Public Health Metrics Research Articles

Regional variation of age-related blood pressure trajectories and their association with blood pressure control rates, disability-adjusted life years and death

Abstract Background/Introduction While the relationship between blood pressure (BP) changes and increasing age is well described, age-related BP trajectories may vary substantially across different populations and associated with differential CV risk and outcomes. Purpose The dataset of the May Measurement Month (MMM) international campaigns enables a unique investigation of regional, socio-economic and sex differences in age-BP trajectories and their association with major public-health outcomes. Method Global BP data from more than 4,5 million adult (≥18 years) screenees obtained during the cross-sectional MMM campaigns in 2017, 2018, 2019, and 2021 were used for analysis. Average BP and slopes of BP changes across age groups were calculated within regional, socio-economic and sex strata. BP slopes of individuals between 30 and 70 years of age were derived from adjusted linear regression models, along with baseline BP estimates at young age. These BP slopes were correlated in regression models adjusted for baseline BP at young age with country-specific all-cause rates of DALYs (Disability-Adjusted Life Years), death (both from Global Burden of Disease Study 2019), life expectancy (World Bank data SP.DYN.LE00.IN 2019) and BP control rates (NCD Risk Factor Collaboration). Results Age-related BP changes differed distinctly across global geographic regions, income levels, and between sexes. The steepest increase in BP with age was observed in populations from Africa and Europe, whereas a more gradual rise in BP with increasing age was seen in South-East Asian populations. Women had lower BP levels than men at younger ages (twenties and thirties), but subsequently experienced a more pronounced age-related BP increase resulting in diminishing sex differences across older age groups. Geographically divergent age-related BP trajectories were significantly associated with major national public health indicators. Globally, the steeper the age-related BP slope, the stronger the association with poor BP control (β = -0.46 [95% CI: -0.74; -0.19], p=0.001), increased DALY’s (β = 66155.68 [95% CI: 44847.0; 87464.36], p<0.001) and death rates. (β = 1017.3 [95% CI: 351.72; 1682.89], p=0.003). Conclusion Age-related BP trajectories vary considerably across global populations and contribute considerably to the variability in BP-related risks and adverse outcomes across regions. Effective public health strategies may require region-specific targeting of lifestyle and other factors contributing to adverse BP trajectories to improve health outcomes.Average blood pressure by ageAssiciations with public health metrics

Project SIGNAL: A Dashboard for Supporting Community Confidence in Making Data-Driven Decisions.

Data dashboards have emerged as critical tools for surveillance and informing resource allocation. Despite their utility and popularity during COVID-19, there is a growing need to understand what tools and training are tailored to nonprofit community-based organizations that may partner with public health officials. In June 2021, the Rhode Island Department of Health and Brown University partnered to create Project SIGNAL (Spatiotemporal Insights to Guide Nuanced Actions Locally), which utilizes spatiotemporal analytics to identify Rhode Island's largest disparities in COVID-19-related outcomes (eg, testing, diagnosis, vaccinations) at the neighborhood level. Results were hosted in an interactive online dashboard (signal-ri.org) designed using principles of the CDC Clear Communication Index. The target audience included a network of 15 geographic areas called Health Equity Zones, funded by the health department to provide critical grassroots public health programs to address social, health, and economic outcomes in their communities. To disseminate the dashboard, a 6-hour virtual workshop series was created to train leaders to use the dashboard and increase their confidence in understanding common public health data terminology and concepts and better prepare attendees for rapid decision making during future public health emergencies. The Project SIGNAL dashboard was launched in August 2022 and has been accessed over 7500 times. A total of 84 community leaders were trained to use this dashboard, increasing their confidence in applying common public health metrics to make decisions about their COVID-19-related activities. While several studies have outlined best practices for data dashboards, this is among the first to examine incorporating these practices into a spatiotemporal decision tool designed specifically for community organizations. Project SIGNAL demonstrates that by incorporating design best practices and pairing data dashboards with hands-on training, we can empower community leaders to utilize advanced spatiotemporal methods to identify health disparities and take localized action.

