Summarise clinical tables records • OmopSketch

Introduction

In this vignette, we will explore the OmopSketch functions designed to provide an overview of the clinical tables within a CDM object (e.g. visit_occurrence, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, and death). Specifically, there are four key functions that facilitate this:

summariseClinicalRecords() and tableClinicalRecords(): Use them to create a summary statistics with key basic information of the clinical table (e.g., number of records, number of concepts mapped, etc.)
summariseRecordCount(), plotRecordCount() and tableRecordCount(): Use them to summarise the number of records within specific time intervals.

Create a mock cdm

Let’s see an example of its functionalities. To start with, we will load essential packages and create a mock cdm using the mockOmopSketch() database.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(OmopSketch)

# Connect to mock database
cdm <- mockOmopSketch()

Summarise clinical tables

Let’s now use summariseClinicalTables()from the OmopSketch package to help us have an overview of one of the clinical tables of the cdm (i.e., condition_occurrence).

summarisedResult <- summariseClinicalRecords(cdm, "condition_occurrence")
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |> print()
#> # A tibble: 74 × 13
#>    result_id cdm_name       group_name group_level      strata_name strata_level
#>        <int> <chr>          <chr>      <chr>            <chr>       <chr>       
#>  1         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  2         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  3         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  4         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  5         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  6         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  7         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  8         1 mockOmopSketch omop_table condition_occur… overall     overall     
#>  9         1 mockOmopSketch omop_table condition_occur… overall     overall     
#> 10         1 mockOmopSketch omop_table condition_occur… overall     overall     
#> # ℹ 64 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>

Notice that the output is in the summarised result format.

We can use the arguments to specify which statistics we want to perform. For example, use the argument recordsPerPerson to indicate which estimates you are interested regarding the number of records per person.

summarisedResult <- summariseClinicalRecords(cdm,
  "condition_occurrence",
  recordsPerPerson = c("mean", "sd", "q05", "q95")
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |>
  filter(variable_name == "records_per_person") |>
  select(variable_name, estimate_name, estimate_value)
#> # A tibble: 4 × 3
#>   variable_name      estimate_name estimate_value
#>   <chr>              <chr>         <chr>         
#> 1 records_per_person mean          84            
#> 2 records_per_person sd            10.1802       
#> 3 records_per_person q05           68            
#> 4 records_per_person q95           102

You can further specify if you want to include the number of records in observation (inObservation = TRUE), the number of concepts mapped (standardConcept = TRUE), which types of source vocabulary does the table contain (sourceVocabulary = TRUE), which types of domain does the vocabulary have (domainId = TRUE) or the concept’s type (typeConcept = TRUE).

summarisedResult <- summariseClinicalRecords(cdm,
  "condition_occurrence",
  recordsPerPerson = c("mean", "sd", "q05", "q95"), 
  conceptSummary = TRUE, 
  quality = TRUE
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |>
  select(variable_name, estimate_name, estimate_value) |>
  glimpse()
#> Rows: 71
#> Columns: 3
#> $ variable_name  <chr> "Number subjects", "Number subjects", "records_per_pers…
#> $ estimate_name  <chr> "count", "percentage", "mean", "sd", "q05", "q95", "cou…
#> $ estimate_value <chr> "100", "100", "84", "10.1802", "68", "102", "8400", "84…

Additionally, you can also stratify the previous results by sex and age groups:

summarisedResult <- summariseClinicalRecords(cdm,
  "condition_occurrence",
  recordsPerPerson = c("mean", "sd", "q05", "q95"),
  quality = TRUE, 
  conceptSummary = TRUE,
  sex = TRUE,
  ageGroup = list("<35" = c(0, 34), ">=35" = c(35, Inf))
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |>
  select(variable_name, strata_level, estimate_name, estimate_value) |>
  glimpse()
#> Rows: 607
#> Columns: 4
#> $ variable_name  <chr> "Number subjects", "Number subjects", "records_per_pers…
#> $ strata_level   <chr> "overall", "overall", "overall", "overall", "overall", …
#> $ estimate_name  <chr> "count", "percentage", "mean", "sd", "q05", "q95", "cou…
#> $ estimate_value <chr> "100", "100", "84", "10.1802", "68", "102.0500", "8400"…

Notice that, by default, the “overall” group will be also included, as well as crossed strata (that means, sex == “Female” and ageGroup == “>35”).

