Covid-19 pandEmic impacts on mental health Related conditions Via multi-database nEtwork: a LongitutinaL Observational study (CERVELLO)

Analytics use case(s): Characterization
Study type: Clinical Application
Tags: psychiatryWG, mental-health, COVID-19
Study lead: Carmen Olga Torre, Hao Luo
Study lead forums tag: CarmenOT haoluo429
Study start date: 21st January, 2021
Study end date: 15th February 2021
Protocol: pdf Doc
Publications: Rates of Antipsychotic Drug Prescribing Among People Living With Dementia During the COVID-19 Pandemic
Results explorer: Shiny app

The primary objective of this study is to describe the baseline demographic, clinical characteristics, treatments and outcomes of interest among individuals with mental health conditions during the COVID-19 pandemic overall and stratified by sex, age, race and specific comorbidities.

FAQ

What do I need to do to run the package?

OHDSI study repos are designed to have information in the README.md (where you are now) to provide you with instructions on how to navigate the repo. This package has CohortDiagnostics as main component: It is an OHDSI R package used to perform diagnostics around the fitness of use of the study phenotypes on your CDM. By running this package you will allow study leads to understand: cohort inclusion rule attrition, inspect source code lists for a phenotype, find orphan codes that should be in a particular concept set but are not, compute incidnece across calendar years, age and gender, break down index events into specific concepts that triggered then, compute overlap of two cohorts and compute basic characteristics of selected cohorts. This package will be requested of all sites. It is run on all available data. This allows us to understand how the study phenotypes perform in your database and identify any potential gaps in the phenotype definitions.

I don't understand the organization of this Github Repo.

The study repo has the following major pieces:

R folder = the folder which will provide the R library the scripts it needs to execute this study
documents folder = the folder where you will find study documents (protocols, 1-sliders to explain the study, etc)
extras folder = the folder where we store a copy of the instructions (called CodeToRun.R) below and other files that the study needs to do things like package maintenance or talk with the Shiny app. Aside from CodeToRun.R, you can largely ignore the rest of these files.
inst folder = This is the "install" folder. It contains the most important parts of the study: the study cohort JSONs (analogous to what ATLAS shows you in the Export tab), the study settings, a sub-folder that contains information to the Shiny app, and the study cohort SQL scripts that SqlRender will use to translate these into your RDBMS.

Below you will find instructions for how to bring this package into your R/ RStudio environment. Note that if you are not able to connect to the internet in R/ RStudio to download pacakges, you will have to pull the TAR file.

What should I do when I finish?

If you finish running a study package, send the file diagnosticsExport/Results_<DatabaseId>.zip in the output folder to the study coordinator Carmen O. Torre. The study team will be in touch within 24 hours to acknowledge receipt of your results and review results. If there are no issues, the results will be pushed to the RShiny app. If any errors occur in this process, the study lead will communicate with you and work to resolve this.

Package Requirements

A database in Common Data Model version 5 in one of these platforms: SQL Server, Oracle, PostgreSQL, IBM Netezza, Apache Impala, Amazon RedShift, or Microsoft APS.
R version 3.5.0 or newer
On Windows: RTools
Java
Suggested: 25 GB of free disk space

How to Run the Study

In R, you will build an .Renviron file. An .Renviron is an R environment file that sets variables you will be using in your code. It is encouraged to store these inside your environment so that you can protect sensitive information. Below are brief instructions on how to do this:

# The code below makes use of R environment variables (denoted by "Sys.getenv(<setting>)") to 
# allow for protection of sensitive information. If you'd like to use R environment variables stored
# in an external file, this can be done by creating an .Renviron file in the root of the folder
# where you have cloned this code. For more information on setting environment variables please refer to: 
# https://stat.ethz.ch/R-manual/R-devel/library/base/html/readRenviron.html
#
# Below is an example .Renviron file's contents: (please remove)
# the "#" below as these too are interprted as comments in the .Renviron file:
#
#    DBMS = "postgresql"
#    DB_SERVER = "database.server.com"
#    DB_PORT = 5432
#    DB_USER = "database_user_name_goes_here"
#    DB_PASSWORD = "your_secret_password"
#    FFTEMP_DIR = "E:/fftemp"
#    USE_SUBSET = FALSE
#
# The following describes the settings
#    DBMS, DB_SERVER, DB_PORT, DB_USER, DB_PASSWORD := These are the details used to connect
#    to your database server. For more information on how these are set, please refer to:
#    http://ohdsi.github.io/DatabaseConnector/
#
#    FFTEMP_DIR = A directory where temporary files used by the FF package are stored while running.
#
#    USE_SUBSET = TRUE/FALSE. When set to TRUE, this will allow for runnning this package with a 
#    subset of the cohorts/features. This is used for testing. PLEASE NOTE: This is only enabled
#    by setting this environment variable.
#
# Once you have established an .Renviron file, you must restart your R session for R to pick up these new
# variables.

To install the study package, type the following into a new R script and run. You can also retrieve this code from extras/CodeToRun.R.

# Make sure to install all dependencies (not needed if already done):
# install.packages("SqlRender")
# install.packages("DatabaseConnector")
# install.packages("ggplot2")
# install.packages("ParallelLogger")
# install.packages("readr")
# install.packages("tibble")
# install.packages("dplyr")
# install.packages("RJSONIO")
# install.packages("devtools")
# devtools::install_github("FeatureExtraction")
# devtools::install_github("ROhdsiWebApi")
# devtools::install_github("CohortDiagnostics")

# When asked to update packages, select '1' ('update all') (could be multiple times)
# When asked whether to install from source, select 'No' (could be multiple times)

#devtools::install_github("ohdsi-studies/Cervello")

# Load the package
library(cervello)

Note: if you have issues using devtools, you can still install the package downloading it and then installing it locally.

Now you have set-up your environment and installed the library that will run the package. You can use the following R script to load in your library and configure your environment connection details:

library(cervello)

# Optional: specify where the temporary files (used by the ff package) will be created:
fftempdir <- if (Sys.getenv("FFTEMP_DIR") == "") "~/fftemp" else Sys.getenv("FFTEMP_DIR")
options(fftempdir = fftempdir)

# Details for connecting to the server:
dbms = Sys.getenv("DBMS")
user <- if (Sys.getenv("DB_USER") == "") NULL else Sys.getenv("DB_USER")
password <- if (Sys.getenv("DB_PASSWORD") == "") NULL else Sys.getenv("DB_PASSWORD")
server = Sys.getenv("DB_SERVER")
port = Sys.getenv("DB_PORT")
connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = dbms,
                                                                server = server,
                                                                user = user,
                                                                password = password,
                                                                port = port)


# Details specific to the database:
outputFolder <- "s:/cervello/mydb"
cdmDatabaseSchema <- "CDM_mydb_V1247.dbo"
cohortDatabaseSchema <- "mydb.dbo"
cohortTable <- "cervello_mydatabase"
databaseId <- "mydb"
databaseName <- "MYDATABASE Medical Claims and Records Database"
databaseDescription <- " MYDATABASE represent data from individuals enrolled in United States employer-sponsored insurance health plans. The data includes adjudicated health insurance claims (e.g. inpatient, outpatient, and outpatient pharmacy) as well as enrollment data from large employers and health plans who provide private healthcare coverage to employees, their spouses, and dependents."

You can now run the characterization package.

# Use this to run the cohorttDiagnostics. The results will be stored in the diagnosticsExport subfolder of the outputFolder. This can be shared between sites.
cervello::runCohortDiagnostics(connectionDetails = connectionDetails,
                                     cdmDatabaseSchema = cdmDatabaseSchema,
                                     cohortDatabaseSchema = cohortDatabaseSchema,
                                     cohortTable = cohortTable,
                                     oracleTempSchem

Cervello

Install / Use

README