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There is growing interest and investment in electronic immunization registries (EIRs) in low- and middle-income countries. EIRs provide ready access to patient- and aggregate-level service delivery data that can be used to improve patient care, identify spatiotemporal trends in vaccination coverage and dropout, inform resource allocation and program operations, and target quality improvement measures. The Government of Tanzania introduced the Tanzania Immunization Registry (TImR) in 2017, and the system has since been rolled out in 3736 facilities in 15 regions.
The aims of this study are to conceptualize the additional ways in which EIRs can add value to immunization programs (beyond measuring vaccine coverage) and assess the potential value-add using EIR data from Tanzania as a case study.
This study comprised 2 sequential phases. First, a comprehensive list of ways EIRs can potentially add value to immunization programs was developed through stakeholder interviews. Second, the added value was evaluated using descriptive and regression analyses of TImR data for a prioritized subset of program needs.
The analysis areas prioritized through stakeholder interviews were population movement, missed opportunities for vaccination (MOVs), continuum of care, and continuous quality improvement. The included TImR data comprised 958,870 visits for 559,542 patients from 2359 health facilities. Our analyses revealed that few patients sought care outside their assigned facility (44,733/810,568, 5.52% of applicable visits); however, this varied by region; facility urbanicity, type, ownership, patient volume, and duration of TImR system use; density of facilities in the immediate area; and patient age. Analyses further showed that MOVs were highest among children aged <12 months (215,576/831,018, 25.94% of visits included an MOV and were applicable visits); however, there were few significant differences based on other individual or facility characteristics. Nearly half (133,337/294,464, 45.28%) of the children aged 12 to 35 months were fully vaccinated or had received all doses except measles-containing vaccine–1 of the 14-dose under-12-month schedule (ie, through measles-containing vaccine–1), and facility and patient characteristics associated with dropout varied by vaccine. The continuous quality improvement analysis showed that most quality issues (eg, MOVs) were concentrated in <10% of facilities, indicating the potential for EIRs to target quality improvement efforts.
EIRs have the potential to add value to immunization stakeholders at all levels of the health system. Individual-level electronic data can enable new analyses to understand service delivery or care-seeking patterns, potential risk factors for underimmunization, and where challenges occur. However, to achieve this potential, country programs need to leverage and strengthen the capacity to collect, analyze, interpret, and act on the data. As EIRs are introduced and scaled in low- and middle-income countries, implementers and researchers should continue to share real-world examples and build an evidence base for how EIRs can add value to immunization programs, particularly for innovative uses.
With the increasing digitalization of health systems worldwide, there is growing interest and investment in electronic immunization registries (EIRs). EIRs are “confidential, computerized, population-based systems that collect and consolidate vaccination data from vaccination providers for better immunization strategies” [
Vaccine coverage has historically been the primary metric for evaluating immunization programs. As an increasing number of low- and middle-income countries (LMICs) have begun implementing EIRs, vaccine coverage has been measured as a key outcome for assessing EIR effectiveness. Pre–post studies in Vietnam, Bangladesh, and Pakistan have demonstrated significant increases in child vaccination coverage after the introduction of EIRs that included SMS text message reminders and, in addition, in Pakistan, decision support systems [
In addition to improving vaccine coverage, other benefits of EIRs have been identified for individuals, immunization program performance and management, research, and population health [
In some settings where EIRs are being considered or introduced, immunization coverage may already be high and, therefore, not an appropriate metric for EIR added value. The Early-Stage Digital Health Investment Tool was developed to assist ministries of health in determining their readiness to introduce a digital health tool, such as an EIR, by assessing the core building blocks of digital health [
The aims of this study are to (1) conceptualize additional ways that EIRs can add value to immunization programs and (2) assess the feasibility and potential value-add using Tanzania as a case study.
This study comprised 2 sequential phases. First, a comprehensive list of ways EIRs can potentially add value for immunization programs was developed through stakeholder interviews. Second, the added value was evaluated using Tanzania Immunization Registry (TImR) data for a prioritized subset of program needs.
A comprehensive list of common barriers that country immunization programs face in achieving coverage and equity goals was used to identify the ways in which EIRs can add value. The list was adapted from a July 2019 Gavi workshop on
Stakeholder interviews were conducted to refine the framework of the immunization program barriers and potential EIR solutions. Stakeholders were purposively selected based on their expertise in research, policy, or implementation of EIRs. A total of 7 stakeholders participated in semistructured web-based interviews facilitated by the study team (EC) from November 2019 to January 2020. A total of 4 stakeholders were government officials from countries in Sub-Saharan Africa, identified through the BID (Better Immunization Data) Learning Network [
Data from Tanzania’s EIR were analyzed to illustrate how an EIR can add value to each of the prioritized topic areas. The Government of Tanzania partnered with the BID Initiative, funded by the Bill & Melinda Gates Foundation and launched in 2013, to design and implement a package of solutions to improve immunization data quality and use [
Immunization, facility, and patient data were extracted from the TImR system with permission from the Government of Tanzania. Data were deidentified after extraction, and all analyses were conducted using deidentified data. The development and implementation of the TImR system have been discussed in detail elsewhere [
This study received nonresearch determination from the PATH. The Government of Tanzania and the PATH have data-sharing permissions in place that guided the use of TImR data for this study.
The analyses focused on services provided between 2017 and 2019 and the vaccine doses that were included in the official Tanzania vaccine schedule, specifically Bacillus Calmette–Guérin (BCG); oral polio vaccine (OPV); diphtheria, tetanus, pertussis, hepatitis B, and
Tanzania vaccine schedule.a
Vaccine dose | Scheduled visit number | Age eligibility |
BCGb-0 and OPVc-0 | 1 | Birth or first contact |
OPV-1, Pentad-1, PCVe-1, and Rotaf-1 | 2 | 6 weeks |
OPV-2, Penta-2, PCV-2, and Rota-2 | 3 | 10 weeks |
OPV-3, Penta-3, and PCV-3 | 4 | 14 weeks |
MCVg-1 | 5 | 9 months |
MCV-2 | 6 | 18 months |
aInactivated polio vaccine immunization was excluded from our analyses as it was introduced partway through the analysis period. It would normally be received during visit 4 at the age of 14 weeks.
bBCG: Bacillus Calmette–Guérin.
cOPV: oral polio vaccine.
dPenta: diphtheria, tetanus, pertussis, hepatitis B, and
ePCV: pneumococcal conjugate vaccine.
fRota: rotavirus.
gMCV: measles-containing vaccine.
