Drivers of Inequality in Millennium Development Goal Progress: A Statistical Analysis
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{"title"=>"Drivers of inequality in millennium development goal progress: A statistical analysis", "type"=>"journal", "authors"=>[{"first_name"=>"David", "last_name"=>"Stuckler", "scopus_author_id"=>"23568944000"}, {"first_name"=>"Sanjay", "last_name"=>"Basu", "scopus_author_id"=>"9335409500"}, {"first_name"=>"Martin", "last_name"=>"McKee", "scopus_author_id"=>"7202304775"}], "year"=>2010, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "scopus"=>"2-s2.0-77950688150", "sgr"=>"77950688150", "pui"=>"358607688", "isbn"=>"1549-1676 (Electronic)\\n1549-1277 (Linking)", "pmid"=>"20209000", "doi"=>"10.1371/journal.pmed.1000241"}, "id"=>"091a7a58-b9f6-3bab-a597-83523c641be3", "abstract"=>"BACKGROUND: Many low- and middle-income countries are not on track to reach the public health targets set out in the Millennium Development Goals (MDGs). We evaluated whether differential progress towards health MDGs was associated with economic development, public health funding (both overall and as percentage of available domestic funds), or health system infrastructure. We also examined the impact of joint epidemics of HIV/AIDS and noncommunicable diseases (NCDs), which may limit the ability of households to address child mortality and increase risks of infectious diseases.\\n\\nMETHODS AND FINDINGS: We calculated each country's distance from its MDG goals for HIV/AIDS, tuberculosis, and infant and child mortality targets for the year 2005 using the United Nations MDG database for 227 countries from 1990 to the present. We studied the association of economic development (gross domestic product [GDP] per capita in purchasing-power-parity), the relative priority placed on health (health spending as a percentage of GDP), real health spending (health system expenditures in purchasing-power-parity), HIV/AIDS burden (prevalence rates among ages 15-49 y), and NCD burden (age-standardised chronic disease mortality rates), with measures of distance from attainment of health MDGs. To avoid spurious correlations that may exist simply because countries with high disease burdens would be expected to have low MDG progress, and to adjust for potential confounding arising from differences in countries' initial disease burdens, we analysed the variations in rates of change in MDG progress versus expected rates for each country. While economic development, health priority, health spending, and health infrastructure did not explain more than one-fifth of the differences in progress to health MDGs among countries, burdens of HIV and NCDs explained more than half of between-country inequalities in child mortality progress (R(2)-infant mortality = 0.57, R(2)-under 5 mortality = 0.54). HIV/AIDS and NCD burdens were also the strongest correlates of unequal progress towards tuberculosis goals (R(2) = 0.57), with NCDs having an effect independent of HIV/AIDS, consistent with micro-level studies of the influence of tobacco and diabetes on tuberculosis risks. Even after correcting for health system variables, initial child mortality, and tuberculosis diseases, we found that lower burdens of HIV/AIDS and NCDs were associated with much greater progress towards attainment of child mortality and tuberculosis MDGs than were gains in GDP. An estimated 1% lower HIV prevalence or 10% lower mortality rate from NCDs would have a similar impact on progress towards the tuberculosis MDG as an 80% or greater rise in GDP, corresponding to at least a decade of economic growth in low-income countries.\\n\\nCONCLUSIONS: Unequal progress in health MDGs in low-income countries appears significantly related to burdens of HIV and NCDs in a population, after correcting for potentially confounding socioeconomic, disease burden, political, and health system variables. The common separation between NCDs, child mortality, and infectious syndromes among development programs may obscure interrelationships of illness affecting those living in poor households--whether economic (e.g., as money spent on tobacco is lost from child health expenditures) or biological (e.g., as diabetes or HIV enhance the risk of tuberculosis).", "link"=>"http://www.mendeley.com/research/drivers-inequality-millennium-development-goal-progress-statistical-analysis", "reader_count"=>186, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>7, "Librarian"=>1, "Researcher"=>37, "Student > Doctoral Student"=>8, "Student > Ph. D. 