injurytools (version 2.0.1)

calc_prevalence: Calculate prevalence proportion

Description

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)

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.

Arguments

injd

Prepared data. An injd object.

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.

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")
calc_prevalence(injd, time_period = "monthly")
calc_prevalence(injd, time_period = "season", by = "injury_type")
calc_prevalence(injd, time_period = "season")