Characterization of immunization secondary analyses using demographic and health surveys (DHS) and multiple indicator cluster surveys (MICS), 2006–2018

Abstract

Background Infant immunization coverage worldwide has plateaued at about 85%. Using existing survey data to conduct analyses beyond estimating coverage may help immunization programmes better tailor strategies to reach un- and under-immunized children. The Demographic and Health Survey (DHS) and the Multiple Indicators Cluster Survey (MICS), routinely conducted in low and middle-income countries (LMICs), collect immunization data, yet vaccination coverage is often the only indicator reported and used. We conducted a review of published immunization-related analyses to characterize and quantify immunization secondary analyses done using DHS and MICS databases. Methods We conducted a systematic search of the literature, of immunization-related secondary analyses from DHS or MICS published between 2006 and August 2018. We searched 15 electronic databases without language restrictions. For the articles included, relevant information was extracted and analyzed to summarize the characteristics of immunization-related secondary analyses. Results are presented following the PRISMA guidelines. Results Among 1411 papers identified, 115 met our eligibility criteria; additionally, one article was supplemented by the Pan American Health Organization. The majority were published since 2012 (77.6%), and most (68.9%) had a first or corresponding author affiliated with institutions in high-income countries (as opposed to LMICs where these surveys are conducted). The median delay between survey implementation and publication of the secondary analysis was 5.4 years, with papers with authors affiliated to institutions in LMIC having a longer median publication delay (p < 0.001). Over 80% of the published analyses looked at factors associated with a specific vaccine or with full immunization. Quality proxies, such as reporting percent of immunization data from cards vs recall; occurrence and handling of missing data; whether survey analyses were weighted; and listing of potential biases or limitations of the original survey or analyses, were infrequently mentioned. Conclusion Our review suggests that more needs to be done to increase the increase the utilization of existing DHS and MICS datasets and improve the quality of the analyses to inform immunization programmes. This would include increasing the proportion of analyses done in LMICs, reducing the time lag between survey implementation and publication of additional analyses, and including more qualitative information about the survey in the publications to better interpret the results.

Inequities in childhood immunisation coverage associated with socioeconomic, geographic, maternal, child, and place of birth characteristics in Kenya

“…Our findings complement the evidence from related cross-sectional studies [26]. Calhoun et al analysed data from Gem, Nyanza province, Kenya in 2003 and found that lower immunisation coverage among children aged 12-23 months was associated with lower maternal income, lower maternal education, and households with an absent parent [21].…”

Estimating global and regional disruptions to routine childhood vaccine coverage during the COVID-19 pandemic in 2020: a modelling study

“… 69 Survey data might more fully capture both disruptions to and recovery of routine immunisation services amid COVID-19, especially as fieldwork resumes; 70 , 71 however, due to time lags in collection and processing of survey data, results might not be available for months or even years. 72 Locally tailored strategies are needed now, but in the absence of timely data on past and current trends in doses and coverage, many countries could face a prolonged path to immunisation recovery. By improving routine immunisation data systems and delivery models, there could be an opportunity to build back stronger, more equitable health services for all populations.…”

Trends, projection and inequalities in full immunization coverage in Ethiopia: in the period 2000-2019

“…In addition to place of residence, children from neighborhoods with high poverty, illiteracy and unemployment rates were more likely to be unimmunized [ 8 ]. A study based on secondary analysis of Demographic and Health Surveys (DHSs) and Multiple Indicator Cluster Surveys (MICS) also indicated that place of residence, maternal education, socioeconomic status and birth order are the most common factors associated with lower immunization coverage [ 16 ]. A WHO report on the state of inequality in childhood immunization showed higher inequalities by mothers’ education level and household economic status in low-income countries where full immunization coverage was at least 20 percentage points higher in the richest wealth quantile compared to the poorest quantile [ 17 ].…”