Disability Prevalence and Associated Factors in Nigeria: Policy Implications and the Path Towards Inclusive Development

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Understanding their characteristics is important for developing effective policies and interventions that promote inclusion and well-being. Methods This analysis drawn data from the 2018 Nigeria Demographic and Health Survey, a nationally representative survey that included a disability module. The analytical sample comprised 67,663 household members aged 15 and older. Disability was assessed using the Washington Group Short Set (WGSS) questions. Descriptive analysis was used to determine the prevalence of disability among household members, and a logistic regression model examined the associated socio-demographic factors. Results Nearly 12% of Nigerians aged 15 and older living in households have some level of disability, with 2% experiencing severe disabilities. The most prevalent types of disability were visual (8.8%) and mobility-related (3.7%). Disability prevalence varied by state and region, with Imo State reporting the highest rate (26.4%) and Kebbi State the lowest (2.6%). Factors such as those age 65+ (aOR; 23.79, 95% CI: 20.22–27.97), primary (aOR: 1.14, 95% CI: 1.03–1.27) and post-secondary education (aOR: 1.73, 95% CI: 1.59–1.99), lower wealth (first quintile aOR: 0.84, 95% CI: 0.72–0.99), residing in the Southeast (aOR: 1.35, 95% CI: 1.13–1.61) or South-South region (aOR: 1.71, 95% CI: 1.43–2.05), being widowed (aOR: 1.52, 95% CI: 1.36–1.71), living in female-headed households (aOR: 1.18, 95% CI: 1.06–1.33), and household size (aOR: 0.96, 95% CI: 0.95–0.98) were significantly associated with disability. Conclusion and Implications The study underscores the necessity of tailored policies and programs to meet the diverse needs of people with disabilities in Nigeria. Interventions should prioritize equitable access to education, healthcare, employment, and public spaces, specifically addressing vulnerable subgroups. To achieve inclusion and well-being, in line with the Sustainable Development Goals, it is essential to address socio-economic disparities, promote inclusive practices, and strengthen support systems. Disability Prevalence Nigeria Demographic and Health Survey Regions SDGs Figures Figure 1 Introduction People with disabilities represent a significant, yet often marginalized, segment of the global population, and this is particularly true in low- and middle-income countries (LMICs) like Nigeria [ 1 , 2 ]. In Nigeria, a nation with over 200 million people, the prevalence of disability presents a complex socio-economic and public health challenge. Nigeria, with its diverse socio-cultural landscape, presents distinct ways in which individuals, groups, and families relate to people with disabilities. Understanding the unique characteristics of individuals with disabilities within this context is, therefore, crucial for developing effective policies and program interventions that promote inclusion and well-being. The concept of disability extends beyond physical impairments, encompassing a broad spectrum of conditions, including sensory, intellectual, and psychosocial disabilities [ 3 , 4 ]. In Nigeria, as in many parts of sub-Saharan Africa, traditional beliefs often associate disability with stigma and social exclusion [ 2 , 5 ]. Studies from other African countries, such as Uganda and Kenya, highlight the profound impact of these cultural attitudes on the lives of people with disabilities, restricting their access to education, employment, and social participation [ 6 , 7 ]. These findings underscore the importance of contextualizing the experiences of individuals with disabilities within their specific socio-cultural environments. Moreover, the Nigerian healthcare system, characterized by inadequate infrastructure and resource constraints, often fails to meet the specific needs of individuals with disabilities [ 8 ]. This lack of access to rehabilitation services, assistive technologies, and specialized care further limits their potential for full social participation. Also, in Nigeria, adequate attentions are not yet directed at fashioning the process of systematic inclusion of PWDs in political social, and economic activities to promote the full participation of PWDs and ensure and ensure their equal and equitable access to the available resources and opportunities. In Nigeria, Furthermore, the lack of comprehensive data on the profile of people with disabilities hinders the development of evidence-based interventions. Recent legislative efforts, such as the Discrimination Against Persons with Disabilities (Prohibition) Act, of 2018 [ 9 ], signal a growing recognition of the rights and needs of individuals with disabilities in Nigeria. However, effective implementation of these policies remains a critical challenge, and the availability of data and/or information is essential to that course. Similarly, in Southeast Asia, countries like Vietnam and Cambodia have documented the challenges faced by individuals with disabilities, particularly those resulting from conflict and landmines [ 10 , 11 ]. Research from this region emphasizes the need for comprehensive rehabilitation services and inclusive policies to address the diverse needs of people with disabilities. The parallels between these regions and Nigeria underscore the shared challenges faced by individuals with disabilities in LMICs, including limited access to healthcare, education, and employment opportunities. While several studies have been conducted to examine the prevalence and factors associated with disability, there are few such studies in Nigeria. The prevalence varies from as low as 8.3% in Nigeria to 26.3% in Uganda among household members aged 5 years and above [ 2 ], while WHO estimates that 1 in 6 of the world’s population has any disability [ 12 ]. In addition, [ 13 ] estimated the global prevalence of adults with disability at 14% based on the analysis of the World Health Survey (WHS) data between 2002 and 2004 [ 14 ]. Disability has been found to increase with increasing age and is higher among poor, rural, women, and other marginalized groups such as ethnic minorities [ 1 , 2 , 15 – 21 ]. Assaf (2022) showed that there were significant differences in the level of disabilities by ethnicity and religion in Nigeria. Studies have demonstrated that disability varied by population subgroups and highlighted the possible discrimination people with disability (PWD) face either directly or indirectly through difficulty in accessing services. For instance, Ahmad et al. (2017) observed that the prevalence of disability among people aged 61 and over in Malaysia is sixteen times higher than among those aged 18–30 years. A study in Bangladesh indicates that there is a 14 percent lower likelihood of reporting disabilities among middle- and rich-household families than among poor families [ 20 ]. Regarding education, the prevalence of disability among women with no education was 28.3% in Rwanda and 38.9% in Uganda, respectively. In contrast, the corresponding prevalence among women with secondary or higher education was 12.8% in Rwanda and 15.5% in Uganda [ 2 ]. This implies that individuals with lower education levels were at a higher risk of disability compared to those with post-secondary education in both countries. Regarding marital status in Nigeria, the prevalence of disability was twice as high among married and never-married household members aged 15 years and older, compared to those who were previously married [ 2 ]. This manuscript profiles people with disabilities in Nigeria, drawing on the 2018 Nigeria Demographic and Health Survey data, a nationally representative survey with the opportunity to provide comparable contexts on the level of disability at the state level. By examining the demographic, socio-economic, and health-related characteristics of this population, this study seeks to inform the development of evidence-based strategies that promote inclusion, empowerment, and a better quality of life for individuals with disabilities in Nigeria. Data and Method Data This study presents a further analysis of the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative survey that included a disability module. The 2018 NDHS was conducted to provide policymakers and program implementers data for evaluating and designing program strategies and interventions aimed at improving the health and well-being of Nigerians. The survey data also provides indicators relevant to Nigeria's progress towards the Sustainable Development Goals (SDGs). Notably, the inclusion of the disability module offers an opportunity to assess progress towards the inclusion of people with disabilities, as disability is referenced in five of the seventeen SDGs, specifically in education (SDG4), economic growth and employment (SDG8), reduced inequalities (SDG10), accessible human settlements (SDG11), and data collection and monitoring (SDG17). The 2018 NDHS represented the fifth wave of the survey implemented in Nigeria, but it was the first to collect data on disability and the disability status of all persons aged 5 years and above. The disability module was incorporated into the household questionnaire and administered to the head of the sampled household for each member of the household aged 5 and above. The household questionnaire listed all de facto residents (those who spent the night before the survey in the household). Sampling and study population For the 2018 NDHS, a stratified, two-stage sampling procedure was employed to select respondents. Nigeria comprises thirty-six states and the Federal Capital Territory, and each state and FCT consists of both urban and rural areas. To ensure sample representativeness, a two-stage sampling design was utilized. In the first stage, 1,400 enumeration areas (EAs) were selected with probability proportional to EA size from rural and urban regions. These EAs, developed for the 2006 census by the National Population Commission, served as the sampling frame for the 2018 NDHS. A household listing was conducted in all selected EAs to update the household list, and the resulting lists formed the sampling frame for household selection in the second stage. The second stage involved selecting 30 households from each cluster using equal probability systematic sampling, yielding a total sample size of approximately 42,000 households. From these selected households, 156,532 individuals were enumerated, with a nearly equal distribution between females (50.8%) and males (49.2%). Of the 156,532 individuals enumerated in the households, the disability module covered 101,739 people aged 5 years and above listed in the household questionnaire. While this sample was used for the descriptive analysis, further analysis in this paper was restricted to individuals aged 15 years and above, resulting in a sample size of 67,663. The rationale for restricting the analysis to individuals aged 15 years and older was twofold: firstly, to improve the accuracy of disability status reporting, and secondly, to examine the working-age population, whose functional limitations can have substantial implications for economic and social productivity. Sample weights were applied to the analysis to account for the non-proportional allocation of the sample across different states and potential variations in response rates. Disability measures The NDHS’ disability module assessed difficulties in seeing, walking, hearing, remembering, communicating, and self-care using the Washington Group Short Set (WGSS) of six questions [ 22 ], a standardized tool designed for cross-population comparisons of disability. One advantage of the WGSS questions is a self-reporting rather than clinical assessment which may be expensive to implement and high probability of non-responses because of the stigma and discrimination often associated with disabilities [ 23 ]. The module was administered to the head of the household, who listed all household members, but the disability module was applied to those household members aged 5 years and above. The questions in the module ask whether there is no difficulty, some difficulty, a lot of difficulty, or cannot do at all in six domains of disability: seeing, hearing, walking or climbing steps, remembering or concentrating, washing all over or dressing, and communicating [ 22 ]. Following the Washington Group Disability Statistics guidelines, responses of ‘a lot of difficulty’ or 'cannot do at all' were used to indicate the presence of a disability for this analysis [ 2 , 24 , 25 ]. Consequently, a dichotomous disability variable was created, categorizing individuals as either 'without disability' or 'with