DHIS2 Aggregate Analytics Training Course

Participants can assess and structure core metadata required for consistent aggregate reporting.

• Data elements defined by service, measure, aggregation type and reporting frequency
• Category combinations for sex, age, service modality and clinical disaggregation
• Category option combinations generated from valid disaggregation combinations
• Data sets linking data elements, periods, organisation units and data-entry forms
• Attributes and groups supporting metadata maintenance, reporting and user selection
• Naming conventions, codes and descriptions for maintainable national metadata

Participants can construct and verify indicator definitions for routine health-service monitoring.

• Indicator reference sheets specifying purpose, numerator, denominator, disaggregation and interpretation
• Numerator and denominator selection for service coverage and performance indicators
• DHIS2 indicator expressions using data elements and category option combinations
• Annualisation factors applied only where reporting frequency requires adjustment
• Program indicators distinguished from aggregate indicators and calculated values
• Formula testing against manual calculations and expected value ranges

Participants can select valid periods and organisation-unit scopes for comparable analysis.

• Period types aligned with monthly, quarterly, annual and financial reporting cycles
• Organisation-unit hierarchies representing facilities, districts, regions and national levels
• Level-based aggregation rules and their effect on district and national totals
• Closed periods and data approval stages protecting reported values
• Organisation-unit groups for ownership, facility type, geography and service delivery
• Analysis scope documented through indicator, period, population and geographic selections

Participants can trace aggregate values from source records through DHIS2 reporting structures.

• Data-entry forms configured from data sets and assigned organisation units
• Compulsory data elements and completion checks supporting timely reporting
• Reporting forms compared with facility registers, tally sheets and summary reports
• Data import templates mapped to DHIS2 identifiers and reporting periods
• Source-document verification for sampled facilities and priority data elements
• Reporting instructions preventing double counting and inconsistent service definitions

Participants can assess routine aggregate data quality using defined dimensions and measures.

• WHO Data Quality Review dimensions: completeness, timeliness, consistency and external comparison
• Reporting completeness calculated by expected reports, received reports and organisation units
• Timeliness analysis separating late submission from missing submission patterns
• Internal consistency checks between related indicators, totals and disaggregations
• Plausibility assessment using historical trends, peer facilities and expected ranges
• Data-quality findings classified by likely cause, ownership and corrective action

Participants can configure validation checks and investigate anomalous aggregate values.

• Validation rule expressions comparing totals, components, ratios and service logic
• Absolute and percentage validation results reviewed by period and organisation unit
• Min-max limits set from plausible clinical and operational reporting ranges
• Outlier detection using historical distributions, standard deviations and contextual review
• Anomaly investigation traced through submitted values, source records and correction requests
• Validation results documented in data-quality action logs and supervision plans

Participants can translate management questions into structured DHIS2 analysis specifications.

• Decision questions framed around coverage, quality, access, equity and service continuity
• Analysis plans specifying indicators, comparison groups, periods and organisational scope
• Targets, baselines and thresholds selected from approved programme documents
• Denominator risks assessed before interpreting coverage and performance measures
• Data-quality caveats stated alongside findings and recommended management actions
• Audience needs differentiated for facility teams, district managers and national programmes

Participants can prepare valid data selections and understand how DHIS2 generates aggregate analysis.

• Resource tables linking metadata relationships for analytics and data-entry functions
• Analytics tables generated for efficient aggregate indicator and data-value queries
• Analytics update schedules aligned with reporting deadlines and approval processes
• Data dimension selections combining organisation units, periods, indicators and disaggregations
• Relative periods used for recurring monthly, quarterly and annual reporting products
• Saved favourites preserving reproducible analysis settings and display choices

Participants can create pivot tables that compare aggregate health-service performance accurately.

• Pivot table dimensions arranged for period, organisation unit, indicator and disaggregation comparison
• Rows, columns and filters selected to answer a defined management question
• Data values, indicators and reporting rates distinguished within analytical tables
• Totals and subtotals reviewed for meaningful aggregation and double-counting risks
• Sorting, top-bottom ranking and display options revealing priority performance gaps
• Pivot favourites saved with clear titles, descriptions and reporting instructions

Participants can select and configure DHIS2 charts suited to health-service performance questions.