MOVs were assessed at the visit level using the World Health Organization (WHO) definition: “any contact with health services by an individual (child or person of any age) who is eligible for vaccination (e.g., unvaccinated or partially vaccinated and free of contraindications to vaccination), which does not result in the person receiving one or more of the vaccine doses for which he or she is eligible” [
Vaccine-specific dropout for multidose vaccines was defined as receiving the first but not the last dose in the vaccine schedule (eg, receiving PCV-1 but not PCV-3). OPV dropout was defined as receiving OPV-0 or OPV-1 and not OPV-3; children who did not receive OPV-0 by the age of 2 were eligible for OPV-1 without receiving OPV-0; therefore, either vaccine can be treated as the starting dose. We also assessed dropout between birth doses and first follow-up visit, defined as receiving either of the birth doses (BCG or OPV-0) but none of the first follow-up visit doses (OPV-1, Penta-1, PCV-1, and Rota-1). Finally, we assessed overall dropout, which is defined as receiving at least one scheduled vaccine dose but not completing the full 14-dose schedule. The dropout variables were constructed as binaries (meeting criteria for dropout or not), motivating the use of logistic regression in the models.
Children are assigned a home facility when they are registered in the TImR system based on their preferences and where they plan to receive care. A nonassigned visit is a visit to any health facility other than the assigned visit. This variable was constructed as a binary variable (visit at home facility or not), motivating the use of logistic regression in the models.
A dose was considered timely if it was received within 7 days of the scheduled date (
A facility was designated as urban if the ward in which it is located had a population density of at least 500 persons per square km and rural if otherwise [
Facility stock use, including days of 0 stock, is recorded in TImR by facility and vaccine type. Vaccine-specific stockout was defined as any period in which the stock balance for a given vaccine was zero. A composite indicator was also constructed for the proportion of days with a stockout, with the number of days with a stockout for a primary vaccine (BCG, OPV, PCV, Penta, Rota, or measles-containing vaccine [MCV]) as the numerator and the number of days with facility stock data in the TImR system for each primary vaccine as the denominator.
Age was defined in two ways: static age at the time of data extraction (December 31, 2019) and age at the time of a given visit. The 2 age variables were coded into 1-year categories up to the age of 5 years (ie, <12 months, 12-23 months, 24-35 months, 36-47 months, and 48-59 months), which is the upper limit of standard eligibility for most of the vaccines of interest.
For all analyses, we used mixed-effects logistic regression to assess the factors associated with the various outcomes. In all models, relevant patient and facility characteristics were included as fixed effects, and nested random intercepts for region, district, and facility ID were used to account for clustering.
On the basis of stakeholder input, 4 topics were prioritized for phase 2 analyses:
Denominators and population movements, including patient movement between facilities or geographic areas for care
MOVs, including their frequency and any associated characteristics
Continuum of care, including which children drop out and when in the vaccination schedule
Continuous quality improvement (CQI), including trends or outliers in data quality or service delivery, to inform targeted quality improvement efforts
The remainder of this section provides an overview of the TImR data and then provides results on each of the 4 priority topic areas to illustrate how EIR data can be used to better understand denominators and population movement, MOVs, continuum of care, and CQI.
EIR data can identify un- or underimmunized children and explore drivers of their vaccination status (eg, geography, demographic characteristics, and facility type).
EIR data can be used to analyze at what point children drop out of the continuum of care.
EIRs can have embedded decision support to guide health workers in delivering tailored messages or services to increase acceptance and uptake.
EIRs can be designed to streamline data capture and reduce the burden of data entry.
EIRs can be designed to meet decision-making needs for end users.
Access to data through EIRs can empower and motivate users and strengthen agency.
If EIRs are designed with individual health worker log-ins, EIRs can track human resources based on active health worker profiles.
EIR data can identify error rates of individual health workers and link them to additional training or supportive supervision.
EIRs can have embedded training resources or capacity assessments.
EIR data can be used to forecast service delivery needs by facility or district to optimize the distribution of human resources and session times.
EIR data can identify un- or underimmunized children to explore whether they are concentrated in certain geographic areas and if they have shared demographic characteristics to inform targeted outreach.
EIRs can track an individual’s vaccinations across public and private sector facilities.
EIRs can capture more accurate, timely, and complete denominators to inform microplanning.
EIR data can be used to understand population movement or health-seeking behaviors to inform microplanning (eg, how common it is for children to move between multiple facilities).
EIR data on current vaccine delivery can be used to forecast the necessary stock and human resources to introduce new vaccines.
The process of designing and introducing an EIR can help clarify and document governance structures related to immunization data.
EIR data can provide more accurate denominator estimates to inform costing and budgeting for the EPI.
EIRs can encourage continuous quality improvement by highlighting trends, outliers, or patterns that may require adaptive management.
EIRs provide more timely, detailed data compared with traditional paper-based reporting, which enables timely, responsive action from leaders.
EIRs can provide a platform for remote, web-based supportive supervision.
EIRs can show which facilities are entering data or not and factors associated with reporting.
EIRs can be designed to mimic health worker workflows to streamline data collection and reporting practices.
EIR service delivery data can be triangulated to see how consistent it is with vaccine stock data and to forecast stock needs.
EIR service delivery data can be used to inform decisions about vial size (eg, whether smaller vial sizes are needed in some areas to reduce waste).
