Public Health Data Research Articles

To Address Women's Health Inequity, It Must First Be Measured.

Research and data collection related to what is historically known as "women's health" is consistently underfunded and marginalizes the health risks and experiences of women of color and transgender people. In the wake of the pandemic, the United States has an opportunity to redesign and reimagine a modern public health data infrastructure that centers equity and elevates the health and well-being of under-represented communities, including the full spectrum of gender identities. This piece offers a blueprint for transformational change in how the United States collects, interprets, and shares critical data to deliver greater health justice for all.

Klasterisasi Data Penanganan dan Pelayanan Kesehatan Masyarakat dengan Algoritma K-Means

Quality public health services are one of the characteristics of the country's successful development in the health sector. The Health Office has formulated a number of methods to determine the level of progress of health development at the center to the sub-districts. Every year the Lubuk Pakam Health Office collects public health data for processing so that it produces a ranking of regions with the predicate of healthy districts/cities. Data mining is a process used to extract and identify useful information and obtain some important information from data in analyzing public health data. Furthermore, the algorithm that will be used for data mining management in the case of analyzing public health data and used for cluster formation is the K-Means algorithm. The results obtained in the data grouping there are categories of patient assessment levels Very Satisfied, Satisfied, and Dissatisfied. From the results of the K-Means method, it can be concluded to improve services and health care as for the results of grouping the level of satisfaction

Using Patient-Reported Health Data From Social Media to Identify Diverse Lupus Patients and Assess Their Symptom and Medication Expressions: A Feasibility Study.

Patient communities use social media for peer support and information seeking. This study assessed the feasibility of using public patient-generated health data from the social network Twitter to identify diverse lupus patients and gather their perspectives about disease symptoms and medications. We extracted public lupus-related Twitter messages (n=47,715 tweets) in English posted by users (n=8,446) in the US between September 1, 2017 and October 31, 2018. We analyzed the data to describe lupus patients and the expressed themes (symptoms and medications). Two independent coders analyzed the data; Cohen's kappa coefficient was used to ensure interrater reliability. Differences in symptom and medication expressions were analyzed using 2-tailed Z tests and a combination of 1-way analysis of variance tests and unpaired t-tests. We found that lupus patients on Twitter are diverse in gender and race: approximately one-third (34.64%, 62 of 179) were persons of color (POCs), and 85.47% were female. The expressed disease symptoms and medications varied significantly by gender and race. Most of our findings correlated with documented clinical observations, e.g., expressions of general pain (8.39%, 709 of 8,446), flares (6.05%, 511 of 8,446), and fatigue (4.18%, 353 of 8,446). However, our data also revealed less well-known patient observations, e.g., possible racial disparities within ocular manifestations of lupus. Our results indicate that social media surveillance can provide valuable data of clinical relevance from the perspective of lupus patients. The medical community has the opportunity to harness this information to inform the patient-centered care within underrepresented patient groups, such as POCs.

Data Science in Public Health: Building Next Generation Capacity

Rapidly evolving technology, data, and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills, including data literacy, is particularly prominent given both the potential of novel data types and analysis methods to fill gaps in existing public health research and intervention practices, as well as the potential of such data or methods to perpetuate or augment health disparities. Through a review of public health courses and programs at the top 10 U.S. and globally ranked schools of public health, this article summarizes existing educational efforts in public health data science. These existing practices serve to inform efforts for broadening such curricula to further schools and populations. Data science ethics course offerings are also examined in context of assessing how population health principles can be blended into training across levels of data involvement to augment the traditional core of public health curricula. Parallel findings from domestic and international ‘outside-the-classroom’ training programs are also synthesized to advance approaches for increasing diversity in public health data science. Based on these program reviews and their synthesis, a 4-point formula is distilled for furthering public health data science education efforts toward development of a critical and inclusive mass of practitioners with fluency to leverage data to advance goals of public health and improve quality of life in the digital age.

