Trends in Water Service Equity in Mali: Measuring the Change in Socio-Demographic Predictors of Improved Drinking Water Access Between the 2018 and 2024 Demographic and Health Surveys

Trends in Water Service Equity in Mali: Measuring the Change in Socio-Demographic Predictors of Improved Drinking Water Access Between the 2018 and 2024 Demographic and Health Surveys | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends in Water Service Equity in Mali: Measuring the Change in Socio-Demographic Predictors of Improved Drinking Water Access Between the 2018 and 2024 Demographic and Health Surveys Elizabeth Avosuahi Dania This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8541068/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The lack of access to clean drinking water is a leading global health concern and a critical challenge in many African societies. Access to unimproved drinking water sources is intrinsically associated with poverty and a lack of basic household resources, often contributing indirectly to broader social issues, such as school dropout and incomplete educational attainment. Mali remains one of the most affected countries, struggling to meet the essential needs of its population, particularly the availability and sustained access to clean drinking water. Understanding the demographic and socioeconomic factors affecting access to drinking water is highly significant for achieving Sustainable Development Goals (SDGs) 3: Good health and well-being and 6: Clean water and sanitation. While some previous studies have focused on broad drinking water coverage, fewer have investigated the specific factors influencing the quality and household-level access to improved sources. Therefore, this study aimed to investigate the direct factors affecting household access to improved drinking water quality. Using two successive nationally representative surveys, the 2018 and 2023–2024 Mali Demographic and Health Surveys (DHS), the study analyses trends in access to various drinking water sources by comparing data across the two periods. Descriptive statistics and logistic regression were employed to analyse relationships among age, gender, residential location, educational level, and household wealth index, to identify variations in drinking water access and to assess the contribution of changes in socio-demographic characteristics over time. The results revealed significant shifts in drinking water patterns between 2018 and 2023–2024, particularly highlighting widening urban-rural disparities and persistent inequality within the wealth index quintiles. The findings emphasise the vital role of socio-demographic factors in determining access to essential infrastructure and highlight the need for targeted policies to support vulnerable populations, especially those in rural areas and the poorest wealth quintiles, to improve water quality and work toward achieving the SDG goals for sanitation and health. Drinking water types households socio-demographic characteristics secondary data inequality Mali Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Drinking water is universally acknowledged as an essential resource for human health, well-being, and sustainable development; however, its quality and reliability remain a primary global concern, particularly within low- and middle-income countries (Martínez-Santos, 2017 ). Unsafe drinking water is a major contributor to the global disease burden, leading to high morbidity and mortality from waterborne illnesses, including cholera, typhoid, and dysentery (Seib, 2011 ; Ortiz-Correa et al., 2016 ; WHO, 2023). Globally, access to safely managed drinking water sources remains highly unevenly distributed. According to WHO and UNICEF (2022), nearly 2 billion people lacked access to safely managed drinking water services in 2020. This deficit is acutely pronounced in developing regions, where unreliable infrastructure and rapid urbanisation often outpace the capacity of existing water supply systems (Seib, 2011 ; Jain et al., 2014 ; Martínez-Santos, 2017 ; Toure et al., 2019 ). The consequences of this deficit are severe, frequently manifesting in sporadic outbreaks of disease and hindering progress across multiple development indicators (Jain et al., 2014 ). This variation in household access and use of improved water sources is demonstrably influenced by a complex interplay of socio-economic and demographic characteristics (Martínez-Santos, 2017 ; Dania et al., 2025 ). The Context of Water Scarcity in Sub-Saharan Africa and Mali The Sub-Saharan African region faces some of the most profound challenges related to water security (Calkins et al., 2002 ). In this region, insufficient water quality and unstable access often translate directly into public health crises (Leuprecht & Roseberry, 2018 ). Infrastructure, such as broken and degraded drinking water pipes, further compromises water safety, even when the source itself is initially clean (Toure et al., 2019 ). Consequently, the lack of improved access to drinking water and its limited availability are significant causes of death and chronic illness from waterborne diseases, particularly in fragile environments and rural areas (Jain et al., 2014 ; Ortiz-Correa et al., 2016 ). Mali exemplifies these challenges within West Africa (Calkins et al., 2002 ). The country relies heavily on groundwater accessed through sources such as dug wells, hand pumps, and traditional shallow wells, which serve as a primary, though often unprotected, source of drinking water for a large portion of the population (Jones, 2011 ; Díaz-Alcaide et al., 2017 ; Keita & Zhonghua, 2017 ). Improved drinking water will alleviate the burden of water-related diseases on household members (Ortiz-Correa et al., 2016 ). Despite extensive efforts by various service bodies, including the World Health Organisation, national governments, and contributing agencies, to implement and expand water distribution systems (Toure et al., 2019 ), significant disparities persist between urban and rural populations and across wealth strata. Recognising the vital link between water and development, the international community has enshrined these issues in the Sustainable Development Goals (SDGs), specifically SDG 6: Clean Water and Sanitation. This goal aims to ensure the availability and sustainable management of water and sanitation for all by 2030. Achieving this ambitious target requires a detailed understanding of the localised factors that impede progress. Therefore, investigating how core socio-demographic variables, such as age, gender, residential location, education, and household wealth, influence access to and quality of drinking water in countries such as Mali is crucial for informing evidence-based policy and targeted interventions. The following research questions were formulated to guide the study If there are differences in drinking water quality among the household members in urban and rural areas. To measure if the types of drinking water facilities household utilise in Mali is impacted by their socio-demographic characteristics, such as age, educational attainment and wealth index. Case study Mali, a landlocked nation situated in West Africa, is classified as a developing country (Díaz-Alcaide et al., 2017 ; Keita & Zhonghua, 2017 ; Leuprecht & Roseberry, 2018 ). Furthermore, despite its vast geographical size, the country is characterised by high population density in certain areas. Economically, Mali faces significant challenges, remaining one of the world's poorest countries (Toure, 2010 ; Ortiz-Correa et al., 2016 ; Leuprecht & Roseberry, 2018 ). Figure 1 illustrates Mali, which shares extensive borders with Algeria, Burkina Faso, Guinea, Côte d'Ivoire, Mauritania, Niger, and Senegal. The country is predominantly rural (Toure 2010 ). The country is experiencing rapid demographic growth, with the population estimated at 23,769,127 in 2023. This figure is projected to increase substantially, rising 94% to 46,154,079 inhabitants by 2050 (WHO, 2024). Source : Demographic and Health Survey 2023 -24 report. https://dhsprogram.com/pubs/pdf/FR394/FR394.pdf . Description of variables A range of water supply technologies is employed globally to provide drinking water. These sources are broadly categorised by their structural safety and their potential to deliver potable water (WHO & UNICEF, 2022). The specific types include piped water into the dwelling, communal/public standpipes, boreholes, protected dug wells, protected springs, packaged or delivered water, and rainwater collection. Conversely, sources designated as high-risk include unimproved dug wells, unprotected springs, water collected in carts with small tanks or drums, tanker trucks, and surface waters (such as rivers, dams, lakes, ponds, streams, canals, or irrigation channels). These sources are assessed for suitability for human consumption according to two standard classifications established by the World Health Organisation (WHO) and UNICEF: improved water sources and unimproved drinking water sources. According to the WHO/UNICEF Joint Monitoring Programme (JMP) definition (2022), improved drinking water sources are those that are designed to deliver safe water inherently, characterised by being "accessible on premises, available when needed, and free from contamination." Conversely, unimproved drinking water sources are inherently vulnerable to external contamination. Specifically, these include unprotected dug wells, unprotected springs, water supplied by vendors, and all forms of surface water. The following variables were included in the analyses: Drinking water sources were categorised into two level (improved and unimproved drinking water sources), age ( 15-24years, 24–34 years, 35–44 years, 44–54 years, 55+), gender (males and females), residential place (urban and rural areas), educational attainment (no school/preschool, primary/ secondary education, higher education and unspecified), wealth index (poor, middle and rich). Source of data used and study participants Data for this study were drawn from two successive, nationally representative household surveys: the 2018 and 2023–2024 Demographic and Health Surveys (DHSs). The 2018 DHS collected data between August and November 2018. The subsequent 2023–2024 DHS field collection phase ran from November 27, 2023, to February 29, 2024. These surveys utilised a sampling frame of randomly selected households, yielding a total sample of 9,510 household members in 2018 and 9,667 in 2023–2024. The overall study included both male and female respondents. The questionnaire covered essential demographic and socioeconomic indicators, including age, educational attainment, household income, gender, residential location, and sources of household drinking water. The subsequent analysis employed the collected household-level dataset. Methodology The methodology employed in this research utilised an analytical approach, specifically involving descriptive analysis, bivariate analysis (Chi-square testing), and multivariate analysis (Binary Logistic Regression), to investigate the factors influencing access to household drinking water thoroughly. The foundation of this study rests on secondary data from the nationally representative Mali Demographic and Health Survey (DHS), incorporating datasets from both the 2018 and the 2023-24 surveys. Initially, the study performed a descriptive (univariate) analysis to summarise the basic characteristics and distribution of the study population drawn from the Mali DHS. Providing a comprehensive overview of the key socio-economic and demographic variables, such as age groups, gender, residential location, educational attainment, and wealth index, as well as the distribution of the primary outcome variable: access to improved vs. unimproved drinking water sources. The analysis was conducted to identify the underlying patterns and trends in the Mali context. Following the descriptive analysis, the research moved to bivariate analysis to examine the relationship between the independent variables (socio-economic and demographic characteristics) and the primary dependent variable (access to household drinking water). Chi-square test was employed to determine whether a statistically significant association existed between each explanatory variable (e.g., educational level, wealth status) and the drinking water source utilised by the household members. A significant Chi-square result (typically p < 0.05) indicated that the distribution of drinking water access was dependent on the independent variable's category, establishing an association between the dependent and independent variables. The final stage involved a multivariate analysis using Binary Logistic Regression. This advanced statistical technique was employed to identify the determinants of access to improved drinking water while controlling for the influence of all other variables in the model. Binary Logistic Regression was specifically chosen because the outcome variable (access to drinking water) was dichotomous (improved and unimproved). The analysis generated Odds Ratios (ORs), which provided essential insights into the likelihood that households had access to improved or unimproved drinking water for each independent factor. By presenting these odds ratios, the study could identify the most influential socio-economic and demographic factors, such as specific age groups, residential location, or wealth quintiles, that significantly predicted the probability of utilising safe and reliable drinking water sources in Mali. Results The data below, in Fig. 1.1 , show a positive trend in access to improved drinking water sources in Mali between 2018 and 2023–2024. The proportion of the population accessing improved drinking water sources increased substantially from 69.4% in 2018 to 81.4% in 2023–2024. This represents a 12% increase. Correspondingly, the reliance on unimproved drinking water sources dropped significantly, falling from 30.6% in 2018 to 18.6% in 2023–2024. Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Frequency distribution of socio-demographic variables. Table 1 presents the frequency and percentage distributions of demographic and socioeconomic characteristics across two survey periods (2018 and 2023–2024), with slightly different total sample sizes ( n = 9510 in 2018 and n = 9667 in 2023–2024). The population appears to be slightly older, and the proportion of younger age groups has decreased. The youngest group (15–24) significantly dropped from 4.5% to 2.8%, and the 25–34 group also saw a substantial decrease (from 22.1% to 15.6%). Conversely, the proportion of older individuals increased. The 55 + age group became the largest category, increasing from 29.9% to 35.0%. The 45–54 group also increased (from 19.3% to 22.0%). The 35–44 age group remained relatively stable in Mali (24.2% to 24.7%). There was a noticeable shift toward a higher proportion of males in the sampled population. The percentage of males increased from 81.7% to 86.2% while the percentage of females decreased from 18.3% to 13.8%. The distribution between urban and rural areas remained highly consistent. Both urban (31.0% to 30.1%) and rural (69.0% to 69.9%) proportions remained nearly identical across the two periods. There is an overall trend towards higher educational attainment. The largest category, no schooling/preschool, decreased from 69.4% to 65.2%. Both the Primary/secondary school group (25.9% to 27.7%) and the Higher education group (4.0% to 5.7%) saw increases. The wealth distribution shows a shift from the poor category to the middle category, while the rich category slightly increased its dominance. The proportion categorised as poor decreased substantially from 38.3% to 29.2%. The Middle wealth category rose significantly, from 19.6% to 27.5%. The proportion of the rich category slightly increased, from 42.1% to 43.3%, maintaining its status as the largest wealth group. Table 1 Descriptive analysis of study participants Variables 2018 2023–2024 Frequency (%) n = 9510 Frequency n = 9667 Age 15–24 426 (4,5) 267 (2,8) 25–34 2102 (22,1) 1508 (15,6) 35–44 2305 (24,2) 2389 (24,7) 45–54 1833 (19,3) 2124 (22,0) 55+ 2844 (29,9) 3379 (35,0) Gender Males 7769 (81,7) 8333 (86,2) Females 1741 (18,3) 1334 (13,8) Residential place Urban 2945 (31,0) 2912 (30,1) Rural 6565 (69,0) 6755 (69,9) Educational level No schooling/preschool 6602 (69,4) 6305 (65,2) Primary/secondary school 2462 (25,9) 2681 (27,7) Higher 376 (4,0) 547 (5,7) Unspecified 70 (0,7) 134 (1,4) Wealth index Poor 3642 (38,3) 2820 (29,2) Middle 1861 (19,6) 2661 (27,5) Rich 4007 (42,1) 4186 (43,3) Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Age There was a significant improvement in access to improved drinking water sources across all age groups from 2018 to 2023–2024 (Fig. 1.2 ). Access to improved sources generally peaked among middle-aged women (35–44 years: 72.1% and 45–54 years: 71.2%) and was lowest among the youngest (15–24 years: 65.3%) and (25–34 years: 67.6%) in 2018. The Chi-square test = 19.812, p < 0.001, indicates a highly significant relationship between age group and access to drinking water sources in 2018. Age remains a highly significant determinant, Chi-square = 17.798, p < 0.001 in 2023–2024. While all groups improved, the overall distribution changed. The 25–34 and 35–44 age groups demonstrated the highest access rates (83.1% and 83.6%, respectively). The 15–24, 55+, and 45–54 groups showed slightly lower, but still very high, access (79.8%, 79.8%, and 80.6%, respectively). The gap between the highest-and lowest-accessing age groups narrowed significantly compared to 2018, suggesting a more equitable distribution of improved water access. Conversely, trends in unimproved drinking water access show a significant reduction in reliance on unimproved sources across all age groups between 2018 and 2023–2024. Unimproved sources were highest among (34.7%) the youngest group, 15–24, and lowest (27.9%) among the 35–44 age group in 2018. By 2023–2024, reliance decreased in all groups, but the patterns of highest and lowest reliance shifted slightly. The 15–24 and 55 + age groups share the highest reliance on unimproved sources (20.2% each). The 35–44 age group continued to show the lowest reliance (16.4%), closely followed by the 25–34 age group (16.9%). Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Household gender. The dataset below (Fig. 1.3 ) shows the distribution of access to improved and unimproved drinking water sources, categorised by gender, for two time periods: 2018 and 2023-24. The data indicate a decrease in the percentage of both males and females relying on unimproved drinking water sources between 2018 and 2023-24. They experienced a substantial reduction (over 12 percentage points). In 2018, males had a slightly higher proportion with access to unimproved drinking water (30.9%) than females (29.3%). By 2023-24, females had a slightly lower proportion of access to unimproved drinking water (16.9%) than males (18.8%). The Chi-square test was used to examine the relationship between gender and the drinking water source for both years: The p -value was greater than 0.184. This indicates that the observed difference in access to drinking water sources between males and females was not statistically significant in 2018. 2023-24: The p -value was greater than 0.087. This suggests that the relationship between gender and the type of drinking water source remained statistically insignificant in 2023-24. Both genders saw a significant increase in access to improved drinking water between 2018 and 2023-24. There was an increase in males from 69.1% (2018) to 81.2% (2023-24), and in females from 70.7% (2018) to 83.1% (2023-24). In both periods, females reported slightly higher access to improved drinking water than males. The overall reliance on unimproved drinking water sources decreased significantly for both genders; the data suggest that gender itself does not significantly determine the type of drinking water source utilised in the households in either year. Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Residential location In both periods (Fig. 1.4 ), urban residents had significantly greater access to improved drinking water sources than rural residents. In 2018, 82.4% of urban residents accessed improved sources, compared to 63.5% of rural residents, while in 2023-24, 95.4% of urban residents accessed improved sources, compared to 75.4% of rural residents. Rural residents relied much more on unimproved drinking water sources than urban residents. In 2018, the reliance on unimproved sources was 36.5% in rural areas compared to 17.6% in urban areas. The Chi-square test results indicate a highly statistically significant relationship between residential location and the drinking water source used in both years: 2018 (χ² = 339.231) and 2023-24 (χ² = 537.058), with p-values < 0.001. This demonstrates that access to improved drinking water is strongly dependent on whether a household is located in an urban or rural area. The study findings indicate that living in urban areas reduces the possibility of choosing unimproved drinking water. Both urban and rural areas saw substantial increases in access to improved drinking water between 2018 and 2023-24. Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Educational attainment. Figure 1.5 illustrates that household members lacking formal education or preschool education continue to be the most vulnerable group, exhibiting the lowest percentage of improved water access in both years, despite notable progress over time: 63.7% in 2018 and 76.6% in 2023–2024. Additionally, respondents with primary or secondary education in 2018 reported an 80.8% access to improved water sources, whereas 19.2% relied on unimproved sources. In 2023-24, 89.1% utilised improved drinking water, and reliance on unimproved sources dropped to 10.9%. This primary/secondary education group shows much stronger access than the 'No schooling/preschool group, approaching 90% access to improved sources by 2023-24. Access was very high at 91.2%, with only 8.8% using unimproved sources in 2018. Access increased further to 97.6%, achieving near-universal access to improved drinking water in 2023-24. Reliance on unimproved sources dropped dramatically to only 2.4%. Women with higher education consistently have better access to improved drinking water, highlighting education as a key factor in water utilisation. The data demonstrate a strong, statistically significant association between educational attainment and access to improved drinking water in both 2018 (χ² = 348.455, p < 0.001) and 2023-24 (χ² = 302.532, p < 0.001). This indicates that the probability of a woman having access to improved drinking water increases significantly with her level of education, while reliance on unimproved drinking water sources decreases. Furthermore, there is a clear trend of overall improvement in access to improved drinking water across all educational levels between 2018 and 2023-24. Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Wealth index In 2018, less than half (46.8%) of impoverished households had access to improved drinking water, indicating that the majority (53.2%) depended on unimproved sources (Fig. 1.6 ). By 2023-24, access had significantly increased to 65.1%, with reliance on unimproved sources decreasing to 34.9%. Despite notable improvement, poor households remain the most disadvantaged, with the highest proportion of women still relying on unimproved water sources in both periods. In 2018, middle-income households utilising improved sources were 74.0%, and 26% used unimproved drinking water sources. On the other hand, in 2023-24, access increased to 77.6%, and 22.4% relied on unimproved drinking water sources. Middle-income households have significantly better access than poor households, but still lag behind the rich, highlighting the ongoing economic constraints on drinking water access. Improved drinking water was extremely high at 87.8%, with only 12.2% using unimproved sources in 2018. Five years later, in 2023-24, access reached near-universal levels at 94.9%, with unimproved sources dropping to a minimal 5.1%. Rich households consistently enjoy the highest access to improved drinking water, confirming that wealth effectively mitigates the risk of utilising unsafe water sources. The high Chi-square values (1534.218 in 2018 and 1026.858 in 2023-24) and p -values < 0.001 confirm an influential, statistically significant association between wealth status and the type of drinking water accessed. The data clearly establishes that the Wealth Index is a powerful and highly significant determinant of access to drinking water sources in both analysed periods (2018 and 2023-24). Households with greater wealth consistently show a much higher probability of accessing improved drinking water sources. Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Factors associated with household access to drinking water. Table 2 below presents the results of a Binary Logistic Regression analysis for 2018, examining factors associated with access to improved and unimproved drinking water in Mali. The analysis includes various socioeconomic and demographic variables, with the reference category for each variable denoted by an "@" symbol. The table presents two parallel logistic regression models: one for access to improved drinking water and one for access to unimproved drinking water. The coefficient B, the Wald statistic, the P -value, and the Exp(B) (Odds Ratio, OR) are reported for each variable category. Wealth index is the most significant factor determining access to drinking water, showing a highly significant association ( p < 0.001) across both improved and unimproved sources. The reference category is 'Rich'. Poor households are significantly less likely to access improved drinking water, with an odds ratio (Exp(B)) of 0.314. Middle-income households are even less likely to access improved drinking water, with an odds ratio of 0.137. Poor and Middle-income households are drastically disadvantaged compared to rich households in accessing improved water sources. Unimproved drinking water poor households are 3.181 times more likely to access unimproved drinking water sources. Similarly, the middle-income households are 7.319 times more likely to access unimproved drinking water. The odds of utilising unimproved drinking water sources increase dramatically for those in the middle and poor wealth categories. Educational level is significantly associated with water access ( p < 0.001). The reference category is 'Unspecified'. Households with no school/preschool are less likely to access improved sources (OR = 0.642). This is similar to households with primary/secondary school education being least likely to access improved sources among the specified groups (OR = 0.497). Higher education household members show an odds ratio of 0.620, though this category is not statistically significant ( p = 0.219). Households with primary/secondary school education are 2.011 times more likely to use unimproved drinking water than the reference group. Likewise, households without a school/preschool are 1.558 times more likely to use unimproved sources. Age groups show a highly significant association with access to drinking water ( p < 0.001). The reference category is '55+'. All specified age groups (25–34, 35–44, 45–54) are less likely to access improved drinking water compared to the oldest group (55+). The 25–34 group (OR = 0.643), the 35–44 group (OR = 0.638) and the 45–54 group (OR = 0.650) have low odds of accessing improved water. For instance, households aged 35–44 have an OR of 0.638, meaning they are about 36.2% less likely to access improved water than the 55 + group. The 15–24 group is not statistically significant in the model ( p = 0.264). Conversely, these age groups (25–34, 35–44, 45–54) are more likely to access unimproved drinking water (ORs ranging from 1.555 to 1.538). The 35–44 group has the highest likelihood of accessing unimproved water, OR = 1.569 (a 56.9% increase) of accessing unimproved water compared to women aged 55+ Residential place: There is no statistically significant difference in the odds of accessing improved or unimproved drinking water between urban and rural residents Gender: There is no statistically significant difference in the odds of accessing improved or unimproved drinking water between male and female-headed households Table 2 Logistic regression analysis of the 2018 Mali Demography and Health Survey 2018 Improved drinking water Unimproved drinking water Variables B Wald P Exp(B) B Wald P Exp(B) Age - 34.706 < 0.001 - - 34.706 < 0.001 - 15–24 -0.139 1.247 0.264 0.871 0.139 1.247 0.264 1.149 25–34 -0.442 12.495 < 0.001 0.643 0.442 12.495 < 0.001 1.555 35–44 -0.450 12.588 < 0.001 0.638 0.450 12.588 < 0.001 1.569 45–54 -0.431 12.298 < 0.001 0.650 0.431 120,298 < 0.001 1.538 55+ @ - - - - - - - - Gender - - - - - - - - Males -0.103 2.520 0.112 0.902 0.103 2.520 0.112 1.108 Females@ - - - - - - - - Residential place - - - - - - - - Urban -0.063 0.739 0.390 0.939 0.063 0.739 0.390 1.065 Rural@ - - - - - - - - Educational level - 56.300 < 0.001 - - 56.300 < 0.001 - No school/preschool -0.444 47.857 < 0.001 0.642 0.444 47.857 < 0.001 1.558 Primary/secondary school -0.699 13.261 < 0.001 0.497 0.699 13.261 < 0.001 2.011 Higher -0.477 1.509 0.219 0.620 0.477 1.509 0.219 1.612 Unspecified@ - - - - - - - - Wealth index - 899.956 < 0.001 - - 899.956 < 0.001 - Poor 1.157 334.546 < 0.001 0.314 1.157 334.546 < 0.001 3.181 Middle 1.991 740.868 < 0.001 0.137 1.991 740.868 < 0.001 7.319 Rich@ - - - - - - - - Constant 0.627 19.839 < 0.001 1.873 -0.627 19.839 < 0.001 0.534 Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Factors associated with household access to drinking water. Table 3 presents the results for a binary logistic regression for improved and unimproved drinking water in Mali for the period 2023-24. The coefficient (B) and Odds Ratio (Exp(B)) for unimproved drinking water are the inverse of those for improved drinking water, confirming they are models of the inverse outcomes. Overall Significance: The age variable group ( p = 0.009), residential location, educational level and wealth index are statistically significant in the model. All age groups (25–34, 35–44, 45–54) have significantly lower odds of accessing improved drinking water compared to the oldest group (55+). For example, household members aged 25–34 (OR = 0.641) are significantly less likely to access improved drinking water. The odds are reduced by approximately 35.9%. This group, 35–44 years (OR = 0.653), is also significantly less likely to access improved drinking water ( p = 0.016), with odds reduced by about 34.7%. Individuals aged 45–54 have an odds ratio of 0.595 for accessing improved water. This means they are about 40.5% less likely to access improved water compared to those aged 55+. Conversely, they are 1.682 times more likely to use unimproved water sources compared to the 55 + group. Access to unimproved drinking water: 25–34 years (OR = 1.560) is significantly 56% more likely to access unimproved drinking water ( p = 0.012). 35–44 years households (OR = 1.531) are 53.1% more likely to access unimproved drinking water ( p = 0.016). This group of 45–54 years (OR = 1.682) is significantly 68.2% more likely to access unimproved drinking water ( p = 0.003), showing the highest likelihood among all non-reference age groups. Residential location is a highly statistically significant predictor ( p < 0.001). Individuals residing in urban areas have 2.126 times the odds of accessing improved drinking water compared to those in rural areas; that is, they are more than twice as likely (112.6% increase in odds) to have access to clean water. This also means urban residents are significantly less likely to rely on unimproved water (OR = 0.470), with the odds reduced by 53% compared to rural residents. Individuals with no school/preschool (OR = 0.624) are less likely to access improved drinking water than the reference group (p < 0.001), with odds reduced by 37.6%. Primary/secondary school households (OR = 0.315) are significantly much less likely to access improved drinking water ( p < 0.001), with odds reduced by 68.5%. Higher education group (OR = 0.758) is not statistically significant. No school/preschool (OR = 1.602) is significantly 60.2% more likely to utilise unimproved drinking water ( p < 0.001). Primary/secondary school (OR = 3.179) is over three times more likely (217.9% increase in odds) to access unimproved drinking water (p < 0.001). Wealth is the strongest predictor of access to drinking water. Poor households are about 45.1% less likely (OR = 0.549) to utilise improved water than rich households. On the other hand, poor households are 1.821 times more likely to access unimproved water. Middle-income households are dramatically disadvantaged, being about 82.9% less likely (OR = 0.171) to access improved water than rich households. Middle-income (OR = 5.835) are significantly more likely to access unimproved water. Gender is not a statistically significant predictor of access to unimproved drinking water (p = 0.477). Gender does not appear to be a significant determinant of access to drinking water in this model. Table 3 Logistic regression analysis of the 2023-24 Mali Demography and Health Survey 2023-24 Improved drinking water Unimproved drinking water Variables B Wald P Exp(B) B Wald P Exp(B) Age - 13.529 0.009 - - 13.529 0.009 - 15–24 -0.294 2.626 0.105 0.745 0.294 2.626 0.105 1.342 25–34 -0.445 6.335 0.012 0.641 0.445 6.335 0.012 1.560 35–44 -0.426 5.811 0.016 0.653 0.426 5.811 0.016 1.531 45–54 -0.520 8.997 0.003 0.595 0.520 8.997 0.003 1.682 55+ @ - - - - - - - - Gender - - - - - - - - Males 0.060 0.506 0.477 1.062 -0.060 0.506 0.477 0.941 Females@ - - - - - - - - Residential place - - - - - - - - Urban 0.754 47.620 < 0.001 2.126 -0.754 47.620 < 0.001 0.470 Rural@ - - - - - - - - Educational level - 51.772 < 0.001 - - 51.772 < 0.001 - No school/preschool -0.471 40.014 < 0.001 0.624 0.471 40.014 < 0.001 1.602 Primary/secondary school -1.157 15.713 < 0.001 0.315 1.157 15.713 < 0.001 3.179 Higher -0.277 0.800 0.371 0.758 0.277 0.800 0.371 1.319 Unspecified@ - - - - - - - - Wealth index - 350.966 < 0.001 - - 350.966 < 0.001 - Poor -0.600 90.789 < 0.001 0.549 0.600 90.789 < 0.001 1.821 Middle -1.764 337.064 < 0.001 0.171 1.764 337.064 < 0.001 5.835 Rich@ - - - - - - - - Constant -0.835 16.869 < 0.001 0.434 0.835 16.869 < 0.001 2.305 Source Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023 -24 dataset. Discussion The primary objective of this study was to investigate the sociodemographic factors influencing household access to and choice of drinking water quality in Mali. This quantitative study utilised secondary data from two nationally representative datasets: the 2018 and 2023–2024 Demographic and Health Surveys (DHSs). The analysis was restricted to household members aged 15 and above. Statistical analysis was performed using SPSS version 30. We employed cross-tabulation and the Chi-square test to determine the relationship between access to drinking water quality and key variables, including age group, gender, residential location, educational attainment, and wealth index. Furthermore, binary logistic regression was utilised to assess the independent factors affecting the quality of drinking water accessed by household members. Summary of Key Findings Consistent with prior research, our findings confirm a significant influence of socio-demographic variables on access to drinking water quality among household members. The overall statistical models demonstrated strong explanatory power. Specifically, age, educational level, residential location, and the wealth index emerged as the most significant determinants of household drinking water choice in both the 2018 and 2023–2024 surveys ( P < 0.001). These results strongly underscore existing disparities in access to and utilisation of quality drinking water across the Malian population. Further analysis focusing on the age variable reveals that access to improved drinking water remained nearly identical across both survey periods (2018 and 2023–2024). Specifically, household members in the middle-aged categories (25–34, 35–44, and 45–54) consistently showed a statistically significant Odds Ratio (OR) of less than 1 for the likelihood of accessing improved drinking water. This finding indicates that, relative to the reference group (55+), individuals in these economically active age cohorts are less likely to utilise improved sources. The influence of these age categories on unimproved water access remains consistently positive and statistically significant across both the 2018 and 2023–2024 time periods. Our findings highlight the significance of age in the utilisation of drinking water sources in Mali. A significant finding throughout the survey periods is the changing significance of residential location as a determinant of access to improved drinking water quality. The factor representing urban residence transitioned from being statistically non-significant in the 2018 model to emerging as a robust, positive, and statistically significant predictor in the 2023–2024 analysis. This notable shift strongly suggests that infrastructure investment and expansion of water service delivery have been disproportionately concentrated in urban areas over the intervening period. This prioritisation has successfully augmented the likelihood of improved water access for urban populations, but has simultaneously exacerbated the disparity in access between urban and rural populations. Consequently, rural households continue to face systemic challenges in accessing and maintaining improved drinking water sources. Rural communities are frequently characterised by underdevelopment, insufficient maintenance capacity, and inadequate service infrastructure, leading to a persistent deficit in improved water availability (Mahama et al., 2014 ; Dongzagla et al., 2022 ; Sani & Scholz, 2022 ). Our observations suggest an urgent need for targeted educational interventions among Mali’s rural population on the critical importance of utilising and maintaining clean drinking water sources at the household level to ensure maximum safety. Achieving the ambitious targets outlined in the Sustainable Development Goals (SDGs), particularly SDG 5 (Gender Equality, often linked to water access responsibility) and SDG 6 (Clean Water and Sanitation), requires eliminating the established inequalities in the utilisation of clean drinking water necessitates, as a crucial initial measure, ensuring equitable access to safe and improved sources across all residential strata (Patel et al., 2013 ; Dania et al., 2025 ). Therefore, future policy and infrastructure initiatives must be deliberately focused on closing the infrastructural gap currently observed between urban and rural areas. The study findings underscore a critical relationship between household educational attainment and the utilisation of unimproved drinking water sources. Specifically, the analysis reveals that lower levels of household education are statistically and positively associated with the reliance on unimproved water intake. This association was most pronounced across households reporting no formal or preschool education and those with only primary or secondary education as their highest level of attainment. Consequently, targeted public health interventions are imperative. Informing households, particularly those with minimal or less formal education, about the superior health outcomes and disease prevention associated with the use of clean drinking water is essential for effectively boosting the adoption and sustained use of improved water sources (Kausar et al., 2011 ; Patel et al., 2013 ). This educational gap represents a key area for policy focus. As Ortiz-Correa and co-authors (2016) meticulously documented, variability or inadequacy in access to safe drinking water can significantly diminish school enrolment rates among young people. The heightened exposure to waterborne diseases resulting from unimproved water usage is a primary driver of chronic illness, leading to frequent school absenteeism and, consequently, hindering academic performance and educational attainment. The analysis of the household wealth index across the two study periods, 2018 and 2023–24, demonstrated a profound and persistent socioeconomic disparity concerning access to safe drinking water in Mali. Our study findings robustly indicate that households in the lower wealth quintiles (poor and middle-income) exhibited significantly higher reliance on unimproved drinking water sources during both observation periods. Conversely, the wealthiest households (higher-income quintiles) demonstrated a markedly greater propensity to access and utilise improved water infrastructure. Income levels, as one of the most critical socioeconomic variables, directly determine a household's capacity to access clean water. Matos and colleagues ( 2014 ) highlighted, disparities in wealth are expected to heavily influence patterns of water utilisation, dictating whether families can afford to connect to municipal systems, purchase bottled water, or invest in reliable household water. The economic limitations faced by poor and middle-income households restrict their choices, often forcing them to rely on unclean sources (such as unprotected wells and water from public transport vendors) that pose significant health risks. This creates a clear cycle of vulnerability. Furthermore, the findings of Kausar and co-authors (2011) reinforce this disparity, reporting that higher-income households inherently possess greater opportunities to improve their drinking water quality by accessing superior infrastructure. Indicating that wealthier households are better positioned geographically. Conclusion and Recommendation It is essential to understand the intricate pathways through which key sociodemographic factors, including age, gender, residential location, educational attainment, and household wealth, actively influence and stratify household water-use patterns. Our study observed an encouraging trend: the population's overall access to improved drinking water sources demonstrably increased between the 2018 baseline and the 2023–2024 period. This increase is reflected in the corresponding decrease in the number of people exposed to unimproved drinking water sources in Mali during the same timeframe. This dual shift indicates substantive progress toward achieving the targets set forth by Sustainable Development Goal (SDG) 6 (Clean Water and Sanitation). However, while the proportion of households utilising improved drinking water sources has increased, a critical challenge remains: the persistence of significant disparities and the ongoing need to ensure a safe, sufficient, and equitable water supply, particularly for vulnerable rural communities, to preserve health and reduce the incidence of waterborne diseases effectively. Crucially, the analysis reveals that the use of unimproved drinking water remains highly prevalent within specific demographic segments in our study area. This risk is disproportionately concentrated among poor households, those households without formal education, and, most notably, rural households. Therefore, we recommend further findings through additional, geographically focused provincial research studies. By understanding how households currently manage their drinking water, policymakers can accelerate the impact of interventions. Enhanced access to and utilisation of clean, reliable water infrastructure are fundamental drivers that will demonstrably accelerate improvements in overall community wellness. Declarations Ethical Approval Not applicable (The study does not require ethical clearance or approval as it utilises secondary data collected by the Demographic and Health Survey (DHS). Consequently, DHS has addressed all ethical clearance considerations for its users. Funding This research did not receive any external funding. Availability of data and materials The data was obtained from the DHS website following a formal request for access through https://dhsprogram.com/data/using-datasets-for-analysis.cfm. Permission was subsequently granted. Author Contribution E.A. wrote the entire manuscript. References Calkins P, Larue B, Vézina M. Willingness to pay for drinking water in the Sahara: The case of Douentza in Mali. Cahiers d'Economie et de Sociologie Rurales. 2002;64:37–56. Dania EA, Nsengiyumva P, Mfubu A. (2025). Among Women in Burundi: Insights from the 2017 Demographic and Health Survey (DHS2017). Political Governance and the African Peer Review Mechanism: A Comparative Analysis , 173. Demographic and Health Survey. (2023-24) https://dhsprogram.com/pubs/pdf/FR394/FR394.pdf . Accessed on the 19th of December 2025. Díaz-Alcaide S, Martínez-Santos P, Villarroya F. A commune-level groundwater potential map for the republic of Mali. Water. 2017;9(11):839. Dongzagla A, Dordaa F, Agbenyo F. Spatial inequality in safely managed water access in Ghana. J Water Sanitation Hygiene Dev. 2022;12(12):869–82. Jain M, Lim Y, Arce-Nazario JA, Uriarte M. (2014). Perceptional and socio-demographic factors associated with household drinking water management strategies in rural Puerto Rico. PLoS ONE, 9(2), e88059. Jones S. (2011). Participation as citizenship or payment? A case study of rural drinking water governance in Mali. Water Altern, 4 (1). Kausar S, Asghar K, Anwar SM, Shaukat F, Kausar R. Factors affecting drinking water quality and human health at household level in Punjab. Pakistan Statistics. 2011;100(100):0. Keita S, Zhonghua T. The assessment of processes controlling the spatial distribution of hydrogeochemical groundwater types in Mali using multivariate statistics. J Afr Earth Sc. 2017;134:573–89. Leuprecht C, Roseberry P. (2018). Political Demography of Conflict in Mali. Report. Chaire Raoul-Dandurand en études stratégiques et diplomatiques. Montréal: Université du Québec à . Mahama AM, Anaman KA, Osei-Akoto I. Factors influencing householders' access to improved water in low-income urban areas of Accra, Ghana. J Water Health. 2014;12(2):318–31. Martínez-Santos P. Determinants for water consumption from improved sources in rural villages of southern Mali. Appl Geogr. 2017;85:113–25. Matos C, Teixeira CA, Bento R, Varajão J, Bentes I. An exploratory study on the influence of socio-demographic characteristics on water end uses inside buildings. Sci Total Environ. 2014;466:467–74. Ortiz-Correa JS, Filho R, M., Dinar A. Impact of access to water and sanitation services on educational attainment. Water Resour Econ. 2016;14:31–43. Patel AI, Shapiro DJ, Wang YC, Cabana MD. Sociodemographic characteristics and beverage intake of children who drink tap water. Am J Prev Med. 2013;45(1):75–82. Sani Y, Scholz M. Gender and other vulnerabilities to water–energy accessibility in rural households of Katsina State, Northern Nigeria. Sustainability. 2022;14(12):7499. Seib MD. (2011). Assessing drinking water quality at source and point-of-use: a case study of Koila Bamana, Mali, West Africa. Toure A, Wenbiao D, Keita Z, Dembele A, Abdalla Elzaki EE. Drinking water quality and risk for human health in Pelengana commune, Segou, Mali. J Water Health. 2019;17(4):609–21. Toure M. (2010). Demography in Mali. World Bank. State of the world’s drinking water: an urgent call to action to accelerate progress on ensuring safe drinking water for all. Geneva: World Health Organisation; 2022. https://www.who.int/publications/i/item/9789240060807 . Accessed the 19th December 2025. World Health Organisation. (2023-24) https://srhdpeuwpubsa.blob.core.windows.net/whdh/DATADOT/COUNTRY/PDF/466_Mali.pdf . Accessed on the 18th of December 2025. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":253665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of Mali\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: Demographic and Health Survey 2023-24 report. \u003c/strong\u003ehttps://dhsprogram.com/pubs/pdf/FR394/FR394.pdf.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/ea59d83772ca706b0792ce84.png"},{"id":101003264,"identity":"968e6b5d-9aaa-4856-849a-802888ae9d70","added_by":"auto","created_at":"2026-01-23 17:01:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.1: Trend in the use of drinking water sources in Mali.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource:\u003c/em\u003e Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023-24 dataset.\u003c/p\u003e","description":"","filename":"1.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/37527d1af5d26c58c8f8a23a.png"},{"id":101203711,"identity":"d72a37d8-ddb5-42f5-b436-fcfc22196825","added_by":"auto","created_at":"2026-01-27 09:40:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.2: The distribution of age groups and water sources.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource:\u003c/em\u003e Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023-24 dataset.\u003c/p\u003e","description":"","filename":"1.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/8b82db9eeb6da4c7dc05920b.png"},{"id":101003265,"identity":"88583ce9-50b5-4143-acc1-d3efc9d2930c","added_by":"auto","created_at":"2026-01-23 17:01:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.3: The distribution of household gender and water sources.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource:\u003c/em\u003e Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023-24 dataset.\u003c/p\u003e","description":"","filename":"1.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/412b944b2dfb1d7f2a7b6a96.png"},{"id":101203220,"identity":"316187fe-1d50-475d-92a1-7ff27e9fab17","added_by":"auto","created_at":"2026-01-27 09:39:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.4: The distribution of residential places and water sources.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource:\u003c/em\u003e Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023-24 dataset.\u003c/p\u003e","description":"","filename":"1.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/ce4fcc8e69ee081402d3b4e3.png"},{"id":101003267,"identity":"2f603396-4e40-4f30-a311-15342a325e3d","added_by":"auto","created_at":"2026-01-23 17:01:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.5: The distribution of educational attainment and water sources.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource:\u003c/em\u003e Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023-24 dataset.\u003c/p\u003e","description":"","filename":"1.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/3e7c6bd7f34ff124c39b2a56.png"},{"id":101204852,"identity":"41235f4b-d350-4dec-aba5-3aefd80b3b2f","added_by":"auto","created_at":"2026-01-27 09:44:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":32006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.6: The distribution of wealth index and water sources.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource:\u003c/em\u003e Author’s compilation using the Mali Demographic and Health Survey 2018 and 2023-24 dataset.\u003c/p\u003e","description":"","filename":"1.