Precise Health Research Articles

Challenging Aspects to Precise Health Strategies in Native Hawaiian and Other Pacific Islanders Using Statins.

Cardiometabolic disorders (CD), including cardiovascular disease (CVD), diabetes, and obesity, are the leading cause of health concern in the United States (U.S.), disproportionately affecting indigenous populations such a Native Hawaiian and Other Pacific Islanders (NHOPI). Dyslipidemia, a prevalent risk factor for the development and progression of CVD, is more prone to occur in NHOPI than other populations in the U.S. High-intensity statin therapy to reduce low-density lipoprotein cholesterol is associated with the prevention of CVD events. However, significant side-effects, such as muscle disorders, have been associated with its use. Different ethnic groups could experience variation in the prevalence of statin side effects due to sociodemographic, behavioral, and/or biological factors. Therefore, identifying the most impactful determinants that can be modified to prevent or reduce statin side effects for individuals from high-risk ethnic minority groups, such as NHOPI, can lead to more effective strategies to reduce health disparities. Thus, our Mini-Review explores the challenging aspects of public health precise strategies in NHOPI taking statins, including a culturally informed additional therapy that could positively impact the NHOPI population.

Knowledge of Emerging and Reemerging Infectious Diseases in the Public of Guangzhou, Southern China.

ObjectiveThe objective of this study is to get the overall picture about the knowledge of emerging and reemerging infectious diseases in public in Guangzhou and provide a scientific basis for developing health information strategies.MethodsWe used the structured questionnaire to interview 1,000 Guangzhou residents by health enquiry hotline. Descriptive analysis was presented to evaluate the knowledge of the participants. Multiple logistic regression model was performed to determine the influence factors for knowledge of emerging and reemerging infectious diseasesResultsA total of 801 individuals completed the survey. About one-third had heard of Middle East respiratory syndrome (MERS) and Zika, whereas Ebola and plague about 50%. A total of 32.08% participants had never heard of any of the four diseases. Only 2.08% knew the sexual transmission of Zika and 90.17% had no idea about the epidemic region of plague. No more than 15% knew they should check their health status after returning from the epidemic region. Education level and income were the key factors that influenced knowledge rate.ConclusionsThe low-level knowledge called for the improvement in health information to the public, especially those with low level of education and income. Effective and precise health information was urged to carry out to improve the prevention for the emerging and reemerging infectious diseases.

Influence of urban expansion on Lyme disease risk: A case study in the U.S. I-95 Northeastern corridor

Currently Lyme disease (LD) is the most common vector-borne disease in the United States. Understanding the potential effects of urban expansion on LD risk is an emerging global health concern. The U.S. Northeastern corridor has experienced a spatio-temporal increase in Lyme disease (LD) and rapid urban expansion over the past decades. The effects of LULCC factors on LD incidence thus were investigated based on county unit through a geographical detector and statistical methods along the Northeastern I-95 corridor during the year of 2008–2018. Analyses indicated that the Northeastern I-95 corridor was characterized by both higher urbanization and increasing LD cases. About 28% of the study area is covered by urban land, which is more than nine times that of the U.S. average. And LD incidence in the study area was calculated up to 39.1 cases per 100,000 population, nearly five times higher than that of the entire United States (8.3 cases according to CDC). In addition, the similar spatial distribution and spatial pattern between LD cases increase and urban expansion were confirmed. The statistical results showed that their similarity in spatial trend was up to 84.9%. Meanwhile, the correlation coefficients also verified that urban expansion has a significant positive correlation with LD incidence (r = 0.511) at the 99% confidential level. Geographical detector analysis further proved that urban expansion is the dominant factor for LD cases increase (P = 0.21). Moreover, the geographic location where the county is located is also a crucial factor for LD cases' impact (P = 0.15). Regional analysis revealed that LD risk appeared in urbanizing areas rather than urbanized and rural areas. These results suggested that rapidly urbanizing areas may become LD hotspots. Our studies improve understanding of LD occurrence in the context of urbanization and provide a more precise public health message to mitigate the tick-borne disease.

Precise Community-Based Public Health Management: Crucial Experience Responding to COVID-19 in Wuhan, China.