disability.' Household members who reported 'no difficulty' or 'some difficulty' in all six domains were classified as 'without disability.' Conversely, individuals who reported 'a lot of difficulty' or 'cannot do at all' in at least one of the six domains were classified as 'with disability [ 2 ]. The data allowed the examination of the prevalence of disability for each of the six domains (seeing, hearing, walking, cognition, self-care, and communication). Also, it allows analysis by type and severity of disability compared with non-disabled counterparts. The severity of disability is determined by a response of 'a lot of difficulty' or 'cannot do at all' in any of the disability domains. Each domain is scored as '0' for no severe disability and '1' for severe disability. The sum of these domain scores is then converted into a binary variable, 'severity of disability'. A final score of '0' indicates no severe disability, while a score of '1' indicates severe disability in at least one domain. Variables measurement Outcome Variable : The outcome variable, disability status, is binary: 1 indicates a household member with a disability, and 0 indicates a household member without a disability. The construction of the disability variable is described in detail above. Explanatory Variables : Based on existing literature regarding factors associated with disability (Ahmad et al., 2017; Assaf, 2022, Thapa et al., 2025) and the availability of relevant data within the 2018 Nigeria Demographic and Health Survey (NDHS) household member dataset, the following variables were included as potential predictors of disability: age, head of household sex, marital status, education, place of residence, household wealth, region, and number of household members. Age was categorized into seven groups: 14 years or younger, 15–24 years, 25–34 years, 35–44 years, 45–54 years, 55–64 years, and 65 years and above. Head of household sex was defined as male or female. Education was grouped into: no education/preschool, primary, secondary, and post-secondary. Marital status categories were: never married/single, married/cohabiting, widowed, divorced, and separated. Household wealth, a composite measure of socioeconomic status derived from household assets and characteristics, was divided into five quintiles, ranging from the lowest (first) to the highest (fifth). Geographical location was represented by place of residence (urban or rural) and region of residence (North-central, Northeast, Northwest, Southeast, South-South, and Southwest). The number of household members was included as a continuous variable in the logistic regression model. Statistical analysis Descriptive, bivariate, and multivariate analyses were conducted. The descriptive analysis assessed the distribution of the study population and the level of disability across various domains, as well as the type and severity of disability. Chi-square tests were used to examine the associations between disability measures and the characteristics of the study population. Because selected characteristics showed similar associations with disability for both men and women, the multivariate analysis combined their data for logistic modeling. Given the dichotomous nature of the disability variable, a multivariate logistic regression model was employed to determine the effects of selected predictors. Binary logistic regression analysis was performed to estimate the odds ratio (OR) and adjusted odds ratio (aOR) with a 95% confidence interval for individuals aged 15 years and older with disabilities. The multivariate model included statistically significant predictors (p < 0.05) in the univariate analysis. The analysis accounted for the survey sampling weights and sample design. All statistical analyses were performed using Stata version 16, and visualizations were generated using R programming. Results Prevalence of disability by type and States The finding indicates that nearly one in ten household members (12%) aged 15 or older experience some level of disability in at least one functional domain: vision impairment, hearing, communication, cognition, mobility, or self-care. Furthermore, 2% of the study population have severe disabilities in at least one domain. Seeing (8.8%) was the more prevalent disability among Nigerian household members aged 15 and above, followed by mobility (3.7%). Self-care, hearing, cognition, and communication were less common disability among those 15 years and above in the sample population (see Table 1 ). A map illustrating the percentage of people with disabilities by state and federal capital territory reveals significant state variation. Imo state exhibits the highest prevalence of disability, with 26.4%, followed by Akwa Ibom at 20.1%. This pattern is consistent among individuals aged 15 or older, where Imo state again reports the highest disability rate, followed by Akwa Ibom (25%), Cross River (24.5%), Enugu (24.3%), and Ondo (24.3%). Characteristics and distribution of people with disability Table 2 presents the percentage distribution of household members aged 15 and older by socio-demographic characteristics and disability status, using the 2018 NDHS data. The table is disaggregated by sex and provides percentages for people with any disability and those with severe disability. The findings indicate a positive measure of associations between age and disability prevalence. Specifically, the percentage of both men and women with disabilities increases with age. For example, in the 65 + age group, 47% of men and 48.6% of women report a disability. Furthermore, education level demonstrates an inverse relationship with disability. Disability prevalence was higher among individuals in households with no education (14.4%) compared to those with post-secondary education (14.0%). This pattern was observed for both men and women. Household wealth quintiles show that disability prevalence among household members increases with wealth for both men and women. For instance, disability affects 12.8% of men in the poorest quintile compared to 13.9% in the wealthiest. For women, the corresponding rates are 19.8% and 21.6%, respectively. The findings indicate that rural residents have a slightly lower percentage of disability (11.9%) compared to urban residents (12.2%). Regional variations in disability prevalence are also observed, with the Southeast and South-South regions exhibiting higher percentages. Specifically, the Southeast has 17.5% and the South-South has 17.7%. Women who are widowed (41.3%) or divorced (18.8%) have a higher percentage of disability compared to those who are single (4.1%) or currently married or cohabiting (12.5%). Household member sex also shows a slight difference, with women having a slightly lower percentage of disability (11.8%) than men (12.3%). Finally, the sex of the household head is strongly associated with disability prevalence. Households headed by females have a higher percentage of members with disabilities (19.4%) compared to male-headed households (10.8%). Factors influencing disability among Nigerians 15 years and above Table 3 presents a logistic regression model examining socio-demographic predictors of disability among Nigerians aged 15 and older, displaying both unadjusted and adjusted odds ratios (OR, aOR) with 95% confidence intervals (CI). Age significantly increases disability odds (aOR 65 + age group: 23.79, 95% CI: 20.22–27.97). Education level shows a complex relationship; primary education slightly increases odds (aOR: 1.14, 95% CI: 1.03–1.27) compared to no education, while post-secondary education significantly increases odds (aOR: 1.73, 95% CI: 1.59–1.99). Wealthier quintiles (first, third, fourth) indicate lower disability odds than the wealthiest (fifth) quintile (e.g., first quintile aOR: 0.84, 95% CI: 0.72–0.99). Regionally, the Southeast (aOR: 1.35, 95% CI: 1.13–1.61) and South-South (aOR: 1.71, 95% CI: 1.43–2.05) have higher disability odds compared to the Northwest. People who were widowed have significantly higher odds compared to who are married or cohabiting people (aOR: 1.52, 95% CI: 1.36–1.71). Female-headed households have higher disability odds (aOR: 1.18, 95% CI: 1.06–1.33) than male-headed households. Increased household size slightly reduces disability odds (aOR: 0.96, 95% CI: 0.95–0.98). Household member sex is not significantly associated with disability (aOR: 0.97, 95% CI: 0.91–1.03). The logistic model reveals that age, education, wealth, region, marital status, sex of household head, and household size are significantly related to disability status in Nigeria. Discussion This study aimed to present the profile of people with disabilities in Nigeria using the 2018 Nigeria Demographic and Health Survey data. The findings indicate that approximately one in ten individuals aged 15 or older in Nigeria experience some level of disability (12%), with 2% reporting severe disabilities. Seeing (8.8%) was the most prevalent disability, followed by mobility (3.7%). These prevalence rates are generally consistent with previous findings that indicate disability prevalence varies widely, from 8.3% in Nigeria to 26.3% in Uganda, and align with the WHO’s estimate that 15% of the world’s population lives with some form of disability [ 2 , 12 ]. The prevalence of disability increases with age as this study indicates a positive measure of associations between age and disability prevalence. Specifically, the percentage of both men and women with disabilities increases with age. For example, in the 65 + age group, 47% of men and 48.6% of women report a disability. This can be associated with the functional disability of the brain with an increase in age. The ability of the brain to remember and function psychologically reduces with age. This finding is consistent with global trends and previous studies in other settings, including Malaysia and Nepal, where disability prevalence was found to be significantly higher among older adults (Ahmad et al., 2017; Assaf, 2022; Mitra, 2018; Thapa et al., 2025). However, unlike a similar study in Nepal among older people [ 26 ], this study reveals a slightly complex association between education and the prevalence of disability. Education level in this study shows that primary education slightly increases odds (aOR: 1.14, 95% CI: 1.03–1.27) compared to no education, while post-secondary education significantly increases odds (aOR: 1.73, 95% CI: 1.59–1.99). The observed relationship between education and the prevalence of disability among household members in this study aligns with findings from Rwanda and Uganda, where lower education levels were associated with a higher risk of disability [ 2 ]. This suggests that education may serve as a protective factor against disability, possibly through increased access to information, healthcare, and better living conditions. Also, there tends to be a correlation between poverty and disability as families with PWDs may have lower wealth accumulation compared with wealthy families. Poor families are also more prone to financial risks as the onset of disability is an indication of financial commitments. Studies have confirmed this finding that at any given point in time, persons with disabilities are more likely to live in low-income households, hold significantly lower overall net worth and non-housing assets, and experience significant reductions in wealth following disability onset (Maroto & Pettinicchio, 2020; Shah Goda Jialu Liu Streeter et al., 2021). Unlike some studies, the current research observed a slightly higher rate of disability in households with the wealthiest quintile. The higher reporting of disabilities in wealthier households could be due to their increased awareness and access to diagnoses, implying that reported disability rates may not directly reflect the relationship with poverty. Disability also tends to eat deep into the financial resources of households, especially in resource-constraint countries where there is no government support system to cushion the economic effects of disability on the financial base of a family and could lead to under-reporting of those with disability in poor households. The findings revealed variations in the reported prevalence of disability by state, with Imo State reporting the highest rate (26.4%), followed by Akwa Ibom (20.1%), and Kebbi State (2.6%) having the least prevalence rate of disability among people in the household 15 years and above. The findings underscore the importance of contextualizing peculiarity in disability prevalence by state that may be attributed to socio-cultural and healthcare access issues relating to disabilities being discussed. This brings to the fore the position of social construct theorists that disability is more of