULL.

calculate_ci

If NULL, 95% confidence intervals will not be calculated for the performance estimates. Otherwise, options of "wilson" or "clopper-pearson" can be supplied to utilize the corresponding methods to calculate the confidence intervals for the proportions. Defaults to NULL.

...

Arguments passed on to nemsqar::nemsqa_binomial_confint

conf.level

Numeric value between 0 and 1 indicating the confidence level. Defaults to 0.95 (95% confidence interval).

correct

Logical, indicating whether to apply continuity correction for Wilson intervals. Defaults to TRUE.

Value

A tibble summarizing SEQIC Indicator 2 results. Includes numerator, denominator, and performance rate for the indicator. 95% confidence intervals are provided optionally.

Details

This function:

  • Filters trauma records to those with a trauma center level of I–IV.

  • Deduplicates by unique_incident_id to ensure one record per incident.

  • Calculates the proportion of cases missing incident_time.

Note

Users must ensure appropriate column names are passed and data is pre-processed to include the necessary fields without missing critical identifiers or timestamps.

Author

Nicolas Foss, Ed.D., MS

Examples


# Packages
library(dplyr)
library(traumar)

# Data
data <- tibble::tibble(
  incident_id = as.character(101:106),
  trauma_level = c("I", "II", "III", "IV", "II", "I"),
  incident_time = as.POSIXct(c("2023-01-01 12:00", NA, "2023-01-02 14:15",
                               NA, "2023-01-03 09:30", "2023-01-04 16:45"))
)

# Run the function
traumar::seqic_indicator_2(
  data = data,
  unique_incident_id = incident_id,
  level = trauma_level,
  incident_time = incident_time,
  calculate_ci = "clopper-pearson"
)
#> # A tibble: 1 × 6
#>   data              numerator_2 denominator_2 seqic_2 lower_ci_2 upper_ci_2
#>   <chr>                   <int>         <int>   <dbl>      <dbl>      <dbl>
#> 1 population/sample           2             6   0.333     0.0433      0.777