LOCATION AND CAPACITY PLANNING FOR PREVENTIVE HEALTHCARE FACILITIES WITH CONGESTION EFFECTS

A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost importance to governments since it can make massive savings on healthcare expenditure and promote the welfare of the society. Recognizing the importance of preventive healthcare, this research aims to present a methodology for designing a network of preventive healthcare facilities in order to prevent diseases early. The problem is formulated as a bilevel non-linear integer programming model. The upper level is a facility location and capacity planning problem under a limited budget, while the lower level is a user choice problem that determines the allocation of clients to facilities. A genetic algorithm (GA) is developed to solve the upper level problem and a method of successive averages (MSA) is adopted to solve the lower level problem. The model and algorithm is applied to analyze an illustrative case in the Sioux Falls transport network and a number of interesting results and managerial insights are provided. It shows that solutions to medium-scale instances can be obtained in a reasonable time and the marginal benefit of investment is decreasing. © 2023, Journal of Industrial and Management Optimization. All Rights Reserved.

MEDICAL EFFICACY OF A SPONTANEOUS MEDICO-SOCIAL EXPERIMENT (BY THE EXAMPLE OF COVID-19 REGIONAL PREVENTION)

Relevance: studies of the effectiveness of large-scale medico-social experiments (MSE) are quite rare. On the one hand, MSE is, as a rule, a response to force ma-jeure circumstances, which should not be at all in ordinary live. On the other hand, such experiments, as well as their study, are processes characterized by high cost and considerable duration. Nevertheless, the example of COVID-19 regional prevention shows that coordinated interaction of various parts of the social organism can and should be carried out in a critical situation, that the effectiveness of this interaction should be quantified using integrative indicators of sanitary statistics. This interaction illustrates the spontaneous formation of a regional model of preventive healthcare of the future, which is characterized by active socio-economic and information-cognitive support of the main preventive and (or) treatment and diagnostic process aimed at the population of the region using a wide range of activities, including traditional and new educational technologies. Gol: to evaluate the medical effectiveness of the first pre-vaccination year of COVID-19 prevention at the regional level using integrative quantitative indicators of sanitary statistics. Methods: historical, statistical, graphical, linear regression analysis, system analysis and synthesis, organizational and methodological modeling. Results: the slope coefficients in the linear regression formulas corresponding to the annual growth rate (GR) on the graphs of prevalence and incidence in the studied social groups indicated that in the pre-pandemic years, the prevalence in the population was permanently growing-GR was within from +17 to +49 ‰ per year, and the incidence showed relative stability-GR was in the range from +8 to +12 ‰, and even decreased in the children’s population (-17 ‰ annually). Comprehensive preventive measures of the first pandemic year (2020) reversed the general growth trend and accelerated the process significantly. Thus, incidence GR was in the range from-35 to-257 ‰, and prevalence GR-from-179 to-326 ‰. Conclusion: preventive measures against COVID-19, carried out in the form of a large-scale MSE, made it possible to introduce at the regional level a model of systemic integrated preventive healthcare of the future, the medical effectiveness of which is many times greater than the previous organizational and methodological model of “autonomous” healthcare, as far as this can be judged by the emerging trends of change integrative indicators of health (prevalence and incidence). © 2022, Science and Innovation Center Publishing House. All rights reserved.

Explanatory power of the tourist destination competitiveness index on the control of the first wave of COVID-19

Purpose: This article intends to analyse the explanatory power of the Travel and Tourism Competitiveness Index (TTCI) and some of its constituent factors on national success metrics in managing the initial surge of the COVID-19 pandemic. Design/methodology/approach: The authors study the outbreak control effectiveness of 132 countries during the first semester of 2020. The authors apply generalized linear regression models and weighted least squares models using 6 COVID-19-related dependent variables, 9 TTCI-related independent variables and 12 control variables. Findings: The results suggest that countries with superior TTCI values and selected constituent factors have the highest daily averages of coronavirus infections and fatalities per million and the highest speed rates of COVID-19 spread. The authors also find that these countries have the shortest government response time, the lowest daily average of the social restrictions index and the shortest time from the first case reported in China to the first case reported nationally. Originality/value: To the best of the authors' awareness, no previous study exists analysing the statistical relationship between the TTCIB and some of its constituent factors with the selected metrics of national success at managing the initial surge of the COVID-19 pandemic. This fact represents the primary evidence of this article's unique contribution. © 2022, Juan Dempere and Kennedy Modugu.

Emerging real time analytics based health start-ups: opportunities during covid-19

Indian population has potential threat of communicable and non-communicable diseases. The low preventive health measure is a cause of major loss to the economy. Integration of the cloud platform with remote wearable sensors not only helps the health stakeholders to capture the patient vitals but also perform predictive analysis during COVID-19. Raising timely alarms through Internet of Medical Things and Artificial Intelligence has wide application in preventive care through real time analytics. However, Health Merchandise Startups using artificial intelligence and machine learning for timely device delivery face delay in making themselves available and affordable for Remote patients of Tier II and III. This study takes a health service provider perspective and seeks to study problem situation systemically by using a casual loop model. Finally, analysis of the feedback loops is done to be able to come out with suitable strategies for COVID-19 patients of Remote locations. © MEDIC SCIENTIFIC, All rights reserved.

