HIV Prevention in Care and Treatment Settings: Baseline Risk Behaviors among HIV Patients in Kenya, Namibia, and Tanzania.

HIV care and treatment settings provide an opportunity to reach people living with HIV/AIDS (PLHIV) with prevention messages and services. Population-based surveys in sub-Saharan Africa have identified HIV risk behaviors among PLHIV, yet data are limited regarding HIV risk behaviors of PLHIV in clinical care. This paper describes the baseline sociodemographic, HIV transmission risk behaviors, and clinical data of a study evaluating an HIV prevention intervention package for HIV care and treatment clinics in Africa. The study was a longitudinal group-randomized trial in 9 intervention clinics and 9 comparison clinics in Kenya, Namibia, and Tanzania (N = 3538). Baseline participants were mostly female, married, had less than a primary education, and were relatively recently diagnosed with HIV. Fifty-two percent of participants had a partner of negative or unknown status, 24% were not using condoms consistently, and 11% reported STI symptoms in the last 6 months. There were differences in demographic and HIV transmission risk variables by country, indicating the need to consider local context in designing studies and using caution when generalizing findings across African countries. Baseline data from this study indicate that participants were often engaging in HIV transmission risk behaviors, which supports the need for prevention with PLHIV (PwP). TRIAL REGISTRATION: ClinicalTrials.gov NCT01256463

Erratum to: Trachoma Prevalence After Discontinuation of Mass Azithromycin Distribution.

In "Trachoma Prevalence After Discontinuation of Mass Azithromycin Distribution [J Infect Dis. 2020 Feb 13, jiz691, https://doi.org/10.1093/infdis/jiz691]" by Godwin et al., the first sentence of the Results section includes a reference to "ITI database" that is incorrect and should read as "GET2020 Database". In addition, the authors note that major contributors to the GET2020 database include numerous Ministries of Health worldwide as well as the Global Trachoma Mapping Project (Solomon AW, Pavluck AL, Courtright P, et al. The Global Trachoma Mapping Project: Methodology of a 34-Country Population-Based Study. Ophthalmic Epidemiol 2015; 22(3):214 25). The authors regret the error

A Single-Blind randomized controlled trial to evaluate the effect of extended counseling on uptake of pre-antiretroviral care in eastern uganda

<p>Abstract</p> <p>Background</p> <p>Many newly screened people living with HIV (PLHIV) in Sub-Saharan Africa do not understand the importance of regular pre-antiretroviral (ARV) care because most of them have been counseled by staff who lack basic counseling skills. This results in low uptake of pre-ARV care and late treatment initiation in resource-poor settings. The effect of providing post-test counseling by staff equipped with basic counseling skills, combined with home visits by community support agents on uptake of pre-ARV care for newly diagnosed PLHIV was evaluated through a randomized intervention trial in Uganda.</p> <p>Methods</p> <p>An intervention trial was performed consisting of post-test counseling by trained counselors, combined with monthly home visits by community support agents for continued counseling to newly screened PLHIV in Iganga district, Uganda between July 2009 and June 2010, Participants (N = 400) from three public recruitment centres were randomized to receive either the intervention, or the standard care (the existing post-test counseling by ARV clinic staff who lack basic training in counseling skills), the control arm. The outcome measure was the proportion of newly screened and counseled PLHIV in either arm who had been to their nearest health center for clinical check-up in the subsequent three months +2 months. Treatment was randomly assigned using computer-generated random numbers. The statistical significance of differences between the two study arms was assessed using chi-square and t-tests for categorical and quantitative data respectively. Risk ratios and 95% confidence intervals were used to assess the effect of the intervention.</p> <p>Results</p> <p>Participants in the intervention arm were 80% more likely to accept (take up) pre-ARV care compared to those in the control arm (RR 1.8, 95% CI 1.4-2.1). No adverse events were reported.</p> <p>Conclusions</p> <p>Provision of post-test counseling by staff trained in basic counseling skills, combined with home visits by community support agents had a significant effect on uptake of pre-ARV care and appears to be a cost-effective way to increase the prerequisites for timely ARV initiation.</p> <p>Trial registration</p> <p>The trial was registered by Current Controlled Trials Ltd C/OBioMed Central Ltd as <a href="http://www.controlled-trials.com/ISRCTN94133652">ISRCTN94133652</a> and received financial support from Sida and logistical support from the European Commission.</p

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination 'as a public health problem' when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models' predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020

Quantitative analyses and modelling to support achievement of the 2020 goals for nine neglected tropical diseases

Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination ‘as a public health problem’ when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models’ predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from &lt;5% of those younger than 20 years to &gt;66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from &lt;1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds. Funding: UK Department for International Development, Wellcome Trust, Health Data Research UK, Medical Research Council, and National Institute for Health Research

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society\u27s responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak

SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort

Identifying the potential for Severe Acute Respiratory Syndrome : Coronavirus 2 (SARS-CoV-2) reinfection is crucial for understanding possible long-term epidemic dynamics. We analysed longitudinal PCR and serological testing data from a prospective cohort of 4,411 United States employees in 4 states between April 2020 and February 2021. We conducted a multivariable logistic regression investigating the association between baseline serological status and subsequent PCR test result in order to calculate an odds ratio for reinfection. We estimated an odds ratio for reinfection ranging from 0.14 (95% CI: 0.019 to 0.63) to 0.28 (95% CI: 0.05 to 1.1), implying that the presence of SARS-CoV-2 antibodies at baseline is associated with around 72% to 86% reduced odds of a subsequent PCR positive test based on our point estimates. This suggests that primary infection with SARS-CoV-2 provides protection against reinfection in the majority of individuals, at least over a 6-month time period. We also highlight 2 major sources of bias and uncertainty to be considered when estimating the relative risk of reinfection, confounders, and the choice of baseline time point and show how to account for both in reinfection analysis

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

Background: Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. Methods: We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. Results: All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. Conclusions: Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings