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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Water</journal-id>
<journal-title-group>
<journal-title>Frontiers in Water</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Water</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2624-9375</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/frwa.2025.1632720</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Examining households&#x2019; expenditure patterns on water, sanitation, and hygiene services in urban slums in Southeast Nigeria</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes"><name><surname>Ezenwaka</surname> <given-names>Uchenna</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author"><name><surname>Onwujekwe</surname> <given-names>Obinna</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author"><name><surname>Nwokolo</surname> <given-names>Chukwudi</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author"><name><surname>de Siqueira Filha</surname> <given-names>Noemia Teixeira</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<aff id="aff1"><label>1</label><institution>Health Policy Research Group, University of Nigeria, Enugu Campus</institution>, <city>Enugu</city>, <country country="ng">Nigeria</country></aff>
<aff id="aff2"><label>2</label><institution>Department of Health Administration and Management, Enugu Campus</institution>, <city>Enugu</city>, <country country="ng">Nigeria</country></aff>
<aff id="aff3"><label>3</label><institution>Department of Economics, University of Nigeria</institution>, <city>Nsukka</city>, <country country="ng">Nigeria</country></aff>
<aff id="aff4"><label>4</label><institution>Department of Health Sciences, University of York</institution>, <city>York</city>, <country country="gb">United Kingdom</country></aff>
<author-notes>
<corresp id="c001"><label>&#x002A;</label>Correspondence: Uchenna Ezenwaka, <email xlink:href="mailto:ezenwakauche@yahoo.com">ezenwakauche@yahoo.com</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-12-12">
<day>12</day>
<month>12</month>
<year>2025</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>7</volume>
<elocation-id>1632720</elocation-id>
<history>
<date date-type="received">
<day>21</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>07</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>24</day>
<month>11</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Ezenwaka, Onwujekwe, Nwokolo and de Siqueira Filha.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Ezenwaka, Onwujekwe, Nwokolo and de Siqueira Filha</copyright-holder>
<license>
<ali:license_ref start_date="2025-12-12">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Objectives</title>
<p>Access to safe water, sanitation, and hygiene (WASH) is essential for human health and wellbeing. However, many households in slum settlements lack access to WASH and are exposed to adverse health effects. Understanding household expenditure and equality in access to WASH is crucial for developing effective policies and interventions to improve access to such services. This paper offers new insights into household expenditures and equality in access to WASH services in urban slums in Nigeria.</p>
</sec>
<sec>
<title>Methods</title>
<p>A cross-sectional study was conducted in two urban slums in Anambra State, Nigeria. An interviewer-administered questionnaire was used to collect data on household expenditures on WASH services from 421 purposively selected households. Household expenditures on WASH were computed. Multiple regression was used to investigate associations between WASH expenditures and socioeconomic characteristics. Principal component analysis was used to create wealth quintiles. Concentration indices were used to assess equality in expenditures.</p>
</sec>
<sec>
<title>Results</title>
<p>The average monthly and yearly household expenditure on WASH were US$19.9 and US$168.6, respectively. WASH spending accounts for 11.3% of the household expenditure. The mean annual expenditure on hygiene accounted for 57.9% of total expenditures on WASH services, followed by water (36.9%) and sanitation (5.6%). Expenditure on water (CI: &#x2212;0.019, <italic>p</italic>-value: 0.637) and sanitation services (CI: &#x2212;0.011, p-value: 0.800) was slightly concentrated among the poorer population, while expenditure on hygiene services (CI: &#x2212;0.012, <italic>p</italic>-value: 0.561) was slightly concentrated among the wealthier population. Expenditures on WASH services were equitable across wealth quintiles (CI: &#x2212;0.001, <italic>p</italic>-value: 0.946). Larger household size (mean difference: 80,874; 95% CI: 50,621; 111,127) and respondents older than 38&#x202F;years (mean difference: 37,679; 95% CI: 7,137&#x2013;68,220) were associated with higher expenditures.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>This study highlights the significant financial burden that households in urban slums face in accessing WASH, which can lead to a decrease in their use. Decision-makers should consider targeted subsidies for water and sanitation services, promote hygiene awareness in low-income households, and invest in affordable WASH infrastructure.</p>
</sec>
</abstract>
<kwd-group>
<kwd>WASH</kwd>
<kwd>access to WASH services</kwd>
<kwd>equality</kwd>
<kwd>water</kwd>
<kwd>sanitation and hygiene</kwd>
<kwd>expenditures</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declare that financial support was received for the research and/or publication of this article. The study team also acknowledges funding support from the Community-led Responsive and Effective Urban Health Systems (CHORUS) project, funded by UK Aid, from the UK Government, Grant 301132. All views expressed in this article are of the authors only and do not necessarily represent the views of the funders.</funding-statement>
</funding-group>
<counts>
<fig-count count="5"/>
<table-count count="6"/>
<equation-count count="1"/>
<ref-count count="30"/>
<page-count count="11"/>
<word-count count="7037"/>
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<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Water and Human Health</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<title>Introduction</title>
<p>About 2.2 billion people worldwide lack access to safe drinking water, while approximately 4.2 and 3 billion lack access to safe sanitation and basic hygiene, respectively (<xref ref-type="bibr" rid="ref34">WHO and UNICEF, 2019</xref>). Access to appropriate water, sanitation, and hygiene (WASH) services is essential for human health, dignity, and wellbeing (<xref ref-type="bibr" rid="ref34">WHO and UNICEF, 2019</xref>). The severe consequences of inadequate access to WASH services include increased morbidity and mortality, reduced economic productivity, and diminished quality of life (<xref ref-type="bibr" rid="ref33">WHO, 2018</xref>). The scenario in urban slums in developing countries, including Nigeria, is even worse, as many households lack access to high-quality WASH services (<xref ref-type="bibr" rid="ref28">UN-Habitat, 2016</xref>). Only 11% of the Nigerian population has access to basic WASH services, representing a significant gap between access to various WASH components (<xref ref-type="bibr" rid="ref8">FMWR, NBS, and UNICEF, 2021</xref>). About 87% of households lack access to safe drinking water services, while only 13% of the population use improved sanitation facilities with a proper handwashing station that has running water and soap (<xref ref-type="bibr" rid="ref8">FMWR, NBS, and UNICEF, 2021</xref>). The country also records a high prevalence of open defecation, with 23% of households practising open defecation (<xref ref-type="bibr" rid="ref8">FMWR, NBS, and UNICEF, 2021</xref>). In addition, 11% of households spend more than 30&#x202F;min to fetch water, indicating a significant burden of water collection on households (<xref ref-type="bibr" rid="ref30">UNICEF, 2019</xref>).</p>
