Public Health Data Research Articles

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.

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method for identifying toxicological biological indicators of human exposure in smart cities based on public health data and deep learning to achieve accurate assessment and management of exposure risks. Initially, the study used a network of sensors within the smart city infrastructure to collect environmental monitoring data, including indicators such as air quality, water quality, and soil pollution. Using public health data, a database containing information on types and concentrations of environmental pollutants has been established. Convolutional neural network was used to recognize the pattern of environmental monitoring data, identify the relationship between different indicators, and build the correlation model between health indicators and environmental indicators. Identify biological indicators associated with environmental pollution exposure through training optimization. Experimental analysis showed that the prediction accuracy of the model reached 93.45%, which could provide decision support for the government and the health sector. In the recognition of the association pattern between respiratory diseases, cardiovascular diseases and environmental exposure factors such as PM2.5 and SO2, the fitting degree between the model and the simulation value reached more than 0.90. The research design model can play a positive role in public health and provide new decision-making ideas for protecting public health.

Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing.

Background: The COVID-19 pandemic has caused a disproportionate impact on the sexual and gender-diverse (SGD) community. Compared with non-SGD populations, their social relations and health status are more vulnerable, whereas public health data regarding SGD are scarce. Methods: To analyze the concerns and health status of SGD individuals, this cohort study leveraged 471,371,477 tweets from 251,455 SGD and 22,644,411 non-SGD users, spanning from 2020 February 1 to 2022 April 30. The outcome measures comprised the distribution and dynamics of COVID-related topics, attitudes toward vaccines, and the prevalence of symptoms. Results: Topic analysis revealed that SGD users engaged more frequently in discussions related to "friends and family" (20.5% vs. 13.1%, P < 0.001) and "wear masks" (10.1% vs. 8.3%, P < 0.001) compared to non-SGD users. Additionally, SGD users exhibited a marked higher proportion of positive sentiment in tweets about vaccines, including Moderna, Pfizer, AstraZeneca, and Johnson & Johnson. Among 102,464 users who self-reported COVID-19 diagnoses, SGD users disclosed significantly higher frequencies of mentioning 61 out of 69 COVID-related symptoms than non-SGD users, encompassing both physical and mental health challenges. Conclusion: The results provide insights into an understanding of the unique needs and experiences of the SGD community during the pandemic, emphasizing the value of social media data in epidemiological and public health research.

Causal relationship between ankylosing spondylitis and ocular inflammatory diseases: a Mendelian randomization study.

Observational studies have shown an increased risk of ocular inflammatory diseases in patients with ankylosing spondylitis (AS), but the genetically predicted association remains unclear. The aim of this study was to systematically assess the causal relationship between AS and ocular inflammatory diseases. We conducted a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between AS and several common ocular inflammatory diseases based on genome-wide association study (GWAS) data and public health data. Five methods, namely, inverse-variance weighted (IVW), MR-Egger, weighted median, weighted mode, and simple mode, were used. Sensitivity analysis was performed using MR-Egger intercept, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Cochran's Q test, outlier methods, leave-one-out analysis, and funnel plots. The MR analysis showed a significantly increased risk of uveitis (ORIVW = 2.825, 95%CIIVW = 1.709-4.672, and PIVW < 0.001), iridocyclitis (ORIVW = 3.806, 95%CIIVW = 2.809-5.157, and PIVW < 0.001), scleritis (ORIVW = 1.738, 95%CIIVW = 1.190-2.539, and PIVW < 0.001), and episcleritis (ORIVW = 5.113, 95%CIIVW = 2.067-12.645, and PIVW = 0.004) associated with AS. However, no correlation was found between genetically predicted AS and keratitis (ORIVW = 1.041, 95%CIIVW = 0.886-1.222, and PIVW = 0.628) and optic neuritis (ORIVW = 0.868, 95%CIIVW = 0.441-1.709, and PIVW = 0.682). AS increases the genetically predicted risk for uveitis, iridocyclitis, scleritis, and episcleritis. No potential association of AS with keratitis and optic neuritis was found. It may provide clues for the prevention of AS complications.

