Clean Drinking Water in Africa Peer Reviewed Article
Heliyon. 2018 Nov; 4(eleven): e00931.
Access to improved h2o and sanitation in sub-Saharan Africa in a quarter century
Frederick Ato Armah
aDepartment of Ecology Scientific discipline, School of Biological Sciences, Higher of Agriculture and Natural Sciences, University of Greatcoat Declension, Ghana
Bernard Ekumah
aDepartment of Environmental Scientific discipline, School of Biological Sciences, College of Agriculture and Natural Sciences, Academy of Cape Declension, Ghana
David Oscar Yawson
aDepartment of Ecology Science, School of Biological Sciences, College of Agriculture and Natural Sciences, University of Greatcoat Coast, Ghana
Justice O. Odoi
bNature Today, P. O. Box OS 1455, Osu-Accra, Republic of ghana
Abdul-Rahaman Afitiri
aSection of Environmental Science, School of Biological Sciences, College of Agriculture and Natural Sciences, University of Cape Declension, Republic of ghana
Florence Esi Nyieku
cRegional Water and Environmental Sanitation Centre Kumasi (RWESCK), Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Republic of ghana
Received 2018 Aug 19; Revised 2018 Oct 9; Accepted 2018 Nov 8.
Abstruse
The realization of the calibration, magnitude, and complexity of the water and sanitation problem at the global level has compelled international agencies and national governments to increase their resolve to face up the challenge. There is extensive evidence on the independent effects of urbanicity (rural-urban surround) and wealth status on access to water and sanitation services in sub-Saharan Africa. Still, our understanding of the joint effect of urbanicity and wealth on access to water and sanitation services across spatio-temporal scales is nascent. In this written report, a pooled regression analysis of the compositional and contextual factors that systematically vary with access to water and sanitation services over a 25-year fourth dimension period in 15 countries across sub-Saharan Africa (SSA) was carried out. On the whole, substantial improvements have been made in providing access to improved h2o sources in SSA from 1990 to 2015 unlike access to sanitation facilities over the same period. Households were 28.two percentage and 125.2 percent more likely to have access to improved h2o sources in 2000–2005 and 2010–2015 respectively, than in 1990–1995. Urban rich households were 329 percentage more probable to have admission to improved water sources compared with the urban poor. Although admission to improved sanitation facilities increased from 69 percent in 1990–1995 and 74 per centum in 2000–2005 it declined significantly to 53 percent in 2010–2015. Urban rich households were 227 percent more likely to have access to improved sanitation facilities compared with urban poor households. These results were mediated and adulterate by biosocial, socio-cultural and contextual factors and underscore the fact that the challenge of access to h2o and sanitation in sub-Saharan Africa is non only scientific and technical merely interwoven with environment, civilisation, economics and human behaviour necessitating the need for interdisciplinary research and policy interventions.
Keywords: Public health, Environmental science, Geography
one. Introduction
Access to improved h2o and sanitation are central human rights and basic to the health of every person, nonetheless many people effectually the world do non accept admission to these bones needs (WHO/UNICEF, 2006). People who are deprived of access to improved h2o and sanitation services face macerated opportunities to realize their potential (Watkins, 2006). Unimproved drinking water and sanitation are the earth'southward 2d biggest killer of children (Watkins, 2006). Approximately 10,000 people die every day from h2o- and sanitation-related diseases, and thousands more suffer from a range of debilitating illnesses (World Bank, 2003). Access to improved h2o sources and improved sanitation significantly reduce water-borne diseases (Armah, 2014; Pullan et al., 2014).
In 1976, the Un Conference on Human Settlements launched the International Drinking Water Supply and Sanitation Decade (1981–1990), which provided recommendations for urgent activeness on programmes to raise the quality and quantity of water supplies for urban and rural areas by 1990. This led to a commitment to improve water supply and sanitation coverage for the disadvantaged people lacking such services. A broad spectrum of depression-cost water and sanitation options were applied in the course of the decade (Najlis and Edwards, 1991). Nonetheless past 2013, approximately 1.3 billion people in the developing earth lacked admission to adequate quantities of clean h2o, and nigh iii billion people were without adequate sanitation services (Bosch et al., 2002).
As a sequel, the Millennium Development Goals (MDGs) ended in 2015 with significant progress in access to improved drinking water. The global target for drinking h2o was met in 2010 giving 91 percentage of the global population access to improved drinking water as compared to 76 percent in 1990 (Mulenga et al., 2017). By 2015, the Progress Report on Drinking Water, Sanitation and Hygiene (2017) of the World Health Organization (WHO) and the United Nations Children'due south Fund (UNICEF) indicated that 71 percentage of the global population representing 5.2 billion used a safely managed drinking water service; that is, 1 located on premises, bachelor when needed and free from contamination. The written report further indicates that one out of three people using safely managed drinking water services (1.nine billion) lived in rural areas.
The 2015 MDG assessment revealed that five developing regions met the target, but the Caucasus and Key Asia, Northern Africa, Oceania and sub-Saharan Africa (SSA) failed to meet the target (World Health System WHO/UNICEF Joint Water Supply and Sanitation Monitoring Programme, 2015). Global coverage of access to basic sanitation services is lower equally compared to safe drinking water. The MDG target for sanitation was not met with 68 percentage of the global population currently using an improved sanitation facility which is an improvement over the 1990 figure of 54 percent (WHO/UNICEF Joint H2o Supply and Sanitation Monitoring Programme, 2015). Even though there has been significant progress with regards to admission to improved drinking water and sanitation, there are big disparities among countries, within countries and betwixt gender (Osei et al., 2015; UNICEF, 2016).
In order to address the disparities in access to h2o and sanitation, the Sustainable Development Goal (SDG) 6 attempts to accomplish universal and equitable access to improved drinking h2o and sanitation for all by 2030. It is important to track inequalities in admission to drinking water and sanitation in order to appraise progress with regards to universal coverage. The SDGs deal with inequalities, with Goal x aimed at reducing inequalities between and within countries. The Joint Monitoring Plan (JMP) annual reports continually highlight inequalities betwixt rural and urban areas, between rich and poor and between other groups. The 2030 Calendar further commits Member States to 'leave no one backside' and states that SDG indicators should be disaggregated, where necessary, by income, sexual practice, historic period, race, ethnicity, migratory status, inability and geographic location.
Sub Saharan Africa (SSA) is 1 of the regions with low levels of coverage (WHO/UNICEF Joint Water Supply and Sanitation Monitoring Programme, 2015). SSA, like other least developed regions, did not meet the MDG target but progressed during the MDG menses, with 42% of its current population gaining access to improved drinking water since 1990 (WHO/UNICEF Joint Water Supply and Sanitation Monitoring Programme, 2015). The region accomplished a xx percent indicate increase in the use of improved sources of drinking water (United nations – UNICEF, 2015). The population of SSA doubled during the MDG period (1990–2015). Nevertheless, access to improved sanitation facilities increased by merely six percentage points during the same period (WHO/UNICEF Joint Water Supply and Sanitation Monitoring Plan, 2015).
From a spatio-temporal perspective (beyond countries and over time), at that place is picayune empirical evidence on household level factors that systematically and jointly influence access to improved water and sanitation services and which of these factors transcend geopolitical boundaries. Overall, there is likewise incomplete understanding of how to incorporate initiatives at the household level into a wider planning and support framework in sub-Saharan Africa. Consolidating information on such factors would be relevant both nationally and regionally for policymaking. In this study, we conducted a pooled analysis of multi-state information to assistance in the estimation of circuitous, contradictory and quickly changing social contexts related to the water and sanitation problems in SSA. In item, this report assessed household trends in admission to improved water and sanitation for the past 25 years and evaluated the combined effect of relative residential well-existence on access to improved water sources and sanitation facilities in sub Saharan Africa to inform policy and intervention pattern. H2o and sanitation interventions tin be strengthened or undermined by factors that assist or hinder access to safety h2o and acceptable sanitation. Insight into factors that affect admission to safety water and acceptable sanitation tin can help diverse stakeholders to develop and implement solutions in SSA.
