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      Financial toxicity and its risk factors among patients with cancer in China: A nationwide multisite study

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          Abstract

          Objective

          We assessed financial toxicity (FT) among Chinese patients with cancer and investigated associated risk factors guided by a multilevel conceptual framework.

          Methods

          Applying multistage stratified sampling, we selected six tertiary and six secondary hospitals across three economically diverse provinces in China. From February to October 2022, 1208 patients with cancer participated. FT was measured using the COmprehensive Score for financial Toxicity (COST), with 28 potential risk factors identified at multilevel. Multiple regression analysis was used for risk factor identification.

          Results

          FT prevalence was 82.6% (95% confidence interval [CI]: 80.5%, 84.8%), with high FT (COST score ≤ 18.5) observed in 40.9% of participants (95% CI: 38.1%, 43.7%). Significant risk factors included younger age at cancer diagnosis, unmarried status, low annual household income, negative impact of cancer on participants' or family caregiver's work, advanced cancer stage, longer hospital stay for cancer treatment or treatment-related side effects, high perceived stress, poor emotional/informational support, lack of social medical insurance or having urban and rural resident basic medical insurance, lack of commercial medical insurance, tertiary hospital treatment, and inadequate cost discussions with healthcare providers (all P < 0.05).

          Conclusions

          Cancer-related FT is prevalent in China, contributing to disparities in cancer care access and health-related outcomes. The risk factors associated with cancer-related FT encompasses multilevel, including patient/family, provider/practice, and payer/policy levels. There is an urgent need for collective efforts by patients, healthcare providers, policymakers, and insurers to safeguard the financial security and well-being of individuals affected by cancer, promoting health equities in the realm of cancer care.

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          A global measure of perceived stress.

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            Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

            As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n 2.1 Kurtosis is a measure of the peakedness of a distribution. The original kurtosis value is sometimes called kurtosis (proper) and West et al. (1996) proposed a reference of substantial departure from normality as an absolute kurtosis (proper) value > 7.1 For some practical reasons, most statistical packages such as SPSS provide 'excess' kurtosis obtained by subtracting 3 from the kurtosis (proper). The excess kurtosis should be zero for a perfectly normal distribution. Distributions with positive excess kurtosis are called leptokurtic distribution meaning high peak, and distributions with negative excess kurtosis are called platykurtic distribution meaning flat-topped curve. 2) Normality test using skewness and kurtosis A z-test is applied for normality test using skewness and kurtosis. A z-score could be obtained by dividing the skew values or excess kurtosis by their standard errors. As the standard errors get smaller when the sample size increases, z-tests under null hypothesis of normal distribution tend to be easily rejected in large samples with distribution which may not substantially differ from normality, while in small samples null hypothesis of normality tends to be more easily accepted than necessary. Therefore, critical values for rejecting the null hypothesis need to be different according to the sample size as follows: For small samples (n < 50), if absolute z-scores for either skewness or kurtosis are larger than 1.96, which corresponds with a alpha level 0.05, then reject the null hypothesis and conclude the distribution of the sample is non-normal. For medium-sized samples (50 < n < 300), reject the null hypothesis at absolute z-value over 3.29, which corresponds with a alpha level 0.05, and conclude the distribution of the sample is non-normal. For sample sizes greater than 300, depend on the histograms and the absolute values of skewness and kurtosis without considering z-values. Either an absolute skew value larger than 2 or an absolute kurtosis (proper) larger than 7 may be used as reference values for determining substantial non-normality. Referring to Table 1 and Figure 1, we could conclude all the data seem to satisfy the assumption of normality despite that the histogram of the smallest-sized sample doesn't appear as a symmetrical bell shape and the formal normality tests for the largest-sized sample were rejected against the normality null hypothesis. 3) How strict is the assumption of normality? Though the humble t test (assuming equal variances) and analysis of variance (ANOVA) with balanced sample sizes are said to be 'robust' to moderate departure from normality, generally it is not preferable to rely on the feature and to omit data evaluation procedure. A combination of visual inspection, assessment using skewness and kurtosis, and formal normality tests can be used to assess whether assumption of normality is acceptable or not. When we consider the data show substantial departure from normality, we may either transform the data, e.g., transformation by taking logarithms, or select a nonparametric method such that normality assumption is not required.
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              The MOS social support survey

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                Author and article information

                Contributors
                Journal
                Asia Pac J Oncol Nurs
                Asia Pac J Oncol Nurs
                Asia-Pacific Journal of Oncology Nursing
                Elsevier
                2347-5625
                2349-6673
                15 March 2024
                May 2024
                15 March 2024
                : 11
                : 5
                : 100443
                Affiliations
                [a ]School of Nursing, Hunan University of Chinese Medicine, Changsha, China
                [b ]The Nethersole School of Nursing, The Chinese University of Hong Kong, Hong Kong SAR, China
                [c ]The Nursing Department of the Affiliated Cancer Hospital of Guizhou Medical University, Guiyang, China
                [d ]The Infection Control Department of Xuzhou Cancer Hospital, Xuzhou, China
                [e ]The Nursing Department of the Third Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
                [f ]The Nursing Department of Zhuzhou Central Hospital, Zhuzhou, China
                [g ]The Oncology Department of the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
                [h ]The Internal Medicine Nursing Office, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
                [i ]The Nursing Department of Nanjing Pukou People's Hospital, Nanjing, China
                Author notes
                [* ] Corresponding author. winnieso@ 123456cuhk.edu.hk
                Article
                S2347-5625(24)00063-5 100443
                10.1016/j.apjon.2024.100443
                11039943
                38665637
                d31cbe51-ddfa-409c-a655-ec17247c18e8
                © 2024 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 8 February 2024
                : 11 March 2024
                Categories
                Original Article

                cancer,china,financial toxicity,multisite,risk factor,health equity

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