Does the COVID-19 Vaccination Reduce the Risk to Transmit SARS-CoV-2 to Others?

It has been assumed that the COVID-19 vaccination reduces the risk of transmission to others. Results during the delta predominance show that the viral load in thevaccinated population is not consistently lower compared to the unvaccinated, and during the omicron predominance, the viral load was even somewhat higher. Levels of infectious SARS-CoV-2 were partly lower in the vaccinated population. Viral loads were mostly lower in re-infections compared to breakthrough infections. Viral clearance including the detection of infectious virus has mostly been described to be faster in the vaccinated population suggesting a shorter duration as a possible source for transmission. The epidemiological relevance of this finding remains uncertain. Approximately half of the transmission studies found lower secondary attack rates from the fully vaccinated population, but the results are probably best explained by the vaccination status of the contact population. Public health data from the UK show that the number of COVID-19 cases is higher among the fully vaccinated and boosted population who might be possible sources, in contrast to lower case numbers within the first three monthsamong the vaccinated obtained in phase 3 trials on symptomatic cases. Overall, there is no convincing evidence that the COVID-19 vaccination significantly reduces the risk to transmit SARS-CoV-2 to others.

Barriers and Opportunities for Tribal Access to Public Health Data to Advance Health Equity.

Public health authorities (PHAs), including Tribal nations, have the right and responsibility to protect and promote the health of their citizens. Although Tribal nations have the same need and legal authority to access public health data as any other PHA, significant legal challenges continue to impede Tribal data access.

Household air pollution exposure and risk of tuberculosis: a case–control study of women in Lilongwe, Malawi

IntroductionGlobally, 3–4 billion people rely on solid fuels for cooking, and 1 billion use kerosene to light their homes. While household air pollution (HAP) emitted from burning these fuels has...

The promise and performance of data ecosystems: Australia’s COVID-19 response

Ecosystem metaphors are increasingly used to describe complex business and civil society relationships. This article argues that the rise of ecosystem thinking in public policy and administration represents a new, radically decentred model of policy action. It focuses on ecosystems of data-driven modelling and experts who use government public health data. This study presents a conceptual framework that suggests effective data ecosystems undergo organic adaptation, display institutional leadership, and respond instrumentally to policymakers’ information needs. It examines how Australia’s infectious disease data ecosystem performed before and during the COVID-19 pandemic. In-depth interviews with data experts involved in infectious disease modelling in Australia revealed trade-offs within and between organic, institutional and instrumental dimensions, which COVID-19 exacerbated. The article concludes that data ecosystems represent a hybrid form of policymaking in practice and that tensions must be managed to maximise the contributions of data ecosystems to facilitate evidence-informed decision-making.

Unlocking Public Health Data: Navigating New Legal Guardrails and Emerging AI Challenges.

Here, we analyze the public health implications of recent legal developments - including privacy legislation, intergovernmental data exchange, and artificial intelligence governance - with a view toward the future of public health informatics and the potential of diverse data to inform public health actions and drive population health outcomes.

Improving Wildfire Readiness Among Patients With Chronic Obstructive Pulmonary Disease and Asthma: Applying a Population Health Approach to Climate Change.

