Basic information on data and sample demographics
Basic information on DMISE
From 2017 to 2021, the DMISE recorded a total of 10,723 enrollees involved in 12,898 cases. The SMISII identified 7 types of fraud and abuse activities, which included repeated visits or excessive dispensing (96.8%), lending of medical insurance cards (2.6%), impersonation using medical insurance cards (3.4%), selling of drugs covered by medical insurance (0.4%), compensated transfers (0.1%), and forgery or alteration of medical insurance vouchers (<0.01%).
Respondent characteristics
A total of 1,770 individuals who met the eligibility criteria were invited to participate in the survey. Among them, 1,655 participants (93.50%) completed the questionnaire, and the final sample consisted of 965 individuals (58.31%) who passed the response quality assessment. The majority were female (59.69%) and Shanghai natives with local household registration (87.36%). Just over 60% of the participants had obtained a college degree or higher. The distribution of insurance coverage among the respondents revealed that the majority were beneficiaries of the UEBMI. Their marital status was predominantly married (70.78%), with just over 60% of the respondents being employed. Approximately 25% of the respondents reported monthly earnings of between 10,000 and 50,000 yuan. Health status reports indicated that the majority (62.73%) of the respondents were free from any diseases, while a smaller proportion reported experiencing multiple diseases. Additionally, a few respondents reported receiving review notices from regulatory authorities within the past five years. Detailed characteristics are provided in Table 1.
Table 1. Demographic characteristics of the survey population (N=965)
|
Characteristics
|
N
|
%
|
Gender
|
|
|
Male
|
389
|
40.31
|
Female
|
576
|
59.69
|
Age group
|
|
|
18–29 years
|
218
|
22.59
|
30–44 years
|
272
|
28.19
|
45–59 years
|
238
|
24.66
|
60+ years
|
237
|
24.56
|
Household registration location
|
|
|
Shanghai
|
843
|
87.36
|
Other
|
122
|
12.64
|
Education level
|
|
|
Primary school
|
32
|
3.32
|
Secondary school
|
137
|
14.2
|
High school
|
200
|
20.73
|
College degree or above (including associate degree)
|
596
|
61.76
|
Medical insurance
|
|
|
UEBMI
|
763
|
79.07
|
URBMI
|
185
|
19.17
|
Other
|
17
|
1.76
|
Marital status
|
|
|
Married
|
683
|
70.78
|
Unmarried
|
214
|
22.18
|
Other (Such as: divorced, separated, widowed, etc.)
|
68
|
7.05
|
Employment
|
|
|
Employed
|
592
|
61.35
|
Retired
|
259
|
26.84
|
Unemployed
|
114
|
11.81
|
Gross income per month (RMB, ¥) a
|
|
|
≥50,000
|
159
|
23.31
|
>10,000 and ≤50,000
|
170
|
24.93
|
>6,500 and ≤10,000
|
166
|
24.34
|
>3,000 and ≤6,500
|
139
|
20.38
|
<3,000
|
48
|
7.04
|
Health state
|
|
|
Disease-free
|
579
|
62.73
|
Suffering from a disease
|
205
|
22.21
|
Suffering from a variety of diseases
|
139
|
15.06
|
Had received review notice from the regulatory b
|
|
|
Yes
|
29
|
4.14
|
No
|
671
|
95.86
|
Abbreviations: UEBMI: urban employee basic medical insurance; URBMI: urban and rural residents basic medical insurance.
|
Notes.
|
a Not all respondents answered; there were 283 missing samples.
|
b Not all respondents answered; there were 265 missing samples.
|
The deterrence of the basic medical insurance regulatory system
Severity of the regulatory system
Based on the DMISE data, we analyzed the variety and intensity of regulatory measures. Table 2 presents the types of regulatory measures to reflect the variety. The penalties applied to MIFA could be categorized into four types: economic penalties, benefits restrictions, judicial means, and other measures. The majority of cases (88.78%) implemented refunds as the penalty measure. A small proportion of cases (5.24%) involved suspending the settlement. Less than 2% of cases resulted in exemption from penalty, and other measures had been infrequently used in the past five years of regulation.
