Automated wearable cameras for improving recall of diet and time use in Uganda: a cross-sectional feasibility study.

BACKGROUND: Traditional recall approaches of data collection for assessing dietary intake and time use are prone to recall bias. Studies in high- and middle-income countries show that automated wearable cameras are a promising method for collecting objective health behavior data and may improve study participants' recall of foods consumed and daily activities performed. This study aimed to evaluate the feasibility of using automated wearable cameras in rural Eastern Ugandan to collect dietary and time use data. METHODS: Mothers of young children (n = 211) wore an automated wearable camera on 2 non-consecutive days while continuing their usual activities. The day after wearing the camera, participants' dietary diversity and time use was assessed using an image-assisted recall. Their experiences of the method were assessed via a questionnaire. RESULTS: Most study participants reported their experiences with the automated wearable camera and image-assisted recall to be good (36%) or very good (56%) and would participate in a similar study in the future (97%). None of the eight study withdrawals could be definitively attributed to the camera. Fifteen percent of data was lost due to device malfunction, and twelve percent of the images were "uncodable" due to insufficient lighting. Processing and analyzing the images were labor-intensive, time-consuming, and prone to human error. Half (53%) of participants had difficulty interpreting the images captured by the camera. CONCLUSIONS: Using an automated wearable camera in rural Eastern Uganda was feasible, although improvements are needed to overcome the challenges common to rural, low-income country contexts and reduce the burdens posed on both participants and researchers. To improve the quality of data obtained, future automated wearable camera-based image assisted recall studies should use a structured data format to reduce image coding time; electronically code the data in the field, as an output of the image review process, to eliminate ex post facto data entry; and, ideally, use computer-assisted personal interviews software to ensure completion and reduce errors. In-depth formative work in partnership with key local stakeholders (e.g., researchers from low-income countries, representatives from government and/or other institutional review boards, and community representatives and local leaders) is also needed to identify practical approaches to ensuring that the ethical rights of automated wearable camera study participants in low-income countries are adequately protected.

A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis.

BACKGROUND: Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. RESULTS: We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). CONCLUSION: Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Towards targeted dietary support for shift workers with type 2 diabetes (Shift-Diabetes study): A mixed-methods case study protocol.

BACKGROUND: Blood glucose is higher in people working night shifts compared to day workers. Changes to eating behaviour, activity and sleep patterns in addition to circadian disruption are likely to impact glucose management in night-shift workers with type 2 diabetes. AIM: To investigate current dietary intake and glucose variability during night work, including barriers and facilitators to dietary behaviour in this context. METHODS: A mixed-methods case study will be conducted. Shift workers with type 2 diabetes working in a hospital setting will be recruited to this two-part study. Part 1: 70 participants will complete a 10-day observational study collecting data on continuous glucose, diet (self-report diary), sleep and physical activity during a period covering night work, rest days and non-night workdays. Mean glucose concentration and variability, and the mean healthy diet index score, will be compared between days of night work, non-night work and rest, after adjusting for other individual factors (sleep/physical activity/demographics). Part 2: A sample (n~13) will complete semi-structured interviews based on behavioural science frameworks to explore barriers/enablers to dietary behaviour when working night shifts. This will inform a quantitative survey to explore the generalisability of interview findings. DISCUSSION: Findings from Part 1 and 2 will be triangulated to identify potential intervention strategies to address key barriers and enablers to healthier eating, and in turn improved glucose control, in shift workers with type 2 diabetes. This will be facilitated through stakeholder consultation and application of behavioural science frameworks. Shift-Diabetes study registration: ISRCTN11764942.

A Novel Microwave Resonant Sensor for Measuring Cancer Cell Line Aggressiveness.

