INSPIRE-Mental-Health-Project-Integrating-and-Harmonizing-Longitudinal-Data

This repository contains project resources for building a data science platform for integration and harmonization of longitudinal data on mental health in Africa.

Summary

The INSPIRE: Building a Data Science Platform for Integration and Harmonization of Longitudinal Data on Mental Health in East Africa project is designed to improve mental health outcomes for depression, anxiety and psychosis in East African settings, specifically in Kenya. To do this, the project aims to provide mental health datasets from African researches to help ensure data-driven decision making on mental health conditions.

Historically, public health policymakers in Africa have focused on communicable diseases, such as malaria, tuberculosis, and HIV/AIDS. However, non-communicable diseases, such as cancer, heart disease, diabetes, and mental health conditions are increasingly becoming the main cause of mortality in Africa.

There is a continuous need for essential data on mental health prevalence and services in East Africa to assist in improving the appropriateness and accuracy of mental health screenings and to develop a more nuanced and culturally appropriate mental health database. Data on the causes, consequences, and impacts of mental health conditions in the African population, therefore, needs to be collected and harmonized. This data needs to capture and account for physical exposures, socio-economic forces, health opportunities , and lifestyles, often referred to as the external exposome, which impact mental health. This necessitates data from longitudinal research, which could also show the effectiveness of interventions to improve mental health in the Kenyan Region of East Africa.

Primary deliverables

• D1: Discover new and existing data from population and clinical data sources on: longitudinal African mental health (mental health) conditions; mental health signs, symptoms and biomarkers; mental health treatment interventions in the African context; and mental health risk factors in Africa.
• D2: Integrate and augment the vocabularies by mental health researchers in the metadata used by data science applications to FAIRly described mental health observations in Africa.
• D3: Provide a dashboard and central catalog that can be used worldwide to discover and characterize African mental health conditions.
• D4: Identify and answer key questions about causes and management of mental health in African settings through cohorts identified in the OMOP database.
• D5: Conduct advanced causal inferential analyses on the impact of community, household and environmental exposures on mental health across a federated cloud-based environment on the East Africa data web.

Repo Structure

Inspired by Cookie Cutter Data Science.

├── LICENSE
├── README.md              <- The top-level README for users of this project.
├── CODE_OF_CONDUCT.md     <- Guidelines for users and contributors of the project.
├── CONTRIBUTING.md        <- Information on how to contribute to the project.
├── CHANGES.md             <- Information on summary of changes made in this repository
│
├── images                 <- Images folder for any images to be used in the README files
│
├── assets                 <- Folder for evidence products  
│
├── data                   <- Delivarable 1: A brief description of how we obtained the datasets and
│                             how they can be accessed.  
│   ├── primary        
│   └── secondary      
│
├── project_management     <- Project planning resources
│   ├── communication
│   ├── people folder
│   ├── policies
│   ├── project planning
│   ├── project proposals
│   ├── project reports           
│   └── workshops
│
├── src                    <- Delivarable 2 - 5: Source code for use in this project.
│   │
│   ├── A. staging database   
│   │   │                 
│   │   ├── 1. staging db              <- Documentation on design and creation of staging database
│   │   ├── 2. metadata                <- Files generated for various staging db tables
│   │   ├── 3. ETL-source to staging   <- Source code on ETL of various data using the various tools.
│   │   │                                  i.e SQL, Python, Pentaho.
│   │   │   │
│   │   │   ├── population_study_id_1              
│   │   │   ├── population_study_id_2             
│   │   │   ├── population_study_id_3             
│   │   │   ├── population_study_id_4              
│   │   │   ├── population_study_id_5             
│   │   │   ├── population_study_id_6
│   │   │   ├── population_study_id_7              
│   │   │   ├── population_study_id_8             
│   │   │   ├── population_study_id_9             
│   │   │   ├── population_study_id_10              
│   │   │   ├── population_study_id_11
│   │   │   ├── population_study_id_12
│   │   │   ├── population_study_id_13               
│   │   │   └── population_study_id_14
│   │   │                                                 
│   │   ├── 4. Local staging merge to central staging     <- Merging all ETL population studies to central staging
│   │   └── 5. metabase dashboard
│   │
│   ├── B. OMOP-CDM           <- ETL from staging to OMOP
│   │   │                 
│   │   ├── 1. mapping document           <- Staging to OMOP Mapping document
│   │   ├── 2. ETL staging to OMOP        <- Source code on ETL
│   │   │   │
│   │   │   ├── Pentaho
│   │   │   ├── R
│   │   │   └── Python
│   │   │         
│   │   ├── 3. Achilles                   
│   │   └── 4. Data Quality Dashboard
│   │           
│   ├── C. ATLAS Analysis  <- Exploratory and results-oriented analysis and visualization
│   │   │
│   │   ├── 1. WSL Installation           <- Windows Subsystem for Linux (WSL) Installation
│   │   ├── 2. Docker Installation        <- Install Docker Engine on a Linux distribution i.e, Ubuntu
│   │   ├── 3. OHDSI-Broadsea_PgAdmin-Container-Atlas-Configuration          <- Install Broadsea and PgAdmin containers, then connect PgAdmin to AtlasDB
│   │   ├── 4. Restore-CDM-to-AtlasDB_Generate-Results_WebAPIConfiguration    <- Restore CDM to Atlas DB, Generate Results, and WebAPI Configuration
│   │   ├──                   
│   │   └── 
│   │           
│   ├── D. FIPS+FER4LS     <- FAIR Implementation Profiles (FIPs) and FAIR Evaluation Reports (FERs)
│   │   │
│   │   
│   └── E. Staging2Schema  <- The MH Studies Catalog      
│
└──

