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التحصين
إجمالي برنامج التحصين الموسع للتحصين (EPI)

Expanded Programme on Immunization (EPI) - System Design Document

Background and Purpose

This document describes the design of an immunization module in DHIS2 optimized for integration with a national HMIS. The module is most widely used by countries that have integrated their Expanded Programme on Immunization (EPI) reporting into the HMIS.

The module includes standard indicators and dashboards to support core immunization program analyses recommended by the WHO's Guidance on the Analysis & Use of Health Facility Data for Immunization Program Managers. The module contains aggregate monthly data sets which can be used flexibility depending on system maturity:

  • For routine direct reporting into DHIS2 where facility data remain paper-based (data entry can be done at facility or district levels); or
  • Populated by aggregating individual-level electronic data from DHIS2 tracker or another system into the pre-defined aggregate dimensions needed to support the data analysis framework.

Package Structure

Dataset Description
Immunization Monthly dataset.
Reports immunization activities (outreach activities included), adverse events following immunization, stock, and cold chain.

Note

This data set was designed using a custom form in DHIS2, in particular to accommodate the stock section within this data set. If a section form was used, the stock section would extend beyond the width of the majority of user’s screens who do not have a high-resolution monitor, causing them to have to scroll left/right when entering data within this section.

Intended users

  • Health facility users: capture and report key data on immunization activities
  • Program managers: managers at national and sub-national level may be responsible for supporting data entry and analysis.
  • National and local health authorities: to monitor and analyze the surveillance of data through dashboards and analytics tools, to conduct risk assessments and plan response measures; to generate reports for regional and global reporting

Dataset Details

Outreach Sessions

Data entry for outreach sessions

The info for outreach sessions have been designed as individual data elements without any disaggregation, although a catCombo could be added if needed. Should the activities not include outreach sessions, the section can be removed from the dataset.

Vaccinations - Children

Data entry for vaccinated children - the screenshot does not include the full table

This section has been configured using an age disaggregation (<1, >=1) for each data element within the section.From an analytical point of view, this allows to quickly add in the age breakdowns and pivot them as required. The totals are automatically calculated. Some cells, like for example the cell for OPV 0 for children above the year of age, are blocked and grayed out to prevent data entry as per indications of the global guidelines - e.g. OPV 0 is normally administered at birth. The cells can be unblocked in case of need should the local guidelines differ from the global ones.

Vaccinations - Women of Childbearing age

Data entry for vaccinated women

This section has been configured using a disaggregation for pregnant women (Pregnant, Other). From an analytical point of view, this solution allows to quickly add in the breakdown of pregnancy status and pivot them as required. The disaggregation and the list of vaccines can and should be edited locally to better represent the contextual needs if needed.

Adverse Events Following Immunization

Data entry for reported AEFI

This basic table reports the number of AEFIs by severity. Should the AEFIs require a more detailed reporting form, the DHIS2 global packages also include an AEFI tracker that can be used and expanded for the purpose.

Analytics and Indicators

Be it for lack of reliable data or for mistakes in calculations, population data and its use in denominators often sets a difficult task.

Up until any DHIS2 version before 2.35.12 the targets and coverages were simply based on the antigen administered VS the target population through the time (months of a year) cumulatively the target population has been divided by 12 (months) as the target population was entered annually. The indicators should have been adjusted according to the periodicity of the analysis (quarterly/yearly/etc.) and the periodicity with which the target population is reported; while the coverage would have simply been based on the annualization of the indicator. The use of cumulative over-time aggregate indicators is recommended to ensure accurate calculations of coverage. This is possible using the periodOffset function in indicators in versions above 2.35.12. As a result, coverage indicators use the sum of the last 12 months as a numerator instead of annualizing the indicator. This is a more accurate way of calculating coverage indicators than annualizing them.

