Caesarean delivery and its risk

The global rise in caesarean section (CS) rates has resulted in considerable concern. In 1985, the World Health Organization (WHO) first recorded the “highest” CS rate of 15% [1]. However, CS rates remain higher than what is considered reasonable in most areas [2]. In 2016, according to data collected from all continents, the highest CS rates were 40.5% for Latin America and the Caribbean region; these rates were above 15% for all other regions, except for Africa, which has a CS rate of 7.3% [3].

China has experienced remarkable increases in the number of CS procedures, and the CS rate has grown by more than 30% since 1992 [4]. Before the 1990s, the CS rate was below 10% because the development of modern medicine in China was in the early stages. However, this rate rose rapidly. Between 1988 and 2008, the CS rate increased from 3 to 39%, which initially occurred in urban areas and then later in the rural areas with the improvement of the health system [5]. The rapid increase in CS has evoked concern about the health system in China. Therefore, a series of measures have been taken to reduce the unreasonable CS rates, including the reformation of the payment system, the normalization of clinical guidelines, and the strengthening of health education. Accordingly, the growth of the CS rate has slowed and reached 34.9% in 2014 [6].

High CS rates have led to a series of problems. CS is a costly operation compared to vaginal delivery (VD); consequently, it could aggravate the financial burden for households and health care systems and lead to unnecessary risks for maternal and neonatal health [7, 8]. Because CS are subsidized to different degrees, the increased use of CS in many developing countries has resulted in an increase in government funding and a substantially higher proportion of government funding in the total health-care expenditures [9]. From a global perspective, excess use of CS has had considerable costs [10]. Additionally, CS has been linked to a risk of surgical site infections, as described by Eriksen et al. [11], and CS increases the risk of early childhood anaemia compared with VD, as confirmed by Li et al. [12].

Although many objective factors led to the CS rate increase, such as the obstetric care system, provider factors, and pregnant factors, it is likely that social factors, not medical factors, are the main cause for the rate increase in China [13]. These non-medical reasons for the high proportion of CS could be motivated by the preferences of pregnant women and providers. In the absence of medical indications, many pregnant women who insist on requiring CS generally mistakenly believe that CS is less painful and more convenient than VD [14]. Physicians often favour the rapid surgical turnover over a potentially long labour and have incentives to perform CS because they fear charges of malpractice [15]. High medical insurance coverage could also be a key reason for the high CS rates among both providers and patients. Bogg et al. [16] found that the unhealthy increases in CS rates in rural China resulted from the design of the NCMS, the payment systems, and revenue-related bonus systems for doctors. Hu et al. [17] previously emphasised that providers and patients were less likely to avoid excessive tests and medication when a large proportion of the costs were shifted to third-party payers, which leads to increased medical expenses.

To reduce the CS rates and control delivery costs, many countries have adopted various measures and methods, which can be roughly classified into 3 strategies: (1) health education and teaching correct delivery concepts in society, (2) payment reform, and (3) medical malpractice reform [18]. China aims to modify and improve the medical insurance payment system in many places to reduce the CS rates and control delivery costs.

Medical insurance in China and payment reform in Xi County

Through the end of 2016, the health insurance system in China has had three main parts: (1) the Urban Employee Basic Medical Insurance Scheme, (2) the Urban Resident Basic Medical Insurance Scheme, and (3) the NCMS. The NCMS, which was established in 2003, aims to cover the medical expenses of rural residents through financial assistance from the central government, local government, and rural residents themselves, with a separate pool of funds for each county. The annual premium per capita of the NCMS was raised from 30 Chinese Yuan (¥) (USD 4.35, according to the exchange rate of USD 1 = ¥6.89 in April 1, 2017) in 2003, of which ¥20 (USD 2.90) came s from the government and ¥10 (USD 1.45) came from the rural residents, to an average of ¥410(USD 59.50) in 2014 and ¥570 (USD 82.72) in 2016, of which ¥420 (USD 60.95) came from the government and ¥150 (USD 21.77) came from the rural residents. Until 2015, the NCMS covered 99% of all rural residents. However, in 2017, the NCMS began gradually being integrated into the Urban Resident Basic Medical Insurance scheme.

