Respiratory Health Upshots due to Contaminated Living Environment: A Cross-Sectional Study of the Industrial Belt of Kanpur City, India

Kashyap, Gyan Chandra; Puri, Parul; Singh, Shri Kant

doi:10.21272/sec.4(1).17-27.2020

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Besides, general unhealthy environmental conditions provoke most diseases and injuries. It is worth mentioning that non-communicable diseases, including coronary heart disease, chronic respiratory disease, and cancer, are the most common consequences of global environmental changes (WHO, 2021a; Kashyap et al, 2020).…”

Section: Methodology and Research Methodsmentioning

confidence: 99%

An overview of the elemental and functional content of the health indicators system as an economic category

Koibichuk¹,

Jakubowska²,

Drozd³

et al. 2021

HEM

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This paper summarizes the arguments and counterarguments within the scientific discussion on defining the essence of health as an economic category. Systematization of the scientific works to defining health as an economic category requires a clear formation and a detailed description of the health determinants. For achieving the research goal, the study was carried out in the following logical sequence: 1) defining the general research problem; 2) theoretical analysis on the relevant publications; 3) classifying the health determinants by item functioning content; 4) defining areas of public policy concerning the investigated issue; 5) determining the global changes due to COVID-19 impact. The methodological base of this study was the methods of systematization, comparison, structural analysis, logical generalization, and bibliometric analysis. The study involved the VOSviewer 1.6.15 software in visualizing the obtained results. The study sample consists of 610 documents indexed in the Scopus database from 2010 to May 2021. The paper presents the classification of the health determinants by the item functioning content as follows: 1) medical and demographic orientation determinants; 2) morbidity determinants, their composition, a list of the number of appeals to medical institutions, reports of medical examinations; 3) disability determinants; 4) determinants of physical development such as somatometric (average height, weight, chest volume, geometric shape of organs and body tissues), somatotopic (geometry of the spine, legs, arms, skeletal development, etc.), and psychometric (arm strength, respiratory rate, blood pressure, etc.); 5) determinants of natural population movement (births, deaths, natural population growth, life expectancy). The authors noted that quantitative determinants mentioned above allowed determining in detail and systematically the level of health as an economic category. The findings showed that the global COVID-19 pandemic changes the legislation support of health care. Besides, the healthcare guarantees program ensures transparent government support in the medical sector of Ukraine. The authors indicated that using an «Analytical panels» (dashboards) service on the website of the National Health Service allowed the analytical data processing on medical costs. The authors highlighted the improvement in healthcare institutions’ performance and financial transparency. The obtained results could be helpful for scientists and students interested in this research issue.

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Section: Methodology and Research Methodsmentioning

confidence: 99%

An overview of the elemental and functional content of the health indicators system as an economic category

Koibichuk¹,

Jakubowska²,

Drozd³

et al. 2021

HEM

View full text Add to dashboard Cite

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“…The authors (Lewandowska et al, 2020;Dutta et al, 2020;Jafarzadeh et al, 2019;Vasylieva et al, 2020) examined the institutional and educational determinants. Authors (Çera et al, 2019;Kashyap et al, 2020) used the principal component analysis and non-parametric methods to explore the linkages between entrepreneurial and social factors. The paper (Didenko et al, 2020a) investigated the relationship between behavioral aspects of populations' financial inclusion and social safety level.…”

Section: Literature Reviewmentioning

confidence: 99%

Why do regions differ in vulnerability to СOVID-19? Spatial nonlinear modeling of social and economic patterns

Kuzmenko¹,

Vasylieva²,

Vojtovič³

et al. 2020

Economics &Amp; Sociology

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Certain groups of determinants (economic, environmental, social, healthcare) with the highest vulnerability identify the reasons for regional differentiation in morbidity and mortality from COVID-19. This defines the necessity to find appropriate combinations of factors characterizing the vulnerability of a region. The methodology and tools to explain the regional specifics of population vulnerability to COVID-19 are investigated through a systematic consideration of many public health factors, environmental, social and economic specific nature of regions. The aim of the article is to study the reasons for regional differentiation of population vulnerability (morbidity and mortality rates) from COVID-19. The authors investigate a nonlinear spatial model in which the stepwise algorithm of individual factor variables is added/removed from the model specifications step by step by the Aitken method depending on their correlation with morbidity and mortality from COVID-19 in the region. The Farrar-Glober method is used to eliminate the multicollinearity of factors, the Spearman test is used to detect the heteroskedastic effect, and the Darbin-Watson test is used to check the presence of autocorrelation between the residues. As a result, the specification of the model with the highest adequacy in terms of p-value and tstatistics is formed. Relevant socioecological-economic vulnerability indices of regions to mortality and morbidity from COVID-19 are identified. The obtained results allow making adjustments in the state and regional programs concerning the mobilization of economic and healthcare systems.

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Healthcare Financing and Budgeting: The Regional Policy Priorities in Response to Covid-19

Kuzmenko¹,

Kashcha²,

Shvindina³

et al. 2021

ФКДПТП

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Abstract. This paper summarizes the arguments and counter-arguments in the scholarly debates on transformations in healthcare budgeting that should consider the differentiated regional vulnerability in responding to the pandemic. The primary purpose of the study is to identify priorities for local health development programs. The urgency of solving this problem is that the pandemic has revealed the unprecedented unpreparedness of the health care system to respond effectively to challenges; also, hidden problems accumulated during the last decades, which increase the emerging risks. The study is carried out in the following logical sequence: 1) collection, processing, and analysis of statistical data; 2) conducting a cluster analysis for group regions by vulnerability to different classes of diseases; 3) conducting correlation and regression analysis to compare the effects of the COVID-19 pandemic (cases and deaths) and the state of the region; 4) selection of the most significant features of the vulnerability of the region; 5) designing the matrix of the choice of priorities for financing targeted programs in the field of health care. Methodological tools of the study were methods of correlation and regression analysis, cluster analysis, testing for autocorrelation by Darbin — Watson method, sigma limited parameterization to identify the most significant coefficients. The method is tested for 25 regions of Ukraine (including Kyiv), as they can serve as pilots for other regions with similar demographic and economic characteristics. The article presents the results of an empirical analysis of the readiness of regions for critical conditions, such as COVID-19. Identifying such readiness and appropriate distribution of regions by disease classes allows to make decisions in financing and budgeting and improve the quality of health care. Keywords: COVID-19, regional vulnerability to COVID-19, step-by-step nonlinear regression, morbidity, mortality, regional profile, pandemic, multicollinearity, targeting budgeting. JEL Classification C21, C51, C31, C12, I15, I18, R58, R11 Formulas: 9; fig.: 5; tabl.: 7; bibl.: 36.

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Respiratory Health Upshots due to Contaminated Living Environment: A Cross-Sectional Study of the Industrial Belt of Kanpur City, India

Cited by 7 publications

References 20 publications

An overview of the elemental and functional content of the health indicators system as an economic category

An overview of the elemental and functional content of the health indicators system as an economic category

Why do regions differ in vulnerability to СOVID-19? Spatial nonlinear modeling of social and economic patterns

Healthcare Financing and Budgeting: The Regional Policy Priorities in Response to Covid-19

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