Public Health Data Research Articles

A Critical Opportunity to Improve Public Health Data.

A Critical Opportunity to Improve Public Health Data.

WATERFALL METHOD OF MEDICAL RECORD APPLICATION DEVELOPMENT USING PHP AND MYSQL PROGRAMMING LANGUAGE

The use of computer technology and involvement in people's lives are involved in many aspects of life. One of the applications of computer technology in the health sector is to assist in detecting health problems suffered by the community. Modern society whose daily activities move dynamically certainly needs devices that help in reminding health conditions. One solution to this problem is the use of mobile health applications or M-Health, which tends to increase every year. Based on estimates in the United States, the number of users using these devices is 4500 in 2018 and an estimated increase is at 50,000 users in 2025. Another problem is that most of the current public health history is still recorded manually or not at all. Recording of public health data still uses books and for a long time, of course, these archives can be lost or damaged. One of the uses of this mobile medical history recording application is that the data will always be there. If at any time the user wants to look back at his history, he can just search in this system without having to look in the book and can save time. Current health history as one of the most important conditions in people's lives will provide services. Designing an application is carried out in research based on this problem which can make it easier for users to get their own health information quickly and flexibly using a browser that can be accessed via mobile phones or PCs. As a result of this research, this application will be developed using the waterfall process model and the SDLC (System Development Life Cycle) methodology.

Integrating human-centered design in public health data dashboards: lessons from the development of a data dashboard of sexually transmitted infections in New York State.

The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs. We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data. Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data. Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic. Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere.

Innovative platforms for data aggregation, linkage and analysis in the context of pandemic and epidemic intelligence.

During the COVID-19 pandemic, open-access platforms that aggregate, link and analyse data were transformative for global public health surveillance. This perspective explores the work of three of these platforms: Our World In Data (OWID), Johns Hopkins University (JHU) COVID-19 Dashboard (later complemented by the Coronavirus Resource Center), and Global.Health, which were presented in the second World Health Organization (WHO) Pandemic and Epidemic Intelligence Innovation Forum. These platforms, operating mostly within academic institutions, added value to public health data that are collected by government agencies by providing additional real-time public health intelligence about the spread of the virus and the evolution of the public health emergency. Information from these platforms was used by health professionals, political decision-makers and members of the public alike. Further engagement between government and non-governmental surveillance efforts can accelerate the improvements needed in public health surveillance overall. Increasing the diversity of public health surveillance initiatives beyond the government sector comes with several benefits: technology innovation in data science, engagement of additional highly skilled professionals, greater transparency and accountability for government agencies, and new opportunities to engage with members of society.

Measuring bereavement prevalence in a complex sampling survey: the 2019 Georgia Behavioral Risk Factor Surveillance System (BRFSS)

BackgroundThe Behavioral Risk Factor Surveillance System (BRFSS) is an annual survey designed to identify trends in the public’s health. In its 2019 field survey, the U.S. state of GA tested a new 3 – item module to measure the numbers of bereaved, resident adults aged 18 years and older. Participants were eligible if they answered ‘Yes’ to the item ‘Have you experienced the death of a family member or close friend in the years 2018 or 2019?’. This analysis explores two research questions. Can estimates for bereavement prevalence be derived without large sampling errors, low precision, and small subsamples? Can multiple imputation techniques be applied to overcome non-response and missing data to support multivariate modeling?MethodsBRFSS is a non-institutionalized sample of adults aged 18 years and older living in the U.S. state of Georgia. Analyses in this study were conducted under two scenarios. Scenario 1 applies the complex sample weights created by the Centers for Disease Control and imputes values for missing responses. Scenario 2 treats the data as a panel – no weighting combined with removal of persons with missing data. Scenario 1 reflects the use of BRFSS data for public health and policy, while Scenario 2 reflects data as it is commonly used in social science research studies.ResultsThe bereavement screening item has a response rate (RR) of 69.1% (5206 of 7534 persons). Demographic subgroups and categories of health have RR of 55% or more. Under Scenario 1, the estimated prevalence of bereavement is 45.38%, meaning that 3,739,120 adults reported bereaved in 2018 or 2019. The estimated prevalence is 46.02% with Scenario 2 which removes persons with any missing data (4,289 persons). Scenario 2 overestimates the bereavement prevalence by 1.39%. An illustrative logistic model is presented to show the performance of exposure to bereavement under the two data scenarios.ConclusionsRecent bereavement can be ascertained in a surveillance survey accounting for biases in response. Estimating bereavement prevalence is needed for measuring population health. This survey is limited to one US state in a single year and excludes persons aged 17 years and younger.

Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data.

Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.

New Web-Based System for Recording Public Health Nursing Practices and Determining Best Practices: Protocol of an Exploratory Sequential Design

Background Digitalization and information and communication technology (ICT) promote effective, efficient individual and community care. Clinical terminology or taxonomy and its framework visualize individual patients’ and nursing interventions’ classifications to improve their outcomes and care quality. Public health nurses (PHNs) provide lifelong individual care and community-based activities while developing projects to promote community health. The linkage between these practices and clinical assessment remains tacit. Owing to Japan’s lagging digitalization, supervisory PHNs face difficulties in monitoring each department’s activities and staff members’ performances and competencies. Randomly selected prefectural or municipal PHNs collect data on daily activities and required hours every 3 years. No study has adopted these data for public health nursing care management. PHNs need ICTs to manage their work and improve care quality; it may help identify health needs and suggest best public health nursing practices. Objective We aim to develop and validate an electronic recording and management system for evaluating different public health nursing practice needs, including individual care, community-based activities, and project development, and for determining their best practices. Methods We used a 2-phase exploratory sequential design (in Japan) comprising 2 phases. In phase 1, we developed the system’s architectural framework and a hypothetical algorithm to determine the need for practice review through a literature review and a panel discussion. We designed a cloud-based practice recording system, including a daily record system and a termly review system. The panels included 3 supervisors who were prior PHNs at the prefectural or municipal government, and 1 was the executive director of the Japanese Nursing Association. The panels agreed that the draft architectural framework and hypothetical algorithm were reasonable. The system was not linked to electronic nursing records to protect patient privacy. Phase 2 validated each item through interviews with supervisory PHNs using a web-based meeting system. A nationwide survey was distributed to supervisory and midcareer PHNs across local governments. Results This study was funded in March 2022 and approved by all ethics review boards from July to September and November 2022. Data collection was completed in January 2023. Five PHNs participated in the interviews. In the nationwide survey, responses were obtained from 177 local governments of supervisory PHNs and 196 midcareer ones. Conclusions This study will reveal PHNs’ tacit knowledge about their practices, assess needs for different approaches, and determine best practices. Additionally, this study will promote ICT-based practices in public health nursing. The system will enable PHNs to record their daily activities and share them with their supervisors to reflect on and improve their performance, and the quality of care to promote health equity in community settings. The system will support supervisory PHNs in creating performance benchmarks for their staff and departments to promote evidence-based human resource development and management. Trial Registration UMIN-ICDR UMIN000049411; https://tinyurl.com/yfvxscfm International Registered Report Identifier (IRRID) DERR1-10.2196/45342

Leveraging mHealth Technologies for Public Health.

Traditional public health surveillance efforts are generally based on self-reported data. Although well validated, these methods may nevertheless be subjected to limitations such as biases, delays, and costs or logistical challenges. An alternative is the use of smart technologies (eg, smartphones and smartwatches) to complement self-report indicators. Having embedded sensors that provide zero-effort, passive, and continuous monitoring of health variables, these devices generate data that could be leveraged for cases in which the data are related to the same self-report metric of interest. However, some challenges must be considered when discussing the use of mobile health technologies for public health to ensure digital health equity, privacy, and best practices. This paper provides, through a review of major Canadian surveys and mobile health studies, an overview of research involving mobile data for public health, including a mapping of variables currently collected by public health surveys that could be complemented with self-report, challenges to technology adoption, and considerations on digital health equity, with a specific focus on the Canadian context. Population characteristics from major smart technology brands-Apple, Fitbit, and Samsung-and demographic barriers to the use of technology are provided. We conclude with public health implications and present our view that public health agencies and researchers should leverage mobile health data while being mindful of the current barriers and limitations to device use and access. In this manner, data ecosystems that leverage personal smart devices for public health can be put in place as appropriate, as we move toward a future in which barriers to technology adoption are decreasing.

Machine learning and data analysis-based study on the health issues post-pandemic.

