Course Introduction
Noncommunicable disease programmes generate extensive data across prevention, screening, diagnosis, treatment, follow-up, chronic disease management, referral, risk-factor surveillance, medicine availability, and health-system performance. Noncommunicable Disease Programme Data Analytics Training Course equips participants with practical skills to transform these datasets into actionable programme intelligence for planning, resource allocation, service improvement, population health monitoring, and evidence-based management of chronic disease programmes.
Effective NCD analytics requires understanding how information is generated across primary healthcare, hospitals, specialist services, community programmes, laboratories, pharmacies, screening campaigns, registries, surveys, and health information systems. Participants will examine data relating to conditions such as cardiovascular disease, diabetes, cancer, chronic respiratory diseases, and other priority NCDs, alongside behavioural and metabolic risk factors. Particular attention is given to data quality, completeness, timeliness, consistency, disease classification, denominators, duplicate records, linkage challenges, and integration of routine and population-level information.
The course develops practical approaches for analysing NCD screening, diagnosis, treatment, retention, disease control, complications, referrals, mortality, and service utilization. Participants will learn how to calculate and interpret indicators covering screening coverage, positivity, diagnosis, treatment initiation, treatment continuity, follow-up, control rates, missed appointments, hospital utilization, and programme reach. They will explore trends and disaggregated data to identify service gaps, population disparities, changing disease burdens, treatment bottlenecks, and opportunities for strengthening chronic disease management.
Modern NCD programmes increasingly require dashboards, risk stratification, geographic analysis, patient cohort monitoring, forecasting, and integrated information systems. The course therefore introduces spreadsheet analytics, databases, business intelligence, GIS, data visualization, cohort analysis, trend analysis, and performance dashboards relevant to chronic disease programmes. Participants will learn how analytical outputs can support screening strategies, medicine planning, workforce allocation, referral management, community outreach, programme budgeting, performance reviews, and integration of NCD services into primary healthcare.
Emerging technologies are creating new possibilities through artificial intelligence, predictive analytics, digital health platforms, electronic medical records, remote monitoring, mobile health applications, wearable devices, automated risk assessment, and population health intelligence. Participants will examine these developments while considering privacy, cybersecurity, data governance, algorithmic bias, interoperability, digital exclusion, and responsible use of health information. The course also considers how ageing populations, urbanization, socioeconomic conditions, climate-related pressures, lifestyle changes, and disruptions to health services can influence NCD programme demand and performance.
By the end of the course, participants will be equipped to transform NCD programme data into clear and actionable management intelligence. They will be able to assess data quality, analyse disease and risk-factor trends, monitor screening and treatment cascades, evaluate service coverage and equity, assess programme performance, develop dashboards, and communicate evidence-based recommendations. The practical orientation enables participants to apply analytics to real-world NCD programme challenges and strengthen planning, resource allocation, chronic care management, prevention strategies, and continuous performance improvement.
Duration
5 days
Who Should Attend
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Noncommunicable disease programme managers and coordinators
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NCD technical officers and public health specialists
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Chronic disease programme managers
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Primary healthcare programme managers
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Hospital and clinical service managers
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Epidemiologists and public health analysts
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Monitoring and evaluation specialists
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Health information and data management officers
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Diabetes and cardiovascular disease programme teams
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Cancer prevention and control programme officers
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Chronic respiratory disease programme personnel
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Community health and NCD prevention coordinators
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Health data analysts and business intelligence professionals
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NGO and development-partner NCD programme staff
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Health planning, quality improvement, and performance management professionals
Course Objectives
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Develop practical capabilities for collecting, managing, analysing, and interpreting NCD programme data from clinical, community, facility, population, laboratory, pharmacy, and health information sources.
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Apply appropriate analytical methods to assess NCD screening, diagnosis, treatment initiation, treatment continuity, disease control, follow-up, referral, and service utilization.
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Analyse trends in NCD burden and programme performance to identify emerging disease patterns, service gaps, population disparities, and changing healthcare demands.
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Strengthen the ability to assess NCD data quality, completeness, timeliness, consistency, disease classification, denominators, duplication, and reporting reliability.
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Use spreadsheets, databases, GIS, dashboards, and business intelligence tools to transform NCD datasets into practical programme planning and performance intelligence.
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Conduct geographic, demographic, and equity-focused analysis to identify underserved populations, high-risk communities, service access gaps, and opportunities for targeted NCD interventions.
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Analyse screening, treatment, medicine availability, workforce, referral, and facility capacity information to support efficient chronic disease programme planning and resource allocation.
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Explore artificial intelligence, predictive analytics, digital health, remote monitoring, mobile technologies, and other emerging tools relevant to NCD prevention and programme management.
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Strengthen responsible management of NCD information through privacy, cybersecurity, data governance, ethical analytics, interoperability, appropriate disaggregation, and controlled use of sensitive health data.
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Develop integrated NCD analytics products that translate complex programme data into meaningful dashboards, performance insights, planning scenarios, and actionable recommendations.
Comprehensive Course Outline
Module 1: Foundations of Noncommunicable Disease Programme Data Analytics
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Understanding the role of data analytics in NCD prevention, screening, treatment, chronic care, programme planning, resource allocation, and health-system decision-making.
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Exploring major NCD programme indicators covering cardiovascular disease, diabetes, cancer, chronic respiratory disease, risk factors, service coverage, treatment, and disease control.
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Mapping NCD data flows across community services, primary healthcare, hospitals, specialist facilities, laboratories, pharmacies, registries, surveys, and population health information systems.
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Examining how programme intelligence supports prevention strategies, service integration, chronic disease management, budgeting, performance monitoring, and evidence-based programme improvement.