As a result of climate change, wildfire frequency, duration, and severity are increasing in the United States. Exposure to wildfire-related air pollutants can lead to negative health outcomes, particularly among patients with preexisting respiratory diseases (e.g., asthma and chronic obstructive pulmonary disease) and those who are at higher risk for developing these conditions. Underserved communities are disproportionately affected for multiple reasons, including lack of financial and social resources, increased exposure to air pollutants at home and at work, and impaired access to health care. To best serve clinically high-risk and underserved populations, health systems must leverage community public health data, develop and mobilize a wildfire preparedness action plan to identify populations at high risk, and implement interventions to mitigate the consequences of poor air quality. University of California, Davis Health, located at the epicenter of the largest wildfires in California's history, has developed the 5 pillar Wildfire Population Health Approach: (1) identify clinically at-risk and underserved patient populations using well-validated, condition-targeted registries; (2) assemble multidisciplinary care teams to understand the needs of these communities and patients; (3) create custom analytics and wildfire-risk stratification; (4) develop care pathways based on wildfire-risk tiers by disease, risk of exposure, and health care access; and (5) identify outcome measures tailored to interventions with a commitment to continuous, iterative improvement efforts. The Wildfire Population Health Approach provides an action plan for health systems and care teams to meet the needs of clinically at-risk and underserved patients affected by the increasing health threat posed by climate change-related wildfires.

Considerations for Defining Homelessness in Public Health Data Collection.

Considerations for Defining Homelessness in Public Health Data Collection.

Socio-economic factors and medical conditions affecting regular stomach cancer screening in Korea: a retrospective longitudinal study using national public health data for 11 years

ObjectiveThis study aimed to explore socio-economic factors and medical conditions that affect regular stomach cancer (SC) screening among Korean adults. Study designThis was a retrospective observational study. MethodsStudy subjects were 5545 adults aged ≥40 years who participated in the 2007–2012 Korean National Health and Nutrition Examination Survey and were followed up to year 2017 based on data linking to the Korean National Health Insurance Service and Korean Health Insurance Review and Assessment. Socio-economic factors included sex, age, residential area, education, occupation, marital status, disability, public and private health insurance, service through local public health organizations, history of cancer except for SC, and family history of SC. Medical factors included six gastric lesions with the possibility of facilitating SC screening, including benign gastric neoplasm, chronic atrophic gastritis, gastric polyp, Helicobacter pylori infection, intestinal metaplasia, and peptic ulcers. The outcome was adherence to SC screening, which was divided into non-adherence, irregular adherence, and regular adherence. ResultsAfter adjusting for the effects of socio-economic factors, multivariate ordinal logistic regression revealed that participants with a history of four types of gastric lesions were more likely to regularly participate in SC screening: chronic atrophic gastritis (odds ratio [OR] 1.567; 95% confidence interval [CI] = 1.276–1.923), gastric polyps (OR 1.565; 95% CI = 1.223–2.003), H. pylori infection (OR 1.637; 95% CI = 1.338–2.003), and peptic ulcer (OR 2.226; 95% CI 1.750–2.831). ConclusionsTo improve participation in SC screening, it is necessary to implement personalized strategies for individuals at risk for gastric cancer in addition to population-based strategies for vulnerable groups.

Assessing the role of social determinants of health in health disparities: The need for data on work.

Work is a key social determinant of health. Without the collection of work-related information in public health data systems, the role of social determinants in creating and reinforcing health disparities cannot be fully assessed. The Centers for Disease Control and Prevention (CDC) maintains or supports a number of public health surveillance and health monitoring systems, including surveys, case-based disease and exposure systems, vital status records, and administrative data systems. We evaluated a convenience sample of these systems for inclusion of information in three work-related domains: employment status, industry and occupation, and working conditions. While 12 of 39 data systems were identified as collecting work-related data, this information was often minimal (e.g., only employment status), restricted to a subset of respondents, or only gathered periodically. Information on working conditions was particularly sparse. Historically, the limited and inconsistent collection of work-related information in public health data systems has hindered understanding of the role work plays in health disparities. Current CDC data modernization efforts present opportunities to enhance the identification and mitigation of health disparities by prioritizing inclusion of an expanded set of work-related data elements.

The causal association between COVID-19 and herpes simplex virus: a Mendelian randomization study.

