Approaches to quantify the contribution of multiple anemia risk factors in children and women from cross-sectional national surveys

Abstract

BackgroundAttributable fractions (AF) of anemia are often used to understand the multifactorial etiologies of anemia, despite challenges interpreting them in cross-sectional studies. We aimed to compare different statistical approaches for estimating AF for anemia due to inflammation, malaria, and micronutrient deficiencies including iron, vitamin A, vitamin B12, and folate.MethodsAF were calculated using nationally representative survey data among preschool children (10 countries, total N = 7,973) and nonpregnant women of reproductive age (11 countries, total N = 15,141) from the Biomarkers Reflecting Inflammation and Nutrition Determinants of Anemia (BRINDA) project. We used the following strategies to calculate AF: 1) Levin’s formula with prevalence ratio (PR) in place of relative risk (RR), 2) Levin’s formula with odds ratio (OR) in place of RR, and 3) average (sequential) AF considering all possible removal sequences of risk factors. PR was obtained by 1) modified Poisson regression with robust variance estimation, 2) Kleinman-Norton’s approach, and 3) estimation from OR using Zhang-Yu’s approach. Survey weighted country-specific analysis was performed with and without adjustment for age, sex, socioeconomic status, and other risk factors.ResultsAbout 20–70% of children and 20–50% of women suffered from anemia, depending on the survey. Using OR yielded the highest and potentially biased AF, in some cases double those using PR. Adjusted AF using different PR estimations (Poisson regression, Kleinman-Norton, Zhang-Yu) were nearly identical. Average AF estimates were similar to those using Levin’s formula with PR. Estimated anemia AF for children and women were 2–36% and 3–46% for iron deficiency, <24% and <12% for inflammation, and 2–36% and 1–16% for malaria. Unadjusted AF substantially differed from adjusted AF in most countries.ConclusionAF of anemia can be estimated from survey data using Levin’s formula or average AF. While different approaches exist to estimate adjusted PR, Poisson regression is likely the easiest to implement. AF are a useful metric to prioritize interventions to reduce anemia prevalence, and the similarity across methods provides researchers flexibility in selecting AF approaches.