Big Data's Role in Precision Public Health

The Solutions in Health Analytics for Rural Equity Across the Northwest (SHARE-NW) Dashboard for Health Equity in Rural Public Health: Usability Evaluation

BACKGROUND

Given the dearth of resources to support rural public health practice, the solutions in health analytics for rural equity across the northwest dashboard (SHAREdash) was created to support rural county public health departments in northwestern United States with accessible and relevant data to identify and address health disparities in their jurisdictions. To ensure the development of useful dashboards, assessment of usability should occur at multiple stages throughout the system development life cycle. SHAREdash was refined via user-centered design methods, and upon completion, it is critical to evaluate the usability of SHAREdash.

OBJECTIVE

This study aims to evaluate the usability of SHAREdash based on the system development lifecycle stage 3 evaluation goals of efficiency, satisfaction, and validity.

METHODS

Public health professionals from rural health departments from Washington, Idaho, Oregon, and Alaska were enrolled in the usability study from January to April 2022. The web-based evaluation consisted of 2 think-aloud tasks and a semistructured qualitative interview. Think-aloud tasks assessed efficiency and effectiveness, and the interview investigated satisfaction and overall usability. Verbatim transcripts from the tasks and interviews were analyzed using directed content analysis.

RESULTS

Of the 9 participants, all were female and most worked at a local health department (7/9, 78%). A mean of 10.1 (SD 1.4) clicks for task 1 (could be completed in 7 clicks) and 11.4 (SD 2.0) clicks for task 2 (could be completed in 9 clicks) were recorded. For both tasks, most participants required no prompting-89% (n=8) participants for task 1 and 67% (n=6) participants for task 2, respectively. For effectiveness, all participants were able to complete each task accurately and comprehensively. Overall, the participants were highly satisfied with the dashboard with everyone remarking on the utility of using it to support their work, particularly to compare their jurisdiction to others. Finally, half of the participants stated that the ability to share the graphs from the dashboard would be "extremely useful" for their work. The only aspect of the dashboard cited as problematic is the amount of missing data that was present, which was a constraint of the data available about rural jurisdictions.

CONCLUSIONS

Think-aloud tasks showed that the SHAREdash allows users to complete tasks efficiently. Overall, participants reported being very satisfied with the dashboard and provided multiple ways they planned to use it to support their work. The main usability issue identified was the lack of available data indicating the importance of addressing the ongoing issues of missing and fragmented public health data, particularly for rural communities.

Citizens' digital footprints to support health promotion at the local level-PUHTI study, Finland

BACKGROUND

We aimed to explore to the possibilities of utilizing automatically accumulating data on health-owned for example by local companies and non-governmental organizations-to complement traditional health data sources in health promotion work at the local level.

METHODS

Data for the PUHTI study consisted of postal code level information on sport license holders, drug purchase and sales advertisements in a TOR online underground marketplace, and grocery sales in Tampere. Additionally, open population register data were utilized. An interactive reporting tool was prepared to show the well-being profile for each postal code area. Feedback from the tool's end-users was collected in interviews.

RESULTS

The study showed that buying unhealthy food and alcohol, selling or buying drugs, and participating in organized sport activities differed by postal code areas according to its socioeconomic profile in the city of Tampere. The health and well-being planners and managers of Tampere found that the new type of data brought added value for the health promotion work at the local level. They perceived the interactive reporting tool as a good tool for planning, managing, allocating resources and preparing forecasts.

CONCLUSIONS

Traditional health data collection methods-administrative registers and health surveys-are the cornerstone of local health promotion work. Digital footprints, including data accumulated about people's everyday lives outside the health service system, can provide additional information on health behaviour for various population groups. Combining new sources with traditional health data opens a new perspective for health promotion work at local and regional levels.

Advancing Newborn Screening Long-Term Follow-Up: Integration of Epic-Based Registries, Dashboards, and Efficient Workflows

The Connecticut Newborn Screening (NBS) Network, in partnership with the Connecticut Department of Public Health, strategically utilized the Epic electronic health record (EHR) system to establish registries for tracking long-term follow-up (LTFU) of NBS patients. After launching the LTFU registry in 2019, the Network obtained funding from the Health Resources and Services Administration to address the slow adoption by specialty care teams. An LTFU model was implemented in the three highest-volume specialty care teams at Connecticut Children's, involving an early childhood cohort diagnosed with an NBS-identified disorder since the formation of the Network in March 2019. This cohort grew from 87 to 115 over the two-year project. Methods included optimizing registries, capturing external data from Health Information Exchanges, incorporating evidence-based guidelines, and conducting qualitative and quantitative evaluations. The early childhood cohort demonstrated significant and sustainable improvements in the percentage of visits up-to-date (%UTD) compared to the non-intervention legacy cohort of patients diagnosed with an NBS disorder before the formation of the Network. Positive trends in the early childhood cohort, including %UTD for visits and condition-specific performance metrics, were observed. The qualitative evaluation highlighted the achievability of practice behavior changes for specialty care teams through responsive support from the nurse analyst. The Network's model serves as a use case for applying and achieving the adoption of population health tools within an EHR system to track care delivery and quickly fill identified care gaps, with the aim of improving long-term health for NBS patients.

The effect of data balancing approaches on the prediction of metabolic syndrome using non-invasive parameters based on random forest

BACKGROUND

Metabolic syndrome (MetS) is a cluster of metabolic abnormalities (including obesity, insulin resistance, hypertension, and dyslipidemia), which can be used to identify at-risk populations for diabetes and cardiovascular diseases, the main causes of morbidity and mortality worldwide. The achievement of a simple approach for diagnosing MetS without needing biochemical tests is so valuable. The present study aimed to predict MetS using non-invasive features based on a successful random forest learning algorithm. Also, to deal with the problem of data imbalance that naturally exists in this type of data, the effect of two different data balancing approaches, including the Synthetic Minority Over-sampling Technique (SMOTE) and Random Splitting data balancing (SplitBal), on model performance is investigated.

RESULTS

The most important determinant for MetS prediction was waist circumference. Applying a random forest learning algorithm to imbalanced data, the trained models reach 86.9% and 79.4% accuracies and 37.1% and 38.2% sensitivities in men and women, respectively. However, by applying the SplitBal data balancing technique, the best results were obtained, and despite that the accuracy of the trained models decreased by 7.8% and 11.3%, but their sensitivity improved significantly to 82.3% and 73.7% in men and women, respectively.

CONCLUSIONS

The random forest learning method, along with data balancing techniques, especially SplitBal, could create MetS prediction models with promising results that can be applied as a useful prognostic tool in health screening programs.

