This quasi-experimental study focused on Hebei Province, a region adjacent to Beijing and Tianjin, where high-quality medical resources are concentrated in northern China. The intervention analyzed was the health insurance deregulation policy implemented on September 1, 2021, which eliminated administrative barriers for cross-regional healthcare access within the province. Prior to this policy, patients seeking care outside their coordinated regions faced higher deductibles and reduced reimbursement rates under the Urban–Rural Resident Basic Medical Insurance (URRBMI). Post-policy, URRBMI enrollees could freely access same-tier hospitals across Hebei without reimbursement penalties, aligning intra-provincial reimbursement standards with local care.

The study focused on URRBMI-insured patients, who constitute 68.3% of China’s insured population in 2023 (14) and face higher out-of-pocket (OOP) expenses and catastrophic health expenditure risks compared to Urban Employee Basic Medical Insurance (UEBMI) enrollees. Individual-level inpatient claims data were extracted from the medical insurance settlement platform across three counties in Hebei Province, covering January 2021 to July 2023. Ethical approval was obtained from the Institutional Review Board of the Institute of Medical Information, Chinese Academy of Medical Sciences, with anonymized data ensuring participant confidentiality.

Individual inpatient data were obtained from the medical insurance settlement platform (health insurance claims) in three counties in Hebei province, covering all inpatient settlement data during the period from January 2021 to July 2023. According to the International Classification of Diseases, 10th revision (ICD-10) (15), IHD is classified under codes I20 to I25, which encompass conditions such as coronary heart disease, angina pectoris, and myocardial infarction. The analysis focused on the first hospitalization for IHD among residents with medical insurance who choose intra-provincial care rather than care in coordinated regions. The intra-provincial means the different cities in one province but not the coordinated region.

Demographic characteristics, including gender and age at admission, were included in the datasets. Hospital information comprised ICD-10 codes, primary diagnoses, hospital tiers, dates of visits and discharge, and lengths of stay. Expense-related data included total expenses, insurance reimbursement costs, and out-of-pocket expenditures. The individual-level data reflected various individuals at different points in time, representing a repeated cross-section. The policy intervention occurred at the group level.

In the study, one hospitalizations visit means one single hospital admissions for the patient, not hospital encounters, e.g., ambulatory services provided in a hospital, brief hospital stays awaiting diagnostic information. The total expenses include the direct medical expenses incurred in the hospital, e.g., examination fee, drug cost, operation fee, bed fee, material fee, nursing fee, laboratory testing fee, etc.

The categorical variables were presented in terms of counts and percentages, and the chi-squared test was utilized to compare these variables. An interrupted time series analysis (ITSA) was employed to investigate the impacts of the policy on hospitalization visits and expenses for patients with IHD. ITSA is regarded as a quasi-experimental research design for establishing causality without randomization frequently applied to assess intervention effects (16), and often utilizes existing time-series data that have been collected routinely over an extended period. This method evaluates both the instantaneous and trend impacts of interventions by analyzing data collected at multiple time points before and after the implementation of the policy (17). This design accounted for autocorrelation and seasonal trends while isolating policy impacts from concurrent events, such as COVID-19 restriction adjustments. In the study, a weekly interval was used to analyze the change in trend (slope) and the change in level of the data indicators before and after the implementation of deregulation policy. The intervention point was designated as September 1st, 2021, marking the official implementation of deregulation policy. The ITSA regression model is specified as follows:

In Equation 1, Y t represents the weekly outcome indicator for the period spanning from week 1of 2021 to week 26 of 2023, time is treated as a continuous variable that denotes the specific week; intervention is a binary indicator, taking the value of 1 following the implementation of the policy and 0 otherwise. Additionally, time after intervention is a continuous measure that counts the number of weeks post- intervention, assigning a value of 0 for periods preceding the intervention. Moreover, in this model, β 0 estimates the baseline level or intercept at time 0, β 1 estimates the trend or slope of change prior to the introduction of the intervention. β 2 estimates the instantaneous change following the intervention, and β 3 estimates the change in the trend or slope after the policy. Consequently, β 1 + β 3 reflects the actual trend of the outcome, representing the net effect of the policy intervention. e t is a random error term at moment t, which is not explained in the model. The Durbin–Watson (D-W) test was applied to assess first-order autocorrelation in the error terms, while the Paris-Winsten estimation method was employed to correct the D-W value. All data analyses were conducted using StataMP-64 V.17.0 software. The level of significance was set at 0.05.

