Who is More Likely to Report Medical Insurance Fraud in the Two Scenarios of Whether It Results in a Direct Loss of Individual Benefit? A Cross-Sectional Survey in China

Hongyu Zhang1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Ting Zhang1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Qi Shi1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Jian Liu1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Jinpeng Xu1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Bokai Zhang1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Haixin Wang1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Guomei Tian2 Department of Nuclear Medicine, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaView further author information

Qunhong Wu1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of China

https://orcid.org/0000-0002-2873-5266 View further author information

Zheng Kang1 School of Health Management, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaCorrespondence[email protected]
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Who is More Likely to Report Medical Insurance Fraud in the Two Scenarios of Whether It Results in a Direct Loss of Individual Benefit? A Cross-Sectional Survey in China

Table 1 Demographic Characteristics of the Participants (n = 571)

Table 2 Logistic Regression Results When Medical Insurance Fraud Causes Loss of Direct Benefits to Individuals (n = 571)

Table 3 Logistic Regression Results When Medical Insurance Fraud Does Not Result in a Direct Loss of Personal Benefit (n = 571)

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Who is More Likely to Report Medical Insurance Fraud in the Two Scenarios of Whether It Results in a Direct Loss of Individual Benefit? A Cross-Sectional Survey in China

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Table 1 Demographic Characteristics of the Participants (n = 571)

Table 2 Logistic Regression Results When Medical Insurance Fraud Causes Loss of Direct Benefits to Individuals (n = 571)

Table 3 Logistic Regression Results When Medical Insurance Fraud Does Not Result in a Direct Loss of Personal Benefit (n = 571)

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