Epidemiological study on Covid-19 frequency, severity, and territorial spreading

This study analyzes the descriptive epidemiology and testing performance in India has been thoroughly analyzed, revealing a robust and effective approach to reporting and managing COVID-19 cases. COVID-19 scenario reports from January 22, 2020 and April 30, 2020 in India. This research examines COVID-19 incidence, severity and spread. The examination frequency was explained spatially and temporally. Along with public health metrics and events, we showed trends and characterized instances by period location and individuals. During January 22, 2020 and April 30, 2020, people were tested for SARS-CoV-2; 40 184 (3,9 %) of the tests produced positive findings in India. Age-specific assault rates were lowest among children under 10 and highest among those aged 50-69. Positive instances were more common in encounters with complaints and in non-responsive people than in patients with SARI, overseas travel experiences or healthcare professionals. Males had a greater assault rate (41,64 %) over females (24,32 %). Six percent of attacks were secondary. In all, 71,1 % of 736 districts reported COVID-19 instances and 99 % percent of districts confirmed treatment.

Integrating Sexual and Reproductive Health Equity Into Public Health Goals and Metrics: Comparative Analysis of Healthy People 2030's Approach and a Person-Centered Approach to Contraceptive Access Using Population-Based Data.

The Healthy People initiative is a national effort to lay out public health goals in the United States every decade. In its latest iteration, Healthy People 2030, key goals related to contraception focus on increasing the use of effective birth control (contraceptive methods classified as most or moderately effective for pregnancy prevention) among women at risk of unintended pregnancy. This narrow focus is misaligned with sexual and reproductive health equity, which recognizes that individuals' self-defined contraceptive needs are critical for monitoring contraceptive access and designing policy and programmatic strategies to increase access. We aimed to compare 2 population-level metrics of contraceptive access: a conventional metric, use of contraceptive methods considered most or moderately effective for pregnancy prevention among those considered at risk of unintended pregnancy (approximating the Healthy People 2030 approach), and a person-centered metric, use of preferred contraceptive method among current and prospective contraceptive users. We used nationally representative data collected in 2022 to construct the 2 metrics of contraceptive access; the overall sample included individuals assigned female at birth not using female sterilization or otherwise infecund and who were not pregnant or trying to become pregnant (unweighted N=2760; population estimate: 43.9 million). We conducted a comparative analysis to examine the convergence and divergence of the metrics by examining whether individuals met the inclusion criteria for the denominators of both metrics, neither metric, only the conventional metric, or only the person-centered metric. Comparing the 2 approaches to measuring contraceptive access, we found that 79% of respondents were either included in or excluded from both metrics (reflecting that the metrics converged when individuals were treated the same by both). The remaining 21% represented divergence in the metrics, with an estimated 5.7 million individuals who did not want to use contraception included only in the conventional metric denominator and an estimated 3.5 million individuals who were using or wanted to use contraception but had never had penile-vaginal sex included only in the person-centered metric denominator. Among those included only in the conventional metric, 100% were content nonusers-individuals who were not using contraception, nor did they want to. Among those included only in the person-centered metric, 68% were currently using contraception. Despite their current or desired contraceptive use, these individuals were excluded from the conventional metric because they had never had penile-vaginal sex. Our analysis highlights that a frequently used metric of contraceptive access misses the needs of millions of people by simultaneously including content nonusers and excluding those who are using or want to use contraception who have never had sex. Documenting and quantifying the gap between current approaches to assessing contraceptive access and more person-centered ones helps clearly identify where programmatic and policy efforts should focus going forward.

Integrating community-based partnerships for enhanced health outcomes: A collaborative model with healthcare providers, clinics, and pharmacies across the USA

The Community Health Enhancement Initiative represents a significant advancement in the field of public health within the United States, targeting the improvement of health outcomes through a comprehensive, collaborative approach. This initiative, in partnership with an array of healthcare entities including renowned hospitals (e.g., Cleveland Clinic, Mayo Clinic, Johns Hopkins Hospital), federally qualified community health centers (e.g., Henry J. Austin Health Center, Chase Brexton Health Care), primary care practices (e.g., Kaiser Permanente, Geisinger Health System, Providence Medical Group), specialized clinics (e.g., Dana-Farber Cancer Institute, Cleveland Clinic Heart and Vascular Institute), and pharmacies with healthcare services (e.g., CVS Health, Walgreens, Rite Aid), aims to revolutionize community health services across the nation. This paper delves into the strategic framework of the initiative, which is designed to foster health literacy, disease prevention, and wellness in diverse American communities. It examines how the collaborative model not only leverages the strengths of various healthcare providers to offer comprehensive care but also actively involves communities in their health management through educational workshops and accessible services. By bridging the gap between healthcare providers and the public, the initiative seeks to build a more health-literate society that is better equipped to manage and prevent diseases. Furthermore, the paper evaluates the impact of this nationwide initiative on public health metrics, showcasing evidence-based outcomes and the potential for scalability in different regions. The initiative's alignment with national health objectives, its innovative use of partnerships to extend healthcare access, and its emphasis on health literacy as a pivotal component of community well-being underscore its relevance and importance to the broader health policy landscape in the U.S. In addition, the research addresses the challenges and opportunities presented by this model, including the integration of technology in healthcare delivery, the sustainability of partnerships, and the continuous engagement of communities. It concludes with policy recommendations for enhancing the initiative's effectiveness and potential for replication, contributing to the ongoing discourse on improving public health infrastructure and outcomes across the United States.

Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.

The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data. Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets. Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic. Utilizing real-time public health metrics, including estimated Rt from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas. Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings.

A pilot recruitment strategy to enhance ethical and equitable access to Covid-19 pediatric vaccine trials.

The SARS-CoV-2 pandemic disproportionately impacted communities with lower access to health care in the United States, particularly before vaccines were widely available. These same communities are often underrepresented in clinical trials. Efforts to ensure equitable enrollment of participants in trials related to treatment and prevention of Covid-19 can raise concerns about exploitation if communities with lower access to health care are targeted for recruitment. To enhance equity while avoiding exploitation, our site developed and implemented a three-part recruitment strategy for pediatric Covid-19 vaccine studies. First, we publicized a registry for potentially interested participants. Next, we applied public health community and social vulnerability indices to categorize the residence of families who had signed up for the registry into three levels to reflect the relative impact of the pandemic on their community: high, medium, and low. Finally, we preferentially offered study participation to interested families living in areas categorized by these indices as having high impact of the Covid-19 pandemic on their community. This approach allowed us to meet goals for study recruitment based on public health metrics related to disease burden, which contributed to a racially diverse study population that mirrored the surrounding community demographics. While this three-part recruitment strategy improved representation of minoritized groups from areas heavily impacted by the Covid-19 pandemic, important limitations were identified that would benefit from further study. Future use of this approach to enhance equitable access to research while avoiding exploitation should test different methods to build trust and communicate with underserved communities more effectively.

Mapping the timeliness of routine childhood vaccination in The Gambia: A spatial modelling study

IntroductionTimeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and makeitdifficultto identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants. MethodsWe used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019–20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation—stochastic partial differential equation (INLA-SPDE) approach. ResultsWe found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specificdistricts in the central and eastern regions of The Gambia consistentlyexhibited the highest prevalence of delayed vaccination, while the coastal districts showed alower prevalence forallthree vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants. ConclusionsOur approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact.

Operationalizing an open-source dashboard for communicating results of wastewater-based surveillance

COVID-19 saw the expansion of public health tools to manage the pandemic. One tool that saw extensive use was the public health dashboard, web-based visualization tools that communicate information to users in easy-to-read graphics. Dashboards were widely used prior to the pandemic, but COVID-19 saw expanded use and development. To date, dashboards have become an important part of public health surveillance programs around the world helping decisionmakers use data to evaluate different public health metrics including caseloads, hospitalizations, and environmental surveillance results from testing wastewater. Wastewater surveillance provides community-based, spatially relevant data on disease trends within communities to assess the scale of infection in a region, which makes it an excellent candidate for dashboard development to improve public health. We developed a dashboard for New York State's wastewater surveillance program using open-source, reproducible web programming. The dashboard we developed has been used for the COVID-19 response in New York, and our methods can be adapted to other programs and pathogens. We provide:•descriptions of how the dashboard was developed and maintained•specific guidance for reproducing our dashboard in other areas and for other pathogens•fully reproducible code with step-by-step instructions for researchers and professionals to make their own data dashboards

Establishing an Approach for Designation of Evolving and Emerging Drug Threats.

Establishing an Approach for Designation of Evolving and Emerging Drug Threats.