This study included 8,522 IHD inpatients from three counties in Hebei province. Among these, 2,241 were admitted prior to the implementation of the policy, and 6,281 were admitted afterward. The sociodemographic characteristics of the IHD inpatients, categorized by gender, age, and tiers of hospitals before and after the policy implementation, are displayed in Table 1. Male patients accounted for 55.3%. Patients aged ≥60 years constituted the largest proportion of hospitalizations at both time points, accounting for 64.7% before the policy and 63.7% after its implementation. Overall, the age distribution was relatively consistent. The highest proportion of inpatients was treated at tertiary hospitals (tier 3). However, following the policy implementation, the percentage of admissions to tertiary hospitals decreased from 79.47 to 74.96%, while the percentage of patients admitted to primary and secondary hospitals (tier 1 and 2) increased. Additionally, the highest proportion of IHD inpatients had a length of stay of 7–9 days, which declined from 43.42 to 42.64% after the policy implementation. Conversely, the proportion of IHD inpatients with a length of stay of 7 days or fewer increased from 30.25 to 35.71% (p < 0.001).

The proportion of IHD inpatients with total hospitalization expenses per visit of 10,000 CNY or less was the most substantial. After the policy implementation, this group increased from 40.70 to 48.97%, while the percentages of IHD inpatients with hospitalization expenses between 10,000 and 20,000 CNY and above both declined (p < 0.001). Figure 2 depicts the weekly trends of hospitalization numbers and average hospitalization expense per visit from January 2021 to July 2023. There are fluctuations that exhibit similar patterns for the weekly trends of hospitalization numbers, initially increasing and subsequently decreasing. There was a rapid increase beginning in 2023, followed by another downward trend. A consistent downward trajectory was observed in per-visit hospitalization expenses on a weekly basis.

Considering the transition from Category A to Category B management for COVID-19, effective from January 8, 2023, which may impact the accessibility of cross-regional medical services, a multiple treatment periods ITSA model was conducted, utilizing the weekly number of hospitalizations as the dependent variable. In this model, September 1, 2021 was considered as the initial time point for the implementation of insurance deregulation policy, and continued until the aforementioned date, signifying the lifting of COVID-19 interventions (18). The adjusted Durbin-Watson (DW) value was 1.84, indicating that the data satisfied the autocorrelation test requirements (19). Table 2 presents the model parameters. The results revealed that the estimated initial weekly number of hospitalizations was 74.21 (β₀ = 74.21, p < 0.001), with a decreasing trend of 0.57 hospitalizations per week prior to the policy implementation (β₁ = −0.57, p = 0.066). In the first week post-policy, a significant increase of 20.27 hospitalizations was observed (β₂ = 20.27, p = 0.006), followed by a non-significant weekly increase of 0.17 hospitalizations (β₃ = 0.17, p = 0.619). Additionally, immediately following the lifting of COVID-19 interventions, there was a significant increase of 46.92 hospitalizations in the first week (β₄ = 46.92, p = 0.001), followed by a subsequent decrease of 1.47 hospitalizations in weekly trend (β₅ = −1.47, p = 0.050). Figure 3 provides a visual presentation of these results.

Hospitalization expenses and insurance reimbursement costs are influenced by policy changes through health insurance reimbursement mechanisms, but the management level of COVID-19 does not have an impact. In this model, September 1, 2021 is designated as the initial time point for the implementation of the health insurance deregulation policy. The model parameters are summarized in Table 3. The adjusted D-W value ranged from 1.81 to 1.98, with values close to 2 indicating no autocorrelation (20). The results revealed that the estimated initial hospitalization expenses per visit was 20422.34 CNY (β₀ = 20422.34, p < 0.001). Prior to the implementation of the policy, hospitalization expenses per visit exhibited a significant downward trend, decreasing by 126.71 CNY per visit weekly (β₁ = −126.71, p = 0.037). In the first week following the implementation of this policy, hospitalization expenses per visit instantaneously increased by 478.43 CNY (β₂ = 478.43, p = 0.723), followed by an increase of 94.67 (β₃ = 94.67, p = 0.121) in weekly trend. After the implementation of the deregulation policy, total hospitalization expenses per visit maintained a downward trend of 32.04 per week (β₁ + β₃ = −32.04). Figure 4A provides a visual display of these results.

The cost reimbursed by medical insurance include not only those covered by basic medical insurance, but also that covered by catastrophic medical insurance (also known as critical illness insurance or Da Bing Yi Bao), and the medical aid program (also refer to as medical financial assistance or Yi Liao Jiu Zhu). The insurance reimbursement cost per visit was 9341.78 CNY (β₀ = 9341.78, p < 0.001), with a weekly decrease of 81.98 CNY prior to the implementation of the policy (β₁ = −81.98, p = 0.004). In the first week following the implementation of the policy, insurance reimbursement costs per visit increased instantaneously by 490.74 CNY (β₂ = 490.74, p = 0.417). After the health insurance deregulation policy, there was an increase of 83.93 (β₃ = 83.93, p = 0.004) insurance reimbursement cost per visit in weekly trend. The trend shifted from a downward to an upward trajectory, increasing by 1.95 per week (β₁ + β₃ = 1.95). Although the instantaneous increase was statistically insignificant (p = 0.417), the upward trend is statistically significant (p = 0.004). Figure 4B provides a visual display of these results.