Also, see that the analysis can be conducted for multiple OMOP tables at the same time:

summarisedResult <- summariseClinicalRecords(cdm,
  c("visit_occurrence", "drug_exposure"),
  recordsPerPerson = c("mean", "sd"),
  quality = FALSE,
  conceptSummary = FALSE,
  missingData = FALSE
)
#> ℹ Adding variables of interest to visit_occurrence.
#> ℹ Summarising records per person in visit_occurrence.
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.

summarisedResult |>
  select(group_level, variable_name, estimate_name, estimate_value) |>
  glimpse()
#> Rows: 10
#> Columns: 4
#> $ group_level    <chr> "visit_occurrence", "visit_occurrence", "visit_occurren…
#> $ variable_name  <chr> "Number subjects", "Number subjects", "records_per_pers…
#> $ estimate_name  <chr> "count", "percentage", "mean", "sd", "count", "count", …
#> $ estimate_value <chr> "100", "100", "346.1100", "116.9244", "34611", "100", "…

We can also filter the clinical table to a specific time window by setting the dateRange argument.

summarisedResult <- summariseClinicalRecords(cdm, "drug_exposure",
  dateRange = as.Date(c("1990-01-01", "2010-01-01"))) 
#> ℹ Adding variables of interest to drug_exposure.
#> ℹ Summarising records per person in drug_exposure.
#> ℹ Summarising records in observation in drug_exposure.
#> ℹ Summarising records with start before birth date in drug_exposure.
#> ℹ Summarising records with end date before start date in drug_exposure.
#> ℹ Summarising domains in drug_exposure.
#> ℹ Summarising standard concepts in drug_exposure.
#> ℹ Summarising source vocabularies in drug_exposure.
#> ℹ Summarising concept types in drug_exposure.
#> ℹ Summarising missing data in drug_exposure.

summarisedResult |>
  omopgenerics::settings()|>
  glimpse()
#> Rows: 1
#> Columns: 10
#> $ result_id          <int> 1
#> $ result_type        <chr> "summarise_clinical_records"
#> $ package_name       <chr> "OmopSketch"
#> $ package_version    <chr> "0.5.1"
#> $ group              <chr> "omop_table"
#> $ strata             <chr> ""
#> $ additional         <chr> ""
#> $ min_cell_count     <chr> "0"
#> $ study_period_end   <chr> "2010-01-01"
#> $ study_period_start <chr> "1990-01-01"

Tidy the summarised object

tableClinicalRecords() will help you to tidy the previous results and create a gt table.

summarisedResult <- summariseClinicalRecords(cdm,
  "condition_occurrence",
  recordsPerPerson = c("mean", "sd", "q05", "q95"),
  quality = T, 
  conceptSummary = T,
  sex = TRUE
)
#> ℹ Adding variables of interest to condition_occurrence.
#> ℹ Summarising records per person in condition_occurrence.
#> ℹ Summarising records in observation in condition_occurrence.
#> ℹ Summarising records with start before birth date in condition_occurrence.
#> ℹ Summarising records with end date before start date in condition_occurrence.
#> ℹ Summarising domains in condition_occurrence.
#> ℹ Summarising standard concepts in condition_occurrence.
#> ℹ Summarising source vocabularies in condition_occurrence.
#> ℹ Summarising concept types in condition_occurrence.
#> ℹ Summarising missing data in condition_occurrence.

summarisedResult |>
  tableClinicalRecords()