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/2d7a6d4ebe4f30d9f58bec49.png"},{"id":101299018,"identity":"f7a92a84-1ceb-4817-9760-1992cf32429a","added_by":"auto","created_at":"2026-01-28 09:39:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1686051,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8541068/v1/a619c8c3-b78b-41b5-a096-2a098c1f890b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends in Water Service Equity in Mali: Measuring the Change in Socio-Demographic Predictors of Improved Drinking Water Access Between the 2018 and 2024 Demographic and Health Surveys","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDrinking water is universally acknowledged as an essential resource for human health, well-being, and sustainable development; however, its quality and reliability remain a primary global concern, particularly within low- and middle-income countries (Mart\u0026iacute;nez-Santos, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Unsafe drinking water is a major contributor to the global disease burden, leading to high morbidity and mortality from waterborne illnesses, including cholera, typhoid, and dysentery (Seib, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ortiz-Correa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; WHO, 2023). Globally, access to safely managed drinking water sources remains highly unevenly distributed. According to WHO and UNICEF (2022), nearly 2\u0026nbsp;billion people lacked access to safely managed drinking water services in 2020. This deficit is acutely pronounced in developing regions, where unreliable infrastructure and rapid urbanisation often outpace the capacity of existing water supply systems (Seib, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mart\u0026iacute;nez-Santos, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Toure et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The consequences of this deficit are severe, frequently manifesting in sporadic outbreaks of disease and hindering progress across multiple development indicators (Jain et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This variation in household access and use of improved water sources is demonstrably influenced by a complex interplay of socio-economic and demographic characteristics (Mart\u0026iacute;nez-Santos, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dania et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe Context of Water Scarcity in Sub-Saharan Africa and Mali\u003c/h3\u003e\n\u003cp\u003eThe Sub-Saharan African region faces some of the most profound challenges related to water security (Calkins et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In this region, insufficient water quality and unstable access often translate directly into public health crises (Leuprecht \u0026amp; Roseberry, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Infrastructure, such as broken and degraded drinking water pipes, further compromises water safety, even when the source itself is initially clean (Toure et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, the lack of improved access to drinking water and its limited availability are significant causes of death and chronic illness from waterborne diseases, particularly in fragile environments and rural areas (Jain et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ortiz-Correa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMali exemplifies these challenges within West Africa (Calkins et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The country relies heavily on groundwater accessed through sources such as dug wells, hand pumps, and traditional shallow wells, which serve as a primary, though often unprotected, source of drinking water for a large portion of the population (Jones, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; D\u0026iacute;az-Alcaide et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Keita \u0026amp; Zhonghua, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Improved drinking water will alleviate the burden of water-related diseases on household members (Ortiz-Correa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite extensive efforts by various service bodies, including the World Health Organisation, national governments, and contributing agencies, to implement and expand water distribution systems (Toure et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), significant disparities persist between urban and rural populations and across wealth strata.\u003c/p\u003e \u003cp\u003eRecognising the vital link between water and development, the international community has enshrined these issues in the Sustainable Development Goals (SDGs), specifically SDG 6: Clean Water and Sanitation. This goal aims to ensure the availability and sustainable management of water and sanitation for all by 2030. Achieving this ambitious target requires a detailed understanding of the localised factors that impede progress. Therefore, investigating how core socio-demographic variables, such as age, gender, residential location, education, and household wealth, influence access to and quality of drinking water in countries such as Mali is crucial for informing evidence-based policy and targeted interventions. The following research questions were formulated to guide the study\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIf there are differences in drinking water quality among the household members in urban and rural areas.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo measure if the types of drinking water facilities household utilise in Mali is impacted by their socio-demographic characteristics, such as age, educational attainment and wealth index.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCase study\u003c/h2\u003e \u003cp\u003eMali, a landlocked nation situated in West Africa, is classified as a developing country (D\u0026iacute;az-Alcaide et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Keita \u0026amp; Zhonghua, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Leuprecht \u0026amp; Roseberry, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, despite its vast geographical size, the country is characterised by high population density in certain areas. Economically, Mali faces significant challenges, remaining one of the world's poorest countries (Toure, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ortiz-Correa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Leuprecht \u0026amp; Roseberry, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates Mali, which shares extensive borders with Algeria, Burkina Faso, Guinea, C\u0026ocirc;te d'Ivoire, Mauritania, Niger, and Senegal. The country is predominantly rural (Toure \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The country is experiencing rapid demographic growth, with the population estimated at 23,769,127 in 2023. This figure is projected to increase substantially, rising 94% to 46,154,079 inhabitants by 2050 (WHO, 2024).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource\u003c/b\u003e: Demographic and Health Survey \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003cb\u003e-24 report.\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com/pubs/pdf/FR394/FR394.pdf\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com/pubs/pdf/FR394/FR394.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescription of variables\u003c/h3\u003e\n\u003cp\u003eA range of water supply technologies is employed globally to provide drinking water. These sources are broadly categorised by their structural safety and their potential to deliver potable water (WHO \u0026amp; UNICEF, 2022). The specific types include piped water into the dwelling, communal/public standpipes, boreholes, protected dug wells, protected springs, packaged or delivered water, and rainwater collection. Conversely, sources designated as high-risk include unimproved dug wells, unprotected springs, water collected in carts with small tanks or drums, tanker trucks, and surface waters (such as rivers, dams, lakes, ponds, streams, canals, or irrigation channels).\u003c/p\u003e \u003cp\u003eThese sources are assessed for suitability for human consumption according to two standard classifications established by the World Health Organisation (WHO) and UNICEF: improved water sources and unimproved drinking water sources.\u003c/p\u003e \u003cp\u003eAccording to the WHO/UNICEF Joint Monitoring Programme (JMP) definition (2022), improved drinking water sources are those that are designed to deliver safe water inherently, characterised by being \"accessible on premises, available when needed, and free from contamination.\" Conversely, unimproved drinking water sources are inherently vulnerable to external contamination. Specifically, these include unprotected dug wells, unprotected springs, water supplied by vendors, and all forms of surface water.\u003c/p\u003e \u003cp\u003eThe following variables were included in the analyses: Drinking water sources were categorised into two level (improved and unimproved drinking water sources), age ( 15-24years, 24\u0026ndash;34 years, 35\u0026ndash;44 years, 44\u0026ndash;54 years, 55+), gender (males and females), residential place (urban and rural areas), educational attainment (no school/preschool, primary/ secondary education, higher education and unspecified), wealth index (poor, middle and rich).\u003c/p\u003e\n\u003ch3\u003eSource of data used and study participants\u003c/h3\u003e\n\u003cp\u003eData for this study were drawn from two successive, nationally representative household surveys: the 2018 and 2023\u0026ndash;2024 Demographic and Health Surveys (DHSs). The 2018 DHS collected data between August and November 2018. The subsequent 2023\u0026ndash;2024 DHS field collection phase ran from November 27, 2023, to February 29, 2024.\u003c/p\u003e \u003cp\u003eThese surveys utilised a sampling frame of randomly selected households, yielding a total sample of 9,510 household members in 2018 and 9,667 in 2023\u0026ndash;2024. The overall study included both male and female respondents. The questionnaire covered essential demographic and socioeconomic indicators, including age, educational attainment, household income, gender, residential location, and sources of household drinking water. The subsequent analysis employed the collected household-level dataset.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe methodology employed in this research utilised an analytical approach, specifically involving descriptive analysis, bivariate analysis (Chi-square testing), and multivariate analysis (Binary Logistic Regression), to investigate the factors influencing access to household drinking water thoroughly. The foundation of this study rests on secondary data from the nationally representative Mali Demographic and Health Survey (DHS), incorporating datasets from both the 2018 and the 2023-24 surveys.\u003c/p\u003e \u003cp\u003eInitially, the study performed a descriptive (univariate) analysis to summarise the basic characteristics and distribution of the study population drawn from the Mali DHS. Providing a comprehensive overview of the key socio-economic and demographic variables, such as age groups, gender, residential location, educational attainment, and wealth index, as well as the distribution of the primary outcome variable: access to improved vs. unimproved drinking water sources. The analysis was conducted to identify the underlying patterns and trends in the Mali context.\u003c/p\u003e \u003cp\u003eFollowing the descriptive analysis, the research moved to bivariate analysis to examine the relationship between the independent variables (socio-economic and demographic characteristics) and the primary dependent variable (access to household drinking water). Chi-square test was employed to determine whether a statistically significant association existed between each explanatory variable (e.g., educational level, wealth status) and the drinking water source utilised by the household members. A significant Chi-square result (typically \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicated that the distribution of drinking water access was dependent on the independent variable's category, establishing an association between the dependent and independent variables.\u003c/p\u003e \u003cp\u003eThe final stage involved a multivariate analysis using Binary Logistic Regression. This advanced statistical technique was employed to identify the determinants of access to improved drinking water while controlling for the influence of all other variables in the model. Binary Logistic Regression was specifically chosen because the outcome variable (access to drinking water) was dichotomous (improved and unimproved). The analysis generated Odds Ratios (ORs), which provided essential insights into the likelihood that households had access to improved or unimproved drinking water for each independent factor. By presenting these odds ratios, the study could identify the most influential socio-economic and demographic factors, such as specific age groups, residential location, or wealth quintiles, that significantly predicted the probability of utilising safe and reliable drinking water sources in Mali.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe data below, in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1.1\u003c/span\u003e, show a positive trend in access to improved drinking water sources in Mali between 2018 and 2023\u0026ndash;2024. The proportion of the population accessing improved drinking water sources increased substantially from 69.4% in 2018 to 81.4% in 2023\u0026ndash;2024. This represents a 12% increase. Correspondingly, the reliance on unimproved drinking water sources dropped significantly, falling from 30.6% in 2018 to 18.6% in 2023\u0026ndash;2024.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFrequency distribution of socio-demographic variables.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the frequency and percentage distributions of demographic and socioeconomic characteristics across two survey periods (2018 and 2023\u0026ndash;2024), with slightly different total sample sizes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9510 in 2018 and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9667 in 2023\u0026ndash;2024). The population appears to be slightly older, and the proportion of younger age groups has decreased. The youngest group (15\u0026ndash;24) significantly dropped from 4.5% to 2.8%, and the 25\u0026ndash;34 group also saw a substantial decrease (from 22.1% to 15.6%). Conversely, the proportion of older individuals increased. The 55\u0026thinsp;+\u0026thinsp;age group became the largest category, increasing from 29.9% to 35.0%. The 45\u0026ndash;54 group also increased (from 19.3% to 22.0%). The 35\u0026ndash;44 age group remained relatively stable in Mali (24.2% to 24.7%).