Comparison of Seven Healthy Lifestyle Scores Cardiometabolic Health: Age, Sex, and Lifestyle Interactions in the NutrIMDEA Web-Based Study

BACKGROUND

Global health status concerns both the assessment of wellbeing as well as the associated individualized determinants including quality of life and lifestyle factors. This study aimed to evaluate seven cardiometabolic health related scores and the influence, as well as interactions of lifestyle, heart-related and health-related quality of life (HRQoL) factors in order to inform the future implementation of precision public health (PPH).

METHODS

Data collected from 17,333 participants who were enrolled of the NutrIMDEA study. The data collection period was between May 2020 and November 2020 through an online survey. The baseline questionnaire collected information on socio-demographic data, cardiometabolic history, anthropometric variables and lifestyle aspects. Also, physical and mental component scores of SF12 Health Survey (PCS12/MCS12) were assessed as HRQoL features, which were applied to estimated seven scores (LS7, HLS, 20-years DRS %, FBS, CLI, WAI derived, LWB-I).

RESULTS

Most indices (except FBS, CLI, 20-years DRS % and WAI derived) showed that cardiometabolic outcomes and HRQoL measures were dependent on interactions by age and sex. The largest ponderal effect was found in PA total and Mediterranean Diet Score (MEDAS-14) interaction using LS7 as reference. However, using LWB-I as standard, the greatest effect was found in the quality-of-life feature MCS12. Noteworthy, LS7 showed good discrimination against PCS12, while LWB-I demonstrated excellent discrimination to MCS12.

CONCLUSIONS

A major finding was the interplay between MEDAS-14 and PA on the LS7 scale as well as major effects of lifestyle factors and MCS12/PCS12 among scores, which need to be accounted with precision when implementing cardiometabolic screenings with PPH purposes.

Advancing precision public health for obesity in children

Worldwide, far too many children and adolescents are living with the disease of obesity. Despite decades of public health initiatives, rates are still rising in many countries. This raises the question of whether precision public health may be a more successful approach to preventing obesity in youth. In this review, the objective was to review the literature on precision public health in the context of childhood obesity prevention and to discuss how precision public health may advance the field of childhood obesity prevention. As precision public health is a concept that is still evolving and not fully identifiable in the literature, a lack of published studies precluded a formal review. Therefore, the approach of using a broad interpretation of precision public health was used and recent advances in childhood obesity research in the areas of surveillance and risk factor identification as well as intervention, evaluation and implementation using selected studies were summarized. Encouragingly, big data from a multitude of designed and organic sources are being used in new and innovative ways to provide more granular surveillance and risk factor identification in obesity in children. Challenges were identified in terms of data access, completeness, and integration, ensuring inclusion of all members of society, ethics, and translation to policy. As precision public health advances, it may yield novel insights that can contribute to strong policies acting in concert that ultimately lead to the prevention of obesity in children.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Population-level, patient-reported outcomes: a case study regarding a public health intervention that involves patients with life-limiting illnesses

Introduction

Dying and death are public health concerns, but little is known about public health interventions that target populations living with life-limiting illnesses. This gap makes it difficult to identify best-practice public health interventions for this population and to achieve public health objectives. The study aimed to describe a public health intervention that intends to improve population-level outcomes using point-of-care and patient-reported outcomes.

Methods

A case study approach, informed by the Organization for Economic Co-operation and Development's (OECD) Best-Practice Public Health Framework, was used to describe coverage, effectiveness, and equity using mixed methods. Data from 2012 to 2022 were analyzed.

Results

Over the 10-year period, the number of deaths recorded in the programme (n = 16,358 to 32,421, +98.2%) as well as the percentage of the population that might benefit from palliative care increased (14.8% to 25.1%). The median age of those admitted for care (74 to 77 years) and the proportion of services participating in the programme located in outer regional and remote areas of Australia increased (2012: 59; 2022: 94; +5.4%). The access by patients that experience the greatest socioeconomic disadvantage decreased (2012: 18.2% n = 4,918; 2022: 15.9% n = 9,525). Improvements in relation to moderate distress related to pain were identified (2012: 63% n = 8,751, 2022: 69% n = 13,700), and one in five instances of severe distress related to pain did not improve (2012: 20% n = 781; 2022: 19% n = 635).

Conclusion

Population-level, patient-reported outcome data are useful and necessary in addressing public health objectives in populations with life-limiting illnesses. Our application of the OECD's Best-Practice Public Health Framework has helped to identify and describe a national intervention that may be transferred to other settings to address health promotion objectives. This may help improve the targeting of treatments to improve pain and issues related to equity.

Forecasting virus outbreaks with social media data via neural ordinary differential equations

During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.

Community- and data-driven homelessness prevention and service delivery: optimizing for equity

OBJECTIVE

The study tests a community- and data-driven approach to homelessness prevention. Federal policies call for efficient and equitable local responses to homelessness. However, the overwhelming demand for limited homeless assistance is challenging without empirically supported decision-making tools and raises questions of whom to serve with scarce resources.

MATERIALS AND METHODS

System-wide administrative records capture the delivery of an array of homeless services (prevention, shelter, short-term housing, supportive housing) and whether households reenter the system within 2 years. Counterfactual machine learning identifies which service most likely prevents reentry for each household. Based on community input, predictions are aggregated for subpopulations of interest (race/ethnicity, gender, families, youth, and health conditions) to generate transparent prioritization rules for whom to serve first. Simulations of households entering the system during the study period evaluate whether reallocating services based on prioritization rules compared with services-as-usual.

RESULTS

Homelessness prevention benefited households who could access it, while differential effects exist for homeless households that partially align with community interests. Households with comorbid health conditions avoid homelessness most when provided longer-term supportive housing, and families with children fare best in short-term rentals. No additional differential effects existed for intersectional subgroups. Prioritization rules reduce community-wide homelessness in simulations. Moreover, prioritization mitigated observed reentry disparities for female and unaccompanied youth without excluding Black and families with children.

DISCUSSION

Leveraging administrative records with machine learning supplements local decision-making and enables ongoing evaluation of data- and equity-driven homeless services.

CONCLUSIONS

Community- and data-driven prioritization rules more equitably target scarce homeless resources.

Midwifery learning and forecasting: Predicting content demand with user-generated logs

Every day, 800 women and 6700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help improve their learning competencies. In this work, we evaluate various forecasting methods to determine the future interest of users for the different types of content available in the Safe Delivery App, a digital training tool for skilled birth attendants, broken down by profession and region. This first attempt at health content demand forecasting for midwifery learning shows that DeepAR can accurately anticipate content demand in operational settings, and could therefore be used to offer users personalized content and to provide an adaptive learning journey.

Neighbourhood-related socioeconomic perinatal health inequalities: An illustration of the mediational g-formula and considerations for the big data context

BACKGROUND

Advances in computing power have enabled the collection, linkage and processing of big data. Big data in conjunction with robust causal inference methods can be used to answer research questions regarding the mechanisms underlying an exposure-outcome relationship. The g-formula is a flexible approach to perform causal mediation analysis that is suited for the big data context. Although this approach has many advantages, it is underused in perinatal epidemiology and didactic explanation for its implementation is still limited.

OBJECTIVE

The aim of this was to provide a didactic application of the mediational g-formula by means of perinatal health inequalities research.

METHODS

The analytical procedure of the mediational g-formula is illustrated by investigating whether the relationship between neighbourhood socioeconomic status (SES) and small for gestational age (SGA) is mediated by neighbourhood social environment. Data on singleton births that occurred in the Netherlands between 2010 and 2017 (n = 1,217,626) were obtained from the Netherlands Perinatal Registry and linked to sociodemographic national registry data and neighbourhood-level data. The g-formula settings corresponded to a hypothetical improvement in neighbourhood SES from disadvantaged to non-disadvantaged.

RESULTS

At the population level, a hypothetical improvement in neighbourhood SES resulted in a 6.3% (95% confidence interval [CI] 5.2, 7.5) relative reduction in the proportion of SGA, that is the total effect. The total effect was decomposed into the natural direct effect (5.6%, 95% CI 5.1, 6.1) and the natural indirect effect (0.7%, 95% CI 0.6, 0.9). In terms of the magnitude of mediation, it was observed the natural indirect effect accounted for 11.4% (95% CI 9.2, 13.6) of the total effect of neighbourhood SES on SGA.

CONCLUSIONS

The mediational g-formula is a flexible approach to perform causal mediation analysis that is suited for big data contexts in perinatal health research. Its application can contribute to providing valuable insights for the development of policy and public health interventions.

Doctor-patient bilateral matching considering diagnosis and treatment perception in the absence of public health resources

Introduction

The public health crisis is one of the main threats affecting the sustainable development of the economy and strengthening the rational allocation of medical resources is essential for building a strong public health system. Therefore, the study of the doctor-patient bilateral matching has important theoretical and practical significance and perception of diagnosis and treatment is taken as a key consideration in the research.

Methods

Based on the current situation of the medical industry and the main contradiction between supply and demand of medical services, an evaluation index of doctor-patient satisfaction is constructed in this paper. Then, based on the different forms of evaluation, calculate the doctor's satisfaction and patient's satisfaction respectively. Taking maximizing the overall satisfaction of doctors and patients, maximizing the number of patients and minimizing the workload difference between doctors as the decision-making objectives, considering the upper limit of doctors' working hours as the constraint condition, a multi-objective decision-making model is constructed and solved by NSGA-II algorithm to realize the matching between doctors and patients.

Conclusion

Finally, through the comparison with NSGA-III algorithm in three dimensions: the degree of convergence to the reference set, the propagation range of the solution and the running time of the algorithm, it is proved that NSGA-II algorithm has good performance in solving the matching problem of medical service supply and demand.

European national health plans and the monitoring of online searches for information on diabetes mellitus in different European healthcare systems

Diabetes mellitus (DM) is a serious non-communicable disease (NCD) and relies on the patient being aware of their condition, proactive, and having adequate medical care. European countries healthcare models are aware of the impact of these variables. This study evaluates the impact of online health information seeking behavior (OHISB) during World Diabetes Mellitus Day (WDMD) in European countries from 2014 to 2019 by grouping countries according to the changes in citizens' search behavior, diabetes mellitus prevalence, the existence of National Health Plans (NHP), and their respective healthcare systems. We extracted data from Global Burden of Disease, Google Trends (GT), Public Health European Commission, European Coalition for Diabetes, and the Spanish Ministry of Health. First, we used the broken-line models to analyze significant changes in search trends (GT) in European Union member countries in the 30-day intervals before and after the WDMD (November 14) from 2014 to 2019. Then the results obtained were used in the second phase to group these countries by factor analysis of mixed data (FAMD) using the prevalence of DM, the existence of NHP, and health models in each country. The calculations were processed using R software (gtrendsR, segmented, Factoextra, and FactoMineR). We established changes in search trends before and after WDMD, highlighting unevenness among European countries. However, significant changes were mostly observed among countries with NHP. These changes in search trends, in addition to being significant, were reiterated over time and occurred especially in countries belonging to the Beveridge Model (Portugal, Spain, and Sweden) and with NHPs in place. Greater awareness of diabetes mellitus among the population and continuous improvements in NHP can improve the patients' quality of life, thus impacting in disease management and healthcare expenditure.

Real-world data for precision public health of noncommunicable diseases: a scoping review

BACKGROUND

Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation.

RESULTS

Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear.

CONCLUSIONS

Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.

Spatial distribution and computational modeling for mapping of tuberculosis in Pakistan

Background

Tuberculosis (TB) like many other infectious diseases has a strong relationship with climatic parameters.

Methods

The present study has been carried out on the newly diagnosed sputum smear-positive pulmonary TB cases reported to National TB Control Program across Pakistan from 2007 to 2020. In this study, spatial and temporal distribution of the disease was observed through detailed district wise mapping and clustered regions were also identified. Potential risk factors associated with this disease depending upon population and climatic variables, i.e. temperature and precipitation were also identified.

Results

Nationwide, the incidence rate of TB was observed to be rising from 7.03% to 11.91% in the years 2007–2018, which then started to decline. However, a declining trend was observed after 2018–2020. The most populous provinces, Punjab and Sindh, have reported maximum number of cases and showed a temporal association as the climatic temperature of these two provinces is higher with comparison to other provinces. Machine learning algorithms Maxent, Support Vector Machine (SVM), Environmental Distance (ED) and Climate Space Model (CSM) predict high risk of the disease with14.02%, 24.75%, 34.81% and 43.89% area, respectively.

Conclusion

SVM has a higher significant probability of prediction in the diseased area with a 1.86 partial receiver-operating characteristics (ROC) value as compared with other models.

Priorities for successful use of artificial intelligence by public health organizations: a literature review

Artificial intelligence (AI) has the potential to improve public health's ability to promote the health of all people in all communities. To successfully realize this potential and use AI for public health functions it is important for public health organizations to thoughtfully develop strategies for AI implementation. Six key priorities for successful use of AI technologies by public health organizations are discussed: 1) Contemporary data governance; 2) Investment in modernized data and analytic infrastructure and procedures; 3) Addressing the skills gap in the workforce; 4) Development of strategic collaborative partnerships; 5) Use of good AI practices for transparency and reproducibility, and; 6) Explicit consideration of equity and bias.

PREVIDE: A Qualitative Study to Develop a Decision-Making Framework (PREVention decIDE) for Noncommunicable Disease Prevention in Healthcare Organisations

Noncommunicable diseases (NCDs), including obesity, remain a significant global public health challenge. Prevention and public health innovation are needed to effectively address NCDs; however, understanding of how healthcare organisations make prevention decisions is immature. This study aimed to (1) explore how healthcare organisations make decisions for NCD prevention in Queensland, Australia (2) develop a contemporary decision-making framework to guide NCD prevention in healthcare organisations. Cross-sectional and qualitative design, comprising individual semi-structured interviews. Participants (n = 14) were recruited from two organisations: the state public health care system (CareQ) and health promotion/disease prevention agency (PrevQ). Participants held executive, director/manager or project/clinical lead roles. Data were analysed in two phases (1) automated content analysis using machine learning (Leximancer v4.5) (2) researcher-led interpretation of the text analytics. Final themes were consolidated into a proposed decision-making framework (PREVIDE, PREvention decIDE) for NCD prevention in healthcare organisations. Decision-making was driven by four themes: Data, Evidence, Ethics and Health, i.e., data, its quality and the story it tells; traditional and non-traditional sources of evidence; ethical grounding in fairness and equity; and long-term value generated across multiple determinants of health. The strength of evidence was directly proportional to confidence in the ethics of a decision. PREVIDE can be adapted by public health practitioners and policymakers to guide real-world policy, practice and investment decisions for obesity prevention and with further validation, other NCDs and priority settings (e.g., healthcare).

Can information infrastructure development improve the health care environment? Evidence from China

Existing studies ignore the importance of information infrastructure development in improving regional health care environment. This paper adopts a spatial difference-in-difference (DID) model to assess the impact of information infrastructure development on urban health care environment based on a quasi-natural experiment of the "Broadband China" city pilots (BCCP). A balanced panel of 259 cities from 2010 to 2019 is selected for empirical analysis in this paper. Our findings show that the implementation of BCCP resulted in a 4.1 and 2.9% improvement in local medical workforce and medical infrastructure. In addition, there is significant spatial spillover effects of the implementation of BCCP, with 7.2 and 12.5% improvement in medical workforce and medical infrastructure in the surrounding areas. Our findings also suggest that information infrastructure development enhances the health care environment by driving industrial upgrading and education levels. Further analysis shows that BCCP has the strongest improvement on medical workforce in the eastern region and non-ordinary prefecture-level cities. For medical infrastructure, BCCP has stronger improvement in central region, western region, and non-ordinary prefecture-level cities. Finally, the paper conducts a series of robustness tests to ensure the reliability of the analysis results, including parallel trend tests, placebo tests, and re-estimation with different methods. Policies to improve the health care environment through information infrastructure development are proposed.

Reconnecting health through innovation

Learning health systems identify appropriate data to improve their performance and population health. The pandemic has shown that a proper response depends on using data from patients' needs, scientific research, hospital capacity, digital innovations, and stakeholder knowledge. Academic health centres play a role in data collection, information synthesis, and decision making supported by digital innovations. The results obtained by an academic centre and network in Quebec have demonstrated the value of integrating these elements during the pandemic and beyond.

Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases

Non-communicable diseases (NCDs) remain the largest global public health threat. The emerging field of precision public health (PPH) offers a transformative opportunity to capitalize on digital health data to create an agile, responsive and data-driven public health system to actively prevent NCDs. Using learnings from digital health, our aim is to propose a vision toward PPH for NCDs across three horizons of digital health transformation: Horizon 1—digital public health workflows; Horizon 2—population health data and analytics; Horizon 3—precision public health. This perspective provides a high-level strategic roadmap for public health practitioners and policymakers, health system stakeholders and researchers to achieving PPH for NCDs. Two multinational use cases are presented to contextualize our roadmap in pragmatic action: ESP and RiskScape (USA), a mature PPH platform for multiple NCDs, and PopHQ (Australia), a proof-of-concept population health informatics tool to monitor and prevent obesity. Our intent is to provide a strategic foundation to guide new health policy, investment and research in the rapidly emerging but nascent area of PPH to reduce the public health burden of NCDs.

mHealth interventions targeting movement behaviors in Asia: A scoping review

mHealth interventions can promote healthy movement behaviors (physical activity, sedentary behavior, and sleep). However, recent reviews include few studies from Asia, despite it being home to over 60% of the world population. The aim is to map the current evidence for mHealth interventions targeting movement behaviors in Asia. Six databases were searched up until August 2021. Included studies described an mHealth intervention targeting one or more movement behaviors, delivered in a country/territory in Asia, to a general population. A total of 3986 unique records were screened for eligibility in duplicate. Eighty studies with 1,413,652 participants were included. Most were randomized (38.8%) or quasi-experimental (27.5%) trials. Studies were from 17 countries/territories (out of 55); majority were high- (65.0%) or upper middle-income (28.7%). Physical activity was targeted most often (93.8%), few targeted sedentary behavior (7.5%), or sleep (8.8%). Most targeted one movement behavior (90.0%), and none targeted all three together. Interventions typically incorporated a single mHealth component (70.0%; app, pedometer, text messages, wearable) and were delivered remotely (66.3%). The average intervention length was 121.8 (SD 127.6) days. mHealth interventions in Asia have primarily targeted physical activity in high- and upper middle-income countries. There are few interventions targeting sedentary behavior or sleep, and no interventions in low-income countries.

Guideline-directed device therapies in heart failure: A clinical practice-based analysis using electronic health record data

Background

Guideline-directed device therapies (GDDT) improve outcomes for eligible patients with heart failure (HF) with reduced ejection fraction (HFrEF). Utilization rates of device therapies in HFrEF after the 2012 ACCF/AHA/HRS Focused Update for Device-based Therapies of Cardiac Rhythm Abnormalities have not been well studied.

Objective

Characterize the use of GDDT in newly indicated HFrEF patients from 2012 to 2019 using aggregated electronic health record (EHR) data.