Variable name	Variable level	Estimate name	Database name
Variable name	Variable level	Estimate name	mockOmopSketch
condition_occurrence; overall
Number records	-	N	8,400.00
Number subjects	-	N (%)	100 (100.00%)
Records per person	-	Mean (SD)	84.00 (10.18)
		q05	68.00
		q95	102.05
In observation	Yes	N (%)	8,400 (100.00%)
Domain	-	N (%)	8,400 (100.00%)
Source vocabulary	No matching concept	N (%)	8,400 (100.00%)
Standard concept	-	N (%)	8,400 (100.00%)
Type concept id	Unknown type concept: 1	N (%)	8,400 (100.00%)
Start date before birth date	-	N (%)	0 (0.00%)
End date before start date	-	N (%)	0 (0.00%)
Column name	Condition concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition end date	N missing data (%)	0 (0.00%)
	Condition end datetime	N missing data (%)	8,400 (100.00%)
	Condition occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source concept id	N missing data (%)	8,400 (100.00%)
		N zeros (%)	0 (0.00%)
	Condition source value	N missing data (%)	8,400 (100.00%)
	Condition start date	N missing data (%)	0 (0.00%)
	Condition start datetime	N missing data (%)	8,400 (100.00%)
	Condition status concept id	N missing data (%)	8,400 (100.00%)
		N zeros (%)	0 (0.00%)
	Condition status source value	N missing data (%)	8,400 (100.00%)
	Condition type concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Person id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Provider id	N missing data (%)	8,400 (100.00%)
		N zeros (%)	0 (0.00%)
	Stop reason	N missing data (%)	8,400 (100.00%)
	Visit detail id	N missing data (%)	8,400 (100.00%)
		N zeros (%)	0 (0.00%)
	Visit occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
condition_occurrence; Female
Number records	-	N	4,737.00
Number subjects	-	N (%)	57 (100.00%)
Records per person	-	Mean (SD)	83.11 (10.36)
		q05	68.00
		q95	103.40
In observation	Yes	N (%)	4,737 (100.00%)
Domain	-	N (%)	4,737 (100.00%)
Source vocabulary	No matching concept	N (%)	4,737 (100.00%)
Standard concept	-	N (%)	4,737 (100.00%)
Type concept id	Unknown type concept: 1	N (%)	4,737 (100.00%)
Column name	Condition concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition end date	N missing data (%)	0 (0.00%)
	Condition end datetime	N missing data (%)	4,737 (100.00%)
	Condition occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source concept id	N missing data (%)	4,737 (100.00%)
		N zeros (%)	0 (0.00%)
	Condition source value	N missing data (%)	4,737 (100.00%)
	Condition start date	N missing data (%)	0 (0.00%)
	Condition start datetime	N missing data (%)	4,737 (100.00%)
	Condition status concept id	N missing data (%)	4,737 (100.00%)
		N zeros (%)	0 (0.00%)
	Condition status source value	N missing data (%)	4,737 (100.00%)
	Condition type concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Person id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Provider id	N missing data (%)	4,737 (100.00%)
		N zeros (%)	0 (0.00%)
	Stop reason	N missing data (%)	4,737 (100.00%)
	Visit detail id	N missing data (%)	4,737 (100.00%)
		N zeros (%)	0 (0.00%)
	Visit occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
condition_occurrence; Male
Number records	-	N	3,663.00
Number subjects	-	N (%)	43 (100.00%)
Records per person	-	Mean (SD)	85.19 (9.93)
		q05	71.00
		q95	100.00
In observation	Yes	N (%)	3,663 (100.00%)
Domain	-	N (%)	3,663 (100.00%)
Source vocabulary	No matching concept	N (%)	3,663 (100.00%)
Standard concept	-	N (%)	3,663 (100.00%)
Type concept id	Unknown type concept: 1	N (%)	3,663 (100.00%)
Column name	Condition concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition end date	N missing data (%)	0 (0.00%)
	Condition end datetime	N missing data (%)	3,663 (100.00%)
	Condition occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Condition source concept id	N missing data (%)	3,663 (100.00%)
		N zeros (%)	0 (0.00%)
	Condition source value	N missing data (%)	3,663 (100.00%)
	Condition start date	N missing data (%)	0 (0.00%)
	Condition start datetime	N missing data (%)	3,663 (100.00%)
	Condition status concept id	N missing data (%)	3,663 (100.00%)
		N zeros (%)	0 (0.00%)
	Condition status source value	N missing data (%)	3,663 (100.00%)
	Condition type concept id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Person id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)
	Provider id	N missing data (%)	3,663 (100.00%)
		N zeros (%)	0 (0.00%)
	Stop reason	N missing data (%)	3,663 (100.00%)
	Visit detail id	N missing data (%)	3,663 (100.00%)
		N zeros (%)	0 (0.00%)
	Visit occurrence id	N missing data (%)	0 (0.00%)
		N zeros (%)	0 (0.00%)

