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{"title"=>"Mapping the risk of anaemia in preschool-age children: The contribution of malnutrition, malaria, and helminth infections in West Africa", "type"=>"journal", "authors"=>[{"first_name"=>"Ricardo J Soares", "last_name"=>"Magalhães", "scopus_author_id"=>"8863883300"}, {"first_name"=>"Archie C A", "last_name"=>"Clements", "scopus_author_id"=>"35583466600"}], "year"=>2011, "source"=>"PLoS Medicine", "identifiers"=>{"sgr"=>"79959800820", "pmid"=>"21687688", "isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)", "scopus"=>"2-s2.0-79959800820", "issn"=>"15491277", "pui"=>"362049925", "doi"=>"10.1371/journal.pmed.1000438"}, "id"=>"0fcc3dbf-7119-3ccb-8988-f578433c9c31", "abstract"=>"BACKGROUND: Childhood anaemia is considered a severe public health problem in most countries of sub-Saharan Africa. We investigated the geographical distribution of prevalence of anaemia and mean haemoglobin concentration (Hb) in children aged 1-4 y (preschool children) in West Africa. The aim was to estimate the geographical risk profile of anaemia accounting for malnutrition, malaria, and helminth infections, the risk of anaemia attributable to these factors, and the number of anaemia cases in preschool children for 2011.\\n\\nMETHODS AND FINDINGS: National cross-sectional household-based demographic health surveys were conducted in 7,147 children aged 1-4 y in Burkina Faso, Ghana, and Mali in 2003-2006. Bayesian geostatistical models were developed to predict the geographical distribution of mean Hb and anaemia risk, adjusting for the nutritional status of preschool children, the location of their residence, predicted Plasmodium falciparum parasite rate in the 2- to 10-y age group (Pf PR(2-10)), and predicted prevalence of Schistosoma haematobium and hookworm infections. In the four countries, prevalence of mild, moderate, and severe anaemia was 21%, 66%, and 13% in Burkina Faso; 28%, 65%, and 7% in Ghana, and 26%, 62%, and 12% in Mali. The mean Hb was lowest in Burkina Faso (89 g/l), in males (93 g/l), and for children 1-2 y (88 g/l). In West Africa, severe malnutrition, Pf PR(2-10), and biological synergisms between S. haematobium and hookworm infections were significantly associated with anaemia risk; an estimated 36.8%, 14.9%, 3.7%, 4.2%, and 0.9% of anaemia cases could be averted by treating malnutrition, malaria, S. haematobium infections, hookworm infections, and S. haematobium/hookworm coinfections, respectively. A large spatial cluster of low mean Hb (<80 g/l) and maximal risk of anaemia (>95%) was predicted for an area shared by Burkina Faso and Mali. We estimate that in 2011, approximately 6.7 million children aged 1-4 y are anaemic in the three study countries.\\n\\nCONCLUSIONS: By mapping the distribution of anaemia risk in preschool children adjusted for malnutrition and parasitic infections, we provide a means to identify the geographical limits of anaemia burden and the contribution that malnutrition and parasites make to anaemia. Spatial targeting of ancillary micronutrient supplementation and control of other anaemia causes, such as malaria and helminth infection, can contribute to efficiently reducing the burden of anaemia in preschool children in Africa.", "link"=>"http://www.mendeley.com/research/mapping-risk-anaemia-preschoolage-children-contribution-malnutrition-malaria-helminth-infections-wes", "reader_count"=>207, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Librarian"=>1, "Researcher"=>41, "Student > Doctoral Student"=>8, "Student > Ph. D. 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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/768289"], "description"=>"a<p>The observed values were compared to the mean of the posterior distribution of the each predicted value of prevalence of anaemia and Hb. The estimates in parenthesis are the percentage of the overall mean attributed to the error estimate.</p><p>CI, confidence interval; ROC, receiver operating characteristic.</p>", "links"=>[], "tags"=>["validation", "predictive", "models", "anaemia", "prevalence", "haemoglobin", "burkina"], "article_id"=>438656, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.t003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_validation_statistics_for_predictive_models_of_anaemia_prevalence_and_haemoglobin_concentration_in_Burkina_Faso_Ghana_and_Mali_/438656", "title"=>"Summary of validation statistics for predictive models of anaemia prevalence and haemoglobin concentration in Burkina Faso, Ghana, and Mali.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-07 02:24:16"}
• {"files"=>["https://ndownloader.figshare.com/files/767672"], "description"=>"<p>Surveys conducted in Burkina Faso (2003), Ghana (2003), and Mali (2006).</p>", "links"=>[], "tags"=>["prevalence", "anaemia", "dhs"], "article_id"=>438046, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.g001", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_prevalence_of_anaemia_at_1_192_DHS_survey_sites_/438046", "title"=>"Mean prevalence of anaemia at 1,192 DHS survey sites.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-07 02:14:06"}
• {"files"=>["https://ndownloader.figshare.com/files/768097"], "description"=>"<p>Predictive geographical variation of mean haemoglobin concentration in children aged 1–4 y, based on a model-based geostatistical Gaussian model.</p>", "links"=>[], "tags"=>["geographical", "haemoglobin", "children", "aged", "model-based", "geostatistical", "gaussian"], "article_id"=>438458, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictive_geographical_variation_of_mean_haemoglobin_concentration_in_children_aged_1_8211_4_y_based_on_a_model_based_geostatistical_Gaussian_model_/438458", "title"=>"Predictive geographical variation of mean haemoglobin concentration in children aged 1–4 y, based on a model-based geostatistical Gaussian model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-07 02:20:58"}
• {"files"=>["https://ndownloader.figshare.com/files/767808"], "description"=>"<p>Anaemia (<i>y</i>-axis; Hb<110 g/l) by age in months (<i>x</i>-axis; in months) with smooth fit line (red line) generated by a loess smoother, in children aged 1–4 y in the DHS surveys for Burkina Faso (2003), Ghana (2003), and Mali (2006).</p>", "links"=>[], "tags"=>["anaemia", "burkina"], "article_id"=>438183, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Profile_of_anaemia_by_age_in_Burkina_Faso_Ghana_and_Mali_/438183", "title"=>"Profile of anaemia by age in Burkina Faso, Ghana, and Mali.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-07 02:16:23"}
• {"files"=>["https://ndownloader.figshare.com/files/768008"], "description"=>"<p>Predictive geographical risk of anaemia in children aged 1–4 y, based on a model-based geostatistical Bernoulli model.</p>", "links"=>[], "tags"=>["geographical", "anaemia", "children", "aged", "model-based", "geostatistical", "bernoulli"], "article_id"=>438374, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.g004", "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictive_geographical_risk_of_anaemia_in_children_aged_1_8211_4_y_based_on_a_model_based_geostatistical_Bernoulli_model_/438374", "title"=>"Predictive geographical risk of anaemia in children aged 1–4 y, based on a model-based geostatistical Bernoulli model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-07 02:19:34"}
• {"files"=>["https://ndownloader.figshare.com/files/768176"], "description"=>"<p>Predictive geographical variation of number of children aged 1–4 y with anaemia, for 2011.</p>", "links"=>[], "tags"=>["geographical", "children", "aged"], "article_id"=>438548, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.g006", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predictive_geographical_variation_of_number_of_children_aged_1_8211_4_y_with_anaemia_for_2011_/438548", "title"=>"Predictive geographical variation of number of children aged 1–4 y with anaemia, for 2011.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-07 02:22:28"}
• {"files"=>["https://ndownloader.figshare.com/files/768329"], "description"=>"<p>Mild anaemia defined as 100–109 g/l; moderate anaemia, as 70–99 g/l, and severe anaemia, as <70 g/l. West Africa region includes Burkina Faso (<i>n = </i>1,911), Ghana (<i>n = </i>1,828), and Mali (<i>n = </i>2,149).</p>a<p>Numbers in parentheses are the proportion of anaemia cases in the country.</p>", "links"=>[], "tags"=>["children", "aged", "anaemia", "anaemic", "africa"], "article_id"=>438690, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_and_proportion_of_children_aged_1_8211_4_y_with_mild_anaemia_moderate_anaemia_and_severe_anaemia_in_5_888_anaemic_children_in_the_West_Africa_region_/438690", "title"=>"Number and proportion of children aged 1–4 y with mild anaemia, moderate anaemia, and severe anaemia in 5,888 anaemic children in the West Africa region.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-07 02:24:50"}
