A common problem in health registry research is to collapse overlapping hospital stays to a single stay while retaining all information registered for the patient. Let’s start with looking at some example data:

pat_id    <- c(1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
               3, 3, 3, 3, 4, 4, 4,4, 5, 5, 5, 5, 5, 5,
               5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 
               7, 7, 7, 7)

hosp_stay_id <- 1:44

enter_date <- as.Date(c(19324, 19363, 19375, 19380, 19356, 
                19359, 19362,19368, 19369, 19373, 19375, 
                19376, 19382, 19423, 19423, 19425, 19429, 
                19373, 19395, 19403, 19437, 19321, 19422,
                19437, 19438, 19443, 19444, 19445, 19454, 
                19454, 19458, 19459, 19460, 19464, 19467, 
                19468, 19510, 19510, 19511, 19511, 
                19360, 19397, 19432, 19439), origin="1970-01-01")

exit_date <- as.Date(c(19380, 19363, 19375, 19380, 19359, 
                19382, 19362, 19368, 19369, 19373, 19375, 
                19376, 19382,  19423, 19429, 19425, 19507, 
                47117, 19395, 19403,  19437, 19445, 19422, 
                19437, 19438, 19443, 19444, 19445, 19454, 
                 19468, 19458, 19459, NA, 19464, 
                19467, 19468, 19510, 19511, 19511, 19513, 19450, 
                19397, 19432, 19439), origin="1970-01-01")

example_data <- data.frame(pat_id,hosp_stay_id,
                     enter_date,exit_date)

In the example data, patient nr. 1 has 4 hospital episodes that we would like to identify as a single consecutive hospital stay. We still want to retain all the other information (in this case only the unique hosp_stay_id).

Since we want to keep all the other information, we can’t simply collapse the information for patient 1 to a single line if information with enter date 2022-11-28 and exit date 2023-01-23.

Let’s start by evoking data.table (my very favorite R package!) and change the structure of the data frame to the lovely data table structure:

library(data.table)
setDT(example_data)

# The code below will run but give strange results with missing data in exit date. Missing in exit date usually means patients are still hospitalized, and we could replace the missing date with the 31st December of the reporting year. Let's just exclude this entry for now:  

example_data <- example_data[!is.na(exit_date)]


# Then order the datatable by patient id, enter date and exit date:

setorder(example_data,pat_id,enter_date,exit_date)

# We need a unique identifier per group of overlapping hospital stays.
# Let the magic begin!

example_data[, group_id:=cumsum(
  cummax(shift(as.integer(enter_date), 
  fill=as.integer(exit_date)[1])) < as.integer(enter_date)) + 1,
             by=pat_id]

# The group id is now unique per patient and group of overlapping stays
# Let's turn it make it unique for each group of overlapping stays over the entire dataset:

example_data[,group_id := ifelse(seq(.N)==1,1,0),
             by=.(pat_id,group_id) ][,
              group_id := cumsum(group_id)]

# Let's make our example data a little prettier and easier to read by changing the column order:

setcolorder(example_data, 
        c("pat_id", "hosp_stay_id","group_id"))

# Ready!

Now we can conduct our analyses.

In this simple example, we can only do simple things like counting the number of non-overlapping hospital stays or calculating the total length of stay per patient.

In more realistic examples, we will be able to solve more complex problems, like looking into medical information that might be stored in a separate table, with the hospital_stay_id as the link between the two tables.

R data table makes life so much easier for analysts of health registry data!

Acknowledgement: This solution was inspired by this Stack overflow post: https://stackoverflow.com/questions/28938147/how-to-flatten-merge-overlapping-time-periods

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Linking overlapping hospital stays was first posted on February 21, 2024 at 6:56 pm.
Continue reading: Linking overlapping hospital stays