The COVID-19 pandemic has had significant impacts on the health of individuals and communities around the world. While the immediate health impacts of the virus itself are well-known, there are also a number of post-pandemic health issues that have emerged as a result of the pandemic. The pandemic has caused increased levels of anxiety, depression, and other mental health issues among people of all ages. The isolation, uncertainty, and grief caused by the pandemic have taken a toll on people's mental well-being, and there is a growing concern that the long-term effects of the pandemic on mental health could be severe. Many people have delayed or avoided medical care during the pandemic, which could lead to long-term health problems. Additionally, people who have contracted COVID-19 may experience ongoing symptoms, such as fatigue, shortness of breath, and muscle weakness, which could impact their long-term health. Machine learning (ML) can be a powerful tool to analyze the health impact of the post-pandemic period. With the vast amounts of data available from electronic health records, public health databases, and other sources, this article is making use of ML methods which can help identify patterns and insights to conclude the study. The proposed ML models can analyze health data to identify trends and patterns that may indicate future health problems. By monitoring patterns in medical records and public health data, the proposed ML model can help public health officials detect and respond to outbreaks more quickly. The survey outcome reveals that the level of physical activities has been decreased by 22% during COVID-19-outbreak. The variance is shown at49% during COVID-19 outbreak. The absence ofphysical activity (PA)and perceived stress (PS) are observed to be suggestivelycorrelatedwiththe QoL (quality of life) of adults. Deteriorated mental health also disrupts the normal lives and impacts the sleeping quality of people. The analysis of the data is performed using statistical analytical tools to depict the consequences of pandemic on the health of individuals aged between 50 to 80years.

The public availability of hospital CHNA reports: limitations and potential to study hospital investments in the next phase of public health.

Nonprofit hospitals have been required to complete and make publicly available their community benefit reports for more than a decade, a sign of changing expectations for private health care organizations to explicitly collaborate with public health departments to improve community health. Despite these important changes to practice and policy, no governmental agency provides statistics regarding compliance with this process. To better understand the nature and usefulness of the data provided through these processes, we led a research team that collected and coded Community Health Needs Assessment (CHNA) and Implementation Strategy (IS) Reports for a nationally representative sample of hospitals between 2018 and 2022. We utilized descriptive statistics to understand the frequency of noncompliance; t-tests and chi-square tests were employed to identify characteristics associated with incomplete documents. Approximately 95% of hospitals provided a public CHNA, and approximately 86% made their IS available. The extent of compliance with the CHNA/IS mandate indicates that these documents, paired with existing public health and policy data, offer considerable potential for understanding the investments nonprofit hospitals make to improve health outcomes and health equity in the communities they serve.

HealthE: Recognizing Health Advice & Entities in Online Health Communities

The task of extracting and classifying entities is at the core of important Health-NLP systems such as misinformation detection, medical dialogue modeling, and patient-centric information tools. Granular knowledge of textual entities allows these systems to utilize knowledge bases, retrieve relevant information, and build graphical representations of texts. Unfortunately, most existing works on health entity recognition are trained on clinical notes, which are both lexically and semantically different from public health information found in online health resources or social media. In other words, existing health entity recognizers vastly under-represent the entities relevant to public health data, such as those provided by sites like WebMD. It is crucial that future Health-NLP systems be able to model such information, as people rely on online health advice for personal health management and clinically relevant decision making. In this work, we release a new annotated dataset, HealthE, which facilitates the large-scale analysis of online textual health advice. HealthE consists of 3,400 health advice statements with token-level entity annotations. Additionally, we release 2,256 health statements which are not health advice to facilitate health advice mining. HealthE is the first dataset with an entity-recognition label space designed for the modeling of online health advice. We motivate the need for HealthE by demonstrating the limitations of five widely-used health entity recognizers on HealthE, such as those offered by Google and Amazon. We additionally benchmark three pre-trained language models on our dataset as reference for future research. All data is made publicly available.

A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data

Chronic obstructive pulmonary disease (COPD) is a serious chronic respiratory disease. Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease. With the continuous development of medical digitization, the application of big data informatization in the medical and health fields has become possible. Recently, applying innovative technologies such as big data analysis, machine learning, and artificial intelligence-assisted decision-making in the medical field has become an interdisciplinary research hotspot. Based on the identification and diagnosis of COPD in the high-risk population, this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions. The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.

