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Nominal Dropout Rate

PAHO/CIM

April 22, 2025

Source: vignettes/nominal_dropout_en.Rmd
nominal_dropout_en.Rmd

Rationale

The dropout rate is an indicator used to evaluate the loss of follow-up in vaccination schedules. It reflects the percentage of individuals who did not receive a final dose (i.e, DTP3) among those who received an initial dose (i.e, DTP1). In aggregated systems, it is estimated using the following formula:

Performing the analysis with aggregated data does not guarantee that the individuals who received both doses are the same, as shown in Figure 1a. Electronic nominal vaccination records enable a person-to-person analysis, ensuring that the individuals who received both doses are indeed the same, as illustrated in Figure 1b.

Figure 1. Visual representation of a nominal dropout analysis.
Figure 1. Visual representation of a nominal dropout analysis.


Note

All functions used for nominal dropout analyses within PAHOabc begin with the nod_ prefix.

Glossary

Political Geographic Boundaries

Throughout this vignette, we will make reference to different levels of political geographic boundaries. These levels are defined by the country/territory and can have different names from country to country. PAHOabc is agnostic to these names and requires the user to recode their variable names to fit the PAHOabc structure.

Namely, PAHOabc can distinguish three administrative levels, which listed from top to bottom level are: ADM0, ADM1 and ADM2.

  1. ADM0: The country. This is the top-most administrative level.
  2. ADM1: The first geographic subdivision in the country. ADM0 contains several ADM1 subdivisions.
  3. ADM2: The second geographic subdivision in the country. Each ADM1 contains several ADM2 subdivisions.

Vaccine Names

Our example datasets use a specific naming convention for the vaccine doses. This might not match your own naming schemes, but it will not affect the usage of the package as long as you make sure to be consistent when naming vaccine doses.

For example, the vaccine names used in the PAHOabc package are:

Abbreviation Full Name
SRP1 Measles, Rubella, and Mumps Vaccine (1st dose)
DTP1 Diphtheria, Tetanus, and Pertussis Vaccine (1st dose)
DTP2 Diphtheria, Tetanus, and Pertussis Vaccine (2nd dose)
DTP3 Diphtheria, Tetanus, and Pertussis Vaccine (3rd dose)
BCG RN Bacillus Calmette–Guérin Vaccine (at birth)
YFV1 Yellow Fever Vaccine (1st dose)

Usage

Install Package

The first step to run the nominal dropout rate analysis is to install the PAHOabc package available on Github

devtools::install_github("IM-Data-PAHO/pahoabc")

Load Data

The functions in this module require you to provide your EIR dataset in a specific format.

To make it easy for you to test out PAHOabc’s functionality and understand the structure we require for your datasets, we will now explore the example dataset provided by PAHOabc that is required by this module.

EIR

The pahoabc::pahoabc.EIR data frame provides a simulated, nominal-level table representing individual vaccination events from an Electronic Immunization Registry (EIR). Each row corresponds to a single vaccination act for a person, including information on their residence, where they were vaccinated (occurrence), their date of birth, and the vaccine dose received.

pahoabc.EIR %>% head() %>% kable(caption = "Example Electronic Immunization Registry")
Example Electronic Immunization Registry
ID date_birth date_vax ADM1_residence ADM2_residence ADM1_occurrence ADM2_occurrence dose
191997 2023-08-08 2023-12-26 ADM1_4 ADM2_4_35 ADM1_4 ADM2_4_35 DTP2
212189 2023-12-20 2023-12-26 ADM1_5 ADM2_5_61 ADM1_5 ADM2_5_61 BCG RN
118063 2022-09-15 2023-12-26 ADM1_2 ADM2_2_5 ADM1_2 ADM2_2_5 DTP1
118063 2022-09-15 2023-12-26 ADM1_2 ADM2_2_5 ADM1_2 ADM2_2_5 YFV1
130751 2022-10-27 2023-12-12 ADM1_5 ADM2_5_55 ADM1_5 ADM2_5_55 YFV1
136532 2021-09-21 2023-12-26 ADM1_3 ADM2_3_12 ADM1_3 ADM2_3_12 SRP1
  • ID: Unique person identification number.
  • date_birth: Date of birth of person.
  • date_vax: Date of vaccination event.
  • ADM1: Refers to the first geographic administrative level of the country.
  • ADM2: Refers to the second geographic administrative level of the country.
  • Residence: Refers to the place where the person lives.
  • Occurrence: Refers to the place where the vaccination event occurred.
  • dose: A combined variable representing the vaccine type and its corresponding dose number. For example, DTP1 refers to the first dose of a vaccine containing diphtheria, tetanus, and pertussis components.

Nominal Dropout Analysis

PAHOabc includes the functionality to estimate the nominal dropout rate, on a per-person basis. This allows for the analysis of any pair of doses by specifying an initial dose and a final dose. For correct interpretation, the initial dose must precede the final dose in the vaccination schedule, for example: DTP1 – DTP3 or DTP1 – MCV. Additionally, it allows users to select the level of aggregation for the results, which can be at the national level or subnational levels (ADM1 or ADM2).

