Coping With Big Health Data Challenges
1. Missing Data
Accept that missing data are common due to paper records, displacement, and weak follow-up.
Define minimum essential data for key programs (NCDs, TB, MCH).
Use simple methods always and apply complex, multiple imputation, and mixed models only when needed.
Improve data collection using simple digital tools (DHIS2, Kobo, ODK).
2. Selection Bias
Large datasets might not be representative of the whole population due to selection bias.
Clearly state who is included and excluded in analyses.
Use triangulation (routine data + surveys + qualitative data).
Treat findings as hypothesis-generating, not practice-changing.
3. Data Analysis & Skills
Limited local skills are a major challenge.
Train clinicians and public health staff in basic data analysis and interpretation.
Use standard analysis templates to reduce errors.
Avoid advanced AI or machine learning unless data quality is strong.
4. Interpretation & Use of Results
Translate results into simple, actionable messages for decision-makers.
Involve program staff in interpreting findings.
Use big data to identify trends and gaps, not to dictate clinical care.
5. Privacy & Ethics
Build trust through clear explanations of how data are used.
Collect only necessary data and limit access.
Use broad consent models suitable for conflict-affected settings.