The measurement of biological tissues' dielectric properties plays a crucial role in determining the state of health, and recent studies have reported microwave biosensing to be an innovative method with great potential in this field. Research has been conducted from the tissue level to the cellular level but, to date, cellular adhesion has never been considered. In addition, conventional systems for diagnosing tumor aggressiveness, such as a biopsy, are rather expensive and invasive. Here, we propose a novel microwave approach for biosensing adherent cancer cells with different malignancy degrees. A circular patch resonator was designed adjusting its structure to a standard Petri dish and a network analyzer was employed. Then, the resonator was realized and used to test two groups of different cancer cell lines, based on various tumor types and aggressiveness: low- and high-aggressive osteosarcoma cell lines (SaOS-2 and 143B, respectively), and low- and high-aggressive breast cancer cell lines (MCF-7 and MDA-MB-231, respectively). The experimental results showed that the sensitivity of the sensor was high, in particular when measuring the resonant frequency. Finally, the sensor showed a good ability to distinguish low-metastatic and high-metastatic cells, paving the way to the development of more complex measurement systems for noninvasive tissue diagnosis.

Using deep learning to identify recent positive selection in malaria parasite sequence data.

BACKGROUND: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. METHODS: A deep learning-based approach (called "DeepSweep") was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep . RESULTS: Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60-75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). CONCLUSION: The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making.

Validation of a life-logging wearable camera method and the 24-h diet recall method for assessing maternal and child dietary diversity.

Accurate and timely data are essential for identifying populations at risk for undernutrition due to poor-quality diets, for implementing appropriate interventions and for evaluating change. Life-logging wearable cameras (LLWC) have been used to prospectively capture food/beverage consumed by adults in high-income countries. This study aimed to evaluate the concurrent criterion validity, for assessing maternal and child dietary diversity scores (DDS), of a LLWC-based image-assisted recall (IAR) and 24-h recall (24HR). Direct observation was the criterion method. Food/beverage consumption of rural Eastern Ugandan mothers and their 12-23-month-old child (n 211) was assessed, for the same day for each method, and the IAR and 24HR DDS were compared with the weighed food record DDS using the Bland-Altman limits of agreement (LOA) method of analysis and Cohen's κ. The relative bias was low for the 24HR (-0·1801 for mothers; -0·1358 for children) and the IAR (0·1227 for mothers; 0·1104 for children), but the LOA were wide (-1·6615 to 1·3012 and -1·6883 to 1·4167 for mothers and children via 24HR, respectively; -2·1322 to 1·8868 and -1·7130 to 1·4921 for mothers and children via IAR, respectively). Cohen's κ, for DDS via 24HR and IAR, was 0·68 and 0·59, respectively, for mothers, and 0·60 and 0·59, respectively, for children. Both the 24HR and IAR provide an accurate estimate of median dietary diversity, for mothers and their young child, but non-differential measurement error would attenuate associations between DDS and outcomes, thereby under-estimating the true associations between DDS - where estimated via 24HR or IAR - and outcomes measured.

A logistic regression analysis of risk factors in ME/CFS pathogenesis.

BACKGROUND: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex disease, whose exact cause remains unclear. A wide range of risk factors has been proposed that helps understanding potential disease pathogenesis. However, there is little consistency for many risk factor associations, thus we undertook an exploratory study of risk factors using data from the UK ME/CFS Biobank participants. We report on risk factor associations in ME/CFS compared with multiple sclerosis participants and healthy controls. METHODS: This was a cross-sectional study of 269 people with ME/CFS, including 214 with mild/moderate and 55 with severe symptoms, 74 people with multiple sclerosis (MS), and 134 healthy controls, who were recruited from primary and secondary health services. Data were collected from participants using a standardised written questionnaire. Data analyses consisted of univariate and multivariable regression analysis (by levels of proximity to disease onset). RESULTS: A history of frequent colds (OR = 8.26, P <= 0.001) and infections (OR = 25.5, P = 0.015) before onset were the strongest factors associated with a higher risk of ME/CFS compared to healthy controls. Being single (OR = 4.41, P <= 0.001), having lower income (OR = 3.71, P <= 0.001), and a family history of anxiety is associated with a higher risk of ME/CFS compared to healthy controls only (OR = 3.77, P < 0.001). History of frequent colds (OR = 6.31, P < 0.001) and infections before disease onset (OR = 5.12, P = 0.005), being single (OR = 3.66, P = 0.003) and having lower income (OR = 3.48, P = 0.001), are associated with a higher risk of ME/CFS than MS. Severe ME/CFS cases were associated with lower age of ME/CFS onset (OR = 0.63, P = 0.022) and a family history of neurological illness (OR = 6.1, P = 0.001). CONCLUSIONS: Notable differences in risk profiles were found between ME/CFS and healthy controls, ME/CFS and MS, and mild-moderate and severe ME/CFS. However, we found some commensurate overlap in risk associations between all cohorts. The most notable difference between ME/CFS and MS in our study is a history of recent infection prior to disease onset. Even recognising that our results are limited by the choice of factors we selected to investigate, our findings are consistent with the increasing body of evidence that has been published about the potential role of infections in the pathogenesis of ME/CFS, including common colds/flu.