a function of the attitudes and perceptions within the context where PWDs find themselves. The theory suggests that disability is a label or category that is created and reinforced by society, rather than being a natural or inherent characteristic. Regional variations were evident, with the Southeast and South-South regions exhibiting higher disability prevalence, potentially reflecting regional socio-economic disparities or other environmental and social factors. The findings show that the Southeast (aOR: 1.35, 95% CI: 1.13–1.61) and South-South (aOR: 1.71, 95% CI: 1.43–2.05) have higher odds of having household members with disabilities compared to households in the Northwest. It is therefore not surprising that in many Northern states of Nigeria, the reported prevalence of disability was lower compared with some of the states in the South. It could be inferred that perceptions of disability differ across the regions in Nigeria, importantly because PWDs are probably more acceptable and seen as part of the communities in Northern Nigeria than in the Southern part. This understanding might influence how cases of impairments are reported across the regions by households or family members. Related to the region is the place of residence of a person which in this study is classified either to be in rural or urban areas. The findings indicate that rural residents have a slightly lower percentage of disability (11.9%) compared to urban residents (12.2%) and this could be linked to changes in the environment that are often associated with urban settings. Contrary to some studies[ 17 , 19 ] that suggest higher disability rates in rural areas due to limited access to services, this study found a lower prevalence of disability reported for household members in rural areas compared to urban areas. The findings show the prevalence of disability was significantly higher among widows compared to those who are currently married or in cohabiting relationships (aOR: 1.52, 95% CI: 1.36–1.71). This is consistent with previous research [ 2 , 26 ]. This could be linked to the stress and social isolation associated with widowhood, as well as the increased likelihood of older age and related health conditions in this group. Household head sex was also a significant factor, with female-headed households showing a higher percentage of members with disabilities. This finding may indicate the socio-economic vulnerabilities of female-headed households, which may face greater challenges in accessing resources and support for members with disabilities. The slight reduction in disability odds with increased household size (aOR: 0.96, 95% CI: 0.95–0.98) is an interesting finding that warrants careful consideration alongside existing literature. While prior research has shown a higher prevalence of household-level disability in smaller households [ 22 , 27 ], this study suggests the opposite. Potential explanations include a dilution effect, variations in household age structure, or greater informal support in larger families [ 28 – 30 ]. Conversely, research on disability's impact on household economics [ 31 , 32 ] may not directly address the odds of household disability by size. The small magnitude of the observed effect (aOR: 0.96), despite statistically significant, also necessitates consideration of its practical implications. Policy and Programmatic Implications Analysis of the 2018 NDHS data on disability in Nigeria reveals critical policy and program implications. Findings underscore a vital need for transforming policies and societal attitudes to achieve the full inclusion and improved well-being of people with disabilities, aligning with the SDGs. Firstly, equitable access to fundamental aspects of life, including education, healthcare, employment, and public spaces, is paramount for the full participation of people with disabilities. This necessitates developing inclusive infrastructure, support services, and urban planning, benefiting people with disabilities and the broader society. Secondly, interventions must be tailored to the diverse needs of vulnerable subgroups within the disability population, such as older adults, widowed individuals, and those in specific regions. Contextualized approaches, utilizing participatory methods to identify needs, are crucial for effective well-being and social inclusion. Thirdly, protecting the rights and fostering inclusion requires reviewing and reforming legal frameworks, alongside a fundamental shift in societal attitudes. Effective implementation and enforcement of laws like the Discrimination Against Persons with Disabilities (Prohibition) Act, 2018, coupled with public awareness campaigns to combat stigma, are essential. The study aligns with social constructivist perspectives, highlighting disability as shaped by societal attitudes, policies, and environmental barriers, not solely a medical issue. This calls for development and humanitarian organizations to prioritize inclusive programs addressing these social and environmental factors. Strengthening coordination platforms is crucial for improved inclusion. Development partners play a vital role in providing technical support through partnerships, advocacy, resource mobilization, and capacity development. While Nigeria has enacted the Persons with Disabilities Act (2018), its effective implementation remains a challenge. Concerted efforts are needed to secure the buy-in of government officials at all levels to translate legislation into concrete actions. Reliable disability data is paramount for effective policymaking and targeted programs. Investing in robust national data collection systems, and employing standardized tools like the WGSS, is crucial for evidence-based decision-making, resource allocation, and monitoring progress towards SDGs. In conclusion, this study underscores the need for a multi-faceted approach to addressing socio-economic disparities, promoting inclusive practices, and strengthening support systems for people with disabilities. Implementing evidence-based policies and programs will enable Nigeria to make significant strides towards achieving the SDGs and ensuring the inclusion and well-being of all citizens. Study strengths and limitations The data are self-related and technically such data in research are prone to limitations among which is respondent-selected biases as respondents might decide to answer questions in a particular way. Also, considering the sensitive nature of research on disability, culture and belief systems can influence how people respond to questions on disabilities to protect family members and themselves from stigma and social discrimination. Thus, reporting the disability status of household members may be influenced by social stigma, cultural beliefs, and variations in how individuals perceive and report their disabilities. In addition, asking the household head about household members’ disability status has a high tendency to generate low-quality data. The head of the household may not always have complete or accurate information about the disability status of other household members. Disability affects all age groups; however, the analysis was restricted to people aged 15 years and above, thus excluding children, which is an important population to consider in disability research and policy. The study uses cross-sectional data, which limits the ability to establish causality. While the study identified associations between various factors and disability, it cannot determine the direction of cause-and-effect relationships. The study primarily focuses on functional limitations as measured by the WGSS. This may not capture the full complexity of disability, which also includes social, environmental, and attitudinal barriers. Despite the aforementioned limitations, this study provides valuable data and information to inform policy and programmatic interventions. The use of the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative survey, provided an opportunity for generalizations and discussing disability prevalence and correlates at a national level in Nigeria. The 2018 NDHS included a disability module, offering improved insights into the situation of people with disabilities in Nigeria, which was lacking in previous NDHS waves. Disability status was assessed using the Washington Group Short Set (WGSS) of questions, a standardized tool for cross-population comparisons, enhancing the study's comparability with other studies and countries. The study explicitly links its findings to the Sustainable Development Goals (SDGs), emphasizing the importance of disability inclusion in achieving these global goals. Declarations Funding This research, its authorship, and publication were conducted without financial support from any external source. Data availability The datasets utilized in this study were publicly accessible through the MEASURE DHS program (funded by the United States Government) prior to its foreign aid review. These data remain downloadable, contingent on the website's active status: https://www.dhsprogram.com/data/available-datasets.cfm . Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Human Ethics and Consent to Participate declarations Not applicable. Competing interests The authors declare that they have no known conflicts of interest, whether financial or personal, that could be perceived as influencing the research presented in this paper. Author Details 1 International Health and Development Analysis Centre (IHDAC), Abuja, Nigeria. 2 Data for Implementation (Data.FI), Abuja, Nigeria. Author Contribution GPI, JK, and KAO developed the study concept and design; KAO wrote the main manuscript text; KAO, JK, and GPI conducted data analysis and interpretation; SB and KAO carried out statistical analysis and data visualization; all authors critically reviewed the manuscript for important intellectual content. Acknowledgement The authors gratefully acknowledge MEASURE DHS for granting permission to use the 2018 Nigeria Demographic and Health Survey data. They also extend their sincere appreciation to the anonymous peer reviewers for their invaluable insights that significantly enhanced this manuscript. It is important to note that the views and opinions expressed in this paper are solely those of the authors and do not necessarily represent the positions of their respective organizations or the Nigerian government. References World Health Organization. World Report on Disability. 2011. https://iris.who.int/handle/10665/44575. Accessed 10 Apr 2025. Assaf S. 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World Health Survey (WHS): Data Catalog (2002 - 2004). https://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/whs/?page=1&from=2002&to=2004&ps=15&repo=whs. Accessed 11 Apr 2025. Ahmad NA, Mohamad Kasim N, Mahmud NA, Mohd Yusof Y, Othman S, Chan YY, et al. Prevalence and determinants of disability among adults in Malaysia: Results from the National Health and Morbidity Survey (NHMS) 2015. BMC Public Health. 2017;17:1–10. Bachani AM, Galiwango E, Kadobera D, Bentley JA, Bishai D, Wegener S, et al. A new screening instrument for disability in low-income and middle-income settings: application at the Iganga-Mayuge Demographic Surveillance System (IM-DSS), Uganda. BMJ Open. 2014;4:e005795. Mitra S. Disability, Health and Human Development. Disability, Health and Human Development. 2018. https://doi.org/10.1057/978-1-137-53638-9. Pinilla-Roncancio M, Alkire S. How Poor Are People With Disabilities? Evidence Based on the Global Multidimensional Poverty Index. J Disabil Policy Stud. 2021;31:206–16. Priebe J. Disability and Its Correlates in a Developing Country Context: Evidence from Multiple Datasets and Measures. J Dev Stud. 2018;54:657–81. Tareque MI, Begum S, Saito Y. Inequality in Disability in Bangladesh. PLoS One. 2014;9:e103681. World Health Organization. WHO Global Disability Action Plan 2014–2021: Better Health for All People with Disability. 2015. https://iris.who.int/bitstream/handle/10665/199544/?sequence=1. Accessed 10 Apr 2025. Altman BM. International Measurement of Disability: Purpose, Method and Application. Cham: Springer International Publishing; 2016. Groce NE, Mont D. Counting disability: emerging consensus on the Washington Group questionnaire. Lancet Glob Health. 2017;5:e649–50. Mactaggart I, Bek AH, Banks LM, Bright T, Dionicio C, Hameed S, et al. Interrogating and reflecting on disability prevalence data collected using the washington group tools: Results from population-based surveys in cameroon, guatemala, india, maldives, nepal, turkey and vanuatu. Int J Environ Res Public Health. 2021;18:9213. Washington Group on Disability Statistics (WG). The Washington Group Short Set on Functioning (WG-SS). 