Geographical Inequalities in Use of Improved Drinking Water Supply and Sanitation across Sub-Saharan Africa: Mapping and Spatial Analysis of Cross-sectional Survey Data
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{"title"=>"Geographical Inequalities in Use of Improved Drinking Water Supply and Sanitation across Sub-Saharan Africa: Mapping and Spatial Analysis of Cross-sectional Survey Data", "type"=>"journal", "authors"=>[{"first_name"=>"Rachel L.", "last_name"=>"Pullan", "scopus_author_id"=>"57190211779"}, {"first_name"=>"Matthew C.", "last_name"=>"Freeman", "scopus_author_id"=>"23099628300"}, {"first_name"=>"Peter W.", "last_name"=>"Gething", "scopus_author_id"=>"57193559189"}, {"first_name"=>"Simon J.", "last_name"=>"Brooker", "scopus_author_id"=>"7005926568"}], "year"=>2014, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491676", "scopus"=>"2-s2.0-84900443256", "sgr"=>"84900443256", "pui"=>"373076980", "isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)", "pmid"=>"24714528", "doi"=>"10.1371/journal.pmed.1001626"}, "id"=>"2c0bc62d-7363-317e-902b-41af1786c0e8", "abstract"=>"BACKGROUND: Understanding geographic inequalities in coverage of drinking-water supply and sanitation (WSS) will help track progress towards universal coverage of water and sanitation by identifying marginalized populations, thus helping to control a large number of infectious diseases. This paper uses household survey data to develop comprehensive maps of WSS coverage at high spatial resolution for sub-Saharan Africa (SSA). Analysis is extended to investigate geographic heterogeneity and relative geographic inequality within countries.\\n\\nMETHODS AND FINDINGS: Cluster-level data on household reported use of improved drinking-water supply, sanitation, and open defecation were abstracted from 138 national surveys undertaken from 1991-2012 in 41 countries. Spatially explicit logistic regression models were developed and fitted within a Bayesian framework, and used to predict coverage at the second administrative level (admin2, e.g., district) across SSA for 2012. Results reveal substantial geographical inequalities in predicted use of water and sanitation that exceed urban-rural disparities. The average range in coverage seen between admin2 within countries was 55% for improved drinking water, 54% for use of improved sanitation, and 59% for dependence upon open defecation. There was also some evidence that countries with higher levels of inequality relative to coverage in use of an improved drinking-water source also experienced higher levels of inequality in use of improved sanitation (rural populations r = 0.47, p = 0.002; urban populations r = 0.39, p = 0.01). Results are limited by the quantity of WSS data available, which varies considerably by country, and by the reliability and utility of available indicators.\\n\\nCONCLUSIONS: This study identifies important geographic inequalities in use of WSS previously hidden within national statistics, confirming the necessity for targeted policies and metrics that reach the most marginalized populations. The presented maps and analysis approach can provide a mechanism for monitoring future reductions in inequality within countries, reflecting priorities of the post-2015 development agenda. Please see later in the article for the Editors' Summary.", "link"=>"http://www.mendeley.com/research/geographical-inequalities-improved-drinking-water-supply-sanitation-across-subsaharan-africa-mapping-6", "reader_count"=>178, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>26, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>42, "Student > Postgraduate"=>10, "Student > Master"=>48, "Other"=>7, "Student > Bachelor"=>18, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>2, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>26, "Student > Doctoral Student"=>7, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1454586"], "description"=>"<p>Data are linked to second administrative areas where possible and if not to the first administrative level; administrative boundaries are provided by the United Nations Second Administrative Level Boundaries (SALB) project.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "nationally", "sanitation", "sub-saharan", "africa"], "article_id"=>990856, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Availability_of_nationally_representative_cluster_survey_data_on_improved_drinking_water_and_sanitation_across_sub_Saharan_Africa_for_the_period_1990_8211_2012_/990856", "title"=>"Availability of nationally representative, cluster survey data on improved drinking water and sanitation across sub-Saharan Africa for the period 1990–2012.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454587"], "description"=>"<p>Access to improved drinking-water supply in (A) rural and (B) urban populations; access to improved sanitation in (C) rural and (D) urban populations; and open defecation in (E) rural and (F) urban populations. Model results showing posterior median predicted coverage (i.e. most likely value) for each second administrative area. No data was available for Botswana and Eritrea (hatched). Each indicator was modelled independently.