BackgroundHIV testing with counseling is an integral component of most national HIV and AIDS prevention strategies in southern Africa. Equity in testing implies that people at higher risk for HIV such as women; those who do not use condoms consistently; those with multiple partners; those who have suffered gender based violence; and those who are unable to implement prevention choices (the choice-disabled) are tested and can have access to treatment.MethodsWe conducted a household survey of 24,069 people in nationally stratified random samples of communities in Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe. We asked about testing for HIV in the last 12 months, intention to test, and about HIV risk behaviour, socioeconomic indicators, access to information, and attitudes related to stigma.ResultsAcross the ten countries, seven out of every ten people said they planned to have an HIV test but the actual proportion tested in the last 12 months varied from 24% in Mozambique to 64% in Botswana. Generally, people at higher risk of HIV were not more likely to have been tested in the last year than those at lower risk, although women were more likely than men to have been tested in six of the ten countries. In Swaziland, those who experienced partner violence were more likely to test, but in Botswana those who were choice-disabled for condom use were less likely to be tested. The two most consistent factors associated with HIV testing across the countries were having heard about HIV/AIDS from a clinic or health centre, and having talked to someone about HIV and AIDS.ConclusionsHIV testing programmes need to encourage people at higher risk of HIV to get tested, particularly those who do not interact regularly with the health system. Service providers need to recognise that some people are not able to implement HIV preventive actions and may not feel empowered to get themselves tested.

Weight loss from exercise-induced energy deficits is usually less than expected. The objective of this systematic review was to investigate predictors of energy compensation, which is defined as body energy changes (fat mass and fat-free mass) over the total amount of exercise energy expenditure. A search was conducted in multiple databases without date limits. Of 4745 studies found, 61 were included in this systematic review with a total of 928 subjects. The overall mean energy compensation was 18% ± 93%. The analyses indicated that 48% of the variance of energy compensation is explained by the interaction between initial fat mass, age and duration of exercise interventions. Sex, frequency, intensity and dose of exercise energy expenditure were not significant predictors of energy compensation. The fitted model suggested that for a shorter study duration, lower energy compensation was observed in younger individuals with higher initial fat mass (FM). In contrast, higher energy compensation was noted for younger individuals with lower initial FM. From 25 weeks onward, energy compensation was no longer different for these predictors. For studies of longer duration (about 80 weeks), the energy compensation approached 84%. Lower energy compensation occurs with short-term exercise, and a much higher level of energy compensation accompanies long-term exercise interventions.

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Evidence suggests that fat-free mass and resting metabolic rate (RMR), but not fat mass, are strong predictors of energy intake (EI). However, body composition and RMR do not explain the entire variance in EI, suggesting that other factors may contribute to this variance. We aimed to investigate the associations between body mass index (in kg/m), fat mass, fat-free mass, and RMR with acute (1 meal) and daily (24-h) EI and between fasting appetite ratings and certain eating behavior traits with daily EI. We also evaluated whether RMR is a predictor of the error variance in acute and daily EI. Data collected during the control condition of 7 studies conducted in Ottawa, Ontario, Canada, were included in these analyses ( = 191 and 55 for acute and daily EI, respectively). These data include RMR (indirect calorimetry), body composition (dual-energy X-ray absorptiometry), fasting appetite ratings (visual analog scales), eating behavior traits (Three-Factor Eating Questionnaire), and EI (food buffet or menu). Fat-free mass was the best predictor of acute EI ( = 0.46; < 0.0001). The combination of fasting prospective food consumption ratings and RMR was the best predictor of daily EI ( = 0.44; < 0.0001). RMR was a statistically significant positive predictor of the error variance for acute ( = 0.20; < 0.0001) and daily ( = 0.23; < 0.0001) EI. RMR did, however, remain a statistically significant predictor of acute ( = 0.32; < 0.0001) and daily ( = 0.30; < 0.0001) EI after controlling for this error variance. Our findings suggest that combined measurements of appetite ratings and RMR could be used to estimate EI in weight-stable individuals. However, greater error variance in acute and daily EI with increasing RMR values was observed. Future studies are needed to identify whether greater fluctuations in daily EI over time occur with increasing RMR values. This trial was registered at clinicaltrials.gov as NCT02653378.

