Anemia-related survey data can inform programmatic decision-making. You can collect hemoglobin data alongside data about conditions known to cause anemia, to inform public health anemia control programs. Quantifying the contributions of multiple anemia causes that often overlap with one another can be complex. Examining prevalence estimates of conditions known to cause anemia (e.g., inherited blood disorders, infectious diseases, or micronutrient deficiencies) is a first step in understanding the ecology of the anemia.

A frequent next step is to examine the bivariate associations between the risk factors and anemia (the outcome), and then build multivariable models predicting likely causes of anemia controlling for the overlap and interrelation between these conditions. Accounting for confounding and correlation amongst exposure variables is important to prevent overestimation of attribution of risk factors in regression models, Therefore, measurement of multiple underlying risk factors for anemia within surveys is also useful.