Here below, an example of the type of indicators that are now available to better calculate the coverage.
Indicator EPI - BCG coverage (%) - period offset
UID lnC0fWd3sD4
Numerator BCG doses < 1 year this month plus last 11 months
EPI - BCG doses given <1 year + EPI - BCG doses given <1 year.periodOffset(-1) + EPI - BCG doses given <1 year.periodOffset(-2) + EPI - BCG doses given <1 year.periodOffset(-3) + EPI - BCG doses given <1 year.periodOffset(-4) + EPI - BCG doses given <1 year.periodOffset(-5) + EPI - BCG doses given <1 year.periodOffset(-6) + EPI - BCG doses given <1 year.periodOffset(-7) + EPI - BCG doses given <1 year.periodOffset(-8) + EPI - BCG doses given <1 year.periodOffset(-9) + EPI - BCG doses given <1 year.periodOffset(-10) + EPI - BCG doses given <1 year.periodOffset(-11)
Denominator Live birth
GEN - Population live births

On the front of immunization performance, the package now contains a score assigned to the differtent OUs. The score is based on the combination of coverage and dropout rate of the antigens through the use of a logic statement in the formula (possible from 2.36 version).

Validation rules

The validation rules for the Immunization package are grouped in the “Immunization” validation rule group. The rules will run at data entry level (Run validation button) to flag any potential error in data entry.

Example of errors flagged by data validation

Dashboards

The Immunization package includes one predefined dashboard. The table below lists only the items containing notes of interest for the implementation of the package and the set-up of the dashboard.

Item Comment
Categorization of immunization performance by district Categorization of OrgUnits into four categories (1,2,3,4) depending on their performance:
- 1: Coverage > 90% / Dropout rate < 10%
- 2: Coverage > 90% / Dropout rate > 10%
- 3: Coverage < 90% / Dropout rate < 10%
- 4: Coverage < 90% / Dropout rate > 10%

Visualizations from the Immunization App

Over the past few DHIS2 releases there has been ongoing work on extending core analytics features to be able to meet the requirements that led to the development of the custom Immunization analysis. The representative visualizations of the “Immunization analysis app” present in the dashboard are:

  • EPI - Doses and coverage by antigens - months this year
  • Monitoring chart for antigens administered <1 years old child - Months this year
  • Monitoring chart for antigens administered in newborns - Months this year
  • Coverage and dropout rate categorization - last 12 months
  • Categorization of immunization performance by district - last 12 months

In the description of each visualization you can find the relevant information on the content and structure

User groups

As part of the package configuration, user groups have been created to be used to manage sharing settings in the metadata for all the modules. Core metadata that use these sharing settings include mainly the dataSets, dashboard, indicators and data Elements.

The three user groups created include:

User group Dashboard Program Metadata Program data
EPI-Admin Can edit and view Can edit and view Can view only
EPI-Access Can view only Can view only Can view only
EPI-Data capture No access Can view only Can capture and view
  • EPI-Admin - users in this group will have sharing setting with view/edit access metadata and view only access to data values in dataSets
  • EPI-Access - users in this group will have sharing setting with view only access to metadata and data values in dataSets
  • EPI-Data capture - users in this group will have sharing setting with view only access to metadata and edit access to data values in dataSets

Whereas it is important to maintain these userGroups while installing this package, feel free to review them in line with any existing userGroups setup or policy in the host instance.

Special considerations

Population data

Accurate target population estimates are vital for calculating vaccination coverage. For vaccines administered beyond the first year, the target is typically the estimate of surviving children from the birth cohort WHO, 2020

If not already in use, the dataset should be coupled with a second dataset to collect the population data necessary to calculate key indicators such as coverage and to monitor important targets for activities and/or key populations (eg, children under 5 years of age or pregnant women). Alternatively, a linkage with the Vital Events tracker or the national CRVS system could provide the updated birth cohort.

Please refer to the Indicators section for more information on the population data.

Vaccination Coverage and Age Groups

Vaccination coverage is often disaggregated by age groups, crucial for monitoring and improving immunization programs. Common disaggregation includes age ranges like 0-11 months, 12-23 months, and ≥24 months​ PAHO, 2017. Recording vaccinations accurately based on the child's age is essential. For instance, a child receiving the first dose of the MR vaccine at 15 months should be recorded as MR1 (12-23 months)​ WHO, 2020.

The WHO Handbook emphasizes the importance of immunization beyond infancy. Initially, the Expanded Programme on Immunizations (EPI) focused on children under one year of age, but it has since evolved to include new vaccines, booster doses, and vaccinations throughout the life course. As new vaccines are developed and recommended, immunization schedules should be updated accordingly to protect populations against emerging health threats and maintain immunity throughout life. Countries should adopt integrated immunization schedules that account for vaccinations from birth through adolescence and into adulthood. This approach ensures that booster doses and new vaccines are administered at appropriate ages, maintaining continuous immunity and reducing the number of zero-dose children. A lifespan approach is crucial for ensuring ongoing protection against vaccine-preventable diseases. Additionally, leveraging community-based information systems and integrating health data to track immunization status over time is essential. Monitoring systems must adapt to track vaccinations given to different age groups, ensuring comprehensive coverage across the lifespan PAHO, 2017, USAID.