EIRs can identify service delivery patterns to optimize health worker allocation and session timing to match demand.
EIRs that capture check-in time and vaccination time can calculate patient wait times.
EIRs can identify missed opportunities for vaccination.
EIRs can include stock reorder alerts to reduce stockout frequency.
EIR data triangulated with patient-level data on adverse events following immunization or surveillance data can answer questions about the effectiveness of vaccines given at different times.
The sample size for the individual analyses varied because of differing inclusion criteria and missing data. In full, our sample comprised 2,444,803 vaccine doses over 958,870 visits for 559,542 patients. These visits occurred in 2359 health facilities covering 57 districts in 10 regions. The median (IQR) number of provided doses per facility per month was 40 (9-123), and the median number of visits was 17 (4-49).
Patient and facility characteristics.a
Level and covariate | Number of visits, n (%) | Number of patients, n (%) | Number of facilities, n (%) | |||||
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Female | 472,782 (49.35) | 275,605 (49.31) | N/Ab | |||
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Male | 485,195 (50.65) | 283,361 (50.69) | N/A | |||
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<12 months | 235,387 (24.55) | 153,857 (21.61) | N/A | |||
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12-23 months | 300,948 (31.39) | 183,618 (25.8) | N/A | |||
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24-35 months | 300,646 (31.35) | 143,976 (20.23) | N/A | |||
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36-47 months | 106,673 (11.12) | 64,360 (9.04) | N/A | |||
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48-59 months | 13,389 (1.4) | 12,153 (1.71) | N/A | |||
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≥5 years | 1,828 (0.19) | 153,857 (21.61) | N/A | |||
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<12 | 833,349 (86.91) | N/A | N/A | |||
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12-23 | 111,259 (11.6) | N/A | N/A | |||
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24-35 | 10,138 (1.06) | N/A | N/A | |||
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36-47 | 2811 (0.29) | N/A | N/A | |||
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48-59 | 1283 (0.13) | N/A | N/A | |||
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Rural | 624,726 (66.2) | 365,459 (66.38) | N/A | |||
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Urban | 318,972 (33.8) | 185,106 (33.62) | N/A | |||
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Dispensary | 343,525 (60.39) | 1,953 (82.79) | 343,525 (60.39) | |||
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Health center | 152,496 (26.81) | 311 (13.18) | 152,496 (26.81) | |||
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Hospital | 72,786 (12.8) | 95 (4.03) | 72,786 (12.8) | |||
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Rural | 621,375 (65.78) | 364,817 (65.67) | 1873 (81.01) | |||
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Urban | 323,284 (34.22) | 190,689 (34.33) | 439 (18.99) | |||
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0-5 months | 5038 (0.53) | N/A | 104 (4.45) | |||
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6-11 months | 282,993 (29.63) | N/A | 1041 (44.56) | |||
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1 year | 183,826 (19.25) | N/A | 625 (26.76) | |||
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≥2 years | 483,201 (50.59) | N/A | 566 (24.23) | |||
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Arusha | 270,099 (28.25) | 112,963 (20.22) | 271 (11.56) | |||
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Dar es Salaam | 3062 (0.32) | 2726 (0.49) | 48 (2.05) | |||
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Dodoma | 96,059 (10.05) | 61,033 (10.93) | 321 (13.69) | |||
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Geita | 25,807 (2.7) | 21,152 (3.79) | 123 (5.25) | |||
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Kilimanjaro | 93,231 (9.75) | 52,367 (9.37) | 294 (12.54) | |||
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Lindi | 12,532 (1.31) | 10,101 (1.81) | 162 (6.91) | |||
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Morogoro | 86,815 (9.08) | 61,697 (11.04) | 298 (12.71) | |||
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Mwanza | 152,914 (15.99) | 113,953 (20.4) | 318 (13.56) | |||
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Njombe | 11,469 (1.2) | 9645 (1.73) | 184 (7.85) | |||
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Tanga | 204,246 (21.36) | 113,002 (20.23) | 326 (13.9) |
aSome categories will add up to more or less than the total number of visits, patients, or facilities because of missing data or patients having repeat visits or visits at multiple facilities.
bN/A: not applicable.
cTImR: Tanzania Immunization Registry.
This analysis explored population movement, that is, care seeking at alternative (nonassigned) facilities, which affects the accuracy of facility denominators. Of 810,568 total visits, 765,835 (94.48%) were at a child’s assigned facility, 15,575 (1.92%) were at a nonassigned facility within 5 km of the child’s assigned facility, 14,147 (1.82%) at facilities located >5 km from the assigned facility but within the same district, 12,267 (1.51%) in a different district within the same region, and 2926 (0.36%) in a different region.
Visits to assigned and nonassigned facilities.
Visits to assigned and nonassigned facilities by patient and assigned facility characteristics (N=810,568).
Covariate | Total visits, n | At nonassigned facility, n (%) | At assigned facility, n (%) | |
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Female | 400,507 | 22,028 (5.5) | 378,479 (94.5) |
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Male | 409,164 | 22,504 (5.5) | 386,660 (94.5) |
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<12 | 232,653 | 10,935 (4.7) | 221,718 (95.3) |
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12-23 | 296,680 | 16,317 (5.5) | 280,363 (94.5) |
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24-35 | 220,613 | 12,796 (5.8) | 207,817 (94.2) |
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36-47 | 53,190 | 3989 (7.5) | 49,201 (92.5) |
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48-59 | 5908 | 520 (8.8) | 5388 (91.2) |
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Dispensary | 490,965 | 25,530 (5.2) | 465,435 (94.8) |
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Health center | 223,344 | 13,401 (6.0) | 209,943 (94.0) |
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Hospital | 96,259 | 5679 (5.9) | 90,580 (94.1) |
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Rural | 598,804 | 25,150 (4.2) | 573,654 (95.8) |
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Urban | 205,070 | 19,482 (9.5) | 185,588 (90.5) |
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Private | 148,088 | 9922 (6.7) | 138,166 (93.3) |
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Public | 655,786 | 34,101 (5.2) | 621,685 (94.8) |
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0-5 months | 461,325 | 20,760 (4.5) | 440,565 (95.5) |
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6-11 months | 196,127 | 14,513 (7.4) | 181,614 (92.6) |
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1 year | 133,746 | 8693 (6.5) | 125,053 (93.5) |
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≥2 years | 19,370 | 697 (3.6) | 18,673 (96.4) |
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0 | 277,803 | 9167 (3.3) | 268,636 (96.7) |
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1 | 157,322 | 7551 (4.8) | 149,771 (95.2) |
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2-5 | 171,457 | 13,888 (8.1) | 157,569 (91.9) |
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>5 | 192,290 | 20,767 (10.8) | 171,523 (89.2) |
aTImR: Tanzania Immunization Registry.
