Framework Overview
This page summarizes the CD2030 DataSuite analytical framework. It is intended to help teams move from raw facility and survey data to defensible national and sub-national coverage findings.
End-to-end workflow
The framework follows a practical sequence:
- Load and configure facility and survey data.
- Calibrate key national benchmark rates.
- Assess data quality before estimating coverage.
- Adjust incomplete, unstable, or unreliable data.
- Select the best-performing denominator.
- Generate and interprete coverage , equity, sub-national, mortality, Service utilisation and Health system performance results.
Framework modules
Data Quality Assessment
Review reporting completeness, outliers, missingness, internal consistency, and annual quality scores.
Data Adjustment
Remove unreliable periods, apply adjustment factors, and compare values before and after cleaning.
Denominator Selection
Compare DHIS2, UN, ANC1-derived, and Penta1-derived denominator options.
Coverage Estimation
Estimate levels and trends, compare with survey benchmarks, and review progress toward targets.
Equity Analysis
Assess inequality by residence, wealth, and maternal education using survey-based comparisons.
Sub-National Analysis
Examine regional and district variation, target attainment, and geographic inequality.
The final purpose of the framework is practical use. Findings should inform country review processes, planning discussions, and technical working groups so that the analysis leads to concrete RMNCAH action rather than standing alone as a report.