BackgroundFocus groups, rapid assessment procedures, key informant interviews and institutional reviews of local health services provide valuable insights on health service resources and performance. A long-standing challenge of health planning is to combine this sort of qualitative evidence in a unified analysis with quantitative evidence from household surveys. A particular challenge in this regard is to take account of the neighbourhood or clustering effects, recognising that these can be informative or incidental.MethodsAn example of food aid and food sufficiency from the Bosnian emergency (1995-96) illustrates two Lamothe cluster-adjustments of the Mantel Haenszel (MH) procedure, one assuming a fixed odds ratio and the other allowing for informative clustering by not assuming a fixed odds ratio. We compared these with conventional generalised estimating equations and a generalised linear mixed (GLMM) model, using a Laplace adjustment.ResultsThe MH adjustment assuming incidental clustering generated a final model very similar to GEE. The adjustment that does not assume a fixed odds ratio produced a final multivariate model and effect sizes very similar to GLMM.DiscussionIn medium or large data sets with stratified last stage random sampling, the cluster adjusted MH is substantially more conservative than the naïve MH computation. In the example of food aid in the Bosnian crisis, the cluster adjusted MH that does not assume a fixed odds ratio produced similar results to the GLMM, which identified informative clustering.

nature publishing group short communications integrative PhysiologyThe physiological changes that take place during weight loss may work in concert in order to re-establish depleted energy stores. One such change may be the sustained depression of resting EE (1). Weight loss may be accompanied by greater than expected decreases in EE during weight loss (2) which are sustained over time (3). Although controversy persists (4), the changes in resting EE during weight loss may be explained by factors beyond what would be expected given the concomitant changes in body composition, i.e., fat mass (FM) and fat-free mass (FFM).The purpose of this study was to establish whether a greater than predicted change in resting EE exists as a consequence of weight loss. This study draws from the same pool of subjects as our previously published systematic review (5) and uses peer reviewed weight loss literature in a large cohort of adults over the last 20 years. To our knowledge, this is the largest study sample to investigate whether there are greater than predicted changes in resting EE after weight loss. The first objective was to study to what degree the variance in resting EE upon weight loss was associated with changes in FM and FFM and whether FM and FFM alone could predict changes in resting EE. The second objective was to compare actual changes in resting EE to those obtained with the Harris-Benedict (HB) equation (6). Based on previous results, we hypothesize that the depression of resting EE will be greater than what can be predicted from the changes in body composition alone. MethodsFor the first objective, data from 2,977 subjects that were included in the analysis of our previously published systematic review was used (5). The selection of papers in the previous review was carried out systematically through specific collection criteria. In order to be included in the study, the publications had: (i) To include specific information on the weight loss interventions; (ii) To be performed on overweight or obese adults who were otherwise healthy, except in the case of surgical interventions where individuals were only considered candidates for some of the procedures if they had comorbidities such as diabetes and blood pressure, and (iii) To have values of resting EE or resting metabolic rate or basal metabolic rate or sleeping metabolic rate and body weight before and after the intervention. For publications dealing with more than one study group, all those groups that fit the inclusion criteria within that study were included and treated as individual sets of data. Of the 2,977 subjects, a total of 815 were included (714 females and 101 males) from 35 study groups based on information that provided the sex, change in body weight, change in FM, change in FFM, and change in resting EE. The data was then used to establish a relationship between the changes in body composition and changes in resting EE.For the second objective, data was again selected from the 2,977 subject of the original systematic review and excluded if...

Background/Objectives Body composition (BC) does not always vary as a function of exercise induced energy expenditure (exercise EE – resting EE). Energy balance variables were measured to understand energy compensation (EC) in response to an exercise intervention performed at low (LOW) or moderate (MOD) intensity. Subjects/Methods Twenty-one women with overweight/obesity (33 ± 5 kg/m 2 ; 29 ± 10 yrs; 31 ± 4 ml O 2 /kg/min) were randomized to a 3-month LOW or MOD (40 or 60% of VȮ 2reserve , respectively) matched to expend 1500 kcal/week (compliance = 97 ± 5%). Body energy stores (DXA), energy intake (EI) (food menu and food diaries), resting EE (indirect calorimetry), total EE (doubly-labeled water), time spent in different activities (accelerometers), appetite (visual analog scale), eating behavior traits and food reward (liking and wanting) were assessed at baseline, after weeks 1 and 2 and at the end of the 3-month exercise intervention. Results EC based on BC changes (fat mass and fat-free mass) was 49 ± 79% and 161 ± 88% in LOW and MOD groups, respectively ( p = 0.010). EI did not change significantly during the intervention. However, eating behavior traits and food reward had changed by the end of the 3-month supervised exercise. Non-structured physical activity (NSPA) decreased across the intervention ( p < 0.002), independent of the intensity of the exercise training. Conclusion Women with overweight/obesity training at LOW presented lower EC for a given energy cost of exercise. Our results strongly suggest that NSPA plays a major role in mediating the effects of exercise on energy balance and ultimately on changes in BC. Clinical Trial Registration www.ClinicalTrials.gov , identifier ISRCTN31641049.

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