• Line charts used for monthly trends, seasonality and reporting disruption
• Bar charts comparing facilities, districts, targets and peer performance
• Stacked charts displaying service composition and disaggregated client groups
• Chart axes, labels and legends configured for accurate executive interpretation
• Reference lines applied to targets, thresholds and baseline performance values
• Trend narratives separating observed change, probable causes and verification needs

Participants can create maps that identify geographic variation and priority service gaps.

• Organisation-unit coordinates and boundary files supporting valid geographic visualisation
• Thematic maps displaying indicator values through classified colour ranges
• Equal interval, quantile and natural breaks classification compared for interpretability
• Bubble maps showing volume alongside performance, coverage or reporting completeness
• Map layers combined to compare service results with administrative boundaries
• Geographic findings translated into supervision, outreach and resource-allocation questions

Participants can design dashboards that support recurring operational and strategic decisions.

• Dashboard purpose defined by decision owner, review cycle and action required
• Visualisation selection matched to monitoring, comparison, trend and geographic questions
• Dashboard layouts prioritising key indicators, exceptions and data-quality signals
• Relative periods configured for recurring monthly and quarterly management reviews
• Text widgets documenting interpretation guidance, thresholds and data caveats
• Sharing settings applied through user groups, public access and edit permissions

Participants can analyse disaggregated aggregate data to identify inequitable service outcomes.

• Sex and age disaggregations tested for completeness and mutually exclusive categories
• Facility ownership and location groups compared for access and performance differences
• Equity questions framed around population need, service reach and outcome gaps
• Disaggregated denominators assessed for alignment with reported service populations
• Small numbers handled through suppression, aggregation and cautious interpretation
• Priority populations identified without overstating causality from routine aggregate data

Participants can analyse common service-delivery measures and explain their management implications.

• Service coverage calculated from valid service counts and target populations
• Utilisation rates interpreted against catchment population, capacity and referral patterns
• Immunisation dropout calculated between sequential doses and reporting periods
• Antenatal care continuity assessed across first contact and completion measures
• Testing, treatment and positivity indicators interpreted as linked service pathways
• Indicator triangulation identifies contradictory results requiring data-quality investigation

Participants can present evidence-based findings with clear limitations and recommended actions.

• Interpretation framework linking observed patterns, plausible explanations, evidence and actions
• Data stories structured around the management question and priority finding
• Comparisons made against targets, prior periods, peers and service benchmarks
• Confidence statements distinguish verified findings from preliminary reporting signals
• Action recommendations assigned to accountable owners, deadlines and follow-up measures
• Feedback messages tailored for facility teams, district managers and programme leaders

Participants can produce recurring DHIS2 reporting products aligned with management review cycles.

• Monthly performance packs combining data-quality, coverage and service continuity measures
• District review agendas structured around exceptions, root causes and agreed actions
• Standard report templates linking pivot tables, charts, maps and interpretation notes
• Reporting calendars aligned with submission deadlines, validation and decision meetings
• Version control applied to indicator definitions, dashboard designs and briefing products
• Action trackers connecting review findings with supervision and corrective activities

Participants can apply appropriate access and sharing controls to aggregate analytics products.

• User roles separated between data entry, approval, analysis and system administration
• Organisation-unit data capture permissions matched to reporting responsibility and hierarchy
• User groups assigned for dashboard access, favourites sharing and collaboration
• Public sharing assessed against disclosure risk, policy and intended audience
• Metadata change control documented through requests, testing, approval and release records
• Aggregate data governance responsibilities defined for quality, interpretation and publication

Participants can document and reproduce aggregate analyses for review and recurring reporting.

• CSV and spreadsheet exports prepared with metadata, filters and extraction dates
• Analysis documentation records indicator definitions, selections, exclusions and data limitations
• Saved favourites catalogued by business purpose, owner and reporting frequency
• Quality assurance checklists reviewed before dashboard publication or management distribution
• Reproducible workflows separate source extraction, calculation, visualisation and interpretation steps
• Change logs capture revised metadata, corrected data and updated analytical conclusions

Participants can build, review and present a complete DHIS2 aggregate analytics product addressing a priority health-service decision.

• Management question selected from a defined health-service performance or equity problem
• Indicator reference sheet completed with formulas, sources, periods and disaggregations
• Data-quality assessment applied using completeness, timeliness, consistency and anomaly checks
• Pivot table, chart and map configured for complementary evidence presentation
• Decision dashboard assembled with audience-specific sharing, guidance and action prompts
• Peer review tests calculations, visual clarity, caveats and recommended management actions