Global Health Informatics and Visualization

Yu Cheng Hsu, PhD

Learning Objectives

  • Understand the principles of global health informatics
    • Explore applications in healthcare
    • Identify challenges in implementation
  • Recognize key data sources for global health
  • Learn effective data presentation and visualization techniques

What is Global Health Informatics?

  • Global Health: The development, adoption, and application of an area for study, research, and practice that places a priority on improving health and achieving equity in health for all people worldwide (Koplan et al. 2009).

Scope of global health informatics

Scope of global health informatics

Applications of Global Health Informatics

  • Disease Surveillance:
    • Communicable disease monitoring
    • Prediction
  • Enhancing access and equity in healthcare delivery:
    • eHR
    • mHealth
    • Telemedicine
  • Policy advocacy and evaluation

Enhancing access and equity in healthcare delivery

  • Enhancing healthcare access for LMICs and rural populations
  • Health education
  • Personalized monitoring

Enhancing access and equity in healthcare delivery

HIV education app in Kenya

HIV education app in Kenya

HIV education in Kenya

HIV education in Kenya
  • Non-Prep HIV prevention in Kenya (Mwaisaka et al. 2021): Improving on
    • Enhanced knowledge
    • Self-efficacy
    • Behavioral intention

Mental health promotion app in India

Mental health promotion app in India

Disease surveillance

  • Enhancing information transparency
  • Evidence foundation
  • Monitoring disease transmission
  • AI prediction of future trends

Before we go through example

  • Have you tried visiting surveillance tools?
    • What kind of information do you usually look for?
    • How will you use these data?

Disease surveillance

COVID dashboard

COVID dashboard

COVID dashboard

COVID dashboard
  • Dashboard for COVID-19 (Everts 2020):
    • Intervention
    • Surveillance
  • Arguments:
    • Nationalism
  • Neglecting social determinants of health

HIV dashboard in Kenya

HIV dashboard in Kenya
  • Dashboard for HIV in Kenya (Gesicho and Babic 2022):
    • Leveraging DHIS2 system (WHO system) for integrating reporting and surveillance

Decision support and policy

Data-driven intervention and policy advocacy

Data-driven intervention and policy advocacy

Data-driven intervention and policy advocacy

Data-driven intervention and policy advocacy

Challenges in Global Health Informatics

Challenges in global health informatics

Change management framework

Change management framework

Getting Global Data

Role of the data in the global health context

  • Detect disease outbreaks
  • Monitor health trends
  • Improve service coverage
  • Track risk factors
  • Guide evidence-based actions
  • Inform and evaluate public health policies

Fantastic data and where to find them

Measuring dimensions

  • Health economics
  • Mortality and life expectancy
  • Global issues
  • Social determinants of health
  • Child and women’s health
    • Infectious disease

Global Burden of Disease (GBD)

  • GBD dataset focuses on health loss across:
    • Diseases
    • Places
    • Time
  • Additional items:
    • Demographic
    • Fertility
    • Life expectancy
    • Socio-Demographic Index
    • DALY/HLY

Global burden of disease

Global burden of disease

Global Health Expenditure Database (GHED)

  • Tracking and analyzing health spending trends
  • Assessing health expenditure by different levels:
    • Sources (e.g., government, households, donors)
    • Financing mechanisms (compulsory vs. voluntary)
    • Country-wise comparison

GHED

GHED

Global Health Observatory (GHO)

Global Health Observatory is the data hub from WHO aggregating all the data under the WHO umbrella

Global Health Observatory (GHO)

Global Health Observatory (GHO)

Multiple Income Cluster Survey (MICS)

MICS is a survey under UNICEF focusing on women and children in LMIC:

  • Child health
  • Child development
  • Literacy
  • Protection
  • HIV
  • Well-being

MICS surveying

MICS surveying

Challenges in global health data

Discussion

  • Is WHO data reliable?
  • Will there be any potential bias in global data?