Module 2: NCD Data Collection, Quality, Integration, and Management
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Identifying routine and population-level NCD data sources including clinical records, screening registers, electronic systems, laboratory records, pharmacy information, surveys, registries, and community datasets.
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Assessing data completeness, accuracy, consistency, timeliness, duplication, missing information, classification quality, denominator reliability, and reporting variation.
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Applying data cleaning, validation, standardization, reconciliation, deduplication, aggregation, disaggregation, and documentation techniques to NCD datasets.
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Exploring interoperability and integration between NCD information systems, electronic medical records, laboratory platforms, pharmacy systems, registries, community health systems, and national health information platforms.
Module 3: Spreadsheet, Database, GIS, and Business Intelligence Analytics
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Using spreadsheets for NCD data preparation, indicator calculation, cohort analysis, trend analysis, pivot analysis, data-quality checks, and routine reporting.
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Developing structured databases and analytical datasets for managing screening records, patient cohorts, diagnoses, treatment information, referrals, and programme indicators.
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Applying GIS and spatial analysis to visualize NCD service availability, disease patterns, risk-factor distribution, population density, and geographic coverage gaps.
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Designing business intelligence dashboards that communicate screening, treatment, disease control, service utilization, equity, and programme performance indicators.
Module 4: NCD Screening, Diagnosis, Treatment, and Care Cascade Analytics
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Analysing screening coverage, screening uptake, positive findings, diagnostic confirmation, treatment initiation, and progression through the NCD care cascade.
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Examining patient cohorts to understand treatment continuity, follow-up attendance, missed appointments, loss to follow-up, and long-term engagement with chronic care.
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Assessing disease control indicators for conditions such as hypertension and diabetes while identifying differences across facilities, populations, service levels, and time periods.
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Using cascade and cohort analytics to identify bottlenecks and opportunities for improving early detection, treatment access, continuity of care, and chronic disease outcomes.
Module 5: NCD Risk Factors, Population Health, and Prevention Analytics
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Analysing behavioural and metabolic risk-factor information including tobacco use, harmful alcohol consumption, physical inactivity, unhealthy diets, obesity, hypertension, and related risk patterns.
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Integrating population, survey, clinical, and routine programme data to understand relationships between risk-factor patterns and NCD service demand.
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Identifying high-risk population groups and communities to support targeted prevention, screening, health promotion, community engagement, and early intervention strategies.
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Developing risk-factor dashboards and analytical profiles that support population health planning, prevention programming, advocacy, and resource prioritization.
Module 6: Medicines, Workforce, Referral, and NCD Service Performance Analytics
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Analysing essential NCD medicine availability, stock-outs, consumption, prescribing, dispensing, treatment continuity, and supply-related barriers to chronic disease management.
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Assessing NCD workforce availability, workload, productivity, skill mix, service capacity, training requirements, and geographic distribution across levels of care.
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Analysing referral pathways between community, primary, secondary, and specialist services to identify delays, incomplete referrals, capacity constraints, and coordination gaps.
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Developing integrated service performance indicators covering access, utilization, medicine availability, workforce capacity, referral efficiency, quality, and continuity of NCD care.
Module 7: Geographic, Equity, Disease Burden, and Programme Performance Analytics
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Applying geographic analysis to identify NCD burden patterns, service coverage gaps, underserved populations, referral barriers, and communities requiring intensified interventions.
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Conducting equity analysis across age, sex, socioeconomic circumstances, geography, disability, urban and rural populations, and other relevant demographic characteristics.
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Analysing disease burden, hospitalization, complications, mortality, and service utilization trends to identify emerging programme priorities and health-system pressures.
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Developing performance scorecards, exception reports, and analytical review processes that support programme supervision, accountability, resource allocation, and continuous improvement.
Module 8: AI, Predictive Analytics, Digital Health, and Emerging NCD Technologies
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Exploring artificial intelligence and machine learning applications for NCD risk stratification, demand forecasting, patient follow-up, resource allocation, and identification of emerging programme risks.
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Examining electronic chronic care platforms, mobile health applications, telehealth, remote monitoring, wearable devices, digital screening, and automated patient reminders.
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Applying predictive analytics to anticipate disease service demand, treatment interruptions, medicine requirements, workforce needs, and potential pressure on healthcare facilities.
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Assessing emerging technologies while considering privacy, algorithmic bias, explainability, digital exclusion, interoperability, cybersecurity, clinical oversight, and responsible implementation.
Module 9: NCD Data Governance, Privacy, Cybersecurity, and Responsible Analytics
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Understanding governance requirements for NCD information covering data ownership, stewardship, access rights, documentation, retention, information sharing, and analytical accountability.
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Applying privacy and confidentiality principles when handling patient identifiers, diagnoses, treatment records, laboratory results, risk profiles, and linked chronic disease datasets.
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Identifying cybersecurity risks affecting electronic medical records, NCD registries, mobile applications, dashboards, remote monitoring systems, and integrated health platforms.
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Promoting responsible analytics through appropriate consent, de-identification, data minimization, bias awareness, transparent methods, controlled disclosure, and careful communication of findings.
Module 10: Integrated Noncommunicable Disease Programme Analytics Project
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Developing an end-to-end NCD analytics workflow covering data preparation, quality assessment, indicator development, cohort analysis, visualization, interpretation, and programme reporting.
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Building a practical NCD dashboard covering screening, diagnosis, treatment, disease control, risk factors, medicine availability, referrals, service utilization, and selected equity indicators.
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Conducting an integrated NCD programme performance assessment to identify priority populations, service bottlenecks, capacity gaps, emerging risks, and evidence-based improvement opportunities.
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Presenting analytical findings and programme recommendations to NCD managers and health-system decision-makers using concise narratives, meaningful visualizations, and actionable intelligence.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
