Socio‐economic and demographic determinants of childhood anemia

Who is Anaemic in India? Intersections of class, caste, and gender

Anaemia severely impacts physical and mental abilities, raises health risks, and diminishes the quality of life and work capacity. It is a leading cause of adverse pregnancy outcomes and maternal mortality, especially in developing nations like India, where recent data on anaemia from National Family and Health Survey (NFHS-4) (2015-16) and NFHS-5 (2019-21) indicate a tremendous rise. Anaemia is a marker of poor nutrition and health, and socio-economic factors such as gender norms, race, income, and living conditions influence its impact. As a result, there are disparities in how anaemia affects different segments of society. However, existing research on health inequity and anaemia often employs a single-axis analytical framework of social power. These studies operate under the assumption that gender, economic class, ethnicity, and caste are inherently distinct and mutually exclusive categories and fail to provide a comprehensive understanding of anaemia prevalence. Therefore, the study has adopted the theoretical framework of intersectionality and analysed the NFHS-5 (2019-21) data using bivariate cross-tabulations and binary logistic regression models to understand how gender, class, caste, and place of residence are associated with the prevalence of anaemia. The results suggest that the women of Scheduled Tribes (ST) and Scheduled Castes (SC) share a disproportionate burden of anaemia. This study confirms that economic class and gender, geographical location, level of education, and body mass index significantly determine the prevalence of anaemia. The ST and SC women who are economically marginalised and reside in rural areas with high levels of poverty, exclusion, and poor nutritional status have a higher prevalence of anaemia than other population groups. Thus, the study suggests that intersections of multiple factors such as caste, class, gender, and place of residence significantly determine 'who is anaemic in India'.

Risk factors of anemia among preschool children in Ethiopia: a Bayesian geo-statistical model

BACKGROUND

The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model.

METHODS

We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization's cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05.

RESULT

The 9267 children in our study were between 6 and 59 months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4-59.4). We found that child stunting (OR = 1.26, 95% BCI (1.14-1.39), wasting (OR = 1.35, 95% BCI (1.15-1.57), maternal anemia (OR = 1.61, 95% BCI (1.44-1.79), mothers having two under five children (OR = 1.2, 95% BCI (1.08-1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR = 0.73, 95% BCI (0.62-0.85).

CONCLUSION

Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.