<p>The provision of appropriate WASH services in Nigeria is bedevilled with significant challenges that include high household expenditures on WASH services, inadequate funding, poor infrastructure, and weak institutions (<xref ref-type="bibr" rid="ref6">Federal Ministry of Water Resources, 2021</xref>). Hence, many residents of urban slums rely on informal and substandard WASH services (<xref ref-type="bibr" rid="ref16">National Bureau of Statistics, 2021</xref>). Consequently, poor access to appropriate WASH services is a major contributing factor to high morbidity and mortality rates among children under five (CU5) as a result of increased vulnerability to water-borne diseases, including diarrhoea, which leads to deaths of more than 70,000 CU5 in Nigeria (<xref ref-type="bibr" rid="ref29">UNICEF, 2018</xref>).</p>
<p>Household expenditure on WASH services is a critical indicator of financial access, as it reflects the economic burden of securing these essential services. Many households in urban slums face significant financial constraints in access to WASH services, making it difficult for them to afford appropriate services (<xref ref-type="bibr" rid="ref2">Abdulhadi et al., 2024</xref>; <xref ref-type="bibr" rid="ref3">Acheampong et al., 2024</xref>). Also, there is inequality in the distribution of household expenditure on WASH services, with the poorest households paying the highest proportional prices for services compared to their income (<xref ref-type="bibr" rid="ref3">Acheampong et al., 2024</xref>; <xref ref-type="bibr" rid="ref5">Azeez et al., 2023</xref>).</p>
<p>However, the level of household expenditures and effects on access to WASH remain understudied in urban slums in Nigeria. There is also limited research on household expenditure on WASH at the micro-level. Current research has primarily focused on evidence around access, infrastructure, and the impact of poor access on health outcomes (<xref ref-type="bibr" rid="ref34">WHO and UNICEF, 2019</xref>; <xref ref-type="bibr" rid="ref27">UNESCO, 2019</xref>). While these studies have contributed significantly to our understanding of WASH challenges, they have largely overlooked the critical issue of household expenditure on WASH services.</p>
<p>Consequently, there is a need for evidence on household expenditure and equitable access to WASH services within urban slums in Nigeria. Such information is essential for informing policy and investment decisions aimed at promoting equitable access to improved WASH services. Evidence on WASH expenditures and access also contributes to the discussion surrounding the achievement of Sustainable Development Goal (SDG) 6 (Ensure access to water and sanitation for all), particularly targets 6.1 (Achieving universal and equitable access to safe and affordable drinking water for all) and 6.2 (End open defecation and provide access to sanitation and hygiene). This paper offers new insights into household expenditures and equality in access to WASH services in urban slums in Nigeria.</p>
</sec>
<sec sec-type="methods" id="sec2">
<title>Methods</title>
<sec id="sec3">
<title>Conceptual framework</title>
<p>This study was guided by the principles of the Distributional Impact Analysis (DIA) framework (<xref ref-type="bibr" rid="ref1008">Bedoya et al., 2017</xref>) to investigate how water, sanitation, and hygiene (WASH) expenditures affect various income groups in urban slums. DIA is grounded in the principles of equality, which emphasize the need to ensure that all households, regardless of socio-economic status, have access to safe and affordable WASH services. The framework posits that household WASH expenditures are a critical factor in determining access to these essential services, particularly for low-income households.</p>
<p>It assesses the distribution of WASH resources and services across different SES groups, focusing on factors like expenditure proportions, access to WASH services, and service quality while highlighting potential inequalities. The DIA framework, therefore, provides a lens to understand the relationships between WASH expenditures, SES groups, and access to services in urban slums. Exploring these dynamics to identify the most vulnerable income groups and inform policies that promote equitable access to WASH services, and ultimately enhancing the wellbeing of urban slum residents.</p>
</sec>
<sec id="sec4">
<title>Study design and setting</title>
<p>A cross-sectional study was conducted in two urban slums in Okpoko, situated in the Ogbaru Local Government Area (LGA), Anambra State, South-eastern Nigeria. The State is one of the most densely populated in the country, with an estimated population of 6.1 million (<xref ref-type="bibr" rid="ref17">National Population Commission, 2016</xref>). The urban slums in Anambra face significant WASH challenges, including inadequate access to clean water, poor sanitation, and limited hygiene facilities (<xref ref-type="bibr" rid="ref4">Anambra State Ministry of Public Utilities, 2019</xref>).</p>
</sec>
<sec id="sec5">
<title>Study population</title>
<p>The overall target population consisted of households containing at least one woman of reproductive age and/or at least one child aged 5&#x202F;years or younger. Participants were eligible if they were community members living in an eligible household within the study sites. Within eligible households, we primarily sought the main female caregiver as the eligible respondent, but if such an individual was not available, we allowed the male head of the household to be the eligible respondent if they had sufficient knowledge of household WASH expenditures.</p>
</sec>
<sec id="sec6">
<title>Sampling and sample size</title>
<p>Two out of six slums were randomly selected in Okpoko. Within each selected slum, data collection began with a random walk from the household closest to the public primary health centre, and continued to the next-nearest household. The next succeeding household was then selected from the recruited ones until the desired sample size was reached. We conveniently selected the households in line with the eligibility criteria. Households not willing to participate were excluded. In each household, the head or primary caregiver was identified for the interview. A revisit was made to each household to recruit primary caregivers who were absent during the initial visit.</p>
<p>To determine the required sample size for exploring household expenditures on WASH services in slums in Nigeria, we conducted a power analysis using standard statistical methods. Assuming a 95% confidence level (<italic>&#x03B1;</italic>&#x202F;=&#x202F;0.05) and 80% power (1-<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.80), the minimum required sample size was estimated based on the assumption of a proportion of households incurring WASH expenditures (<italic>p</italic>&#x202F;=&#x202F;0.5) to maximize variability, as well as an effect size of d&#x202F;=&#x202F;0.1. Using the standard formula for sample size estimation for proportions:</p>
<disp-formula id="E1">
<mml:math id="M1">
<mml:mi mathvariant="normal">n</mml:mi>
<mml:mo>=</mml:mo>
<mml:msup>
<mml:mi mathvariant="normal">Z</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
<mml:mi mathvariant="normal">p</mml:mi>
<mml:mo stretchy="true">(</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>&#x2212;</mml:mo>
<mml:mi mathvariant="normal">p</mml:mi>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>/</mml:mo>
<mml:msup>
<mml:mi mathvariant="normal">d</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:math>
</disp-formula>
<p>Where:</p>
<list list-type="bullet">
<list-item>
<p><italic>Z</italic>&#x202F;=&#x202F;1.96 (Z-score for 95% confidence level)</p>
</list-item>
<list-item>
<p><italic>p</italic>&#x202F;=&#x202F;0.5 (assumed proportion)</p>
</list-item>
<list-item>
<p><italic>d</italic>&#x202F;=&#x202F;0.05 (margin of error)</p>
</list-item>
</list>
