Measuring Adult Mortality Using Sibling Survival: A New Analytical Method and New Results for 44 Countries, 1974–2006
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{"title"=>"Measuring adult mortality using sibling survival: A new analytical method and new results for 44 countries, 1974-2006", "type"=>"journal", "authors"=>[{"first_name"=>"Ziad", "last_name"=>"Obermeyer", "scopus_author_id"=>"15848778500"}, {"first_name"=>"Julie Knoll", "last_name"=>"Rajaratnam", "scopus_author_id"=>"35932745800"}, {"first_name"=>"Chang H.", "last_name"=>"Park", "scopus_author_id"=>"7408416325"}, {"first_name"=>"Emmanuela", "last_name"=>"Gakidou", "scopus_author_id"=>"6507025547"}, {"first_name"=>"Margaret C.", "last_name"=>"Hogan", "scopus_author_id"=>"14030077300"}, {"first_name"=>"Alan D.", "last_name"=>"Lopez", "scopus_author_id"=>"7401455668"}, {"first_name"=>"Christopher J.L.", "last_name"=>"Murray", "scopus_author_id"=>"55481130700"}], "year"=>2010, "source"=>"PLoS Medicine", "identifiers"=>{"isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)", "pui"=>"358744952", "pmid"=>"20405004", "doi"=>"10.1371/journal.pmed.1000260", "sgr"=>"77951754865", "issn"=>"15491277", "scopus"=>"2-s2.0-77951754865"}, "id"=>"56118b89-2b97-3d47-bedf-08c0ebcab4b8", "abstract"=>"BACKGROUND: For several decades, global public health efforts have focused on the development and application of disease control programs to improve child survival in developing populations. The need to reliably monitor the impact of such intervention programs in countries has led to significant advances in demographic methods and data sources, particularly with large-scale, cross-national survey programs such as the Demographic and Health Surveys (DHS). Although no comparable effort has been undertaken for adult mortality, the availability of large datasets with information on adult survival from censuses and household surveys offers an important opportunity to dramatically improve our knowledge about levels and trends in adult mortality in countries without good vital registration. To date, attempts to measure adult mortality from questions in censuses and surveys have generally led to implausibly low levels of adult mortality owing to biases inherent in survey data such as survival and recall bias. Recent methodological developments and the increasing availability of large surveys with information on sibling survival suggest that it may well be timely to reassess the pessimism that has prevailed around the use of sibling histories to measure adult mortality.\\n\\nMETHODS AND FINDINGS: We present the Corrected Sibling Survival (CSS) method, which addresses both the survival and recall biases that have plagued the use of survey data to estimate adult mortality. Using logistic regression, our method directly estimates the probability of dying in a given country, by age, sex, and time period from sibling history data. The logistic regression framework borrows strength across surveys and time periods for the estimation of the age patterns of mortality, and facilitates the implementation of solutions for the underrepresentation of high-mortality families and recall bias. We apply the method to generate estimates of and trends in adult mortality, using the summary measure (45)q(15)-the probability of a 15-y old dying before his or her 60th birthday-for 44 countries with DHS sibling survival data. Our findings suggest that levels of adult mortality prevailing in many developing countries are substantially higher than previously suggested by other analyses of sibling history data. Generally, our estimates show the risk of adult death between ages 15 and 60 y to be about 20%-35% for females and 25%-45% for males in sub-Saharan African populations largely unaffected by HIV. In countries of Southern Africa, where the HIV epidemic has been most pronounced, as many as eight out of ten men alive at age 15 y will be dead by age 60, as will six out of ten women. Adult mortality levels in populations of Asia and Latin America are generally lower than in Africa, particularly for women. The exceptions are Haiti and Cambodia, where mortality risks are comparable to many countries in Africa. In all other countries with data, the probability of dying between ages 15 and 60 y was typically around 10% for women and 20% for men, not much higher than the levels prevailing in several more developed countries.\\n\\nCONCLUSIONS: Our results represent an expansion of direct knowledge of levels and trends in adult mortality in the developing world. The CSS method provides grounds for renewed optimism in collecting sibling survival data. We suggest that all nationally representative survey programs with adequate sample size ought to implement this critical module for tracking adult mortality in order to more reliably understand the levels and patterns of adult mortality, and how they are changing. Please see later in the article for the Editors' Summary.", "link"=>"http://www.mendeley.com/research/measuring-adult-mortality-using-sibling-survival-new-analytical-method-new-results-44-countries-1974", "reader_count"=>71, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>23, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>6, "Other"=>7, "Student > Bachelor"=>3, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Researcher"=>23, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>1, "Student > Master"=>6, "Other"=>7, "Student > Bachelor"=>3, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Environmental Science"=>2, "Nursing and Health Professions"=>4, "Mathematics"=>1, "Medicine and Dentistry"=>23, "Agricultural and Biological Sciences"=>2, "Business, Management and Accounting"=>1, "Psychology"=>5, "Social Sciences"=>24, "Computer Science"=>1, "Economics, Econometrics and Finance"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>23}, "Social Sciences"=>{"Social Sciences"=>24}, "Psychology"=>{"Psychology"=>5}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>2}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "United States"=>3, "United Kingdom"=>3, "Italy"=>1, "South Africa"=>1, "Ghana"=>3, "Kenya"=>1, "France"=>1, "Switzerland"=>1, "Ethiopia"=>1}, "group_count"=>8}

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  • {"files"=>["https://ndownloader.figshare.com/files/855989"], "description"=>"<p>Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Zambia, Zimbabwe.</p>", "links"=>[], "tags"=>["css", "compared", "estimates", "generated", "census"], "article_id"=>526430, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g010", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_45_q_15_from_the_CSS_method_compared_to_estimates_generated_from_vital_registration_DSS_and_census_household_death_estimates_Zambia_Zimbabwe_/526430", "title"=>"Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Zambia, Zimbabwe.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:47:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/855855"], "description"=>"<p>Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Senegal, Tanzania, South Africa.</p>", "links"=>[], "tags"=>["css", "compared", "estimates", "generated", "census"], "article_id"=>526298, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g009", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_45_q_15_from_the_CSS_method_compared_to_estimates_generated_from_vital_registration_DSS_and_census_household_death_estimates_Senegal_Tanzania_South_Africa_/526298", "title"=>"Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Senegal, Tanzania, South Africa.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:44:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/855364"], "description"=>"<p>Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Burkina Faso, Brazil, Dominican Republic.</p>", "links"=>[], "tags"=>["css", "compared", "estimates", "generated", "census", "burkina", "dominican"], "article_id"=>525804, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_45_q_15_from_the_CSS_method_compared_to_estimates_generated_from_vital_registration_DSS_and_census_household_death_estimates_Burkina_Faso_Brazil_Dominican_Republic_/525804", "title"=>"Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Burkina Faso, Brazil, Dominican Republic.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:36:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/856143"], "description"=>"<p>CSS-generated estimates of <sub>45</sub><i>q</i><sub>15</sub> for African countries with DHS data, 1990 and 2000.</p>", "links"=>[], "tags"=>["estimates", "african", "countries", "dhs", "1990"], "article_id"=>526586, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g011", "stats"=>{"downloads"=>6, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CSS_generated_estimates_of_45_q_15_for_African_countries_with_DHS_data_1990_and_2000_/526586", "title"=>"CSS-generated estimates of <sub>45</sub><i>q</i><sub>15</sub> for African countries with DHS data, 1990 and 2000.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:49:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/856240"], "description"=>"<p>Percent of deaths missing due to zero-female-survivors and the factor by which estimates of <sub>n</sub><i>q</i><sub>x</sub> are corrected upward as a result.</p>", "links"=>[], "tags"=>["deaths", "zero-female-survivors", "estimates", "corrected", "upward"], "article_id"=>526682, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.t004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percent_of_deaths_missing_due_to_zero_female_survivors_and_the_factor_by_which_estimates_of_n_q_x_are_corrected_upward_as_a_result_/526682", "title"=>"Percent of deaths missing due to zero-female-survivors and the factor by which estimates of <sub>n</sub><i>q</i><sub>x</sub> are corrected upward as a result.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-04-13 01:51:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/855729"], "description"=>"<p>Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Malawi, Peru, The Philippines.</p>", "links"=>[], "tags"=>["css", "compared", "estimates", "generated", "census"], "article_id"=>526170, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_45_q_15_from_the_CSS_method_compared_to_estimates_generated_from_vital_registration_DSS_and_census_household_death_estimates_Malawi_Peru_The_Philippines_/526170", "title"=>"Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Malawi, Peru, The Philippines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:42:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/855587"], "description"=>"<p>Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Haiti, Mozambique, Mauritania.</p>", "links"=>[], "tags"=>["css", "compared", "estimates", "generated", "census"], "article_id"=>526022, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g007", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_45_q_15_from_the_CSS_method_compared_to_estimates_generated_from_vital_registration_DSS_and_census_household_death_estimates_Haiti_Mozambique_Mauritania_/526022", "title"=>"Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Haiti, Mozambique, Mauritania.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:40:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/425140", "https://ndownloader.figshare.com/files/425172", "https://ndownloader.figshare.com/files/425339", "https://ndownloader.figshare.com/files/425355", "https://ndownloader.figshare.com/files/425369", "https://ndownloader.figshare.com/files/425391"], "description"=>"<div><h3>Background</h3><p>For several decades, global public health efforts have focused on the development and application of disease control programs to improve child survival in developing populations. The need to reliably monitor the impact of such intervention programs in countries has led to significant advances in demographic methods and data sources, particularly with large-scale, cross-national survey programs such as the Demographic and Health Surveys (DHS). Although no comparable effort has been undertaken for adult mortality, the availability of large datasets with information on adult survival from censuses and household surveys offers an important opportunity to dramatically improve our knowledge about levels and trends in adult mortality in countries without good vital registration. To date, attempts to measure adult mortality from questions in censuses and surveys have generally led to implausibly low levels of adult mortality owing to biases inherent in survey data such as survival and recall bias. Recent methodological developments and the increasing availability of large surveys with information on sibling survival suggest that it may well be timely to reassess the pessimism that has prevailed around the use of sibling histories to measure adult mortality.</p><h3>Methods and Findings</h3><p>We present the Corrected Sibling Survival (CSS) method, which addresses both the survival and recall biases that have plagued the use of survey data to estimate adult mortality. Using logistic regression, our method directly estimates the probability of dying in a given country, by age, sex, and time period from sibling history data. The logistic regression framework borrows strength across surveys and time periods for the estimation of the age patterns of mortality, and facilitates the implementation of solutions for the underrepresentation of high-mortality families and recall bias. We apply the method to generate estimates of and trends in adult mortality, using the summary measure <sub>45</sub><em>q</em><sub>15</sub>—the probability of a 15-y old dying before his or her 60th birthday—for 44 countries with DHS sibling survival data. Our findings suggest that levels of adult mortality prevailing in many developing countries are substantially higher than previously suggested by other analyses of sibling history data. Generally, our estimates show the risk of adult death between ages 15 and 60 y to be about 20%–35% for females and 25%–45% for males in sub-Saharan African populations largely unaffected by HIV. In countries of Southern Africa, where the HIV epidemic has been most pronounced, as many as eight out of ten men alive at age 15 y will be dead by age 60, as will six out of ten women. Adult mortality levels in populations of Asia and Latin America are generally lower than in Africa, particularly for women. The exceptions are Haiti and Cambodia, where mortality risks are comparable to many countries in Africa. In all other countries with data, the probability of dying between ages 15 and 60 y was typically around 10% for women and 20% for men, not much higher than the levels prevailing in several more developed countries.</p><h3>Conclusions</h3><p>Our results represent an expansion of direct knowledge of levels and trends in adult mortality in the developing world. The CSS method provides grounds for renewed optimism in collecting sibling survival data. We suggest that all nationally representative survey programs with adequate sample size ought to implement this critical module for tracking adult mortality in order to more reliably understand the levels and patterns of adult mortality, and how they are changing.</p><h3></h3><p><em>Please see later in the article for the Editors' Summary</em></p></div>", "links"=>[], "tags"=>["measuring", "analytical", "results", "44"], "article_id"=>143924, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000260.s001", "https://dx.doi.org/10.1371/journal.pmed.1000260.s002", "https://dx.doi.org/10.1371/journal.pmed.1000260.s003", "https://dx.doi.org/10.1371/journal.pmed.1000260.s004", "https://dx.doi.org/10.1371/journal.pmed.1000260.s005", "https://dx.doi.org/10.1371/journal.pmed.1000260.s006"], "stats"=>{"downloads"=>20, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Measuring_Adult_Mortality_Using_Sibling_Survival_A_New_Analytical_Method_and_New_Results_for_44_Countries_1974_2006/143924", "title"=>"Measuring Adult Mortality Using Sibling Survival: A New Analytical Method and New Results for 44 Countries, 1974–2006", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-04-13 01:05:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/855492"], "description"=>"<p>Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Ethiopia, Ghana, Guatemala.</p>", "links"=>[], "tags"=>["css", "compared", "estimates", "generated", "census"], "article_id"=>525937, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimates_of_45_q_15_from_the_CSS_method_compared_to_estimates_generated_from_vital_registration_DSS_and_census_household_death_estimates_Ethiopia_Ghana_Guatemala_/525937", "title"=>"Estimates of <sub>45</sub><i>q</i><sub>15</sub> from the CSS method compared to estimates generated from vital registration, DSS, and census household death estimates: Ethiopia, Ghana, Guatemala.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:38:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/856277"], "description"=>"<p>N/A, not applicable.</p>", "links"=>[], "tags"=>["classifying", "country-periods", "age-pattern"], "article_id"=>526722, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.t001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Criteria_for_classifying_country_periods_into_different_age_pattern_groups_/526722", "title"=>"Criteria for classifying country-periods into different age-pattern groups.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-04-13 01:52:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/855270"], "description"=>"<p>Country-specific estimates of recall bias as measured by the <i>TiPS</i> coefficient compared to the overall estimate from CSS model (red line).</p>", "links"=>[], "tags"=>["estimates", "coefficient", "compared", "css"], "article_id"=>525713, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Country_specific_estimates_of_recall_bias_as_measured_by_the_TiPS_coefficient_compared_to_the_overall_estimate_from_CSS_model_red_line_/525713", "title"=>"Country-specific estimates of recall bias as measured by the <i>TiPS</i> coefficient compared to the overall estimate from CSS model (red line).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:35:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/855086"], "description"=>"<p>The effects on mortality estimates of the GK survival bias adjustment, adjusting for recall, and the zero-female-survivor correction are shown.</p>", "links"=>[], "tags"=>["step-by-step", "adjustments", "css"], "article_id"=>525533, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_step_by_step_look_at_each_of_the_adjustments_in_the_CSS_method_Indonesia_Peru_and_Tanzania_/525533", "title"=>"A step-by-step look at each of the adjustments in the CSS method: Indonesia, Peru, and Tanzania.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:32:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/856360"], "description"=>"<p>RMSE of: differences between model-predicted age-specific probabilities of death (<sub>n</sub><i>q</i><sub>x</sub>) and observed <sub>n</sub><i>q</i><sub>x</sub>, differences in predicted and observed <sub>n</sub><i>q</i><sub>x</sub> in log space, and differences in predicted and observed <sub>45</sub><i>q</i><sub>15</sub> are shown. Models are ranked for each metric of fit within each sex, with 1 being best. The summary rank score is the sum of the ranks for each metric, across both sexes.</p>", "links"=>[], "tags"=>["applying", "age-pattern", "groupings"], "article_id"=>526811, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.t002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_fit_results_from_applying_different_age_pattern_groupings_to_sibling_survival_data_/526811", "title"=>"Model fit results from applying different age-pattern groupings to sibling survival data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-04-13 01:53:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/855177"], "description"=>"<p>The effects on mortality estimates of the GK survival bias adjustment, adjusting for recall, and the zero-female-survivor correction are shown.</p>", "links"=>[], "tags"=>["step-by-step", "adjustments", "css"], "article_id"=>525623, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g003", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_step_by_step_look_at_each_of_the_adjustments_in_the_CSS_Method_Senegal_Uganda_and_Zimbabwe_/525623", "title"=>"A step-by-step look at each of the adjustments in the CSS Method: Senegal, Uganda, and Zimbabwe.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:33:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/854966"], "description"=>"<p>Model 1 groups country-periods into a single age pattern. Model 2 groups country-periods according to level of HIV seroprevalence. Model 3 groups country-periods by history of war and levels of <sub>5</sub><i>q</i><sub>0</sub>, and model 4 is a hybrid model containing one group of country-periods with history of war, one group with high levels (≥7%) of HIV seroprevalence, and two groups based on <sub>5</sub><i>q</i><sub>0</sub> levels, low and high.</p>", "links"=>[], "tags"=>["patterns"], "article_id"=>525413, "categories"=>["Cancer"], "users"=>["Ziad Obermeyer", "Julie Knoll Rajaratnam", "Chang H. Park", "Emmanuela Gakidou", "Margaret C. Hogan", "Alan D. Lopez", "Christopher J. L. Murray"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000260.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Age_patterns_from_all_four_models_/525413", "title"=>"Age patterns from all four models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-13 01:30:13"}

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  • {"unique-ip"=>"9", "full-text"=>"13", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2015", "month"=>"4"}
  • {"unique-ip"=>"14", "full-text"=>"19", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"5"}
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  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"3", "year"=>"2015", "month"=>"11"}
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  • {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
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  • {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2016", "month"=>"4"}
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  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
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  • {"unique-ip"=>"8", "full-text"=>"10", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
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  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
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  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
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Relative Metric

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