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      Association between menarche and iron deficiency in non-anemic young women

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          Abstract

          Background

          The prevalence of iron deficiency (ID) among non-pregnant, reproductive-age US women significantly exceeds rates among males. In clinical practice ID screening relies on hemoglobin, a late-stage indicator of ID. As a single, low-cost laboratory test to diagnose ID before anemia develops is lacking, the study objective was to improve ID screening by identifying risk factors among non-anemic, iron-deficient reproductive age women.

          Methods

          Cross-sectional data were from the National Health and Nutrition Examination Survey (NHANES) 2003–2010. Hemoglobin identified non-anemic women. ID was defined using the body iron formula, requiring ferritin and transferrin receptor values. Logistic regression assessed the association of sociodemographic, behavioral, and reproductive risk factors in an anemia-based conceptual framework with non-anemic reproductive age women (12–49 years) with ID, as well as subsets of younger (12–21 years) and older (22–49 years) women, recognizing that risks may differ by age.

          Results

          Among 6216 women, 494 had ID (prevalence was 8.0%, 95% CI 7.3%, 8.6%). Among non-anemic younger women, 250 (8.7%, 95% CI 7.7%, 9.8%) had ID, compared to 244 (7.3%, 95% CI 6.4%, 8.2%) older women. Among younger women, menstruation for over 3 years was the only variable significantly associated with non-anemic ID (risk ratio 3.18, 95% CI 2.03, 4.96). No other significant risk factors were identified.

          Conclusion

          Menstrual years was the only risk factor significantly associated with ID among non-anemic younger women. The negative results suggest ID risk factors among non-anemic women may need to be considered separately from those associated with ID anemia.

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          Most cited references28

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          Applied Logistic Regression

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            Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data.

            There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc., Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments. Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate these concepts.
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              Prevalence of iron deficiency in the United States.

              To determine the prevalence of iron deficiency and iron deficiency anemia in the US population. Nationally representative cross-sectional health examination survey that included venous blood measurements of iron status. Iron deficiency, defined as having an abnormal value for at least 2 of 3 laboratory tests of iron status (erythrocyte protoporphyrin, transferrin saturation, or serum ferritin); and iron deficiency anemia, defined as iron deficiency plus low hemoglobin. A total of 24,894 persons aged 1 year and older examined in the third National Health and Nutrition Examination Survey (1988-1994). Nine percent of toddlers aged 1 to 2 years and 9% to 11% of adolescent girls and women of childbearing age were iron deficient; of these, iron deficiency anemia was found in 3% and 2% to 5%, respectively. These prevalences correspond to approximately 700,000 toddlers and 7.8 million women with iron deficiency; of these, approximately 240,000 toddlers and 3.3 million women have iron deficiency anemia. Iron deficiency occurred in no more than 7% of older children or those older than 50 years, and in no more than 1% of teenage boys and young men. Among women of childbearing age, iron deficiency was more likely in those who are minority, low income, and multiparous. Iron deficiency and iron deficiency anemia are still relatively common in toddlers, adolescent girls, and women of childbearing age.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                9 May 2017
                2017
                : 12
                : 5
                : e0177183
                Affiliations
                [1 ]Department of Pediatrics, Penn State College of Medicine, Hershey, Pennsylvania, United States
                [2 ]Department of Nutritional Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States
                [3 ]Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States
                [4 ]Department of Obstetrics and Gynecology, Penn State College of Medicine, Hershey, Pennsylvania, United States
                Universidade de Sao Paulo, BRAZIL
                Author notes

                Competing Interests: ARK discloses stock ownership in a pharmaceutical company, Merck & Co. This had no bearing on the research involved in the submitted manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                • Conceptualization: DLS ARK CSW IMP.

                • Data curation: DLS ARK.

                • Formal analysis: DLS LEM ARK CSW IMP.

                • Funding acquisition: DLS CSW.

                • Investigation: DLS ARK.

                • Methodology: DLS LEM ARK CSW IMP.

                • Project administration: DLS.

                • Resources: DLS ARK.

                • Software: ARK.

                • Supervision: DLS CSW IMP.

                • Validation: ARK.

                • Visualization: DLS LEM ARK CSW IMP.

                • Writing – original draft: DLS.

                • Writing – review & editing: DLS LEM ARK CSW IMP.

                Article
                PONE-D-16-41882
                10.1371/journal.pone.0177183
                5423639
                28486542
                4eae488d-0172-4de8-9529-af6cd529507d
                © 2017 Sekhar et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 October 2016
                : 23 April 2017
                Page count
                Figures: 0, Tables: 4, Pages: 11
                Funding
                Funded by: National Institutes of Health
                Award ID: K12HD055882
                Award Recipient :
                Dr. Sekhar’s research is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under BIRCWH award number K12HD055882, “Career Development Program in Women’s Health Research at Penn State.” The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
                Categories
                Research Article
                Medicine and Health Sciences
                Hematology
                Anemia
                Biology and Life Sciences
                Biochemistry
                Proteins
                Hemoglobin
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Complexes
                Ferritin
                Biology and Life Sciences
                Nutrition
                Nutritional Deficiencies
                Iron Deficiency
                Medicine and Health Sciences
                Nutrition
                Nutritional Deficiencies
                Iron Deficiency
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Contraception
                Female Contraception
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Medicine and Health Sciences
                Hematology
                Anemia
                Iron Deficiency Anemia
                Medicine and Health Sciences
                Pharmaceutics
                Drug Therapy
                Contraceptive Therapy
                Oral Contraceptive Therapy
                Custom metadata
                Data used in this study are publicly available from the National Health and Nutrition Examination Survey 2003-2010 ( https://www.cdc.gov/nchs/nhanes/).

                Uncategorized
                Uncategorized

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