Package 'HospitalNetwork' reference manual

Title:	Building Networks of Hospitals Through Patients Transfers
Description:	Set of tools to help interested researchers to build hospital networks from data on hospitalized patients transferred between hospitals. Methods provided have been used in Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715>, and Nekkab N, Crépey P, Astagneau P, Opatowski L, Temime L. (2020) <doi:10.1038/s41598-020-71212-6>.
Authors:	Pascal Crépey [aut, cre, cph], Tjibbe Donker [aut], Clément Massonnaud [aut], Michael Lydeamore [aut]
Maintainer:	Pascal Crépey <[email protected]>
License:	GPL-3
Version:	0.9.4.9000
Built:	2025-02-25 05:17:35 UTC
Source:	https://github.com/pascalcrepey/hospitalnetwork

Check and fix overlapping admissions.

Description

This function checks if a discharge (n) is not later than the next (n+1) admission. If this is the case, it sets the date of discharge n to date of discharge n+1, and creates an extra record running from discharge n+1 to discharge n. If the length of stay of this record is negative, it removes it. It is possible that one pass of this algorithm doesn't clear all overlapping admissions (e.g. when one admission overlaps with more than one other admission), it is therefore iterated until no overlapping admissions are found. Returns the corrected database.

Usage

adjust_overlapping_stays(
  report,
  maxIteration = 25,
  verbose = FALSE,
  retainAuxData = TRUE,
  ...
)
adjust_overlapping_stays(
  report,
  maxIteration = 25,
  verbose = FALSE,
  retainAuxData = TRUE,
  ...
)

Arguments

`report`	(list). A list containing the base and in which will be stored reporting variables. The base is a patient discharge database, in the form of a data.table. The data.table should have at least the following columns: sID: subjectID (character) fID: facilityID (character) Adate: admission date (POSIXct, but character can be converted to POSIXct) Ddate: discharge date (POSIXct, but character can be converted to POSIXct)
`maxIteration`	(integer) the maximum number of times the function will try and remove overlapping admissions.
`verbose`	(boolean) print diagnostic messages. Default is FALSE.
`retainAuxData`	(boolean) allow retaining additional data provided in the database. Default is TRUE.
`...`	other parameters passed on to internal functions

Value

The corrected database as data.table.

Summary statistics on entire database

Description

Function that extracts summary statistics from entire database

Usage

all_admissions_summary(base, verbose = FALSE, ...)
all_admissions_summary(base, verbose = FALSE, ...)

Arguments

`base`	(data.table). A subject discharge database, in the form of a data.table. The data.table should have at least the following columns: sID: subjectID (character) fID: facilityID (character) Adate: admission date (date) Ddate: discharge date (date)
`verbose`	(boolean) print diagnostic messages. Default is TRUE.
`...`	other parameters passed on to internal functions

Value

a list of summary statistics: - meanLOS: The mean length of stay, in days - meanTBA: The mean time between admissions, in days - totalAdmissions: Total number of admissions (i.e. number of records in the database) - numSubjects: Number of unique subjects - numFacilities: Number of unique facilities - LOSdistribution: Distribution of length of stay - TBAdistribution: Distribution of time between admissions

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
all_admissions_summary(myBase)
mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
all_admissions_summary(myBase)

General check function

Description

Function that performs various checks to ensure the database is correctly formatted, and adjusts overlapping patient records.

Usage

checkBase(
  base,
  convertDates = FALSE,
  dateFormat = NULL,
  deleteMissing = NULL,
  deleteErrors = NULL,
  subjectID = "sID",
  facilityID = "fID",
  disDate = "Ddate",
  admDate = "Adate",
  maxIteration = 25,
  retainAuxData = TRUE,
  verbose = TRUE,
  ...
)
checkBase(
  base,
  convertDates = FALSE,
  dateFormat = NULL,
  deleteMissing = NULL,
  deleteErrors = NULL,
  subjectID = "sID",
  facilityID = "fID",
  disDate = "Ddate",
  admDate = "Adate",
  maxIteration = 25,
  retainAuxData = TRUE,
  verbose = TRUE,
  ...
)

Arguments

`base`	(data.table). A patient discharge database, in the form of a data.table. The data.table should have at least the following columns: sID: patientID (character) fID: facilityID (character) Adate: admission date (POSIXct, but character can be converted to POSIXct) Ddate: discharge date (POSIXct, but character can be converted to POSIXct)
`convertDates`	(boolean) indicating if dates need to be converted to POSIXct if they are not
`dateFormat`	(character) giving the input format of the date character string (e.g. "ymd" for dates like "2019-10-30") See `parse_date_time` for more information on the format.
`deleteMissing`	(character) How to handle records that contain a missing value in at least one of the four mandatory variables: NULL (default): do not delete. Stops the function with an error message. "record": deletes just the incorrect record. "patient": deletes all records of each patient with one or more incorrect records.
`deleteErrors`	(character) How incorrect records should be deleted: "record" deletes just the incorrect record "patient" deletes all records of each patient with one or more incorrect records.
`subjectID`	(character) the columns name containing the subject ID. Default is "sID"
`facilityID`	(character) the columns name containing the facility ID. Default is "fID"
`disDate`	(character) the columns name containing the discharge date. Default is "Ddate"
`admDate`	(character) the columns name containing the admission date. Default is "Adate"
`maxIteration`	(integer) the maximum number of times the function will try and remove overlapping admissions
`retainAuxData`	(boolean) allow retaining additional data provided in the database. Default is TRUE.
`verbose`	(boolean) print diagnostic messages. Default is TRUE.
`...`	other parameters passed on to internal functions