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Student"=>21, "Student > Postgraduate"=>8, "Student > Master"=>53, "Other"=>12, "Student > Bachelor"=>20, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>4, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>15, "Agricultural and Biological Sciences"=>12, "Arts and Humanities"=>3, "Business, Management and Accounting"=>3, "Chemistry"=>3, "Computer Science"=>1, "Economics, Econometrics and Finance"=>10, "Engineering"=>2, "Environmental Science"=>4, "Nursing and Health Professions"=>11, "Mathematics"=>1, "Medicine and Dentistry"=>88, "Psychology"=>8, "Social Sciences"=>24}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>88}, "Social Sciences"=>{"Social Sciences"=>24}, "Psychology"=>{"Psychology"=>8}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>15}, "Environmental Science"=>{"Environmental Science"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Computer Science"=>{"Computer Science"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>11}}, "reader_count_by_country"=>{"Saudi Arabia"=>1, "Colombia"=>2, "Sweden"=>1, "United States"=>2, "Brazil"=>1, "South Africa"=>5, "United Kingdom"=>1, "Spain"=>1, "India"=>1, "Indonesia"=>1}, "group_count"=>5}

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  • {"files"=>["https://ndownloader.figshare.com/files/860991"], "description"=>"<p><i>Notes</i>: Authors' calculations. Unmet MDG Progress is calculated in percentage terms as 100 * [1–(Actual ΔMR/Expected ΔMR)], for years 1990 and 2005 for infant, child, and tuberculosis mortality; 2001 and 2007 for HIV prevalence. Scores include negative values (i.e., greater than 100% progress). Source of data: Millennium Development Goals Indicators, available at <a href=\"http://mdgs.un.org/unsd/mdg/Default.aspx\" target=\"_blank\">http://mdgs.un.org/unsd/mdg/Default.aspx</a>. MDG #4 aims to reduce infant and child mortality by two-thirds by 2015. MDG #6 aims to halt and reverse the incidence, prevalence and mortality of HIV, malaria and other diseases, including tuberculosis. Box 1 further defines MDG targets. Zero percent denotes complete progress. Geographic classification based on World Bank geographic categories for the year 2009: <a href=\"http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20420458~menuPK:64133156~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html\" target=\"_blank\">http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20420458~menuPK:64133156~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html</a></p>", "links"=>[], "tags"=>["millennium", "geographic"], "article_id"=>531454, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unmet_progress_towards_Millennium_Development_Goals_by_geographic_region_/531454", "title"=>"Unmet progress towards Millennium Development Goals, by geographic region.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-02 00:24:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/861104"], "description"=>"<p><i>Notes:</i> Results presented from four separate regression models. Constant estimated but not reported. 95% confidence intervals based on heteroskedasticity robust standard errors in parentheses. Unmet MDG Progress is calculated in percentage terms as 100×[1–(Actual ΔMR/Expected ΔMR)]. Multiplying each coefficient by 40% transforms the coefficient to describe the associations with the percentage change in each outcome. Progress towards reducing infant mortality rates and child mortality rates reflects MDG 4.1 and 4.2 and modelled using a linear standard regression model. Progress towards halting or reversing tuberculosis mortality rates reflects MDG 6.9, and modelled using a linear probability model. Progress towards halting or reversing HIV prevalence reflects MDG 6.1, and modelled using a linear probability model. Data are from UN Millennium Development Goals Indicators 2008 edition.</p><p>*<i>p</i><0.05.</p><p>**<i>p</i><0.01.</p><p>***<i>p</i><0.001.