Using Data Analytics to Strengthen Monitoring and Surveillance of

Routine Immunization Coverage for Children under One Year in

Uganda

Bartha Alexandra Nantongo, Josephine Nabukenya and Peter Nabende

Department of Information System, College of Computing and Information Systems, Makerere University,

P.O. Box 7062 Kampala, Uganda

Keywords: Data Analytics, Monitoring, Routine Immunization, Coverage, Performance.

Abstract: Immunization coverage is a traditional key performance indicator that enables stakeholders to monitor child

health, investigate gaps, and take remedial actions. It is continuously challenged by validity due to the neglect

of unstructured data and process indicators that track small changes/milestones. While empirical evidence

indicates digitalized immunization systems establish coverage from structured data, renowned administrative

and household survey estimates are often inaccurate/untimely. Government instituted awareness,

accessibility, and results-based performance approaches, but stakeholders are challenged by accurate

monitoring of performance against Global Vaccination Action Plan coverage targets. This heightens

inappropriate strategy implementation leading to persistent low coverage and declining trends. There is scanty

literature substantiating the essence of comprehensive immunization indicators in monitoring evidence-based

and timely interventions. For this reason, health workers failed to appreciate immunization process indicators

and monitoring role. The study aims at developing a real-time immunization coverage monitoring framework

that supports evidence-based strategy implementation using prescriptive analytics. The envisaged artifact

analyzes a variety of data and monitors immunization performance against comprehensive indicators. It is a

less resource-demanding strategy that prompts accurate and real-time insights to support intervention

implementation decisions. This study will follow an explanatory research approach by first collecting

quantitative data and later qualitative for in-depth analysis.

1 INTRODUCTION

Strong evidence indicates that big data analytics have

a positive impact on organization performance

(Bogdan and Borza, 2019), decision enhancing, and

agility gains (Ghasemaghaei, et al., 2017). In

healthcare, big data refers to structured, semi-

structured, and unstructured electronic health data

sets that are complex and difficult to manage with

classical software, data management tools, or

internet-based platforms. (Raghupathi and

Raghupathi, 2014; Dash et al., 2019). The healthcare

industry generates enormous data, scaling from

clinical decisions, patient-care, compliance, and

regulatory requirements (SoleimaniRoozbahani, et

al., 2019; Liang and Kelemen, 2016). The United

States health system alone by 2011 had generated 150

exabytes soon reaching yottabyte (Raghupathi and

Raghupathi, 2014; SoleimaniRoozbahani et al.,

2019). A substantial amount of data in the health

industry are stored in hard copy form, however, rapid

data digitization trend, for example, Digital Health

Management System (DHIS2) in Uganda, Electronic

Immunization Registries (EIR) piloted in Zambia,

Tanzania (Dolan, et al., 2020; Villagereach, 2020),

Immunization Information Systems (IIS) in Mexico

(Derrough, et al,, 2017), and Online Real-Time

Immunization System (OTRIS) among others,

generate volumes of data that can facilitate extracting

useful insights to support decision making for

efficient operations. Big data in healthcare is

overwhelming not only because of its volume but also

because of the diversity of data types and speed at

which it must be managed (Dash et al., 2019;

Palanisamy and Thirunavukarasu, 2017).



In public health, big data encompasses patient

information gathered from electronic health records

and participatory surveillance systems, as well as

mining of digital traces like social media and internet

Nantongo, B., Nabukenya, J. and Nabende, P.

Using Data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda.

DOI: 10.5220/0010301805490556

In Proceedings of the 14th Inter national Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 549-556

ISBN: 978-989-758-490-9

549

searches (Bansal et al., 2016). Driven by the potential

to improve quality of healthcare delivery while

reducing costs, these massive quantities of data hold

the promise of supporting a wide range of healthcare

functions such as clinical decisions, gaining valuable

insights in monitoring and surveillance of disease

prevention strategies like immunization.

(SoleimaniRoozbahani et al., 2019).



Immunization is a cost-effective public health

intervention guaranteeing the safety of children

against preventable diseases and a right for every

child (Karami et al., 2019; Wariri et al., 2019). It has

numerous activities grouped into service delivery,

programme management, surveillance and

monitoring, advocacy and communication, vaccine

supply, quality and logistics components

(WHO/IVB/08.05, 2008). Immunization deters 2–3

million deaths yearly and an additional 1.5 million

could be avoided if immunization coverage is

improved worldwide (Bhatti, et al., 2017).

Immunization coverage (IC) is a cornerstone of

Primary Health Care and a key immunization

programme performance indicator that shows how far

a country is from preventable disease outbreaks

(Roux, et al., 2017; Derrough, et al., 2017). Coverage

is calculated as the percentage of persons in the target

age group who received a particular vaccine dose by

a specified age and is globally the most closely

followed indicator annually tracked (Sodha & Dietz,

2015). Indicators like the third dose of diphtheria-

tetanus-pertussis (DTP3) coverage is used to measure

the strength & reach of routine immunization (RI)

(Mihigo et al., 2016). RI ensures that children below

one year receive recommended vaccination on time,

at the right age, in accordance to the recommended

schedule by age, gender and with ease of access to the

point of vaccination (MOH-UG, 2017).



It is important to note that, the 194 GVAP

member states including Uganda have instituted

accessibility and awareness strategies like free

immunization, outreaches, traditional leader

engagement, mass campaigns among others to

increase IC (Bhatti, et al., 2017; Malande, et al.,

2019). Additionally, development partners like

GAVI, global fund, master card, Orange, have

subsidized vaccine costs and supported private health

facilities to ease community service accessibility

(Villagereach, 2020). Similarly, technological

strategies like IIS monitor vaccine coverage and

effectiveness (Derrough, et al., 2017); EIR are

capable of child enrolment at birth, unique

identifition, structured data aggregation, among

others (Dolan, et al., 2020; Villagereach, 2020).



However, an estimated 19.7 million children

under the age of one year never received basic

vaccines according to the global immunization

coverage 2019. Correspondingly, Uganda at 73%

DPT3, 88% BCG (WHO/UNICEF, 2020) and 55% of

fully immunized children coverage (MOH-UG, 2017)

lag below the GVAP 90% national coverage target by

2020 (Mihigo et al., 2016). Awareness/accessibility

strategies target caretakers while digital interventions

known to reduce health worker data burden are a

replicate of the current paper-based system (Dolan, et

al., 2020; Villagereach, 2020) aggregating structured

data only. Currently, government and development

partners are concerned about Uganda’s immunization

declining trends (WHO/UNICEF, 2020) as illustrated.

Figure 1: Current immunization coverage estimates.

The achieved IC gains were reversed due to reported

high infant mortality rate attributed to Vaccine-

Preventable Diseases -VPD (MOH-UG, 2017) and

sporadic measles and rubella 2018 outbreaks in 55

and 13 districts respectively (Opendi, 2018). In fact,

100% IC is still the target to avoid further mortality

(Bhatti, et al., 2017) which is hard to achieve without

constant monitoring.

Monitoring is a systematic and continuous

process of examining data, procedures and practices

to measure progress, identify problems, develop

solutions, and guide policies. It is an important tool

for mid-level managers to improve the quality of the

immunization programme by ensuring that: (1) all

infants are immunized; (2) vaccines and safe injection

equipment are delivered in correct quantities and on

time; (3) staff are well trained and adequately

supervised; (4) information on disease incidence and

adverse events following immunization (AEFI) are

collected and analyzed, (5) community has

confidence in the vaccines delivered and

immunization service they receive (WHO/IVB/08.05,

2008).



BCG

DPT3

100

2016 2017 2018 2019

BCG

94 85 88 88

DPT1

99 99 95 91

DPT3

93 94 79 73

Coverage

Ugandaimmunisationcoverageestimates

2016‐ 2019byWHO/UNICEFasper1stJuly

2020

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550

African ministers collectively and individually

committed themselves to monitor progress towards

achieving the goal of the global and regional

immunization plans having recognized that Africa

despite the progress, was largely off track compared

to the 2020 90% target (Desalegn, 2016). At national

and subnational levels, monitoring and assessment of

coverage rates are critical for countries to prioritize

and customize strategies or operation plans to address

immunization gaps and reach life-saving vaccine to

every child (Dicko, 2020; Derrough, et al., 2017).



