Public Health Policy Decisions Research Articles

A Modelling Framework for Evidence-Based Public Health Policy Making.

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision-making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020.

Stroke and Risk of Mental Disorders Compared With Matched General Population and Myocardial Infarction Comparators.

Accurate estimates of risks of poststroke outcomes from large population-based studies can provide a basis for public health policy decisions. We examined the absolute and relative risks of a spectrum of incident mental disorders following ischemic stroke and intracerebral hemorrhage. During 2004 to 2018, we used Danish registries to identify patients (≥18 years and with no hospital history of mental disorders), with a first-time ischemic stroke (n=76 767) or intracerebral hemorrhage (n=9344), as well as age-,sex-, and calendar year-matched general population (n=464 840) and myocardial infarction (n=92 968) comparators. We computed risk differences, considering death a competing event, and hazard ratios adjusted for income, occupation, education, and history of cardiovascular and noncardiovascular comorbidity. Compared with the general population, following ischemic stroke, the 1-year risk difference was 7.3% (95% CI, 7.0-7.5) for mood disorders (driven by depression), 1.4% (95% CI, 1.3-1.5) for organic brain disorders (driven by dementia and delirium), 0.8% (95% CI, 0.7-0.8) for substance abuse disorders (driven by alcohol and tobacco abuse), and 0.5% (95% CI, 0.4-0.5) for neurotic disorders (driven by anxiety and stress disorders). For suicide, risk differences were near null. Hazard ratios were particularly elevated in the first year of follow-up, ranging from a 2- to a 4-fold increased hazard, decreasing thereafter. Compared with myocardial infarction patients, the 1-year risk difference was 4.9% (95% CI, 4.6 to 5.3) for mood disorders, 1.0% (95% CI, 0.8 to 1.1) for organic brain disorders, 0.1% (95% CI, 0.0 to 0.2) for substance abuse disorders, but -0.2% (95% CI, -0.2 to -0.1) for neurotic disorders. Hazard ratios during the first year of follow-up were elevated 1.1- to 1.8-fold for mood, organic brain, and neurotic disorders, while decreased 0.8-fold for neurotic disorders. The considerably greater risks of mental disorders following a stroke, particularly mood disorders, underline the importance of mental health evaluation after stroke.

Spatiotemporal heterogeneity and determinants of canine rabies evidence at Local Government Area Level in Nigeria: Implications for rabies prevention and control

Canine rabies poses a significant risk to humans and animals in Nigeria. However, the lack of reliable tools to evaluate the performance of existing canine rabies control programs to inform public health policy decisions poses a severe obstacle. We obtained canine rabies surveillance data from the National Veterinary Research Institute (NVRI) and supplemented these data with rabies diagnoses reported in the published studies from Nigeria. To uncover contextual factors (i.e., environmental and sociodemographic) associated with canine rabies evidence at the Local Government Area (LGA) level, we classified LGAs in Nigeria into four categories based on evidence availability (i.e., LGAs with NVRI data or published studies, both, or no evidence). We described the geographical and temporal variation in coverage. We fitted a multinomial regression model to examine the association between LGA level canine rabies evidence and potential sociodemographic and ecological determinants of canine rabies evidence. The effective annual testing during the 19 years was less than one dog/100,000 Nigerian resident-year. Our results showed that 58% of Nigerian LGAs (450/774) had not been targeted by the existing national rabies surveillance or studies on rabies, including ten states capitals with high human populations. While 16% (122/774) of Nigerian LGAs concentrated in Taraba, Adamawa, and Abia had canine rabies evidence from published studies, none of these LGAs was represented in the NVRI rabies surveillance data. We also observed an increasing trend in rabies evidence over time towards the eastern part of Nigeria. Our multinomial regression model indicated that education level, poverty, population density, land use and temperature were significantly associated with canine rabies evidence at the LGA level. This study underscores the value of combining canine rabies evidence from different sources to better understand the current disease situation for targeted intervention.

Protection Duration of COVID-19 Vaccines: Waning Effectiveness and Future Perspective.

The coronavirus disease 2019 (COVID-19) vaccines have very successfully decreased the disease risk as we know; some key information remains unknown due to the short development history and the lack of long-term follow-up studies in vaccinated populations. One of the unanswered issues is the protection duration conferred after COVID-19 vaccination, which appears to play a pivotal role in the future impact of pathogens and is critical to inform the public health response and policy decisions. Here, we review current information on the long-term effectiveness of different COVID-19 vaccines, persistence of immunogenicity, and gaps in knowledge. Meanwhile, we also discuss the influencing factors and future study prospects on this topic.

Cascading effects of COVID-19 on population mobility and air quality: An exploration including place characteristics using geovisualization.

This study hypothesizes that public health responses to coronavirus disease 2019 (COVID-19), including a mandated restriction of activity (commonly called a 'lockdown') resulted in reduced transportation activities and changes in air quality in Texas, USA. This presented a natural experiment where population mobility and air quality before and after the lockdown could be compared. Changes in mobility were measured by SafeGraph mobility data (from opt-in smart phone applications that transmit location data) and air quality changes were based on NO2 concentrations measured by the European Space Agency's Sentinel-5 Precursor satellite (from the TROPOspheric Monitoring Instrument). The changes in population mobility and NO2 concentration between mid-March 2020 (lockdown initiated) and the end of 2020, as compared to the same time window in 2019, were the basis of exploring the lockdown hypothesis. Additionally, numerous socio-economic (place based) indicators were hypothesized to follow public health vulnerability assumptions based on COVID- 19 incidence patterns. This hypothesis was subjected to geovisualization techniques in order to find potential patterns and insights into the complex combinations of these place-based data. Our results suggest that simultaneously visualizing COVID-19, mobility, air quality and socio-economic data yields insights in underlying spatial processes related to public health policy decisions. The hypothesis that the lockdown resulted in reduced mobility and NO2 concentrations was found partially correct - this trend was observed in highly urbanized areas, but not in less populated areas. Data related public health vulnerability assumptions (e.g. a region's age, poverty, education, etc.) were agreed with in part, but disagreed with in part.

Inference of the SARS-CoV-2 generation time using UK household data.

The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March to November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2-7.0 days) and a similar standard deviation (4.8 days, 4.0-6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.

Community seroprevalence and risk factors for SARS-CoV-2 infection in different subpopulations in Vellore, India, and their implications for future prevention

ObjectivesThe aim of this study was to inform public health policy decisions through the assessment of IgG antibody seroprevalence in the population and the risk factors for SARS-CoV-2 infection. MethodsThe seroprevalence of IgG antibodies among different subpopulations at the end of the first and second waves of the pandemic was estimated. Various risk factors associated with seropositivity, including sociodemography, IgG antibodies against endemic human coronavirus, and vaccination status, were also assessed. ResultsFor all 2433 consenting participants, the overall estimated seroprevalences at the end of first and second waves were 28.5% (95% CI 22.3–33.7%) and 71.5% (95% CI 62.8–80.5%), respectively. The accrual of IgG positivity was heterogeneous, with the highest seroprevalences found in urban slum populations (75.1%). Vaccine uptake varied among the subpopulations, with low rates (< 10%) among rural and urban slum residents. The majority of seropositive individuals (75%) were asymptomatic. Residence in urban slums (OR 2.02, 95% CI 1.57–2.6; p < 0.001), middle socioeconomic status (OR 1.77, 95% CI 1.17–2.67; p = 0.007), presence of diabetes (OR 1.721, 95% CI 1.148–2.581; p = 0.009), and hypertension (OR 1.75, 95% CI 1.16–2.64; p = 0.008) were associated with seropositivity in multivariable analyses. ConclusionAlthough considerable population immunity has been reached, with more than two-thirds seropositive, improved vaccination strategies among unreached subpopulations and high-risk individuals are suggested for better preparedness in future.

Public health spending, primary care, and perceived risk promoted vaccination against H1N1

The purpose of this study is to examine factors affecting the intent to vaccinate during the 2009 H1N1 pandemic and to leverage the results to inform public health policy decisions aimed at increasing vaccine uptake during the COVID-19 pandemic. Using the National 2009 H1N1 Flu Survey data and state-level administrative data, we employ logistic regression and mediation models to estimate the association between vaccine uptake and state level public health spending, political ideology, and H1N1 case and death rates as well as a set of individual and household characteristics. We find that higher public health spending can significantly increase the intent to vaccinate, mainly through raising concerns about the pandemic and promoting vaccine relevant doctor patient interactions. We conclude that physicians, especially primary care physicians, should play more important roles in the ongoing vaccination efforts against the COVID-19 virus.

