Measles is a notifiable disease, but not everyone infected seeks care, nor is every consultation reported. We estimated the completeness of reporting during a measles outbreak in The Netherlands in 2013–2014. Children below 15 years of age in a low vaccination coverage community (n = 3422) received a questionnaire to identify measles cases. Cases found in the survey were matched with the register of notifiable diseases to estimate the completeness of reporting. Second, completeness of reporting was assessed by comparing the number of susceptible individuals prior to the outbreak with the number of reported cases in the surveyed community and on a national level.

We found 307 (15%) self-identified measles cases among 2077 returned questionnaires (61%), of which 27 could be matched to a case reported to the national register; completeness of reporting was 8.8%. Based on the number of susceptible individuals and number of reported cases in the surveyed community and on national level, the completeness of reporting was estimated to be 9.1% and 8.6%, respectively. Estimating the completeness of reporting gave almost identical estimates, which lends support to the credibility and validity of both approaches. The size of the 2013–2014 outbreak approximated 31 400 measles infections.

Routine, passive surveillance systems tend to underestimate the burden of communicable diseases such as dengue. When empirical methods are unavailable, complimentary opinion-based or extrapolative methods have been employed. Here, an expert Delphi panel estimated the proportion of dengue captured by the Indonesian surveillance system, and associated health system parameters. Following presentation of medical and epidemiological data and subsequent discussions, the panel made iterative estimates from which expansion factors (EF), the ratio of total:reported cases, were calculated. Panelists estimated that of all symptomatic Indonesian dengue episodes, 57·8% (95% confidence interval (CI) 46·6–59·8) enter healthcare facilities to seek treatment; 39·3% (95% CI 32·8–42·0) are diagnosed as dengue; and 20·3% (95% CI 16·1–24·3) are subsequently reported in the surveillance system. They estimated most hospitalizations occur in the public sector, while ~55% of ambulatory episodes are seen privately. These estimates gave an overall EF of 5·00; hospitalized EF of 1·66; and ambulatory EF of 34·01 which, when combined with passive surveillance data, equates to an annual average (2006–2015) of 612 005 dengue cases, and 183 297 hospitalizations. These estimates are lower than those published elsewhere, perhaps due to case definitions, local clinical perceptions and treatment-seeking behavior. These findings complement global burden estimates, support health economic analyses, and can be used to inform decision-making.

Subjective energy intake (sEI) is often misreported, providing unreliable estimates of energy consumed. Therefore, relating sEI data to health outcomes is difficult. Recently, Börnhorst et al. compared various methods to correct sEI-based energy intake estimates. They criticised approaches that categorise participants as under-reporters, plausible reporters and over-reporters based on the sEI:total energy expenditure (TEE) ratio, and thereafter use these categories as statistical covariates or exclusion criteria. Instead, they recommended using external predictors of sEI misreporting as statistical covariates. We sought to confirm and extend these findings. Using a sample of 190 adolescent boys (mean age=14), we demonstrated that dual-energy X-ray absorptiometry-measured fat-free mass is strongly associated with objective energy intake data (onsite weighted breakfast), but the association with sEI (previous 3-d dietary interview) is weak. Comparing sEI with TEE revealed that sEI was mostly under-reported (74 %). Interestingly, statistically controlling for dietary reporting groups or restricting samples to plausible reporters created a stronger-than-expected association between fat-free mass and sEI. However, the association was an artifact caused by selection bias – that is, data re-sampling and simulations showed that these methods overestimated the effect size because fat-free mass was related to sEI both directly and indirectly via TEE. A more realistic association between sEI and fat-free mass was obtained when the model included common predictors of misreporting (e.g. BMI, restraint). To conclude, restricting sEI data only to plausible reporters can cause selection bias and inflated associations in later analyses. Therefore, we further support statistically correcting sEI data in nutritional analyses. The script for running simulations is provided.

