Antimicrobial Resistance: The Major Contribution of Poor Governance and Corruption to This Growing Problem
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{"title"=>"Antimicrobial resistance: The major contribution of poor governance and corruption to this growing problem", "type"=>"journal", "authors"=>[{"first_name"=>"Peter", "last_name"=>"Collignon", "scopus_author_id"=>"35377810400"}, {"first_name"=>"Prema Chandra", "last_name"=>"Athukorala", "scopus_author_id"=>"7003816319"}, {"first_name"=>"Sanjaya", "last_name"=>"Senanayake", "scopus_author_id"=>"7004008804"}, {"first_name"=>"Fahad", "last_name"=>"Khan", "scopus_author_id"=>"56567283000"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84925428021", "doi"=>"10.1371/journal.pone.0116746", "pui"=>"603271357", "pmid"=>"25786027", "scopus"=>"2-s2.0-84925428021", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "arxiv"=>"e0116746"}, "id"=>"b541329f-4bc1-366f-a722-ae6f0abe3a6d", "abstract"=>"OBJECTIVES: To determine how important governmental, social, and economic factors are in driving antibiotic resistance compared to the factors usually considered the main driving factors-antibiotic usage and levels of economic development.\\n\\nDESIGN: A retrospective multivariate analysis of the variation of antibiotic resistance in Europe in terms of human antibiotic usage, private health care expenditure, tertiary education, the level of economic advancement (per capita GDP), and quality of governance (corruption). The model was estimated using a panel data set involving 7 common human bloodstream isolates and covering 28 European countries for the period 1998-2010.\\n\\nRESULTS: Only 28% of the total variation in antibiotic resistance among countries is attributable to variation in antibiotic usage. If time effects are included the explanatory power increases to 33%. However when the control of corruption indicator is included as an additional variable, 63% of the total variation in antibiotic resistance is now explained by the regression. The complete multivariate regression only accomplishes an additional 7% in terms of goodness of fit, indicating that corruption is the main socioeconomic factor that explains antibiotic resistance. The income level of a country appeared to have no effect on resistance rates in the multivariate analysis. The estimated impact of corruption was statistically significant (p< 0.01). The coefficient indicates that an improvement of one unit in the corruption indicator is associated with a reduction in antibiotic resistance by approximately 0.7 units. The estimated coefficient of private health expenditure showed that one unit reduction is associated with a 0.2 unit decrease in antibiotic resistance.\\n\\nCONCLUSIONS: These findings support the hypothesis that poor governance and corruption contributes to levels of antibiotic resistance and correlate better than antibiotic usage volumes with resistance rates. We conclude that addressing corruption and improving governance will lead to a reduction in antibiotic resistance.", "link"=>"http://www.mendeley.com/research/antimicrobial-resistance-major-contribution-poor-governance-corruption-growing-problem", "reader_count"=>79, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>5, "Student > Doctoral Student"=>3, "Researcher"=>13, "Student > Ph. D. 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Student"=>16, "Student > Postgraduate"=>6, "Other"=>4, "Student > Master"=>15, "Student > Bachelor"=>4, "Lecturer > Senior Lecturer"=>2, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Agricultural and Biological Sciences"=>10, "Veterinary Science and Veterinary Medicine"=>3, "Business, Management and Accounting"=>2, "Earth and Planetary Sciences"=>2, "Economics, Econometrics and Finance"=>6, "Engineering"=>2, "Environmental Science"=>3, "Nursing and Health Professions"=>3, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>2, "Medicine and Dentistry"=>21, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>1, "Social Sciences"=>7, "Immunology and Microbiology"=>1, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>21}, "Social Sciences"=>{"Social Sciences"=>7}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>11}, "Environmental Science"=>{"Environmental Science"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Engineering"=>{"Engineering"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>6}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>3}}, "reader_count_by_country"=>{"Sweden"=>1, "Argentina"=>1, "Belgium"=>1, "United States"=>1, "China"=>1, "United Kingdom"=>3, "France"=>1, "Spain"=>1}, "group_count"=>5}