<p>The minimum required sample size was calculated as 385 households. Given that the two slums under study are similar in size, the sample was divided equally, resulting in approximately 193 households per slum. However, the sample size was increased to 420 (210 households per slum) to enhance the precision, reliability, and generalizability of the research findings, as well as to ensure sufficient statistical power and account for missing data.</p>
</sec>
<sec id="sec7">
<title>Data collection</title>
<p>Data was collected using a pre-tested interviewer-administered questionnaire that elicited information on socio-demographic characteristics, household expenditure on WASH services, and attributes of WASH facilities. The questionnaire was administered using both hard copies and tablets with Open Data Kit (ODK) software installed. Trained enumerators with a minimum of secondary education completed and administered the questionnaire. Data was collected in April 2024.</p>
<p>The study achieved a response rate of 98%, with 421 households participating out of 428 approached. The response rate is considered high, likely due to strong community mobilization and engagement of community leaders before the survey&#x2019;s commencement, as well as the study&#x2019;s relevance to the setting. No attrition was observed during data collection.</p>
<p>Data on various expenditure categories, including food and non-food items such as clothing, housing rent, durable household goods, healthcare, cooking fuel, recreation and entertainment, education, food, and WASH, were collected from the households. Data was also collected separately on households&#x2019; monthly and annual expenditure on WASH services: water household spending with storage facilities (e.g., tank and its stand, containers, bucket), bills/levies/payments for water supply or purchase and water treatment (e.g., water guard, chlorine, purification tablets, cooking fuel for boiling water), sanitation facilities such as toilet maintenance or repair (e.g., doors, roof, seat, floor), waste bin container (e.g., Sulo or basket), bills/levies/payments for toilet use, bills/levies/payments for sanitation exercise (e.g., Anambra State Waste Management Authority (ASWAMA), community sanitation exercise, waste service provider pick-ups), waste bin and bags, toilet emptying (e.g., soak away, or pit toilet), and hygiene products (e.g., purchase of hand washing equipment such as buckets, stand, tap), washing/laundry soap or detergent and bathing soap, toothpaste and brush/chewing stick, sanitary pads, tissue paper/wipes, and other washing materials (e.g., hypo/bleach/Jik, dettol/izal sponge).</p>
</sec>
<sec id="sec8">
<title>Data analysis</title>
<p>The data were cleaned and analyzed using Stata version 17. Descriptive statistics were used to describe the socio-economic characteristics of the study respondents by calculating frequencies for each slum and overall. We also applied this approach to characterize household WASH practices and facilities. Monthly and yearly household expenditures on WASH services and components were calculated as mean (95% CI) and median (IQR). The expenditure data were collected in Nigerian Naira (&#x20A6;) and converted to United States Dollars (US$) using the exchange rate (&#x20A6;1,450&#x202F;=&#x202F;US$1) (OANDA currency converter) at the time of data collection.</p>
<p>Key variables:</p>
<list list-type="bullet">
<list-item>
<p>Outcome or dependent variable &#x2013; Total expenditure on WASH, which was measured as the amount of money spent by the household while purchasing WASH services.</p>
</list-item>
<list-item>
<p>Explanatory or independent variables &#x2013; Socio-economic variables: age of the respondent in the household, gender, educational level, occupation or major source of income, household size, and wealth quintiles where the household falls.</p>
</list-item>
</list>
<p>Gamma regression analysis was also conducted to investigate the relationship between WASH annual expenditures and socioeconomic characteristics (age, household size, occupation, education, and wealth quintile). WASH spending as a percentage of household expenditure was calculated by dividing the total WASH expenditure by the total household expenditure and multiplying by 100. The principal component analysis (PCA) was used to create the wealth index of the respondents using household living assets and living conditions. The concentration index (CI) was used to measure the degree of inequality in the distribution of WASH expenditures according to wealth quintiles (<xref ref-type="bibr" rid="ref19">O'Donnell et al., 2008</xref>; <xref ref-type="bibr" rid="ref31">Wagstaff et al., 1991</xref>). The index varies from &#x2212;1 and +1, with (&#x2212;) denoting the concentration of expenditures amongst the poorest (regressivity) and (+) the concentration amongst the richest (progressivity). We applied the concentration index to measure socioeconomic inequalities in WASH expenditure, allowing us to quantify the distribution of WASH spending across different wealth quintiles. It quantifies the degree to which a variable is concentrated among the poor or the rich, providing a summary measure of inequality. This approach enables us to identify potential disparities in WASH expenditure and inform targeted interventions that promote more equitable access to WASH services. Our analytical focus is on examining inequality rather than inequity, as we aim to measure the distribution of WASH expenditure without making normative judgments about fairness (<xref ref-type="fig" rid="fig1">Figure 1</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Schematic diagram of the study methodology.</p>
</caption>
<graphic xlink:href="frwa-07-1632720-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Flowchart depicting a study on WASH expenditures in urban slums, Okpoko, Anambra State, Nigeria. The study design is cross-sectional, targeting all households. Two slums were randomly selected using a random walk method starting from a public primary health center. A total of 420 households were selected, 210 per slum. Data collection involved interviewer-administered questionnaires on socio-demographics, WASH expenditures, and facility attributes, with a 98% response rate. Data analysis includes statistical analysis of expenditures and equality of access to WASH services.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec9">
<title>Ethical considerations</title>
<p>The study was approved by the Ethics Committee of the University of Nigeria Teaching Hospital, Ituku Ozalla, Enugu (UNTH/HREC/2023/10/639) and the University of Leeds (MREC 23&#x2013;013). Informed consent was obtained from all respondents before administering the questionnaire. Respondents were assured of confidentiality and anonymity.</p>
</sec>
</sec>
<sec sec-type="results" id="sec10">
<title>Results</title>
<sec id="sec11">
<title>Demographics, socioeconomic profile, and WASH facilities</title>
<p><xref ref-type="table" rid="tab1">Table 1</xref> shows the socioeconomic characteristics of the respondents and households. Most respondents were female (99.3%). The majority had secondary education (67.5%) and were employed (93.6%). The mean household size was six, while the mean age of the respondents was 38&#x202F;years.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Distribution of the characteristics of the respondent (<italic>N</italic>&#x202F;=&#x202F;421).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="middle" rowspan="2">Variables</th>
<th align="center" valign="middle">Slum 1</th>
<th align="center" valign="middle">Slum 2</th>
<th align="center" valign="middle">Total</th>
</tr>
<tr>
<th align="center" valign="middle"><italic>n</italic> (%)</th>
<th align="center" valign="middle"><italic>n</italic> (%)</th>
<th align="center" valign="middle"><italic>n</italic> (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Respondents</td>
<td align="center" valign="middle">211 (50.1)</td>
<td align="center" valign="middle">210 (49.9)</td>
<td align="center" valign="middle">421 (100)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Gender</td>
