Modeling Age-Specific Mortality for Countries with Generalized HIV Epidemics
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{"title"=>"Modeling age-specific mortality for countries with generalized HIV epidemics", "type"=>"journal", "authors"=>[{"first_name"=>"David J.", "last_name"=>"Sharrow", "scopus_author_id"=>"56178592200"}, {"first_name"=>"Samuel J.", "last_name"=>"Clark", "scopus_author_id"=>"8838786400"}, {"first_name"=>"Adrian E.", "last_name"=>"Raftery", "scopus_author_id"=>"7006212242"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"373161094", "sgr"=>"84901327793", "pmid"=>"24853081", "scopus"=>"2-s2.0-84901327793", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0096447", "issn"=>"19326203"}, "id"=>"c87bfcde-2c62-31d1-9843-f1e3658b9900", "abstract"=>"BACKGROUND: In a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique 'humped' age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics.\\n\\nMETHODS AND FINDINGS: The model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems.\\n\\nCONCLUSIONS: We present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.", "link"=>"http://www.mendeley.com/research/modeling-agespecific-mortality-countries-generalized-hiv-epidemics", "reader_count"=>15, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>3, "Other"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Master"=>3, "Other"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Mathematics"=>3, "Agricultural and Biological Sciences"=>1, "Medicine and Dentistry"=>7, "Arts and Humanities"=>1, "Business, Management and Accounting"=>1, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Social Sciences"=>{"Social Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Norway"=>1, "United States"=>1}, "group_count"=>3}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/1507818"], "description"=>"<p>1) HIV prevalence with life expectancy at birth [solid black line] 2) HIV prevalence and child mortality [dotted black line] 3) HIV prevalence with child mortality and adult mortality [dashed black line]. For comparison, fits from the WHO modified logit model [red solid line], Coale and Demeny model life tables [green solid line], UN model life tables for developing countries [teal solid line], and the Log-Quad model [purple solid line] are also shown.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "mlt", "combinations", "lesotho", "females"], "article_id"=>1034304, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096447.g002", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fits_of_HIV_MLT_model_with_three_different_input_combinations_for_Lesotho_females_2005_8211_2010_/1034304", "title"=>"Fits of HIV MLT model with three different input combinations for Lesotho females 2005–2010.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-22 03:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1507827"], "description"=>"<p>HIV MLT refers to the model presented in this paper with three possible input combinations: 1) HIV prevalence with life expectancy at birth [input: ], 2) HIV prevalence with child mortality [input: ], or 3) HIV prevalence with child mortality and adult mortality [input: and ]. MAE for ‘All-ages’, , and expressed per 1,000 and the smallest number in each column is bolded. For HIV MLT model, ‘–’ indicates MAE 0.001 (or 0.01 for ).</p><p>‘All-ages’ refers to the mean absolute error for the non-logged mortality rates across age groups (0, 1–4, 5–9, 10–14,…, 75) and amongst all life tables ().</p><p>‘WHO’, ‘CD’, and ‘Log-Quad’ contain blank spaces as these quantities are inputs to these systems and thus have no error.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "indicators", "amongst", "all-ages", "tables", "iterations"], "article_id"=>1034313, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096447.t002", "stats"=>{"downloads"=>4, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_validation_results_showing_the_mean_of_the_distribution_of_mean_absolute_error_MAE_for_three_mortality_indicators_and_amongst_all_ages_and_life_tables_after_1_000_iterations_of_cross_validation_/1034313", "title"=>"Cross-validation results showing the mean of the distribution of mean absolute error (MAE) for three mortality indicators and amongst all-ages and life tables after 1,000 iterations of cross validation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-22 03:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1507825"], "description"=>"<p>Model output for HIV MLT male Africa model at varying prevalence and varying .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "mlt", "africa", "varying", "prevalence"], "article_id"=>1034311, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096447.g005", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_output_for_HIV_MLT_male_Africa_model_at_varying_prevalence_and_varying_/1034311", "title"=>"Model output for HIV MLT male Africa model at varying prevalence and varying .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-22 03:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1507826"], "description"=>"<p>Summary of the distribution of values for African (n = 260) and non-African life tables (n = 40).</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "african", "non-african", "tables"], "article_id"=>1034312, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096447.t001", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_the_distribution_of_values_for_African_n_260_and_non_African_life_tables_n_40_/1034312", "title"=>"Summary of the distribution of values for African (n = 260) and non-African life tables (n = 40).