Proportion of visits at assigned facilities by facility geocode.
Population movement regression model results.
Covariate | Unadjusted model | Adjusted model | |||||||
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ORa (95% CI) | aORb (95% CI) | |||||||
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Female | Reference | N/Ac | Reference | N/A | ||||
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Male | 0.98 (0.96-1.00) | .08 | 0.99 (0.97-1.01) | .21 | ||||
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<12 | Reference | N/A | Reference | N/A | ||||
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12-23 | 0.71 (0.69-0.74) | <.001 | 0.79 (0.76-0.82) | <.001 | ||||
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24-35 | 0.64 (0.61-0.66) | <.001 | 0.87 (0.83-0.91) | <.001 | ||||
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36-47 | 0.85 (0.81-0.89) | <.001 | 1.30 (1.23-1.37) | <.001 | ||||
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48-59 | 1.20 (1.08-1.33) | <.001 | 1.89 (1.69-2.11) | <.001 | ||||
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Rural | Reference | N/A | Reference | N/A | ||||
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Urban | 1.42 (1.11-1.82) | .01 | 0.93 (0.74-1.18) | .56 | ||||
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Private | Reference | N/A | Reference | N/A | ||||
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Public | N/A | N/A | 1.37 (1.13-1.66) | .001 | ||||
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Dispensary | Reference | N/A | Reference | N/A | ||||
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Health center | 1.16 (0.94-1.43) | .17 | 1.71 (1.41-2.07) | <.001 | ||||
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Hospital | 1.53 (1.07-2.21) | .02 | 2.13 (1.52-2.98) | <.001 | ||||
Assigned facility stockout (% of days) | 1.00 (1.00-1.01) | .48 | 1.00 (0.99-1.01) | .94 | |||||
Total assigned visits (log) | 0.28 (0.26-0.32) | <.001 | 0.24 (0.21-0.27) | <.001 | |||||
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0-5 months | Reference | N/A | Reference | N/A | ||||
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6-11 months | 2.40 (1.17-4.94) | .02 | 1.55 (1.44-1.68) | <.001 | ||||
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12-23 months | 2.84 (1.17-6.88) | .02 | 7.29 (6.75-7.87) | <.001 | ||||
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≥2 years | 2.15 (0.88-5.28) | .09 | 8.15 (7.48-8.89) | <.001 | ||||
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0 | Reference | N/A | Reference | N/A | ||||
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1 | 1.62 (1.50-1.76) | <.001 | 2.03 (1.97-2.09) | <.001 | ||||
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2-5 | 8.38 (7.75-9.05) | <.001 | 2.06 (1.98-2.15) | <.001 | ||||
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>5 | 9.55 (8.76-10.40) | <.001 | 1.48 (1.35-1.64) | <.001 |
aOR: odds ratio.
baOR: adjusted odds ratio.
cN/A: not applicable.
dTImR: Tanzania Immunization Registry.
MOVs, where the patient did not receive at least one vaccine for which they were eligible, were observed in 23.69% (226,525/956,195) of visits. Although we found little variation in the likelihood of an MOV based on sex, there was notable heterogeneity across age groups, facility urbanicity, facility type, and duration of TImR use at the facility (
Visits with missed opportunities for vaccination (MOVs) by vaccine type and patient and facility characteristics.a
Covariate | Number of visits | Visits with an MOV by vaccine, n (%) | |||||||
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Any vaccine | Pentab | OPVc | BCGd | MCVe | Rotaf | PCVg | |
Overall | 956,195 | 226,525 (23.69) | 60,364 (6.31) | 58,040 (6.07) | 54,924 (5.74) | 5781 (0.60) | 95,651 (10.00) | 63,684 (6.66) | |
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Female | 471,406 | 111,636 (23.68) | 29,692 (6.30) | 28,570 (6.06) | 27,234 (5.78) | 2794 (0.59) | 47,011 (9.97) | 31,358 (6.65) |
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Male | 483,896 | 114,582 (23.68) | 30,594 (6.32) | 29,409 (6.08) | 27,583 (5.70) | 2953 (0.61) | 48,488 (10.02) | 32,236 (6.66) |
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<12 | 831,018 | 215,576 (25.94) | 55,453 (6.67) | 53,492 (6.44) | 51,461 (6.19) | 2080 (0.25) | 95,651 (11.51) | 58,627 (7.05) |
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12-23 | 110,968 | 9298 (8.38) | 4376 (3.94) | 4027 (3.63) | 2913 (2.63) | 2973 (2.68) | —h | 4492 (4.05) |
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24-35 | 10,123 | 1239 (12.24) | 374 (3.69) | 350 (3.46) | 354 (3.5) | 609 (6.02) | — | 417 (4.12) |
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36-47 | 2805 | 293 (10.45) | 114 (4.06) | 125 (4.46) | 145 (5.17) | 83 (2.96) | — | 104 (3.71) |