IntroductionFacing a grim public challenge caused by COVID-19, many countries decide to live with it for a long time, whereas China continues to enforce precise community-based public health management. This paper summarized China’s approaches and aimed to provide illumination for health services decision-makers.MethodsWe systematically reviewed the construction of precise community-based public health management from three aspects: organizational structure, service content, and flexible adjustment, and summed up four essential elements for success. Then, we selected 9 typical countries to compare their non-pharmaceutical strategies and daily new cases.ResultsChina’s community-based public health management has a clear four-level organizational structure. It has worked on infection control, surveillance for new cases, management of contacts, health education, medicare service, outdoor environment disinfection, and living material supply in different stages, and the daily new confirmed cases in Wuhan sustainedly declined to zero. The outbreak was relatively well contained in China, Italy, and Germany as of June 2020, as they adopted stricter movement restrictions, social distance, and patient tracking.ConclusionChina’s experience has proved the feasibility of non-pharmaceutical strategies responding to COVID-19. The precise community-based public health management strategy can be considered, as it’s pretty much the same as public health and social measures (PHSMs) advocated by WHO.

Towards health-aware energy management strategies in fuel cell hybrid electric vehicles: A review

An energy management strategy (EMS) is responsible for distributing the power between the electrochemical power sources of a fuel cell hybrid electric vehicle (FCHEV) with a view to minimizing the hydrogen consumption and maximizing the lifetime of the system. However, the energetic characteristics of the electrochemical devices (fuel cell, battery, and supercapacitor) are time-varying due to the influence of ageing, and different ambient and operating conditions. Any drift in the characteristics of the power sources can lead to the mismanagement of an EMS. According to the literature, ignorance of health adaptation can increase the hydrogen consumption from almost 6.5%–24% depending on the EMS. Therefore, it is necessary to develop a strategy which is aware of the actual state of the components while conducting the power split. Health monitoring techniques are potential candidates to deal with the uncertainties arising from the mentioned factors. In this respect, this paper first puts forward a concise review of the general modeling techniques which are essential for developing precise health monitoring techniques and in turn EMSs. Subsequently, the utilized methods for prognosis, diagnosis, and health state tracking of each of the mentioned power sources in a FCHEV are introduced. Then, a new taxonomy for the classification of the EMSs based on their health-awareness is proposed based on which three categories of prognostic-based, diagnostic-based, and systemic EMSs are formed. Each category is thoroughly explained, and a state-of-the-art review of these health-aware EMSs is presented. Finally, future perspectives of this new line of research and development are discussed before drawing a conclusion. • Health-awareness necessity in energy management strategy design is highlighted. • Health unawareness could lead to hydrogen consumption increase (6.5%–24%). • Online estimation techniques in electrochemical devices are discussed. • New categories for energy management of hybrid fuel cell vehicles are introduced. • Concepts of prognostic-, diagnostic-, and systemic-based strategies are explained.

Structural effects of 3D printing resolution on the gauge factor of microcrack-based strain gauges for health care monitoring

Measurements of physiological parameters such as pulse rate, voice, and motion for precise health care monitoring requires highly sensitive sensors. Flexible strain gauges are useful sensors that can be used in human health care devices. In this study, we propose a crack-based strain gauge fabricated by fused deposition modeling (FDM)-based three-dimensional (3D)-printing. The strain gauge combined a 3D-printed thermoplastic polyurethane layer and a platinum layer as the flexible substrate and conductive layer, respectively. Through a layer-by-layer deposition process, self-aligned crack arrays were easily formed along the groove patterns resulting from stress concentration during stretching motions. Strain gauges with a 200-µm printing thickness exhibited the most sensitive performance (~442% increase in gauge factor compared with that of a flat sensor) and the fastest recovery time (~99% decrease in recovery time compared with that of a flat sensor). In addition, 500 cycling tests were conducted to demonstrate the reliability of the sensor. Finally, various applications of the strain gauge as wearable devices used to monitor human health and motion were demonstrated. These results support the facile fabrication of sensitive strain gauges for the development of smart devices by additive manufacturing.

Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records.

A risk prediction model for major cardiovascular events was developed using population survey data linked to National Health Insurance (NHI) claim data and the death registry. Another set of population survey data were used to validate the model. The model was built using the Nutrition and Health Survey in Taiwan (NAHSIT) collected from 1993–1996 and linked with 10 years of events from NHI data. Major adverse cardiovascular events (MACEs) were identified based on hospital admission or death from coronary heart disease or stroke. The Taiwanese Survey on Hypertension, Hyperglycemia, and Hyperlipidemia (TwSHHH), conducted in 2002 was used for external validation. The NAHSIT data consisted of 1658 men and 1652 women aged 35–70 years. The incidence rates for MACE per 1000 person-years were 13.77 for men and 7.76 for women. These incidence rates for the TwSHHH were 7.27 for men and 3.58 for women. The model had reasonable discrimination (C-indexes: 0.76 for men; 0.75 for women), thus can be used to predict MACE risks in the general population. NHI data can be used to identify disease statuses if the definition and algorithm are clearly defined. Precise preventive health services in Taiwan can be based on this model.