2. Materials and methods
2.one. Data source
This study uses nationally representative household survey information from Demographic and Wellness Surveys (DHS) for selected sub Saharan African (SSA) countries. DHS data are secondary data which provide several indicators for monitoring and impact assessment in the areas of population, health, and diet. DHS data are open source and can be accessed on DHS website (world wide web.dhsprogram.com). The questionnaire of the DHS are standardized and pre-tested to ensure comparability across populations and over time. One important advantage of the DHS data is the vastness of information that are nerveless including demographic, social, wealth and wellness attributes and allow for in-depth analysis of the information, that goes beyond the count of prevalence and examine complex causal relationships or associations betwixt social characteristics and wellness (Corsi et al., 2012). Water and sanitation data are collected at the household level in the DHS. The surveys are based on probability sampling using existing sampling frames primarily, population censuses. The selection criteria for including a country in this study were as follows: (i) the country should exist found in SSA based on the United nations regional groupings; (ii) should have DHS dataset with standardised questions on sources of drinking water and type of toilet facility at the household level; (3) should have datasets in all the iii-timeframes for the written report (i.e. 1990–1995, 2000–2005, and 2010–2015) (iv) should contain information on size of population without admission to improved h2o sources and sanitation facilities.
2.ii. Study countries
A total of 15 countries in SSA met the criteria (see Fig. 1). Where multiple datasets were available for onetime frame for the aforementioned country, the most recent survey was used (run across Table 1). Table one gives detail information on countries included in this study and the year of bachelor information.
The selected study countries in sub-Saharan Africa.
Table ane
Study country and available dataset.
| Country | Available Dataset |
|---|---|
| Senegal | 1992–1993, 2005, 2010–2011 |
| Cote d'Ivoire | 1994, 2005, 2011–2012 |
| Republic of cameroon | 1991, 2004, 2011 |
| Ghana | 1993, 2003, 2014 |
| Kenya | 1993, 2003, 2014 |
| Madagascar | 1992, 2003–2004, 2011 |
| Mali | 1995, 2001, 2012–2013 |
| Malawi | 1992, 2004, 2015 |
| Namibia | 1992, 2000, 2013 |
| Rwanda | 1992, 2000, 2014–2015 |
| Burkina Faso | 1993, 2003, 2010 |
| Tanzania | 1992–1993, 2004–2005, 2010 |
| Uganda | 1995, 2000–2001, 2011 |
| Zambia | 1992, 2001–2002, 2013–2014 |
| Zimbabwe | 1994, 2005, 2015 |
2.three. Definitions of improved and unimproved water sources and sanitation facilities
The WHO/UNICEF Articulation Monitoring Plan (JMP) 2017 report has reviewed the definition of improved and unimproved water sources and sanitation facilities and has established boosted criteria relating to service levels. For drinking water, improved sources are those that take the potential to deliver safe h2o by nature of their design and construction. Co-ordinate to the report, an improved source should run into these three criteria: (i) information technology should be accessible on premises (2) water should be available when needed (3) the h2o supplied should be complimentary from contamination. Packaged water (bottled h2o and sachets of h2o) and delivered h2o are now classified as improved but these were previously considered as unimproved as a result of lack of information on accessibility, availability and quality.
For sanitation, improved facilities are those designed to hygienically carve up excreta from human being contact. The iii main criteria for having a safely managed sanitation service are: (i) treated and disposed of in situ (ii) stored temporarily and and so emptied, transported and treated off-site (three) transported through a sewer with wastewater and and so treated off-site. Some examples of improved water sources and sanitation facilities from WHO/UNICEF Joint Monitoring Plan (JMP) 2017 report are shown in Table ii.
Table 2
Definition of improved and unimproved facilities (WHO/UNICEF Articulation Water Supply and Sanitation Monitoring Program, 2017).
| Service | Improved | Unimproved |
|---|---|---|
| Drinking water sources | Piped water, boreholes or tubewells, protected dug wells, protected springs, rainwater, and packaged or delivered water. | Unprotected dug well, unprotected spring, river, dam, lake, pond, stream, canal and irrigation culvert |
| Sanitation facilities | Flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, composting toilets or pit latrines with slabs. | Pit latrines without a slab or platform, hanging latrines or bucket latrines and open up defecation. |
2.four. Measures
2.iv.one. Response variable
The response/dependent or event variables considered in this study were improved drinking water sources and improved sanitation facilities. Improved and unimproved water sources or sanitation facilities were represented every bit dichotomous variables, with'i' representing 'improved' and '0' representing 'unimproved', respectively for both water sources and sanitation.
ii.4.2. Key predictor variable
The predictor or independent variable was selected based on literature review, parsimony, practical significance and theoretical relevance. The predictor variable was derived from type of residence (rural–urban) and wealth status (poorer, poor, middle, rich and richer). The wealth index is a composite measure out of a household's cumulative living standard. The wealth index was calculated from data nerveless on ownership of durable avails, housing characteristics and access to services (Howe et al., 2009). Main components analysis (PCA) was used to assign the indicator weights. The wealth alphabetize places individual households on a continuous scale of relative wealth. The DHS separates all interviewed households into 5 wealth quintiles to compare the influence of wealth on various populations. For parsimony, the observations under poorer and poor were combined and recoded equally 'poor'. Observations under richer and rich were also combined and recoded every bit 'rich'. This produced the predictor variable called urbanicity wealth status with six mutually exclusive groups: the urban poor (poor households in urban areas), urban middle (heart quintile households in urban areas), urban rich (rich quintile households in urban areas), rural poor (poor households in rural areas), rural middle (middle quintile households in rural areas) and rural rich (rich households in rural areas).
2.iv.3. Compositional and contextual factors
Compositional factors refer to variables relating to the socio-demographic characteristics of individuals (Collins et al., 2017; Political leader and Thomas, 2000). Compositional factors comprise biosocial and socio-cultural factors. Biosocial characteristics are factors with an underlying biological or physical component which are characteristics present at birth and non amenable to change (Politician and Thomas, 2000). Socio-cultural factors are community, behavior, lifestyles and values. In this study, the compositional factors included gender of household head (male person or female), age of household (immature adult: below 35years, centre-age adult: 35–55 years, erstwhile historic period adult: above 55 years), household size (small: 1–v, medium: six–10, large: above 10), level of instruction of household head (no didactics/preschool, principal, secondary, college). Contextual factors are defined as the broader neighbourhood attributes or location-specific opportunities in a region, such as availability of and access to services (Collins et al., 2017; Ross and Mirowsky, 2008). In this study, the contextual factors considered were land and year (1990–1995, 2000–2005, 2010–2015).
2.4.four. Data analysis
All statistical analyses were performed in STATA 13 MP (StataCorp, College Station, TX, USA). Descriptive assay was carried out to draw the status and trend of access to improved water sources and sanitation facilities over 25 year period in the study countries and the type of residence (urban/rural). Inferential and multivariate techniques were used to assess associations between the admission to improved water and sanitation and residential wellbeing (urbanicity wealth) status of households while controlling for theoretically relevant compositional factors (biosocial, socio-cultural) and contextual factors (year, land).
2.4.five. Univariate analysis
Univariate analysis of predictors of access to improved h2o and sanitation was carried out using Pearson chi-square and Cramer's V statistic. Pearson chi-foursquare was used to guess associations between chiselled variables. The chi-foursquare examination of independence is a nonparametric statistical test that is used to determine if two or more groups of samples are independent or not.
