# Introduction to healthdb

## 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 as `dplyr::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_def` creates codes of definitions (which is chain of the interactive functions, e.g., `define_case()`) that are not immediately ran. `execute_def` runs built definitions with different input data.

- Miscellaneous functions such as computing age `compute_duration()`, collapsing records within a time range into one episode `collapse_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.

    library(dplyr)
    library(dbplyr)
    library(lubridate)
    library(glue)
    library(purrr)
    library(healthdb)

## 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:

1. 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 [?? x 6]
       #> # Database: sqlite 3.51.0 [:memory:]
       #>     uid clnt_id dates diagx diagx_1 diagx_2
       #>   <int>   <int> <dbl> <chr> <chr>   <chr>
       #> 1    17       1 16684 999   3031    <NA>
       #> 2    54       1 17280 999   999     <NA>
       #> 3    51       1 17650 999   999     <NA>
       #> 4    55       1 18353 999   <NA>    <NA>
       #> 5    83       2 16611 999   <NA>    <NA>
       #> 6    95       2 16822 999   <NA>    <NA>

2. 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  74       1 2017-07-13   999     999     999
       #> 2  84       2 2016-10-25   999    <NA>     999
       #> 3   6       2 2018-06-04  F166    F192     999
       #> 4  78       2 2019-05-05   999     999    <NA>
       #> 5   9       3 2015-03-10  F166    F143     999
       #> 6  14       3 2015-10-17   F15     999     999

       # convert Date to numeric to be consistent with claim_db
       hosp_df <- hosp_df %>%
         mutate(dates = julian(dates))

## 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)
    ## end

Here’s how you could use `healthdb` to achieve these steps:

1. Identify rows contains the target codes. Use `?identify_row` to 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().

2. 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 (see `dplyr::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"

3. 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.

4. 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.

5. 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] 28

   Our 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 the `fetch_var()` function to make the codes more concise. Note that the input must be a named object and not from a pipe (i.e., don’t do this `data %>% some_action %>% fetch_var()`).

       # 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
       fetch_var(result_df,
         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    22       4 17472    19 F           119
       #> 2    39       4 18445    19 F           119
       #> 3    40      11 16843    72 F            58
       #> 4    20      11 16943    72 F            58
       #> 5     8      11 17645    72 F            58
       #> 6    21      11 18608    72 F            58

## 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 F100 F101 F104 F106 F108 F109 F112 F113 F116 F119  F12 F120 F121 F122
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F126 F127 F128 F129 F130 F131 F132 F133 F134 F135 F136 F138 F139  F14 F141 F142
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F143 F145 F147  F15 F150 F155 F156 F158  F16 F160 F163 F166 F168 F169  F17 F171
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F172 F173 F175 F177 F180 F181 F182 F183 F184 F185 F186 F188 F189  F19 F192 F195
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F196 F197 F198 F199 NA's
    #>    1    1    1    1    1
    #> → -------------- Output all records--------------

Let’s check the results!

    # view the results
    purrr::walk(result_list, ~ head(.) %>% print())
    #> # Source:   SQL [?? x 10]
    #> # Database: sqlite 3.51.0 [: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    17       1 16684 999   3031    <NA>                  0
    #> 2 SUD   claim     6       2 18290 304   2920    999                   0
    #> 3 SUD   claim    22       4 17472 2910  2929    <NA>                  1
    #> 4 SUD   claim    39       4 18445 3052  3047    <NA>                  1
    #> 5 SUD   claim    11       5 16807 3042  3045    999                   1
    #> 6 SUD   claim    28       5 16842 2914  111     999                   1
    #> # ℹ 1 more variable: flag_restrict_date <int>
    #>   def  src uid clnt_id dates diagx diagx_1 diagx_2
    #> 1 SUD hosp   6       2 17686  F166    F192     999
    #> 2 SUD hosp   9       3 16504  F166    F143     999
    #> 3 SUD hosp  14       3 16725   F15     999     999
    #> 4 SUD hosp  47       3 18495  F195    F171     F12
    #> 5 SUD hosp  42       6 16598  F126    F171    F138
    #> 6 SUD hosp  20       6 17841  F121     F14    <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    17       1     0
    #>  2      1 claim     6       2     0
    #>  3      1 claim    22       4     0
    #>  4      1 claim    39       4     0
    #>  5      1 claim    11       5     1
    #>  6      1 claim    28       5     0
    #>  7      1 claim    43       6     0
    #>  8      1 claim    25       7     0
    #>  9      1 claim    23      10     0
    #> 10      1 claim    40      11     1
    #> # ℹ 90 more rows

`pool_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: 34 × 10
    #>    def   clnt_id first_valid_date first_valid_src last_entry_date last_entry_src
    #>    <chr>   <int>            <dbl> <chr>                     <dbl> <chr>
    #>  1 SUD         2            17686 hosp                      17686 hosp
    #>  2 SUD         3            16504 hosp                      18495 hosp
    #>  3 SUD         5            16807 claim                     16842 claim
    #>  4 SUD         6            16598 hosp                      18362 hosp
    #>  5 SUD         7            16803 hosp                      16803 hosp
    #>  6 SUD         9            17367 hosp                      17367 hosp
    #>  7 SUD        10            16704 hosp                      17465 hosp
    #>  8 SUD        11            16843 claim                     18608 hosp
    #>  9 SUD        14            17430 hosp                      17430 hosp
    #> 10 SUD        16            17083 hosp                      17083 hosp
    #> # ℹ 24 more rows
    #> # ℹ 4 more variables: raw_in_claim <dbl>, raw_in_hosp <dbl>,
    #> #   valid_in_claim <int>, valid_in_hosp <int>