fragmented_care_density {CareDensity} | R Documentation |
Calculate the Fragmented Care Density for all Patients
Description
This function calculates the Fragmented Care Density Index as defined by Engels et al. (2024) for each patient in the supplied dataset. Works well with large patient-sharing networks.
Usage
fragmented_care_density(data, pat_col=1, weights, type,
by_connection=FALSE, data_frame=TRUE)
Arguments
data |
A |
pat_col |
Specifies which column of |
weights |
A |
type |
A |
by_connection |
Either |
data_frame |
Set this argument to |
Details
The Fragmented Care Density is an extension of the classic Care Density (see care_density
) and was proposed by Engels et al. (2024). It is also a measure of care coordination, but it allows a lot more flexibility by using different weights for different provider-type connections. For example, it may make sense to weight the amount of patients shared by two general providers differently than the amount of patients shared by a general provider and a specialist. Formally, the fragmented care density is defined as:
FC_p = \sum_{j = 1}^{k} w_j \frac{s_j}{n_p(n_p - 1) /2},
where n_p
is the number of different providers patient p
visited and w_j
are some connection specific weights. k
is defined as:
k = {l \choose 2} + l,
with l
being the number of different provider types. Finally, s_j
is the sum of the number of patients shared by all doctors of a specific connection type. See Engels et al. (2024) for more information.
Under the hood, this function uses the igraph
package to construct a patient-sharing network from the provided data
to calculate the weights. It then uses the data.table
package to efficiently calculate the care densities from a resulting edge list with weights.
Value
Returns a single data.frame
(or data.table
) containing output depending on the specification of the by_connection
argument.
When by_connection=FALSE
was used the output only includes the patient id ("PatID"
) and the calculated fragmented care densities ("fragmented_care_density"
).
When by_connection=TRUE
was used instead, the output includes the patient id ("PatID"
), the connection-type ("connection"
) the sum of all weights ("sum_weights"
), the number of providers seen by each patient ("n"
) and the calculated simple care density ("care_density"
).
Author(s)
Robin Denz
References
Pollack, Craig Evan, Gary E. Weissman, Klaus W. Lemke, Peter S. Hussey, and Jonathan P. Weiner. (2013). "Patient Sharing Among Physicians and Costs of Care: A Network Analytic Approach to Care Coordination Using Claims Data". Journal of General Internal Medicine 28 (3), pp. 459-465.
Engels, Alexander, Claudia Konnopka, Espen Henken, Martin Härter, and Hans-Helmut König. (2024). "A Flexible Approach to Measure Care Coordination Based on Patient-Sharing Networks". BMC Medical Research Methodology 24 (1), pp. 1-12.
See Also
Examples
library(CareDensity)
library(data.table)
library(igraph)
# some arbitrary patient-provider contact data
data <- data.frame(PatID=c("1", "1", "1", "2", "2", "3", "3", "4", "5"),
ArztID=c("A", "C", "D", "A", "D", "A", "D", "D", "C"))
# defining the provider types
d_type <- data.frame(ID=c("A", "C", "D"),
Type=c("GP", "GP", "Psychiatrist"))
# defining the connection-specific weights
d_weights <- data.frame(from=c("GP", "GP", "Psychiatrist"),
to=c("GP", "Psychiatrist", "Psychiatrist"),
weight=c(1.1, 0.8, 1.3))
# calculate the fragmented care densities
fragmented_care_density(data, type=d_type, weights=d_weights)
# calculate only the connection-specific sums and care-densities per patient
# NOTE: "weights" can be set to NULL here because they won't be used
fragmented_care_density(data, type=d_type, weights=NULL, by_connection=TRUE)