2022. Thapa NR, Subedi G, KC VK. Disability and its sociodemographic correlates among older people in Nepal: evidence from the 2022 Nepal Demographic Health Survey. BMC Public Health. 2025;25:1–10. von Reichert C. Disability and the household context: Findings for the United States from the public Use Microdata Sample of the American Community Survey. Frontiers in Rehabilitation Sciences. 2022;3:875966. Ruggiero CF, McHale SM, Paul IM, Savage JS. Learned Experience and Resource Dilution: Conceptualizing Sibling Influences on Parents’ Feeding Practices. International Journal of Environmental Research and Public Health 2021, Vol 18, Page 5739. 2021;18:5739. Riswick T, Engelen T. Siblings and life transitions: investigating the resource dilution hypothesis across historical contexts and outcomes. The History of the Family. 2018;23:521–32. Wolf DA. Getting Help From Others: The Effects of Demand and Supply. The Journals of Gerontology: Series B. 2014;69 Suppl_1:S59–64. Mitra S, Palmer M, Kim H, Mont D, Groce N. Extra costs of living with a disability: A review and agenda for research. Disabil Health J. 2017;10:475–84. Friedman C. Financial hardship experienced by people with disabilities during the COVID-19 pandemic. Disabil Health J. 2022;15:101359. Tables Table 1: Percentage distribution of household members 15 years and above by type and severity of disability (N=67,663) Domain of Disability disability severe disability Seeing 8.8 0.9 Hearing 1.8 0.3 Communication 1.3 0.2 Cognition 1.4 0.3 Mobility 3.7 0.7 Self-care 2.0 0.5 Any disability (severe) 12.0 2.0 Table 2: Percentage distribution of household members 15 years and older by socio-demographic characteristics and disability status, NDHS 2018 Characteristics Men Women Total % % with disability % with severe disability % % with disability % with severe disability % % with disability % with severe disability Age group 15-24 25-34 35-44 45-54 55-64 65+ 28.9 22.2 18.5 12.8 8.7 9.0 3.3*** 4.4 7.6 17.2 29.0 47.0 0.5*** 0.8 1.0 1.7 3.7 11.4 29.9 25.0 17.4 13.0 7.8 7.0 3.4*** 4.0 9.3 19.5 29.4 48.6 0.4*** 0.6 0.9 1.9 4.4 12.1 29.4 23.6 17.9 12.9 8.3 8.0 3.3*** 8.2 8.4 18.4 29.2 47.7 0.5*** 0.7 0.9 1.8 4.0 11.7 Education None Primary Secondary Post-secondary 25.4 16.9 41.3 16.4 15.5*** 17.8 7.6 13.5 4.1*** 2.9 1.0 0.9 38.7 17.8 33.7 9.7 13.7*** 14.2 7.5 14.8 3.3*** 1.7 0.7 0.6 32.2 17.4 37.5 13.0 14.4*** 15.9 7.6 14.0 3.6*** 2.3 0.8 0.9 Wealth First quintile Second quintile Third quintile Fourth quintile Fifth quintile 19.3 18.9 19.4 19.6 22.9 12.8*** 11.4 11.5 11.6 13.8 3.3*** 2.2 1.8 1.7 1.4 19.8 19.3 19.8 19.5 21.6 10.4*** 10.7 11.3 12.6 13.9 2.3*** 1.9 1.9 1.9 1.4 19.5 19.1 19.6 19.5 22.2 11.6*** 11.1 11.4 12.1 13.9 2.8*** 2.0 1.9 1.8 1.4 Place of residence Urban Rural 40.4 59.6 12.1 12.4 1.7*** 2.4 40.6 59.4 12.3* 11.5 1.7* 2.0 40.5 59.5 12.2 11.9 1.7*** 2.2 Region North central North east North west South east South south South west 18.3 18.8 23.6 11.2 13.4 14.7 12.0*** 11.7 9.3 16.9 15.3 11.8 1.6*** 2.7 2.0 3.6 2.0 1.1 17.9 18.0 23.4 14.4 12.4 13.9 10.7*** 9.7 5.9 17.9 20.3 12.1 1.5*** 1.7 1.5 3.0 3.0 1.0 18.1 18.4 23.5 12.8 12.9 14.3 11.4*** 10.7 7.6 17.5 17.7 11.9 1.5*** 2.2 1.7 3.2 2.5 1.1 Marital Status Never married/Single Currently married Widowed Divorced 37.7 59.0 1.7 1.6 3.8*** 16.5 47.6 18.4 0.8*** 2.6 11.5 4.6 20.8 66.0 10.9 2.4 4.7*** 9.1 40.4 19.0 0.8*** 1.0 8.5 3.1 29.2 62.5 6.4 2.0 4.1*** 12.5 41.3 18.8 0.8*** 1.7 8.9 3.7 Sex of H member Male Female NA NA NA NA NA NA 49.4 50.6 12.3 11.8 2.1* 1.9 Sex of HH+ Male Female 93.8 6.2 12.7*** 6.6 2.1 1.9 78.2 21.8 8.7*** 23.0 1.3*** 4.0 85.9 14.1 10.8*** 19.4 1.7*** 3.5 Total 100.00 12.3 2.1 100 11.8 1.9 100 12.0 2.0 Note: HH = Household Head; H = Household; * p <0.05; ** p <0.01; *** p <0.001 Table 3: Logistic regression model of disability on socio-demographic characteristics among household members 15 years and above in Nigeria Characteristics Total (combined men and women) OR (95% CI) aOR (95% CI) Age group 15-24 (ref) 25-34 35-44 45-54 55-64 65+ 1.00 1.33*** (1.17 – 1.52) 2.60*** (2.28 – 2.95) 6.45*** (5.75 – 7.23) 12.08*** (10.73 -13.59) 26.73*** (23.52 – 30.39) 1.00 1.22** (1.06 – 1.39) 2.46*** (2.12 – 2.85) 6.04*** (5.23 – 6.97) 11.06*** (9.49 – 12.89) 23.79*** (20.22 -27.97) Education None (ref) Primary Secondary Post-secondary 1.00 1.09* (1.00 – 1.20) 0.48*** (0.43 – 0.53) 1.00 (0.88 – 1.14) 1.00 1.14* (1.03 – 1.27) 1.12 (0.99 – 1.26) 1.73*** (1.59 – 1.99) Wealth First quintile Second quintile Third quintile Fourth quintile Fifth quintile (ref) 0.82** (0.72 – 0.93) 0.78*** (0.69 – 0.89) 0.73*** (0.65 – 0.82) 0.79*** (0.71 – 0.88) 1.00 0.84* (0.72 – 0.99) 0.91 (0.79 – 1.05) 0.87* (0.76 – 0.99) 0.92 (0.82 – 1.04) 1.00 Place of residence Urban Rural (ref) 1.08 (0.99 – 1.19) 1.00 0.98 (0.89 – 1.09) 1.00 Region North central North east North west South east South south South west 1.43*** (1.24 – 1.66) 1.56*** (1.36 –1.78) 1.00 2.46*** (2.14 – 2.84) 2.52*** (2.18 - 2.91) 1.54*** (1.30 – 1.84) 1.13 (0.96 – 1.32) 1.55*** (1.34 – 1.79) 1.00 1.35*** (1.13 – 1.61) 1.71*** (1.43 – 2.05) 0.86 (0.71 – 1.04) Marital Status Never married/Single Currently married Widowed Divorced 0.32*** (0.29 – 0.36) 1.00 5.01*** (4.60 – 5.47) 1.46*** (1.19 – 1.78) 1.04 (0.91 – 1.18) 1.00 1.52*** (1.36 – 1.71) 1.45*** (1.19 – 1.78) Sex of household member Male Female 1.00 0.92** (0.88 – 0.97) 1.00 0.97 (0.91 – 1.03) Sex of head of household Male Female 1.00 1.97*** (1.83 – 2.13) 1.00 1.18** (1.06 – 1.33) Number of household members 0.91*** (0.90 – 0.92) 0.96*** (0.95 -0.98) Note: OR=unadjusted odds ratio, aOR=adjusted odds ratio; CI=confidence interval; ref =reference group; * p <0.05; ** p <0.01; *** p <0.001 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 24 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviews received at journal 14 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 23 Apr, 2025 Submission checks completed at journal 23 Apr, 2025 First submitted to journal 19 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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In Nigeria, a nation with over 200\u0026nbsp;million people, the prevalence of disability presents a complex socio-economic and public health challenge. Nigeria, with its diverse socio-cultural landscape, presents distinct ways in which individuals, groups, and families relate to people with disabilities. Understanding the unique characteristics of individuals with disabilities within this context is, therefore, crucial for developing effective policies and program interventions that promote inclusion and well-being.\u003c/p\u003e \u003cp\u003eThe concept of disability extends beyond physical impairments, encompassing a broad spectrum of conditions, including sensory, intellectual, and psychosocial disabilities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Nigeria, as in many parts of sub-Saharan Africa, traditional beliefs often associate disability with stigma and social exclusion [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies from other African countries, such as Uganda and Kenya, highlight the profound impact of these cultural attitudes on the lives of people with disabilities, restricting their access to education, employment, and social participation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings underscore the importance of contextualizing the experiences of individuals with disabilities within their specific socio-cultural environments.\u003c/p\u003e \u003cp\u003eMoreover, the Nigerian healthcare system, characterized by inadequate infrastructure and resource constraints, often fails to meet the specific needs of individuals with disabilities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This lack of access to rehabilitation services, assistive technologies, and specialized care further limits their potential for full social participation. Also, in Nigeria, adequate attentions are not yet directed at fashioning the process of systematic inclusion of PWDs in political social, and economic activities to promote the full participation of PWDs and ensure and ensure their equal and equitable access to the available resources and opportunities. In Nigeria, Furthermore, the lack of comprehensive data on the profile of people with disabilities hinders the development of evidence-based interventions. Recent legislative efforts, such as the Discrimination Against Persons with Disabilities (Prohibition) Act, of 2018 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], signal a growing recognition of the rights and needs of individuals with disabilities in Nigeria. However, effective implementation of these policies remains a critical challenge, and the availability of data and/or information is essential to that course.\u003c/p\u003e \u003cp\u003eSimilarly, in Southeast Asia, countries like Vietnam and Cambodia have documented the challenges faced by individuals with disabilities, particularly those resulting from conflict and landmines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Research from this region emphasizes the need for comprehensive rehabilitation services and inclusive policies to address the diverse needs of people with disabilities. The parallels between these regions and Nigeria underscore the shared challenges faced by individuals with disabilities in LMICs, including limited access to healthcare, education, and employment opportunities.\u003c/p\u003e \u003cp\u003eWhile several studies have been conducted to examine the prevalence and factors associated with disability, there are few such studies in Nigeria. The prevalence varies from as low as 8.3% in Nigeria to 26.3% in Uganda among household members aged 5 years and above [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], while WHO estimates that 1 in 6 of the world\u0026rsquo;s population has any disability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] estimated the global prevalence of adults with disability at 14% based on the analysis of the World Health Survey (WHS) data between 2002 and 2004 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Disability has been found to increase with increasing age and is higher among poor, rural, women, and other marginalized groups such as ethnic minorities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Assaf (2022) showed that there were significant differences in the level of disabilities by ethnicity and religion in Nigeria.\u003c/p\u003e \u003cp\u003eStudies have demonstrated that disability varied by population subgroups and highlighted the possible discrimination people with disability (PWD) face either directly or indirectly through difficulty in accessing services. For instance, Ahmad et al. (2017) observed that the prevalence of disability among people aged 61 and over in Malaysia is sixteen times higher than among those aged 18\u0026ndash;30 years. A study in Bangladesh indicates that there is a 14 percent lower likelihood of reporting disabilities among middle- and rich-household families than among poor families [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Regarding education, the prevalence of disability among women with no education was 28.3% in Rwanda and 38.9% in Uganda, respectively. In contrast, the corresponding prevalence among women with secondary or higher education was 12.8% in Rwanda and 15.5% in Uganda [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This implies that individuals with lower education levels were at a higher risk of disability compared to those with post-secondary education in both countries. Regarding marital status in Nigeria, the prevalence of disability was twice as high among married and never-married household members aged 15 years and older, compared to those who were previously married [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis manuscript profiles people with disabilities in Nigeria, drawing on the 2018 Nigeria Demographic and Health Survey data, a nationally representative survey with the opportunity to provide comparable contexts on the level of disability at the state level. By examining the demographic, socio-economic, and health-related characteristics of this population, this study seeks to inform the development of evidence-based strategies that promote inclusion, empowerment, and a better quality of life for individuals with disabilities in Nigeria.