However, IC validity is continuously questionable

as different tools report differing coverage for the

same population and time (Liu, et al., 2017; Murray,

et al., 2018). Known coverage determination methods

of administrative overestimate or underestimate

coverage while surveys are costly and provide

untimely information to guide programmes (Sodha &

Dietz, 2015. This is attributed to low-quality data.

Surveillance and monitoring data is largely captured

passively from structured DHMIS2. These traditional

passive monthly reports do not capture much

information on the advocacy and communication, and

programme-management components of the

immunization system (WHO/IVB/08.05, 2008.

Active supervision like physical supportive visits is

the instituted way to collect uncaptured data. It is

known to increase reporting of measles, rubella, and

hepatitis in demonstration projects but is generally

too expensive to be performed routinely (Roush,

2017), notorious for severe time lags and challenged

when aggregating variety of data (Bansal et al., 2016).

WHO asserts that monitoring requires a combination

of passive and active data collection measured against

indicators.



“Additional indicators that describe immunization

system functioning in real-time can provide managers

with essential information to guide their actions for

improving IC. Process indicators like a micro plan,

supervision, outreach conducted among others have

long been proposed but not highly valued by health

personnel or promoted as useful tools for

management” (USAID_MCSP, 2018). As the need

for evidence-based policies grows, big data hold the

key to rapid improvements to promote health/prevent

disease (Gall and Suzuki, 2019).

A new era is dawning where

monitoring/surveillance systems are strengthened by

big-data streams, from legacy systems and non-

traditional digital data sources, like social media

(Bansal, et al., 2017). Big data analytics technologies

and techniques can analyze large, diverse and

dynamic data sets intended to enhance firm decision

making/performance (Al-Shiakhli, 2019). They

discover associations, understand patterns and trends

within the data to improve care, save lives and lower

costs (Raghupathi and Raghupathi, 2014). “The

impact of big data in healthcare lies in identifying

new data sources such as wearable devices in addition

to the data in legacy sources”. Coupling analytics and

all data sources provide valuable insights for

researchers to attain novel health care solutions

(Palanisamy and Thirunavukarasu, 2017). Big data

analytics is beneficial to public health by turning

large amounts of data into actionable information that

can be used to identify needs, provide services,

predict and prevent crises especially for the benefit of

populations (Raghupathi and Raghupathi, 2014). Big

data analytics has been mainly used to predict (Heart

Attack, Disease Outcome/Outbreak), on fewer

occasions diagnosis and vaccine development. (Das

et al., 2018). Using prescriptive analytics,

Performance of public health intervention can be

monitored in real-time.



“Prescriptive analytics includes functions as a

decision support tool that explores a set of possible

actions and suggests decision based on descriptive

and predictive analysis of complex data” (Liang &

Kelemen, 2016). Using tools like optimization,

simulation, business rules, algorithms, and machine

learning (Al-Shiakhli, 2019), prescriptive analysis

conducts real-time analytics using point-of-care data

to present immediate and actionable information to

providers (Liang and Kelemen, 2016).

1.1 General Research Question

How can data analytics strengthen monitoring and

surveillance of routine immunization coverage?



1.2 Research Questions 

To what extent does data analytics influence

immunization coverage validity?

How can big data analytics enhance

comprehensive indicators monitoring?



What components must the immunization

coverage monitoring framework have to support

evidence strategy implementation?



How will the developed coverage monitoring

framework be evaluated?



Using Data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda

551

1.3 General Objective

To develop a real-time routine immunization

coverage monitoring framework that supports

evidence-based strategy implementation to improve

coverage, uptake and completion of routine

immunization.



1.4 Research Objectives

1. To establish the extent to which data analytics

can influence immunization coverage validity.

To determine how big data analytics can enhance

comprehensive indicators monitoring.



To design a real-time immunization coverage

monitoring framework.