CARTOGRAPHY AND MAPPING FOR PRECISION HEALTH: REVIEW OF ESSENTIAL ELEMENTS FOR DISEASE MAPPING

Abstract. A well-developed healthcare system, decent access to clean water and sanitation, and programmes to eliminate poverty and build modern infrastructure are essential components to create healthier Malaysia's population. Non-communicable diseases currently account for most of the mortality and morbidity, although communicable diseases such as dengue fever, avian flu and covid-19 still pose a threat. The World Health Organization (WHO) identified COVID-19 is a rare pneumonia disease that originated in Wuhan, on January 12, 2020, before it became an outbreak in all countries including Malaysia. The requirement of a precise mapping and Cartography for the accurate disease mapping and data management are crucial due to a precise map gives higher resolution of the data and for more specific data analysis, interpretation and decision making process. In Malaysia, there no specific report on precise mapping for health applications, and it is therefore this paper is to identify the potential criteria and factors needed for precise health mapping applications. A precise health mapping is essential to create a precise risk map towards the surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease phenomena.

Is precision public health the future — or a contradiction?

Some public-health researchers are embracing data and technology to target small groups with precise health interventions. Others fear that these tactics could fail millions. Some public-health researchers are embracing data and technology to target small groups with precise health interventions. Others fear that these tactics could fail millions.

Ethnicity and Perceived Influence of Social Media-Based Health Information on Health Decisions and Behaviors: A Test of the Social Diversification Hypothesis

The current study examined ethnic differences in the perceived influence of health information found on social media websites on health decisions and behaviors. These differences were examined through the lens of the social diversification hypothesis. The data were collected through a telephone survey. The sample included Israeli adult social media users who reported engaging in health information seeking on social media websites (n = 234). The results of the logistic regression analyses suggest that respondents from the Arab group were more likely than respondents from the Jewish group to report that health information on social media websites has persuaded them to stop or cut down on smoking, undertake medical tests, and purchase private health insurance. In addition, respondents from the Arab group were more likely than respondents from the Jewish group to report being influenced by health information on social media websites in multiple health domains. These findings provide extensive support for the social diversification hypothesis. They point to the need for increased investment in the provision of up-to-date and precise health information to members of disadvantaged population groups in a given society.

The northeast glucose drift: Stratification of post-breakfast dysglycemia among predominantly Hispanic/Latino adults at-risk or with type 2 diabetes.

SummaryBackgroundThere is minimal experience in continuous glucose monitoring (CGM) among underserved racial/ethnic minority populations with or at risk of type 2 diabetes (T2D), and therefore a lack of CGM-driven insight for these individuals. We analyzed breakfast-related CGM profiles of free-living, predominantly Hispanic/Latino individuals at-risk of T2D, with pre-T2D, or with non-insulin treated T2D.MethodsStarting February 2019, 119 participants in Santa Barbara, CA, USA, (93 female, 87% Hispanic/Latino [predominantly Mexican-American], age 54·4 [±12·1] years), stratified by HbA1c levels into (i) at-risk of T2D, (ii) with pre-T2D, and (iii) with non-insulin treated T2D, wore blinded CGMs for two weeks. We compared valid CGM profiles from 106 of these participants representing glucose response to breakfast using four parameters.FindingsA “northeast drift” was observed in breakfast glucose responses comparing at-risk to pre-T2D to T2D participants. T2D participants had a significantly higher pre-breakfast glucose level, glucose rise, glucose incremental area under the curve (all p < 0·0001), and time to glucose peak (p < 0·05) compared to pre-T2D and at-risk participants. After adjusting for demographic and clinical covariates, pre-breakfast glucose and time to peak (p < 0·0001) were significantly associated with HbA1c. The model predicted HbA1c within (0·550·67)% of true laboratory HbA1c values.InterpretationFor predominantly Hispanic/Latino adults, the average two-week breakfast glucose response shows a progression of dysglycemia from at-risk of T2D to pre-T2D to T2D. CGM-based breakfast metrics have the potential to predict HbA1c levels and monitor diabetes progression.FundingUS Department of Agriculture (Grant #2018–33800–28404), a seed grant from the industry board fees of the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (Award #1648451), and the Elsevier foundation.

