Objectives: To describe changes in public risk perception and risky behaviours during the first wave (W1) and second wave (W2) of COVID-19 in Nigeria, associated factors and observed trend of the outbreak.

Design: A secondary data analysis of cross-sectional telephone-based surveys conducted during the W1 and W2 of COVID-19 in Nigeria.

Setting: Nigeria.

Participants: Data from participants randomly selected from all states in Nigeria.

Primary Outcome: Risk perception for COVID-19 infection categorised as risk perceived and risk not perceived.

Secondary Outcome: Compliance to public health and social measures (PHSMs) categorised as compliant; non-compliant and indifferent.

Analysis: Comparison of frequencies during both waves using χ statistic to test for associations. Univariate and multivariate logistic regression analyses helped estimate the unadjusted and adjusted odds of risk perception of oneself contracting COVID-19. Level of statistical significance was set at p<0.05.

Results: Triangulated datasets had a total of 6401 respondents, majority (49.5%) aged 25-35 years. Overall, 55.4% and 56.1% perceived themselves to be at risk of COVID-19 infection during the W1 and W2, respectively. A higher proportion of males than females perceived themselves to be at risk during the W1 (60.3% vs 50.3%, p<0.001) and the W2 (58.3% vs 52.6%, p<0.05). Residing in the south-west was associated with not perceiving oneself at risk of COVID-19 infection (W1-AOdds Ratio (AOR) 0.28; 95% CI 0.20 to 0.40; W2-AOR 0.71; 95% CI 0.52 to 0.97). There was significant increase in non-compliance to PHSMs in the W2 compared with W1. Non-compliance rate was higher among individuals who perceived themselves not to be at risk of getting infected (p<0.001).

Conclusion: Risk communication and community engagement geared towards increasing risk perception of COVID-19 should be implemented, particularly among the identified population groups. This could increase adherence to PHSMs and potentially reduce the burden of COVID-19 in Nigeria.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977458PMC
http://dx.doi.org/10.1136/bmjopen-2021-058747DOI Listing

Publication Analysis

Top Keywords

risk perception
16
public risk
8
secondary data
8
data analysis
8
covid-19
5
risk
5
perception
4
perception behaviours
4
behaviours covid-19
4
covid-19 second
4

Similar Publications

This study investigated whether parental socialization of negative emotions moderated the relationship between adolescents' low executive function or high impulsivity and their current or subsequent emotion dysregulation. Emotion dysregulation, characterized by difficulties in managing the intensity and duration of emotions, is a transdiagnostic factor linked to adverse outcomes. Youth with poor executive functioning and/or high impulsivity are at risk for emotion dysregulation; however, the role of parenting in influencing this trajectory warrants exploration.

View Article and Find Full Text PDF

Background: Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in health care.

Objective: This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.

View Article and Find Full Text PDF

Public health interventions reduce infection risk, while imposing significant costs on both individuals and the society. Interventions can also lead to behavioral changes, as individuals weigh the cost and benefits of avoiding infection. Aggregate epidemiological models typically focus on the population-level consequences of interventions, often not incorporating the mechanisms driving behavioral adaptations associated with interventions compliance.

View Article and Find Full Text PDF
Article Synopsis
  • E-commerce struggles with issues like content sameness and user anxiety about making purchases, prompting a study on perceived risk based on online reviews.
  • The study used a dataset of over 262,000 reviews and a predictive model that effectively identified 11 key factors impacting perceived risk, achieving high accuracy metrics (precision of 84%, recall of 86%, F1 score of 85%).
  • Key features influencing perceived risk vary by product type; for electronics, quality, functionality, and price are crucial, while for skincare, skin safety is the top concern, highlighting differences in risk perception.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!