Table 2. Types of punitive measures in medical insurance regulation
|
Types
|
Measures
|
Number of cases
|
Percentage
|
Economic penalties
|
Ordering the returning of funds
|
11,447
|
88.75%
|
Fines a
|
4
|
0.03%
|
Benefits restrictions
|
Suspending the settlement b
|
676
|
5.24%
|
Judicial means
|
Transfer to judicial departments
|
1
|
0.01%
|
Other measures
|
Immunity from penalties
|
213
|
1.65%
|
Other
|
671
|
5.20%
|
Notes: a Refers to a monetary penalty imposed for fraudulent claims, calculated as two-to-ten times the defrauded amount as stipulated by regulations. b Refers to the temporary suspension of networked medical cost settlements for individuals who violate regulations, with the suspension period ranging from three to twelve months.
|
Regarding the intensity of regulatory enforcement, Table 3 shows that repeat offenders received significantly higher mean estimates than first-time offenders in terms of the number of punitive measures and the amount of refunds. However, no significant difference was observed in other measures, such as the amount of fines or the duration of settlement suspension. Nearly all cases (99.12%) were addressed with a single measure, while a small proportion (0.88%) involved two measures.
Table 3. Comparative analysis of penalties between first-time and repeat offenders
|
Measures
|
First-time offenders
|
Repeat offenders
|
P
|
N
|
Mean
|
SD
|
N
|
Mean
|
SD
|
Number of Punitive measures
|
10,723
|
1.008
|
0.001
|
2,175
|
1.014
|
0.003
|
<0.01
|
Amount of refunds a
|
9,493
|
1634.5
|
26.04
|
1,954
|
2016.88
|
70.267
|
<0.001
|
Amount of fines a
|
2
|
750
|
250
|
2
|
1500
|
500
|
--
|
Suspending the settlement b
|
583
|
147.92
|
7.275
|
93
|
143.46
|
21.844
|
0.413
|
Abbreviations: SD standard deviation.
|
Notes: a The unit is in Chinese Yuan (CNY). b The unit is in days.
|
According to the survey results from the enrollees, as shown in Appendix Table 3 and Figure 4, the perceived severity of medical insurance regulation was the highest among all dimensions of the scale. On average, the perceived severity of regulations was rated as 4.445 (±0.691) out of 5, with the vast majority of participants scoring a severity above 4. Moreover, itemized analysis within Appendix Table 4 reveals that enrollees perceived psychological pressure as the most intense, with an average score of 4.486 out of 5. In contrast, the perceived severity of economic penalties scored was rated the lowest at 4.413 out of 5, closely followed by the impact on social reputation, which scored 4.417 out of 5.
Certainty of the regulatory system
In evaluating the certainty of detection of the basic medical insurance regulatory system, our analysis focused on the procedures of regulatory review, the annual change in the number of cases, and the experience of regulatory managers, based primarily on Shanghai's medical insurance regulation practices.
The review process in Shanghai involves three main steps, shown in Figure 3. Initially, regulatory authorities conduct broad monitoring of all enrollees through three channels: system audits for two specific anomalies, real-time monitoring with intelligent medical insurance systems, and investigation of reports. If anomalous behavior is detected, further manual examination of the enrollee's medical records assesses potential violations. Subsequently, if both automated and manual reviews suggest a violation, the individual is summoned for a "face-to-face" audit at a medical insurance office to confirm the specific infraction. This comprehensive monitoring, enhanced by intelligent systems, allows for accurate identification of violations. The manual review builds on this to ensure precision. Except in special cases, the majority of those notified complied with the face-to-face audit, despite some violators managing to evade detection. Through these rigorous steps, approximately 80–90% of violators could be identified.
