---
title: "Socioeconomic inequalities in COVID-19 vaccination uptake among Nigerian women of reproductive age"
id: "plos-one-10-decomposing-socioeconomic-inequalities-in-covid-19-vaccination-uptake-among"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-10-decomposing-socioeconomic-inequalities-in-covid-19-vaccination-uptake-among"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357171"
published_at: "2026-08-28T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Socioeconomic inequalities in COVID-19 vaccination uptake among Nigerian women of reproductive age
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-10-decomposing-socioeconomic-inequalities-in-covid-19-vaccination-uptake-among
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357171)
- **Published At:** 2026-08-28T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This population-based analysis used data from the 2024 Nigeria Demographic and Health Survey (NDHS) to examine **socioeconomic inequality** in **COVID-19 vaccination** uptake among women aged 15–49 years. The analytic sample comprised 36,555 women who responded to the vaccination question. - Overall uptake was low and unevenly distributed across socioeconomic groups: the least disadvantaged women reported 31.6% vaccination, the moderately disadvantaged about one-quarter, and the most disadvantaged 19.2%. - A concentration index and curve indicated vaccination coverage was significantly concentrated among the **least disadvantaged** women, confirming a pro-rich inequality in uptake. - Decomposition analysis identified positive contributors to this socioeconomic inequality: **higher education** and **secondary education**, **mass media exposure**, being **employed**, **health insurance** coverage, being aged ≥30 years, and residence in the **South West**, **South South**, and **South East** geopolitical zones. - The study used a two-stage stratified NDHS sampling design conducted between December 1, 2023 and May 7, 2024, achieving a 93.6% response rate on the vaccine question. Institutional populations were excluded. - The author concludes that achieving equitable vaccination coverage will require targeted strategies prioritizing disadvantaged women and context-specific policies to reduce socioeconomic barriers to access. - Data and the analytic dataset are publicly available via DHS and a figshare DOI provided in the article. The study reported no specific funding and no competing interests.
## Clinical Analysis & Structured Key Points
Decomposing socioeconomic inequalities in COVID-19 vaccination uptake among Nigerian women of reproductive age: A further analysis of population-based data | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Background Understanding COVID-19 vaccine uptake is particularly important in studies of inequity in vaccination access, as differences in uptake may reflect underlying social and structural inequalities. Evidence suggests that socioeconomic status is a key driver for COVID-19 vaccination uptake in Nigeria. This study examines the socioeconomic inequality in COVID-19 vaccination uptake among reproductive-age Nigerian women. Methods Data from the 2024 Nigeria Demographic and Health Survey (NDHS) was analyzed for a sample of 36,555 women of reproductive age (15–49 years). COVID-19 vaccination uptake was the outcome variable measured in this study. A socioeconomic decomposition analysis was conducted to identify the factors contributing to inequalities in COVID-19 vaccination uptake across the socioeconomic gradient. Results The least disadvantaged women had the highest COVID-19 vaccination uptake (31.6%), whereas the most disadvantaged women had the lowest COVID-19 vaccination uptake (19.2%). The concentration index curve showed that COVID-19 vaccination uptake among Nigerian women was significantly higher among the least disadvantaged group. In addition, having higher education, secondary education, being exposed to mass media, employed, covered by health insurance, being aged 30 years or older, from South West, South South and South East geopolitical zones respectively, were positive contributors to socioeconomic inequality in COVID-19 vaccination uptake. Conclusion There was low uptake rate of COVID-19 vaccination among Nigerian women, with approximately one-third of the least disadvantaged, one-quarter of the moderately disadvantaged, and one-fifth of the most disadvantaged women reported receiving a COVID-19 vaccine. This shows that the dream of achieving equitable vaccination coverage will require concerted efforts and targeted strategies that prioritize disadvantaged women, including context-specific policies aimed at reducing socioeconomic barriers to access. Citation: Ekholuenetale M (2026) Decomposing socioeconomic inequalities in COVID-19 vaccination uptake among Nigerian women of reproductive age: A further analysis of population-based data. PLoS One 21(8): e0357171. https://doi.org/10.1371/journal.pone.0357171 Editor: Morufu Olalekan Raimi, Federal University Otuoke, NIGERIA Received: April 28, 2026; Accepted: August 13, 2026; Published: August 28, 2026 Copyright: © 2026 Michael Ekholuenetale. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Data are available in a public, open access repository. The extracted analytic dataset for this study can be accessed here: https://doi.org/10.6084/m9.figshare.32936522 . The original DHS data are available at: https://www.dhsprogram.com/data/dataset/Nigeria_Standard-DHS_2024.cfm?flag=1 . Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Introduction The development of effective and safe vaccines against SARS-CoV-2 has greatly mitigated morbidity and mortality of the COVID-19 pandemic. The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to global health systems [ 1 ], with vaccination emerging as the most effective strategy for reducing infection, severe illness, and mortality [ 2 , 3 ]. Evidence from previous studies suggests that socioeconomic status is a key driver for COVID-19 vaccination uptake in Nigeria in particular and Sub-Saharan Africa at large [ 4 – 6 ]. Since the introduction of COVID-19 vaccines in 2020, several countries have made significant progress in scaling up the vaccination programme [ 7 ]. However, inequitable access and uptake remain major barriers, particularly in low- and middle-income countries (LMICs) [ 8 ]. Despite the global efforts to ensure equitable vaccine distribution through initiatives like COVAX [ 9 ], disparities in vaccination coverage persist across and within countries, often reflecting underlying socioeconomic inequalities [ 10 ]. Nigeria, the most populous country in Africa [ 11 ], presents a particularly important context for examining these inequalities. The country has faced longstanding challenges related to health system capacity, uneven distribution of healthcare resources, and high levels of poverty and income inequality. Health financing in Nigeria is largely driven by out-of-pocket expenditure, which disproportionately affects low-income households and limits access to essential health services. These structural inequities have been further exacerbated by the COVID-19 pandemic, which disrupted health services and deepened existing socioeconomic vulnerabilities. Women represent a key population group in the context of COVID-19 vaccination [ 12 , 13 ], particularly in Nigeria where gender norms and socioeconomic conditions significantly influence health-seeking behaviour. Women often face unique barriers to accessing healthcare services, including limited financial autonomy, lower educational attainment, and little decision-making power within households. COVID-19 vaccination rollout in Nigeria began in early 2021 [ 14 , 15 ], yet coverage has remained relatively low compared to the global targets. Preliminary analyses indicated that only a small proportion of the population had been fully vaccinated during the initial phases of the rollout, highlighting gaps in access and uptake. Studies from West Africa demonstrate that gender can shape COVID-19 vaccination through distinct social and structural pathways. In Ghana, a qualitative study among women in Greater Accra and Ashanti identified fear of adverse effects, vaccine misconceptions, long queues and vaccine shortages as important barriers, while access to information and protecting family members facilitated uptake [ 16 ]. In Nigeria, there is evidence that female sex is associated with greater COVID-19 vaccine hesitancy, alongside socioeconomic disadvantage and rural residence [ 17 ]. Albeit, a multi-country study found no significant sex difference in self-reported uptake across Nigeria and Senegal, highlighting context-specific gender effects and the need for sex-disaggregated analysis [ 18 ]. The COVID-19 pandemic has further amplified gendered health inequalities [ 12 , 13 ]. Nigerian women, especially those in vulnerable situations, experienced heightened socioeconomic and health inequities. These inequities may translate into disparities in access to COVID-19 vaccines, as women in lower socioeconomic strata are more likely to face financial, informational, and logistical barriers. Socioeconomic inequalities in health care are well documented in Nigeria. For example, a study on high-risk fertility behaviour among Nigerian women have shown that the socioeconomic disadvantaged is strongly associated with adverse health outcomes [ 19 ], underscoring the importance of addressing inequality in health interventions. These patterns are likely to extend to COVID-19 vaccination uptake, where similar structural determinants influence access and utilization. Understanding socioeconomic inequality in COVID-19 vaccination uptake among Nigerian women is therefore crucial for several reasons. First, it provides insights into the distributional equity of vaccination programmes and identifies groups that are being left behind. Second, it informs targeted interventions aimed at improving vaccine coverage among disadvantaged populations. Also, it contributes to broader efforts to achieve health equity and universal health coverage, which are central to global health agendas such as the Sustainable Development Goals (SDGs) [ 20 ]. Moreover, examining inequality using robust quantitative measures, such as concentration indices and decomposition analysis, can help to disentangle the relative contributions of different socioeconomic factors in observed disparities. Such analyses are essential for designing evidence-based policies that address both demand-side and supply-side barriers to vaccination. It is in the light of the above that this study was conducted to examine the socioeconomic inequalities in COVID-19 vaccination uptake among Nigerian women of reproductive age. Methods Data source Of the total 39,050 women of reproductive age recruited during the 2024 Nigeria Demographic and Health Survey (NDHS), 36,555 women of reproductive age (15–49 years) were interviewed and responded to the question