Alignment between first edition JEE, SPAR, and WHO Global Health Observatory data.

(DOCX)</p

Cost estimates for country-level pandemic preparedness across sources, reported in billions of 2021 USD.

McKinsey and IHR costing estimates are rounded to the nearest billion. Note that these results do not include estimates from WHO or McKinsey for capacity building at the regional or global level in addition to the country level; McKinsey findings estimate that "73% percent [of total costs] would take place at the country level"[3].</p

Comparison of rates of nausea side effects for prescription medications from an online patient community versus medication labels: an exploratory analysis

Abstract Background While medication labels are considered the authoritative resource for medication information, emerging research suggests that patient-generated health data (PGHD) are a valuable tool to understand the ways in which patients experience medications in real world settings. However, the relationship between these two data sources has not been closely examined. Methods To understand how rates of medication side effects compare between a source of PGHD and medication labels, the current study compares adverse drug reaction rates from FDA medication labels with those self-reported by patients from an online patient community, PatientsLikeMe (PLM). The linear association between medication label and PLM nausea rates was evaluated using Spearman correlation, with an associated 95% confidence interval calculated based on 10,000 bootstrap iterations. The reporting ratio of PLM nausea rates to medication label nausea rates was defined for all treatments with non-zero medication label nausea rates. Lognormality of the distribution of this reporting ratio was assessed based on a Kolmogorov-Smirnov test (α = 0.05). Results Nausea rates for 163 medications were compared between the two data sources. Overall rates ranged from 0 to 60% for medication labels and 0 to 36% for PLM data with median rates of 6.4 and 3.7%, respectively. In general, nausea rates reported by patients in the online community were lower than those found in medication labels. This inconsistency was attributed to a variety of factors, including differences in data collection mechanisms and product use factors. Conclusions Quantifiable and consistent differences exist between side effect rates reported on medication labels and those self-reported by patients based on real-world use. In general, self-reported rates of nausea associated with medication use were lower than those reported in medication labels. Although considered a definitive resource for medication information, this discrepancy demonstrates that medication labels may not comprehensively describe the patient experience. Results suggest that a combination of information from different sources may provide a more rounded and holistic view on medication safety and tolerability

Select cost drivers of total 5-year costs.

All costs reported in approximate 2021 USD and rounded to the nearest tenth of a billion.</p

Costs by World Health Organization Regional Office.

All costs are reported in approximate 2021 USD, and rounded to the nearest hundred million, as such, numbers reported may not sum precisely to total. Regional average (mean) SPAR data based on 2020 SPAR summary data reported by WHO. Per capita costs calculated based on total cost divided by estimated country population.</p

Overall and per capita costs by World Bank income group.

Per capita costs reflect the costs of investments over a period of five years. All costs reported in approximate 2021 USD, and rounded to the nearest hundred million, as such, numbers reported may not sum precisely to total.</p

Distribution of 5-year costs, by pillar.

Each cell corresponds to a costed line item and is scaled by cost and colored by pillar (e.g., prevent, detect, respond).</p

Mapping stakeholders and policies in response to deliberate biological events

Background: Recent infectious disease outbreaks have brought increased attention to the need to strengthen global capacity to prevent, detect, and respond to natural biological threats. However, deliberate biological events also represent a significant global threat, but have received relatively little attention. While the Biological Weapons Convention provides a foundation for the response to deliberate biological events, the political mechanisms to respond to and recover from such an event are poorly defined. Methods: We performed an analysis of the epidemiological timeline, the international policies triggered as a notional deliberate biological event unfolds, and the corresponding stakeholders and mandates assigned by each policy. Findings: The results of this analysis identify a significant gap in both policy and stakeholder mandates: there is no single policy nor stakeholder mandate for leading and coordinating response activities associated with a deliberate biological event. These results were visualized using an open source web-based tool published at https://dbe.talusanalytics.com. Interpretation: While there are organizations and stakeholders responsible for leading security or public health response, these roles are non-overlapping and are led by organizations not with limited interaction outside such events. The lack of mandates highlights a gap in the mechanisms available to coordinate response and a gap in guidance for managing the response. The results of the analysis corroborate anecdotal evidence from stakeholder meetings and highlight a critical need and gap in deliberate biological response policy

Understanding and comparing HIV-related law & policy environments: cross-national data and accountability for the global AIDS response

Law and policy differences help explain why, as HIV-related science has advanced swiftly, some countries have realised remarkable progress on AIDS while others see expanding epidemics. We describe the structure and findings of a new dataset and research platform, the HIV Policy Lab, which fills an important knowledge gap by measuring the HIV-related policy environment across 33 indicators and 194 countries over time, with online access and visualisation. Cross-national indicators can be critical tools in international governance—building social power to monitor state behaviour with the potential to change policy and improve domestic accountability. This new and evolving effort collects data about policy through review of legal documents, official government reports and systematic review of secondary sources. Alignment between national policy environments and global norms is demonstrated through comparison with international public health guidance and agreements. We demonstrate substantial variation in the content of law and policies between countries, regions and policy areas. Given progress in basic and implementation science, it would be tempting to believe most countries have adopted policies aligned with global norms, with a few outliers. Data show this is not the case. Globally, alignment is higher on clinical and treatment policies than on prevention, testing and structural policies. Policy-makers, researchers, civil society, finance agencies and others can use these data to better understand the policy environment within and across countries and support reform. Longitudinal analysis enables evaluation of the impact of laws and policies on HIV outcomes and research about the political drivers of policy choice

Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students.

Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to limitations in how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by targeting high school students from underrepresented backgrounds in AI, giving them a chance to learn about AI with a focus on biomedicine, and promoting diversity and inclusion. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus, students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at the final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There were also nominally significant increases in the students' knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater diversity in AI. This work can guide AI training programs aspiring to engage and educate students entirely online, and motivate people in AI to strive towards increasing diversity and inclusion in this field