Phase I control chart for individual autocorrelated data: application to prescription opioid monitoring

Phase I or retrospective process monitoring plays a key part in an overall statistical process monitoring (SPM) regime and is increasingly emphasized in the recent literature. At present, a lot of the data in a variety of settings (public and private sector organizations) are collected individually and sequentially and thus are serially correlated (or autocorrelated). Though a reasonable amount of work is available in the control charting literature for prospective (Phase II) autocorrelated data monitoring, very little work exists for the retrospective phase (Phase I). In this article, we present a Shewhart-type control chart for Phase I monitoring of individual autocorrelated data, assuming normality, with estimated parameters. The methodology, while developed and presented for the first-order autoregressive (AR(1)) model for simplicity, may be adapted to more general time series models. The correct charting constants, adjusted for autocorrelation and parameter estimation, are derived, and tabulated for a nominal in-control (IC) false alarm probability (FAP). Simulation results show that the proposed chart is favorably IC FAP robust and effective for reasonably small sample sizes, moderate autocorrelation, and some model miss-specifications, compared to other approaches. An illustration using some public health data involving prescription fentanyl transactions is provided to show the potential for broader areas of applications of the proposed methodology. Along with a summary and recommendations, some future research areas are indicated. An R package is developed and made available for implementing the proposed methodology on demand.

Making blind spots visible: the lack of race-conscious research and data in public and global health

Abstract In Continental Europe, race-conscious research and data (RCRD) is not adequately deployed in support of policy-making and social change to tackle health disparities. One reason is a lack of national data systems based on race in many countries, another is that researchers or policymakers are unaccustomed to taking them into account. One consequence of this for public and global health is the adoption and implementation of policies that are ostensibly race-neutral but which in fact reproduce “methodological whiteness” (Bhambra, 2017) and create or exacerbate racial health inequities. Calls to decolonise public health recognize the importance of RCRD, yet race or ethnicity as a variable in research continues to be seen as a contentious issue for a number of reasons. First, while some researchers see it as a valuable tool for addressing health inequalities or the impact of institutionalized and systemic racism on racialized/ethnic minority groups, others oppose its use on the ground that it can lead to stigmatization or racial stereotyping. Second, RCRD is often seen as too difficult to implement in practice, due to challenges of finding appropriate approaches to conceptualisation, operationalisation, data (non)collection, and interpretation. Third, many researchers fear that the use of race and ethnicity in research may be instrumentalized against minorities in ways that are sometimes difficult to anticipate. This project aims to contribute to addressing this, by building expertise for RCRD and creating a knowledge network and community of practice for researchers working in Public and Global Health in Continental Europe. The project begins by examining how RCRD is used at the Institute of Tropical Medicine Antwerp, which is located in Belgium, Europe but has research collaborations on other continents. It then continues with a scoping review on the use of RCRD to address racial and ethnic health disparities more generally across Europe. Key messages • Race-conscious research and data are lacking in public and global health research and policy-making, leading to racial health disparities. • Need to develop expertise and a network of researchers in race-conscious research and data in Europe.

Plenary 2 Benefits and challenges of the European Health Data Space

Abstract The creation of a European Health Data Space (EHDS) is one of the key components of a strong European Health Union. The objectives of the EHDS are: i) Empower individuals through better digital access to their personal health data; support free movement by ensuring that health data follow people; ii) Unleash the data economy by fostering a genuine single market for digital health services and products; and iii) Set up strict rules for the use of individual’s non-identifiable health data for research, innovation, policy-making and regulatory activities. As such, the EHDS aims to improve and support healthcare delivery within Europe by allowing public health data to be accessible throughout Europe. The EHDS also aims to promote better access and exchange of different types of health data for research and policy purposes. The aim is to have the EHDS up and running in 2025. The EHDS is expected to bring great benefit, but it also brings challenges related to technology, governance and privacy. The exchange of data at European level means that health data from different sources need to be able to talk to each other. Making the data Findable, Accessible, Interoperable, and Re-usable is key to the success of the EHDS. Moreover, the diversity of Europe’s health information systems need to be taken in account. The EHDS will also have to be transparent to ensure privacy of personal information included in the EHDS. Speakers: Fulvia Raffaelli European Commission, Brussels, Belgium Petronille Bogaert Sciensano, Brussels, Belgium Irene Schlünder TMF, Germany Emmanuel Bacry Health Data Hub, France

Exploring Public Discussions Regarding COVID-19 Vaccinations on Microblogs in China: Findings from Machine Learning Algorithms.

