injprev {injurytools}

Calculate injury prevalence

Description

Calculate the prevalence of injured players and the proportion of non-injured (available) players 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

injprev(injd, by = c("monthly", "season"), var_type_injury = NULL)

Arguments

injd

Prepared data. An injd object.

by

Character. One of "monthly" or "season", specifying the periodicity according to which to calculate the proportions of available and injured players/athletes.

var_type_injury

Character specifying the name of the column on the basis of which to classify the injuries and calculate proportions of the injured players. Defaults to NULL.

Value

A data frame containing one row for each combination of season, month (optionally) and injury type (if var_type_injury not specified, then this variable has two categories: Available and Injured). Plus, three more columns, specifying the proportion of players (prop) satisfying the corresponding row's combination of values, i.e. prevalence, how many players were injured at that moment with the type of injury of the corresponding row (n), over how many players were at that time in the cohort (n_player). See Note section.

Note

If var_type_injury is specified (and not NULL), it may happen that a player 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 players 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.

Examples

df_exposures <- prepare_exp(raw_df_exposures, player = "player_name",
                            date = "year", time_expo = "minutes_played")
df_injuries  <- prepare_inj(raw_df_injuries, player = "player_name",
                            date_injured = "from", date_recovered = "until")
injd         <- prepare_all(data_exposures = df_exposures,
                            data_injuries  = df_injuries,
                            exp_unit = "matches_minutes")


injprev(injd, by = "monthly", var_type_injury = "injury_type")
injprev(injd, by = "monthly")
injprev(injd, by = "season", var_type_injury = "injury_type")
injprev(injd, by = "season")