Global Research on Coronaviruses: Metadata-Based Analysis for Public Health Policies.

BackgroundWithin the context of the COVID-19 pandemic, this paper suggests a data science strategy for analyzing global research on coronaviruses. The application of reproducible research principles founded on text-as-data information, open science, the dissemination of scientific data, and easy access to scientific production may aid public health in the fight against the virus.ObjectiveThe primary goal of this paper was to use global research on coronaviruses to identify critical elements that can help inform public health policy decisions. We present a data science framework to assist policy makers in implementing cutting-edge data science techniques for the purpose of developing evidence-based public health policies.MethodsWe used the EpiBibR (epidemiology-based bibliography for R) package to gain access to coronavirus research documents worldwide (N=121,231) and their associated metadata. To analyze these data, we first employed a theoretical framework to group the findings into three categories: conceptual, intellectual, and social. Second, we mapped the results of our analysis in these three dimensions using machine learning techniques (ie, natural language processing) and social network analysis.ResultsOur findings, firstly, were methodological in nature. They demonstrated the potential for the proposed data science framework to be applied to public health policies. Additionally, our findings indicated that the United States and China were the primary contributors to global coronavirus research during the study period. They also demonstrated that India and Europe were significant contributors, albeit in a secondary position. University collaborations in this domain were strong between the United States, Canada, and the United Kingdom, confirming the country-level findings.ConclusionsOur findings argue for a data-driven approach to public health policy, particularly when efficient and relevant research is required. Text mining techniques can assist policy makers in calculating evidence-based indices and informing their decision-making process regarding specific actions necessary for effective health responses.

Genomic Epidemiology Reveals Multiple Introductions of Severe Acute Respiratory Syndrome Coronavirus 2 in Niigata City, Japan, Between February and May 2020.

The coronavirus disease 2019 (COVID-19) has caused a serious disease burden and poses a tremendous public health challenge worldwide. Here, we report a comprehensive epidemiological and genomic analysis of SARS-CoV-2 from 63 patients in Niigata City, a medium-sized Japanese city, during the early phase of the pandemic, between February and May 2020. Among the 63 patients, 32 (51%) were female, with a mean (±standard deviation) age of 47.9 ± 22.3 years. Fever (65%, 41/63), malaise (51%, 32/63), and cough (35%, 22/63) were the most common clinical symptoms. The median Ct value after the onset of symptoms lowered within 9 days at 20.9 cycles (interquartile range, 17–26 cycles), but after 10 days, the median Ct value exceeded 30 cycles (p < 0.001). Of the 63 cases, 27 were distributed in the first epidemic wave and 33 in the second, and between the two waves, three cases from abroad were identified. The first wave was epidemiologically characterized by a single cluster related to indoor sports activity spread in closed settings, which included mixing indoors with families, relatives, and colleagues. The second wave showed more epidemiologically diversified events, with most index cases not related to each other. Almost all secondary cases were infected by droplets or aerosols from closed indoor settings, but at least two cases in the first wave were suspected to be contact infections. Results of the genomic analysis identified two possible clusters in Niigata City, the first of which was attributed to clade S (19B by Nexstrain clade) with a monophyletic group derived from the Wuhan prototype strain but that of the second wave was polyphyletic suggesting multiple introductions, and the clade was changed to GR (20B), which mainly spread in Europe in early 2020. These findings depict characteristics of SARS-CoV-2 transmission in the early stages in local community settings during February to May 2020 in Japan, and this integrated approach of epidemiological and genomic analysis may provide valuable information for public health policy decision-making for successful containment of chains of infection.

Strong Association of Angiotensin Converting Enzyme-2 Gene Insertion/Deletion Polymorphism with Susceptibility to SARS-CoV-2, Hypertension, Coronary Artery Disease and COVID-19 Disease Mortality.

