Public Health Data Research Articles

Counting the Dead in Nursing Homes during the COVID-19 Pandemic

This article analyzes the genesis and consolidation of the “statistical argument” (Desrosières 2008) of mortality during the pandemic. Public health data are approached from the perspective of “biopower” which can be read as a statization of life, combining the power of both science and the state. The authors explore the social conditions of the production and dissemination of mortality data in French nursing homes in a period of strong uncertainty. The web of interactions between agencies of public health generates vagueness and uncertainty, but also weak and fragile data, in a period nevertheless marked by the centralization of power. The fragility of mortality data is mirrored by the fragility of the nursing home as institution — an expression of numerous fallibilities, in particular economic (lack of resources), symbolic (out‐of‐sight situations) and institutional (tension between health and social care).

Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View.

Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.

Global Estimate of Human Brucellosis Incidence.

Brucellosis is a major public health concern worldwide, especially for persons living in resource-limited settings. Historically, an evidence-based estimate of the global annual incidence of human cases has been elusive. We used international public health data to fill this information gap through application of risk metrics to worldwide and regional at-risk populations. We performed estimations using 3 statistical models (weighted average interpolation, bootstrap resampling, and Bayesian inference) and considered missing information. An evidence-based conservative estimate of the annual global incidence is 2.1 million, significantly higher than was previously assumed. Our models indicate Africa and Asia sustain most of the global risk and cases, although areas within the Americas and Europe remain of concern. This study reveals that disease risk and incidence are higher than previously suggested and lie mainly within resource-limited settings. Clarification of both misdiagnosis and underdiagnosis is required because those factors will amplify case estimates.

Open Government Data (OGD) sites and the sharing of country-specific real-time pandemic information: An investigation into COVID-19 datasets available on worldwide OGDs

In this study, we investigate the key characteristics of more than 1500 COVID-19 datasets appearing on 35 Open Government Data (OGD) sites around the world. In addition to examining the number of datasets, data formats, star levels (a measure of data formats), and data levels, we also investigated the topical focus of individual dataset content by analyzing the text words of dataset titles and descriptions. Significant differences among the world regions were found in both the number of datasets and the number of data formats. Regional differences were also found among various star levels. In addition, regional differences were found in the topic contributions to two specific clusters. As one of the first empirical studies examining various characteristics of COVID-19 datasets on OGDs, our research fills the gaps in this area. The findings of our study will benefit not only the government in improving their OGDs’ public health data provision by ensuring accuracy, consistency, and timeliness, but also benefit those OGD users who are public health professionals who can assess the progress and identify gaps in the community, or everyday citizens who access OGD datasets to obtain an understanding of the development and risk levels of the crisis. Future research may use the same method to expand the examination to the general “health” datasets accessible from OGDs worldwide to help improve the sharing and reuse of health datasets for the general public.

A Florida urban heat risk index: Assessing weighting and aggregation approaches

Extreme heat is a burgeoning public health concern facing cities, thus composite indicators (CI) are useful identifying at-risk populations and directing resources. Nonetheless, heat risk assessments regularly overlook fundamental CI components like weighting, aggregation, and exploring links to other models. This analysis examined how weighting and aggregation alter CI scores, associated spatial distribution, and performance. Models were validated with heat-related mortality data. The Florida urban heat risk index (FUHRI) demonstrated how weighting and aggregation influence model outputs. Applying uncommon statistical weights performed better than traditional approaches, and multiplicative aggregation outperformed conventional additive aggregation. Results support further exploring non-traditional methods to enhance extreme heat risk assessment while underscoring importance of comparing multiple CI. Despite FUHRI score and performance variation, there was often agreement about at-risk geographies; however, consistency does not guarantee accuracy identifying areas needing adaptation and mitigation. Decision makers should use caution interpreting CI unless validation analysis demonstrates adequate performance, and even then, models must be continuously refined using the most recent public health data and desirable methods available.

Mass spectrometry as a lens into molecular human nutrition and health.

Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.

