Anaemia and Its Relation to Demographic, Socio-economic and Anthropometric Factors in Rural Primary School Children in Hai Phong City, Vietnam

Abstract

Little is known about the prevalence of anaemia and associated factors in school children in Vietnam. In this cross-sectional study, we aimed to determine the prevalence of anaemia and its subtypes, and the associations of types of anaemia with demographic, socio-economic and anthropometric factors among 6–9-year-old primary school children in rural areas of Hai Phong City, Vietnam. Haemoglobin (Hb) and mean corpuscular volume (MCV) were measured, and demographic, socio-economic and anthropometric data were collected in 893 children from eight primary schools. The prevalence of anaemia (Hb < 115 g/L) was 12.9% (95% CI: 8.1%, 19.9%), microcytic anaemia (Hb < 115 g/L and MCV < 80 fL) was 7.9% (95% CI: 5.3%, 11.6%) and normocytic anaemia (Hb < 115 g/L and MCV 80–90 fL) was 5.3% (95% CI: 2.9%, 9.5%). No child presented with macrocytic anaemia (Hb < 115 g/L and MCV > 90 fL). Children who were underweight, wasted, or in anthropometric failure (either underweight, stunted or wasted) were more likely to be anaemic (all p ≤ 0.004), and specifically, to have normocytic anaemia (all p ≤ 0.006), than those who were not underweight, wasted or in anthropometric failure. Stunted children were more likely to be anaemic (p = 0.018) than those who were not stunted. Overweight/obese children were less likely to be anaemic (p = 0.026) or have normocytic anaemia (p = 0.038) compared with children who were not overweight/obese. No anthropometric status indicator was associated with the risk of microcytic anaemia. No demographic or socio-economic factor was associated with any type of anaemia. Anaemia remains a public health issue in rural areas in Hai Phong City, Vietnam, and future approaches for its prevention and control should target undernourished primary school children.

Prevalence of Anemia and its Associated Factors among Chinese 9-, 12-, and 14-Year-Old Children: Results from 2014 Chinese National Survey on Students Constitution and Health

“…Understanding factors correlated with anemia can provide more insight to the measures that can be applied to combat anemia. In the present study, we found that overweight and obese children were less likely to have anemia, which was consistent with a study conducted by Hoang [26], although other studies had detected both positive [27,28] and insignificant [29,30] correlations between anemia and BMI. Hoang et al [26] found that overweight/obese children may have had a higher hemoglobin concentration compared to normal weight or underweight children for some reasons that have not yet been explored.…”

Anaemia Types and Severity in Patients aged 1 to 14 years at the Children’s Hospital of the University Teaching Hospitals in Zambia

“…This distribution of moderate to severe anaemia in our study could be because the University Teaching Hospital where the study was conducted is a level 3 referral hospital receiving patients whose conditions cannot be handled by lower levels of hospitals in the city. Furthermore, Zambia is a developing country where most households may not afford foods rich in iron and other minerals required for the formation of blood cells [26]. This study was conducted in an urban area and agreed with findings of some studies that found that children in such areas are more likely to be anaemic [27].…”

Multiple micronutrient supplementation improves micronutrient status in primary school children in Hai Phong City, Vietnam: a randomised controlled trial

“…This 22-week parallel, double-blind, randomised, controlled trial was conducted from December 2016 to May 2017 in rural areas of Hai Phong City, Vietnam. Eight schools were selected for participation using a multistage sampling approach described in detail elsewhere 13 , 26 . Briefly, two of the eight rural districts in Hai Phong City were selected at random in the first stage, followed by the selection of four schools in each district among primary schools with student enrolment of ≥ 300.…”

“…To account for the clustering induced by schools, the sample size was multiplied by a design effect of 1.42 [DE = 1 + ICC × (m − 1), where m = 8, the number of schools; and ICC = 0.06; the intracluster correlation coefficient]. The intracluster correlation coefficient was estimated from Hb data obtained during screening for eligibility 26 . Assuming 15% attrition rate, the target sample size for recruitment was 348 children [approximately 29 children in each strata defined by sex (boy/girl) and anthropometric status (undernourished/normal weight/overweight-obese)].…”