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  • {"files"=>["https://ndownloader.figshare.com/files/1958684"], "description"=>"<p>‘Average Microbial Resistance’ against ‘Antibiotic Use.’</p>", "links"=>[], "tags"=>["development.DesignA retrospective multivariate analysis", "resistance rates", "health care expenditure", "0.2 unit decrease", "antibiotic resistance.ConclusionsThese findings support", "antibiotic usage volumes", "gdp", "Antibiotic resistance", "corruption indicator", "antibiotic usage"], "article_id"=>1341374, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Peter Collignon", "Prema-chandra Athukorala", "Sanjaya Senanayake", "Fahad Khan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116746.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_8216_Average_Microbial_Resistance_8217_against_8216_Antibiotic_Use_8217_/1341374", "title"=>"‘Average Microbial Resistance’ against ‘Antibiotic Use.’", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:38:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958692"], "description"=>"<div><p>Objectives</p><p>To determine how important governmental, social, and economic factors are in driving antibiotic resistance compared to the factors usually considered the main driving factors—antibiotic usage and levels of economic development.</p><p>Design</p><p>A retrospective multivariate analysis of the variation of antibiotic resistance in Europe in terms of human antibiotic usage, private health care expenditure, tertiary education, the level of economic advancement (per capita GDP), and quality of governance (corruption). The model was estimated using a panel data set involving 7 common human bloodstream isolates and covering 28 European countries for the period 1998–2010.</p><p>Results</p><p>Only 28% of the total variation in antibiotic resistance among countries is attributable to variation in antibiotic usage. If time effects are included the explanatory power increases to 33%. However when the control of corruption indicator is included as an additional variable, 63% of the total variation in antibiotic resistance is now explained by the regression. The complete multivariate regression only accomplishes an additional 7% in terms of goodness of fit, indicating that corruption is the main socioeconomic factor that explains antibiotic resistance. The income level of a country appeared to have no effect on resistance rates in the multivariate analysis. The estimated impact of corruption was statistically significant (p< 0.01). The coefficient indicates that an improvement of one unit in the corruption indicator is associated with a reduction in antibiotic resistance by approximately 0.7 units. The estimated coefficient of private health expenditure showed that one unit reduction is associated with a 0.2 unit decrease in antibiotic resistance.</p><p>Conclusions</p><p>These findings support the hypothesis that poor governance and corruption contributes to levels of antibiotic resistance and correlate better than antibiotic usage volumes with resistance rates. We conclude that addressing corruption and improving governance will lead to a reduction in antibiotic resistance.</p></div>", "links"=>[], "tags"=>["development.DesignA retrospective multivariate analysis", "resistance rates", "health care expenditure", "0.2 unit decrease", "antibiotic resistance.ConclusionsThese findings support", "antibiotic usage volumes", "gdp", "Antibiotic resistance", "corruption indicator", "antibiotic usage"], "article_id"=>1341382, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Peter Collignon", "Prema-chandra Athukorala", "Sanjaya Senanayake", "Fahad Khan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116746", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Antimicrobial_Resistance_The_Major_Contribution_of_Poor_Governance_and_Corruption_to_This_Growing_Problem_/1341382", "title"=>"Antimicrobial Resistance: The Major Contribution of Poor Governance and Corruption to This Growing Problem", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-18 04:38:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958688"], "description"=>"<p>Note: Standard Deviations are reported in parentheses.</p><p>Descriptive Statistics.</p>", "links"=>[], "tags"=>["development.DesignA retrospective multivariate analysis", "resistance rates", "health care expenditure", "0.2 unit decrease", "antibiotic resistance.ConclusionsThese findings support", "antibiotic usage volumes", "gdp", "Antibiotic resistance", "corruption indicator", "antibiotic usage"], "article_id"=>1341378, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Peter Collignon", "Prema-chandra Athukorala", "Sanjaya Senanayake", "Fahad Khan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116746.