Birth Cohort Coverage

Rationale

The objective of this indicator is to monitor a specific birth cohort of newborns to determine their vaccination status and estimate the level of population protection against vaccine-preventable diseases. This analysis is essential for preventing the accumulation of susceptible individuals, maximizing herd immunity, and developing targeted vaccination strategies for those who have not received the necessary doses.

Vaccination coverage by birth cohort is calculates using the following formula:

$\text{Coverage by birth cohort (\%)} = \frac{\text{# of vaccines administered to people in a specific birth cohort}}{\text{Total people in the specific birth cohort}} \times 100$

Note

All functions used for complete schedule coverage analyses within PAHOabc begin with the bc_ prefix.

Glossary

Usage

Install Package

The first step to run the birth cohort coverage analyses 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 three datasets in a specific format.

Your EIR.
The vaccination schedule related to this EIR.
Optionally, a table with population denominators at the same geographic level of disaggregation as your analysis.

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 datasets provided by PAHOabc that are 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.

Vaccination Schedule

The pahoabc::pahoabc.schedule dataset defines the national immunization schedule, listing each vaccine dose and its recommended age of administration (in days). It helps the package determine whether an individual is up to date with their vaccinations.

pahoabc.schedule %>% kable(caption = "Example Immunization Schedule")

Example Immunization Schedule
dose	age_schedule	age_schedule_low	age_schedule_high
SRP1	365	360	420
DTP1	60	54	90
DTP2	120	116	150
DTP3	180	176	210
BCG RN	0	0	28
YFV1	365	360	420

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.
age_schedule: The recommended age of administration of the corresponding dose in days.
age_schedule_low: The lower limit for the target age in days.
age_schedule_high: The upper limit for the target age in days.

Note

The dose names must match exactly those in the dose column of the pahoabc.EIR dataset.

Population Denominators

While not mandatory, this module will lets you provide a population table to use as denominator for the coverage calculation. The geographic level of this table will be directly dependent on the geographic level of your analysis. For example, if you were to perform an analysis at ADM1 level, the required population denominator table must contain the following.

pahoabc.pop.ADM1 %>% head() %>% kable(caption = "Example Population at ADM1")

Example Population at ADM1
ADM1	year	age	population
ADM1_1	2022	0	1718.189
ADM1_1	2022	1	1808.964
ADM1_1	2023	0	1703.145
ADM1_1	2023	1	1792.443
ADM1_2	2022	0	6575.261
ADM1_2	2022	1	6922.644

Note that this table is in long format and contains the population of children of a specific age during a given year for a certain ADM1 level.

Note

Please take into account that since this analysis is performed with birth cohorts, only the rows with age=0 are of interest in the population table, since these represent the number of children that belong to that specific birth cohort (year).

Birth cohort coverage analysis

This module lets users estimate the vaccination coverage by birth cohort. This allows for the analysis of any vaccine among a specific birth cohort. For correct interpretation, you must take into account that it represents the percentage of people vaccinated among a specific year of birth, regardless of the year in which they were vaccinated, so it could be different from the administrative coverage of a particular year. 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 bc_coverage() and bc_barplot() functions provide a way of analyzing and visualizing coverage by birth cohort. Their relationships are shown in Figure 1.

Figure 1. Expected workflow for the birth cohort coverage analysis.

Example

Here is a simple use case of the bc_coverage() function, where we use our example dataset to compute vaccination coverage for the first dose of the measles-containing vaccine, for the 2022 birth year cohort.

# compute coverage
coverage_df <- bc_coverage(
  data.EIR = pahoabc.EIR,
  data.schedule = pahoabc.schedule,
  geo_level = "ADM1",
  vaccines = "SRP1", # this can be a vector of vaccines
  birth_cohorts = 2022 # this can be a vector of years
)

# show results in table
coverage_df %>%
  kable(digits = 2, caption = "Coverage by birth cohort by ADM1 level")

Coverage by birth cohort by ADM1 level
dose	year_cohort	ADM1	numerator	population	coverage
SRP1	2022	ADM1_1	558	1733	32.20
SRP1	2022	ADM1_2	3138	12428	25.25
SRP1	2022	ADM1_3	13967	50238	27.80
SRP1	2022	ADM1_4	2977	6382	46.65
SRP1	2022	ADM1_5	1362	3488	39.05

We can visually observe these results by calling the bc_barplot() function as shown below.

# visualize output from cs_coverage()
coverage_plot <- bc_barplot(data = coverage_df, vaccine = "SRP1")

# show
coverage_plot

A review of the different subnational levels reveals a significant disparity: the ADM1_4 region records the best performance in vaccination (~47%), while the ADM1_2 area exhibits the poorest result (~25%).

Summary

By incorporating vaccination coverage based on birth cohort, PAHOabc enables public health officials to accurately assess the immunity level of specific population groups within a given territory. This deep, cohort-specific insight is essential for effective strategic planning, including the execution of measles and rubella follow-up campaigns and the development of targeted HPV vaccine catch-up strategies.

PAHO/CIM

February 12, 2026