The autopsy is not dead: ongoing relevance of the autopsy

Autopsy requests have been trending downward for a variety of factors. There are differences between pre- and postmortem diagnoses. Autopsies remain a tool for education, public health research, quality control, and closure for families.We report two cases that illustrate the utility of autopsy for uncovering contributing factors in the death of these patients and highlight their ongoing importance.Clinical and autopsy investigation of two individuals and illustration of the importance of autopsy findings which, had they been diagnosed premortem, could have changed the outcome. Cases were evaluated using the Goldman criteria for discrepancies between premortem clinical diagnoses and postmortem autopsy findings.In the first case, the patient had been previously admitted due to a non-ST elevation myocardial infarction months before the fatal event. The autopsy showed an undiagnosed clear cell carcinoma of the ovary. She expired due to a massive myocardial infarction secondary to neoplasm induced hypercoagulable state. The degree of pre-mortem/postmortem diagnostic discrepancy makes this a Goldman Class I error.In the second case, the patient presented to the emergency department with symptoms of Guillain-Barré Syndrome (GBS), for which he was treated. Abdominal masses were discovered; however, the patient decompensated before workup was completed. A high-grade B-cell lymphoma was confirmed but would not have altered the outcome, making this a Goldman class II error.The autopsy remains a relevant and necessary tool for physicians and society. It assists in the establishment of diagnoses, measurement of treatment quality, the providence of public health metrics, and closure to the survivors.

It Is Not Just the Southeast-Geographically Pervasive Racial Disparities in Neisseria gonorrhoeae Between Non-Hispanic Black and White US Women.

Racial disparities in female gonorrhea rates are not confined to the Southeast; both relative and absolute disparities are equivalent or larger in areas of the Northeast, Midwest, and West. Background Spatial analyses of gonorrhea morbidity among women often highlight the Southeastern United States but may not provide information on geographic variation in the magnitude of racial disparities; such maps also focus on geographic space, obscuring underlying population characteristics. We created a series of visualizations depicting both county-level racial disparities in female gonorrhea diagnoses and variations in population size. Methods We calculated county- and region-level race-specific relative rates (RelR) and between-race rate differences (RDs) and rate ratios (RRs) comparing gonorrhea case rates in non-Hispanic Black (NHB) versus non-Hispanic White (NHW) women. We then created proportional symbol maps with color representing counties' RelR/RD/RR category and symbol size representing counties' female population. Results Gonorrhea rates among NHB women were highest in the Midwest (718.7/100,000) and West (504.8), rates among NHW women were highest in the West (74.1) and Southeast (72.1). The RDs were highest in the Midwest (654.6 excess cases/100,000) and West (430.7), whereas the RRs were highest in the Northeast (12.4) and Midwest (11.2). Nearly all US counties had NHB female rates ≥3× those in NHW women, with NHB women in most highly populated counties experiencing ≥9-fold difference in gonorrhea rates. Conclusions Racial disparities in gonorrhea were not confined to the Southeast; both relative and absolute disparities were equivalent or larger in magnitude in areas of the Northeast, Midwest, and West. Our findings help counter damaging regional stereotypes, provide evidence to refocus prevention efforts to areas of highest disparities, and suggest a useful template for monitoring racial disparities as an actionable public health metric.

Re-assessing the late HIV diagnosis surveillance definition in the era of increased and frequent testing.

Late HIV diagnosis (CD4 <350 cells/mm3 ) is a key public health metric. In an era of more frequent testing, the likelihood of HIV diagnosis occurring during seroconversion, when CD4 counts may dip below 350, is greater. We applied a correction, considering markers of recent infection, and re-assessed 1-year mortality following late diagnosis. We used national epidemiological and laboratory surveillance data from all people diagnosed with HIV in England, Wales, and Northern Ireland (EW&NI). Those with a baseline CD4 <350 were reclassified as 'not late' if they had evidence of recent infection (recency test and/or negative test within 24 months). A correction factor (CF) was the number reclassified divided by the number with a CD4 <350. Of the 32 227 people diagnosed with HIV in EW&NI between 2011 and 2019 with a baseline CD4 (81% of total), 46% had a CD4 <350 (uncorrected late diagnosis rate): 34% of gay and bisexual men (GBM), 65% of heterosexual men, and 56% of heterosexual women. Accounting for recency test and/or prior negative tests gave a 'corrected' late diagnosis rate of 39% and corresponding CF of 14%. The CF increased from 10% to 18% during 2011-2015, then plateaued, and was larger among GBM (25%) than heterosexual men and women (6% and 7%, respectively). One-year mortality among people diagnosed late was 329 per 10 000 after reclassification (an increase from 288/10 000). The case-surveillance definition of late diagnosis increasingly overestimates late presentation, the extent of which differs by key populations. Adjustment of late diagnosis is recommended, particularly for frequent testers such as GBM.

Wastewater-based epidemiology (WBE) for SARS-CoV-2 - A review focussing on the significance of the sewer network using a Dublin city catchment case study.