</p>", "links"=>[], "tags"=>["gdp", "physicians", "capita", "unmet"], "article_id"=>531564, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.t005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_of_GDP_per_capita_health_spending_GDP_health_spending_and_physicians_per_capita_with_percentage_of_unmet_progress_toward_health_MDGs_/531564", "title"=>"Associations of GDP per capita, health spending/GDP, health spending, and physicians per capita with percentage of unmet progress toward health MDGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-02 00:26:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/427513", "https://ndownloader.figshare.com/files/427575", "https://ndownloader.figshare.com/files/427681", "https://ndownloader.figshare.com/files/427781", "https://ndownloader.figshare.com/files/427837", "https://ndownloader.figshare.com/files/427941", "https://ndownloader.figshare.com/files/428014", "https://ndownloader.figshare.com/files/428086", "https://ndownloader.figshare.com/files/428126"], "description"=>"<div><h3>Background</h3><p>Many low- and middle-income countries are not on track to reach the public health targets set out in the Millennium Development Goals (MDGs). We evaluated whether differential progress towards health MDGs was associated with economic development, public health funding (both overall and as percentage of available domestic funds), or health system infrastructure. We also examined the impact of joint epidemics of HIV/AIDS and noncommunicable diseases (NCDs), which may limit the ability of households to address child mortality and increase risks of infectious diseases.</p><h3>Methods and Findings</h3><p>We calculated each country's distance from its MDG goals for HIV/AIDS, tuberculosis, and infant and child mortality targets for the year 2005 using the United Nations MDG database for 227 countries from 1990 to the present. We studied the association of economic development (gross domestic product [GDP] per capita in purchasing-power-parity), the relative priority placed on health (health spending as a percentage of GDP), real health spending (health system expenditures in purchasing-power-parity), HIV/AIDS burden (prevalence rates among ages 15–49 y), and NCD burden (age-standardised chronic disease mortality rates), with measures of distance from attainment of health MDGs. To avoid spurious correlations that may exist simply because countries with high disease burdens would be expected to have low MDG progress, and to adjust for potential confounding arising from differences in countries' initial disease burdens, we analysed the variations in rates of <em>change</em> in MDG progress versus expected rates for each country. While economic development, health priority, health spending, and health infrastructure did not explain more than one-fifth of the differences in progress to health MDGs among countries, burdens of HIV and NCDs explained more than half of between-country inequalities in child mortality progress (<em>R</em><sup>2</sup>-infant mortality  = 0.57, <em>R</em><sup>2</sup>-under 5 mortality  = 0.54). HIV/AIDS and NCD burdens were also the strongest correlates of unequal progress towards tuberculosis goals (<em>R</em><sup>2</sup> = 0.57), with NCDs having an effect independent of HIV/AIDS, consistent with micro-level studies of the influence of tobacco and diabetes on tuberculosis risks. Even after correcting for health system variables, initial child mortality, and tuberculosis diseases, we found that lower burdens of HIV/AIDS and NCDs were associated with much greater progress towards attainment of child mortality and tuberculosis MDGs than were gains in GDP. An estimated 1% lower HIV prevalence or 10% lower mortality rate from NCDs would have a similar impact on progress towards the tuberculosis MDG as an 80% or greater rise in GDP, corresponding to at least a decade of economic growth in low-income countries.</p><h3>Conclusions</h3><p>Unequal progress in health MDGs in low-income countries appears significantly related to burdens of HIV and NCDs in a population, after correcting for potentially confounding socioeconomic, disease burden, political, and health system variables. The common separation between NCDs, child mortality, and infectious syndromes among development programs may obscure interrelationships of illness affecting those living in poor households—whether economic (e.g., as money spent on tobacco is lost from child health expenditures) or biological (e.g., as diabetes or HIV enhance the risk of tuberculosis).</p><h3></h3><p><em>Please see later in the article for the Editors' Summary</em></p></div>", "links"=>[], "tags"=>["drivers", "inequality", "millennium"], "article_id"=>144392, "categories"=>["Cancer", "Biological Sciences", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000241.s001", "https://dx.doi.org/10.1371/journal.pmed.1000241.s002", "https://dx.doi.org/10.1371/journal.pmed.1000241.s003", "https://dx.doi.org/10.1371/journal.pmed.1000241.s004", "https://dx.doi.org/10.1371/journal.pmed.1000241.s005", "https://dx.doi.org/10.1371/journal.pmed.1000241.s006", "https://dx.doi.org/10.1371/journal.pmed.1000241.s007", "https://dx.doi.org/10.1371/journal.pmed.1000241.s008", "https://dx.doi.org/10.1371/journal.pmed.1000241.s009"], "stats"=>{"downloads"=>42, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Drivers_of_Inequality_in_Millennium_Development_Goal_Progress_A_Statistical_Analysis/144392", "title"=>"Drivers of Inequality in Millennium Development Goal Progress: A Statistical Analysis", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-03-02 01:13:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/860911"], "description"=>"<p>Notes: Authors' calculations. Unmet MDG Progress is calculated in percentage terms as 100 * [1–(Actual ΔMR/Expected ΔMR)], for years 1990 and 2005 for infant, child, and tuberculosis mortality; 2001 and 2007 for HIV prevalence. Scores include negative values (i.e., greater than 100% progress). Source of data: Millennium Development Goals Indicators, available at <a href=\"http://mdgs.un.org/unsd/mdg/Default.aspx\" target=\"_blank\">http://mdgs.un.org/unsd/mdg/Default.aspx</a>. MDG #4 aims to reduce infant and child mortality by two-thirds by 2015. MDG #6 aims to halt and reverse the incidence, prevalence and mortality of HIV, malaria and other diseases, including tuberculosis. Box 1 further defines MDG targets. Income groups categorized based on the World Bank Atlas method based on their average GDP per capita from 1990 to 2005. For Box plots see <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000241#pmed.1000241.s008\" target=\"_blank\">Text S8</a></p>", "links"=>[], "tags"=>["millennium"], "article_id"=>531376, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unmet_progress_towards_Millennium_Development_Goals_by_income_group_/531376", "title"=>"Unmet progress towards Millennium Development Goals, by income group.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-02 00:22:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/861146"], "description"=>"<p><i>Notes:</i> Results presented from four separate regression models. Constant estimated but not reported. 95% confidence intervals based on heteroskedasticity robust standard errors in parentheses. Unmet MDG Progress is calculated in percentage terms as 100×[1–(Actual ΔMR/Expected ΔMR)]. Multiplying each coefficient by 40% transforms the coefficient to describe the associations with the percentage change in each outcome. Progress towards reducing infant mortality rates and child mortality rates reflects MDG 4.1 and 4.2 and modelled using a linear standard regression model. Progress towards halting or reversing tuberculosis mortality rates reflects MDG 6.9, and modelled using a linear probability model. Progress towards halting or reversing HIV prevalence reflects MDG 6.1, and modelled using a linear probability model. Data are from UN Millennium Development Goals Indicators 2008 edition.</p><p>*<i>p</i><0.05.</p><p>**<i>p</i><0.01.</p><p>***<i>p</i><0.001</p>", "links"=>[], "tags"=>["gdp", "physicians", "hiv", "prevalence", "unmet"], "article_id"=>531608, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.t006", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_of_GDP_per_capita_health_spending_GDP_health_spending_physicians_per_capita_and_HIV_prevalence_with_percentage_of_unmet_progress_toward_health_MDGs_/531608", "title"=>"Associations of GDP per capita, health spending/GDP, health spending, physicians per capita, and HIV prevalence with percentage of unmet progress toward health MDGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-02 00:26:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/861074"], "description"=>"<p><i>Notes</i>: Results presented from four separate regression models. Constant estimated but not reported. 95% confidence intervals based on heteroskedasticity robust standard errors in parentheses. Unmet MDG Progress is calculated in percentage terms as 100×[1–(Actual ΔMR/Expected ΔMR)]. Multiplying each coefficient by 40% transforms the coefficient to describe the associations with the percentage change in each outcome. Progress towards reducing infant mortality rates and child mortality rates reflects MDG 4.1 and 4.2 and modelled using a linear standard regression model. Progress towards halting or reversing tuberculosis mortality rates reflects MDG 6.9, and modelled using a linear probability model. Progress towards halting or reversing HIV prevalence reflects MDG 6.1, and modelled using a linear probability model. Data are from UN Millennium Development Goals Indicators 2008 edition.</p><p>*<i>p</i><0.05.</p><p>**<i>p</i><0.01.</p><p>***<i>p</i><0.001.</p>", "links"=>[], "tags"=>["gdp", "unmet"], "article_id"=>531531, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.t004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_of_GDP_per_capita_health_spending_GDP_and_health_spending_with_percentage_of_unmet_progress_toward_health_MDGs_/531531", "title"=>"Associations of GDP per capita, health spending/GDP, and health spending with percentage of unmet progress toward health MDGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-02 00:25:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/861279"], "description"=>"<p><i>Notes</i>: Results presented from four separate regression models. Constant estimated but not reported. 95% confidence intervals based on heteroskedasticity robust standard errors in parentheses. Unmet MDG Progress is calculated in percentage terms as 100×[1−(Actual ΔMR/Expected ΔMR)]. Multiplying each coefficient by 40% transforms the coefficient to describe the associations with the percentage change in each outcome. Progress towards reducing infant mortality rates and child mortality rates reflects MDG 4.1 and 4.2 and modelled using a linear standard regression model. Progress towards halting or reversing tuberculosis mortality rates reflects MDG 6.9, and modelled using a linear probability model. Progress towards halting or reversing HIV prevalence reflects MDG 6.1, and modelled using a linear probability model. Data are from UN Millennium Development Goals Indicators 2008 edition.</p><p>*<i>p</i><0.05.</p><p>**<i>p</i><0.01.</p><p>***<i>p</i><0.001.</p>", "links"=>[], "tags"=>["gdp", "capita", "unmet", "health-related"], "article_id"=>531740, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.t003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_of_GDP_per_capita_and_health_spending_GDP_with_percentage_of_unmet_progress_towards_health_related_MDGs_/531740", "title"=>"Associations of GDP per capita and health spending/GDP with percentage of unmet progress towards health-related MDGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-02 00:29:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/861183"], "description"=>"<p><i>Notes</i>: Results presented from four separate regression models. Constant estimated but not reported. 95% confidence intervals based on heteroskedasticity robust standard errors in parentheses. Unmet MDG Progress is calculated in percentage terms as 100 [1−(Actual ΔMR/Expected ΔMR)]. Multiplying each coefficient by 40% transforms the coefficient to describe the associations with the percentage change in each outcome. Progress towards reducing infant mortality rates and child mortality rates reflects MDG 4.1 and 4.2 and modelled using a linear standard regression model. Progress towards halting or reversing tuberculosis mortality rates reflects MDG 6.9, and modelled using a linear probability model. Progress towards halting or reversing HIV prevalence reflects MDG 6.1, and modelled using a linear probability model. Data are from UN Millennium Development Goals Indicators 2008 edition.</p><p>*<i>p</i><0.05.</p><p>**<i>p</i><0.01.</p><p>***<i>p</i><0.001.</p>", "links"=>[], "tags"=>["gdp", "physicians", "hiv", "ncd", "rates", "unmet"], "article_id"=>531640, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.t007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_of_GDP_per_capita_health_spending_GDP_health_spending_physicians_per_capita_HIV_prevalence_and_NCD_mortality_rates_with_percentage_of_unmet_progress_toward_health_MDGs_/531640", "title"=>"Associations of GDP per capita, health spending/GDP, health spending, physicians per capita, HIV prevalence, and NCD mortality rates with percentage of unmet progress toward health MDGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-02 00:27:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/861242"], "description"=>"<p><i>Notes</i>: Results presented from four separate regression models. Constant estimated but not reported. 