What it does
This package is designed for identifying disease cases from admin data for epidemiological studies. The implementation focused on code readability and re-usability. Three types of functions are included:
Interactive functions (e.g.,
identify_row(),exclude(),fetch_var()) based on filter and joins from dplyr with tweaks that fix SQL translation or add features that are not natively support by SQL. They also work for local data.frame, and some use ‘data.table’ package (vignette("datatable-intro", package = "data.table")) to speed up processing time for large data. These functions are not as flexible asdplyr::filter(), but they are general enough to be useful even outside health research.Call-building functions (e.g.,
build_def(),execute_def()) that facilitate batch execution and re-use of case definitions. In essence,build_defcreates codes of definitions (which is chain of the interactive functions, e.g.,define_case()) that are not immediately ran.execute_defruns built definitions with different input data.Miscellaneous functions such as computing age
compute_duration(), collapsing records within a time range into one episodecollapse_episode(), and more (on-going effort). Most of these functions have built-in checks signalling when things might go wrong, e.g., missing values in calculated ages.
Motivation
In health research and surveillance, identifying diseases or events from administrative databases is often the initial step. However, crafting case-finding algorithms is a complex task. Existing algorithms, often written in SAS by experienced analysts, can be complex and difficult to decipher for the growing number of analysts trained primarily in R.
These algorithms may also affect performance if they depend on Data Step in SAS, due to a lack of translation between Data Step and SQL. This can result in SAS downloading data from a remote database to a local machine, leading to poor performance when handling large, population-based databases.
The ‘healthdb’ R package was created to address these challenges. It
minimizes the need to download data and offers an easy-to-use interface
for working with healthcare databases. It also includes capabilities not
supported by ‘SQL’, such as matching strings by ‘stringr’ style regular
expressions, and can compute comorbidity scores
(compute_comorbidity()) directly on a database server. This
vignette will present an example of common use cases.
Installation
Simply run:
install.packages("healthdb")We will need the following packages for this demo.
Intended use case
Consider the case definition of substance use disorder (SUD) from British Columbia Centre for Disease Control’s Chronic Disease Dashboard,
One or more hospitalization with a substance use disorder diagnostic code, OR Two or more physician visits with a substance use disorder diagnostic code within one year.
We are going to implement this definition. First, let’s make a demo data sets for the two sources:
-
Physician claims with multiple columns of ICD-9 diagnostic codes
# make_test_dat() makes either a toy data.frame or database table in memory with known number of rows that satisfy the query we will show later claim_db <- make_test_dat(vals_kept = c("303", "304", "305", "291", "292", glue("30{30:59}"), glue("29{10:29}"), noise_val = c("999", "111")), type = "database") # this is a database table # note that in-memory SQLite database stores dates as numbers claim_db %>% head() #> # Source: SQL [6 x 6] #> # Database: sqlite 3.45.2 [:memory:] #> uid clnt_id dates diagx diagx_1 diagx_2 #> <int> <int> <dbl> <chr> <chr> <chr> #> 1 25 3 17909 3030 2921 NA #> 2 46 4 18497 2925 3047 2925 #> 3 79 5 16559 999 999 NA #> 4 40 5 17134 3056 3042 NA #> 5 63 5 17213 999 999 999 #> 6 77 5 17976 999 NA NA -
Hospitalization with ICD-10 codes
hosp_df <- make_test_dat(vals_kept = c(glue("F{10:19}"), glue("F{100:199}"), noise_val = "999"), type = "data.frame") # this is a local data.frame/tibble hosp_df %>% head() #> uid clnt_id dates diagx diagx_1 diagx_2 #> 1 100 1 2015-02-01 999 999 999 #> 2 85 1 2017-07-17 999 <NA> 999 #> 3 3 1 2019-06-17 F133 F159 999 #> 4 65 2 2019-01-08 999 <NA> 999 #> 5 52 2 2019-06-01 999 <NA> <NA> #> 6 75 3 2016-07-11 999 <NA> <NA>
Interactive functions
Let’s focus on the physician claims. Extracting clients with at least two records within a year is not difficult, and involves only a few steps. The codes could look like the following using dplyr, however, it does not work because: 1. SQL does not support multiple patterns in one LIKE operation, 2. dbply currently have issue with translating n_distinct.