Wastewater-based epidemiology (WBE) has been employed by many countries globally since the beginning of the COVID-19 pandemic in order to assess the benefits of this surveillance tool in the context of informing public health measures. WBE has been successfully employed to detect SARS-CoV-2 at wastewater treatment plants for community-wide surveillance, as well as in smaller catchments and institutions for targeted surveillance of COVID-19. In addition, WBE has been successfully used to detect new variants, identify areas of high infection levels, as well as to detect new infection outbreaks. However, due to to the large number of inherent uncertainties in the WBE process, including the inherent intricacies of the sewer network, decay of the virus en route to a monitoring point, levels of recovery from sampling and quantification methods, levels of faecal shedding among the infected population, as well as population normalisation methods, the usefulness of wastewater samples as a means of accurately quantifying SARS-CoV-2 infection levels among a population remains less clear. The current WBE programmes in place globally will help to identify new areas of research aimed at reducing the levels of uncertainty in the WBE process, thus improving WBE as a public health monitoring tool for future pandemics. In the meantime, such programmes can provide valuable comparisons to clinical testing data and other public health metrics, as well being an effective early warning tool for new variants and new infection outbreaks. This review includes a case study of sampled wastewater from the sewer network in Dublin, Ireland, during a peak infection period of COVID-19 in the city, which evaluates the different uncertainties in the WBE process.

All Public Health is Local: Lessons From Eagle County During the First 2 Years of the Coronavirus Disease-2019 Pandemic.

Background:During the coronavirus disease-2019 (COVID-19) pandemic cumulative United States COVID-19 deaths per capita were higher than all other large, high-income nations, but with substantial variation across the country.Objective:The aim was to detail the public health response during the pandemic in Eagle County, Colorado.Research Design and Measures:Observational study using pre-COVID-19 county public health metrics. Pandemic actions were recorded from a narrative summary of daily phone consultations by a county-wide taskforce and interviews. Outcomes obtained from local, state, and nationally reported databases.Methods:Eagle County began with a life expectancy of 85.9, low all-cause age-adjusted death rates equal for both White and Latinx populations, a high household median income, and other prepandemic advantages. It also launched an innovative, independent county-wide taskforce lead by experienced mid-level managers. The taskforce implemented rapid communication of decision consequences, made immediate course corrections without traditional organizational approvals or contradictory political pressures.Results:Eagle County was first in Colorado to obtain Personal Protective Equipment and to establish a drive-through testing facility. The COVID-19 case fatality rate was 0.34%. The sole intensive care unit never reached maximum capacity. By March 2022, Eagle County had administered at least 1 vaccine dose to 100% of the population and 83% were fully vaccinated.Conclusions:It is not possible to directly attribute superior outcomes to either the baseline characteristics of Eagle County or its innovative taskforce design and deployment. Rather this report highlights the potential impact that improving the baseline health status of US citizens and permitting novel problem-solving approaches by local public health officials might have for the next pandemic.

Inequality in researchers’ minds: Four guiding questions for studying subjective perceptions of economic inequality

AbstractSubjective perceptions of inequality can substantially influence policy attitudes, public health metrics, and societal well‐being, but the lack of consensus in the scientific community on how to best operationalize and measure these perceptions may impede progress on the topic. Here, we provide a theoretical framework for the study of subjective perceptions of inequality, which brings critical differences to light. This framework—which we conceptualize as a series of four guiding questions for studying subjective perceptions of economic inequality—serves as a blueprint for the theoretical and empirical decisions researchers need to address in the study of when, how, and why subjective perceptions of inequality are consequential for individuals, groups, and societies. To lay the foundation for a comprehensive approach to the topic, we offer four theoretical and empirical decisions in studying subjective perceptions of inequality, urging researchers to specify: (1) What kind of inequality? (2) What level of analysis? (3) What part of the distribution? and (4) What comparison group? We subsequently discuss how this framework can be used to organize existing research and highlight its utility in guiding future research across the social sciences in both the theory and measurement of subjective perceptions of inequality.