Value

The adjusted database as a data.table with a new class attribute "hospinet.base" and an attribute "report" containing information related to the quality of the database.

Examples

## create a "fake and custom" data base
mydb = create_fake_subjectDB(n_subjects = 100, n_facilities = 100)
setnames(mydb, 1:4, c("myPatientId", "myHealthCareCenterID", "DateOfAdmission", "DateOfDischarge"))
mydb[,DateOfAdmission:= as.character(DateOfAdmission)]
mydb[,DateOfDischarge:= as.character(DateOfDischarge)]

head(mydb)
#   myPatientId myHealthCareCenterID DateOfAdmission DateOfDischarge
#1:        s001                 f078      2019-01-26      2019-02-01
#2:        s002                 f053      2019-01-18      2019-01-21
#3:        s002                 f049      2019-02-25      2019-03-05
#4:        s002                 f033      2019-04-17      2019-04-21
#5:        s003                 f045      2019-02-02      2019-02-04
#6:        s003                 f087      2019-03-12      2019-03-19

str(mydb)
#Classes ‘data.table’ and 'data.frame':	262 obs. of  4 variables:
# $ myPatientId         : chr  "s001" "s002" "s002" "s002" ...
# $ myHealthCareCenterID: chr  "f078" "f053" "f049" "f033" ...
# $ DateOfAdmission     : chr  "2019-01-26" "2019-01-18" "2019-02-25" "2019-04-17" ...
# $ DateOfDischarge     : chr  "2019-02-01" "2019-01-21" "2019-03-05" "2019-04-21" ...
#- attr(*, ".internal.selfref")=<externalptr> 

my_checked_db = checkBase(mydb, 
     subjectID = "myPatientId", 
     facilityID = "myHealthCareCenterID", 
     disDate = "DateOfDischarge",
     admDate = "DateOfAdmission", 
     convertDates = TRUE, 
     dateFormat = "ymd")

#Converting Adate, Ddate to Date format
#Checking for missing values...
#Checking for duplicated records...
#Removed 0 duplicates
#Done.

head(my_checked_db)
#    sID  fID      Adate      Ddate
#1: s001 f078 2019-01-26 2019-02-01
#2: s002 f053 2019-01-18 2019-01-21
#3: s002 f049 2019-02-25 2019-03-05
#4: s002 f033 2019-04-17 2019-04-21
#5: s003 f045 2019-02-02 2019-02-04
#6: s003 f087 2019-03-12 2019-03-19
str(my_checked_db)
#Classes ‘hospinet.base’, ‘data.table’ and 'data.frame':	262 obs. of  4 variables:
#$ sID  : chr  "s001" "s002" "s002" "s002" ...
#$ fID  : chr  "f078" "f053" "f049" "f033" ...
#$ Adate: POSIXct, format: "2019-01-26" "2019-01-18" "2019-02-25" "2019-04-17" ...
#$ Ddate: POSIXct, format: "2019-02-01" "2019-01-21" "2019-03-05" "2019-04-21" ...
# ...

## Show the quality report
attr(my_checked_db, "report")
## create a "fake and custom" data base
mydb = create_fake_subjectDB(n_subjects = 100, n_facilities = 100)
setnames(mydb, 1:4, c("myPatientId", "myHealthCareCenterID", "DateOfAdmission", "DateOfDischarge"))
mydb[,DateOfAdmission:= as.character(DateOfAdmission)]
mydb[,DateOfDischarge:= as.character(DateOfDischarge)]

head(mydb)
#   myPatientId myHealthCareCenterID DateOfAdmission DateOfDischarge
#1:        s001                 f078      2019-01-26      2019-02-01
#2:        s002                 f053      2019-01-18      2019-01-21
#3:        s002                 f049      2019-02-25      2019-03-05
#4:        s002                 f033      2019-04-17      2019-04-21
#5:        s003                 f045      2019-02-02      2019-02-04
#6:        s003                 f087      2019-03-12      2019-03-19

str(mydb)
#Classes ‘data.table’ and 'data.frame':	262 obs. of  4 variables:
# $ myPatientId         : chr  "s001" "s002" "s002" "s002" ...
# $ myHealthCareCenterID: chr  "f078" "f053" "f049" "f033" ...
# $ DateOfAdmission     : chr  "2019-01-26" "2019-01-18" "2019-02-25" "2019-04-17" ...
# $ DateOfDischarge     : chr  "2019-02-01" "2019-01-21" "2019-03-05" "2019-04-21" ...
#- attr(*, ".internal.selfref")=<externalptr> 

my_checked_db = checkBase(mydb, 
     subjectID = "myPatientId", 
     facilityID = "myHealthCareCenterID", 
     disDate = "DateOfDischarge",
     admDate = "DateOfAdmission", 
     convertDates = TRUE, 
     dateFormat = "ymd")

