Food Insecurity across Age Groups in the United States during the COVID-19 Pandemic.

Food insecurity increased during the COVID-19 pandemic, but the impact varied across different age groups during the prolonged public health emergency. This study sought to describe national food insecurity prevalence by adult age group at multiple stages of the pandemic and explore differences by demographic characteristics. Data were from the nationally representative US Census Bureau's Household Pulse Survey from April 2020 to May 2023 (N = 4,153,462). Locally weighted scatterplot smoothing (LOESS) regression analysis identified change points in food insecurity trends, segmenting the timeline into three periods: (1) April 2020-March 2021, (2) April 2021-May 2022, and (3) June 2022-May 2023. Logistic regression models examined associations between age, time period, and self-reported household food insecurity; covariates included demographics, socioeconomic status, household structure, and food support program usage. Overall, 9.3% of respondents experienced food insecurity, ranging from 3.5% among those aged ≥75 to 12.2% for ages 35-44 years. Significant interaction between age group and time period indicated inconsistency in the age-food insecurity association during the pandemic (p < 0.001). From Period 1 to 3, the proportion of food-insecure adults aged ≥65 rose from 9.2% to 13.9%. Across all age groups, higher odds of food insecurity were found among Black, Hispanic/Latino, or Other/Multiracial respondents; those with less than a Bachelor's degree; those with incomes below USD 35,000; those unemployed for reasons other than retirement; and non-homeowners (p < 0.001). The results show that trends and characteristics associated with food insecurity varied across age groups and time periods. Continuous monitoring of food insecurity during emergencies is critical to identify vulnerable populations and timely interventions.

Corrigendum: Patients with juvenile idiopathic arthritis have decreased clonal diversity in the CD8+ T cell repertoire response to influenza vaccination.

[This corrects the article DOI: 10.3389/fimmu.2024.1306490.].

Patients with juvenile idiopathic arthritis have decreased clonal diversity in the CD8+ T cell repertoire response to influenza vaccination.

Recurrent exposures to a pathogenic antigen remodel the CD8+ T cell compartment and generate a functional memory repertoire that is polyclonal and complex. At the clonotype level, the response to the conserved influenza antigen, M158-66 has been well characterized in healthy individuals, but not in patients receiving immunosuppressive therapy or with aberrant immunity, such as those with juvenile idiopathic arthritis (JIA). Here we show that patients with JIA have a reduced number of M158-66 specific RS/RA clonotypes, indicating decreased clonal richness and, as a result, have lower repertoire diversity. By using a rank-frequency approach to analyze the distribution of the repertoire, we found several characteristics of the JIA T cell repertoire to be akin to repertoires seen in healthy adults, including an amplified RS/RA-specific antigen response, representing greater clonal unevenness. Unlike mature repertoires, however, there is more fluctuation in clonotype distribution, less clonotype stability, and more variable IFNy response of the M158-66 specific RS/RA clonotypes in JIA. This indicates that functional clonal expansion is altered in patients with JIA on immunosuppressive therapies. We propose that the response to the influenza M158-66 epitope described here is a general phenomenon for JIA patients receiving immunosuppressive therapy, and that the changes in clonal richness and unevenness indicate a retarded and uneven generation of a mature immune response.

Missingness and algorithmic bias: an example from the United States National Outbreak Reporting System, 2009-2019.

Growing debates about algorithmic bias in public health surveillance lack specific examples. We tested a common assumption that exposure and illness periods coincide and demonstrated how algorithmic bias can arise due to missingness of critical information related to illness and exposure durations. We examined 9407 outbreaks recorded by the United States National Outbreak Reporting System (NORS) from January 1, 2009 through December 31, 2019 and detected algorithmic bias, a systematic over- or under-estimation of foodborne disease outbreak (FBDO) durations due to missing start and end dates. For 7037 (75%) FBDOs with complete date-time information, ~ 60% reported that the exposure period ended before the illness period started. For 2079 (87.7%) FBDOs with missing exposure dates, average illness durations were ~ 5.3 times longer (p < 0.001) than those with complete information, prompting the potential for algorithmic bias. Modern surveillance systems must be equipped with investigative capacities to examine and assess structural data missingness that can lead to bias.

How well global dietary intake estimates agree: a case of sodium consumption.

Global dietary data repositories are key components of nutrition surveillance. The two most comprehensive databases, the Global Dietary Database (GDD) and the Global Burden Disease (GBD), provide national dietary intake estimates but use different data sources and models to generate estimates. To explore the agreement between GDD and GBD estimates, we compared country-specific average daily sodium intakes in 169 countries over a 28-year period using descriptive statistics, the Bland-Altman method, and prevalence exceeding the intake reference level of 2.3 g/day. We detected a staggering 36% difference between GDD and GBD estimates of global mean intakes (2.68 ± 0.74 vs. 3.88 ± 1.15 g/day, respectively; p < 0.0001). As 104 (61.5%) countries reported to have over-consumed sodium by both databases, the development of standardized approaches for national dietary intake estimation is critical for monitoring global sodium intake in a systematic and comprehensive way and for implementing global strategies to reduce sodium intake.