on whether or not they have received COVID-19 vaccine. This accounted for 93.6% response rate and analyzed for this study. The National Population Commission (NPC), acted on behalf of the Federal Ministry of Health and Social Welfare (FMoHSW) to support the data collection process, which took place between December 1, 2023, and May 7, 2024. The survey was funded by the United States Agency for International Development’s (USAID). In addition, ICF, the World Health Organisation (WHO), the United Nations Population Fund (UNFPA), the United Nations Children’s Fund (UNICEF), and the Global Fund to Fight AIDS, Tuberculosis, and Malaria (Global Fund) were among the other organisations and agencies that provided financial or technical assistance to enable the survey’s successful implementation. The data analyzed for this study can be accessed here [ 21 ]. Exclusion criteria Institutional populations, including those in hotels, barracks and prisons, were not included in the survey. Sample design The 2024 NDHS used a two-stage stratified sample design. Nigeria is separated into six administrative zones: North Central, North East, North West, South East, South South, and South West. The Federal Capital Territory (FCT) and 36 states make up the zones, which result in 37 subnational entities. Local government areas (LGAs) make up each state, while communities make up each LGA. During the delineation process for the impending census, each locality is further divided into suitable areas known as enumeration areas (EAs). These EAs served as the basis for primary sampling units (PSUs), also known as clusters in the 2024 NDHS. By dividing each of the 36 states and FCT into urban and rural areas, stratification was accomplished. There were 74 sampling strata in all. In each stratum, samples were chosen separately using a two-stage selection process. The initial step was to choose sample locations, or clusters, made up of EAs. Within each sampling stratum, EAs were selected with a probability that was proportionate to their size. There were 701 urban and 699 rural clusters out of the 1,400 total that were chosen. A systematic sample of households was used in the second step. All of the chosen clusters underwent a household listing procedure, and a systematic equal probability selection process was used to choose a set number of 30 households per cluster, for a total sample size of about 42,000 households. At the time of listing and during interviews, Global Positioning System (GPS) data were gathered for every household. The report of the sampling design, training of field assistants and data collection process has been published previously by NDHS [ https://www.dhsprogram.com/data/dataset/Nigeria_Standard-DHS_2024.cfm?flag=1 ] [ 22 ]. Selection and measurements of variables Outcome variable. The outcome variable examined in this study was COVID-19 vaccine uptake among Nigerian women of reproductive age. This variable was obtained from the question: “ S1112P: Received COVID-19 vaccination ”. COVID-19 vaccination data was self-reported by the women based on their uptake status during and after the global COVID-19 pandemic. The self-reported uptake was verified with information provided by the respondents on the number of doses received, only those who reported uptake and receipt of ≥1 dose were coded “1” and “0” if otherwise. Creating socioeconomic disadvantage variable. A socioeconomic disadvantage index was constructed using selected indicators reflecting participants’ social and economic circumstances. The indicators included rural residence, belonging to the poorest household wealth quintile, having no formal education, and not being currently employed. Principal component analysis (PCA) was used to combine these correlated indicators into a single composite measure of socioeconomic disadvantage. Prior to PCA, the component variables were appropriately coded and standardised to ensure comparability across indicators measured on different scales. The first principal component, which captured the greatest proportion of the common variance among the selected indicators, was used to generate an overall socioeconomic disadvantage score for each participant. The resulting scores were standardised as z-scores, with higher values indicating greater socioeconomic disadvantage. Based on the distribution of the standardised scores, participants were subsequently categorised into three groups, as low, medium, and high socioeconomic disadvantage to facilitate analysis of inequalities in COVID-19 vaccination uptake among women of reproductive age in Nigeria. Therefore, higher score indicates being more disadvantaged, while the lower score indicates the less disadvantaged respectively. Explanatory variables. Age (in years): 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49; education: none, primary, secondary, higher; religion: Christianity, Islam, traditional/no religion; covered by health insurance scheme: yes vs no; marital status: single, currently married, formerly married; employment status: not working vs working; exposure to media (read newspaper, listen to radio, watch tv, use internet): yes vs no. The cultural norms about wife-beating was created by aggregating responses from women in each community. The items used include: “beating justified if wife goes out without telling husband”, “beating justified if wife neglects the children”, “beating justified if wife argues with husband”, “beating justified if wife refuses to have