## not run
claim_db %>%
# identify the target codes
filter(if_any(starts_with("diagx"), ~ str_like(., c("291%", "292%", "303%", "304%", "305%")))) %>%
# each clnt has at least 2 records on different dates
group_by(clnt_id) %>%
# the n_distinct step is mainly for reducing computation in the next step
filter(n_distinct(dates) >= 2) %>%
# any two dates within one year?
filter((max(dates) - min(dates)) <= 365)
## endHere’s how you could use healthdb to achieve these
steps:
-
Identify rows contains the target codes. Use
?identify_rowto see a list of supported matching types.result1 <- claim_db %>% identify_row( vars = starts_with("diagx"), match = "start", vals = c(291:292, 303:305) ) #> ℹ Identify records with condition(s): #> • where at least one of the diagx, diagx_1, diagx_2 column(s) in each record #> • contains a value satisfied SQL LIKE pattern: 291% OR 292% OR 303% OR 304% OR 305% #> ℹ To see the final query generated by 'dbplyr', use dplyr::show_query() on the output. #> To extract the SQL string, use dbplyr::remote_query(). -
Bonus: remove clients with exclusion codes
This step is not in the substance use disorder definition, but other disease definitions often require exclusion of some ICDs that contradicts the ones of interest. Let’s say we want to remove clients with code “111” here.
We first identify “111” from the source, then exclude clients in the output from the previous step’s result.
exclude()take either a data set (via the excl argument) or expression (condition argument) as input. For the former, it performs an anti join matching on the by argument (seedplyr::join_by()). For the latter, it is the opposite of filter, i.e.,filter(!(some_expression)).result2 <- result1 %>% exclude( excl = identify_row(claim_db, starts_with("diagx"), "in", "111"), by = "clnt_id" ) #> ℹ Identify records with condition(s): #> • where at least one of the diagx, diagx_1, diagx_2 column(s) in each record #> • contains a value exactly matched values in set: "111" #> ℹ Exclude records in `data` through anti_join with `excl` matching on (by argument): "clnt_id" -
Restrict the number of records per client
result3 <- result2 %>% restrict_n( clnt_id = clnt_id, n_per_clnt = 2, count_by = dates, # here we use filter mode to remove records that failed the restriction mode = "filter" ) #> ℹ Apply restriction that each client must have at least 2 records with #> distinct dates. Clients/groups which did not met the condition were excluded. -
Restrict the temporal pattern of diagnoses
restrict_date()supports more complicated patterns like having n diagnoses at least i days apart within j years. Note that when SQL interpret order of dates, the result could be not deterministic if there were duplicate dates within client. Therefore, a unique row id (uid) has to be supplied to get consistent result.result4 <- result3 %>% restrict_date( clnt_id = clnt_id, date_var = dates, n = 2, within = 365, uid = uid, # here we use flag mode to flag records that met the restriction instead of removing those mode = "flag" ) #> ℹ Apply restriction that each client must have 2 records that were #> within 365 days. Records that met the condition were flagged. -
Fetch variables from other tables by matching common keys
Up to this point, the result is only a query and have not been downloaded. Hopefully, the data has been shrunken to a manageable size for collection.
# Class of result4 class(result4) #> [1] "tbl_SQLiteConnection" "tbl_dbi" "tbl_sql" #> [4] "tbl_lazy" "tbl"# execute query and download the result result_df <- result4 %>% collect() # Number of rows in source nrow(claim_db %>% collect()) #> [1] 100# Number of rows in the current result nrow(result_df) #> [1] 27Our data now only contains diagnoses which are probably not enough for further analyses. Let’s say we want to gather client demographics such as age and sex from other sources. This certainly can be done with multiple
dplyr::left_join()calls. Here we provide thefetch_var()function to make the codes more concise.# make two look up tables age_tab <- data.frame( clnt_id = 1:50, age = sample(1:90, 50), sex = sample(c("F", "M"), 50, replace = TRUE) ) address_tab <- data.frame( clnt_id = rep(1:50, 5), year = rep(2016:2020, each = 50), area_code = sample(0:200, 50, replace = TRUE) ) # get year from dates for matching result_df <- result_df %>% mutate(year = lubridate::year(as.Date(dates, origin = "1970-01-01"))) # note that keys must be present in all tables result_df %>% fetch_var( keys = c(clnt_id, year), linkage = list( # the formula means from_table ~ get_variable # |clnt_id means matching on clnt_id only age_tab ~ c(age, sex) | clnt_id, address_tab ~ area_code ) ) %>% select(uid, clnt_id, dates, age, sex, area_code) %>% head() #> # A tibble: 6 × 6 #> uid clnt_id dates age sex area_code #> <int> <int> <dbl> <int> <chr> <int> #> 1 1 18 16988 4 M 193 #> 2 34 18 17810 4 M 193 #> 3 45 20 16517 62 M NA #> 4 39 20 17305 62 M 154 #> 5 33 21 16451 8 F NA #> 6 50 21 16818 8 F 196
Call-building functions
To complete the definition, we need to repeat the process shown above
with hospitalization data. Some studies may use more than a handful of
data sources to define their sample. We packed steps 1-4 in one function
define_case(), and provide tools to perform batch execution
with different data and parameters to meet those needs.
