Assessing the optimal time interval between growth measurements using a combined data set of weights and heights from 5948 infants

Background: Current guidance on the optimum interval between measurements in infancy is not evidence based. We used routine data to explore how measurement error and short-term variation (‘noise’) might affect interpretation of infant weight and length gain (‘signal’) over different time intervals. / Method: Using a database of weights and lengths from 5948 infants aged 0–12 months, all pairs of measurements per child 2, 4 and 8 weeks apart were extracted. Separately, 20 babies aged 2–10 months were weighed on six occasions over 3 days to estimate the SD of the weight difference between adjacent measurements (=116 g). Values of 116 g and 0.5 cm for ‘noise’ were then used to model its impact on (a) the estimated velocity centile and (b) the chance of seeing no growth during the interval, in individuals. / Results: The average gain in weight and length was much larger than the corresponding SD over 8-week and 4-week time intervals, but not over 2 weeks. Noise tended to make apparent velocity less extreme; after age 6 months, a 2-week velocity that appeared to be on to the ninth centile, would truly be on the second–third centile if measured with no noise. For 2-week intervals, there was a 16% risk of no apparent growth by age 10 months. / Conclusions: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. Growth in infancy is so rapid that the change in measurements 4–8 weeks apart is unlikely ever to be obscured by noise, but after age 6 months, measurements 2 weeks or less apart should be treated with caution when assessing growth faltering

Physical growth during the first year of life. A longitudinal study in rural and urban areas of Hanoi, Vietnam

<p>Abstract</p> <p>Background</p> <p>Good infant growth is important for future health. Assessing growth is common in pediatric care all over the world, both at the population and individual level. There are few studies of birth weight and growth studies comparing urban and rural communities in Vietnam. The first aim is to describe and compare the birth weight distributions and physical growth (weight and length) of children during their first year in one rural and one urban area of Hanoi Vietnam. The second aim is to study associations between the anthropometric outcomes and indicators of the economic and educational situations.</p> <p>Methods</p> <p>Totally 1,466 children, born from 1^stMarch, 2009 to June 2010, were followed monthly from birth to 12 months of age in two Health and Demographic Surveillance Sites; one rural and one urban. In all, 14,199 measurements each of weight and length were made. Birth weight was recorded separately. Information about demographic conditions, education, occupation and economic conditions of persons and households was obtained from household surveys. Fractional Polynomial models and standard statistical methods were used for description and analysis.</p> <p>Results</p> <p>Urban infants have higher birth weight and gain weight faster than rural infants. The mean birth weight for urban boys and girls were 3,298 grams and 3,203 grams as compared to 3,105 grams and 3,057 grams for rural children. At 90 days, the urban boys were estimated to be 4.1% heavier than rural boys. This difference increased to 7.2% at 360 days. The corresponding difference for girls was 3.4% and 10.5%. The differences for length were comparatively smaller. Both birth weight and growth were statistically significantly and positively associated with economic conditions and mother education.</p> <p>Conclusion</p> <p>Birth weight was lower and the growth, weight and length, considerably slower in the rural area, for boys as well as for girls. The results support the hypothesis that the rather drastic differences in maternal education and economic conditions lead to poor nutrition for mothers and children in turn causing inferior birth weight and growth.</p

Distinguishing the signals of gingivitis and periodontitis in supragingival plaque: a cross-sectional cohort study in Malawi

Periodontal disease ranges from gingival inflammation (gingivitis) to the inflammation and loss of tooth-supporting tissues (periodontitis). The bacterial composition of supragingival plaque across a range of periodontal severities has not previously been explored with high-throughput sequencing. Furthermore, quantitative modelling of bacterial abundances in supragingival plaque as a function of both gingivitis and periodontitis has not previously been attempted. We assessed a cross-sectional cohort of 962 Malawian women for periodontal disease and used 16S rRNA gene amplicon sequencing (V5-V7 region) to characterise the bacterial composition of supragingival plaque samples. Associations between bacterial relative abundances and gingivitis/periodontitis were investigated by using negative binomial models, adjusting for epidemiological factors. We also examined bacterial co-occurrence networks to assess community structure. The main differences in supragingival plaque composition were associated more with gingivitis than periodontitis, including higher bacterial diversity and greater abundance of particular species. However, even after controlling for gingivitis, the presence of subgingival periodontitis was associated with an altered supragingival plaque. A small number of species were associated with periodontitis but not gingivitis, including members of Prevotella, Treponema, and Selemonas, supporting a more complex disease model than linear progression following on from gingivitis. Co-occurrence networks of periodontitis-associated taxa clustered according to periodontitis across all gingivitis severities. Species including Filifactor alocis and Fusobacterium nucleatum were central to this network, supporting their role in co-aggregation of periodontal biofilms during disease progression. Our findings confirm that periodontitis cannot be considered simply an advanced stage of gingivitis, even when only considering supragingival plaque

Co-causation of reduced newborn size by maternal undernutrition, infections, and inflammation

More than 20 million babies are born with low birthweight annually. Small newborns have an increased risk for mortality, growth failure, and other adverse outcomes. Numerous antenatal risk factors for small newborn size have been identified, but individual interventions addressing them have not markedly improved the health outcomes of interest. We tested a hypothesis that in low-income settings, newborn size is influenced jointly by multiple maternal exposures and characterized pathways associating these exposures with newborn size. This was a prospective cohort study of pregnant women and their offspring nested in an intervention trial in rural Malawi. We collected information on maternal and placental characteristics and used regression analyses, structural equation modelling, and random forest models to build pathway maps for direct and indirect associations between these characteristics and newborn weight-for-age Z-score and length-for-age Z-score. We used multiple imputation to infer values for any missing data. Among 1,179 pregnant women and their babies, newborn weight-for-age Z-score was directly predicted by maternal primiparity, body mass index, and plasma alpha-1-acid glycoprotein concentration before 20 weeks of gestation, gestational weight gain, duration of pregnancy, placental weight, and newborn length-for-age Z-score (p < .05). The latter 5 variables were interconnected and were predicted by several more distal determinants. In low-income conditions like rural Malawi, maternal infections, inflammation, nutrition, and certain constitutional factors jointly influence newborn size. Because of this complex network, comprehensive interventions that concurrently address multiple adverse exposures are more likely to increase mean newborn size than focused interventions targeting only maternal nutrition or specific infections