Weight Loss Trajectories and Short-Term Prediction in an Online Weight Management Program.

The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss.

Peak timing of slowest growth velocity among young children coincides with highest ambient temperatures in Burkina Faso: a longitudinal study.

BACKGROUND: Seasonal cycles in climatic factors affect drivers of child growth and contribute to seasonal fluctuations in undernutrition. Current growth seasonality models are limited by categorical definitions of seasons that rely on assumptions about their timing and fail to consider their magnitude. OBJECTIVE: We disentangle the relationship between climatic factors and growth indicators, using harmonic regression to determine how child growth is related to peaks in temperature, precipitation, and vegetation. METHODS: Longitudinal anthropometric data collected between August 2014 and December 2016 from 5039 Burkinabè children measured monthly from age 6 to 28 mo (108,580 observations) were linked with remotely sensed daily precipitation, vegetation, and maximum air temperature. Our models parsimoniously extract a cyclic signal with multiple potential peaks, to compare the magnitude and timing of seasonal peaks in climatic factors and morbidity with that of nadirs in growth velocity (cm/mo, kg/mo). RESULTS: Length and weight velocity were slowest twice a year, coinciding both times with the highest temperatures, and peak fever incidence. Length velocity is slowest 13 d after the first temperature peak in April, and 5 d after the second. Similarly, weight velocity is slowest 13 d before the first temperature peak, and 11 d before the second. The statistical relationship between temperature and anthropometry shows that when the current temperature is higher, weight velocity is lower (ß = -0.0048; 95% CI: -0.0059, -0.0038), and length velocity is higher (ß = 0.0088; 95% CI: 0.0070, 0.0105). CONCLUSIONS: Results suggest that child health and development are more affected by high temperatures than by other aspects of climatic seasonality such as rainfall. Emerging shifts in climatic conditions will pose challenges to optimal growth, highlighting the importance of changes that optimize the timing of nutrition interventions and address environmental growth-limiting conditions. CLINICAL TRIAL REGISTRY: Clinicaltrials.gov: NCT02071563.

Reducing the risks of nuclear war - The role of health professionals.

No abstract available.

Seasonality of Acute Malnutrition in African Drylands: Evidence From 15 Years of SMART Surveys.

Reduction of wasting, or low weight-for-height, is a critical target for the Zero Hunger Sustainable Development Goal, yet robust evidence establishing continuous seasonal patterns of wasting is presently lacking. The current consensus of greatest hunger during the preharvest period is based on survey designs and analytical methods, which discretize time frame into preharvest/postharvest, dry/wet, or lean/plenty seasons. We present a spatiotemporally nuanced study of acute malnutrition seasonality in African drylands using a 15-year data set of Standardized Monitoring and Assessment of Relief and Transition surveys (n = 412,370). Climatological similarity was ensured by selecting subnational survey regions with 1 rainy season and by spatially matching each survey to aridity and livelihood zones. Harmonic logit regression models indicate 2 peaks of wasting during the calendar year. Greatest wasting prevalence is estimated in April to May, coincident with the primary peak of temperature. A secondary peak of wasting is observed in August to October, coinciding with the primary peak of rainfall and secondary peak of temperature. This pattern is retained across aridity and livelihood zones and is sensitive to temperature, precipitation, and vegetation. Improved subnational estimation of acute malnutrition seasonality can thus assist decision makers and practitioners in data-sparse settings and facilitate global progress toward Zero Hunger.

Plain language titleFifteen Years of Rapid Assessment Surveys Indicate Seasonal Variability in Prevalence of Acute Malnutrition Among Children Younger Than 5 Years in African DrylandsPlain language summaryWasting or low weight-for-height is a key indicator of short-term or acute malnutrition. The timing of highest wasting prevalence, particularly among children younger than 5 years, is of interest for humanitarian efforts to reduce hunger. Current knowledge about this timing derives from survey designs, which discretize continuous time into preharvest/postharvest, dry/wet, or lean/plenty seasons. Instead of this categorical approach, we utilize harmonic regressions that allow for modeling of continuous time in our analysis of 15 years of Standardized Monitoring and Assessment of Relief and Transition surveys. Surveys conducted in parts of North Africa with 1 rainy season (unimodal regions) were selected for similar climate, and survey locations were further subdivided by aridity and livelihood zones. The seasonal pattern of extreme wasting prevalence in each group was modeled using survey data for a total of 412,370 children. We identified 2 periods of highest wasting prevalence in April to May and August to October. The April to May peak occurs during highest temperatures, and the August to October peak occurs during periods of highest rainfall and warmer temperatures in the study area. These findings can inform the timing of nutrition programs in unimodal dryland regions and guide future quantitative models of acute malnutrition seasonality.