2.4.6. Multivariate regression
A complementary log-log regression model was fitted to the data at the multivariate level. The link function of this model is apt for binary outcomes that are symmetrical unlike the logit or probit models that are appropriate for modeling symmetrical binary outcomes (meet Ajibade et al., 2014). The complementary log-log transformation is expressed as
which is the inverse of the cumulative distribution function of the extreme value (or log-Weibull) distribution, with cumulative distribution:
For minor values of πi, the complementary log-log transformation is close to the logit. As the probability increases, the transformation approaches infinity more than slowly that either the probit or logit. Although the complementary log-log link differs from the probit and logit, one would need extremely big sample sizes, as in this study, to be able to discriminate empirically betwixt these links.
In Eqs. (1) and (2), the contributory role of urbanicity wealth status in determining access to improved water sources and sanitation facilities was estimated using a complementary log-log model and reported as exponentiated coefficients or odds ratios (OR). An OR of ane means that predictor does not touch odds of access to improved water sources or improved sanitation facilities; OR > 1 means that predictor is associated with college odds of access to improved water sources or improved sanitation facilities; and OR < ane means that predictor is associated with lower odds of access to improved h2o sources or improved sanitation facilities. The study accounted for clustering of observations in units of household, and robust estimates of variance was used to correct for this and any statistical outliers in the estimation of standard errors. The report employed 95% confidence interval (CI) and the level of statistical significance was set up at 0.05. Some compositional (sex of household head, age of household head, household size, level of pedagogy of household head) and contextual (year, country) variables that are known in literature to affect household access to improved water sources and sanitation facilities were controlled for in the models. The model was run separately for access to improved water sources and improved sanitation facilities. Three models namely urbanicity wealth of household head and biosocial (model ane), socio-cultural (model 2), and contextual (model 3) were ran. The analyses were performed separately for improved water sources and improved sanitation facilities. Selection of reference groups for the independent variables in the models was based on theory, literature and parsimony. Urban poor was chosen as the reference group for the central predictor, urbanicity wealth condition. Urban poor are considered equally vulnerable, marginalized and dwell in slums every bit well as lack access to improved water and sanitation (Armah et al., 2017a, 2017b; Hawkins et al., 2013). The reference group selected for the sex was "male". Studies take shown that male person in households are relatively less concerned about h2o and sanitation issues (Mulenga et al., 2017). The young adult grouping was chosen as the reference group as they are commonly in transition and may non be able to afford improved h2o and sanitations services. Lack of formal didactics was chosen as the reference category since it has a direct influence on affordability and decision-making chapters of households regarding access to water and sanitation services. The reference flow "1990–1995" was selected as baseline for temporal cess on inequality in access to water and sanitation services. The reference country selected for country variable was "Senegal". The water, sanitation and hygiene (Wash) performance index report, 2015 ranked Senegal to a higher place all the other fourteen countries included in this written report (Cronk et al., 2015).
2.five. Upstanding statement
The information used in this study was obtained using procedures and questionnaires that have been reviewed and approved past ICF Institutional Review Lath (IRB). Besides, ICF IRB ensures that the survey complies with the United States Department of Health and Human Services regulations for the protection of homo subjects CFR 46. The survey protocols for countries also complied with various host country laws.
three. Results
The study countries made meaning progress in terms of access to improved h2o sources. Namibia had the highest (91%) admission to improved water sources in 2010–2015 and Madagascar had the lowest admission of 47 percent. Rwanda recorded the highest increase in access by 45 percent from 1990-1995 to 2010–2015. With regards to access to improved sanitation facilities, Republic of zimbabwe was the merely country which increased continuously from 1990-1995 to 2010–2015. All the remaining fourteen countries studied increased in access from 1990-1995 to 2000–2005 only declined in 2010–2015.
Among the studied countries, Malawi had the highest (83%) admission in 2010–2015 and Madagascar recorded the lowest of fifteen percent. Details of the trend in access to improved water sources and sanitation facilities are shown in Figs. 2 and three respectively.
Access to improved water sources for study countries in 2010–2015.
Admission to improved sanitation facilities for written report countries in 2010–2015.
On aggregate, access to improved water sources past urban dwellers increased from 86 percentage in 1990–1995 to 92 per centum in 2010–2015 (Fig. 4). Access to improved water sources in rural areas also increased from 31.57 percent in 1990–1995 to 63.79 percent in 2010–2015. However, access to unimproved h2o sources remains high in rural areas.
Residential inequalities in access to water sources for study countries.
Access to improved sanitation increased for both urban and rural populations between 1990-1995 and 2000–2005, but declined significantly in 2010–2015 for both urban and rural populations, respectively (Fig. 5).
Residential inequalities in access to sanitation facilities for written report countries.
Pearson chi-square and Cramer'due south Five statistic were used to determine whether the observed differences in access to improved h2o sources and sanitation facilities, urbanicity wealth as well as the compositional factors and contextual factors were independent. The contingency tables (Tables three and 4) bear witness the detailed results. The Pearson chi-squared statistic consequence rejected the hypotheses that access to improved water sources and sanitation facilities are independent of the urbanicity wealth of household, compositional and contextual factors. This means that urbanicity wealth condition influences access to improved h2o sources and sanitation facilities. Besides, the probability values indicate that figures obtained for improved water sources and sanitation facilities were not past chance and that if the analyses were repeatedly ran aforementioned results volition be obtained. The Cramer's V statistic indicated strong association between admission to improved water sources and urbanicity wealth of household for the 25 year period, aforementioned association was observed for access to improved sanitation facilities and urbanicity wealth of household. Cramer'southward V statistic for contextual factors (country) indicated strong associations, withal, that of the compositional factors showed very weak associations.
Tabular array 3
Percentage distribution of access to water sources by predictor variables.