Background: The ongoing outbreak of SARS-CoV-2 represents a significant challenge to international health. Several reports have highlighted the importance of ACE2 on the pathogenesis of COVID-19. The spike protein of SARS-CoV-2 efficiently binds to the angiotensin-converting enzyme 2 (ACE2) receptors and facilitates virus entry into the host cell. In the present study, we hypothesize that a functional insertion/deletion polymorphism-rs4646994 I/D and rs4240157 T > C in the ACE gene could be associated with SARS-CoV-2 infection and mortality. Methodology: This study included 117 consecutive COVID-19 patients and 150 age matched healthy controls (ACE2-rs4646994 I/D) and 100 age matched healthy controls with ACE2 rs4240157 T > C. We used Mutation specific PCR (MSP) for ACE2-rs4646994 I/D genotyping and amplification refractory mutation system (ARMS-PCR) for ACE2 rs4240157 T > C genotyping. Results: Results indicated that there were significant differences in the genotype distributions of ACE2-rs4646994 I/D polymorphisms (p < 0.030) and ACE2 rs4240157 T > C between COVID-19 patients and controls (p-values < 0.05). Higher frequency of DD genotype (48.71%) and D allele (0.67) was reported in COVID-19 patients than controls. Our results showed that the ACE2-DD genotype was strongly associated with increased COVID-19 severity (OR 2.37 (95%) CI = (1.19–4.70), RR = 1.39 (1.09–1.77), p < 0.013) and also a strong association was seen with ACE2-ID genotype with COVID-19 severity (OR 2.20 (95%) CI = (1.08–4.46), p < 0.020) in the codominant model. In allelic comparison, the D allele was strongly associated with COVID-19 severity (OR 1.58 (95% CI) (1.11–2.27), RR 1.21 (1.05–1.41) p < 0.010). A significant correlation of ACE2-I/D genotypes was reported with Age (p < 0.035), T2D (p < 0.0013), hypertension (p < 0.0031) and coronary artery disease (p < 0.0001). Our results indicated ACE2-DD genotype was strongly associated with increased COVID-19 mortality (OR 8.25 (95%) CI = (2.40 to 28.34), p < 0.008) and also ACE2-DD + DI genotype was strongly associated with increased COVID-19 mortality with OR 4.74 (95%) CI = (1.5214 to 14.7915), p < 0.007. A significant correlation was reported between COVID-19 patients and age matched controls (p < 0.0007). Higher frequency of heterozygosity TC (40%) followed by ACE2-CC genotype (24.78%) was reported among COVID-19 patients. Using multivariate analysis, ACE2–CT genotype was strong associated with SARS-CoV-2 severity with an OR 2.18 (95% CI) (1.92–3.99), p < 0.010 and also ACE2–CC genotype was linked with COVID-19 severity with an OR 2.66 (95% CI) (1.53–4.62), p < 0.005. A significant correlation of ACE2-T > C genotypes was reported with gender (p < 0.04), T2D (p < 0.035). ACE2-CC genotype was strongly associated with increased COVID-19 mortality OR 3.66 (95%) CI = (1.34 to 9.97), p < 0.011 and also ACE2-C allele was associated with COVID-19 mortality OR 2, 01 (1.1761–3.45), p < 0.010. Conclusions: It is concluded that ACE-DD genotype and D allele was strongly associated with increased COVID-19 patient severity. In addition, ACE I/D polymorphism were strongly associated with advanced age, diabetes and ischemic heart disease in COVID-19 patients whereas ACE-II genotype was a protective factor against the development of severe COVID-19. ACE2-DD genotype was strongly associated with increased COVID-19 mortality. Additionally, ACE2–CC and CT genotypes were strongly associated with COVID-19 severity. Therefore, our study might be useful for identifying the susceptible population groups for targeted interventions and for making relevant public health policy decisions.

2.H. Workshop: Role of Health impact assessment in the policy-making process for the control of COVID-19 pandemic