Identifying patients at high risk for carbapenem-resistant Enterobacterales carriage upon admission to acute-care hospitals

Background: Prompt identification of patients colonized or infected with carbapenem-resistant Enterobacterales (CRE) upon admission can help ensure rapid initiation of infection prevention measures and may reduce intrafacility transmission of CRE. The Chicago CDC Prevention Epicenters Program previously created a CRE prediction model using state-wide public health data (doi: 10.1093/ofid/ofz483). We evaluated how well a similar model performed using data from a single academic healthcare system in Atlanta, Georgia, and we sought to determine whether including additional variables improved performance. Methods: We performed a case–control study using electronic medical record data. We defined cases as adult encounters to acute-care hospitals in a 4-hospital academic healthcare system from January 1, 2014, to December 31, 2021, with CRE identified from a clinical culture within the first 3 hospital days. Only the first qualifying encounter per patient was included. We frequency matched cases to control admissions (no CRE identified) from the same hospital and year. Using multivariable logistic regression, we compared 2 models. The “public health model” included 4 variables from the Chicago Epicenters model (age, number of hospitalizations in the prior 365 days, mean length of stay in hospitalizations in the prior 365 days, and hospital admission with an infection diagnosis in the prior 365 days). The “healthcare system model” added 4 additional variables (admission to the ICU in the prior 365 days, malignancy diagnosis, Elixhauser score and inpatient antibiotic days of therapy in the prior 365 days) to the public health model. We used billing codes to determine Elixhauser score, malignancy status, and recent infection diagnoses. We compared model performance using the area under the receiver operating curve (AUC). Results: We identified 105 cases and 441,460 controls (Table 1). CRE was most frequently identified in urine cultures (46%). All 4 variables included in the public health model and the 4 additional variables in the healthcare system model were all significantly associated with being a case in unadjusted analyses (Table 1). The AUC for the public health model was 0.76, and the AUC for the healthcare system model was 0.79 (Table 2; Fig. 1). In both models, a prior admission with an infection diagnosis was the most significant risk factor. Conclusions: A modified CRE prediction model developed using public health data and focused on prior healthcare exposures performed reasonably well when applied to a different academic healthcare system. The addition of variables accessible in large healthcare networks did not meaningfully improve model discrimination.Disclosures: None

Analyzing the Effectiveness of Data-Linked Projects for Health Promotion in Public Health Centers of South Korea.

The purpose of this study lies in verifying the effectiveness of the health promotion project which the public health center at the local level conducted by systematically linking the health examination results from the Health Insurance Corporation. We intend to emphasize the importance of linking the health-related public data. A survey was conducted to measure the effect of improving health behavior using EQ-5D-5L and demographic variables. As a result of the analysis, the residents (3.13) who had experienced the use of public health centers recognized more necessity for the service linked systematically with health checkup data than those (2.93) who had not. In addition, the residents who had experienced the use of public health centers responded that their chronic diseases had improved compared to a year ago (2.78→2.93). Next, those (3.04) who had experienced the services linked with health checkup data recognized that their chronic diseases and health conditions had been improved compared to those (2.81) who had not. However, in EQ-5D-5L, after using the service, mobility showed no difference between those who had used the service and those who had not. Furthermore, even in terms of self-management, daily life, etc., the management ability was further improved compared to those who had not used it, before using the service. This study showed the improved health level when the health promotion service of the public health center was provided by systematically linking the health checkup data of the Health Insurance Corporation in Korea. In order to increase the effectiveness of health data-linked projects, it is necessary to prepare guidelines for linking the public health data and to expand the data-linked project. It will be needed to further subdivide the health checkup results to provide customized services, and to secure dedicated personnel to reinforce the system link.

Improving success of non-communicable diseases mobile phone surveys: Results of two randomized trials testing interviewer gender and message valence in Bangladesh and Uganda.