\u003c/p\u003e"},{"header":"Data and Method","content":"\u003cp\u003eData\u003c/p\u003e \u003cp\u003eThis study presents a further analysis of the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative survey that included a disability module. The 2018 NDHS was conducted to provide policymakers and program implementers data for evaluating and designing program strategies and interventions aimed at improving the health and well-being of Nigerians. The survey data also provides indicators relevant to Nigeria's progress towards the Sustainable Development Goals (SDGs). Notably, the inclusion of the disability module offers an opportunity to assess progress towards the inclusion of people with disabilities, as disability is referenced in five of the seventeen SDGs, specifically in education (SDG4), economic growth and employment (SDG8), reduced inequalities (SDG10), accessible human settlements (SDG11), and data collection and monitoring (SDG17). The 2018 NDHS represented the fifth wave of the survey implemented in Nigeria, but it was the first to collect data on disability and the disability status of all persons aged 5 years and above. The disability module was incorporated into the household questionnaire and administered to the head of the sampled household for each member of the household aged 5 and above. The household questionnaire listed all de facto residents (those who spent the night before the survey in the household).\u003c/p\u003e \u003cp\u003eSampling and study population\u003c/p\u003e \u003cp\u003eFor the 2018 NDHS, a stratified, two-stage sampling procedure was employed to select respondents. Nigeria comprises thirty-six states and the Federal Capital Territory, and each state and FCT consists of both urban and rural areas. To ensure sample representativeness, a two-stage sampling design was utilized. In the first stage, 1,400 enumeration areas (EAs) were selected with probability proportional to EA size from rural and urban regions. These EAs, developed for the 2006 census by the National Population Commission, served as the sampling frame for the 2018 NDHS. A household listing was conducted in all selected EAs to update the household list, and the resulting lists formed the sampling frame for household selection in the second stage. The second stage involved selecting 30 households from each cluster using equal probability systematic sampling, yielding a total sample size of approximately 42,000 households. From these selected households, 156,532 individuals were enumerated, with a nearly equal distribution between females (50.8%) and males (49.2%). Of the 156,532 individuals enumerated in the households, the disability module covered 101,739 people aged 5 years and above listed in the household questionnaire. While this sample was used for the descriptive analysis, further analysis in this paper was restricted to individuals aged 15 years and above, resulting in a sample size of 67,663. The rationale for restricting the analysis to individuals aged 15 years and older was twofold: firstly, to improve the accuracy of disability status reporting, and secondly, to examine the working-age population, whose functional limitations can have substantial implications for economic and social productivity. Sample weights were applied to the analysis to account for the non-proportional allocation of the sample across different states and potential variations in response rates.\u003c/p\u003e \u003cp\u003eDisability measures\u003c/p\u003e \u003cp\u003eThe NDHS\u0026rsquo; disability module assessed difficulties in seeing, walking, hearing, remembering, communicating, and self-care using the Washington Group Short Set (WGSS) of six questions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a standardized tool designed for cross-population comparisons of disability. One advantage of the WGSS questions is a self-reporting rather than clinical assessment which may be expensive to implement and high probability of non-responses because of the stigma and discrimination often associated with disabilities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The module was administered to the head of the household, who listed all household members, but the disability module was applied to those household members aged 5 years and above. The questions in the module ask whether there is no difficulty, some difficulty, a lot of difficulty, or cannot do at all in six domains of disability: seeing, hearing, walking or climbing steps, remembering or concentrating, washing all over or dressing, and communicating [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFollowing the Washington Group Disability Statistics guidelines, responses of \u0026lsquo;a lot of difficulty\u0026rsquo; or 'cannot do at all' were used to indicate the presence of a disability for this analysis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consequently, a dichotomous disability variable was created, categorizing individuals as either 'without disability' or 'with disability.' Household members who reported 'no difficulty' or 'some difficulty' in all six domains were classified as 'without disability.' Conversely, individuals who reported 'a lot of difficulty' or 'cannot do at all' in at least one of the six domains were classified as 'with disability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The data allowed the examination of the prevalence of disability for each of the six domains (seeing, hearing, walking, cognition, self-care, and communication). Also, it allows analysis by type and severity of disability compared with non-disabled counterparts. The severity of disability is determined by a response of 'a lot of difficulty' or 'cannot do at all' in any of the disability domains. Each domain is scored as '0' for no severe disability and '1' for severe disability. The sum of these domain scores is then converted into a binary variable, 'severity of disability'. A final score of '0' indicates no severe disability, while a score of '1' indicates severe disability in at least one domain.\u003c/p\u003e \u003cp\u003eVariables measurement\u003c/p\u003e \u003cp\u003e \u003cem\u003eOutcome Variable\u003c/em\u003e: The outcome variable, disability status, is binary: 1 indicates a household member with a disability, and 0 indicates a household member without a disability. The construction of the disability variable is described in detail above.\u003c/p\u003e \u003cp\u003e \u003cem\u003eExplanatory Variables\u003c/em\u003e: Based on existing literature regarding factors associated with disability (Ahmad et al., 2017; Assaf, 2022, Thapa et al., 2025) and the availability of relevant data within the 2018 Nigeria Demographic and Health Survey (NDHS) household member dataset, the following variables were included as potential predictors of disability: age, head of household sex, marital status, education, place of residence, household wealth, region, and number of household members. Age was categorized into seven groups: 14 years or younger, 15\u0026ndash;24 years, 25\u0026ndash;34 years, 35\u0026ndash;44 years, 45\u0026ndash;54 years, 55\u0026ndash;64 years, and 65 years and above. Head of household sex was defined as male or female. Education was grouped into: no education/preschool, primary, secondary, and post-secondary. Marital status categories were: never married/single, married/cohabiting, widowed, divorced, and separated. Household wealth, a composite measure of socioeconomic status derived from household assets and characteristics, was divided into five quintiles, ranging from the lowest (first) to the highest (fifth). Geographical location was represented by place of residence (urban or rural) and region of residence (North-central, Northeast, Northwest, Southeast, South-South, and Southwest). The number of household members was included as a continuous variable in the logistic regression model.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive, bivariate, and multivariate analyses were conducted. The descriptive analysis assessed the distribution of the study population and the level of disability across various domains, as well as the type and severity of disability. Chi-square tests were used to examine the associations between disability measures and the characteristics of the study population. Because selected characteristics showed similar associations with disability for both men and women, the multivariate analysis combined their data for logistic modeling. Given the dichotomous nature of the disability variable, a multivariate logistic regression model was employed to determine the effects of selected predictors. Binary logistic regression analysis was performed to estimate the odds ratio (OR) and adjusted odds ratio (aOR) with a 95% confidence interval for individuals aged 15 years and older with disabilities. The multivariate model included statistically significant predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the univariate analysis. The analysis accounted for the survey sampling weights and sample design. All statistical analyses were performed using Stata version 16, and visualizations were generated using R programming.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePrevalence of disability by type and States\u003c/p\u003e \u003cp\u003eThe finding indicates that nearly one in ten household members (12%) aged 15 or older experience some level of disability in at least one functional domain: vision impairment, hearing, communication, cognition, mobility, or self-care. Furthermore, 2% of the study population have severe disabilities in at least one domain. Seeing (8.8%) was the more prevalent disability among Nigerian household members aged 15 and above, followed by mobility (3.7%). Self-care, hearing, cognition, and communication were less common disability among those 15 years and above in the sample population (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A map illustrating the percentage of people with disabilities by state and federal capital territory reveals significant state variation. Imo state exhibits the highest prevalence of disability, with 26.4%, followed by Akwa Ibom at 20.1%. This pattern is consistent among individuals aged 15 or older, where Imo state again reports the highest disability rate, followed by Akwa Ibom (25%), Cross River (24.5%), Enugu (24.3%), and Ondo (24.3%).\u003c/p\u003e \u003cp\u003eCharacteristics and distribution of people with disability\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the percentage distribution of household members aged 15 and older by socio-demographic characteristics and disability status, using the 2018 NDHS data. The table is disaggregated by sex and provides percentages for people with any disability and those with severe disability. The findings indicate a positive measure of associations between age and disability prevalence. Specifically, the percentage of both men and women with disabilities increases with age. For example, in the 65\u0026thinsp;+\u0026thinsp;age group, 47% of men and 48.6% of women report a disability. Furthermore, education level demonstrates an inverse relationship with disability. Disability prevalence was higher among individuals in households with no education (14.4%) compared to those with post-secondary education (14.0%). This pattern was observed for both men and women.\u003c/p\u003e \u003cp\u003eHousehold wealth quintiles show that disability prevalence among household members increases with wealth for both men and women. For instance, disability affects 12.8% of men in the poorest quintile compared to 13.9% in the wealthiest. For women, the corresponding rates are 19.8% and 21.6%, respectively. The findings indicate that rural residents have a slightly lower percentage of disability (11.9%) compared to urban residents (12.2%). Regional variations in disability prevalence are also observed, with the Southeast and South-South regions exhibiting higher percentages. Specifically, the Southeast has 17.5% and the South-South has 17.7%. Women who are widowed (41.3%) or divorced (18.8%) have a higher percentage of disability compared to those who are single (4.1%) or currently married or cohabiting (12.5%). Household member sex also shows a slight difference, with women having a slightly lower percentage of disability (11.8%) than men (12.3%). Finally, the sex of the household head is strongly associated with disability prevalence. Households headed by females have a higher percentage of members with disabilities (19.4%) compared to male-headed households (10.8%).\u003c/p\u003e \u003cp\u003eFactors influencing disability among Nigerians 15 years and above\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a logistic regression model examining socio-demographic predictors of disability among Nigerians aged 15 and older, displaying both unadjusted and adjusted odds ratios (OR, aOR) with 95% confidence intervals (CI). Age significantly increases disability odds (aOR 65\u0026thinsp;+\u0026thinsp;age group: 23.79, 95% CI: 20.22\u0026ndash;27.97). Education level shows a complex relationship; primary education slightly increases odds (aOR: 1.14, 95% CI: 1.03\u0026ndash;1.27) compared to no education, while post-secondary education significantly increases odds (aOR: 1.73, 95% CI: 1.59\u0026ndash;1.99). Wealthier quintiles (first, third, fourth) indicate lower disability odds than the wealthiest (fifth) quintile (e.g., first quintile aOR: 0.84, 95% CI: 0.72\u0026ndash;0.99).