Effect of HIV-related factors and LR-HPV on AGW prevalence at enrolment among 572 WLHIV in BF and 613 in SA.

<p>Effect of HIV-related factors and LR-HPV on AGW prevalence at enrolment among 572 WLHIV in BF and 613 in SA.</p

Type specific LR-HPV infection.

<p>(A) Prevalence at enrolment. (B) Incidence over 16 months. (C) Persistence over 16 months. *Significantly different between the two countries (i.e. p-value <0.05). **Persistence as a proportion of those positive for that specific type at enrolment.</p

Associations between prevalent LR-HPV^a and HIV-related factors among 570 WLHIV in BF and 613 in SA.

<p>Associations between prevalent LR-HPV<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196018#t002fn001" target="_blank">^a</a> and HIV-related factors among 570 WLHIV in BF and 613 in SA.</p

Effect of HIV-related factors and LR-HPV infection on incident AGW among 468 WLHIV in BF and 524 in SA.

<p>Effect of HIV-related factors and LR-HPV infection on incident AGW among 468 WLHIV in BF and 524 in SA.</p

Inventory on the dietary assessment tools available and needed in africa: a prerequisite for setting up a common methodological research infrastructure for nutritional surveillance, research, and prevention of diet-related non-communicable diseases

<p><i>Objective:</i> To carry out an inventory on the availability, challenges, and needs of dietary assessment (DA) methods in Africa as a pre-requisite to provide evidence, and set directions (strategies) for implementing common dietary methods and support web-research infrastructure across countries. <i>Methods:</i> The inventory was performed within the framework of the “Africa's Study on Physical Activity and Dietary Assessment Methods” (AS-PADAM) project. It involves international institutional and African networks. An inventory questionnaire was developed and disseminated through the networks. Eighteen countries responded to the dietary inventory questionnaire. <i>Results:</i> Various DA tools were reported in Africa; 24-Hour Dietary Recall and Food Frequency Questionnaire were the most commonly used tools. Few tools were validated and tested for reliability. Face-to-face interview was the common method of administration. No computerized software or other new (web) technologies were reported. No tools were standardized across countries. <i>Conclusions:</i> The lack of comparable DA methods across represented countries is a major obstacle to implement comprehensive and joint nutrition-related programmes for surveillance, programme evaluation, research, and prevention. There is a need to develop new or adapt existing DA methods across countries by employing related research infrastructure that has been validated and standardized in other settings, with the view to standardizing methods for wider use.</p

Nachhaltigkeit in der Gemeinschaftsverpflegung : das Forschungsprojekt NAHGAST

<p>Means are adjusted for age, sex, height, weight and energy and weighted for day and season of recall (N = 34,436). The reference circle of the radius (100%) correspond to the ‘EPIC means’ and the spikes indicate the deviation of the specific nutrient mean in quintiles of pattern scores from the reference ‘EPIC means’.</p

Proportion of the variance in each EPIC center captured in an overall PCA on combined data by the number of PC retained.

<p>Proportion of the variance in each EPIC center captured in an overall PCA on combined data by the number of PC retained.</p

Deviation (%) of the 24-HDR mean intakes from the overall EPIC means among participants in the quintiles of PC1 scores for nutrients (A) and foods (B).

<p>Means are adjusted for age, sex, height, weight and energy and weighted for day and season of recall (N = 34,436). The reference circle of the radius (100%) correspond to the ‘EPIC means’ and the spikes indicate the deviation of the specific nutrient mean in quintiles of pattern scores from the reference ‘EPIC means’.</p

P-values of F-test on type III sum of squares estimate.

<p>*The scores had means of 0 but are standardized to unit variance; PC scores calculated on the country-specific FFQ derived intake levels of 23 nutrients, n = 477,312.</p>†<p>10 units increase.</p>‡<p>10 years increase.</p>#<p>Degree of Freedom.</p

Loading matrix and explained variances for the first four Principal Components (PC) identified by PCA^*.

<p>*Estimates from a EPIC-Wide PCA done on the country-specific FFQ derived intake levels of 23 nutrients (log-transformed and energy adjusted using the energy density method, using Alcohol-free Energy).</p