Methods

Computable phenotyping algorithms for implantable cardioverter defibrillator/cardiac resynchronization therapy-defibrillator (ICD/CRT-D) indications from the GuideLine Indications Detected in EHR for Heart Failure program (GLIDE-HF) used diagnoses, procedures, measures, prescriptions, and the output of natural language processed provider notes from de-identified Optum® EHR data. Patients had a diagnosis of HF, dilated cardiomyopathy, or prior infarct, and were included if they had HFrEF with >1 year of records prior to a new Class 1 or Class 2a indication for an ICD or cardiac resynchronization therapy with defibrillator (CRT-D) from 2012 to 2019.

Results

Records showed 137,476 HFrEF patients were newly indicated for an ICD or CRT-D. GDDT was used in 14,892 of 36,358 (41.0%) CRT-D indicated patients and in 14,904 of 101,118 (14.7%) ICD-indicated patients. While GDDT use was low, 95.7% had echocardiography and 92.1% had prescriptions for beta-blockers or angiotensin-converting enzyme/angiotensin-receptor blockers medications.

Conclusions

In this modern cohort of HF patients, a large proportion of eligible patients did not receive ICDs or CRT-Ds, while frequently receiving other indicated cardiovascular interventions and treatments.

Intra-urban variation in tuberculosis and community socioeconomic deprivation in Lisbon metropolitan area: a Bayesian approach

Background

Multidrug resistant tuberculosis (MDR-TB) is a recognized threat to global efforts to TB control and remains a priority of the National Tuberculosis Programs. Additionally, social determinants and socioeconomic deprivation have since long been associated with worse health and perceived as important risk factors for TB. This study aimed to analyze the spatial distribution of non-MDR-TB and MDR-TB across parishes of the Lisbon metropolitan area of Portugal and to estimate the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.

Methods

In this study, we used hierarchical Bayesian spatial models to analyze the spatial distribution of notification of non-MDR-TB and MDR-TB cases for the period from 2000 to 2016 across 127 parishes of the seven municipalities of the Lisbon metropolitan area (Almada, Amadora, Lisboa, Loures, Odivelas, Oeiras, Sintra), using the Portuguese TB Surveillance System (SVIG-TB). In order to characterise the populations, we used the European Deprivation Index for Portugal (EDI-PT) as an indicator of poverty and estimated the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.

Results

The notification rates per 10,000 population of non-MDR TB ranged from 18.95 to 217.49 notifications and that of MDR TB ranged from 0.83 to 3.70. We identified 54 high-risk areas for non-MDR-TB and 13 high-risk areas for MDR-TB. Parishes in the third [relative risk (RR) = 1.281, 95% credible interval (CrI): 1.021–1.606], fourth (RR = 1.786, 95% CrI: 1.420–2.241) and fifth (RR = 1.935, 95% CrI: 1.536–2.438) quintile of socioeconomic deprivation presented higher non-MDR-TB notifications rates. Parishes in the fourth (RR = 2.246, 95% CrI: 1.374–3.684) and fifth (RR = 1.828, 95% CrI: 1.049–3.155) quintile of socioeconomic deprivation also presented higher MDR-TB notifications rates.

Conclusions

We demonstrated significant heterogeneity in the spatial distribution of both non-MDR-TB and MDR-TB at the parish level and we found that socioeconomically disadvantaged parishes are disproportionally affected by both non-MDR-TB and MDR-TB. Our findings suggest that the emergence of MDR-TB and transmission are specific from each location and often different from the non-MDR-TB settings. We identified priority areas for intervention for a more efficient plan of control and prevention of non-MDR-TB and MDR-TB.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00949-1.

Data sources for precision public health of obesity: a scoping review, evidence map and use case in Queensland, Australia

Background

Global action to reduce obesity prevalence requires digital transformation of the public health sector to enable precision public health (PPH). Useable data for PPH of obesity is yet to be identified, collated and appraised and there is currently no accepted approach to creating this single source of truth. This scoping review aims to address this globally generic problem by using the State of Queensland (Australia) (population > 5 million) as a use case to determine (1) availability of primary data sources usable for PPH for obesity (2) quality of identified sources (3) general implications for public health policymakers.

Methods

The Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Unique search strategies were implemented for ‘designed’ (e.g. surveys) and ‘organic’ (e.g. electronic health records) data sources. Only primary sources of data (with stratification to Queensland) with evidence-based determinants of obesity were included. Primary data source type, availability, sample size, frequency of collection and coverage of determinants of obesity were extracted and curated into an evidence map. Data source quality was qualitatively assessed.

Results

We identified 38 primary sources of preventive data for obesity: 33 designed and 5 organic. Most designed sources were survey (n 20) or administrative (n 10) sources and publicly available but generally were not contemporaneous (> 2 years old) and had small sample sizes (10-100 k) relative to organic sources (> 1 M). Organic sources were identified as the electronic medical record (ieMR), wearables, environmental (Google Maps, Crime Map) and billing/claims. Data on social, biomedical and behavioural determinants of obesity typically co-occurred across sources. Environmental and commercial data was sparse and interpreted as low quality. One organic source (ieMR) was highly contemporaneous (routinely updated), had a large sample size (5 M) and represented all determinants of obesity but is not currently used for public health decision-making in Queensland.

Conclusions

This review provides a (1) comprehensive data map for PPH for obesity in Queensland and (2) globally translatable framework to identify, collate and appraise primary data sources to advance PPH for obesity and other noncommunicable diseases. Significant challenges must be addressed to achieve PPH, including: using designed and organic data harmoniously, digital infrastructure for high-quality organic data, and the ethical and social implications of using consumer-centred health data to improve public health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12939-x.

A Scalable Quality Assurance Process for Curating Oncology Electronic Health Records: The Project GENIE Biopharma Collaborative Approach

The American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative is a multi-institution effort to build a pan-cancer repository of genomic and clinical data curated from the electronic health record. For the research community to be confident that data extracted from electronic health record text are reliable, transparency of the approach used to ensure data quality is essential.

Transparent QA processes for GENIE BPC ensure that the data can be used to support advances in precision oncology OR @jessicalavs of @MSKBiostats & coauthors discuss @AACR Project GENIE BPC, a multi-institution effort to aggregate clinical plus genomic data for patients with cancer. Transparent QA processes for GENIE BPC ensure that the data can be used to support advances in precision oncology.

Mapping precision public health definitions, terminology and applications: a scoping review protocol

INTRODUCTION

Precision public health is an emerging and evolving field. Academic communities are divided regarding terminology and definitions, and what the scope, parameters and goals of precision public health should include. This protocol summarises the procedure for a scoping review which aims to identify and describe definitions, terminology, uses of the term and concepts in current literature.