Large-scale, widespread COVID-19 vaccination is the most effective means of cutting off the spread of the novel coronavirus and establishing an immune barrier. Due to the large population base in China, it has been a very difficult task to establish such an immune barrier. Therefore, this study aims to explore the public’s discussions related to COVID-19 vaccinations on microblogs and to detect their sentiments toward COVID-19 vaccination so as to improve the vaccination rate in China. This study employed machine learning methods in the field of artificial intelligence to analyze mass data obtained from SinaWeibo. A total of 1,478,875 valid microblog texts were collected between December 2020 and June 2022, the results of which indicated that: (1) overall, negative texts (38.7%) slightly outweighed positive texts (36.1%); “Good” (63%) dominated positive texts, while “disgust” (44.6%) and “fear” (35.8%) dominated negative texts; (2) six overarching themes related to COVID-19 vaccination were identified: public trust in the Chinese government, changes in daily work and study, vaccine economy, international COVID-19 vaccination, the COVID-19 vaccine’s R&D, and COVID-19 vaccination for special groups. These themes and sentiments can clarify the public’s reactions to COVID-19 vaccination and help Chinese officials’ response to vaccine hesitancy. Furthermore, this study seeks to make up for the lack of focus on big data in public health and epidemiology research, and to provide novel insights for future studies.

Evaluation of the Homeless Management Information System for COVID-19 Surveillance Among People Experiencing Homelessness.

Variable and incomplete reporting of housing status creates challenges in the surveillance of coronavirus disease 2019 (COVID-19) among the homeless population in Los Angeles County (LA County) and nationwide. We developed standard investigation procedures to assess the housing status of LA County COVID-19 patients. Using data sharing procedures, we matched COVID-19 patients to Homeless Management Information System (HMIS) client profiles and supplemented with additional data sources for contributory data points and to further housing status ascertainment. We identified 10 586 COVID-19 patients among people experiencing homelessness (PEH) between 30 March 2020 and 30 December 2021; 2801 (26.5%) patients were first identified from HMIS profile matches, 1877 (17.7%) from quarantine/isolation housing intake rosters, 573 (5.4%) from hospital records, 749 (7.1%) from case and contact interviews, 3659 (34.6%) directly from PEH medical and service providers, and 927 (8.8%) had unknown sources. Among COVID-19 patients matched to HMIS profiles, 5351 (42.5%) were confirmed to be PEH at the time of COVID-19 diagnosis. Interoperability between public health data, HMIS, and external partners have been critical components in evaluating the impact of COVID-19 among the LA County homeless population. No one data source was complete for COVID-19 surveillance in this population.

Counting: How We Use Numbers to Decide What Matters

Counting: How We Use Numbers to Decide What Matters

A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study.

BackgroundDisease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards.ObjectiveWe sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources.MethodsFollowing a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems—a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds.ResultsData from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability.ConclusionsWe described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

A global epidemiological analysis of COVID-19 vaccine types and clinical outcomes

ObjectivesTo compare messenger RNA (mRNA)–based and adenovirus-vectored vaccines (ADVVs) with inactivated virus vaccines (IVVs) using real-world aggregate data. MethodsWe performed longitudinal analyses of publicly accessible epidemiological, clinical, virological, vaccine-related, and other public health data from 41 eligible countries during the first half of 2021. The relationships between vaccination coverage and clinical outcomes were analyzed using repeated measures correlation analyses and mixed-effects modeling to adjust for potential mediating and confounding factors. ResultsCountries that used mRNA and/or ADVV (n = 31) vs IVV, among other vaccine types (n = 10), had different distributions of age (42.4 vs 33.9 years, respectively; P-value = 0.0006), gross domestic product per capita ($ 38,606 vs $ 20,422, respectively; P <0.0001), and population sizes (8,655,541 vs 5,139,162, respectively; P-value = 0.36). After adjustment for country differences, the stringency of nonpharmaceutical interventions, and dominant SARS-CoV-2 variant types, populations that received mRNA and/or ADVV had significantly lower rates of cases and deaths over time (P <0.001 for each analysis). Populations vaccinated with IVV, among others, had significantly higher rates of cases and deaths over time (P <0.05 for each analysis). ConclusionThe real-world effectiveness of IVV may be inferior to mRNA and/or ADVV, and prospective comparative studies are needed to critically evaluate the role of IVV in the context of contemporary SARS-CoV-2 variants.

Toxoplasma gondii Antibodies in Raw Milk and Sera of Cows in China.

