Perceived Needs Versus Predisposing/Enabling Characteristics in Relation to Internet Cancer Information Seeking Among the US and Chinese Public: Comparative Survey Research

Abstract

Background Since the rise of the internet, online health information seeking has become a worldwide phenomenon. However, health and health communication are inherently culture bound. A data-driven cross-country comparison enables us to better understand how cultural factors moderate the association between individual-level determinants and online health information seeking. Objective The objective of the study was to examine similarities and differences in determinants of internet cancer information seeking between the US and Chinese general public (excluding cancer patients and survivors) under the framework of a behavioral model of health services use. Methods This study used Health Information National Trends Survey (HINTS) 2017 (US data) and HINTS-China 2017 data to answer the research question. It focused on people with no cancer history and with internet access. For the HINTS 2017, the sample size was 2153; for the HINTS-China 2017, the sample size was 2358. To compare China and the United States, the researchers selected the same set of study variables for each dataset. Under the framework of the behavioral model of health services use, these predictors were predisposing factors, enabling factors, and need factors. Results In terms of the predisposing factors, a higher age, college degree or above, being currently unemployed, and having a family history of cancer were associated with internet cancer information seeking for the Chinese respondents; none of these factors were related to information seeking for the US respondents, although a lower age was associated with information seeking. Regarding the enabling conditions, lower trust in family members and friends as reliable information sources was the only factor associated with information seeking for the Chinese respondents, while no enabling factor was related to information seeking for the US respondents. Regarding the need factors, perceived health status was not related to information seeking for the Chinese respondents, while perception of poorer health condition was related to information seeking for the US respondents. Higher cancer fear was related to information seeking for both groups, but the magnitude of association was smaller for the Chinese respondents than for the US respondents. Conclusions Overall, under the framework of the behavioral model of health services use, the results based on multivariate logistic regression reveal clear patterns of cross-country/cultural differences in the factors associated with internet cancer information seeking behaviors: predisposing characteristics and enabling conditions are more important in China, while perceived needs are more significant in the US. Such differences might reflect possible US-China differences in job environment (eg, job pressure) and culture (individualism vs collectivism and family structure).

Race/ethnicity, online information and COVID-19 vaccination: Study of minority immigrants’ internet use for health-related information

“…However, the authors also identified resistance to adapting to the new health culture with participants of the study continuing to use health sources hosted in their original culture due to comfort and familiarity. Additionally, another study conducted by Zhang et al (2021) found that immigrants use the internet for health information and that they are more likely to trust family and friends as reliable information sources as compared with U.S. respondents.…”

Consumption of Alcoholic Beverages Associated With Physical Health Status in Adults: Secondary Analysis of the Health Information National Trends Survey Data

“…Similarly, occupational status was re-coded to “currently employed,” with 1 = “Yes” and 0 = “No.” Annual Household Income was originally measured based on the categorical income used in HINTS 2020. A recode into quartiles was considered: “$0-$ 19 999,” “$20 000-$49 999,” “$50 000-$99 999,” and “$100 000 or more.” 19 …”

“…A recode into quartiles was considered: "$0-$ 19 999," "$20 000-$49 999," "$50 000-$99 999," and "$100 000 or more." 19 Statistical analysis. Structural equation modeling was used to evaluate the fit of the model, which included the prediction of alcohol consumption measures in physical health (BMI and chronic conditions).…”