The coronavirus disease 2019 (COVID-19) has emerged as a main global public health challenge. Additionally, herpes simplex virus type-1 (HSV-1) and type 2 (HSV-2) are widespread viruses that can cause orolabial herpes and genital herpes. Several clinical case reports have declared a possible association between the two, however, the causal relationship between them has not been clarified. This study utilized a Mendelian randomization (MR) approach for causality assessment between COVID-19 infection and HSV infection based on the latest public health data and Genome-Wide Association Study (GWAS) data. Multiple causal estimation methods, such as IVW, weighted median, simple mode, and weighted mode, were employed to validate the causal relation between COVID-19 infection and HSV infection, with COVID-19 infection, COVID-19 hospitalization, and severe COVID-19 as exposures, and HSV1/2 infection as the outcome. A reverse MR analysis was subsequently performed. MR analysis exhibited that COVID-19 infection was relevant to a reduced risk of HSV1 infection (p=7.603239e-152, OR=0.5690, 95%CI=0.5455-0.5935, IVW). Regarding the effect of COVID-19 infection on HSV2, MR analysis suggested that COVID-19 infection was correlated with an augmented risk of HSV2 infection (p=6.46735e-11, OR=1.1137, 95%CI=1.0782-1.1502, IVW). The reverse MR analysis did not demonstrate a reverse causal relationship between HSV and COVID-19. Altogether, COVID-19 infection might cause a decreased risk of HSV1 infection and an elevated risk of HSV2 infection.

Comparison of Paper-and-Pencil Versus Tablet Administration of the 2021 National Youth Risk Behavior Survey (YRBS)

PurposeAs part of efforts to modernize the Youth Risk Behavior Surveillance System, the national Youth Risk Behavior Survey (YRBS) is moving from paper-and-pencil instrument (PAPI) administration to electronic administration using tablets. This study aimed to examine differences in demographic characteristics and the reporting of health behaviors and experiences between the PAPI- and tablet-administered 2021 national YRBS questionnaire. MethodsHigh school students (grades 9–12) in classrooms from 57 schools participating in the 2021 national YRBS were assigned randomly to complete the survey using PAPI (n = 4,684 students) or using tablets (n = 3,645 students). Eighty-nine behavior and experience items were examined to compare the missingness in reporting and the prevalence estimation (i.e., proportions) by administration mode. ResultsDemographic characteristics (sex, race/ethnicity, grade, and sexual identity) did not differ by mode (PAPI vs. tablet). For the majority (93.2%, 83 out of 89) of YRBS behavior and experience items, mode was not significantly associated with the reported proportions, adjusting for sex, race/ethnicity, grade, and sexual identity. However, 30 out of 89 (33.7%) items showed significant variation in missingness by mode; 10 items had higher missingness with PAPI administration while 20 had higher missingness with tablet administration. DiscussionSurvey administration mode was not significantly associated with behavior and experience reporting among high school students. More research is needed to understand differential patterns of missingness by mode. Aligning with Centers for Disease Control and Prevention's Public Health Data Modernization Initiative, findings from this study provide evidence to support electronic survey administration for the national YRBS, particularly using tablet data collection.

Social Determinants of Health, Quality of Public Health Data, and Health Equity in the United States

In recent years, increasing attention has been paid to the impact social determinants of health (SDOH) can have on health equity in the United States. In this essay, we provide a framework for considering the upstream structural factors that affect the distribution of SDOH as well as the downstream consequences for individuals and groups. Improving health equity in the United States will require multiple policy streams, each requiring comprehensive data for policy development, implementation, and evaluation. Although much progress has been made in improving these data, there remain considerable gaps and opportunities for improvement. (Am J Public Health. 2023;113(12):1301–1308. https://doi.org/10.2105/AJPH.2023.307423 )