Although interactive voice response (IVR) is a promising mobile phone survey (MPS) method for public health data collection in low- and middle-income countries (LMICs), participation rates for this method remain lower than traditional methods. This study tested whether using different introductory messages increases the participation rates of IVR surveys in two LMICs, Bangladesh and Uganda. We conducted two randomized, controlled micro-trials using fully-automated random digit dialing to test the impact of (1) the gender of the speaker recording the survey (i.e., survey voice); and (2) the valence of the invitation to participate in the survey (i.e., survey introduction) on response and cooperation rates. Participants indicated their consent by using the keypad of cellphones. Four study arms were compared: (1) male and informational (MI); (2) female and information (FI); (3) male and motivational (MM); and (4) female and motivational (FM). Bangladesh and Uganda had 1705 and 1732 complete surveys, respectively. In both countries, a majority of the respondents were males, young adults (i.e., 18-29-year-olds), urban residents, and had O-level/above education level. In Bangladesh, the contact rate was higher in FI (48.9%), MM (50.0%), and FM (55.2%) groups than in MI (43.0%); the response rate was higher in FI (32.3%) and FM (33.1%) but not in MM (27.2%) and MI (27.1%). Some differences in cooperation and refusal rates were also observed. In Uganda, MM (65.4%) and FM (67.9%) had higher contact rates than MI (60.8%). The response rate was only higher in MI (52.5%) compared to MI (45.9%). Refusal and cooperation rates were similar. In Bangladesh, after pooling by introductions, female arms had higher contact (52.1% vs 46.5%), response (32.7% vs 27.1%), and cooperation (47.8% vs 40.4%) rates than male arms. Pooling by gender showed higher contact (52.3% vs 45.6%) and refusal (22.5% vs 16.3%) rates but lower cooperation rate (40.0% vs 48.2%) in motivational arms than informational arms. In Uganda, pooling intros did not show any difference in survey rates by gender; however, pooling by intros showed higher contact (66.5% vs 61.5%) and response (50.0% vs 45.2%) rates in motivational arms than informational arms. Overall, we found higher survey rates among female voice and motivational introduction arms compared to male voice and informational introduction arm in Bangladesh. However, Uganda had higher rates for motivational intro arms only compared to informational arms. Gender and valence must be considered for successful IVR surveys. Name of the registry: ClinicalTrials.gov. Trial registration number: NCT03772431. Date of registration: 12/11/2018, Retrospectively Registered. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT03772431?term=03772431&cond=Non-Communicable+Disease&draw=2&rank=1. Protocol Availability: https://www.researchprotocols.org/2017/5/e81.

A systematic review of the quality and timeliness of public health data

The quality and timeliness of public health data is a topic of prime concern in this information age. Many epidemiologists, health scientists and researchers in the public health domain have consistently emphasized on the importance of the need for the right timely data for the right decision-making at the right time. In other words, there is an urgent need to ensure that the right data reaches the right people at the right time. However, this urgent need appears to be misleading and not achievable in the current public health practices and workflow processes. The workflow processes in the current healthcare environments enable data collection to be delayed and only to be captured when the events have already occurred. In this paper, a systematic review of relevant scientific literature was used to not only explore the complexity and uniqueness of public health data, but also explain why improving the quality and timeliness of public health data is a challenging endeavor for many epidemiologists, health scientists and researchers. Recommendations for streamlining the public health workflow processes to support the generation of high-quality and timely public health data were also discussed in the paper.

Use of Community SARS-CoV-2 Case Counts and Instantaneous Reproductive Number to Predict Short-Term COVID-19 Hospital Admission Volumes.

The severe acute respiratory syndrome (SARS-CoV-2) pandemic and high hospitalization rates placed a tremendous strain on hospital resources, necessitating the use of models to predict hospital volumes and the associated resource requirements. Complex epidemiologic models have been developed and published, but many require continued adjustment of input parameters. We developed a simplified model for short-term bed need predictions that self-adjusts to changing patterns of disease in the community and admission rates. The model utilizes public health data on community new case counts for SARS-CoV-2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV-2 in New York, New York (October 2020-April 2021) for its accuracy in predicting numbers of coronavirus disease 2019 (COVID-19) admissions 3, 5, 7, and 10 days into the future, comparing predicted admissions with actual admissions for each day at a large integrated health-care delivery network. The mean absolute percent error of the model was found to be low when evaluated across the entire health system, for a single region of the health system or for a single large hospital (6.1%-7.6% for 3-day predictions, 9.2%-10.4% for 5-day predictions, 12.4%-13.2% for 7-day predictions, and 17.1%-17.8% for 10-day predictions).

COVID-19 Surveillance After Expiration of the Public Health Emergency Declaration - United States, May 11, 2023.