Summarise record counts

OmopSketch can also help you to summarise the trend of the records of an OMOP table. See the example below, where we use summariseRecordCount() to count the number of records within each year, and then, we use plotRecordCount() to create a ggplot with the trend. We can also use tableRecordCount() to display results in a table of type gt, reactable or datatable. By default it creates a gt table.

summarisedResult <- summariseRecordCount(cdm, "drug_exposure", interval = "years")

summarisedResult |> tableRecordCount(type = "gt")

Variable name	Time interval	Estimate name	Database name
Variable name	Time interval	Estimate name	mockOmopSketch
episode; drug_exposure
Records in observation	1955-01-01 to 1955-12-31	N (%)	15 (0.07%)
	1956-01-01 to 1956-12-31	N (%)	42 (0.19%)
	1957-01-01 to 1957-12-31	N (%)	95 (0.44%)
	1958-01-01 to 1958-12-31	N (%)	406 (1.88%)
	1959-01-01 to 1959-12-31	N (%)	197 (0.91%)
	1960-01-01 to 1960-12-31	N (%)	300 (1.39%)
	1961-01-01 to 1961-12-31	N (%)	584 (2.70%)
	1962-01-01 to 1962-12-31	N (%)	408 (1.89%)
	1963-01-01 to 1963-12-31	N (%)	368 (1.70%)
	1964-01-01 to 1964-12-31	N (%)	319 (1.48%)
	1965-01-01 to 1965-12-31	N (%)	237 (1.10%)
	1966-01-01 to 1966-12-31	N (%)	219 (1.01%)
	1967-01-01 to 1967-12-31	N (%)	221 (1.02%)
	1968-01-01 to 1968-12-31	N (%)	226 (1.05%)
	1969-01-01 to 1969-12-31	N (%)	230 (1.06%)
	1970-01-01 to 1970-12-31	N (%)	215 (1.00%)
	1971-01-01 to 1971-12-31	N (%)	182 (0.84%)
	1972-01-01 to 1972-12-31	N (%)	195 (0.90%)
	1973-01-01 to 1973-12-31	N (%)	255 (1.18%)
	1974-01-01 to 1974-12-31	N (%)	319 (1.48%)
	1975-01-01 to 1975-12-31	N (%)	348 (1.61%)
	1976-01-01 to 1976-12-31	N (%)	388 (1.80%)
	1977-01-01 to 1977-12-31	N (%)	390 (1.81%)
	1978-01-01 to 1978-12-31	N (%)	370 (1.71%)
	1979-01-01 to 1979-12-31	N (%)	420 (1.94%)
	1980-01-01 to 1980-12-31	N (%)	430 (1.99%)
	1981-01-01 to 1981-12-31	N (%)	303 (1.40%)
	1982-01-01 to 1982-12-31	N (%)	310 (1.44%)
	1983-01-01 to 1983-12-31	N (%)	344 (1.59%)
	1984-01-01 to 1984-12-31	N (%)	389 (1.80%)
	1985-01-01 to 1985-12-31	N (%)	399 (1.85%)
	1986-01-01 to 1986-12-31	N (%)	421 (1.95%)
	1987-01-01 to 1987-12-31	N (%)	497 (2.30%)
	1988-01-01 to 1988-12-31	N (%)	585 (2.71%)
	1989-01-01 to 1989-12-31	N (%)	651 (3.01%)
	1990-01-01 to 1990-12-31	N (%)	754 (3.49%)
	1991-01-01 to 1991-12-31	N (%)	916 (4.24%)
	1992-01-01 to 1992-12-31	N (%)	1,062 (4.92%)
	1993-01-01 to 1993-12-31	N (%)	1,217 (5.63%)
	1994-01-01 to 1994-12-31	N (%)	1,405 (6.50%)
	1995-01-01 to 1995-12-31	N (%)	1,599 (7.40%)
	1996-01-01 to 1996-12-31	N (%)	1,968 (9.11%)
	1997-01-01 to 1997-12-31	N (%)	1,925 (8.91%)
	1998-01-01 to 1998-12-31	N (%)	1,944 (9.00%)
	1999-01-01 to 1999-12-31	N (%)	1,825 (8.45%)
	2000-01-01 to 2000-12-31	N (%)	1,841 (8.52%)
	2001-01-01 to 2001-12-31	N (%)	1,988 (9.20%)
	2002-01-01 to 2002-12-31	N (%)	2,269 (10.50%)
	2003-01-01 to 2003-12-31	N (%)	2,479 (11.48%)
	2004-01-01 to 2004-12-31	N (%)	2,668 (12.35%)
	2005-01-01 to 2005-12-31	N (%)	2,755 (12.75%)
	2006-01-01 to 2006-12-31	N (%)	2,643 (12.24%)
	2007-01-01 to 2007-12-31	N (%)	2,440 (11.30%)
	2008-01-01 to 2008-12-31	N (%)	2,544 (11.78%)
	2009-01-01 to 2009-12-31	N (%)	2,637 (12.21%)
	2010-01-01 to 2010-12-31	N (%)	2,555 (11.83%)
	2011-01-01 to 2011-12-31	N (%)	2,463 (11.40%)
	2012-01-01 to 2012-12-31	N (%)	2,285 (10.58%)
	2013-01-01 to 2013-12-31	N (%)	2,136 (9.89%)
	2014-01-01 to 2014-12-31	N (%)	2,147 (9.94%)
	2015-01-01 to 2015-12-31	N (%)	2,595 (12.01%)
	2016-01-01 to 2016-12-31	N (%)	1,831 (8.48%)
	2017-01-01 to 2017-12-31	N (%)	2,056 (9.52%)
	2018-01-01 to 2018-12-31	N (%)	2,079 (9.62%)
	2019-01-01 to 2019-12-31	N (%)	2,040 (9.44%)
	overall	N (%)	21,600 (100.00%)

