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      Identification and analysis of influencing factors of multidimensional health poverty in rural areas of Northwest China

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          Abstract

          Poverty alleviation remains an urgent issue in China’s rural areas, with health poverty being a critical dimension. This study analyzed the multidimensional health poverty status of 9,052 rural residents in Ningxia in 2019 and 7,910 in 2022, aiming to provide a reference for optimizing health poverty alleviation strategies. The A-F dual cutoff method was used to identify and measure multidimensional health poverty through dimensions such as health status, health service utilization capacity poverty, and health spending and security poverty. Logistic regression analysis was further applied to examine the influencing factors of multidimensional health poverty. The results indicate that when the k-value is set at 0.3, the incidence of multidimensional health poverty was 22.3% in 2019 and 7.7% in 2022. The corresponding multidimensional health poverty indices were 0.091 and 0.028, respectively. Furthermore, the level of multidimensional health poverty was higher among women than men. Chronic diseases were identified as a significant indicator of multidimensional health poverty. At the individual level, gender, age, education, government subsidies, family size, housing, and digital divide significantly effected health poverty. Although multidimensional health poverty has improved in rural northwest China, targeted measures remain essential, especially for rural women, individuals with chronic illnesses, and those facing challenges in accessing healthcare and digital connectivity. Sustainable multidimensional health poverty alleviation should prioritize remote rural areas, especially by enhancing healthcare resource utilization for rural women and individuals with chronic diseases. Additionally, increasing medical subsidies for marginalized groups is essential. Improving rural housing conditions, strengthening digital infrastructure, and raising digital health literacy are also critical steps.

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          Multicollinearity and misleading statistical results

          Jong Kim (2019)
          Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (Rh 2) of a multiple regression model with one explanatory variable (Xh ) as the model’s response variable and the others (Xi [i≠h] as its explanatory variables. The variance (σh 2) of the regression coefficients constituting the final regression model are proportional to the VIF ( 1 1 - R h 2 ) . Hence, an increase in Rh 2 (strong multicollinearity) increases σh 2. The larger σh 2 produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σh 2 according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.
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            Counting and multidimensional poverty measurement

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              What are the economic consequences for households of illness and of paying for health care in low- and middle-income country contexts?

              This paper presents the findings of a critical review of studies carried out in low- and middle-income countries (LMICs) focusing on the economic consequences for households of illness and health care use. These include household level impacts of direct costs (medical treatment and related financial costs), indirect costs (productive time losses resulting from illness) and subsequent household responses. It highlights that health care financing strategies that place considerable emphasis on out-of-pocket payments can impoverish households. There is growing evidence of households being pushed into poverty or forced into deeper poverty when faced with substantial medical expenses, particularly when combined with a loss of household income due to ill-health. Health sector reforms in LMICs since the late 1980s have particularly focused on promoting user fees for public sector health services and increasing the role of the private for-profit sector in health care provision. This has increasingly placed the burden of paying for health care on individuals experiencing poor health. This trend seems to continue even though some countries and international organisations are considering a shift away from their previous pro-user fee agenda. Research into alternative health care financing strategies and related mechanisms for coping with the direct and indirect costs of illness is urgently required to inform the development of appropriate social policies to improve access to essential health services and break the vicious cycle between illness and poverty.
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                Author and article information

                Contributors
                qiaohui71@163.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 November 2024
                22 November 2024
                2024
                : 14
                : 28952
                Affiliations
                [1 ]School of Nursing, Ningxia Medical University, ( https://ror.org/02h8a1848) Yinchuan, China
                [2 ]Ningxia Key Laboratory of Craniocerebral Diseases, Ningxia Medical University, ( https://ror.org/02h8a1848) Yinchuan, China
                [3 ]Ningxia Medical University, ( https://ror.org/02h8a1848) Yinchuan, China
                [4 ]Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, China
                [5 ]School of Humanities and Management, Ningxia Medical University, ( https://ror.org/02h8a1848) Yinchuan, China
                Article
                80628
                10.1038/s41598-024-80628-3
                11584661
                39578607
                96df5173-4ae5-494a-935d-6fccbe183d95
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 10 September 2024
                : 21 November 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004772, Natural Science Foundation of Ningxia Province;
                Award ID: 2023AAC03224
                Award ID: NYG2022049
                Award ID: 2022AAC02036
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 72164033
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                multidimensional health poverty,identification,influence mechanism,rural residents,panel data,china,health policy,health services,public health,health care,risk factors

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