Abstract The COVID-19 pandemic represents one of the most challenging health emergencies faced by humanity in modern history, with global effects on the economy and social well-being of people. This outbreak has abruptly brought public health back to the top of the policy agenda, making even more evident the need for adopting the comprehensive social determinants of health (SDOH) framework in designing effective mitigation and prevention measures. The novelty of the COVID-19 virus and the associated uncertainties about its health consequences and spread led to a massive generation of information from different sources, in some cases conflicting and contradictory. Conducting a systematic analysis and synthesis of existence scientific-evidence, considering all SDOH potentially affected, is crucial for supporting proportionated policy-decisions that reverse the spread of the virus and support health systems across Europe and worldwide. Health Impact Assessment (HIA) aims precisely at supporting the decision-making process by providing information and scientific evidence on the positive and negative effects that any new proposal may have on health and health equity. Its prospective nature also allows the introduction of corrective measures as an ongoing learning process, managing the estimated impacts and optimizing the health results of each proposal. HIA was launched as a methodology/tool by the World Health Organization (WHO) in 1999 and adopted by the EU in 2006 for supporting the development of healthier policies, projects and programs, related to both health and non-health sectors. However, its implementation in Europe is uneven across countries. The COVID-19 outbreak provides a good scenario for analysing the extent to which decision-makers are basing their decisions on the best available scientific evidence, and on the benefits of using the HIA approach. The present workshop will analyse and debate several experiences of using HIA as a supporting tool in the formulation of some of the most conflictive measures adopted by European governments related to the COVID-19 pandemic, such as ‘Lockdowns', ‘Staying at Home' and social distancing policies and requirements to work from home. Some of the questions that we intend to address in this workshop are: To which extent have decisions for managing the COVID-19 pandemic been based on the best scientific evidence available? What are the advantages of adopting an HIA approach in public health policy decisions? Are science and public health experts providing quick, synthetic and useful information for decision-making in the special circumstances of the current pandemic? Key messages COVID-19 pandemic reinforce the need for policy-makers based their decisions on best available scientific evidence through systematic processes such as HIA. Mitigation measures for the control of COVID-19 should go beyond healthcare assistance, making a more extensive implementation of HIA and a comprehensive approach to health.

Comparison of all-cause mortality in England with Europe and the USA: January 2020 to February 2021

BackgroundAssessing mortality during the COVID-19 pandemic is vital for informing public health strategies and policy decision making. All-cause excess mortality provides an objective measure of the impact of the pandemic including both the direct and indirect effects. Our study considers the burden of mortality in the UK, Europe and the USA. We examine variation between countries, by age and sex. We explore the extent to which this variation is associated with COVID-19 case rates and other population characteristics.MethodsThe study is a secondary analysis of routine administrative population and mortality data. Weekly death occurrences and population estimates were obtained from Eurostat and national statistical agencies. Contextual information on COVID-19 case rates, population-level risk factors and healthcare were obtained from various open-source databases. Weekly age-standardised mortality rates (ASMRs) were calculated and presented relative to a baseline average from the preceding 5-year period. Relative cumulative (rc) ASMRs were then calculated to provide a comparable assessment of excess mortality at a point in time.ResultsPreliminary results show that, by end of the analysis period, England had an overall rcASMR of 10.09%. Higher excess mortality was identified for some countries (eg USA 14.58%) and lower - even below average mortality - for others (eg Norway -6.8%). Under 65 rcASMR showed substantial variation between countries. Cumulative COVID-19 case rates showed a moderate effect size (R2 = 0.51) when used to explain the proportion of variation observed between rcASMRs. Other population factors showed a smaller effect.ConclusionsThe burden of mortality experienced between countries and populations over the COVID-19 pandemic period has shown significant variation. Factors which may have contributed to the position of some countries should be further explored in order to inform ongoing management of Covid-19 and future pandemic events.Key messages Significant variation in all-cause excess mortality has been identified across the COVID-19 pandemic period between nations and particularly in younger age groups.COVID-19 case rates are associated with relative cumulative all-cause excess mortality among the nations assessed.

6.B. Workshop: Fostering population health research on COVID-19 to support policy making

The COVID-19 pandemic demonstrated the urgent need for a structured European mechanism for COVID-19 exchange to organize and share accurate information between countries, especially in the area of population health, to inform decision-makers, researchers and the public. Within PHIRI (Population Health Information Research Infrastructure) the best available evidence for research on health and well-being of populations as impacted by the COVID-19 pandemic is generated and facilitated by a research infrastructure on population health. It supports research across Europe through the identification, access, assessment and reuse of population health and non-health data to underpin public health policy decisions. PHIRI lays the foundation for ultimately developing a distributed infrastructure on population health (DIPoH). In order to foster population health research on COVID-19, the access to and use of COVID-19 relevant population health information and health data should be facilitated, together with services and tools necessary for researchers to link different data sources and to use Pan-European data in a GDPR compliant, federated way. Furthermore, to tackle health information inequalities, capacity building is of eminent importance. Finally, a way to facilitate swift responses to research and policy questions that are raised in countries is needed to tackle the COVID-19 pandemic. In this workshop, the tools and approaches gained within PHIRI are presented and showcased. The workshop will begin with a short presentation on the purpose of this research infrastructure and the prospective distributed infrastructure on population health. This will be followed by four presentations that highlight key activities in PHIRI: The first presentation showcases the Health Information Portal, the one-stop-shop facilitating access to data sources, population health studies, training material and courses, considering ethical and legal aspects. The second presentation shows the technological substrate for the development of a federated research infrastructure. The third presentation demonstrates the European School on Health Information, that aims to contribute to increasing knowledge on availability and use of standardized Health Information methods to common practices in EU countries. The final presentation informs on the Rapid Exchange Forum, establishing a sustainable infrastructure to support rapid exchange between actors from competent authorities, their advisors, researchers and stakeholders in the joint efforts to handle the COVID-19 pandemic. Knowledge and expertise developed across Europe is shared in this workshop, supporting countries to address the COVID-19 crisis now and in its aftermath. Exchange with the audience will facilitate knowledge and opinion exchange through an interactive voting poll during the session. A poll will offer the delegates to share, in real time, their perspectives on selected questions based on the four presentations.Key messages This workshop will leverage the tools and methodologies that are developed in PHIRI to foster population health research across Europe to underpin public health policy decisions.The support of exchange of knowledge and expertise across Europe is key, and facilitated by capacity building on data management and research methodologies; and the rapid exchange of urgent questions.

3.B. Workshop: Monitoring the wider impacts of COVID-19 on population health

COVID-19 has clearly shown that a structured European mechanism for COVID-19 exchange to organize and share accurate information between countries is urgently needed, especially in the area of population health, to inform decision-makers, researchers and the public. The implications of the COVID-19 pandemic reaches far beyond the immediate mortality and morbidity aspects caused by the virus directly. There are many indirect effects of the outbreak that affect health through various pathways. This includes secondary consequences of COVID-19 on long-term health and wellbeing, including consequences due to delayed prevention, diagnosis, and medical treatment. Within the Population Health Information Research Infrastructure (PHIRI), best available evidence for research on health and well-being of populations as impacted by COVID-19 is generated and facilitated by a research infrastructure on population health. It supports research across Europe through the identification, access, assessment and reuse of population health and non-health data to underpin public health policy decisions. PHIRI is a practical use case and lays the foundation for ultimately developing a Distributed Infrastructure on Population Health (DIPoH). The joint workshop aims to raise the awareness and ensure a better understanding of the complexity of the wider effects of COVID-19 on population health across Europe. To provide insights of how the wider impacts of the COVID-19 pandemic could be assessed, tools and methodological approaches gained within PHIRI and WHO are presented. Knowledge and expertise developed across Europe is shared in this workshop which is key to supported countries to address the COVID-19 crisis. The workshop will begin with a presentation on monitoring the wider effects of COVID-19 on population health as set out by the WHO. This will be followed, after a short introduction on PHIRI, by three presentations which highlight key activities of assessing the wider impact of COVID-19 by PHIRI. The first on research methodologies evaluated across Europe to assess the direct and indirect impacts of COVID-19 on population wellbeing, morbidity and mortality across European countries. The second at COVID-19 impacts in specific subgroups and risk settings by conducting research through four use cases of immediate relevance. This facilitates research by making scalable, reproducible methods available within PHIRI. Finally, the third on insights in possible future health impacts of the COVID-19 outbreak by foresight and modelling scenarios for national situations as well as the provision of research capacity to strengthen the evidence-informed policy decisions on direct/indirect effects of COVID-19 will be presented. Exchange with the audience will facilitate knowledge and opinion exchange through an interactive Mentimeter poll during the session, to offer the delegates to share, in real time, their perspectives on selected questions based on the four presentations.Key messages The main outputs will support European countries to develop tools and methodologies for measuring the wider impacts of COVID-19 on population health within and across countries.The results will support the exchange of knowledge and expertise across Europe by facilitating insights in the assessment of direct and indirect effects of COVID-19 on population health.

Characteristics of Online Crowdfunding Campaigns for Urological Cancers in the United States.

We examined the characteristics and financial outcomes of online crowdfunding campaigns for patients with major urological cancers in the U.S. This cross-sectional study analyzed publicly available data from GoFundMe, the largest online medical crowdfunding service, via automated web scraping. Online campaigns from 2010 to 2018 with the following primary cancer types were included: kidney, prostate, bladder and testicular. Financial outcomes were compared using Kruskal-Wallis and Wilcoxon rank-sum tests. Multivariable analyses were utilized to identify predictors of campaign financial outcomes. Kidney cancers were the most frequent online campaign type (478), followed by prostate (379), bladder (202) and testicular (175) malignancies. Urological cancer campaign recipients frequently requested funding for medical expenses (71%) during active treatment (57%). After adjustment, testicular cancer and children's cancer campaigns generated more donations than other urological and adult cancer campaigns (p <0.05). Family and friend-authored campaigns generated more donations and average donation amounts than self-authored campaigns (p <0.05). Campaign narratives focused on disheartening circumstances received fewer donations than narratives focused on the recipient's high moral character or contributions to society (p <0.05), and unclear narratives received the smallest donation amounts (p <0.05). Urological cancer crowdfunding in the U.S. is primarily used to finance uncovered costs associated with medical care during active treatment. Crowdfunding financial outcomes are likely related to the campaign recipient's age, malignancy type, social network and primary appeal of the narrative. Urologists should be aware of trends in medical crowdfunding in order to better understand the financial burden this patient population faces.

A climate-dependent spatial epidemiological model for the transmission risk of West Nile virus at local scale

In this study, initial elements of a modelling framework aimed to become a spatial forecasting model for the transmission risk of West Nile virus (WNV) are presented. The model describes the dynamics of a WNV epidemic in population health states of mosquitoes, birds and humans and was applied to the case of Greece for the period 2010–2019. Calibration was performed with the available epidemiological data from the Hellenic Centre for Disease Control and Prevention and the environmental data from the European Union's earth observation program, Copernicus. Numerical results of the model for each municipality were evaluated against observations. Specifically, the occurrence of WNV, the number of infected humans and the week of incidence predicted from the model were compared to the corresponding numbers from observations. The results suggest that dynamic downscaling of a climate-dependent epidemiological model is feasible down-to roughly 80km2. This below nomenclature of territorial units for statistics (NUTS) 3 level represents the municipalities being the lowest level of administrative units, able to cope with WNV and take actions. The average detection probability in hindcast mode was 72%, improving strongly as the number of infected humans increased. Using the developed model, we were also able to show the fundamental importance of the May temperatures in shaping the WNV dynamics. The modeling framework couples epidemiological and environmental dynamical variables with surveillance data producing risk maps downscaled at a local level. The approach can be expanded to provide targeted early warning probabilistic forecasts that can be used to inform public health policy decision making.

Annual dementia incidence and socioeconomic costs attributable to fine particulate matter (PM2.5) exposure in Sweden

BACKGROUND AND AIM: Alzheimer’s disease (AD) and other dementias currently represent the fifth most common cause of death in the world, according to the World Health Organization, with a projected future increase as the proportion of the elderly in the population is growing. Air pollution has emerged as a plausible risk factor for AD, but studies estimating dementia cases attributable to exposure to fine particulate matter (PM2.5) air pollution and resulting socioeconomic costs are lacking. METHODS: We used data on average population-weighted exposure to ambient PM2.5 for the entire population of Sweden above 30 years of age. To estimate the annual number of dementia cases in Sweden attributable to air pollution we used the latest concentration response functions (CRF) between PM2.5 exposure and dementia incidence, based on ten longitudinal cohort studies. To estimate the socioeconomic costs of dementia, we calculated average life-time costs related to dementia, including formal health care costs, informal care, indirect costs, and intangible costs by including quality-adjusted life years (QALYs) lost. Two applied monetary valuations of QALYs in Sweden decision-making process was used to estimate the monetary value of reduced quality-of-life. RESULTS:The annual number of dementia cases attributable to PM2.5 exposure was estimated to be 820, which represents 5% of the annual dementia cases in Sweden. Formal life-time average cost per incidence of dementia was calculated to correspond € 213,000. From a societal perspective, this corresponds to monetarized annual dementia burden of €215 per individual at risk. Reducing PM2.5 by 1 µg/m3 would correspond to 0.6% of Swedish GDP in opportunity costs due to reduced incident of dementia. CONCLUSIONS:This study estimated that 5% of annual dementia cases could be attributed to PM2.5 exposure, and that the resulting socioeconomic costs were substantial. These findings suggest the need to consider airborne toxic pollutants associated with dementia incidence in public health policy decisions. KEYWORDS: Air pollution, Policy, particulate matter,