Requirements

Hardware

A laptop/desktop with the following specifications is recommended:

• Processor: Intel Core i5 or higher (higher core count is beneficial for parallel processing)
• RAM: 16GB (recommended for most OHDSI workloads) if you anticipate running multiple containers or handling large datasets, consider upgrading to 32GB.
• Storage: Minimum 500GB, but more is better with Solid-State Drive SSD
• Operating System: Windows 10 version 2004 and higher (Build 19041 and higher) or Windows 11

Software

Maintainers

This repository has been set up and maintained by Bylhah Mugotitsa and Reinpeter Momanyi to centralise resources used, developed and maintained under the project.

♻️ License

This work is licensed under the MIT license (code) and Creative Commons Attribution 4.0 International license (for documentation). You are free to share and adapt the material for any purpose, even commercially, as long as you provide attribution (give appropriate credit, provide a link to the license, and indicate if changes were made) in any reasonable manner, but not in any way that suggests the license or endorses you or your use and with no additional restrictions.

🤝 Acknowledgement

This repository uses the template created by Malvika and members of The Turing Way team, shared under CC-BY 4.0 for reuse: https://github.com/the-turing-way/reproducible-project-template.

Contributors ✨

Thanks goes to these wonderful people:

Emoji Type/Represents	Role	Contributor	Institution	Profile Link
💵 Financial	Financial Support	Wellcome Trust	Wellcome Trust	https://wellcome.org/
🔍 Grant Finders	Principal Investigator	Agnes Kiragga	African Population & Health Research Centre (APHRC)
🔍 Grant Finders	Principal Investigator	Jim Todd	London School of Hygiene & Tropical Medicine
📆 Project Management 🔬 Research 📖 doc 💻 code 🔣 analysis	Project management, Research, Documentation of project, ETLs & Atlas analysis	Bylhah Mugotitsa	African Population & Health Research Centre (APHRC)
🤔 Ideas 👀 Review 🔣 analysis	Ideas, review & Atlas analysis	Emma Slaymaker	London School of Hygiene & Tropical Medicine
🤔 Ideas 🎨 Design 👀 Review 🔣 Analysis	Ideas, staging database design, review & Atlas analysis	Jay Greenfield	Committee Data of the International Science Council (CODATA)
🤔 Ideas 🎨 Design 👀 Review 💻 code	Ideas, staging database design, review & ETLs	Tathagata Bhattacharjee	London School of Hygiene & Tropical Medicine
🚇 Infrastructure 📦 platform 💻 code	Build-Tools i.e staging database, visualization platform & ETLs	Dorothy Mailosi	Committee Data of the International Science Council (CODATA)
🎨 Design 🚇 Infrastructure 📦 platform 💻 code	Staging database design, Build-Tools i.e staging database, visualization platform & ETLs	Michael Ochola	African Population & Health Research Centre (APHRC)
📖 doc 💻 code	Documentation of project & ETLs	David Amadi	London School of Hygiene & Tropical Medicine
📖 doc 💻 code	Documentation of project & ETLs	Reinpeter Ondeyo	African Population & Health Research Centre (APHRC)
💻 code 🔣 analysis	ETLs & Atlas analysis	Pauline Andeso	African Population & Health Research Centre (APHRC)
💻 code	ETLs	Joseph Kuria	African Population & Health Research Centre (APHRC)

The full list of the committee members for the project is available in the people folder.