DHIS2 Monitoring Data Analysis for Health Programmes Training Course

Course overview

Health programmes frequently collect large volumes of service-delivery data in DHIS2 but struggle to turn monthly submissions into defensible management decisions. Incomplete facility reporting, inconsistent indicator interpretation, late data entry, denominator problems and unexamined outliers can distort performance reviews and donor reports. Programme teams need to know not only how to retrieve figures, but how to test whether they are credible, interpret changes against targets and equity questions, and present findings that prompt action at district, facility and national level.

This course builds practical capability in analysing routine monitoring data in DHIS2 for health programmes. Participants work with data elements, indicators, organisational-unit hierarchies, reporting rates, completeness measures and disaggregated results. They use DHIS2 Pivot Tables, Data Visualizer, dashboards, maps and the Data Quality App to investigate trends, compare locations, identify outliers and assess data quality. The course also covers indicator interpretation, target tracking, denominator checks, data triangulation with external sources, and the production of clear charts, tables and narrative findings for performance review meetings.

Delivery combines instructor demonstrations in a DHIS2 training environment with guided analysis of a realistic health programme dataset. Participants complete daily exercises that mirror common monitoring tasks: checking reporting completeness, analysing coverage and service trends, investigating poor-performing districts, and building management-ready visualisations. By the final day, each participant produces a DHIS2 monitoring analysis pack containing a data-quality review, selected indicator analyses, dashboard specifications, priority findings and recommended follow-up actions.

The course is designed for professionals who already work with routine health information and need stronger analytical control over the DHIS2 data used for programme monitoring, supervision, planning and reporting.

Course objectives

By the end of this course, participants will be able to:

• Configure DHIS2 Pivot Tables and Data Visualizer outputs for routine health programme indicators
• Calculate and interpret reporting completeness, reporting timeliness and facility submission rates
• Assess indicator numerators, denominators, disaggregations and target definitions before drawing conclusions
• Detect outliers, missing values, duplicate patterns and improbable trends using the DHIS2 Data Quality App
• Analyse time trends, geographic variation and subgroup disparities through DHIS2 charts and thematic maps
• Triangulate DHIS2 results with population estimates, supervision findings and selected external datasets
• Build a role-specific DHIS2 dashboard for district or programme performance review
• Produce a monitoring analysis pack with documented findings, caveats, actions and data-quality follow-up questions

Benefits of attending

Target competencies

Who should attend

• Monitoring and Evaluation Officers — who must convert routine DHIS2 data into credible programme performance evidence
• Health Programme Managers — who need to identify underperforming locations and direct corrective action
• HMIS Officers — who manage reporting workflows and investigate data-quality issues in DHIS2
• District Health Management Teams — who review facility submissions, coverage trends and local service gaps
• Data Analysts — who prepare health-sector dashboards, reports and visualisations for decision-makers
• Donor-Funded Programme Coordinators — who must validate results before submitting indicator reports to partners

Requirements and prerequisites

Participants should be comfortable navigating a DHIS2 instance, selecting organisational units and periods, and recognising common health-service indicators such as ANC visits, immunisation doses, outpatient attendance or stock-out rates. They should understand the difference between a numerator, denominator and percentage, and be able to work with basic spreadsheets, including sorting, filtering and simple formulas in Microsoft Excel. Previous experience entering data is useful but not essential. DHIS2 system-administration, SQL, programming, advanced statistics and dashboard configuration experience are not required. Participants should bring examples of indicators or reporting questions from their own programme where permitted.

Training methodology

The instructor uses a configured DHIS2 training database containing realistic facility, district and programme data. Short demonstrations introduce each analytical feature, followed by individual tasks in Pivot Tables, Data Visualizer, Maps, Dashboards and the Data Quality App. Participants work in small groups on a district performance-review case, comparing interpretations and challenging unsupported conclusions. Facilitated debriefs connect each output to programme decisions, supervision and reporting. The final session is an application-planning workshop in which participants adapt a monitoring analysis pack and dashboard plan to their own reporting cycle.

Course outline

Day 1: DHIS2 monitoring architecture and indicator logic

• Routine health monitoring questions and decision-use cases
• DHIS2 data elements, indicators and program indicators
• Organisation-unit hierarchies and facility reporting structures
• Periods, frequency and reporting-cycle alignment
• Numerators, denominators, targets and indicator interpretation
• Disaggregations by age, sex, service point and geography
• DHIS2 Analytics app navigation and data-selection rules

Workshop: Participants map a programme question to the required DHIS2 indicator, organisational units, period, disaggregation and interpretation caveats in an indicator analysis worksheet.

Day 2: Data quality assessment before analysis

• Reporting completeness and timeliness metrics
• Expected reporting rates and facility reporting denominators
• DHIS2 Data Quality App validation rules
• Outlier detection using minimum-maximum and standard-deviation checks
• Missing values, zero values and null-value interpretation
• Cross-checking numerator-denominator relationships
• Data-quality investigation logs and escalation questions

Workshop: Participants run a data-quality review for a selected district, identify priority anomalies and produce an investigation log for district follow-up.

Day 3: Trend, comparison and equity analysis

• DHIS2 Pivot Table configuration and export options
• Time-series analysis for service-utilisation indicators
• District and facility comparison using rates and absolute counts
• Coverage analysis and denominator plausibility checks
• Seasonality, campaign effects and interpretation of abrupt change
• Disaggregation analysis for age, sex and service-delivery inequalities
• Triangulation with population projections and supervision evidence

Workshop: Participants analyse twelve months of maternal and child health data, then prepare a comparison table identifying three findings that require verification or action.

Day 4: Visualisation and performance-review reporting

• DHIS2 Data Visualizer chart selection and configuration
• Thematic maps for geographic performance variation
• Chart titles, axis choices and annotation for non-technical audiences
• Dashboard design for national, district and facility users
• Target lines, thresholds and traffic-light interpretation risks
• Exporting tables and charts for Excel and Power BI workflows
• Writing evidence-based findings, caveats and management actions

Workshop: Participants build a district performance-review dashboard with a map, trend chart, data-quality view and action-oriented narrative note.

Day 5: Applied DHIS2 monitoring analysis

• End-to-end analysis workflow from question to action
• Prioritising signals by programme risk and decision urgency
• WHO Data Quality Review Toolkit dimensions
• Reconciling DHIS2 findings with external reports and field intelligence
• Facilitating data review meetings and documenting decisions
• Monitoring action completion and feedback to reporting facilities
• Personal monitoring analysis pack and dashboard implementation plan

Workshop: Participants complete and present a DHIS2 monitoring analysis pack containing data-quality findings, indicator results, dashboard specifications and a 90-day follow-up action plan.

Tools & standards covered

DHIS2, Microsoft Excel, Microsoft Power BI, WHO Data Quality Review Toolkit

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions