Data use in decision-making for immunisation: role of an electronic immunisation register in Vanuatu

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Cheung, Rachel Takoar, Chris Gauthier-Coles, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6200226/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Discover Public Health → Version 1 posted 9 You are reading this latest preprint version Abstract Background Electronic immunisation registers (EIRs) can strengthen immunisation programs by increasing access to rich data for policy-making. We examined data use from paper-based systems for routine immunisation compared with an EIR for COVID-19 vaccination in Vanuatu. Methods We conducted a qualitative study, interviewing 16 key informants working within the Vanuatu Ministry of Health (n = 11) and international development agencies (n = 5). We thematically identified data use actions and factors determining data use with the Performance of Routine Information System Management framework. We verified findings through document review and quality assessment of EIR data. Results Routine immunisation coverage data were used to identify coverage gaps, but used inconsistently in planning service delivery or strategic decision-making. In contrast, decision-makers used COVID-19 vaccination coverage data regularly to monitor and plan program rollout, allocate resources, and assess adverse events following immunisation. The EIR streamlined data processes, allowing data to be entered, analysed and shared at a faster pace. Barriers to using routine immunisation data included inadequate data management processes, minimal performance feedback, lack of data use culture, poor data literacy and workers’ heavy workload. For COVID-19 vaccination, EIR data use was enabled by increased resources, greater demand and accountability for data, and urgency to achieve high COVID-19 vaccination coverage during the pandemic. Conclusions The increased resourcing, emergency context and focus on COVID-19 fostered an environment for greater data use. While the EIR enabled rapid access to data, health leadership, regular feedback and accountability to achieve targets were necessary to increase data use in decision-making. Immunisation data use evidence-based decision-making electronic immunisation register routine health information systems immunisation information systems Figures Figure 1 Figure 2 Introduction A robust health information system that provides actionable data can inform decision-making and guide strategies to improve health and immunisation systems. 1 , 2 Substantial volumes of immunisation data are collected and reported by countries each year. 3 However, the use of data to drive decision-making is limited, especially in low-and-middle-income countries (LMICs). Enablers, as well as barriers, to using data span behavioural (i.e. health workers’ motivation, confidence and competence to use health information systems), organisational (i.e. system processes and structures such as human and financial resources and management of health information systems) and technical (i.e. specialised knowledge and technology to develop and manage health information systems) determinants. 4 For example, in Mozambique, efforts to increase accessibility to and the perceived value of data among workers increased data use in planning actions, but capacity constrains limited healthcare workers’ ability to record and review data. 5 Additional factors constraining the use of data in decision-making across LMICs include poor quality of data, lack of availability when it is needed, and its lack of relevance to policy makers’ and program planners’ considerations. 4 , 6 – 8 Shifting from manual methods of data collection and reporting to using digital systems, whereby data is collected and reported electronically, can streamline and automate data collection, improving the accessibility and quality of health data. 9 – 11 Here we examine the potential benefits of using electronic immunisation registers (EIRs). EIRs are confidential, computerised, population-based routine system used to capture, store, access, and share individual-level data on vaccination. 12 , 13 The uptake of EIRs by LMICs had been slow, but increased rapidly during the COVID-19 pandemic. 14 – 16 EIRs enable the collection of rich data and granular analyses, which can enable easier tracking of individuals to ensure complete vaccination and provide insights on immunisation coverage relevant to policy making and program planning. 13 , 17 – 19 The emerging body of evidence on EIR use in LMICs indicates successes in using them to improve vaccination coverage, streamline vaccine management, reduce costs to the health system through administrative efficiencies (particularly healthcare worker time), and inform actions in response to outbreaks of vaccine-preventable diseases. 20 – 27 However empirical evidence on the role of EIRs in facilitating public health decision-making and planning is limited. Understanding the role of EIRs in addressing barriers to data use can help to enhance their effectiveness in translating data into evidence-based decisions and actions. Vanuatu is a lower-middle income small island developing state in the South Pacific, with a population of approximately 300,000 people living across 83 geographically dispersed islands. 28 It has a decentralised health system, with immunisation services largely delivered via health centres and dispensaries in the public sector, and often relies on supplementary immunisation activities to catch-up on vaccination . 29 A team at the national level are responsible for program oversight and strategy, including governance, management, coordination, and vaccine procurement and distribution to provinces. Provincial offices are responsible for oversight of immunisation service delivery through primary healthcare services, and are the primary liaisons with workers at health facilities. For COVID-19 vaccination, the program was centrally managed by a national health emergency team, due to differences in speed, scale and target populations compared to routine immunisation. Additionally, resources across the health system were redirected away from routine services towards the emergency response. Immunisation coverage in Vanuatu is consistently lower than other countries in the Pacific region. 29 A Multiple Indicator Cluster Survey in 2023 reported coverage of DTP3 at 57.9% and of the first dose of measles-containing vaccine at 46.1% among children aged 12–23 months of age (i.e. birth cohorts from 2021 and 2022), reflecting the decline in immunisation coverage during the COVID-19 pandemic. 30 The country’s ability to reach geographically remote populations to deliver immunisation and other primary healthcare services is limited by workforce capacity, logistical difficulties and infrastructural challenges. 31 – 33 Health information systems in Vanuatu, including those for immunisation, are largely paper-based, with data aggregated by healthcare workers at facilities reported via provincial offices to the national level. At the national level, a health information management system, the District Health Information System 2 (DHIS2), is used to manage health data for multiple public health programs. In 2021, Vanuatu Ministry of Health introduced an EIR exclusively for COVID-19 vaccination using DHIS2 Tracker (an application to DHIS2). Following the successes of the COVID-19 EIR, the Ministry of Health was motivated to implement the same EIR system for routine immunisation and sought to identify best practices from the COVID-19 EIR experience. At the time of writing, the scope and transition to a routine immunisation EIR were being planned, with paper-based systems still in place. In this study, we aimed to examine data use from paper-based systems for routine immunisation compared with an EIR for COVID-19 vaccination in Vanuatu. Materials and methods Definitions As this study focuses on information systems for immunisations, we use the following definitions in this paper: Data: raw information on vaccinations recorded by healthcare workers Coverage data: estimates of vaccination coverage (derived following analysis of raw data on administered vaccinations) that can be used by decision-makers Data use: where health workers use raw data and/or coverage data to make decisions and take actions. Study design and conceptual framework We conducted a qualitative study, informed by a theory of change based on the Performance of Routine Information System Management Framework (PRISM), 34 , 35 adapted with elements from the Theory of Change for Supporting Data-Informed Decision-Making for Immunization Programs developed by Osterman et al. 9 Our study framework (Fig. 1 ) shows how an intervention to increase data use (i.e. the EIR in this case) can improve data collection and reporting processes and increase the availability of timely, high-quality coverage data. These can then be used in decision making, with improvements in immunisation coverage and equity expected to follow. To be successful, individuals must have the skills, knowledge and motivation to use the EIR and data outputs, and the appropriate health and information system structures (including governance, resourcing and technical infrastructure) to support data use must be in place. Our framework reflects the feedback loops between data collection, its use and health benefits, i.e. greater data use leads to improved data processes and actions to improve data quality. We used our framework to inform data collection tools, analysis and interpretation of findings. Study participants We purposively recruited health system actors responsible for managing immunisation programs or health information systems in Vanuatu. Participants included immunisation program managers at the national and provincial level, health information managers and officers at the national level, and staff from global development partner agencies. As the COVID-19 vaccination program was operationally managed nationally, we focused on recruiting national-level system actors. Data collection We conducted interviews with key informants, supplemented by a document review and a data quality assessment. Key informant interviews : We conducted 16 semi-structured interviews with key informants to examine health system actors’ experiences with collecting, analysing and using data. Interviews were conducted face-to-face from 18th March – 29th March 2024. Questions were guided by our research aims and informed by published tools of routine health information system assessments, specifically the PRISM tools. 36 Our interview guide (Appendix 1) covered three domains: 1) the individuals and processes involved in collecting, analysing and reporting data; 2) use of coverage data in decisions about immunisation programs; 3) perceptions of the EIR in facilitating data use. We asked questions separately for routine immunisation and COVID-19 vaccination given the vast differences in context and processes. We tailored questions during the interview based on the interviewee’s role and degree of involvement in routine immunisation and/or COVID-19 vaccination programs. Document review : Data were extracted from documents related to data flow for routine immunisation, and the implementation and use of the EIR. Key documents included: Strategies or plans for immunisation or health information systems in Vanuatu Standard operating procedures for collecting immunisation data Examples of outputs/reports following syntheses of data, such as coverage reports Documents published within 5 years or currently in use. Study team members at the Vanuatu Ministry of Health provided documents meeting these criteria. Data quality assessment : We obtained and analysed de-identified individual line-listed data on all COVID-19 vaccination encounters recorded in the EIR since its inception in May 2021 (earliest vaccination recorded on 27 May 2021) and the date of data extraction (23 April 2024). Data analyses The interviews were our primary source of data. We used findings from the document reviews and data quality assessment to cross-validate information provided by interviewees, e.g. to verify claims of improved data quality with the EIR, or the presence/absence of a policy for a specified data process. Key informant interviews: We recorded and transcribed all interviews and analysed findings thematically based on our study framework (Fig. 1 ). 38 Themes included data processes, data use and factors determining data use. Two researchers (CP and OC) initially coded a sample of 25% of interviews based on pre-agreed definitions of themes. Following discussion, a further 25% of interviews were coded by both researchers, with final adjustments to theme definitions (see Appendix 2) agreed amongst the study team. The primary researcher (CP) then coded the remaining 50% of interviews. Analyses were conducted using NVivo v.12. Findings were categorised under themes to describe how routine immunisation and COVID-19 vaccination data were collected, reported and used, factors explaining data use, differences between COVID-19 vaccination and routine immunisation, and the role of DHIS2. Immunisation data flows were diagrammatically summarised. Instances of data use and factors enabling or hindering data use were described narratively. Document review: Documents were analysed using the READ approach described by Dalglish et al 37 : 1) ready materials, 2) extract data, 3) analyse data, 4) distil findings. Documents were categorised as contextual (i.e. pertaining to the policy and/or health system environment), data process (i.e. pertaining to data collection, reporting and synthesis processes), and/or data use (i.e. outputs generated following data analysis). Relevant information on the purpose of each document, its characteristics (e.g. author, audience, date of production, etc.) content, relevance to the research questions, and its strengths and limitations were extracted using Microsoft Excel. Findings were synthesised narratively, categorised by document type. EIR data quality assessment: We calculated descriptive statistics on the completeness (i.e. absence of missing data), validity (i.e. absence of errors), and timeliness (i.e. data recorded in the EIR within a specified timeframe since vaccination) of COVID-19 vaccination data recorded in the EIR. We conducted subgroup analyses by age of recipient, province, COVID-19 vaccination dose number, and vaccine brand. Details on the definitions and methods used to assess data quality are in Appendix 3. Analyses were conducted using RStudio (R version 4.4.1, 2024). Results We interviewed 16 participants including 11 individuals (68.75%) from the Ministry of Health and five (31.25%) from development partner organisations. Most participants worked at the national level of the health system (n = 12, 75%) and were highly experienced (4/16 [25%] with 2–5 years and 7/16 [43.75%] with ≥ 5 years of experience in their current role). Table 1 presents interviewee characteristics. Table 1 summarises the demographic and professional characteristics of the 16 key informants interviewed in this study. Characteristic n % Gender Male 9 56.25 Female 7 43.75 Age group 18–29 years 4 25.0 30–39 years 4 25.0 40–49 years 3 18.75 ≥ 50 years 5 31.25 Ethnicity Ni-Vanuatu 10 62.5 Other Pacific Islander 2 12.5 Other 4 25.0 Level of education completed Tertiary diploma 1 6.25 Bachelor's degree 6 43.75 Post-graduate training 6 43.75 Professional degree* 3 18.75 Self-reported computer literacy Sufficient 2 12.5 Good 7 43.75 Very Good 7 43.75 Type of role Immunisation/public health 11 68.75 Health information 5 31.25 Health system level National 12 75.0 Provincial 3 18.75 Health facility 1 6.25 Organisation Ministry of Health 11 68.75 Partner organisations 5 31.25 Time working in current role < 1 year 3 18.75 1–2 years 2 12.5 2–5 years 4 25.0 ≥ 5 years 7 43.75 *Includes clinical degrees i.e. medical and nursing We reviewed 23 documents, summarised in Appendix 4. We did not identify any documents containing information on a digital health strategy or policies related to data governance and security. Immunisation data processes Figure 2 shows the processes for recording, collating and transferring immunisation data through the health system. For routine immunisation, healthcare workers recorded data on child health registers, hand-held vaccination cards and tally sheets, and compiled data monthly into aggregated paper reporting forms for reporting to provinces. During document review, we noted replication across different reporting forms. Provincial officers then transferred the data into an Excel spreadsheet, though this was transitioning to a DHIS2-based aggregate system (Vanuatu Health Management Information System database – VanHMIS). Data from provinces were aggregated by the national level EPI team with assistance from development partners. The process was simplified for COVID-19 vaccination due to the EIR: data officers recorded data on a paper form and the EIR, where it could be directly accessed by those analysing the data. Analysis for both routine immunisation and COVID-19 vaccination data were conducted exclusively at the national level, but by different teams. Interviewees discussed that coverage data were generated much faster and were more detailed for COVID-19 vaccination, with summary reports regularly and publicly available: “That [i.e. analysis and reporting for COVID-19 vaccination] was done very well. It was done on a real time basis. I think we had a new report every other week. Very well disaggregated. Male, female, different age groups, different target populations. So it was very easy to synthesize. And also, because it was being used in dashboards all over the place for public consumption. So it was excellently done. And there were no delays.” Participant 5 In contrast, participants’ perspectives differed on the extent to which coverage data on routine immunisation were fed back to health system actors. Some national level participants stated that they fed back data through following up in areas of low coverage or where issues were suspected, and that there were some meetings (e.g. annual review meetings with provincial officers) where coverage data were reviewed. However, the extent to which feedback was discussed and used in planning was unclear, and considered limited and irregular by some participants. “As far as feedback and data back to facilities, and I think that goes, like that can go for not only for VDI [vaccine-preventable diseases and immunisation], but all other public health programs. Currently, it's just about strengthening that unidirectional process of data going from health facilities to province and national. But yeah, eventually it will be how can we reconnect that back down to the health facility so it can promote good planning, effective decision making.” Participant 2 Interviewees identified several issues with data processes for routine immunisation compared with COVID-19 vaccination. Data were often incomplete or delayed, affecting analysis and availability of coverage data. Coverage data quality was affected by inconsistent sources of population denominator data, absence of data on immunisations administered by non-public sector providers (namely non-governmental organisations), lost data due to damaged paper records, discordance between data sources (e.g. coverage versus stock management), and difficulties in transferring data from health facilities to provincial offices in remote areas or due to inclement weather. There was lack of clarity amongst interviewees on whether there were standardised procedures for routine immunisation data collection and reporting, despite a manual for recording data in VanHMIS which verified some of the roles and responsibilities being identified during document review. Interviewees perceived that data quality, particularly in terms of its timeliness and accuracy, were vastly improved for COVID-19 vaccination compared with routine immunisation. In our assessment of EIR data quality, we found a high proportion of completeness; all fields included in the analysis were ≥ 99% complete except for the field identifying an individual as being pregnant (missing for 61.3%, 163,927/267,252). Dates of data entry into the EIR were invalid (i.e. before vaccination dates) in 45.6% of records (121,995/267,252), with dates largely valid for first doses of COVID-19 vaccination (97.6%) but mostly invalid for second, third and booster doses (> 93%, see Table 2 ), suggesting a systemic rather than user error. Among records with valid dates of entry, 68.9% were entered on the day of vaccination, 82.7% within 3 days, and 87.7% within 7 days (see Table 2 ). Table 2 summarises the findings of the assessment of quality of COVID-19 vaccination data extracted from the COVID-19 vaccination electronic immunisation register in Vanuatu. It includes data recorded between May 2021 and April 2024. Key indicators include validity of data values and timeliness of data entry into the EIR (relative to date of vaccination). Results are shown by COVID-19 vaccination dose number. COVID-19 vaccination dose Number of records Validity of dates of data entry into EIR* Timeliness of data entry, % # No. with valid dates No. with invalid dates % with valid dates % with invalid dates Same day Within 3 days Within 7 days Within 14 days Within 30 days All doses 267,252 145,257 121,995 54.4% 45.6% 68.9% 82.7% 87.7% 92.2% 96.5% By dose number First doses 146,949 143,428 3,521 97.6% 2.4% 69.3% 83.1% 88.1% 92.5% 96.8% Second doses 102,595 1,170 101,425 1.1% 98.9% 42.3% 50.3% 57.5% 62.3% 73.8% Third doses 819 55 764 6.7% 93.3% 20.0% 23.6% 27.3% 92.7% 94.5% Booster doses 16,480 409 16,071 2.5% 97.5% 50.4% 60.4% 72.1% 81.2% 89.0% Unspecified 409 195 214 47.7% 52.3% 37.9% 43.6% 51.3% 74.4% 87.2% * A date of data entry into the EIR was considered valid if the date of data entry was the day of vaccination or later (dates of data entry before vaccination were considered invalid). Denominator for proportions is the total number of records in the EIR. # Timeliness of data entry defined as the proportion of records where the date that the vaccination was recorded on the EIR was within the specified timeframe (e.g. same day, within 3 days, etc.) of the date of vaccination. Denominator for proportions is the number of records in the EIR with valid dates of entry. Use of immunisation data Interviewees perceived that there was limited use of routine immunisation coverage data in decision-making, with use occurring largely at the national level. Interviewees described using coverage data to identify areas of low routine immunisation coverage and allocate resources, including vaccine stock, and implement additional activities. For example, interviewees reported using coverage data to plan mobile vaccination activities or implement a vaccination campaign, citing the example of a recent measles vaccination campaign that was conducted in response to ongoing low coverage. However, other interviewees were concerned that coverage data were not actually being used to plan immunisation services: “The outreach has used a lot of resources: you used time, you used transport, all these things. But when we went to the field, we saw a very small number of children, for the kinds of input that they are putting. So that means that that particular facility has not used the data to actually plan where they're going. They're just going because it's a default for outreach, they're not going because that is the area with a lot of dropout children.” Participant 5 In contrast, COVID-19 vaccination coverage data were used regularly to monitor the rollout, identify areas that needed more resources, and plan actions to deliver the program. The ability to generate reports containing almost real-time coverage data allowed program planners to identify areas where there was low vaccine uptake and target resources appropriately: “The registry allowed us to monitor the stock levels of vaccines by province and to adjust the levels of vaccine being sent out to individual provinces based on the number of recorded immunisations.” Participant 2 “For example, in , there was a bit of hesitancy which was obvious from the data. So, for example, the health promotion, you need to put in more work, in terms of advocacy and demand generation activities in that province. So there was that synthesis of the data… by the Ministry of Health officials, and then using that to make decisions.” Participant 5 Interviewees discussed the advantages of having access to individual-level data that allowed detailed analyses of vaccination coverage (e.g. coverage maps) disaggregated data by age, gender and risk groups. Having individual-level data enabled health workers to trace and recall people due for a second dose of COVID-19 vaccine. It also facilitated rapid investigations of causality of adverse events following immunisation (AEFIs) or claims that a death or serious illness were attributable to COVID-19 vaccination, which was especially useful in a political climate where rumours about COVID-19 vaccination were spreading: “They were thinking vaccine, this lowers the immune system... it's one or two of them that has been requesting data, wanting to find out how many people were immunised. Out of these people immunised, how many of them has died from COVID? How many of them has those adverse, you know, those reactions? How many of them? So this is where the data is very useful.” Participant 13 Determinants of effective data use of immunisation coverage Table 3 summarises the barriers to effective data use for routine immunisation, and factors that facilitated data use for COVID-19 vaccination. These are discussed in detail below. Table 3 summarises the barriers to use of routine immunisation coverage data and facilitators for use of COVID-19 vaccination data in Vanuatu, by the type of factor i.e. behavioural factor, organisational factor, technical factor and public health context (i.e. routine immunisation vs COVID-19 vaccination during the COVID-19 pandemic). The table includes quotes from key informants demonstrating the barrier or facilitator identified. Type of factor Barriers for routine immunisation Facilitators for COVID-19 vaccination Description of barriers Representative quotes Description of facilitators Representative quotes Behavioural factors ● Limited data literacy and skill to record immunisation data ● Limited value placed on recording and reporting immunisation data ● No/limited motivation to produce data, considered in large part due to the absence of feedback to healthcare workers “We need to train and build the capacity of staff at different levels to look at those data to understanding what it is, as the first step… this investigation of supervision skills, just to try to understand why some areas or health services have low coverage or low reporting rate. So again, it's developing those analytical skills in the first place.” Participant 10 “That's one of the reasons why I think that many of the people in the health facilities, not only don't they have a lot of time to spend to do reports, because they want to take care of their patients. But they don't really see the need, they don't understand why, you know, they don't see the result of their input. You know, because no one is actually giving them any feedback, or very little of it, you know, so it doesn't act as an incentive for them to, to, you know, to start reporting on time.” Participant 15 ● Data officers were well trained on data recording requirements ● Regular, public feedback on COVID-19 vaccination performance encouraged demand to view data, resulting in greater efforts to record data “And it was expected, if you go out there, you have to record and you are following this data closely. The government was following this data closely. There was huge interest in the performance of the program, not just by the program itself, but even above political and even not just politically within the country, even globally.” Participant 5 “During the time that the nurses were very smart and more concentrate on vaccination to the COVID vaccine, and they were like having this kind of interest, this kind of interest was very high. And then once we drop I mean come back to the normal.” Participant 16 Organisational factors ● Constrained capacity of healthcare workers to record immunisation data due to conflicting clinical responsibilities ● Absence of regular feedback and mechanisms to review and embed coverage data into decision-making and planning ● Turnover and poor retention of healthcare workers, requiring further training on data recording requirements to be conducted ● Perception of lack of accountability for producing and using immunisation coverage data or holding health workers responsible for achieving targets ● Lack of demand for data on routine immunisation coverage at all levels of the health system; absence of a culture of using coverage data to monitor performance and to adjust programmatic planning and strategies (i.e. lack of a monitoring and evaluation cycle) “So the data is absolutely useful, but it's only useful if it gets back to the producer. So I think that is still a gap in terms of the feedback loop to the provinces, for them to be able to use the data for, you know, day to day action, and state accountability for their work.” Participant 5 “….Continuous engagement, and feedback loops, and annual reviews and interactions with them, and showing them examples from other countries, all these kind of things, then the system will work. But if you just give them a training and tell them to go and do it. They have not done it six months later. They haven't. Yeah, nobody's following up.” Participant 5 “The system has to be locally owned.” Participant 14 ● Recruitment of dedicated data officers responsible for recording immunisation data; healthcare workers only administered vaccinations ● Data officers were trained on how to use the data collection tools ● Clarity and accountability for recording and reporting data throughout the health systems ● Strong demand to have regular updates on COVID-19 vaccination coverage performance with the health system (from Ministry of Health and partners), as well as from members of the public and politicians ● Demand for COVID-19 vaccination coverage data by individuals in leadership roles encouraged ownership of immunisation data and a culture of data demand and generation ● Influx of external resources (human, financial, material and technical) provided by development partners and donors, including to establish and implement the EIR “There was a lot of investment in human resource to the program, which is the main issue here in Vanuatu, human resource for health. So as a result, it was easy for the system to produce data, good quality data, within a short time, because those extra hands were there. And also, there was a lot of training. And there was also a lot of quality assurance across the whole system. So those kind of investments made the system successful.” Participant 5 “At the time COVID-19, HR was not an issue because everybody was mobilised, including extra staff, volunteers and all of this… with the resources existing here, I don't think you can reach the same level for routine immunisations, not in the short term.” Participant 10 “It's like the partners who are more interested in the routine data. Not really the government. Yeah. That's a difference. But for the COVID. The government itself was interested.” Participant 5 “Most of the Ministry of Health staff was mobilised and repurposed for this, while at the same time even the financial resources of the Ministry, whatever was able to be repurpose for this. And then there were additional resources from partners to put in for the rollout of the vaccination as well as to cover the costs for all these extra staff.” Participant 10 Technical factors ● Perception of poor data management practices, particularly consistency in recording and reporting data, and lack of feedback on immunisation coverage especially to healthcare workers ● Paper-based system is labour-intensive and duplicative due to use of multiple data collection tools for immunisation ● Lack of consistency and clarity in how reports are transferred from health facilities to provincial offices, leading to loss of data especially in remote areas ● Aggregate data collection prevents in-depth analysis of vaccination coverage data by additional variables of interest ● Lack of consistent use of population denominator data and estimates of catchment area, leading to issues with the quality of coverage data ● Communication infrastructure, especially poor internet connectivity in remote areas, affects the ability and mechanisms to report immunisation data “Tools, everybody using a different way, like using the same but they change their, you know, the template in a way, like the rows were changed and the columns were up and down. And so it was very difficult to analyse.” Participant 11 “Usually when they need to send the reports over not only for EPI, but also the HIS. the same recording is in the HIS. So when they, they have to send and if there's a delay, like rough seas or registration and then so, transportation there, then the delay will be like, reports for three months won’t receive on time.” Participant 7 “There are transportation, logistic issues and then someone has to just to get the report back to the provincial level, where usually to be entered and then sent to central level. So generally, communication infrastructure is obviously affecting reporting.” Participant 10 “Not every health facility will be having access to network. That's why on monthly health information reporting form they have to fill in hardcopy at health facility levels and send it on every first week of second month to the provincial official that the HIS officer at the provincial levels can input into the system.” Participant 14 “I know it sounds really basic, but I think the most advanced thing that we can ever do here in Vanuatu, is to make sure that everyone is consistent. Consistency in doing is really the issue here. Yeah, yeah. So it's not the technologies, just the consistency of resources, consistency of focus consistency of entering the information.” Participant 15 ● Clear procedures for reporting immunisation data, first on paper and then on the EIR, using a single standardised form ● Streamlined processes for data analysis and synthesis into reports with coverage data using standardised templates; consistency in reporting ● Individual-level data enabled detailed analyses of COVID-19 vaccination coverage and disaggregation by variables of interest, allowing better targeting of vaccination strategies ● Consistent use of population denominator data allowing for better comparisons across time ● Establishment of temporary satellite connections where internet was poor, allowing data to be entered into the EIR “One of the barrier is communication infrastructure. But again, only for COVID-19, it was overcome because there were specific just like satellite dishes that were just like installed and internet connection that were set up just for COVID-19 vaccination, which doesn't exist in every health facilities.” Participant 10 “For the COVID-19 with the electronic record, we can generate data weekly, daily and weekly…. for routine immunisation, we have to wait for all the Excel sheets to be analysed.” Participant 3 “Then there was also the disaggregation based on the different target or rather the risk groups. We had all these people who had been classified as the risk groups, the healthcare workers, the older people, the children with comorbidities, etc. So the, the COVID data was separated in like it was disaggregated, and those kind of things. Also, there was the male female data, which is sometimes not available for the routine immunisation. And that's actually I mean, that's one of the challenges of the routine immunisation is the sex disaggregation. But for COVID, we had that readily available.” Participant 5 Public health context ● Routine immunisation is ongoing and occurs at a steady “slow burn” pace over time ● Constrained resources, with healthcare workers responsible for delivering immunisation programs alongside all other health programs ● Negligible public interest in routine immunisation coverage ● Limited accountability if immunisation coverage targets are not achieved “When you get into the routine side. Now you're back again, to a skeleton crew of one or two people, you know, who has to do the job, who has to do the writing, and who has to enter the data... unlike COVID, where, you know, you had an army of people.” Participant 15 “I would say, I didn't get as much pushback, because a lot of our resources were being focused on COVID-19. And whereas after, when I had to expand to include other vaccinations, even though I had a lot more time, and I would say it wasn't as hectic to get done, I faced a lot more pushback then.” Participant 6 ● COVID-19 vaccination was a short-term intense campaign, and implemented with a sense of urgency due to the threat of an outbreak ● Influx of resources from external partners and donors, and redirection of health system resources from routine programs to COVID-19 vaccination ● Intense media attention, public scrutiny and political interest in achieving high COVID-19 vaccination ● Achievement of COVID-19 vaccination targets tied to other events such as border re-openings, creating accountability to achieve targets “The focus is a lot more sharper with COVID. Because we were right in the middle of a pandemic, right. So you had everybody's attention.” Participant 15 “It was really because all units, the Ministry wants it, everybody was just like, looking at COVID-19 vaccination. And I think that's what made it possible as, as it was.” Participant 10 DHIS2: District Health Information System 2; EIR: Electronic immunisation register; EPI: Expanded Programme on Immunization; HIS: Health Information System; HR: Human resources Behavioural factors Interviewees perceived that health workers at all levels of the health system lacked the capacity and capability to effectively record, analyse, interpret and use immunisation data. Workers, especially at health facilities, were often overwhelmed, placed little value on recording and reporting data, and lacked motivation to do so. This was exacerbated by the lack of feedback on coverage data to healthcare workers. “They don't receive any feedback at the health facilities. And that's something that demoralizes them. Because they've done a lot of data entry. And then they say, we didn't see any importance of this data and what is this data entry doing, because the feedback mechanism hasn't been working.” Participant 13 In contrast, for COVID-19 vaccination, individuals were recruited and trained on how to record data, leaving healthcare workers to only administer vaccinations. Interviewees said that workers viewed the data as being useful for decision-making to increase vaccination coverage, increasing workers’ motivation to accurately enter data in a timely manner. Organisational factors Interviewees discussed how system-level issues with the health workforce affected the availability and use of immunisation data, particularly the conflicting responsibilities of providing clinical care and recording and reporting data. Many facilities had only one nurse responsible for delivering care for all health programs. Interviewees also described problems with turnover and staff retention, with some participants estimating that 25–30% of facilities had no qualified nurse at a given time. In another example, a vacancy in a provincial office meant that data for that province had not been reported for almost a year, and so it was unclear if low coverage estimates for the province were real or due to absent reporting. Some suggested embedding an additional nurse aid or administrator to record and report data rather than relying on nurses to record data. Interviewees also unanimously highlighted that ongoing training to build data literacy and supportive supervision was required to ensure proper recording and reporting of data and to use data to plan and target immunisation activities. “The thing is, we have to train them on the system, because it's not just giving them the access, but train them how to go into the system, where to go to see the data. I mean, the dashboards, where to go to do the data entry, how they can own, they themselves can study, looking at their own data.” Participant 13 Interviewees also discussed that there was a lack of accountability for recording and reporting routine immunisation data throughout the health system, and that training alone was insufficient to address the lack of data culture. They discussed the need for greater leadership in driving the demand for data and its use. Interviewees cited this as a key enabler for data use during the COVID-19 vaccination rollout, where staff in leadership roles in the Ministry of Health drove the demand to see COVID-19 vaccination coverage data, encouraging accountability. “It is the user to see the need. And the user… does not necessarily mean the user at the lowest level, it also means at the leadership level… unless these people actually advocate for the system, then it becomes difficult for the system to work. Because there is no ownership, there is no ownership then there's no sustainability.” Participant 5 Some interviewees perceived that a monitoring and evaluation cycle, where coverage data are reviewed and plans and resources to improve performance follow, was absent, resulting in an absence of accountability to improve performance. They considered that health workers needed to see where gaps were and whether coverage data reflected their expectations, and that reports on coverage data addressed the needs of users. Thus, implementing processes where coverage data were regularly reviewed and considered in planning would drive workers to improve recording, reporting and managing immunisation data. “The only reason why we started doing this is to develop the culture of accountability. Looking at your data, looking at the performance… The whole idea of review meeting is to support them. If they're not performing, how the higher level can support them. Sometimes financial needs requirements, sometimes the HR [human resource] things and more training required… it's a cycle you know... monitoring and evaluating. We need to give feedback and review, support them. Then continue the cycle. This is the thing they need to establish.” Participant 11 “I believe that if you give them the tools, and you allow them to, you know, to be able to access the information easily… and it's usable, that information culture would be good.” Participant 15 Interviewees consistently referred to the role of partner organisations in facilitating data management and resourcing, and were frequent users of data to determine where gaps were and where vaccines and other resources should be allocated. They acknowledged that much of the work partners did was to fill gaps in workforce capacity and capabilities. “It's not at an advisory role alone, but with the government, for short, they usually ask, can you do this? Can you please do the report? Okay. So you’re actually doing a government officer’s role, either because of capacity gap, or constraints in multiple tasks… We are slowly weaning off from the partners to the government to take on the leading role. But we still have the constant challenges of the human resource of government… But there's lapses in our contract lapses. So it returns to the partner. And then the processes takes time.” Participant 9 Interviewees credited partners and donors during the COVID-19 pandemic with providing additional resources, including financial, human, technical expertise and material resources (e.g. hardware), that enabled the implementation of the EIR and improvements in data processes for COVID-19 vaccination. Interviewees highlighted funding for dedicated data entry officers, and stated that all the resources of the Ministry and partners were focused on COVID-19 vaccination during this time. While this was possible for COVID-19, interviewees did not consider having the same level of resourcing for routine immunisation as feasible. “At the time COVID-19, HR [human resources] was not an issue because everybody was mobilised, including extra staff, volunteers and all of this… with the resources existing here, I don't think you can reach the same level for routine immunisations, not in the short term.” Participant 10 Technical factors Interviewees discussed their concern that basic data management practices across all health programs needed to be improved. While substantial effort had already been invested in consolidating data collection tools for routine immunisation, there was still duplication and lack of consistency in recording and reporting. Manual transfer of paper documents from health facilities to provincial offices meant that documents could be lost along the way or did not reach the correct individual. There was a reliance on ad hoc measures to transfer data especially in more remote areas, such as waiting for a passing boat to send documents to the next island. Some interviewees discussed that it was unclear if the data collected and coverage data generated were useful for decision-making, and that work needed to be undertaken to determine what was useful and adjust data collection tools based on users’ needs and capacity. “So I think what we'll have to do is, again, part of the requirements and design process, is we need to really sit down with the EPI [Expanded Programme on Immunization] team and say, listen, tell us how can we design the screen, so that it's designed for high throughput? Especially for looking at 2000 patient records to be entered each month. Yeah, so the user interface design needs to be done properly.” Participant 15 During the COVID-19 pandemic, many of the data management issues were resolved by establishing clear roles and responsibilities, reporting pathways and eliminating manual document transfer through using the EIR. However, the EIR raised new challenges related to the communication infrastructure and lack of internet access in many parts of the country. These issues were largely addressed through the setup of temporary satellite connections, made possible by resources from donors, which interviewees acknowledged was neither feasible nor sustainable for routine immunisation without permanent improvements in the communications infrastructure. Public health context Interviewees unanimously agreed that the context during the COVID-19 pandemic was vastly different to business-as-usual, and that the urgency of the pandemic increased the demand for and use of coverage data within the health sector and beyond. There was media and public scrutiny of COVID-19 vaccination coverage data within Vanuatu due to economic and political interests, particularly as Vanuatu closed their borders to control importation of COVID-19 infections and reopening borders was tied to achieving coverage targets. This, along with the global attention on COVID-19 vaccination and control, increased the desire to be able to rapidly and accurately calculate COVID-19 vaccination coverage on a real-time basis and ensure progress towards achieving pre-determined targets. Interviewees also discussed the influx of resources from partners and donors for all aspects of program delivery, and how all the resources of the health system were focused on COVID-19 vaccination at the expense of routine health programs. Thus, the capacity to deliver and monitor COVID-19 vaccination performance was much greater than for routine immunisation. Interviewees identified additional unique features of the pandemic including needing to generate individual vaccination certificates for travel and to monitor and assess AEFIs (a system to monitor AEFI was not previously in place), necessitating changes to the traditional paper-based systems. Interviewees discussed that during the COVID-19 pandemic, all the necessary resources and systems aligned with the interests of actors both within and beyond the health system, and that the same scenario could not be easily replicated for routine immunisation: “I think it shows that, yes, if we have all the staff in the system, and a standardised format for data collection, standardised tool for data entry with proper training, the staff doing it. The properly designed software just us to manage, analyse, and then produce some output. That can help just to make decisions. And then yes, people just like using those data, just to make decisions. Yeah, it's works. But it's like everything with COVID. It's a bit like kind of the ideal situation, the way that everybody was focused on this. The whole resources of the Ministry, at national level, and at provincial level, were focused on this. Every other program was dropped out, everything else stopped. So this whole thing that it worked well for COVID-19 vaccine was really because of this situation. And again, I'm not sure how it could work in a different, for routine immunisation in the way that was used for COVID-19 vaccination.” Participant 10 Broader country context Interviewees acknowledged that the country’s geography affected health information system structures and thus processes for routine immunisation data. They provided examples of destroyed and lost paper-based immunisation records and disrupted reporting due to climate-related disasters. Interviewees discussed that the context varies across the country, with urban centres like Port Vila and Luganville being very different to the isolated and remote islands in Torba and Tafea provinces. Some areas were difficult to reach and had poor or no internet access, affecting the transfer of immunisation data. The differing contexts and factors affecting health systems across the country meant that interviewees considered that a one-size-fits-all solution to data process issues may not be appropriate: “That's why I say these challenges might be the same but how we approach it depends on the context.” Participant 14 Interviewees discussed differences between a small island developing nation like Vanuatu and larger countries, and that the small population and workforce size meant that solutions to similar problems in other countries will not necessarily work in Vanuatu. “This is the problem in a small island setting like this, is that unlike places like Cambodia, or you know, the Philippines… they have an army of people that can do these things… trying to superimpose best practices in Australia does not mean to say it can work here...” Participant 15 Role of an electronic immunisation register Interviewees cited several advantages of using an EIR for COVID-19 vaccination, including streamlined data recording and reporting, availability of real-time feedback, improved and partially automated analysis through the EIR (a function of the DHIS2 Tracker), and ability to generate granular coverage data by various population characteristics (e.g. gender). Interviewees also noted the EIR improved the ability to track and recall individuals for vaccination and follow-up after reported AEFI to establish causality, and enabled vaccine recipients to be able to generate their own vaccination certificates for the first time. These benefits are described further in Table 4 . Table 4 summarises the benefits of using the EIR for COVID-19 vaccination and potential role for an EIR for routine immunisation in Vanuatu, identified based on key informant interviews. The table includes quotes from key informants that provide evidence of the benefits observed for COVID-19 vaccination and the potential to improve routine immunisation or address barriers currently faced for routine immunisation. Benefit of EIR for COVID-19 vaccination Potential role of EIR for routine immunisation Representative quotes Data recording and reporting were streamlined, avoiding the need to manually tally and transfer data between different levels of the health system • Potential to reduce healthcare workers’ workload through reducing need to tally vaccination information each month and enter data into multiple reporting forms • Some of the issues related to transferring data between health system levels could be alleviated, assuming an appropriate communications infrastructure was in place • Issues related to loss of data due to loss or destruction of paper records could be reduced. • Could improve issues related to completeness and timeliness of data reporting “I think it will make work much easier because most nurses they... we don't ask them to fill up all the forms, because we have HIS [health information system] and then we have immunisation. And we have malaria and we have NTD [neglected tropical diseases] reports to fill up. So that's too much for us. So maybe electronic will be much easier for them to send in all the data.” Participant 8 Real-time feedback were available and public reports generated frequently – coverage reports were generated weekly, but estimates of vaccines administered could be generated daily on request • Having the EIR linked to a dashboard could enable immediate feedback to workers, addressing some of the current issues related to lack of feedback and visibility of data in the current routine immunisation information system • Real-time feedback that can be accessed on-demand through a dashboard could contribute to building culture of looking at data to drive performance “I wish if we could use the system of COVID because data is in time. And then when data is in time, data processing is in time, and then we can react with the findings. But if data are late than we all will be late.” Participant 7 “The thing is that we're able to just to… go back to the area of the dashboard and all of this, is that, then we're able to have like on a daily basis, we're able just to know how many people were vaccinated. So that will be helping us just to assess any issue or problem faced, as well as being able just to produce reports.” Participant 10 Data analysis was improved due to a standardising and centralising data analysis, and through partly automating the process through the EIR • Analysis of routine immunisation data could be expedited and partially automated “ was putting together coverage maps per area council from the different areas. So that would allow us just to monitor a bit, or the rollout was going and areas that needed more support… the tables were generated just quickly… So I think this is really a great advantage of, of DHIS2.” Participant 10 Coverage data were available by subgroups (e.g. by subnational levels including to the level of area council, gender and various priority population groups) • Currently subgroup analysis is only possible by subnational level (e.g. by province, area council), and are unavailable by certain variables as data are reported in aggregate • With an EIR, analyses could be conducted by various subgroups, with further analyses conducted to answer policy-specific questions “So for now, if you come in and ask I want the number of people this age group. Females only, you know, that are vaccinated for COVID, we can get that information from the DHIS2 COVID Tracker. But if you go up to EPI and tell them that we want this number of people that are vaccinated for BCG, females only, this age group, I don't think they'll be able to give you that information because it's an aggregated data… they don't have the gender disaggregation and the age disaggregation. So once we start looking at those age disaggregation and sex disaggregation, I think this will be very, very useful.” Participant 13 Individuals were traceable, so individuals who required a second dose of COVID-19 vaccine could be recalled for vaccination • Potential to improve the ability to track and recall children for vaccination and to plan activities to reach under- or unvaccinated children, especially outreach vaccination “The system will allow us to identify defaulters, so people who are taking their first dose but hadn't returned to take their second dose. We were able to generate lists of defaulters and the Red Cross volunteers were recruited to call, do follow up calls.” Participant 2 Assessments of adverse events following immunisation (AEFIs) to establish causality were easier and faster due to the ability to immediately check vaccination status in the EIR • AEFI causality assessment is challenging with the current paper-based system and can take several weeks due to individual records being held only in facility-based paper registers “So a requirement of being able to apply for compensation [i.e. for an AEFI], if that occurred would be to have a patient level record. Without that [i.e. individual level record], I don't think that would have been possible.” Participant 2 “It was very helpful in terms of just verifying reports, because we did have cases of people and these ones were mostly I would say, event based reports. So we would just get reports from outside. Someone, like maybe someone died. And they would say, Oh, it was because of the vaccine. There was a lot of vaccine hesitancy so that, that created an influx of reports as well. And DHIS2 [i.e. COVID-19 EIR], we were able to use it to sometimes verify the report. If we had a case who got severely ill in one island or a village, we would be able to verify if they were in fact, they had, in fact, been vaccinated, or if there were even any vaccinations carried out in that island at the time.” Participant 6 Vaccine recipients could electronically access and generate their own vaccination certificates • Ability for individuals to access their own immunisation records, which is increasing in importance for the purposes of proving immunisation for travel • Currently individuals only receive a paper-based immunisation card, which if lost means there is no record of immunisation • Cited as an example of a health system innovated that can enable patient-centred care “I can imagine, in the future when a routine immunisation registry comes online, that same level of accessibility will be made available to recipients, so children who receive their immunisations, once they grow up, or their parents, will be able to go online, download the vaccine records, without having necessarily to come back to us and ask us to dig up their records. So I feel that that was quite great to see that, that was something we were able to make work... I think that was a first in Vanuatu in terms of that level of patient centricity.” Participant 2 AEFI: Adverse event following immunisation; DHIS2: District Health Information System 2; EIR: Electronic immunisation register; EPI: Expanded Programme on Immunization; HIS: Health Information System; NTD: Neglected tropical diseases However, interviewees expressed their concern that an EIR could not address many of the issues with the routine immunisation information system, particularly given the differences in context and resource availability to the COVID-19 pandemic. One interviewee stated: “if something is not working on paper, introducing digital electronic tools won’t make it better.” (Participant 10) This was echoed by others, who stated their concern that introducing a digital tool alone cannot rectify the underlying behavioural and organisational constraints. An EIR would also introduce additional challenges, such as lack of digital literacy and ability to use the EIR and reliance on internet connectivity which will limit access given the current digital infrastructure. Interviewees discussed that several aspects of the health system had to work together to create a synergistic ecosystem where the EIR could enable increased use of coverage data in decision-making: “You can have the best software... If nobody is entering the data accurately at the beginning, as we say, well, we won't get anything out of it. So even if we get something out of it, then we still have issues, people actually using it to make a decision. So yeah, it's just a tool. I think it can help. But again, depending on the context, I don't think it will be helpful in every context. There's still a lot of situations, I think, where using paper works better than trying just to introduce those tools. Again, as we say, you need the enabling environment just to have this work.” Participant 10 Discussion This study examines health system factors that impact data use in immunisation decision-making, and the potential for EIRs to increase data-driven decision-making through improvements in the synthesis, quality and availability of immunisation data. We found evidence that the EIR facilitated decision-making at the service delivery level by enabling follow-up of individuals due for vaccination, and decision-making at the health system level to take targeted actions to improve delivery of mass COVID-19 vaccination. Use of COVID-19 vaccination coverage data was strongly influenced by a sense of urgency during the COVID-19 pandemic and desire to achieve coverage objectives. In contrast, we found evidence of limited and inconsistent routine immunisation coverage data use at the national level; data use was almost non-existent at other levels of the health system. Motivation and accountability to produce and use routine immunisation data was lacking throughout the health system. Factors that hindered the use of routine immunisation coverage data included inadequate data management processes, lack of performance feedback, limited demand and perceived value of data, lack of accountability for performance, poor data literacy and the heavy workload of healthcare workers. These issues are similar to those identified in studies examining the use of routine health information systems data in other LMICs, particularly challenges with human resource capacity and training, data governance, technical and procedural barriers in health information systems, and lack of leadership and ownership of data. 4 , 5 , 8 , 38 , 39 A single intervention is rarely sufficient to address these barriers. 4 , 39 , 40 Improving data use in decision-making at any level of the health system requires a multi-pronged approach with a suite of interventions to address the various behavioural and organisational issues preventing use. 4 , 35 , 39 – 41 Experience from digitising health systems in five African countries showed that efforts to advance data use go beyond the digital tools themselves, and depend on policies, infrastructure and workforce capacity-building. 42 Some strategies to increase data use include training health workers, improving data governance (e.g. strengthening supportive supervision or reinforcing and communicating management structures), digitalising health information systems (especially through developing easily accessible dashboards), streamlining data collection tools and processes (e.g. reducing repetition across forms), and improving data management including through introducing dedicated monitoring and evaluation staff positions. 4 , 39 , 40 Reducing the amount of data collected to focus on what is important for programmatic use rather than reporting on a wide variety of indicators can also improve data quality and use. 4 EIRs can contribute towards addressing some of the barriers to data use including healthcare worker capacity issues. Time-and-motion studies conducted pre- and post-EIR implementation in Tanzania and Kenya (EIRs implemented in 2017 and 2018, respectively) have found that shifting from paper-based to electronic-only systems can save up to 50% of time in an immunisation visit. 24 , 43 In our study, the process for recording and reporting COVID-19 vaccination data was much more streamlined and standardised compared to routine immunisation – having a single data entry form avoided confusion and work involved in completing multiple forms typical for routine immunisation. There are similarities in the benefits of EIRs with digitalised disease and outbreak surveillance systems, which have allowed more rapid data collection and automated analysis leading to faster detection of outbreaks and thus a more timely public health response. 44 , 45 One of the key findings of our study was that increased data use was directly related to the public health context at the time, i.e. the COVID-19 pandemic emergency context. There was not just demand for data but an expectation that decision-makers would be constantly monitoring performance. This led to a self-sustaining cycle of data use, whereby increased demand for coverage data led to implementation of systems to improve the quality of recording and reporting, which increased data availability and use. This pattern was observed in other settings and theories of data use in routine health information systems, with the Data Use Acceleration and Learning model describing it as a critical component. 42 , 43 Interviewees in our study also highlighted the importance of health leadership in fostering a culture of data use and accountability for immunisation performance, citing the need to view this as a monitoring and evaluation cycle rather than a unidirectional administrative requirement. Our findings are corroborated by other studies that emphasise the role of performance feedback to build a data use culture – when workers have access to information on their performance, they are more motivated to use data in planning services to achieve goals and to review and implement quality assurance measures to improve data quality. 4 , 43 This study also demonstrated that an EIR can facilitate and increase data use in decision-making. This occurred within a public health context where multiple health system factors came together to create a synergistic ecosystem, revealing the interlinks between what is needed for an enabling EIR ecosystem and what is necessary for a well-performing immunisation system. Participants in our study frequently discussed the connections between the two, such as how system-wide issues with the workforce affect the ability to record and report immunisation data (due to limited capacity) and the ability to deliver immunisation services (e.g. poor staff retention with vacancies at health facilities resulting in no vaccinations administered). Poor data management affects other parts of the immunisation system, such as supply management which could lead to stockouts and missed opportunities for vaccination. This co-dependency means that interventions to build the EIR ecosystem can possibly strengthen other capacities within the health system, indirectly leading to further improvements in immunisation performance and possibly having spillover effects for other primary care programs. For example, in the Democratic Republic of Congo, health workers reported that changes to workflow following adoption of the DHIS2 COVID-19 EIR Tracker led to efficiencies for other health services. 41 Future research examining the impact of EIRs should also examine the potential effects on other aspects of immunisation system performance and broader health systems. This case study highlights that the influx of resources, all-hands-on-deck approach and heightened political will during a public health emergency presented an ideal opportunity to strengthen health and information systems. However, integrating EIRs or other innovations from the emergency to routine setting is neither automatic nor guaranteed – our study shows how the public health context differ hugely, and integrating the EIR requires dedicated effort, planning and resourcing. COVID-19 vaccination programs were arguably simpler in that a single vaccine is being administered as a 2-dose course, whereas routine immunisation requires administering multiple vaccines at specific timepoints requiring children to be recalled several times with catch-up schedules being more complex and dependent on a variety of factors. Furthermore, many countries were under pressure to achieve COVID-19 vaccination coverage goals and ended up implementing systems that could not be easily integrated for routine immunisation, representing lost opportunities for long term health system strengthening. 46 In 2022, the Maldives and Lao People’s Democratic Republic implemented EIRs for routine immunisation that used the same DHIS2 platform but were separate from the COVID-19 vaccination registers implemented during the pandemic. 47 , 48 This is reminiscent of experiences in past public health emergencies; during the Ebola virus crisis in West Africa in 2014–2016, a multitude of digital interventions were implemented but lacked a coordinated approach resulting in duplicative efforts, lack of interoperability and disenfranchisement among overwhelmed healthcare workers, resulting in few tools being integrated into routine systems and sustained in the long run. 49 Additional research is needed to understand what have been enabling factors for implementing innovative solutions, digital technologies or otherwise, during the pandemic that were then sustained, and the contextual factors influencing outcomes. The lessons learned can be used to develop a framework or decision-making tool to identify and implement activities and innovations that can help to optimise benefits during the crisis while strengthening health systems and be sustained in the future. 50 Our study is amongst the first to present empirical evidence on the role of an EIR in facilitating decision-making about COVID-19 vaccination programs. Nevertheless, our study had several limitations. Our study focused on experiences at the national level, given that decision-making about the COVID-19 vaccination program and EIR rollout was highly centralised. We captured some perspectives from those working at the provincial level, but had limited input from those working at the health facility level. Exploring the perspectives of those working at health facilities would be an important progression of this work and necessary in planning for a future nationwide EIR for routine immunisation, given the varying contextual factors across the country. Our study is also not necessarily representative of the experience of all provinces, especially those that are more remote. We interviewed a variety of health system actors involved in both immunisation and health information systems, but it is possible we inadvertently excluded some individuals. Many with leadership roles during the pandemic had moved to other roles by the time of data collection and were unavailable for interviews. Our study is also subject to recall bias as we asked people to relay what happened during the COVID-19 pandemic which was a very busy period. Desirability bias may have also influenced some responses, especially where individuals were expected to use coverage data as part of their current roles. We could not objectively verify what some interviewees said particularly how data was used, regularity of follow up, the effectiveness of recalling individuals for immunisation and/or the quality and frequency of feedback of coverage data through observations or other means. The diversity of perspectives captured and cross-validation of responses provides a high degree of confidence in our findings. Finally, our interpretation of findings is affected by our previous experiences, biases, beliefs about EIRs and use of immunisation data. Conclusion Our study adds to the limited body of evidence on the barriers and facilitators of data use for routine health programs, and contributes to the case for using EIRs to strengthen evidence-based decision-making for immunisation programs. Our study highlights that while EIRs have the potential to improve immunisation decision-making, their success relies upon having an enabling environment, driven by strong leadership and a culture of demand for data and its use. Digital tools like EIRs should not be viewed as a panacea that will automatically address issues related to poor data use and immunisation coverage – alone, they are simply a tool that may go unused if underlying issues in the health system, like lack of accountability for performance, remain. Taking a holistic health systems lens to EIR implementation, supplemented by steps to increase data use tailored to workers’ needs, can result in a self-sustaining cycle of data production and use. Abbreviations AEFI Adverse event following immunisation DHIS2 District Health Information System 2 EIR Electronic immunisation register EPI Expanded Programme on Immunization HIS Health information system LMIC Low-or-middle-income country VanHMIS Vanuatu Health Management Information System VDI Vaccine-preventable diseases and immunisation Declarations Clinical trial number : Not applicable Consent to Publish declaration : Not applicable Acknowledgements We would like to thank all key informants who participated in this study for their time and contributions. We would also like to thank the Vanuatu Ministry of Health for providing access to de-identified data from their COVID-19 immunisation register. Authors’ contributions Conception: CP, MS Study design: CP, MS, GS Data collection: CP, RT, CGC Data analysis and interpretation: CP, OC Supervision: MS Validation of findings: RT, CGC, SS, EM, PG, MFH, LV Writing – first draft: CP Critical revision: all authors Final approval: all authors Declarations of interest The authors declare no competing interests. Funding CP is supported by an Australian Government Research Training Program (RTP) Scholarship. CP received travel funding from the Australian National University. Ethical approval and consent to participate Ethical approval was obtained from the Vanuatu Ministry of Health Research Ethics Officer (approved 26 September 2023) and the Australian National University Human Research Ethics Committee (protocol no. 2023/568). All participants provided written informed consent prior to interviews. As part of the informed consent process, participants agreed that their quotes may be used in publicly-available reports in a de-identified manner; participants were given the option to decline to be quoted. Participants were provided with the option to review information sheets and provide written consent in English and Bislama (an English-based pidgin language spoken in Vanuatu). All interviews were conducted in English. Data availability Summary findings from this study are reported in the manuscript and tables. The coding framework used for qualitative data analysis are included in the supplementary materials. Individual-level data, i.e. full transcripts of interviews, cannot be released as per the conditions of ethical approval and to which participants agreed when consenting to participate in this study. The conditions of this study were reviewed and approved by the Vanuatu Ministry of Health Research Ethics Officer (approved 26 September 2023) and the Australian National University Human Research Ethics Committee (protocol no. 2023/568). Any requests for data not already available in the manuscript and supplementary materials must be approved by the Ethics Committee. Requests can be sent to [email protected] . Clinical trial number: not applicable. References Immunization, Agenda. Immunization Agenda. 2030: A global strategy to leave no one behind [Internet]. 2020 [cited 2021 May 26]. Available from: https://www.who.int/teams/immunization-vaccines-and-biologicals/strategies/ia2030 O’Brien KL, Lemango E, Nandy R, Lindstrand A. The immunization Agenda 2030: A vision of global impact, reaching all, grounded in the realities of a changing world. 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Supplementary Files VanuatudatauseEIRsuppmaterials.docx Cite Share Download PDF Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Discover Public Health → Version 1 posted Editorial decision: Revision requested 22 May, 2025 Reviews received at journal 14 May, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Editor assigned by journal 14 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6200226","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447646191,"identity":"2d5cafe3-bb0e-4bf7-8e57-f985831b26d6","order_by":0,"name":"Cyra Patel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFADduYDEMYBIjVIMDCzJZCshceAOC38M3IffmDcYVfHz8zz8XNhG4Mc340Exs88+Ey/kW4swXgmWUKymXez9Mw2BmPJGwnM0vi0GEikMUgwtjFLGBzm3SDN28aQuOFGAgMhLcw/GNvqJewP8zz+DdRSD9TC/JuAFjagLYclDJh52EC2JBjcSGDDa4vEmWdsFolnjkvOOMxmZs1zTsJw5pmHbZZz8Gjhb09jvvFxRzU/f3vz49s8ZTbyfMeTD994g0cLg0ACA0NiA8JWIGZswKEWZs0BwmpGwSgYBaNghAMAPUtDLcZlbdsAAAAASUVORK5CYII=","orcid":"","institution":"Australian National University","correspondingAuthor":true,"prefix":"","firstName":"Cyra","middleName":"","lastName":"Patel","suffix":""},{"id":447646192,"identity":"5bd3ac3e-7972-4169-ab3a-e92115b7cb05","order_by":1,"name":"Olivia Y. 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Sargent","email":"","orcid":"","institution":"Australian National University","correspondingAuthor":false,"prefix":"","firstName":"Ginny","middleName":"M.","lastName":"Sargent","suffix":""},{"id":447646196,"identity":"a4c06498-e368-4251-bc0e-9e805857d045","order_by":5,"name":"Esther Makanga","email":"","orcid":"","institution":"UNICEF","correspondingAuthor":false,"prefix":"","firstName":"Esther","middleName":"","lastName":"Makanga","suffix":""},{"id":447646197,"identity":"a01fa5fe-5ced-47b1-8528-a750a3ac5a76","order_by":6,"name":"Simon Samson","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Samson","suffix":""},{"id":447646198,"identity":"5a938278-8f12-4dff-96b9-63e1948e0750","order_by":7,"name":"Phillippe Guyant","email":"","orcid":"","institution":"World Health Organization Country Liaison Office","correspondingAuthor":false,"prefix":"","firstName":"Phillippe","middleName":"","lastName":"Guyant","suffix":""},{"id":447646199,"identity":"67e8084c-8161-4ede-9486-72569862eea4","order_by":8,"name":"Syed M Faizan ul Haque","email":"","orcid":"","institution":"World Health Organization Country Liaison Office","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"M Faizan ul","lastName":"Haque","suffix":""},{"id":447646200,"identity":"f7753992-a2a7-4459-a3a4-21b9af96e879","order_by":9,"name":"Lauro Vives","email":"","orcid":"","institution":"World Health Organization Country Liaison Office","correspondingAuthor":false,"prefix":"","firstName":"Lauro","middleName":"","lastName":"Vives","suffix":""},{"id":447646201,"identity":"8519ac0a-c6a3-401e-b81b-0033b3511be8","order_by":10,"name":"Meru Sheel","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Meru","middleName":"","lastName":"Sheel","suffix":""}],"badges":[],"createdAt":"2025-03-11 05:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6200226/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6200226/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-025-00949-0","type":"published","date":"2025-09-17T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82137073,"identity":"7a9daa6e-6856-4b67-88c3-b5be09fdc9fe","added_by":"auto","created_at":"2025-05-07 06:16:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258221,"visible":true,"origin":"","legend":"\u003cp\u003eTheory of change used in this study, based on the Performance of Routine Information System Management (PRISM) Framework\u003c/p\u003e\n\u003cp\u003eFigure 1 shows our theory of change for how immunisation data can lead to greater data use, and then in turn to public health benefits such as improved immunisation coverage and reduced burden of vaccine-preventable diseases. Several factors determine the extent to which this occurs, as shown in the outer circle of the diagram (i.e. behavioural factor, organisational factor, technical factor, the public health context and country factors).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6200226/v1/2b7f02cb6f647b8a4d775da5.jpeg"},{"id":82137072,"identity":"2ddfa480-adc0-4037-b348-1b416b1f401d","added_by":"auto","created_at":"2025-05-07 06:16:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271314,"visible":true,"origin":"","legend":"\u003cp\u003eData flow processes for childhood routine immunisation coverage data compared with COVID-19 vaccination coverage data\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the flow of immunisation data from the time of collection to the point at which data are used. Figure 2A shows this process for routine immunisation, and Figure 2B shows this process for COVID-19 vaccination.\u003c/p\u003e\n\u003cp\u003eEIR: Electronic immunisation register; EPI: Expanded Programme on Immunization; HIS: Health Information System\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6200226/v1/6fe2ab1db4505de6f133755e.png"},{"id":91889773,"identity":"1c770cb2-c075-41d6-9448-9570524c6f2e","added_by":"auto","created_at":"2025-09-22 16:01:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1995917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6200226/v1/4cedd88f-40b2-4c6e-b498-6e9903f25b57.pdf"},{"id":82134956,"identity":"4cc130ac-5ea4-48dc-9cde-a4e5b5b48d9c","added_by":"auto","created_at":"2025-05-07 06:08:01","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3615803,"visible":true,"origin":"","legend":"","description":"","filename":"VanuatudatauseEIRsuppmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6200226/v1/df8af63bbbce9cb365f95db7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data use in decision-making for immunisation: role of an electronic immunisation register in Vanuatu","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA robust health information system that provides actionable data can inform decision-making and guide strategies to improve health and immunisation systems.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Substantial volumes of immunisation data are collected and reported by countries each year.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e However, the use of data to drive decision-making is limited, especially in low-and-middle-income countries (LMICs). Enablers, as well as barriers, to using data span behavioural (i.e. health workers\u0026rsquo; motivation, confidence and competence to use health information systems), organisational (i.e. system processes and structures such as human and financial resources and management of health information systems) and technical (i.e. specialised knowledge and technology to develop and manage health information systems) determinants.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e For example, in Mozambique, efforts to increase accessibility to and the perceived value of data among workers increased data use in planning actions, but capacity constrains limited healthcare workers\u0026rsquo; ability to record and review data.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Additional factors constraining the use of data in decision-making across LMICs include poor quality of data, lack of availability when it is needed, and its lack of relevance to policy makers\u0026rsquo; and program planners\u0026rsquo; considerations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eShifting from manual methods of data collection and reporting to using digital systems, whereby data is collected and reported electronically, can streamline and automate data collection, improving the accessibility and quality of health data.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Here we examine the potential benefits of using electronic immunisation registers (EIRs). EIRs are confidential, computerised, population-based routine system used to capture, store, access, and share individual-level data on vaccination.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The uptake of EIRs by LMICs had been slow, but increased rapidly during the COVID-19 pandemic.\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e EIRs enable the collection of rich data and granular analyses, which can enable easier tracking of individuals to ensure complete vaccination and provide insights on immunisation coverage relevant to policy making and program planning.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The emerging body of evidence on EIR use in LMICs indicates successes in using them to improve vaccination coverage, streamline vaccine management, reduce costs to the health system through administrative efficiencies (particularly healthcare worker time), and inform actions in response to outbreaks of vaccine-preventable diseases.\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25 CR26\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e However empirical evidence on the role of EIRs in facilitating public health decision-making and planning is limited. Understanding the role of EIRs in addressing barriers to data use can help to enhance their effectiveness in translating data into evidence-based decisions and actions.\u003c/p\u003e \u003cp\u003eVanuatu is a lower-middle income small island developing state in the South Pacific, with a population of approximately 300,000 people living across 83 geographically dispersed islands.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e It has a decentralised health system, with immunisation services largely delivered via health centres and dispensaries in the public sector, and often relies on supplementary immunisation activities to catch-up on vaccination .\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e A team at the national level are responsible for program oversight and strategy, including governance, management, coordination, and vaccine procurement and distribution to provinces. Provincial offices are responsible for oversight of immunisation service delivery through primary healthcare services, and are the primary liaisons with workers at health facilities. For COVID-19 vaccination, the program was centrally managed by a national health emergency team, due to differences in speed, scale and target populations compared to routine immunisation. Additionally, resources across the health system were redirected away from routine services towards the emergency response.\u003c/p\u003e \u003cp\u003eImmunisation coverage in Vanuatu is consistently lower than other countries in the Pacific region.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e A Multiple Indicator Cluster Survey in 2023 reported coverage of DTP3 at 57.9% and of the first dose of measles-containing vaccine at 46.1% among children aged 12\u0026ndash;23 months of age (i.e. birth cohorts from 2021 and 2022), reflecting the decline in immunisation coverage during the COVID-19 pandemic.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The country\u0026rsquo;s ability to reach geographically remote populations to deliver immunisation and other primary healthcare services is limited by workforce capacity, logistical difficulties and infrastructural challenges.\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHealth information systems in Vanuatu, including those for immunisation, are largely paper-based, with data aggregated by healthcare workers at facilities reported via provincial offices to the national level. At the national level, a health information management system, the District Health Information System 2 (DHIS2), is used to manage health data for multiple public health programs. In 2021, Vanuatu Ministry of Health introduced an EIR exclusively for COVID-19 vaccination using DHIS2 Tracker (an application to DHIS2). Following the successes of the COVID-19 EIR, the Ministry of Health was motivated to implement the same EIR system for routine immunisation and sought to identify best practices from the COVID-19 EIR experience. At the time of writing, the scope and transition to a routine immunisation EIR were being planned, with paper-based systems still in place. In this study, we aimed to examine data use from paper-based systems for routine immunisation compared with an EIR for COVID-19 vaccination in Vanuatu.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions\u003c/h2\u003e \u003cp\u003eAs this study focuses on information systems for immunisations, we use the following definitions in this paper:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eData: raw information on vaccinations recorded by healthcare workers\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoverage data: estimates of vaccination coverage (derived following analysis of raw data on administered vaccinations) that can be used by decision-makers\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eData use: where health workers use raw data and/or coverage data to make decisions and take actions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and conceptual framework\u003c/h3\u003e\n\u003cp\u003eWe conducted a qualitative study, informed by a theory of change based on the Performance of Routine Information System Management Framework (PRISM),\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e adapted with elements from the Theory of Change for Supporting Data-Informed Decision-Making for Immunization Programs developed by Osterman et al.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) shows how an intervention to increase data use (i.e. the EIR in this case) can improve data collection and reporting processes and increase the availability of timely, high-quality coverage data. These can then be used in decision making, with improvements in immunisation coverage and equity expected to follow. To be successful, individuals must have the skills, knowledge and motivation to use the EIR and data outputs, and the appropriate health and information system structures (including governance, resourcing and technical infrastructure) to support data use must be in place. Our framework reflects the feedback loops between data collection, its use and health benefits, i.e. greater data use leads to improved data processes and actions to improve data quality. We used our framework to inform data collection tools, analysis and interpretation of findings.\u003c/p\u003e\n\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eWe purposively recruited health system actors responsible for managing immunisation programs or health information systems in Vanuatu. Participants included immunisation program managers at the national and provincial level, health information managers and officers at the national level, and staff from global development partner agencies. As the COVID-19 vaccination program was operationally managed nationally, we focused on recruiting national-level system actors.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e We conducted interviews with key informants, supplemented by a document review and a data quality assessment.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eKey informant interviews\u003c/span\u003e: We conducted 16 semi-structured interviews with key informants to examine health system actors\u0026rsquo; experiences with collecting, analysing and using data. Interviews were conducted face-to-face from 18th March \u0026ndash; 29th March 2024. Questions were guided by our research aims and informed by published tools of routine health information system assessments, specifically the PRISM tools.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Our interview guide (Appendix 1) covered three domains: 1) the individuals and processes involved in collecting, analysing and reporting data; 2) use of coverage data in decisions about immunisation programs; 3) perceptions of the EIR in facilitating data use. We asked questions separately for routine immunisation and COVID-19 vaccination given the vast differences in context and processes. We tailored questions during the interview based on the interviewee\u0026rsquo;s role and degree of involvement in routine immunisation and/or COVID-19 vaccination programs.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDocument review\u003c/span\u003e: Data were extracted from documents related to data flow for routine immunisation, and the implementation and use of the EIR. Key documents included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStrategies or plans for immunisation or health information systems in Vanuatu\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStandard operating procedures for collecting immunisation data\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExamples of outputs/reports following syntheses of data, such as coverage reports\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDocuments published within 5 years or currently in use.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eStudy team members at the Vanuatu Ministry of Health provided documents meeting these criteria.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData quality assessment\u003c/span\u003e: We obtained and analysed de-identified individual line-listed data on all COVID-19 vaccination encounters recorded in the EIR since its inception in May 2021 (earliest vaccination recorded on 27 May 2021) and the date of data extraction (23 April 2024).\u003c/p\u003e\n\u003ch3\u003eData analyses\u003c/h3\u003e\n\u003cp\u003eThe interviews were our primary source of data. We used findings from the document reviews and data quality assessment to cross-validate information provided by interviewees, e.g. to verify claims of improved data quality with the EIR, or the presence/absence of a policy for a specified data process.\u003c/p\u003e \u003cp\u003eKey informant interviews: We recorded and transcribed all interviews and analysed findings thematically based on our study framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Themes included data processes, data use and factors determining data use. Two researchers (CP and OC) initially coded a sample of 25% of interviews based on pre-agreed definitions of themes. Following discussion, a further 25% of interviews were coded by both researchers, with final adjustments to theme definitions (see Appendix 2) agreed amongst the study team. The primary researcher (CP) then coded the remaining 50% of interviews. Analyses were conducted using NVivo v.12. Findings were categorised under themes to describe how routine immunisation and COVID-19 vaccination data were collected, reported and used, factors explaining data use, differences between COVID-19 vaccination and routine immunisation, and the role of DHIS2. Immunisation data flows were diagrammatically summarised. Instances of data use and factors enabling or hindering data use were described narratively.\u003c/p\u003e \u003cp\u003eDocument review: Documents were analysed using the READ approach described by Dalglish et al\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e: 1) ready materials, 2) extract data, 3) analyse data, 4) distil findings. Documents were categorised as contextual (i.e. pertaining to the policy and/or health system environment), data process (i.e. pertaining to data collection, reporting and synthesis processes), and/or data use (i.e. outputs generated following data analysis). Relevant information on the purpose of each document, its characteristics (e.g. author, audience, date of production, etc.) content, relevance to the research questions, and its strengths and limitations were extracted using Microsoft Excel. Findings were synthesised narratively, categorised by document type.\u003c/p\u003e \u003cp\u003eEIR data quality assessment: We calculated descriptive statistics on the completeness (i.e. absence of missing data), validity (i.e. absence of errors), and timeliness (i.e. data recorded in the EIR within a specified timeframe since vaccination) of COVID-19 vaccination data recorded in the EIR. We conducted subgroup analyses by age of recipient, province, COVID-19 vaccination dose number, and vaccine brand. Details on the definitions and methods used to assess data quality are in Appendix 3. Analyses were conducted using RStudio (R version 4.4.1, 2024).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe interviewed 16 participants including 11 individuals (68.75%) from the Ministry of Health and five (31.25%) from development partner organisations. Most participants worked at the national level of the health system (n\u0026thinsp;=\u0026thinsp;12, 75%) and were highly experienced (4/16 [25%] with 2\u0026ndash;5 years and 7/16 [43.75%] with \u0026ge;\u0026thinsp;5 years of experience in their current role). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents interviewee characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummarises the demographic and professional characteristics of the 16 key informants interviewed in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi-Vanuatu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of education completed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor's degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-graduate training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional degree*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-reported computer literacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of role\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunisation/public health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth system level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvincial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganisation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinistry of Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartner organisations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime working in current role\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Includes clinical degrees i.e. medical and nursing\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe reviewed 23 documents, summarised in Appendix 4. We did not identify any documents containing information on a digital health strategy or policies related to data governance and security.\u003c/p\u003e\n\u003ch3\u003eImmunisation data processes\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the processes for recording, collating and transferring immunisation data through the health system. For routine immunisation, healthcare workers recorded data on child health registers, hand-held vaccination cards and tally sheets, and compiled data monthly into aggregated paper reporting forms for reporting to provinces. During document review, we noted replication across different reporting forms. Provincial officers then transferred the data into an Excel spreadsheet, though this was transitioning to a DHIS2-based aggregate system (Vanuatu Health Management Information System database \u0026ndash; VanHMIS). Data from provinces were aggregated by the national level EPI team with assistance from development partners. The process was simplified for COVID-19 vaccination due to the EIR: data officers recorded data on a paper form and the EIR, where it could be directly accessed by those analysing the data. Analysis for both routine immunisation and COVID-19 vaccination data were conducted exclusively at the national level, but by different teams.\u003c/p\u003e \u003cp\u003eInterviewees discussed that coverage data were generated much faster and were more detailed for COVID-19 vaccination, with summary reports regularly and publicly available:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;That [i.e. analysis and reporting for COVID-19 vaccination] was done very well. It was done on a real time basis. I think we had a new report every other week. Very well disaggregated. Male, female, different age groups, different target populations. So it was very easy to synthesize. And also, because it was being used in dashboards all over the place for public consumption. So it was excellently done. And there were no delays.\u0026rdquo; Participant 5\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn contrast, participants\u0026rsquo; perspectives differed on the extent to which coverage data on routine immunisation were fed back to health system actors. Some national level participants stated that they fed back data through following up in areas of low coverage or where issues were suspected, and that there were some meetings (e.g. annual review meetings with provincial officers) where coverage data were reviewed. However, the extent to which feedback was discussed and used in planning was unclear, and considered limited and irregular by some participants.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;As far as feedback and data back to facilities, and I think that goes, like that can go for not only for VDI [vaccine-preventable diseases and immunisation], but all other public health programs. Currently, it's just about strengthening that unidirectional process of data going from health facilities to province and national. But yeah, eventually it will be how can we reconnect that back down to the health facility so it can promote good planning, effective decision making.\u0026rdquo; Participant 2\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInterviewees identified several issues with data processes for routine immunisation compared with COVID-19 vaccination. Data were often incomplete or delayed, affecting analysis and availability of coverage data. Coverage data quality was affected by inconsistent sources of population denominator data, absence of data on immunisations administered by non-public sector providers (namely non-governmental organisations), lost data due to damaged paper records, discordance between data sources (e.g. coverage versus stock management), and difficulties in transferring data from health facilities to provincial offices in remote areas or due to inclement weather. There was lack of clarity amongst interviewees on whether there were standardised procedures for routine immunisation data collection and reporting, despite a manual for recording data in VanHMIS which verified some of the roles and responsibilities being identified during document review.\u003c/p\u003e \u003cp\u003eInterviewees perceived that data quality, particularly in terms of its timeliness and accuracy, were vastly improved for COVID-19 vaccination compared with routine immunisation. In our assessment of EIR data quality, we found a high proportion of completeness; all fields included in the analysis were \u0026ge;\u0026thinsp;99% complete except for the field identifying an individual as being pregnant (missing for 61.3%, 163,927/267,252). Dates of data entry into the EIR were invalid (i.e. before vaccination dates) in 45.6% of records (121,995/267,252), with dates largely valid for first doses of COVID-19 vaccination (97.6%) but mostly invalid for second, third and booster doses (\u0026gt;\u0026thinsp;93%, see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting a systemic rather than user error. Among records with valid dates of entry, 68.9% were entered on the day of vaccination, 82.7% within 3 days, and 87.7% within 7 days (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummarises the findings of the assessment of quality of COVID-19 vaccination data extracted from the COVID-19 vaccination electronic immunisation register in Vanuatu. It includes data recorded between May 2021 and April 2024. Key indicators include validity of data values and timeliness of data entry into the EIR (relative to date of vaccination). Results are shown by COVID-19 vaccination dose number.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOVID-19 vaccination dose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of records\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eValidity of dates of data entry into EIR*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eTimeliness of data entry, %\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. with valid dates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. with invalid dates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% with valid dates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% with invalid dates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSame day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWithin 3 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWithin 7 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWithin 14 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eWithin 30 days\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267,252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121,995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e96.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBy dose number\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146,949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e96.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102,595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101,425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e73.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e94.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBooster doses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e81.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e74.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e* A date of data entry into the EIR was considered valid if the date of data entry was the day of vaccination or later (dates of data entry before vaccination were considered invalid). Denominator for proportions is the total number of records in the EIR.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e# Timeliness of data entry defined as the proportion of records where the date that the vaccination was recorded on the EIR was within the specified timeframe (e.g. same day, within 3 days, etc.) of the date of vaccination. Denominator for proportions is the number of records in the EIR with valid dates of entry.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eUse of immunisation data\u003c/h3\u003e\n\u003cp\u003eInterviewees perceived that there was limited use of routine immunisation coverage data in decision-making, with use occurring largely at the national level. Interviewees described using coverage data to identify areas of low routine immunisation coverage and allocate resources, including vaccine stock, and implement additional activities. For example, interviewees reported using coverage data to plan mobile vaccination activities or implement a vaccination campaign, citing the example of a recent measles vaccination campaign that was conducted in response to ongoing low coverage. However, other interviewees were concerned that coverage data were not actually being used to plan immunisation services:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The outreach has used a lot of resources: you used time, you used transport, all these things. But when we went to the field, we saw a very small number of children, for the kinds of input that they are putting. So that means that that particular facility has not used the data to actually plan where they're going. They're just going because it's a default for outreach, they're not going because that is the area with a lot of dropout children.\u0026rdquo; Participant 5\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn contrast, COVID-19 vaccination coverage data were used regularly to monitor the rollout, identify areas that needed more resources, and plan actions to deliver the program. The ability to generate reports containing almost real-time coverage data allowed program planners to identify areas where there was low vaccine uptake and target resources appropriately:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The registry allowed us to monitor the stock levels of vaccines by province and to adjust the levels of vaccine being sent out to individual provinces based on the number of recorded immunisations.\u0026rdquo; Participant 2\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;For example, in \u0026lt;\u0026thinsp;province\u0026gt;, there was a bit of hesitancy which was obvious from the data. So, for example, the health promotion, you need to put in more work, in terms of advocacy and demand generation activities in that province. So there was that synthesis of the data\u0026hellip; by the Ministry of Health officials, and then using that to make decisions.\u0026rdquo; Participant 5\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInterviewees discussed the advantages of having access to individual-level data that allowed detailed analyses of vaccination coverage (e.g. coverage maps) disaggregated data by age, gender and risk groups. Having individual-level data enabled health workers to trace and recall people due for a second dose of COVID-19 vaccine. It also facilitated rapid investigations of causality of adverse events following immunisation (AEFIs) or claims that a death or serious illness were attributable to COVID-19 vaccination, which was especially useful in a political climate where rumours about COVID-19 vaccination were spreading:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;They were thinking vaccine, this lowers the immune system... it's one or two of them that has been requesting data, wanting to find out how many people were immunised. Out of these people immunised, how many of them has died from COVID? How many of them has those adverse, you know, those reactions? How many of them? So this is where the data is very useful.\u0026rdquo; Participant 13\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDeterminants of effective data use of immunisation coverage\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the barriers to effective data use for routine immunisation, and factors that facilitated data use for COVID-19 vaccination. These are discussed in detail below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummarises the barriers to use of routine immunisation coverage data and facilitators for use of COVID-19 vaccination data in Vanuatu, by the type of factor i.e. behavioural factor, organisational factor, technical factor and public health context (i.e. routine immunisation vs COVID-19 vaccination during the COVID-19 pandemic). The table includes quotes from key informants demonstrating the barrier or facilitator identified.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBarriers for routine immunisation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFacilitators for COVID-19 vaccination\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription of barriers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepresentative quotes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription of facilitators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRepresentative quotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioural factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e● Limited data literacy and skill to record immunisation data\u003c/p\u003e \u003cp\u003e● Limited value placed on recording and reporting immunisation data\u003c/p\u003e \u003cp\u003e● No/limited motivation to produce data, considered in large part due to the absence of feedback to healthcare workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;We need to train and build the capacity of staff at different levels to look at those data to understanding what it is, as the first step\u0026hellip; this investigation of supervision skills, just to try to understand why some areas or health services have low coverage or low reporting rate. So again, it's developing those analytical skills in the first place.\u0026rdquo; \u003cem\u003eParticipant 10\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;That's one of the reasons why I think that many of the people in the health facilities, not only don't they have a lot of time to spend to do reports, because they want to take care of their patients. But they don't really see the need, they don't understand why, you know, they don't see the result of their input. You know, because no one is actually giving them any feedback, or very little of it, you know, so it doesn't act as an incentive for them to, to, you know, to start reporting on time.\u0026rdquo; \u003cem\u003eParticipant 15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● Data officers were well trained on data recording requirements\u003c/p\u003e \u003cp\u003e● Regular, public feedback on COVID-19 vaccination performance encouraged demand to view data, resulting in greater efforts to record data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ldquo;And it was expected, if you go out there, you have to record and you are following this data closely. The government was following this data closely. There was huge interest in the performance of the program, not just by the program itself, but even above political and even not just politically within the country, even globally.\u0026rdquo; \u003cem\u003eParticipant 5\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;During the time that the nurses were very smart and more concentrate on vaccination to the COVID vaccine, and they were like having this kind of interest, this kind of interest was very high. And then once we drop I mean come back to the normal.\u0026rdquo; \u003cem\u003eParticipant 16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganisational factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e● Constrained capacity of healthcare workers to record immunisation data due to conflicting clinical responsibilities\u003c/p\u003e \u003cp\u003e● Absence of regular feedback and mechanisms to review and embed coverage data into decision-making and planning\u003c/p\u003e \u003cp\u003e● Turnover and poor retention of healthcare workers, requiring further training on data recording requirements to be conducted\u003c/p\u003e \u003cp\u003e● Perception of lack of accountability for producing and using immunisation coverage data or holding health workers responsible for achieving targets\u003c/p\u003e \u003cp\u003e● Lack of demand for data on routine immunisation coverage at all levels of the health system; absence of a culture of using coverage data to monitor performance and to adjust programmatic planning and strategies (i.e. lack of a monitoring and evaluation cycle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;So the data is absolutely useful, but it's only useful if it gets back to the producer. So I think that is still a gap in terms of the feedback loop to the provinces, for them to be able to use the data for, you know, day to day action, and state accountability for their work.\u0026rdquo; \u003cem\u003eParticipant 5\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u0026hellip;.Continuous engagement, and feedback loops, and annual reviews and interactions with them, and showing them examples from other countries, all these kind of things, then the system will work. But if you just give them a training and tell them to go and do it. They have not done it six months later. They haven't. Yeah, nobody's following up.\u0026rdquo; \u003cem\u003eParticipant 5\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;The system has to be locally owned.\u0026rdquo; \u003cem\u003eParticipant 14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● Recruitment of dedicated data officers responsible for recording immunisation data; healthcare workers only administered vaccinations\u003c/p\u003e \u003cp\u003e● Data officers were trained on how to use the data collection tools\u003c/p\u003e \u003cp\u003e● Clarity and accountability for recording and reporting data throughout the health systems\u003c/p\u003e \u003cp\u003e● Strong demand to have regular updates on COVID-19 vaccination coverage performance with the health system (from Ministry of Health and partners), as well as from members of the public and politicians\u003c/p\u003e \u003cp\u003e● Demand for COVID-19 vaccination coverage data by individuals in leadership roles encouraged ownership of immunisation data and a culture of data demand and generation\u003c/p\u003e \u003cp\u003e● Influx of external resources (human, financial, material and technical) provided by development partners and donors, including to establish and implement the EIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ldquo;There was a lot of investment in human resource to the program, which is the main issue here in Vanuatu, human resource for health. So as a result, it was easy for the system to produce data, good quality data, within a short time, because those extra hands were there. And also, there was a lot of training. And there was also a lot of quality assurance across the whole system. So those kind of investments made the system successful.\u0026rdquo; \u003cem\u003eParticipant 5\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;At the time COVID-19, HR was not an issue because everybody was mobilised, including extra staff, volunteers and all of this\u0026hellip; with the resources existing here, I don't think you can reach the same level for routine immunisations, not in the short term.\u0026rdquo; \u003cem\u003eParticipant 10\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;It's like the partners who are more interested in the routine data. Not really the government. Yeah. That's a difference. But for the COVID. The government itself was interested.\u0026rdquo; \u003cem\u003eParticipant 5\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;Most of the Ministry of Health staff was mobilised and repurposed for this, while at the same time even the financial resources of the Ministry, whatever was able to be repurpose for this. And then there were additional resources from partners to put in for the rollout of the vaccination as well as to cover the costs for all these extra staff.\u0026rdquo; \u003cem\u003eParticipant 10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e● Perception of poor data management practices, particularly consistency in recording and reporting data, and lack of feedback on immunisation coverage especially to healthcare workers\u003c/p\u003e \u003cp\u003e● Paper-based system is labour-intensive and duplicative due to use of multiple data collection tools for immunisation\u003c/p\u003e \u003cp\u003e● Lack of consistency and clarity in how reports are transferred from health facilities to provincial offices, leading to loss of data especially in remote areas\u003c/p\u003e \u003cp\u003e● Aggregate data collection prevents in-depth analysis of vaccination coverage data by additional variables of interest\u003c/p\u003e \u003cp\u003e● Lack of consistent use of population denominator data and estimates of catchment area, leading to issues with the quality of coverage data\u003c/p\u003e \u003cp\u003e● Communication infrastructure, especially poor internet connectivity in remote areas, affects the ability and mechanisms to report immunisation data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Tools, everybody using a different way, like using the same but they change their, you know, the template in a way, like the rows were changed and the columns were up and down. And so it was very difficult to analyse.\u0026rdquo; \u003cem\u003eParticipant 11\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;Usually when they need to send the reports over not only for EPI, but also the HIS. the same recording is in the HIS. So when they, they have to send and if there's a delay, like rough seas or registration and then so, transportation there, then the delay will be like, reports for three months won\u0026rsquo;t receive on time.\u0026rdquo; \u003cem\u003eParticipant 7\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;There are transportation, logistic issues and then someone has to just to get the report back to the provincial level, where usually to be entered and then sent to central level. So generally, communication infrastructure is obviously affecting reporting.\u0026rdquo; \u003cem\u003eParticipant 10\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;Not every health facility will be having access to network. That's why on monthly health information reporting form they have to fill in hardcopy at health facility levels and send it on every first week of second month to the provincial official that the HIS officer at the provincial levels can input into the system.\u0026rdquo; \u003cem\u003eParticipant 14\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;I know it sounds really basic, but I think the most advanced thing that we can ever do here in Vanuatu, is to make sure that everyone is consistent. Consistency in doing is really the issue here. Yeah, yeah. So it's not the technologies, just the consistency of resources, consistency of focus consistency of entering the information.\u0026rdquo; \u003cem\u003eParticipant 15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● Clear procedures for reporting immunisation data, first on paper and then on the EIR, using a single standardised form\u003c/p\u003e \u003cp\u003e● Streamlined processes for data analysis and synthesis into reports with coverage data using standardised templates; consistency in reporting\u003c/p\u003e \u003cp\u003e● Individual-level data enabled detailed analyses of COVID-19 vaccination coverage and disaggregation by variables of interest, allowing better targeting of vaccination strategies\u003c/p\u003e \u003cp\u003e● Consistent use of population denominator data allowing for better comparisons across time\u003c/p\u003e \u003cp\u003e● Establishment of temporary satellite connections where internet was poor, allowing data to be entered into the EIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ldquo;One of the barrier is communication infrastructure. But again, only for COVID-19, it was overcome because there were specific just like satellite dishes that were just like installed and internet connection that were set up just for COVID-19 vaccination, which doesn't exist in every health facilities.\u0026rdquo; \u003cem\u003eParticipant 10\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;For the COVID-19 with the electronic record, we can generate data weekly, daily and weekly\u0026hellip;. for routine immunisation, we have to wait for all the Excel sheets to be analysed.\u0026rdquo; \u003cem\u003eParticipant 3\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;Then there was also the disaggregation based on the different target or rather the risk groups. We had all these people who had been classified as the risk groups, the healthcare workers, the older people, the children with comorbidities, etc. So the, the COVID data was separated in like it was disaggregated, and those kind of things. Also, there was the male female data, which is sometimes not available for the routine immunisation. And that's actually I mean, that's one of the challenges of the routine immunisation is the sex disaggregation. But for COVID, we had that readily available.\u0026rdquo; \u003cem\u003eParticipant 5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic health context\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e● Routine immunisation is ongoing and occurs at a steady \u0026ldquo;slow burn\u0026rdquo; pace over time\u003c/p\u003e \u003cp\u003e● Constrained resources, with healthcare workers responsible for delivering immunisation programs alongside all other health programs\u003c/p\u003e \u003cp\u003e● Negligible public interest in routine immunisation coverage\u003c/p\u003e \u003cp\u003e● Limited accountability if immunisation coverage targets are not achieved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;When you get into the routine side. Now you're back again, to a skeleton crew of one or two people, you know, who has to do the job, who has to do the writing, and who has to enter the data... unlike COVID, where, you know, you had an army of people.\u0026rdquo; \u003cem\u003eParticipant 15\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;I would say, I didn't get as much pushback, because a lot of our resources were being focused on COVID-19. And whereas after, when I had to expand to include other vaccinations, even though I had a lot more time, and I would say it wasn't as hectic to get done, I faced a lot more pushback then.\u0026rdquo; \u003cem\u003eParticipant 6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● COVID-19 vaccination was a short-term intense campaign, and implemented with a sense of urgency due to the threat of an outbreak\u003c/p\u003e \u003cp\u003e● Influx of resources from external partners and donors, and redirection of health system resources from routine programs to COVID-19 vaccination\u003c/p\u003e \u003cp\u003e● Intense media attention, public scrutiny and political interest in achieving high COVID-19 vaccination\u003c/p\u003e \u003cp\u003e● Achievement of COVID-19 vaccination targets tied to other events such as border re-openings, creating accountability to achieve targets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ldquo;The focus is a lot more sharper with COVID. Because we were right in the middle of a pandemic, right. So you had everybody's attention.\u0026rdquo; \u003cem\u003eParticipant 15\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;It was really because all units, the Ministry wants it, everybody was just like, looking at COVID-19 vaccination. And I think that's what made it possible as, as it was.\u0026rdquo; \u003cem\u003eParticipant 10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDHIS2: District Health Information System 2; EIR: Electronic immunisation register; EPI: Expanded Programme on Immunization; HIS: Health Information System; HR: Human resources\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBehavioural factors\u003c/h2\u003e \u003cp\u003eInterviewees perceived that health workers at all levels of the health system lacked the capacity and capability to effectively record, analyse, interpret and use immunisation data. Workers, especially at health facilities, were often overwhelmed, placed little value on recording and reporting data, and lacked motivation to do so. This was exacerbated by the lack of feedback on coverage data to healthcare workers.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;They don't receive any feedback at the health facilities. And that's something that demoralizes them. Because they've done a lot of data entry. And then they say, we didn't see any importance of this data and what is this data entry doing, because the feedback mechanism hasn't been working.\u0026rdquo; Participant 13\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn contrast, for COVID-19 vaccination, individuals were recruited and trained on how to record data, leaving healthcare workers to only administer vaccinations. Interviewees said that workers viewed the data as being useful for decision-making to increase vaccination coverage, increasing workers\u0026rsquo; motivation to accurately enter data in a timely manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOrganisational factors\u003c/h2\u003e \u003cp\u003eInterviewees discussed how system-level issues with the health workforce affected the availability and use of immunisation data, particularly the conflicting responsibilities of providing clinical care and recording and reporting data. Many facilities had only one nurse responsible for delivering care for all health programs. Interviewees also described problems with turnover and staff retention, with some participants estimating that 25\u0026ndash;30% of facilities had no qualified nurse at a given time. In another example, a vacancy in a provincial office meant that data for that province had not been reported for almost a year, and so it was unclear if low coverage estimates for the province were real or due to absent reporting. Some suggested embedding an additional nurse aid or administrator to record and report data rather than relying on nurses to record data. Interviewees also unanimously highlighted that ongoing training to build data literacy and supportive supervision was required to ensure proper recording and reporting of data and to use data to plan and target immunisation activities.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The thing is, we have to train them on the system, because it's not just giving them the access, but train them how to go into the system, where to go to see the data. I mean, the dashboards, where to go to do the data entry, how they can own, they themselves can study, looking at their own data.\u0026rdquo; Participant 13\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInterviewees also discussed that there was a lack of accountability for recording and reporting routine immunisation data throughout the health system, and that training alone was insufficient to address the lack of data culture. They discussed the need for greater leadership in driving the demand for data and its use. Interviewees cited this as a key enabler for data use during the COVID-19 vaccination rollout, where staff in leadership roles in the Ministry of Health drove the demand to see COVID-19 vaccination coverage data, encouraging accountability.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;It is the user to see the need. And the user\u0026hellip; does not necessarily mean the user at the lowest level, it also means at the leadership level\u0026hellip; unless these people actually advocate for the system, then it becomes difficult for the system to work. Because there is no ownership, there is no ownership then there's no sustainability.\u0026rdquo; Participant 5\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSome interviewees perceived that a monitoring and evaluation cycle, where coverage data are reviewed and plans and resources to improve performance follow, was absent, resulting in an absence of accountability to improve performance. They considered that health workers needed to see where gaps were and whether coverage data reflected their expectations, and that reports on coverage data addressed the needs of users. Thus, implementing processes where coverage data were regularly reviewed and considered in planning would drive workers to improve recording, reporting and managing immunisation data.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The only reason why we started doing this is to develop the culture of accountability. Looking at your data, looking at the performance\u0026hellip; The whole idea of review meeting is to support them. If they're not performing, how the higher level can support them. Sometimes financial needs requirements, sometimes the HR [human resource] things and more training required\u0026hellip; it's a cycle you know... monitoring and evaluating. We need to give feedback and review, support them. Then continue the cycle. This is the thing they need to establish.\u0026rdquo; Participant 11\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I believe that if you give them the tools, and you allow them to, you know, to be able to access the information easily\u0026hellip; and it's usable, that information culture would be good.\u0026rdquo; Participant 15\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInterviewees consistently referred to the role of partner organisations in facilitating data management and resourcing, and were frequent users of data to determine where gaps were and where vaccines and other resources should be allocated. They acknowledged that much of the work partners did was to fill gaps in workforce capacity and capabilities.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;It's not at an advisory role alone, but with the government, for short, they usually ask, can you do this? Can you please do the report? Okay. So you\u0026rsquo;re actually doing a government officer\u0026rsquo;s role, either because of capacity gap, or constraints in multiple tasks\u0026hellip; We are slowly weaning off from the partners to the government to take on the leading role. But we still have the constant challenges of the human resource of government\u0026hellip; But there's lapses in our contract lapses. So it returns to the partner. And then the processes takes time.\u0026rdquo; Participant 9\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInterviewees credited partners and donors during the COVID-19 pandemic with providing additional resources, including financial, human, technical expertise and material resources (e.g. hardware), that enabled the implementation of the EIR and improvements in data processes for COVID-19 vaccination. Interviewees highlighted funding for dedicated data entry officers, and stated that all the resources of the Ministry and partners were focused on COVID-19 vaccination during this time. While this was possible for COVID-19, interviewees did not consider having the same level of resourcing for routine immunisation as feasible.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;At the time COVID-19, HR [human resources] was not an issue because everybody was mobilised, including extra staff, volunteers and all of this\u0026hellip; with the resources existing here, I don't think you can reach the same level for routine immunisations, not in the short term.\u0026rdquo; Participant 10\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTechnical factors\u003c/h2\u003e \u003cp\u003eInterviewees discussed their concern that basic data management practices across all health programs needed to be improved. While substantial effort had already been invested in consolidating data collection tools for routine immunisation, there was still duplication and lack of consistency in recording and reporting. Manual transfer of paper documents from health facilities to provincial offices meant that documents could be lost along the way or did not reach the correct individual. There was a reliance on \u003cem\u003ead hoc\u003c/em\u003e measures to transfer data especially in more remote areas, such as waiting for a passing boat to send documents to the next island.\u003c/p\u003e \u003cp\u003eSome interviewees discussed that it was unclear if the data collected and coverage data generated were useful for decision-making, and that work needed to be undertaken to determine what was useful and adjust data collection tools based on users\u0026rsquo; needs and capacity.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;So I think what we'll have to do is, again, part of the requirements and design process, is we need to really sit down with the EPI [Expanded Programme on Immunization] team and say, listen, tell us how can we design the screen, so that it's designed for high throughput? Especially for looking at 2000 patient records to be entered each month. Yeah, so the user interface design needs to be done properly.\u0026rdquo; Participant 15\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDuring the COVID-19 pandemic, many of the data management issues were resolved by establishing clear roles and responsibilities, reporting pathways and eliminating manual document transfer through using the EIR. However, the EIR raised new challenges related to the communication infrastructure and lack of internet access in many parts of the country. These issues were largely addressed through the setup of temporary satellite connections, made possible by resources from donors, which interviewees acknowledged was neither feasible nor sustainable for routine immunisation without permanent improvements in the communications infrastructure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePublic health context\u003c/h2\u003e \u003cp\u003eInterviewees unanimously agreed that the context during the COVID-19 pandemic was vastly different to business-as-usual, and that the urgency of the pandemic increased the demand for and use of coverage data within the health sector and beyond. There was media and public scrutiny of COVID-19 vaccination coverage data within Vanuatu due to economic and political interests, particularly as Vanuatu closed their borders to control importation of COVID-19 infections and reopening borders was tied to achieving coverage targets. This, along with the global attention on COVID-19 vaccination and control, increased the desire to be able to rapidly and accurately calculate COVID-19 vaccination coverage on a real-time basis and ensure progress towards achieving pre-determined targets. Interviewees also discussed the influx of resources from partners and donors for all aspects of program delivery, and how all the resources of the health system were focused on COVID-19 vaccination at the expense of routine health programs. Thus, the capacity to deliver and monitor COVID-19 vaccination performance was much greater than for routine immunisation. Interviewees identified additional unique features of the pandemic including needing to generate individual vaccination certificates for travel and to monitor and assess AEFIs (a system to monitor AEFI was not previously in place), necessitating changes to the traditional paper-based systems.\u003c/p\u003e \u003cp\u003eInterviewees discussed that during the COVID-19 pandemic, all the necessary resources and systems aligned with the interests of actors both within and beyond the health system, and that the same scenario could not be easily replicated for routine immunisation:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I think it shows that, yes, if we have all the staff in the system, and a standardised format for data collection, standardised tool for data entry with proper training, the staff doing it. The properly designed software just us to manage, analyse, and then produce some output. That can help just to make decisions. And then yes, people just like using those data, just to make decisions. Yeah, it's works. But it's like everything with COVID. It's a bit like kind of the ideal situation, the way that everybody was focused on this. The whole resources of the Ministry, at national level, and at provincial level, were focused on this. Every other program was dropped out, everything else stopped. So this whole thing that it worked well for COVID-19 vaccine was really because of this situation. And again, I'm not sure how it could work in a different, for routine immunisation in the way that was used for COVID-19 vaccination.\u0026rdquo; Participant 10\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBroader country context\u003c/h2\u003e \u003cp\u003eInterviewees acknowledged that the country\u0026rsquo;s geography affected health information system structures and thus processes for routine immunisation data. They provided examples of destroyed and lost paper-based immunisation records and disrupted reporting due to climate-related disasters. Interviewees discussed that the context varies across the country, with urban centres like Port Vila and Luganville being very different to the isolated and remote islands in Torba and Tafea provinces. Some areas were difficult to reach and had poor or no internet access, affecting the transfer of immunisation data. The differing contexts and factors affecting health systems across the country meant that interviewees considered that a one-size-fits-all solution to data process issues may not be appropriate:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;That's why I say these challenges might be the same but how we approach it depends on the context.\u0026rdquo; Participant 14\u003c/em\u003e \u003c/p\u003e \u003cp\u003eInterviewees discussed differences between a small island developing nation like Vanuatu and larger countries, and that the small population and workforce size meant that solutions to similar problems in other countries will not necessarily work in Vanuatu.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;This is the problem in a small island setting like this, is that unlike places like Cambodia, or you know, the Philippines\u0026hellip; they have an army of people that can do these things\u0026hellip; trying to superimpose best practices in Australia does not mean to say it can work here...\u0026rdquo; Participant 15\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRole of an electronic immunisation register\u003c/h2\u003e \u003cp\u003eInterviewees cited several advantages of using an EIR for COVID-19 vaccination, including streamlined data recording and reporting, availability of real-time feedback, improved and partially automated analysis through the EIR (a function of the DHIS2 Tracker), and ability to generate granular coverage data by various population characteristics (e.g. gender). Interviewees also noted the EIR improved the ability to track and recall individuals for vaccination and follow-up after reported AEFI to establish causality, and enabled vaccine recipients to be able to generate their own vaccination certificates for the first time. These benefits are described further in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummarises the benefits of using the EIR for COVID-19 vaccination and potential role for an EIR for routine immunisation in Vanuatu, identified based on key informant interviews. The table includes quotes from key informants that provide evidence of the benefits observed for COVID-19 vaccination and the potential to improve routine immunisation or address barriers currently faced for routine immunisation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenefit of EIR for COVID-19 vaccination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential role of EIR for routine immunisation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepresentative quotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData recording and reporting were streamlined, avoiding the need to manually tally and transfer data between different levels of the health system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Potential to reduce healthcare workers\u0026rsquo; workload through reducing need to tally vaccination information each month and enter data into multiple reporting forms\u003c/p\u003e \u003cp\u003e\u0026bull; Some of the issues related to transferring data between health system levels could be alleviated, assuming an appropriate communications infrastructure was in place\u003c/p\u003e \u003cp\u003e\u0026bull; Issues related to loss of data due to loss or destruction of paper records could be reduced.\u003c/p\u003e \u003cp\u003e\u0026bull; Could improve issues related to completeness and timeliness of data reporting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I think it will make work much easier because most nurses they... we don't ask them to fill up all the forms, because we have HIS [health information system] and then we have immunisation. And we have malaria and we have NTD [neglected tropical diseases] reports to fill up. So that's too much for us. So maybe electronic will be much easier for them to send in all the data.\u0026rdquo; Participant 8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReal-time feedback were available and public reports generated frequently \u0026ndash; coverage reports were generated weekly, but estimates of vaccines administered could be generated daily on request\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Having the EIR linked to a dashboard could enable immediate feedback to workers, addressing some of the current issues related to lack of feedback and visibility of data in the current routine immunisation information system\u003c/p\u003e \u003cp\u003e\u0026bull; Real-time feedback that can be accessed on-demand through a dashboard could contribute to building culture of looking at data to drive performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I wish if we could use the system of COVID because data is in time. And then when data is in time, data processing is in time, and then we can react with the findings. But if data are late than we all will be late.\u0026rdquo; Participant 7\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;The thing is that we're able to just to\u0026hellip; go back to the area of the dashboard and all of this, is that, then we're able to have like on a daily basis, we're able just to know how many people were vaccinated. So that will be helping us just to assess any issue or problem faced, as well as being able just to produce reports.\u0026rdquo; Participant 10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData analysis was improved due to a standardising and centralising data analysis, and through partly automating the process through the EIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Analysis of routine immunisation data could be expedited and partially automated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;\u0026lt;Name\u0026thinsp;\u0026gt;\u0026thinsp;was putting together coverage maps per area council from the different areas. So that would allow us just to monitor a bit, or the rollout was going and areas that needed more support\u0026hellip; the tables were generated just quickly\u0026hellip; So I think this is really a great advantage of, of DHIS2.\u0026rdquo; Participant 10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage data were available by subgroups (e.g. by subnational levels including to the level of area council, gender and various priority population groups)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Currently subgroup analysis is only possible by subnational level (e.g. by province, area council), and are unavailable by certain variables as data are reported in aggregate\u003c/p\u003e \u003cp\u003e\u0026bull; With an EIR, analyses could be conducted by various subgroups, with further analyses conducted to answer policy-specific questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;So for now, if you come in and ask I want the number of people this age group. Females only, you know, that are vaccinated for COVID, we can get that information from the DHIS2 COVID Tracker. But if you go up to EPI and tell them that we want this number of people that are vaccinated for BCG, females only, this age group, I don't think they'll be able to give you that information because it's an aggregated data\u0026hellip; they don't have the gender disaggregation and the age disaggregation. So once we start looking at those age disaggregation and sex disaggregation, I think this will be very, very useful.\u0026rdquo; Participant 13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividuals were traceable, so individuals who required a second dose of COVID-19 vaccine could be recalled for vaccination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Potential to improve the ability to track and recall children for vaccination and to plan activities to reach under- or unvaccinated children, especially outreach vaccination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;The system will allow us to identify defaulters, so people who are taking their first dose but hadn't returned to take their second dose. We were able to generate lists of defaulters and the Red Cross volunteers were recruited to call, do follow up calls.\u0026rdquo; Participant 2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssessments of adverse events following immunisation (AEFIs) to establish causality were easier and faster due to the ability to immediately check vaccination status in the EIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; AEFI causality assessment is challenging with the current paper-based system and can take several weeks due to individual records being held only in facility-based paper registers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;So a requirement of being able to apply for compensation [i.e. for an AEFI], if that occurred would be to have a patient level record. Without that [i.e. individual level record], I don't think that would have been possible.\u0026rdquo; Participant 2\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;It was very helpful in terms of just verifying reports, because we did have cases of people and these ones were mostly I would say, event based reports. So we would just get reports from outside. Someone, like maybe someone died. And they would say, Oh, it was because of the vaccine. There was a lot of vaccine hesitancy so that, that created an influx of reports as well. And DHIS2 [i.e. COVID-19 EIR], we were able to use it to sometimes verify the report. If we had a case who got severely ill in one island or a village, we would be able to verify if they were in fact, they had, in fact, been vaccinated, or if there were even any vaccinations carried out in that island at the time.\u0026rdquo; Participant 6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccine recipients could electronically access and generate their own vaccination certificates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Ability for individuals to access their own immunisation records, which is increasing in importance for the purposes of proving immunisation for travel\u003c/p\u003e \u003cp\u003e\u0026bull; Currently individuals only receive a paper-based immunisation card, which if lost means there is no record of immunisation\u003c/p\u003e \u003cp\u003e\u0026bull; Cited as an example of a health system innovated that can enable patient-centred care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026ldquo;I can imagine, in the future when a routine immunisation registry comes online, that same level of accessibility will be made available to recipients, so children who receive their immunisations, once they grow up, or their parents, will be able to go online, download the vaccine records, without having necessarily to come back to us and ask us to dig up their records. So I feel that that was quite great to see that, that was something we were able to make work... I think that was a first in Vanuatu in terms of that level of patient centricity.\u0026rdquo; Participant 2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAEFI: Adverse event following immunisation; DHIS2: District Health Information System 2; EIR: Electronic immunisation register; EPI: Expanded Programme on Immunization; HIS: Health Information System; NTD: Neglected tropical diseases\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, interviewees expressed their concern that an EIR could not address many of the issues with the routine immunisation information system, particularly given the differences in context and resource availability to the COVID-19 pandemic. One interviewee stated: \u003cem\u003e\u0026ldquo;if something is not working on paper, introducing digital electronic tools won\u0026rsquo;t make it better.\u0026rdquo; (Participant 10)\u003c/em\u003e This was echoed by others, who stated their concern that introducing a digital tool alone cannot rectify the underlying behavioural and organisational constraints. An EIR would also introduce additional challenges, such as lack of digital literacy and ability to use the EIR and reliance on internet connectivity which will limit access given the current digital infrastructure. Interviewees discussed that several aspects of the health system had to work together to create a synergistic ecosystem where the EIR could enable increased use of coverage data in decision-making:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;You can have the best software... If nobody is entering the data accurately at the beginning, as we say, well, we won't get anything out of it. So even if we get something out of it, then we still have issues, people actually using it to make a decision. So yeah, it's just a tool. I think it can help. But again, depending on the context, I don't think it will be helpful in every context. There's still a lot of situations, I think, where using paper works better than trying just to introduce those tools. Again, as we say, you need the enabling environment just to have this work.\u0026rdquo; Participant 10\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examines health system factors that impact data use in immunisation decision-making, and the potential for EIRs to increase data-driven decision-making through improvements in the synthesis, quality and availability of immunisation data. We found evidence that the EIR facilitated decision-making at the service delivery level by enabling follow-up of individuals due for vaccination, and decision-making at the health system level to take targeted actions to improve delivery of mass COVID-19 vaccination. Use of COVID-19 vaccination coverage data was strongly influenced by a sense of urgency during the COVID-19 pandemic and desire to achieve coverage objectives. In contrast, we found evidence of limited and inconsistent routine immunisation coverage data use at the national level; data use was almost non-existent at other levels of the health system. Motivation and accountability to produce and use routine immunisation data was lacking throughout the health system.\u003c/p\u003e \u003cp\u003eFactors that hindered the use of routine immunisation coverage data included inadequate data management processes, lack of performance feedback, limited demand and perceived value of data, lack of accountability for performance, poor data literacy and the heavy workload of healthcare workers. These issues are similar to those identified in studies examining the use of routine health information systems data in other LMICs, particularly challenges with human resource capacity and training, data governance, technical and procedural barriers in health information systems, and lack of leadership and ownership of data.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e A single intervention is rarely sufficient to address these barriers.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Improving data use in decision-making at any level of the health system requires a multi-pronged approach with a suite of interventions to address the various behavioural and organisational issues preventing use.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Experience from digitising health systems in five African countries showed that efforts to advance data use go beyond the digital tools themselves, and depend on policies, infrastructure and workforce capacity-building.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Some strategies to increase data use include training health workers, improving data governance (e.g. strengthening supportive supervision or reinforcing and communicating management structures), digitalising health information systems (especially through developing easily accessible dashboards), streamlining data collection tools and processes (e.g. reducing repetition across forms), and improving data management including through introducing dedicated monitoring and evaluation staff positions.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Reducing the amount of data collected to focus on what is important for programmatic use rather than reporting on a wide variety of indicators can also improve data quality and use.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEIRs can contribute towards addressing some of the barriers to data use including healthcare worker capacity issues. Time-and-motion studies conducted pre- and post-EIR implementation in Tanzania and Kenya (EIRs implemented in 2017 and 2018, respectively) have found that shifting from paper-based to electronic-only systems can save up to 50% of time in an immunisation visit.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e In our study, the process for recording and reporting COVID-19 vaccination data was much more streamlined and standardised compared to routine immunisation \u0026ndash; having a single data entry form avoided confusion and work involved in completing multiple forms typical for routine immunisation. There are similarities in the benefits of EIRs with digitalised disease and outbreak surveillance systems, which have allowed more rapid data collection and automated analysis leading to faster detection of outbreaks and thus a more timely public health response.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOne of the key findings of our study was that increased data use was directly related to the public health context at the time, i.e. the COVID-19 pandemic emergency context. There was not just demand for data but an expectation that decision-makers would be constantly monitoring performance. This led to a self-sustaining cycle of data use, whereby increased demand for coverage data led to implementation of systems to improve the quality of recording and reporting, which increased data availability and use. This pattern was observed in other settings and theories of data use in routine health information systems, with the Data Use Acceleration and Learning model describing it as a critical component.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Interviewees in our study also highlighted the importance of health leadership in fostering a culture of data use and accountability for immunisation performance, citing the need to view this as a monitoring and evaluation cycle rather than a unidirectional administrative requirement. Our findings are corroborated by other studies that emphasise the role of performance feedback to build a data use culture \u0026ndash; when workers have access to information on their performance, they are more motivated to use data in planning services to achieve goals and to review and implement quality assurance measures to improve data quality.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study also demonstrated that an EIR can facilitate and increase data use in decision-making. This occurred within a public health context where multiple health system factors came together to create a synergistic ecosystem, revealing the interlinks between what is needed for an enabling EIR ecosystem and what is necessary for a well-performing immunisation system. Participants in our study frequently discussed the connections between the two, such as how system-wide issues with the workforce affect the ability to record and report immunisation data (due to limited capacity) and the ability to deliver immunisation services (e.g. poor staff retention with vacancies at health facilities resulting in no vaccinations administered). Poor data management affects other parts of the immunisation system, such as supply management which could lead to stockouts and missed opportunities for vaccination. This co-dependency means that interventions to build the EIR ecosystem can possibly strengthen other capacities within the health system, indirectly leading to further improvements in immunisation performance and possibly having spillover effects for other primary care programs. For example, in the Democratic Republic of Congo, health workers reported that changes to workflow following adoption of the DHIS2 COVID-19 EIR Tracker led to efficiencies for other health services.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Future research examining the impact of EIRs should also examine the potential effects on other aspects of immunisation system performance and broader health systems.\u003c/p\u003e \u003cp\u003eThis case study highlights that the influx of resources, all-hands-on-deck approach and heightened political will during a public health emergency presented an ideal opportunity to strengthen health and information systems. However, integrating EIRs or other innovations from the emergency to routine setting is neither automatic nor guaranteed \u0026ndash; our study shows how the public health context differ hugely, and integrating the EIR requires dedicated effort, planning and resourcing. COVID-19 vaccination programs were arguably simpler in that a single vaccine is being administered as a 2-dose course, whereas routine immunisation requires administering multiple vaccines at specific timepoints requiring children to be recalled several times with catch-up schedules being more complex and dependent on a variety of factors. Furthermore, many countries were under pressure to achieve COVID-19 vaccination coverage goals and ended up implementing systems that could not be easily integrated for routine immunisation, representing lost opportunities for long term health system strengthening.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e In 2022, the Maldives and Lao People\u0026rsquo;s Democratic Republic implemented EIRs for routine immunisation that used the same DHIS2 platform but were separate from the COVID-19 vaccination registers implemented during the pandemic.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e This is reminiscent of experiences in past public health emergencies; during the Ebola virus crisis in West Africa in 2014\u0026ndash;2016, a multitude of digital interventions were implemented but lacked a coordinated approach resulting in duplicative efforts, lack of interoperability and disenfranchisement among overwhelmed healthcare workers, resulting in few tools being integrated into routine systems and sustained in the long run.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e Additional research is needed to understand what have been enabling factors for implementing innovative solutions, digital technologies or otherwise, during the pandemic that were then sustained, and the contextual factors influencing outcomes. The lessons learned can be used to develop a framework or decision-making tool to identify and implement activities and innovations that can help to optimise benefits during the crisis while strengthening health systems and be sustained in the future.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study is amongst the first to present empirical evidence on the role of an EIR in facilitating decision-making about COVID-19 vaccination programs. Nevertheless, our study had several limitations. Our study focused on experiences at the national level, given that decision-making about the COVID-19 vaccination program and EIR rollout was highly centralised. We captured some perspectives from those working at the provincial level, but had limited input from those working at the health facility level. Exploring the perspectives of those working at health facilities would be an important progression of this work and necessary in planning for a future nationwide EIR for routine immunisation, given the varying contextual factors across the country. Our study is also not necessarily representative of the experience of all provinces, especially those that are more remote. We interviewed a variety of health system actors involved in both immunisation and health information systems, but it is possible we inadvertently excluded some individuals. Many with leadership roles during the pandemic had moved to other roles by the time of data collection and were unavailable for interviews. Our study is also subject to recall bias as we asked people to relay what happened during the COVID-19 pandemic which was a very busy period. Desirability bias may have also influenced some responses, especially where individuals were expected to use coverage data as part of their current roles. We could not objectively verify what some interviewees said particularly how data was used, regularity of follow up, the effectiveness of recalling individuals for immunisation and/or the quality and frequency of feedback of coverage data through observations or other means. The diversity of perspectives captured and cross-validation of responses provides a high degree of confidence in our findings. Finally, our interpretation of findings is affected by our previous experiences, biases, beliefs about EIRs and use of immunisation data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study adds to the limited body of evidence on the barriers and facilitators of data use for routine health programs, and contributes to the case for using EIRs to strengthen evidence-based decision-making for immunisation programs. Our study highlights that while EIRs have the potential to improve immunisation decision-making, their success relies upon having an enabling environment, driven by strong leadership and a culture of demand for data and its use. Digital tools like EIRs should not be viewed as a panacea that will automatically address issues related to poor data use and immunisation coverage \u0026ndash; alone, they are simply a tool that may go unused if underlying issues in the health system, like lack of accountability for performance, remain. Taking a holistic health systems lens to EIR implementation, supplemented by steps to increase data use tailored to workers\u0026rsquo; needs, can result in a self-sustaining cycle of data production and use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAEFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdverse event following immunisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDHIS2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDistrict Health Information System 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic immunisation register\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpanded Programme on Immunization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth information system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-or-middle-income country\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVanHMIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVanuatu Health Management Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVaccine-preventable diseases and immunisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eWe would like to thank all key informants who participated in this study for their time and contributions. We would also like to thank the Vanuatu Ministry of Health for providing access to de-identified data from their COVID-19 immunisation register.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003eConception: CP, MS\u003c/p\u003e\n\u003cp\u003eStudy design: CP, MS, GS\u003c/p\u003e\n\u003cp\u003eData collection: CP, RT, CGC\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation: CP, OC\u003c/p\u003e\n\u003cp\u003eSupervision: MS\u003c/p\u003e\n\u003cp\u003eValidation of findings: RT, CGC, SS, EM, PG, MFH, LV\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; first draft: CP\u003c/p\u003e\n\u003cp\u003eCritical revision: all authors\u003c/p\u003e\n\u003cp\u003eFinal approval: all authors\u003c/p\u003e\n\u003ch3\u003eDeclarations of interest\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eCP is supported by an Australian Government Research Training Program (RTP) Scholarship. CP received travel funding from the Australian National University.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eEthical approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eEthical approval was obtained from the Vanuatu Ministry of Health Research Ethics Officer (approved 26 September 2023) and the Australian National University Human Research Ethics Committee (protocol no. 2023/568). All participants provided written informed consent prior to interviews. As part of the informed consent process, participants agreed that their quotes may be used in publicly-available reports in a de-identified manner; participants were given the option to decline to be quoted. Participants were provided with the option to review information sheets and provide written consent in English and Bislama (an English-based pidgin language spoken in Vanuatu). All interviews were conducted in English.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eSummary findings from this study are reported in the manuscript and tables. The coding framework used for qualitative data analysis are included in the supplementary materials. Individual-level data, i.e. full transcripts of interviews, cannot be released as per the conditions of ethical approval and to which participants agreed when consenting to participate in this study. The conditions of this study were reviewed and approved by the Vanuatu Ministry of Health Research Ethics Officer (approved 26 September 2023) and the Australian National University Human Research Ethics Committee (protocol no. 2023/568). Any requests for data not already available in the manuscript and supplementary materials must be approved by the Ethics Committee. Requests can be sent to [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eImmunization, Agenda. Immunization Agenda. 2030: A global strategy to leave no one behind [Internet]. 2020 [cited 2021 May 26]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/teams/immunization-vaccines-and-biologicals/strategies/ia2030\u003c/span\u003e\u003cspan address=\"https://www.who.int/teams/immunization-vaccines-and-biologicals/strategies/ia2030\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Brien KL, Lemango E, Nandy R, Lindstrand A. The immunization Agenda 2030: A vision of global impact, reaching all, grounded in the realities of a changing world. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gh.bmj.com/lookup/doi/\u003c/span\u003e\u003cspan address=\"https://gh.bmj.com/lookup/doi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjgh-2020-002499\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2020-002499\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immunisation, data use, evidence-based decision-making, electronic immunisation register, routine health information systems, immunisation information systems","lastPublishedDoi":"10.21203/rs.3.rs-6200226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6200226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElectronic immunisation registers (EIRs) can strengthen immunisation programs by increasing access to rich data for policy-making. We examined data use from paper-based systems for routine immunisation compared with an EIR for COVID-19 vaccination in Vanuatu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a qualitative study, interviewing 16 key informants working within the Vanuatu Ministry of Health (n = 11) and international development agencies (n = 5). We thematically identified data use actions and factors determining data use with the Performance of Routine Information System Management framework. We verified findings through document review and quality assessment of EIR data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRoutine immunisation coverage data were used to identify coverage gaps, but used inconsistently in planning service delivery or strategic decision-making. In contrast, decision-makers used COVID-19 vaccination coverage data regularly to monitor and plan program rollout, allocate resources, and assess adverse events following immunisation. The EIR streamlined data processes, allowing data to be entered, analysed and shared at a faster pace. Barriers to using routine immunisation data included inadequate data management processes, minimal performance feedback, lack of data use culture, poor data literacy and workers’ heavy workload. For COVID-19 vaccination, EIR data use was enabled by increased resources, greater demand and accountability for data, and urgency to achieve high COVID-19 vaccination coverage during the pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe increased resourcing, emergency context and focus on COVID-19 fostered an environment for greater data use. While the EIR enabled rapid access to data, health leadership, regular feedback and accountability to achieve targets were necessary to increase data use in decision-making.\u003c/p\u003e","manuscriptTitle":"Data use in decision-making for immunisation: role of an electronic immunisation register in Vanuatu","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:07:56","doi":"10.21203/rs.3.rs-6200226/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-22T18:05:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-14T23:05:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T22:26:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38674872787951178929115606464288458354","date":"2025-04-23T06:46:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28983121714287076635772960738739907426","date":"2025-04-22T18:47:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-15T06:03:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T16:44:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T01:35:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-04-10T01:34:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec973194-f32b-4e44-b969-df13d9082257","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T15:58:30+00:00","versionOfRecord":{"articleIdentity":"rs-6200226","link":"https://doi.org/10.1186/s12982-025-00949-0","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2025-09-17 15:56:55","publishedOnDateReadable":"September 17th, 2025"},"versionCreatedAt":"2025-05-07 06:07:56","video":"","vorDoi":"10.1186/s12982-025-00949-0","vorDoiUrl":"https://doi.org/10.1186/s12982-025-00949-0","workflowStages":[]},"version":"v1","identity":"rs-6200226","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6200226","identity":"rs-6200226","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}