Note that you can adjust the time interval period using the interval argument, which can be set to either “years”, “months” or “quarters”. See the example below, where it shows the number of records every 18 months:

summariseRecordCount(cdm, "drug_exposure", interval = "quarters") |>
  plotRecordCount()

We can further stratify our counts by sex (setting argument sex = TRUE) or by age (providing an age group). Notice that in both cases, the function will automatically create a group called overall with all the sex groups and all the age groups.

summariseRecordCount(cdm, "drug_exposure",
  interval = "months",
  sex = TRUE,
  ageGroup = list(
    "<30" = c(0, 29),
    ">=30" = c(30, Inf)
  )
) |>
  plotRecordCount()

By default, plotRecordCount() does not apply faceting or colour to any variables. This can result confusing when stratifying by different variables, as seen in the previous picture. We can use VisOmopResults package to help us know by which columns we can colour or face by:

summariseRecordCount(cdm, "drug_exposure",
  interval = "months",
  sex = TRUE,
  ageGroup = list(
    "0-29" = c(0, 29),
    "30-Inf" = c(30, Inf)
  )
) |>
  visOmopResults::tidyColumns()
#>  [1] "cdm_name"       "omop_table"     "sex"            "age_group"     
#>  [5] "variable_name"  "variable_level" "count"          "percentage"    
#>  [9] "time_interval"  "interval"       "type"

Then, we can simply specify this by using the facet and colour arguments from plotRecordCount()

summariseRecordCount(cdm, "drug_exposure",
  interval = "months",
  sex = TRUE,
  ageGroup = list(
    "0-29" = c(0, 29),
    "30-Inf" = c(30, Inf)
  )
) |>
  plotRecordCount(facet = omop_table ~ age_group, colour = "sex")

We can also filter the clinical table to a specific time window by setting the dateRange argument.

summariseRecordCount(cdm, "drug_exposure",
  interval = "years",
  sex = TRUE, 
  dateRange = as.Date(c("1990-01-01", "2010-01-01"))) |>
  tableRecordCount(type = "gt")

