Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response.

During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.

A synthesis of evidence for policy from behavioural science during COVID-19.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

Digital measurement of SARS-CoV-2 transmission risk from 7 million contacts.

How likely is it to become infected by SARS-CoV-2 after being exposed? Almost everyone wondered about this question during the COVID-19 pandemic. Contact-tracing apps1,2 recorded measurements of proximity3 and duration between nearby smartphones. Contacts-individuals exposed to confirmed cases-were notified according to public health policies such as the 2 m, 15 min guideline4,5, despite limited evidence supporting this threshold. Here we analysed 7 million contacts notified by the National Health Service COVID-19 app6,7 in England and Wales to infer how app measurements translated to actual transmissions. Empirical metrics and statistical modelling showed a strong relation between app-computed risk scores and actual transmission probability. Longer exposures at greater distances had risk similar to that of shorter exposures at closer distances. The probability of transmission confirmed by a reported positive test increased initially linearly with duration of exposure (1.1% per hour) and continued increasing over several days. Whereas most exposures were short (median 0.7 h, interquartile range 0.4-1.6), transmissions typically resulted from exposures lasting between 1 h and several days (median 6 h, interquartile range 1.4-28). Households accounted for about 6% of contacts but 40% of transmissions. With sufficient preparation, privacy-preserving yet precise analyses of risk that would inform public health measures, based on digital contact tracing, could be performed within weeks of the emergence of a new pathogen.

Historical narratives about the COVID-19 pandemic are motivationally biased.

How people recall the SARS-CoV-2 pandemic is likely to prove crucial in future societal debates on pandemic preparedness and appropriate political action. Beyond simple forgetting, previous research suggests that recall may be distorted by strong motivations and anchoring perceptions on the current situation1-6. Here, using 4 studies across 11 countries (total n = 10,776), we show that recall of perceived risk, trust in institutions and protective behaviours depended strongly on current evaluations. Although both vaccinated and unvaccinated individuals were affected by this bias, people who identified strongly with their vaccination status-whether vaccinated or unvaccinated-tended to exhibit greater and, notably, opposite distortions of recall. Biased recall was not reduced by providing information about common recall errors or small monetary incentives for accurate recall, but was partially reduced by high incentives. Thus, it seems that motivation and identity influence the direction in which the recall of the past is distorted. Biased recall was further related to the evaluation of past political action and future behavioural intent, including adhering to regulations during a future pandemic or punishing politicians and scientists. Together, the findings indicate that historical narratives about the COVID-19 pandemic are motivationally biased, sustain societal polarization and affect preparation for future pandemics. Consequently, future measures must look beyond immediate public-health implications to the longer-term consequences for societal cohesion and trust.

The effects of cash transfers on adult and child mortality in low- and middle-income countries.

Poverty is an important social determinant of health that is associated with increased risk of death1-5. Cash transfer programmes provide non-contributory monetary transfers to individuals or households, with or without behavioural conditions such as children's school attendance6,7. Over recent decades, cash transfer programmes have emerged as central components of poverty reduction strategies of many governments in low- and middle-income countries6,7. The effects of these programmes on adult and child mortality rates remains an important gap in the literature, however, with existing evidence limited to a few specific conditional cash transfer programmes, primarily in Latin America8-14. Here we evaluated the effects of large-scale, government-led cash transfer programmes on all-cause adult and child mortality using individual-level longitudinal mortality datasets from many low- and middle-income countries. We found that cash transfer programmes were associated with significant reductions in mortality among children under five years of age and women. Secondary heterogeneity analyses suggested similar effects for conditional and unconditional programmes, and larger effects for programmes that covered a larger share of the population and provided larger transfer amounts, and in countries with lower health expenditures, lower baseline life expectancy, and higher perceived regulatory quality. Our findings support the use of anti-poverty programmes such as cash transfers, which many countries have introduced or expanded during the COVID-19 pandemic, to improve population health.

Tendência de mortalidade por doença de Alzheimer no Brasil, 2000 a 2019 / Tendencias de mortalidad por enfermedad de Alzheimer en Brasil, 2000 a 2019 / Trends in mortality from Alzheimer's disease in Brazil, 2000-2019

Objetivo: analisar as tendências das taxas de mortalidade por doença de Alzheimer no Brasil e nas suas macrorregiões, por faixa etária e sexo, no período de 2000 a 2019. Métodos: estudo de séries temporais sobre mortalidade por doença de Alzheimer no Brasil e suas macrorregiões por faixa etária e sexo; os dados foram extraídos do Sistema de Informação sobre Mortalidade (SIM); o modelo de Prais-Winsten foi utilizado para análise das tendências. Resultados: houve 211.658 óbitos no período analisado, com tendência crescente na mortalidade por doença de Alzheimer no país em idosos de 60-69 anos (VPA = 4,3; IC95% 2,9;5,9), 70-79 anos (VPA = 8,1; IC95% 4,8;11,5) e ≥ 80 anos (VPA = 11,3; IC95% 8,1;14,6), e em todas as macrorregiões, faixas etárias e sexo. Conclusão: o Brasil e todas as suas macrorregiões apresentaram tendência crescente nas taxas de mortalidade por doença de Alzheimer, seguindo a tendência mundial.

Objective: to analyze trends in mortality rates due to Alzheimer's disease in Brazil and its macro-regions by age and sex, from 2000 to 2019. Methods: this was a time-series study on mortality from Alzheimer's disease in Brazil and its macro-regions by age and sex; data were obtained from the Mortality Information System; a Prais-Winsten model was used to analyze trends. Results: there were 211,658 deaths in the period analyzed, with an increasing trend in Alzheimer's disease mortality in Brazil in elderly people aged 60-69 years (APC = 4.3; 95%CI 2.9;5.9), 70-79 years (APC = 8.1; 95%CI 4.8;11.5) and ≥ 80 years (APC = 11.3; 95%CI 8.1;14.6) and in all macro-regions, age groups and sexes. Conclusion: Brazil and all its macro-regions showed a rising trend in Alzheimer's disease mortality rates, following the global trend.

Objetivo: analizar las tendencias en las tasas de mortalidad por enfermedad de Alzheimer en Brasil y sus macrorregiones por grupo de edad y sexo, de 2000 a 2019. Métodos: estudio de series temporales de mortalidad por enfermedad de Alzheimer en Brasil y sus macrorregiones por grupo de edad y sexo; los datos se obtuvieron del Sistema de Información sobre Mortalidad del Ministerio de Salud de Brasil; se utilizó el modelo Prais-Winsten para analizar tendencias. Resultados: hubo 211.658 óbitos, con tendencia creciente en la mortalidad por enfermedad de Alzheimer en el país, en adultos mayores de 60-69 años (VPA = 4,3; IC95% 2,9;5,9), 70-79 años (VPA = 8,1; IC95%: 4,8;11,5) y ≥ 80 años (VPA = 11,3; IC95% 8,1;14,6) y en todas las macrorregiones, grupos de edad y sexo. Conclusión: Brasil y todas sus macrorregiones mostraron una tendencia creciente en las tasas de mortalidad por enfermedad de Alzheimer siguiendo la tendencia mundial.

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Nonfatal opioid overdoses before and after Covid-19: Regional variation in rates of change.

BACKGROUND: The Covid-19 pandemic and its accompanying public-health orders (PHOs) have led to (potentially countervailing) changes in various risk factors for overdose. To assess whether the net effects of these factors varied geographically, we examined regional variation in the impact of the PHOs on counts of nonfatal overdoses, which have received less attention than fatal overdoses, despite their public health significance. METHODS: Data were collected from the Overdose Detection Mapping Application Program (ODMAP), which recorded suspected overdoses between July 1, 2018 and October 25, 2020. We used segmented regression models to assess the impact of PHOs on nonfatal-overdose trends in Washington DC and the five geographical regions of Maryland, using a historical control time series to adjust for normative changes in overdoses that occurred around mid-March (when the PHOs were issued). RESULTS: The mean level change in nonfatal opioid overdoses immediately after mid-March was not reliably different in the Covid-19 year versus the preceding control time series for any region. However, the rate of increase in nonfatal overdose was steeper after mid-March in the Covid-19 year versus the preceding year for Maryland as a whole (B = 2.36; 95% CI, 0.65 to 4.06; p = .007) and for certain subregions. No differences were observed for Washington DC. CONCLUSIONS: The pandemic and its accompanying PHOs were associated with steeper increases in nonfatal opioid overdoses in most but not all of the regions we assessed, with a net effect that was deleterious for the Maryland region as a whole.