Identifying temporal variations in burn admissions

Abstract

Background: Variations in admission patterns have been previously identified in non-elective surgical services, but minimal data on the subject exists with respect to burn admissions. Improved understanding of the temporal pattern of burn admissions could inform resource utilization and clinical staffing. We hypothesize that burn admissions have a predictable temporal distribution with regard to the time of day, day of week, and season of year in which they present.

Study design: A retrospective, cohort observational study of a single burn center from 7/1/2016 to 3/31/2021 was performed on all admissions to the burn surgery service. Demographics, burn characteristics, and temporal data of burn admissions were collected. Bivariate absolute and relative frequency data was captured and plotted for all patients who met inclusion criteria. Heat-maps were created to visually represent the relative admission frequency by time of day and day of week. Frequency analysis grouped by total body surface area against time of day and relative encounters against day of year was performed.

Results: 2213 burn patient encounters were analyzed, averaging 1.28 burns per day. The nadir of burn admissions was from 07:00 and 08:00, with progressive increase in the rate of admissions over the day. Admissions peaked in the 15:00 hour and then plateaued until midnight (p<0.001). There was no association between day of week in the burn admission distribution (p>0.05), though weekend admissions skewed slightly later (p = 0.025). No annual, cyclical trend in burn admissions was identified, suggesting that there is no predictable seasonality to burn admissions, though individual holidays were not assessed.

Conclusion: Temporal variations in burn admissions exist, including a peak admission window late in the day. Furthermore, we did not find a predictable annual pattern to use in guiding staffing and resource allocation. This differs from findings in trauma, which identified admission peaks on the weekends and an annual cycle that peaks in spring and summer.

Fig 1. Heatmap of burn admissions by time of day versus day of week.

Each block of the heatmap represents the absolute number of admissions in one hour of one day of the week, over the nearly five-year study period. Warm colors represent higher burn admission frequency and cool colors represent lower frequency, as described by the color bar to the right. Late evenings and the early night, (15:00–00:00) have more burn admissions than the rest of the day on all days of the week.

Fig 2. Circular heatmap of relative burn admissions by time of day versus day of week.

Each block represents one hour of relative burn admissions normalized to the mean, where 1 represents the mean number of burn admissions per hour. The start of each day was set to the 07:00–08:00 hour, to align with both the timing of clinical handoff and the nadir of daily admissions. On average, half of the weekend daily admissions are done slightly later than the weekday admissions.

Fig 3. Relative frequency of burn admissions versus time of day partitioned by TBSA<20% and TBSA ≥20%.

The height of the bar corresponds to the relative frequency of burn admissions, normalized for the mean number of daily burn admissions. The increase in burn admissions is shown to be primarily driven by burns below 20% TBSA. Larger burns occurred too infrequently to impact the overall pattern of admissions.

Fig 4. Heatmap of burn admissions by time of day versus total body surface area.

The smallest burns, specifically those ≤ 5% TBSA burns account for much of the pattern of burn admissions. The concentration of burn admissions to the late evening and early night (15:00–00:00) is again demonstrated, suggesting that that factors other than TBSA led to the decision to admit patients with burns ≤5%.

Fig 5. Relative frequency of burn admissions by time of day versus mechanism of injury.

The most common burn mechanisms, including flame and scald burns, generate most of the burn admissions and, subsequently, the pattern of burn admission timing. The peak admission window 15:00–00:00) is again demonstrated.

Fig 6. Normalized relative burn frequency per day throughout the year.

Relative burn admissions are plotted against the day of the year. The 1 on the y-axis represents the average number of daily burn admissions (1.28 in our data). Each red asterisk represents relative deviations from the mean on that day. A linear regression of those daily variations (blue line) has a slope of 0, indicating that there is no change throughout the year.