The Optimising treatment for acute MAlnutrition (OptiMA) strategy trains mothers to use mid upper arm circumference (MUAC) bracelets for screening and targets treatment to children with MUAC < 125 mm or oedema with one therapeutic food at a gradually reduced dose. This study seeks to determine whether OptiMA conforms to SPHERE standards (recovery rate > 75 %). A single-arm proof-of-concept trial was conducted in 2017 in Yako district, Burkina Faso including children aged 6–59 months in outpatient health centres with MUAC < 125 mm or oedema. Outcomes were stratified by MUAC category at admission. Multivariate survival analysis was carried out to identify variables predictive of recovery. Among 4958 children included, 824 (16·6 %) were admitted with MUAC < 115 mm or oedema, 1070 (21·6 %) with MUAC 115–119 mm and 3064 (61·8 %) with MUAC 120–124 mm. The new dosage was correctly implemented at all visits for 75·9 % of children. Global recovery was 86·3 (95 % CI 85·4, 87·2) % and 70·5 (95 % CI 67·5, 73·5) % for children admitted with MUAC < 115 mm or oedema. Average therapeutic food consumption was 60·8 sachets per child treated. Recovery was positively associated with mothers trained to use MUAC prior to child’s admission (adjusted hazard ratio 1·09; 95 % CI 1·01, 1·19). OptiMA was successfully implemented at the scale of an entire district under ‘real-life’ conditions. Programme outcomes exceeded SPHERE standards, but further study is needed to determine if increasing therapeutic food dosages for the most severely malnourished will improve recovery.

Bacillary dysentery continues to be a major health issue in developing countries and ambient temperature is a possible environmental determinant. However, evidence about the risk of bacillary dysentery attributable to ambient temperature under climate change scenarios is scarce. We examined the attributable fraction (AF) of temperature-related bacillary dysentery in urban and rural Hefei, China during 2006–2012 and projected its shifting pattern under climate change scenarios using a distributed lag non-linear model. The risk of bacillary dysentery increased with the temperature rise above a threshold (18·4 °C), and the temperature effects appeared to be acute. The proportion of bacillary dysentery attributable to hot temperatures was 18·74% (95 empirical confidence interval (eCI): 8·36–27·44%). Apparent difference of AF was observed between urban and rural areas, with AF varying from 26·87% (95% eCI 16·21–36·68%) in urban area to −1·90% (95 eCI −25·03 to 16·05%) in rural area. Under the climate change scenarios alone (1–4 °C rise), the AF from extreme hot temperatures (>31·2 °C) would rise greatly accompanied by the relatively stable AF from moderate hot temperatures (18·4–31·2 °C). If climate change proceeds, urban area may be more likely to suffer from rapidly increasing burden of disease from extreme hot temperatures in the absence of effective mitigation and adaptation strategies.

Human illness attribution is recognized as an important metric for prioritizing and informing food-safety decisions and for monitoring progress towards long-term food-safety goals. Inferences regarding the proportion of illnesses attributed to a specific commodity class are often based on analyses of datasets describing the number of outbreaks in a given year or combination of years. In many countries, the total number of pathogen-related outbreaks reported nationwide for an implicated food source is often fewer than 50 instances in a given year and the number of years for which data are available can be fewer than 10. Therefore, a high degree of uncertainty is associated with the estimated fraction of pathogen-related outbreaks attributed to a general food commodity. Although it is possible to make inferences using only data from the most recent year, this type of estimation strategy ignores the data collected in previous years. Thus, a strong argument exists for an estimator that could ‘borrow strength’ from data collected in the previous years by combining the current data with the data from previous years. While many estimators exist for combining multiple years of data, most either require more data than is currently available or lack an objective and biologically plausible theoretical basis. This study introduces an estimation strategy that progressively reduces the influence of data collected in past years in accordance with the degree of departure from a Poisson process. The methodology is applied to the estimation of the attribution fraction for Salmonella and Escherichia coli O157:H7 for common food commodities and the estimates are compared against two alternative estimators.

The incidence of fever among infants in the village of Idete in the Morogoro region of Tanzania was analysed in relation to densities of Plasmodium falciparum parasites in the peripheral blood. Parasite densities in both fever cases and in asymptomatic infants, were compared and a Bayesian non-parametric mixture decomposition algorithm was used to estimate the proportion of fevers attributable to malaria and hence the incidence of clinical malaria. Age group-specific densities of peripheral parasitaemia showed little seasonality, but the clinical malaria incidence showed a clear peak in the wet season in children aged less than 9 months. Estimates of the parasitaemia-specific incidence of clinical malaria were used to quantify apparent tolerance of parasites, and indicated that clinical episodes occurred on average at lower parasite densities during the wet season than in the dry season. These patterns could reflect differences in levels of anti-toxic immunity, but the nature of the seasonal differences supports the alternative explanation that the variation in apparent tolerance may be an effect of changes in the ratio of peripheral parasite densities to the sequestered mass.