Effect of Time and Temperature on SARS-CoV-2 in Municipal Wastewater Conveyance Systems

Wastewater surveillance for SARS-CoV-2 is becoming a widespread public health metric, but little is known about pre-analytical influences on these measurements. We examined SARS-CoV-2 loads from two sewer service areas with different travel times that were within the same metropolitan area. Throughout the one-year study, case rates were nearly identical between the two service areas allowing us to compare differences in empirical concentrations relative to conveyance system characteristics and wastewater treatment plant parameters. We found time did not have a significant effect on degradation of SARS-CoV-2 when using average transit times (22 vs. 7.5 h) (p = 0.08), or under low flow conditions when transit times are greater (p = 0.14). Flow increased rather than decreased SARS-CoV-2 case-adjusted concentrations, but this increase was only significant in one service area. Warmer temperatures (16.8–19.8 °C) compared with colder (8.4–12.3 °C) reduced SARS-CoV-2 case-adjusted loads by ~50% in both plants (p &lt; 0.05). Decreased concentrations in warmer temperatures may be an important factor to consider when comparing seasonal dynamics. Oxygen demand and suspended solids had no significant effect on SARS-CoV-2 case-adjusted loads overall. Understanding wastewater conveyance system influences prior to sample collection will improve comparisons of regional or national data for SARS-CoV-2 community infections.

LCIA Formatter.

SummaryLife Cycle Assessment (LCA) is an established and standardized methodology to comprehensively assess environmental and public health metrics across industries and products (International Organization for Standardization, 2006). The United States Environmental Protection Agency (USEPA) is developing an open source LCA tool ecosystem (Ingwersen, 2019). The ecosystem includes tools to automate the creation of life cycle inventory (LCI) datasets, which account for flows to and from nature for steps across the life cycle of products or services, and tools for life cycle impact assessment (LCIA) to support classification and characterization of the cumulative LCI to potential impacts. Impacts are expressed via indicators, either midpoint or endpoint, corresponding to different points on the environmental cause-effect chain model (Frischknecht & Jolliet, 2016). This paper describes a USEPA LCA ecosystem tool ‘LCIA formatter’ that extracts LCIA information from original source methods and converts the data for interoperability with the rest of the USEPA LCA ecosystem tools.

A Descriptive Analysis of the Scientific Literature on Meteorological and Air Quality Factors and COVID-19.

The role of meteorological and air quality factors in moderating the transmission of SARS‐CoV‐2 and severity of COVID‐19 is a critical topic as an opportunity for targeted intervention and relevant public health messaging. Studies conducted in early 2020 suggested that temperature, humidity, ultraviolet radiation, and other meteorological factors have an influence on the transmissibility and viral dynamics of COVID‐19. Previous reviews of the literature have found significant heterogeneity in associations but did not examine many factors relating to epidemiological quality of the analyses such as rigor of data collection and statistical analysis, or consideration of potential confounding factors. To provide greater insight into the current state of the literature from an epidemiological standpoint, the authors conducted a rapid descriptive analysis with a strong focus on the characterization of COVID‐19 health outcomes and use of controls for confounding social and demographic variables such as population movement and age. We have found that few studies adequately considered the challenges posed by the use of governmental reporting of laboratory testing as a proxy for disease transmission, including timeliness and consistency. In addition, very few studies attempted to control for confounding factors, including timing and implementation of public health interventions and metrics of population compliance with those interventions. Ongoing research should give greater consideration to the measures used to quantify COVID‐19 transmission and health outcomes as well as how to control for the confounding influences of public health measures and personal behaviors.

Using Network Analysis and Machine Learning to Identify Virus Spread Trends in COVID-19

The outbreak of Coronavirus Disease 2019 (COVID-19) has infected and killed millions of people globally, resulting in a pandemic with enormous global impact. This disease affects the respiratory system, and the viral agent that causes it, SARS-CoV-2, spreads through droplets of saliva, as well as through coughing and sneezing. As an extremely transmissible viral infection, COVID-19 is causing significant damage to the economies of both developed and lower- and middle-income countries because of its direct impact on the health of citizens and the containment measures taken to curtail the virus. Methods to reduce or control the spread of the virus and protect the global population are needed to avoid further deaths, long-term health issues, and prolonged economic impact. The most effective approach to reduce viral spread and avoid a substantial collapse of the health system, in the absence of vaccines, is nonpharmaceutical interventions (NPI) such as enforcing social containment restrictions, monitoring overall population mobility, implementing widespread viral testing, and increasing hygiene measures. Our approach consists of combining network analytics with machine learning models by using a combination of anonymized health and telecommunications data to better understand the correlation between population movements and virus spread. This approach, called location network analysis (LNA), allows for accurate prediction of possible new outbreaks. It gives governments and health authorities a crucial tool that can help define more accurate public health metrics and can be used either to intensify social containment policies to avoid further spread or to ease them to reopen the economy. LNA can also help to retrospectively evaluate the effectiveness of policy responses to COVID-19.