ArticleBMC public health2025
Factors that influence anemia prevalence: a comparative study of datasets from Russia and South Africa.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe prevalence of anemia is heterogeneous: different countries and population groups face varying risks of the disease. By identifying social, demographic, and economic factors, policymakers can define risk groups based on lifestyle and tailor measures to address the disease. This study examines and compares socioeconomic factors associated with anemia using data from two national surveys. The Russian survey relied solely on questionnaires, while the South African survey included medical examinations to confirm anemia cases.
methodsMultinomial regression was employed to estimate the risks of anemia using a combination of socioeconomic factors.
resultsAn inverse relationship was observed between bad habits and the risk of anemia in both samples. Education, income, and regular food consumption were found to be insignificant variables in both samples. However, household property ownership emerged as a significant factor. In South Africa, an inverse relationship with anemia risk was identified for households owning electric/gas ovens (OR = 0.769, 95% CI: 0.613-0.967, p ≤ 0.05) and washing machine (OR = 0.699, 95% CI: 0.564-0.866, p ≤ 0.01. Increased efforts for housekeeping also manifest themselves as increased risk to be anemic if an individual grows vegetables and fruits (OR = 1.333, 95% CI: 1.063-1.671, p ≤ 0.05). In Russia, factors associated with a higher socioeconomic status-such as owning a computer (OR = 0.754, 95% CI: 0.629-0.905, p ≤ 0.01), car (OR = 0.757, 95% CI: 0.610-0.938, p ≤ 0.05), or DVD player (OR = 0.819, 95% CI: 0.684-0.981, p ≤ 0.05) - were linked to a lower risk of anemia. Additionally, the habit of seeking medical help rather than self-medicating was negatively associated with anemia in the Russian sample (OR = 0.774, 95% CI: 0.704-0.850, p ≤ 0.01).
conclusionsThe comparison of socio-economic factors influencing the prevalence of anemia between Russian and South African samples has validated self-assessments as a reliable proxy for health status in the context of Russia. This methodological advancement underpins current and future research based on the extensive database of the Russia Longitudinal Monitoring Survey, encompassing approximately 2,500 indicators, to investigate disease prevalence.
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