</tr>
<tr>
<td align="left" valign="middle">Female</td>
<td align="center" valign="middle">209(99.1)</td>
<td align="center" valign="middle">209 (99.5)</td>
<td align="center" valign="middle">418 (99.3)</td>
</tr>
<tr>
<td align="left" valign="middle">Male</td>
<td align="center" valign="middle">2 (0.9)</td>
<td align="center" valign="middle">1(0.5)</td>
<td align="center" valign="middle">3 (0.7)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Highest educational level</td>
</tr>
<tr>
<td align="left" valign="middle">Primary</td>
<td align="center" valign="middle">48 (22.7)</td>
<td align="center" valign="middle">47 (22.4)</td>
<td align="center" valign="middle">95 (22.6)</td>
</tr>
<tr>
<td align="left" valign="middle">Secondary</td>
<td align="center" valign="middle">135 (64.0)</td>
<td align="center" valign="middle">149 (71.0)</td>
<td align="center" valign="middle">284 (67.5)</td>
</tr>
<tr>
<td align="left" valign="middle">Tertiary</td>
<td align="center" valign="middle">22 (10.4)</td>
<td align="center" valign="middle">11 (5.2)</td>
<td align="center" valign="middle">33 (7.8)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Mean age</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2264;38</td>
<td align="center" valign="middle">161 (55.9)</td>
<td align="center" valign="middle">73 (34.7)</td>
<td align="center" valign="middle">234 (55.6)</td>
</tr>
<tr>
<td align="left" valign="middle">&#x003E;38</td>
<td align="center" valign="middle">50 (23.7)</td>
<td align="center" valign="middle">137 (65.2)</td>
<td align="center" valign="middle">187 (44.4)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Mean household size</td>
</tr>
<tr>
<td align="left" valign="middle">&#x2264;6</td>
<td align="center" valign="middle">118 (55.9)</td>
<td align="center" valign="middle">113 (53.8)</td>
<td align="center" valign="middle">231 (54.9)</td>
</tr>
<tr>
<td align="left" valign="middle">&#x003E;6</td>
<td align="center" valign="middle">93 (44.1)</td>
<td/>
<td align="center" valign="middle">190 (45.1)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Employment status</td>
</tr>
<tr>
<td align="left" valign="middle">Employed (farming, trading, artisan, government worker)</td>
<td align="center" valign="middle">205 (97.2)</td>
<td align="center" valign="middle">189 (90.0)</td>
<td align="center" valign="middle">394 (93.6)</td>
</tr>
<tr>
<td align="left" valign="middle">Unemployed (pensioner, student, retired, housewife)</td>
<td align="center" valign="middle">6 (2.8)</td>
<td align="center" valign="middle">21 (10.0)</td>
<td align="center" valign="middle">27 (6.4)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Wealth index</td>
</tr>
<tr>
<td align="left" valign="middle">Very poor</td>
<td align="center" valign="middle">43 (51.2)</td>
<td align="center" valign="middle">41 (48.81)</td>
<td align="center" valign="middle">84 (19.9)</td>
</tr>
<tr>
<td align="left" valign="middle">Poor</td>
<td align="center" valign="middle">38 (44.7)</td>
<td align="center" valign="middle">47 (55.3)</td>
<td align="center" valign="middle">85(20.1)</td>
</tr>
<tr>
<td align="left" valign="middle">Average</td>
<td align="center" valign="middle">40 (47.6)</td>
<td align="center" valign="middle">44 (52.4)</td>
<td align="center" valign="middle">84(19.9)</td>
</tr>
<tr>
<td align="left" valign="middle">Rich</td>
<td align="center" valign="middle">44 (52.4)</td>
<td align="center" valign="middle">40 (47.6)</td>
<td align="center" valign="middle">84(19.9)</td>
</tr>
<tr>
<td align="left" valign="middle">Very rich</td>
<td align="center" valign="middle">46 (54.8)</td>
<td align="center" valign="middle">38 (45.2)</td>
<td align="center" valign="middle">84(19.9)</td>
</tr>
</tbody>
</table>
</table-wrap>
<p><xref ref-type="table" rid="tab2">Table 2</xref> shows the WASH practices and facilities of the households disaggregated by the study communities. The source of drinking water for the majority (83%) was boreholes. Almost all the respondents purchased their water (96.9%). The most common toilet facility was a flush or pour/flush toilet (77%). Only 4% of the respondents had handwashing facilities in their households.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Characteristics of WASH practices and facilities.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="middle" rowspan="2">Variables</th>
<th align="center" valign="middle">Slum 1</th>
<th align="center" valign="middle">Slum 2</th>
<th align="center" valign="middle">Total</th>
</tr>
<tr>
<th align="center" valign="middle"><italic>n</italic> (%)</th>
<th align="center" valign="middle"><italic>n</italic> (%)</th>
<th align="center" valign="middle"><italic>n</italic> (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="4">The major source of domestic drinking water</td>
</tr>
<tr>
<td align="left" valign="middle">Bottled/Sachet water</td>
<td align="center" valign="middle">34 (16.1)</td>
<td align="center" valign="middle">30 (14.3)</td>
<td align="center" valign="middle">64 (15.2)</td>
</tr>
<tr>
<td align="left" valign="middle">Handpumps/boreholes (private and public)</td>
<td align="center" valign="middle">173 (82.0)</td>
<td align="center" valign="middle">178 (84.8)</td>
<td align="center" valign="middle">351 (83.4)</td>
</tr>
<tr>
<td align="left" valign="middle">Piped connection to the house</td>
<td align="center" valign="middle">1 (0.5)</td>
<td align="center" valign="middle">0 (0.0)</td>
<td align="center" valign="middle">1 (0.2)</td>
</tr>
<tr>
<td align="left" valign="middle">Rainwater collection</td>
<td align="center" valign="middle">1(0.5)</td>
<td align="center" valign="middle">0 (0.0)</td>
<td align="center" valign="middle">1 (0.2)</td>
</tr>
<tr>
<td align="left" valign="middle">Water seller</td>
<td align="center" valign="middle">2 (0.9)</td>
<td align="center" valign="middle">2 (0.9)</td>
<td align="center" valign="middle">4 (1.0)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">A major source of water for domestic use</td>
</tr>
<tr>
<td align="left" valign="middle">Handpumps/boreholes (private and public)</td>
<td align="center" valign="middle">202 (95.7)</td>
<td align="center" valign="middle">207 (98.6)</td>
<td align="center" valign="middle">409 (97.1)</td>
</tr>
<tr>
<td align="left" valign="middle">Others (Protected well, Water sellers/kiosks)</td>
<td align="center" valign="middle">5 (2.4)</td>
<td align="center" valign="middle">1(0.5)</td>
<td align="center" valign="middle">6 (1.1)</td>
</tr>
<tr>
<td align="left" valign="middle">Shortage of drinking water for the last one month</td>
<td align="center" valign="middle">131 (62.1)</td>
<td align="center" valign="middle">151 (71.9)</td>
<td align="center" valign="middle">282 (67.0)</td>
</tr>
<tr>
<td align="left" valign="middle">Payment for drinking water</td>
<td align="center" valign="middle">200 (94.8)</td>
<td align="center" valign="middle">208 (99.0)</td>
<td align="center" valign="middle">408 (96.9)</td>
</tr>
<tr>
<td align="left" valign="middle">Treatment of drinking water</td>
<td align="center" valign="middle">10 (4.7)</td>
<td align="center" valign="middle">7 (3.3)</td>
<td align="center" valign="middle">17 (4.0)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Availability of soap in the household</td>
</tr>
<tr>
<td align="left" valign="middle">Yes, but not presented</td>
<td align="center" valign="middle">93 (44.1)</td>
<td align="center" valign="middle">95 (45.2)</td>
<td align="center" valign="middle">188 (44.7)</td>
</tr>
<tr>
<td align="left" valign="middle">Yes, presented</td>
<td align="center" valign="middle">115 (54.5)</td>
<td align="center" valign="middle">113 (53.8)</td>
<td align="center" valign="middle">228 (54.20)</td>
</tr>
<tr>
<td align="left" valign="middle">Availability of a specific hand washing device/station in the household</td>
<td align="center" valign="middle">13 (6.2)</td>
<td align="center" valign="middle">4 (1.9)</td>
<td align="center" valign="middle">17 (4.0)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Type of Toilet/latrine</td>