95% confidence intervals based on heteroskedasticity robust standard errors in parentheses. Unmet MDG Progress is calculated in percentage terms as 100×[1−(Actual ΔMR/Expected ΔMR)]. Multiplying each coefficient by 40% transforms the coefficient to describe the associations with the percentage change in each outcome. Progress towards reducing infant mortality rates and child mortality rates reflects MDG 4.1 and 4.2 and modelled using a linear standard regression model. Progress towards halting or reversing tuberculosis mortality rates reflects MDG 6.9, and modelled using a linear probability model. Progress towards halting or reversing HIV prevalence reflects MDG 6.1, and modelled using a linear probability model. Data are from UN Millennium Development Goals Indicators 2008 edition.</p><p>*<i>p</i><0.05.</p><p>**<i>p</i><0.01.</p><p>***<i>p</i><0.001.</p>", "links"=>[], "tags"=>["gdp", "capita", "unmet", "health-related"], "article_id"=>531702, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_of_GDP_per_capita_with_percentage_of_unmet_progress_towards_health_related_MDGs_/531702", "title"=>"Associations of GDP per capita with percentage of unmet progress towards health-related MDGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-02 00:28:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/861212"], "description"=>"<p><i>Notes</i>: See <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000241#pmed.1000241.s006\" target=\"_blank\">Text S6</a> for more details.</p>", "links"=>[], "tags"=>["ncds", "injuries", "factors"], "article_id"=>531670, "categories"=>["Cancer", "Computational Biology", "Information And Computing Sciences"], "users"=>["David Stuckler", "Sanjay Basu", "Martin McKee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000241.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selected_Effects_of_NCDs_and_injuries_and_their_risk_factors_on_health_MDGs_/531670", "title"=>"Selected Effects of NCDs and injuries and their risk factors on health MDGs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-02 00:27:50"}

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  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"9", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"12", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"12", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"13", "full-text"=>"14", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"13", "full-text"=>"11", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"16", "full-text"=>"5", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"4", "full-text"=>"2", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"8"}

Relative Metric

{"start_date"=>"2010-01-01T00:00:00Z", "end_date"=>"2010-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Medicine and health sciences", "average_usage"=>[277, 558, 715, 845, 963, 1070, 1166, 1258, 1336, 1410, 1485, 1561, 1638, 1707, 1767, 1834, 1896, 1960, 2030, 2096, 2165, 2229, 2300, 2368, 2425, 2489, 2553, 2617, 2679, 2743, 2814, 2873, 2941, 2995, 3050, 3111, 3169, 3235, 3300, 3363, 3428, 3487, 3548, 3605, 3670, 3726, 3785, 3844]}, {"subject_area"=>"/Medicine and health sciences/Pediatrics", "average_usage"=>[290, 559, 706, 802, 937, 1071, 1195, 1291, 1385, 1484, 1588, 1646, 1776, 1846, 1915, 2023, 2145, 2248, 2290, 2367, 2442, 2519, 2624, 2702, 2737, 2808, 2856, 2910, 2967, 3038, 3079, 3136, 3187, 3234, 3281, 3322, 3387, 3452, 3515, 3569, 3645, 3775, 3854, 3936, 4028, 4095, 4168, 4229, 4304]}, {"subject_area"=>"/People and places/Demography", "average_usage"=>[261, 552, 705, 818, 947, 1060, 1175, 1250, 1325, 1412, 1492, 1562, 1621, 1697, 1808, 1898, 1989, 2068, 2145, 2212, 2296, 2407, 2522, 2603, 2651, 2717, 2791, 2845, 2900, 2949, 3014, 3069, 3135, 3204, 3256, 3307, 3349, 3417, 3464, 3533, 3570, 3605, 3664, 3713, 3769, 3844, 3889, 3924, 3974]}, {"subject_area"=>"/Social sciences", "average_usage"=>[365, 646, 813, 929, 1044, 1126, 1205, 1294, 1377, 1449, 1517, 1586, 1660, 1746, 1812, 1886, 1945, 2022, 2094, 2179, 2255, 2320, 2417, 2490, 2571, 2650, 2693, 2787, 2850, 2927, 2994, 3072, 3138, 3216, 3261, 3337, 3393, 3448, 3512, 3606, 3667, 3746, 3811, 3865, 3928, 3988, 4031, 4086]}, {"subject_area"=>"/Social sciences/Economics", "average_usage"=>[339, 630, 827, 965, 1074, 1205, 1274, 1329, 1409, 1464, 1506, 1583, 1671, 1786, 1912, 2028, 2086, 2171, 2267, 2378, 2495, 2564, 2627, 2752, 2855, 2934, 3002, 3073, 3133, 3195, 3250, 3329, 3391, 3516, 3608, 3703, 3748, 3797, 3853, 3911, 3961, 4004, 4038, 4096, 4146, 4209, 4281, 4371, 4449]}]}

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