To evaluate the designed framework.

2 LITERATURE REVIEW

2.1 Immunization Data Quality

Immunisation programme performance management

must be built on a foundation of accurate and

complete data collection. Data checks and

management are crucial to improving performance

management, evaluation and form the basis of

evidence-based advocacy to politicians and donors.

(Stokes-Prindle et al., 2012). Poor quality and

underuse of data remain a persistent problem,

affecting the ability of countries and partners to

monitor progress against the GVAP goals as well as

supporting optimal changes to immunization

programmes (SAGE, 2019). The lack of adequate

skills in data collection, analysis, interpretation and

use among health workers are key factors limiting the

quality and use of data (SAGE, 2019).



In Uganda, vaccine dose administration data are

often not available or low-quality to optimally plan,

monitor, evaluate program performance (Ward, et al.,

2017) and target missed populations (Carnahan,

2020). Optimal immunization coverage relies on

high-quality immunization data, which are a

prerequisite for effective and efficient public health

action to improved population immunity against

VPDs (Ward, et al., 2017; Nzaji, et al., 2019).

Administrative IC estimates enable programme

managers to monitor, investigate gaps and take

remedial action. However, population denominator

used in the computation is often inaccurate (Ward, et

al., 2017).

In-accurate IC estimates are evident in many

countries like; inflation in administrative coverage

data in 2013 according to national data quality self-

assessment (DQS) in Uganda attributed to sub-

optimal data quality. Similarly, in Nigeria,

administrative data were indicated as unreliable to

accurately represent RI coverage levels hence

difficulty to evaluate programme performance

(Stokes-Prindle, et al., 2012). Overestimation and

underestimation occur in rural and near urban areas

districts respectively, therefore national coverage

estimates may not accurately reflect the true situation

(Wetherill, et al., 2017). In Democratic Republic of

Congo over-reporting on the administration of third-

dose of the pentavalent vaccine was identified in

Tshiaba, Mukeba and Ditalala and measles antigen in

Tshiaba and Tshibombo health facilities, while under-

reporting was evident in Ditalala and Mukeba (Nzaji,

et al., 2019). Surveys that never depend on census

population as dominator are known to be more

reliable. However, they are costly and provide

untimely (after 3-4 years) information to guide

programmes (Sodha and Dietz, 2015; Cutts et al.,

2016). Unreliable estimates undermine national and

international investments, prevents accurate

monitoring of global immunization initiatives, and

can increase the risk of VPDs outbreaks. (Wetherill,

et al., 2017).

2.2 Performance Indicators Monitoring

Strategies

2.2.1 Results-based Approaches

Results-based Approaches (RBA) and Results-based

financing and incentives (RBF) are government tools

to disburse a portion of its health budget in cash or

goods conditional on measurable actions taken or

performance target achieved by health workers

(Naimoli and Brenzel, 2009; Pearson, et al., 2010).

For example, GAVI funded programs receive results-

based funds after two years of an initial investment

(Stokes-Prindle, et al., 2012). RBF implemented in

Rwanda, Zambia and Ghana, between 2009-2014

indicated improved health services (Naimoli and

Brenzel, 2009; Stokes-Prindle, et al., 2012).

Conditional Cash transfer programs targeting

users of services began in Latin America/Caribbean

region in the 1990s; where a cash transfer to

household conditional on completing certain actions

statistically indicated significant IC increment in

Mexico and Nicaragua (Naimoli and Brenzel, 2009).



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These strategies, however, have unintended

pitfalls like non-remunerated services neglect, falsify

reporting, propagating a culture of monetization

among health workers, sustainability and cost-

effectiveness challenges (Stokes-Prindle, et al.,

2012). Pearson asserts that there is a risk of these

schemes to focus on results that are measurable

instead of important (Pearson, et al., 2010).



2.2.2 Other Approaches

The Reach Every District (RED) approach

implemented since 2002, emerged from WHO and

partners in an attempt to devise an innovative strategy

to improve stagnating immunization coverage in

Africa. It offers planning for better management of

resources, supportive supervision, link communities

with service delivery and ensures monitoring for

action (Mahigo, 2009). RED’s passive monitoring

tools like drop out chart, timeliness and completeness

reports, and data analysis increased IC. However,

RED is challenged by funding, lack of qualified staff

and immunization declining trends.



2.3 Comprehensive (Process and Basic)

Indicators’ Role 

The capacity to attain and sustain recommended IC is

a great challenge facing expanded programs on

immunization (EPI). Bicaba et al. indicated that full

immunization coverage (FIC) is sufficient neither to

evaluate EPI performance nor to help identify the

broad strategies that must be implemented to improve

performance. The study asserts that FIC is a

restrictive process that only accounts for several

vaccines received, but not the age of the child at the

time of vaccination and adherence to schedule. They

recommended a tripartite performance view

including FIC, the adherence to vaccination schedule

and status of children not completely vaccinated. It

yields better-targeted interventions, inequity

reduction and vaccination accessibility. Conversely,

the study never investigated the reasons underlying

performance deficiencies (Bicaba, et al., 2009).



Similarly, Naimoli and Brenzel indicated there is

need for a comprehensive approach in monitoring the

immunization programme to draw attention to low

performing areas instead of the classical coverage

indicators (Naimoli and Brenzel, 2009). To

emphasize, Better Immunization Data (BID)

prioritization exercise team arrived at four

challenging areas that could be informed by EIR data

analysis namely: Denominators and population

movement, Missed opportunities, Continuum of Care

and Continuous quality improvement (CQI). CQI is

“an iterative data-driven process of empowering

health care workers to improve health service

delivery by identifying challenges, trends,

consistencies, outliers in coverage and dropout rate”.

BID is meant to share findings with stakeholders

(Carnahan, 2020). This approach, however, lasts for

a period, never informs stakeholders in real-time and

emphasis is put on basic performance indicators.



2.4 Big Data Analytics 

The success of public health big data applications

entirely depends on underlying architecture and

utilization of appropriate tools. Data curation plays a

vital role in transforming big data into actionable

knowledge (Palanisamy and Thirunavukarasu, 2017).

Big data generate more revenue while reducing risk

and predicting future outcomes with greater

confidence at low cost. Big data management cycle

includes capture, organize, integrate, analyze and act

(Hadi, et al., 2015). However, validation,

interpretation, and visualization are crucial in

extracting actionable knowledge for decision making

(Liang and Kelemen, 2016). Besides,

 real-time big

data analytics is a key requirement in healthcare to

address the lag between data collection and

processing (Raghupathi and Raghupathi, 2014).



Information systems design theory (ISDT)

underpins this study. ISDT’s strength is the

theoretical basis of “vigilance denoting the ability of

an information system to help an executive remain

alertly watchful for weak signals, discontinuities and

opportunities”. (Walls, et al., 1992)

. ISDT indicates

how to design an artifact on principles of function,

methods and justificatory theoretical knowledge.

3 METHODOLOGY

Pragmatism will form the basis of the study. It

integrates both inductive and deductive research and

accepts concepts to be relevant only if they support

action (Ågerfalk et al., 2008). The explanatory

approach will be used because it provides a greater

depth and breadth of information. (Venkatesh et al.,

2013). The research strategy is both qualitative

(useful to provide a detailed description as it occurs

in context) and quantitative methods (searches for

significant relationships, patterns or correlations

between variables) (Nowell and Albrecht, 2018)

following design science.

Using Data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda

553

Table 1: Case study population.

Figure 2: Envisaged Artifact (Adapted from “Applied conceptual architecture of big data analytics” by (Ravikumaran and

Vimala, 2016)).

