The Influence of Front-of-Package Nutrition Labeling on Consumer Behavior and Product Reformulation.

Countries worldwide have implemented mandatory or voluntary front-of-package nutrition labeling systems. We provide a narrative review of (a) real-world evaluations of front-of-package nutrition labels that analyze objective sales data and (b) studies that objectively assess product reformulation in response to a front-of-package nutrition label implementation. We argue that there is sufficient scientific evidence to recommend that governments implement mandatory front-of-package nutrition labeling systems to improvepopulation health. We also present a conceptual framework to describe front-of-package label influence and provide recommendations for the optimal label design, emphasizing that labeling systems should be highly visible and salient, be simple and easy to understand, leverage automatic associations, and integrate informational and emotional messaging. The existing research suggests that Guideline Daily Amount labels should be avoided and that the Health Star Rating and Nutri-Score systems are promising but that systems with warning labels like the one in Chile are likely to produce the largest public health benefits.

Towards a liquid healthcare: primary care organisational and management strategies during the COVID-19 pandemic - a qualitative study.

BACKGROUND: The COVID-19 pandemic has changed the organisational and management strategies of healthcare institutions such as primary care centres. Organisational culture as well as leadership style are key issues for the success of these institutions. Due to the multidimensional nature of identity processes, it is necessary to explore the changes experienced by health professionals from these perspectives. This study explores health professionals' organisational and management strategies in primary care settings during the COVID-19 pandemic. DESIGN: Qualitative, exploratory study based on the analysis of participants' accounts within a hermeneutic phenomenologicaly approach. METHODS: Research was conducted in primary care settings in two neighbouring Spanish healthcare regions. The sample included participants with different demographics (gender, age), professional roles (practice managers, general practitioners, paediatricians), employment status (permanent, temporary, zero-hours), and years of experience (under or over ten years' experience). Data were collected between July and December 2020 through focus groups and in-depth, semi-structured individual interviews. RESULTS: A total of 53 primary care workers participated in the study, of which 38 were individually interviewed and 15 participated in three focus groups. Of these, 78.4% were healthcare professionals, 49% were female nurses, and 70.5% had more than 10 years of work experience in primary care. Two main themes emerged: "liquid" healthcare and "the best healthcare system in the world". During the first wave of the COVID-19 pandemic, new, more fluid organisational and management models were implemented in primary care settings, which have remained in place since. Primary care workers' perceived a lack of appreciation and inclusion in decision-making that risked their alienation and disengagement. CONCLUSION: Primary care workers' professional identity became gradually blurred due to shifting perceptions of their professional roles in a context of increasing improvisation and flexible working practices. This affected their professional performance. TRIAL REGISTRATION: The study was approved by the Clinical Research Ethical Committee of the Talavera de la Reina Integrated Management Area (CEIm del AGI de Talavera de la Reina in Spain, Hospital Nuestra Señora del Prado, ref: 23/2020).

Management of burnout among the staff of primary care centres in Spain during the pandemic caused by the SARS-CoV-2.

BACKGROUND: The provision of healthcare during the pandemic caused by the SARS-CoV-2 virus represented a challenge for the management of the resources in the primary care centres. We proposed assessing burnout among the staff of those centres and identifying factors that contributed to its appearance and those that limited it. METHODS: An observational study which, by means of anonymous questionnaires, collected information about: (i) demographic variables; (ii) the characteristics of each position; (iii) the measures implemented by the medical decision-makers in order to provide care during the pandemic; and (iv) the Burnout Clinical Subtype Questionnaire (BCSQ-36). We performed a descriptive analysis of the burnout mentioned by the staff, and, by means of a multivariate analysis, we identified the factors which influenced it. Using logit models, we analysed whether receiving specific training in COVID-19, feeling involved in decision-making processes, and/or working within different healthcare systems had effects on the development of burnout. RESULTS: We analysed the replies of 252 employees of primary care centres in Spain with an average age of 45 (SD = 15.7) and 22 (SD = 11.4) years of experience. 68% of the participants (n = 173) indicated burnout of the frenetic subtype. 79% (n = 200) of the employees had high scores in at least one burnout subtype, and 62% (n = 156) in at least two. Women older than 45 had a lower probability of suffering burnout. Receiving specific training (OR = 0.28; CI95%: 0.11-0.73) and feeling involved in decision-making (OR = 0.32; CI95%:0.15-0.70) each reduced the probability of developing burnout. Working in a different department increased the likelihood of developing burnout of at least one clinical subtype (OR = 2.85; CI95%: 1.38-5.86). CONCLUSIONS: The staff in primary care centres have developed high levels of burnout. Participation in decision-making and receiving specific training are revealed as factors that protect against the development of burnout. The measures taken to contain the adverse effects of a heavy workload appear to be insufficient. Certain factors that were not observed, but which are related to decisions taken by the healthcare management, appear to have had an effect on the development of some burnout subtypes.

SMOOTH DENSITY SPATIAL QUANTILE REGRESSION.

We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling the quantile function as a combination of I-spline basis functions and Pareto tail distributions, we allow for flexible parametric modeling of the extremes while preserving non-parametric flexibility in the center of the distribution. We further establish that the model guarantees the desired degree of differentiability in the density function and enables the estimation of non-stationary covariance functions dependent on the predictors. We demonstrate through a simulation study that the proposed method produces more efficient estimates of the effects of predictors than other methods, particularly in distributions with heavy tails. To illustrate the utility of the model we apply it to measurements of benzene collected around an oil refinery to determine the effect of an emission source within the refinery on the distribution of the fence line measurements.

Spatiotemporal signal detection using continuous shrinkage priors.

Periodontal disease (PD) is a chronic inflammatory disease that affects the gum tissue and bone supporting the teeth. Although tooth-site level PD progression is believed to be spatio-temporally referenced, the whole-mouth average periodontal pocket depth (PPD) has been commonly used as an indicator of the current/active status of PD. This leads to imminent loss of information, and imprecise parameter estimates. Despite availability of statistical methods that accommodates spatiotemporal information for responses collected at the tooth-site level, the enormity of longitudinal databases derived from oral health practice-based settings render them unscalable for application. To mitigate this, we introduce a Bayesian spatiotemporal model to detect problematic/diseased tooth-sites dynamically inside the mouth for any subject obtained from large databases. This is achieved via a spatial continuous sparsity-inducing shrinkage prior on spatially varying linear-trend regression coefficients. A low-rank representation captures the nonstationary covariance structure of the PPD outcomes, and facilitates the relevant Markov chain Monte Carlo computing steps applicable to thousands of study subjects. Application of our method to both simulated data and to a rich database of electronic dental records from the HealthPartners ® Institute reveal improved prediction performances, compared with alternative models with usual Gaussian priors for regression parameters and conditionally autoregressive specification of the covariance structure.

The impact of population mobility on estimates of environmental exposure effects in a case-control study.

In many studies of environmental risk factors for disease, researchers use the location at diagnosis as a geographic reference for environmental exposures. However, many environmental pollutants change continuously over space and time. The dynamic characteristics of these pollutants coupled with population mobility in the United States suggest that for diseases with long latencies like cancer, historic exposures may be more relevant than exposure at the time of diagnosis. In this article, we evaluated to what extent the commonly used assumption of no population mobility results in increased bias in the estimates of the relationship between environmental exposures and long-latency health outcomes disease in a case-control study. We conducted a simulation study using the residential histories of a random sample from the National Institutes of Health-AARP (formerly American Association of Retired Persons) Diet and Health Study. We simulated case-control status based on subject exposure and true exposure effects that varied temporally. We compared estimates from models using only subject location at diagnosis to estimates where subjects were assumed to be mobile. Ignoring population mobility resulted in underestimates of subject exposure, with largest deviations observed at time points further away from study enrollment. In general, the effect of population mobility on the bias of the estimates of the relationship between the exposure and the outcome was more prominent with exposures that showed substantial spatial and temporal variability. Based on our results, we recommend using residential histories when environmental exposures and disease latencies span a long enough time period that mobility is important.

Multi-element effects on arsenate accumulation in a geochemical matrix determined using µ-XRF, µ-XANES and spatial statistics.

Soils regulate the environmental impacts of trace elements, but direct measurements of reaction mechanisms in these complex, multi-component systems can be challenging. The objective of this work was to develop approaches for assessing effects of co-localized geochemical matrix elements on the accumulation and chemical speciation of arsenate applied to a soil matrix. Synchrotron X-ray fluorescence microprobe (µ-XRF) images collected across 100 µm × 100 µm and 10 µm × 10 µm regions of a naturally weathered soil sand-grain coating before and after treatment with As(V) solution showed strong positive partial correlations (r' = 0.77 and 0.64, respectively) between accumulated As and soil Fe, with weaker partial correlations (r' > 0.1) between As and Ca, and As and Zn in the larger image. Spatial and non-spatial regression models revealed a dominant contribution of Fe and minor contributions of Ca and Ti in predicting accumulated As, depending on the size of the sample area analyzed. Time-of-flight secondary ion mass spectrometry analysis of an area of the sand grain showed a significant correlation (r = 0.51) between Fe and Al, so effects of Fe versus Al (hydr)oxides on accumulated As could not be separated. Fitting results from 25 As K-edge microscale X-ray absorption near-edge structure (µ-XANES) spectra collected across a separate 10 µm × 10 µm region showed ∼60% variation in proportions of Fe(III) and Al(III)-bound As(V) standards, and fits to µ-XANES spectra collected across the 100 µm × 100 µm region were more variable. Consistent with insights from studies on model systems, the results obtained here indicate a dominance of Fe and possibly Al (hydr)oxides in controlling As(V) accumulation within microsites of the soil matrix analyzed, but the analyses inferred minor augmentation from co-localized Ti, Ca and possibly Zn.

Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects.

Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post-conception gestational weeks 2-8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5 ) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post-conception gestational weeks 2-8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post-conception gestational weeks 2-8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post-conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd.

Spatial variable selection methods for investigating acute health effects of fine particulate matter components.

Multi-site time series studies have reported evidence of an association between short term exposure to particulate matter (PM) and adverse health effects, but the effect size varies across the United States. Variability in the effect may partially be due to differing community level exposure and health characteristics, but also due to the chemical composition of PM which is known to vary greatly by location and time. The objective of this article is to identify particularly harmful components of this chemical mixture. Because of the large number of highly-correlated components, we must incorporate some regularization into a statistical model. We assume that, at each spatial location, the regression coefficients come from a mixture model with the flavor of stochastic search variable selection, but utilize a copula to share information about variable inclusion and effect magnitude across locations. The model differs from current spatial variable selection techniques by accommodating both local and global variable selection. The model is used to study the association between fine PM (PM <2.5µm) components, measured at 115 counties nationally over the period 2000-2008, and cardiovascular emergency room admissions among Medicare patients.

Multilevel quantile function modeling with application to birth outcomes.

Infants born preterm or small for gestational age have elevated rates of morbidity and mortality. Using birth certificate records in Texas from 2002 to 2004 and Environmental Protection Agency air pollution estimates, we relate the quantile functions of birth weight and gestational age to ozone exposure and multiple predictors, including parental age, race, and education level. We introduce a semi-parametric Bayesian quantile approach that models the full quantile function rather than just a few quantile levels. Our multilevel quantile function model establishes relationships between birth weight and the predictors separately for each week of gestational age and between gestational age and the predictors separately across Texas Public Health Regions. We permit these relationships to vary nonlinearly across gestational age, spatial domain and quantile level and we unite them in a hierarchical model via a basis expansion on the regression coefficients that preserves interpretability. Very low birth weight is a primary concern, so we leverage extreme value theory to supplement our model in the tail of the distribution. Gestational ages are recorded in completed weeks of gestation (integer-valued), so we present methodology for modeling quantile functions of discrete response data. In a simulation study we show that pooling information across gestational age and quantile level substantially reduces MSE of predictor effects. We find that ozone is negatively associated with the lower tail of gestational age in south Texas and across the distribution of birth weight for high gestational ages. Our methods are available in the R package BSquare.

Multivariate spatial nonparametric modelling via kernel processes mixing.

In this paper we develop a nonparametric multivariate spatial model that avoids specifying a Gaussian distribution for spatial random effects. Our nonparametric model extends the stick-breaking (SB) prior of Sethuraman (1994), which is frequently used in Bayesian modelling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended here to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of space-dependent kernel functions that have a space-varying bandwidth parameter. This results in a flexible non-stationary spatial model, as different kernel functions lead to different relationships between the distributions at nearby locations. This approach is the first to allow both the probabilities and the point mass values of the SB prior to depend on space. Thus, there is no need for replications and we obtain a continuous process in the limit. We extend the model to the multivariate setting by having for each process a different kernel function, but sharing the location of the kernel knots across the different processes. The resulting covariance for the multivariate process is in general nonstationary and nonseparable. The modelling framework proposed here is also computationally efficient because it avoids inverting large matrices and calculating determinants, which often hinders the spatial analysis of large data sets. We study the theoretical properties of the proposed multivariate spatial process. The methods are illustrated using simulated examples and an air pollution application to model components of fine particulate matter.

Nonparametric Spatial Models for Extremes: Application to Extreme Temperature Data.

Estimating the probability of extreme temperature events is difficult because of limited records across time and the need to extrapolate the distributions of these events, as opposed to just the mean, to locations where observations are not available. Another related issue is the need to characterize the uncertainty in the estimated probability of extreme events at different locations. Although the tools for statistical modeling of univariate extremes are well-developed, extending these tools to model spatial extreme data is an active area of research. In this paper, in order to make inference about spatial extreme events, we introduce a new nonparametric model for extremes. We present a Dirichlet-based copula model that is a flexible alternative to parametric copula models such as the normal and t-copula. The proposed modelling approach is fitted using a Bayesian framework that allow us to take into account different sources of uncertainty in the data and models. We apply our methods to annual maximum temperature values in the east-south-central United States.

Front-of-Package Nutrition Labeling and Its Impact on Food Industry Practices: A Systematic Review of the Evidence.

The obesity epidemic has become a major public health concern globally, and the food supply is a significant driver of this trend. Front-of-package (FOP) labels have been implemented in many countries to encourage healthier food choices. This systematic review aimed to examine the effect of FOP label implementation on food manufacturers' practices. A comprehensive search of multiple databases was conducted following PRISMA guidelines, identifying 39 relevant articles from 1990 to 2021. The studies indicated that FOP labels conveying intuitive information influenced product reformulation, whereas those with numerical information without specific guidance had no impact on reducing unhealthy nutrients. The most common outcomes were sodium, sugar, and calorie reduction. Mandatory policies reported higher and more consistent effects on product reformulation compared to voluntary approaches. Voluntary FOP labeling resulted in low uptake and tended to be applied to healthier products. Food manufacturers responded to FOP labeling heterogeneously, depending on the label design and type of enforcement. FOP label implementation can reduce nutrients of concern but food manufacturers behave strategically by labeling healthier choices. This review provides recommendations for maximizing the benefits of using FOP labels to prevent obesity, and findings can inform future public health research and policymaking.

Resilience among primary care professionals in a time of pandemic: a qualitative study in the Spanish context.

OBJECTIVES: This study explores the impact of the COVID-19 pandemic on the Spanish primary care structure and services and the mechanisms implemented by the primary care workforce to restore and reinforce their reference care model. DESIGN: An exploratory, qualitative study with semistructured interviews and a focus group discussion conducted during the fall semester of 2020. SETTING: Primary health centres in Madrid (Spain), chosen based on factors such as infection rates during the earliest stages of the pandemic and demographic and socioeconomic aspects. PARTICIPANTS: A total of 19 primary health and social care professionals were purposively selected. Criteria for inclusion were gender (male/female), at least 5 years of experience in their current position, category (health/social/administrative worker), and whether they worked in a rural or urban healthcare setting. RESULTS: Two main themes were identified: (1) reflecting on a model in crisis-particularly the reopening of centres to users and the proactive, participative strategies implemented by primary care professionals to reach their community; and (2) regaining a sense of purpose-how healthcare professionals implemented strategies to sustain their vision of their reference model. The COVID-19 pandemic exposed leadership deficiencies that, together with the initial unavailability of resources and difficulties maintaining face-to-face contact with users, triggered a sense of loss of professional identity. On the other hand, the analysis revealed potential strategies to restore and reinforce the traditional model, such as the adoption of digital technologies and reliance on community networks. CONCLUSION: This study highlights the importance of a solid reference framework and enhances the strengths and skills of the workforce to reinforce the community-based service provision model.

Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure.

