Prevalence and Severity of Food Insecurity Before and During the Coronavirus Disease 2019 Pandemic Among Adults and Youth in Australia, Canada, Chile, Mexico, the United Kingdom, and the United States.

BACKGROUND: Disruptions from the coronavirus disease 2019 (COVID-19) pandemic potentially exacerbated food insecurity among adults and youth. OBJECTIVES: The objective was to examine changes in the prevalence and severity of food insecurity among adults and youth from before (2019) to during (2020) the pandemic in multiple countries. METHODS: Repeated cross-sectional data were collected among adults aged 18-100 y (n = 63,278) in 5 countries in November to December in 2018-2020 and among youth aged 10-17 y (n = 23,107) in 6 countries in November to December in 2019 and 2020. Food insecurity in the past year was captured using the Household Food Security Survey Module and the Child Food Insecurity Experiences Scale. Changes in the prevalence and severity of food insecurity were examined using logistic and generalized logit regression models, respectively. Models included age, gender, racial-ethnic identity, and other sociodemographic characteristics associated with food insecurity to adjust for possible sample differences across waves. Models were weighted to reflect each country's population. RESULTS: Adults [adjusted OR (AOR): 1.15; 95% CI: 1.02, 1.31] and youth (AOR: 1.43; 95% CI: 1.19, 1.71) in Mexico were more likely to live in food-insecure households in 2020 compared to 2019. Adults in Australia (AOR: 0.81; 95% CI: 0.72, 0.92) and Canada (AOR: 0.87; 95% CI: 0.77, 0.99) were less likely to live in food-insecure households in 2020. Trends in severity aligned with changes in prevalence, with some exceptions. Youth in Australia (AOR: 2.24; 95% CI: 1.65, 3.02) and the United States (AOR: 1.39; 95% CI: 1.04, 1.86) were more likely to have many compared with no experiences of food insecurity in 2020 compared to 2019. There was no evidence of change among adults and youth in the remaining countries. CONCLUSIONS: Except for Mexico, few changes in food insecurity among adults and youth were observed from before to during the COVID-19 pandemic. Action is needed to support households at risk of food insecurity.

Food Security Status in Relation to Co-operative Enrolment Among University of Waterloo Undergraduate Students: A Cross-Sectional Analysis.

Purpose: Co-operative (co-op) education facilitates development of workplace competencies but may have unintended consequences for financial stability and food security. This study examined the association between co-op program enrolment and food security status among a sample of undergraduate students. Financial insufficiency and strategies to cope with it were also characterized.Methods: Cross-sectional data were collected from 167 co-op and 89 non-co-op students at the University of Waterloo from January to March 2019. Logistic regression assessed associations between co-op program enrolment and food insecurity.Results: Twenty-four percent of co-op and 39.3% of non-co-op students lived in moderately or severely food insecure households. Adjusting for confounders, the odds of living in moderately or severely food insecure households were lower among co-op students (adjusted odds ratio: 0.51, 95% CI: 0.27-0.97), though no association was observed when marginal food insecurity was included within the food insecure category. One-quarter (26.3%) of co-op students and 38.2% of non-co-op students reported financial insufficiency, which they tried to cope with by asking parents or friends for assistance or initiating paid work.Conclusions: Co-op program enrolment was weakly associated with lower odds of living in moderately or severely food insecure households, and food insecurity prevalence was high overall. Efforts are needed to alleviate food insecurity among postsecondary students.

Measurement error and precision medicine: Error-prone tailoring covariates in dynamic treatment regimes.

Precision medicine incorporates patient-level covariates to tailor treatment decisions, seeking to improve outcomes. In longitudinal studies with time-varying covariates and sequential treatment decisions, precision medicine can be formalized with dynamic treatment regimes (DTRs): sequences of covariate-dependent treatment rules. To date, the precision medicine literature has not addressed a ubiquitous concern in health research-measurement error-where observed data deviate from the truth. We discuss the consequences of ignoring measurement error in the context of DTRs, focusing on challenges unique to precision medicine. We show-through simulation and theoretical results-that relatively simple measurement error correction techniques can lead to substantial improvements over uncorrected analyses, and apply these findings to the sequenced treatment alternatives to relieve depression study.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics.

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Model selection for G-estimation of dynamic treatment regimes.

Dynamic treatment regimes (DTRs) aim to formalize personalized medicine by tailoring treatment decisions to individual patient characteristics. G-estimation for DTR identification targets the parameters of a structural nested mean model, known as the blip function, from which the optimal DTR is derived. Despite its potential, G-estimation has not seen widespread use in the literature, owing in part to its often complex presentation and implementation, but also due to the necessity for correct specification of the blip. Using a quadratic approximation approach inspired by iteratively reweighted least squares, we derive a quasi-likelihood function for G-estimation within the DTR framework, and show how it can be used to form an information criterion for blip model selection. We outline the theoretical properties of this model selection criterion and demonstrate its application in a variety of simulation studies as well as in data from the Sequenced Treatment Alternatives to Relieve Depression study.

Reward ignorant modeling of dynamic treatment regimes.

Personalized medicine optimizes patient outcome by tailoring treatments to patient-level characteristics. This approach is formalized by dynamic treatment regimes (DTRs): decision rules that take patient information as input and output recommended treatment decisions. The DTR literature has seen the development of increasingly sophisticated causal inference techniques that attempt to address the limitations of our typically observational datasets. Often overlooked, however, is that in practice most patients may be expected to receive optimal or near-optimal treatment, and so the outcome used as part of a typical DTR analysis may provide limited information. In light of this, we propose considering a more standard analysis: ignore the outcome and elicit an optimal DTR by modeling the observed treatment as a function of relevant covariates. This offers a far simpler analysis and, in some settings, improved optimal treatment identification. To distinguish this approach from more traditional DTR analyses, we term it reward ignorant modeling, and also introduce the concept of multimethod analysis, whereby different analysis methods are used in settings with multiple treatment decisions. We demonstrate this concept through a variety of simulation studies, and through analysis of data from the International Warfarin Pharmacogenetics Consortium, which also serve as motivation for this work.

Model assessment in dynamic treatment regimen estimation via double robustness.

Dynamic treatment regimens (DTRs) recommend treatments based on evolving subject-level data. The optimal DTR is that which maximizes expected patient outcome and as such its identification is of primary interest in the personalized medicine setting. When analyzing data from observational studies using semi-parametric approaches, there are two primary components which can be modeled: the expected level of treatment and the expected outcome for a patient given their other covariates. In an effort to offer greater flexibility, the so-called doubly robust methods have been developed which offer consistent parameter estimators as long as at least one of these two models is correctly specified. However, in practice it can be difficult to be confident if this is the case. Using G-estimation as our example method, we demonstrate how the property of double robustness itself can be used to provide evidence that a specified model is or is not correct. This approach is illustrated through simulation studies as well as data from the Multicenter AIDS Cohort Study.

Doubly-robust dynamic treatment regimen estimation via weighted least squares.

Personalized medicine is a rapidly expanding area of health research wherein patient level information is used to inform their treatment. Dynamic treatment regimens (DTRs) are a means of formalizing the sequence of treatment decisions that characterize personalized management plans. Identifying the DTR which optimizes expected patient outcome is of obvious interest and numerous methods have been proposed for this purpose. We present a new approach which builds on two established methods: Q-learning and G-estimation, offering the doubly robust property of the latter but with ease of implementation much more akin to the former. We outline the underlying theory, provide simulation studies that demonstrate the double-robustness and efficiency properties of our approach, and illustrate its use on data from the Promotion of Breastfeeding Intervention Trial.

Statistical method use in public health research.

AIMS: The content of public health research is often statistically complex. This review seeks to assess the breadth of statistical literacy required to understand this material, with a view to informing practitioners' statistical training. METHODS: We review the statistical content of original research articles published in 2011 in four major public health journals. Categories of statistical methodologies are identified and their frequency of use recorded. Methods' "usefulness" in terms of the extent to which their understanding increases accessibility to the literature is assessed. RESULTS: A total of 482 articles were reviewed and 30 categories of methods identified. Along with descriptive statistics (467 articles), regression analyses were also common, with logistic regression (206 articles) more than twice as prevalent as linear regression (95 articles). More complex regression models for use with clustered data were also commonly encountered, appearing in 96 articles. CONCLUSIONS: The public health literature features a wide variety of statistical methods, some of which are advanced. To ensure the literature remains accessible, training for public health practitioners should include statistical training that maximizes breadth as well as depth of understanding.

Personalizing medicine: a review of adaptive treatment strategies.

Much of current pharmacological practice focuses on identifying the single 'best' treatment (or course of treatments) for a particular disease. Recently, however, focus has begun to shift towards a more patient-centric rather than disease-centric approach, where personal characteristics are used to identify the optimal treatment for an individual. Adaptive treatment strategies (also known as dynamic treatment regimes) are part of a rapidly expanding area of research whereby such personalized treatments can be identified. These methods can lead to improved results over standard 'one size fits all' approaches, as well as provide a route to formalizing a common practice of using ad hoc approaches when deciding or updating management plans. Here, we provide an introduction to adaptive treatment strategies, explaining their background, their purpose, and how they can be employed in practice.

Advances in methods for characterizing dietary patterns: A scoping review.

There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.

Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment.

Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, and fixed tissues. Our objective was to determine whether PARS could provide an image quality sufficient for the diagnostic assessment of breast cancer needle core biopsies (NCB). We PARS imaged and virtually H&E stained seven independent unstained formalin-fixed paraffin-embedded breast NCB sections. These identical tissue sections were subsequently stained with standard H&E and digitally scanned. Both the 40× PARS and H&E whole-slide images were assessed by seven breast cancer pathologists, masked to the origin of the images. A concordance analysis was performed to quantify the diagnostic performances of standard H&E and PARS virtual H&E. The PARS images were deemed to be of diagnostic quality, and pathologists were unable to distinguish the image origin, above that expected by chance. The diagnostic concordance on cancer vs. benign was high between PARS and conventional H&E (98% agreement) and there was complete agreement for within-PARS images. Similarly, agreement was substantial (kappa > 0.6) for specific cancer subtypes. PARS virtual H&E inter-rater reliability was broadly consistent with the published literature on diagnostic performance of conventional histology NCBs across all tested histologic features. PARS was able to image unstained tissues slides that were diagnostically equivalent to conventional H&E. Due to its ability to non-destructively image fixed and fresh tissues, and the suitability of the PARS output for artificial intelligence assistance in diagnosis, this technology has the potential to improve the speed and accuracy of breast cancer diagnosis.

Nitrogen and phosphorus removal using tile-treatment wetlands: A 12-year study from the midwestern United States.

Nutrient enrichment from tile-drained agricultural lands to the Mississippi River is a leading cause of hypoxia in the Gulf of Mexico. Small edge-of-field wetlands can effectively treat nitrate-nitrogen (NO3 -N) export from tiles, although less research exists on their capacity to treat phosphorus (P). Additionally, long-term data are needed to incorporate variability of weather and farming practices into assessments of wetland performance longevity. Research conducted over 12 yr quantified size-effectiveness of wetlands to reduce NO3 -N and dissolved P (orthophosphate [ORP]) loadings from subsurface tile systems. Nitrate-N export was significantly higher during corn (Zea mays L.) than soybean [Glycine max (L.) Merr.] production years, during which 80-84% of mean annual loadings were exported during spring. Wetlands representing 3% (W1) of tile-drained farmland area reduced 15-38% of NO3 -N export, with cumulative reductions of 39-49 and 49-57% observed in wetlands representing 6 (W2) and 9% (W3) areas, respectively. Mass NO3 -N removal ranged from 28 to 52%. Twelve-year total ORP load reductions for W1 ranged from 53 to 81%, with cumulative reductions of 35-91% and 32-95% for W2 and W3 wetlands, respectively. Mass ORP removal ranged from 71 to 85%. Results emphasize how incorporating constructed wetlands into state and watershed-level conservation planning can significantly contribute toward reducing excess N and P export to river systems and ultimately to the Gulf of Mexico.

Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares.

Model assessment is a standard component of statistical analysis, but it has received relatively little attention within the dynamic treatment regime literature. In this paper, we focus on the dynamic-weighted ordinary least squares approach to optimal dynamic treatment regime estimation, introducing how its double-robustness property may be leveraged for model assessment, and how quasilikelihood may be used for model selection. These ideas are demonstrated through simulation studies, as well as through application to data from the sequenced treatment alternatives to relieve depression study.

Treatment of Amblyopia Using Personalized Dosing Strategies: Statistical Modelling and Clinical Implementation.

PURPOSE: To generate a statistical model for personalizing a patient's occlusion therapy regimen. METHODS: Statistical modelling was undertaken on a combined data set of the Monitored Occlusion Treatment of Amblyopia Study (MOTAS) and the Randomized Occlusion Treatment of Amblyopia Study (ROTAS). This exercise permits the calculation of future patients' total effective dose (TED)-that predicted to achieve their best attainable visual acuity. Daily patching regimens (hours/day) can be calculated from the TED. RESULTS: Occlusion data for 149 study participants with amblyopia (anisometropic in 50, strabismic in 43, and mixed in 56) were analyzed. Median time to best observed visual acuity was 63 days (25% and 75% quartiles; 28 and 91 days). Median visual acuity in the amblyopic eye at start of occlusion was 0.40 logMAR (quartiles 0.22 and 0.68 logMAR) and at end of occlusion was 0.12 (quartiles 0.025 and 0.32 logMAR). Median lower and upper estimates of TED were 120 hours (quartiles 34 and 242 hours), and 176 hours (quartiles 84 and 316 hours). The data suggest a piecewise linear relationship (P = 0.008) between patching dose-rate (hours/day) and TED with a single breakpoint estimated at 2.16 (standard error 0.51) hours/day, suggesting doses below 2.16 hours/day are less effective. CONCLUSION: We introduce the concept of TED of occlusion. Predictors for TED are visual acuity deficit, amblyopia type, and age at start of occlusion therapy. Dose-rates prescribed within the model range from 2.5 to 12 hours/day and can be revised dynamically throughout treatment in response to recorded patient compliance: a personalized dosing strategy.

