Type: Package
Title: Building Networks of Hospitals Through Patients Transfers
Version: 0.9.4
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>.
URL: https://pascalcrepey.github.io/HospitalNetwork/
BugReports: https://github.com/PascalCrepey/HospitalNetwork/issues
License: GPL-3
Encoding: UTF-8
LazyLoad: true
Imports: checkmate, igraph, lubridate, R6, ggplot2, ggraph
Depends: data.table
RoxygenNote: 7.3.2
Suggests: knitr, rmarkdown, testthat (≥ 2.1.0), shiny, shinyWidgets, shinydashboard, DT, shinyalert, shinyjs, vdiffr, pander, glue, golem, htmltools
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2024-12-22 01:15:55 UTC; pascalcrepey
Author: Pascal Crépey [aut, cre, cph], Tjibbe Donker [aut], Clément Massonnaud [aut], Michael Lydeamore [aut]
Maintainer: Pascal Crépey <pascal.crepey@ehesp.fr>
Repository: CRAN
Date/Publication: 2024-12-22 04:30:02 UTC

Class providing the HospiNet object with its methods

Description

Class providing the HospiNet object with its methods

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

Public 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")

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,
  ...
)

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, ...)

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)

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,
  ...
)

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.

See Also

parse_date_time

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")

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)

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
)

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

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
)

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

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
)

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,
  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.

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).

See Also

matrix_from_edgelist, matrix_from_base

Examples

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)

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")

Arguments

graph

an igraph object

modes

option passed on to igraph::closeness : "out", "in", "all", "total"

Value

a data.table containing the closeness measure

See Also

closeness

Compute the clusters

Description

Compute the clusters

Usage

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"))

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, ...)

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()'

See Also

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, ...)

Arguments

graph

An igraph graph

...

other arguments to be passed to igraph function hits_scores()

See Also

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)

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"))
)

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

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,
  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.

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.

See Also

HospiNet

Examples

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
)

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.

See Also

edgelist_from_base, matrix_from_edgelist

Examples

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
)

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

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 numeric matrix, the adjacency matrix of the network.

See Also

edgelist_from_base, matrix_from_base

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")

Function that extracts summary statistics from entire database

Description

Function that extracts summary statistics from entire database

Usage

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)