| Variable | 1990–1995 | North = 75842 | 2000–2005 | N = 107452 | 2010–2015 | N = 186073 | |||
|---|---|---|---|---|---|---|---|---|---|
| Unimproved (%) | Improved (%) | Inferential Statistics | Unimproved (%) | Improved (%) | Inferential Statistics | Unimproved (%) | Improved (%) | Inferential Statistics | |
| Urbanicity Wealth | |||||||||
| Urban Poor | 34 | 66 | Pearson chi2 = 3.6e+04 P value = 0.000 Cramér's V = 0.6856 | 55 | 45 | Pearson chi2 = 2.3e+04 P value = 0.000 Cramér's V = 0.4654 | 34 | 66 | Pearson chi2 = 2.3e+04 P value = 0.000 Cramér'due south 5 = 0.3525 |
| Rural Poor | 93 | seven | 64 | 36 | 41 | 59 | |||
| Urban Centre | 30 | 70 | 36 | 64 | 10 | 90 | |||
| Rural Middle | 45 | 55 | 53 | 47 | 28 | 72 | |||
| Urban Rich | 6 | 94 | 7 | 93 | iii | 97 | |||
| Rural Rich | 33 | 67 | 33 | 67 | eighteen | 82 | |||
| Sex of household head | |||||||||
| Male | 52 | 48 | Pearson chi2 = fifteen.4561 P value = 0.000 Cramér's V = 0.0133 | 41 | 59 | Pearson chi2 = 166.1230 P value = 0.000 Cramér'due south V = 0.0368 | 26 | 74 | Pearson chi2 = 90.3536 P value = 0.000 Cramér'due south 5 = 0.0210 |
| Female | 50 | 50 | 37 | 63 | 24 | 76 | |||
| Age of household caput | |||||||||
| Young Developed (Below 35yrs) | 48 | 52 | Pearson chi2 = 842.2819 P value = 0.000 Cramér's V = 0.0979 | 38 | 62 | Pearson chi2 = 285.5426 P value = 0.000 Cramér'due south V = 0.0483 | 24 | 76 | Pearson chi2 = 432.7012 P value = 0.000 Cramér's V = 0.0460 |
| Centre-aged Adult (35–55yrs) | 50 | fifty | 39 | 61 | 25 | 75 | |||
| Older-aged Developed (Above 55yrs) | lx | 40 | 40 | threescore | 29 | 71 | |||
| Household size | |||||||||
| Small (1–5 members) | 51 | 49 | Pearson chi2 = 86.2787 P value = 0.000 Cramér'due south V = 0.0313 | 39 | 61 | Pearson chi2 = 171.0617 | 23 | 77 | Pearson chi2 = 985.6094 |
| Medium (half dozen–10 members) | 53 | 47 | 43 | 57 | P value = 0.000 | 30 | 70 | P value = 0.000 | |
| Large (Higher up 10 members) | 48 | 52 | 39 | 61 | Cramér'due south V = 0.0374 | 30 | 70 | Cramér's Five = 0.0694 | |
| Highest instruction level of household head | |||||||||
| No education/Preschool | 60 | 40 | Pearson chi2 = vi.2e+03 P value = 0.000 Cramér's V = 0.2661 | 51 | 49 | Pearson chi2 = 8.4e+03 P value = 0.000 Cramér'southward V = 0.2622 | 32 | 68 | Pearson chi2 = 8.7e+03 P value = 0.000 Cramér's V = 0.1997 |
| Primary | 54 | 46 | 44 | 56 | 29 | 71 | |||
| Secondary | 26 | 74 | 21 | 79 | 14 | 86 | |||
| Higher | 9 | 91 | 9 | 91 | 5 | 95 | |||
| Land | |||||||||
| Senegal | 42 | 58 | Pearson chi2 = 6.2e+03 P value = 0.000 Cramér'southward V = 0.2659 | 35 | 65 | Pearson chi2 = 7.4e+03 P value = 0.000 Cramér's Five = 0.2457 | 28 | 72 | Pearson chi2 = 1.1e+04 P value = 0.000 Cramér'south V = 0.2365 |
| Cote d'Ivoire | 35 | 65 | 33 | 67 | 20 | fourscore | |||
| Republic of cameroon | 47 | 53 | 35 | 65 | 30 | 70 | |||
| Ghana | 43 | 57 | 32 | 68 | 12 | 88 | |||
| Kenya | 55 | 45 | 48 | 52 | 31 | 69 | |||
| Republic of madagascar | 64 | 36 | 44 | 56 | 53 | 47 | |||
| Mali | 48 | 52 | 59 | 41 | 32 | 68 | |||
| Malawi | 46 | 54 | 37 | 63 | 13 | 87 | |||
| Namibia | 37 | 63 | xiii | 87 | 9 | 91 | |||
| Rwanda | 71 | 29 | 56 | 44 | 26 | 74 | |||
| Burkina Faso | 58 | 42 | 38 | 62 | 21 | 79 | |||
| Tanzania | 66 | 34 | 31 | 69 | 38 | 62 | |||
| Uganda | 66 | 34 | 41 | 59 | 24 | 76 | |||
| Republic of zambia | 56 | 44 | 54 | 46 | 37 | 63 | |||
| Zimbabwe | 22 | 78 | 22 | 78 | xviii | 82 | |||
Table four
Percentage distribution of access to sanitation facilities past predictor variables.
| Variable | 1990–1995 | Due north = 75842 | 2000–2005 | N = 107452 | 2010–2015 | N = 186073 | |||
|---|---|---|---|---|---|---|---|---|---|
| Unimproved (%) | Improved (%) | Inferential Statistics (%) | Unimproved (%) | Improved (%) | Inferential Statistics (%) | Unimproved (%) | Improved (%) | Inferential Statistics (%) | |
| Urbanicity Wealth | |||||||||
| Urban Poor | 12 | 88 | Pearson chi2 = 6.9e+03 P value = 0.000 Cramér's V = 0.3015 | 41 | 59 | Pearson chi2 = 1.9e+04 P value = 0.000 Cramér'southward V = 0.4217 | 68 | 32 | Pearson chi2 = 3.9e+04 P value = 0.000 Cramér's Five = 0.4559 |
| Rural Poor | 45 | 55 | 46 | 54 | 70 | 30 | |||
| Urban Center | 25 | 75 | xv | 85 | thirty | 70 | |||
| Rural Middle | 37 | 63 | 25 | 75 | 49 | 51 | |||
| Urban Rich | 10 | 90 | three | 97 | 15 | 85 | |||
| Rural Rich | 24 | 76 | 10 | 90 | 30 | seventy | |||
| Sexual practice of household head | |||||||||
| Male | 31 | 69 | Pearson chi2 = 22.6812 P value = 0.000 Cramér's Five = -0.0161 | 26 | 74 | Pearson chi2 = 10.2874 P value = 0.001 Cramér's V = 0.0092 | 47 | 53 | Pearson chi2 = 103.3761 P value = 0.000 Cramér's Five = 0.0225 |
| Female | 33 | 67 | 25 | 75 | 45 | 55 | |||
| Age of household head | |||||||||
| Young Adult (Beneath 35yrs) | 28 | 72 | Pearson chi2 = 842.2819 P value = 0.000 Cramér's V = 0.0979 | 23 | 77 | Pearson chi2 = 586.9827 P value = 0.000 Cramér's V = 0.0693 | 44 | 56 | Pearson chi2 = 880.1444 P value = 0.000 Cramér'south V = 0.0656 |
| Eye-anile Adult (35–55yrs) | 31 | 69 | 25 | 75 | 46 | 54 | |||
| Older-aged Adult (Above 55yrs) | 38 | 62 | 31 | 69 | 52 | 48 | |||
| Household size | |||||||||
| Small (ane–5 members) | 31 | 69 | Pearson chi2 = 57.8144 P value = 0.000 Cramér'south V = 0.0256 | 24 | 76 | Pearson chi2 = 222.0830 P value = 0.000 Cramér'south 5 = 0.0426 | 44 | 56 | Pearson chi2 = 1.3e+03 P vale = 0.000 Cramér'due south 5 = 0.0805 |
| Medium (6–10 members) | 32 | 68 | 27 | 73 | 51 | 49 | |||
| Big (Above 10 members) | 36 | 64 | 31 | 69 | 56 | 44 | |||
| Highest education level of household head | |||||||||
| No education/Preschool | 43 | 57 | Pearson chi2 = 4.5e+03 P value = 0.000 Cramér'southward 5 = 0.2256 | 40 | 60 | Pearson chi2 = nine.4e+03 P value = 0.000 Cramér'southward 5 = 0.2781 | 64 | 37 | Pearson chi2(iii) = 1.9e+04 P value = 0.000 Cramér's 5 = 0.3089 |
| Primary | 27 | 73 | 22 | 78 | 47 | 53 | |||
| Secondary | 17 | 83 | 11 | 89 | thirty | 70 | |||
| Higher | 6 | 94 | 2 | 98 | 13 | 87 | |||
| Country | |||||||||
| Senegal | 41 | 59 | Pearson chi2 = one.0e+04 P value = 0.000 Cramér's V = 0.3424 | 26 | 74 | Pearson chi2 = 1.7e+04 P value = 0.000 Cramér's V = 0.3713 | 56 | 44 | Pearson chi2 = 2.4e+04 P value = 0.000 Cramér'southward V = 0.3418 |
| Cote d'ivoire | 41 | 59 | 33 | 67 | 55 | 45 | |||
| Cameroon | 53 | 47 | 6 | 94 | 44 | 56 | |||
| Ghana | 31 | 69 | 29 | 71 | 32 | 68 | |||
| Kenya | xvi | 84 | 17 | 83 | 52 | 48 | |||
| Madagascar | 56 | 44 | 33 | 67 | 85 | 15 | |||
| Republic of mali | 30 | 70 | 26 | 74 | 57 | 44 | |||
| Malawi | 23 | 77 | 16 | 84 | 17 | 83 | |||
| Namibia | 64 | 36 | 47 | 53 | fifty | 50 | |||
| Rwanda | 7 | 93 | 4 | 96 | 28 | 72 | |||
| Burkina Faso | 56 | 44 | 69 | 31 | 67 | 33 | |||
| Tanzania | 17 | 83 | eighteen | 82 | 62 | 38 | |||
| Uganda | sixteen | 84 | 13 | 87 | 48 | 52 | |||
| Zambia | 33 | 67 | 31 | 69 | 58 | 42 | |||
| Republic of zimbabwe | 38 | 62 | 32 | 68 | 30 | seventy | |||
3.1. Urbanicity wealth status and access to improved water sources
Table v shows the odds ratios, robust standard errors, probability values and confidence intervals associated with urbanicity wealth status of households, as well as compositional and contextual factors. Model 1 shows that rural poor (OR = 0.540, P < 0.0001) and rural center (OR = 0.974, P < 0.0001) households were less likely to have admission to improved water sources compared to poor urban households. The urban middle (OR = ane.759, P < 0.0001), urban rich (OR = three.105, P < 0.0001) and rural rich (OR = ane.410, P < 0.0001) households were more likely to have access to improved h2o sources than urban poor households. Female-headed households were 17.half-dozen percent more probable to have access to improved h2o sources compared to male-headed households. Model 1 revealed that households with heart-aged adult (OR = 0.977, P < 0.0001) and older-aged developed (OR = 0.973, P < 0.0001) heads were less probable to accept admission to improved water sources than households with young adult heads.
