Working Paper: Measuring Job Creation in Private Sector Development

The Donor Committee for Enterprise Development (DCED) Standard offers a best practice by outlining the key elements for practically and credibly estimating the results of Private Sector Development programmes, in a process which can be managed by programmes internally. It involves a few common impact indicators to ensure that donors will be able to add up their results across country programmes. The Standard is being piloted on a multi-agency basis; the DCED invites new programmes to join in adopting the approach

Correction for Johansson et al., An open challenge to advance probabilistic forecasting for dengue epidemics.

Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116. The authors note that the affiliation for Xavier Rodó should instead appear as Catalan Institution for Research and Advanced Studies (ICREA) and Climate and Health Program, Barcelona Institute for Global Health (ISGlobal). The corrected author and affiliation lines appear below. The online version has been corrected

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue

Towards a Holarctic synthesis of peatland testate amoeba ecology: Development of a new continental-scale palaeohydrological transfer function for North America and comparison to European data

Fossil testate amoeba assemblages have been used to reconstruct peatland palaeohydrology for more than two decades. While transfer function training sets are typically of local-to regional-scale in extent, combining those data to cover broad ecohydrological gradients, from the regional-to continental- and hemispheric-scales, is useful to assess if ecological optima of species vary geographically and therefore may have also varied over time. Continental-scale transfer functions can also maximise modern analogue quality without losing reconstructive skill, providing the opportunity to contextualise understanding of purely statistical outputs with greater insight into the biogeography of organisms. Here, we compiled, at moderate taxonomic resolution, a dataset of nearly 2000 modern surface peatland testate amoeba samples from 137 peatlands throughout North America. We developed transfer functions using four model types, tested them statistically and applied them to independent palaeoenvironmental data. By subdividing the dataset into eco-regions, we examined biogeographical patterns of hydrological optima and species distribution across North America. We combined our new dataset with data from Europe to create a combined transfer function. The performance of our North-American transfer function was equivalent to published models and reconstructions were comparable to those developed using regional training sets. The new model can therefore be used as an effective tool to reconstruct peatland palaeohydrology throughout the North American continent. Some eco-regions exhibited lower taxonomic diversity and some key indicator taxa had restricted ranges. However, these patterns occurred against a background of general cosmopolitanism, at the moderate taxonomic resolution used. Likely biogeographical patterns at higher taxonomic resolution therefore do not affect transfer function performance. Output from the combined North American and European model suggested that any geographical limit of scale beyond which further compilation of peatland testate amoeba data would not be valid has not yet been reached, therefore advocating the potential for a Holarctic synthesis of peatland testate amoeba data. Extending data synthesis to the tropics and the Southern Hemisphere would be more challenging due to higher regional endemism in those areas

Tracheostomy in the COVID-19 era: global and multidisciplinary guidance

Global health care is experiencing an unprecedented surge in the number of critically ill patients who require mechanical ventilation due to the COVID-19 pandemic. The requirement for relatively long periods of ventilation in those who survive means that many are considered for tracheostomy to free patients from ventilatory support and maximise scarce resources. COVID-19 provides unique challenges for tracheostomy care: health-care workers need to safely undertake tracheostomy procedures and manage patients afterwards, minimising risks of nosocomial transmission and compromises in the quality of care. Conflicting recommendations exist about case selection, the timing and performance of tracheostomy, and the subsequent management of patients. In response, we convened an international working group of individuals with relevant expertise in tracheostomy. We did a literature and internet search for reports of research pertaining to tracheostomy during the COVID-19 pandemic, supplemented by sources comprising statements and guidance on tracheostomy care. By synthesising early experiences from countries that have managed a surge in patient numbers, emerging virological data, and international, multidisciplinary expert opinion, we aim to provide consensus guidelines and recommendations on the conduct and management of tracheostomy during the COVID-19 pandemic

Comparing test-specific distress of susceptibility versus deterministic genetic testing for Alzheimer's disease

BACKGROUND: Genetic risk for Alzheimer’s disease (AD) may be conferred by the susceptibility polymorphism apolipoprotein E (APOE), where the ε4 allele increases the risk of developing late-onset Alzheimer’s disease but is not a definitive predictor of the disease, or by autosomal dominant mutations (e.g., the presenilins), which almost inevitably result in early-onset familial Alzheimer’s disease. The purpose of this study was to compare the psychological impact of using these two different types of genetic information to disclose genetic risk for AD to family members of affected patients. METHODS: Data were compared from two separate protocols. The Risk Evaluation and Education for Alzheimer’s Disease (REVEAL) Study is a randomized, multi-site clinical trial that evaluated the impact of susceptibility testing for Alzheimer’s disease with APOE in 101 adult children of Alzheimer’s disease patients. A separate study, conducted at the University of Washington, assessed the impact of deterministic genetic testing by disclosing presenilin-1, presenilin-2, or TAU genotype to 22 individuals at risk for familial Alzheimer’s disease or frontotemporal dementia. In both protocols, participants received genetic counseling and completed the Impact of Event Scale (IES), a measure of test-specific distress. Scores were analyzed at the time point closest to one year post-disclosure at which IES data were available. The role of genetic test result (positive vs. negative) and type of genetic testing (deterministic vs. susceptibility) in predicting log-transformed IES scores was assessed with linear regression, controlling for age, gender, and time from disclosure. RESULTS: Subjects from the REVEAL Study who learned that they were positive for the susceptibility gene APOE ε4+ experienced similar, low levels of test-specific distress compared to those who received positive results of deterministic testing in the University of Washington study (p= 0.78). APOE ε4+ individuals in the susceptibility protocol experienced more test-specific distress than those who tested ε4− in the same study (p= 0.04); however, among those receiving deterministic test disclosure, the subjects who received positive results did not experience significantly higher levels of distress when compared to those who received negative results (p= 0.88). CONCLUSIONS: The findings of this preliminary study, with limited sample size, suggest that the test-related distress experienced by those receiving positive results for a deterministic mutation is similar to the distress experienced by those receiving positive results from genetic susceptibility testing, and that the majority of participants receiving genotype disclosure do not experience clinically significant distress as indicated by IES scores one year after learning of their test results

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue