Ethnic disparities in liver fat accumulation in school-aged children.

OBJECTIVE: Nonalcoholic fatty liver disease (NAFLD) has a different prevalence in adults from different ethnic groups. This study examined whether these ethnic differences originate in early life and could be explained by early-life factors. METHODS: This observational study was embedded in a population-based prospective cohort study from fetal life onward among 2,570 children born in Rotterdam, the Netherlands. Information about prepregnancy, pregnancy, and childhood factors, as well as childhood BMI, was obtained from questionnaires and physical examinations. Liver fat was assessed by magnetic resonance imaging at age 10 years. RESULTS: Median liver fat fraction was 2.0% (95% CI: 1.2%-5.3%), and NAFLD prevalence was 2.8%. Children from a Turkish background had the highest median liver fat percentage (2.5%, 95% CI: 1.2%-10.7%) and NAFLD prevalence (9.1%). Children of Cape Verdean, Dutch Antillean, Surinamese-Creole, or Turkish background had a higher total liver fat fraction compared with children with a Dutch background (p < 0.05). After controlling for early-life factors, these differences persisted only in children with a Turkish background. CONCLUSIONS: Prevalence of liver fat accumulation and NAFLD differs between ethnic subgroups living in the Netherlands, especially for those with a Turkish background. Early-life factors have a strong influence on these associations and may hold clues for future preventive strategies.

Do risk visualizations improve the understanding of numerical risks? A randomized, investigator-blinded general population survey.

BACKGROUND: Risk visualizations are often employed to support risk communication. However, their effectiveness in communication of single absolute risks remains unclear. We investigated the effectiveness of risk visualizations in conveying verbatim knowledge of single absolute risks among the general population. METHODS: Randomly sampled members of the general Dutch population completed four basic risk conversions from percentages to natural frequencies and vice versa. By random investigator-blinded allocation, these conversions were supported by either icon arrays, pie charts, bar graphs or no visualization. Verbatim risk knowledge was scored as the number of conversions completed correctly. RESULTS: 393 subjects were included. Overall, 60% of respondents answered all four questions correctly. Risk format (percentages vs. natural frequencies, p = 0.677) and risk magnitude (p = 0.532) were not associated with verbatim risk knowledge score. Younger age (p = 0.001) and higher education level (p < 0.001) were independently associated with higher scores. The use of risk visualizations was not associated with higher scores (OR = 1.08; 95% confidence interval: 0.69-1.69; p = 0.745). All three forms of risk visualization were equally ineffective. These findings held when stratifying by risk format, risk magnitude and user preference for a certain form of risk visualization. There were no significant interactions with age or education level. CONCLUSION: Risk visualizations did not improve conveyance of verbatim knowledge of single absolute risks, irrespective of age, education level, risk magnitude, risk format and form of risk visualization. Risk visualizations may therefore be less suitable for settings in which detailed conveyance of single absolute risks is the main objective, although their effect on user experience and perception of risk communication and subsequent patient activation and participation remains to be elucidated.