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These functions determine which items in a vector can be considered (the start of) a new episode. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns TRUE for every new get_episode() index. Both absolute and relative episode determination are supported.

Usage

get_episode(x, episode_days = NULL, case_free_days = NULL, ...)

is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)

Arguments

x

vector of dates (class Date or POSIXt), will be sorted internally to determine episodes

episode_days

episode length in days to specify the time period after which a new episode begins, can also be less than a day or Inf, see Details

case_free_days

(inter-epidemic) interval length in days after which a new episode will start, can also be less than a day or Inf, see Details

...

ignored, only in place to allow future extensions

Value

Details

Episodes can be determined in two ways: absolute and relative.

  1. Absolute

    This method uses episode_days to define an episode length in days, after which a new episode will start. A common use case in AMR data analysis is microbial epidemiology: episodes of S. aureus bacteraemia in ICU patients for example. The episode length could then be 30 days, so that new S. aureus isolates after an ICU episode of 30 days will be considered a different (or new) episode.

    Thus, this method counts since the start of the previous episode.

  2. Relative

    This method uses case_free_days to quantify the duration of case-free days (the inter-epidemic interval), after which a new episode will start. A common use case is infectious disease epidemiology: episodes of norovirus outbreaks in a hospital for example. The case-free period could then be 14 days, so that new norovirus cases after that time will be considered a different (or new) episode.

    Thus, this methods counts since the last case in the previous episode.

In a table:

DateUsing episode_days = 7Using case_free_days = 7
2023-01-0111
2023-01-0211
2023-01-0511
2023-01-082**1
2023-02-2132***
2023-02-2232
2023-02-2332
2023-02-2432
2023-03-0142

** This marks the start of a new episode, because 8 January 2023 is more than 7 days since the start of the previous episode (1 January 2023).
*** This marks the start of a new episode, because 21 January 2023 is more than 7 days since the last case in the previous episode (8 January 2023).

Either episode_days or case_free_days must be provided in the function.

Difference between get_episode() and is_new_episode()

The get_episode() function returns the index number of the episode, so all cases/patients/isolates in the first episode will have the number 1, all cases/patients/isolates in the second episode will have the number 2, etc.

The is_new_episode() function on the other hand, returns TRUE for every new get_episode() index.

To specify, when setting episode_days = 365 (using method 1 as explained above), this is how the two functions differ:

patientdateget_episode()is_new_episode()
A2019-01-011TRUE
A2019-03-011FALSE
A2021-01-012TRUE
B2008-01-011TRUE
B2008-01-011FALSE
C2020-01-011TRUE

Other

The first_isolate() function is a wrapper around the is_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.

The dplyr package is not required for these functions to work, but these episode functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise().

See also

Examples

# difference between absolute and relative determination of episodes:
x <- data.frame(dates = as.Date(c(
  "2021-01-01",
  "2021-01-02",
  "2021-01-05",
  "2021-01-08",
  "2021-02-21",
  "2021-02-22",
  "2021-02-23",
  "2021-02-24",
  "2021-03-01",
  "2021-03-01"
)))
x$absolute <- get_episode(x$dates, episode_days = 7)
x$relative <- get_episode(x$dates, case_free_days = 7)
x
#>         dates absolute relative
#> 1  2021-01-01        1        1
#> 2  2021-01-02        1        1
#> 3  2021-01-05        1        1
#> 4  2021-01-08        2        1
#> 5  2021-02-21        3        2
#> 6  2021-02-22        3        2
#> 7  2021-02-23        3        2
#> 8  2021-02-24        3        2
#> 9  2021-03-01        4        2
#> 10 2021-03-01        4        2


# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates
df <- example_isolates[sample(seq_len(2000), size = 100), ]

get_episode(df$date, episode_days = 60) # indices
#>   [1] 26 32 34 46  9 45 34 39 30 56 35 24 31 52  7 48 28 26 57 20 22 13  2 11 11
#>  [26] 40 26 50 36 47 12 25  6 45 43 28 47 53 33 17 49 36 12 18 55 26 40 13  8 10
#>  [51] 34 52 12 12 22 50 54 33  5  4  8 41 19 49 42 32 50 32 27 27 14 44 53 48 51
#>  [76] 15 16 34 38 57  5 21 16 20  1 46 37 23  5 27 24 29 21 39 16  3 45 17 55  9
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [25] FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#>  [37] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
#>  [49]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#>  [61] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
#>  [73] FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [85]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [97] FALSE FALSE FALSE FALSE

# filter on results from the third 60-day episode only, using base R
df[which(get_episode(df$date, 60) == 3), ]
#> # A tibble: 1 × 46
#>   date       patient   age gender ward      mo           PEN   OXA   FLC   AMX  
#>   <date>     <chr>   <dbl> <chr>  <chr>     <mo>         <sir> <sir> <sir> <sir>
#> 1 2002-06-07 710157     76 M      Outpatie… B_STPHY_CONS S     NA    S     NA   
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>

# the functions also work for less than a day, e.g. to include one per hour:
get_episode(
  c(
    Sys.time(),
    Sys.time() + 60 * 60
  ),
  episode_days = 1 / 24
)
#> [1] 1 2

# \donttest{
if (require("dplyr")) {
  # is_new_episode() can also be used in dplyr verbs to determine patient
  # episodes based on any (combination of) grouping variables:
  df %>%
    mutate(condition = sample(
      x = c("A", "B", "C"),
      size = 100,
      replace = TRUE
    )) %>%
    group_by(patient, condition) %>%
    mutate(new_episode = is_new_episode(date, 365)) %>%
    select(patient, date, condition, new_episode) %>%
    arrange(patient, condition, date)
}
#> # A tibble: 100 × 4
#> # Groups:   patient, condition [99]
#>    patient date       condition new_episode
#>    <chr>   <date>     <chr>     <lgl>      
#>  1 034010  2010-02-26 C         TRUE       
#>  2 04C169  2012-07-18 A         TRUE       
#>  3 058917  2002-11-14 C         TRUE       
#>  4 078381  2014-06-28 C         TRUE       
#>  5 097186  2015-10-28 B         TRUE       
#>  6 0E2483  2007-06-21 A         TRUE       
#>  7 136315  2004-02-02 A         TRUE       
#>  8 179451  2007-09-15 C         TRUE       
#>  9 181687  2008-05-04 A         TRUE       
#> 10 183220  2008-11-14 B         TRUE       
#> # ℹ 90 more rows

if (require("dplyr")) {
  df %>%
    group_by(ward, patient) %>%
    transmute(date,
      patient,
      new_index = get_episode(date, 60),
      new_logical = is_new_episode(date, 60)
    ) %>%
    arrange(patient, ward, date)
}
#> # A tibble: 100 × 5
#> # Groups:   ward, patient [95]
#>    ward       date       patient new_index new_logical
#>    <chr>      <date>     <chr>       <int> <lgl>      
#>  1 Clinical   2010-02-26 034010          1 TRUE       
#>  2 Clinical   2012-07-18 04C169          1 TRUE       
#>  3 ICU        2002-11-14 058917          1 TRUE       
#>  4 ICU        2014-06-28 078381          1 TRUE       
#>  5 Clinical   2015-10-28 097186          1 TRUE       
#>  6 ICU        2007-06-21 0E2483          1 TRUE       
#>  7 Clinical   2004-02-02 136315          1 TRUE       
#>  8 ICU        2007-09-15 179451          1 TRUE       
#>  9 Outpatient 2008-05-04 181687          1 TRUE       
#> 10 Clinical   2008-11-14 183220          1 TRUE       
#> # ℹ 90 more rows

if (require("dplyr")) {
  df %>%
    group_by(ward) %>%
    summarise(
      n_patients = n_distinct(patient),
      n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
      n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),
      n_episodes_30 = sum(is_new_episode(date, episode_days = 30))
    )
}
#> # A tibble: 3 × 5
#>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30
#>   <chr>           <int>          <int>         <int>         <int>
#> 1 Clinical           55             14            43            48
#> 2 ICU                30             12            25            30
#> 3 Outpatient         10              7            10            10

# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
if (require("dplyr")) {
  x <- df %>%
    filter_first_isolate(
      include_unknown = TRUE,
      method = "episode-based"
    )

  y <- df %>%
    group_by(patient, mo) %>%
    filter(is_new_episode(date, 365)) %>%
    ungroup()

  identical(x, y)
}
#> [1] TRUE

# but is_new_episode() has a lot more flexibility than first_isolate(),
# since you can now group on anything that seems relevant:
if (require("dplyr")) {
  df %>%
    group_by(patient, mo, ward) %>%
    mutate(flag_episode = is_new_episode(date, 365)) %>%
    select(group_vars(.), flag_episode)
}
#> # A tibble: 100 × 4
#> # Groups:   patient, mo, ward [98]
#>    patient mo           ward       flag_episode
#>    <chr>   <mo>         <chr>      <lgl>       
#>  1 181687  B_STRPT_ANGN Outpatient TRUE        
#>  2 823896  B_STPHY_CONS Clinical   TRUE        
#>  3 802237  B_ESCHR_COLI Clinical   TRUE        
#>  4 280834  B_STPHY_EPDR Clinical   TRUE        
#>  5 1B0933  B_STPHY_AURS Clinical   TRUE        
#>  6 078381  B_STPHY_EPDR ICU        TRUE        
#>  7 662978  B_ESCHR_COLI Clinical   TRUE        
#>  8 695956  B_STRPT_PNMN Outpatient TRUE        
#>  9 AB0003  B_ESCHR_COLI Clinical   TRUE        
#> 10 763991  B_STPHY_AURS ICU        TRUE        
#> # ℹ 90 more rows
# }