Iodine is the key element for thyroid hormone synthesis, and its deficiency, even moderate, is harmful in pregnancy, when needs are increased, because of its potential deleterious effects on fetal brain development. In Portugal, no recent data on iodine intake exists. The objective of this countrywide study was to analyze iodine status in pregnant Portuguese women in order to propose adequate measures to the health authorities. Using a fast colorimetric method, urine iodine concentration (UIC) was evaluated in 3631 pregnant women followed in 17 maternity hospitals from hinterland and coastal areas in Continental Portugal and the Portuguese islands of Açores and Madeira. Median UIC value was 84.9 μg/l (range 67.6-124.1) in Continental Portugal, 69.5 μg/l in Madeira, and 50.0 μg/l in Açores. The percentage of satisfactory values (>150 μg/l) was 16.8, ranging from 8.8 to 34.1 in the Continent, and being 8.2 in Madeira and 2.3 in Açores. The percentage of values below 50 μg/l was 23.7, ranging from 14.0 to 37.4 in the Continent, 33.7 in Madeira, and 50.0 in Açores. Our results point to an inadequate iodine intake in pregnant women assisted in most Portuguese maternity hospitals. Considering the potential deleterious effects of inadequate iodine supply in pregnancy, iodine supplementation is strongly recommended in this period of life.

The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality’s population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.