A Fast Method that Uses Polygenic Scores to Estimate the Variance Explained by Genome-wide Marker Panels and the Proportion of Variants Affecting a Trait.

Several methods have been proposed to estimate the variance in disease liability explained by large sets of genetic markers. However, current methods do not scale up well to large sample sizes. Linear mixed models require solving high-dimensional matrix equations, and methods that use polygenic scores are very computationally intensive. Here we propose a fast analytic method that uses polygenic scores, based on the formula for the non-centrality parameter of the association test of the score. We estimate model parameters from the results of multiple polygenic score tests based on markers with p values in different intervals. We estimate parameters by maximum likelihood and use profile likelihood to compute confidence intervals. We compare various options for constructing polygenic scores, based on nested or disjoint intervals of p values, weighted or unweighted effect sizes, and different numbers of intervals, in estimating the variance explained by a set of markers, the proportion of markers with effects, and the genetic covariance between a pair of traits. Our method provides nearly unbiased estimates and confidence intervals with good coverage, although estimation of the variance is less reliable when jointly estimated with the covariance. We find that disjoint p value intervals perform better than nested intervals, but the weighting did not affect our results. A particular advantage of our method is that it can be applied to summary statistics from single markers, and so can be quickly applied to large consortium datasets. Our method, named AVENGEME (Additive Variance Explained and Number of Genetic Effects Method of Estimation), is implemented in R software.

Breakfast consumption and nutrient intakes in 4-18-year-olds: UK National Diet and Nutrition Survey Rolling Programme (2008-2012).

Although breakfast consumption is widely considered to be an important component of a healthy lifestyle, few UK studies have examined differences in nutrient intakes between breakfast consumers and breakfast skippers among children and adolescents. We investigated associations between breakfast skipping in 4-18-year-olds and their nutrient intakes using data from the UK's National Diet and Nutrition Survey Rolling Programme. Dietary data were derived from 4-d estimated food diaries of 802 children aged 4-10 years and 884 children aged 11-18 years (1686 in total). Daily nutrient intakes of children with different breakfast habits were compared by one-way ANCOVA adjusting for relevant covariates (sex, age, ethnicity, equivalised household income and BMI). Within-person analysis was carried out on children with an irregular breakfast habit (n 879) comparing nutrient intakes on breakfast days with those on non-breakfast days using repeated measures ANCOVA. We observed that the overall nutritional profile of the children in terms of fibre and micronutrient intake was superior in frequent breakfast consumers (micronutrients: folate, Ca, Fe and I (P<0·01)) and, for the 4-10 years age group, on breakfast days (micronutrients: folate, vitamin C, Ca and I (P<0·01)). Also, significantly higher proportions of breakfast-consuming children met their reference nutrient intakes of folate, vitamin C, Ca, Fe and I compared with breakfast skippers (χ 2 analysis, P<0·001). Our study adds to the body of data linking breakfast consumption with higher quality dietary intake in school-age children, supporting the promotion of breakfast as an important element of a healthy dietary pattern in children.

Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

BACKGROUND: Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS: The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS: The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.

The spread in time and space of COVID-19 pandemic waves: the Italian experience from mortality data analyses.