Tabular array v
Complementary log-log regression model showing the relationship between access to improved h2o sources and household characteristics.
| Variable | Urbanicity wealth + Bisocial factors | + Socio-cultural factors | + Contextual factors | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR | SE | P value | Conf. Interval | OR | SE | P value | Conf. Interval | OR | SE | P value | Conf. Interval | ||||
| Model 1 | Model ii | Model iii | |||||||||||||
| Urbanicity wealth (ref: Urban poor) | |||||||||||||||
| Rural Poor | 0.540 | 0.006 | 0.000 | 0.529 | 0.553 | 0.575 | 0.007 | 0.000 | 0.563 | 0.589 | 0.708 | 0.009 | 0.000 | 0.690 | 0.726 |
| Urban Centre | ane.759 | 0.024 | 0.000 | 1.712 | ane.808 | i.707 | 0.024 | 0.000 | one.661 | 1.754 | 1.887 | 0.029 | 0.000 | 1.831 | 1.944 |
| Rural Center | 0.974 | 0.012 | 0.029 | 0.952 | 0.997 | ane.010 | 0.012 | 0.413 | 0.986 | 1.034 | 1.291 | 0.017 | 0.000 | ane.258 | one.326 |
| Urban Rich | 3.105 | 0.036 | 0.000 | 3.035 | three.177 | 2.877 | 0.034 | 0.000 | ii.811 | ii.944 | iv.294 | 0.061 | 0.000 | four.177 | 4.415 |
| Rural Rich | ane.410 | 0.017 | 0.000 | ane.378 | 1.443 | 1.422 | 0.017 | 0.000 | 1.389 | 1.455 | one.916 | 0.026 | 0.000 | i.866 | ane.968 |
| Sex of household head (ref: Male person) | |||||||||||||||
| Female person | 1.176 | 0.006 | 0.000 | i.164 | 1.189 | ane.181 | 0.007 | 0.000 | 1.169 | 1.194 | 1.106 | 0.007 | 0.000 | one.093 | 1.120 |
| Age group of household head (ref: Young adult) | |||||||||||||||
| Heart-aged Adult | 0.977 | 0.005 | 0.000 | 0.966 | 0.987 | 1.035 | 0.006 | 0.000 | i.024 | 1.047 | 1.005 | 0.006 | 0.396 | 0.993 | 1.018 |
| Older-aged Developed | 0.973 | 0.006 | 0.000 | 0.961 | 0.985 | one.074 | 0.007 | 0.000 | ane.060 | ane.088 | 1.002 | 0.007 | 0.782 | 0.988 | 1.016 |
| Household size (ref: Small) | |||||||||||||||
| Medium | 0.902 | 0.005 | 0.000 | 0.892 | 0.911 | 0.938 | 0.005 | 0.000 | 0.927 | 0.949 | |||||
| Large | 0.891 | 0.009 | 0.000 | 0.874 | 0.909 | 0.916 | 0.010 | 0.000 | 0.897 | 0.937 | |||||
| Education level of household head (ref: No education) | |||||||||||||||
| Primary | ane.069 | 0.006 | 0.000 | 1.057 | 1.081 | i.024 | 0.007 | 0.001 | 1.010 | 1.038 | |||||
| Secondary | ane.400 | 0.010 | 0.000 | i.381 | one.420 | 1.168 | 0.010 | 0.000 | i.148 | 1.188 | |||||
| College | 1.685 | 0.022 | 0.000 | one.642 | 1.729 | 1.398 | 0.022 | 0.000 | 1.355 | 1.442 | |||||
| Yr (ref: 1990 – 1995) | |||||||||||||||
| 2000–2005 | 1.282 | 0.009 | 0.000 | i.264 | 1.300 | ||||||||||
| 2010–2015 | two.252 | 0.015 | 0.000 | 2.223 | 2.282 | ||||||||||
| Country (ref: Senegal) | |||||||||||||||
| Ivory coast | 1.025 | 0.017 | 0.137 | 0.992 | 1.060 | ||||||||||
| Cameroon | 0.711 | 0.011 | 0.000 | 0.690 | 0.732 | ||||||||||
| Republic of ghana | i.134 | 0.018 | 0.000 | 1.098 | one.170 | ||||||||||
| Kenya | 0.630 | 0.009 | 0.000 | 0.612 | 0.648 | ||||||||||
| Madagascar | 0.491 | 0.009 | 0.000 | 0.473 | 0.510 | ||||||||||
| Mali | 0.682 | 0.010 | 0.000 | 0.662 | 0.702 | ||||||||||
| Republic of malaŵi | 1.177 | 0.017 | 0.000 | 1.144 | 1.211 | ||||||||||
| Namibia | 1.574 | 0.026 | 0.000 | 1.523 | 1.627 | ||||||||||
| Rwanda | 0.649 | 0.010 | 0.000 | 0.630 | 0.669 | ||||||||||
| Burkina Faso | 0.856 | 0.013 | 0.000 | 0.832 | 0.882 | ||||||||||
| Tanzania | 0.604 | 0.009 | 0.000 | 0.586 | 0.623 | ||||||||||
| Uganda | 0.608 | 0.010 | 0.000 | 0.589 | 0.629 | ||||||||||
| Republic of zambia | 0.657 | 0.012 | 0.000 | 0.634 | 0.680 | ||||||||||
| Zimbabwe | 1.274 | 0.022 | 0.000 | 1.232 | i.318 | ||||||||||
| N | 379000 | 369732 | 369732 | ||||||||||||
The results from model ii, in which socio-cultural factors were controlled for, show that rural poor households were 42.five percent less likely to accept admission to improved h2o sources compared to urban poor households. Again, urban middle (OR = one.707, P < 0.0001), urban rich (OR = 2.877, P < 0.0001) and rural rich (OR = i.422, P < 0.0001) households were more probable to take access to improved water sources than urban poor households. Model 2 also shows that female-headed households were eighteen.one percentage more than likely to have access to improved h2o sources compared to male-headed households. Information technology was revealed in model ii that households with middle-aged adult (OR = 1.035, P < 0.0001) and older-aged adult (OR = 1.074, P < 0.0001) heads were now slightly more likely to have access to improved h2o sources than households with young adult heads.
Medium- (OR = 0.902, P < 0.0001) and large- (OR = 0.891, P < 0.0001) size households were less likely to accept access to improved h2o source compared to small-scale-size households. Regarding level of didactics, households with heads that accept primary level (OR = 1.069, P < 0.0001), secondary level (OR = ane.400, P < 0.0001) and college education (OR = ane.685, P < 0.0001) were more probable to have admission to improved water sources than households with uneducated heads.