\u003c/p\u003e \u003cp\u003eRegionally, the Southeast (aOR: 1.35, 95% CI: 1.13\u0026ndash;1.61) and South-South (aOR: 1.71, 95% CI: 1.43\u0026ndash;2.05) have higher disability odds compared to the Northwest. People who were widowed have significantly higher odds compared to who are married or cohabiting people (aOR: 1.52, 95% CI: 1.36\u0026ndash;1.71). Female-headed households have higher disability odds (aOR: 1.18, 95% CI: 1.06\u0026ndash;1.33) than male-headed households. Increased household size slightly reduces disability odds (aOR: 0.96, 95% CI: 0.95\u0026ndash;0.98). Household member sex is not significantly associated with disability (aOR: 0.97, 95% CI: 0.91\u0026ndash;1.03). The logistic model reveals that age, education, wealth, region, marital status, sex of household head, and household size are significantly related to disability status in Nigeria.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to present the profile of people with disabilities in Nigeria using the 2018 Nigeria Demographic and Health Survey data. The findings indicate that approximately one in ten individuals aged 15 or older in Nigeria experience some level of disability (12%), with 2% reporting severe disabilities. Seeing (8.8%) was the most prevalent disability, followed by mobility (3.7%). These prevalence rates are generally consistent with previous findings that indicate disability prevalence varies widely, from 8.3% in Nigeria to 26.3% in Uganda, and align with the WHO\u0026rsquo;s estimate that 15% of the world\u0026rsquo;s population lives with some form of disability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of disability increases with age as this study indicates a positive measure of associations between age and disability prevalence. Specifically, the percentage of both men and women with disabilities increases with age. For example, in the 65\u0026thinsp;+\u0026thinsp;age group, 47% of men and 48.6% of women report a disability. This can be associated with the functional disability of the brain with an increase in age. The ability of the brain to remember and function psychologically reduces with age. This finding is consistent with global trends and previous studies in other settings, including Malaysia and Nepal, where disability prevalence was found to be significantly higher among older adults (Ahmad et al., 2017; Assaf, 2022; Mitra, 2018; Thapa et al., 2025).\u003c/p\u003e \u003cp\u003eHowever, unlike a similar study in Nepal among older people [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], this study reveals a slightly complex association between education and the prevalence of disability. Education level in this study shows that primary education slightly increases odds (aOR: 1.14, 95% CI: 1.03\u0026ndash;1.27) compared to no education, while post-secondary education significantly increases odds (aOR: 1.73, 95% CI: 1.59\u0026ndash;1.99). The observed relationship between education and the prevalence of disability among household members in this study aligns with findings from Rwanda and Uganda, where lower education levels were associated with a higher risk of disability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This suggests that education may serve as a protective factor against disability, possibly through increased access to information, healthcare, and better living conditions.\u003c/p\u003e \u003cp\u003eAlso, there tends to be a correlation between poverty and disability as families with PWDs may have lower wealth accumulation compared with wealthy families. Poor families are also more prone to financial risks as the onset of disability is an indication of financial commitments. Studies have confirmed this finding that at any given point in time, persons with disabilities are more likely to live in low-income households, hold significantly lower overall net worth and non-housing assets, and experience significant reductions in wealth following disability onset (Maroto \u0026amp; Pettinicchio, 2020; Shah Goda Jialu Liu Streeter et al., 2021). Unlike some studies, the current research observed a slightly higher rate of disability in households with the wealthiest quintile. The higher reporting of disabilities in wealthier households could be due to their increased awareness and access to diagnoses, implying that reported disability rates may not directly reflect the relationship with poverty. Disability also tends to eat deep into the financial resources of households, especially in resource-constraint countries where there is no government support system to cushion the economic effects of disability on the financial base of a family and could lead to under-reporting of those with disability in poor households.\u003c/p\u003e \u003cp\u003e The findings revealed variations in the reported prevalence of disability by state, with Imo State reporting the highest rate (26.4%), followed by Akwa Ibom (20.1%), and Kebbi State (2.6%) having the least prevalence rate of disability among people in the household 15 years and above. The findings underscore the importance of contextualizing peculiarity in disability prevalence by state that may be attributed to socio-cultural and healthcare access issues relating to disabilities being discussed. This brings to the fore the position of social construct theorists that disability is more of a function of the attitudes and perceptions within the context where PWDs find themselves. The theory suggests that disability is a label or category that is created and reinforced by society, rather than being a natural or inherent characteristic.\u003c/p\u003e \u003cp\u003e Regional variations were evident, with the Southeast and South-South regions exhibiting higher disability prevalence, potentially reflecting regional socio-economic disparities or other environmental and social factors. The findings show that the Southeast (aOR: 1.35, 95% CI: 1.13\u0026ndash;1.61) and South-South (aOR: 1.71, 95% CI: 1.43\u0026ndash;2.05) have higher odds of having household members with disabilities compared to households in the Northwest. It is therefore not surprising that in many Northern states of Nigeria, the reported prevalence of disability was lower compared with some of the states in the South. It could be inferred that perceptions of disability differ across the regions in Nigeria, importantly because PWDs are probably more acceptable and seen as part of the communities in Northern Nigeria than in the Southern part. This understanding might influence how cases of impairments are reported across the regions by households or family members.\u003c/p\u003e \u003cp\u003eRelated to the region is the place of residence of a person which in this study is classified either to be in rural or urban areas. The findings indicate that rural residents have a slightly lower percentage of disability (11.9%) compared to urban residents (12.2%) and this could be linked to changes in the environment that are often associated with urban settings. Contrary to some studies[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] that suggest higher disability rates in rural areas due to limited access to services, this study found a lower prevalence of disability reported for household members in rural areas compared to urban areas.\u003c/p\u003e \u003cp\u003eThe findings show the prevalence of disability was significantly higher among widows compared to those who are currently married or in cohabiting relationships (aOR: 1.52, 95% CI: 1.36\u0026ndash;1.71). This is consistent with previous research [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This could be linked to the stress and social isolation associated with widowhood, as well as the increased likelihood of older age and related health conditions in this group. Household head sex was also a significant factor, with female-headed households showing a higher percentage of members with disabilities. This finding may indicate the socio-economic vulnerabilities of female-headed households, which may face greater challenges in accessing resources and support for members with disabilities.\u003c/p\u003e \u003cp\u003eThe slight reduction in disability odds with increased household size (aOR: 0.96, 95% CI: 0.95\u0026ndash;0.98) is an interesting finding that warrants careful consideration alongside existing literature. While prior research has shown a higher prevalence of household-level disability in smaller households [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], this study suggests the opposite. Potential explanations include a dilution effect, variations in household age structure, or greater informal support in larger families [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conversely, research on disability's impact on household economics [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] may not directly address the odds of household disability by size. The small magnitude of the observed effect (aOR: 0.96), despite statistically significant, also necessitates consideration of its practical implications.\u003c/p\u003e\n\u003ch3\u003ePolicy and Programmatic Implications\u003c/h3\u003e\n\u003cp\u003eAnalysis of the 2018 NDHS data on disability in Nigeria reveals critical policy and program implications. Findings underscore a vital need for transforming policies and societal attitudes to achieve the full inclusion and improved well-being of people with disabilities, aligning with the SDGs. Firstly, equitable access to fundamental aspects of life, including education, healthcare, employment, and public spaces, is paramount for the full participation of people with disabilities. This necessitates developing inclusive infrastructure, support services, and urban planning, benefiting people with disabilities and the broader society.\u003c/p\u003e \u003cp\u003eSecondly, interventions must be tailored to the diverse needs of vulnerable subgroups within the disability population, such as older adults, widowed individuals, and those in specific regions. Contextualized approaches, utilizing participatory methods to identify needs, are crucial for effective well-being and social inclusion. Thirdly, protecting the rights and fostering inclusion requires reviewing and reforming legal frameworks, alongside a fundamental shift in societal attitudes. Effective implementation and enforcement of laws like the Discrimination Against Persons with Disabilities (Prohibition) Act, 2018, coupled with public awareness campaigns to combat stigma, are essential.\u003c/p\u003e \u003cp\u003eThe study aligns with social constructivist perspectives, highlighting disability as shaped by societal attitudes, policies, and environmental barriers, not solely a medical issue. This calls for development and humanitarian organizations to prioritize inclusive programs addressing these social and environmental factors. Strengthening coordination platforms is crucial for improved inclusion. Development partners play a vital role in providing technical support through partnerships, advocacy, resource mobilization, and capacity development.\u003c/p\u003e \u003cp\u003eWhile Nigeria has enacted the Persons with Disabilities Act (2018), its effective implementation remains a challenge. Concerted efforts are needed to secure the buy-in of government officials at all levels to translate legislation into concrete actions. Reliable disability data is paramount for effective policymaking and targeted programs. Investing in robust national data collection systems, and employing standardized tools like the WGSS, is crucial for evidence-based decision-making, resource allocation, and monitoring progress towards SDGs.\u003c/p\u003e \u003cp\u003eIn conclusion, this study underscores the need for a multi-faceted approach to addressing socio-economic disparities, promoting inclusive practices, and strengthening support systems for people with disabilities. Implementing evidence-based policies and programs will enable Nigeria to make significant strides towards achieving the SDGs and ensuring the inclusion and well-being of all citizens.