METHODS AND ANALYSIS

A scoping review will be undertaken to gather existing literature on precision public health. We will search CINAHL, PubMed, Scopus, Web of Science and Google Scholar, and include all documents published in English that mention precision public health. A critical discourse analysis of the resulting papers will generate an account of precision public health terminology, definitions and uses of the term and the use and meaning of language. The analysis will occur in stages: first, descriptive information will be extracted and descriptive statistics will be calculated in order to characterise the literature. Second, occurrences of the phrase 'precision public health' and alternative terms in documents will be enumerated and mapped, and definitions collected. The third stage of discourse analysis will involve analysis and interpretation of the meaning of precision public health, including the composition, organisation and function of discourses. Finally, discourse analysis of alternative phrases to precision public health will be undertaken. This will include analysis and interpretation of what alternative phrases to precision public health are used to mean, how the phrases relate to each other and how they are compared or contrasted to precision public health. Results will be grouped under headings according to how they answer the research questions.

ETHICS AND DISSEMINATION

No ethical approval will be required for the scoping review. Results of the scoping review will be used as part of a doctoral thesis, and may be published in journals, conference proceedings or elsewhere.

Applying a public health approach to identify priorities for regulating child product safety

OBJECTIVE

To identify leading injury risk factors and jurisdictional differences in Australian and US child-related product safety regulatory responses to inform the development of Australian policy and reform priorities.

METHODS

The study established and evaluated a knowledge base of child-related product safety regulatory responses (recalls, bans, standards and warnings) made in Australia and the US over the period 2011-17 to identify risk factors and potential regulatory gaps.

RESULTS

The research identified 1,540 Australian and US child-related product safety regulatory responses with the most common response type being product safety recall, and the leading product hazards in responses being choking, fire, fall, strangulation and chemical hazards. Jurisdictional differences identified potential regulatory gaps in Australia related to chemical hazards and high-risk durable infant and toddler products, and some data deficiencies in Australian responses.

CONCLUSIONS

Priorities include the need to improve the prevention orientation of the Australian product safety framework, to create an intelligence platform to assess injury risks more precisely and to address regulatory gaps related to the use of toxic chemicals in children's products and high-risk durable infant and toddler products. Implications for public health: The study demonstrates the identification of policy and reform priorities for child product safety using a public health lens.

Public health surveillance in the U.S. Department of Veterans Affairs: evaluation of the Praedico surveillance system

Background

Early threat detection and situational awareness are vital to achieving a comprehensive and accurate view of health-related events for federal, state, and local health agencies. Key to this are public health and syndromic surveillance systems that can analyze large data sets to discover patterns, trends, and correlations of public health significance. In 2020, Department of Veterans Affairs (VA) evaluated its public health surveillance system and identified areas for improvement.

Methods

Using the Centers for Disease Control and Prevention (CDC) Guidelines for Evaluating Public Health Surveillance Systems, we assessed the ability of the Praedico Surveillance System to perform public health surveillance for a variety of health issues and evaluated its performance compared to an enterprise data solution (VA Corporate Data Warehouse), legacy surveillance system (VA ESSENCE) and a national, collaborative syndromic surveillance platform (CDC NSSP BioSense).

Results

Review of system attributes found that the system was simple, flexible, and stable. Representativeness, timeliness, sensitivity, and Predictive Value Positive were acceptable but could be further improved. Data quality issues and acceptability present challenges that potentially affect the overall usefulness of the system.

Conclusions

Praedico is a customizable surveillance and data analytics platform built on big data technologies. Functionality is straightforward, with rapid query generation and runtimes. Data can be graphed, mapped, analyzed, and shared with key decision makers and stakeholders. Evaluation findings suggest that future development and system enhancements should focus on addressing Praedico data quality issues and improving user acceptability. Because Praedico is designed to handle big data queries and work with data from a variety of sources, it could be enlisted as a tool for interdepartmental and interagency collaboration and public health data sharing. We suggest that future system evaluations include measurements of value and effectiveness along with additional organizations and functional assessments.

Data Integration Challenges for Machine Learning in Precision Medicine

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

Advancing understanding of dietary and movement behaviours in an Asian population through real-time monitoring: Protocol of the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA)

Background

Modifiable risk factors for non-communicable diseases, including eating an unhealthy diet and being physically inactive, are influenced by complex and dynamic interactions between people and their social and physical environment. Therefore, understanding patterns and determinants of these risk factors as they occur in real life is essential to enable the design of precision public health interventions.

Objective

This paper describes the protocol for the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA). The study uses real-time data capture methods to gain a comprehensive understanding of eating and movement behaviours, including how these differ by socio-demographic characteristics and are shaped by people's interaction with their social and physical environment.

Methods

COBRA is an observational study in free-living conditions. We will recruit 1500 adults aged 21-69 years from a large prospective cohort study. Real-time data capture methods will be used for nine consecutive days: an ecological momentary assessment app with a global positioning system enabled to collect location data, accelerometers to measure movement, and wearable sensors to monitor blood glucose levels. Participants receive six EMA surveys per day between 8 a.m. and 9.30 p.m. to capture information on behavioural risk factors including eating behaviours and diet composition movement behaviours (physical activity, sedentary behaviour, sleep), and related contextual factors. The second wave of ecological momentary assessment surveys with a global positioning system enabled will be sent 6 months later. Data will be analysed using generalised linear models to examine associations between behavioural risk factors and contextual determinants.

Discussion

Findings from this study will advance our understanding of dietary and movement behaviours as they occur in real-life and inform the development of personalised interventions to prevent chronic diseases.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

Rising Mental Health Incidence Among Adolescents in Westchester, NY

CONTEXT

Many governments have publicly released healthcare data, which can be mined for insights about disease conditions, and their impact on society.

METHODS

We present a big-data analytics approach to investigate data in the New York Statewide Planning and Research Cooperative System (SPARCS) consisting of 20 million patient records.

FINDINGS

Whereas the age group 30-48 years exhibited an 18% decline in mental health (MH) disorders from 2009 to 2016, the age group 0-17 years showed a 5.4% increase. MH issues amongst the age group 0-17 years comprise a significant expenditure in New York State. Within this age group, we find a higher prevalence of MH disorders in females and minority populations. Westchester County has seen a 32% increase in incidences and a 41% increase in costs.

CONCLUSIONS

Our approach is scalable to data from multiple government agencies and provides an independent perspective on health care issues, which can prove valuable to policy and decision-makers.

Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment

BACKGROUND

Longer retention in opioid agonist treatment (OAT) is associated with improved treatment outcomes but 12-month retention rates are often low. Innovative approaches are needed to strengthen retention in OAT. We develop and compare traditional and deep learning-extensions of Cox regression to examine the potential for predicting time in OAT at individuals' first episode entry.