In model three, we considered some contextual factors that can influence access to improved water sources. The year and the country where the households are located were controlled for in the model. These contextual factors mediated the human relationship between the master predictor and access to improved water sources. Observations nether rural centre households were non statistically significant in model one and 2 but became pregnant when the contextual variables were added in model 3. Conversely, yr and country variables adulterate the effect of age grouping of household caput on access to improved water sources. Historic period group of household head variable was statistically significant in model 1 and 2 simply ceased to be significant when the two contextual factors were considered in model 3. The results show rural poor households (OR = 0.708, P < 0.0001) were still less probable to have admission to improved water sources compared to poor households. We found that urban centre (OR = 1.887, P < 0.0001), rural middle (OR = 1.291, P < 0.0001), urban rich (OR = four.294, P < 0.0001) and rural rich (OR = i.916, P < 0.0001) households were more likely to have access to improved h2o sources than urban poor households. Model 3 also shows that people in female-headed households were all the same 10.six pct more likely to take access to improved water sources than male-headed households. With regards to household size, medium (OR = 0.938, P < 0.0001) and large (OR = 0.916, P < 0.0001) size households were nevertheless less likely to accept access to improved water sources as compared to those in small-scale size households. Information technology was also observed that households with heads that have chief level (OR = 1.024, P < 0.0001), secondary level (OR = 1.168, P < 0.0001) and college instruction (OR = 1.398, P < 0.0001) were more likely to take access to improved water sources than households with heads who have no educational activity. Temporally, households were 28.ii percent and 125.2 percentage more likely to have access to improved h2o sources in 2000–2005 and 2010–2015 respectively, than in 1990–1995. In terms of country, households in the following countries: Cameroon (OR = 0.711, P < 0.0001), Republic of kenya (OR = 0.630, P < 0.0001), Madagascar (OR = 0.491, P < 0.0001), Republic of mali (OR = 0.682, P < 0.0001), Rwanda (OR = 0.649, P < 0.0001), Burkina Faso (OR = 0.856, P < 0.0001), Tanzania (OR = 0.604, P < 0.0001), Uganda (OR = 0.608, P < 0.0001), Zambia (OR = 0.657, P < 0.0001) were less probable to have access to improved h2o sources compared to those in Senegal. Households in countries such equally Ghana (OR = 1.134, P < 0.0001), Malawi (OR = 1.177, P < 0.0001), Namibia (OR = 1.574, P < 0.0001), Zimbabwe (OR = one.274, P < 0.0001) were more likely to have admission to improve h2o sources compared with those in Senegal.
three.2. Urbanicity wealth condition and admission to improved sanitation facilities
Table 6 shows the three results for the multivariate analyses that were ran for admission to improved sanitation facilities. Model one indicates that rural poor households (OR = 0.847, P < 0.0001) were less likely to have access to improved sanitation facilities compared to urban poor households. We observed that urban middle (OR = two.023, P < 0.0001), rural centre (OR = ane.444, P < 0.0001) urban rich (OR = 3.389, P < 0.0001) and rural rich (OR = ii.254, P < 0.0001) households were more likely to have access to improved sanitation facilities than urban poor households. It was also observed that female-headed households (OR = ane.047, P < 0.0001) were slightly more probable to have access to improved sanitation facilities than male-headed households. Model ane shows that households with center-aged adult (OR = i.059, P < 0.0001) and older-aged adult (OR = 1.128, P < 0.0001) heads were more likely to have access to improved sanitation facilities than households with immature adult heads.
Table 6
Complementary log-log regression model showing the relationship between access to improved sanitation facilities and household characteristics.
| Variable | Urbanicity wealth + Bisocial factors | + Socio-cultural factors | + Contextual factors | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR | SE | P value | Conf. Interval | OR | SE | P value | Conf. Interval | OR | SE | P value | Conf. Interval | ||||
| Model 1 | Model two | Model 3 | |||||||||||||
| Urbanicity wealth (ref: Urban poor) | |||||||||||||||
| Rural Poor | 0.847 | 0.010 | 0.000 | 0.827 | 0.868 | 0.934 | 0.012 | 0.001 | 0.912 | 0.958 | 0.745 | 0.010 | 0.000 | 0.726 | 0.764 |
| Urban Middle | 2.023 | 0.030 | 0.000 | 1.966 | 2.082 | ii.003 | 0.030 | 0.000 | 1.946 | 2.063 | 2.091 | 0.032 | 0.000 | two.030 | ii.154 |
| Rural Middle | 1.444 | 0.019 | 0.000 | i.408 | 1.481 | 1.524 | 0.020 | 0.000 | one.485 | 1.563 | 1.255 | 0.017 | 0.000 | 1.222 | 1.289 |
| Urban Rich | iii.389 | 0.042 | 0.000 | 3.308 | iii.473 | 3.305 | 0.042 | 0.000 | three.224 | iii.389 | iii.266 | 0.045 | 0.000 | 3.180 | 3.355 |
| Rural Rich | 2.254 | 0.029 | 0.000 | 2.199 | 2.311 | two.370 | 0.031 | 0.000 | 2.311 | 2.431 | i.915 | 0.027 | 0.000 | 1.864 | i.968 |
| Sex activity of household head (ref: Male person) | |||||||||||||||
| Female | 1.047 | 0.005 | 0.000 | 1.036 | one.057 | ane.065 | 0.006 | 0.000 | 1.054 | 1.076 | 1.050 | 0.006 | 0.000 | 1.038 | 1.062 |
| Age group of household head (ref: Young adult) | |||||||||||||||
| Middle-aged Adult | 0.967 | 0.005 | 0.000 | 0.957 | 0.977 | 1.017 | 0.006 | 0.003 | one.006 | 1.028 | ane.059 | 0.006 | 0.000 | i.047 | 1.072 |
| Older-aged Adult | 0.929 | 0.006 | 0.000 | 0.918 | 0.939 | 1.060 | 0.007 | 0.000 | ane.047 | 1.073 | i.128 | 0.008 | 0.000 | ane.113 | one.144 |
| Household size (ref: Small) | |||||||||||||||
| Medium | 0.988 | 0.005 | 0.020 | 0.978 | 0.998 | 0.992 | 0.006 | 0.152 | 0.981 | one.003 | |||||
| Large | 0.933 | 0.009 | 0.000 | 0.915 | 0.950 | 1.020 | 0.011 | 0.074 | 0.998 | i.041 | |||||
| Highest education level of household head (ref: No didactics) | |||||||||||||||
| Main | ane.419 | 0.008 | 0.000 | 1.404 | 1.434 | 1.269 | 0.008 | 0.000 | 1.252 | 1.285 | |||||
| Secondary | i.509 | 0.010 | 0.000 | 1.489 | 1.530 | one.637 | 0.014 | 0.000 | ane.610 | 1.665 | |||||
| Higher | 1.814 | 0.021 | 0.000 | one.774 | 1.856 | ii.129 | 0.031 | 0.000 | 2.069 | 2.190 | |||||
| Year (ref: 1990 – 1995) | |||||||||||||||
| 2000–2005 | i.273 | one.273 | 0.000 | 1.254 | 1.291 | ||||||||||
| 2010–2015 | 0.552 | 0.552 | 0.000 | 0.544 | 0.559 | ||||||||||
| Land (ref: Senegal) | |||||||||||||||
| Republic of cote d'ivoire | 0.794 | 0.012 | 0.000 | 0.771 | 0.818 | ||||||||||
| Republic of cameroon | 0.986 | 0.015 | 0.335 | 0.958 | 1.015 | ||||||||||
| Ghana | 0.983 | 0.015 | 0.266 | 0.953 | ane.013 | ||||||||||
| Kenya | 0.967 | 0.013 | 0.000 | 0.942 | 0.992 | ||||||||||
| Republic of madagascar | 0.510 | 0.009 | 0.000 | 0.492 | 0.528 | ||||||||||
| Republic of mali | 1.168 | 0.016 | 0.000 | 1.137 | 1.199 | ||||||||||
| Republic of malaŵi | 2.068 | 0.029 | 0.000 | 2.012 | 2.126 | ||||||||||
| Namibia | 0.522 | 0.008 | 0.000 | 0.506 | 0.538 | ||||||||||
| Rwanda | 2.400 | 0.036 | 0.000 | 2.330 | 2.471 | ||||||||||
| Burkina Faso | 0.428 | 0.006 | 0.000 | 0.416 | 0.440 | ||||||||||
| Tanzania | 1.299 | 0.018 | 0.000 | one.263 | ane.336 | ||||||||||
| Uganda | 1.114 | 0.017 | 0.000 | 1.081 | 1.149 | ||||||||||
| Zambia | 0.895 | 0.015 | 0.000 | 0.866 | 0.926 | ||||||||||
| Zimbabwe | ane.276 | 0.021 | 0.000 | 1.236 | i.318 | ||||||||||
| Due north | 379000 | 369732 | 369732 | ||||||||||||