\u003c/p\u003e \u003cp\u003eThere was a noticeable shift toward a higher proportion of males in the sampled population. The percentage of males increased from 81.7% to 86.2% while the percentage of females decreased from 18.3% to 13.8%.\u003c/p\u003e \u003cp\u003eThe distribution between urban and rural areas remained highly consistent. Both urban (31.0% to 30.1%) and rural (69.0% to 69.9%) proportions remained nearly identical across the two periods.\u003c/p\u003e \u003cp\u003eThere is an overall trend towards higher educational attainment. The largest category, no schooling/preschool, decreased from 69.4% to 65.2%. Both the Primary/secondary school group (25.9% to 27.7%) and the Higher education group (4.0% to 5.7%) saw increases.\u003c/p\u003e \u003cp\u003eThe wealth distribution shows a shift from the poor category to the middle category, while the rich category slightly increased its dominance. The proportion categorised as poor decreased substantially from 38.3% to 29.2%. The Middle wealth category rose significantly, from 19.6% to 27.5%. The proportion of the rich category slightly increased, from 42.1% to 43.3%, maintaining its status as the largest wealth group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive analysis of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023\u0026ndash;2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9510\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9667\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (4,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (2,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2102 (22,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1508 (15,6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2305 (24,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2389 (24,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1833 (19,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2124 (22,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2844 (29,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3379 (35,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7769 (81,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8333 (86,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1741 (18,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1334 (13,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential place\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2945 (31,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2912 (30,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6565 (69,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6755 (69,9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo schooling/preschool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6602 (69,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6305 (65,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary/secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2462 (25,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2681 (27,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376 (4,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e547 (5,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (1,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3642 (38,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2820 (29,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1861 (19,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2661 (27,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4007 (42,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4186 (43,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAge\u003c/h2\u003e \u003cp\u003eThere was a significant improvement in access to improved drinking water sources across all age groups from 2018 to 2023\u0026ndash;2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1.2\u003c/span\u003e). Access to improved sources generally peaked among middle-aged women (35\u0026ndash;44 years: 72.1% and 45\u0026ndash;54 years: 71.2%) and was lowest among the youngest (15\u0026ndash;24 years: 65.3%) and (25\u0026ndash;34 years: 67.6%) in 2018. The Chi-square test\u0026thinsp;=\u0026thinsp;19.812, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicates a highly significant relationship between age group and access to drinking water sources in 2018.\u003c/p\u003e \u003cp\u003eAge remains a highly significant determinant, Chi-square\u0026thinsp;=\u0026thinsp;17.798, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in 2023\u0026ndash;2024. While all groups improved, the overall distribution changed. The 25\u0026ndash;34 and 35\u0026ndash;44 age groups demonstrated the highest access rates (83.1% and 83.6%, respectively). The 15\u0026ndash;24, 55+, and 45\u0026ndash;54 groups showed slightly lower, but still very high, access (79.8%, 79.8%, and 80.6%, respectively). The gap between the highest-and lowest-accessing age groups narrowed significantly compared to 2018, suggesting a more equitable distribution of improved water access.\u003c/p\u003e \u003cp\u003eConversely, trends in unimproved drinking water access show a significant reduction in reliance on unimproved sources across all age groups between 2018 and 2023\u0026ndash;2024. Unimproved sources were highest among (34.7%) the youngest group, 15\u0026ndash;24, and lowest (27.9%) among the 35\u0026ndash;44 age group in 2018. By 2023\u0026ndash;2024, reliance decreased in all groups, but the patterns of highest and lowest reliance shifted slightly. The 15\u0026ndash;24 and 55\u0026thinsp;+\u0026thinsp;age groups share the highest reliance on unimproved sources (20.2% each). The 35\u0026ndash;44 age group continued to show the lowest reliance (16.4%), closely followed by the 25\u0026ndash;34 age group (16.9%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHousehold gender.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe dataset below (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1.3\u003c/span\u003e) shows the distribution of access to improved and unimproved drinking water sources, categorised by gender, for two time periods: 2018 and 2023-24. The data indicate a decrease in the percentage of both males and females relying on unimproved drinking water sources between 2018 and 2023-24. They experienced a substantial reduction (over 12 percentage points). In 2018, males had a slightly higher proportion with access to unimproved drinking water (30.9%) than females (29.3%). By 2023-24, females had a slightly lower proportion of access to unimproved drinking water (16.9%) than males (18.8%).\u003c/p\u003e \u003cp\u003eThe Chi-square test was used to examine the relationship between gender and the drinking water source for both years: The \u003cem\u003ep\u003c/em\u003e-value was greater than 0.184. This indicates that the observed difference in access to drinking water sources between males and females was not statistically significant in 2018.\u003c/p\u003e \u003cp\u003e2023-24: The \u003cem\u003ep\u003c/em\u003e-value was greater than 0.087. This suggests that the relationship between gender and the type of drinking water source remained statistically insignificant in 2023-24. Both genders saw a significant increase in access to improved drinking water between 2018 and 2023-24. There was an increase in males from 69.1% (2018) to 81.2% (2023-24), and in females from 70.7% (2018) to 83.1% (2023-24). In both periods, females reported slightly higher access to improved drinking water than males. The overall reliance on unimproved drinking water sources decreased significantly for both genders; the data suggest that gender itself does not significantly determine the type of drinking water source utilised in the households in either year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResidential location\u003c/h3\u003e\n\u003cp\u003eIn both periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1.4\u003c/span\u003e), urban residents had significantly greater access to improved drinking water sources than rural residents. In 2018, 82.4% of urban residents accessed improved sources, compared to 63.5% of rural residents, while in 2023-24, 95.4% of urban residents accessed improved sources, compared to 75.4% of rural residents.\u003c/p\u003e \u003cp\u003eRural residents relied much more on unimproved drinking water sources than urban residents. In 2018, the reliance on unimproved sources was 36.5% in rural areas compared to 17.6% in urban areas. The Chi-square test results indicate a highly statistically significant relationship between residential location and the drinking water source used in both years: 2018 (χ\u0026sup2; = 339.231) and 2023-24 (χ\u0026sup2; = 537.058), with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This demonstrates that access to improved drinking water is strongly dependent on whether a household is located in an urban or rural area. The study findings indicate that living in urban areas reduces the possibility of choosing unimproved drinking water. Both urban and rural areas saw substantial increases in access to improved drinking water between 2018 and 2023-24.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEducational attainment.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1.5\u003c/span\u003e illustrates that household members lacking formal education or preschool education continue to be the most vulnerable group, exhibiting the lowest percentage of improved water access in both years, despite notable progress over time: 63.7% in 2018 and 76.6% in 2023\u0026ndash;2024. Additionally, respondents with primary or secondary education in 2018 reported an 80.8% access to improved water sources, whereas 19.2% relied on unimproved sources. In 2023-24, 89.1% utilised improved drinking water, and reliance on unimproved sources dropped to 10.9%. This primary/secondary education group shows much stronger access than the 'No schooling/preschool group, approaching 90% access to improved sources by 2023-24.\u003c/p\u003e \u003cp\u003eAccess was very high at 91.2%, with only 8.8% using unimproved sources in 2018. Access increased further to 97.6%, achieving near-universal access to improved drinking water in 2023-24. Reliance on unimproved sources dropped dramatically to only 2.4%. Women with higher education consistently have better access to improved drinking water, highlighting education as a key factor in water utilisation. The data demonstrate a strong, statistically significant association between educational attainment and access to improved drinking water in both 2018 (χ\u0026sup2; = 348.455, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 2023-24 (χ\u0026sup2; = 302.532, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that the probability of a woman having access to improved drinking water increases significantly with her level of education, while reliance on unimproved drinking water sources decreases. Furthermore, there is a clear trend of overall improvement in access to improved drinking water across all educational levels between 2018 and 2023-24.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eWealth index\u003c/h3\u003e\n\u003cp\u003eIn 2018, less than half (46.8%) of impoverished households had access to improved drinking water, indicating that the majority (53.2%) depended on unimproved sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1.6\u003c/span\u003e). By 2023-24, access had significantly increased to 65.1%, with reliance on unimproved sources decreasing to 34.9%. Despite notable improvement, poor households remain the most disadvantaged, with the highest proportion of women still relying on unimproved water sources in both periods.\u003c/p\u003e \u003cp\u003eIn 2018, middle-income households utilising improved sources were 74.0%, and 26% used unimproved drinking water sources. On the other hand, in 2023-24, access increased to 77.6%, and 22.4% relied on unimproved drinking water sources. Middle-income households have significantly better access than poor households, but still lag behind the rich, highlighting the ongoing economic constraints on drinking water access.\u003c/p\u003e \u003cp\u003eImproved drinking water was extremely high at 87.8%, with only 12.2% using unimproved sources in 2018. Five years later, in 2023-24, access reached near-universal levels at 94.9%, with unimproved sources dropping to a minimal 5.1%. Rich households consistently enjoy the highest access to improved drinking water, confirming that wealth effectively mitigates the risk of utilising unsafe water sources. The high Chi-square values (1534.218 in 2018 and 1026.858 in 2023-24) and \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001 confirm an influential, statistically significant association between wealth status and the type of drinking water accessed.\u003c/p\u003e \u003cp\u003eThe data clearly establishes that the Wealth Index is a powerful and highly significant determinant of access to drinking water sources in both analysed periods (2018 and 2023-24). Households with greater wealth consistently show a much higher probability of accessing improved drinking water sources.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFactors associated with household access to drinking water.