Additionally, Figure 4 illustrates the fluctuation in the number of cases handled. In 2017, a total of 1,958 cases were addressed, with about 3,000 cases each in 2018 and 2019. Due to the COVID-19 pandemic, which restricted manual checks and face-to-face audits in 2020, only 799 cases were processed. However, as the pandemic situation in China eased, this number increased to 4,000 cases in 2021. This trend underlines how the certainty of detecting violations is greatly affected by the review system's functionality, highlighting that regulatory certainty remains stable when the review system operates normally.
In terms of the certainty of enforcement, statistical analysis shows that from 2017 to 2021, all 12,898 cases received certain handling opinions, with only 1.65% of cases being exempt from sanctions due to minor violations. This indicates the high certainty in the regulatory system’s enforcement. In other words, once enrollees were found to have MIFA behaviors, they had a high probability of receiving a penalty.
Survey results from enrollees, presented in Appendix Table 3 and Figure 4, indicated that the perceived certainty of medical insurance regulation ranked second among all dimensions of the scale. The certainty of the regulatory system received an average score of 4.436 (±0.777) out of 5, with a median score of 5. Additionally, an itemized analysis, shown in Appendix Table 4, revealed that the certainty of enforcement (4.436±0.823) was marginally higher than that of the certainty of detection (4.435±0.809).
Celerity of the regulatory system
The celerity of the basic medical insurance regulatory system, shown in Table 4, demonstrates that the average duration for the celerity of detection fluctuated around 218 days from 2017 to 2021. The data from 2017 to 2019 show minimal variation in detection times. However, the year 2020 exhibited a pronounced anomaly in the timeline, primarily due to the disruptions caused by the COVID-19 pandemic. Notably, the celerity of detection significantly improved in 2021, with the duration shortening to an average of 171 days.
Furthermore, the period for the celerity of enforcement, as shown in Table 4, maintained an average of approximately 29 days across the same timeframe. Over the five-year period, except for 2020, there was a gradual reduction in the average number of days for sanctions. The enforcement timespan showed considerable variability, with durations ranging from 0 to 1,420 days, indicating that certain cases experienced unduly prolonged penalty phases.
Survey findings on the enrollees’ perceptions of the regulatory system's celerity identified it as the lowest-ranked dimension among those assessed, with an average rating of 4.225 (±0.905) out of 5, and a median score of 4.5, as shown in Appendix Table 3 and Figure 4. Furthermore, an itemized analysis, shown in Appendix Table 4, revealed that the perceived celerity of detection (4.228±0.946) was higher than the perceived celerity of enforcement (4.222±0.944).
Table 4. Annual distribution of celerity of detection and enforcement from 2017 to 2021
|
Year
|
N
|
Celerity of detection
|
Celerity of enforcement
|
Average days
|
SD
|
Max
|
Min
|
Average days
|
SD
|
Max
|
Min
|
2017–2021
|
12,898
|
217.8
|
101.48
|
42
|
499
|
29.2
|
72.04
|
0
|
1,421
|
2017
|
1,958
|
209.1
|
82.25
|
83
|
324
|
39.7
|
103.8
|
0
|
1,421
|
2018
|
3,153
|
228.1
|
95.02
|
49
|
429
|
37.3
|
101.2
|
0
|
1,275
|
2019
|
2,988
|
240.5
|
93.78
|
43
|
385
|
26.2
|
59.28
|
0
|
928
|
2020
|
799
|
350.2
|
117.29
|
49
|
499
|
18.3
|
14.59
|
1
|
116
|
2021
|
4,000
|
170.6
|
85.03
|
42
|
391
|
22
|
23.22
|
0
|
291
|
Examining the overall deterrence of the medical insurance regulatory system, the survey results showed that enrollees distinctly perceived its effectiveness. The mean deterrence score—integrating the aspects of severity, certainty, celerity, and subjective norms—was 4.379 (±0.675) on a 5-point scale. The median score was 4.615, with the 25th and 75th percentiles at 4 and 5, respectively. Notably, 31.09% of respondents gave the highest score of 5, indicating substantial agreement on the system's deterrent effect (Appendix Table 3 and Figure 4).