\u003c/p\u003e\n\u003ch3\u003eStudy strengths and limitations\u003c/h3\u003e\n\u003cp\u003eThe data are self-related and technically such data in research are prone to limitations among which is respondent-selected biases as respondents might decide to answer questions in a particular way. Also, considering the sensitive nature of research on disability, culture and belief systems can influence how people respond to questions on disabilities to protect family members and themselves from stigma and social discrimination. Thus, reporting the disability status of household members may be influenced by social stigma, cultural beliefs, and variations in how individuals perceive and report their disabilities. In addition, asking the household head about household members\u0026rsquo; disability status has a high tendency to generate low-quality data. The head of the household may not always have complete or accurate information about the disability status of other household members. Disability affects all age groups; however, the analysis was restricted to people aged 15 years and above, thus excluding children, which is an important population to consider in disability research and policy. The study uses cross-sectional data, which limits the ability to establish causality. While the study identified associations between various factors and disability, it cannot determine the direction of cause-and-effect relationships. The study primarily focuses on functional limitations as measured by the WGSS. This may not capture the full complexity of disability, which also includes social, environmental, and attitudinal barriers.\u003c/p\u003e \u003cp\u003eDespite the aforementioned limitations, this study provides valuable data and information to inform policy and programmatic interventions. The use of the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative survey, provided an opportunity for generalizations and discussing disability prevalence and correlates at a national level in Nigeria. The 2018 NDHS included a disability module, offering improved insights into the situation of people with disabilities in Nigeria, which was lacking in previous NDHS waves. Disability status was assessed using the Washington Group Short Set (WGSS) of questions, a standardized tool for cross-population comparisons, enhancing the study's comparability with other studies and countries. The study explicitly links its findings to the Sustainable Development Goals (SDGs), emphasizing the importance of disability inclusion in achieving these global goals.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research, its authorship, and publication were conducted without financial support from any external source.\u003c/p\u003e \u003cp\u003eData availability\u003c/p\u003e \u003cp\u003eThe datasets utilized in this study were publicly accessible through the MEASURE DHS program (funded by the United States Government) prior to its foreign aid review. These data remain downloadable, contingent on the website's active status: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dhsprogram.com/data/available-datasets.cfm\u003c/span\u003e\u003cspan address=\"https://www.dhsprogram.com/data/available-datasets.cfm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eEthics approval and consent to participate\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e Consent for publication\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003eHuman Ethics and Consent to Participate declarations\u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003eCompeting interests\u003c/p\u003e \u003cp\u003eThe authors declare that they have no known conflicts of interest, whether financial or personal, that could be perceived as influencing the research presented in this paper.\u003c/p\u003e \u003cp\u003eAuthor Details\u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e International Health and Development Analysis Centre (IHDAC), Abuja, Nigeria.\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003e Data for Implementation (Data.FI), Abuja, Nigeria.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGPI, JK, and KAO developed the study concept and design; KAO wrote the main manuscript text; KAO, JK, and GPI conducted data analysis and interpretation; SB and KAO carried out statistical analysis and data visualization; all authors critically reviewed the manuscript for important intellectual content.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge MEASURE DHS for granting permission to use the 2018 Nigeria Demographic and Health Survey data. They also extend their sincere appreciation to the anonymous peer reviewers for their invaluable insights that significantly enhanced this manuscript. It is important to note that the views and opinions expressed in this paper are solely those of the authors and do not necessarily represent the positions of their respective organizations or the Nigerian government.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. World Report on Disability. 2011. https://iris.who.int/handle/10665/44575. Accessed 10 Apr 2025.\u003c/li\u003e\n\u003cli\u003eAssaf S. Profile and Correlates of Persons Living with Disability. 2022.\u003c/li\u003e\n\u003cli\u003eUnited Nations. Convention on the Rights of Persons with Disabilities and Optional Protocol. 2006.\u003c/li\u003e\n\u003cli\u003eMason R, Munn-Rivard L, Walker J. 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Disability prevalence among adults: estimates for 54 countries and progress toward a global estimate. Disabil Rehabil. 2014;36:940\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. World Health Survey (WHS): Data Catalog (2002 - 2004). https://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/whs/?page=1\u0026amp;from=2002\u0026amp;to=2004\u0026amp;ps=15\u0026amp;repo=whs. Accessed 11 Apr 2025.\u003c/li\u003e\n\u003cli\u003eAhmad NA, Mohamad Kasim N, Mahmud NA, Mohd Yusof Y, Othman S, Chan YY, et al. Prevalence and determinants of disability among adults in Malaysia: Results from the National Health and Morbidity Survey (NHMS) 2015. BMC Public Health. 2017;17:1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eBachani AM, Galiwango E, Kadobera D, Bentley JA, Bishai D, Wegener S, et al. A new screening instrument for disability in low-income and middle-income settings: application at the Iganga-Mayuge Demographic Surveillance System (IM-DSS), Uganda. BMJ Open. 2014;4:e005795.\u003c/li\u003e\n\u003cli\u003eMitra S. Disability, Health and Human Development. Disability, Health and Human Development. 2018. https://doi.org/10.1057/978-1-137-53638-9.\u003c/li\u003e\n\u003cli\u003ePinilla-Roncancio M, Alkire S. How Poor Are People With Disabilities? Evidence Based on the Global Multidimensional Poverty Index. J Disabil Policy Stud. 2021;31:206\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003ePriebe J. Disability and Its Correlates in a Developing Country Context: Evidence from Multiple Datasets and Measures. J Dev Stud. 2018;54:657\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eTareque MI, Begum S, Saito Y. Inequality in Disability in Bangladesh. PLoS One. 2014;9:e103681.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO Global Disability Action Plan 2014\u0026ndash;2021: Better Health for All People with Disability. 2015. https://iris.who.int/bitstream/handle/10665/199544/?sequence=1. Accessed 10 Apr 2025.\u003c/li\u003e\n\u003cli\u003eAltman BM. International Measurement of Disability: Purpose, Method and Application. Cham: Springer International Publishing; 2016.\u003c/li\u003e\n\u003cli\u003eGroce NE, Mont D. Counting disability: emerging consensus on the Washington Group questionnaire. Lancet Glob Health. 2017;5:e649\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eMactaggart I, Bek AH, Banks LM, Bright T, Dionicio C, Hameed S, et al. Interrogating and reflecting on disability prevalence data collected using the washington group tools: Results from population-based surveys in cameroon, guatemala, india, maldives, nepal, turkey and vanuatu. Int J Environ Res Public Health. 2021;18:9213.\u003c/li\u003e\n\u003cli\u003eWashington Group on Disability Statistics (WG). The Washington Group Short Set on Functioning (WG-SS). 2022.\u003c/li\u003e\n\u003cli\u003eThapa NR, Subedi G, KC VK. Disability and its sociodemographic correlates among older people in Nepal: evidence from the 2022 Nepal Demographic Health Survey. BMC Public Health. 2025;25:1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003evon Reichert C. Disability and the household context: Findings for the United States from the public Use Microdata Sample of the American Community Survey. Frontiers in Rehabilitation Sciences. 2022;3:875966.\u003c/li\u003e\n\u003cli\u003eRuggiero CF, McHale SM, Paul IM, Savage JS. Learned Experience and Resource Dilution: Conceptualizing Sibling Influences on Parents\u0026rsquo; Feeding Practices. International Journal of Environmental Research and Public Health 2021, Vol 18, Page 5739. 2021;18:5739.\u003c/li\u003e\n\u003cli\u003eRiswick T, Engelen T. Siblings and life transitions: investigating the resource dilution hypothesis across historical contexts and outcomes. The History of the Family. 2018;23:521\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eWolf DA. Getting Help From Others: The Effects of Demand and Supply. The Journals of Gerontology: Series B. 2014;69 Suppl_1:S59\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eMitra S, Palmer M, Kim H, Mont D, Groce N. Extra costs of living with a disability: A review and agenda for research. Disabil Health J. 2017;10:475\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eFriedman C. Financial hardship experienced by people with disabilities during the COVID-19 pandemic. Disabil Health J. 2022;15:101359.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Percentage distribution of household members 15 years and above by type and severity of disability (N=67,663)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eDomain of Disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003edisability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003esevere disability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eSeeing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eHearing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eCognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eSelf-care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eAny disability (severe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: Percentage distribution of household members 15 years and older by socio-demographic characteristics and disability status, NDHS 2018\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e% with disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e% with severe disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e% with disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e% with severe disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e% with disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e% with severe disability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.3***\u003c/p\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.5***\u003c/p\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.4***\u003c/p\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4***\u003c/p\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.3***\u003c/p\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.5***\u003c/p\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003ePost-secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15.5***\u003c/p\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.1***\u003c/p\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38.7\u003c/p\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.7***\u003c/p\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.3***\u003c/p\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14.4***\u003c/p\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.6***\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eWealth\u003c/p\u003e\n \u003cp\u003eFirst quintile\u003c/p\u003e\n \u003cp\u003eSecond quintile\u003c/p\u003e\n \u003cp\u003eThird quintile\u003c/p\u003e\n \u003cp\u003eFourth quintile\u003c/p\u003e\n \u003cp\u003eFifth quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.8***\u003c/p\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.3***\u003c/p\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.4***\u003c/p\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.3***\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.6***\u003c/p\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.8***\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.7***\u003c/p\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003cp\u003e59.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.3*\u003c/p\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.7*\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003cp\u003e59.