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below presents the results of a Binary Logistic Regression analysis for 2018, examining factors associated with access to improved and unimproved drinking water in Mali. The analysis includes various socioeconomic and demographic variables, with the reference category for each variable denoted by an \"@\" symbol. The table presents two parallel logistic regression models: one for access to improved drinking water and one for access to unimproved drinking water. The coefficient B, the Wald statistic, the \u003cem\u003eP\u003c/em\u003e-value, and the Exp(B) (Odds Ratio, OR) are reported for each variable category.\u003c/p\u003e \u003cp\u003eWealth index is the most significant factor determining access to drinking water, showing a highly significant association (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across both improved and unimproved sources. The reference category is 'Rich'. Poor households are significantly less likely to access improved drinking water, with an odds ratio (Exp(B)) of 0.314. Middle-income households are even less likely to access improved drinking water, with an odds ratio of 0.137. Poor and Middle-income households are drastically disadvantaged compared to rich households in accessing improved water sources. Unimproved drinking water poor households are 3.181 times more likely to access unimproved drinking water sources. Similarly, the middle-income households are 7.319 times more likely to access unimproved drinking water. The odds of utilising unimproved drinking water sources increase dramatically for those in the middle and poor wealth categories.\u003c/p\u003e \u003cp\u003eEducational level is significantly associated with water access (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The reference category is 'Unspecified'. Households with no school/preschool are less likely to access improved sources (OR\u0026thinsp;=\u0026thinsp;0.642). This is similar to households with primary/secondary school education being least likely to access improved sources among the specified groups (OR\u0026thinsp;=\u0026thinsp;0.497). Higher education household members show an odds ratio of 0.620, though this category is not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.219). Households with primary/secondary school education are 2.011 times more likely to use unimproved drinking water than the reference group. Likewise, households without a school/preschool are 1.558 times more likely to use unimproved sources.\u003c/p\u003e \u003cp\u003eAge groups show a highly significant association with access to drinking water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The reference category is '55+'. All specified age groups (25\u0026ndash;34, 35\u0026ndash;44, 45\u0026ndash;54) are less likely to access improved drinking water compared to the oldest group (55+). The 25\u0026ndash;34 group (OR\u0026thinsp;=\u0026thinsp;0.643), the 35\u0026ndash;44 group (OR\u0026thinsp;=\u0026thinsp;0.638) and the 45\u0026ndash;54 group (OR\u0026thinsp;=\u0026thinsp;0.650) have low odds of accessing improved water. For instance, households aged 35\u0026ndash;44 have an OR of 0.638, meaning they are about 36.2% less likely to access improved water than the 55\u0026thinsp;+\u0026thinsp;group. The 15\u0026ndash;24 group is not statistically significant in the model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.264).\u003c/p\u003e \u003cp\u003eConversely, these age groups (25\u0026ndash;34, 35\u0026ndash;44, 45\u0026ndash;54) are more likely to access unimproved drinking water (ORs ranging from 1.555 to 1.538). The 35\u0026ndash;44 group has the highest likelihood of accessing unimproved water, OR\u0026thinsp;=\u0026thinsp;1.569 (a 56.9% increase) of accessing unimproved water compared to women aged 55+\u003c/p\u003e \u003cp\u003eResidential place: There is no statistically significant difference in the odds of accessing improved or unimproved drinking water between urban and rural residents\u003c/p\u003e \u003cp\u003eGender: There is no statistically significant difference in the odds of accessing improved or unimproved drinking water between male and female-headed households\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of the 2018 Mali Demography and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eImproved drinking water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eUnimproved drinking water\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55+ @\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential place\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo school/preschool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary/secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e899.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e899.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e334.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e740.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e740.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFactors associated with household access to drinking water.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results for a binary logistic regression for improved and unimproved drinking water in Mali for the period 2023-24. The coefficient (B) and Odds Ratio (Exp(B)) for unimproved drinking water are the inverse of those for improved drinking water, confirming they are models of the inverse outcomes.\u003c/p\u003e \u003cp\u003eOverall Significance: The age variable group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), residential location, educational level and wealth index are statistically significant in the model. All age groups (25\u0026ndash;34, 35\u0026ndash;44, 45\u0026ndash;54) have significantly lower odds of accessing improved drinking water compared to the oldest group (55+). For example, household members aged 25\u0026ndash;34 (OR\u0026thinsp;=\u0026thinsp;0.641) are significantly less likely to access improved drinking water. The odds are reduced by approximately 35.9%. This group, 35\u0026ndash;44 years (OR\u0026thinsp;=\u0026thinsp;0.653), is also significantly less likely to access improved drinking water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), with odds reduced by about 34.7%. Individuals aged 45\u0026ndash;54 have an odds ratio of 0.595 for accessing improved water. This means they are about 40.5% less likely to access improved water compared to those aged 55+. Conversely, they are 1.682 times more likely to use unimproved water sources compared to the 55\u0026thinsp;+\u0026thinsp;group.\u003c/p\u003e \u003cp\u003eAccess to unimproved drinking water: 25\u0026ndash;34 years (OR\u0026thinsp;=\u0026thinsp;1.560) is significantly 56% more likely to access unimproved drinking water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). 35\u0026ndash;44 years households (OR\u0026thinsp;=\u0026thinsp;1.531) are 53.1% more likely to access unimproved drinking water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). This group of 45\u0026ndash;54 years (OR\u0026thinsp;=\u0026thinsp;1.682) is significantly 68.2% more likely to access unimproved drinking water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), showing the highest likelihood among all non-reference age groups.\u003c/p\u003e \u003cp\u003eResidential location is a highly statistically significant predictor (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Individuals residing in urban areas have 2.126 times the odds of accessing improved drinking water compared to those in rural areas; that is, they are more than twice as likely (112.6% increase in odds) to have access to clean water. This also means urban residents are significantly less likely to rely on unimproved water (OR\u0026thinsp;=\u0026thinsp;0.470), with the odds reduced by 53% compared to rural residents.\u003c/p\u003e \u003cp\u003eIndividuals with no school/preschool (OR\u0026thinsp;=\u0026thinsp;0.624) are less likely to access improved drinking water than the reference group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with odds reduced by 37.6%. Primary/secondary school households (OR\u0026thinsp;=\u0026thinsp;0.315) are significantly much less likely to access improved drinking water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with odds reduced by 68.5%. Higher education group (OR\u0026thinsp;=\u0026thinsp;0.758) is not statistically significant. No school/preschool (OR\u0026thinsp;=\u0026thinsp;1.602) is significantly 60.2% more likely to utilise unimproved drinking water (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Primary/secondary school (OR\u0026thinsp;=\u0026thinsp;3.179) is over three times more likely (217.9% increase in odds) to access unimproved drinking water (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWealth is the strongest predictor of access to drinking water. Poor households are about 45.1% less likely (OR\u0026thinsp;=\u0026thinsp;0.549) to utilise improved water than rich households. On the other hand, poor households are 1.821 times more likely to access unimproved water. Middle-income households are dramatically disadvantaged, being about 82.9% less likely (OR\u0026thinsp;=\u0026thinsp;0.171) to access improved water than rich households. Middle-income (OR\u0026thinsp;=\u0026thinsp;5.835) are significantly more likely to access unimproved water.\u003c/p\u003e \u003cp\u003eGender is not a statistically significant predictor of access to unimproved drinking water (p\u0026thinsp;=\u0026thinsp;0.477). Gender does not appear to be a significant determinant of access to drinking water in this model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of the 2023-24 Mali Demography and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e2023-24\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eImproved drinking water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eUnimproved drinking water\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55+ @\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential place\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo school/preschool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary/secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e350.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e337.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich@\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eAuthor\u0026rsquo;s compilation using the Mali Demographic and Health Survey 2018 and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e-24 dataset.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary objective of this study was to investigate the sociodemographic factors influencing household access to and choice of drinking water quality in Mali. This quantitative study utilised secondary data from two nationally representative datasets: the 2018 and 2023\u0026ndash;2024 Demographic and Health Surveys (DHSs). The analysis was restricted to household members aged 15 and above. Statistical analysis was performed using SPSS version 30. We employed cross-tabulation and the Chi-square test to determine the relationship between access to drinking water quality and key variables, including age group, gender, residential location, educational attainment, and wealth index. Furthermore, binary logistic regression was utilised to assess the independent factors affecting the quality of drinking water accessed by household members.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Key Findings\u003c/h2\u003e \u003cp\u003eConsistent with prior research, our findings confirm a significant influence of socio-demographic variables on access to drinking water quality among household members. The overall statistical models demonstrated strong explanatory power. Specifically, age, educational level, residential location, and the wealth index emerged as the most significant determinants of household drinking water choice in both the 2018 and 2023\u0026ndash;2024 surveys (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results strongly underscore existing disparities in access to and utilisation of quality drinking water across the Malian population.\u003c/p\u003e \u003cp\u003eFurther analysis focusing on the age variable reveals that access to improved drinking water remained nearly identical across both survey periods (2018 and 2023\u0026ndash;2024). Specifically, household members in the middle-aged categories (25\u0026ndash;34, 35\u0026ndash;44, and 45\u0026ndash;54) consistently showed a statistically significant Odds Ratio (OR) of less than 1 for the likelihood of accessing improved drinking water. This finding indicates that, relative to the reference group (55+), individuals in these economically active age cohorts are less likely to utilise improved sources. The influence of these age categories on unimproved water access remains consistently positive and statistically significant across both the 2018 and 2023\u0026ndash;2024 time periods. Our findings highlight the significance of age in the utilisation of drinking water sources in Mali.