OBJECTIVE: The current study seeks to (1) estimate the prevalence of EDs and ED risk status among adults in Malaysia using an established diagnostic screen; (2) examine gender and ethnic differences between ED diagnostic/risk status groups; and (3) characterize the clinical profile of individuals who screen positive for an ED.
METHOD: We administered the Stanford-Washington University Eating Disorder Screen, an online ED screening tool, to adults in Malaysia in September 2020.
RESULTS: ED risk/diagnostic categories were assigned to 818 participants (ages 18-73 years) of which, 0.8% screened positive for anorexia nervosa, 1.4% for bulimia nervosa, 0.1% for binge-ED, 51.4% for other specified feeding or ED, and 4.8% for avoidant/restrictive food intake disorder. There was gender parity in the high risk and the overall ED categories. The point prevalence of positive eating pathology screening among Malays was significantly higher than Chinese but no different from Indians.
DISCUSSION: This is the first study to estimate the prevalence of EDs using a diagnostic screen in a population-based sample of Malaysians. It is concerning that over 50% of Malaysians reported symptoms of EDs. This study highlights the need to invest more resources in understanding and managing eating pathology in Malaysia.
PUBLIC SIGNIFICANCE: This study estimates the prevalence of EDs among adults in Malaysia using an online EDs screen. Over 50% of Malaysians report symptoms of EDs. The study highlights the need for more resources and funding to address this important public health issue through surveillance, prevention, and treatment of EDs in Malaysia.
METHODS: Text ads from Google searches in eight countries (Bahamas, Germany, India, Malaysia, Mexico, South Africa, United Arab Emirates, and United States) were collected in 2022, totaling 1,974 prepolicy and 3,262 post-policy ads, and analyzed in 2023. A gold standard database was established by two coders who labeled 707 ads, which trained five natural language processing models to label the ads, covering content and target demographics. The descriptive statistics and multivariable logistic models were applied to analyze content before versus after policy implementation, both globally and by country.
RESULTS: Vertex AI emerged as the best natural language processing model with the highest F1 score of 0.87. There were significant decreases from pre- to post-policy implementation in the prevalence of labels of "Racial or Ethnic Identification" and "Ingredients: Natural" by 47% and 66%, respectively. Notable differences were identified from pre- to post-policy implementation in India, Mexico, and Germany.
CONCLUSIONS: The study observed changes in skin-lightening product advertisement labels from pre- to post-policy implementation, both globally and within countries. Considering the influence of digital advertising on colorist norms, assessing digital ad policy changes is crucial for public health surveillance. This study presents a computational method to help monitor digital platform policies for consumer product advertisements that affect public health.
METHODS: A systematic search of PubMed, PsycInfo, and Embase was conducted. Studies published between 2007 and 2022 were included if they reported treatment rates among college students with mental health problems, stratified by sex, gender, race-ethnicity, sexual orientation, student type, student year, or student status. Random-effects models were used to calculate pooled prevalence ratios (PRs) of having a perceived need for treatment and of receiving treatment for each sociodemographic subgroup.
RESULTS: Twenty-one studies qualified for inclusion. Among students experiencing mental health problems, consistent and significant sociodemographic differences were identified in perceived need for treatment and treatment receipt. Students from racial-ethnic minority groups (in particular, Asian students [PR=0.49]) and international students (PR=0.63) reported lower rates of treatment receipt than White students and domestic students, respectively. Students identifying as female (sex) or as women (gender) (combined PR=1.33) reported higher rates of treatment receipt than students identifying as male or as men. Differences in perceived need appeared to contribute to some disparities; in particular, students identifying as male or as men reported considerably lower rates of perceived need than students identifying as female or as women.
CONCLUSIONS: Findings highlight the need for policy makers to address barriers throughout the treatment-seeking pathway and to tailor efforts to student subgroups to reduce treatment disparities.