Review of the impact of menopause symptoms on longevity

AbstractBackgroundEmerging evidence suggests that experiencing specific symptoms during menopause can heighten risks of developing conditions which can impact lifespan and long‐term vitality including dementia, cardiovascular disease, psychiatric conditions and cancer. In addition, it is now recognised that, while women live on average about 5 years longer than men, they often have poorer health which could be linked to this increased risk. The aim of the present review was to assess past literature to understand how distinct menopausal symptom presentations can impact lifespan and propensity for developing life‐limiting conditions.MethodThe following databases were searched: Google Scholar, EMBASE, MEDLINE, Cochrane Library, Scopus, PubMed, and Web of Science. Keys words aimed to capture factors relating to menopause, menopausal symptoms, mortality, and life limiting conditions such as Alzheimer’s disease, dementia, cardiovascular disease, stroke, cancer, liver disease, and suicide susceptibility.ResultFindings suggested that cognitive symptoms experienced during menopause were related to heightened risks of developing Alzheimer’s disease, mild cognitive impairment, dementia, and premature mortality. Vasomotor symptoms were related to increased risks of stroke and cardiovascular disease mortality. Psychological symptoms during the menopause transition were related to increased suicide risks and cardiovascular disease. Sleeping problems during menopause were related to heightened risks of cardiovascular mortality, cancer‐related mortality, and premature death from all causes.ConclusionThese findings warrant further investigation through robust analyses of public health data, to evaluate whether these outcomes are replicated within the general population. Should these findings endure, these outcomes suggest a need to improve the health of women during midlife, as doing so may prolong wellness in later life and also have consequences for socioeconomic costs. Furthermore, clarifying these associations may enable women and their health providers to recognise whether they are at risk of developing life‐limiting conditions, thus enhancing their vitality by increasing their likelihood of accessing preventative or early interventions and treatments.

Abstract B077: Understanding the relationship between neighborhood deprivation and breast cancer mortality among Black and White women

Abstract Background: Neighborhood deprivation has been associated with increased breast cancer mortality among White women, but findings are inconsistent among Black women, who are more likely to die from the disease. To improve our understanding of the relation, we investigated the joint effects of race and neighborhood racial composition, residential mobility, and rurality on the neighborhood deprivation-breast cancer mortality association among White and Black women. Methods: In 2010-2017, 31,358 non-Hispanic White and Black women diagnosed with invasive stage I-IIIA breast cancer were identified by the Georgia Cancer Registry and were followed through 2019 for breast cancer mortality. The neighborhood deprivation index (NDI) was derived through principal component analysis of 2011-2015 block group-level American Community Survey (ACS) data on poverty, income, housing, unemployment, occupation, and education. Neighborhood racial composition and residential mobility were measured using 2011-2015 ACS data. Rurality was measured using Georgia Department of Public Health data. We used Cox proportional hazards regression to estimate multivariable hazard ratios (HR) and 95% confidence intervals (CI) for the association between NDI, in quintiles, and breast cancer mortality, overall and according to joint effects of race and neighborhood characteristics (neighborhood racial composition, residential mobility, and rurality). Results: During follow-up, 2,353 (1,347 non-Hispanic White, 1,006 non-Hispanic Black) women died from breast cancer. Living in the most deprived neighborhoods was associated with a 30% increased risk of breast cancer mortality (quintile 5 vs. 1: HR=1.30, 95% CI 1.13-1.49). The association was present among non-Hispanic White (quintile 5 vs. 1: HR=1.47, 95% CI 1.20-1.79) but not non-Hispanic Black women. We observed similar race-specific patterns when assessing jointly stratified associations. For example, among non-Hispanic White women, neighborhood deprivation was associated with breast cancer mortality irrespective of the additional neighborhood characteristics considered, with the strongest association observed among non-Hispanic White women living in the most deprived rural neighborhoods compared to those living in the least deprived urban neighborhoods (HR=1.70, 95% CI 1.30-2.22). In contrast, no association was found among non-Hispanic Black women in any strata of neighborhood racial composition, residential mobility, or rurality (non-Hispanic Black women living in the most deprived rural neighborhoods vs. those living in the least deprived urban neighborhoods: HR=1.05, 95% CI 0.78-1.41). Conclusions: Consistent with previous studies, our study found that living in a deprived neighborhood may increase breast cancer mortality among non-Hispanic White but not non-Hispanic Black women. Neighborhood racial composition, residential mobility, and rurality did not explain the lack of association among non-Hispanic Black women. Other factors beyond neighborhood may contribute to the high breast cancer mortality rate among this racial group. Citation Format: Lauren E. Barber, Leah Moubadder, Maret L. Maliniak, Jasmine M. Miller-Kleinhenz, Jeffrey M. Switchenko, Lauren E. McCullough. Understanding the relationship between neighborhood deprivation and breast cancer mortality among Black and White women [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B077.