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48-59 | 1281 | 119 (9.29) | 47 (3.67) | 46 (3.59) | 51 (3.98) | 36 (2.81) | — | 44 (3.43) |
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Dispensary | 563,186 | 138,714 (24.63) | 38,917 (6.91) | 34,295 (6.09) | 30,271 (5.37) | 3646 (0.65) | 61,852 (10.98) | 43,948 (7.80) |
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Health center | 270,290 | 58,821 (21.76) | 14,359 (5.31) | 16,463 (6.09) | 15,233 (5.64) | 1465 (0.54) | 22,830 (8.45) | 14,035 (5.19) |
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Hospital | 122,719 | 28,990 (23.62) | 7088 (5.78) | 7282 (5.93) | 9420 (7.68) | 670 (0.55) | 10,969 (8.94) | 5701 (4.65) |
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Rural | 620,214 | 156,768 (25.28) | 41,729 (6.73) | 41,956 (6.76) | 35,968 (5.8) | 4178 (0.67) | 67,467 (10.88) | 47,301 (7.63) |
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Urban | 322,199 | 65,247 (20.25) | 16,588 (5.15) | 14,540 (4.51) | 18,082 (5.61) | 1540 (0.48) | 26,318 (8.17) | 15,165 (4.71) |
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0-5 months | 600,234 | 130,595 (21.76) | 23,894 (3.98) | 37,593 (6.26) | 36,829 (6.14) | 2340 (0.39) | 53,416 (8.90) | 36,747 (6.12) |
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6-11 months | 197,188 | 50,634 (25.68) | 18,569 (9.42) | 11,582 (5.87) | 10,329 (5.24) | 1321 (0.67) | 21,660 (10.98) | 12,598 (6.39) |
|
1 year | 135,342 | 37,630 (27.8) | 15,231 (11.25) | 7277 (5.38) | 5864 (4.33) | 1827 (1.35) | 17,584 (12.99) | 11,410 (8.43) |
|
≥2 years | 19,705 | 6576 (33.37) | 2430 (12.33) | 1140 (5.79) | 1188 (6.03) | 248 (1.26) | 2871 (14.57) | 2641 (13.40) |
|
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|
<10% | 666,531 | 155,134 (23.27) | 40,379 (6.06) | 41,754 (6.26) | 34,730 (5.21) | 3990 (0.60) | 66,733 (10.01) | 46,524 (6.98) |
|
10%-19% | 153,392 | 36,516 (23.81) | 9888 (6.45) | 8543 (5.57) | 10,200 (6.65) | 960 (0.63) | 15,191 (9.90) | 8672 (5.65) |
|
20%-29% | 77,730 | 21,007 (27.03) | 6297 (8.10) | 4226 (5.44) | 7103 (9.14) | 490 (0.63) | 7957 (10.24) | 4291 (5.52) |
|
≥30% | 54,072 | 12,203 (22.57) | 2998 (5.54) | 3031 (5.61) | 2683 (4.96) | 306 (0.57) | 5090 (9.41) | 3552 (6.57) |
aVaccine-specific percentages do not add up to the total missed opportunity for vaccination (MOV) percentage as patients can have MOVs for multiple vaccine types in a single visit.
bPenta: diphtheria, tetanus, pertussis, hepatitis B, and
cOPV: oral polio vaccine.
dBCG: Bacillus Calmette–Guérin.
eMCV: measles-containing vaccine.
fRota: rotavirus.
gPCV: pneumococcal conjugate vaccine.
hChildren are not considered eligible for rotavirus immunization after the first year of life.
iTImR: Tanzania Immunization Registry.
Of the 557,674 children included in the analysis, 167,115 (29.97%) had ≥1 MOVs. The mean number of MOVs per child was 0.61 (SD 1.20). Among the 167,115 children with an MOV, 85,697 (51.28%) had ≥1 MOV (range 1-15). Of 338,439 recorded MOVs, rotavirus was the most likely to have an MOV (accounting for 28.26% of all MOVs; n=95,650), followed by PCV (18.82%, 63,682), Penta (17.84%, 60,363), OPV (17.15%, 58,039), BCG (16.23%, 54,924), and MCV (1.71%, 5781). The lower MOV proportion for MCV was likely because of fewer visits where children were age-eligible for MCV (aged at least 9 months).
The TImR system allows providers to indicate the reasons why a scheduled and eligible dose was not provided. However, the reason will only be noted if a dose is knowingly not given and thus is absent for doses for which providers did not recognize the patient’s eligibility. For eligible doses that the provider logged as missed, the data indicated the mechanisms behind MOVs.
Reasons for missed opportunities for vaccination (MOVs).
Vaccine type | Number of recorded MOVs | MOV reason (MOVs for given vaccine type), n (%) | |||||
|
|
Stockout | Medical contraindication | Late | Refusal | Expired stock | No reason provided |
Overall | 338,439 | 177,624 (52.48) | 2474 (0.73) | 3184 (0.94) | 178 (0.05) | 163 (0.05) | 154,816 (45.74) |
Rotaa | 95,650 | 34,315 (35.88) | 913 (0.95) | 761 (0.8) | 34 (0.04) | 38 (0.04) | 59,589 (62.3) |
OPVb | 58,039 | 37,056 (63.85) | 296 (0.51) | 1118 (1.93) | 37 (0.06) | 31 (0.05) | 19,501 (33.6) |
Pentac | 60,363 | 46,133 (76.43) | 309 (0.51) | 558 (0.92) | 36 (0.06) | 62 (0.1) | 13,265 (21.98) |
PCVd | 63,682 | 47,712 (74.92) | 434 (0.68) | 834 (1.31) | 39 (0.06) | 37 (0.06) | 14,626 (22.97) |
BCGe | 54,924 | 24,430 (44.48) | 126 (0.23) | 513 (0.93) | 40 (0.07) | 11 (0.02) | 29,804 (54.26) |
MCVf | 5781 | 2694 (46.6) | 12 (0.21) | 117 (2.02) | 15 (0.26) | 5 (0.09) | 2938 (50.82) |
aRota: rotavirus.
bOPV: oral polio vaccine.
cPenta: diphtheria, tetanus, pertussis, hepatitis B, and
dPCV: pneumococcal conjugate vaccine.
eBCG: Bacillus Calmette–Guérin.
fMCV: measles-containing vaccine.