Risk score-embedded deep learning for biological age estimation: Development and validation

The health index measures a person’s overall health status which provides useful information for people to manage their health, so developing a precise and relevant health index is urgent. Currently, many researchers have studied the biological age (BA) estimation, one of the beneficial health indices, by applying machine learning and deep learning techniques to health data. However, most of them have focused on the chronological age prediction or basic latent feature extraction methods. In this paper, we present a new algorithm to estimate BA, called Risk Score-Embedded Autoencoder-based BA (RSAE-BA). RSAE-BA can provide an accurate health index by using deep representation learning with an individual’s health risk. We first proposed a notion of risk score (RS) calculation to monitor a person’s health risk. Then we extracted latent features by using an autoencoder embedding the RS, and used them to generate BA. To evaluate RSAE-BA, we presented a new BA validation method using the RS, which is applicable to both unlabeled and labeled data. We compared the results of RSAE-BA with existing methods, and demonstrated the accuracy of RSAE-BA and its applicability to predict disease incidence. We believe that RSAE-BA will be a useful alternative method to measure a person’s health.

SISTEM INFORMASI PENGOLAHAN DATA PASIEN PADA PUSKESMAS TENAM MUARA BULIAN

Pusat Kesehatan Masyarakat (Puskesmas) is a provider of public health services to always provide fast, precise and accurate health services. Pusat Kesehatan Masyarakat (Puskesmas) have the main function to provide medical services in the form of perfect care and treatment to the community Data processing is a function of administrative activities used by agencies or offices to provide information services to organizations to deal with many managements. This health center is the object of this research, because at this time the patient data processing system is still manual, this is due to the absence of an integrated data processing system and mechanism and the limited facilities and infrastructure of information services that are produced not as expected. The author tries to find a solution to the problem in processing this data by implementing a computer-based data processing system&#x0D;

Multiple Indicators-Based Health Diagnostics and Prognostics for Energy Storage Technologies Using Fuzzy Comprehensive Evaluation and Improved Multivariate Grey Model

Precise health diagnostics and prognostics for batteries, which can improve the reliability and efficiency of energy storage technologies are significant. It is still a challenge to predict and diagnose state-of-health (SOH) of batteries due to the complicated and unobservable electrochemical reaction inside the batteries. In this article, a novel battery health estimation framework based on an optimized multiple health indicators (MHIs) system using fuzzy comprehensive evaluation (FCE) and improved multivariate grey model (IMGM) is proposed. The proposed MHIs system, which considers different characteristics of batteries is introduced. Health indicators (HIs) including partial incremental capacity curve peak area (PICA) and partial charge time period are extracted and optimized based on the Box-Cox transformation method. On the basis of the MHIs system, the FCE method is proposed for SOH diagnosis, which decreases the impact of dispersion of different batteries. In addition, an IMGM method is proposed for battery health prognostics considering the coupling relationship between MHIs and battery aging. The MHIs work together on the health prognostics, reducing the impact of the error of any HI on the overall prediction result. The experiments results indicate that the proposed methods show good performance on battery online health diagnostics and prognostics.

Novel Materials for Urban Farming.

Scarcity of natural resources, shifting demographics, climate change, and increasing waste are four major challenges in the quest to feed the exploding world population. These challenges serve as the impetus to harness novel technologies to improve agriculture, productivity, and sustainability. Urban farming has several advantages over conventional farming: higher productivity, improved sustainability, and the ability to provide fresh food all year round. Novel materials are key to accelerating the evolution of urban farming - with their ability to facilitate controlled release of nutrients and pesticides, improved seed health, substrates with better water retention capability, more efficient recycling of agricultural waste, and precise plant health monitoring. Materials science enables environmental sustainability and higher harvest yields in urban farms. Here, Singapore is used as an example of a land-scarce city where urban farming may be the solution for future food production. Potential research directions and challenges in urban farming are highlighted, and how material optimization and innovation drive the development of urban farming to meet national and global food demands is briefly discussed. This review serves as a guide for researchers and a reference for stakeholders of urban farms, policy makers, and other interested parties.

Precision Nursing.

Precision Nursing.

Impacts of Incarceration on Health Focusing on Minority Males: Considerations for COVID-19 and Future Pandemics

Long-standing health and social inequalities associated with minorities have increased their risk for infection, hospitalization, and death related to COVID-19. This disparity is further exacerbated with incarcerated individuals, yet little attention, both prepandemic and presently, has been devoted to collecting up-to-date data. This study uses the 2004 Survey of Inmates in State Correctional Facilities (SISCF), the most recent data, to explore the general offender population and self-reported chronic and medical health conditions to highlight how COVID-19 will impact marginalized populations. Results from the four binary regression models found that minority males are more likely to have chronic illnesses which increase in probability with longer sentences, number of incarcerations, and advancement in age. Our findings advocate for the development of recent data sets on inmate health, particularly minority individuals, as well as the construction of more precise health measures to address these health disparities, including COVID-19.