Toxoplasma gondii is a worldwide food-borne protozoa that has harmful influences on animal and human health. Raw milk containing T. gondii has been considered as one of the possible infectious sources for humans. Although China is one of the world’s leading milk consumers, there is still no study to investigate the seroprevalence of T. gondii in raw cow milk in China; especially for cows in rural areas. Thus, we conducted this study to examine the specific anti-T. gondii IgG-antibody in the raw milk and sera of domestic cows in China. In total, 894 cows were randomly selected from rural areas in northeastern China. The positive rate of T. gondii in the milk and serum samples were 6.38% (57/894) and 7.16% (64/894), respectively. Moreover, a history of abortion (OR = 2.03, 95% CI: 1.11–3.72, p = 0.022) was identified as the only risk factor for T. gondii infection in the studied cows. This study investigated the seroprevalence of T. gondii in the raw milk and sera of cows in China; it provided timely and useful data for public health and food safety, especially in rural areas.

The Relationships Between Social Media and Human Papillomavirus Awareness and Knowledge: Cross-sectional Study.

BackgroundHuman papillomavirus (HPV) is the most common sexually transmitted infection. HPV can infect both females and males, and it can cause many cancers, including anal, cervical, vaginal, vulvar, and penile cancers. HPV vaccination rates are lower than vaccination rates within other national vaccination programs, despite its importance. Research literature indicates that people obtain health-related information from internet sources and social media; however, the association between such health-seeking behavior on social media and HPV-related behaviors has not been consistently demonstrated in the literature.ObjectiveThis study aims to examine the association between social media usage and HPV knowledge and HPV awareness.MethodsThis study analyzed public health data collected through the Health Information National Trends Survey (HINTS) conducted by the US National Cancer Institute. The analysis used data collected in 2020; in total, 2948 responses were included in the analysis. Six HPV-related questions were used to identify HPV awareness, HPV vaccine awareness, and HPV knowledge about HPV-related cancers. Four questions about social media usage and one question about online health information–seeking behavior were used to analyze the associations between social media usage and HPV-related behaviors. Initially, six logistic regressions were conducted using replicate weights. Based on the results, significant factors were included in a second set of regression analyses that also included demographic variables.ResultsAbout half of the respondents were aware of HPV (68.40%), the HPV vaccine (64.04%), and the relationship between HPV and cervical cancer (48.00%). However, fewer respondents were knowledgeable about the relationships between HPV and penile cancer (19.18%), anal cancer (18.33%), and oral cancer (19.86%). Although social media usage is associated with HPV awareness, HPV vaccine awareness, and knowledge of cervical cancer, these associations were not significant after adjusting for demographic variables. Those less likely to report HPV awareness and knowledge included older participants, males, those with a household income of less than US $20,000, those with a formal education equal to or less than high school, or those who resided in a household where adults are not fluent in English.ConclusionsAfter adjusting for demographic variables, social media use was not related to HPV knowledge and awareness, and survey respondents were generally not aware that HPV can lead to specific types of cancer, other than cervical cancer. These results suggest that perhaps a lack of high-quality information on social media may impede HPV awareness and knowledge. Efforts to educate the public about HPV via social media might be improved by using techniques like storytelling or infographics, especially targeting vulnerable populations, such as older participants, males, those with low incomes, those with less formal education, or those who reside in the United States but are not fluent in English.

International Network for Public Health and Environmental Tracking (INPHET) and its role to Environmental Epidemiology

Background and aim: INPHET was formed by a community of environmental public health practitioners to address common interests and activities that advance environmental public health surveillance (EPHS) and data systems, and their uses to inform public health actions. Our aim is to show how this has supported environmental epidemiology practice. Methods: A &amp;#x22;Community of Practice”, INPHET main goals focus on sharing experiences and expertise; addressing common challenges to implementation and enhancing the science of EPHS/tracking; improving understanding of the link between the environment and health; and providing the appropriate evidence that informs public policy. Results: The network, initiated in ISEE meetings in 2012-13 and formally launched in Modena, Italy in 2014, has carried out several activities which aim at sharing expertise and learning from different models of environmental and health data integration and surveillance systems that directly inform policy. These include Twinning cooperation with Euro-Asian and Balkan countries and sharing of expertise with African Public Health Agencies. Some experiences are presented in this Symposium (Georgia, Ghana, Zambia), and highlight the EPHS/Tracking (EPHT) model adopted by several national public health agencies. It promotes that such integration and uses of environmental and health data is key for EPHS, including their accessibility and usability for communities of public health researchers, practitioners and policy makers. Conclusions: Environment and health monitoring and information systems are fundamental infrastructure for evidence provision for policy making. INPHET aims to address the vast inequalities across countries and communities in their ability to monitor, assess, inform and mitigate environmental and health issues. Its objectives and work support environmental epidemiology in its role as the pillar science that provides evidence for decision making and intervention aimed at reducing and preventing environmental related diseases, and contributes to an overall vision for public health.