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.7***\u003c/p\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eRegion\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNorth central\u003c/p\u003e\n \u003cp\u003eNorth east\u003c/p\u003e\n \u003cp\u003eNorth west\u003c/p\u003e\n \u003cp\u003eSouth east\u003c/p\u003e\n \u003cp\u003eSouth south\u003c/p\u003e\n \u003cp\u003eSouth west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.0***\u003c/p\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.6***\u003c/p\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.7***\u003c/p\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.5***\u003c/p\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.4***\u003c/p\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.5***\u003c/p\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003cp\u003eNever married/Single\u003c/p\u003e\n \u003cp\u003eCurrently married\u003c/p\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.8***\u003c/p\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.8***\u003c/p\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003cp\u003e66.0\u003c/p\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.7***\u003c/p\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.8***\u003c/p\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.1***\u003c/p\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.8***\u003c/p\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eSex of H member\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.1*\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eSex of HH+\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.8\u003c/p\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.7***\u003c/p\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.7***\u003c/p\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.3***\u003c/p\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.8***\u003c/p\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.7***\u003c/p\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: HH = Household Head; H = Household; *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Logistic regression model of disability on socio-demographic characteristics among household members 15 years and above in Nigeria\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 342px;\"\u003e\n \u003cp\u003eTotal (combined men and women)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eaOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003cp\u003e15-24 (ref)\u003c/p\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003cp\u003e45-54\u003c/p\u003e\n \u003cp\u003e55-64\u003c/p\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.33*** \u0026nbsp; (1.17 \u0026ndash; 1.52)\u003c/p\u003e\n \u003cp\u003e2.60*** \u0026nbsp; (2.28 \u0026ndash; 2.95)\u003c/p\u003e\n \u003cp\u003e6.45*** \u0026nbsp; (5.75 \u0026ndash; 7.23)\u003c/p\u003e\n \u003cp\u003e12.08*** (10.73 -13.59)\u003c/p\u003e\n \u003cp\u003e26.73*** (23.52 \u0026ndash; 30.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.22** \u0026nbsp; \u0026nbsp; (1.06 \u0026ndash; 1.39)\u003c/p\u003e\n \u003cp\u003e2.46*** \u0026nbsp; (2.12 \u0026ndash; 2.85)\u003c/p\u003e\n \u003cp\u003e6.04*** \u0026nbsp; (5.23 \u0026ndash; 6.97)\u003c/p\u003e\n \u003cp\u003e11.06*** (9.49 \u0026ndash; 12.89)\u003c/p\u003e\n \u003cp\u003e23.79*** (20.22 -27.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003cp\u003eNone (ref)\u003c/p\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003ePost-secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.09* \u0026nbsp; \u0026nbsp; \u0026nbsp;(1.00 \u0026ndash; 1.20)\u003c/p\u003e\n \u003cp\u003e0.48*** \u0026nbsp;(0.43 \u0026ndash; 0.53)\u003c/p\u003e\n \u003cp\u003e1.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.88 \u0026ndash; 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.14* \u0026nbsp; \u0026nbsp; \u0026nbsp; (1.03 \u0026ndash; 1.27)\u003c/p\u003e\n \u003cp\u003e1.12 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.99 \u0026ndash; 1.26)\u003c/p\u003e\n \u003cp\u003e1.73*** \u0026nbsp; (1.59 \u0026ndash; 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eWealth\u003c/p\u003e\n \u003cp\u003eFirst quintile\u003c/p\u003e\n \u003cp\u003eSecond quintile\u003c/p\u003e\n \u003cp\u003eThird quintile\u003c/p\u003e\n \u003cp\u003eFourth quintile\u003c/p\u003e\n \u003cp\u003eFifth quintile (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.82** \u0026nbsp; \u0026nbsp; (0.72 \u0026ndash; 0.93)\u003c/p\u003e\n \u003cp\u003e0.78*** \u0026nbsp;(0.69 \u0026ndash; 0.89)\u003c/p\u003e\n \u003cp\u003e0.73*** \u0026nbsp;(0.65 \u0026ndash; 0.82)\u003c/p\u003e\n \u003cp\u003e0.79*** \u0026nbsp;(0.71 \u0026ndash; 0.88)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.84* \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.72 \u0026ndash; 0.99)\u003c/p\u003e\n \u003cp\u003e0.91 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.79 \u0026ndash; 1.05)\u003c/p\u003e\n \u003cp\u003e0.87* \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.76 \u0026ndash; 0.99)\u003c/p\u003e\n \u003cp\u003e0.92 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.82 \u0026ndash; 1.04)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003ePlace of residence\u003c/p\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003eRural (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.99 \u0026ndash; 1.19)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98 \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.89 \u0026ndash; 1.09)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eRegion\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNorth central\u003c/p\u003e\n \u003cp\u003eNorth east\u003c/p\u003e\n \u003cp\u003eNorth west\u003c/p\u003e\n \u003cp\u003eSouth east\u003c/p\u003e\n \u003cp\u003eSouth south\u003c/p\u003e\n \u003cp\u003eSouth west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.43*** \u0026nbsp; (1.24 \u0026ndash; 1.66)\u003c/p\u003e\n \u003cp\u003e1.56*** \u0026nbsp; (1.36 \u0026ndash;1.78)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e2.46*** \u0026nbsp; (2.14 \u0026ndash; 2.84)\u003c/p\u003e\n \u003cp\u003e2.52*** \u0026nbsp; (2.18 - 2.91)\u003c/p\u003e\n \u003cp\u003e1.54*** \u0026nbsp; (1.30 \u0026ndash; 1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.13 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.96 \u0026ndash; 1.32)\u003c/p\u003e\n \u003cp\u003e1.55*** \u0026nbsp;(1.34 \u0026ndash; 1.79)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.35*** \u0026nbsp;(1.13 \u0026ndash; 1.61)\u003c/p\u003e\n \u003cp\u003e1.71*** \u0026nbsp;(1.43 \u0026ndash; 2.05)\u003c/p\u003e\n \u003cp\u003e0.86 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.71 \u0026ndash; 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003cp\u003eNever married/Single\u003c/p\u003e\n \u003cp\u003eCurrently married\u003c/p\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.32*** \u0026nbsp; (0.29 \u0026ndash; 0.36)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e5.01*** \u0026nbsp; (4.60 \u0026ndash; 5.47)\u003c/p\u003e\n \u003cp\u003e1.46*** \u0026nbsp; (1.19 \u0026ndash; 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.04 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.91 \u0026ndash; 1.18)\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.52*** (1.36 \u0026ndash; 1.71)\u003c/p\u003e\n \u003cp\u003e1.45*** (1.19 \u0026ndash; 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eSex of household member\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e0.92** \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.88 \u0026ndash; 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e0.97 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.91 \u0026ndash; 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eSex of head of household\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.97*** \u0026nbsp; \u0026nbsp; (1.83 \u0026ndash; 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e1.18** \u0026nbsp; (1.06 \u0026ndash; 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003eNumber of household members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.91*** \u0026nbsp; \u0026nbsp; (0.90 \u0026ndash; 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.96*** (0.95 -0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: OR=unadjusted odds ratio, aOR=adjusted odds ratio; CI=confidence interval; ref =reference group; *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Disability, Prevalence, Nigeria, Demographic and Health Survey, Regions, SDGs","lastPublishedDoi":"10.21203/rs.3.rs-6484923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6484923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeople with disabilities are a marginalized group, facing significant socio-economic and public health challenges in low- and middle-income countries like Nigeria. Understanding their characteristics is important for developing effective policies and interventions that promote inclusion and well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis drawn data from the 2018 Nigeria Demographic and Health Survey, a nationally representative survey that included a disability module. The analytical sample comprised 67,663 household members aged 15 and older. Disability was assessed using the Washington Group Short Set (WGSS) questions. Descriptive analysis was used to determine the prevalence of disability among household members, and a logistic regression model examined the associated socio-demographic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNearly 12% of Nigerians aged 15 and older living in households have some level of disability, with 2% experiencing severe disabilities. The most prevalent types of disability were visual (8.8%) and mobility-related (3.7%). Disability prevalence varied by state and region, with Imo State reporting the highest rate (26.4%) and Kebbi State the lowest (2.6%). Factors such as those age 65+ (aOR; 23.79, 95% CI: 20.22–27.97), primary (aOR: 1.14, 95% CI: 1.03–1.27) and post-secondary education (aOR: 1.73, 95% CI: 1.59–1.99), lower wealth (first quintile aOR: 0.84, 95% CI: 0.72–0.99), residing in the Southeast (aOR: 1.35, 95% CI: 1.13–1.61) or South-South region (aOR: 1.71, 95% CI: 1.43–2.05), being widowed (aOR: 1.52, 95% CI: 1.36–1.71), living in female-headed households (aOR: 1.18, 95% CI: 1.06–1.33), and household size (aOR: 0.96, 95% CI: 0.95–0.98) were significantly associated with disability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion and Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study underscores the necessity of tailored policies and programs to meet the diverse needs of people with disabilities in Nigeria. Interventions should prioritize equitable access to education, healthcare, employment, and public spaces, specifically addressing vulnerable subgroups. To achieve inclusion and well-being, in line with the Sustainable Development Goals, it is essential to address socio-economic disparities, promote inclusive practices, and strengthen support systems.\u003c/p\u003e","manuscriptTitle":"Disability Prevalence and Associated Factors in Nigeria: Policy Implications and the Path Towards Inclusive Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 11:41:38","doi":"10.21203/rs.3.rs-6484923/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-24T10:49:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T20:35:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T17:24:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T20:16:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144909940412534675283999246656706320562","date":"2025-06-17T17:11:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-14T14:39:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280328248146671120108883948402278299639","date":"2025-06-07T19:21:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149559790155004198435763374631998852397","date":"2025-06-07T18:08:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T14:08:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-23T09:39:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-23T09:38:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-19T13:07:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a82e30f-b98e-4c61-a509-1fe6484984cd","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:09:56+00:00","versionOfRecord":{"articleIdentity":"rs-6484923","link":"https://doi.org/10.1186/s12889-025-25492-0","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-11-26 15:57:59","publishedOnDateReadable":"November 26th, 2025"},"versionCreatedAt":"2025-04-30 11:41:38","video":"","vorDoi":"10.1186/s12889-025-25492-0","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25492-0","workflowStages":[]},"version":"v1","identity":"rs-6484923","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6484923","identity":"rs-6484923","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}