Robust response rates are essential for effective survey-based strategies. Researchers can improve survey validity by addressing both response rates and nonresponse bias. In this AMEE Guide, we explain response rate calculations and discuss methods for improving response rates to surveys as a whole (unit nonresponse) and to questions within a survey (item nonresponse). Finally, we introduce the concept of nonresponse bias and provide simple methods to measure it.

The ACR Task Force on Medical Student Education in Radiology, in partnership with the Alliance of Medical Student Educators in Radiology, investigated the current status of how and to what extent medical imaging was being taught in medical schools. The task force executed a 3-part survey of medical school deans, radiology department chairs, and intern physicians. The results provided an updated understanding of the status of radiology education in medical schools in the United States. This summary includes recommendations about how individual radiology departments and ACR members can assist in advancing the specialty of diagnostic radiology through medical student education.

Disease progression in HIV-infected individuals varies greatly, and while the environmental and host factors influencing this variation have been widely investigated, the viral contribution to variation in set-point viral load, a predictor of disease progression, is less clear. Previous studies, using transmission-pairs and analysis of phylogenetic signal in small numbers of individuals, have produced a wide range of viral genetic effect estimates. Here we present a novel application of a population-scale method based in quantitative genetics to estimate the viral genetic effect on set-point viral load in the UK subtype B HIV-1 epidemic, based on a very large data set. Analyzing the initial viral load and associated pol sequence, both taken before anti-retroviral therapy, of 8,483 patients, we estimate the proportion of variance in viral load explained by viral genetic effects to be 5.7% (CI 2.8–8.6%). We also estimated the change in viral load over time due to selection on the virus and environmental effects to be a decline of 0.05 log10 copies/mL/year, in contrast to recent studies which suggested a reported small increase in viral load over the last 20 years might be due to evolutionary changes in the virus. Our results suggest that in the UK epidemic, subtype B has a small but significant viral genetic effect on viral load. By allowing the analysis of large sample sizes, we expect our approach to be applicable to the estimation of the genetic contribution to traits in many organisms.

We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log 10 increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV—CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences—is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence.

Human immunodeficiency virus type 1 (HIV-1) strains dually resistant to zidovudine and lamivudine (3TC) may arise during zidovudine-3TC combination therapy. The objective of this cross-sectional study (n = 43 patients) was to test the association between therapy response (clinical and immunologic) to zidovudine-3TC and the level of phenotypic zidovudine resistance and zidovudine resistance-associated genotype of 3TC-resistant isolates. Other variables included were baseline CD4+ cell count, baseline Centers for Disease Control and Prevention (CDC) classification, virus load, and time receiving zidovudine. Phenotypic resistance was assessed using a recombinant virus assay. Genotypic analysis was based on population sequencing of plasma HIV-1. In a univariate analysis using a logistic regression model, it was found that therapy response was significantly associated with phenotypic and genotypic zidovudine resistance, baseline CD4+ cell count, and virus load. After adjustment for all variables, phenotypic resistance to zidovudine remained the only significantly associated factor, independent of baseline CD4+ cell count, baseline CDC classification, and virus load.

Similar CD4 cell count criteria for initiation of antiretroviral therapy can be applied to adults and children > or = 5 years of age.

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Approximately half of the articles evaluated used a survey as part of their methods. Limited data are available to establish a baseline response rate among health professions trainees and inform researchers which strategies are associated with higher response rates. Journals publishing survey-based health professions education research should improve reporting of response rate, nonresponse bias, and other survey factors.