Stable Carbon Isotope Evidence for Neolithic and Bronze Age Crop Water Management in the Eastern Mediterranean and Southwest Asia.

In a large study on early crop water management, stable carbon isotope discrimination was determined for 275 charred grain samples from nine archaeological sites, dating primarily to the Neolithic and Bronze Age, from the Eastern Mediterranean and Western Asia. This has revealed that wheat (Triticum spp.) was regularly grown in wetter conditions than barley (Hordeum sp.), indicating systematic preferential treatment of wheat that may reflect a cultural preference for wheat over barley. Isotopic analysis of pulse crops (Lens culinaris, Pisum sativum and Vicia ervilia) indicates cultivation in highly varied water conditions at some sites, possibly as a result of opportunistic watering practices. The results have also provided evidence for local land-use and changing agricultural practices.

Personalized versus standardized dosing strategies for the treatment of childhood amblyopia: study protocol for a randomized controlled trial.

BACKGROUND: Amblyopia is the commonest visual disorder of childhood in Western societies, affecting, predominantly, spatial visual function. Treatment typically requires a period of refractive correction ('optical treatment') followed by occlusion: covering the nonamblyopic eye with a fabric patch for varying daily durations. Recent studies have provided insight into the optimal amount of patching ('dose'), leading to the adoption of standardized dosing strategies, which, though an advance on previous ad-hoc regimens, take little account of individual patient characteristics. This trial compares the effectiveness of a standardized dosing strategy (that is, a fixed daily occlusion dose based on disease severity) with a personalized dosing strategy (derived from known treatment dose-response functions), in which an initially prescribed occlusion dose is modulated, in a systematic manner, dependent on treatment compliance. METHODS/DESIGN: A total of 120 children aged between 3 and 8 years of age diagnosed with amblyopia in association with either anisometropia or strabismus, or both, will be randomized to receive either a standardized or a personalized occlusion dose regimen. To avoid confounding by the known benefits of refractive correction, participants will not be randomized until they have completed an optical treatment phase. The primary study objective is to determine whether, at trial endpoint, participants receiving a personalized dosing strategy require fewer hours of occlusion than those in receipt of a standardized dosing strategy. Secondary objectives are to quantify the relationship between observed changes in visual acuity (logMAR, logarithm of the Minimum Angle of Resolution) with age, amblyopia type, and severity of amblyopic visual acuity deficit. DISCUSSION: This is the first randomized controlled trial of occlusion therapy for amblyopia to compare a treatment arm representative of current best practice with an arm representative of an entirely novel treatment regimen based on statistical modelling of previous trial outcome data. Should the personalized dosing strategy demonstrate superiority over the standardized dosing strategy, then its adoption into routine practice could bring practical benefits in reducing the duration of treatment needed to achieve an optimal outcome. TRIAL REGISTRATION: ISRCTN ISRCTN12292232.

The effect of amblyopia treatment on stereoacuity.

PURPOSE: To explore how stereoacuity changes in patients while they are being treated for amblyopia. METHODS: The Monitored Occlusion Treatment for Amblyopia Study (MOTAS) comprised 3 distinct phases. In the first phase, baseline, assessments of visual function were made to confirm the initial visual and binocular visual deficit. The second phase, refractive adaptation, now commonly termed "optical treatment," was an 18-week period of spectacle wear with measurements of logMAR visual acuity and stereoacuity with the Frisby test at weeks 0, 6, 12, and 18. In the third phase, occlusion, participants were prescribed 6 hours of patching per day. RESULTS: A total of 85 children were enrolled (mean age, 5.1 ± 1.5 years). In 21 children amblyopia was associated with anisometropia; in 29, with strabismus; and in 35, with both. At study entry, poor stereoacuity was associated with poor visual acuity (P < 0.001) in the amblyopic eye and greater angle of strabismus (P < 0.001). Of 66 participants, 25 (38%) who received refractive adaptation and 19 (29%) who received occlusion improved by at least one octave in stereoacuity, exceeding test-retest variability. Overall, 38 (45%) improved one or more octaves across both treatment phases. Unmeasureable stereoacuity was observed in 56 participants (66%) at study entry and in 37 (43%) at study exit. CONCLUSIONS: Stereoacuity improved for almost one half of the study participants. Improvement was observed in both treatment phases. Factors associated with poor or nil stereoacuity at study entry and exit were poor visual acuity of the amblyopic eye and large-angle strabismus.