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptive_Statistics_/1341378", "title"=>"Descriptive Statistics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-18 04:38:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958691"], "description"=>"<p>Notes:</p><p>The standardized (beta) regression coefficients are reported in the table. TE refers to the set of time dummy variables i.e. the time effects. The estimated coefficient for the constant term and TE are not reported. ‘ABU = -Cor’ refers to the F test for equality of the magnitude for the coefficient of <i>Antibiotic Usage</i> and <i>Control of Corruption</i>. <i>P</i> value is the probability of obtaining the observed test statistic. We reject the null hypothesis if the p-value is less than the level of statistical significance at which the test is conducted. For Pooled OLS and Fixed Effects regressions, standard errors clustered by countries are reported in parenthesis. Annual observations are used from 1998–2010. The Adjusted R<sup>2</sup> value of the regression in Column (1) without including the time effects is 0.28. The R<sup>2</sup> in Columns (4), (5) and (6) refers to the coefficient of determination from estimation of the equivalent Least Squares Dummy Variable Model (LSDV).</p><p>For System GMM, Windmeijer-Corrected Robust standard errors from the two-step GMM estimation are reported in parenthesis. Observations at 2 year intervals used from 1998–2010. Orthogonal forward deviations are used to purge fixed effects. The main explanatory variables (<i>Antibiotic Usage</i> and <i>Control of Corruption</i>) are treated as endogenous and instrumented by the collapsed matrix of all available lags.</p><p>*Significant at the 10% level</p><p>**Significant at the 5% level</p><p>***Significant at the 1% level</p><p>Regression Results for Average Antibiotic Resistance.</p>", "links"=>[], "tags"=>["development.DesignA retrospective multivariate analysis", "resistance rates", "health care expenditure", "0.2 unit decrease", "antibiotic resistance.ConclusionsThese findings support", "antibiotic usage volumes", "gdp", "Antibiotic resistance", "corruption indicator", "antibiotic usage"], "article_id"=>1341381, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Peter Collignon", "Prema-chandra Athukorala", "Sanjaya Senanayake", "Fahad Khan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116746.t003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regression_Results_for_Average_Antibiotic_Resistance_/1341381", "title"=>"Regression Results for Average Antibiotic Resistance.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-18 04:38:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958690"], "description"=>"<p>Pair-wise Correlation Coefficients.</p>", "links"=>[], "tags"=>["development.DesignA retrospective multivariate analysis", "resistance rates", "health care expenditure", "0.2 unit decrease", "antibiotic resistance.ConclusionsThese findings support", "antibiotic usage volumes", "gdp", "Antibiotic resistance", "corruption indicator", "antibiotic usage"], "article_id"=>1341380, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Peter Collignon", "Prema-chandra Athukorala", "Sanjaya Senanayake", "Fahad Khan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116746.t002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pair_wise_Correlation_Coefficients_/1341380", "title"=>"Pair-wise Correlation Coefficients.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-18 04:38:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958687"], "description"=>"<p>‘Average Microbial Resistance’ against ‘Control of Corruption.’</p>", "links"=>[], "tags"=>["development.DesignA retrospective multivariate analysis", "resistance rates", "health care expenditure", "0.2 unit decrease", "antibiotic resistance.ConclusionsThese findings support", "antibiotic usage volumes", "gdp", "Antibiotic resistance", "corruption indicator", "antibiotic usage"], "article_id"=>1341377, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Peter Collignon", "Prema-chandra Athukorala", "Sanjaya Senanayake", "Fahad Khan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116746.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_8216_Average_Microbial_Resistance_8217_against_8216_Control_of_Corruption_8217_/1341377", "title"=>"‘Average Microbial Resistance’ against ‘Control of Corruption.’", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:38:47"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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