Risking Your Health to Help Others: The Effect of Pandemic Severity on Volunteering

The COVID-19 pandemic affected the provision of voluntary work across the globe. We study informal volunteers who buy and deliver groceries for people in a high-risk group or in quarantine. Using data from a volunteering grocery delivering app in Switzerland that coordinated these volunteers, we are able to track volunteering during the pandemic. Combined with public health data on cases and deaths, we test how the severity of the pandemic affects the provision of voluntary work in the form of neighborhood grocery deliveries. We find a positive effect of the number of deaths on voluntary deliveries. However, in contrast to the literature studying the effect of the severity of the pandemic on giving, this effect is concave. We suggest that this concave effect is due to the signal of risk of infection implied by rising death rates, which is at odds with the signal of need to help others.

Towards a Swiss health study with human biomonitoring: Learnings from the pilot phase about participation and design.

A large-scale national cohort aiming at investigating the health status and determinants in the general population is essential for high-quality public health research and regulatory decision-making. We present the protocol and first results of the pilot phase to a Swiss national cohort aiming at establishing the study procedures, evaluating feasibility, and assessing participation and willingness to participate. The pilot phase 2020/21 included 3 components recruited via different channels: a population-based cross-sectional study targeting the adult population (20-69 years) of the Vaud and Bern cantons via personal invitation, a sub-study on selenium in a convenience sample of vegans and vegetarians via non-personal invitation in vegan/vegetarian networks, and a self-selected sample via news promotion (restricted protocol). Along with a participatory approach and participation, we tested the study procedures including online questionnaires, onsite health examination, food intake, physical activity assessments and biosample collection following high-quality standards. The population-based study and the selenium sub-study had 638 (participation rate: 14%) and 109 participants, respectively, both with an over-representation of women. Of altogether 1349 recruited participants over 90% expressed interest in participating to a national health study, over 75% to contribute to medicine progress and help improving others' health, whereas about one third expressed concerns over data protection and data misuse. Publicly accessible high-quality public health data and human biomonitoring samples were collected. There is high interest of the general population in taking part in a national cohort on health. Challenges reside in achieving a higher participation rate and external validity. For project management clear governance is key.

Disaster and public health emergency health data collection and management: A scoping review.

The World Health Organization (WHO) developed the Emergency Medical Team (EMT) Minimum Data Set (MDS) to provide a structured, data-based approach to health data collection and management during disasters and public health emergencies. Given recent creation of the EMT MDS, we conducted a scoping review to gauge current practices surrounding health data collection and sharing in emergent settings. An English-based scoping review of PubMed and Embase databases of publications before June 28, 2021. The review aimed to identify facilitators and barriers to the implementation of the WHO-standardized health data collection systems in the context of disasters and public health emergencies; characterize best practices regarding implementation of an MDS to improve health data collection capacity in differing settings; and highlight internationally accepted, standardized tools or methods for setting up essential public health data for disaster response. A total of 8,038 citations from PubMed and Embase were imported into Covidence with 46 duplicates removed. Among these, 7,992 citations underwent title screening and abstract review, with 161 articles proceeding to full-text article review where an additional 109 articles were excluded. Fifty-two citations were included in final data abstraction. Findings revealed a range of critical operational, structural, and functional insights of relevance to implementation of the EMT MDS. The literature identified facilitators and barriers to collecting and storing disaster-based datasets, gaps in standardization of data collection resulting in poor data quality during the transition from the acute to post-acute phase, and best practices in the collection of EMT MDS.

Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater.

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

How Canada's decentralised covid-19 response affected public health data and decision making.

How Canada's decentralised covid-19 response affected public health data and decision making.

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis

BackgroundCOVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance.MethodsWe evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic.ResultsThe simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods.ConclusionWe evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Modernizing the United States' Public Health Infrastructure: A Position Paper From the American College of Physicians.

The United States' public health sector plays a crucial role in preventing illness and promoting health. Public health drove massive gains in life expectancy during the 20th century by supporting vaccination campaigns, promoting motor vehicle safety, and preventing and treating tobacco use. However, public health is underfunded and underappreciated, forcing the field to do more with fewer resources. In this position paper, the American College of Physicians (ACP) updates its 2012 policy recommendations on strengthening the nation's public health infrastructure. ACP calls for effective coordination of public health activities, robust and stable year-to-year funding of public health services, a renewed and well-supported public health workforce, action to address health-related dis- and misinformation, modernized public health data systems, and greater coordination between public health and medical sectors.

