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2022, Number 3-4

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MEDICC Review 2022; 24 (3-4)

Adjusting Iron Deficiency for Inflammation in Cuban Children Aged Under Five Years: New Approaches Using Quadratic and Quantile Regression

Montero-Díaz M, Chávez-Chong C, Rodríguez-Martínez E, Pita-Rodríguez GM, Lambert-Lamazares B, Basabe-Tuero B, Alfonso-Sagué K

Language: English
References: 29
Page: 36-45
PDF size: 627.77 Kb.


ABSTRACT

INTRODUCTION Ferritin is the best biomarker for assessing iron defi ciency, but ferritin concentrations increase with infl ammation. Several adjustment methods have been proposed to account for infl ammation’s eff ect on iron biomarker interpretation. The most recent and highly recommended method uses linear regression models, but more research is needed on other models that may better defi ne iron status in children, particularly when distributions are heterogenous and in contexts where the eff ect of infl ammation on ferritin is not linear.
OBJECTIVES Assess the utility and relevance of quadratic regression models and quantile quadratic regression models in adjusting ferritin concentration in the presence of infl ammation.
METHODS We used data from children aged under fi ve years, taken from Cuba’s national anemia and iron defi ciency survey, which was carried out from 2015–2018 by the National Hygiene, Epidemiology and Microbiology Institute. We included data from 1375 children aged 6 to 59 months and collected ferritin concentrations and two biomarkers for infl ammation: C-reactive protein and α-1 acid glycoprotein. Quadratic regression and quantile regression models were used to adjust for changes in ferritin concentration in the presence of infl ammation.
RESULTS Unadjusted iron defi ciency prevalence was 23% (316/1375). Infl ammation-adjusted ferritin values increased iron-defi ciency prevalence by 2.6–4.5 percentage points when quadratic regression correction model was used, and by 2.8–6.2 when quantile regression was used. The increase when using the quantile regression correction model was more pronounced and statistically signifi cant when both infl ammation biomarkers were considered, but adjusted prevalence was similar between the two correction methods when only one biomarker was analyzed.
CONCLUSIONS The use of quadratic regression and quantile quadratic regression models is a complementary strategy in adjusting ferritin for infl ammation, and is preferable to standard regression analysis when the linear model’s basic assumptions are not met, or when it can be assumed that ferritin–infl ammation relationships within a subpopulation may deviate from average trends.


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