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EHR Prevalence and Co-occurrence Frequencies

This project analyzes an OMOP database to measure EHR prevalence and co-occurrence frequencies of all observed conditions, drugs, procedures and patient demographics (ethnicity, race, and gender).

Count is determined as the number of patients with the given concept or pair of concepts. EHR prevalence and co-occurrence frequency are calculated as count / number of patients in the time range.

Options are available to exclude concepts with counts below a specified threshold (e.g., <= 10 patients for HIPAA protection) and to randomize counts for additional protection.

This is the analysis performed to generate the Columbia Open Health Data (COHD).

Pre-requisites

  • Python 2
  • Numpy: pip install numpy

Running

Analyses involve patient identifiable information (PII) and should be handled in accordance with your institution's rules and regulations.

Export from OMOP tables

Export data from the OMOP database to tab-delimited data files for further processing in Python. Code is provided to extract from SQL Server and MySQL. See cohd_omop_export_sql_server.sql or cohd_omop_export_mysql.sql.

SQL Server (using SQL Server Management Studio):

  1. Update settings in SQL Server Manangement Studio so that Results to Text saves tab-delimited files
    Tools > Options > Query Results > SQL Server > Results to Text
    Output format: tab delimited
    Include column headers in the result set: enabled
    Restart SSMS for new settings to take effect
  2. Open cohd_omop_export_sql_server.sql
  3. Enable SQLCMD mode:
    Query > SQLCMD Mode
  4. Update the output paths in the :OUT command
  5. Execute

MySQL (using mysql command line):

  1. Optional: Open cohd_omop_export_mysql.sh and update the connection settings (user and database)
  2. Execute ./cohd_omop_export_mysql.sh

The following files are produced. All files should be tab-delimited and include a header.

  1. concepts.txt
    Extract from the OMOP concept table. Does not contain PII. Columns: concept_id, concept_name, domain_id, concept_class_id
  2. person.txt
    Extract from the OMOP person table. Contains PII. Columns: person_id, gender_concept_id, race_concept_id, ethnicity_concept_id
  3. unique_patient_concept_pairs_date.txt
    Extract and union from the OMOP condition_occurrence, drug_exposure, and procedure_occurrence tables. Contains PII. Columns:
    person_id
    date: year of condition_start_date, drug_exposure_start_date, or procedure_date
    concept_id: condition_concept_id, drug_concept_id, or procedure_concept_id
    domain_id: "Condition", "Drug", or "Procedure"

Synthetic example files can be found in the synthetic_example_files folder.

EHR prevalence and co-occurrence analyses

Occurrence and co-occurrence analyses are performed in Python

  1. Open ehr_prevalence_script.py
  2. Update the settings (paths, file names, database, year ranges, etc) as needed.
    The default configuration produces a set of basic data quality analyses, the EHR prevalence analysis restricted to data from a 5-year range ("5-year dataset"), and the EHR prevalence analyses over the entire dataset ("lifetime dataset").
    For the 5-year dataset, we suggest using the most recent 5 complete years in the OMOP database, e.g., if the OMOP database covers up to mid-2017, then use the range range_5year = (2012, 2016)
    To share data: we recommend min_count = 11 and randomize = True (default).
  3. If needed, update the code for reading in the text files if your database writes the text files in a different format
  4. run python ./ehr_prevalence_script.py

Results

Exporting from the OMOP database produces files containing PII (person.txt and unique_patient_concept_pairs_date.txt). Please do not share these files.

Running ehr_prevalence_script.py with default settings will produce the following files.

Data quality files: these files contain annual counts of patients, concepts, and prevalence counts for basic consistency checks.

  1. dq_patients_year.txt
    The number of patients per year
  2. dq_domain_year_total_count.txt
    The sum of all counts within each domain per year
  3. dq_domain_year_num_concepts.txt
    The number of distinct concepts in each domain per year

Concept counts files: these files contain the prevalence data of each concept or pair of concepts.

  1. concept_counts_<settings>.txt
    Single concept counts and frequencies (1 file for the 5-year dataset and 1 file for the lifetime dataset)
  2. concept_pair_counts_<settings>.txt
    Paired concept counts and frequencies (1 file for the 5-year dataset and 1 file for the lifetime dataset)
  3. concept_counts_yearly_<settings>.txt
    Counts and frequencies of each single concept per year (default settings do not generate this analysis)

Synthetic example files can be found in the synthetic_example_files folder.

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