This study examined the correlates of dietary energy under-reporting (UR) and over-reporting (OV) in European adolescents. Two self-administered computerised 24-h dietary recalls and physical activity data using accelerometry were collected from 1512 adolescents aged 12·5–17·5 years from eight European countries. Objective measurements of height and weight were obtained. BMI was categorised according to Cole/International Obesity Task Force (IOTF) cut-off points. Diet-related attitudes were assessed via self-administered questionnaires. Reported energy intake (EI) was compared with predicted total energy expenditure to identify UR and OV using individual physical activity objective measures. Associations between misreporting and covariates were examined by multilevel logistic regression analyses. Among all, 33·3 % of the adolescents were UR and 15·6 % were OV when considering mean EI. Overweight (OR 3·25; 95 % CI 2·01, 5·27) and obese (OR 4·31; 95 % CI 1·92, 9·65) adolescents had higher odds for UR, whereas underweight individuals were more likely to over-report (OR 1·67; 95 % CI 1·01, 2·76). Being content with their own figures (OR 0·61; 95 % CI 0·41, 0·89) decreased the odds for UR, whereas frequently skipping breakfast (OR 2·14; 95 % CI 1·53, 2·99) was linked with higher odds for UR. Those being worried about gaining weight (OR 0·55; 95 % CI 0·33, 0·92) were less likely to OV. Weight status and psychosocial weight-related factors were found to be the major correlates of misreporting. Misreporting may reflect socially desirable answers and low ability to report own dietary intakes, but also may reflect real under-eating in an attempt to lose weight or real over-eating to reflect higher intakes due to growth spurts. Factors influencing misreporting should be identified in youths to clarify or better understand diet–disease associations.

The objectives of the present study were to identify childhood energy misreporting, and evaluate characteristics that are associated with its prevalence in a nationwide cross-sectional sample of Greek schoolchildren. Under the context of the GRECO (Greek Childhood Obesity) study, data from a total of 4547 children aged 10–12 years and 2318 parents were included in the analysis. Anthropometric, lifestyle and parental characteristics plus psychological concerns were investigated in relation to the prevalence of energy misreporting. Of the included children, 36 % were classified as energy under-reporters and 16 % as over-reporters. Multinomial logistic regression analysis revealed that the most important predictors of energy under-reporting (URP) were children's BMI (OR 1·11, 95 % CI 1·09, 1·14) and weight satisfaction (OR 0·87, 95 % CI 0·78, 0·97). In the case of energy over-reporting (ORP), children's BMI (OR 0·87, 95 % CI 0·84, 0·90), meal and snack consumption frequency (OR 1·52, 95 % CI 1·32, 1·75), female sex (OR 0·65, 95 % CI 0·45, 0·90), and maternal education (OR 0·95, 95 % CI 0·91, 0·99) remained as significant predictors. Additionally, parental perception that the body weight of their children was normal reduced the odds of URP (OR 0·69, 95 % CI 0·48, 0·99) and ORP (OR 0·53, 95 % CI 0·31, 0·93). In conclusion, the present study confirms that the issue of URP and ORP in childhood populations is evident and quite serious. Although there are no definite guidelines on how to use data obtained from misreporters in an epidemiological dataset, validity of reported energy intake seems to be influenced by children's BMI and weight satisfaction, as well as by parental perceptions regarding their children's weight.