On January 31, 2020, the U.S. Department of Health and Human Services (HHS) declared, under Section 319 of the Public Health Service Act, a U.S. public health emergency because of the emergence of a novel virus, SARS-CoV-2.* After 13 renewals, the public health emergency will expire on May 11, 2023. Authorizations to collect certain public health data will expire on that date as well. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority, and a number of surveillance indicators have been identified to facilitate ongoing monitoring. After expiration of the public health emergency, COVID-19-associated hospital admission levels will be the primary indicator of COVID-19 trends to help guide community and personal decisions related to risk and prevention behaviors; the percentage of COVID-19-associated deaths among all reported deaths, based on provisional death certificate data, will be the primary indicator used to monitor COVID-19 mortality. Emergency department (ED) visits with a COVID-19 diagnosis and the percentage of positive SARS-CoV-2 test results, derived from an established sentinel network, will help detect early changes in trends. National genomic surveillance will continue to be used to estimate SARS-CoV-2 variant proportions; wastewater surveillance and traveler-based genomic surveillance will also continue to be used to monitor SARS-CoV-2 variants. Disease severity and hospitalization-related outcomes are monitored via sentinel surveillance and large health care databases. Monitoring of COVID-19 vaccination coverage, vaccine effectiveness (VE), and vaccine safety will also continue. Integrated strategies for surveillance of COVID-19 and other respiratory viruses can further guide prevention efforts. COVID-19-associated hospitalizations and deaths are largely preventable through receipt of updated vaccines and timely administration of therapeutics (1-4).

Changing spatiotemporal patterns for hepatitis of unspecified aetiology in China, 2004-2021: a population-based surveillance study.

As global efforts continue toward the target of eliminating viral hepatitis by 2030, the emergence of acute hepatitis of unspecified aetiology (HUA) remains a concern. This study assesses the overall trends and changes in spatiotemporal patterns in HUA in China from 2004 to 2021. We extracted the incidence and mortality rates of HUA from the Public Health Data Center, the official website of the National Health Commission of the People's Republic of China, and the National Notifiable Infectious Disease Surveillance System from 2004 to 2021. We used R software, ArcGIS, Moran's statistical analysis, and joinpoint regression to examine the spatiotemporal patterns and annual percentage change in incidence and mortality of the HUA across China. From 2004 to 2021, a total of 707,559 cases of HUA have been diagnosed, including 636 deaths. The proportion of HUA in viral hepatitis gradually decreased from 7.55% in 2004 to 0.72% in 2021. The annual incidence of HUA decreased sharply from 6.6957 per 100,000 population in 2004 to 0.6302 per 100,000 population in 2021, with an average annual percentage change (APC) reduction of -13.1% (p < 0.001). The same result was seen in the mortality (APC, -22.14%, from 0.0089/100,000 in 2004 to 0.0002/100,000 in 2021, p < 0.001). All Chinese provinces saw a decline in incidence and mortality. Longitudinal analysis identified the age distribution in the incidence and mortality of HUA did not change and was highest in persons aged 15-59 years, accounting for 70% of all reported cases. During the COVID-19 pandemic, no significant increase was seen in pediatric HUA cases in China. China is experiencing an unprecedented decline in HUA, with the lowest incidence and mortality for 18 years. However, it is still important to sensitively monitor the overall trends of HUA and further improve HUA public health policy and practice in China.

Validation of immunoassays for the Chlamydia trachomatis antigen Pgp3 using a chimeric monoclonal antibody

Seroepidemiology, or measuring antibodies to pathogens to estimate population-level exposure, can provide useful public health data. The tests used, however, often lack sufficient validation data due to absence of a gold standard. For many pathogens, serum antibodies can be detected long after resolution of infection, but infection status is often used as a gold standard for antibody positivity. To ensure that recently developed antibody tests for seroepidemiology of Chlamydia trachomatis (Ct), the causative agent of urogenital chlamydia and the blinding eye disease trachoma, have high performance, we generated a chimeric antibody to the immunodominant Ct antigen Pgp3. Two clones were selected to evaluate the test performance of three assays to measure antibodies to Pgp3: multiplex bead assay (MBA), enzyme-linked immunosorbent assay (ELISA), and lateral flow assay (LFA). Overall, each assay demonstrated high accuracy and precision when tested using either clone, and the clones were stable when stored at − 20 °C and 4 °C for almost 2 years. The limit of detection was similar for MBA and LFA, but almost a log-fold higher (i.e. less sensitive) using ELISA. Overall, the chimeric antibodies represent stable control reagents for tests with robust performance and will facilitate deployment of these tests to other laboratories.