#Converting Adate, Ddate to Date format
#Checking for missing values...
#Checking for duplicated records...
#Removed 0 duplicates
#Done.

head(my_checked_db)
#    sID  fID      Adate      Ddate
#1: s001 f078 2019-01-26 2019-02-01
#2: s002 f053 2019-01-18 2019-01-21
#3: s002 f049 2019-02-25 2019-03-05
#4: s002 f033 2019-04-17 2019-04-21
#5: s003 f045 2019-02-02 2019-02-04
#6: s003 f087 2019-03-12 2019-03-19
str(my_checked_db)
#Classes ‘hospinet.base’, ‘data.table’ and 'data.frame':	262 obs. of  4 variables:
#$ sID  : chr  "s001" "s002" "s002" "s002" ...
#$ fID  : chr  "f078" "f053" "f049" "f033" ...
#$ Adate: POSIXct, format: "2019-01-26" "2019-01-18" "2019-02-25" "2019-04-17" ...
#$ Ddate: POSIXct, format: "2019-02-01" "2019-01-21" "2019-03-05" "2019-04-21" ...
# ...

## Show the quality report
attr(my_checked_db, "report")

Check database format

Description

Function that performs various generic checks to ensure that the database has the correct format

Usage

checkFormat(report, convertDates = FALSE, dateFormat = NULL, verbose = TRUE)
checkFormat(report, convertDates = FALSE, dateFormat = NULL, verbose = TRUE)

Arguments

`report`	(list). A list containing the base and in which will be stored reporting variables. The base is a patient discharge database, in the form of a data.table. The data.table should have at least the following columns: sID: subjectID (character) fID: facilityID (character) Adate: admission date (POSIXct, but character can be converted to POSIXct) Ddate: discharge date (POSIXct, but character can be converted to POSIXct)
`convertDates`	(boolean) TRUE/FALSE: whether the dates should converted. Default is TRUE.
`dateFormat`	(boolean) The format of date as a character string (e.g. %y%m%d for 20190524, or %d-%m-%y for 24-05-2019).
`verbose`	(boolean) print diagnostic messages. Default is FALSE.

Value

Returns either an error message, or the database (modified if need be).

Create a fake subject database

Description

Create a fake subject database

Usage

create_fake_subjectDB(
  n_subjects = 100,
  n_facilities = 10,
  avg_n_stays = 3,
  days_since_discharge = NULL,
  length_of_stay = NULL,
  start_id_subjects = 1,
  start_id_facilities = 1,
  with_errors = FALSE
)
create_fake_subjectDB(
  n_subjects = 100,
  n_facilities = 10,
  avg_n_stays = 3,
  days_since_discharge = NULL,
  length_of_stay = NULL,
  start_id_subjects = 1,
  start_id_facilities = 1,
  with_errors = FALSE
)

Arguments

`n_subjects`	the number of different subjects in the database
`n_facilities`	the number of facility present in the database
`avg_n_stays`	the average number of stays per subject
`days_since_discharge`	the number of days between a discharge date and an admission date (default: max(0, rnorm(1, mean = 30, sd = 10)))
`length_of_stay`	the length of stay (default: max(1, rnorm(1, mean = 5, sd = 3) )
`start_id_subjects`, `start_id_facilities`	change starting ids (used for clustered network)
`with_errors`	(boolean) introduce or not random errors in the database. Default to FALSE.

Value

a data.table containing all subjects stays

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
mydb
mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
mydb

Create a fake subject database with clustering

Description

Create a fake subject database with clustering

Usage

create_fake_subjectDB_clustered(
  n_subjects = 50,
  n_facilities = 10,
  avg_n_stays = 3,
  days_since_discharge = NULL,
  length_of_stay = NULL,
  n_clusters = 3
)
create_fake_subjectDB_clustered(
  n_subjects = 50,
  n_facilities = 10,
  avg_n_stays = 3,
  days_since_discharge = NULL,
  length_of_stay = NULL,
  n_clusters = 3
)

Arguments

`n_subjects`	the number of different subjects in the database
`n_facilities`	the number of facility present in the database
`avg_n_stays`	the average number of stays per subject
`days_since_discharge`	the number of days between a discharge date and an admission date (default: max(0, rnorm(1, mean = 30, sd = 10)))
`length_of_stay`	the length of stay (default: max(1, rnorm(1, mean = 5, sd = 3) )
`n_clusters`	the number of cluster in the network

Value

a data.table containing all subjects stays

Examples

mydb <- create_fake_subjectDB_clustered(n_subjects = 100, n_facilities = 10)
mydb
mydb <- create_fake_subjectDB_clustered(n_subjects = 100, n_facilities = 10)
mydb

Create a fake subject stay

Description

create_subject_stay is an internal function used by create_fake_subjectDB.

Usage

create_subject_stay(
  sID,
  fID,
  last_discharge_date = NULL,
  days_since_discharge = NULL,
  length_of_stay = NULL
)
create_subject_stay(
  sID,
  fID,
  last_discharge_date = NULL,
  days_since_discharge = NULL,
  length_of_stay = NULL
)

Arguments

`sID`	the subject ID
`fID`	the facility ID
`last_discharge_date`	the last discharge date
`days_since_discharge`	the number of days since last discharge (default: max(0, rnorm(1, mean = 30, sd = 10)))
`length_of_stay`	the length of stay (default: max(1, rnorm(1, mean = 5, sd = 3))

Value

a one row data.table corresponding to the subject stay.

Compute the edgelist of a network from a database of movements records.

Description

This function computes the edgelist of a network of facilities across which subjects can be transferred. The edgelist is computed from a database that contains the records of the subjects' stays in the facilities.

Usage

edgelist_from_base(
  base,
  window_threshold = 365,
  count_option = "successive",
  prob_params = c(0.0036, 1/365, 0.128),
  condition = "dates",
  noloops = TRUE,
  nmoves_threshold = NULL,
  flag_vars = NULL,
  flag_values = NULL,
  keep_nodes = FALSE,
  verbose = FALSE
)
edgelist_from_base(
  base,
  window_threshold = 365,
  count_option = "successive",
  prob_params = c(0.0036, 1/365, 0.128),
  condition = "dates",
  noloops = TRUE,
  nmoves_threshold = NULL,
  flag_vars = NULL,
  flag_values = NULL,
  keep_nodes = FALSE,
  verbose = FALSE
)

Arguments

`base`	(data.table) A database of records of stays of subjects in facilities. The table should have at least the following columns: subjectID (character) unique subject identifier facilityID (character) unique facility identifier admDate (POSIXct) date of admission in the facility disDate (POSIXct) date of discharge of the facility
`window_threshold`	(integer) A number of days. If two stays of a subject at two facilities occurred within this window, this constitutes a connection between the two facilities (given that potential other conditions are met).
`count_option`	(character) How to count connections. Options are "successive", "probability" or "all". See details.
`prob_params`	(vector of numeric) Three numerical values to calculate the probability that a movement causes an introduction from hospital A to hospital B. See Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715> for more details. For use with count_option="probability". prob_params[1] is the rate of acquisition in hospital A (related to LOS in hospital A). Default: 0.0036 prob_params[2] is the rate of loss of colonisation (related to time between admissions). Default: 1/365 prob_params[4] is the rate of transmission to other patients in hospital B (related to LOS in hospital B). Default: 0.128
`condition`	(character) Condition(s) used to decide what constitutes a connection. Can be "dates", "flags", or "both". See details.
`noloops`	(boolean). Should transfers within the same nodes (loops) be kept or set to 0. Defaults to TRUE, removing loops (setting matrix diagonal to 0).
`nmoves_threshold`	(numeric) A threshold for the minimum number of subject transfer between two facilities. Set to NULL to deactivate, default to NULL.
`flag_vars`	(list) Additional variables that can help flag a transfer, besides the dates of admission and discharge. Must be a named list of two character vectors which are the names of the columns that can flag a transfer: the column that can flag a potential origin, and the column that can flag a potential target. The list must be named with "origin" and "transfer". Eg: list("origin" = "var1", "target" = "var2"). See details.
`flag_values`	(list) A named list of two character vectors which contain the values of the variables in flag_var that are matched to flag a potential transfer. The list must be named with "origin" and "transfer". The character vectors might be of length greater than one. Eg: list("origin" = c("value1", "value2"), "target" = c("value2", "value2")). The values in 'origin' and 'target' are the values that flag a potential origin of a transfer, or a potential target, respectively. See details.
`keep_nodes`	(logical) Should nodes with no connections be kept in the edgelist? Defaults to FALSE.
`verbose`	TRUE to print computation steps

Details

Value

A list of two data.tables, which are the edgelists. One in long format (el_long), and one aggregated by pair of nodes (el_aggr).

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
edgelist_from_base(myBase)
mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
edgelist_from_base(myBase)

Compute the betweenness centrality

Description

Compute the betweenness centrality

Usage

get_betweenness(graph)
get_betweenness(graph)

Arguments

graph

an igraph object

Value

a data.table containing the centrality measure

Compute closeness

Description

Compute one or several closeness measure for facility networks.

Usage

get_closeness(graph, modes = "total")
get_closeness(graph, modes = "total")

Arguments

`graph`	an igraph object
`modes`	option passed on to igraph::closeness : "out", "in", "all", "total"

Value

a data.table containing the closeness measure

Compute the clusters

Description

Compute the clusters

Usage

get_clusters(graph, algos, undirected, ...)
get_clusters(graph, algos, undirected, ...)

Arguments

`graph`	an igraph object
`algos`	the type of algorithm, single argument describing a cluster function from the igraph package
`undirected`	either "mutual" or "arbitrary"
`...`	other arguments to be passed on to the algorithm

Value

a data.table

Compute the degree of each nodes in the network

Description

Compute the degree of each nodes in the network

Usage

get_degree(graph, modes = c("in", "out", "total"))
get_degree(graph, modes = c("in", "out", "total"))

Arguments

`graph`	an igraph object
`modes`	the type of degree: "in", "out", "total"

Value

a data.table of nodes degree

Function computing hub scores of nodes by group

Description

Function computing hub scores of nodes by group

Usage

get_hubs_bycluster(graphs, name, ...)
get_hubs_bycluster(graphs, name, ...)

Arguments

`graphs`	A list of igraph graphs, one for each group within which the hub scores will be computed
`name`	[character (1)] The name of grouping variable (used only for naming the column of the DT)
`...`	Optional arguments to be passed to igraph function 'hits_scores()'

Function computing hub scores for each node. If bycluster = TRUE, hub scores are computed by cluster

Description

Function computing hub scores for each node. If bycluster = TRUE, hub scores are computed by cluster

Usage

get_hubs_global(graph, ...)
get_hubs_global(graph, ...)

Arguments

`graph`	An igraph graph
`...`	other arguments to be passed to igraph function hits_scores()

Function returning matrices of transfers within each by clusters

Description

Function returning matrices of transfers within each by clusters

Usage

get_matrix_bycluster(mat, DT, clusters)
get_matrix_bycluster(mat, DT, clusters)

Arguments

`mat`	The adjacency matrix of the network
`DT`	A data table with at least a column 'node' and a factor column identifying the node's cluster
`clusters`	A unique character vector of the name of the column identifying the nodes' clusters

Compute network metrics

Description

Function computing different network analysis metrics.

Usage

get_metrics(
  network,
  mode = "directed",
  weighted = TRUE,
  transfers = TRUE,
  metrics = c("degree", "closeness", "clusters", "betweenness"),
  clusters = c("cluster_fast_greedy", "cluster_infomap"),
  hubs = "all_clusters",
  options = list(degree = list(modes = c("in", "out", "total")), closeness = list(modes =
    "total"), betweenness = list(), cluster_fast_greedy = list(undirected = "collapse"),
    cluster_infomap = list(undirected = "collapse"), clusters = list(algos =
    c("cluster_fast_greedy", "cluster_infomap"), undirected = "collapse"))
)
get_metrics(
  network,
  mode = "directed",
  weighted = TRUE,
  transfers = TRUE,
  metrics = c("degree", "closeness", "clusters", "betweenness"),
  clusters = c("cluster_fast_greedy", "cluster_infomap"),
  hubs = "all_clusters",
  options = list(degree = list(modes = c("in", "out", "total")), closeness = list(modes =
    "total"), betweenness = list(), cluster_fast_greedy = list(undirected = "collapse"),
    cluster_infomap = list(undirected = "collapse"), clusters = list(algos =
    c("cluster_fast_greedy", "cluster_infomap"), undirected = "collapse"))
)

Arguments

`network`	the network to analyze. Must be an igraph, HospiNet or a square adjacency matrix (n*n).
`mode`	either "directed" or "undirected" network measures
`weighted`	TRUE if the network is weighted
`transfers`	TRUE if metrics specific to subject transfers must be computed
`metrics`	list of the metrics to compute
`clusters`	choose between cluster algorithm: cluster_fast_greedy or cluster_infomap
`hubs`	choose between getting hubs from "all_clusters" or "global"
`options`	named list of options to be passed to the igraph functions

Class providing the HospiNet object with its methods

Description

Class providing the HospiNet object with its methods

Format

R6::R6Class object.

Value

Object of R6::R6Class with methods for accessing facility networks.

Methods

new(edgelist, window_threshold, nmoves_threshold, noloops)

This method is used to create an object of this class with edgelist as the necessary information to create the network. The other arguments window_threshold, nmoves_threshold, and noloops are specific to the edgelist and need to be provided. For ease of use, it is preferable to use the function hospinet_from_subject_database().

print()

This method prints basic information about the object.

plot(type = "matrix")

This method plots the network matrix by default. The argument type can take the following values:

matrix: plot the network matrix,
clustered_matrix: identify and plot cluster(s) in the matrix using the infomap algorithm (from igraph),
degree: plot the histogram of the number of neighbors by facility,
circular_network: plot the network by clusters using a "spaghetti-like" layout. Only works when there are at least 2 clusters.