To control the increase in medical expenses, the NCMS agencies in some counties have attempted to implement payment reform for providers by transforming the fee-for-service (FFS) payment into case payment. Because of the FFS payment, which is the most popular method of paying providers in the NCMS, incentivizes health providers to generate unnecessary procedures for high profits and resulted in the inefficient utilization of health services, it has played an important role in the increasing medical expenses in China [19–21]. Therefore, case payment, which is a prospective payment system (PPS), is considered a useful measure for regulating the behaviour of health providers and ultimately controlling the unreasonable increases in medical expenses. Both experts and governors consider the use of case payment more suitable than the use of diagnosis-related groups in rural China because of the limitation of the medical information system and providers’ ability to code procedures. McCue et al. [22] testified that the case payment approach reduces costs by approximately 6%–10% compared with FFS arrangements.

Accordingly, the NCMS agencies in some counties, such as Xi County, Henan Province, China, have explored the use of case payment through some support policies to control the increase in medical expenses. In Xi County, which is predominantly occupied by rural residents who are covered by the NCMS, a pilot CPR for inpatients in the NCMS began in 2009 and was implemented for the entire county in 2011. Delivery was the first case covered by the CPR in Xi County. The effectiveness of the CPR to control costs in Xi County requires further examination.

To provide insight into the reform and determine its value as a model for other districts that intend to implement such policies, we take delivery as a research object and investigated the effectiveness of the CPR on CS rate reduction and delivery cost control. In the present study, we aim to (1) observe the changes in the CS rates and delivery costs before and after the CPR, (2) compare the changes in delivery-related variables between CS and VD before and after the CPR, and (3) identify variables related to delivery costs in Xi County.

Data collection

Data were obtained from the inpatient information database of the Xi County NCMS agency. This database contains the claim information of individuals residing in Xi County, which was covered by the NCMS from January 1, 2009, to December 31, 2010, and from January 1, 2014, to December 31, 2015. In total, 34,210 admissions for delivery were found among a total 295,796 inpatient admission cases over the past four years. In China, township hospitals are grass-root institutions, only providing basic medical services and treating. County hospitals are higher level institutions than township hospital in the county. County hospital could provide comprehensive medical services and treat both ordinary delivery without complication and severe case with complication, because their medical service capacity is stronger than township hospitals.

To focus on studying the effectiveness of the CPR, we excluded patients who were not covered by the case payment for unknown reasons in the hospital, patients for whom the delivery method (VD vs. CS) could not be determined, and patients who visited from outside Xi County. A total of 28,314 deliveries were analysed.

We considered hospital deliveries from January 1, 2009, to December 31, 2010, as the pre-CPR patients and deliveries from January 1, 2014, to December 31, 2015, as the post-CPR patients. There were 12,705 deliveries in the pre-CPR group and 15,609 in the post-CPR group.

Statistical analysis

To ensure the comparability of the medical costs of delivery, we adjusted the medical costs using the consumer price index (2010, 2014, and 2015 = 102.8, 101.8, and 101.1, respectively) in Henan Province.

The variables in this study included inpatient age, LOS, total medical cost, CPR, hospital type, and delivery type. The inpatient age, LOS, and total medical cost were the continuous variables, whereas CPR, hospital type and delivery type were the categorical variables. Total medical cost refers to the full costs of hospitalization. CPR includes the pre-CPR and the post-CPR, and there were two types of hospitals: township and county hospitals. Two types of delivery: VD and CS.

We compared the variables of the VD and CS groups between the pre-CPR and post-CPR, respectively. The variables include the inpatient age, LOS, total medical cost and hospital type. The continuous variables were analysed using a one-way ANOVA to compare the differences in the means, and the categorical variables were analysed using a chi-squared test to compare the differences in proportions.

We applied multivariate linear regressions for the total medical cost of the VD and CS groups and total samples to identify the relationship between medical expenses and variables. The variables include age, CPR, LOS, hospital type and delivery type. Delivery type was only taken into Model 3 (total samples). CPR, hospital type and delivery type are dichotomy variables. Dummy variables can quantify the dichotomy variables and be incorporated in regression models [23]. In order to set reference variables for these three dichotomy variables in multivariate linear regressions, we set (1. the pre-CPR = 0 versus the post-CPR = 1, (2. township hospital = 0 versus county hospitals = 1, and (3. VD = 0 versus and CS = 1, respectively [24]. All data were analysed using SPSS 19.0. All P values used in these tests were two-tailed, with 0.05 indicating statistical significance.