The Power and Peril of Precise vs. Round Health Message Interventions to Increase Stair Use.

Taking the stairs vs. an elevator generate benefits for the individual by increasing overall physical activity, health, and wellbeing. In the present paper, we report two pre-registered field intervention studies that examine how health message interventions can motivate individuals to change their behavior. We empirically contrasted opposing predictions from the literature as to whether numerically round (60.00%) or precise (61.87%) health messages are more effective in causing people to use the stairs over taking the elevator. Both interventions were compared to a control condition (no-health message). Contrary to our hypotheses and extant findings, both intervention studies did not produce a significant positive effect of the interventions relative to the control condition. In recent years such null findings have received increasingly more appreciation, particularly in the light of evident downsides of file-drawered studies. We discuss a number of moderating factors that may determine when and why nudging interventions are (in-) effective (e.g., a priori behavioral prevalence, pre-established habits, ceiling effects, and building infrastructure), as well as limitations and avenues for future research.

Achieving Equity in Asian American Healthcare: Critical Issues and Solutions.

Equity is a fundamental goal of the US health care system. Asians comprise 6% of the US population, and 60% of the world's population. Less than 1% of National Institutes of Health funding is directed toward Asian health. Asian health outcomes are often worse than non-Hispanic Whites (NHWS) in America. Increasing federal and foundation resources and funding toward Asian health research could illuminate these risks and develop precision interventions to reduce known health disparities. When disaggregated by race/ethnicity, Asian health risks are even more apparent. Here, we discuss critical health outcome differences between the six major Asian American subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese) comprising 80% of the US Asian population, highlighting demographic, pharmacologic, disease prevalence, and mortality outcomes. We then outline seven critical issues contributing to Asian American health disparities, including aggregated Asian health data, undersampling, invalid extrapolations, underrepresentation in clinical trials, lack of funding and awareness of disparities, and the model minority myth. Building on the successes of national public health initiatives, we propose nine leverage points to improve Asian American health including the following: obtaining disaggregated Asian health data, improved Asian health research (oversampling Asians, improving clinical trial participation, and increasing research funding), stakeholder collaboration (national and with Asian nations), community engagement, providing culturally precise health care, and expansion of the Asian American research ecosystem. Achieving health equity takes deliberate practice and does not occur by accident. By addressing critical issues that perpetuate Asian health disparities, we grow closer to understanding how to effectively improve Asian health and build a nationally unified mindset toward action that emphasizes equitable care for all.

Prevalence and Related Factors of Depression, Anxiety, Acute Stress, and Insomnia Symptoms Among Medical Staffs Experiencing the Second Wave of COVID-19 Pandemic in Xinjiang, China.

The prevalence and related factors of mental health impact among medical staffs who experienced the second wave of the COVID-19 pandemic in China is unknown. Therefore, this survey was conducted to investigate the prevalence and related factors of depressive, anxiety, acute stress, and insomnia symptoms in medical staffs in Kashi, Xinjiang, China during the second wave of the COVID-19 pandemic. A cross-sectional online survey was conducted among medical staffs working in First People's Hospital of Kashi, Xinjiang. The questionnaire collected demographic data and self-design questions related to the COVID-19 pandemic. The Impact of Events Scale-6, the Insomnia Severity Index, the Patient Health Questionnaire-9, the Generalized Anxiety Disorder Scale-7, the Perceived Social Support Scale, the Chinese Big Five Personality Inventory-15, and the Trait Coping Style Questionnaire were used to measure psychological symptoms or characteristics. Binary logistic regression was carried out to examine the associations between socio-demographic factors and symptoms of depression, anxiety, stress, and insomnia. In total, data from 123 participants were finally included, among which the prevalence rate of depressive, anxiety, acute stress, and insomnia symptoms is 60.2, 49.6, 43.1, and 41.1%, respectively. The regression model revealed that minority ethnicity, being worried about infection, spending more time on following pandemic information, and neurotic personality were positively associated with the mental health symptoms, while extraversion personality, higher education level, and better social support were negatively associated. In our study, the prevalence of mental health impact was high among medical staffs in Kashi, China who experienced the second wave of the COVID-19 pandemic. Several factors were found to be associated with mental health conditions. These findings could help identify medical staffs at risk for mental health problems and be helpful for making precise mental health intervention policies during the resurgence. Our study may pave way for more research into Xinjiang during the COVID-19 pandemic.