Active bindings

edgelist: (data.table) the list of edges (origin, target) and their associated number of movements (N) (read-only)
edgelist_long: (data.table) edgelist with additional information (read-only)
matrix: (matrix) the transfer matrix (active binding, read-only)
igraph: (igraph) the igraph object corresponding to the network (active binding, read-only)
n_facilities: the number of facilities in the network (read-only)
n_movements: the total number of subject movements in the network (read-only)
window_threshold: the window threshold used to compute the network (read-only)
nmoves_threshold: the nmoves threshold used to compute the network (read-only)
noloops: TRUE if loops have been removed (read-only)
hist_degrees: histogram data of the number of connections per facility
LOSPerHosp: the mean length of stay for each facility (read-only)
admissionsPerHosp: the number of admissions to each facility (read-only)
subjectsPerHosp: the number of unique subjects admitted to each facility (read-only)
degrees: number of connections for each facilities (total, in, and out)(read-only)
closenesss: the closeness centrality of each facility (read-only)
betweennesss: the betweenness centrality of each facility (read-only)
cluster_infomap: the assigned community for each facility, based on the infomap algorithm (read-only)
cluster_fast_greedy: the assigned community for each facility, based on the greedy modularity optimization algorithm (read-only)
hubs_global: Kleinberg's hub centrality scores, based on the entire network (read-only)
hubs_infomap: same as hubs_global, but computed per community based on the infomap algorithm (read-only)
hubs_fast_greedy: same as hubs_global, but computed per community based on the infomap algorithm (read-only)
metricsTable: (data.table) all of the above metrics for each facility (read-only)

Methods

Method `new()`

Create a new HospiNet object.

Usage

HospiNet$new(
  edgelist,
  edgelist_long,
  window_threshold,
  nmoves_threshold,
  noloops,
  prob_params,
  fsummary = NULL,
  create_MetricsTable = FALSE
)

Arguments

edgelist: Short format edgelist
edgelist_long: Long format edgelist
window_threshold: The window threshold used to compute the network
nmoves_threshold: The nmoves threshold used to compute the network
noloops: TRUE if loops have been removed
prob_params: Currently unused
fsummary: A pre-built data.table with the LOSPerHosp, subjectsPerHosp and admissionsPerHosp that don't need to be recomputed.
create_MetricsTable: all of the metrics for each facility

Returns

A new 'HospiNet' object

Method `print()`

Prints a basic description of the number of facilities and movements of a HospiNet object.

Usage

HospiNet$print()

Returns

NULL

Method `plot()`

Plots various representations of the HospiNet network

Usage

HospiNet$plot(type = "matrix", ...)

Arguments

type: One of "matrix", "degree", "clustered_matrix", "circular network" Choose what you would like to plot - the connectivity matrix, degree distribution, the clusters, or the network in a circle.
...: Additional arguments to be provided. Only supported for ‘type == ’circular_network''.

Returns

a 'ggplot2' object

Method `clone()`

The objects of this class are cloneable with this method.

Usage

HospiNet$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

mydbsmall <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)

hn <- hospinet_from_subject_database(
  base = checkBase(mydbsmall),
  window_threshold = 10,
  count_option = "successive",
  condition = "dates"
)

hn

plot(hn)
plot(hn, type = "clustered_matrix")
mydbsmall <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)

hn <- hospinet_from_subject_database(
  base = checkBase(mydbsmall),
  window_threshold = 10,
  count_option = "successive",
  condition = "dates"
)

hn

plot(hn)
plot(hn, type = "clustered_matrix")

Create HospiNet object from subject database

Description

This function creates a HospiNet object from the database containing subjects stays.

Usage

hospinet_from_subject_database(
  base,
  window_threshold = 365,
  count_option = "successive",
  condition = "dates",
  prob_params = c(0.0036, 1/365, 0.128),
  noloops = TRUE,
  nmoves_threshold = NULL,
  flag_vars = NULL,
  flag_values = NULL,
  keep_nodes = FALSE,
  create_MetricsTable = TRUE,
  verbose = FALSE,
  shinySession = NULL,
  ...
)
hospinet_from_subject_database(
  base,
  window_threshold = 365,
  count_option = "successive",
  condition = "dates",
  prob_params = c(0.0036, 1/365, 0.128),
  noloops = TRUE,
  nmoves_threshold = NULL,
  flag_vars = NULL,
  flag_values = NULL,
  keep_nodes = FALSE,
  create_MetricsTable = TRUE,
  verbose = FALSE,
  shinySession = NULL,
  ...
)

