Does diet map with mortality? Ecological association of dietary patterns with chronic disease mortality and its spatial dependence in Switzerland. Issue 7 (14th April 2022) - Pestoni, Giulia; Karavasiloglou, Nena; Braun, Julia; Krieger, Jean-Philippe; Sych, Janice M.; Bopp, Matthias; Faeh, David; Gruebner, Oliver; Rohrmann, Sabine
Abstract: We investigated the associations between dietary patterns and chronic disease mortality in Switzerland using an ecological design and explored their spatial dependence, i.e. the tendency of near locations to present more similar and distant locations to present more different values than randomly expected. Data of the National Nutrition Survey menuCH ( n 2057) were used to compute hypothesis- (Alternate Healthy Eating Index (AHEI)) and data-driven dietary patterns. District-level standardised mortality ratios (SMR) were calculated using the Swiss Federal Statistical Office mortality data and linked to dietary data geographically. Quasipoisson regression models were fitted to investigate the associations between dietary patterns and chronic disease mortality; Moran's I statistics were used to explore spatial dependence. Compared with the first, the fifth AHEI quintile (highest diet quality) was associated with district-level SMR of 0·95 (95 % CI 0·93, 0·97) for CVD, 0·91 (95 % CI 0·88, 0·95) for ischaemic heart disease (IHD), 0·97 (95 % CI 0·95, 0·99) for stroke, 0·99 (95 % CI 0·98, 1·00) for all-cancer, 0·98 (95 % CI 0·96, 0·99) for colorectal cancer and 0·93 (95 % CI 0·89, 0·96) for diabetes. The Swiss traditional and Western-like patterns were associated with significantly higher district-level SMR for CVD, IHD, stroke and diabetes (ranging from 1·02 to 1·08) compared with the Prudent pattern. Significant global and local spatial dependence was identified, with similar results across hypothesis- and data-driven dietary patterns. Our study suggests that dietary patterns partly contribute to the explanation of geographic disparities in chronic disease mortality in Switzerland. Further analyses including spatial components in regression models would allow identifying regions where nutritional interventions are particularly needed.