# build the full definition of SUD
sud_def <- build_def(
# name of definition
def_lab = "SUD",
# place holder names for sources
src_labs = c("claim", "hosp"),
def_fn = define_case, # you could alter it and supply your own function
# below are argumets of define_case
fn_args = list(
# if length = 1, the single element will be use for every source
vars = list(starts_with("diagx")),
match = "start", # match ICD starts with vals
vals = list(c(291:292, 303:305), glue("F{10:19}")),
clnt_id = clnt_id,
n_per_clnt = c(2, 1),
date_var = dates,
within = c(365, NULL),
uid = uid,
mode = "flag"
)
)
sud_def
#> # A tibble: 2 × 5
#> def_lab src_labs def_fn fn_args fn_call
#> <chr> <chr> <chr> <list> <list>
#> 1 SUD claim define_case <named list [9]> <language>
#> 2 SUD hosp define_case <named list [9]> <language>Let’s look inside the fn_call list column. Two calls of
define_case() have been made with different parameters. The
data arguments are left empty on purpose for re-usability. For example,
you may want to repeat the analysis with data from different regions or
study periods.
sud_def$fn_call
#> [[1]]
#> define_case(data = , vars = starts_with("diagx"), match = "start",
#> vals = c(291:292, 303:305), clnt_id = clnt_id, n_per_clnt = 2,
#> date_var = dates, within = 365, uid = uid, mode = "flag")
#>
#> [[2]]
#> define_case(data = , vars = starts_with("diagx"), match = "start",
#> vals = glue("F{10:19}"), clnt_id = clnt_id, n_per_clnt = 1,
#> date_var = dates, within = NULL, uid = uid, mode = "flag")Executing the definition is simply a call of
execute_def(). If verbose option is not turned off by
options(healthdb.verbose = FALSE), the output message will
explain what has been done. You could append multiple
build_def() outputs together and execute them all at once.
Definition and source labels will be added to the result to identify
outputs from different calls.
# execute the definition
result_list <- sud_def %>%
execute_def(with_data = list(
claim = claim_db,
hosp = hosp_df
))
#>
#> Actions for definition SUD using source claim_db:
#> → --------------Inclusion step--------------
#> ℹ Identify records with condition(s):
#> • where at least one of the diagx, diagx_1, diagx_2 column(s) in each record
#> • contains a value satisfied SQL LIKE pattern: 291% OR 292% OR 303% OR 304% OR 305%
#> → --------------No. rows restriction--------------
#>
#> ℹ Apply restriction that each client must have at least 2 records with distinct dates. Records that met the condition were flagged.
#> → --------------Time span restriction--------------
#>
#> ℹ Apply restriction that each client must have 2 records that were within 365 days. Records that met the condition were flagged.