Subsequently decision-making for the socio-cultural factors in model 2, the results show that rural poor households were 6.six per centum less likely to have access to improved sanitation facilities compared to urban poor households. Nosotros found out that urban middle (OR = 2.003, P < 0.0001), rural center (OR = 1.524, P < 0.0001) urban rich (OR = iii.305, P < 0.0001) and rural rich (OR = ii.370, P < 0.0001) households were more probable to have admission to improved sanitation facilities than urban poor households. Female-headed households were 6.5 percentage marginally more than likely to have admission to improved sanitation facilities compared to male-headed households. Households with middle-anile adult (OR = 1.017, P < 0.003) and older-aged adult (OR = 1.060, P < 0.0001) heads were more likely to have access to improved sanitation facilities than households with young adult heads. Medium (OR = 0.988 P < 0.020) and large (OR = 0.933, P < 0.0001) size households were less likely to accept access to improved sanitation facilities than minor size households. With regards to level of education of household caput, we observed that households with heads that have principal level (OR = 1.419, P < 0.0001), secondary level (OR = ane.509, P < 0.0001) and higher education (OR = ane.814, P < 0.0001) education were more likely to have admission to improved sanitation facilities than those who reside in households with heads that have no instruction.
Contextual factors (yr and country) were controlled for in model three. We observed that rural poor households (OR = 0.745, P < 0.0001) were all the same less probable to have access to improved sanitation facilities compared to urban poor households. The model iii shows that urban middle (OR = two.091, P < 0.0001), rural heart (OR = 1.255, P < 0.0001), urban rich (OR = 3.266, P < 0.0001) and rural rich (OR = 1.915, P < 0.0001) households were more probable to have access to improved sanitation facilities than urban poor households. Female person-headed households were 5 percent more than probable to take admission to improved sanitation facilities compared to male-headed households. Model 3 indicates that households with centre-anile developed (OR = 1.059, P < 0.0001) and older-aged adult (ane.128, P < 0.0001) heads were slightly more likely to accept admission to improved sanitation facilities than households with young adult heads. Regarding didactics, households with heads that accept primary level (OR = one.269, P < 0.0001), secondary level (OR = 1.637, P < 0.0001) and higher (OR = 2.129, P < 0.0001) teaching were more likely to have admission to improved sanitation facilities than households with heads that have no education. Fifty-fifty though household size was statistically significant in model 2, contextual factors attenuated its upshot in model 3. Considering the year in which the surveys were carried out, households in 2000–2005 (OR = 1.273, P < 0.0001) were more likely to accept access to improved sanitation facilities compared to those in 1990–1995. On the other hand, households in 2010–2015 (OR = 0.552, P < 0.0001) were less likely to have access to improved sanitation facilities than those in 1990–1995. When countries were controlled for, it was observed that households in the following countries: Cote d'Ivoire (OR = 0.771, P < 0.0001), Kenya (OR = 0.939, P < 0.011), Republic of madagascar (OR = 0.510, P < 0.0001), Namibia (OR = 0.522, P < 0.0001), Burkina Faso (OR = 0.428, P < 0.0001) and Zambia (OR = 0.895, P < 0.0001) were less probable to take access to improved sanitation facilities compared to those in Senegal. Households in countries such as Mali (OR = 1.168, P < 0.0001), Malawi (OR = 2.013, P < 0.0001), Rwanda (OR = two.400, P < 0.0001), Tanzania (OR = 1.299, P < 0.0001), Uganda (OR = one.114, P < 0.0001) and Zimbabwe (OR = 1.276, P < 0.0001) were more probable to have access to improved sanitation facilities compared to those in Senegal.
iv. Give-and-take
Comparative analyses on access to water and sanitation facilities in developing countries is fundamentally nigh using comparison across different units of assay to delineate the mechanisms that explicate variation amid environmental, social, economic and health outcomes in those units and beyond them. The greater ease of acquiring comparable quantitative indicators, and the potential for exploiting both temporal and spatial variation through regression techniques that use pooled cross sectional time series, are technological advances that requite acceptance to the value of multi-country studies. Both national comparisons and advanced statistical techniques using such data accept moved knowledge forward in a diverseness of fields of enquiry within water and sanitation. We conducted a pooled analysis of data to assess the household level factors that determine access to water and sanitation services in fifteen countries across sub-Saharan Africa. Unlike previous studies which assessed the effect of rural-urban location and wealth on access to improved water sources and sanitation separately (see Roche et al., 2017; Tuyet-Hanh et al., 2016; Osei et al., 2015; Pullan et al., 2014), the current written report examined the joint effect of relative residential well-existence (urbanicity and wealth status) on access to improved water sources and sanitation facilities in selected SSA countries.
Based on our findings, great improvements have been made in providing access to improved water sources in SSA from 1990 to 2015. The results show a consequent increase in access to improved h2o sources over the 25-year period, thus the odds of having admission to improved water sources increased over the 25-yr period as indicated in the multivariate analyses. Information technology was observed that 74 per centum of the population of the SSA countries studied had access to improved water sources in 2010–2015 which is college than the MDG 2015 figure of 68 percent (Un, 2015). The difference may be attributed to the time periods considered and the number of countries included in this written report. On the opposite, same cannot be said about sanitation. Admission to improved sanitation facilities increased from 69 per centum in 1990–1995 to 74 percent in 2000–2005 still, it declined significantly to 53 percent in 2010–2015. The decline can exist attributed to high population growth and urbanization in SSA without complementary expansion in sanitation facilities during the MDG period (1990–2015). The 2015 JMP written report indicated that the population of SSA doubled during the MDG catamenia (1990–2015). Nonetheless, admission to improved sanitation facilities increased by only six percentage points during the same period (Earth Health Arrangement WHO/UNICEF Joint H2o Supply and Sanitation Monitoring Programme, 2015). Africa's urban population was projected to reach 1.2 billion by 2050, with an urbanization charge per unit of 58 percent (UNDESA, 2014) simply, in SSA, urbanization is not accompanied by the level of per capita economic growth or housing investment equally seen in global trends (World Bank Group, 2015). This, in combination with poorly planned human settlements, put many urban poor in SSA into slums. The slum population is growing at 4.5 percent annually, and expected to double in 15 years (Marx, Stoker and Suri, 2013). A study by Ndikumana and Pickbourn (2015), has also shown that foreign aid to the h2o and sanitation sector has a non-linear effect on the per centum of the rural population that has access to improved sanitation. Generally, admission to improved sanitation facilities increased with increase in foreign help to a threshold beyond which farther increases in aid are associated with declining access to sanitation. As shown by the results, both the urban and rural poor households are least likely to take access to improved h2o and sanitation facilities. This raises two fundamental issues-- economic access and spatial access. Economically, the poor cannot beget the initial loftier price of both water and sanitation facilities, regardless of whether they are in urban or rural areas. Spatially, the urban poor, especially, are frequently confined to slums or areas without municipal services. And fifty-fifty if they can afford, they might be quite a distance away from improved facilities, resulting in boosted high transaction cost. The urban rich, on the other mitt, regardless of their locations, can have both economic and spatial access to improved facilities as they might accept meliorate access to transportation.