Several epidemiological studies in adults have suggested a favourable effect of frequent eating on blood lipid profiles, but evidence in younger populations is lacking. In the present cross-sectional study, we examined the associations of eating frequency (EF) with metabolic risk factors in British children aged 4–10 years (n 818) and adolescents aged 11–18 years (n 818). Dietary intake was assessed using a 7 d weighed dietary record. EF was calculated based on all eating occasions, except for those providing < 210 kJ of energy. Metabolic risk factors examined were total, HDL-cholesterol and LDL-cholesterol concentrations, TAG concentration, BMI z-score, waist:height ratio (WHtR; only adolescents), and systolic and diastolic blood pressures. Adjustment was made for age, sex, social class, physical activity levels, intakes of protein, fat, total sugar and dietary fibre, ratio of reported energy intake to estimated energy requirement (EI:EER) and BMI z-score (except for BMI z-score and WHtR). In children, EF was inversely associated with total cholesterol and LDL-cholesterol concentrations (n 324, P= 0·01 and 0·04, respectively). Conversely, EF was positively associated with BMI z-score in adolescents (P= 0·004). There were no associations between EF and other metabolic risk factors. In analyses in which only plausible energy reporters (EI:EER: 0·72–1·28) were included, similar results were obtained, except for an inverse association between EF and diastolic blood pressure in children. In conclusion, a higher EF is associated with lower total cholesterol and LDL-cholesterol concentrations in children but with a higher BMI z-score in adolescents.

To date, no study has directly and simultaneously measured the discrepancy between what people actually eat and what they report eating under observation in the context of energy balance (EB). The present study aimed to objectively measure the ‘extent’ and ‘nature’ of misreporting of dietary intakes under conditions in which EB and feeding behaviour were continuously monitored. For this purpose, a total of fifty-nine adults were recruited for 12 d, involving two 3 d overt phases and two 3 d covert phases of food intake measurement in a randomised cross-over design. Subjects had ad libitum access to a variety of familiar foods. Food intake was covertly measured using a feeding behaviour suite to establish actual energy and nutrient intakes. During the overt phases, subjects were instructed to self-report food intake using widely accepted methods. Misreporting comprised two separate and synchronous phenomena. Subjects decreased energy intake (EI) when asked to record their food intake (observation effect). The effect was significant in women ( − 8 %, P< 0·001) but not in men ( − 3 %, P< 0·277). The reported EI was 5 to 21 % lower (reporting effect) than the actual intake, depending on the reporting method used. Semi-quantitative techniques gave larger discrepancies. These discrepancies were identical in men and women and non-macronutrient specific. The ‘observation’ and ‘reporting’ effects combined to constitute total misreporting, which ranged from 10 to 25 %, depending on the intake measurement assessed. When studied in a laboratory environment and EB was closely monitored, subjects under-reported their food intake and decreased the actual intake when they were aware that their intake was being monitored.

Energy intake under-reporting (UR) is a concern in nutritional epidemiological studies, as it may distort the relationships between dietary habits and health. Although UR is known to be associated with certain characteristics, few studies have investigated them in France. Therefore, the goal of the present study was to assess the prevalence and characteristics of UR in French adults. UR was defined according to Goldberg's classification. A sample of 1567 adults was drawn from the nationally representative French dietary survey (Individuelle Nationale des Consommations Alimentaires 2 2006–7). Food intake (7 d record), dietary habits, socio-economic status, region of residence, sedentary behaviour and weight perception variables were assessed. Multivariate logistic regression was used to investigate the associations between UR and a number of covariates. The overall prevalence of UR was 22·5 %, similar in men and women. In both sexes, UR was positively associated with overweight and protein intake and inversely associated with age. In women, UR was associated with eating lunch in the office, poor perception of diet quality and sedentary behaviour and was inversely associated with educational level, residence in the Paris region, cereal product intake and eating lunch in a friend's or family member's home. In men, UR was positively associated with a history of slimming and inversely associated with dairy product intake and eating lunch at a staff canteen. In conclusion, UR is prevalent in French adults and is associated with several different characteristics. It is important to take account of UR when investigating diet–disease associations in adults.