Differences in Clinical Presentations of Omicron Infections with the Lineages BA.2 and BA.5 in Mecklenburg-Western Pomerania, Germany, between April and July 2022.

Knowledge on differences in the severity and symptoms of infections with the SARS-CoV-2 Omicron variants BA.2 (Pango lineage B.1.529.2) and BA.5 (Pango lineage B.1.529.5) is still scarce. We investigated epidemiological data available from the public health authorities in Mecklenburg-Western Pomerania, Northeast Germany, between April and July 2022 retrospectively. Comparative analyses revealed significant differences between recorded symptoms of BA.2 and BA.5 infected individuals and found strong correlations of associations between symptoms. In particular, the symptoms ‘chills or sweating’, ‘freeze’ and ‘runny nose’ were more frequently reported in BA.2 infections. In contrast, ‘other clinical symptoms’ appeared more frequently in Omicron infections with BA.5. However, the results obtained in this study provide no evidence that BA.5 has a higher pathogenicity or causes a more severe course of infection than BA.2. To our knowledge, this is the first report on clinical differences between the current Omicron variants BA.2 and BA.5 using public health data. Our study highlights the value of timely investigations of data collected by public health authorities to gather detailed information on the clinical presentation of different SARS-CoV-2 subvariants at an early stage.

Mathematical modeling of the dynamics of COVID-19 variants of concern: Asymptotic and finite-time perspectives.

The COVID-19 pandemic has seen multiple waves, in part due to the implementation and relaxation of social distancing measures by the public health authorities around the world, and also caused by the emergence of new variants of concern (VOCs) of the SARS-Cov-2 virus. As the COVID-19 pandemic is expected to transition into an endemic state, how to manage outbreaks caused by newly emerging VOCs has become one of the primary public health issues. Using mathematical modeling tools, we investigated the dynamics of VOCs, both in a general theoretical framework and based on observations from public health data of past COVID-19 waves, with the objective of understanding key factors that determine the dominance and coexistence of VOCs. Our results show that the transmissibility advantage of a new VOC is a main factor for it to become dominant. Additionally, our modeling study indicates that the initial number of people infected with the new VOC plays an important role in determining the size of the epidemic. Our results also support the evidence that public health measures targeting the newly emerging VOC taken in the early phase of its spread can limit the size of the epidemic caused by the new VOC (Wu et al., 2139Wu, Scarabel, Majeed, Bragazzi, & Orbinski, ; Wu et al., 2021).

Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study

The level of unpredictability of the COVID-19 pandemics poses a challenge to effectively model its dynamic evolution. In this study we incorporate the inherent stochasticity of the SARS-CoV-2 virus spread by reinterpreting the classical compartmental models of infectious diseases (SIR type) as chemical reaction systems modeled via the Chemical Master Equation and solved by Monte Carlo Methods. Our model predicts the evolution of the pandemics at the level of municipalities, incorporating for the first time (i) a variable infection rate to capture the effect of mitigation policies on the dynamic evolution of the pandemics (ii) SIR-with-jumps taking into account the possibility of multiple infections from a single infected person and (iii) data of viral load quantified by RT-qPCR from samples taken from Wastewater Treatment Plants. The model has been successfully employed for the prediction of the COVID-19 pandemics evolution in small and medium size municipalities of Galicia (Northwest of Spain).

REIN : un outil au service de la recherche

On the occasion of the 20th anniversary of the REIN (French Renal Epidemiology and Information Network), a summary work on the contributions of the national French ESKD register was carried out. On the issue its role in research, the following key messages were retained. The growing number of publications, dissertations, theses and teams involved shows that the REIN registry has attained its objective of being a shared research infrastructure, in the field of epidemiology as well as in public health, health economics or medical data processing. REIN is a valuable tool for the study of specific kidney diseases, particularly for epidemiological data pertaining to rare diseases that may lead to stage 5 of a chronic kidney disease. The linkage with the data from the National Healthcare Data System enables integrating and analysing the medical treatments received by patients, before the initiation of the replacement therapy and towards the end, as well as the details of the hospital stays.

Connecting food supply chains

Connecting food supply chains