</tr>
<tr>
<td align="left" valign="middle">VIP Toilet</td>
<td align="center" valign="middle">2 (9.0)</td>
<td align="center" valign="middle">0 (0.0)</td>
<td align="center" valign="middle">2 (0.5)</td>
</tr>
<tr>
<td align="left" valign="middle">Composting toilet</td>
<td align="center" valign="middle">1 (0.5)</td>
<td align="center" valign="middle">0 (0.0)</td>
<td align="center" valign="middle">1 (0.2)</td>
</tr>
<tr>
<td align="left" valign="middle">Flush or pour/flush toilet</td>
<td align="center" valign="middle">166 (79.7)</td>
<td align="center" valign="middle">158 (75.2)</td>
<td align="center" valign="middle">324 (77.0)</td>
</tr>
<tr>
<td align="left" valign="middle">Pit latrine</td>
<td align="center" valign="middle">32 (16.2)</td>
<td align="center" valign="middle">48 (22.9)</td>
<td align="center" valign="middle">80 (19.0)</td>
</tr>
<tr>
<td align="left" valign="middle">No toilet</td>
<td align="center" valign="middle">10 (4.7)</td>
<td align="center" valign="middle">4 (1.9)</td>
<td align="center" valign="middle">14 (3.3)</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="4">Place of domestic waste disposal</td>
</tr>
<tr>
<td align="left" valign="middle">Burn it</td>
<td align="center" valign="middle">1 (0.5)</td>
<td align="center" valign="middle">8 (3.8)</td>
<td align="center" valign="middle">9 (2.1)</td>
</tr>
<tr>
<td align="left" valign="middle">Communal pit</td>
<td align="center" valign="middle">2 (0.9)</td>
<td align="center" valign="middle">1 (0.5)</td>
<td align="center" valign="middle">3 (0.7)</td>
</tr>
<tr>
<td align="left" valign="middle">Household pit</td>
<td align="center" valign="middle">32 (15.2)</td>
<td align="center" valign="middle">37 (17.6)</td>
<td align="center" valign="middle">69 (16.4)</td>
</tr>
<tr>
<td align="left" valign="middle">Street bin/container for garbage collection</td>
<td align="center" valign="middle">149 (70.6)</td>
<td align="center" valign="middle">92 (43.8)</td>
<td align="center" valign="middle">241 (57.2)</td>
</tr>
<tr>
<td align="left" valign="middle">Undesignated open area</td>
<td align="center" valign="middle">27 (12.8)</td>
<td align="center" valign="middle">72 (34.3)</td>
<td align="center" valign="middle">99 (23.5)</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec12">
<title>Household expenditure on WASH services</title>
<p>The mean (95% CI) and median (IQR) monthly and yearly household expenditure on WASH services are presented in <xref ref-type="table" rid="tab3">Table 3</xref>. The median expenditure on WASH services varied across different categories, with the highest median expenditure being reported on hygiene (monthly: US$6.95; yearly: US$82.76), followed by water (monthly: US$4.63; yearly: US$46.69) and then sanitation (monthly: US$3.17; yearly: US$6.07). The monthly and yearly WASH median expenditures were US$15.79 and US$142.21, respectively. The annual mean expenditures on hygiene accounted for 57.9% of total WASH spending, while water (36.9%) and sanitation (5.6%) expenditures were lower.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Mean and median monthly and annual expenditure (in US$) on WASH services.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variable</th>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">95% CI</th>
<th align="center" valign="top">Median</th>
<th align="center" valign="top">IQR</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" style="background-color:#e7e6e6" colspan="5">Per month</td>
</tr>
<tr>
<td align="left" valign="top">Water</td>
<td align="char" valign="top" char=".">6.11</td>
<td align="char" valign="top" char="&#x2013;">5.29&#x2013;6.93</td>
<td align="char" valign="top" char=".">4.63</td>
<td align="char" valign="top" char="&#x2013;">2.76&#x2013;7.17</td>
</tr>
<tr>
<td align="left" valign="top">Sanitation</td>
<td align="char" valign="top" char=".">5.55</td>
<td align="char" valign="top" char="&#x2013;">4.52&#x2013;6.57</td>
<td align="char" valign="top" char=".">3.17</td>
<td align="char" valign="top" char="&#x2013;">0.55&#x2013;6.76</td>
</tr>
<tr>
<td align="left" valign="top">Hygiene</td>
<td align="char" valign="top" char=".">8.42</td>
<td align="char" valign="top" char="&#x2013;">7.85&#x2013;8.98</td>
<td align="char" valign="top" char=".">6.95</td>
<td align="char" valign="top" char="&#x2013;">3.10&#x2013;9.72</td>
</tr>
<tr>
<td align="left" valign="top">WASH</td>
<td align="char" valign="top" char=".">19.94</td>
<td align="char" valign="top" char="&#x2013;">18.45&#x2013;21.43</td>
<td align="char" valign="top" char=".">15.79</td>
<td align="char" valign="top" char="&#x2013;">11.52&#x2013;24.34</td>
</tr>
<tr>
<td align="left" valign="top" style="background-color:#e7e6e6" colspan="5">Per year</td>
</tr>
<tr>
<td align="left" valign="top">Water</td>
<td align="char" valign="top" char=".">62.38</td>
<td align="char" valign="top" char="&#x2013;">55.21&#x2013;69.57</td>
<td align="char" valign="top" char=".">46.69</td>
<td align="char" valign="top" char="&#x2013;">24.69&#x2013;72.97</td>
</tr>
<tr>
<td align="left" valign="top">Sanitation</td>
<td align="char" valign="top" char=".">9.53</td>
<td align="char" valign="top" char="&#x2013;">8.24&#x2013;10.82</td>
<td align="char" valign="top" char=".">6.07</td>
<td align="char" valign="top" char="&#x2013;">3.10&#x2013;9.72</td>
</tr>
<tr>
<td align="left" valign="top">Hygiene</td>
<td align="char" valign="top" char=".">97.67</td>
<td align="char" valign="top" char="&#x2013;">91.34&#x2013;104.00</td>
<td align="char" valign="top" char=".">82.76</td>
<td align="char" valign="top" char="&#x2013;">60.41&#x2013;116.69</td>
</tr>
<tr>
<td align="left" valign="top">WASH</td>
<td align="char" valign="top" char=".">168.61</td>
<td align="char" valign="top" char="&#x2013;">18.45&#x2013;21.43</td>
<td align="char" valign="top" char=".">142.21</td>
<td align="char" valign="top" char="&#x2013;">105.37&#x2013;200.14</td>
</tr>
</tbody>
</table>
</table-wrap>
<p><xref ref-type="table" rid="tab4">Table 4</xref> presents a snapshot of household expenditure and WASH spending among the households. On average, households spend US$168.61 on WASH services, which accounts for approximately 11.27% of their total household expenditure. The total WASH spending for the households surveyed amounts to US$70,983.54, while the average household expenditure is US$1,496.23, totaling US$629,772.64.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Annual household spending and WASH spending (in US$).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">Total</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">Total WASH Spending</td>
<td align="char" valign="bottom" char=".">168.61</td>
<td align="char" valign="bottom" char=".">70,983.54</td>
</tr>
<tr>
<td align="left" valign="bottom">Total household expenditure</td>
<td align="char" valign="bottom" char=".">1,496.23</td>
<td align="char" valign="bottom" char=".">629,772.64</td>
</tr>
<tr>
<td align="left" valign="bottom">WASH spending as a % of household expenditure</td>
<td align="center" valign="bottom" colspan="2">11.27</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec13">
<title>Expenditure pattern on WASH services</title>
<p><xref ref-type="table" rid="tab5">Table 5</xref> presents the relationship between yearly expenditures on WASH and various socio-economic characteristics. There was a statistically significant difference in terms of household size and respondents&#x2019; age. Respondents from larger households (mean difference: US$55.77; 95% CI: US$34.91-US$76.64) and older than 38&#x202F;years (mean difference: US$25.98; 95% CI: US$4.92 - US$47.05) reported higher yearly expenditures on WASH. No other socio-economic characteristics demonstrated statistically significant differences (<xref ref-type="table" rid="tab6">Table 6</xref>).</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Association between WASH yearly expenditures (in US$) and socio-economic characteristics.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Mean expenditure, NGN (95% CI)</th>
