Height of Nations: A Socioeconomic Analysis of Cohort Differences and Patterns among Women in 54 Low- to Middle-Income Countries
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{"title"=>"Height of nations: A socioeconomic analysis of cohort differences and patterns among women in 54 low- to middle-income countries", "type"=>"journal", "authors"=>[{"first_name"=>"S. V.", "last_name"=>"Subramanian", "scopus_author_id"=>"55433328900"}, {"first_name"=>"Emre", "last_name"=>"Özaltin", "scopus_author_id"=>"35094456500"}, {"first_name"=>"Jocelyn E.", "last_name"=>"Finlay", "scopus_author_id"=>"23008230900"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"361671384", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0018962", "scopus"=>"2-s2.0-79955368241", "pmid"=>"21533104", "sgr"=>"79955368241"}, "id"=>"30cd1e4b-2d4e-3724-a801-e3706d48d8b7", "abstract"=>"BACKGROUND: Adult height is a useful biological measure of long term population health and well being. We examined the cohort differences and socioeconomic patterning in adult height in low- to middle-income countries.\\n\\nMETHODS/FINDINGS: We analyzed cross-sectional, representative samples of 364,538 women aged 25-49 years drawn from 54 Demographic and Health Surveys (DHS) conducted between 1994 and 2008. Linear multilevel regression models included year of birth, household wealth, education, and area of residence, and accounted for clustering by primary sampling units and countries. Attained height was measured using an adjustable measuring board. A yearly change in birth cohorts starting with those born in 1945 was associated with a 0.0138 cm (95% CI 0.0107, 0.0169) increase in height. Increases in heights in more recent birth year cohorts were largely concentrated in women from the richer wealth quintiles. 35 of the 54 countries experienced a decline (14) or stagnation (21) in height. The decline in heights was largely concentrated among the poorest wealth quintiles. There was a strong positive association between height and household wealth; those in two richest quintiles of household wealth were 1.988 cm (95% CI 1.886, 2.090) and 1.018 cm (95% CI 0.916, 1.120) taller, compared to those in the poorest wealth quintile. The strength of the association between wealth and height was positive (0.05 to 1.16) in 96% (52/54) countries.\\n\\nCONCLUSIONS: Socioeconomic inequalities in height remain persistent. Height has stagnated or declined over the last decades in low- to middle-income countries, particularly in Africa, suggesting worsening nutritional and environmental circumstances during childhood.", "link"=>"http://www.mendeley.com/research/height-nations-socioeconomic-analysis-cohort-differences-patterns-among-women-54-low-middleincome-co-10", "reader_count"=>42, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>1, "Researcher"=>11, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>1, "Researcher"=>11, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>3, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>15, "Agricultural and Biological Sciences"=>7, "Arts and Humanities"=>1, "Business, Management and Accounting"=>1, "Psychology"=>2, "Social Sciences"=>8, "Economics, Econometrics and Finance"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>15}, "Social Sciences"=>{"Social Sciences"=>8}, "Psychology"=>{"Psychology"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"United States"=>3, "Russia"=>1}, "group_count"=>6}

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  • {"files"=>["https://ndownloader.figshare.com/files/783474"], "description"=>"<p>Note: Countries are presented from shortest to tallest differential from the global mean; Adjusted  =  adjusted for year of birth, household wealth, education, and place of residence (urban or rural); SE  =  Standard Error.</p>", "links"=>[], "tags"=>["differentials"], "article_id"=>453856, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Country_specific_differentials_in_height_around_the_global_mean_for_adult_women_/453856", "title"=>"Country specific differentials in height around the global mean for adult women.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:04:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/783625"], "description"=>"<p>Note: Reference group are women born in 1945 or earlier who is in the lowest wealth quintile, with no education, and lives in a rural area.</p><p>*** p<0.01,</p><p>** p<0.05,</p><p>CI  =  Confidence Interval, ref  =  Reference.</p>", "links"=>[], "tags"=>["mutually", "adjusted"], "article_id"=>453997, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unadjusted_and_mutually_adjusted_effects_of_year_of_birth_wealth_quintiles_schooling_and_place_of_residence_on_height_accounting_for_within_and_between_country_variation_for_adult_women_/453997", "title"=>"Unadjusted and mutually adjusted effects of year of birth, wealth quintiles, schooling, and place of residence on height accounting for within and between country variation for adult women.