Calculate the prevalence proportion of injured athletes and the proportion of non-injured (available) athletes in the cohort, on a monthly or season basis. Further information on the type of injury may be specified so that the injury-specific prevalences are reported according to this variable.
Usage
calc_prevalence(injd, time_period = c("monthly", "season"), by = NULL)Arguments
- injd
Prepared data. An
injdobject.- time_period
Character. One of "monthly" or "season", specifying the periodicity according to which to calculate the proportions of available and injured athletes.
- by
Character specifying the name of the column on the basis of which to classify the injuries and calculate proportions of the injured athletes. Defaults to
NULL.
Value
A data frame containing one row for each combination of season, month
(optionally) and injury type (if by not specified, then this variable has
two categories: Available and Injured). Plus, three more columns,
specifying the proportion of athletes (prop) satisfying the corresponding
row's combination of values, i.e. prevalence, how many athletes were injured
at that moment with the type of injury of the corresponding row (n), over
how many athletes were at that time in the cohort (n_athlete). See Note
section.
Note
If by is specified (and not NULL), it may happen that an athlete in
one month suffers two different types of injuries. For example, a muscle and
a ligament injury. In this case, this two injuries contribute to the
proportions of muscle and ligament injuries for that month, resulting in an
overall proportion that exceeds 100%. Besides, the athletes in Available
category are those that did not suffer any injury in that moment
(season-month), that is, they were healthy all the time that the period
lasted.
References
Bahr R, Clarsen B, Derman W, et al. International Olympic Committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS)) British Journal of Sports Medicine 2020; 54:372-389.
Nielsen RO, Debes-Kristensen K, Hulme A, et al. Are prevalence measures better than incidence measures in sports injury research? British Journal of Sports Medicine 2019; 54:396-397.
Examples
# \donttest{
df_exposures <- prepare_exp(raw_df_exposures, person_id = "player_name",
date = "year", time_expo = "minutes_played")
df_injuries <- prepare_inj(raw_df_injuries, person_id = "player_name",
date_injured = "from", date_recovered = "until")
injd <- prepare_all(data_exposures = df_exposures,
data_injuries = df_injuries,
exp_unit = "matches_minutes")
# }
calc_prevalence(injd, time_period = "monthly", by = "injury_type")
#> # A tibble: 98 × 6
#> season month status n n_athlete prop
#> <fct> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Jul Available 21 23 91.3
#> 2 season 2017/2018 Jul Muscle 2 23 8.7
#> 3 season 2017/2018 Aug Available 18 23 78.3
#> 4 season 2017/2018 Aug Muscle 3 23 13
#> 5 season 2017/2018 Aug Unknown 2 23 8.7
#> 6 season 2017/2018 Sep Available 22 23 95.7
#> 7 season 2017/2018 Sep Muscle 1 23 4.3
#> 8 season 2017/2018 Oct Available 19 23 82.6
#> 9 season 2017/2018 Oct Concussion 1 23 4.3
#> 10 season 2017/2018 Oct Muscle 2 23 8.7
#> # ℹ 88 more rows
calc_prevalence(injd, time_period = "monthly")
#> # A tibble: 48 × 6
#> season month status n n_athlete prop
#> <fct> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Jul Available 21 23 91.3
#> 2 season 2017/2018 Jul Injured 2 23 8.7
#> 3 season 2017/2018 Aug Available 18 23 78.3
#> 4 season 2017/2018 Aug Injured 5 23 21.7
#> 5 season 2017/2018 Sep Available 22 23 95.7
#> 6 season 2017/2018 Sep Injured 1 23 4.3
#> 7 season 2017/2018 Oct Available 19 23 82.6
#> 8 season 2017/2018 Oct Injured 4 23 17.4
#> 9 season 2017/2018 Nov Available 18 23 78.3
#> 10 season 2017/2018 Nov Injured 5 23 21.7
#> # ℹ 38 more rows
calc_prevalence(injd, time_period = "season", by = "injury_type")
#> # A tibble: 11 × 5
#> season status n n_athlete prop
#> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Available 7 23 30.4
#> 2 season 2017/2018 Concussion 3 23 13
#> 3 season 2017/2018 Ligament 4 23 17.4
#> 4 season 2017/2018 Muscle 11 23 47.8
#> 5 season 2017/2018 Unknown 8 23 34.8
#> 6 season 2018/2019 Available 2 19 10.5
#> 7 season 2018/2019 Bone 11 19 57.9
#> 8 season 2018/2019 Concussion 13 19 68.4
#> 9 season 2018/2019 Ligament 6 19 31.6
#> 10 season 2018/2019 Muscle 15 19 78.9
#> 11 season 2018/2019 Unknown 13 19 68.4
calc_prevalence(injd, time_period = "season")
#> # A tibble: 4 × 5
#> season status n n_athlete prop
#> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Available 7 23 30.4
#> 2 season 2017/2018 Injured 16 23 69.6
#> 3 season 2018/2019 Available 2 19 10.5
#> 4 season 2018/2019 Injured 17 19 89.5