In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.

Pharmacological treatment may become a barrier for a mother’s breastfeeding goals. We aimed to investigate maternal medication intake as a factor for non-initiation and cessation of breastfeeding and the effect of professional counseling on maternal decision-making. Throughout 2020, 847 women were recruited from five healthcare institutions. Information was gathered prospectively with an organized questionnaire through interview during hospitalization and through telephone at 1, 3 and 6 months postpartum. Results revealed that from the 57 cases of breastfeeding cessation due to medication intake, only 10.5% received evidence-based counseling from a physician. Unfortunately, 68.4% (n = 39/57) of the participants ceased breastfeeding due to erroneous professional advice. The compatibility of medicines with breastfeeding was examined according to the Lactmed and Hale classification systems, which showed discrepancy in 8 out of 114 medicines used, while 17.5% and 13.2% of the medicines, respectively, were not classified. Educational level, employment at six months postpartum, mode of delivery, previous breastfeeding experience, medication intake for chronic diseases, physician’s recommendation and smoking before pregnancy were factors significantly correlated with breastfeeding discontinuation due to medication intake. The COVID-19 restrictions protected women from ceasing breastfeeding due to medication intake. Maternal and lactation consultancy should be strictly related to evidence-based approaches.

(1) Background: This study assesses the impact of mothers’ illness perceptions about autism spectrum disorder and their coping strategies on the family’s quality of life during the initial period following diagnosis and one year afterward. (2) Method: The sample consisted of 53 mothers of children newly diagnosed with autism spectrum disorder and having communication difficulties who completed the following: the Beach Center Family Quality of Life Scale, the Brief Illness Perception Questionnaire, and the Brief-COPE. (3) Results: The findings revealed a moderate family quality of life in the initial assessment and a lack of a statistically significant change one year later. Notably, statistically significant changes were observed in coping strategies, as in the second assessment, and the score in denial and self-blame decreased. Pearson and Eta analyses indicated several correlations between socio-demographic characteristics, illness perceptions, coping strategies, and family quality of life. Multiple regression analysis showed that positive reframing was positively associated with total family quality of life in the initial period following diagnosis and one year afterward, while self-blame was associated with poorer quality of life in the time after diagnosis. Furthermore, the belief about the controllability of the disorder was correlated with better family quality of life one year after the diagnosis. (4) Conclusions: Illness perceptions and coping can be considered as predictors of family quality of life outcomes one year after the diagnosis of autism spectrum disorder. The focus of interventions, apart from controlling the disorder’s symptoms, should aim to strengthen specific strategies and weaken others.