Introduction: Italy was the first European country affected by COVID-19. Thanks to governmental containment measures (9 March 2020), the spread of COVID-19 was limited. However, in this context, accurate data assessment is crucial and mortality is a more reliable indicator of the virus spread compared to the count of positive cases. This study aimed to retrospectively evaluate the impact of the pandemic in different areas of Italy using the time series analysis of official deaths and excess COVID-19 deaths. Methods: Mortality data (23 February-30 April 2022) by Istituto Nazionale di Statistica (ISTAT) were analyzed, including four waves of COVID-19. Previous mortality data (January 2015-November 2019) were used to estimate a Poisson regression model of the pre-pandemic mortality pattern and derive the excess COVID-19 deaths as the difference between the actual deaths number and the extrapolation of the previous mortality pattern to the pandemic period, separately for Northern, Central, and Southern Italy, to compare the impact of mortality across time periods and geographical areas. Results: Estimated excess compared with official COVID-19 mortality shows that, during the first wave, there was an underestimation of deaths. COVID-19 mortality rate almost doubled the official rate in the North (1.60‰ vs. 0.86‰) and nearly tripled it in the South (0.22‰ vs. 0.08‰). In late 2020-early 2021, official and estimated mortality curves are closer, displaying just a small gap at the start of the second wave. During the fourth wave (end of 2021-early 2022), Northern and Central Italy show reasonable agreement; the South presents a large relative underestimation of deaths (+90% increase), with a large increase in its excess deaths national quota, 9% in the first wave to 42% in the fourth. Discussion: The results provide a measure of the COVID-19 excess deaths and an unbiased estimate of Italian mortality rates. In the first wave, the gap between official COVID-19 and excess mortality was particularly high and lockdown measures may have reduced the spread of the infection. In the fourth wave, the gap for the South increases again, probably because the healthcare system may not have coped with the prolonged pressure of the pandemic, or for a decreased compliance with the official paper-based mortality surveillance system that could be overcome in the future by digitalizing the process.

Sutureless Repair for Open Treatment of Inguinal Hernia: Three Techniques in Comparison.

Currently, groin hernia repair is mostly performed with application of mesh prostheses fixed with or without suture. However, views on safety and efficacy of different surgical approaches are still partly discordant. In this multicentre retrospective study, three sutureless procedures, i.e., mesh fixation with glue, application of self-gripping mesh, and Trabucco's technique, were compared in 1034 patients with primary unilateral non-complicated inguinal hernia subjected to open anterior surgery. Patient-related features, comorbidities, and drugs potentially affecting the intervention outcomes were also examined. The incidence of postoperative complications, acute and chronic pain, and time until discharge were assessed. A multivariate logistic regression was used to compare the odds ratio of the surgical techniques adjusting for other risk factors. The application of standard/heavy mesh, performed in the Trabucco's technique, was found to significantly increase the odds ratio of hematomas (p = 0.014) and, most notably, of acute postoperative pain (p < 0.001). Among the clinical parameters, antithrombotic therapy and large hernia size were independent risk factors for hematomas and longer hospital stay, whilst small hernias were an independent predictor of pain. Overall, our findings suggest that the Trabucco's technique should not be preferred in patients with a large hernia and on antithrombotic therapy.

Is there a sex difference in mortality rates in paediatric intensive care units?: a systematic review.

Introduction: Mortality rates in infancy and childhood are lower in females than males. However, for children admitted to Paediatric Intensive Care Units (PICU), mortality has been reported to be lower in males, although males have higher admission rates. This female mortality excess for the subgroup of children admitted in intensive care is not well understood. To address this, we carried out a systematic literature review to summarise the available evidence. Our review studies the differences in mortality between males and females aged 0 to <18 years, while in a PICU, to examine whether there was a clear difference (in either direction) in PICU mortality between the two sexes, and, if present, to describe the magnitude and direction of this difference. Methods: Any studies that directly or indirectly reported the rates of mortality in children admitted to intensive care by sex were eligible for inclusion. The search strings were based on terms related to the population (those admitted into a paediatric intensive care unit), the exposure (sex), and the outcome (mortality). We used the search databases MEDLINE, Embase, and Web of Science as these cover relevant clinical publications. We assessed the reliability of included studies using a modified version of the risk of bias in observational studies of exposures (ROBINS-E) tool. We considered estimating a pooled effect if there were at least three studies with similar populations, periods of follow-up while in PICU, and adjustment variables. Results: We identified 124 studies of which 114 reported counts of deaths by males and females which gave a population of 278,274 children for analysis, involving 121,800 (44%) females and 156,474 males (56%). The number of deaths and mortality rate for females were 5,614 (4.61%), and for males 6,828 (4.36%). In the pooled analysis, the odds ratio of female to male mortality was 1.06 [1.01 to 1.11] for the fixed effect model, and 1.10 [1.00 to 1.21] for the random effects model. Discussion: Overall, males have a higher admission rate to PCU, and potentially lower overall mortality in PICU than females. Systematic Review Registration: www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=203009, identifier (CRD42020203009).

Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: the EPIC-InterAct case-cohort study.

BACKGROUND: Waist circumference (WC) is a simple and reliable measure of fat distribution that may add to the prediction of type 2 diabetes (T2D), but previous studies have been too small to reliably quantify the relative and absolute risk of future diabetes by WC at different levels of body mass index (BMI). METHODS AND FINDINGS: The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. We used Prentice-weighted Cox regression and random effects meta-analysis methods to estimate hazard ratios for T2D. Kaplan-Meier estimates of the cumulative incidence of T2D were calculated. BMI and WC were each independently associated with T2D, with WC being a stronger risk factor in women than in men. Risk increased across groups defined by BMI and WC; compared to low normal weight individuals (BMI 18.5-22.4 kg/m(2)) with a low WC (<94/80 cm in men/women), the hazard ratio of T2D was 22.0 (95% confidence interval 14.3; 33.8) in men and 31.8 (25.2; 40.2) in women with grade 2 obesity (BMI≥35 kg/m(2)) and a high WC (>102/88 cm). Among the large group of overweight individuals, WC measurement was highly informative and facilitated the identification of a subgroup of overweight people with high WC whose 10-y T2D cumulative incidence (men, 70 per 1,000 person-years; women, 44 per 1,000 person-years) was comparable to that of the obese group (50-103 per 1,000 person-years in men and 28-74 per 1,000 person-years in women). CONCLUSIONS: WC is independently and strongly associated with T2D, particularly in women, and should be more widely measured for risk stratification. If targeted measurement is necessary for reasons of resource scarcity, measuring WC in overweight individuals may be an effective strategy, since it identifies a high-risk subgroup of individuals who could benefit from individualised preventive action.

Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.

Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.

Validation of an Automated Wearable Camera-Based Image-Assisted Recall Method and the 24-h Recall Method for Assessing Women's Time Allocation in a Nutritionally Vulnerable Population: The Case of Rural Uganda.

Accurate data are essential for investigating relationships between maternal time-use patterns and nutritional outcomes. The 24 h recall (24HR) has traditionally been used to collect time-use data, however, automated wearable cameras (AWCs) with an image-assisted recall (IAR) may reduce recall bias. This study aimed to evaluate their concurrent criterion validity for assessing women's time use in rural Eastern Ugandan. Women's (n = 211) time allocations estimated via the AWC-IAR and 24HR methods were compared with direct observation (criterion method) using the Bland-Altman limits of agreement (LOA) method of analysis and Cronbach's coefficient alpha (time allocation) or Cohen's κ (concurrent activities). Systematic bias varied from 1 min (domestic chores) to 226 min (caregiving) for 24HR and 1 min (own production) to 109 min (socializing) for AWC-IAR. The LOAs were within 2 h for employment, own production, and self-care for 24HR and AWC-IAR but exceeded 11 h (24HR) and 9 h (AWC-IAR) for caregiving and socializing. The LOAs were within four concurrent activities for 24HR (-1.1 to 3.7) and AWC-IAR (-3.2 to 3.2). Cronbach's alpha for time allocation ranged from 0.1728 (socializing) to 0.8056 (own production) for 24HR and 0.2270 (socializing) to 0.7938 (own production) for AWC-IAR. For assessing women's time allocations at the population level, the 24HR and AWC-IAR methods are accurate and reliable for employment, own production, and domestic chores but poor for caregiving and socializing. The results of this study suggest the need to revisit previously published research investigating the associations between women's time allocations and nutrition outcomes.