Social and built environment antecedents to disparities in adult nature engagement

BACKGROUND AND AIM: Antecedent factors which forge adult nature engagement are underexplored given the human health benefits strongly associated with nature exposure. This qualitative study contributes first-hand insights into the phenomenon of nature engagement to explain how health disparities partially attributable to uneven nature access unfold far upstream of quantitative measure-based analyses. METHODS: Using a phenomenological approach to qualitative research, we probed experiential pathways and attitudes toward nature engagement among adults purposefully sampled across U.S. region, age span, and population density. We conducted ten semi-structured focus group interviews with consenting participants (N=126) in four U.S. metropolitan regions, with discussions recorded, transcribed, and analyzed qualitatively after Braun and Clarke’s phases of thematic analysis (2006). RESULTS:Findings show the existence of a general—though not universal—exposure model which structures adult nature engagement for most individuals. Experiential factors including early life exposures outdoors, parental or other personal mentorship, and organizational affiliation were found highly influential in socializing individuals to nature and in soldering attachment to nature which continued to manifest into adulthood. In contrast, changing demographics, social dynamics, metropolitan growth, urban renewal, and modern lifestyle demands explained alienation from nature. Urban, low-income individuals encountered a distinct model which precludes opportunities to benefit equally from nature contact. CONCLUSIONS:Formative influences and causes of separation remain valid under each exposure model but in different combinations and weights, framing who gets to experience nature and how this occurs as an environmental justice as well as epidemiological concern. Identifying patterns and uniquenesses in nature access across regions and subpopulations can inform intervention potentials in public health policy and urban planning decisions targeted to specific use. KEYWORDS: Green space, built environment, environmental disparities, methodological study design, socio-economic factors

Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments.

During the COVID-19 pandemic, governments globally had to impose severe contact restriction measures and social mobility limitations in order to limit the exposure of the population to COVID-19. These public health policy decisions were informed by statistical models for infection rates in national populations. In this work, we are interested in modelling the temporal evolution of national-level infection counts for the United Kingdom (UK-Wales, England, Scotland), Germany (GM), Italy (IT), Spain (SP), Japan (JP), Australia (AU) and the United States (US). We model the national-level infection counts for the period January 2020 to January 2021, thus covering both the pre- and post-vaccine roll-out periods, in order to better understand the most reliable model structure for the COVID-19 epidemic growth curve. We achieve this by exploring a variety of stochastic population growth models and comparing their calibration, with respect to in-sample fitting and out-of-sample forecasting, both with and without exposure adjustment, to the most widely used and reported growth model, the Gompertz population model, often referred to in the public health policy discourse during the COVID-19 pandemic. Model risk as we explore it in this work manifests in the inability to adequately capture the behaviour of the disease progression growth rate curve. Therefore, our concept of model risk is formed relative to the standard reference Gompertz model used by decision-makers, and then we can characterise model risk mathematically as having two components: the dispersion of the observation distribution, and the structure of the intensity function over time for cumulative counts of new infections daily (i.e. the force of infection) attributed directly to the COVID-19 pandemic. We also explore how to incorporate in these population models the effect that governmental interventions have had on the number of infected cases. This is achieved through the development of an exposure adjustment to the force of infection comprised of a purpose-built sentiment index, which we construct from various authoritative public health news reporting. The news reporting media we employed were the New York Times, the Guardian, the Telegraph, Reuters global blog, as well as national and international health authorities: the European Centre for Disease Prevention and Control, the United Nations Economic Commission for Europe, the United States Centres for Disease Control and Prevention, and the World Health Organisation. We find that exposure adjustments that incorporate sentiment are better able to calibrate to early stages of infection spread in all countries under study.