Results from the any-vaccine MOV and OPV-specific MOV models were selected as illustrative examples of interest and are shown in
Missed opportunity for vaccination (MOV) regression model results.
Covariate | Any MOV | OPVa MOV | |||
|
aORb (95% CI) | aOR (95% CI) | |||
|
|||||
|
Female | Reference | N/Ac | Reference | N/A |
|
Male | 1.00 (0.99-1.01) | .90 | 1.00 (0.98-1.02) | .88 |
|
|||||
|
0-11 | Reference | N/A | Reference | N/A |
|
12-23 | 0.19 (0.18-0.19) | <.001 | 0.41 (0.40-0.43) | <.001 |
|
24-35 | 0.25 (0.23-0.26) | <.001 | 0.33 (0.29-0.37) | <.001 |
|
36-47 | 0.19 (0.17-0.22) | <.001 | 0.40 (0.33-0.49) | <.001 |
|
48-59 | 0.18 (0.15-0.22) | <.001 | 0.33 (0.24-0.46) | <.001 |
|
|||||
|
Rural | Reference | N/A | Reference | N/A |
|
Urban | 0.90 (0.75-1.08) | .25 | 0.96 (0.73-1.26) | .78 |
|
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|
Private | Reference | N/A | Reference | N/A |
|
Public | 1.02 (0.88-1.18) | .85 | 0.89 (0.71-1.12) | .31 |
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|
Dispensary | Reference | N/A | Reference | N/A |
|
Health center | 0.89 (0.77-1.03) | .11 | 1.13 (0.91-1.40) | .28 |
|
Hospital | 0.99 (0.76-1.28) | .91 | 0.92 (0.62-1.36) | .68 |
|
|||||
|
0-5 months | Reference | N/A | Reference | N/A |
|
6-11 months | 1.61 (1.58-1.63) | <.001 | 0.90 (0.88-0.93) | <.001 |
|
12-23 months | 2.27 (2.22-2.31) | <.001 | 0.73 (0.71-0.76) | <.001 |
|
≥2 years | 3.15 (3.03-3.27) | <.001 | 0.67 (0.62-0.72) | <.001 |
aOPV: oral polio vaccine.
baOR: adjusted odds ratio.
cN/A: not applicable.
dTImR: Tanzania Immunization Registry.
This analysis explored the vaccine dropout. To ensure common eligibility for doses, this analysis was restricted to children aged 12 to 47 months at the end of 2019 and focused on the 14 doses scheduled for the first year of life (ie, through MCV-1;
Overall, 93,619 (31.79%) of 294,464 children in our sample were fully immunized for doses scheduled in the first year of life (inclusive of OPV-0), with a further 39,718 (13.48%) receiving all scheduled doses, except for MCV-1.
Vaccine coverage and dose timeliness. BCG: Bacillus Calmette–Guérin; MCV: measles-containing vaccine; OPV: oral polio vaccine; PCV: pneumococcal conjugate vaccine.
Dropout by patient and facility characteristics.
Covariate | Children dropped out, n (%) | ||||||||||||
|
Pentaa | OPVb | Rotac | PCVd | Birth or first | Overall dropout | |||||||
Overall | 76,659 (28.57) | 66,798 (30.67) | 52,086 (20.4) | 78,767 (29.33) | 16,414 (5.79) | 194,765 (66.14) | |||||||
|
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|
Female | 37,591 (28.47) | 32,735 (30.6) | 25,547 (20.31) | 38,652 (29.23) | 8073 (5.78) | 95,793 (66.1) | ||||||
|
Male | 38,962 (28.66) | 33,955 (30.69) | 26,466 (20.45) | 40,006 (29.4) | 8262 (5.75) | 98,633 (66.14) | ||||||
|
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12-23 | 51,057 (31.9) | 46,743 (34.42) | 34,951 (22.84) | 52,795 (32.87) | 12,159 (6.99) | 124,599 (69.99) | ||||||
|
24-35 | 25,602 (23.65) | 20,055 (24.46) | 17,135 (16.75) | 25,972 (24.06) | 4255 (3.88) | 70,166 (60.26) | ||||||
|
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Dispensary | 46,833 (27.82) | 38,737 (28.63) | 32,216 (20.08) | 48,415 (28.72) | 8338 (4.72) | 118,279 (64.57) | ||||||
|
Health center | 21,375 (29.52) | 20,176 (33.41) | 14,236 (20.62) | 21,790 (30.06) | 5161 (6.74) | 54,369 (68.01) | ||||||
|
Hospital | 8451 (30.69) | 7885 (35.68) | 5634 (21.77) | 8562 (31.15) | 2915 (9.63) | 22,117 (70.55) | ||||||
|
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Rural | 51,651 (28.88) | 41,372 (30.23) | 35,904 (21.19) | 53,075 (29.68) | 10,448 (5.54) | 131,395 (67) | ||||||
|
Urban | 23,375 (27.46) | 23,778 (30.94) | 15,051 (18.44) | 24,077 (28.25) | 5711 (6.34) | 59,872 (64.1) | ||||||
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Private | 14,514 (30.83) | 11,976 (32.64) | 9272 (20.78) | 14,567 (30.89) | 3072 (6.16) | 35,856 (68.61) | ||||||
|
Public | 61,289 (28.03) | 53,867 (30.13) | 42,184 (20.26) | 63,223 (28.9) | 13,186 (5.71) | 156,847 (65.54) |
aPenta: diphtheria, tetanus, pertussis, hepatitis B, and
bOPV: oral polio vaccine.
cRota: rotavirus.
dPCV: pneumococcal conjugate vaccine.