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "2012"], "article_id"=>990857, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_population_coverage_in_2012_for_rural_and_urban_populations_by_second_administrative_area_/990857", "title"=>"Predicted population coverage in 2012 for rural and urban populations, by second administrative area.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454588"], "description"=>"<p>Dots show national comparisons for urban (red) and rural (blue) populations. An accessible drinking water supply is defined as one within 15 minutes of the household; private sanitation is defined as a facility used by only one household.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "sanitation"], "article_id"=>990858, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_A_any_improved_drinking_water_source_against_accessible_improved_drinking_water_source_and_B_any_improved_sanitation_against_private_improved_sanitation_/990858", "title"=>"Comparison of (A) any improved drinking water source against accessible, improved drinking water source and (B) any improved sanitation against private improved sanitation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454589"], "description"=>"<p>Second administrative areas shaded red have significantly lower coverage than the national average, based on 95% BCI, for either both (dark red) or one (light red) of improved drinking water and improved sanitation; administrative areas shaded blue have significantly higher coverage rates than the national average. Administrative areas shaded grey are not significantly different from the national mean.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "areas"], "article_id"=>990859, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_second_administrative_areas_that_differ_significantly_from_national_mean_coverage_/990859", "title"=>"Predicted second administrative areas that differ significantly from national mean coverage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454590"], "description"=>"<p>Comparisons are made for (<b>A</b>) Nigeria, (<b>B</b>) Mozambique and (<b>C</b>) Uganda. <i>r</i> is the Pearson pairwise correlation coefficient. Each dot represents one administrative area.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "sanitation"], "article_id"=>990860, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_coverage_in_improved_drinking_water_against_improved_sanitation_in_overall_population_by_second_administrative_area_/990860", "title"=>"Comparison of coverage in improved drinking water against improved sanitation in overall population, by second administrative area.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454591"], "description"=>"<p>Second administrative areas were stratified into quintiles based on coverage of each indicator. Dots show median proportion of households with access for each quintile; lines show the full range in coverage.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "modelled", "wss", "areas"], "article_id"=>990861, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_modelled_WSS_coverage_across_administrative_areas_by_country_/990861", "title"=>"Distribution of modelled WSS coverage across administrative areas by country.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454592"], "description"=>"<p>Plots are shown for (<b>A</b>) use of improved drinking water, (<b>B</b>) use of improved sanitation facilities, and (<b>C</b>) use of any type of sanitation. All plots show the linear regression prediction (solid line) with 95% confidence interval (shaded area). Labelled countries (by 3-letter ISO codes) are those with GINI scores significantly higher or lower than would be expected, given national coverage.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "inequality"], "article_id"=>990862, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_relationship_between_inequality_GINI_score_as_a_function_of_national_coverage_/990862", "title"=>"Empirical relationship between inequality (GINI score) as a function of national coverage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454593"], "description"=>"<p>(<b>A</b>) rural populations (correlation (<i>r</i>) = 0.47, <i>p</i> = 0.002) and (<b>B</b>) urban populations (<i>r</i> = 0.39, <i>p</i> = 0.01).</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "geographical", "inequality", "rgi", "sanitation"], "article_id"=>990863, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.g008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_relative_geographical_inequality_for_use_of_improved_drinking_water_and_RGI_for_use_of_improved_sanitation_for_/990863", "title"=>"Relationship between relative geographical inequality for use of improved drinking water and RGI for use of improved sanitation for", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454594"], "description"=>"a<p>MDG 7C is to halve the proportion of the population without sustainable access to safe drinking water and basic sanitation. Classification of achievement is based on figures produced by the WHO/UNICEF JMP for Water Supply and Sanitation for 1990 and 2010 <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001626#pmed.1001626-WHOUNICEF4\" target=\"_blank\">[91]</a>.</p>b<p>Median administrative area standard deviation (SD) is taken from the posterior distribution for standard deviation at the administrative area level generated by the hierarchical model, and reflects the amount of data available for a given country (increased SD reflects greater uncertainty and less data).