Blood glucose level (BGL) is routinely assessed by paramedics in the out-of-hospital setting. Most commonly, BGL is measured using a blood sample of capillary origin analyzed by a hand-held, point-of-care glucometer. In some clinical circumstances, the capillary sample may be replaced by blood of venous origin. Given most point-of-care glucometers are engineered to analyze capillary blood samples, the use of venous blood instead of capillary may lead to inaccurate or misleading measurements.

The aim of this prospective study was to compare mean difference in BGL between venous and capillary blood from healthy volunteers when measured using a capillary-based, hand-held, point-of-care glucometer.

Using a prospective observational comparison design, 36 healthy participants provided paired samples of blood, one venous and the other capillary, taken near simultaneously. The BGL values were similar between the two groups. The capillary group had a range of 4.3mmol/l, with the lowest value being 4.4mmol/l and 8.7mmol/l the highest. The venous group had a range of 2.7mmol/l, with the lowest value being 4.1mmol/l and 7.0mmol/l the highest.

For the primary research question, the mean BGL for the venous sample group was 5.3mmol/l (SD = 0.6), compared to 5.6mmol/l (SD = 0.8) for the capillary group. This represented a statistically significant difference of 0.3mmol/l (P = .04), but it did not reach the a priori established point of clinical significance (1.0mmol/l). Pearson’s correlation coefficient for capillary versus venous indicated moderate correlation (r = 0.42).

In healthy, non-fasted people in a non-clinical setting, a statistically significant, but not clinically significant, difference was found between venous- and capillary-derived BGL when measured using a point-of-care, capillary-based glucometer. Correlation between the two was moderate. In this context, using venous samples in a capillary-based glucometer is reasonable providing the venous sample can be gathered without exposure of the clinician to risk of needle-stick injury. In clinical settings where physiological derangement or acute illness is present, capillary sampling would remain the optimal approach.

In June 2012, the Botswana Ministry of Health and Wellness (MOHW; Gaborone, Botswana) initiated a national Emergency Medical Services (EMS) system in response to significant morbidity and mortality associated with prehospital emergencies. The MOHW requested external expertise to train its developing workforce. Simulation-based training was planned to equip these health care providers with clinical knowledge, procedural skills, and communication techniques.

The objective of this study was to assess the educational needs of the pioneer Botswana MOHW EMS providers based on retrospective EMS logbook review and EMS provider feedback to guide development of a novel educational curriculum.

Data were abstracted from a representative sample of the Gaborone, Botswana MOHW EMS response log from 2013-2014 and were quantified into the five most common call types for both adults and children. Informal focus groups with health professionals and EMS staff, as well as surveys, were used to rank common response call types and self-perceived educational needs.

Based on 1,506 calls, the most common adult response calls were for obstetric emergencies, altered mental status, gastrointestinal/abdominal pain, trauma, gynecological emergencies, and cardiovascular and respiratory distress-related emergencies. The most common pediatric response calls were for respiratory distress, gastrointestinal complaints/dehydration, trauma and musculoskeletal injuries, newborn delivery, seizures, and toxic ingestion/exposure. The EMS providers identified these same chief complaints as priorities for training using the qualitative approach. A locally relevant, simulation-based curriculum for the Botswana MOHW EMS system was developed and implemented based on these data.

: Trauma, respiratory distress, gastrointestinal complaints, and puerperal/perinatal emergencies were common conditions for all age groups. Other age-specific conditions were also identified as educational needs based on epidemiologic data and provider feedback. This needs assessment may be useful when designing locally relevant EMS curricula in other low-income and middle-income countries.GlombNW, KosokoAA, DoughtyCB, RusMC, ShahMI, CoxM, GalapiC, ParkesPS, KumarS, LabaB.Needs Assessment for Simulation Training for Prehospital Providers in Botswana. Prehosp Disaster Med. 2018;33(6):621–626.

To examine timing of eating across ten European countries.

Cross-sectional analysis of the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study using standardized 24 h diet recalls collected during 1995–2000. Eleven predefined food consumption occasions were assessed during the recall interview. We present time of consumption of meals and snacks as well as the later:earlier energy intake ratio, with earlier and later intakes defined as 06.00–14.00 and 15.00–24.00 hours, respectively. Type III tests were used to examine associations of sociodemographic, lifestyle and health variables with timing of energy intake.

Ten Western European countries.

In total, 22 985 women and 13 035 men aged 35–74 years (n 36 020).

A south–north gradient was observed for timing of eating, with later consumption of meals and snacks in Mediterranean countries compared with Central and Northern European countries. However, the energy load was reversed, with the later:earlier energy intake ratio ranging from 0·68 (France) to 1·39 (Norway) among women, and from 0·71 (Greece) to 1·35 (the Netherlands) among men. Among women, country, age, education, marital status, smoking, day of recall and season were all independently associated with timing of energy intake (all P<0·05). Among men, the corresponding variables were country, age, education, smoking, physical activity, BMI and day of recall (all P<0·05).

We found pronounced differences in timing of eating across Europe, with later meal timetables but greater energy load earlier during the day in Mediterranean countries compared with Central and Northern European countries.

In the Netherlands, various FFQs have been administered in large cohort studies, which hampers comparison and pooling of dietary data. The present study aimed to describe the development of a standardized Dutch FFQ, FFQ-NL1.0, and assess its compatibility with existing Dutch FFQs.

Dutch FFQTOOLTM was used to develop the FFQ-NL1.0 by selecting food items with the largest contributions to total intake and explained variance in intake of energy and thirty-nine nutrients in adults aged 25–69 years from the Dutch National Food Consumption Survey (DNFCS) 2007–2010. Compatibility with the Maastricht-FFQ, Wageningen-FFQ and EPICNL-FFQ was assessed by comparing the number of food items, the covered energy and nutrient intake, and the covered variance in intake.

FFQ-NL1.0 comprised 160 food items, v. 253, 183 and 154 food items for the Maastricht-FFQ, Wageningen-FFQ and EPICNL-FFQ, respectively. FFQ-NL1.0 covered ≥85 % of energy and all nutrients reported in the DNFCS. Covered variance in intake ranged from 57 to 99 % for energy and macronutrients, and from 45 to 93 % for micronutrients. Differences between FFQ-NL1.0 and the other FFQs in covered nutrient intake and covered variance in intake were <5 % for energy and all macronutrients. For micronutrients, differences between FFQ-NL and other FFQs in covered level of intake were <15 %, but differences in covered variance were much larger, the maximum difference being 36 %.

The FFQ-NL1.0 was compatible with other FFQs regarding energy and macronutrient intake. However, compatibility for covered variance of intake was limited for some of the micronutrients. If implemented in existing cohorts, it is advised to administer the old and the new FFQ in combination to derive calibration factors.

The present study tested the combination of an established and a validated food-choice research method (the ‘fake food buffet’) with a new food-matching technology to automate the data collection and analysis.

The methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors.

The final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %.

The present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.