<th align="center" valign="top">Mean difference (95% CI)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" colspan="3">Mean HH size</td>
</tr>
<tr>
<td align="left" valign="top">&#x2264;6</td>
<td align="char" valign="top" char="(">143.44 (132.03&#x2013;154.85)</td>
<td align="center" valign="top">Ref</td>
</tr>
<tr>
<td align="left" valign="top">&#x003E;6</td>
<td align="char" valign="top" char="(">199.21 (181.75&#x2013;216.68)</td>
<td align="center" valign="top">55.77 (34.91; 76.64) &#x002A;</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3">Age group</td>
</tr>
<tr>
<td align="left" valign="top">38&#x202F;years and below</td>
<td align="char" valign="top" char="(">157.07 (144.25&#x2013;168.89)</td>
<td align="center" valign="top">Ref</td>
</tr>
<tr>
<td align="left" valign="top">Above 38&#x202F;years</td>
<td align="char" valign="top" char="(">183.06 (116.34&#x2013;199.77)</td>
<td align="center" valign="top">25.98 (4.92; 47.05) &#x002A;</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3">Completed formal education</td>
</tr>
<tr>
<td align="left" valign="top">Primary education</td>
<td align="char" valign="middle" char="(">158.15 (137.44&#x2013;178.86)</td>
<td align="center" valign="middle">Ref</td>
</tr>
<tr>
<td align="left" valign="top">Secondary education</td>
<td align="char" valign="middle" char="(">175.38 (162.09&#x2013;188.66)</td>
<td align="center" valign="middle">17.23 (&#x2212;7.38; 41.83)</td>
</tr>
<tr>
<td align="left" valign="top">Tertiary education</td>
<td align="char" valign="middle" char="(">151.43 (117.79&#x2013;185.08)</td>
<td align="center" valign="middle">&#x2212;6.72 (&#x2212;46.23; 32.79)</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3">Employment status</td>
</tr>
<tr>
<td align="left" valign="top">Unemployed</td>
<td align="char" valign="top" char="(">156.57 (117.83&#x2013;195.31)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Employed</td>
<td align="char" valign="top" char="(">169.44 (158.46&#x2013;180.41)</td>
<td align="center" valign="top">12.86 (&#x2212;27.40; 53.13)</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3">Wealth index</td>
</tr>
<tr>
<td align="left" valign="top">Very poor and poor</td>
<td align="char" valign="top" char="(">169.17 (152.27&#x2013;186.08)</td>
<td align="center" valign="top">Ref</td>
</tr>
<tr>
<td align="left" valign="top">Average</td>
<td align="char" valign="top" char="(">158.75 (136.25&#x2013;181.25)</td>
<td align="center" valign="top">&#x2212;10.42 (&#x2212;38.57; 17.72)</td>
</tr>
<tr>
<td align="left" valign="top">Rich and very rich</td>
<td align="char" valign="top" char="(">172.98 (155.64&#x2013;190.31)</td>
<td align="center" valign="top">3.80 (&#x2212;14.19; 42.63)</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Equality analysis of expenditure on WASH services and wealth Index.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variable</th>
<th align="center" valign="top">Concentration index (Estimate)</th>
<th align="center" valign="top"><italic>P</italic>&#x202F;&#x003E;&#x202F;|t|</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Water</td>
<td align="char" valign="top" char=".">&#x2212;0.019</td>
<td align="char" valign="top" char=".">0.637</td>
</tr>
<tr>
<td align="left" valign="top">Sanitation</td>
<td align="char" valign="top" char=".">&#x2212;0.011</td>
<td align="char" valign="top" char=".">0.800</td>
</tr>
<tr>
<td align="left" valign="top">Hygiene</td>
<td align="char" valign="top" char=".">0.012</td>
<td align="char" valign="top" char=".">0.561</td>
</tr>
<tr>
<td align="left" valign="top">WASH</td>
<td align="char" valign="top" char=".">&#x2212;0.001</td>
<td align="char" valign="top" char=".">0.946</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec14">
<title>Equality in access to WASH services</title>
<p>The concentration index of &#x2212;0.019 indicates that expenditure on water services was slightly concentrated among the poorer population. For sanitation services, the concentration index of &#x2212;0.011 shows that expenditure was also slightly concentrated among the poorer population, while the concentration index of 0.012 indicates that expenditure on hygiene services is slightly concentrated among the wealthier population. Overall, the concentration index for WASH was &#x2212;0.001. However, the <italic>p</italic>-values were not statistically significant. <xref ref-type="fig" rid="fig2">Figures 2</xref>&#x2013;<xref ref-type="fig" rid="fig5">5</xref> show the concentration index of WASH expenditure by wealth quintiles.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Concentration curves of households&#x2019; yearly expenditure on water services by wealth quintiles.</p>
</caption>
<graphic xlink:href="frwa-07-1632720-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Graph showing the concentration curve for water expenditure. The x-axis represents the cumulative percentage of the wealth index, and the y-axis shows the cumulative percentage of water expenditure. A line of equality in blue runs diagonally, indicating equal distribution, while a red line represents actual water expenditure distribution, closely following the line of equality.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Concentration curve of households&#x2019; yearly expenditure on sanitation services by wealth quintiles.</p>
</caption>
<graphic xlink:href="frwa-07-1632720-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Line graph titled "Concentration curve for sanitation expenditure" comparing cumulative percentage of sanitation expenditure to wealth index. A blue line represents equality, while a red line shows actual sanitation expenditure. Both axes range from 0 to 1.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Concentration curve of households&#x2019; yearly expenditure on hygiene services by wealth quintiles.</p>
</caption>
<graphic xlink:href="frwa-07-1632720-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Chart titled "Concentration curve for hygiene expenditure," showing two lines on a graph. X-axis represents cumulative percentage of wealth index; y-axis represents cumulative percentage of hygiene expenditure. A blue line denotes the "Line of equality," and a red line represents "Hygiene expenditure." Both lines closely track each other along the diagonal from the origin to the top right.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Concentration curve of households&#x2019; yearly expenditure on WASH by wealth quintiles.</p>
</caption>
<graphic xlink:href="frwa-07-1632720-g005.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Concentration curve graph for total WASH expenditure showing cumulative percentage of total WASH expenditure on the vertical axis and cumulative percentage of wealth index on the horizontal axis. It includes a blue line representing equality and a red line for total WASH expenditure. The lines run closely together, indicating expenditure distribution.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec15">
<title>Discussion</title>
<p>The study provides valuable insights into household expenditure patterns and inequalities in accessing WASH services in urban slums in Nigeria. There were varying estimates of household expenditure on different components of WASH services. The findings of this study highlight the significant expenditure on WASH services by households in urban slums, with hygiene services accounting for the largest proportion of total WASH spending. The higher percentage of expenditure on hygiene services may indicate that households in urban slums prioritize personal hygiene practices, potentially due to awareness of its importance in preventing illnesses and maintaining personal cleanliness. This finding suggests that households are investing in hygiene products and practices to protect their health. However, this prioritization of hygiene over sanitation services may also highlight the need for a more balanced approach to WASH investments, ensuring that both hygiene and sanitation services are adequately addressed to promote overall health and wellbeing. The relatively low expenditure on sanitation services is concerning and may reflect the filthy nature of many slum environments, where inadequate sanitation infrastructure and poor waste management practices are common. This low expenditure on sanitation services may perpetuate the cycle of poor health outcomes and environmental degradation in these settings.</p>
