A Bayesian Model Based on Local Phenotypic Resistance Data to Inform Empiric Antibiotic Escalation Decisions.

INTRODUCTION: Clinicians commonly escalate empiric antibiotic therapy due to poor clinical progress without microbiology guidance. When escalating, they should take account of how resistance to an initial antibiotic affects the probability of resistance to subsequent options. The term "escalation antibiogram" (EA) has been coined to describe this concept. One difficulty when applying the EA concept to clinical practice is understanding the uncertainty in results and how this changes for specific patient subgroups. METHODS: A Bayesian model was developed to estimate antibiotic resistance rates in Gram-negative bloodstream infections based on phenotypic resistance data. The model generates a series of "credible" curves to fit the resistance data, each with the same probability of representing the true rate given the inherent uncertainty. To avoid overfitting, an integrated penalisation term adaptively smooths the curves given the level of evidence. RESULTS: Rates of resistance to empiric first-choice and potential escalation antibiotics were calculated for the whole hospitalised population based on 10,486 individual bloodstream infections, and for a range of specific patient groups, including ICU (intensive care unit), haematolo-oncology, and paediatric patients. The model generated an expected value (posterior mean) with 95% credible interval to illustrate uncertainty, based on the size of the patient subgroup. For example, the posterior means of piperacillin/tazobactam resistance rates in Gram-negative bloodstream infection are different between patients on ICU and the general hospital population: 27.3% (95% CI 18.1-37.2 vs. 13.4% 95% CI 11.0-16.1) respectively. The model can also estimate the probability of inferiority between two antibiotics for a specific patient population. Differences in optimal escalation antibiotic options between specific patient groups were noted. CONCLUSIONS: EA analysis informed by our Bayesian model is a useful tool to support empiric antibiotic switches, providing an estimate of local resistance rates, and a comparison of antibiotic options with a measure of the uncertainty in the data. We demonstrate that EAs calculated for the whole hospital population cannot be assumed to apply to specific patient group.

The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries.

The Rural Household Multiple Indicator Survey (RHoMIS) is a standardized farm household survey approach which collects information on 758 variables covering household demographics, farm area, crops grown and their production, livestock holdings and their production, agricultural product use and variables underlying standard socio-economic and food security indicators such as the Probability of Poverty Index, the Household Food Insecurity Access Scale, and household dietary diversity. These variables are used to quantify more than 40 different indicators on farm and household characteristics, welfare, productivity, and economic performance. Between 2015 and the beginning of 2018, the survey instrument was applied in 21 countries in Central America, sub-Saharan Africa and Asia. The data presented here include the raw survey response data, the indicator calculation code, and the resulting indicator values. These data can be used to quantify on- and off-farm pathways to food security, diverse diets, and changes in poverty for rural smallholder farm households.