Corruption Kills: Estimating the Global Impact of Corruption on Children Deaths
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{"title"=>"Corruption kills: Estimating the global impact of corruption on children deaths", "type"=>"journal", "authors"=>[{"first_name"=>"Matthieu", "last_name"=>"Hanf", "scopus_author_id"=>"35279874300"}, {"first_name"=>"Astrid", "last_name"=>"Van-Melle", "scopus_author_id"=>"36244753800"}, {"first_name"=>"Florence", "last_name"=>"Fraisse", "scopus_author_id"=>"54404601700"}, {"first_name"=>"Amaury", "last_name"=>"Roger", "scopus_author_id"=>"54405080400"}, {"first_name"=>"Bernard", "last_name"=>"Carme", "scopus_author_id"=>"7103224732"}, {"first_name"=>"Mathieu", "last_name"=>"Nacher", "scopus_author_id"=>"7006076854"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-80355146199", "doi"=>"10.1371/journal.pone.0026990", "issn"=>"19326203", "pui"=>"362861833", "sgr"=>"80355146199", "isbn"=>"1932-6203", "pmid"=>"22073233"}, "id"=>"19460f42-ddef-3491-b34d-fbb83db6da37", "abstract"=>"BACKGROUND: Information on the global risk factors of children mortality is crucial to guide global efforts to improve survival. Corruption has been previously shown to significantly impact on child mortality. However no recent quantification of its current impact is available.\\n\\nMETHODS: The impact of corruption was assessed through crude Pearson's correlation, univariate and multivariate linear models coupling national under-five mortality rates in 2008 to the national \"perceived level of corruption\" (CPI) and a large set of adjustment variables measured during the same period.\\n\\nFINDINGS: The final multivariable model (adjusted R(2)= 0.89) included the following significant variables: percentage of people with improved sanitation (p.value<0.001), logarithm of total health expenditure (p.value = 0.006), Corruption Perception Index (p.value<0.001), presence of an arid climate on the national territory (p = 0.006), and the dependency ratio (p.value<0.001). A decrease in CPI of one point (i.e. a more important perceived corruption) was associated with an increase in the log of national under-five mortality rate of 0.0644. According to this result, it could be roughly hypothesized that more than 140000 annual children deaths could be indirectly attributed to corruption.\\n\\nINTERPRETATIONS: Global response to children mortality must involve a necessary increase in funds available to develop water and sanitation access and purchase new methods for prevention, management, and treatment of major diseases drawing the global pattern of children deaths. However without paying regard to the anti-corruption mechanisms needed to ensure their proper use, it will also provide further opportunity for corruption. Policies and interventions supported by governments and donors must integrate initiatives that recognise how they are inter-related.", "link"=>"http://www.mendeley.com/research/corruption-kills-estimating-global-impact-corruption-children-deaths", "reader_count"=>32, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>3, "Researcher"=>4, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>2}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>3, "Researcher"=>4, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Agricultural and Biological Sciences"=>4, "Arts and Humanities"=>2, "Business, Management and Accounting"=>3, "Computer Science"=>1, "Economics, Econometrics and Finance"=>4, "Engineering"=>2, "Environmental Science"=>1, "Nursing and Health Professions"=>2, "Medicine and Dentistry"=>4, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>1, "Social Sciences"=>6}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>6}, "Psychology"=>{"Psychology"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Engineering"=>{"Engineering"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}}, "reader_count_by_country"=>{"Argentina"=>1, "Malaysia"=>1, "Australia"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/716947"], "description"=>"<p>Representation obtained with a Focused Principal Components Analysis. Pearson's correlations between the mortality rate in children and the other variables are represented faithfully. Positive correlations with child mortality are plotted on the left and negative ones on the right. Correlations which are significantly different to zero are inside the pink circle. The relationships between the other variables are interpreted like in a PCA: correlated variables are close or diametrically opposite (for negative correlations), independent variables make a right angle with the origin. Colours indicate the nature of explicative variables (red: political and societal factors, blue: socio economic factors, orange: health and medical factors and green: environmental factors).</p>", "links"=>[], "tags"=>["correlations", "children", "variables"], "article_id"=>387309, "categories"=>["Mathematics", "Biotechnology"], "users"=>["Matthieu Hanf", "Astrid Van-Melle", "Florence Fraisse", "Amaury Roger", "Bernard Carme", "Mathieu Nacher"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0026990.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pearson_s_correlations_between_the_mortality_rate_in_children_and_the_other_variables_used_in_the_analysis_/387309", "title"=>"Pearson's correlations between the mortality rate in children and the other variables used in the analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 12:04:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/717124"], "description"=>"<p>Available variables that can potentially explain inter-country variation in the rate of deaths in children in 178 countries, 2008.</p>", "links"=>[], "tags"=>["variables", "inter-country", "deaths", "children", "178"], "article_id"=>387475, "categories"=>["Mathematics", "Biotechnology"], "users"=>["Matthieu Hanf", "Astrid Van-Melle", "Florence Fraisse", "Amaury Roger", "Bernard Carme", "Mathieu Nacher"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0026990.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Available_variables_that_can_potentially_explain_inter_country_variation_in_the_rate_of_deaths_in_children_in_178_countries_2008_/387475", "title"=>"Available variables that can potentially explain inter-country variation in the rate of deaths in children in 178 countries, 2008.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 12:05:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/716846"], "description"=>"<p>A) Deaths of children younger than 5 years per 1000 live births in 2008. Derivated from data compiled by Black and colleagues <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026990#pone.0026990-Black2\" target=\"_blank\">[11]</a>. B) Corruption Perceptions Index (CPI) in 2008. Derivated from data compiled by Transparency International <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0026990#pone.0026990-Santosham1\" target=\"_blank\">[10]</a>.</p>", "links"=>[], "tags"=>["repartition", "children", "deaths"], "article_id"=>387196, "categories"=>["Mathematics", "Biotechnology"], "users"=>["Matthieu Hanf", "Astrid Van-Melle", "Florence Fraisse", "Amaury Roger", "Bernard Carme", "Mathieu Nacher"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0026990.g001", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_World_repartition_of_children_deaths_and_perceived_corruption_/387196", "title"=>"World repartition of children deaths and perceived corruption.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 12:04:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/717091"], "description"=>"<p>£: Analysis of deaths in children from a multivariate linear regression model. Only variables with a p.value<0·1 in univariate regression were incorporated in the final multivariate analysis. Backward stepwise procedure with a stop criterion fixed at 0·1 was then applied to determinate the final multivariate model.</p>", "links"=>[], "tags"=>["multivariate", "linear", "regressions", "predicting", "children", "deaths", "1000", "births", "178", "2008"], "article_id"=>387437, "categories"=>["Mathematics", "Biotechnology"], "users"=>["Matthieu Hanf", "Astrid Van-Melle", "Florence Fraisse", "Amaury Roger", "Bernard Carme", "Mathieu Nacher"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0026990.t002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Univariate_and_multivariate_linear_regressions_predicting_Log_of_children_deaths_per_1000_live_births_in_178_countries_2008_163_/387437", "title"=>"Univariate and multivariate linear regressions predicting Log of children deaths per 1000 live births in 178 countries, 2008 (£).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 12:05:22"}

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Relative Metric

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