Arguments

`base`	(hospinet.base) A database of records of stays of subjects in facilities. This can be obtained using the function `checkBase`.
`window_threshold`	(numeric) A threshold for the number of days between discharge and admission to be counted as a transfer. Set to 0 for same day transfer, default is 365 days.
`count_option`	(character) TODO. Default is "successive".
`condition`	(character) TODO. Default is "dates".
`prob_params`	(vector of numeric) Three numerical values to calculate the probability that a movement causes an introduction from hospital A to hospital B. See Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715> for more details. prob_params[1] is the rate of acquisition in hospital A (related to LOS in hospital A). Default: 0.0036 prob_params[2] is the rate of loss of colonisation (related to time between admissions). Default: 1/365 prob_params[4] is the rate of transmission to other patients in hospital B (related to LOS in hospital B). Default: 0.128
`noloops`	(boolean). Should transfers within the same nodes (loops) be kept or set to 0. Defaults to TRUE, removing loops (setting matrix diagonal to 0).
`nmoves_threshold`	(numeric) A threshold for the minimum number of subject transfer between two facilities. Set to NULL to deactivate, default to NULL.
`flag_vars`	(list) Additional variables that can help flag a transfer, besides the dates of admission and discharge. Must be a named list of two character vectors which are the names of the columns that can flag a transfer: the column that can flag a potential origin, and the column that can flag a potential target. The list must be named with "origin" and "transfer". Eg: list("origin" = "var1", "target" = "var2"). See details.
`flag_values`	(list) A named list of two character vectors which contain the values of the variables in flag_var that are matched to flag a potential transfer. The list must be named with "origin" and "transfer". The character vectors might be of length greater than one. Eg: list("origin" = c("value1", "value2"), "target" = c("value2", "value2")). The values in 'origin' and 'target' are the values that flag a potential origin of a transfer, or a potential target, respectively. See details.
`keep_nodes`	(logical) Should nodes with no connections be kept in the edgelist? Defaults to FALSE.
`create_MetricsTable`	(boolean) Should the metrics table be created along with the network. Setting to FALSE will speed up the results. Default is TRUE.
`verbose`	TRUE to print computation steps
`shinySession`	(NULL) internal variable to deal with the progress bar
`...`	Additional parameters to be sent to checkBase in case the database has not been checked yet.

Details

This function will build a HospiNet object from a line-listed subject database. The HospiNet object has all of the functions stored as active bindings which can be accessed in the usual way. For more info, see HospiNet. Note that the subject database will need to be run through checkBase before going into this function.

Value

The function returns a HospiNet object.

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
hospinet_from_subject_database(myBase)
mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
hospinet_from_subject_database(myBase)

Compute the adjacency matrix of a network from a database of movements records.

Description

This function computes the adjacency matrix of a network of facilities across which subjects can be transferred. The matrix is computed from a database that contains the records of the subjects' stays in the facilities. This function is a simple wrapper around the two functions edgelist_from_base, which computes the edgelist of the network from the database, and matrix_from_edgelist, which converts the edgelist into the adjacency matrix.

Usage

matrix_from_base(
  base,
  window_threshold = 365,
  count_option = "successive",
  prob_params = c(0.0036, 1/365, 0.128),
  condition = "dates",
  noloops = TRUE,
  nmoves_threshold = NULL,
  flag_vars = NULL,
  flag_values = NULL,
  verbose = FALSE
)
matrix_from_base(
  base,
  window_threshold = 365,
  count_option = "successive",
  prob_params = c(0.0036, 1/365, 0.128),
  condition = "dates",
  noloops = TRUE,
  nmoves_threshold = NULL,
  flag_vars = NULL,
  flag_values = NULL,
  verbose = FALSE
)

Arguments

`base`	(data.table) A database of records of stays of subjects in facilities. The table should have at least the following columns: subjectID (character) unique subject identifier facilityID (character) unique facility identifier admDate (POSIXct) date of admission in the facility disDate (POSIXct) date of discharge of the facility
`window_threshold`	(integer) A number of days. If two stays of a subject at two facilities occurred within this window, this constitutes a connection between the two facilities (given that potential other conditions are met).
`count_option`	(character) How to count connections. Options are "successive", "probability" or "all". See details.
`prob_params`	(vector of numeric) Three numerical values to calculate the probability that a movement causes an introduction from hospital A to hospital B. See Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715> for more details. For use with count_option="probability". prob_params[1] is the rate of acquisition in hospital A (related to LOS in hospital A). Default: 0.0036 prob_params[2] is the rate of loss of colonisation (related to time between admissions). Default: 1/365 prob_params[4] is the rate of transmission to other patients in hospital B (related to LOS in hospital B). Default: 0.128
`condition`	(character) Condition(s) used to decide what constitutes a connection. Can be "dates", "flags", or "both". See details.
`noloops`	(boolean). Should transfers within the same nodes (loops) be kept or set to 0. Defaults to TRUE, removing loops (setting matrix diagonal to 0).
`nmoves_threshold`	(numeric) A threshold for the minimum number of subject transfer between two facilities. Set to NULL to deactivate, default to NULL.
`flag_vars`	(list) Additional variables that can help flag a transfer, besides the dates of admission and discharge. Must be a named list of two character vectors which are the names of the columns that can flag a transfer: the column that can flag a potential origin, and the column that can flag a potential target. The list must be named with "origin" and "transfer". Eg: list("origin" = "var1", "target" = "var2"). See details.
`flag_values`	(list) A named list of two character vectors which contain the values of the variables in flag_var that are matched to flag a potential transfer. The list must be named with "origin" and "transfer". The character vectors might be of length greater than one. Eg: list("origin" = c("value1", "value2"), "target" = c("value2", "value2")). The values in 'origin' and 'target' are the values that flag a potential origin of a transfer, or a potential target, respectively. See details.
`verbose`	TRUE to print computation steps