4 ENVISAGED ARTIFACT:

ROUTINE IMMUNIZATION

COVERAGE MONITOR

FRAMEWORK

The Routine Immunization Coverage Monitor

Framework in Figure 2 is an extension of

Ravikumaran and Vimala, 2016 applied architecture

of big data analytics. The adapted framework initiates

at a big data layer (A) by pulling/integrating

structured data from legacy systems and unstructured

data from immunization generating activities (data

curation) in real-time. These raw data are transformed

and validated at component (B). The transformed data

is subjected to big data tools and platforms like

Hadoop a “NoSQL” open source distributed data

processing technology. MapReduce provides the

interface for the distribution of sub-tasks and tracks

processing of each server/node. Mahout generates

machine learning algorithms while Lucene supports

text search and analytics. Using prescriptive

analytics, data mining and querying will generate

performance reports. The reports are fed into a data

discovery component (C). This component houses

variables like performance indicators. Comparing

immunization performance indicated by insights

Unit of

Analysis

Particular Kampala region details Kampala attributes

Unit of

inquiry

(per

Health

Facility)

Hospitals: 22 General hospitals, 2 National referrals, 3

Regional referrals, 13 Health Centre IV and 48 Health Centre

III. Total : 88

(

MOH U

anda

2018

)

-Key informants: In charge (health facility), Head of

immunization department, Community personnel (Village

health team), Head immunization outreaches, Records officer,

Nurse/vaccinator; Total study population: 528

-Representative sample size : 226 (Krejcie & Morgan, 1970).

-Sampling techniques: Stratified, simple random, purposive.

-Methods: Interview, questionnaire, focus group and document

review

-Data analysis: Epinfo for quantitative Text for qualitative

-Kampala covers 189.3

square kilo meter.

-Divisions/strata:

Kawempe, Rubaga; Central,

Nakawa, Makidye, -

Parishes 96 and 1285

villages. (UBoS; 2019).

-74,913 births per year.

-6,242 children immunized

(UNICEF-Uganda, 2015)

per month(formula

indicated by

(WHO/IVB/08.05, 2008)).

Knowledge Discovery (C)

Big data sources (A)

Immunization Variety

data

RDMS (DHIS2, IIS,

EIR, ORTIS)

Programme management,

advocacy &

communication

monitoring

TEXT

-SMS, social media

-Community mobilization

-Community meetings

-Supervisory visits

-Session/ outreach reports

-Training sessions

-Staff movement

-Transport schedule

-meetings

recommendation

Big Data techniques (B)

Platforms &

tools

- Hadoop

- Mahout

-MapReduce

-Lucene

Prescriptive

Analytics

-data

mining

-Queries

Comprehensive

Performance

indicators

 Process

 Basic

Gap

Strength

Appropriate

customized

Intervention

Outcome: Real

time performance

monitoring (D)

-Coverage validity

-Timely

interventions

-Increased coverage

-Low child mortality

-Increased hard

immunity, Less costs

-Low dropout rate

-Increased uptake

Data

Validation

Transform

ation

-Extract

Transform

Load

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from reports against set process and basics indicators,

strength and gaps are easily identified to inform

conclusion and support decisions for appropriate

customized child health intervention. It is this

intervention that is implemented national or

subnational level to attain/sustain recommended

immunization coverage preserving optimal validity.

5 EXPECTED RESEARCH

LIMITATION AND

MITIGATION

The researchers anticipate self-reporting to limit the

study, especially for selected key informants. This

will be mitigated by verifying given information with

doses administered in reports, unique child identifier

from Vital records management systems and National

Identification Regulatory Authority(NIRA) records.

6 CONCLUSION

Monitoring and surveillance of IC rates are critical at

national and subnational levels for countries to

prioritize and customize strategies to address

immunization gaps and reach life-saving vaccine to

every child. The study suggests that the success of the

immunization programme lies in real-time

monitoring of its performance against all set targets.

This study indicates that the envisaged framework

integrates structure and unstructured immunization

data to generate real-time programme performance

actionable knowledge to guide child health

interventions using prescriptive analytics. Monitoring

these insights against comprehensive health facility

indicators like micro-plans, process and national

basic indicators, is important in identifying evidence-

based gaps/strengths that inform conclusion and

support customized remedial action/interventions in

real-time to eliminate VPDs. This artifact also

strengthens monitoring and surveillance of IC with

greater validity confidence. Empirically establishing

the extent to which data analytics influences

immunization coverage validity will be the next step

in our research.



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