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:06:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/783507"], "description"=>"<p>Note:</p><p>*** p<0.01,</p><p>** p<0.05,</p><p>* p<0.1; CI = Confidence Interval.</p>", "links"=>[], "tags"=>["1-year", "quintile", "models"], "article_id"=>453883, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t006", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Change_in_height_for_a_1_year_increase_in_birth_year_and_a_1_quintile_increase_in_wealth_from_separate_models_for_adult_women_/453883", "title"=>"Change in height for a 1-year increase in birth year and a 1 quintile increase in wealth from separate models for adult women.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:04:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/783412"], "description"=>"<p>Note: * t-test (Poorest/Second Poorest Quintile)  =  (Richest/Second Richest Quintile);</p><p>*** p<0.01,</p><p>** p<0.05,</p><p>* p<0.1: for the test that the coefficient is significantly different from zero.</p>", "links"=>[], "tags"=>["1-year", "poorest", "richest", "quintiles"], "article_id"=>453789, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t008", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Country_specific_predicted_effects_of_change_in_height_for_a_1_year_increase_in_birth_year_in_the_two_poorest_and_two_richest_wealth_quintiles_among_adult_women_/453789", "title"=>"Country-specific predicted effects of change in height for a 1-year increase in birth year in the two poorest and two richest wealth quintiles among adult women.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:03:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/783334"], "description"=>"<p>Percent of variation in height attributable to the individual and context (primary sampling units (PSU) and country) before and after accounting for the distribution of individual characteristics among adult women.</p>", "links"=>[], "tags"=>["attributable", "sampling", "units", "characteristics"], "article_id"=>453704, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percent_of_variation_in_height_attributable_to_the_individual_and_context_primary_sampling_units_PSU_and_country_before_and_after_accounting_for_the_distribution_of_individual_characteristics_among_adult_women_/453704", "title"=>"Percent of variation in height attributable to the individual and context (primary sampling units (PSU) and country) before and after accounting for the distribution of individual characteristics among adult women.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-04-20 01:01:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/783602"], "description"=>"<p>Note: SD  =  Standard Deviation.</p>", "links"=>[], "tags"=>["categories", "variables"], "article_id"=>453973, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Frequency_and_percentage_distribution_of_sample_by_independent_variables_and_mean_height_by_categories_of_independent_variables_for_adult_women_/453973", "title"=>"Frequency and percentage distribution of sample by independent variables, and mean height by categories of independent variables for adult women.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/783075"], "description"=>"<p>Country-level scatter plot between mean height (y axis) and gap in height between the richest and poorest wealth quintile (x axis) among adult women.</p>", "links"=>[], "tags"=>["richest", "poorest", "quintile"], "article_id"=>453446, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.g001", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Country_level_scatter_plot_between_mean_height_y_axis_and_gap_in_height_between_the_richest_and_poorest_wealth_quintile_x_axis_among_adult_women_/453446", "title"=>"Country-level scatter plot between mean height (y axis) and gap in height between the richest and poorest wealth quintile (x axis) among adult women.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-04-20 00:57:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/783567"], "description"=>"<p>Note: PSU  =  Primary Sampling Units, SD  =  Standard Deviation.</p>", "links"=>[], "tags"=>["public health and epidemiology"], "article_id"=>453949, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Survey_year_sample_size_and_mean_height_and_year_of_birth_for_adult_women_/453949", "title"=>"Survey year, sample size, and mean height and year of birth for adult women.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:05:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/391902", "https://ndownloader.figshare.com/files/391945"], "description"=>"<div><h3>Background</h3><p>Adult height is a useful biological measure of long term population health and well being. We examined the cohort differences and socioeconomic patterning in adult height in low- to middle-income countries.