METHODS

Retrospective cohort study in New South Wales, Australia including 16,576 people entering OAT for the first time between January 2006 and December 2017. We develop 12-month OAT cessation prediction models using traditional and deep learning-extensions of the Cox regression algorithm with predictors evaluated from linked administrative datasets. Proportion of explained variation, calibration, and discrimination are compared using 5 × 2 cross-validation.

RESULTS

Twelve-month cessation rate was 58.4%. The largest hazard ratios for earlier cessation from the deep learning model were observed for treatment factors, including private dosing points (HR=1.54, 95% CI=1.49-1.60) and buprenorphine medication (HR=1.43, 95% CI=1.39-1.46). Diagnostic codes for homelessness (HR=1.09, 95% CI=1.04-1.13), outpatient treatment for drug use disorders (HR=1.10, 95% CI=1.06-1.15), and occupant of vehicle accident (HR=1.04, 95% CI=1.01-1.07) from past-year health service presentations were identified as significant predictors of retention. We observed no improvement in performance of the deep learning model over traditional Cox regression.

CONCLUSIONS

Deep learning may be more useful in identifying novel risk factors of OAT retention from administrative data than evaluating individual-level risk. An increased focus on addressing structural issues at the population level and considering alternate models of care may be more effective at improving retention than delivering fully personalised OAT.

Diagnosing Neurally Mediated Syncope Using Classification Techniques

Syncope is a medical condition resulting in the spontaneous transient loss of consciousness and postural tone with spontaneous recovery. The diagnosis of syncope is a challenging task, as similar types of symptoms are observed in seizures, vertigo, stroke, coma, etc. The advent of Healthcare 4.0, which facilitates the usage of artificial intelligence and big data, has been widely used for diagnosing various diseases based on past historical data. In this paper, classification-based machine learning is used to diagnose syncope based on data collected through a head-up tilt test carried out in a purely clinical setting. This work is concerned with the use of classification techniques for diagnosing neurally mediated syncope triggered by a number of neurocardiogenic or cardiac-related factors. Experimental results show the effectiveness of using classification-based machine learning techniques for an early diagnosis and proactive treatment of neurally mediated syncope.

Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol

Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals' health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations.

Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test

Syncope is the medical condition of loss of consciousness triggered by the momentary cessation of blood flow to the brain. Machine learning techniques have been established to be very effective way to address such problems, where a class label is predicted for given input data. This work presents a Support Vector Machine (SVM) based classification of neuro-mediated syncope evaluated using train-test-split and K-fold cross-validation methods using the patient's physiological data collected through the Head-up Tilt Test in pure clinical settings. The performance of the model has been analyzed over standard statistical performance indices. The experimental results prove the effectiveness of using SVM-based classification for the proactive diagnosis of syncope.

Characterization of Rookie Season Injury and Illness and Career Longevity Among National Basketball Association Players

IMPORTANCE

There is limited research investigating injury and illness among professional basketball players during their rookie season. By improving the understanding of injury incidence and risk specific to rookie players, sports medicine clinicians may be able to further individualize injury mitigation programs that address the unique needs of rookie players.

OBJECTIVE

To compare incidence and rate ratio (RR) of injury and illness among professional National Basketball Association (NBA) players in their rookie season with veteran players and to explore the association of sustaining an injury rookie season with career longevity.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used an online data repository and extracted publicly available data about NBA players between the 2007 and 2008 season to the 2018 and 2019 season. Available data for initial injury and all subsequent injuries were extracted during this time frame.

EXPOSURES

Injury and illness based on injury status during the rookie season of professional NBA players.

MAIN OUTCOMES AND MEASURES

Injury and illness incidence and RR. Association of injury during the rookie season with career longevity was assessed via Poisson regressions.

RESULTS

Of the 12 basketball seasons analyzed, 904 NBA players were included (mean [SD] age, 24.6 [3.9] years; body mass index, 24.8 [1.8]). The injury and illness incidence for rookie players was 14.28 per 1000 athlete game exposures (AGEs). Among all body regions, ankle injuries had the greatest injury incidence among players injured during their rookie season (3.17 [95% CI, 3.15-3.19] per 1000 AGEs). Rookie athletes demonstrated higher RR compared with veterans across multiple regions of the body (ankle: 1.32; 95% CI, 1.12 to 1.52; foot/toe: 1.29; 95% CI, 0.97 to 1.61; shoulder/arm/elbow: 1.43; 95% CI, 1.10 to 1.77; head/neck: 1.21; 95% CI, 0.61 to 1.81; concussions: 2.39; 95% CI, 1.89 to 2.90; illness: 1.14; 95% CI, 0.87 to 1.40), and demonstrated a higher rate of initial injuries compared with veteran players (1.41; 95% CI, 1.29 to 1.53). Players who sustained an injury rookie season demonstrated an unadjusted decrease in total seasons played (-0.4 [95% CI, -0.5 to -0.3] log years; P < .001), but this decrease was not observed within adjusted analysis (0.1 [95% CI, -0.1 to 0.2] log years; P = .36).

CONCLUSIONS AND RELEVANCE

In this study, rookie athletes demonstrated the highest injury incidence at the ankle and increased RR across multiple regions. These findings may reflect differences in preseason conditioning or load variables impacting rookie athletes and warrant further investigation. Future research is needed to determine the association of cumulative injury burden vs a singular injury event on career longevity.

An Intelligent Control Model of Credit Line Computing in Intelligence Health-Care Systems

Technologies such as machine learning and artificial intelligence have brought about a tremendous change to biomedical computing and intelligence health care. As a principal component of the intelligence healthcare system, the hospital information system (HIS) has provided great convenience to hospitals and patients, but incidents of leaking private information of patients through HIS occasionally occur at times. Therefore, it is necessary to properly control excessive access behavior. To reduce the risk of patient privacy leakage when medical data are accessed, this article proposes a dynamic permission intelligent access control model that introduces credit line calculation. According to the target given by the doctor in HIS and the actual access record, the International Classification of Diseases (ICD)-10 code is used to describe the degree of correlation, and the rationality of the access is formally described by a mathematical formula. The concept of intelligence healthcare credit lines is redefined with relevance and time Windows. The access control policy matches the corresponding credit limit and credit interval according to the authorization rules to achieve the purpose of intelligent control. Finally, with the actual data provided by a Grade-III Level-A hospital in Kunming, the program code is written through machine learning and biomedical computing-related technologies to complete the experimental test. The experiment proves that the intelligent access control model based on credit computing proposed in this study can play a role in protecting the privacy of patients to a certain extent.