• {"files"=>["https://ndownloader.figshare.com/files/767903"], "description"=>"<p>Age (<i>x</i>-axis; in months) patterns of mean Hb (<i>y</i>-axis; g/l) with smooth fit line (red line) generated by a loess smoother, in children aged 1–4 y in the DHS surveys for Burkina Faso (2003), Ghana (2003), and Mali (2006).</p>", "links"=>[], "tags"=>["patterns", "haemoglobin"], "article_id"=>438273, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Age_patterns_of_mean_haemoglobin_concentration_/438273", "title"=>"Age patterns of mean haemoglobin concentration.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-06-07 02:17:53"}
• {"files"=>["https://ndownloader.figshare.com/files/768398"], "description"=>"a<p>Source: based on a 2.5 arc-minute resolution Gridded Population of the World (GPWv3) map for 2009.</p>b<p>Source: World Population Prospects 2008 Revision Population Database (Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat; <a href=\"http://esa.un.org/unpp\" target=\"_blank\">http://esa.un.org/unpp</a>).</p>c<p>The estimates provided by the World Population Prospects database are for children aged 0–4.99 y. To obtain estimates of the percentage of children aged 1–4 y (the age group included in our analysis), the estimates presented by the World Population Prospects database were discounted by a factor of 0.2.</p>", "links"=>[], "tags"=>["children", "aged", "anaemia", "burkina", "mali"], "article_id"=>438768, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.t005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_number_of_children_aged_1_8211_4_y_with_anaemia_in_Burkina_Faso_Ghana_and_Mali_in_2011_/438768", "title"=>"Predicted number of children aged 1–4 y with anaemia in Burkina Faso, Ghana, and Mali in 2011.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-07 02:26:08"}
• {"files"=>["https://ndownloader.figshare.com/files/385717"], "description"=>"<div><h3>Background</h3><p>Childhood anaemia is considered a severe public health problem in most countries of sub-Saharan Africa. We investigated the geographical distribution of prevalence of anaemia and mean haemoglobin concentration (Hb) in children aged 1–4 y (preschool children) in West Africa. The aim was to estimate the geographical risk profile of anaemia accounting for malnutrition, malaria, and helminth infections, the risk of anaemia attributable to these factors, and the number of anaemia cases in preschool children for 2011.</p><h3>Methods and Findings</h3><p>National cross-sectional household-based demographic health surveys were conducted in 7,147 children aged 1–4 y in Burkina Faso, Ghana, and Mali in 2003–2006. Bayesian geostatistical models were developed to predict the geographical distribution of mean Hb and anaemia risk, adjusting for the nutritional status of preschool children, the location of their residence, predicted <em>Plasmodium falciparum</em> parasite rate in the 2- to 10-y age group (<em>Pf</em> PR_2–10), and predicted prevalence of <em>Schistosoma haematobium</em> and hookworm infections. In the four countries, prevalence of mild, moderate, and severe anaemia was 21%, 66%, and 13% in Burkina Faso; 28%, 65%, and 7% in Ghana, and 26%, 62%, and 12% in Mali. The mean Hb was lowest in Burkina Faso (89 g/l), in males (93 g/l), and for children 1–2 y (88 g/l). In West Africa, severe malnutrition, <em>Pf</em> PR_2–10, and biological synergisms between <em>S. haematobium</em> and hookworm infections were significantly associated with anaemia risk; an estimated 36.8%, 14.9%, 3.7%, 4.2%, and 0.9% of anaemia cases could be averted by treating malnutrition, malaria, <em>S. haematobium</em> infections, hookworm infections, and <em>S. haematobium</em>/hookworm coinfections, respectively. A large spatial cluster of low mean Hb (<80 g/l) and maximal risk of anaemia (>95%) was predicted for an area shared by Burkina Faso and Mali. We estimate that in 2011, approximately 6.7 million children aged 1–4 y are anaemic in the three study countries.</p><h3>Conclusions</h3><p>By mapping the distribution of anaemia risk in preschool children adjusted for malnutrition and parasitic infections, we provide a means to identify the geographical limits of anaemia burden and the contribution that malnutrition and parasites make to anaemia. Spatial targeting of ancillary micronutrient supplementation and control of other anaemia causes, such as malaria and helminth infection, can contribute to efficiently reducing the burden of anaemia in preschool children in Africa.