Variable name	Time interval	Sex	Estimate name	Database name
Variable name	Time interval	Sex	Estimate name	mockOmopSketch
episode; drug_exposure
Records in observation	1990-01-01 to 1990-12-31	overall	N (%)	754 (7.44%)
		Female	N (%)	532 (5.25%)
		Male	N (%)	222 (2.19%)
	1991-01-01 to 1991-12-31	overall	N (%)	916 (9.04%)
		Female	N (%)	641 (6.32%)
		Male	N (%)	275 (2.71%)
	1992-01-01 to 1992-12-31	overall	N (%)	1,062 (10.48%)
		Female	N (%)	722 (7.12%)
		Male	N (%)	340 (3.35%)
	1993-01-01 to 1993-12-31	overall	N (%)	1,217 (12.01%)
		Male	N (%)	399 (3.94%)
		Female	N (%)	818 (8.07%)
	1994-01-01 to 1994-12-31	overall	N (%)	1,405 (13.86%)
		Male	N (%)	469 (4.63%)
		Female	N (%)	936 (9.24%)
	1995-01-01 to 1995-12-31	overall	N (%)	1,599 (15.78%)
		Female	N (%)	1,121 (11.06%)
		Male	N (%)	478 (4.72%)
	1996-01-01 to 1996-12-31	overall	N (%)	1,968 (19.42%)
		Female	N (%)	1,360 (13.42%)
		Male	N (%)	608 (6.00%)
	1997-01-01 to 1997-12-31	overall	N (%)	1,925 (18.99%)
		Female	N (%)	1,193 (11.77%)
		Male	N (%)	732 (7.22%)
	1998-01-01 to 1998-12-31	overall	N (%)	1,944 (19.18%)
		Female	N (%)	1,231 (12.15%)
		Male	N (%)	713 (7.04%)
	1999-01-01 to 1999-12-31	overall	N (%)	1,825 (18.01%)
		Female	N (%)	1,241 (12.24%)
		Male	N (%)	584 (5.76%)
	2000-01-01 to 2000-12-31	overall	N (%)	1,841 (18.16%)
		Female	N (%)	1,272 (12.55%)
		Male	N (%)	569 (5.61%)
	2001-01-01 to 2001-12-31	overall	N (%)	1,988 (19.62%)
		Female	N (%)	1,390 (13.71%)
		Male	N (%)	598 (5.90%)
	2002-01-01 to 2002-12-31	overall	N (%)	2,269 (22.39%)
		Female	N (%)	1,545 (15.24%)
		Male	N (%)	724 (7.14%)
	2003-01-01 to 2003-12-31	overall	N (%)	2,479 (24.46%)
		Female	N (%)	1,631 (16.09%)
		Male	N (%)	848 (8.37%)
	2004-01-01 to 2004-12-31	overall	N (%)	2,668 (26.32%)
		Female	N (%)	1,714 (16.91%)
		Male	N (%)	954 (9.41%)
	2005-01-01 to 2005-12-31	overall	N (%)	2,755 (27.18%)
		Female	N (%)	1,740 (17.17%)
		Male	N (%)	1,015 (10.01%)
	2006-01-01 to 2006-12-31	overall	N (%)	2,643 (26.08%)
		Female	N (%)	1,916 (18.90%)
		Male	N (%)	727 (7.17%)
	2007-01-01 to 2007-12-31	overall	N (%)	2,440 (24.07%)
		Female	N (%)	1,626 (16.04%)
		Male	N (%)	814 (8.03%)
	2008-01-01 to 2008-12-31	overall	N (%)	2,544 (25.10%)
		Female	N (%)	1,549 (15.28%)
		Male	N (%)	995 (9.82%)
	2009-01-01 to 2009-12-31	overall	N (%)	2,637 (26.02%)
		Female	N (%)	1,570 (15.49%)
		Male	N (%)	1,067 (10.53%)
	2010-01-01 to 2010-12-31	overall	N (%)	1,734 (17.11%)
		Female	N (%)	1,115 (11.00%)
		Male	N (%)	619 (6.11%)
	overall	overall	N (%)	10,135 (100.00%)
		Male	N (%)	4,200 (41.44%)
		Female	N (%)	5,935 (58.56%)

Finally, disconnect from the cdm

PatientProfiles::mockDisconnect(cdm = cdm)