LOCATION AND CAPACITY PLANNING FOR PREVENTIVE HEALTHCARE FACILITIES WITH CONGESTION EFFECTS

A painful lesson got from pandemic COVID-19 is that preventive healthcare service is of utmost importance to governments since it can make massive savings on healthcare expenditure and promote the welfare of the society. Recognizing the importance of preventive healthcare, this research aims to present a methodology for designing a network of preventive healthcare facilities in order to prevent diseases early. The problem is formulated as a bilevel non-linear integer programming model. The upper level is a facility location and capacity planning problem under a limited budget, while the lower level is a user choice problem that determines the allocation of clients to facilities. A genetic algorithm (GA) is developed to solve the upper level problem and a method of successive averages (MSA) is adopted to solve the lower level problem. The model and algorithm is applied to analyze an illustrative case in the Sioux Falls transport network and a number of interesting results and managerial insights are provided. It shows that solutions to medium-scale instances can be obtained in a reasonable time and the marginal benefit of investment is decreasing.

"They Wait until the Disease Has Taking over You and the Doctors Cannot Do Anything about It": Qualitative Insights from Harambee! 2.0.

African immigrants make up a large subgroup of Black/African-Americans in the US. However, because African immigrant groups are typically categorized as "Black," little is known about their preventative healthcare needs. Differences in culture, life and healthcare experiences between African immigrant populations and US-born people may influence preventive health care uptake. Thus, policymakers and healthcare providers lack information needed to make informed decisions around preventive care for African immigrants. This formative study was conducted among the largest East African immigrant communities in King County, WA. We recruited religious leaders, community leaders, health professionals, and lay community members to participate in thirty key informant interviews and five focus group discussions (n = 72 total), to better understand preventative healthcare attitudes in these communities. Through inductive coding and thematic analysis, we identified factors that impact preventative healthcare attitudes of the Somali, Ethiopian and Eritrean immigrant communities and deter them from accessing and utilizing healthcare. Cultural beliefs and attitudes around preventative healthcare, mistrust of westernized healthcare, religious beliefs/views, intersecting identities and shared immigrant experiences all influence how participants view preventative healthcare. Our results suggest that interventions that address these factors are needed to most effectively increase uptake of preventative healthcare in African immigrant communities.

Predictors of parents' infant vaccination decisions: A concept derivation.

The myths surrounding coronavirus disease 2019 (COVID-19) vaccines have prompted scientists to refocus their attention on vaccine hesitancy, which is fuelled by the spread of misinformation. The scientific investigation of behavioural concepts relating to vaccine hesitancy can be enhanced by the examination of behavioural concepts from the field of consumer sciences. South African consumer scientists study personal decisions that contribute to individuals' well-being, including the decisions to prevent ill health. Current data on the predictors of vaccination decisions do not incorporate consumer science constructs imperative in decision-making, which could provide fresh insights in addressing vaccine hesitancy. This study aimed to investigate and illustrate the analogy between concepts of the Health Belief Model (HBM) as parent model, and consumer behaviour that could affect parents' infant vaccination decisions, by applying a concept derivation approach. The HBM was analysed within the context of public health, including literature from consumers' vaccination decisions, medical decisions, paediatrics, vaccinology, virology and nursing. Through a qualitative, theory derivation strategy, six main concepts of the HBM were redefined to consumer sciences, using four iterative concept derivation steps. Concept derivation resulted in consumer behaviour concepts that could be possible predictors of parents' infant vaccination decisions, including consumers' values; risk perception; consideration of immediate and future consequences; self-efficacy; cues to action; demographics; personal information and knowledge. These predictors could be a starting point for a context- and product-specific consumer primary preventive healthcare decisions model. Our findings highlight the opportunities for interdisciplinary collaboration in investigating consumer primary healthcare-related behaviour. CONTRIBUTION: This study introduced interfaces between consumer science and health science literature. Through interdisciplinary collaboration, a better understanding of influences to promote primary preventive healthcare can be achieved.

Women's Lives Matter-The Critical Need for Women to Prioritize Optimal Physical Activity to Reduce COVID-19 Illness Risk and Severity.

Physical activity (PA) is beneficial for the health and wellness of individuals and societies. During an infectious disease pandemic, such as the one caused by COVID-19, social distancing, quarantines, and lockdowns are used to reduce community spread of the disease. Unfortunately, such nonpharmacological interventions or physical risk mitigation measures also make it challenging to engage in PA. Reduced PA could then trigger physiological changes that affect both mental and physical health. In this regard, women are more likely to experience physical and psychological distress. PA is a safe and effective nonpharmacological modality that can help prevent and manage several mental and physical health problems when performed correctly. PA might even confer benefits that are directly related to decreasing COVID-19 morbidity and mortality in women. In this review, we summarize why optimal PA must be a priority for women during the COVID-19 pandemic. We then discuss chronic COVID-19 illness and its impact on women, which further underscores the need for worldwide preventive health strategies that include PA. Finally, we discuss the importance of vaccination against COVID-19 for women, as part of prioritizing preventive healthcare and an active lifestyle.

Alternative strategies of posttransplant influenza vaccination in adult solid organ transplant recipients.

Solid organ transplant (SOT) recipients are at increased risk of influenza disease and associated complications. The mainstay of prevention is the annual standard-dose influenza vaccine, as studies showed decreased influenza-related morbidity and mortality in vaccinated SOT recipients compared to those unvaccinated. Nonetheless, the immune response in this high-risk population is suboptimal compared to healthy individuals. Over the past two decades, several vaccination strategies have been investigated to overcome this inadequate immune response in SOT recipients. Howbeit, the best vaccination strategy and optimal timing of influenza vaccination remain unclear. This review will provide a detailed summary of studies of various influenza vaccination strategies in adult SOT recipients, discussing immunogenicity results, and addressing their limitations and knowledge gaps.

Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics.

Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don't tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures-a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer's V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.