How does WHO get the data

  • Survey
  • Liaise with local government officials

Implication on global health data quality

Integrating data worldwide

Collecting and maintaining data worldwide is challenging Resources:

  • Standard coding (ICD-9/ICD-10/ICD-11/DSM-V)
  • Deidentification

Cultural context and bias in the data

Items with strong cultural stigma or illegal behavior are usually underreported or missing

  • Drug overuse
  • AIDS
  • Mental health

Presenting Health Data

Principle of visualizing data

  • Effective: Information conveyed by one visualization is more readily perceived
  • Expressiveness: The visualizations express all the facts in the set of data, and only the facts in the data

Triangle of visualization

Triangle of visualization

Principle of visualizing data

Visualization cheatsheet

Visualization cheatsheet

Mackinlay’s Ranking

Motivations

  • When more than two variables need to be presented (hopefully in one graph):
    • What is the best way to present them?
    • What are the pre-attentive features (easily perceived by humans)?

Mackinlay’s paper

Mackinlay’s paper

Pre-attentive features

  • Location
  • Colour:
    • Hue
    • Saturation
  • Shape:
    • Length
    • Shape
    • Texture
    • Area

Color guide

Color guide

Combinations of pre-attentive features are not pre-attentive, e.g., Red triangle vs. Green rectangle

Mackinlay’s Ranking

  • Mackinlay developed the ranking to choose the best way to present data
  • Rule of thumb: Don’t go to ranking below 5

Mackinlay’s Ranking

Mackinlay’s Ranking

Social and cultural context consideration

  • Color-blindness friendly Source from Wong (2011) Source from Wong (2011)

Cultural context:

  • Blue and red:
    • Sex
    • Severity
    • Politics
  • Red and green:
    • Chinese and Western stock market

Example

Source

Source

Example

Source

Source

Example

Source

Source

Example

Source

Source

Example

Source

Source

Bibliography

Everts, Jonathan. 2020. “The Dashboard Pandemic.” Dialogues in Human Geography 10 (2): 260–64. https://doi.org/10.1177/2043820620935355.
Gesicho, Milka, and Ankica Babic. 2022. “Designing a Dashboard for HIV-Data Reporting Performance by Facilities: Case Study of Kenya.” In Advances in Informatics, Management and Technology in Healthcare, 238–41. IOS Press.
Gonsalves, Pattie P, Eleanor S Hodgson, Avinash Kumar, Tiara Aurora, Yash Chandak, Rhea Sharma, Daniel Michelson, and Vikram Patel. 2019. “Design and Development of the ‘POD Adventures’ Smartphone Game: A Blended Problem-Solving Intervention for Adolescent Mental Health in India.” Frontiers in Public Health 7: 238.
Koplan, Jeffrey P, T Christopher Bond, Michael H Merson, K Srinath Reddy, Mario Henry Rodriguez, Nelson K Sewankambo, and Judith N Wasserheit. 2009. “Towards a Common Definition of Global Health.” The Lancet 373 (9679): 1993–95.
Mwaisaka, Jefferson, Lianne Gonsalves, Mary Thiongo, Michael Waithaka, Hellen Sidha, Otieno Alfred, Carol Mukiira, Peter Gichangi, et al. 2021. “Young People’s Experiences Using an on-Demand Mobile Health Sexual and Reproductive Health Text Message Intervention in Kenya: Qualitative Study.” JMIR mHealth and uHealth 9 (1): e19109.
“National Information Center on Health Services Research; Health Care Technology (NICHSR) - Hsric.nlm.nih.gov.” https://hsric.nlm.nih.gov/hsric_public/topic/informatics.
Wigand, Dianne. 2007. “Building on Leavitt’s Diamond Model of Organizations: The Organizational Interaction Diamond Model and the Impact of Information Technology on Structure, People, and Tasks.” AMCIS 2007 Proceedings, 287.
Wong, Bang. 2011. “Points of View: Color Blindness.” Nature Publishing Group US New York.