Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Associated Factors Before and During the COVID-19 Pandemic in Nigeria

Version 1 : Received: 27 October 2023 / Approved: 30 October 2023 / Online: 1 November 2023 (02:34:23 CET)

A peer-reviewed article of this Preprint also exists.

Aheto, J.M.K.; Olowe, I.D.; Chan, H.M.T.; Ekeh, A.; Dieng, B.; Fafunmi, B.; Setayesh, H.; Atuhaire, B.; Crawford, J.; Tatem, A.J.; Utazi, C.E. Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria. Vaccines 2023, 11, 1830. Aheto, J.M.K.; Olowe, I.D.; Chan, H.M.T.; Ekeh, A.; Dieng, B.; Fafunmi, B.; Setayesh, H.; Atuhaire, B.; Crawford, J.; Tatem, A.J.; Utazi, C.E. Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria. Vaccines 2023, 11, 1830.

Abstract

The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the COVID-19 pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic in 2018 and during the pandemic in 2021 in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of DTP1, MCV1 and any of the four basic vaccines (BCG, OPV0, DTP1 and MCV1), and corresponding zero-dose estimates independently at 1x1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing areas in greatest need. At the national level, risk factors related to socioeconomic/ demographic status (e.g., maternal education), maternal access to and utilization of health services and remoteness were strongly associated with the odds of zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero dose in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.

Keywords

MCV1 coverage; DTP1 coverage; composite coverage; zero-dose prevalence; demographic and health surveys; multiple indicator cluster survey; Bayesian geostatistical modelling; Bayesian multilevel modelling

Subject

Public Health and Healthcare, Public, Environmental and Occupational Health

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