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-22 03:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1507821"], "description"=>"<p>Derived from the Singular Value Decomposition of the World Population Prospects five-year mortality rate schedules 1970–2010 for countries with generalized HIV epidemics. from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096447#pone.0096447.e016\" target=\"_blank\">Equation 1</a>.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "components"], "article_id"=>1034307, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096447.g003", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_First_three_components_left_singular_vectors_/1034307", "title"=>"First three components (left-singular vectors).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-22 03:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1507834", "https://ndownloader.figshare.com/files/1507836", "https://ndownloader.figshare.com/files/1507837", "https://ndownloader.figshare.com/files/1507838", "https://ndownloader.figshare.com/files/1507839", "https://ndownloader.figshare.com/files/1507840", "https://ndownloader.figshare.com/files/1507841", "https://ndownloader.figshare.com/files/1507842", "https://ndownloader.figshare.com/files/1507843", "https://ndownloader.figshare.com/files/1507844"], "description"=>"<div><p>Background</p><p>In a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique ‘humped’ age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics.</p><p>Methods and Findings</p><p>The model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems.</p><p>Conclusions</p><p>We present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.</p></div>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "age-specific", "countries", "generalized"], "article_id"=>1034320, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0096447.s001", "https://dx.doi.org/10.1371/journal.pone.0096447.s002", "https://dx.doi.org/10.1371/journal.pone.0096447.s003", "https://dx.doi.org/10.1371/journal.pone.0096447.s004", "https://dx.doi.org/10.1371/journal.pone.0096447.s005", "https://dx.doi.org/10.1371/journal.pone.0096447.s006", "https://dx.doi.org/10.1371/journal.pone.0096447.s007", "https://dx.doi.org/10.1371/journal.pone.0096447.s008", "https://dx.doi.org/10.1371/journal.pone.0096447.s009", "https://dx.doi.org/10.1371/journal.pone.0096447.s010"], "stats"=>{"downloads"=>9, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modeling_Age_Specific_Mortality_for_Countries_with_Generalized_HIV_Epidemics_/1034320", "title"=>"Modeling Age-Specific Mortality for Countries with Generalized HIV Epidemics", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-05-22 03:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1507815"], "description"=>"<p>–axis on log scale. Country-periods with a generalized epidemic (>1% HIV prevalence) plotted in red.</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "2010", "five-year", "schedules", "countries", "generalized", "epidemics"], "article_id"=>1034301, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096447.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_WPP_2010_female_five_year_mortality_rate_schedules_for_countries_with_generalized_HIV_epidemics_1970_8211_2010_/1034301", "title"=>"WPP 2010 female five-year mortality rate schedules for countries with generalized HIV epidemics 1970–2010.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-22 03:08:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1507823"], "description"=>"<p>Model output for HIV MLT female Africa model at varying prevalence and varying .</p>", "links"=>[], "tags"=>["Computational biology", "Population modeling", "microbiology", "Medical microbiology", "Microbial pathogens", "Viral pathogens", "Immunodeficiency viruses", "hiv", "Population biology", "Population metrics", "Death rates", "Infectious diseases", "Viral diseases", "aids", "Sexually transmitted diseases", "Public and occupational health", "life expectancy", "demography", "mathematics", "Statistics (mathematics)", "Statistical methods", "Sociology", "mlt", "africa", "varying", "prevalence"], "article_id"=>1034309, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["David J. Sharrow", "Samuel J. Clark", "Adrian E. Raftery"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096447.g004", "stats"=>{"downloads"=>4, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_output_for_HIV_MLT_female_Africa_model_at_varying_prevalence_and_varying_/1034309", "title"=>"Model output for HIV MLT female Africa model at varying prevalence and varying .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-22 03:08:47"}

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  • {"unique-ip"=>"16", "full-text"=>"8", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
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  • {"unique-ip"=>"10", "full-text"=>"7", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
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Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Population biology", "average_usage"=>[312, 475]}, {"subject_area"=>"/Medicine and health sciences/Pediatrics", "average_usage"=>[304, 478]}, {"subject_area"=>"/Medicine and health sciences/Public and occupational health", "average_usage"=>[327, 510]}, {"subject_area"=>"/People and places", "average_usage"=>[302]}, {"subject_area"=>"/People and places/Population groupings", "average_usage"=>[300]}]}
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