Dietary patterns and cardiovascular risk factors among Brazilians: A population-based study in Viçosa, Minas Gerais.

OBJECTIVE: The aim of this study was to identify dietary patterns (DPs) and analyze their association with cardiovascular risk factors including metabolic biomarkers and markers of inflammation and oxidative stress in a cross-sectional population-based study with 959 Brazilian adults from Viçosa. METHODS: Food consumption was assessed by food frequency questionnaire, and DPs were identified by principal component analysis. A self-administered questionnaire was applied to assess sociodemographic and behavioral variables. RESULTS: Four distinct DPs were identified: Western, Snacks and Processed Food, Healthy, and Traditional Brazilian. Comparing the highest to the lowest tertile of each DP: Snacks and Processed Food DP was associated with a significantly higher diastolic blood pressure (ß = 2.81; 95% confidence interval [CI], 0.48-5.14), waist circumference (ß = 4.75; 95% CI, 2.77-6.73), body mass index (ß = 1.65; 95% CI, 0.63-2.67), neck circumference (ß = 0.74; 95% CI, 0.15-1.34), uric acid (ß = 0.32; 95% CI, 0.13-0.51), and C-reactive protein (ß = 0.31; 95% CI, 0.07-0.55). The Healthy DP was associated with lower Homeostatic Model Assessment of Insulin Resistance (HOMA-IR; ß = -0.17; 95% CI, -0.34 to -0.008), lower tumor necrosis factor-α (odds ratio [OR] = 0.46; 95% CI, 0.26-0.84), lower interleukin (IL)-8 (OR = 0.50; 95% CI, 0.28-0.91), and lower catalase (OR = 0.36; 95% CI, 0.16-0.78). The Traditional Brazilian DP was associated with lower high-density lipoprotein (HDL) cholesterol (ß = -5.04; 95% CI, -7.60 to -2.48), non-HDL cholesterol (ß: -10.25; 95% CI, -19.07 to -1.43), and superoxide dismutase (OR = 0.52; 95% CI, 0.32-0.87), and higher uric acid (ß = 0.24; 95% CI, 0.01-0.48), IL-12p70 (OR = 2.55; 95% CI, 1.23-5.32), IL-1ß (OR = 2.22; 95% CI, 1.01-4.89), IL-10 (OR = 2.03; 95% CI, 1.05-3.93), and ferric reducing ability of plasma (OR = 2.23; 95% CI, 1.16-4.27). CONCLUSIONS: The Snacks and Processed Food DP was associated with increases in several risk factors for cardiovascular disease, and the Healthy DP with lower HOMA-IR scores, tumor necrosis factor-α, IL-8, and catalase. A diet based on rice and beans (Traditional Brazilian) may have a protective role against non-HDL cholesterol while presenting other risks related to inflammation and oxidative stress, as shown by a direct association with the interleukins IL-12p70, IL-1ß, and IL-10 and an inverse association with superoxide dismutase.

Measles outbreak in the Philippines: epidemiological and clinical characteristics of hospitalized children, 2016-2019.

BACKGROUND: Measles outbreaks increased worldwide during 2017-19. The largest outbreak in the World Health Organisation Western Pacific region occurred in the Philippines where first-dose measles-containing vaccine (MCV1) coverage had reduced to 75% in 2018. The aim of this study was to summarise paediatric measles admissions to the national infectious diseases referral hospital in Manila during 2016 to 2019. METHODS: A retrospective single-centre observational study including 5,562 children aged under five years admitted with measles from January 2016 to December 2019. We summarised sociodemographic and clinical characteristics, vaccine status, reported exposures, and outcomes. Univariable and multivariable logistic regression analyses were undertaken to assess associations between different characteristics of hospitalised children and death. FINDINGS: The median age of children hospitalised with measles was 11 months (interquartile range: 7-28). 84·5% of cases were reported not to have received any MCV. The risk of mortality was 3·2%, with 41% of deaths occurring among children aged less than 9 months. No children died who had received two MCV. The following characteristics were significantly associated with mortality in the multivariable analysis: age group, residence outside of the national capital region, not having received any MCV, duration between onset of fever and hospital admission of 7-14 days compared with 0-3 days, not receiving vitamin A supplementation, having pneumonia, and gastroenteritis. INTERPRETATION: The Philippines remains at risk of future measles epidemics. Routine immunization needs to be strengthened and earlier timing of MCV1 requires further evaluation to reduce measles incidence and mortality.