Inconsistent associations between dietary glycaemic index (GI) and glycaemic load (GL) and body fatness may be partly due to differences in the underlying dietary patterns or energy under-reporting. In the present study, we examined the cross-sectional associations of dietary GI and GL with food and nutrient intake and general and central obesity, accounting for energy under-reporting. The subjects were 1487 British adults aged 19–64 years. Dietary intake was assessed using a 7 d weighed dietary record. Breads and potatoes were the positive predictive foods for dietary GI, while fruit, other cereals and dairy products were the negative predictors. These foods were similarly identified in the analysis of only acceptable reporters (AR; ratio of reported energy intake:estimated energy requirement within 0·665–1·335) and under-reporters (UR; ratio < 0·665). Dietary GL was closely correlated with carbohydrate intake. Multiple linear regression analyses showed that dietary GI was independently associated with a higher risk of general obesity (BMI ≥ 30 kg/m2) and central obesity (waist circumference ≥ 102 cm in men and ≥ 88 cm in women). Dietary GL was also associated with general (only women) and central obesity. Similarly, in the analysis of AR, the GI showed positive associations with general and central obesity, and, only in women, the GL showed positive associations with general and central obesity. Conversely, in the analysis of UR, the associations were generally weaker and many of them failed to reach statistical significance. In conclusion, we found independent positive associations of dietary GI and GL with general and central obesity in British adults.

The diversity of the associations of dietary glycaemic index (GI) and glycaemic load (GL) with dietary intake and body fatness observed in epidemiological studies may be partly due to the differences in underlying dietary intake patterns. We examined the cross-sectional associations of dietary GI and GL with food and nutrient intakes and indices of body fatness in 818 children aged 4–10 years and 818 adolescents aged 11–18 years in Britain, based on the data from the National Diet and Nutrition Survey. Dietary intake was assessed using a 7 d weighed dietary record. Overweight was defined as BMI ≥ 85th percentile of the age- and sex-specific British growth reference data. Central obesity was defined as waist:height ratio (WHtR) ≥ 0·5 (adolescents only). Breads, breakfast cereals and potatoes were the positive predictive food groups for dietary GI, while dairy products, fruit juice, other cereals and fruit were the negative predictors. Dietary GL was closely correlated with carbohydrate intake. Dietary GI showed no associations with overweight or central obesity. Conversely, dietary GL showed an independent association with a higher risk of overweight in children and a higher risk of central obesity (but not overweight) in adolescents. However, dietary GI and GL were not associated with BMI z-score in children and adolescents or WHtR in adolescents. In conclusion, the present study showed that dietary GL was independently associated with overweight in children and with central obesity in adolescents. Nevertheless, given no associations when body fatness measures were treated as continuous variables, the results must be interpreted cautiously.

Whether there are differences between countries in the validity of self-reported diet in relation to BMI, as evaluated using recovery biomarkers, is not well understood. We aimed to evaluate BMI-related reporting errors on 24 h dietary recalls (24-HDR) and on dietary questionnaires (DQ) using biomarkers for protein and K intake and whether the BMI effect differs between six European countries. Between 1995 and 1999, 1086 men and women participating in the European Prospective Investigation into Cancer and Nutrition completed a single 24-HDR, a DQ and one 24 h urine collection. In regression analysis, controlling for age, sex, education and country, each unit (1 kg/m2) increase in BMI predicted an approximately 1·7 and 1·3 % increase in protein under-reporting on 24-HDR and DQ, respectively (both P < 0·0001). Exclusion of individuals who probably misreported energy intake attenuated BMI-related bias on both instruments. The BMI effect on protein under-reporting did not differ for men and women and neither between countries on both instruments as tested by interaction (all P>0·15). In women, but not in men, the DQ yielded higher mean intakes of protein that were closer to the biomarker-based measurements across BMI groups when compared with 24-HDR. Results for K were similar to those of protein, although BMI-related under-reporting of K was of a smaller magnitude, suggesting differential misreporting of foods. Under-reporting of protein and K appears to be predicted by BMI, but this effect may be driven by ‘low-energy reporters’. The BMI effect on under-reporting seems to be the same across countries.