\u003c/p\u003e \u003cp\u003eA significant finding throughout the survey periods is the changing significance of residential location as a determinant of access to improved drinking water quality. The factor representing urban residence transitioned from being statistically non-significant in the 2018 model to emerging as a robust, positive, and statistically significant predictor in the 2023\u0026ndash;2024 analysis. This notable shift strongly suggests that infrastructure investment and expansion of water service delivery have been disproportionately concentrated in urban areas over the intervening period. This prioritisation has successfully augmented the likelihood of improved water access for urban populations, but has simultaneously exacerbated the disparity in access between urban and rural populations.\u003c/p\u003e \u003cp\u003eConsequently, rural households continue to face systemic challenges in accessing and maintaining improved drinking water sources. Rural communities are frequently characterised by underdevelopment, insufficient maintenance capacity, and inadequate service infrastructure, leading to a persistent deficit in improved water availability (Mahama et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dongzagla et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sani \u0026amp; Scholz, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our observations suggest an urgent need for targeted educational interventions among Mali\u0026rsquo;s rural population on the critical importance of utilising and maintaining clean drinking water sources at the household level to ensure maximum safety.\u003c/p\u003e \u003cp\u003eAchieving the ambitious targets outlined in the Sustainable Development Goals (SDGs), particularly SDG 5 (Gender Equality, often linked to water access responsibility) and SDG 6 (Clean Water and Sanitation), requires eliminating the established inequalities in the utilisation of clean drinking water necessitates, as a crucial initial measure, ensuring equitable access to safe and improved sources across all residential strata (Patel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dania et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, future policy and infrastructure initiatives must be deliberately focused on closing the infrastructural gap currently observed between urban and rural areas.\u003c/p\u003e \u003cp\u003eThe study findings underscore a critical relationship between household educational attainment and the utilisation of unimproved drinking water sources. Specifically, the analysis reveals that lower levels of household education are statistically and positively associated with the reliance on unimproved water intake. This association was most pronounced across households reporting no formal or preschool education and those with only primary or secondary education as their highest level of attainment.\u003c/p\u003e \u003cp\u003eConsequently, targeted public health interventions are imperative. Informing households, particularly those with minimal or less formal education, about the superior health outcomes and disease prevention associated with the use of clean drinking water is essential for effectively boosting the adoption and sustained use of improved water sources (Kausar et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This educational gap represents a key area for policy focus. As Ortiz-Correa and co-authors (2016) meticulously documented, variability or inadequacy in access to safe drinking water can significantly diminish school enrolment rates among young people. The heightened exposure to waterborne diseases resulting from unimproved water usage is a primary driver of chronic illness, leading to frequent school absenteeism and, consequently, hindering academic performance and educational attainment.\u003c/p\u003e \u003cp\u003eThe analysis of the household wealth index across the two study periods, 2018 and 2023\u0026ndash;24, demonstrated a profound and persistent socioeconomic disparity concerning access to safe drinking water in Mali. Our study findings robustly indicate that households in the lower wealth quintiles (poor and middle-income) exhibited significantly higher reliance on unimproved drinking water sources during both observation periods. Conversely, the wealthiest households (higher-income quintiles) demonstrated a markedly greater propensity to access and utilise improved water infrastructure. Income levels, as one of the most critical socioeconomic variables, directly determine a household's capacity to access clean water. Matos and colleagues (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) highlighted, disparities in wealth are expected to heavily influence patterns of water utilisation, dictating whether families can afford to connect to municipal systems, purchase bottled water, or invest in reliable household water. The economic limitations faced by poor and middle-income households restrict their choices, often forcing them to rely on unclean sources (such as unprotected wells and water from public transport vendors) that pose significant health risks. This creates a clear cycle of vulnerability. Furthermore, the findings of Kausar and co-authors (2011) reinforce this disparity, reporting that higher-income households inherently possess greater opportunities to improve their drinking water quality by accessing superior infrastructure. Indicating that wealthier households are better positioned geographically.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and Recommendation","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cp\u003eIt is essential to understand the intricate pathways through which key sociodemographic factors, including age, gender, residential location, educational attainment, and household wealth, actively influence and stratify household water-use patterns. Our study observed an encouraging trend: the population's overall access to improved drinking water sources demonstrably increased between the 2018 baseline and the 2023\u0026ndash;2024 period. This increase is reflected in the corresponding decrease in the number of people exposed to unimproved drinking water sources in Mali during the same timeframe. This dual shift indicates substantive progress toward achieving the targets set forth by Sustainable Development Goal (SDG) 6 (Clean Water and Sanitation).\u003c/p\u003e \u003cp\u003eHowever, while the proportion of households utilising improved drinking water sources has increased, a critical challenge remains: the persistence of significant disparities and the ongoing need to ensure a safe, sufficient, and equitable water supply, particularly for vulnerable rural communities, to preserve health and reduce the incidence of waterborne diseases effectively. Crucially, the analysis reveals that the use of unimproved drinking water remains highly prevalent within specific demographic segments in our study area. This risk is disproportionately concentrated among poor households, those households without formal education, and, most notably, rural households.\u003c/p\u003e \u003cp\u003eTherefore, we recommend further findings through additional, geographically focused provincial research studies. By understanding how households currently manage their drinking water, policymakers can accelerate the impact of interventions. Enhanced access to and utilisation of clean, reliable water infrastructure are fundamental drivers that will demonstrably accelerate improvements in overall community wellness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (The study does not require ethical clearance or approval as it utilises secondary data collected by the Demographic and Health Survey (DHS). Consequently, DHS has addressed all ethical clearance considerations for its users.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data was obtained from the DHS website following a formal request for access through https://dhsprogram.com/data/using-datasets-for-analysis.cfm. Permission was subsequently granted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.A. wrote the entire manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCalkins P, Larue B, V\u0026eacute;zina M. Willingness to pay for drinking water in the Sahara: The case of Douentza in Mali. 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State of the world\u0026rsquo;s drinking water: an urgent call to action to accelerate progress on ensuring safe drinking water for all. Geneva: World Health Organisation; 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240060807\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240060807\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed the 19th December 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organisation. (2023-24) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://srhdpeuwpubsa.blob.core.windows.net/whdh/DATADOT/COUNTRY/PDF/466_Mali.pdf\u003c/span\u003e\u003cspan address=\"https://srhdpeuwpubsa.blob.core.windows.net/whdh/DATADOT/COUNTRY/PDF/466_Mali.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed on the 18th of December 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Drinking water types, households, socio-demographic characteristics, secondary data, inequality, Mali","lastPublishedDoi":"10.21203/rs.3.rs-8541068/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8541068/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe lack of access to clean drinking water is a leading global health concern and a critical challenge in many African societies. Access to unimproved drinking water sources is intrinsically associated with poverty and a lack of basic household resources, often contributing indirectly to broader social issues, such as school dropout and incomplete educational attainment. Mali remains one of the most affected countries, struggling to meet the essential needs of its population, particularly the availability and sustained access to clean drinking water. Understanding the demographic and socioeconomic factors affecting access to drinking water is highly significant for achieving Sustainable Development Goals (SDGs) 3: Good health and well-being and 6: Clean water and sanitation. While some previous studies have focused on broad drinking water coverage, fewer have investigated the specific factors influencing the quality and household-level access to improved sources.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate the direct factors affecting household access to improved drinking water quality. Using two successive nationally representative surveys, the 2018 and 2023\u0026ndash;2024 Mali Demographic and Health Surveys (DHS), the study analyses trends in access to various drinking water sources by comparing data across the two periods. Descriptive statistics and logistic regression were employed to analyse relationships among age, gender, residential location, educational level, and household wealth index, to identify variations in drinking water access and to assess the contribution of changes in socio-demographic characteristics over time.\u003c/p\u003e \u003cp\u003eThe results revealed significant shifts in drinking water patterns between 2018 and 2023\u0026ndash;2024, particularly highlighting widening urban-rural disparities and persistent inequality within the wealth index quintiles. The findings emphasise the vital role of socio-demographic factors in determining access to essential infrastructure and highlight the need for targeted policies to support vulnerable populations, especially those in rural areas and the poorest wealth quintiles, to improve water quality and work toward achieving the SDG goals for sanitation and health.\u003c/p\u003e","manuscriptTitle":"Trends in Water Service Equity in Mali: Measuring the Change in Socio-Demographic Predictors of Improved Drinking Water Access Between the 2018 and 2024 Demographic and Health Surveys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 17:01:12","doi":"10.21203/rs.3.rs-8541068/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T15:27:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T08:35:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45276728859005029321222190850311669783","date":"2026-02-14T10:50:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253993139339746397279576579236164780005","date":"2026-02-11T16:41:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T21:15:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255835807543299109000482587801579604217","date":"2026-01-25T00:30:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75698403020081988022411356052661043325","date":"2026-01-23T01:30:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T15:10:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T15:30:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T00:55:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T00:54:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-07T11:37:13+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":"e19c0341-0f6b-4e76-8e5b-cc5d0df9f3e4","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T13:39:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 17:01:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8541068","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8541068","identity":"rs-8541068","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}