Promoting lung cancer screening of high-risk patients by primary care providers.

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) of the chest of eligible patients remains low. Accordingly, augmentation of appropriate LCS referrals by primary care providers (PCPs) was sought. The quality improvement (QI) project was performed between April 2021 and June 2022. It incorporated patient education, shared decision-making (SDM) with PCPs, and tracking of initial LDCT completion. In each case, lag time (LT) to LCS and pack-years (PYs) were calculated from initial LCS eligibility. The cohort's scores were compared to national scores. Patient zip codes were used to create a geographic map of our cohort for comparison with public health data. An immediate and sustained increase in weekly LCS referrals from PCPs was recorded. Of 337 initial referrals, 95% were men, consisting of 66.2% Black, 28.4% White, and 5.4% other. Mean PY was less for minorities (45.3 vs. 37.3years; p=.0002) but mean LT was greater for Whites (7.9 vs. 6.2years; p=.03). Twenty-five percent of veterans failed to report to their scheduled screening, and two declined referrals. Notably, most no-show patients lived in transit deserts. Furthermore, Lung-RADS scores 4B/4X were more than double the expected prevalence (p=.008). The PCPs in this study successfully augmented LCS referrals. A substantial proportion of these patients were no-shows, and our data suggest complex racial and socioeconomic factors as contributing variables. In addition, a higher-than-expected number of initial Lung-RADS scores 4B/4X were reported. A large, multisite QI project is warranted to address overcoming potential transportation barriers in high-risk patient populations.

Community collaboration in the face of the COVID-19 Pandemic: Examples of How Remote First Nations in Northern Ontario Managed the Pandemic

At the outset of the COVID-19 pandemic, it was believed that Indigenous peoples in Canada would be disproportionately affected due to inequities across social determinants of health as a result of the ongoing processes of colonization. High levels of overcrowding, higher burden of chronic disease, reduced access to clean drinking water, healthcare, and food security in many rural and remote First Nations across northern Canada increased vulnerability to COVID-19. In the Nishnawbe Aski Region of northern Ontario, data from the Sioux Lookout First Nations Health Authority indicates that First Nations communities were able to limit COVID -19 infection and had an overall fatality rate that was lower than the general Canadian population. The focus of this research was to analyze public health data, media reports, and research to determine how the pandemic impacted First Nations throughout northern Ontario. The research highlights that as a direct result of rapid and strength-based responses, First Nations in Northern Ontario have managed the pandemic with limited serious illness, hospitalizations, and fatalities.

Public health data quality and evidence use in developing countries: a call to action.

Public health data quality and evidence use in developing countries: a call to action.

A population-level post-screening treatment cost framework to help inform vision screening choices for children under the age of seven

ABSTRACT Purpose/Background Visual acuity (VA) screening in children primarily detects low VA and amblyopia between 3 and 6 years of age. Photoscreening is a low-cost, lower-expertise alternative which can be carried out on younger children and looks instead for refractive amblyopia risk factors so that early glasses may prevent or mitigate the conditions. The long-term benefits and costs of providing many children with glasses in an attempt to avoid development of amblyopia for some of them needs clarification. This paper presents a framework for modeling potential post-referral costs of different screening models once referred children reach specialist services. Methods The EUSCREEN Screening Cost-Effectiveness Model was used together with published literature to estimate referral rates and case mix of referrals from different screening modalities (photoscreening and VA screening at 2, 3–4 years and 4–5 years). UK 2019–20 published National Health Service (NHS) costings were used across all scenarios to model the comparative post-referral costs to the point of discharge from specialist services. Potential costs were compared between a) orthoptist, b) state funded ophthalmologist and c) private ophthalmologist care. Results Earlier VA screening and photoscreening yield higher numbers of referrals because of lower sensitivity and specificity for disease, and a different case mix, compared to later VA screening. Photoscreening referrals are a mixture of reduced VA caused by amblyopia and refractive error, and children with amblyopia risk factors, most of which are treated with glasses. Costs relate mainly to the secondary care providers and the number of visits per child. Treatment by an ophthalmologist of a referral at 2 years of age can be more than x10 more expensive than an orthoptist service receiving referrals at 5 years, but outcomes can still be good from referrals aged 5. Conclusions All children should be screened for amblyopia and low vision before the age of 6. Very early detection of amblyopia refractive risk factors may prevent or mitigate amblyopia for some affected children, but population-level outcomes from a single high-quality VA screening at 4–5 years can also be very good. Total patient-journey costs incurred by earlier detection and treatment are much higher than if screening is carried out later because younger children need more professional input before discharge, so early screening is less cost-effective in the long term. Population coverage, local healthcare models, local case-mix, public health awareness, training, data monitoring and audit are critical factors to consider when planning, evaluating, or changing any screening programme.