</p><h3></h3><p><em>Please see later in the article for the Editors' Summary</em></p></div>", "links"=>[], "tags"=>["anaemia", "preschool-age", "helminth", "infections", "africa"], "article_id"=>136142, "categories"=>["Neuroscience", "Biological Sciences", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438", "stats"=>{"downloads"=>10, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Mapping_the_Risk_of_Anaemia_in_Preschool_Age_Children_The_Contribution_of_Malnutrition_Malaria_and_Helminth_Infections_in_West_Africa/136142", "title"=>"Mapping the Risk of Anaemia in Preschool-Age Children: The Contribution of Malnutrition, Malaria, and Helminth Infections in West Africa", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-07 01:42:22"}
• {"files"=>["https://ndownloader.figshare.com/files/768360"], "description"=>"<p>Association values are given as posterior mean (95% Bayesian credible interval).</p>a<p>Variables were standardised to have mean<i> = </i>0 and SD<i> = </i>1.</p>", "links"=>[], "tags"=>["altitude-adjusted", "haemoglobin", "model-based", "geostatistical", "gaussian"], "article_id"=>438725, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.t004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_with_altitude_adjusted_haemoglobin_concentration_based_on_model_based_geostatistical_Gaussian_models_/438725", "title"=>"Associations with altitude-adjusted haemoglobin concentration, based on model-based geostatistical Gaussian models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-07 02:25:25"}
• {"files"=>["https://ndownloader.figshare.com/files/768254"], "description"=>"<p>Association values are given as posterior mean (95% Bayesian credible interval).</p>a<p>Variables were standardised to have mean<i> = </i>0 and SD<i> = </i>1.</p>", "links"=>[], "tags"=>["anaemia", "model-based", "geostatistical", "bernoulli"], "article_id"=>438625, "categories"=>["Neuroscience", "Computational Biology", "Medicine", "Biotechnology"], "users"=>["Ricardo J. Soares Magalhães", "Archie C. A. Clements"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000438.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Associations_with_anaemia_risk_based_on_model_based_geostatistical_Bernoulli_models_/438625", "title"=>"Associations with anaemia risk, based on model-based geostatistical Bernoulli models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-06-07 02:23:45"}

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• {"unique-ip"=>"15", "full-text"=>"17", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
• {"unique-ip"=>"8", "full-text"=>"5", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
• {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
• {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2021", "month"=>"1"}

Relative Metric

{"start_date"=>"2011-01-01T00:00:00Z", "end_date"=>"2011-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[304, 568, 702, 818, 927, 1027, 1118, 1206, 1285, 1357, 1427, 1500, 1564, 1636, 1705, 1773, 1840, 1909, 1974, 2039, 2106, 2170, 2234, 2296, 2358, 2423, 2484, 2546, 2606, 2673, 2734, 2795, 2857, 2921, 2984, 3046, 3100]}, {"subject_area"=>"/Biology and life sciences/Nutrition", "average_usage"=>[286, 567, 722, 851, 990, 1079, 1199, 1303, 1398, 1498, 1567, 1657, 1736, 1806, 1884, 1963, 2037, 2120, 2202, 2264, 2352, 2408, 2453, 2534, 2597, 2658, 2707, 2779, 2848, 2915, 2998, 3081, 3147, 3201, 3248, 3314, 3379]}, {"subject_area"=>"/Medicine and health sciences/Hematology", "average_usage"=>[318, 581, 714, 833, 954, 1053, 1145, 1241, 1327, 1405, 1490, 1556, 1632, 1699, 1767, 1834, 1898, 1978, 2041, 2103, 2165, 2225, 2282, 2345, 2403, 2460, 2530, 2582, 2659, 2711, 2768, 2819, 2879, 2939, 2997, 3052, 3100]}, {"subject_area"=>"/Medicine and health sciences/Parasitic diseases", "average_usage"=>[362, 736, 861, 965, 1072, 1194, 1301, 1377, 1465, 1535, 1624, 1694, 1745, 1817, 1877, 1958, 2038, 2106, 2167, 2241, 2288, 2381, 2447, 2508, 2566, 2622, 2687, 2781, 2853, 2913, 2984, 3057, 3141, 3186, 3250, 3302, 3367]}, {"subject_area"=>"/Medicine and health sciences/Tropical diseases", "average_usage"=>[338, 662, 813, 915, 1030, 1125, 1242, 1315, 1390, 1447, 1525, 1593, 1653, 1750, 1827, 1887, 1972, 2030, 2103, 2153, 2215, 2279, 2336, 2397, 2468, 2528, 2590, 2670, 2746, 2820, 2871, 2936, 2986, 3043, 3080, 3133, 3170]}]}

F1000Prime