Calculate the case burden rate of a sports-related health problem (e.g. disease, injury) in a cohort.
Usage
calc_burden(
injd,
by = NULL,
overall = TRUE,
method = c("poisson", "negbin", "zinfpois", "zinfnb"),
se = TRUE,
conf_level = 0.95,
scale = TRUE,
quiet = FALSE
)Arguments
- injd
injdS3 object (seeprepare_all()).- by
Character specifying the name of the column according to which compute summary statistics. It should refer to a (categorical) variable that describes a grouping factor (e.g. "type of case or injury", "injury location", "sports club"). Optional, defaults to
NULL.- overall
Logical, whether to calculate overall (for all the cohort) or athlete-wise summary statistic (i.e. number of cases per cohort of per athlete). Defaults to
TRUE.- method
Method to estimate the incidence (burden) rate. One of "poisson", "negbin", "zinfpois" or "zinfnb"; that stand for Poisson method, negative binomial method, zero-inflated Poisson and zero-inflated negative binomial.
- se
Logical, whether to calculate the confidence interval related to the rate.
- conf_level
Confidence level (defaults to 0.95).
- scale
Logical, whether to transform the incidence and burden rates output according to the unit of exposure (defaults to
TRUE).- quiet
Logical, whether or not to silence the warning messages (defaults to
FALSE).
Value
The case burden rate. Either a numeric value (if overall
TRUE) or a data frame indicating the case burden rate per
athlete.
References
Bahr R., Clarsen B., & Ekstrand J. (2018). Why we should focus on the burden of injuries and illnesses, not just their incidence. British Journal of Sports Medicine, 52(16), 1018–1021. https://doi.org/10.1136/bjsports-2017-098160
Waldén M., Mountjoy M., McCall A., Serner A., Massey A., Tol J. L., ... & Andersen T. E. (2023). Football-specific extension of the IOC consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020. British journal of sports medicine.
Examples
calc_burden(injd)
#> Warning:
#> Exposure time unit is matches_minutes
#> Case incidence and case burden are calculated per 100 athlete-matches of exposure (i.e. 90 minutes times 100)
#>
#> # A tibble: 1 × 6
#> totalexpo ndayslost burden burden_sd burden_lower burden_upper
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 74690 2049 247. 5.45 236. 258.
calc_burden(injd, overall = FALSE)
#> Warning:
#> Exposure time unit is matches_minutes
#> Case incidence and case burden are calculated per 100 athlete-matches of exposure (i.e. 90 minutes times 100)
#>
#> # A tibble: 28 × 7
#> person_id totalexpo ndayslost burden burden_sd burden_lower burden_upper
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 adam-lallana 700 302 3883. 223. 3445. 4321.
#> 2 alberto-moreno 1264 50 356. 50.3 257. 455.
#> 3 alex-oxlade-c… 1483 316 1918. 108. 1706. 2129.
#> 4 alisson 3420 0 0 0 0 0
#> 5 andrew-robert… 5162 22 38.4 8.18 22.3 54.4
#> 6 daniel-sturri… 927 122 1184. 107. 974. 1395.
#> 7 danny-ings 265 0 0 0 0 0
#> 8 dejan-lovren 3109 160 463. 36.6 391. 535.
#> 9 divock-origi 366 5 123. 55.0 15.2 231.
#> 10 dominic-solan… 581 0 0 0 0 0
#> # ℹ 18 more rows
calc_burden(injd, by = "injury_type")
#> Warning:
#> Exposure time unit is matches_minutes
#> Case incidence and case burden are calculated per 100 athlete-matches of exposure (i.e. 90 minutes times 100)
#>
#> # A tibble: 5 × 7
#> injury_type totalexpo ndayslost burden burden_sd burden_lower burden_upper
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Bone 74690 173 20.8 1.58 17.7 24.0
#> 2 Concussion 74690 213 25.7 1.76 22.2 29.1
#> 3 Ligament 74690 596 71.8 2.94 66.1 77.6
#> 4 Muscle 74690 735 88.6 3.27 82.2 95.0
#> 5 Unknown 74690 332 40.0 2.20 35.7 44.3