#> → -------------- Output all records--------------
#>
#>
#> Actions for definition SUD using source hosp_df:
#>
#> → --------------Inclusion step--------------
#>
#> ℹ Identify records with condition(s):
#> • where at least one of the diagx, diagx_1, diagx_2 column(s) in each record
#> • contains a value satisfied regular expression: ^F10|^F11|^F12|^F13|^F14|^F15|^F16|^F17|^F18|^F19
#>
#> All unique value(s) and frequency in the result (as the conditions require just one of the columns containing target values; irrelevant values may come from other vars columns):
#> 999 F10 F101 F103 F104 F105 F107 F108 F109 F111 F112 F113 F114 F115 F116 F117
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> F118 F119 F12 F121 F122 F123 F124 F125 F128 F129 F13 F132 F133 F134 F135 F137
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> F138 F14 F140 F141 F143 F144 F145 F146 F147 F15 F154 F155 F159 F16 F160 F161
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> F162 F164 F165 F166 F168 F17 F170 F172 F173 F174 F175 F176 F179 F181 F183 F184
#> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> F185 F187 F188 F189 F19 F191 F192 F193 F196 F197 F198 NA's
#> 1 1 1 1 1 1 1 1 1 1 1 1
#> → -------------- Output all records--------------Let’s check the results!
# view the results
purrr::walk(result_list, ~ head(.) %>% print())
#> # Source: SQL [6 x 10]
#> # Database: sqlite 3.45.2 [:memory:]
#> def src uid clnt_id dates diagx diagx_1 diagx_2 flag_restrict_n
#> <chr> <chr> <int> <int> <dbl> <chr> <chr> <chr> <int>
#> 1 SUD claim 25 3 17909 3030 2921 NA 0
#> 2 SUD claim 46 4 18497 2925 3047 2925 0
#> 3 SUD claim 40 5 17134 3056 3042 NA 0
#> 4 SUD claim 10 8 17237 2912 3057 999 0
#> 5 SUD claim 41 12 17520 3047 3049 2915 0
#> 6 SUD claim 22 13 18489 3045 3042 NA 0
#> # ℹ 1 more variable: flag_restrict_date <int>
#> def src uid clnt_id dates diagx diagx_1 diagx_2
#> 1 SUD hosp 3 1 18064 F133 F159 999
#> 2 SUD hosp 38 4 16625 F191 F147 999
#> 3 SUD hosp 17 4 17279 F114 F154 999
#> 4 SUD hosp 30 5 16614 F101 F107 <NA>
#> 5 SUD hosp 27 5 17079 F165 F174 <NA>
#> 6 SUD hosp 24 5 17414 F118 F111 <NA>At this point, the result from the claim database
(result[[1]]) has not been collected locally. You could
collect it manually, do further filtering, and then combine with the
result from hospitalization data in any way you want. If you just need a
simple row bind, we have bind_source() with convenient
naming feature.
bind_source(result_list,
# output_name = c(names in the list elements)
src = "src",
uid = "uid",
clnt_id = "clnt_id",
flag = c("flag_restrict_date", NA),
# force_proceed is needed to collect remote tables to local memory
force_proceed = TRUE
)
#> # A tibble: 100 × 5
#> src_No src uid clnt_id flag
#> <int> <chr> <int> <int> <int>
#> 1 1 claim 25 3 0
#> 2 1 claim 46 4 0
#> 3 1 claim 40 5 0
#> 4 1 claim 10 8 0
#> 5 1 claim 41 12 0
#> 6 1 claim 22 13 0
#> 7 1 claim 3 15 0
#> 8 1 claim 14 16 0
#> 9 1 claim 26 17 0
#> 10 1 claim 1 18 0
#> # ℹ 90 more rowspool_case() goes a few steps further than row bind. It
also filters records with valid flags and can summarize by client/group.
Since we had to decide which variables to be summarized in advance, the
output may not be flexible enough to meet your needs.
pool_case(result_list,
def = sud_def,
# your could skip summary with output_lvl = "raw"
output_lvl = "clnt",
# include records only from sources having valid records, see function documentation for more detail and other options
include_src = "has_valid",
force_proceed = TRUE)
#> # A tibble: 29 × 8
#> def clnt_id first_valid_date last_entry_date raw_in_claim raw_in_hosp
#> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 SUD 1 18064 18064 0 1
#> 2 SUD 4 16625 17279 0 2
#> 3 SUD 5 16614 17414 0 3
#> 4 SUD 7 17700 17865 0 2
#> 5 SUD 8 17821 18135 0 2
#> 6 SUD 9 17637 18344 0 3
#> 7 SUD 11 17675 18395 0 3
#> 8 SUD 12 17888 17888 0 1
#> 9 SUD 13 18342 18342 0 1
#> 10 SUD 16 18088 18088 0 1
#> # ℹ 19 more rows
#> # ℹ 2 more variables: valid_in_claim <int>, valid_in_hosp <int>