Equity and Socio-economic Characteristics

Coverage surveys often measure coverage along socio-economic dimensions such as wealth, ethnicity, religion, and caretaker’s educational attainment. This helps in understanding and addressing immunization inequities​ WHO, 2020. In Latin America, electronic immunization registries and other data sources are used to disaggregate immunization data by equity dimensions, identifying sub-groups or localities with more zero-dose and under-immunized children USAID.

Timeliness (Recovered Children) Analyses

Timeliness in vaccination refers to administering vaccines according to recommended schedules, which is crucial for maximizing their effectiveness and minimizing periods when children are vulnerable to preventable diseases.Vaccination schedules outline the minimum ages for the first dose and the required intervals between subsequent doses. Adhering to these schedules is essential for maintaining continuous protection against infectious diseases. Non-timely doses, while still valid, are administered significantly later than the recommended age, potentially leaving children unprotected for longer periods.

Monitoring timeliness is also essential for effective decision-making in immunization programs. Timeliness rates, calculated as the percentage of reports submitted on time out of the expected reports, are vital for timely decision-making and reducing errors. The timeliness and completeness of data are critical for accurate monitoring and decision-making. Delayed or missing reports can lead to inaccurate coverage assessments and negatively impact program effectiveness.

Surveys and electronic immunization registries (EIR) are more effective in monitoring timeliness compared to administrative systems, which tend to aggregate vaccinations into broad age categories, obscuring detailed timing information. Definitions of timeliness can vary, but vaccinations administered within three months of the recommended age are generally considered timely.

Improving timeliness in vaccination involves ensuring that children receive vaccines at the optimal ages specified by health authorities, maximizing the vaccines' protective benefits and reducing the risk of disease outbreaks. Accurate and timely reporting is essential for assessing vaccine coverage accurately and making informed decisions to enhance program effectiveness. WHO, 2020, PAHO, 2017, GAVI

Country Examples PAHO, 2017: - In Guatemala, an analysis revealed significant variation in the timeliness of DTP1 and DTP3 doses across different regions, highlighting the need for targeted interventions to improve timely vaccination coverage - In Costa Rica, a data quality self-assessment found that improving timeliness was essential for accurate coverage data and effective immunization programs​

Use of Data for Strategy Design

Effective data use involves integrating multiple data sources, including administrative records, surveys, and population data, to triangulate information and make informed decisions​ WHO, 2020. Immunization programs can improve by analyzing administrative, surveillance, and GIS data to understand immunization inequities and design strategies to reach zero-dose and under-immunized children. For example, geospatial technology helps create accurate program targets and identifies missed communities PAHO, 2017, USAID

Country Examples: - Geospatial technology has been extensively used in Nigeria for polio eradication efforts, resulting in more accurate population estimates and identification of missed populations. This technology has shown promise in improving routine immunization through better microplanning and identifying undocumented settlements WHO, 2020 - GIS tools have been used in polio eradication efforts in Kenya, including creating digital microplans for vaccination campaigns. A targeted door-to-door mobile strategy based on GIS data increased the number of fully immunized children by 33.5% in one year in poorly performing facilities and unreached rural-urban populations​WHO, 2020 - GIS-based microplanning combined with community mobilization, strengthened data use, and logistics planning has helped double routine immunization coverage in urban settings in India. This approach shows the potential for similar strategies in other regions​ ​WHO, 2020 - Vaccine distribution planners used GIS data to respond to the Ebola virus outbreak and are exploring its use for routine immunization planning to update health zone maps and population estimates in DRC. Participatory mapping approaches involving local health workers have been implemented to inform microplanning ​WHO, 2020

Data quality assessments are crucial for ensuring the reliability of the data used in strategy design. Regular data quality checks help in maintaining accurate records and improving the overall effectiveness of immunization programs​. More information can be accessed in the DHIS2 Data Quality user guide.

References