</p> <h3>Methods/Findings</h3><p>We analyzed cross-sectional, representative samples of 364538 women aged 25-49 years drawn from 54 Demographic and Health Surveys (DHS) conducted between 1994 and 2008. Linear multilevel regression models included year of birth, household wealth, education, and area of residence, and accounted for clustering by primary sampling units and countries. Attained height was measured using an adjustable measuring board. A yearly change in birth cohorts starting with those born in 1945 was associated with a 0.0138 cm (95% CI 0.0107, 0.0169) increase in height. Increases in heights in more recent birth year cohorts were largely concentrated in women from the richer wealth quintiles. 35 of the 54 countries experienced a decline (14) or stagnation (21) in height. The decline in heights was largely concentrated among the poorest wealth quintiles. There was a strong positive association between height and household wealth; those in two richest quintiles of household wealth were 1.988 cm (95% CI 1.886, 2.090) and 1.018 cm (95% CI 0.916, 1.120) taller, compared to those in the poorest wealth quintile. The strength of the association between wealth and height was positive (0.05 to 1.16) in 96% (52/54) countries.</p> <h3>Conclusions</h3><p>Socioeconomic inequalities in height remain persistent. Height has stagnated or declined over the last decades in low- to middle-income countries, particularly in Africa, suggesting worsening nutritional and environmental circumstances during childhood.</p> </div>", "links"=>[], "tags"=>["socioeconomic", "cohort", "differences", "patterns", "women", "54", "low-", "middle-income", "countries"], "article_id"=>137404, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0018962.s001", "https://dx.doi.org/10.1371/journal.pone.0018962.s002"], "stats"=>{"downloads"=>10, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Height_of_Nations_A_Socioeconomic_Analysis_of_Cohort_Differences_and_Patterns_among_Women_in_54_Low_to_Middle_Income_Countries/137404", "title"=>"Height of Nations: A Socioeconomic Analysis of Cohort Differences and Patterns among Women in 54 Low- to Middle-Income Countries", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-04-20 02:03:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/783231"], "description"=>"<p>Predicted association between height and year of birth across wealth quintiles among adult women.</p>", "links"=>[], "tags"=>["quintiles"], "article_id"=>453610, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_association_between_height_and_year_of_birth_across_wealth_quintiles_among_adult_women_/453610", "title"=>"Predicted association between height and year of birth across wealth quintiles among adult women.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-04-20 01:00:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/783536"], "description"=>"<p>Note:</p><p>* Based on significance at p = 0.05</p><p>** <b>Africa</b>: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Comoros, Congo (Brazzaville), Congo (DRC), Cote d'Ivoire, Ethiopia, Gabon, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe; <b>Americas</b>: Bolivia, Brazil, Colombia, Dominican Republic, Guatemala, Haiti, Honduras, Nicaragua, Peru; <b>Eastern Mediterranean</b>: Egypt, Jordan, Morocco; <b>Europe</b>: Armenia, Azerbaijan, Kazakhstan, Kyrgyz Republic, Moldova, Turkey, Uzbekistan; <b>South-East Asia/West Pacific</b>: Bangladesh, Cambodia, India, Nepal.</p>", "links"=>[], "tags"=>["countries", "regions"], "article_id"=>453911, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_table_showing_frequency_and_percentage_of_countries_across_different_WHO_regions_that_experienced_a_statistically_significant_decline_or_no_change_or_increase_in_height_among_adult_women_/453911", "title"=>"Summary table showing frequency and percentage of countries across different WHO regions that experienced a statistically significant decline, or no change or increase in height among adult women.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:05:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/783453"], "description"=>"<p>Note: The height values were calculated by adding or subtracing 1.96 times the square root of the variance at each level to the global mean height (155.75 cm) from the adjusted model for the reference group. PSU  =  Primary Sampling Unit.</p>", "links"=>[], "tags"=>["bounds", "heights", "between-countries", "within-country"], "article_id"=>453820, "categories"=>["Biotechnology"], "users"=>["S. V. Subramanian", "Emre Özaltin", "Jocelyn E. Finlay"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0018962.t004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_height_and_95_Coverage_Bounds_in_heights_in_cm_between_individuals_between_PSUs_and_between_countries_after_accounting_for_within_country_covariates_/453820", "title"=>"Mean height and 95% Coverage Bounds in heights (in cm) between individuals, between-PSUs, and between-countries after accounting for within-country covariates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-04-20 01:03:40"}

PMC Usage Stats | Further Information

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