</p>c<p>RGI is a measure of relative inequality in access sub-nationally within a given country given national coverage levels. Negative values indicate a lower than expected inequality, whilst positive values indicate greater than expected inequality; <b>↓</b> indicates a score significantly lower than 0 and <b>↑</b> indicates a score significantly higher than 0.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "estimates", "admin2", "summaries", "procedures", "drinking-water"], "article_id"=>990864, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_National_estimates_and_admin2_summaries_resulting_from_the_modelling_procedures_for_access_to_improved_drinking_water_source_improved_sanitation_and_open_defecation_/990864", "title"=>"National estimates and admin2 summaries resulting from the modelling procedures for access to improved drinking-water source, improved sanitation, and open defecation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454595"], "description"=>"<p>Sub-Saharan countries not included are Botswana, Cape Verde, Comoros, Djibouti, Eritrea, Reunion, Sao Tome and Principe, and Seychelles, representing <1.0% of the population of SSA in 2012.</p><p>AIS, AIDs Indicator Surveys; DHS, Demographic and Health Surveys; LSMS, Living Standard Measurement Studies; MICs, Multiple Indicator Cluster Surveys; MIS, Malaria Indicator Surveys.</p>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "sanitation", "sources", "41", "sub-saharan", "african"], "article_id"=>990865, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regional_summary_of_water_and_sanitation_coverage_data_sources_and_quantity_for_41_sub_Saharan_African_countries_/990865", "title"=>"Regional summary of water and sanitation coverage data sources and quantity for 41 sub-Saharan African countries.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-08 03:40:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1454596"], "description"=>"<div><p>Background</p><p>Understanding geographic inequalities in coverage of drinking-water supply and sanitation (WSS) will help track progress towards universal coverage of water and sanitation by identifying marginalized populations, thus helping to control a large number of infectious diseases. This paper uses household survey data to develop comprehensive maps of WSS coverage at high spatial resolution for sub-Saharan Africa (SSA). Analysis is extended to investigate geographic heterogeneity and relative geographic inequality within countries.</p><p>Methods and Findings</p><p>Cluster-level data on household reported use of improved drinking-water supply, sanitation, and open defecation were abstracted from 138 national surveys undertaken from 1991–2012 in 41 countries. Spatially explicit logistic regression models were developed and fitted within a Bayesian framework, and used to predict coverage at the second administrative level (admin2, e.g., district) across SSA for 2012. Results reveal substantial geographical inequalities in predicted use of water and sanitation that exceed urban-rural disparities. The average range in coverage seen between admin2 within countries was 55% for improved drinking water, 54% for use of improved sanitation, and 59% for dependence upon open defecation. There was also some evidence that countries with higher levels of inequality relative to coverage in use of an improved drinking-water source also experienced higher levels of inequality in use of improved sanitation (rural populations <i>r</i> = 0.47, <i>p</i> = 0.002; urban populations <i>r</i> = 0.39, <i>p</i> = 0.01). Results are limited by the quantity of WSS data available, which varies considerably by country, and by the reliability and utility of available indicators.</p><p>Conclusions</p><p>This study identifies important geographic inequalities in use of WSS previously hidden within national statistics, confirming the necessity for targeted policies and metrics that reach the most marginalized populations. The presented maps and analysis approach can provide a mechanism for monitoring future reductions in inequality within countries, reflecting priorities of the post-2015 development agenda.</p><p><i>Please see later in the article for the Editors' Summary</i></p></div>", "links"=>[], "tags"=>["health care", "Environmental health", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "inequalities", "sanitation", "sub-saharan", "spatial", "cross-sectional"], "article_id"=>990866, "categories"=>["Biological Sciences"], "users"=>["Rachel L. Pullan", "Matthew C. Freeman", "Peter W. Gething", "Simon J. Brooker"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1001626"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Geographical_Inequalities_in_Use_of_Improved_Drinking_Water_Supply_and_Sanitation_across_Sub_Saharan_Africa_Mapping_and_Spatial_Analysis_of_Cross_sectional_Survey_Data_/990866", "title"=>"Geographical Inequalities in Use of Improved Drinking Water Supply and Sanitation across Sub-Saharan Africa: Mapping and Spatial Analysis of Cross-sectional Survey Data", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-08 03:40:25"}

PMC Usage Stats | Further Information

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