To better understand vaccination profiles, we constructed immunization archetypes using all possible combinations of eligible scheduled doses. Patients were fit into these archetypes based on their immunization history.
Immunization typologies (10 most common).
Vaccine and dose | Children, n (%) | |||||||||||||||||||
BCGa | OPVb | Pentac | PCVd | Rotae | MCVf |
|
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0 | 0 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 3 | 1 | 2 | 1 |
|
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Rg | R | R | R | R | R | R | R | R | R | R | R | R | R | 93,619 (31.79) | ||||||
R | R | R | R | R | R | R | R | R | R | R | R | R | NRg | 39,718 (13.49) | ||||||
R | NR | NR | NR | NR | R | R | R | R | R | R | R | R | R | 19,322 (6.56) | ||||||
R | R | R | NR | NR | R | NR | NR | R | NR | NR | R | NR | NR | 13,270 (4.51) | ||||||
R | R | R | R | N | R | R | NR | R | R | NR | R | R | NR | 13,102 (4.45) | ||||||
R | R | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR | NR | 10,156 (3.45) | ||||||
R | NR | NR | NR | NR | R | R | R | R | R | R | R | R | NR | 10,064 (3.42) | ||||||
R | NR | NR | NR | NR | R | NR | NR | R | NR | NR | R | NR | NR | 5587 (1.9) | ||||||
R | NR | NR | NR | NR | R | R | NR | R | R | NR | R | R | NR | 3861 (1.31) | ||||||
R | R | R | R | NR | R | R | NR | R | R | NR | R | NR | NR | 3842 (1.31) |
aBCG: Bacillus Calmette–Guérin.
bOPV: oral polio vaccine.
cPenta: diphtheria, tetanus, pertussis, hepatitis B, and
dPCV: pneumococcal conjugate vaccine.
eRota: rotavirus.
fMCV: measles-containing vaccine.
g"R" indicates a given dose was received, while "NR" indicates the dose was not received.
To understand dropout between different scheduled visits, we analyzed the proportion of children that had received any vaccine from each of the 5 scheduled touchpoints with the immunization system in the first year of life (
The results from the Penta and overall dropout models were selected as illustrative examples of interest and are shown below. Unadjusted results can be found in
Dropout regression model results.
Covariate | Penta dropout | Overall dropout | |||||||
|
aORa (95% CI) | aOR (95% CI) | |||||||
|
|||||||||
|
Female | Reference | N/Ab | Reference | N/A | ||||
|
Male | 1.02 (1.00-1.04) | .06 | 1.01 (0.99-1.03) | .21 | ||||
|
|||||||||
|
12-23 | Reference | N/A | Reference | N/A | ||||
|
24-35 | 0.23 (0.22-0.23) | <.001 | 0.19 (0.19-0.19) | <.001 | ||||
|
|||||||||
|
Rural | Reference | N/A | Reference | N/A | ||||
|
Urban | 0.86 (0.71-1.04) | .11 | 0.83 (0.70-0.99) | .03 | ||||
|
|||||||||
|
Private | Reference | N/A | Reference | N/A | ||||
|
Public | 1.04 (0.90-1.22) | .57 | 1.15 (1.00-1.33) | .047 | ||||
|
|||||||||
|
Dispensary | Reference | N/A | Reference | N/A | ||||
|
Health center | 0.95 (0.81-1.10) | .49 | 1.04 (0.91-1.22) | .58 | ||||
|
Hospital | 1.19 (0.91-1.54) | .20 | 1.27 (1.00-1.61) | .06 | ||||
Assigned facility stockout (% of days) | 1.00 (1.00-1.01) | .12 | 1.00 (1.00-1.01) | .28 |
aaOR: adjusted odds ratio.
bN/A: not applicable.
EIRs provide data for the rapid assessment of CQI improvement measures. These assessments can help improve service provision by identifying areas in need of targeted training or other quality improvement interventions. As shown in
Continuous quality improvement.
All facilities (n=2345) | Issues accounted for, n (%) | ||
|
Visits to a nonassigned facilitya |
Visits with an MOVb |
Children who have dropped out |
10% (n=134) | 36,307 (81.16) | 126,226 (55.72) | 112,895 (57.96) |
25% (n=586) | 42,937 (95.99) | 180,752 (79.79) | 159,584 (81.94) |
50% (n=1172) | 44,715 (99.96) | 215,989 (95.35) | 188,281 (96.67) |
75% (n=1758) | 44,733 (100) | 225,569 (99.58) | 193,971 (99.59) |
aAggregated by child’s assigned facility.
bMOV: missed opportunity for vaccination.
