Contrary to what would be expected, smoking habits of asthmatics do not differ from those of the general population: approximately 30% of asthmatic patients smoke cigarettes. Although the relationship between smoking and the incidence of asthma has been well explored, little attention has been paid to documenting the relationship between smoking and asthma symptoms among adults with asthma. The objective of this study was to assess the association of cigarette smoking with asthma symptom severity. The present report is of a cross-sectional study of 225 asthmatics, aged 20-54 years, from six general practice clinics in East Anglia, U.K. The outcome measures are overall asthma symptom score (range 6.3-28) and three asthma symptom domains: respiratory (range 1.3-8), daily activity interference (range 2-8), and physical activity interference (range 3-12), generated from the sum of ordinal responses to questions on asthma symptom severity. Of the sample, 27.0% were current and 22.1% were former smokers. Current smokers more frequently had bothersome asthma symptoms than nonsmokers in both unadjusted analyses and analyses controlling for age, gender, recent visits to the general practitioner for asthma, and asthma medication use (p = 0.06). Respiratory symptoms (p = 0.03) and symptoms that affect daily activities (p = 0.03) were more strongly associated with smoking than symptoms that affect physical activities (p = 0.62). Our data suggest that smoking hastens asthma progression or affects disease control. Increased frequency of symptoms may be an indicator for potential morbidity among asthmatics, especially those who smoke cigarettes. The hazards associated with smoking among asthmatics need to be more clearly emphasized by physicians and public health officials in order to convince people with asthma who smoke to stop.

Our estimates for almost all groups in each country fall within the range of other regional and national estimates, indicating that the service multiplier method, assuming all assumptions are met, can produce informative estimates. We suggest using multiple multipliers whenever possible, garnering program data from the widest possible range of services, projects, and studies. A median of several estimates is likely more robust to potential biases than a single estimate.

Background An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent. Building on diverse research disciplines and expanding the discipline of infodemiology, more evidence-based interventions are needed to design infodemic management interventions and tools and implement them by health emergency responders. Objective The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics. Methods As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions. Results The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions. Conclusions Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider.

The need to obtain unbiased information among hard-to-reach and hidden populations for behavioural and biological surveillance, epidemiological studies, and intervention program evaluations has led researchers to search for a suitable sampling method. One method that has been tested among IDU and MSM recently is respondent-driven sampling (RDS). We used RDS to conduct a behavioural survey among FSWs and MSM in two urban centres in Papua New Guinea (PNG). In this paper we present the lessons learned implementing RDS in a developing country setting. We also present comparisons of RDSAT-adjusted versus unadjusted crude estimates of some key socio-demographic indicators as well as comparisons between the estimates from RDS and a hypothetical time-location sample (TLS). Overall, the use of RDS among the MSM and FSWs in PNG had numerous advantages in terms of collecting a required sample size in a short time period, minimizing costs and maximising security for staff and respondents. Although there were a few problems these were easily remedied and we would recommend RDS for other similar studies in PNG and other developing countries.

IntroductionThe UNAIDS 90‐90‐90 targets to diagnose 90% of people living with HIV, put 90% of them on treatment, and for 90% of them to have suppressed viral load have focused the international HIV response on the goal of eliminating HIV by 2030. They are also a constructive tool for measuring progress toward reaching this goal but their utility is dependent upon data availability. Though more than 25% of new infections are among key populations (KP)‐ sex workers, men who have sex with men, transgender people, people who inject drugs, and prisoners‐ and their sex partners, there is a dearth of treatment cascade data for KP. We assess the availability of cascade data and review the opportunities offered by biobehavioral and programme data to inform the HIV response.DiscussionThe emphasis on the collection of treatment cascade data among the general population in higher prevalence countries has not led to a similar increase in the availability of cascade data for KP. The limited data available for KP highlight large gaps in service uptake across the cascade, particularly in the first 90, awareness of HIV status. Biobehavioral surveys (BBS), with linked population size estimation, provide population‐based data on the treatment cascade and should be conducted every two to three years in locations with services for KP. With the inclusion of viral load testing, these surveys are able to monitor the entire treatment cascade among KP regardless of whether these populations access HIV services targeting the general population or KP. BBS also reach people accessing services and those who do not, thereby providing a unique opportunity to learn about barriers to service uptake including stigma and discrimination. At the same time high‐quality programme data can play a complementary role in identifying missed opportunities that can be addressed in real‐time.ConclusionsData are more important than ever for guiding the HIV response toward reaching 90‐90‐90 targets and eliminating HIV, particularly in the face of decreased funding for HIV and specifically for KP. Timely high‐quality BBS data can be triangulated with high‐quality programme data to provide a comprehensive picture of the epidemic response for KP.