Assessing the Electronic Evidence System Needs of Canadian Public Health Professionals: Cross-sectional Study

BACKGROUND

True evidence-informed decision-making in public health relies on incorporating evidence from a number of sources in addition to traditional scientific evidence. Lack of access to these types of data as well as ease of use and interpretability of scientific evidence contribute to limited uptake of evidence-informed decision-making in practice. An electronic evidence system that includes multiple sources of evidence and potentially novel computational processing approaches or artificial intelligence holds promise as a solution to overcoming barriers to evidence-informed decision-making in public health.

OBJECTIVE

This study aims to understand the needs and preferences for an electronic evidence system among public health professionals in Canada.

METHODS

An invitation to participate in an anonymous web-based survey was distributed via listservs of 2 Canadian public health organizations in February 2019. Eligible participants were English- or French-speaking individuals currently working in public health. The survey contained both multiple-choice and open-ended questions about the needs and preferences relevant to an electronic evidence system. Quantitative responses were analyzed to explore differences by public health role. Inductive and deductive analysis methods were used to code and interpret the qualitative data. Ethics review was not required by the host institution.

RESULTS

Respondents (N=371) were heterogeneous, spanning organizations, positions, and areas of practice within public health. Nearly all (364/371, 98.1%) respondents indicated that an electronic evidence system would support their work. Respondents had high preferences for local contextual data, research and intervention evidence, and information about human and financial resources. Qualitative analyses identified several concerns, needs, and suggestions for the development of such a system. Concerns ranged from the personal use of such a system to the ability of their organization to use such a system. Recognized needs spanned the different sources of evidence, including local context, research and intervention evidence, and resources and tools. Additional suggestions were identified to improve system usability.

CONCLUSIONS

Canadian public health professionals have positive perceptions toward an electronic evidence system that would bring together evidence from the local context, scientific research, and resources. Elements were also identified to increase the usability of an electronic evidence system.

Identifying non-traditional electronic datasets for population-level surveillance and prevention of cardiometabolic diseases: a scoping review protocol

INTRODUCTION

Cardiometabolic diseases, including cardiovascular disease, obesity and diabetes, are leading causes of death and disability worldwide. Modern advances in population-level disease surveillance are necessary and may inform novel opportunities for precision public health approaches to disease prevention. Electronic data sources, such as social media and consumer rewards points systems, have expanded dramatically in recent decades. These non-traditional datasets may enhance traditional clinical and public health datasets and inform cardiometabolic disease surveillance and population health interventions. However, the scope of non-traditional electronic datasets and their use for cardiometabolic disease surveillance and population health interventions has not been previously reviewed. The primary objective of this review is to describe the scope of non-traditional electronic datasets, and how they are being used for cardiometabolic disease surveillance and to inform interventions. The secondary objective is to describe the methods, such as machine learning and natural language processing, that have been applied to leverage these datasets.

METHODS AND ANALYSIS

We will conduct a scoping review following recommended methodology. Search terms will be based on the three central concepts of non-traditional electronic datasets, cardiometabolic diseases and population health. We will search EMBASE, MEDLINE, CINAHL, Scopus, Web of Science and Cochrane Library peer-reviewed databases and will also conduct a grey literature search. Articles published from 2000 to present will be independently screened by two reviewers for inclusion at abstract and full-text stages, and conflicts will be resolved by a separate reviewer. We will report this data as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

ETHICS AND DISSEMINATION

No ethics approval is required for this protocol and scoping review, as data will be used only from published studies with appropriate ethics approval. Results will be disseminated in a peer-reviewed publication.

Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health

INTRODUCTION

Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India.

METHODS

Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups.

RESULTS

Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning.

CONCLUSION

These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.

Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective

The field of precision medicine explores disease treatments by looking at genetic, socio-environmental, and clinical factors, thus trying to provide a holistic view of a person's health. Public health, on the other hand, is focused on improving the health of populations through preventive strategies and timely interventions. With recent advances in technology, we are able to collect, analyze and store for the first-time large volumes of real-time, diverse and continuous health data. Typically, the field of precision medicine deals with a huge amount of data from few individuals; public health, on the other hand, deals with limited data from a population. With the coming of Big Data, the fields of precision medicine and public health are converging into precision public health, the study of biological and genetic factors supported by large amounts of population data. In this paper, we explore through a comprehensive review the data types and use cases found in precision medicine and public health. We also discuss how these data types and use cases can converge toward precision public health, as well as challenges and opportunities provided by research and analyses of health data.

The Contribution of Clinical Pharmacologists in Precision Medicine: An Opportunity for Health Care Improvement

Background

Clinical pharmacologists play an important role and have professional value in the field, especially regarding their role within precision medicine (PM) and personalized therapies.

Objective

In this work, we sought to stimulate debate on the role of clinical pharmacologists.

Methods

A literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, through electronic consultation of 2 databases, PubMed/Medline and Embase, and Google Scholar with manual research taking into account the peer-reviewed literature such as observational studies, reviews, original research articles, comments, mini-reviews, and opinion papers published in English between 2010 and February 2020. Titles and abstracts were screened by 1 author, and studies identified for full-text analysis and selected according to inclusion criteria were agreed on by 2 reviewers.

Results

We identified a total of 535 peer-reviewed articles and the number of full texts eligible for the project was 43. Several publications highlight the clinical value of pharmacologists in highly complex hospitals, where the strategies of PM are implemented. Although there are still no studies measuring the clinical efficiency and the efficacy of clinical pharmacology services, and the applicability of PM protocols, this review shows the considerable debate around the future mission of clinical pharmacology services as a bridging discipline capable of combining the complex knowledge and different professional skills needed to fully implement PM.

Conclusions

Various strategies have been conceived and planned to facilitate the transition from mainstream medicine to PM, which will enable patients to be treated more accurately, with significant advantages in terms of safety and effectiveness of treatments. Therefore, in the future, to ensure that the evolutionary process of medicine can involve as many patients and caregivers as possible, infrastructures capable of bringing together different multidisciplinary skills among health professionals will have to be implemented. Clinical pharmacologists could be the main drivers of this strategy because they already, with their multidisciplinary training, operate in a series of services in high-level hospitals, facilitating the clinical governance of the most challenging patients. The implementation of these strategies will lastly allow national health organizations to adequately address the management and therapeutic challenges related to the advent of new drugs and cell and gene therapies by facilitating the removal of economic and organizational barriers to ensure equitable access to PM. (Curr Ther Res Clin Exp. 2021; 82:XXX–XXX)