Under-reporting (UR) of food intake is an issue of concern, as it may distort the relationships studied between diet and health. This topic has been scarcely addressed in children. The objective of the study was to assess the extent of UR in French children and investigate associated covariates. A total of 1455 children aged 3–17 years were taken from the nationally representative cross-sectional French étude Individuelle Nationale des Consommations Alimentaires (INCA2) dietary survey (2006–7). Food intake was reported in a 7 d diet record. Socio-economic status, sedentary behaviour, weight perception variables and food habits were collected by questionnaires. Weight and height were measured. Under-reporters were identified according to the Goldberg criterion adapted to children. Multivariate logistic regressions investigated the associations between UR and covariates. Rates of under-reporters were 4·9 and 26·0 % in children aged 3–10 and 11–17 years, respectively (P < 0·0001), without significant differences between boys and girls. Overall, UR was positively associated with a lower socio-economic status, overweight, skipping breakfast and dinner, a higher contribution of proteins to energy intake (EI), and a lower contribution of simple carbohydrates to EI. Under-reporters aged 3–10 years also had a higher sedentary behaviour and a lower snack-eating frequency. In adolescents, UR was also associated with a less-frequent school canteen attendance, a perception of being overweight, a wish to weigh less, and current and past restrictive diets. In conclusion, under-reporters differ from plausible reporters in several characteristics related to diet, lifestyle, weight status and socio-economic status. Therefore, it is important to consider this differential UR bias when investigating diet–disease associations in children.

The aim of the present study was to determine whether under-reporting rates vary between dietary pattern clusters. Subjects were sixty-five Brazilian women. During 3 weeks, anthropometric data were collected, total energy expenditure (TEE) was determined by the doubly labelled water method and diet was measured. Energy intake (EI) and the daily frequency of consumption per 1000 kJ of twenty-two food groups were obtained from a FFQ. These frequencies were entered into a cluster analysis procedure in order to obtain dietary patterns. Under-reporters were defined as those who did not lose more than 1 kg of body weight during the study and presented EI:TEE less than 0·82. Three dietary pattern clusters were identified and named according to their most recurrent food groups: sweet foods (SW), starchy foods (ST) and healthy (H). Subjects from the healthy cluster had the lowest mean EI:TEE (SW = 0·86, ST = 0·71 and H = 0·58; P = 0·003) and EI − TEE (SW = − 0·49 MJ, ST = − 3·20 MJ and H = − 5·08 MJ; P = 0·008). The proportion of under-reporters was 45·2 (95 % CI 35·5, 55·0) % in the SW cluster; 58·3 (95 % CI 48·6, 68·0) % in the ST cluster and 70·0 (95 % CI 61·0, 79) % in the H cluster (P = 0·34). Thus, in Brazilian women, under-reporting of EI is not uniformly distributed among dietary pattern clusters and tends to be more severe among subjects from the healthy cluster. This cluster is more consistent with both dietary guidelines and with what lay individuals usually consider ‘healthy eating’.

The objective of the present study was to examine the associations between the portion sizes of food groups consumed with measures of adiposity using data from the National Diet and Nutrition Survey of British adults. Seven-day weighed dietary records, physical activity diaries and anthropometric measurements were used. Foods eaten were assigned to thirty different food groups and analyses were undertaken separately for men and women. The median daily portion size of each food group consumed was calculated. The potential misreporting of dietary energy intake (EI) was identified using the following equation: EI − estimated energy requirements × 100 = percentage of under-reporting (UR) of energy needs. Multinomial logistic regression (adjusted for age, social class, physical activity level and UR) was used to determine the portion sizes of food groups most strongly associated with obesity status. Few positive associations between the portion sizes of food groups consumed and obesity status were found. However, UR was prevalent, with a median UR of predicted energy needs of 34 and 33 % in men and women, respectively. After the adjustment was made for UR, more associations between the food groups and obesity status became apparent in both sexes. The present study suggests that the true effect of increased portion size of foods on obesity status may be masked by high levels of UR. Alternatively, these data may indicate that an increased risk of obesity is not associated with specific foods/food groups but rather with an overall increase in the range of foods and food groups being consumed.