Immunization Coverage

Table 1 summarizes the four steps of the method and its application in Kenya. The first step is to obtain data and statistics from different sources. The national bureau of statistics provides the official population projections, by age, sex and subnational unit. The most recent population-based survey provides statistics on the coverage of key interventions at national and subnational levels for a specified time period before the survey. Subnational levels include provinces, regions and counties but usually not districts. At the health facility level, data for key maternal and child health interventions – such as ANC first and fourth visit, place of delivery, Caesarean section, first and third dose of pentavalent vaccination and measles vaccination – are obtained for multiple years (preferably at least three years) to be able to assess consistency over time. In most countries using DHIS 2, these data are derived from paper-based recording and reporting in almost all facilities. The monthly facility reports are then sent to the district or subcounty health office where the data are entered into DHIS 2 and uploaded to the Internet. However, some facilities (mainly hospitals) enter the data directly into DHIS 2.
Step 2 starts with assessing the quality of the numerator of the coverage indicator by analysing completeness of reporting and consistency over time. High levels of reporting (over 80% of health facilities reporting a specific indicator) are essential to be able to compute coverage rates. Internal consistency is checked in terms of trends over time for coverage of each indicator, as well as between first antenatal visit and first pentavalent vaccination, and between first and third pentavalent vaccinations, as recommended by the World Health Organization.¹⁵ Outliers, defined as more than two standard deviations from the mean values of the numerators for the multi-year period, are identified and corrected if no satisfactory explanation is found for the outlier value.
For the coverage calculations, we need to adjust for incomplete reporting by facilities. This involves making assumptions about the number of service outputs (pregnancy care, vaccinations, etc.) provided at facilities which did not report compared with those that reported. The adjustment can be expressed as follows:
where n  is the number of service outputs, c is the reporting completeness, k is the adjustment factor. If we consider the missing reports an indication that no services were provided during the reporting period, then k = 0, and no adjustment is made for incomplete reporting. However, if facilities provided services but not at the same level as before reporting periods, the apparent incomplete reporting is an indication of a lower level of service provision; k in this case is between 0 and 1. In other cases, it may be assumed that services were provided at the same rate in non-reporting facilities as in reporting facilities, and so k = 1. Important considerations in the selection of a value of k are the extent to which large health facilities and private health facilities are reporting and engaged in the provision of the specific services. This is likely to be different for different services, resulting in different adjustment factors. Subsequently, the selection of the most likely value of k is done through a comparison of facility reports with the survey results, by selecting a value of k that brings the adjusted health facility statistic close to the survey statistic for a particular year with data from both sources.
Step 3 is about finding the best possible denominator or target population size. This is usually obtained from census projections by the country’s national bureau of statistics. Often, problems with the projected subnational denominators lead to unexpectedly high or low coverage rates. An alternative approach is to derive the population size from health facility data on indicators with near-universal coverage (at least 90%), such as the first antenatal visit or the first dose of pentavalent vaccine (normally given at 6 weeks of age). If the health facility reports are of good quality, and almost all children are vaccinated, the first vaccination or first antenatal visit numbers should be very close to the actual target populations. Only a small proportion is added to the reported first pentavalent vaccination or first antenatal visit numbers to account for those who did not receive them (< 10% of people, according to household surveys in many countries).^{16 ,17} The estimated young infant target population can then be used to obtain target populations for other maternal and child health coverage indicators (e.g. live births, deliveries, pregnancies and older infants), based on available statistics from recent surveys or other sources.
In step 4, the adjusted numbers and denominators are used to calculate the subnational coverages of immunizations, antenatal care (first and fourth visits) and facility-based deliveries.
In the second half of 2016, we used data from Kenya to apply and refine the method. Health-facility data were analysed across the 47 counties for the fiscal years (1 July to 30 June) 2012/13, 2013/14, 2014/15 and 2015/16 and compared with survey results from the most recent Kenya demographic and health survey in 2014.¹⁸ Population projections were obtained from the 2009 Kenya national census.¹⁹
All calculations were done using Microsoft Office 365 Excel software version 1705 (Microsoft Corporation, Redmond, United States of America). The spreadsheet with data by county and the adjustment procedure are available from the corresponding author.