Precision Medicine for the Population—The Hope and Hype of Public Health Genomics

Public health is the most recent of the biomedical sciences to embrace “precision.” Advocates for “precision public health” (PPH) call for a data-driven, computa¬tional approach to public health, leveraging genomic and other data to inform public health decision-making. Yet, like precision medicine, PPH risks overselling the value of genomic data to deter¬mine health outcomes, but on a population level. History has shown that over-emphasizing heredity tends to disproportion¬ately harm underserved minorities and disadvantaged communities. Comparing and contrasting cur¬rent PPH with an earlier attempt at using genetics to inform pub¬lic health during the Progressive era (1890–1920) highlights some potential risks of genotype-driven preventive public health.

Expansion of wastewater-based disease surveillance to improve health equity in California's Central Valley: sequential shifts in case-to-wastewater and hospitalization-to-wastewater ratios.

Over a third of the communities (39%) in the Central Valley of California, a richly diverse and important agricultural region, are classified as disadvantaged-with inadequate access to healthcare, lower socio-economic status, and higher exposure to air and water pollution. The majority of racial and ethnic minorities are also at higher risk of COVID-19 infection, hospitalization, and death according to the Centers for Disease Control and Prevention. Healthy Central Valley Together established a wastewater-based disease surveillance (WDS) program that aims to achieve greater health equity in the region through partnership with Central Valley communities and the Sewer Coronavirus Alert Network. WDS offers a cost-effective strategy to monitor trends in SARS-CoV-2 community infection rates. In this study, we evaluated correlations between public health and wastewater data (represented as SARS-CoV-2 target gene copies normalized by pepper mild mottle virus target gene copies) collected for three Central Valley communities over two periods of COVID-19 infection waves between October 2021 and September 2022. Public health data included clinical case counts at county and sewershed scales as well as COVID-19 hospitalization and intensive care unit admissions. Lag-adjusted hospitalization:wastewater ratios were also evaluated as a retrospective metric of disease severity and corollary to hospitalization:case ratios. Consistent with other studies, strong correlations were found between wastewater and public health data. However, a significant reduction in case:wastewater ratios was observed for all three communities from the first to the second wave of infections, decreasing from an average of 4.7 ± 1.4 over the first infection wave to 0.8 ± 0.4 over the second. The decline in case:wastewater ratios was likely due to reduced clinical testing availability and test seeking behavior, highlighting how WDS can fill data gaps associated with under-reporting of cases. Overall, the hospitalization:wastewater ratios remained more stable through the two waves of infections, averaging 0.5 ± 0.3 and 0.3 ± 0.4 over the first and second waves, respectively.

The Perilous Mix of Populism and Pandemics: Lessons from COVID-19

Populist leaders have consistently rejected evidence-based policies in responding to the ongoing COVID-19 pandemic. They acted later and with less intensity than non-populists in implementing public health measures such as physical distancing, lockdowns, and developing public health data sets. We describe the responses of ten large countries with populist leadership at the onset of the pandemic (Brazil, Hungary, India, Mexico, the Philippines, Poland, Russia, Turkey, the United Kingdom, and the United States). Together, these countries account for a disproportionately large number of cases and deaths associated with COVID-19 relative to their population. We categorize the policy responses into two types: (1) slow and ineffective, and (2) strict and illiberal. We conclude that while not all countries that responded poorly to the pandemic were led by populists, no countries with populist leadership performed well in either applying public health measures or achieving desirable health outcomes.