Objective

To propose a tool to identify local communities that require public health work priority, taking into account factors that influence adherence to risk minimization guidelines, especially in lock-down environments and unconventional workplaces.

Methodology

An ordering algorithm, based on the theory of uncertainty, was applied to classify population zones exhibiting high levels of infection and non-compliance with regulations in Guayaquil, during the last 'weekend' lockdown episode in July 2021. Seven urban sectors showed the highest number of infections (more than 70% of the local population): Vergeles (A1), Samanes (A2), Socio Vivienda (A3), Guasmo Norte (A4), Fertisa (A5), Alborada (A6), Urdesa (A7). Seven risk factors were identified after a path analysis of compliance hypothesis (x2/gl=3,6; CFI≥0,91; TLI≥0,90, RMSEA≤0,05), based on a random sample of n=515 adults living in the affected areas.

Results

Adherence to norms is influenced by the safety climate, perceived risk and risk understanding. The ordering threshold (h) leaded unidirectional relationships between variables.

Conclusions:

Adding more factors are believed to increases the differentiation path. The results showed that Vergeles, Norte and Fertisa were the areas with the highest priority for public health care {A4,A5,A6}>{A2}>{A3}>{A1}>{A7}.