JMIR Publications

ABSTRACT

Background:

COVID-19 risk perception is a factor that influences the pandemic spread. Understanding the potential behavioral responses to COVID-19, including preparedness and adoption of preventive measures, can inform interventions to curtail its spread.

Objective:

We assessed self-perceived and latent class analysis (LCA) based risks of COVID-19 and their associations with preparedness, misconception, information gap, and preventive practices among residents of a densely populated city in Nigeria.

Methods:

We used data from a cross-sectional survey conducted among residents (n=140) of Onitsha City, Nigeria, in March 2020 before the government-mandated lockdown. Using an iterative Expectation-Maximization algorithm, we applied a latent class analysis (LCA) to systematically segment participants into the most likely distinct risk clusters. Furthermore, we used bivariate and multivariable logistic regression models to determine the associations between knowledge, attitude, preventive practice, perceived preparedness, misconception, COVID-19 information gap, and self-perceived and LCA-based COVID-19 risks.

Results:

Most participants (60.7%) had good knowledge and did not perceive themselves as at risk of contracting COVID-19. Three-quarters of the participants (74.6%; P<.0001) experienced COVID-19-related information gaps, while 62.9% (P<.05) of the participants had some misconceptions about the disease. Conversely, most participants (66.4%, P<.0001) indicated they were prepared for the COVID-19 pandemic. Using the LCA, we identified three distinct risk clusters (P<.0001), namely, prudent/low-risk takers, skeptics/high-risk takers, and carefree/very high-risk takers with prevalence rates (γc) of 47.5% (95%CI:40.0-55.0), 16.2% (95%CI:11.4-20.9) and 36.4% (95%CI:28.8-43.9), respectively. We recorded a significant negative agreement between self-perceived risk and LCA-based segmentation of COVID-19 risk (Kappa Coefficient = -0.218 ± 0.067, P<.01). Knowledge, attitude, and perceived need for COVID-19 information were significant predictors of COVID-19 preventive practices among the city residents.

Conclusions:

The clustering patterns highlight the impact of modifiable risk behaviors on COVID-19 preventive practices, which can provide strong empirical support for health prevention policies. Consequently, clusters with individuals at high risk of contracting COVID-19 would benefit from multi-component interventions delivered in diverse settings to improve the population-based response to the pandemic.