sex with husband” and “beating justified if wife burns the food”. A binary variable was created by aggregating responses from women for acceptance of wife beating, where “no” indicates empowered women and “yes” to wife beating justified, indicates women not empowered. The “wife-beating justified” variable was included as a proxy measure of gender-related social norms and women’s empowerment, which may shape access to and uptake of health services. Attitudes that legitimise wife-beating may reflect the internalisation of patriarchal norms and reduced autonomy in household and personal health-related decision-making. These factors are relevant to vaccination because women’s autonomy, access to information, and ability to make independent healthcare decisions can influence vaccine confidence and service utilisation. The World Health Organization identifies limited autonomy and decision-making power as important gender-related barriers to COVID-19 vaccination [ 23 ]. Evidence from Nigeria further shows that gender attitudes, including attitudes towards wife-beating, are associated with vaccination uptake, even after accounting for socioeconomic factors [ 24 ]. Moreover, Nigerian research has demonstrated that acceptance of wife-beating is socially patterned and associated with broader socioeconomic and demographic characteristics [ 25 ]. Accordingly, this variable was included to assess whether gender norms contribute to socioeconomic inequalities in COVID-19 vaccination uptake. Higher acceptance of wife-beating is considered an indicator of lower women’s empowerment because it reflects the adherence to gender norms that allows male dominance and violence against women. Women who justify wife-beating are generally less likely to perceive themselves as entitled to autonomy, equal decision-making, and freedom from violence, all of which are fundamental dimensions of empowerment. Consequently, greater acceptance of wife-beating is interpreted as lower empowerment, whereas rejection of all justifications for wife-beating is regarded as an indicator of higher empowerment. Sex of household head: male vs female; household size: 1–2, 3–4, 5–6, 7 + ; household wealth: wealth indicator weights were assigned using PCA. The wealth indicator variables (bicycle, motorcycle/scooter, car/truck, main floor material, main wall material, main roof material, sanitation facilities, water source, radio, television, electricity, refrigerator, cooking fuel, furniture and number of people per room) were standardised and scores were assigned. Factor loadings, or factor coefficient scores, and z-scores were computed. To determine each household’s wealth index value, the indicator values were multiplied by the loadings and added together. The overall scores were divided into the poorest/poorer/middle/richer/richest groups using the standardised z-score. Since the DHS did not gather aggregate-level data at the community-level, we used EAs to represent communities. Therefore, the analysis’s community-level factors were centred on women’s characteristics, especially those that affect the coverage of COVID-19 vaccinations. Community-level women’s acceptance of wife beating was created from the individual woman cultural norms about wife-beating justification by aggregating responses from women in each community; hence, community-level acceptance of wife beating was categorized as more acceptance (less empowered) if the proportion of women who reported wife beating justified in a given community was 79–100% and less acceptance (more empowered) if the proportion was 0–78% (median = 79%; IQR = 28%; minimum = 0%; maximum = 100%). Community-level willingness for COVID-19 vaccine uptake was created from question S1112S “ Willing to be vaccinated against COVID-19 ” and categorized as high if the proportion of women who reported willingness to COVID-19 vaccine uptake in a given community was 47–100% and low if the proportion was 0–46% (median = 47%; IQR = 34%; minimum = 0%; maximum = 100%). Community-level nativity was categorized as high if the proportion of women who had lived 5 years or more in a given community was 88–100% and low if the proportion was 0–87% (median = 88%; IQR = 17%; minimum = 0%; maximum = 100%). Community-level urbanicity was categorized as high if proportion of women who reside in urban areas was 50–100% and low if the proportion was 0–49% (median = 50%; IQR = 50%; minimum = 0%; maximum = 100%). Community-level illiteracy was categorized as high if proportion of women who cannot read at all in a given community was 40–100% and as low if the proportion was 0–39% (median = 40%; IQR = 59%; minimum = 0%; maximum = 100%). Community-level media use was categorized as high if the proportion of women who are exposed to media use in a given community was 66–100% and as low if the proportion was 0–65% (median = 66%; IQR = 58%; minimum = 0%; maximum = 100%). Community-level poverty was categorized as high if the proportion of women from the two lowest wealth quintiles in a given community was 34–100% and low if the proportion was 0–33% (median = 34%; IQR = 74%; minimum = 0%; maximum = 100%). Geopolitical zone: North West, North East, North Central, South East, South South, South West. This approach was used in a previous study [ 26 – 28 ]. Cut-offs and rationale for constructing community-level variables. The community-level variables were derived from individual-level responses using an ag
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