Temporal trends of food consumption patterns in Tuvalu under the context of climate change: COMmunity-based Behavior and Attitude survey in Tuvalu (COMBAT) since 2020.

AIM: The aim of this study was to analyze temporal trends of food consumption patterns, attitudes, and health-related knowledge in Tuvalu, a small Pacific Island country facing the triple threat of obesity, climate change, and food insecurity. METHODS: Two waves of the COMmunity-based Behavior and Attitude (COMBAT) survey were conducted in 2020 and 2022. Descriptive characteristics of changes in obesity proportion, food intake, and sociodemographic factors were assessed. Additionally, this study also integrates individual climate data utilizing satellite-based prediction models, and estimates historical temperature, precipitation, and sea level trends among all islands in Tuvalu. RESULTS: The study revealed a high obesity proportion among adults (69.5% in 2020, 73.2% in 2022) and an increase in the percentage of adolescents with a high waist circumference. Variations in food intake were also observed between the two waves of the survey. CONCLUSIONS: The data collected in the COMBAT study provides valuable insights for future epidemiological research to elucidate the associations and causal relationships between climate change, food security, and non-communicable diseases in Tuvalu.

Higher Imported Food Patterns Are Associated with Obesity and Severe Obesity in Tuvalu: A Latent Class Analysis.

Background: Tuvalu is a Pacific Island country within the small island developing states that has observed a significant and alarming increase in obesity rates over the past 40 years, affecting ∼60 %-70 % of the current population. Objectives: This study aimed to investigate the association between food patterns and the proportion of obesity in a Pacific Island country. Methods: The 2022 COMmunity-based Behavior and Attitude survey in Tuvalu (COMBAT) included 985 adults with complete data on sociodemographic information and the frequency of consumption of 25 common foods. A latent class analysis determined 4 food patterns. Bayesian multilevel logistic and linear regression models estimated the association between food patterns and the proportion of obesity [body mass index (BMI) ≥30 kg/m2], severe obesity (BMI ≥40 kg/m2), and weight (kg), adjusting for potential confounders and accounting for clustering by region. Results: The latent class analysis revealed 4 food patterns with an entropy of 0.94 and an average posterior probability of class assignment for each individual of 0.97, described as follows: 1) local: locally produced foods with moderate food diversity (proportion of individuals = 28 %); 2) diverse-local: local with greater food diversity (17 %); 3) restricted-imported: more imported with restricted diversity (29 %); and 4) imported: heavily imported with high diversity (26 %). Compared to those following the diverse-local pattern, the odds of having obesity were greater for those classified with the imported pattern [odds ratio (OR): 2.52; 95 % credible interval (CrI): 1.59, 3.99], restricted-imported pattern (OR: 1.89; 95 % CrI: 1.59, 3.99), and local pattern (OR: 1.54; 95 % CrI: 0.94, 2.50). Similar trends were observed for severe obesity while body weight was positively associated with both restricted-imported and imported food patterns. Conclusions: The high consumption of imported foods, together with the low consumption of plant-based foods and protein-rich foods, could be a relevant modifiable lifestyle factor explaining the high levels of obesity and severe obesity in Tuvalu, a Pacific Island country.