Exposure to high levels of air pollution during the pregnancy is associated with increased probability of preterm birth (PTB), a major cause of infant morbidity and mortality. New statistical methodology is required to specifically determine when a particular pollutant impacts the PTB outcome, to determine the role of different pollutants, and to characterize the spatial variability in these results. We develop a new Bayesian spatial model for PTB which identifies susceptible windows throughout the pregnancy jointly for multiple pollutants (PM(2.5) , ozone) while allowing these windows to vary continuously across space and time. We geo-code vital record birth data from Texas (2002-2004) and link them with standard pollution monitoring data and a newly introduced EPA product of calibrated air pollution model output. We apply the fully spatial model to a region of 13 counties in eastern Texas consisting of highly urban as well as rural areas. Our results indicate significant signal in the first two trimesters of pregnancy with different pollutants leading to different critical windows. Introducing the spatial aspect uncovers critical windows previously unidentified when space is ignored. A proper inference procedure is introduced to correctly analyze these windows.

Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation.

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM(2.5)) and ozone (O(3)) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM(2.5) and O(3), respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) had an annual normalized mean bias of -2.09%, although bias varied seasonally, from 32% in November to -27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.

Nonparametric Bayesian models for a spatial covariance.

A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.

Bayesian Spatial-temporal Model for Cardiac Congenital Anomalies and Ambient Air Pollution Risk Assessment.

We introduce a Bayesian spatial-temporal hierarchical multivariate probit regression model that identifies weeks during the first trimester of pregnancy which are impactful in terms of cardiac congenital anomaly development. The model is able to consider multiple pollutants and a multivariate cardiac anomaly grouping outcome jointly while allowing the critical windows to vary in a continuous manner across time and space. We utilize a dataset of numerical chemical model output which contains information regarding multiple species of PM2.5. Our introduction of an innovative spatial-temporal semiparametric prior distribution for the pollution risk effects allows for greater flexibility to identify critical weeks during pregnancy which are missed when more standard models are applied. The multivariate kernel stick-breaking prior is extended to include space and time simultaneously in both the locations and the masses in order to accommodate complex data settings. Simulation study results suggest that our prior distribution has the flexibility to outperform competitor models in a number of data settings. When applied to the geo-coded Texas birth data, weeks 3, 7 and 8 of the pregnancy are identified as being impactful in terms of cardiac defect development for multiple pollutants across the spatial domain.

Federal Food Assistance Program Participation during the COVID-19 Pandemic: Participant Perspectives and Reasons for Discontinuing.

This study aims to describe reasons for discontinuing participation and experiences participating in the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC) during the COVID-19 pandemic. We analyzed data from a cross-sectional online survey distributed to a national sample, restricted to (1) households that discontinued participating in SNAP (n = 146) or WIC (n = 149) during the pandemic and (2) households that participated in SNAP (n = 501) or WIC (n = 141) during spring 2021-approximately one year into the pandemic. We conducted thematic analyses of open-ended survey questions and descriptive statistics for Likert-scale items. Themes raised by respondents who discontinued participating in SNAP or WIC included difficulty recertifying and virus exposure concerns. Former WIC participants reported the program was not worth the effort and former SNAP participants reported failing to requalify. Respondents participating in WIC or SNAP during the pandemic mentioned transportation barriers and insufficient benefit value. WIC participants had trouble redeeming benefits in stores and SNAP participants desired improved online grocery purchasing experiences. These results suggest that enhancements to WIC and SNAP, such as expanded online purchasing options, program flexibilities, and benefit increases, can improve program participation to ensure access to critical nutrition supports, especially during emergencies.

School Garden Prevalence Before and After the Implementation of the Healthy Hunger-Free Kids Act.

BACKGROUND: The Healthy Hunger-Free Kids Act (HHFKA) of 2010 supported implementation of school gardens for promoting fruit and vegetable consumption. We examined school garden prevalence over time by school-level factors during the period before and after the implementation of HHFKA. METHODS: Using data from the New Jersey Child Health Study, conducted in 4 low-income New Jersey cities, prevalence of school gardens among K-12 schools (n = 148) was assessed between school year 2010-2011 and 2017-2018. Multivariable analysis estimated changes in garden prevalence over time adjusting for school-level factors. RESULTS: Overall, the sample included 97 elementary and 51 middle/high schools. Multivariable logistic regression showed that compared to 2010-2011 (19%) a higher proportion of schools reported having a garden in 2013-2014 (32%, p = 0.025). Over the entire study period, schools with majority Hispanic student enrollment had approximately half the odds of having a garden compared to schools with majority Black students (p = 0.036). CONCLUSION: School garden prevalence increased in the year immediately following the implementation of the HHFKA but this increase was not sustained over time. Future research should investigate the reasons for this decline and potential disparities by race/ethnicity.