Details

The edgelist contains the information on the connections between nodes of the network, that is the movements of subjects between facilities. The edgelist can be in two different formats: long or aggregated. In long format, each row corresponds to a single movement between two facilities, therefore only two columns are needed, one containing the origin facilities of a movement, the other containing the target facilities. In aggregated format, the edgelist is aggregated by unique pairs of origin-target facilities. Thus, each row corresponds to a unique connection between two facilities, and the table contains an additional variable which is the count of the number of movements recorded for the pair. If the edgelist is provided in long format, it will be aggregated to compute the matrix.

Value

A square matrix, the adjacency matrix of the network.

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
matrix_from_base(myBase)
mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
matrix_from_base(myBase)

Compute the adjacency matrix of a network from its edgelist

Description

Compute the adjacency matrix of a network from its edgelist

Usage

matrix_from_edgelist(
  edgelist,
  origin_name = "origin",
  target_name = "target",
  count,
  format_long = FALSE
)
matrix_from_edgelist(
  edgelist,
  origin_name = "origin",
  target_name = "target",
  count,
  format_long = FALSE
)

Arguments

`edgelist`	(data.table) A table containing the edges (or links) of the network, i.e. representing the movements of subjects between facilities. Either in long format with at least two columns (origin and target facilities of a link), each row corresponding to a single movement, or aggregated by unique pairs of origin/target, therefore with an additional variable for movements count (default). See details.
`origin_name`	(character) Column of the origin facilities of the links.
`target_name`	(character) Column of the target facilities of the links.
`count`	(character) Column of the counts of movements by unique pair of facilities.
`format_long`	(logical) Whether the edgelist is in long format, with each row corresponding to a single movement. If TRUE, the edgelist will be aggregated by unique pairs of facilities to compute the matrix.

Details

Value

A square numeric matrix, the adjacency matrix of the network.

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
hospinet <- hospinet_from_subject_database(myBase)
matrix_from_edgelist(hospinet$edgelist, count = "N")
mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
hospinet <- hospinet_from_subject_database(myBase)
matrix_from_edgelist(hospinet$edgelist, count = "N")

Function that extracts summary statistics from entire database

Description

Function that extracts summary statistics from entire database

Usage

per_facility_summary(base, verbose = FALSE, ...)
per_facility_summary(base, verbose = FALSE, ...)

Arguments

`base`	(data.table). A subject discharge database, in the form of a data.table. The data.table should have at least the following columns: sID: subjectID (character) fID: facilityID (character) Adate: admission date (date) Ddate: discharge date (date)
`verbose`	(boolean) print diagnostic messages. Default is TRUE.
`...`	other parameters passed on to internal functions

Value

a data table with one row per facility, showing mean LOS, number of subjects, and number of admissions

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
per_facility_summary(myBase)
mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
per_facility_summary(myBase)

Package 'HospitalNetwork'

Help Index

Check and fix overlapping admissions.

Description

Usage

Arguments

Value

Summary statistics on entire database

Description

Usage

Arguments

Value

Examples

General check function

Description

Usage

Arguments

Value

See Also

Examples

Check database format

Description

Usage

Arguments

Value

Create a fake subject database

Description

Usage

Arguments

Value

Examples

Create a fake subject database with clustering

Description

Usage

Arguments

Value

Examples

Create a fake subject stay

Description

Usage

Arguments

Value

Compute the edgelist of a network from a database of movements records.

Description

Usage

Arguments

Details

Value

See Also

Examples

Compute the betweenness centrality

Description

Usage

Arguments

Value

Compute closeness

Description

Usage

Arguments

Value

See Also

Compute the clusters

Description

Usage

Arguments

Value

Compute the degree of each nodes in the network

Description

Usage

Arguments

Value

Function computing hub scores of nodes by group

Description

Usage

Arguments

See Also

Function computing hub scores for each node. If bycluster = TRUE, hub scores are computed by cluster

Description

Usage

Arguments

Method `new()`

Method `print()`

Method `plot()`

Method `clone()`