<p>The substantial financial burden of WASH services on households in urban slums, accounting for 11.3% of household expenditure, may also have broader implications for household welfare, potentially diverting resources away from other essential expenses such as healthcare and education. Chronic challenges, including underinvestment, fragmented institutional responsibilities, and inadequate regulations, exacerbate the issue (<xref ref-type="bibr" rid="ref1003">FMWR, NBS, and UNICEF, 2021</xref>). These findings are consistent with research highlighting access inequities in WASH services in LMICs (<xref ref-type="bibr" rid="ref2">Abdulhadi et al., 2024</xref>). The findings underscore the need for increased investments in WASH infrastructure and targeted support (<xref ref-type="bibr" rid="ref8">FMWR, NBS, and UNICEF, 2021</xref>; <xref ref-type="bibr" rid="ref34">WHO and UNICEF, 2019</xref>) for the underserved areas to achieve SDG 6, particularly since household spending on WASH may not necessarily translate to access to quality, safe, and sustainable services and practices. This requires a multifaceted approach that prioritizes the needs of underserved areas and populations.</p>
<p>The result for households with larger members spending more on WASH services is expected. This is probably due to comparatively increased demands for water, sanitation, and hygiene-related services, which translates into higher expenditures (<xref ref-type="bibr" rid="ref30">UNICEF, 2019</xref>). Also, older household heads, often with more experience and awareness of the importance of WASH services, may prioritise it, which can lead to comparatively increased expenditure. Our findings are consistent with studies showing that household size and/or age were significant predictors of WASH expenditure (<xref ref-type="bibr" rid="ref9">Gaffan et al., 2022</xref>; <xref ref-type="bibr" rid="ref34">WHO and UNICEF, 2019</xref>; <xref ref-type="bibr" rid="ref26">Tanzania National Bureau of Statistics, 2019</xref>; <xref ref-type="bibr" rid="ref12">India National Sample Survey Office, 2019</xref>). Other studies have also reported mixed findings in terms of the demographic factors influencing WASH expenditure. For example, a study in Nairobi found that household size was not a significant predictor of WASH expenditure (<xref ref-type="bibr" rid="ref24">Sarkar, 2020</xref>). On the other hand, a study in Uganda found that the age of the household head was a significant predictor of WASH expenditure (<xref ref-type="bibr" rid="ref32">Whittington et al., 2012</xref>), which is consistent with the current finding. The difference in findings may be due to differences in methodology or sampling frames.</p>
<p>Our study reveals interesting patterns in WASH expenditures concerning socio-economic characteristics. Notably, households with secondary education had higher mean WASH expenditures compared to those with primary education. This may suggest that households with secondary education are more aware of the importance of WASH services or have better access to resources to invest in these services. However, households with tertiary education did not exhibit significantly higher expenditures, which may indicate that other factors, such as income or occupation, play a more significant role in determining WASH expenditures. Furthermore, our study shows that employed households had higher mean WASH expenditures compared to unemployed households. This finding highlights the importance of economic stability in accessing WASH services. Households with stable employment may have more financial resources to invest in WASH services, which can have positive impacts on their health and wellbeing.</p>
<p>These findings have important implications for policy and practice. They suggest that WASH interventions should consider the socio-economic context of households and target those who are most vulnerable. For example, households with lower levels of education or unemployment may require additional support to access WASH services. Additionally, policymakers should explore ways to make WASH services more affordable and accessible to all households, regardless of their socio-economic status. These also highlight the importance of considering intersectional vulnerabilities when designing WASH interventions. Households with multiple disadvantages, such as low education and unemployment, may face compounded challenges in accessing WASH services.</p>
<p>Our analysis of equality revealed that expenditures on the water and sanitation components were slightly more concentrated among the poorer population, while expenditures on the hygiene component were slightly more concentrated among the wealthier population. One possible explanation is that poorer households may prioritise spending on basic WASH services such as water and sanitation. In comparison, wealthier households may have more disposable income to spend on hygiene services and materials such as soap, toothpaste, and other personal hygiene products (<xref ref-type="bibr" rid="ref36">World Bank, 2020</xref>). Our findings also suggest that poorer households are disproportionately burdened with expenditure on water and sanitation services. This is consistent with previous studies showing that poorer households often pay more for water and sanitation services due to poor access to affordable and reliable services (<xref ref-type="bibr" rid="ref30">WHO and UNICEF, 2019</xref>; <xref ref-type="bibr" rid="ref1001">Hutton, 2012</xref>; <xref ref-type="bibr" rid="ref1002">Andres et al., 2020</xref>). Secondly, wealthier households often prioritise hygiene practices due to greater awareness and knowledge of the importance of hygiene for health (<xref ref-type="bibr" rid="ref3">Acheampong et al., 2024</xref>) and the associated costs.</p>
<p>Despite these variations, the overall distribution of expenditure on WASH services is relatively equitable, suggesting that households across different income levels are spending similar proportions of their income on WASH services. Our finding is consistent with studies that reported that WASH expenditure was not disproportionately concentrated among wealthy or poor households (<xref ref-type="bibr" rid="ref23">RMH, 2015</xref>; <xref ref-type="bibr" rid="ref10">GON, 2019</xref>). Nevertheless, a study reported less equitable distributions of WASH expenditure, with the wealthiest households spending more than twice as much on WASH services as the poorest households (<xref ref-type="bibr" rid="ref1003">UNICEF, 2021</xref>). However, the equal distribution of WASH expenditure could imply vertical inequity in access to WASH services among households. This is because households with lower SES are disproportionately disadvantaged in terms of their WASH expenditure and needs. Hence, future research could build upon this analysis by examining the equity implications of WASH expenditure, going beyond equality measures to assess fairness and justice in the distribution of WASH services. This will inform policymakers and practitioners to consider the implications of WASH expenditure patterns while designing more targeted and effective interventions to improve access to WASH services.</p>
