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SEQIC Indicator 13 – Validation of Trauma Registry Records

Source: R/seqic_13.R
seqic_indicator_13.Rd

[Experimental]

Calculates the proportion of trauma records that meet or exceed a threshold for data validity among facilities at the specified trauma center levels. Optionally computes confidence intervals.

Usage

seqic_indicator_13(
  data,
  level,
  included_levels = c("I", "II", "III", "IV"),
  unique_incident_id,
  validity_score,
  validity_threshold = 85,
  groups = NULL,
  calculate_ci = NULL,
  ...
)

Arguments

data

A data frame containing trauma incident records.

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").

unique_incident_id

Unique identifier for each record.

validity_score

Numeric. The proportion of each trauma registry record that is valid, expressed as a percentage (0–100). Typically calculated by the registry system.

validity_threshold

Numeric. The minimum acceptable validity percentage threshold for records to be counted in the numerator. Defaults to 85.

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 13 results. Includes numerator, denominator, and performance rate 95% confidence intervals are included if requested.

Details

This function:

  • Filters to include only patients treated at trauma centers with levels specified in included_levels (default: Levels I–IV).

  • Deduplicates the dataset using unique_incident_id to ensure each incident is counted only once.

  • Flags records with a validity_score greater than or equal to the specified validity_threshold threshold (default: 85).

  • Calculates the proportion of valid records among all included records.

  • Optionally calculates binomial confidence intervals using the method specified in calculate_ci via nemsqa_binomial_confint().

  • Adds a "Population/Sample" label unless grouping is applied via groups.

Users must ensure that appropriate column names are passed using tidy evaluation (bare column names) and that the input data has been cleaned and includes no missing or malformed identifiers, trauma level classifications, or validity scores.

Author

Nicolas Foss, Ed.D., MS

Examples

# Packages
library(dplyr)
library(traumar)

# Simulated data for SEQIC Indicator 13
test_data <- tibble::tibble(
  id = as.character(1:12),
  trauma_level = c("I", "II", "III", "IV", "I", "II", "III", "IV", "I", "II",
  "III", "IV"),
  validity = c(90, 80, 88, 92, 86, 75, 89, 70, 95, 85, 83, 87)
)

# Run the function
traumar::seqic_indicator_13(
  data = test_data,
  level = trauma_level,
  included_levels = c("I", "II", "III", "IV"),
  unique_incident_id = id,
  validity_score = validity,
  validity_threshold = 85,
  calculate_ci = "wilson"
)
#> # A tibble: 1 × 6
#>   data              numerator_13 denominator_13 seqic_13 lower_ci_13 upper_ci_13
#>   <chr>                    <int>          <int>    <dbl>       <dbl>       <dbl>
#> 1 population/sample            8             12    0.667       0.354       0.887