We retrieved data on DTP, OPV, and Haemophilus influenzae type b (Hib) immunisation coverage using existing surveys [9 (link),15 ,20 ,21 (link)]. DTP, OPV, and the Hib vaccine are all scheduled during the first 6 months of life (at 2, 4, and 6 months), which is the peak age range for SIDS [22 (link)]. To ensure comparability over time, we used the reported coverage of at least three doses of the respective vaccine. From 1995 onwards, any pertussis vaccine was reported including acellular pertussis vaccine (DTaP) [16 ].
The following three surveys provided data on immunisation coverage in the United States: the United States Immunization Survey (USIS; 1968–1985), the National Health Interview Survey (NHIS; 1991–1993), and the National Immunization Survey (NIS; 1994–2009) [9 (link),15 ,20 ,21 (link)]. The USIS started as an area-probability household survey using face-to-face interviews and became a telephone survey in 1971 [9 (link)]. Until 1978, the USIS assessed the immunisation of children between 1 and 4 years of age. Between 1979 and 1985, the survey included only children aged 24–35 months. The collected information was based on either parental recall or an immunisation record that was maintained at home. There was a lack of information on immunisation coverage from 1986–1990. From 1991 onwards, the NHIS assessed the immunisation status of children aged 19–35 months [20 ,23 ,24 ]. The NHIS examined a representative probability sample of households in the United States using face-to-face interviews. If a child’s immunisation records were available, the data were abstracted from the records; otherwise, the collected information was based on parental recall. Then, in 1994, the CDC implemented the NIS for continuous monitoring of immunisation coverage [15 ,21 (link)]. For 1994, we used the Morbidity and Mortality Weekly Report, and for the years 1995–2009, we used the public use files that are published on the CDC website [15 ,16 ]. The NIS is a random-digit-dialling telephone survey of households with children aged 19–35 months. The data were validated with the immunisation history of the child, which was obtained from the family’s health care provider [25 ]. The NIS and NHIS yielded similar results for estimated immunisation coverage levels [15 ]. In the current study, immunisation coverage is presented graphically as the percentage of children who were immunised with DTP, OPV, and the Hib vaccine in each year during the time period from 1968 to 2009.

Four types of surveys are commonly employed to estimate vaccination coverage (Table 1). The Demographic and Health Surveys (DHS) [16] and Multiple Indicator Cluster Surveys (MICS) [17] are probability sample surveys, in which each household has a known and nonzero probability of being selected in the sample. There have been about 10–15 DHS and 20 MICS per year since 1995. These large, important, and generally well-conducted household surveys, which are used to collect data about many aspects of health, are described in detail in a companion paper in this Collection [18] (link).
The Expanded Programme on Immunization (EPI) cluster survey was developed by the WHO and was described in 1982 as a practical tool to quickly estimate coverage to within ±10 percentage points of the point estimate [19] (link). The original EPI survey method selects 30 clusters from which seven children in each cluster are selected using the “random start, systematic search” method. Specifically, a starting dwelling is chosen by starting at a central location in the village or town, selecting a direction at random, counting the dwellings lying in that direction up to the edge of the village, and selecting one of them randomly; adjacent households are then visited until seven children aged 12–23 months have been enrolled [20] ,[21] . The central starting location may bias the method to include households with good access to vaccination, so it is difficult to assign unbiased probabilities of selection to the households using this method, which does not meet the above criteria for a probability sample and is, therefore, a “non-probability sampling” survey method [22] (link). EPI surveys are widely used at national and sub-national levels, but there is no central database of results, so the total number of surveys conducted is unknown. Adaptations of the EPI survey have incorporated probability sampling at the final stage of sample selection [22] (link)–[26] (link), and the updated WHO guidelines [21] as well as a recent companion manual on hepatitis B immunization surveys emphasize the need for probability sampling for scientifically robust estimates of coverage [27] .
The main design differences between EPI surveys (if probability sampling is used) and DHS or MICS surveys is that EPI surveys focus specifically on vaccination data while DHS and MICS surveys cover a wide range of population and health topics and include a much larger sample size. In addition, field implementation of EPI surveys is variable and often done without external technical assistance, while the DHS and MICS are highly standardized and have substantial technical assistance and quality control.
A final household survey method commonly used to estimate health intervention coverage in low- and middle-income countries is Lot Quality Assurance Sampling (LQAS). LQAS surveys use a stratified sampling approach to classify “lots,” which might be districts, health units, or catchment areas, as having either “adequate” or “inadequate” coverage of various public health interventions. For vaccination coverage measurement, LQAS is “nested” within a cluster survey to evaluate neonatal tetanus elimination [28] (link), coverage of yellow fever vaccination [29] (link), and coverage of meningococcal vaccine campaigns [30] (link), and to monitor polio vaccination coverage after supplementary immunization activities [31] .

