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Calculate case incidence rate

Source: R/calc_incidence.R
calc_incidence.Rd

Calculate the case incidence rate of a sports-related health problem (e.g. disease, injury) in a cohort.

Usage

calc_incidence(
  injd,
  by = NULL,
  overall = TRUE,
  method = c("poisson", "negbin", "zinfpois", "zinfnb"),
  se = TRUE,
  conf_level = 0.95,
  scale = TRUE,
  quiet = FALSE
)

Arguments

injd

injd S3 object (see prepare_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 incidence rate. Either a numeric value (if overall TRUE) or a data frame indicating the case incidence 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_incidence(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 ncases incidence incidence_sd incidence_lower incidence_upper
#>       <dbl>  <dbl>     <dbl>        <dbl>           <dbl>           <dbl>
#> 1     74690     82      9.88         1.09            7.74            12.0
calc_incidence(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 ncases incidence incidence_sd incidence_lower
#>    <fct>                     <dbl>  <dbl>     <dbl>        <dbl>           <dbl>
#>  1 adam-lallana                700      6     77.1         31.5            15.4 
#>  2 alberto-moreno             1264      1      7.12         7.12           -6.84
#>  3 alex-oxlade-chamberl…      1483      1      6.07         6.07           -5.83
#>  4 alisson                    3420      0      0            0               0   
#>  5 andrew-robertson           5162      5      8.72         3.90            1.08
#>  6 daniel-sturridge            927      3     29.1         16.8            -3.83
#>  7 danny-ings                  265      0      0            0               0   
#>  8 dejan-lovren               3109      6     17.4          7.09            3.47
#>  9 divock-origi                366      1     24.6         24.6           -23.6 
#> 10 dominic-solanke             581      0      0            0               0   
#> # ℹ 18 more rows
#> # ℹ 1 more variable: incidence_upper <dbl>
calc_incidence(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 ncases incidence incidence_sd incidence_lower
#>   <chr>           <dbl>  <dbl>     <dbl>        <dbl>           <dbl>
#> 1 Bone            74690     11      1.33        0.400           0.542
#> 2 Concussion      74690     16      1.93        0.482           0.983
#> 3 Ligament        74690      9      1.08        0.361           0.376
#> 4 Muscle          74690     25      3.01        0.602           1.83 
#> 5 Unknown         74690     21      2.53        0.552           1.45 
#> # ℹ 1 more variable: incidence_upper <dbl>
calc_incidence(injd, by = "injury_type", scale = FALSE)
#> # A tibble: 5 × 7
#>   injury_type totalexpo ncases incidence incidence_sd incidence_lower
#>   <chr>           <dbl>  <dbl>     <dbl>        <dbl>           <dbl>
#> 1 Bone            74690     11  0.000147    0.0000444       0.0000602
#> 2 Concussion      74690     16  0.000214    0.0000536       0.000109 
#> 3 Ligament        74690      9  0.000120    0.0000402       0.0000418
#> 4 Muscle          74690     25  0.000335    0.0000669       0.000204 
#> 5 Unknown         74690     21  0.000281    0.0000614       0.000161 
#> # ℹ 1 more variable: incidence_upper <dbl>