Relationships Between Food Groups and Eating Time Slots According to Diabetes Status in Adults From the UK National Diet and Nutrition Survey (2008-2017).

Time of eating is associated with diabetes and obesity but little is known about less healthy foods and specific time of their intake over the 24 h of the day. In this study, we aimed to identify potential relationships between foods and their eating time and to see whether these associations may vary by diabetes status. The National Diet and Nutrition Survey (NDNS) including 6,802 adults (age ≥ 19 years old) collected 749,026 food recordings by a 4-day-diary. The contingency table cross-classifying 60 food groups with 7 pre-defined eating time slots (6-9 a.m., 9 a.m.-12 p.m., 12-2 p.m., 2-5 p.m., 8-10 p.m., 10 p.m.-6 a.m.) was analyzed by Correspondence Analysis (CA). CA biplots were generated for all adults and separately by diabetes status (self-reported, pre-diabetes, undiagnosed-diabetes, and non-diabetics) to visually explore the associations between food groups and time of eating across diabetes strata. For selected food groups, odds ratios (OR, 99% CI) were derived of consuming unhealthy foods at evening/night (8 p.m.-6 a.m.) vs. earlier time in the day, by logistic regression models with generalized estimating equations. The biplots suggested positive associations between evening/night and consumption of puddings, regular soft drinks, sugar confectioneries, chocolates, beers, ice cream, biscuits, and crisps for all adults in the UK. The OR (99% CIs) of consuming these foods at evening/night were, respectively, 1.43 (1.06, 1.94), 1.72 (1.44, 2.05), 1.84 (1.31, 2.59), 3.08 (2.62, 3.62), 7.26 (5.91, 8.92), 2.45 (1.84, 3.25), 1.90 (1.68, 2.16), and 1.49 (1.22, 1.82) vs. earlier time in the day adjusted for age, sex, body mass index (BMI), and social-economic levels. Stratified biplots found that sweetened beverages, sugar-confectioneries appeared more strongly associated with evening/night among undiagnosed diabetics. Foods consumed in the evening/night time tend to be highly processed, easily accessible, and rich in added sugar or saturated fat. Individuals with undiagnosed diabetes are more likely to consume unhealthy foods at night. Further longitudinal studies are required to ascertain the causal direction of the association between late-eating and diabetes status.

A study of sex difference in infant mortality in UK pediatric intensive care admissions over an 11-year period.

Within the UK, child mortality from all causes has declined for all ages over the last three decades. However, distinct inequality remains, as child mortality rates are generally found to be higher in males. A significant proportion of childhood deaths in the UK occur in Paediatric Intensive Care Units (PICU). We studied the association of sex with infant mortality in PICUs. We included all infants (0 to 12 months old) admitted to UK PICUs from 01/01/2005 to 31/12/2015 using the Paediatric Intensive Care Audit Network (PICANet) dataset. We considered first admissions to PICU and fitted a cause-specific-hazard-ratio (CSHR) model, and a logistic model to estimate the adjusted association between sex and mortality in PICU. Pre-defined subgroups were children less than 56-days old, and those with a primary diagnosis of infection. Of 71,243 cases, 1,411/29,520 (4.8%) of females, and 1,809/41,723 (4.3%) of males died. The adjusted male/female CSHR was 0.87 (95%-CI 0.81 to 0.92) representing a 13% higher risk of death for females. The adjusted OR for male to female mortality is 0.86 (95%-CI 0.80 to 0.93). Analyses in subgroups yielded similar findings. In our analysis, female infants have a higher rate of PICU mortality compared to male infants.