<p>The findings of this study highlight the need for further research on the economics of WASH services in Nigerian urban slums. While the study provides valuable insights into the expenditure patterns of households on WASH services, further research is needed to understand the drivers of expenditure and the impact of improved WASH services on household health and wellbeing. In addition, future studies should investigate the value for money, reliability, and quality of WASH services to provide a more comprehensive understanding of these services in urban slums. This would enable policymakers to make informed decisions and improve the effectiveness of WASH interventions.</p>
<p>It is crucial to highlight that availability of water plays a vital role in maintaining proper sanitation and hygiene. Without reliable water supply, achieving and sustaining proper hygiene and sanitation practices becomes significantly challenging, impacting overall health and wellbeing. Hence, availability of water is tied to expenditure on water infrastructure and services. Inadequate water supply system can limit access to hygiene and sanitation. Studies has shown that availability of safe drinking water improves health outcomes, reducing diarrheal diseases and improving maternal and child health (<xref ref-type="bibr" rid="ref20">Okesanya et al., 2024</xref>; <xref ref-type="bibr" rid="ref20">Okesanya et al., 2024</xref>; <xref ref-type="bibr" rid="ref21">Osisiogu et al., 2024</xref>). Water availability has been reported to have to positive impacts on health, hygiene, time-saving, productivity, food supply and nutrition, school performance and reduced stress on people especially women (Abnyie2023 et al., 2021).</p>
<p>This study has some strengths. First, the study focused on vulnerable urban slums, usually neglected or underrepresented in national surveys and statistics. The study&#x2019;s focus on household expenditure on WASH services provides valuable insights into the economic burden of accessing these services. In addition, using a structured, interviewer-administered questionnaire allowed for the collection of quantitative data, which can be easily analysed and compared. Last, the selection of two urban slums from a larger pool of six slums allows for some generalisability of the findings to other urban slum settings. Finally, the study is part of CHORUS, a large consortium focusing on urban health, which allowed for interdisciplinary support for the study design and implementation.</p>
<p>We noted some limitations of the study. As the study relied on self-reported cost data, recall bias could be a concern, especially for a long-term time horizon such as one year. Respondents may have difficulty accurately recalling their expenditures on WASH services, particularly if they do not keep track of their expenses. However, it is worth noting that recall bias can be difficult to eliminate. To mitigate this, the researchers attempted to minimise the bias by asking respondents to report their expenditures over a shorter period of a month, rather than a longer one. In addition, the study only collected quantitative data, which may not provide a complete understanding of the drivers and complexities surrounding household expenditure on WASH services. The high percentage of female respondents may limit the generalizability of our findings to male heads of households. However, this skewness in representation is largely since females were more available and likely to recall household expenditures accurately, given their role in managing household finances. While this approach ensured the collection of reliable data, future research should aim to capture a more balanced representation of male and female household heads to provide a more nuanced understanding of WASH expenditure patterns in households. Finally, the study only focused on household expenditure on WASH services and did not consider other important factors, such as the quality of WASH services or the impact of WASH services on health outcomes.</p>
<sec id="sec16">
<title>Policy implications and recommendations</title>
<p>Policymakers should prioritise the development of affordable and sustainable WASH services for the urban slums in Nigeria. This can be achieved by developing targeted subsidies or other forms of support to help households with larger sizes access WASH services. In addition, the development of affordable and sustainable WASH services that cater to the needs of households in urban slums, regardless of demographic and socio-economic groups, should be implemented to address disparities in access to WASH in Nigeria. This can help improve access to these essential services, promoting health, wellbeing, and economic development.</p>
</sec>
</sec>
<sec sec-type="conclusions" id="sec17">
<title>Conclusion</title>
<p>This study has provided valuable insights into the household&#x2019;s expenditure patterns and inequality in access to WASH services in urban slums in Nigeria. Our findings reveal a significant economic burden associated with WASH access. Also, while expenditure on water and sanitation is pro-poor, and hygiene services are skewed toward wealthier households, the overall distribution of WASH expenditure remains relatively equitable across income groups. This study revealed vertical inequities in access to WASH services, with poorer households facing a disproportionate burden. To address these issues, decision-makers should consider chronic barriers to improve WASH access in urban slums. Targeted subsidies for water and sanitation services, promoting hygiene awareness among low-income households, and investments in WASH infrastructure in urban slum settlements should be prioritised. These efforts are crucial for improving access to affordable and equitable WASH services for vulnerable populations in urban slums.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec18">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec sec-type="ethics-statement" id="sec19">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Ethics Committee of the University of Nigeria Teaching Hospital Ituku Ozalla, Enugu (UNTH/HREC/2023/10/639) and the University of Leeds (MREC 23-013). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="sec20">
<title>Author contributions</title>
<p>UE: Investigation, Data curation, Methodology, Visualization, Validation, Conceptualization, Project administration, Supervision, Writing &#x2013; original draft, Software, Formal analysis, Writing &#x2013; review &#x0026; editing, Funding acquisition. OO: Supervision, Investigation, Conceptualization, Methodology, Writing &#x2013; review &#x0026; editing, Visualization, Project administration, Funding acquisition, Validation. CN: Formal analysis, Visualization, Writing &#x2013; review &#x0026; editing, Data curation, Methodology, Software, Validation. NS: Validation, Writing &#x2013; review &#x0026; editing, Visualization, Data curation, Formal analysis, Software.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The study team acknowledges the approval of the Anambra State Ministry of Health to conduct the study. The team appreciates all the key respondents who participated in the interviews and focus group discussions, whose insights and experience improved the quality of the study output.</p>
</ack>
<sec sec-type="COI-statement" id="sec21">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
<p>The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.</p>
</sec>
<sec sec-type="ai-statement" id="sec22">
<title>Generative AI statement</title>
<p>The authors declare that no Gen AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec23">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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<fn-group>
<fn fn-type="custom" custom-type="edited-by" id="fn0001">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2225997/overview">Manoj Roy</ext-link>, Lancaster University, United Kingdom</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by" id="fn0002">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2788526/overview">Sunitha Vangala</ext-link>, Yogi Vemana University, India</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3083045/overview">Vishal Kamboj</ext-link>, BFIT Group of Institutions, India</p>
</fn>
</fn-group>
</back>
</article>