A flowchart of all methods is provided in Fig. 1. Routine immunisation coverage was modelled for twelve meningitis belt countries. These countries are: Burkina Faso, Central African Republic, Chad, Côte d’Ivoire, Eritrea, the Gambia, Ghana, Guinea, Mali, Niger, Nigeria, and Sudan. Total coverage of campaign data plus routine immunisation was modelled for the twenty-four of the twenty-six meningitis belt countries which have introduced any MenA immunisation as of 2021. These countries are those listed for RI modelling, plus: Benin, Burundi, Cameroon, Democratic Republic of the Congo, Ethiopia, Guinea Bissau, Kenya, Mauritania, Senegal, South Sudan, Togo and Uganda.¹⁵ Rwanda and Tanzania are in the meningitis belt but have not introduced any immunisation activity, so are not included in modelling.Fig. 1

Diagram of methods. Green squares indicate a modelling step, blue parallelograms indicate results, light green ovals indicate data, and grey cylinders indicate GBD (Global Burden of Disease Study) results used as input to this model.

Data were not obtained from subjects for the Global Burden of Diseases, Injuries, and Risk Factors Study. Instead, we used pre-existing, publicly available, de-identified datasets that include but are not limited to administrative and survey-based vaccine coverage reports. Data are identified through online searches, through outreach to institutions that hold relevant data such as ministries of health, or through individual collaborator reference and identification. Most data used are publicly available. Therefore, informed consent is not required. This study was approved by University of Washington's Human Subjects Division Study ID: STUDY00009060.
Our study follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER; Supplementary Table S3). The findings of this study are supported by data available in public online repositories and data publicly available upon request of the data provider. Details of data sources and availability are publicly available in the Global Health Data Exchange (https://ghdx.healthdata.org/record/ihme-data/sub-saharan-africa-menafrivac-estimates-2010-2021). The full output of the analyses can be found in Supplementary Table S4 and at the abovementioned website.

We conducted a multicountry study at 6 immunization centers, 3 each in Pakistan and Bangladesh. Both countries vary in terms of full immunization coverage rate (Pakistan 66%; Bangladesh 84%) and infant mortality rates (81/1000 live births in Pakistan; 43/1000 live births in Bangladesh) [21 ,22 ]. Immunization centers were selected based on the influx of children, availability of vaccines and vaccinators, and absence of any kind of decision support system at these sites. The selected study sites had high penetration of mobile phones (>90%) and the presence of cellular networks (data connectivity) among the population [23 ,24 ].
Two of the selected immunization centers in Pakistan were located in Gilgit district in Gilgit Baltistan territory, which had a full immunization coverage rate of 57% among children aged 12 to 23 months in 2018 [21 ]. The third selected immunization center in Pakistan was a private center located in the Rahim Yar Khan district in Punjab province, with a full immunization coverage rate of 65% in 2019 [23 ].
In Bangladesh, all 3 immunization centers were located in Dhaka city in Dhaka district. The district had a full immunization coverage rate of 85% among children aged 12 to 23 months as of 2019 [25 (link)].