This paper evaluates the diagnostic accuracy of Onomap in identifying population groups by ethnicity, and discusses applications to public health practice.
Spatial analysis of molecular variance was performed using SAMOVA 1.0 to identify population groups [ 57].
Firstly, baseline personal risk domain data could identify population groups with differential personal risk thereby allowing efforts to be targeted.
Thus larger and more inclusive studies are needed to identify population groups with higher burden of hypertension.
To prevent from premature deaths and illnesses, it is necessary to evaluate the magnitude of metabolic risk factors and identify population groups at risk of chronic diseases.
Further consideration of what cooking skill is, how to measure it, and studies of population prevalence is required both to identify population groups where further interventions could be targeted, and evaluation of such interventions.
The method implemented in SAMOVA employs a simulated annealing procedure and uses allele frequency data along with geographic coordinates of the sampled populations to identify population groups maximizing genetic differentiation.
As depressive symptoms are known to be predictive of poor quality of life and increased morbidity and mortality, it is important to identify population groups that may be especially vulnerable.
In addition, predictive models of utilization have been designed that can identify population groups with the potential for very high levels of resource consumption [ 10, 11], classify the types of patients attended, and expedite costs forecasting.