EIRs can add value in multiple ways. Access to individual-level data that captures all touchpoints with the immunization program allows for new analyses that can benefit immunization programs, national and regional ministry staff, health care providers and administrators, funders, and other stakeholders [
Inaccurate population denominators are a common challenge for monitoring coverage, improving implementation, and informing planning, such as projecting vaccine stock and staffing needs. A recent scoping review of immunization data quality in LMICs found that denominators were often inaccurate, infrequently adjusted, and inconsistent between the district and national levels [
EIRs greatly simplify tracking patients who seek care at multiple facilities, enabling a more nuanced understanding of population movement across both geography and time and allowing for more robust coverage estimates. The use of the TImR data allowed us to explore both the magnitude of and factors associated with seeking care at facilities other than the patient’s assigned, or
Identifying and avoiding MOVs is an important and cost-effective method for achieving greater vaccination coverage. The challenge is in identifying when, where, and among which children or facilities MOVs are experienced to address them. Integration of clinical decision support systems within EIRs can automate the determination of child dose eligibility and alert the provider, which has been shown to reduce MOVs for routine childhood immunizations [
This information can be used by providers to identify children who may be at higher risk of experiencing an MOV. In addition, it can be used by managers to identify providers and facilities with higher rates of MOVs for supportive supervision or refresher training or identify areas with high rates of vaccine hesitancy for outreach campaigns. In addition, EIRs can provide insight into the mechanisms behind MOVs, such as stock issues and vaccine-specific hesitancy. Where data were available, stockouts were the primary reason for MOVs, whereas mechanisms such as vaccine hesitancy and medical contraindications were relatively rare. The TImR data also showed that rotavirus was the most likely to have an MOV, which may indicate that eligibility requirements should be reviewed or refresher training provided. For additional insights, analysis of EIR data can be complemented by other tools such as those included in the WHO MOV strategy toolkit [
Identifying where in the vaccine schedule some children drop out and why they drop out is another key challenge for achieving high levels of vaccine coverage. Understanding which vaccine doses and child and facility characteristics are associated with failure to complete a vaccine sequence or the full vaccine schedule can help inform service provision, training, and quality improvement measures at the facility, regional, and national levels. In the TImR data, nearly half of children aged 12 to 35 months were fully vaccinated or had received all doses except MCV-1 of the 14-dose under-12-month schedule (ie, through MCV-1). Among children who did not complete the vaccine schedule, levels of dropout varied by vaccine. Facility characteristics associated with dropout also varied by vaccine; for example, assigned facility urbanicity was significantly associated with a lower likelihood of overall dropout (ie, starting but not finishing the 14-dose schedule) but not Penta-specific dropout, suggesting that the mechanisms behind dropout may vary by vaccine. Continuum of care analyses could be further expanded if the EIR data were linked to a birth registration system. In the Tanzania case study, 5.79% (16,414/283,548) of children dropped out between birth and the first immunization dose; however, this may be an underestimation if some children are not registered at birth. In countries with a strong civil registration and vital statistics system, linking the EIR to birth registration or an antenatal care registry could expand the continuum of care analysis. Using EIRs to explore immunization typologies can also provide insight into which vaccines and visits require greater care. For example, in the TImR data, 6.56% (19,322/294,464) of children were fully vaccinated through MCV-1 except for the 3 to 4 OPV doses, highlighting the need for greater research into barriers to OPV coverage.
The CQI analysis showed that most issues (eg, MOVs) came from a minority of facilities. EIRs enable decision-makers at the national and subnational levels to quickly assess and identify providers, facilities, or geographic areas for targeted quality improvement measures, thereby improving the quality of care and increasing improvement in intervention effectiveness.
These analyses were designed to show the potential of EIRs to allow for a more nuanced, rapid, and cost-effective evaluation of vaccine program data to facilitate data use for decision-making. For example, automated dashboards of key indicators (eg, vaccine-specific coverage, stockouts, and child dropout) can inform planning and clinical practice at the facility level without the need for on-site data analysis. Providers can also use EIRs to simplify the tracking of individual patients, particularly those seeking care at multiple facilities, to improve the quality of care and reduce issues such as MOVs [
Designed well, EIRs can democratize immunization data. However, they require the necessary support to function effectively. The Early-Stage Digital Health Investment Tool has identified 6 building blocks for effective digital health systems: human capacity, investments and funding, data capture and use, infrastructure, standards and interoperability, and governance and policy [
EIR is a solution that aims to improve immunization program performance. The efficiency and impact of EIRs can be maximized by introducing them in combination with other interventions, such as capacity strengthening for data use, vaccine stock management systems, data governance frameworks, or SMS text messaging reminders for caregivers. Interventions that use multiple mechanisms to address various barriers to data use have been found to be more successful in achieving immunization data use and action [
The TImR results are intended to illustrate the ways EIRs can add value to immunization programs by providing actionable information for health care providers and managers. The results are not intended to be generalizable to Tanzania as a whole because of several data limitations. First, regions and districts implemented TImR at various points in time, meaning that some geographies are over- or underrepresented in the results. Second, and relatedly, only a subset of regions in Tanzania have introduced TImR; therefore, immunization services delivered outside the TImR coverage area are not captured in the results. Third, children who may live within the TImR coverage area but have not had a touchpoint with the immunization delivery system (also known as
EIRs have the potential to add substantial value to immunization stakeholders at all levels of the health system beyond measuring vaccine coverage. Individual-level data captured through EIRs can enable new analyses to understand immunization service delivery or care-seeking patterns, potential risk factors for underimmunization, and where challenges occur. Notably, most issues (eg, occurrence of MOVs, visits to a nonassigned facility, and number of defaulters) occur in a minority of facilities, highlighting the potential for EIRs to inform targeted quality improvement efforts. However, to achieve this potential, country programs need to leverage and strengthen their capacity for collecting, analyzing, and interpreting the data. Measures and analyses should be prioritized to match the needs and capabilities of the immunization program. Ideally, the prioritized measures should be integrated into routine systems to facilitate ongoing CQI efforts. As EIRs are introduced and scaled in LMICs, implementers and researchers should continue to share real-world examples and build an evidence base for how EIRs can add value to immunization programs, particularly for innovative uses.
Complete missed opportunity for vaccination (any vaccine) regression model results.
Complete missed opportunity for vaccination (oral polio vaccine) regression model results.
Complete dropout (Penta) regression model results.
Complete dropout (overall) regression model results.
Bacillus Calmette–Guérin
Better Immunization Data
continuous quality improvement
electronic immunization registry
Database of Global Administrative Areas
low- and middle-income country
measles-containing vaccine
missed opportunity for vaccination
oral polio vaccine
Tanzania Immunization Registry
World Health Organization
The authors thank the Ministry of Health in Tanzania, particularly the staff in the Immunization and Vaccines Development program, for their insights on the analyses and use of the Tanzania Immunization Registry data. The authors also thank the stakeholders who shared their insights and expertise through interviews and web-based surveys. Finally, the authors would like to thank the Bill & Melinda Gates Foundation for providing support for this study.
TKR provided funding for this research in her role as a senior program officer at the Bill & Melinda Gates Foundation.