We observed that urbanicity wealth status of households had a strong clan with access to improved water and sanitation facilities. Urban rich households were 329 percent more probable to have admission to improved water sources and 227 per centum more likely to have access to improved sanitation facilities compared to urban poor households. This means that access to improved water sources and sanitation is more than concentrated in the rich households compared to the poor ones and this finding is in understanding with several studies (Mulenga et al., 2017; Tuyet-Hanh et al., 2016; Yang et al., 2013; Lawrence and Meigh, 2003). The reason is that having wealth increases the ability to pay for municipal services, such as water and sanitation, fifty-fifty when the local authority or government is not providing these services. Rural poor households were found to exist 29 percent less likely to have access to improved water sources and 25 percent less likely to have access to improved sanitation facilities compared to urban poor households. This suggests that the urban poor might be spatially closer to facilities or services but might not be able to beget, compared to their rural counterpart who might not have these services or facilities at all. This conspicuously shows the disparities betwixt rural-urban population in terms of access to improved h2o and sanitation facilities. This is in consonance with literature which suggest that urban households stand a ameliorate chance of having admission to improved water sources and sanitation facilities (Tuyet-Hanh et al., 2016; Yang et al., 2013; WHO and UNICEF, 2014; UN, 2015).
The results testify that gender of household head has association with access to improved water sources and sanitation facilities. Female-headed households had higher odds of access to improved h2o sources and sanitation. In many homes in SSA, women have the responsibility of managing water, sanitation and hygiene (Wash), cooking and other household chores. This direct connection with water and sanitation suggests that women could pay more attention to such bug than their male counterparts, and especially when women are the household heads. This finding is consistent with Mulenga et al. (2017) and Osei et al. (2015).
Age of household caput had no association with admission to improved water sources but had a weak association with improved sanitation facilities. Households with heart-aged and older-anile adult heads were more likely to accept access to improved sanitation facilities than immature adult headed households. Older people were able beget bones services every bit compared to young ones perhaps considering of their higher economic status.
The number of household members is one of the socio-cultural factors that were assessed in the multivariate analyses. Households with smaller size were observed to have higher odds of having access to improved water sources and sanitation facilities. This is in understanding with a study carried out by Armand and Fotu (2013) in Cameroon where they observed that increasing the size of a household decreases the likelihood of using improved water sources. The authors suggested that household wealth decreases with increasing size.
Furthermore, households with more educated heads were more than likely to have admission to improved water sources and sanitation facilities compared to households with less educated heads and this is consequent with previous studies (see Abubakar, 2017; Prasetyoputra and Irianti, 2013; Ordinioha and Owhondah, 2013; Okurut et al., 2015). It may be attributed to the fact that educated people appreciate the respective benefits and cost of using improved and unimproved water sources or sanitation facilities. Therefore, sensation increases the likelihood of having access to improved sanitation facilities (Kema et al., 2012).
The strong association between land and access to improved h2o sources and sanitation facilities suggests that geographical inequalities surpass rural-urban disparities and tin can exist likened to urbanicity wealth inequalities (Pullan et al., 2014). The big inter-country disparities in coverage of improved water sources and sanitation facilities in SSA has been reported (UNICEF/WHO, 2013). Our results suggest that there are substantial geographical inequalities in access of improved h2o sources and sanitation facilities beyond SSA that exceed simple urban-rural disparities and are of like magnitude to the large socio-economical inequalities highlighted in a number of national studies (e.yard. Pullan et al., 2014; Fehr et al., 2013; WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation, 2011; Khan et al., 2011). The differences in coverage amongst countries can be attributed to divergence in economic growth, infrastructure evolution, housing investment, government and nongovernmental organizations interventions etc. Studies testify that unless governments and relevant stakeholders deliberately adopt strategies that target deprived areas and population groups, it is unlikely that countries will achieve universal coverage (Pullan et al., 2014; Taylor-Robinson et al., 2012; Murray et al., 2012; Laxminarayan et al., 2006).
iv.1. Limitations of the written report
The study limitations may event in nether-interpretation of inequality in access to improved water sources and sanitation facilities. The DHS records type of drinking h2o and type of toilet facility by households instead of individuals. This presupposes that our analyses does non pay attention to inequality in access to improved water sources and sanitation facilities among members in the same household (intra-household) and therefore may underestimate inequality. With regards to wealth index, the assets recorded in the DHS were not intended to measure economical status of households only were included for other purposes. Some studies accept stated that such asset-based measures have weak association with other measures such as consumption (Howe et al., 2009). Even though, the criteria used in classifying improved drinking water sources and sanitation facilities in the Articulation Monitoring Programme of the WHO/UNICEF are backed past empirical data, it is probable to overestimate or underestimate compliance.
Nearly all data collected in DHS are subject to reporting and call back biases. This may affect some variables (age grouping of household heads, household size, and level of teaching) used in this report. However, detailed evaluation of DHS information has shown that these data are reasonably well reported (Boerma and Sommerfeltb, 1993). DHS are conducted on an ongoing basis and independently within countries, meaning that the bulk of participating countries are not measured at the same fourth dimension, limiting the contemporaneous cantankerous-national comparisons.
5. Decision
Admission to improved water sources has increased over the terminal 25 years in the SSA countries studied. Access to improved sanitation facilities as well increased from 1990-1995 to 2000–2005 yet, information technology declined significantly in the 2010–2015 period. The written report shows that the improvement observed in access to improved sanitation facilities is gradually existence eroded. The region has experienced a high population growth charge per unit and urbanization which were not accompanied by economical growth and investment in housing, water and sanitation infrastructure. This has resulted in mushrooming of slum communities which lack basic amenities and social services. Access to improved water sources was non affected considering of the growing use of packaged and delivered h2o. The combined upshot of residential wellbeing (urbanicity wealth status) had magnified event on admission to improved water sources and sanitation facilities. Compositional factors such as sexual practice, age and level of educational activity of household caput too every bit the size of household are strong and significantly contribute to the magnified disparities in access to improved water sources and sanitation facilities in SSA. This suggests that concerted policy initiatives are required to increase admission to improved h2o sources and sanitation facilities in the households giving special attention to the underserved populations. Extensive inequalities in coverage of improved water sources and sanitation facilities amongst countries in the region are discernible from the results of this study. International bodies and policy makers responsible for h2o and sanitation programmes should take notation that a common intervention approach volition not be favourable for all countries in sub-Saharan Africa rather; interventions should be designed to run into the peculiar needs of specific countries. On the whole, compositional and contextual factors mediated or adulterate the magnitude and direction of the relationship between residential wealth status and access to improved water sources and sanitation facilities indicating that access to water and sanitation facilities in SSA is a circuitous and multifaceted issue that needs to be tackled holistically taking into consideration interdisciplinary enquiry and policy interventions covering environment, culture, economics and human behaviour.
Declarations
Author contribution statement
Frederick A. Armah, Bernard Ekumah: Conceived and designed the assay; Analyzed and interpreted the data; Contributed analysis tools or data; Wrote the paper.
David O. Yawson, Justice O. Odoi: Analyzed and interpreted the data; Contributed analysis tools or data; Wrote the paper.
Abdul-Rahaman Afitiri, Florence Eastward. Nyieku: Analyzed and interpreted the data; Wrote the paper.
Funding statement
This research did not receive whatever specific grant from funding agencies in the public, commercial, or not-for-turn a profit sectors.
Competing interest statement
The authors declare no conflict of interest.
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