Determine Clinical or Epidemic Episodes

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

get_episode(): an integer vector
is_new_episode(): a logical vector

Details

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

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.
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:


Date	Using `episode_days = 7`	Using `case_free_days = 7`
2023-01-01	1	1
2023-01-02	1	1
2023-01-05	1	1
2023-01-08	2**	1
2023-02-21	3	2***
2023-02-22	3	2
2023-02-23	3	2
2023-02-24	3	2
2023-03-01	4	2

** 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:


patient	date	`get_episode()`	`is_new_episode()`
A	2019-01-01	1	TRUE
A	2019-03-01	1	FALSE
A	2021-01-01	2	TRUE
B	2008-01-01	1	TRUE
B	2008-01-01	1	FALSE
C	2020-01-01	1	TRUE

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().

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$date, episode_days = 60) # indices
#>   [1] 28 47  7  7  6 17  9  4 37 11 43 43 14 38 26 38 12 39 49 18 15 27  5 22 25
#>  [26] 36 11 18 22 41 42 38 33 35 18 45 11 30 40 31 46 19 24 18 17 16 43 46  1 23
#>  [51]  2 18 34 45 21  3 45 12 48 30 10 13 29 40 48 30  2 20  9 19 14 36 19 32 36
#>  [76] 10 44 20  4  4 36  8 48 43 46  9 32  6  8 29 13  6 45  9 12 38 45 44 35  5
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#>   [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
#>  [13]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [25]  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE  TRUE
#>  [37] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#>  [49]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
#>  [61]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
#>  [73] FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
#>  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 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: 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 [95]
#>    patient date       condition new_episode
#>    <chr>   <date>     <chr>     <lgl>      
#>  1 005088  2017-09-28 B         TRUE       
#>  2 010257  2004-04-03 C         TRUE       
#>  3 080086  2007-10-26 A         TRUE       
#>  4 083080  2012-04-16 B         TRUE       
#>  5 0E2483  2008-07-22 B         TRUE       
#>  6 141061  2014-10-22 B         TRUE       
#>  7 16DC39  2015-11-19 A         TRUE       
#>  8 204562  2010-07-03 B         TRUE       
#>  9 22B987  2009-10-19 C         TRUE       
#> 10 257844  2011-05-22 C         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 [91]
#>    ward       date       patient new_index new_logical
#>    <chr>      <date>     <chr>       <int> <lgl>      
#>  1 Clinical   2017-09-28 005088          1 TRUE       
#>  2 Clinical   2004-04-03 010257          1 TRUE       
#>  3 Clinical   2007-10-26 080086          1 TRUE       
#>  4 Clinical   2012-04-16 083080          1 TRUE       
#>  5 Clinical   2008-07-22 0E2483          1 TRUE       
#>  6 Clinical   2014-10-22 141061          1 TRUE       
#>  7 ICU        2015-11-19 16DC39          1 TRUE       
#>  8 Outpatient 2010-07-03 204562          1 TRUE       
#>  9 Clinical   2009-10-19 22B987          1 TRUE       
#> 10 Clinical   2011-05-22 257844          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           60             13            39            49
#> 2 ICU                26             11            23            24
#> 3 Outpatient          5              5             5             5

# 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] FALSE

# 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 [94]
#>    patient mo           ward     flag_episode
#>    <chr>   <mo>         <chr>    <lgl>       
#>  1 D91230  B_STPHY_EPDR Clinical TRUE        
#>  2 F5F794  B_STPHY_AURS ICU      TRUE        
#>  3 419655  B_STPHY_EPDR Clinical TRUE        
#>  4 010257  B_ESCHR_COLI Clinical TRUE        
#>  5 E60130  B_KLBSL_OXYT Clinical TRUE        
#>  6 693505  B_STPHY_AURS ICU      TRUE        
#>  7 E1C9D4  B_STPHY_CONS Clinical TRUE        
#>  8 762305  B_PROTS_MRBL Clinical TRUE        
#>  9 545388  B_STPHY_HMNS Clinical TRUE        
#> 10 E02001  B_ESCHR_COLI Clinical TRUE        
#> # ℹ 90 more rows
# }

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