Expected workflow

The nod_dropout() and nod_barplot() functions work together to perform nominal dropout analysis and visualization. The nod_dropout() function computes the nominal dropout and complete cases by residence across selected administrative levels, birth cohorts, and vaccines. Its output (a data table) is structured to be directly compatible with nod_barplot(), which generates a visualization for the data computed.

Figure 2. Expected workflow for nominal dropout rate analysis.
Figure 2. Expected workflow for nominal dropout rate analysis.


Example

Here is a simple use case of the nod_dropout() function, where we compute nominal dropout for ADM 1 for DTP1 and DTP3 in our example dataset.

#Runs the nominal dropout function
example_dropout <- nod_dropout(data.EIR = pahoabc.EIR,
                               vaccine_init = "DTP1", #Starting vaccine based on your dataset
                               vaccine_end= "DTP3", #Ending vaccine based on your dataset
                               geo_level = "ADM1", #Can be ADM0, ADM1 or ADM2
                               birth_cohorts = NULL #Can be a vector of years or a year
                               )

# show results in table
example_dropout %>%
  kable(digits = 3, caption = "Nominal dropout rate between DTP1 and DTP3 by ADM1 level")
Nominal dropout rate between DTP1 and DTP3 by ADM1 level
ADM1_residence indicator num denom percent
ADM1_1 complete 1491 2607 57.192
ADM1_1 dropout 1116 2607 42.808
ADM1_2 complete 6967 12429 56.054
ADM1_2 dropout 5462 12429 43.946
ADM1_3 complete 30999 55608 55.746
ADM1_3 dropout 24609 55608 44.254
ADM1_4 complete 5304 7574 70.029
ADM1_4 dropout 2270 7574 29.971
ADM1_5 complete 2703 4040 66.906
ADM1_5 dropout 1337 4040 33.094
Parameters accepted
  • data: R Structured dataframe with the variables listed above.
  • vaccine_init: The name (string) of the vaccine to use as the start point of the analysis as stored in the dose column of the dataframe.
  • vaccine_end: The name (string) of the vaccine to use as the end point of the analysis as stored in the dose column of the dataframe.
  • geo_level: Administrative level to use, this is an optional parameter. Can be “ADM0”, “ADM1” or “ADM2” as strings. Defaults to “ADM0” if ignored.
  • birth_cohorts (years): Parameter to segment the analysis based on the year of birth of the persons in the dataframe. This function uses the date_birth column in the dataframe and can be a single year or a vector of years.

The data, vaccine_init, vaccine_end parameters are mandatory. geo_level and birth_cohorts are optional and help to segment the data for more precise analyses.

Check the documentation of the function using ?nod_dropout() in R for additional information.

This output may be fed directly into the nod_barplot() function as shown below.

# Runs the nod_barplot function
example_dropout_plot <- nod_barplot(data=example_dropout, order="alpha")

#show plot
example_dropout_plot

Parameters accepted

The function nod_barplot() accepts the following parameters:

  • data: Dataframeformatted from the result of thenod_dropout()` function.
  • order: Organizes the bars based on three options: “alpha”, “desc” or “asc”, default is “alpha”.
  • within_ADM1 Allows to filter within an ADM1 to filter and display only the ADM2 data corresponding to the specified ADM1.
Interpretation

This chart shows the nominal dropout per Administrative level 1 as well as a 5% line to indicate a threshold of nominal dropout. Having a high dropout rate means a large percentage of the persons analyzed are not registering for the follow up doses of their schedule. Ideally, every person that has received a dose of a multi-dose schedule should return to receive the follow up dose. In this case the administrative level ADM1_1 is the one doing the worst, whilst the ADM1_4 is doing the best.

All of the administrative units have a nominal dropout rate above 25% meaning, at least 25% of the persons in that target group did not come back for their follow up dose.

Summary

Accurately monitoring continuity in multi-dose vaccination schedules is vital for immunisation programmes. The nominal dropout rate—the proportion of individuals who begin a series (e.g., DTP1) but fail to receive its final dose (e.g., DTP3)—offers a person-level view of programme performance that aggregated counts cannot. This vignette introduces PAHOabc, an R package that streamlines nominal dropout analysis by harnessing electronic immunisation registry (EIR) data.

After defining key concepts—administrative units (ADM0–2) and standardised vaccine-dose codes—the document walks the reader through a reproducible workflow: installing the package, exploring the bundled example EIR, computing dropout with nod_dropout(), and visualising results with nod_barplot(). A worked example estimates DTP1→DTP3 dropout at the ADM1 level, revealing that every subdivision exceeds the 25 % dropout threshold; ADM1_1 performs the worst, while ADM1_4 performs the best relative to a 5 % target line.

By coupling granular EIR data with a transparent analytical pipeline, PAHOabc enables precise, geographically disaggregated insights into follow-up losses, equipping health authorities to target interventions where they are most needed.