Lessons Learned From Applying a Monitoring and Evaluation Framework to Economic, Social, and Other Health Impacts of the COVID-19 Pandemic.

Individual and community-level COVID-19 mitigation policies can have effects beyond direct COVID-19 health outcomes, including social, behavioral, and economic outcomes. These social, behavioral, and economic outcomes can extend beyond the pandemic period and have disparate effects on populations. Public Health-Seattle & King County (PHSKC) built on the Centers for Disease Control and Prevention's community mitigation strategy framework to create a local project tracking near-real-time data to understand factors affected by mitigation approaches, inform decision-making, and monitor and evaluate community-level disparities during the pandemic. This case study describes the framework and lessons learned from PHSKC's collation, use, and dissemination of local data from 20 data sources to guide community and public health decision-making. Social, behavioral, economic, and health indicators were regularly updated and disseminated through interactive dashboards and products that examined data in the context of applicable policies. Data disaggregated by demographic characteristics and geography highlighted inequities, but not all datasets contained the same details; local surveys or qualitative data were used to fill gaps. Project outcomes included informing city and county emergency response planning related to implementation of financial and food assistance programs. Key lessons learned included the need to (1) build on existing processes and use automated processes and (2) partner with other sectors to use nontraditional public health data for active dissemination and data disaggregation and for real-time data contextualized by policy changes. This project provided programs and communities with timely, reliable data to understand where to invest recovery funding. A similar framework could position other health departments to examine social and economic effects during future public health emergencies.

Application of life course trajectory methods to public health data: A comparison of sequence analysis and group-based multi-trajectory modeling for modelling childhood adversity trajectories

There is increasing awareness of the importance of modelling life course trajectories to unravel how social, economic and health factors relate to health over time. Different methods have been developed and applied in public health to classify individuals into groups based on characteristics of their life course. However, the application and results of different methods are rarely compared. We compared the application and results of two methods to classify life course trajectories of individuals, i.e. sequence analysis and group-based multi-trajectory modeling (GBTM), using public health data. We used high-resolution Danish nationwide register data on 926,160 individuals born between 1987 and 2001, including information on the yearly occurrence of 7 childhood adversities in 2 dimensions (i.e. family poverty and family dynamics). We constructed childhood adversity trajectories from 0 to 15 years by applying (1) sequence analysis using optimal matching and cluster analysis using Ward's method and (2) GBTM using logistic and zero-inflated Poisson regressions. We identified 2 to 8 cluster solutions using both methods and determined the optimal solution for both methods. Both methods generated a low adversity, a poverty, and a consistent or high adversity cluster. The 5-cluster solution using sequence analysis additionally included a household psychiatric illness and a late adversity cluster. The 4-group solution using GBTM additionally included a moderate adversity cluster. Compared with the solution obtained through sequence analysis, the solution obtained through GBTM contained fewer individuals in the low adversity cluster and more in the other clusters. We find that the two methods generate qualitatively similar solutions, but the quantitative distributions of children over the groups are different. The method of choice depends on the type of data available and the research question of interest. We provide a comprehensive overview of important considerations and benefits and drawbacks of both methods.