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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$date, episode_days = 60) # indices
#>   [1] 42 11 13 43 29 19  8 20 30 45 40 18 12 46 20 18 23 21 32 16 31 12 14 46 13
#>  [26] 22  5 37 45 16 41 19 17  8 21 31  7  8 29 32 34 22 25 41 15  3 17 17  2 25
#>  [51]  8  9  4 46 25 36  3 39 43  4 43 38 10 21 44 11 15  4 37 25 41 13 10 18 20
#>  [76]  6 35 10 36 24 18 24 41 27 37 21 26  1  6 22 12 15 14 33 28 44  7 23 46  5
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [13]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
#>  [25] FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#>  [37]  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
#>  [49]  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
#>  [61] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [73] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
#>  [85] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
#>  [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: 2 × 46
#>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
#>   <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
#> 1 2002-10-18 E55128     57 F      ICU      B_STPHY_AURS R     NA    S     R    
#> 2 2002-08-28 390178     57 M      Clinical B_STRPT_SLVR S     NA    NA    S    
#> # … with 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 [98]
#>    patient date       condition new_episode
#>    <chr>   <date>     <chr>     <lgl>      
#>  1 022060  2004-05-04 A         TRUE       
#>  2 059414  2006-07-21 B         TRUE       
#>  3 065133  2011-12-19 C         TRUE       
#>  4 080086  2007-10-26 B         TRUE       
#>  5 083080  2010-02-25 B         TRUE       
#>  6 083080  2012-04-16 B         TRUE       
#>  7 0DBF93  2015-12-03 C         TRUE       
#>  8 151041  2003-01-31 A         TRUE       
#>  9 167474  2008-02-15 C         TRUE       
#> 10 174209  2011-10-03 C         TRUE       
#> # … with 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 [93]
#>    ward       date       patient new_index new_logical
#>    <chr>      <date>     <chr>       <int> <lgl>      
#>  1 ICU        2004-05-04 022060          1 TRUE       
#>  2 Clinical   2006-07-21 059414          1 TRUE       
#>  3 Clinical   2011-12-19 065133          1 TRUE       
#>  4 Clinical   2007-10-26 080086          1 TRUE       
#>  5 Clinical   2010-02-25 083080          1 TRUE       
#>  6 Clinical   2012-04-16 083080          2 TRUE       
#>  7 ICU        2015-12-03 0DBF93          1 TRUE       
#>  8 Clinical   2003-01-31 151041          1 TRUE       
#>  9 Clinical   2008-02-15 167474          1 TRUE       
#> 10 Outpatient 2011-10-03 174209          1 TRUE       
#> # … with 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           56             14            38            48
#> 2 ICU                34             11            28            29
#> 3 Outpatient          3              2             3             3

# 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 [95]
#>    patient mo            ward     flag_episode
#>    <chr>   <mo>          <chr>    <lgl>       
#>  1 953838  B_STPHY_AURS  Clinical TRUE        
#>  2 B27238  B_STRPT_MITS  Clinical TRUE        
#>  3 838434  B_STPHY_CONS  Clinical TRUE        
#>  4 5C1947  B_ESCHR_COLI  Clinical TRUE        
#>  5 570226  B_ESCHR_COLI  Clinical TRUE        
#>  6 CB6026  B_STPHY_CONS  Clinical TRUE        
#>  7 022060  B_ENTRBC_CLOC ICU      TRUE        
#>  8 80C025  B_ESCHR_COLI  Clinical TRUE        
#>  9 065133  B_STPHY_CONS  Clinical TRUE        
#> 10 6FC260  B_STPHY_HMNS  ICU      TRUE        
#> # … with 90 more rows
# }