# 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)
    #> Warning: package 'dplyr' was built under R version 4.5.2
    library(dbplyr)
    #> Warning: package 'dbplyr' was built under R version 4.5.2
    library(lubridate)
    #> Warning: package 'lubridate' was built under R version 4.5.2
    library(glue)
    #> Warning: package 'glue' was built under R version 4.5.2
    library(purrr)
    #> Warning: package 'purrr' was built under R version 4.5.2
    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 3.53.1 [:memory:]
       #>     uid clnt_id dates diagx diagx_1 diagx_2
       #>   <int>   <int> <dbl> <chr> <chr>   <chr>
       #> 1     9       2 16819 3038  3042    999
       #> 2    98       3 16539 999   <NA>    999
       #> 3     4       3 17224 3050  2921    999
       #> 4    43       4 17749 3049  3044    2928
       #> 5    92       4 17872 999   <NA>    <NA>
       #> 6    35       5 16468 3054  3037    <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  80       1 2018-11-11   999    <NA>    <NA>
       #> 2  60       1 2020-12-11   999     999     999
       #> 3  28       2 2016-07-28  F199    F157    <NA>
       #> 4  13       2 2017-07-04  F132    F157     999
       #> 5  77       2 2018-12-27   999     999    <NA>
       #> 6  55       3 2015-10-16   999     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    21      16 18129    12 M           132
       #> 2    16      16 18288    12 M           132
       #> 3    30      16 18409    12 M           132
       #> 4    47      22 16712    19 M            NA
       #> 5    31      22 16899    19 M           136
       #> 6    14      22 16969    19 M           136

## 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 F103 F106 F107 F108 F110 F112 F113 F114 F115 F117 F118  F12 F122 F124
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F125 F126 F127  F13 F131 F132 F133 F135 F138 F139 F140 F141 F142 F145 F146 F148
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F149 F151 F153 F155 F156 F157 F158 F159  F16 F160 F161 F162 F163 F165 F166 F168
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F169  F17 F170 F172 F173 F174 F178 F179  F18 F181 F182 F183 F184 F185 F186 F189
    #>    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1
    #> F190 F194 F195 F196 F197 F198 F199 NA's
    #>    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 [?? x 10]
    #> # Database: sqlite 3.53.1 [: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     9       2 16819 3038  3042    999                   0
    #> 2 SUD   claim     4       3 17224 3050  2921    999                   0
    #> 3 SUD   claim    43       4 17749 3049  3044    2928                  0
    #> 4 SUD   claim    35       5 16468 3054  3037    <NA>                  1
    #> 5 SUD   claim    12       5 16506 111   3054    <NA>                  1
    #> 6 SUD   claim     5       5 16974 2925  2924    <NA>                  1
    #> # ℹ 1 more variable: flag_restrict_date <int>
    #>   def  src uid clnt_id dates diagx diagx_1 diagx_2
    #> 1 SUD hosp  28       2 17010  F199    F157    <NA>
    #> 2 SUD hosp  13       2 17351  F132    F157     999
    #> 3 SUD hosp  35       3 17518   F18    F174    <NA>
    #> 4 SUD hosp  20       3 18442  F110    F110     999
    #> 5 SUD hosp  40       5 16754  F182    F181    <NA>
    #> 6 SUD hosp   3       5 18070  F106    F108     999

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     9       2     0
    #>  2      1 claim     4       3     0
    #>  3      1 claim    43       4     0
    #>  4      1 claim    35       5     1
    #>  5      1 claim    12       5     0
    #>  6      1 claim     5       5     0
    #>  7      1 claim    44       6     0
    #>  8      1 claim    22       8     1
    #>  9      1 claim    48       8     1
    #> 10      1 claim    18       8     0
    #> # 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: 33 × 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            17010 hosp                      17351 hosp
    #>  2 SUD         3            17518 hosp                      18442 hosp
    #>  3 SUD         5            16468 claim                     18070 hosp
    #>  4 SUD         7            18122 hosp                      18209 hosp
    #>  5 SUD         8            18048 claim                     18458 claim
    #>  6 SUD        14            16895 hosp                      16895 hosp
    #>  7 SUD        15            17169 hosp                      17184 hosp
    #>  8 SUD        16            16589 claim                     18409 hosp
    #>  9 SUD        17            18422 hosp                      18493 hosp
    #> 10 SUD        18            16450 hosp                      18514 hosp
    #> # ℹ 23 more rows
    #> # ℹ 4 more variables: raw_in_claim <dbl>, raw_in_hosp <dbl>,
    #> #   valid_in_claim <int>, valid_in_hosp <int>