The COVID-19 outbreak: a snapshot from down under

Associations between patient-level health literacy and diagnostic time intervals for head and neck cancer: A prospective cohort study

BACKGROUND

Health literacy (HL) comprises skills and knowledge required to understand, access, and make decisions about healthcare. Our aim was to examine associations between patient HL and time intervals (defined in the Aarhus statement) along the pathway to treatment of head and neck cancer (HNC).

METHODS

A prospective cohort study was conducted from October 2018 to March 2020. Participants completed the Health Literacy Questionnaire (HLQ®) and described key events and dates along the pathway to treatment using validated questionnaires. Correlations between six diagnostic time intervals and domains of HL were explored, and factors predicting exceeding maximum acceptable timeframes were assessed using logistic regression.

RESULTS

One hundred patients with a diagnosis of HNC within the preceding 6 months were recruited. HLQ® Domain 2 (sufficient information to manage health) was significantly negatively associated with four intervals: the patient interval (first symptom to first presentation), primary care interval (first presentation to referral to secondary care), diagnostic interval (first presentation to diagnosis), and total interval (first symptom to treatment onset); correlation coefficients -0.25 to -0.27 (P < 0.05). Domain 8 (ability to find good information) was significantly negatively associated with three intervals (primary care interval, diagnostic interval, and total interval; correlation coefficients -0.23 to -0.34; P < 0.05). Higher education, age, and comorbidity levels were associated with shorter patient and diagnostic intervals.

CONCLUSIONS

HL may be a potential target to improve timeliness of HNC diagnosis and reduce disparities in outcomes.

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.